Facilitating smarter homes. Appendix to Final Report

H3 / H5 Opportunity Assessment

Facilitating smarter homes Appendix to Final report

RACE for Homes

Project team

Research Theme H3: Facilitating smarter homes

University of New South Wales •

Simon Heslop

•

Zahra Rahimpour

Industry Report

•

Charlie Zhang

•

Anna Bruce

An Opportunity Assessment for RACE for 2030

•

Iain MacGill

ISBN: 978-1-922746-48-1

University of Technology Sydney

November 2023

•

Steve Beletich

Citations

•

Jaysson Guerrero Orbe

•

Kaveh Khalilpour

Heslop, S., Beletich, S., Guerrero Orbe, J., Pang, B., Rahimpour, Z., Zhang, C., Khalilpour, K., Dwyer, S., Berry, A., Bruce, A., MacGill, I., Rundle-Thiele, S. (2023) . Facilitating smarter homes. Prepared for RACE for 2030 CRC.

•

Scott Dwyer

•

Adam Berry

Project Partners

Griffith University •

Bo Pang

•

Sharyn Rundle-Thiele

Acknowledgements The Project team would like to acknowledge and thank the IRG members who provided their time and expertise during the scoping and development of the report including the review of findings and opportunities: The industry reference group comprised of individuals from Ausgrid, Department of Energy, Environment and Climate Action (DEECA), Endeavour Energy, Mirvac, Redgrid and Simble.

Acknowledgement of Country The authors of this report would like to respectfully acknowledge the Traditional Owners of the ancestral lands throughout Australia and their connection to land, sea and community. We recognise their continuing connection to the land, waters and culture and pay our respects to them, their cultures and to their Elders past, present, and emerging.

What is RACE for 2030?

RACE for 2030 CRC is a 10-year cooperative research centre with AUD350 million of resources to fund research towards a reliable, affordable, and clean energy future. https://www.racefor2030.com.au

Disclaimer The authors have used all due care and skill to ensure the material is accurate as at the date of this report. The authors do not accept any responsibility for any loss that may arise by anyone relying upon its contents.

1

Contents 1

RESEARCH QUESTIONS, RESEARCH ACTIVITIES AND RECCOMENDATIONS __________________ 5

1.1 1.2 1.3 1.4 1.5 1.6 1.7

Enabling projects ____________________________________________________________________________ 5 Effective Dynamic Operating Envelope (DOEs) __________________________________________________ 6 Effective HEMS operation ____________________________________________________________________ 8 Engaged and informed consumers ____________________________________________________________ 12 Appropriate design of AS/NZS 4755.2 __________________________________________________________ 16 Uninhibited data access _____________________________________________________________________ 17 Informed industry decision makers ___________________________________________________________ 20

2

TECHNOLOGY AND MARKET POTENTIAL SCENARIO MODELLING ________________________ 24

2.1 2.2 2.3 2.4 2.5

Introduction _______________________________________________________________________________ 24 Methodology ______________________________________________________________________________ 24 Results ____________________________________________________________________________________ 28 Discussion _________________________________________________________________________________ 31 Assumptions _______________________________________________________________________________ 33

3

STATE OF THE MARKET AND RESEARCH LITERATURE REVIEW __________________________ 34

3.1

RT.1 Use of data and IT ______________________________________________________________________ 35

3.2

2

3.1.1

Introduction _________________________________________________________________________ 35

3.1.2

Review of Regulatory Framework for Metering Services ____________________________________ 35

3.1.3

Energy Security Board (ESB) Data Strategy—Independent DER data platform ________________ 49

3.1.4

Consumer Data Right (CDR) for Energy __________________________________________________ 50

3.1.5

Findings _____________________________________________________________________________ 53

3.1.6

My Energy Marketplace (MEM) _________________________________________________________ 62

3.1.7

EU policy on SM rollouts _______________________________________________________________ 65

3.1.8

Green button (GB) ____________________________________________________________________ 68

RT.2 Dynamic Operating Envelopes (DOE) _____________________________________________________ 69 3.2.1

Introduction _________________________________________________________________________ 69

3.2.2

Review of work done in previous RACE for 2030 OAs ______________________________________ 70

3.2.3

General DOE explanation ______________________________________________________________ 70

3.2.4

DOE allocation principles ______________________________________________________________ 72

3.2.5

Pilot/trial DOE projects ________________________________________________________________ 75

3.2.6

Regulatory Framework ________________________________________________________________ 79

3.2.7

Household considerations _____________________________________________________________ 83

3.3

3.4

3.5

3.6

3.2.8

Technical considerations for DOE implementation ________________________________________ 86

3.2.9

Implementation pathway of DOEs across Australian distribution networks. ___________________ 88

3.2.10

Academic relevant literature _________________________________________________________ 93

3.2.11

International precedents _______________________________________________________________ 96

3.2.12

Research questions _________________________________________________________________ 96

RT.3 Household level appliance/PV integration model comparison _______________________________ 100 3.3.1

Introduction ________________________________________________________________________ 100

3.3.2

Review of work done in previous OAs __________________________________________________ 100

3.3.3

DER integration potential benefits and opportunities _____________________________________ 101

3.3.4

DER integration models _______________________________________________________________ 102

3.3.5

Market Status ________________________________________________________________________112

3.3.6

Findings ____________________________________________________________________________ 116

RT.4 Standards review _____________________________________________________________________ 124 3.4.1

Introduction ________________________________________________________________________ 124

3.4.2

Review of work done in previous OAs __________________________________________________ 126

3.4.3

Overview of relevant standards ________________________________________________________ 126

3.4.4

State of the Market/Technology ________________________________________________________ 131

3.4.5

State of Research ____________________________________________________________________ 138

3.4.6

Findings and Research Questions ______________________________________________________ 139

RT.5 Algorithms for the control and optimisation of major electric household appliances ___________ 141 3.5.1

Introduction ________________________________________________________________________ 141

3.5.2

Review of work done in previous OAs __________________________________________________ 142

3.5.3

Home Energy Management System and Algorithms ______________________________________ 143

3.5.4

Market status ________________________________________________________________________ 148

3.5.5

Pilot Studies and Projects ______________________________________________________________ 151

3.5.6

State of research ____________________________________________________________________ 152

3.5.7

Summary of Findings and RQs _________________________________________________________ 161

RT.6 Methods to make maximum use of data _________________________________________________ 163 3.6.1

3

Introduction ________________________________________________________________________ 163

3.7

4

4

3.6.2

Review of work done in previous OAs __________________________________________________ 164

3.6.3

Current research and technology status ________________________________________________ 164

3.6.4

Findings _____________________________________________________________________________171

RT.7 Behavioural aspects of energy use and energy savings _____________________________________ 172 3.7.1

Introduction ________________________________________________________________________ 172

3.7.2

Methodology ________________________________________________________________________ 172

3.7.3

Review______________________________________________________________________________ 177

3.7.4

Findings ____________________________________________________________________________ 181

BIBLIOGRAPHY ____________________________________________________________ 184

1

Research questions, research activities and recommendations

1.1

Enabling projects

Two large scale “enabling” projects are proposed which are required to enable certain RAs or alleviate certain barrier. The two projects are: 1. Home Energy Management System (HEMS) test lab 2. Independent DER data platform (iDDP) Many of the recommended RAs would benefit from a HEMS lab and/or iDDP, and in some cases require it. They facilitate the building and testing of consumer facing apps, tariff tools, consumer testing, hardware, firmware, communication testing for HEMS interoperability and standards and enable the simulation of scenarios (HEMS operation and DOE network -> consumer interactions) for extensive consumer testing.

Home Energy Management System (HEMS) test lab. DER technology and functionality are still immature, mostly still at trial stage, and there are many knowledge gaps in relation to DER market services, DER capabilities, communication standards and interoperability. To facilitate testing and trials of DER technology operation, it is recommended that a HEMS test lab be built. The knowledge gaps are specified in Sections 1.2 to 1.7, where it is also mentioned when the associated recommended research activity would benefit from access to a HEMS lab.

Independent DER data platform (iDDP). The ESB Data Strategy gives a number of recommendations which can be addressed through the creation of an independent DER data platform (iDDP), which is open source and owned and managed by a non-commercial entity. It will serve as a free source of DER data for researchers, and as a platform for the development of innovative use cases and services, trialling new technologies, dissolving barriers to data access through the development of a data format, management (collection, consent, sharing), security, and communication standards, and pioneer best practice for DER data platform governance and operation. The authors admit that a project such as the iDDP will be a difficult to successfully fund and arrange. Challenges include sourcing data, which will come from data holders who are trying to monetise their data sets, and who pays for hosting if it’s free to use.

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1.2 Effective Dynamic Operating Envelope (DOEs) DOEs are considered to be the means through which DNSPs can ensure that high penetration DER doesn’t impact negatively on LV network power quality. But DOEs will also place limits on consumer DER adoption and operation, and the participation of consumer DER is integral to the success of Grid 2.0. As such, while DOEs will obviously need to be effective and efficient, they will also need to be equitable.

The ARENA Distributed Energy Integration Program (DEIP) DOE working group have developed DOE allocation principles (transparency, efficiency, fairness, etc.) and consider four location scale levels (network, zone substation, distribution transformer, household) along with four ways to allocate capacity (equal, proportional to PV system size, valuebased allocation, where households get higher allocation if they can provide more value, and pay-for allocation), to guide the design and implementation of DOEs. However, further work is required to fully understand the impact of the DEIP DOE WG guidelines on consumers and the network. There are aspects of the DOE framework design which need to be addressed, as well as the calculation method. Table 9 lists the findings, RQs, RAs, and recommendations associated with this theme.

Table 1. Effective DOE implementation

ID

RAR1.1

6

Topic

Consumer and network impact

Reference

Findings/RQs/RA and Recommendation

3.2.4, 3.2.5, 3.2.6, 3.2.9

The approach for implementing DOEs can consist of a number of different factors, capacity allocation methods, location scale levels, new DOE aligned tariff structures etc., and the approach which best meets the ARENA DEIP DOE guidelines is still to be determined.

- How do these factors impact on consumer behaviour, and therefore load profiles and the potential benefits of DOEs to consumers, at both the household and aggregated level?

- Installers of smart home technologies (HEMS, SMs, and solar) are consumer facing, should they have a DOE communication role?

- What is the impact on network level load profiles?

- Existing research on calculation methods has been focused on rooftop solar and battery systems. How flexible loads might be used to meet DOEs (and ensure their solar isn't curtailed) and meet DOE import setpoints would be of particular interest to consumers. What are the opportunities for future research on flexible loads in the context of DOE? - What potential do well designed tariffs have to reduce reliance on DOEs?

-

-

Trials to evaluate how consumers respond to the various DOE approaches and their impact on network load profiles. In addition to retailers, smart home technology installers should be included in the recruitment and communication process. Each trial should also investigate how flexible loads can assist households from breaching DOE setpoints and avoiding curtailment, this aspect can be coupled with trialling tariffs designed to incentivise households to avoid breaching DOE setpoints.

DOE scenario simulations, incorporating power flow analysis and using a range of assumed behavioural responses, can also be used to assess the impact on network load profiles. The simulation of DOE scenarios would benefit from access to data from an iDDP. Testing of how flexible loads might be able to respond to DOEs, could be facilitated through the HEMS lab.

There are aspects of the DOE framework which could potentially be standardised to ensure more reliable and consistent DOE operation, these include communication systems and protocols, DOE forecasting periods, compliance response, DOE calculation and the contingency procedures in the case of communication systems failure. It has also been suggested that the framework include DOE performance measurement and monitoring. Finally, the initial DOE framework design only considers export limits, which mitigates solar export and minimum demand, but the framework needs to consider import limits to mitigate peak demand.

RAR1.2

DOE 3.2.6, 3.2.8, Framework 3.2.9

-

-

What aspects of the DOE framework could and/or should be standardised? If so, what would be best practice for each?

Should DOE performance measurements and monitoring be included in the Framework? If so what monitoring and measurements are required?

Do the proposed DOE allocation principles also apply to import limits or do new ones need to be determined?

The first step to establishing a standard is to gain a baseline level of understanding of how well current implementations are working and identify where improvements can be made. To achieve this an assessment through survey/feedback of existing DOE implementations, covering communication systems, forecasting periods, compliance response, DOE allocation, contingency response etc. is recommended. Consumer acceptance of the compliance response is important here for gaining social license.

Trials as per RAR1.1 combined with simulation of DOE scenarios. Simulations will certainly be required for assessing the applicability of DOE for import, considering the limited functionality of flexible loads to respond to DOE signals.

Design and establish standardised DOE performance measures and required monitoring for DOEs, to ensure consistent evaluation of DOEs against the ARENA DIEP guidelines. To be done in collaboration with the ARENA DIEP WG on DOEs. This process is informed through trials but also through simulation.

The process of establishing a standard will be an iterative process, requiring ongoing trials and modelling, as well as collaboration from industry and the ARENA DIEP WG on DOEs. The simulation of DOE scenarios would benefit from access to data from an iDDP.

7

RAR1.3

DOE calculation method

3.2.5, 3.2.9, 3.2.10

A comprehensive comparative analysis between the different DOE calculation approaches is needed to develop guidelines for best practice on the calculation method for DOEs. Methods to be compared should include currently implemented (Evolve) and what is proposed in the literature (optimisation, state estimation, data-driven).

- What method, or methods, potentially best meet the DOE allocation principles?

- Are methods proposed in the literature suitable for implementation in real-world distribution networks?

- Are there potential methods that do not require full visibility? And can these methods calculate DOE with reasonable accuracy with only limited visibility?

- To what extent can DOE be applied to unbalanced distribution networks? What additional considerations are needed for DOE to be applied in unbalanced distribution networks?

This RA focusses on assessing more advanced and theoretical DOE calculation methods, which have yet to be properly tested in the field.

This RA would assess how well advanced DOE calculation methods meet DOE allocation principles and their applicability in real-world distribution networks. Methods to be assessed would include those which require only limited visibility and are designed for unbalanced networks. This work will be carried out through simulation and would benefit from access to data from an iDDP and hardware testing through a HEMS lab.

1.3 Effective HEMS operation An effective HEMS enables consumers to be better able to adjust their consumption behaviour to maximise solar self-consumption, respond to time-of-use tariffs to reduce their energy bills, be optimised by an aggregator, or respond to DOE setpoints. Effective HEMS is crucial to the success of Grid 2.0. To achieve effective HEMS requires progress across a number of areas, including standards, interoperability, Information and Communication (ICT) infrastructure performance, device level algorithms and consumer facing apps.

Table 10 lists findings, RQs, RAs, and recommendations associated with this theme.

8

Table 2. Effective HEMS operation

ID

9

Topic

Reference

Findings/RQs/RA and Recommendation

RAR2.1

HEMS 3.3.4, 3.4.1, functionality 3.5.5.3, and 3.5.6.2 performance

Part of an assessment of AS/NZS 4755.2 is to determine what functionality is required by behind-the-meter (BTM) devices, Smart Meters (SMs), and consumer facing apps. A way to determine the required functionality is through a performance assessment. Where different functionality for BTM devices, SMs, and consumer facing apps is for different types of HEMS (architecture and algorithm), in combination with the different types of DER integration models (local, aggregated, extent of automation, market, and network participation). A poorly implemented HEMS may result in unexpected poor performance for the consumer, as well as the aggregator, and may also fail to meet DOE setpoints in an efficient manner.

- How is HEMS performance to be measured?

- What functionality (and their associated parameters) is required by the different types of HEMS devices to achieve a minimum (typical) level of performance?

- How does the associated DER integration model impact on the performance and required functionality of HEMS devices?

- HEMS design is dependent upon a number of factors, including parameters of flexible loads, user preferences, types of HEMS devices, and price/tariff plans, how do these factors influence performance?

- The above performance and functionality assessment assumes a degree of automation. How does performance compare against voluntary control, enabled through well designed consumer facing apps?

- Modelling to determine ranges of HEMS performance, which will vary according to type of HEMS devices and architecture, DER integration model. Assumptions would need to be made around household preferences, extent of automation, functionality of consumer facing apps, tariff plan, household demographics and base load profiles, location, and season. A calculation of a performance range will help to establish a typical level of performance. Performance here could be, for example, measured by energy savings. This modelling could also assess different levels of functionality. This work would benefit from data provided by an iDDP. - Trials are also required, which recruits households with various types of HEMS, to boundary test existing functionality (HEMS devices and cloud, and consumer facing apps) to determine the limits, and the limiting factors, of their performance. These trials could also be replicated through a HEMS lab, using data from an iDDP.

RAR2.2

HEMS algorithms

With rule-based algorithms, control decisions are typically made locally, not cloud-based, are straightforward (making them easier to understand and implement) and suitable for practical HEMS implementation but they have their drawbacks. Those proposed in the

3.5.4.2, 3.5.5.3,

3.5.6.1, 3.5.6.2, 3.5.6.3, 3.5.6.4, 3.5.6.5

literature do not consider economic incentives, tariffs etc, or provision of network services, and therefore a HEMS system using a rulebased algorithm will miss opportunities to reduce energy costs through tariff arbitrage or provision of network services. Those proposed also only use real-time measurements, with no forecasting, and thus don’t optimise scheduling of flexible loads over a period of time (1-3 days for example) limiting their performance.

Centralised optimisation algorithms (using either linear programming) use a cloud-based server and can optimise scheduling of flexible loads over a period of time and be designed to be multi-objective, minimising, for example, thermal discomfort, energy cost, carbon emission, user inconvenience and equipment degradation, simultaneously. But they require large amounts of storage and computing power, and typically require customised design and parameter setting for each household, adding to the cost of implementation.

Calculations are also computationally demanding, and typically made in cloud-based servers, thus making near real-time control challenging.

Hierarchical coordination algorithms are a combination of local rule-based control (using control devices embedded in HEMS appliances) and centralised optimisation. These algorithms can both optimise flexible load schedules over a period of time and manage near real-time control. HEMS which utilise this type of algorithm are however expensive, requiring the installation of proprietary hardware. CarbonTrack and SwitchDin both use a hierarchical control framework, but it is not public whether hierarchical coordination algorithms are used inside their cloud-based platform. Hierarchical coordination algorithms are promising however and would benefit from standardised communication, which would remove the need for proprietary hardware, reduce costs, and a greater range of products giving more options/flexibility to consumers.

Machine learning (ML) algorithms are an alternative to linear programming (used in centralised). They are also typically cloud-based, optimise scheduling of flexible loads over a period of time simultaneously, and require large amounts of storage and computing power.

They are however more flexible (don’t require customised design for each household) and have a faster decision-making response time.

No ML algorithms are currently implemented in commercially available HEMSs. ML algorithms need to be investigated further to determine whether they are suitable for practical HEMSs.

10

-

-

-

RAR2.3

NILM algorithms

3.6.3

What are the comparative ICT infrastructure costs for each HEMS algorithm? What is the trade-off between ICT infrastructure cost and performance?

Is it possible to incorporate economic incentives into rule-based algorithms? And can they be designed, following analysis of historical household data, such that the logic can consider forecasted inputs such as PV power generation, temperature and expected consumption?

If server latency were not a bottleneck, is it possible for centralised optimisation algorithms to be designed such that near real-time control is possible?

To what extent can sophisticated ML-based algorithms be practically used in HEMS? Modelling is required to assess the ICT infrastructure costs versus performance for the various HEMS algorithms to determine the trade-offs. To consider potential improvements in performance, this work would also consider theoretical scenarios which consider more sophisticated rule-based algorithms, levels of reduced server latency for cloud based control, and the practicality of using MLbased algorithms. This work would benefit from access to data from an iDDP. It would be difficult to answer the above research questions related to performance through trials and are better suited to be addressed in a controlled environment such as a HEMS lab. If this work is conducted by researchers, who knowledge-share their findings for other researchers and innovators to analyse, then advancement in this area of research will be accelerated.

Using Non-intrusive Load Monitoring (NILM) algorithms, it is possible to disaggregate the overall energy consumption to the appliance level without having to install additional current transformer (CT) clamps, sensors, or plugs. Interestingly, there are only two commercial apps (Sense, and Samppee) that use NILM methods, and existing Australian government initiative home monitoring apps (such as Powerpal and Wattwatchers) require the installation of extra CT clamps or separate smart plugs to get appliance level measurements.

This may be due to the fact that it is challenging to develop a NILM algorithm capable of identifying different types of appliances within a reasonable amount of time (depending on the appliance and noise level) and training, with acceptable accuracy. Exposure to a sufficient number of on/off cycles is required before appliance signatures can be detected.

Is it possible to effectively disaggregate the overall energy consumption to the appliance level without having to install additional current transformer (CT) clamps, sensors, or plugs using NILM algorithms?

11

-

Analysis is required, using real-world data, to test and evaluate different NILM algorithms and improve their performance. This study would benefit from access to data from an iDPP.

1.4 Engaged and informed consumers Consumers are obviously a key component to the success of Grid 2.0, and they need to be engaged and informed and for this to occur. Table 11 lists the findings, RQs, RAs, and recommendations associated with this theme.

Table 3. Engaged and informed consumers

ID

RAR3.1

Topic

DOE operation

Reference

3.2.6, 3.2.9

Findings/RQs/RA and Recommendation Households need adequate information to make informed decisions about whether to participate or opt-out of DOE programs.

The way this information is conveyed to households will be crucial in gaining acceptance and social licence for the service.

- How will market providers effectively incentivise/engage people to opt in for DOE? - What type of information do households need to make informed decisions to opt-in to DOE? - How can the information be explained or communicated to all households simply and transparently, considering that they have different motivations, attitudes, and expectations?

Desktop analysis to collate all consumer research (CX) on DOEs currently available to determine how well they answer the above RQs. Any CX, existing or future, should increase knowledge of what incentivises consumers to participate in DOEs, what type of information consumers need to make informed decisions on DOEs, and how that information should be communicated.

RAR3.2

Tariff analysis and tool development

3.1.4.2

New retail tariffs are expected to become more complicated, moving away from the historical structures. New retail tariffs may be subscription based, and include exposure to the wholesale energy price, participation in virtual power plants, or the provision of DR. The CDR-E rules will need to ensure that customers are able to make informed tariff decisions. Tariff tools and are needed to assist consumers wanting to manage their energy consumption more actively.

Are existing tariff tools able to assist households make decisions on new retail tariffs?

12

-

Review the methods for calculating savings for new retail tariffs currently proposed in the literature.

Take the range of tariffs (currently on offer, under trial, and proposed) designed for households expected to manage their energy consumption more actively, and design and test methods to calculate savings for each.

Assess the saving calculation methods used by current tariff tools and their ability to calculate savings for new retail tariffs.

It is expected that this work will identify the limitations of tariff tools currently on offer and reveal a best practice method for calculating savings for each type of new retail tariff. Following this it is recommended that a new tariff tool be developed which implements the identified best practice methods, and then be consumer tested. This work would benefit from access to historical energy consumption data from an iDPP.

RAR3.3

Consumer facing apps

3.6.3

Consumers have complained that their apps don’t provide them with all the information they are interested in, with most only provide generation or consumption, they also expect their apps to include information on EVs and batteries in the future.

Consumers seeking convenience have also indicated they would benefit from an appliance control automation feature, a feature that is lacking in most of the available apps.

How can developers be assisted/informed to build better apps?

The facilitation of the development and consumer testing of innovative consumer facing apps is needed. This work would benefit from access to data from an iDPP and HEMS lab.

HEMS operation and algorithms are complicated, and providing understandable information on this is difficult. HEMS is also likely to need input from consumers (parameters), to determine its operation, and some degree of understanding may be required for a consumer to decide on these parameters, it may also be necessary for the HEMS market to gain consumer trust and traction.

RAR3.4

HEMS operation

3.5.5.3

What information do households need to make informed decisions about HEMS? What is the best way to communicate this information?

Desktop analysis to collate all consumer research (CX) on HEMS currently available to determine how well they answer the above RQs. Any CX, existing or future, should increase knowledge of what type of information consumers need to make informed decisions on HEMs, and how that information should be communicated.

13

What are the effects of family dynamics on the energy decision making process? And what is needed to understand and overcome any existing and unwarranted lack of trust consumers may have towards actors (retailers, DNSPS, technology providers, government) in the electricity industry?

RAR3.5

RAR3.6

14

Communication strategy research

3.7.3

Communication strategy 3.7.3 recommendations

To assist in the development of an effective communication strategy directed towards consumers on grid2.0, the following consumer research activities are recommended: • Determine the effect of family dynamics in terms of energy decision making in Australia. Studies from other countries shows the importance of family influence on energy consumption behaviours, studies in Australia on this to date are limited.

• Trust in the actors involved (retailers, DNSPS, technology providers, government) will play a pivotal role on the reception of new products and policies, and the communication strategy implemented needs to consider this. It is expected that lack of trust can be alleviated through knowledge, which help to clarify risks (perceived or real) and the benefits/impacts/outcomes which may come from purchasing a new technology, participating in a new service, or changing to a new tariff. But more research is required to understand and overcome any existing and unwarranted lack of trust consumers may have towards actors in the electricity industry.

• • • •

Should it the communication strategy on grid2.0 be co-designed with consumers?

Should consumers receive personalised information about the environmental impact of their energy consumption to incentivise them to change their consumption behaviour?

Is a mass media campaign the way to gain traction with the wider community?

Should a social norm on the topic be developed by sharing what others are doing (HEMS reviews/examples/testimonials)?

It is recommended that the following be considered and assessed as part of a communication strategy directed towards consumers on grid2.0: • Use co-design when developing the communication strategy, incorporating feedback and input from consumers, rather than the expert-drive approach that has been widely used in the literature. Co-designing solutions are expected to better identify consumer knowledge gaps and results in communication solutions that are more appealing to consumers.

• To draw more attention to the direct (and indirect) impact of their energy consumption on the environment, and thus incentivise them to change their consumption behaviour (in addition to cost, the main reason) to reduce CO2 emissions, the research indicates that consumers should receive personalised information about the environmental impact of their energy consumption.

• For this topic, to gain traction with the wider community, it needs to become embedding into the social consciousness, and mass media campaigns are shown to be effective at this. None on this topic have been implemented in Australia and it is recommended that this occurs. This will include disseminating knowledge on how to get access to educational material on this topic, with the educational material designed to be accessible and understandable to all consumers. Research is also required to identify the most effective forms of mass media communication to ensure educational materials reach a wide audience.

• Research shows that people, in general, tend to be more favourable towards a topic when they have information about what others are doing. Provision of this information develops a social norm on the topic. It is recommended that anonymous consumer information related to this topic (smart home technologies reviews/examples/testimonials and statistics, examples/testimonies on the impacts/benefits of changing consumption behaviour etc) be shared as part of the communication strategy.

• Research shows consumers have a desire to learn about smart energy technologies and are inclined to install smart technology but that access to relatable/personalised information in relation to smart energy technologies and their personal energy consumption behaviour is preventing uptake. It is recommended that any marketing strategy consider this gap.

15

1.5 Appropriate design of AS/NZS 4755.2 The compatibility of SMs, BTM devices, consumer facing apps, and in-home displays is limited, with most using proprietary communication protocols. A lack of communication standards for these technologies adds cost to, and inhibits innovation, meaning less development of services and products, a less competitive market, and less flexibility and reduced options for consumers. Table 12 lists the findings, RQs, RAs, and recommendations associated with this theme.

Table 4. Appropriate design of AS/NZS 4755.2

ID

Topic

Reference

Findings/RQs/RA and Recommendation It is recommended by respondents to the Regulatory Framework for Metering Services that the poor interoperability between grid2.0 consumer technologies (SMs, Behind-the-meter (BTM) devices, HEMS, and in-home displays) could be resolved by mandating that grid2.0 technologies be compliant with IEEE 2030.5.

RAR4.1

Grid2.0 consumer technologies Interoperability

3.1.2.3, 3.4.1, 3.5.4.1, 3.5.4.2, 3.6.3

AS/NZS 4755.2 is currently under review to bring it in to line with international standards OpenADR and IEEE 2030.5, but once released it will need to be tested, both in the field and through simulation How easily and effectively does the new version of AS/NZS 4755.2 enable interoperability between all BTM devices, SMs, HEMS, and consumer facing apps?

-

Review the new version of AS/NZS 4755.2 to identify functionality for testing.

Review the literature on testing of IEEE 2030.5 to learn of any issues already identified.

Field test the new version of AS/NZS 4755.2 using compliant devices to test the specified functionality, and surface functionality design and interoperability issues which may limit the performance of HEMS.

Field testing would benefit from access to a HEMS lab.

16

1.6 Uninhibited data access The success of Grid 2.0 is dependent upon access to energy data, but this energy data needs to be managed (shared, stored, secured, protected) appropriately. Table 13 lists the findings, RQs, RAs, and recommendations associated with this theme.

Table 5. Uninhibited data access

ID

RAR5.1

Topic

Consumer facing apps

Reference

Findings/RQs/RA and Recommendation For the evaluation of different NILM methods, as well as for app development, there are several publicly available international data sets.

However, it is difficult for researchers to develop new algorithms and evaluate them for Australian households without access to datasets representing household usage in Australia. As an example, identifying which household appliances are the top consumers and their types is crucial in developing algorithms to detect each appliance's signature. This varies based on geographical location and we cannot use other nations' datasets for Australian households.

3.6.3

There are few quality household energy consumption data sets available to researchers, required for the evaluation and development of consumer facing apps. How can the creation of these data sets be facilitated?

This issue could be addressed through the creation of an iDDP. For most HEMS algorithms to be effective, they need access to data, such as historical and forecasted weather data, and energy prices, that is typically only available commercially.

RAR5.2

17

HEMS algorithms

3.5.6

What commercial HEMS use historical and forecasted weather data, and energy prices? What is the current state of access to the above data? How does lack of access potentially limit the performance of HEMS? - An assessment of current commercial HEMS, and their use of historical and forecasted weather data, and energy prices.

- An assessment of historical and forecasted weather data, and energy prices products and services to determine accessibility.

- An investigation into how accessibility may potentially impact on HEMS performance.

RAR5.3

CDR-E -Use case analysis

3.1.4

The aim of the Consumer Data Rights for Energy (CDR-E) is to give Australians greater control over their energy data, by empowering consumers to be able to choose to share their energy data with trusted recipients for the purposes the consumer has authorised. The CDR-E still has many decisions to make on what data should be shared and how to share it. Key issues raised by respondents are given below: - Respondents complain that the CDR-E isn’t clear on what the priority use cases are. i.e., what are the most important use cases which the CDR-E is expected to facilitate.

- Respondents point out that behind-the-meter (BTM) data isn’t currently included in the CDR-E, despite, according to respondents, it being required for a number of important use cases, such as assessing the viability of VPPs, energy efficiency assessment, tariff comparisons, and for helping households make informed DER purchasing decisions.

- Respondents want more clarity/specifics on what data should actually be included for Metering, NMI Standing, and DER register data.

- There are differing views on how much historical data is required to meet use case needs. Respondents also argue there are cost implications associated with the length of time that historical data is stored. And historical data may need to come from previous eligible CDR consumers at a property, how to manage access to this data has yet to be addressed by the CDR-E. - There is contention around whether providing metering data is a security risk to the consumer, and whether it has the potential to allow third parties to know the occupancy patterns of a property. This contention exists even though metering data is currently provided for Energy Made Easy and Victorian Energy Compare tariff, and identified by NMI, postcode, and retailer.

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How are priority use cases to be identified?

What are the use cases which come from including BTM data in the CDR-E which wouldn’t be supported otherwise? How to quantify the value they bring to consumers and the network?

What is the minimum Metering, NMI Standing, and DER register data fields required to meet the scope of use cases under the CDRE? How much historical data is required to meet use cases? What is the dependency of the error in a use case assessment and amount of historical data? What methods can be applied to a historical data set, extending it artificially for example, which can decrease error?

Is historical data at a previous property for the eligible CDR consumer relevant to them for use cases at their current property? Is historical data for previous eligible CDR consumers at a property relevant to use cases for the current eligible CDR consumer residing at the property?

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How valid are respondents concerns that the occupancy patterns of a property can be predicted from metering data? Does the prediction accuracy vary according to the property? If metering data is only identified using NMI, postcode, and retailer does it matter?

It is proposed by some respondents that provision of modified or aggregated metering data, as opposed to raw, may reduce the risk it presents to the consumer. Is this true? And is it possible to meet use case requirements using modified or aggregated metering data?

What use case is required to assist the Treasury make decisions on tiered accreditation and data security?

To answer the above RQs, use case analysis for each data type (BTM, Metering, NMI Standing, and DER register data) is required to determine what data is required (data fields, resolution, historical duration) to meet use case needs. The benefits each use case brings to electricity industry stakeholders (consumers, networks, and retailers) then needs to be assessed to quantify their importance.

For Metering data specifically, use case analysis looking at data security, and the risk sharing metering data to third parties poses to consumers, is required to assist the ACCC make informed decisions on tiered accreditation.

Following the above analysis, priority use cases could then be identified. This work would benefit from access to data from an iDPP.

RAR5.4

19

CDR-E - My Energy Marketplace

3.1.6

My Energy Marketplace knowledge sharing reports provided a number of insights relevant to the CDR-E. Key findings from the reports include: - Wattwatchers (WW) is enabling innovative consumer data services through provision of access to the MEM energy data, and they have discovered that these new services require additional metadata.

- WW have developed a short-form commercial term sheet to support new MEM data services customers, enabling them to rapidly develop prototypes and pilot projects without the need for extensive negotiation and legal review.

- WW have found that who “owns” data is complex question, and that it is actually WW who own the consumer energy data measured using their devices.

- WW estimate that the costs to become accredited as a CDR-E data holder or data recipient ranges from $250k to $2M.

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Is it prudent to design the Regulatory Framework for Metering Services and the CDR-E such that the inclusion of non-energy metadata is not excluded? Use case analysis here would be beneficial.

Assuming the importance of making consumer data, such as MEM data, available to third parties to develop consumer services and to researchers for analysis, can the WW “lean agreement”, potentially be developed to either be recognised as best practice or even as a standard? This process may require industry collaboration.

In regard to who owns consumer data, how does the law here compare with the US and UK? Can WW relinquish their ‘ownership’ rights to consumers? If the consumer owns the WW device, and access to the data is made available through sharing communication protocols, does this shift ownership to the consumer?

The cost to become CDR-E accredited is prohibitive for smaller companies. Why is the cost so high? And why is the range estimate so large?

1.7 Informed industry decision makers Industry decision makers need to be well informed to assist with their decision-making process, and knowledge gaps identified (in all RTs) indicated that this wasn’t the case.

Modelling is required to address these knowledge gaps, typically a cost-benefit analysis (CBA) or risk analysis. Table 14 lists the findings, RQs, RAs, and recommendations associated with this theme.

Table 6. Engaged and informed industry decision-makers

ID

RAR6.1

20

Topic

Consumer control of data disruption

Reference

Findings/RQs/RAs/Recommendations

3.1.2.1, 3.1.2.3

It is indicated through feedback from respondents to the Regulatory Framework for Metering review that giving consumers control over their own data will be disruptive, mainly to retailers, as SMs enable a change in consumer energy consumption behaviour which undermines their business models. This argument is supported by the evidence that retailers have not initiated any SM rollouts to date, as it’s not in the retailers' interest to do so. Interestingly, and despite the poor rate of SM uptake to date, retailers still want to continue to manage the rollout, but recommend that they are incentivised to initiate rollouts by having the costs of installing SMs subsidised by consumers through an increase in the Default Market Price (DMO) or Distribution Use of System (DUoS) charge. If retailers require subsidies to install SMs does this not undermine the “competitiveness” component of the Framework?

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Is there a role for the retailer under a 100% SM penetration or are they no longer needed?

What would the business model of a retailer look like under 100% SM penetration?

To answer the above RQs modelling is required to identify the potential negative consequences of this disruption on the electricity industry, and retailers in particular, and to therefore develop strategies to avoid them. Considering that the rollout of SMs potentially undermines the viability of the conventional retail business model. The analysis should examine consumer energy consumption under 100% SM penetration, with assumptions around consumer services, with a comparison against electricity prices, would give an indication as to where a retailer might generate a revenue under a conventional model, assessing its viability. A study such as this would benefit from access to data from an iDDP. There seems to be a lack of evidence in support of the AEMCs decision to recommend legacy retirement plan.

RAR6.2

SM rollout policy

3.15

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What studies, evidence, are EU jurisdictions using to justify their policies on accelerated SM rollouts?

Has Australia implemented similar studies and/or provided similar evidence?

What are the important tenets of each policy? What can Australia learn from SM rollout policies implemented in the EU?

A comparative study on international SM rollout policy is required to answer the above RQs.

RAR6.3

21

CBA on Smart-home technologies

3.1.2.3, 3.2.8, 3.3.4, 3.4.1, 3.5.6.2, 3.5.6.3, 3.5.6.5

Households will need to invest in new equipment, grid2.0 consumer technologies (HEMS, SMs, BTM devices, and in-home displays), to take advantage of new tariffs, participate in energy markets, provide network services, or participate in DOEs. According to respondents to the Regulatory Framework for Metering Services review, cost is still considered the main barrier to the purchase of grid2.0 consumer technologies. It is necessary to properly quantity the costs of investing in grid2.0 consumer technologies against the benefits that their large-scale deployment brings to the electricity industry as a whole, where benefits are expected to come from a smoother residential load profile. Properly quantifying benefits to the network would also help determine pricing thresholds for progressive tariffs and the provision of network support services.

Aggregated DER integration models (which would typically use distributed optimisation algorithms) include participation from a number of households, and the computation and communication requirements are higher than what is required for household level control. Aggregated models require the exchange of information between households and aggregators through two-way communication channels with adequate latency and bandwidth to dynamically receive and send signals.

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Is it cost-effective (and is it required) to subsidise grid2.0 consumer technologies if it results in a greater reduction in network costs and electricity costs overall?

If it be mandated that grid2.0 consumer technologies be IEEE 2030.5 compliant. As the majority of existing homes have legacy flexible loads with no means of communication or control, this would also include the cost effectiveness of upgrading (retro-fitting) existing SMs and BTM devices to be IEEE 2030.5 compliant.

What are the ICT infrastructure requirements for effective household level HEMS operation? How does it vary according to HEMS algorithm? What is the impact (performance and benefits) of inadequate ICT infrastructure, which results in unacceptable latency times and up-time? How do ICT infrastructure requirements vary according to HEMS architecture? What is current capability of typical ICT infrastructure?

What are the ICT infrastructure requirements for aggregated DER integration models? What is the impact (performance and benefits) of inadequate ICT infrastructure, which results in unacceptable latency times and up-time? How do ICT requirements vary according to the type of aggregated DER integration model? What is the current capability of typical ICT infrastructure?

A comprehensive cost-benefit analysis on grid2.0 consumer technologies is required to answer the above RQs. It is recommended that this comprehensive CBA determine the factors which determine cost-effectiveness. And if costs are prohibitive, why is this the case? As results will vary from consumer to consumer, the CBA should consider different demographics which then leads to a standardised approach (with standardised results) for conducting CBAs on grid2.0 consumer technologies investments for consumers. This study would benefit from access to data from an iDDP.

RAR6.4

CBA on Physical LV models

Calculating DOEs currently requires electrical LV network models. However, the creation of accurate network models is timeconsuming and expensive. It is also challenging to create accurate network models due to data errors in topology and lack of data generally.

3.2.9

What is cost benefit of investing in the creation of accurate physical LV models?

A CBA is needed which compares the costs of paying for the creation of physical LV models, and LV models derived from SM voltage and consumption data, versus the benefits to the network.

RAR6.5

22

CBA on Legacy

3.1.2.1

The AEMC has recommended the legacy retirement plan for the accelerated rollout of SMs. The following outlines some concerns related the plan:

retirement plan

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In response to the poor SM rollout to date, respondents are arguing for it to be managed by DNSPs. On this, DNSPs advise they could manage and pay for targeted SM rollouts in areas where network benefits can be derived, or in high-value but high cost-to-install locations, which are not cost-effective for a retailer, such as rural and remote locations. Any DNSP involvement in the rollout does not however seem to be considered as part of legacy retirement plan.

The AEMC recommended changes to the Framework (complementary to the legacy rollout plan) look to be designed to reduce or remove the short-term disincentive retailers have to install SMs. However, there is no recommendation on the remote energise, deenergise service, expected by retailers to reduce costs of install significantly, and it could be argued that the long-term disincentive still remains. It can therefore be argued that retailers are potentially a risk to the success of the legacy retirement plan.

Despite the evidence that industry participants have to date not been cooperative, the success of the legacy retirement plan is dependent upon industry cooperation. It could be argued that the extent to which industry participants cooperate is an unknown quantity, and therefore introduces a risk to the success of the plan.

The AEMC refer to a CBA report by Oakley Greenwood as a justification for an accelerated rollout (and therefore its legacy retirement plan), but the CBA just assumes the legacy retirement plan achieves 100% SM penetration by 2030. It could be argued there is a lack of evidence for the reasons why the AEMC is recommending the legacy retirement plan.

A rollout of SMs at remote and difficult-to-serve geographical areas is going to be more costly to retailers, and likely to require more comprehensive negotiation between industry participants. The legacy retirement plan however doesn’t make any suggestions on how to manage these situations.

Is it more efficient and cost-effective for DNPSs to manage the SM rollout?

Is it cost-effective for DNSPs manage and pay for targeted SM rollouts in areas where network benefits can be derived, or in highvalue but high cost-to-install locations, which are not cost-effective for a retailer, such as rural and remote locations?

How do SM install prices vary according to geographical area and remoteness?

Are there methods to estimate the increase in SM penetration through the various accelerated SM rollout plans (especially the legacy meter retirement plan) proposed by the AEMC?

To assist the AEMC make decisions on how best to implement an accelerated rollout of SMs, a CBA is needed to answer the above RQs. In addition, considering that continuing lack of incentive for retailers, and that poor industry co-operation pose a risk to the success of the legacy meter retirement plan, it is recommended that a study be undertaken to quantify this risk. And what contingencies could be put in place to minimise it.

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2

Technology and market potential scenario modelling

2.1 Introduction The purpose of this modelling is to estimate ‘business-as-usual’ (BaU) and ‘barrier-removal’ scenarios for residential flexible loads (water heaters, pool pumps and A/C). This report includes a detailed methodology for the modelling, a summary of the results, and a discussion of the implications.

2.2 Methodology Overview of modelling method The method used to estimate scenarios for residential flexible loads (water heaters, pool pumps and A/C) is as follows: • Stocks of water heaters, pool pumps and A/C units were taken from an existing dataset published by the Australian Government. = total stock = Stotal • A proportion of stocks will not be technically suited to flexible control, and these proportions were estimated. = technically suited stock = Ssuited • Average annual energy use per device was derived from the same dataset and converted to daily figures. In some case only a proportion of this daily energy use is available for flexible control, and this proportion was estimated. = daily energy available for flexible control, per device = Edevice daily • The total daily ‘shiftable’ energy available from flexible control is the multiple of the previous two estimates = Etotaldaily = Ssuited x Edevice daily • Uptake scenarios were then applied to these calculations, to provide time series estimates of the total daily shiftable energy available from flexible control.

• Separate to the uptake scenarios, customer benefits derived from changing tariffs were estimated.

Stocks At the core of the model are stocks for electric water heaters, pool pumps and A/C units. These are time series estimates of the total numbers of devices installed at a given time. For this model, stocks of electric water heaters, pool pumps and A/C units were taken directly from EnergyConsult (EnergyConsult, 2020). No changes in the BaU stock predictions of water heaters, pool pumps and A/C were modelled – in other words the Australian Government’s estimates were used.

EnergyConsult separates electric water heaters into four categories - med/large, small, heat pump and electric solar. Pool pumps have one singular category and A/C units are segregated by ducted and non-ducted. EnergyConsult stock estimates for these devices, from 2000 to 2040, are shown in Figure 5.

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Figure 1. Estimated total stocks of residential electric water heaters, pool pumps and A/C units, 2000-2040

Within the stocks of the three devices analysed, there will be some units which are not technically suited to being fitted or retrofitted with flexible control. Around 85% of med/large water heaters are connected to an off-peak tariff (Roche et al, 2023) and these are an excellent candidate for flexible control, as the night-time heating load can be shifted to day-time with relative ease (fitting a mains relay with an associated control system). For the other three types of water heaters (small, heat pump and electric solar) it was assumed that only 50% would present good candidates for flexible control – that is their loads are able to be easily shifted with minimal impact on users (e.g., smaller tanks less able to store energy for long periods).

The same estimate of 50% was also applied to pool pumps. This is because basic pool pump setups (no timer or smart control system) can be fitted with a relay which is able to both stop and start the pump. Systems which employ smart controllers (e.g., automated chlorination and acid dosing) can be more difficult. De-energising the pump is straightforward (using a relay) but restarting the pump is more difficult as it may require the smart controller to be over-ridden (e.g., when the controller has stopped the pump). A/C units are similarly complex as they require retrofitting a control regime rather than fitting a simple relay. Trials involving retrofitting A/C units with flexible control have encountered significant difficulty (Heslop, 2021), hence 50% was also used for A/Cs. There was assumed to be no difference between ducted and nonducted units in terms of suitability for flexible control.

The resultant estimates for devices technically suited to flexible control are graphed in Figure 6.

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Figure 2. Estimated stocks of residential electric water heaters, pool pumps and A/C units which are technically suited to flexible control, 20252040

Daily energy consumption Average energy consumption for electric water heaters, pool pumps and A/C units was taken from EnergyConsult. For A/C the annual figure given in EnergyConsult was converted to a daily figure using the assumption that A/C units operate for an average of 120 days per year (summer and winter days combined). The resultant average daily energy consumption for all three products in shown in Figure 7. In this figure we can see daily energy use decreasing in line with improvements in energy efficiency of these devices (due to minimum energy performance standards and the use of inverter motor drive technology, etc.).

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Figure 3. Average daily energy use for residential electric water heaters, pool pumps and A/C units (2000-2040)

For water heaters and pool pumps, it was assumed that all of the energy consumed by the device would become available for flexible control. These devices are well suited to flexible control as the customer is largely agnostic to when the unit is energised. Note however that for pool pumps, care must be taken to ensure that the pump is run for sufficient hours to keep the pool sanitised (Pooled Energy, 2021).

For A/C it was assumed that 20% of the energy consumption would become available for flexible control. In the case of curtailment, A/Cs would be curtailed an average of 20% during the evening peak. (Heslop, 2021) reviewed a number of A/C trials and found that average power reduction per customer ranged from 0.24kW to around 1.5kW. A typical non-ducted A/C system is around 7kW output meaning that its electrical power draw is around 2kW electrical power (coefficient of performance ~3.5). Considering larger ducted systems, the weighted average electrical power might be 3kW. Thus, savings of 0.24 - 1.5kW would be expressed as percentages 8% to 50%. 20% then seems a reasonable estimate, noting that this must reflect the amount of discomfort that customers are willing to bear (during any curtailment).

Solar pre-heating / pre-cooling involves the A/C unit being turned on during the afternoon in order to preheat or precool a space. Modelling in the H1 Opportunity Assessment on residential solar pre-cooling (Wilmot et al, 2021) describes a ‘solar pre-heating and pre-cooling efficiency’. Applying an efficiency of 40% (roughly the national average from H1) would result in 2.5 times the evening energy saving being required during the afternoon to preheat/precool the space. Note however that H1 concludes that there is a very large spread in this efficiency value, depending on location, dwelling type, dwelling energy efficiency, etc.

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Similarly, H1 finds a large spread of % kWh evening savings from pre-heating / precooling. 20% is at the lower end of the range found in H1, but has been used here, conservatively, as pre-heating / precooling is as yet unproven in practice in Australian homes (H1 was a theoretical study).

Note that individual device load curves were not developed for this report, and this appears to be the case for (Roche et al, 2023). This is because control of thousands of flexible loads results in the load shape of each being less important, apart from when it occurred. Whether the load was 1kW for 3 hours or 3kW for 1 hour becomes less important when thousands of units are being controlled. The controller has the granularity to aggregate these loads in whatever way they wish, to build an aggregated load shape of their choosing. At the aggregated scale, the load shape changes for each flexible load can be described as follows: • Water heating: shift GWh from other times (especially night-time off peak) to daytime • Pool pumps: shift GWh from other times to day-time • A/C (curtailment): curtail evening GWh • A/C (preheat/precool): shift GWh from evening to afternoon.

Uptake scenarios The uptake of flexible control for all products was modelled over the period 2025-2040, using 4 different uptake scenarios.

These uptake scenarios are designed to represent the degree to which barriers are removed. The ‘high’ uptake scenario assumes complete, early removal of all barriers, resulting in 100% of all technically feasible stock being fitted with flexible control by 2040. The ‘low’ uptake scenario would result in only around 20% of the technically feasible stock being fitted with flexible control by 2040. These and two additional intermediate uptake scenarios (low/med and med) are depicted in Figure 8. Note that the BaU scenario is assumed to be no uptake of flexible control.

Figure 4. Uptake scenario s-curves 2025-2040

2.3 Results From the datasets and assumptions outlined in the previous section, forecasts of the daily ‘shiftable’ energy available from flexible control of electric water heaters, pool pumps and A/Cs were developed, and the results are graphed in Figure 9.

For A/C systems, a reduction in evening peak is modelled. Applying the 40% preheating/precooling efficiency discussed

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above, this graph would increase in size by a factor of 2.5 to show the quantity of ‘solar soak’ available during the afternoon preheat/precool event. This is depicted in the final graph of Figure 9.

Figure 5. Daily energy available for flexible demand, for residential electric water heaters, pool pumps and A/C units (2000-2040)

The uptake curve was applied to each of the 3 devices modelled for this study, and the results graphed in Figure 10, which shows the amount of daily shiftable energy available from flexible control, according to four uptake scenarios.

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Figure 6. Daily energy available from flexible control of residential loads, by uptake scenario

From the above figure we can see that water heaters represent by far the biggest opportunity for flexible load control.

The total available daily energy available from water heating (~24 GWh per day) agrees well with (Roche et al, 2023), which predicts around 20 GWh per day (in the BaU scenario - BaU in this case referring to stock levels, rather than uptake of flexible control).

Customer benefits were also modelled, based on tariffs. The following tariffs were used: • Peak - $0.57 per kWh • Shoulder - $0.28 per kWh • Off-peak - $0.20 per kWh • Solar soak - $0.05 per kWh For med/large water heaters, the customer benefit is derived from switching from an off peak to a solar soak tariff. For other water heaters and pool pumps, the benefit is derived from switching from a shoulder to a solar soak tariff. For A/C, the customer benefit is derived from decreasing energy used during peak times (peak tariff) with a corresponding increase in energy use during the afternoon (solar soak tariff) noting that the afternoon energy use is 2.5 times higher than the evening saving, owing to a precooling/preheating efficiency of 40% as described previously. The resulting customer benefits are graphed in Figure 11. Note that all results are expressed in real terms (2023 Australian dollars) and that these graphs represent approximate average values.

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Figure 7. Customer benefits (annual benefit per customer) from flexible control of residential devices

As expected, the largest customer benefits are derived from water heating, with average benefits of up to $350 p.a., depending on water heater size/type.

2.4 Discussion The results presented in this analysis represent an approximate national average. As with many energy parameters for households, there is a significant spread, depending on many factors such as climate, dwelling type, household usage patterns, etc. The largest opportunity for flexible control of household loads comes from water heating. There are many reasons for this: • Water heating is one of the largest consumers of energy in a home • Energy can be stored in water heaters for significant periods • Control of electric water heaters is technically straightforward (on/off relay with associated control and communications electronics) • Customers are agnostic to when the water is heated, providing that hot water is available when it is required.

This analysis showed that transitioning to flexible control of water heaters could provide around ~24 GWh per day of shiftable load, under a BaU trajectory of water heating stocks. As was found in (Roche et al, 2023), this figure will increase if gas water heaters are phased out in favour of electric.

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Pool pumps present a smaller opportunity than water heating, as these use less energy, comprise a smaller stock, and have fewer units technically suited to flexible control. Customers are however agnostic to when pool pumps are used, providing that appropriate pool sanitation is maintained.

Air conditioning also presents a smaller opportunity than water heating, due to the difficulty in controlling A/C units and the amount of energy that can be shifted using either curtailment or preheating/precooling (estimated to be around 20% of total A/C energy use). Customers are also more sensitive to changes in operating patterns of A/C than for water heaters and pool pumps.

Average customer financial benefits from changing from peak/shoulder/off-peak tariffs to a ‘solar-soak’ tariff are modest, ranging from $50 p.a. for A/C to $350 p.a. for large off-peak water heaters. This may not be enough to motivate large numbers of customers, and other financial incentives may be required.

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2.5 Assumptions Figure 12 below gives the list of assumptions used in the modelling.

Figure 8. List of assumptions used for forecast modelling

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3

State of the market and research literature review

The literature review consists of a review of the current state of the market and technology and academic literature. The review of current state of the market and technology examined all applicable content, but mainly reports from industry, consumer advocates, and government, to assess current scale of market and cost of the technology/practice, international and Australian industry current practice and best practice, and to identify global and Australian leaders in technology and best practice. Authors also drew on feedback from the Industry Reference Group (IRG). The review of the current state of research examined all academic literature to assess the current state of research both in Australia and internationally.

A key output of this review is the identification of barriers and research questions (RQs) to the expected medium and long-term outputs of the H3 and H5 themes, which in turn will be used to derive future research activities. Note that this OA focuses on rooftop solar, flexible loads, and their integration. Content on household batteries, Electric Vehicles (EVs) and their chargers will only be provided for context or to comment on any synergies. The expected medium and longterm outputs of both themes are given below: • In general, remove barriers (technical, regulatory, policy, standards) to enable flexible load management to increase solar hosting capacity, soak excess solar export and increase self-consumption, while also providing benefits through provision of network services.

• Implementation of the required infrastructure (physical, digital, regulatory, market) to support the implementation of smart homes and smart grids. Development of customer-oriented apps, tools, and resources to assist customers use/understand/manage their own data, and better understand retail offerings, home energy technologies and home energy management products.

• Optimisation of rooftop solar and flexible loads to encourage participation in new network services both directly and through third parties such as Distribution System Operators (DSOs) and aggregators.

• Unlock flexible residential demand (and efficiency) in space heating and cooling, water heating, and pool pumps.

• Forecast of: o expected usage costs to be passed down to the customer due to required changes in inverter and appliance standards and industry practices o how smart home technologies indirectly (overall system wide reduction in cost of electricity) and directly (reduced electricity bills) provide economic benefits to customers.

• A framework (technical, regulatory, standards) which ensures a grid fit for purpose in the energy transition and an equilibrium between the needs of customers and networks.

• Increased industry understanding of: o the relative viability of improving home energy technologies for home and grid support o the role customers can play in demand management in terms of cost versus convenience and choice.

The review is comprised of seven Research Topics (RTs) given below: 1. Use of data and IT. This RT gives a comprehensive review of the Regulatory Framework for Metering Services, the Energy Security Board (ESB) data strategy, the Consumer Data Right for Energy (CDR-E), and US technology Green button.

2. Dynamic operating envelopes (DOEs). This RT provides an overview of the current state-of-the-art of DOE implementation by investigating academic and grey literature, trial DOE projects, and international precedents. In particular, this RT examines the concepts and concerns proposed by key stakeholders across the sector (i.e., industry, consumer advocates, market bodies, regulators, and researchers), including network capacity allocation and equity concerns.

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3.

Household level appliance/PV integration model comparison. Different approaches for integrating DER into power systems have been developed. This RT aims to review and examine the implications of these approaches for households. In doing so, it will compare outcomes (savings to prosumers, benefits to DNPSs, user/prosumer preferences) between different aggregation models. The emphasis will be on models which include using solar and flexible loads. This RT will study the outcomes for the different models and modes of operation in terms of usage profiles, cost effectiveness, and value propositions to both the owners of the DER and the network.

4. Standards review. This RT reviews the progress of standard AS/NZS 4755 and alternative standards such as IEEE 2030.5 alongside updates to the inverter standard AS4777.2 and a recent AEMC determination which disallows curtailing of PV export.

5. Algorithms for the control and optimisation of major electric household appliances. This RT has reviewed the scope for algorithms of home energy management systems (HEMS) adopting the flexible loads with high power ratings and rooftop solar.

6. Methods to make maximum use of data. This RT looks at methods to maximise the use of both synthetic and measured data from Home Energy Management Systems (HEMS) devices (smart plugs, for example), behind-themeter DER monitoring devices, and smart meters. The aim of this research is to answer how maximise the use of this data to inform, advise, and empower customers to reduce their energy usage in their homes and reduce their utility bills.

7. Behavioural aspects of energy use and energy savings. This RT reviewed residential customers’ behaviours about key past technology and service purchases and their future intentions and willingness to pay for technology and service alternatives.

The key findings and RQs for each RT review are included in their respective chapters.

3.1 RT.1 Use of data and IT 3.1.1 Introduction This RT gives a comprehensive review of the Regulatory Framework for Metering Services, the Energy Security Board (ESB) data strategy, the Consumer Data Right for Energy (CDR-E), and US technology Green button, expanding on the work undertaken by the H4 1 and N2 2 OAs.

3.1.2 Review of Regulatory Framework for Metering Services In November 2015, the Australian Energy Market Commission (AEMC) made a rule to introduce a competitive framework for metering services. Termed “The Competition in metering rule”, the rule sought to facilitate a market (consumer) led deployment of smart meters (SMs). The rule commenced operation December 2017. SMs are essential infrastructure, required to enable consumers to access new services, markets and pricing and to facilitate more effective and efficient management of the electricity grid.

The rules made retailers responsible for appointing Meter Co-ordinators (MCs) when a new or replacement meter was required, with such meters needing to be advanced or smart meters (i.e., type 4). Distribution Network Service Providers (DNSPs) remain responsible for legacy meters (type 5 and type 6). MCs have overall responsibility for all aspects of metering installations and must appoint a Meter Provider (MP) for each customer connection point to provide, install,

1 https://issuu.com/racefor2030/docs/h4_oa_final_report_17.11.21 2 https://issuu.com/racefor2030/docs/n2_oa_project_final_report_2021

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and maintain the meter installation and must also appoint a Meter Data Provider (MDP) who is responsible for the collection and processing of metering data.

The regulatory framework for metering services (the Framework) was expected to lead to extensive smart meter uptake, enabling consumers to access new services, and to facilitate more effective and efficient management of the electricity grid. Unfortunately, since the commencement of the rule, there have been several implementation issues, including in relation to customer experience.

The AEMC therefore initiated a review of the rule in response to these issues and asked for feedback from industry stakeholders. Three reports have since been published by the AEMC, the initial consultation paper in December 2020 (AEMC, 2020), followed by a directions paper in September 2021 (AEMC, 2021) and the draft report in November 2022 (AEMC, 2022).

This section consists of the following sub-sections, Section 3.1.2.1 looks at the reasons why the Framework has not met expectations, Section 3.1.2.2 covers the AEMC recommendation to implement a power quality data access and exchange framework, Section 3.1.2.3 covers the AEMC recommendation to enable innovation in access to (near) real-time data, and Section 3.1.2.4 covers recommended cost-benefit analysis.

Why has the Framework not met expectations?

One thing that can be agreed upon by the majority of respondents, in agreement with the AEMC, is that the Framework has not met expectations. Where unmet expectations mean benefits from SMs have not materialised because not enough have been installed and/or the services provided by SMs that have been installed are inadequate.

The key reasons given by respondents as to why not enough SMs have been installed under the Framework are: • Retailers are disincentivised to proceed with an install or initiate a rollout of SMs • Consumer demand for SMs is low.

Some respondents go further and suggest that the Framework is fundamentally flawed. This may be the reason that the AEMC found that the Framework had “a general lack of industry cooperation created by misaligned incentives of market participants and the complexity of the framework.”

Why are retailers disincentivised?

The AEMC found that the incentives for retailers to accelerate meter uptake are not clear and state that “according to stakeholders the combination of a vertically-separated industry structure and the current regulatory setting mean that the benefits of widespread uptake of smart meters are divided between several parties, but the responsibility for the deployment is vested in only one market participant category — the retailers.”

The two main disincentives (for a retailer to install a SM) identified are short-term and long-term.

Short-term. The short-term cost-benefit for a retailer to install a SM is often negative. Costs for installation are prohibitively high due to issues such as a site requiring remediation, difficulty in co-ordinating parties, ad hoc geographic nature of new customer connections and meters requiring replacement, multi-occupancy and shared fuse customers, lack of access, and the expected services of remote reading and isolation capabilities (remote energise and de-energise) not materialising. And according to Gridsight, it’s difficult for retailers to offset the cost of the install through returns from (expected services from SMs) remote reading and isolation capabilities. The Public Interest Advocacy Centre (PIAC) point out that “Retailers have little operational or financial incentive to initiate rollout in difficult-to-serve geographical areas,

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sites with higher service costs, and wherever the potential benefits to do so are more marginal”. And the AEMC agree, stating that “Evidence suggests that retailers only deploy smart meters where there is a clear business case the expected competitive pressures and commercial incentives have not been strong enough.”

Long-term. According to some respondent’s retailers have a long-term disincentive to initiate SM rollouts as their conventional means of revenue will decrease with increased deployment of SMs (as energy consumption will decrease).

SMs are expected to enable more active consumer participation and unlock better bill comparison services, increasing the likelihood of churn, the type of behaviour a retailer would typically prefer to avoid. And according to Gridsight, “while there is potential for retailers to develop (and charge for) innovative services for households using SM data, the data and control aspects are too technically challenging to be built in-house, and the markets on which these innovative services rely on, are not sufficiently mature.”.

EnergyAustralia remove any doubt about the lack of incentive, saying “There is minimal incentive for retailers to hasten the roll out of advanced meters; with retailer specific benefits limited to improvements in meter reading, remote reenergisation and de-energisation, and the potential to participate in demand response.”. And that ultimately “the cost vs benefit is not promoting a faster rollout; retailers are deterred by the financial cost and the reputational risks and have largely not received the purported benefits, particularly in the case of remote re-energisation and de-energisation”.

[RQ] If the rollout of SMs potentially undermines the viability of the conventional retail business model, what is the role of the retailer under a 100% SM penetration scenario? What will their business model be? What is the opinion of retailers on this matter? Are retailers evaluating their existing business models in response to a 100% SM penetration scenario? If their role becomes redundant what are the implications?

Why is consumer demand for SMs low?

According to the Clean Energy Council (CEC) consumers “request a smart meter because it is a mandatory requirement of a new connection or for installation of a Distributed Energy Resource (DER)” and “very few customers request a smart meter because they want a smart meter per se”, meaning there are very few voluntary requests for SMs coming from consumers. The South Australian Power Network (SAPN) confirm this saying “that customer choice of metering related services hasn’t been driving the installation of SMs”, and in their experience it is the “installation of smart meters is being driven by customers needing new meters consequent to requesting either a new network connection or the installation of a solar PV system, rather than by additional services that smart meters provide specifically.”

The potential reasons for there being so little voluntary take-up of SMs are described below.

Cost-benefit. The most immediate benefit to consumers which comes from a SM, being more aware of your energy consumption behaviour and adjusting to reduce your electricity costs as a result, are not necessarily sufficient to offset the cost of purchasing a SM. And for consumers considering purchasing HEMS devices the upfront costs are even higher.

According to PIAC “Consumers have little or no incentive to request a smart meter under current circumstances where it is likely they will bear the cost of doing so with limited direct benefit in return.” It is also still to be confirmed whether current time-of-use and dynamic pricing tariffs unlocked by SMs are sufficiently beneficial.

Lack of services. According to the ACT Council of Social Services (ACOSS), the Smart-er Metering Policy report (Sangeetha Chandrashekeran, 2018) found that retailers are not offering the full range of smart meter enabled services to their customers, and that the minimum services required by a SM mainly benefit retailers. And according to AMS international and PIAC, the retailer is also not incentivised to offer SMs with above-minimum specifications as they are not able to charge consumers for the extra capability and therefore recoup the extra cost.

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Technology immaturity. According to Gridsight “Rooftop solar is the only smart meter-related innovation developed todate that is widely known and can deliver significant value directly to consumers. Other potential innovations, such as Home Energy Management System (HEMS), are not developed. As a consequence, rooftop solar is the only smart meterrelated innovation that has been adopted en masse by households.” And as result, according to PIAC “consumers have limited access to service providers other than their retailer”. And these services may not be incentive enough to purchase a SM. Origin describes the situation succinctly, saying “The pace of the rollout is largely dependent on the degree of customer benefit. To realise the full benefit of demand side participation there needed (and needs) to be advances in other key supporting technologies such as smart home energy management products and distributed energy. These technologies needed to be coupled with pricing signals in the form of cost reflective network prices, network support services and other energy related services. These supporting factors are yet to develop and as a result the uptake of meters to date is primarily driven by replacement meters and solar installations rather than (non-solar) customer driven.”

This does present a chicken and egg problem here.: better service offerings and technologies which deliver consumer benefit based on SMs are not being developed because the consumer base is fractured (and still relatively small). At the same time, consumers aren’t adopting SMs because they don’t see the services and technologies that would deliver them benefit.

RT4, RT5, and RT6 look at the state of technology complementary to SMs, such as HEMS, and HEMS devices and applications.

[RQ] Cost is considered to be a barrier to the purchase of a SM as well as HEMS and HEMS devices. What is known about the cost-benefit of purchasing such technologies? What are the factors which determine their cost-effectiveness? If costs are prohibitive, why is this the case? Are initiatives/subsidies required to make SMs and HEMS more affordable? Should their costs be socialised if they result in an overall reduction in electricity costs?

[RA] Perform a cost-benefit analysis on subsidising the cost of 100% SM penetration against the reduction in overall electricity costs. This RA will quickly get complicated – how many people will have solar, how many have batteries, how many have EVs, what sorts of HEMS are being adopted and how are they controlled, what retail tariffs are in play, etc. The first part of this RA would be developing realistic scenarios.

Is the Framework flawed?

It may be that the Framework is flawed. The AEMC found that the incentives for retailers to accelerate meter uptake are not clear and state that “according to stakeholders the combination of a vertically separated industry structure and the current regulatory setting mean that the benefits of widespread uptake of smart meters are divided between several parties, but the responsibility for the deployment is vested in only one market participant category - the retailers.” AEMC also found “a general lack of industry cooperation created by misaligned incentives of market participants and the complexity of the framework.”

The Australian Energy Regulator (AER) hold a similar stance, saying “The introduction of competition in metering services may have unintentionally created several barriers to the efficient deployment of smart meters”, where there is “additional complexity around the deployment of new and replacement meters for small customers due to the fragmentation of roles and increase in transactional costs associated with retailers, metering co-ordinators, metering providers, and network service providers now co-ordinating work that was previously the sole responsibility of the network service provider”.

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PIAC claims the Framework should consider smart metering as essential infrastructure. According to ACOSS this is because of their: • clear benefits to the energy system and consumers, and • necessity to participate in new proposed markets (two-sided market) and access new services (demand management, managing smart appliances, solar PV, virtual power plant) or new pricing reforms (time-of-use tariffs or demand tariffs) It is worth noting that both these claims are still to be convincingly proven. According to AGL, “the setting of the regulatory framework for digital meters was based on a lift and shift of metering obligations from networks to retailers/MCs/MPs/MDPs.”, and that this has led “to less-than-ideal outcomes as the Power of Choice decision created a new market structure and new market participants as part of creating competitive metering services.”. However, “metering rules that applied to monopoly distribution businesses that had end-to-end visibility and control of the process are not fit for purpose in the new metering structure with multiple participants.”

Finally, The Network of Illawarra Consumers of Energy state that “The Power of Choice 3 was a package of reforms to promote demand side participation. The three elements were the introduction of demand side participation in the wholesale market, introducing flexible pricing options based on introducing cost reflective distribution network tariff structures, and the introduction of competition in metering services. The first two elements were meant to create the platform for consumers to be rewarded for choosing to consume energy when it was cheap to generate and distribute and to be rewarded for reducing consumption when it was dear. While the third, the contestability in metering services, only had meaning in the context of facilitating the deployment of communicating interval meters so that this consumer reward could occur.”

And considering there is currently no platform, SMs have nothing to facilitate, so what role do they (and the Framework) have as part of the Power of Choice reforms?

A recommendation (and associated RQs) that a comparison of the structure of the Framework, combined with the structure of our electricity market, against internationally equivalents, is made in the H4 Opportunity Assessment 4.

Recommendations from respondents A summary of the key recommended changes to the Framework by respondents are given below: • Smart metering should be considered essential infrastructure for all consumers.

• Improve consumer decision-making by providing appropriate consumer information on SMs. • Increase the minimum service specifications to include services that will benefit consumers and enable participation in new markets.

• Reforms to prevent consumers being ‘opted’ in or ‘defaulted to’ retailer time-of-use and demand tariffs upon installation of a smart meter.

• Remove the barriers to, and develop procedures for, the efficient install of SMs. Some respondents (retailers and MCs) suggest changes should be made so there are greater incentives for retailers to install SMs (i.e., create a better return for installing SMs). These changes include (i) incentivising and enabling DNSPs to purchase PQ data and/or (ii) increasing the Default Maximum Offer (DMO). Either way the extra cost burden (retailer

3 https://www.aemc.gov.au/markets-reviews-advice/power-of-choice-stage-3-dsp-review 4 https://issuu.com/racefor2030/docs/h4_oa_final_report_17.11.21

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return) will be passed on to consumers as (i) DNSPs will pass on their extra costs through an increase in the Distribution Use of System (DUoS) charge and (ii) the DMO is a maximum price that retailers can charge electricity customers on default contracts known as standing offer contracts.

While DNSPs generally have reservations with the above recommendation, they have suggested that the Framework enable DNSPs to bear the cost and responsibility for some aspects of the SM rollout. This would be for cases where network benefits can be derived, or in high-value but high cost-to-install locations, which are not cost-effective for a retailer, such as rural and remote locations. A rollout coordinated by a DNSP would also have, according to Endeavour, “the distinct advantage of reducing the effort, costs and risk of poor customer outcomes associated with coordinating multiple parties to install a meter that is commonly encountered under the current framework.”

Finally, some respondents suggest DNSPs managing the deployment of SMs. PIAC give a number of reasons for this: • they have a significant incentive to use SMs to help manage the network more efficiently.

• through appropriate regulatory oversight, can have an incentive to ensure the costs of metering infrastructure and operations are efficient, and have an existing suite of network infrastructure-related resources to do so.

• they have no direct incentive to restrict access to a range of metering data as no service entity represents a competitive threat to their primary operations or incomes.

• they can initiate rollout of SMs by geographical area to achieve scale and scheduling benefits.

• they can identify potential areas of rollout priority to respond to network instability, high solar penetration, network congestion or other significant issues.

[RQ] Two of the reasons given by respondents on why the consumer demand for SMs is low, cost and technology immaturity, are two barriers which this OA seeks to resolve. The only recommendation from respondents which may address these barriers is that SMs be considered essential infrastructure. What is required to achieve this? What are the regulatory and legislative changes required? What is required to convince decision-makers to make these changes?

[RQ] Retailers are (essentially) proposing that the costs of installing SMs be subsidised by consumers through an increase in the DMO or DUoS. If retailers require subsidies to install SMs does this not undermine the “competitiveness” component of Framework? Respondents are arguing for DNSPs to manage the SM rollout, is subsidising DNPSs, as opposed to retailers, a more cost-effective option? What are alternatives? What is the most cost-effective?

[RQ] Will handing over the rollout to DNSPs just lead to a prioritisation of smart metering based on what matters to networks? Which does not necessarily align with what matters to consumers. For example, smart metering in lower socioeconomic areas may not yield much for networks (due to lower solar penetration), even though it could unlock really valuable energy efficiency/bill management services for consumers in those areas.

AEMC response The AEMC admits that the current Framework is unsuitable for delivering an accelerated deployment of SMs and that additional regulatory measures are needed to help support a faster and more efficient deployment. The cost-benefit assessment (CBA) undertaken by Oakley Greenwood found that under the current Framework, the uptake of smart meters in NSW, QLD, and SA is not expected to reach universal or universal levels until 2037, 2036 and 2041, respectively.

Therefore, in addition to the market (consumer) incentives to deploy SMs which currently exist, to facilitate universal deployment by 2030, the AEMC recommends a Legacy retirement plan.

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Legacy retirement plan. DSNPs would be required to work with key stakeholders such as retailers, metering parties and jurisdictional governments to develop and publish a plan to retire their legacy meter fleet. Where upon their retirement they would be replaced with a SM. The AEMC also recommend the following the complement the Legacy retirement plan: • Removing retailer-led deployment opt-out provision and the explicit opt-out provision under the accelerated deployment. This means consumers can’t say no to the installation of a new meter.

• Reduce the number of retail notices.

• Remove requirements for the testing and inspection of legacy meters.

• Consider a process to encourage customers to remediate site defects and track sites that need remediation.

• Consider arrangements to better support vulnerable customers who need to carry out site remediation.

• Improve industry coordination and minimising negative customer impacts in shared fusing.

• Implement appropriate replacement timeframes for meter malfunctions.

• Removing the malfunctions exemptions process currently administered by AEMO. The AEMC note that “There are measures in place to enable the AER to enforce compliance with the requirements for installing new meters and replacing malfunctioning meters.” It’s also worth noting that there is no strong opposition from retailers or MCs to a Legacy retirement plan.

The other key recommendation here is that there be “no change to the current industry infrastructure”. Retailers and metering parties will remain responsible for metering services for small customers. And that “other recommendations seek to enhance the existing metering arrangements and improve coordination between market participants.” This will be referring to the complementary recommendations above, designed to facilitate improved coordination between industry participants. The other recommendations will “also build on the facilitation of commercial and consumer investment in metering technology to support demand-side participation, including unforeseen outcomes of competitive innovation.”

This is referring to the recommendation to “Enable innovations in access to (near) real-time data”. This recommendation is discussed in Section 3.1.2.3.

[RQ] Assuming the complementary recommendations made by the AEMC are successful, then the short-term disincentive for retailers to install SMs, along with the risk of being fined by the AER for not meeting install quotas, may be removed, or reduced. However, there is no recommendation on the remote energise, de-energise service, expected by retailers to reduce costs of install significantly, and it could be argued that the long-term disincentive still remains. It can therefore be argued that retailers are potentially a risk to the success of the legacy retirement plan. Has the AEMC quantified this risk?

If so, what is it? And if there is a risk, what contingencies are in place to remove or minimise it?

[RQ] Despite the evidence that industry participants have to date not been cooperative, the success of the legacy retirement plan relies on the success of the complementary recommendations, which are dependent upon industry cooperation. It could be argued that the extent to which industry participants cooperate is an unknown quantity, and therefore introduces a risk to the legacy retirement plan. Has the AEMC quantified this risk? If so, what is it? And if there is a risk, what contingencies are in place to remove or minimise it?

[RQ] The legacy plan doesn’t give estimates on the number of expected legacy meter retirements. The CBA report by Oakley Greenwood (Oakley Greenwood, 2022) assumes the legacy plan achieves 100% SM penetration by 2030. Outside of the assumption that 100% of SM penetration will be achieved by 2030, does the AEMC have estimates on the number of expected through the legacy retirement plan? If not, how has the AEMC justified the legacy retirement plan?

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[RQ] DNSPs advise they could manage and pay for targeted SM rollouts in areas where network benefits can be derived, or in high-value but high cost-to-install locations, which are not cost-effective for a retailer, such as rural and remote locations. This option does not look to be part of the legacy retirement plan. Why has the AEMC not included this option?

[RQ] A rollout of SMs at remote and difficult-to-serve geographical areas are going to be more costly to retailers, and likely to require more comprehensive negotiation between industry participants. The legacy retirement plan doesn’t seem to have any specific aspects which may help with these situations. What is the AEMCs position on how the legacy retirement plan might facilitate the rollout of SMs in remote and difficult-to-serve geographical areas? Does the AEMC know how install prices vary according to geographical area and remoteness?

Implementation of a power quality data access and exchange framework Many respondents pointed out the benefits DNSPs derive from Power Quality (PQ) data, helping them operate their networks more effectively. However, DNSP procurement of PQ data from SMs under the Framework has been minimal.

Why are DNSPs not procuring PQ data from SMs? According to respondents, under the current Framework, DNSPs are not procuring PQ data from SMs as it is difficult to reach commercial arrangements and requires time consuming negotiations. Also, lack of standardisation in data formats add additional complexity and cost to the exchange of data, and contracts between retailers and MCs can prevent access.

Why commercial arrangements are difficult to reach could be due to a conflict of financial interest: • Solar Analytics points out that “retailers are conflicted in this role as they have a financial interest in preventing the release of data to third parties where that could threaten their business model.” PIAC claims that retailers have an incentive to “restrict the provision of data to networks or other service providers where networks may employ that data to initiate demand reduction, demand response and other projects in competition with the retailer’s own operations.”

• It is also suggested by PIAC that there is a “lack of transparency of the costs of metering products and services.

It is not clear what the range of metering costs are, how they are being recovered and how efficient these costs are. Similarly, the cost of metering services and data provisions are not transparent and easily assessable.”.

• Finally, SAPN claim “The MC provides these services to the DNSP on a monopoly basis, and hence there is no competition to restrain prices to efficient levels”, and Ausgrid point out that DNPSs have limited ability to negotiate pricing as “the market of metering providers can be limited in specific geographic areas. For example, across Ausgrid's entire network more than 90 per cent of meters are owned by only two metering providers.”

Another reason is SM penetration level, where according to Energy Networks Australia (ENA) “the current low penetration of SMs means that the overall volume and dispersion of meters in local areas is insufficient in some areas for DNSPs to draw the volumes of data required to deliver some use cases to enhance outcomes for customers.”

The AEMC agrees, acknowledging that: • Negotiation costs are material and can offer perverse incentives to either ‘hold out’ or bypass the meter.

• Restrictive commercial arrangements, e.g., contractual agreements between parties can explicitly deny access or exchange between market participants.

• Current data prices demanded by metering parties exceed the likely marginal cost of providing the data.

• Prices can vary significantly between data providers for the same request (i.e., a similar number of meters, the volume of data, and the time frame).

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And in response, AEMC commissioned NERA Economic Consulting (NERA) to advise on possible power quality data access and exchange frameworks, the four options proposed by NERA are: 1) Authorising a centralised organisation to provide all metering data — with high prescription on data exchange.

2) Minimum content requirements to standardise contracts and agreements on data exchange between market participants.

3) Exchange architecture to facilitate a common interface for data exchange, with low obligation but a high incentive to participate.

4) A negotiate-arbitrate framework for utilisation in access disputes.

Recommendations from respondents The majority of respondents agree a power quality data access and exchange frameworks is needed. A summary of the key recommendations made by respondents in relation to this are given below: • The cost of providing PQ data be set by an independent pricing regulator such as AER or the Independent pricing and regulatory tribunal (IPART), where the price reflects the incremental cost of providing the data plus a reasonable margin.

• That the data provided should be standardised and be implemented using a standardised structure and acquisition method, across all accessible meter data.

• That data provision should be facilitated by updating the minimum services specification for SMs in Schedule 7.5 of the National Electricity Rules (NER). Where the definitions of the remote on-demand meter read service (c), and the remote scheduled meter read service (d) need to be updated to include PQ data, meaning voltage, and active and reactive energy.

Without referring to one of the four options proposed by NERA, in relation to standardising the acquisition method, respondents suggest a standard API, while Gridsight also suggests data be provided through a common web portal.

AEMC response In response the AEMC recommends that rules be made for PQ Data access and exchange, including: • A new definition of “power quality data” be added to NER chapter 10, which would include current, voltage, and power factor.

• New obligations: o Enabling DNSPs to procure PQ Data from MCs under NER 7.15.5  This procurement would be at least once daily; six hourly is preferred.  This procurement would be commercially determined. o Requiring MCs to provide DNSPs with access to PQD under NER 7.6.1.  This access must include relevant identifiers like National meter identifier (NMI) number, serial number, and the phase, aligned to market time at the ‘basic’ level.  This access should also allow for other services to be procured, like Meter inquiry service and Multi-meter ping.  This access should also allow for more ‘advanced’ PQ Data services determined by commercial negotiation (see appendix D.1.6). o By default, accessing parties should communicate PQ Data directly, invoking NER cl.7.17.1 (f).

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NERA (NERA, 2021) advised the AEMC that the costs charged for supplying data should: • Reflect the marginal cost of providing the data – the marginal value of receiving the data at least exceeds the marginal cost.

• Ensure the data providers can recover at least their average cost – this ensures that any fixed costs associated with providing access are spread across all users.

• Allow for a reasonable level of return – this is to allow the total benefits of a transaction to be represented in the considerably smaller cost of enabling it.

For the ‘basic’ PQD service at a minimum, the AEMC recommends that MCs should exchange with DNSPs along the following parameters: • The formatting language should be JavaScript Object Notation (JSON).

• The communications protocol should follow the shared market protocol (SMP).

• The route should be directly from peer to peer.

[RQ] The recommendation maintains that the procurement of data be commercially determined, despite recommendations from respondents that the price for procuring data be regulated (for various reasons). How does the AEMC expect to ensure that the prices calculated meet the specifications given by NERA? How do industry parties participating in commercial negotiations to know whether a price meets NERA specifications? What recourse does an industry participant have if they believe a price isn’t in accordance with NER specifications? Why did the AEMC not choose to recommend a regulated price?

[RQ] The recommendation updates clauses 7.15.5 and 7.6.1 of the NER, despite recommendations that the minimum services specification for SMs in Schedule 7.5 of the NER be updated. What is the AEMCs position on this? Are the recommended changes equivalent to that suggested by respondents in terms of outcomes?

Enable innovations in access to (near) real-time data The AEMC is considering whether it could make regulatory changes to prepare the market for innovations closer to realtime data that a critical mass of smart meters would otherwise enable in the long term. The AEMC could make changes to enable customer access to a (near) real-time data stream sooner than the market would offer because this outcome is among the most persuasive and credible drivers of the shift in sentiment toward smart meters in customers interviewed

by Newgate Research (Newgate Research, 2021a) Recommendations from respondents on this topic can be placed in two categories: • Mandate compliance with IEEE 2030.5, and • Give consumers control of their own data.

Mandate compliance with IEEE 2030.5 For a SM to manage multiple BTM devices, interact with HEMSs, or to share data with in-home displays, all need to be able to communicate. This can potentially be achieved through hard wiring, or wirelessly, when both are compliant with an existing communications protocol. Unfortunately, this is not typical, according to VRT systems “many SMs use proprietary protocols which make integration with DER or smart grid devices, difficult and/or expensive, especially at a per home level”. This is admitted by one MC who says, “a SM can control a DER device if that device has remote control capability via the internet built into them and the meter that supports a secure internet interface has the capability to have an application installed on it to control that device.”

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It is recommended by respondents that this interoperability issue can be resolved by mandating that all SMs and Behindthe-meter (BTM) devices be compliant with IEEE 2030.5, enabling uninhibited communication between them. See RT4 for more detail on the progress of this standard.

Consumers to control access to their data Many respondents also recommend that consumers, not retailers, or MDPs, or DNSPs, have control over their own data, all of it, where this control extends giving third-parties access. The potential benefits of this are: • The facilitation of the development of innovative services to all energy market participants, including consumers, and DNSPs. • The creation a more competitive environment, again for the benefit of all energy market participants.

Rheem notes that this will require changes to the NER, which currently precludes local, real-time, third-party access to revenue meter energy data. It is also likely that the minimum services specification will need to be expanded.

Solar Analytics points out that consumer data should be able to be received by the third parties, “in an automated near real time manner, with a fully online digital sign-up process. This would require the electricity retailer or Meter data providers to provide secure access to data via an Application Programming Interface (API)”, and where consumers permit third parties “to receive their SM data, where the third party may pay reasonable costs to the metering coordinator.”

Wattwatchers (WW) propose a model “based on interoperable API, security and (consumer-centric) permissioning models, that promotes the usefulness of energy data, whether sourced from utility meters or elsewhere.” They suggest “the adaptation of successful digital-industry models such as OAuth point to a more consumer-centric model of data sharing and availability.”

Research questions (RQs) The following RQs aim to assist the AEMC in their decision-making process on whether to mandate IEEE 2030.5 and give consumers control over their data.

[RQ] Assuming that it is mandated that SMs and BTM devices be IEEE 2030.5 compliant, of interest is how to progress to the next step, the deployment of IEEE 2030.5 compliant SMs and BTM devices in homes. Are incentives, and or subsidies required to encourage purchase? If subsidies, what would a CBA reveal where costs are weighed against the energy savings to consumers? How difficult is it to upgrade existing SMs and BTM devices to compliance?

[RQ] It is clear that giving consumers control over their own data will be disruptive. What are the implications of this disruption? Is there modelling which could identify potential negative consequences and therefore strategies implemented to avoid them?

[RQ] GB use OAuth to allow consumers to control and share their own data, this approach could be used for the Residential Model proposed in the CDR (see Section 3.1.4). What is the case against using OAuth, and the Residential Model, considering the success of GB in the US?

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Recommended cost-benefit analysis A number of respondents request that the AEMC provide justification for their recommendation, mainly through an accurate CBA. Cost calculations which respondents feel are important, but haven’t been given due consideration are as follows: • The costs associated with SM rollouts in rural and remote areas of Australia [AER] • Any cost sharing arrangement be subject to a CBA to ensure the outcome is fair and equitable. [Energy QLD, Endeavour, CEC] • Further study on who the primary beneficiaries of SM benefits are (DNSPs, retailers, or consumers), and the factors which influence this outcome. [AEC] • That a decision to increase the rollout of SMs should be dependent on the net benefit of all impacted stakeholders, if this benefit does not exceed the expected costs, then the roll out should remain linked to customer request or meter malfunction replacement. [EnergyAustralia] • A CBA comparing the speed and cost effectiveness of a mandated universal rollout against the current retailer led arrangements. [Rheem] [RQ] What are the CBA outcomes for the above scenarios?

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Summary of findings Table 7. Summary of findings - Regulatory Framework for Metering review

Category

Why are retailers disincentivised?

Why is consumer demand for SMs low?

Why framework has not met expectation: Recommendations from respondents.

Why framework has not met expectation: Recommendations from respondents.

Why framework has not met expectation: AEMC response

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Section

Finding

3.1.2.1

If the rollout of SMs potentially undermines the viability of the conventional retail business model, what is the role of the retailer under a 100% SM penetration scenario? What will their business model be? What is the opinion of retailers on this matter? Are retailers evaluating their existing business models in response to a 100% SM penetration scenario? If their role becomes redundant what are the implications?

3.1.2.1

Cost is considered to be a barrier to the purchase of a SM as well as HEMS and HEMS devices. What is known about the cost-benefit of purchasing such technologies? What are the factors which determine their cost-effectiveness? If costs are prohibitive, why is this the case?

Are initiatives/subsidies required to make SMs and HEMS more affordable? Should their costs be socialised if they result in an overall reduction in electricity costs?

3.1.2.1

Two of the reasons given by respondents on why the consumer demand for SMs is low, cost and technology immaturity, are two barriers which this OA seeks to resolve. The only recommendation from respondents which may address these barriers is that SMs be considered essential infrastructure. What is required to achieve this? What are the regulatory and legislative changes required? What is required to convince decision-makers to make these changes

3.1.2.1

Retailers are (essentially) proposing that the costs of installing SMs be subsidised by consumers through an increase in the DMO or DUoS. If retailers require subsidies to install SMs does this not undermine the “competitiveness” component of Framework? Respondents are arguing for DNSPs to manage the SM rollout, is subsidising DNPSs, as opposed to retailers, a more cost-effective option? What are options?

What is the most cost-effective?

3.1.2.1

Assuming the complementary recommendations made by the AEMC are successful, then the short-term disincentive for retailers to install SMs, along with the risk of being fined by the AER for not meeting install quotas, may be removed, or reduced. However, there is no recommendation on the remote energise, de-energise service, expected by retailers to reduce costs of install significantly, and it could be argued that the long-term disincentive still remains. It can therefore be argued that retailers are potentially a risk to the success of the legacy retirement plan. Has the AEMC quantified this risk? If so, what is it? And if there is a risk, what contingencies are in place to remove or minimise it?

3.1.2.1

Despite the evidence that industry participants have to date not been cooperative, the success of the legacy retirement plan relies on the success of the complementary recommendations, which are dependent upon industry cooperation. It could be argued that the extent to which industry participants cooperate is an unknown quantity, and therefore introduces a risk to the legacy retirement plan. Has the AEMC quantified this risk? If so, what is it? And if there is a risk, what contingencies are in place to remove or minimise it?

3.1.2.1

The legacy plan doesn’t give estimates on the number of expected legacy meter retirements. The CBA report by Oakley Greenwood (Oakley Greenwood, 2022) assumes the legacy plan achieves 100% SM penetration by 2030. Outside of the assumption that 100% of SM penetration will be achieved by 2030, does the AEMC have estimates on the number of expected through the legacy retirement plan? If not, how has the AEMC justified the legacy retirement plan?

3.1.2.1

DNSPs advise they could manage and pay for targeted SM rollouts in areas where network benefits can be derived, or in high-value but high cost-to-install locations, which are not cost-effective for a retailer, such as rural and remote locations. This option does not look to be part of the legacy retirement plan. Why has the AEMC not included this option?

3.1.2.1

A rollout of SMs at remote and difficult-to-serve geographical areas are going to be more costly to retailers, and likely to require more comprehensive negotiation between industry participants. The legacy retirement plan doesn’t seem to have any specific aspects which may help with these situations. What is the AEMCs position on how the legacy retirement plan might facilitate the rollout of SMs in remote and difficult-to-serve geographical areas? Does the AEMC know how install prices vary according to geographical area and remoteness?

Enable innovations in access to (near) real-time data

3.1.2.3

Assuming that it is mandated that SMs and BTM devices be IEEE 2030.5 compliant, of interest is how to progress to the next step, the deployment of IEEE 2030.5 compliant SMs and BTM devices in homes. Are incentives, and or subsidies required to encourage purchase? If subsidies, what would a CBA reveal where costs are weighed against the energy savings to consumers? How difficult is it to upgrade existing SMs and BTM devices to compliance?

Enable innovations in access to (near) real-time data

3.1.2.3

It is clear that giving consumers control over their own data will be disruptive. What are the implications of this disruption? Is there modelling which could identify potential negative consequences and therefore strategies implemented to avoid them?

Recommended cost-benefit analysis

3.1.2.4

Why framework has not met expectation: AEMC response

Why framework has not met expectation: AEMC response

Why framework has not met expectation: AEMC response

Why framework has not met expectation: AEMC response

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The AEMC has recommended a Legacy retirement plan to accelerate the SM rollout and respondents have requested the AEMC justify this through an accurate CBA, as there are cost calculations which respondents feel haven’t been given due consideration. What are the CBA outcomes for the Legacy retirement plan which consider the cost calculations proposed by respondents?

3.1.3 Energy Security Board (ESB) Data Strategy—Independent DER data platform The ESB Data Strategy is designed to provide direction for the data management needed to: • Manage changing data needs in the energy transition, and • Optimise the long-term interests of energy consumers in a digitalised economy.

The ESB Data Strategy gives a number of recommendations which can be addressed through the creation of an independent DER data platform (iDDP), which is open source and owned and managed by a non-commercial entity. It will serve as a free source of DER data for researchers, and as a platform for the development of innovative use cases and services, trialling new technologies, dissolving barriers to data access through the development of a data format, management (collection, consent, sharing), security, and communication standards, and pioneer best practice for DER data platform governance and operation.

Key findings in the ESB Data Strategy which justify the creation of an iDDP are summarised below: • Public good research. A key gap in the ability of the industry to manage changing data needs in the energy transition is the availability of effective data to inform research, where according to the ESB “an active research sector plays a critical role in how to integrate new technologies, facilitate innovation in services and plan and manage a future system.”. Many publicly funded energy technology research projects (such as through ARENA and RACE) face barriers in accessing the data required. These barriers can be legal, regulatory, due to missing processes and systems needed to share data, or the data simply doesn’t exist. o An iDDP would reassure potential sources of DER data that their data is to be used for public good research, won’t place them at a disadvantage, and increase the likelihood of them contributing data to the platform. It would also be used as a source of free data which researchers can use as part of their application for funding from ARENA or RACE. The iDDP will have established processes in place so data access is unimpeded.

• Development of standards and common guidelines. There is a need for the development of common guidelines on collecting and sharing data, and identification of issues and progress with relevant technical standards. The delay in resolution of standards is a handbrake on effective digitalisation. o The iDDP would be used to test and develop standards and guidelines for DER data transfer, format, measurement resolution, management, analytics, and database architecture.

• Nurturing innovation. Access to good quality data is required for the development of new services and use cases, and optimising integration of emerging technologies continues to depend on technology trials. o The iDDP will be used to test and develop innovative services and use cases, and to trial the integration of new technologies.

• Pioneer best practice. DER data platforms will require advanced skills, and clear processes to navigate, manage and protect them, ensure good governance, and to support safe and efficient access to data. Current systems are not able to keep pace with new DER technologies and consumer decision-making and billing. o The iDDP would be used to test and trial all aspects of DER data platform operation, with the aim of achieving and informing industry on best practice.

The ESB intends to establish a Data Leadership and Coordination Group (DataLAC), a non-statutory body made up of representatives from market bodies, which will coordinate implementation of the Data Strategy and related reforms in an iterative manner, responding to changing needs and reforms across the sector. The DER data platform could be developed in collaboration with the ESB and DataLAC, which intend to:

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o o o o

bring together a focus group of research-sector stakeholders to propose models to improve access to research data sets. consider opportunities to leverage existing research collaborations and sources, such as NEAR, RACE, C4NET, and ARENA’s Knowledge Bank. consider leverage of wider work on research data commons and advanced data approaches such as synthesised data sets and collaborative facilitation. design, cost and recommend a new Data Service model to enable greater access to existing data and value from innovative analytics capabilities.

3.1.4 Consumer Data Right (CDR) for Energy The Competition and Consumer (Consumer Data Right) Rules 2020 (CDR) have been developed to facilitate CDR as an economy-wide right. The Australian Competition and Consumer Commission (ACCC) made the foundational rules for CDR in February 2020, with rule-making powers transferred from the ACCC to Treasury in February 2021. These included rules to govern the application of CDR in the banking sector. The CDR in energy (CDR-E) is being rolled out next and aims to give Australians greater control over their energy data, by empowering consumers to be able to choose to share their energy data with trusted recipients for the purposes the consumer has authorised.

The ACCC asked electricity industry stakeholders to respond to their draft rules for CDR-E in 2020 (ACCC, 2020). This section reviews the stakeholder responses and identifies the issues raised. How these issues will be addressed ultimately comes down to what the Treasury decides, therefore the research activities recommended are primarily designed to assist the Treasury in this decision-making process. Note that first stage consumer data sharing commenced on 15 November 2022. The first stage only includes the three large energy retailers, AGL, Origin and Energy Australia, and is limited to “noncomplex” requests. More information is available on the CDR website. 5 Data sets CDR-E categorises the energy data sets which can be shared by consumers as Generic product data, NMI data, Metering data, DER register data, Customer data, Billing data, and Tailored tariff data. BTM data is not included in the CDR-E. Issues out of scope The CDR-E is an extensive regulatory framework and the issues raised by respondents are mainly regulatory. Many are therefore not relevant to the theme of the OA but are still worth mentioning, as they contribute to an overall understanding of the concerns respondents have with the CDR-E, and therefore help with understanding the issues identified which are related to the theme of the OA. These issues are discussed in this section.

How to define an eligible CDR consumer?

Respondents have expressed concern about the approach the ACCC takes to determine an “eligible CDR consumer”. The CDR defines an eligible CDR consumer, the individual which the energy data for a property is associated with, as individuals who have an account or joint account with a retailer. This therefore doesn’t include consumer accounts with a previous retailer which have become inactive, or consumers who live in a shared household but aren’t on the account or if the account is in the name of the landlord. It also means that any prior data for a property associated with previous eligible CDR consumers isn’t accessible to the current eligible CDR consumer residing at the property. This prevents the current

5 https://www.cdr.gov.au/rollout/cdr-energy-sector

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eligible CDR consumer from using energy consumption by the previous eligible CDR consumer as a proxy for their consumption.

The St Vincent De Paul Society also points out that “…often the energy account holder is not the persons responsible for the energy management activities and the product selection decision of the household. This is often left to other parties, which can include either a resident in the household OR a third party that a household calls on to assist them in household management and budgeting, for example, members of extended family, friends, or members of the SVDP Society or other community welfare organisations.”

It gets further complicated when data such as the NMI standing, and DER register data, can be interpreted as being associated with a property, not an individual. Energy Consumers Australia (ECA) also point out that “The retailer is only relevant to DER register data if the consumer is being paid a feed-in-tariff. If the consumer has a DER configuration that includes the ability to fully self-consume generated electricity, then none of the DER register data relates to the ‘arrangement between the customer and their retailer’. It does, however, relate to the arrangement the consumer has with their network operator (with whom they also have a contract) for the supply of electricity to the connection point.”

Questions which arise from this issue include: • How will this affect use cases which require historical data from an old account which is now inactive?

• How do tenants at a property that aren’t on the retailer account gain access to the benefits of the CDR-E? • How is access to NMI standing and DER register data, that the ECA claims is associated with the property and not an individual, to be managed?

Sensitive data Most respondents agree that certain consumer information to be shared through the CDR-E is more sensitive than others, and that separate consent be required by the consumer before it can be shared.

According to Customer Experience (CX) research conducted by AEMO, consumers: a) were less sensitive about sharing their current plan details such as tariffs, service charges and offered discounts. b) are very sensitive to sharing their personal data (i.e., information that identifies, or can be used to reasonably identify, the eligible CDR Consumer) as they see most benefit to be had by sharing their electricity usage and current plan data; and c) are particularly concerned with sharing their financial status (especially hardship), the details of the concession reason (but not the concession status itself), their medical status (life support or otherwise) and financial assistance plans and information that can indicate this (payment history).

AEMO suggests that the data referenced in c) above be unbundled from the billing data set and require additional consent.

The above position is also supported by KPMG, which found that there are certain types of data consumers consider sensitive and would be concerned about sharing under the CDR-E, including fear of discrimination (KPMG, 2020). AGL also refers to CX research conducted by the ESB, where the ESB found there were concerns among participants in relation to data that they considered sensitive (e.g., hardship, concession and contact details) and that “participants were concerned it could also lead to discrimination “the lack of a clear benefit caused some apprehension and concern”. These consumer concerns are also shared by EQ, who consider that “…data sets that contain customers’ personal information should be treated with as high a level of sensitivity as financial data sets.”, as they can equally be used for identity theft.

The ECA are also concerned that “…this type of information can be used to make inferences about a consumer’s ability to pay and their potential value to a company. It could therefore be used to discriminate against and exclude consumers in ways which leave them worse off.” This concern is also shared by EQ.

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How sensitive data is identified and managed in the CDR-E will have a meaningful impact on its overall design, including accreditation (see Tiered accreditation) and consent practices, and will complicate standardisation of services offered by Accredited Data Recipients (ADRs) which use sensitive data.

[RQ] How has the CDR for banking managed sensitive data?

Tiered accreditation There are three possible authentication models, two are included in the CDR, Model 1, and Model 2, and a third not included in the CDR but which is mentioned in (KPMG, 2020), referred to as the Resident Model. Respondents are divided on what is the best model. The key difference between Model 1 and 2 is that authentication of an eligible CDR consumer, who is authorising access to their energy data from a particular data holder, is managed by their retailer in Model 1, and managed by AEMO in Model 2. Authorisation gives access to all the data sets held by the data holder. There is therefore only one level of accreditation, there are no tiers, referred to as “unrestricted”.

The Resident Model differs from both Model 1 and 2 in that it also allows for a lower level of accreditation where “less sensitive” metering data can be accessed by an ADR for a premises using the NMI, postcode, and retailer as the identifiers, no authentication of an eligible CDR consumer is required. It was proposed by AEMO and is based on a model used by government hosted tariff comparison services EnergyMadeEasy (EME) and Victorian Energy Compare (VEC). It is worth noting that whether metering data is “less sensitive” or not is also disputed by respondents (see Section 3.1.4.2).

The Resident Model facilitates tiered accreditation, and respondents are divided on whether it should be included in the CDR-E model. Respondents not in favour of tiered accreditation claim it is a security risk. Other arguments against the Resident Model by AGL are given below: • ADRs without the unrestricted level of accreditation will only have access to certain data and therefore potentially restricted in the extent and/or quality of services they can offer.

• Tariff offers to consumers are not just energy related, an offer may include other benefits, credits, and other information. If this information isn’t available to an ADR, then they are at a disadvantage when calculating alternative offers. Offers can also include non-price benefits, such as movie tickets, club memberships, reward points etc.

• How would customers know and be ensured to understand that they are unable to access the full benefits of CDR-E through a certain ADR because it doesn’t have unrestricted accreditation? Because the ADR can only access some of their energy data sets so the use cases / services / products are limited.

EnergyAustralia also supports this position “As sensitive data sets would require an unrestricted level of accreditation, this would mean an ADR would need to meet this higher accreditation level anyway, and therefore negate the effect of imposing a lower tier of accreditation for some data sets.” And the AEC are concerned “…that there is a risk that customers who decide to use a third party with ‘restricted’ accreditation will not be properly informed about their status and, most importantly, what it means. This may lower trust in the CDR regime if customers select a sub-optimal offer because they are operating on the false assumption that all their data has been analysed.”

In general, supporters of tiered accreditation do not think there is a security risk, and also give reasons why tiered accreditation needs to be included in the CDR-E. WATTever claims that “low ADR and retailer take-up is the biggest risk to the CDR-E” and suggests that “one-size-fits-all authorisation and authentication processes will result in higher development, maintenance, and compliance costs for ADRs and retailers”, and that if “authentication and authorisation models were greatly simplified for key use cases including personalised comparison, then ADR development and

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compliance costs for CDR integration could be dramatically reduced and allow innovative business such as WATTever to come onboard and deliver benefits to consumers.”

The St Vincent de Paul Society (SVdPS) thinks the Resident Model allows consumers to “…quickly and simply access a service, without having to consent to providing access to additional personal data.” And, that it “…provides access to data for a wider set of consumers, including residents in a premises who are not the account holder with the retailer. Providing this access will foster engagement in the retail competitive market which has been a challenge in the energy sector”.

It is recommended by respondents, both those is support and those against tiered accreditation, that the ACCC weigh up the potential benefits versus the risks of tiered accreditation. Some recommended RAs related to this are given in Section 3.1.4.2.

3.1.5 Findings BTM data not included in the CDR-E A shortcoming of the CDR-E pointed out by respondents is that it doesn’t include BTM data, this includes data measurements of battery, rooftop PV, and flexible loads. Several respondents consider BTM data to be a requirement for important use cases. For example, the ECA state that “BTM data will be combined with historic consumption data, user stated preferences and market price signals and operating characteristics for smart energy systems to control DER operation.” Accurassi envisage there will be a strong appetite for household optimisation of DER, but that financial institutions will need access to large sets of depersonalised BTM data to perform risk analysis and measure the expected returns. Accurassi also point out that BTM data is necessary to identify energy efficiency opportunities. BTM data is also required to assess the viability of Virtual Power Plants (VPPs), by a network or third-party, and assess new tariffs (discussed further in Section 3.1.4.2). The ECA also point out that BTM data is needed to assess whether a household should purchase DER and for larger industry or community studies.

[RQ] To alleviate the need for BTM data, how accurately can disaggregation methods derive an estimation of BTM data from SM data?

[RQ] How do use cases with and without BTM data compare in terms of effectiveness? For use case such as assessing the viability of VPPs, energy efficiency assessment, tariff comparisons, and for helping households make informed DER purchasing decisions.

[RQ] What are the use cases which come from including BTM data in the CDR-E which aren’t supported otherwise? What industry or community scale DER studies require BTM data?

[RA] Conduct a review of the literature to identify findings related to the impact of different data sets, and combinations thereof, on use case effectiveness.

[RA] Conduct use case assessments using different data sets and combinations thereof, to determine the impact on use case effectiveness.

[RA] Conduct a review of the literature to identify use cases (residential, industry, and community level) which are only supported by BTM data.

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New retail tariff types are difficult to compare According to Accurassi, there are already over five thousand retail tariffs just in NSW, and there is a wave of new types of tariffs coming. New types include subscription, exposure to the wholesale price, participation in VPPs, and retail tariffs which include (and may optimise) both electricity supply and generation from solar or battery. Tariffs can also have a “value-add” component, provision of Demand Response (DR) for example. These new tariffs make traditional comparison challenging and there are concerns that current reference pricing tends to only accommodate plans with the most common pricing structures and benchmark usage profiles and doesn’t adequately deal with the new tariff types, including “value-add” components. This makes comparisons difficult and may confuse the customer. Energy bills are also complex, and it will also be difficult for an ADR to recreate a bill accurately. It is recommended by Energy Australia and Accurassi that to avoid customer confusion when being offered alternative tariffs by an ADR, and that energy bill estimates are accurate, the ACCC define Generic Product Data and Tailored Tariff Data in a way which includes these new types of tariffs and includes “value-add” information. It should also benchmark the data to be provided for tariff comparison.

[RQ] New retail tariffs are expected to become more complicated, moving away from the historical structures. New tariffs may be subscription based, and include exposure to the wholesale energy price, participation in virtual power plants, or the provision of DR. How can the CDR-E rules ensure that customers are able to make informed and confident tariff decisions?

[RA] Investigate a standard format for retail tariff comparison which adequately covers the new tariff types. This would likely include customer surveys on what information they need to make an informed and confident tariff decision.

[RA] Investigate benchmarking the data shared with ADRs to ensure the data required for the standard format for retail tariff comparison can be met. This will also include investigation into how much historical data is needed.

[RA] Develop and trial new tools designed educate consumers such that they can make confident and informed tariff decisions.

[RQ] What are the new use cases and potential improvements to known use cases which come from drawing on historical meter data from other eligible CDR consumers with similar demographics? Where demographics might include property type, climate, and socio-economic group.

[RA] Undertake use case analysis which draws on historical meter data from other eligible CDR consumers with similar demographics.

More clarity on CDR-E use cases Several respondents make the criticism that the use cases for the energy data sets shared through the CDR-E need to be clearer, with WATTever saying “It seems that the Energy Rules Framework has been developed without a prioritised list of use cases for energy”. The only mention of potential use cases for energy data shared through the CDR-E are defined

by KPMG in (KPMG, 2020), where expected use cases include: • switching plans and tariffs • purchasing solar panels (and understanding feed-in arrangements) • purchasing batteries • conducting energy efficient audits and assisting with energy rating assessments for buildings The ECA give a description of their expected use cases in their submission and extend the above to purchasing of DER and choosing a plan structure, as opposed to just choosing an offered plan. WATTever recommends that “use cases in

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which energy data is used for the benefit of consumers be fleshed about before rules are set that will restrict them or make them cost prohibitive to deliver.”

[RQ] What are the priority use cases which come through the CDR-E? What are the use cases which should be fleshed out to assist the ACCC make decisions on contentious issues (like lower tiered accreditation and sensitive data) and reduce development costs for ADRs and retailers?

[RA] Undertake a review of the literature, and conduct surveys and hold workshops with electricity industry stakeholders, especially consumer advocacy groups, to determine what should be the priority use cases addressed by the CDR-E? [RQ] How are priority use cases identified? Surveys are one way; another is through a cost-benefit analysis (CBA), where the cost of obtaining the data is compared against the reduction in overall electricity costs. Characteristics of data which would be expected to influence the cost of procurement include device granularity (from BTM data to meter data), latency (from historical to real-time), sample granularity (1-minute, 15-minute, hourly etc.), and aggregation granularity (household level, all households connected to the same distribution transformer, or substation, all households located in the same postcode or located in similar locations).

[RA] For each potential use case, undertake a CBA which compares the cost of obtaining data, which is dependent upon the characteristics of the data required by the use case, against the reduction in overall electricity costs.

NMI standing and DER register data While all respondents are supportive of NMI standing and DER register data being included in the CDR-E there are still aspects that need to be addressed.

AEMO point out that “NMI Standing, and DER register data reflects detail of a connection point at a point in time but, through time, the data is subject to change and such change impacts on the ability to interpret associated data, e.g., metering data. How this might impact a CDR consumer and the usefulness of their data for various use cases should be further considered.”. NMI standing data which can change over time includes meter type, DNSP, network tariff, controlled load, and average daily load. DER register data will change with a change in rooftop solar or a battery installation status, as well a change in the characteristics of each (size etc.).

In addition, AGL want the CDR-E to specify a mandatory set of NMI Standing data fields to which clearly meet the scope of data required for relevant use cases under the CDR-E. As part of the CDR legislation, a consumer data standards API is also being developed. 6 [RQ] What are the minimum NMI Standing, and DER register data fields required to meet the scope of use cases under the CDR-E? And for consumer use cases, how relevant is historical NMI Standing, and DER register data which is associated with a different property?

[RA] Assess the minimum NMI Standing, and DER register data required to meet relevant use cases under the CDR-E [RA] Assess the relevance of historical NMI Standing, and DER register data which is associated with a different property to meet relevant use cases under the CDR-E

6 https://consumerdatastandardsaustralia.github.io/standards/#introduction

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Metering data What Metering data is provided needs further delineation?

Smart meters can take a number of different measurements, including voltage and current to calculate power and energy, but the draft CDR-E doesn’t specify what Metering data should be provided and AEMO believes Metering data needs further delineation.

[RQ] What use cases require what measurements from smart meters? What penetration of smart meters is required to meet a use case need?

[RA] Undertake a review of the literature and conduct use case assessments of metering data to determine what use cases require what metering data measurements.

[RA] Undertake a review of the literature and conduct use case assessments of metering data to determine the penetration level of smart meters required to meet the use case need.

[RA] Undertake a review of the literature and conduct use case assessments of metering data to determine the penetration level of smart meters required to meet the use case need.

How much historical data to keep?

To be consistent with the National Energy Retail Rules, the CDR proposes that two years of historical data be kept for an eligible CDR consumer. Most respondents are supportive but point out that there are costs associated with the amount of historical data a data holder must store and urge against a storage period of greater than two years. It is also evident that there are complications related to accessing historical data for an eligible CDR consumer when they’ve changed retailers (assuming that the retailer is the data holder), and the likelihood of this happening increases with increasing storage period. On this, AEMO asserts that the two years of historical data should be provided even if there has been a change in the CDR Consumer at the premises during that period. This is “because providing seasonally adjusted energy usage at the relevant premises over this period will give consumers a better picture of the energy usage at the relevant premises than giving them energy usage over a shorter period.”

[RQ] Ensuring that data is retained for at least two years will be difficult – as it means data may need to come from eligible CDR consumers who previously resided at the property, who may have since changed retailers, cancelled their account, or moved, and are therefore no longer an eligible CDR consumer. This is where using a definition of “eligible CDR consumer” to determine what data can be shared becomes restrictive. Can this definition be expanded? Can it be removed and replaced with a new mechanism for determining how data is shared?

[RQ] Considering the cost implications associated with the length of time that historical energy data should be stored, how much historical data is required to meet use cases? What is the dependency of the error in a use case assessment and amount of historical data? What methods can be applied to a historical data set, extending it artificially for example, which can decrease error?

[RA] Assess the relationship between the duration of historical data and the accuracy of use cases assessments.

[RA] Investigate methods which improve/extend historical data sets such that the error of use cases is reduced.

[RQ] How important is it to be able to access metering data recorded prior to the consumer living at the property? Is data from a previous property relevant to the use case for the current property?

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[RA] Conduct a literature review to identify findings related to the relevance of metering data from a previous property on use cases.

[RA] Conduct use case assessments using metering data from the existing and previous property to see how relevant data is from the previous property.

What is the security risk of Metering data?

Some respondents are concerned including metering data in the CDR-E is a security risk to consumers as it provides information about usage at a particular location which then potentially “…allows third parties to understand behavioural patterns, appliance use, hours of employment and extended absences from the premises. This could lead to a range of outcomes, such as risks to personal safety or to property (including in the case of a consumer who is experiencing family violence), or to less significant issues such as unsolicited marketing and other communications to the household”, Red Energy. Other respondents disagree however, with WATTever claiming that “Having access to a consumer’s half-hourly interval data is of little benefit if taken and for nefarious purposes. It is not possible to conclusively determine when someone was home or not based on meter data history. While patterns of energy usage can be determined, they are also historical in nature”. WATTever also make the point that “…authentication requests for meter data can use NMI, postcode, and retailer. It is not possible for an ADR or citizen to determine an address from an NMI. This is the way that the Energy Made Easy data works today”.

To mitigate the potential risks associated with metering data, it is suggested by some respondents that it be made available in an aggregated or less granular form, or that it may be appropriate to limit the format (i.e., to a modified, lower-risk format) in which an ADR accredited at a lower level (see Section 3.1.4.2) may receive CDR data.

[RQ] There is contention around whether providing metering data is a security risk to the consumer, and whether it has the potential to allow third parties to know the occupancy patterns of a property. This contention exists even though metering data is currently provided for Energy Made Easy and Victorian Energy Compare tariff, and identified by NMI, postcode, and retailer. How valid are respondents concerns that the occupancy patterns of a property can be predicted from metering data? Does the prediction accuracy vary according to the property? If metering data is only identified using NMI, postcode, and retailer does it matter?

[RA] Review of the literature on the potential to predict occupancy patterns from metering data.

[RA] Analyse historical metering data sets to determine how accurately the occupancy patterns for a property can be predicted, how the accuracy varies according to property, and to characterise metering data sets according to their susceptibility to prediction.

[RQ] It is proposed by some respondents that provision of modified or aggregated metering data, as opposed to raw, may reduce the risk it presents to the consumer. Is this true? And is it possible to meet use case requirements using modified or aggregated metering data?

[RA] Analyse modified or aggregated historical metering data sets to determine how accurately the occupancy patterns for a property can be predicted, how the accuracy varies according to property, and to characterise metering data sets according to their susceptibility to prediction. The accuracy could be verified against survey data by the residents of the property.

[RA] Compare use cases outcomes using modified or aggregated historical metering data against raw.

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Case for the inclusion of Metering data in the CDR-E Some respondents note that the Metering data is already provided by consumers to use tariff comparison services Energy Made Easy and Victorian Energy Compare. These services only require NMI, postcode, and current retailer, with no authentication and no involvement of retailers required. It is therefore questioned why Metering data shouldn’t also be provided through the CDR-E. WATTever also claim that any security issues associated with Metering data can be alleviated through de-identification.

It is also argued that Metering data is required for important use cases. WATTever claim the “predominant use case for CDR-E is the provision of personalised energy comparisons, leveraging consumer's meter data history”, and that current comparison services only use usage information from recent energy bills which give suboptimal advice for consumers.

WATTever believe that having access to a consumer's historical usage will result in more accurate comparison results.

WATTever also note that commercial comparison services (Energy Made Easy and Victorian Energy Compare are government services) have led innovation in comparisons for consumers. Nectr support this position stating that “The energy market is ripe for consumer value creation opportunities for those who approach energy services from the consumer’s perspective”, and that the creation of these opportunities is reliant upon the provision of Metering data through the CDR-E. [RQ] What are the use cases which require Metering data? How important are they to the success of the CDR-E? [RA] Review the literature and conduct surveys/workshops with industry stakeholders to identify the use cases which use Metering data and define their benefits.

[RA] Conduct use case assessments which use Metering data and quantify their benefits.

Consumer dashboard The consumer dashboard is to be the main interface between an eligible CDR consumer and the CDR-E. It will, for example, allow eligible CDR consumers to access their energy data, keep track of what data they have shared with whom, and what consent they have given. There is disagreement between respondents as to whether the consumer dashboard should be hosted and maintained by AEMO, or by the retailer, or that there should be more than one consumer dashboard.

The claim from respondents that support retailers hosting (who are primarily retailers) is that retailers are the only entity with an existing relationship with consumers, while AEMO have no relationship. And therefore, retailers are best placed to host the consumer dashboard.

Respondents that are against retailers hosting, and who are inclined to support AEMO being the host, claim that having retailers host may make it difficult for consumers to receive and consider alternative tariff plans, as it is through their current retailer’s dashboard that they are receiving alternative offers from competitors. It may also give an opportunity for the current retailer to engage in customer win-back or saves if they think there is a risk of losing that customer to a competitor. Having AEMO host is also likely to be the least confusing for consumers as it enables viewing of CDR-E information from multiple parties in a single source view. This includes data held by previous retailers. It is also disputed that retailers actually have a relationship with their customers, with Green Energy Trading (GET) arguing that “…many consumers do not have a particularly active relationship with their current energy provider, and most do not have ready and real-time access to online portals and/or retailer specific online authentication methods.”

Whether AEMO or retailers host, most respondents agree that there should be one dashboard – and therefore if a retailer were to host it there would likely need be some type of “white-label” arrangement. It is also agreed upon that the

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consumer dashboard is crucial to the success of the CDR-E, and that it should be designed to meet consumer needs.

According to Energy QLD, the consumer dashboard should “…feature a simple and easy to use and understand interface to enable customers to quickly and easily navigate to view CDR data sharing consent/authorisations.” Icebreaker make some recommendations on consumer dashboards: • Data sets are clearly explained and includes what different data sets are used for what use cases, and who currently has access.

• The duration that accesses to the data is granted for, a consumer’s rights, and the way which they can manage their data should be clearly explained.

• The ability to revoke consent and notification that this has taken place and how data collected previously will be dealt with.

• Consents should be sortable and clearly outline those which are active, expired or cancelled.

Most respondents recommend more CX research on consumer dashboards is required to ensure that they facilitate inclusion, understanding, visibility, and consumer agency over their data consents.

[RQ] What are consumer needs and expectations regarding how a consumer dashboard should be designed?

[RQ] If retailers manage the (numerous) consumer dashboards, what happens when a consumer change retailers? How is continuation of data maintained?

[RA] Undertake further CX research on consumer dashboards.

Consumer education Most respondents make the point that the new energy landscape and the CDR-E is going to be complex and difficult for consumers to understand, and that an educated consumer is required to ensure the CDR-E is accessible and successful.

Respondents recommend consumer education programs on: • AEMO’s role as a data holder and gateway.

• DER register and NMI standing data and why consumers may want to share it, AGL. • The risks and benefits of sharing energy data and what businesses may infer from it, AGL and Energy QLD. • Tiered accreditation (see Section 3.1.4). This program would ensure that consumers understand the differences between accredited parties, what data they can access, and what the different data sets may mean for different use cases, AGL. [RQ] What awareness programs could be implemented for potential solution providers who could utilise CDR-E data for consumer benefit? And similarly, how do we ensure that consumers are aware of promising consumer-centric data-driven services (i.e., product awareness programs)?

[RQ] What aspects of the CDR-E are consumers most concerned and or confused about?

[RA] Undertake CX research to understand what aspects of the CDR-E consumers are most concerned and or confused about, and to determine the best way to deliver education programs.

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Summary of findings Table 8. Summary of findings - CDR for Energy

Category

Section

Finding

BTM data not included in the CDR-E

3.1.4.2

BTM data isn’t currently included in the CDR-E, and use cases are needed to assess whether it should or should not be? How do use cases with and without BTM data compare in terms of effectiveness? What are the use cases which come from including BTM data in the CDR-E which aren’t supported otherwise? What industry or community scale DER studies require BTM data? For use case such as assessing the viability of VPPs, energy efficiency assessment, tariff comparisons, and for helping households make informed DER purchasing decisions.

New tariff types are difficult to compare.

3.1.4.2

New tariffs are expected to become more complicated, moving away from the historical structures. New tariffs may be subscription based, and include exposure to the wholesale energy price, participation in virtual power plants, or the provision of DR. How can the CDR-E rules ensure that customers are able to make informed and confident tariff decisions?

3.1.4.2

Respondents complain that the CDR-E isn’t clear on what the priority use cases are. What are the priority use cases which come through the CDR-E? What are the use cases which should be fleshed out to assist the ACCC make decisions on contentious issues (like lower tiered accreditation and sensitive data) and reduce development costs for ADRs and retailers?

NMI standing and DER register data

3.1.4.2

Respondents want more clarity on NMI Standing, and DER register data fields. What is the minimum NMI Standing, and DER register data fields required to meet the scope of use cases under the CDR-E? And for consumer use cases, how relevant is historical NMI Standing, and DER register data which is associated with a different property?

Metering data use cases

3.1.4.2

What needs to be measured by SMs for important use cases is not clear in the CDR-E. What use cases require what measurements from SMs? What are the use cases which require Metering data? How important are they to the success of the CDR-E?

3.1.4.2

There are cost implications associated with the length of time that historical energy data should be stored and more information on use case needs versus historical data are needed. How much historical data is required to meet use cases? What is the dependency of the error in a use case assessment and amount of historical data? What methods can be applied to a historical data set, extending it artificially for example, which can decrease error?

More clarity on CDR-E use cases

Cost implications of metering data

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3.1.4.2

Historical data may need to come from previous eligible CDR consumers at a property. How important is the metering data recorded prior to the current eligible CDR consumer living at the property? Is data from a previous property for an eligible CDR consumer relevant to the use case for their current property?

3.1.4.2

There is contention around whether providing metering data is a security risk to the consumer, and whether it has the potential to allow third parties to know the occupancy patterns of a property. This contention exists even though metering data is currently provided for Energy Made Easy and Victorian Energy Compare tariff, and identified by NMI, postcode, and retailer. How valid are respondents concerns that the occupancy patterns of a property can be predicted from metering data? Does the prediction accuracy vary according to the property? If metering data is only identified using NMI, postcode, and retailer does it matter?

Security risk of metering data

3.1.4.2

It is proposed by some respondents that provision of modified or aggregated metering data, as opposed to raw, may reduce the risk it presents to the consumer. Is this true? And is it possible to meet use case requirements using modified or aggregated metering data?

Consumer dashboard

3.1.4.2

The consumer dashboard is an important component of the CDR-E. What are consumer needs and expectations regarding how a consumer dashboard should be designed? What aspects of the CDR-E are consumers most concerned and or confused about?

How much historical metering data?

Security risk of metering data

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3.1.6 My Energy Marketplace (MEM) The My Energy Marketplace (MEM) project 7, led by Wattwatchers (WW), is developing a large new energy-data resource in Australia, along with the ‘soft infrastructure’ for increasing consumer participation in the electricity system as the energy transition unfolds. The MEM is an approximately $9.6 million project, with $2.7 million in grant funding from ARENA being focused mainly on subsidising participation in the project by homes, small businesses, and schools.

This section reviewed the knowledge sharing documents for the project to identify barriers in relation to the RT. Protecting privacy An issue identified by WW is how to provide anonymised access to consumer data via their existing tools, such as the standard Dashboard web portal and API. The risk here is that the bundled metadata can at times (depending on which third-party is supplying the data through a WW device) include identifying information. WW have been required to exercise diligence to ensure that all metadata is de-identified. WW are engaging with members of the MEM Data Advisory Panel (DAP) to identify and employ methods for avoiding re-identification and improve our processes and toolset accordingly.

[RQ] WW is also encountering challenges associated with ensuring consumer data privacy. In reference to the CDR, could the GB approach to ensuring data privacy (split-streaming metadata and consumption data) be applied in Australia? What has WW and the WW DAP found in regard to employing data security methods to avoid re-identification?

OAuth The MEM-specific API developed by WW will employ an OAuth-style model (as per GB, see Section 3.1.6) for providing customer control of data sharing with third parties. Meaning a consumer who wants to give third party access to their MEM data can authorise (using their MEM credentials) WW to provide access. WW can assume that the consumer is aware of how their data will be used by the third-party and has agreed to their T&Cs. WW are obviously proponents of OAuth, also speaking in support of it in their response to the AEMC Review of Regulatory Framework for Metering Services (see Section 3.1.2.3). Related RQs on this are also given in this section.

Consumer ownership of data WW found that who ‘owns’ data is a complex question and learned through legal advice “that in broad terms copyright law considers the entity that generates data as the ‘owner’”, and that as “Wattwatchers provides the sole means of access to most of the data being made available through the MEM for both our service users and to third parties, this means Wattwatchers ‘owns’ the data generated by our devices in legal terms.” Thus, WW endeavour to maximise consumers’ ‘control’ of their data.

[RQ] WW have found that who “owns” data is complex question, and that it is actually WW who own the consumer energy data measured using their devices. How does the law here compare with the US and GB? Can WW relinquish their ‘ownership’ rights to consumers? If the consumer owns the WW device, and access to the data is made available through sharing communication protocols, does this shift ownership to the consumer?

7 https://arena.gov.au/projects/wattwatchers-my-energy-marketplace/

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CDR WW claim that participation in the CDR-E will be costly and estimate that the costs to become accredited are potentially in the order of $250k to $2M as a data holder or data recipient. These costs are prohibitive for smaller companies to participate and WW claim “they have been able to establish the MEM to provide access to near-real time energy data based on a set of similar principles and published APIs at a much lower cost point.”

[RQ] WW estimate that the costs to become accredited as a CDR-E data holder or data recipient ranges from $250k to $2M. Which is prohibitive for smaller companies. If this is the case, why is the cost so high? And why is the range estimate so large?

Cultivating innovation WW have developed a short-form commercial term sheet to support new MEM data services customers, enabling them to rapidly develop prototypes and pilot projects without the need for extensive negotiation and legal review. This lean agreement has streamlined the commercial process to access MEM data and made it easy for MEM Data Services customers to start working with MEM data as quickly as possible. WW is also becoming more frequently selected by research institutions and other grant projects to provide data and point out that “Data availability for research is of high importance for the current and ongoing energy transition because so much change is happening in a short period of time”. Research projects can however be expensive, with added costs for conducting surveys and incentives for consumers to participate for example.

[RQ] Assuming the importance of making consumer data, such as MEM data, available to third parties to develop consumer services and to researchers for analysis, can the WW “lean agreement”, potentially be developed to either be recognised as best practice or even as a standard? This process may require industry collaboration.

Linking energy consumption and metadata WW has found that third party service developers want more linked metadata. An example of this is linking property information with MEM energy consumption and power quality data. This includes property size, number of occupants, occupancy periods, and information on major appliances. WW aims to gather and share this additional metadata in the same way the MEM energy consumption and power quality data is made available.

[RQ] WW is enabling innovative consumer data services through provision of access to the MEM energy data, and that these new services may require additional metadata. With this in mind, is it prudent to design the Regulatory Framework for Metering Services and the CDR-E such that the inclusion of non-energy metadata is not excluded?

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Summary of Findings Table 9. MEM - Summary of findings

Category

Section

Finding

Benefits of trials

3.1.4.3

MEM has identified a number of important issues which would not have otherwise been identified (predicted really) through discussion groups, workshops, consultations. This emphasises the importance of field trials. What additional field trials need funding? What trials are being recommended by recent relevant reports on this theme?

Protecting consumer privacy

3.1.4.3

WW is also encountering challenges associated with ensuring consumer data privacy. In reference to the CDR, could the GB approach to ensuring data privacy (split-streaming metadata and consumption data) be applied in Australia? What has WW and the WW DAP found in regard to employing data security methods to avoid re-identification?

Consumer ‘ownership’ of data

3.1.4.3

WW have found that who “owns” data is complex question, and that it is actually WW who own the consumer energy data measured using their devices. How does the law here compare with the US and GB? Can WW relinquish their ‘ownership’ rights to consumers? If the consumer owns the WW device, and access to the data is made available through sharing communication protocols, does this shift ownership to the consumer?

CDR-E?

3.1.4.3

WW estimate that the costs to become accredited as a CDR-E data holder or data recipient ranges from $250k to $2M. Which is prohibitive for smaller companies. If this is the case, why is the cost so high? And why is the range estimate so large?

3.1.4.3

WW have developed a short-form commercial term sheet to support new MEM data services customers, enabling them to rapidly develop prototypes and pilot projects without the need for extensive negotiation and legal review. Assuming the importance of making consumer data, such as MEM data, available to third parties to develop consumer services and to researchers for analysis, can the WW “lean agreement”, potentially be developed to either be recognised as best practice or even as a standard? This process may require industry collaboration.

3.1.4.3

WW is enabling innovative consumer data services through provision of access to the MEM energy data, and that these new services may require additional metadata. With this in mind, is it prudent to design the Regulatory Framework for Metering Services and the CDR-E such that the inclusion of non-energy metadata is not excluded?

Standardisation of commercial agreements to share data

What non-energy metadata should be included in the CDR-E?

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3.1.7 EU policy on SM rollouts The International Energy Agency (IEA) 8 website provides a tool enabling users to search for policies related to clean energy implemented by different governments. Table 19 lists these policies, all are currently active. Further investigation into each policy showed that a number are encountering similar problems with implementation, resulting in slower than expected rollouts. A detailed analysis into the reasons, and how Australia might learn from mistakes made in EU, is required, and it is recommended that further research be undertaken on this.

Important findings, when comparing against the CDR and Regulatory Framework for Metering Services, on EU SM policy are given below: • Cost for SM rollouts can be covered by either government, networks, or the consumer.

• SM rollouts are also slower than expected in some countries.

• Protection of consumer privacy and data security are also concerns.

• Specification of minimal functions and data provision of smart meters are also an issue to be addressed.

• Financial incentives can be offered to companies to implement innovative pilot projects.

• It can be required that SM rollouts be economically assessed, before, during, and after.

• SM rollout targets are common, and also include similar challenges on how best to achieve them.

[RQ] What studies, evidence, are EQ jurisdictions using to justify their policies on accelerated SM rollouts? Has Australia implemented similar studies and/or provided similar evidence? What are the important tenets of each policy? What can Australia learn from SM rollout policies implemented in the EU?

8 https://www.iea.org/

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Table 10. List of international policies related to SM rollouts

Country

Policy name

Year

Description

Portugal

Recovery and resilience plan

2021

The investment in renewable electricity in Arquipélago da Madeira is worth EUR 69 million. The objective is to prepare a thermoelectrical phaseout. The policy plans to install 130,000 smart meters in the next 5 years, modernise 8,750 public lighting points, and develop charging infrastructure for EV.

Germany

Digitalisation and sector coupling

2020

Help the energy sector “de-bureaucratise” and accelerate the rollout of smart meters, which has been slower than initially planned. The use of the systems in households and companies is expected to increase 80 percent by 2025 9

Belgium

Energy market regulation and deployment of smart meters and flexibility

2018

This regulation sets policy framework in the Wallonia Region of Belgium regarding the following points: • smart meters installation by network operator with different dates/targets according to the customer's eligibility and conditions (after carrying out economic assessments with positive results).

• minimum functions and data requirements of smart meters.

• regulations for offering licensing rights for flexibility providers.

• managing and protecting personal data.

Italy

Approval the rollout of 2nd generation smart meters by DSOs

2017

Authority for Electricity, Gas and Water Systems (ARERA) approves the plan for the commissioning of 2nd generation (2G) smart metering systems presented by the large DSO e-Distribuzione, defining some specific conditions, and sets the standard capital expenditure for the purposes of tariff recognition. The 2G meters will act as smart network sensor, enabling continuous grid quality-of-service monitoring, near realtime identification of network faults and renewable micro-generation.

Ireland

Ireland's National Smart Metering Programme

2017

The Commission for Regulation of Utilities (CRU) announced the details of the delivery plan for the introduction of smart meters into Irish homes and businesses through a phased approach, commencing with an initial delivery of 250,000 in 2019 -2020, and approximately 500,000 meters in each of the four subsequent years.

Germany

Pilot program 'Einsparzaehler'

2016

Program details are:

9 https://www.cleanenergywire.org/news/germany-aims-push-energy-efficiency-digitalisation-strategy

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Financial incentives for companies to implement innovative pilot projects to be given, projects meant to allow for a measurement and display of energy consumption patterns; advice on how to improve the efficiency should be provided, and projects should enable a before-and-after measurement of energy use after the implementation of efficiency measures.

Denmark

Order on meters and measurement

Portugal

Smart metering deployment ordinance

Austria

Regulation for smart meters introduction

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2013

The Order mandates smart meter installation by grid companies before 31 December 2020, mentions relevant technical requirements, and permits network companies to collect incurring additional costs from customers.

2013

This ordinance approves the functional and technical requirements of smart meters and mandates biannually economic assessment of smart meters implementation to be carried out by the Energy Services Regulatory Authority (ERSE). The ordinance doesn't explicitly set plans/targets of smart meters rollout.

2012

This ordinance sets a legislation framework for smart meter rollout plan on national level: • project plan submission by network operators for their customers by end of 2015.

• installation for 80% of customers, by end of 2020.

• installation for 95% of customers, by end of 2022.

3.1.8 Green button (GB) The majority of the content in this section is a summary of (C. T. Nguyen & Roberts, 2019).

The Green Button initiative was led and implemented by the U.S. National Institute of Standards and Technology (NIST), the U.S. Department of Energy (DOE), and industry partners in response to a White House call-to-action to provide consumers with machine-readable energy-usage information in a standard electronic format. The ultimate goal of the initiative was to build an ecosystem that enables consumers to have easy and secure access to their energy-usage information in a consumer-friendly and machine-readable format for electricity, natural gas, and water usage, and to share this data readily and securely with partners identified by the consumer. NIST led the development of the GB technical standards, providing an extensible markup language (XML) format for data exchange. The Green Button Alliance (GBA) 10, a non-profit organisation, was then formed to maintain the development and evolution of the standard and manage-andgrow the ecosystem with support from NIST, The Department of Energy (DOE), and industry partners.

GB has over 60 million customers in the US and 2.6 million in Canada, it can therefore be said that GB standard is a reality with increasing support from utilities and ICT companies (de Uña & Rincón, 2020), and is expected to move into other markets around the world, including France, Germany, Italy, Japan, and South Korea 11 . The GBA offer to assist any jurisdiction considering implementing the GB standard.

There are two main methods for a consumer to obtain their GB data: Download My Data (DMD) and Connect My Data (CMD). DMD provides a mechanism for the customer to view and monitor their energy-usage information directly from a utility website or portal. CMD allows the customer to share their usage information with a third-party service provider.

Note that enhancements to the standard are on-going and managed through an open workgroup known as the OpenADE Task Force 12 (where ADE refers to automated data exchange).

Data-formatting language XML was deemed to be the most appropriate data-formatting language for the GB data files the reasons for this are: • In anticipation that there would be later additions, XML was considered the most flexible and readily available data-formatting language.

• XML can be used with off-the-shelf tools and unaltered Web servers.

The Atom Syndication Format (based on XML) acts as a wrapper to the GB -specific information and facilitates the ease of use in programming and conveyance of data using off-the- shelf tools that could already handle XML. Data sharing model While the GB standard does not cover how data are to be stored by the data custodian (retailer) nor at the third-party providing services for a mutual customer (data in situ), GB focuses on the security of these data in terms of their authorisation and deliverance. GB emphasises five core tenets, please see (C. T. Nguyen & Roberts, 2019) for more detailed information on each of the tenets.

• Multiple streams of data. Data is provided in two streams, energy-usage information (EUI) and personally identifiable information (PII), ensuring a hacker may get the address (for example), but no energy data, and viceversa, but not both.

• Adherence to modern web-transit standards.

10 https://www.greenbuttonalliance.org/ 11 https://www.ul.com/news/green-button-alliance-deliver-valuable-savings-customers-and-energy-service-providers 12 https://www.greenbuttonalliance.org/technical-committee

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• •

•

Verification of party identity.

The authority of the data custodian for customer verification. This means the data custodian (retailer) acts as the identity authority for data-sharing authorisation. This is analogous to Google, Facebook, or LinkedIn acting as identify authorities for disparate companies across the web.

The concept of customer consent and control. This refers to the use of OAuth authorisation 13, an open protocol to allow secure authorisation in a simple and standard method from web, mobile and desktop applications, to manage the secure sharing of data between the data custodian (retailer) and the third-party provider of services.

Lessons learnt According to (C. T. Nguyen & Roberts, 2019) the key lessons learnt to date are that there needs to be a: • concerted and singular place for standardisation efforts. Third party service providers complained that they had to build customised consumer apps for different jurisdictions, adding expense and complication, as GB data files across jurisdictions weren’t consistent.

• way to verify and certify that implementations were compliant to the standard. This means the development of a testing and compliance program to ensure third party consumer apps are compliant. Note that this program is now available 14 • go-to place for finding reference implementations, technical support, educational materials, and collaboration.

Findings The GB data sharing model looks to be similar to the CDR Residential Model (Section 3.1.4). Where the CDR could look to GB is in regard to: • Sensitive data and consent. With GB, a consumer can authorise/restrict access to their meter data according to the interval of reading (e.g., monthly, daily, hourly), the level of personal information, and the duration of the authorisation.

• Standard data file format and included information. GB are using XML, with standard development managed by the OpenADE Task Force.

The application of GB also applies to the Regulatory Framework for Metering Review, specifically to consumers owning and controlling their own data (Section 3.3.1.2.3).

[RQ] Considering the success of GB in the US, that GB or a similar approach to maximise the use of SM data is being rolled out in the EU and other jurisdictions, that data security concerns in relation to the Residential Model, and consumers owning and sharing their data, do not appear to be an issue with GB and OAuth, what are the reasons Australia can’t also take this approach?

3.2 RT.2 Dynamic Operating Envelopes (DOE) 3.2.1 Introduction Dynamic operating envelopes (DOEs) are limits on how much electricity households can import and export over time and location. DOEs have become a significant consideration for DNSPs, e.g. the ARENA Working Group is looking into utility options (Australian Renewable Energy Agency (ARENA), 2022), as DOEs are related to grid constraints, access and fairness.

There is a need to balance efficiency and fairness when applying DOE at different locations in the grid, from households

13 https://oauth.net/ 14 https://www.greenbuttonalliance.org/testing

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to various levels of aggregation 15. Also, there is a need to review the required regulatory framework and technical and social considerations for implementing DOEs. Hence, this RT reviews the implementation of DOE, the implications for households, and balancing outcomes to meet households and network needs.

This RT provides an overview of the current state-of-the-art of DOE implementation by investigating academic and grey literature, trial DOE projects, and international precedents. In particular, this RT examines the concepts and concerns proposed by key stakeholders across the sector (i.e., industry, consumer advocates, market bodies, regulators, and researchers), including network capacity allocation and equity concerns. The report also reviews trial projects that have tested or are currently testing the rollout of DOE and identifies trends and opportunities for future projects. Furthermore, the report examines the evolving regulatory framework and the technical and household considerations for DOE implementation, such as the ability of households to respond to DOE signals. Finally, this report reviews how DNSPs plan to evolve in the coming years to offer DOE to all customers. Given this context, this RT reviews the current state of DOE by examining eight fundamental elements: (i) DOE allocation principles, (ii) existing DOE trial projects, (iii) regulatory framework, (iv) household considerations, (v) technology considerations, (vi) implementation pathway of DOE across DNSPs, (vii) academic relevant literature, and (viii) international precedents.

The RT is organised as follows. Section 3.2.2 briefly presents relevant work done in previous OAs about DOEs and how this RT complements the existing work. Sections 3.2.3 and 3.2.4 describe the DOE concept and the underlying DOE allocation principles, respectively. In Section 3.2.5, practical trial DOE projects are analysed by comparing the similarities and differences and proposing potential areas for future projects. Section 3.26 reviews the emerging regulatory framework to implement DOE. The review of the household perception of DOE and technical considerations for DOE implementation are presented in Sections 3.2.7 and 3.2.8, respectively. Section 3.2.9 reviews the implementation pathway of DNSPs to deploy DOE. The relevant academic literature is summarised in Section 3.2.10, and Section 3.2.11 reviews exemplar international precedents. Finally, this RT concludes by overviewing the identified research questions through this RT.

3.2.2 Review of work done in previous RACE for 2030 OAs Given the strong connection between DOEs and hosting capacity, the concept of DOEs was explored in the RACE N2 OA: Network Visibility and Optimising Hosting Capacity. In particular, the RACE N2 OA presents a high-level description of the calculation process, benefits, challenges, and use cases of hosting capacity, including DOEs. This OA extends the analysis by reviewing the implications of DOEs on households, reviewing the market status, and proposing research opportunities and priorities to advance in the DOE implementation process.

3.2.3 General DOE explanation The concept of DOE has emerged as a viable approach to ensure that the DER power flows remain within network constraints and support efficient network management. As defined in (Australian Renewable Energy Agency (ARENA), 2021), operating envelopes (OE) represent the technical limits within which households can import and export electricity.

DOE vary import and export limits over time and location based on the available capacity of the local network or power system as a whole. Export limits can be related to PV systems and batteries as they have the capability to export power to the grid. Import limits can be related to flexible loads, such as electric heat water tanks and air-conditioning systems, as they are loads that import power from the grid and they can be scheduled in response to DOE to run during periods of low demand.

The process of DOE operation is briefly described in three steps. First, DOE are calculated based on the available network capacity. Second, DOE is communicated to customer devices. Finally, the customer devices will need to ensure that the DER operation respects the prescribed DOE. Figure 13 shows an example of load curves and illustrates the concept of

15 Note that household level is also one of the levels of aggregation as DER can be aggregated behind-the-meter at a household level.

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DOEs, using an export limit use case example. In particular, Figure 13a shows typical demand and generation curves. Figure 13b illustrates the concept of a static export limit. If the net export value exceeds the static limit, the energy is curtailed, and the household can only export at the static limit value. And Figure 13c illustrates the concept of a dynamic export limit.

It can be seen that the limit (purple dash line) varies over time depending on a combination of operating conditions, for instance, the household is curtailed during a period around midday when the network may be closed to reach its technical limits. Although most of the emerging work on DOEs is mainly focused on export limits, note that the concept of DOEs also considers import limits, which may be more relevant in the future with increased DER penetration. For example, import limits can be used to manage EVs charging during peak demand periods.

(a)

(b)

(c) Figure 9. Illustration of operating envelopes. a) Typical demand and generation curves, b) Net load curve, and representation of energy imported, exported, and curtailed when a static limit is implemented, c) Net load curve, and representation of energy imported, exported, and curtailed when a dynamic limit is implemented.

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Overall, DOE is an advanced approach to managing network capacity, and its implementation can bring significant benefits to households and the electricity system. Some potential benefits include (Australian Renewable Energy Agency (ARENA), 2022): • Enabling more solar households and more energy exports, • Improve market efficiency by allowing more DERs and reducing electricity prices, • Reducing the need for costly network investment to manage network capacity, resulting in reduced network charges for all households, • Facilitating the integration of EVs by managing the available capacity to charge and contribute to grid congestion, and • Improving assets and network utilisation.

DOE are a developing concept; therefore, many relevant factors are not fully understood or standardised. The number of DOE studies has increased in the last two years, and there is relevant research that significantly contributes to a deeper understanding of DOE. However, there are still some questions about to what extent DOEs are in operation? Is there a best practice? What are the issues and challenges raised by DOE operators and industry in the literature? What knowledge gaps should be addressed to facilitate the rollout of DOEs?

3.2.4 DOE allocation principles As described above, DOE is an approach to allocate the available network capacity 16 between individual households.

Allocating available network capacity is a technical process that can be complex and challenging. It involves trade-off between technical efficiency, cost, and social equity. Theoretically, the most efficient way to allocate capacity would be through specific and granular allocations for each customer connection point (Australian Renewable Energy Agency (ARENA), 2021). However, this approach may lead to concerns about transparency and fairness among customers, even though it could ultimately benefit the majority. For example, using this approach, two neighbours could receive a different DOE, resulting in a household at the end of the feeder having a tighter export limit than a household closer to the distribution transformer. Hence, households with tighter DOE limit may question the fairness of DOE as neighbours in the same street do not have the same DOE and raise concerns about the method and criteria to calculate DOE. Additionally, the cost of implementing advanced information and communication technology (ICT) systems may outweigh the benefits for consumers.

In light of this complexity of allocating the available network capacity, national allocation principles to guide DOE design and implementation by DNSPs have been developed by ARENA (Australian Renewable Energy Agency (ARENA), 2021).

Through a consultation exercise with key stakeholders, the Distributed Energy Integration Program (DEIP) DOE Working Group (WG) developed a draft of capacity allocation principles for export DOE to guide DNSPs in developing their capacity allocation methods. These are related to the following DOE impact factors: • transparency, as households need simple accessible information about available network capacity, network operating limits to respect, the DOE purpose, how DOE are calculated, the benefits of DOE implementation and the inherent DOE trade-offs.

• efficiency, as network capacity allocation affects the efficiency of network operation and network investment to support increased uptake of DER and offer a reliable service.

• fairness, as some customers may receive tighter DOE which may raise equity concerns.

• demand management, as DOE should promote self-consumption and alleviate load and generation peaks.

• technology neutrality, as customers should have freedom to choose the most preferred technology without dependencies involved, such as compatibility with DOE signals.

• customer choice, as customers should have the options to choose static or dynamic limits, and

16 Defined in the Glossary of Terms.

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•

incentives, as DNSPs could develop incentive-based arrangements to promote better practices and facilitate the transition of legacy systems to DOE.

The DEIP DOE WG discussed with multiple stakeholders preferred allocation principles (Australian Renewable Energy Agency (ARENA), 2021), especially around (i) at what scale should DOE be applied? and (ii) how should available capacity be allocated between households?

In summary, this group considered four locational scale levels 17 for DOE, as described below.

1. All customers: All customers on the network get the same capacity allocation.

2. Zone substation: All customers in a small group get the same capacity allocation.

3. Local LV area: All customers on the street get the same capacity allocation.

4. Individual customers: Each customers receives a different capacity allocation that may be different among neighbours.

There was consensus that each locational scale level as opportunities and challenges across multiple DOE impact factors.

It was suggested that DNSPs should start with a baseline model that applies to all households and then this could be improved as required and/or capable. In particular, the DEIP DOE WG considers that currently a baseline mode is to adopt the individual customer locational scale level. That is, DOE is applied at a customer's point of connection to the network.

The second element discussed among the stakeholders was the method to allocate the available capacity. The group acknowledged that there are multiple ways to do it, but four main methods were discussed in more detail: 1. Equal allocation: All households receive the same capacity allocation, regardless of their system size.

2. Proportional allocation: Households are constrained by a proportion of their system size (i.e., larger systems receive greater allocation).

3. Value-based allocation: Households get higher allocation if they can provide more value (e.g., greater allocation for VPP households).

4. Pay-for more allocation: Households can pay to get greater capacity allocation.

In principle, most stakeholders supported the equal and proportional methods and raised fairness concerns on the valueand pay-based models. In fact, there is evidence that most of the existing literature and trial projects use an equal allocation method as it is simple and scalable (see Section 3.2.5). A fifth allocation approach based on a purely technical optimisation model was suggested; however, it also raised fairness concerns.

Given these findings in relation to location scale levels and capacity allocation methods, the DEIP DOE WG proposed the following five allocation principles to guide export DOE design and implementation (Australian Renewable Energy Agency (ARENA), 2021): 1. DNSPs are responsible for setting DOE limits, with the calculation methodology used to determine the limits being transparent and subject to stakeholder consultation.

2. Allocation should seek to maximise the use of network export hosting capacity while balancing customer expectations regarding transparency, cost, and fairness.

3. Capacity allocation can initially be based on net exports and measured at the customer's point of connection to the network.

4. Capacity should be allocated to small customers irrespective of the size or type of customer technology (e.g., solar or batteries) at the customer premises.

17 As DOE allocates the available network capacity between customers, the locational scale level define how DOE is applied in relation to their

location in the network.

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5.

In the near term, DOEs should be offered on an opt-in basis with capacity reserved only to make good on legacy static limit connection agreements, with efficient incentives provided for customers to transition to DOEs over time.

Although the DOE allocation principles were established through consensus within the DEIP DOE WG, there has been limited research on other important aspects, particularly the effects of these allocation principles on households and how they comprehend the trade-offs between the DOE impact factors.

In addition to the DEIP DOE work, (Fernando, 2022) assessed different allocation approaches as part of Project Symphony to provide information on the network optimisation algorithm. Specifically, this work evaluated capacity allocation levels and methods against seven developed metrics to score efficiency, fairness, financial impact, environmental impact, network security, reliability, and scalability. In summary, the approaches tested in the report were: • Equal allocation at the distribution transformer level, similar to the 'equal allocation' method defined above.

• Proportional allocation based on the inverter rating. Each household has a minimum capacity allocation (e.g., 1.5kW), and any remaining network capacity is allocated proportionally based on their respective inverter rating.

Under this allocation method, larger systems can export more energy.

• Proportional allocation using forecast energy. Each household has a minimum capacity allocation and then any remaining network capacity is allocated proportionally based on their respective forecast energy export. Under this allocation method, households with higher forecast energy export can export more energy.

• Weighted allocation using the inverter rating and the forecast energy. This considers both inverter rating and forecast energy. In this method, high self-consumption and small inverter ratings are prioritised over low selfconsumption and large inverter ratings.

• Multistage criteria allocation. This method utilises a principled iterative allocation to prioritise capacity in descending order based on multiple factors, including forecast energy, self-consumption, inverter rating, and network support required. This method is the most complex.

The analysis showed that proportional, weighted, and multistage allocation methods outperformed the equal allocation method on most of the seven metrics in diverse scenarios. Hence, the proposed methods could increase network utilisation, reduce energy curtailment, and increase household economic benefits. This finding supports future research projects to develop advanced methods to improve the performance of DOE regarding the DOE impact factors.

Findings The developed DOE allocation principles by the DEIP DOE WG establish an initial agreement for guiding the design of DOE and the implementation process by DNSPs. Up to now, key stakeholders have noted the importance of the allocation principles and there is a common agreement that it should be based on the relevant DOE impact factors. Although the initial baseline of individual DOE allocation to customers has been recommended, further work is required to systematically study multiple options of location scale levels and capacity allocation methods and to assess their impact under real conditions. Additionally, note that the export DOE allocation principles require more work to analyse how they can be applied in practice and the relevance for import DOE. Despite the established DOE allocation principles considered relevant aspects for households (i.e., transparency, customer choice and fairness), it remains unclear how households will respond to those principles. Also, there has been no detailed investigation of the influence of these allocation principles on households' decisions in DER investment, as well as DER management and operation. It is expected that DNSPs will continue leading the work on DOE allocation principles and these may need to be revisited in the future when more households are using DOE. Also, the oversight by the AER could increase the consistency and transparency of the DOE allocation methods. Against these findings, the following research questions are proposed for future investigation: • How do different DOE allocation methods and location scale levels, technologies and household behaviour synergistically interact at the individual household level, and how does this translate into system-wide impacts and therefore impact other households?

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• • • • • • •

How different DOE allocation methods should be compared? What are the metrics to compare the methods?

What are the advantages and disadvantages of each method?

What are the consequences, benefits, and other impacts for households of the proposed DOE allocation principles by the DEIP DOE WG? How do different DOE allocation methods and location scale levels impact household decisions regarding DER investment and operation?

To what extent can the proposed DOE allocation principles also be applied for import DOE? How do DOEs allocation principles can affect EVs charging behaviour?

How do allocation methods and location scale level affect the strategy of HEMS and aggregators?

In which conditions are households willing to trade transparency and fairness vs efficiency?

3.2.5 Pilot/trial DOE projects This section reviews Australian trial projects that have tested the implementation OE and/or DOE. It is worth noting that pilot and trial projects are key to facilitate the implementation of DOE. They have provided meaningful insights into different aspects, including technical, regulatory, and social areas. Table 20 presents an overview of relevant trial projects, noting the project's start date, location, a brief description, and current status (i.e., in progress or completed). Also, this table summarises the finding around the following questions: • Does the project consider export DOE, import DOE, or both?

• What is the DOE allocation method being tested in the project?

• Does the project conduct social research to review how households understand and perceive DOE and the underlying implications?

• Do households participate in existing electricity markets, such as wholesale energy, contingency Frequency Control Ancillary Services (FCAS) or Reserve Emergency Reliability Trader (RERT)?

• Do households' DER provide network support services?

• What is the DOE calculation approach 18 being used in the project?

Findings Table 20 shows that the concept of DOEs is being tested in many jurisdictions across Australia, which may help to analyse the DOE implementation under diverse conditions. Furthermore, it is evident from Table 20 most projects are focused on export limits only, noting that import limits will become relevant in the future. Regarding the allocation method, there is a trend to adopt the equal allocation method, but the most recent trials are testing different approaches, which require more complex algorithms and additional tools (e.g., Project Symphony developed some metrics related to the DOE impacts to assess different allocation methods). What stands outs from Table 20 is that despite the importance of social research for rolling out DOE implementation, most of the projects do not consider a social research component.

Also, this review reveals that most projects use the Evolve calculation approach to implement DOE. The new projects, Project Edith and Project Converge, are expanding the Evolve functionality by testing the interface of DOEs with market and network support services. According to these findings, the following research questions are proposed for future work: • Although standardising a common approach for DOE calculation and implementation is a complex task (Australian Renewable Energy Agency (ARENA), 2021), a comprehensive comparative analysis between the different DOE calculation approaches could reveal commonalities that can be used to establish a guideline for DOE implementations and support the DOE allocation principles. What are the main similarities and differences

18 DOE calculation approach refers to the methodology to calculate, allocate and communicate DOE. Note that a detailed description of the DOE

calculation approaches is out of the scope of this OA., Hence, we only refer to the name of the DOE calculation approach developer.

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• • •

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between the DOE calculation approaches? How do the DOE calculation approaches perform against the DOE impact factors? Why is the Evolve DOE calculation approach the most adopted approach?

What are the barriers to incorporating social research in the DOE trial projects, and how can they be overcome to ensure the social study of DOE? Although the DOE calculation approaches can calculate import and export DOE, most projects do not consider import DOE. What are the implications of neglecting import DOE for deploying DOE? As the most recent projects are testing new applications of DOE to deliver market and network services, how do the new applications differ from the initial DOE calculation approach in terms of the DOE impact factors?

Table 11. Overview of Australian trial projects which consider OEs and/or DOEs

Location

Brief description

Export or Import

NSW, SA

Explored implementing dynamic export limits to manage voltage and thermal constraints

Export

2019

Solar Enablement Initiative – Distribution State Estimation

QLD

Developed a state estimation approach to assess operational conditions of electricity networks. This approach can be used to calculate DOEs.

Both

NA

No

NA

NA

State estimation GridQube

2019

evolve DER Project

ACT, NSW, QLD

Proposed and developed the concept of DOEs and the underlying calculation and publication framework.

Both

Equal allocation

No

No

No

Evolve

Completed

2019

Advanced VPP Grid integration

SA

Tested the value that dynamic export limits can create for households and VPP operators

Export

Equal allocation

Yes

Yes

Yes

SAPN engine

Completed

2020

SA Power Networks Flexible Exports for Solar PV Trial

SA, VIC

Test flexible export limits with solar PV households

Export

Equal allocation

Yes

No

Yes

SAPN and AusNet engines

In progress

Start date

Name

2018

Dynamic Limits DER Feasibility Study

DOE allocation method

Social Research

Market services

Network support

DOE calculation approach

Status

NA

No

NA

NA

NA

Completed

Completed

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2020

Jemena Dynamic Electric Vehicle Charging Trial

2021

Project Converge ACT Distributed Energy Resources Demonstration Pilot

2021

2020

2022

Project Symphony

Project EDGE

Project Edith

ACT, TAS, VIC

Dynamically manage EV home charging by coordinating available network capacity in real time

Import

NA

No

ACT

Demonstrate new DER orchestration capabilities using 'shaped OE' to improve network congestion, minimise network expenditure and improve DER market services

Export

TBC

Yes

Yes

WA

DER Orchestration pilot project which considers DOEs so DERs operate within constraints

Export

Testing multiple options

Yes

VIC

Test a marketplace for DER to provide wholesale and local network services within the network constraints (i.e., DOEs)

Both

Testing multiple options

Export

Equal allocation, but it depends on other parameters related to network conditions

NSW

Explores DOEs and dynamic network prices to maximise the opportunities and values for and from DER

TBC

In progress

Yes

Evolve

In progress

Yes

Yes

Evolve

In progress

No

Yes

TBC

University of Melbourne

In progress

No

Yes

Yes

Evolve

In progress

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3.2.6 Regulatory Framework This section describes key aspects of the regulatory framework that need to be reviewed to incorporate the implementation of DOEs. The current regulatory framework allows the implementation of DOEs to some extent. However, the adoption of DOE also creates new challenges that need to be assessed. The work in (FTI consulting, 2022) has identified potential gaps in the regulatory and governance frameworks related to the initial stages of DOE implementation within the NEM. Specifically, the following set of gaps required immediate attention by the AER are: • Capacity allocation methodology: The lack of a common approach and guidance can result in divergent approaches that may be inefficient or unfair to households. The AER may provide some guiding principles to support efficient DER and network utilisation, investment, minimise costs and promote social licence for DOE. • Visibility over distribution networks: Network visibility needs to improve as a base level is required for DOE calculation.

• Data protection and privacy: DOE implementation requires data transference which may raise privacy concerns.

Households' privacy and interest must be protected.

• Contractual mechanism: Some parts of contractual agreements need to be changed in order to recognise DOE. • Monitoring DOE calculation and application: Measuring and monitoring DOE calculation and DOE use cases can be used by the AER to define DOE performance monitoring to ensure consumer protection and transparency in DNSP operation.

• Demonstrating investment need: DNSPs will likely need to demonstrate the investment need to deploy DOE. As part of the Energy Security Board's DERs Implementation Plan 19, the Australian Energy Regulatory (AER) has published an issues paper on the introduction of flexible export limits 20 by DNSPs across the NEM (Australian Energy Regulator (AER), 2022). This paper is ongoing work to identify what is needed to facilitate the DOE implementation and functionality, acknowledging that DNSPs lead this process in the first instance, and the AER would assess the proposed expenditure. In this issue paper, the AER has provided a preliminary position on identified gaps and existing workstreams for implementing DOE, as summarised in Table 21 and Table 22, respectively.

Table 12. Identified gaps for implementing DOE

Identified gaps

AER preliminary position

Capacity allocation principles

Following the DOE DEIP working group capacity allocation principles: • DNSPs are responsible for setting flexible export limits. The calculation methodology needs to be transparent and subject to stakeholder consultation.

• The allocation should maximise network utilisation and balance household expectations regarding transparency, cost, and fairness.

• Capacity allocation can initially be based on net exports and measured at the household's connection point.

• Capacity should be allocated irrespectively of the DER size or type.

• In the short-term, flexible export limits should be offered on an opt-in basis.

19 https://esb-post2025-market-design.aemc.gov.au/ 20 Note that the AER issues paper is mainly focused on flexible export limits, leaving import limits aside. This is because the AER anticipates DOEs

will apply to export services in the first instances, but the acknowledge that flexible import limits will be develop in the future with increased DER integration. For example, the use of import limits to manage EV charging during peak demand periods.

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The methodology should be clear and transparent to promote efficient and fair household outcomes. It is expected that the methodology will be documented.

Capacity allocation methodology

The AER agrees with the DEIP DOE working group that a national and standard methodology may be unnecessary and difficult to achieve at this time. Hence, DNSPs can innovate and test different approaches. Also, it is expected that the methodologies will evolve as more consumers use flexible export limits.

Consumer participation (opt-in or opt-out)

The AER view is that where DNSPs choose to implement flexible export limit connections, they should also offer the choice of static export connection.

Governance of traders (party registered with AEMO in market services) and consumer energy resources

The review of consumer protections for future energy services is underway. This review may help to set governance requirements for retailers, traders, and aggregators.

The AER considers the current connection agreement framework as the most appropriate existing mechanism to set out the terms and conditions for flexible export limits. Some DNSPs have updated their MSOs to enable flexible export limits for certain types of connections.

Connection agreement

The AER agrees that they should review changes to the connection agreements to set sufficient consumer protection. In particular, the following information should be specified to inform consumers and establish rights and obligations: • Operating parameters (interval length, notification period, performance, etc.)

• Communication processes for changes to the flexible export limits • Consumers' compliance obligations, including how DNSPs would identify non-compliance cases.

• Related commercial implications, such as direct compensation, rebates on network charges, if service levels are not achieved.

The future standardisation of connection agreements may be considered when flexible export limits become more common in the NEM. A new governance framework is expected to be necessary to acknowledge the role of a trader, as they may be responsible for ensuring compliance with the export limit.

Governance arrangements for flexible export limits

The AER expects that additional agreement may be needed to reflect the relationship between DNSP, trader and household.

The AER view is that consumers should not be exposed to formal penalties for their devices not responding to a change in the flexible export limit

Notification period for a dynamic limit

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As households are essential to orchestrate DER, they will need some form of a forecast of what dynamic limits will be, with reasonable notice to optimise their operation.

This issue will be revisited in the future as relevant work is underway.

Table 13. Identified gaps that can leverage existing workstreams for implementing DOE (Australian Energy Regulator (AER), 2022)

Gaps that can leverage existing workstreams

AER preliminary position

Communication protocol

CSIP-Aus is intended to provide a national protocol to facilitate communication between DOSNPs and devices to enable DOEs. ESB's Interoperability workstream is currently reviewing the application of CSIP-Aus.

Monitoring export limit performance and information provision

The AER is currently evaluating metrics associated with flexible export limits to measure and incentivise export service performance.

Device capability to respond to flexible export limits

Currently, consumers are not required to install devices that are compatible with certain communication protocols.

A mandate on this could bring benefits to facilitate the rollout of flexible export limits, but it also may represent some extra cost to households to update their devices if necessary or purchase additional equipment.

Interval length

The AER considers that DNSPs are best placed to determine the interval length of flexible limits in the short term.

Demonstrating investment need

The DNSPs need to justify investments related to flexible export limits. The AER is of the view that there is sufficient guidance for DNSPs to facilitate the development of their investment proposals within the existing regulatory framework.

Consumer protections

Households need information and tools to make informed choices about participating in flexible export limit programs. The lack of appropriate communication of the benefits and impacts to households may lead to a slower update of flexible export limits. The Review of Consumer Protections for Future Energy Services and AEMC's review of DERs technical standards in the NEM will consider consumer protections that may help to limit this risk.

Data protection and privacy

Consumer understanding and interest

DNSP will likely require more access and data to implement DOEs. DNSPs are subject to ring-fencing provisions that limit their ability to share households' private information obtained through their provision of services.

The AER view is that consumers must have access to sufficient and fit-for-purpose information to enable them to make informed decisions about whether to participate in flexible export limit programs.

The AER expects that DNSPs will engage with retailers, traders, consumers, and other stakeholders to provide information about export limits.

The AER will leverage the underway work by the ESB's Household Insights collaboration workstream.

Integration with export pricing

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The AER has published guidelines describing how DNSPs can develop and justify two-way pricing proposals. But there is currently a knowledge gap in how feed-in tariffs from retailers, export tariffs and flexible export limits will interact to create efficient incentives and outcomes for consumers.

Compliance and enforcement of technical standards that facilitate flexible export limits

Device monitoring

For the AER, 'compliance' refers to three main elements or interactions of a consumer's device (where the consumer has opted into flexible export limits): • Communication between the device and the DNSP (device is connected, receiving export signals, and staying connected) • The device operates within the limit and performs appropriately.

• In case of lost communication, how the device behaves under these circumstances.

•

Monitoring adherence using the CSIP-Aus functionality is not a difficult undertaking.

Furthermore, as DOE are expected to be a key element in the core of power systems, the AER has proposed future areas for potential work. These are: • Location of flexible export limit application (i.e., location scale level and capacity allocation method) • Types of connection (e.g., residential, commercial properties, community batteries and utility solar farms) to which flexible export limits may apply.

• Incentivising consumers to use flexible export limits.

• DNSPs reporting to AER regarding how flexible export limits are used and how they operate.

• Efficient communication of flexible export limits at scale It is worth noting that there are existing workstreams that are aiming to address some of the identified gaps, including: • ESB interoperability DER Technical Standards – Roles and responsibility workstream, • DEIP Outcomes Report, • Retailer Authorisations and Exemptions Review, • Household insights collaboration, • AER Export tariff guidelines, • AER Incentivising and measuring export services performance, • Flexible trading arrangements rule change, • ESB Data Strategy, and • the final rule determination in Access, Pricing, and Incentive arrangements for DER. Also, the current regulatory framework has already considered some emerging needs. For example, following the AEMC's final determination on updates to the National Electricity Rules and National Energy Retail Rules to integrate DER more efficiently into the network, the AER developed a Customer Export Curtailment Value (CECV) methodology which would guide efficient levels of network expenditure for the provision of export services. In practice, CECV would demonstrate the extent to which network investments to enable more DER exports are valued by customers and the market and, therefore, whether the AER will approve expenditure proposals. The AER's final decision includes guidance on interpreting CECV and its connection with DER integration expenditure, how CECV can be estimated, and practical advice for networks to aggregate CECV to estimate the contribution of the overall benefits calculating for DER investment proposals. The AER is also currently consulting about the approach to incentivise and measure export service performance. This forms a part of the reform program to strength the regulatory oversight of export services provided by network businesses.

Furthermore, the AER released export tariff guidelines that include household protections and the need for any two-way pricing proposals. Other relevant works include the review of measures and incentives for export service performance, and the review of static zero export limits for new rooftop connections (Australian Energy Regulator (AER), 2022).

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Findings It is evident that governance and regulatory bodies have started to assess the need to facilitate the implementation of DOEs. Stakeholder feedback on existing workstreams will be essential to identify gaps and propose solutions to flagged challenges. In summary, the aspects that require immediate attention by the AER are: (i) capacity allocation methods, (ii) network visibility, (iii) data protection and privacy, (iv) contractual mechanisms, (v) monitoring DOE performance and application, (vi) and demonstrating investment need. The initial AER position offers some flexibility which enables different testing approaches for challenging aspects such as, capacity allocation methods, monitoring and compliance. Also, the outcomes from existing and emerging projects will guide how the regulatory framework should evolve. Furthermore, the AER recognises that the regulatory framework will evolve based on the market needs, such as adopting import DOE, standardising connection agreements, and reviewing capacity allocation methods.

Based on these findings, the following research questions are proposed for future work: • Current connection agreements are appropriate to set out conditions for export DOEs, but the AER recognises that some potential changes must be reviewed to protect the customer. What are the risks and benefits of changing connection agreements to recognise DOE? • Some potential changes in the regulation regarding DOE performance and consumers' compliance may be included in the future regulatory framework. How DOE performance and compliance can be measured and monitored? Should households be exposed to penalties in case of no response to DOEs signals? How should noresponse situations be managed? What is a reasonable time frame to receive and respond to a DOE signal?

• The AER follows the DOE allocation principles proposed by the DEIP DOE WG. Regarding how DOE should be offered, relevant emerging questions are: What is the impact of opt-in versus opt-out DOE on (i) household engagement and trust in DNSPs, and (ii) household electricity use and load profiles?

• The implementation of DOEs requires the involvement of multiple parties, including households, installers, retailers, networks, aggregators. What is the role and responsibilities of each party for successfully deploying DOEs? • Although the AER recognises the role of import limits, the initial assessment only considers export limits. How does the regulatory framework need to change to incorporate import DOE?

3.2.7 Household considerations This section reviews relevant aspects of how households may perceive the implementation of DOEs and the underlying considerations. The DEIP DOE WG and initial view of AER agreed on the implementation of DOE will be led by DNSPs, but households are essential to realising the potential benefits. Therefore, households need adequate information to make informed decisions about whether to participate or opt-out of DOE programs. The way this information is conveyed to households will be crucial in gaining acceptance and social licence 21 for the service.

Existing workstreams have explored how customers engage with DER and energy use. Households are diverse, and there are differences in what they value based on their motivations, ability, opportunity to manage their energy use and DER. The ESB Customer Insight Collaboration workstream identified four critical barriers and enablers that need to be addressed for consumers to engage in flexible energy generation and energy use. These barriers and enablers and some challenges they may pose in relation to DOE are (i) inclusion and equity, (ii) incentives and nudges, (iii) communication, and (iv) trust. In particular, these barriers and enablers pose some challenges to the DOE implementation process. For

21 Social licence in this context refers to the informal permission granted by stakeholders for institutions to make decisions on their behalf about

the operation of their DER systems.

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example, there is a risk that DOE could be perceived by households as an unfair burden when neighbours in the same street receive a higher export limit. Likewise, the idea that households under a static limit can always export at the agreed maximum level can give the impression that they are not curtailed and have better access than a dynamic limit. Households may not be aware of the existing curtailment and then may make uninformed ideas regarding the benefits and impacts of DOE vs static limits (Australian Energy Regulator (AER), 2022). In relation to communication and trust, outcomes from trials in South Australia and Victoria demonstrate that active education by DNSPs leads to greater consumer engagement.

Additionally, the solar industry and installers are crucial groups in increasing consumer awareness. Therefore, the solar industry and installers must be trained to provide accurate and relevant information to households about DOE programs.

It is expected that these issues will be explored in more detail in the ESB Customer Insight Collaboration workstream 22, as it seeks to provide insights into communication pathways and consumer requirements. Also, the AER will evaluate where additional consumer protections and measures may be warranted to implement flexible export limits. In doing so, the AER used the ESB's consumer risk assessment tool (Energy Security Board, 2021), which was developed to ensure market bodies explicitly and consistently consider the consumer benefits and risks The DEIP DOE WG also reviewed what DOE means from a household perspective. In particular, the DEIP DOE WG states that a social licence is essential to successfully implement DOEs and expand the DOE applications beyond export management (Australian Renewable Energy Agency (ARENA), 2022). A social licence may help engage households and comply with emerging compliance frameworks. Households should be able to trust in the program to unlock the potential benefits that DOE can bring to the system. To this end, DNSPs and regulators need to provide accessible information about DOE, including what they are, their purpose, and how DOE are being designed and managed over time. In this way, households can make more informed decisions to participate in DOE programs and consider attractive alternatives to simple static limits.

A survey of community attitudes to AEMC reforms reveals that households are usually unaware of the network limitations and the challenges associated with increased solar exports into the grid (Newgate Research, 2021b). Furthermore, households may have the perception that programs to manage network capacity may limit the underlying benefits or control their investment decisions as networks are adding limits to their operation and may limit the opportunity to export energy to the network and receive economic incentives. Additionally, participant project trials' feedback revealed that household’s express scepticism about the need for export limits. Hence, it is essential to better understand consumers' perceptions and attitudes towards network capacity, export limits, curtailment, and network security.

Furthermore, building a social licence for DOE needs a comprehensive view of the multiple parties and the entire consumer journey and experience. In particular, the DEIP DOE WG presents three key considerations for establishing a social licence for DOEs (Australian Renewable Energy Agency (ARENA), 2022), as briefly summarised by Figure 14 below.

22 https://esb-post2025-market-design.aemc.gov.au/customer-insights-collaboration

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Consumer motivations, attitudes and expectations •It is important to understand the motivations and expectations of consumers for their DER. These motivations can be grouped into four categories: (i) Community and Environmental, (ii) Practical and Financial, (iii) Independence and (iv) Leadership and Innovation.

•Households should be given easily accessible and understandable information in order to make informed decisions and choices, such as installing DER and opt-in for DOE. Establishing household protections •Current rules and regulations were created based on the traditional model of power systems with one-way power flow and centralised power generation.

While some of these regulations may still be relevant for DOE, more work is needed to understand how they can protect households in a grid that includes DOE and high levels of DER. •There are two principles relevant to building social license for the DOE implementation journey: (i) access to information, and (ii) dispute resolution.

Supporting household access to information and data •Consumers should have easy access to data on their DOE performance.

•Consumers need access to information in a simple, clear and accessible format.

Figure 10. Key considerations for establishing a social licence for DOE (Australian Renewable Energy Agency (ARENA), 2022)

The DEIP DOE WG has also reviewed the process of new solar households and existing solar households, and how DOE could be offered to customers in this process. For new solar households, the WG recommends that households should receive relevant information as early as possible to make informed decisions to opt-in for DOE. Similarly, existing solar households need important information about DOE so they may choose to opt-in to DOE. Important information includes the benefits of DOE concerning the overall benefits of installing solar systems, and how self-generation can be better utilised. The WG also highlighted the need for DNSPs to work with retailer and installers to set clear messaging and communication on DOEs as they are often households' first contact point. Learnings from the Flexible Export Limits for Solar PV in SA suggest that lack of awareness is one of the main reasons to choose static limits over flexible export limits (SA Power Networks, 2022). Solar retailers are unaware of the flexible export option and do not pass the information to customers. An early survey showed that 7 out of 10 customers were unaware of the flexible export option or did not have enough information to decide on opt-in. Ongoing support and dispute resolution will also play and important role. Some of these recommendations also apply to existing households, but they may also need additional information support.

Households need to be aware of their current level of export limit and how DOE may change the perception of DER benefits. Households should be informed of the changes in the market that may impact current or future decisions in investment and participation in DOE programs.

Findings The DEIP DOE WG and AER reports on DOE have established initial household considerations for implementing DOE. Enabling the potential benefits of DOE strongly depends on the participation of households. In particular, these reports have highlighted the importance of explaining what DOE are, their benefits and their impacts on customers. How this

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information is communicated to households will be crucial in gaining acceptance and social licence for the service.

Moreover, the DEIP DOE WG and findings in initial trials on flexible export limits have revealed that there are still barriers to communicating this information to households. Therefore, further research is needed to help households understand DOEs and their impact simply and transparently. Based on these findings, the following research questions have been identified: • What type of information do households need to know to make informed decisions to opt-in DOE? • How can the information be explained or communicated to all households simply and transparently, considering that they have different motivations, attitudes, and expectations?

• What are the best methods for creating visual aids and measuring progress to effectively communicate the performance and benefits of DOE to customers and the entire system?

• Existing research has also highlighted the need to engage with relevant parties, in addition to households, to upskill and explain the emerging DOE concept and implementation. This includes retailers and installers as the first point of contact with households. What is the most effective way to engage and educate retailers and installers, so they are able to communicate the DOE concept to households?

• Create educational tools to communicate the adopted allocation levels and capacity allocation approaches in a simple and transparent way to households. This may include visualisation tools, metrics, and communication channels to better interact with households.

3.2.8 Technical considerations for DOE implementation Some technical considerations related to households need to be reviewed for implementing DOE. In simple words, DOEs represent signals that DNSPs send to aggregators or households who need to respond to this signal. From a technical perspective, aggregators and households would integrate this signal into their energy management system. That is, a new condition based on the DOE signal is added to the energy management algorithm to operate the DER within the network operating limits (i.e., within the DOE). For example, the Advanced VPP Grid Integration Project 23 considers an architecture in which households can access the DOE via a secure web services Application Programming Interface (API), following the IEEE 2030.5 standard. The OE data can be accessed either in the form of a flexible export limit for each NMI, or as an aggregated limit for a local group of NMIs. Technical compatibility also plays another important role. Ideally, household DER should be able to operate within DOE, without requiring additional equipment or upgrades. Without technical compatibility, households may face additional costs, which can act as barriers to implementing DOE. For example, the Flexible Exports for Solar PV project surveyed customers on their sentiment towards Flexible Exports (i.e., export DOE). Interestingly, limited compatible technology was identified as a primary barrier to picking fixed export limits over flexible export limits in the lessons learnt report of the Flexible Exports for Solar PV project (SA Power Networks, 2022). Currently, the technology that can work with Flexible Exports is limited to solutions provided by trial technology providers, which include market-leading inverter vendors such as Fronius, SMA, Fimer and Growatt. 24 As some solar retailers only sell certain technologies, they may not be able to offer flexible exports to their customers. The evolving regulatory framework can help to largely resolve some of these compatibility challenges. For instance, new technical standards and requirements for smaller generating systems were

23 https://arena.gov.au/projects/advanced-vpp-grid-integration/ 24 It is expected that other equipment will become available in the future. Also, in the Flexible Exports for Solar PV project, inverter vendors and

inverter gateway providers are co-developing an end-to-end technical solution using smart inverter technology that enables inverters to download a DOE from the DNSP interface system. More information is available at https://www.sapowernetworks.com.au/industry/flexible-exports/compatibleequipment/.

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introduced in South Australia to ensure all new or upgraded solar installations are compatible with Flexible Exports. 25 SA Power Networks is collaborating with many technology providers to have a wide range of compatible equipment available before the July 1 2023 (SA Power Networks, 2022).

Another main barrier to choosing flexible exports is that customers may face additional costs as they may need new devices to respond to DOE signals. For example, all households in the Flexible Export Solar PV project require a SwitchDin droplet at additional upfront costs, which not all customers are willing to accept. This issue could be alleviated by introducing native inverter installations that do not require additional hardware.

In addition to this equipment compatibility requirement, communicating the DOE signal to customers' devices is essential for the DOE operation. In 2021, the DEIP released the CSIP-Aus (CSIP: Common Smart Inverter Profile) to provide a national protocol for communication between DNSPs and devices to enable flexible export limits. A standard protocol and operating language would help to minimise costs for parties required to comply with DOE. It is important to note that DOE values are dynamic and need constant communication. If communication is lost, the CSIP-Aus states that devices can be set to a predefined operating limit determined by the DNSPs. For example, if communication is lost, flexible export limits in SA are set to 1.5kW until the internet connection comes back online. 26 The application of CSIP-Aus is currently being considered under the ESB's interoperability workstream, which is expected to set a path to a common communication approach to enable the interoperability of devices in the NEM. Furthermore, through the DEIP DOE WG, ENEA delivers a report on the technology and commercial readiness of household energy management systems for DOE (Strauli, 2022). This report found that there are diverse forms for managing export limits. For example, a single device can be used to receive and respond to a DOE signal with the coordination of multiple devices. Alternatively, the DOE signal can be integrated into the home energy management systems, allowing the option to maximise household objectives subject to network limits. The HEMS providers consulted in the report do not consider receiving and responding to DOE to be a technical challenge, either in the short or long term. Implementing DOE into HEMS can be a straightforward software programming task. However, the ability to comply with a DOE for DER will depend on which customers' DER can be controlled.

This report also identified some potential obstacles to DOE implementation. Since the implementation of DOE is mainly limited to trials, the commercial implications of implementing it on a large scale are mostly uncertain. HEMS providers and retailers also expressed concern about the cost implications of an unstandardised approach to DOE. If each DNSP develop their own platform, it will result in additional costs if not nationally harmonised.

Findings Learning from existing trials, such as VPP SA trial project and Flexible Export Limits for Solar PV, and findings in the DEIP DOE WG have allowed identifying some technical considerations to implement DOE. The main emerging challenges are related to technical compatibility, communication protocols and national harmonisation to the implementation of DOE. Potential areas for further research include: • Equipment compatibility represents a challenge for deploying DOEs. Households may need to invest in new equipment or upgrades, which not all households are willing to accept. How many households currently have equipment that is compatible with DOEs? To what extent are households willing to accept new upfront costs to enable their DER to respond to DOE signals?

25 https://www.energymining.sa.gov.au/industry/modern-energy/solar-batteries-and-smarter-homes/regulatory-changes-for-smarter-homes 26 https://www.sapowernetworks.com.au/connections/connect-solar-and-ev-chargers/flexible-exports-eligibility/

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•

• •

Most of the existing work has been focused on facilitating the compatibility of generation assets or their associated inverters. However, it is unclear how other assets will interact with DOE signals. What is the technical compatibility of flexible loads to respond to DOE signals? What do flexible loads need to respond to DOE signals?

What potential additional costs may the household need to face to adopt DOE? If communication is lost, it has been proposed that DER be set to predefined limits by DNSPs. What are the potential consequences for customers? What alternative solutions should be considered?

Stakeholders have emphasised the need for a nationally consistent approach to DOE across DNSPs, including consistent communication and publishing specifications to avoid additional costs. What would a national approach for communication and publishing DOE involve?

3.2.9 Implementation pathway of DOEs across Australian distribution networks

# OF DNSPS

The review of industry reports has revealed that there is almost a consensus around the relevance of DOE for the future of the electricity grid, an important question to ask is, are DNSPs ready to implement DOEs? This is an important question since DNSPs usually face different challenges around data access and visibility, which are required to deploy most of the proposed DOE calculation methods (Final Report RACE for Networks Program Research Theme N2: Low voltage network visibility and optimising DER hosting capacity, 2021). Recent studies have revealed that most Australian DNSPs are either trialling or planning their DOE framework (CutlerMerz, 2022). As shown in Figure 15, eight DNSPs are currently offering DOE services to all or some households, and it is expected this will increase to eleven DNSPs by 2026, increasing the number of households that could opt for DOEs. 13 12 11 10 9 8 7 6 5 4 3 2 1 0 2021

2022

Offering to all customers

2023 Trial/Partial offering stage

2024 Planning stage

2025

2026

Not defined yet

Figure 11. Timeline of planned small-scale DOE implementation. Source: (CutlerMerz, 2022)

Additionally, the work in (CutlerMerz, 2022) reviewed the current state of DOEs in Australian DNSPs by considering the following three focus areas: i.

Service offering: This focus area covers the nature and type of DOE services that DNSPs offer to their households, both now and in the future. This includes a review of how many DNSPs currently offer DOEs, and the proportion of households eligible for these offers, and the estimated timeframe for implementing DOEs across their households. ii.

Technical characteristics: This focus area covers the power system attributes associated with delivering the service offerings, such as, calculation of export and import limits, DOE forecasting periods, communication

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iii.

systems and protocols, how curtailment is allocated and the contingency procedures in the case of communication systems failure.

Reporting and compliance: This focus area covers the responsible parties for ongoing reporting and compliance of DOE performance, and the regulatory framework to prescribe and delineate roles and responsibilities.

A summary of their findings regarding each focus area and what is currently offered (or will be offered in the future) is presented in Table 23. As shown in Table 23, DNSPs are more aligned in the need to update existing arrangements to allow DOE and household obligations in the National Electricity Rules. And regarding service offerings, there is no consensus around the idea of paying for greater access and lower curtailment and tariff arrangements linked to DOE. It is worth noting that some services such as paying for greater access and new tariff arrangements, are being tested in some trial projects, but they are not part of the business-as-usual service.

Table 14. DOE service offering by DNSPs

Pay for greater access and lower curtailment

Currently offered or tested

Offered/tested in the future

No offered by DNPS

No common consensus but some DNSPs may offered in the future Most DNSPs plan to phase out of existing arrangements

Phasing out of existing arrangements Changes to tariff arrangements for households with DOE

No changes to tariff arrangements

DOE household obligations in the Model Standing Offer (MSO)27

Most DNSPs are in the updating process or considering updating their MSO

Guarantee a minimum performance level of exports

Most DNSPs chose not to provide performance guarantees or service standards for DOE to households

No common consensus but some DNSPs may offered in the future

As shown in Table 24, DNSPs are more aligned with the technical characteristics of DOEs. This includes the communication protocols, forecasting period and contingency procedure. Since trials and networks are testing different DOE calculation approaches, there is no consensus and how DOE should be calculated.

Table 15. DOE technical characteristics

Service

Currently offered or tested

Offered/tested in the future

Communications protocols

IEEE 2030.5 CSIP-AUS standards

IEEE 2030.5 CSIP-AUS standards

Forecasting period to calculate DOE

Most DNSPs calculate DOEs on a 24-hour forecast at the 5-minute interval level

DOE for export, import limits or both

Applied to real power for exports

Applied to real power for exports and imports

27 A model standing offer is a contract between DNSPs and a household as defined in the National Electricity Rules (Chapter 5A)

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DOE should consider locational characteristics

Most DNSPs agree DOEs should be calculated on the local network characteristics

Capacity calculation methods

No consistent approach

No consistent approach DNSPs will revert limits to a safe level or a default setting if the DOEs cannot be communicated. To be standardised through the IEEE 2030.5 CSIP-AUS standards

Contingency procedure

As shown in Table 25, Regarding reporting and compliance considerations, DNSPs have different views on how DOE outcomes should be reported and how compliance should be verified and enforced.

Table 16. DOE regulatory and compliance

Approach to reporting DOE outcomes

Currently offered or tested

Offered/tested in the future

No consensus

Reporting formats could include power and energy outside of limits, curtailment, DOE breaches and technical requirements.

Aggregators may also be responsible for reporting on compliance. DNSPs may present results in their DAPRs.

Party responsible for DOE compliance

Households or aggregator agents acting on their behalf

Approach to verifying DOE compliance

Household metering, power quality data, HEMS, inverters, and data from aggregators

Approach to enforcing DOE compliance

Enforced through contractual agreements or technical requirements for DER connections. May also apply penalties.

Although the rollout of DOEs is expected to increase in the next five years, DNSPs still face many challenges. In particular, DOE usually considers congestion and voltages, which require electrical LV network models. However, the creation of accurate network models is time-consuming and expensive. It is also challenging to create accurate network models due to data errors in topology and lack of data generally.

Australian DNSPs have significant diversity in terms of network data models and monitoring equipment availability. Figure 16 and Figure 17 summarise the findings in (Ochoa, 2022) about the current infrastructure and network data across Australia required for calculating DOE. Specifically, Figure 16 shows the availability spectrum of electrical models of Australian DNSPs. 28 It can be seen that twelve DNSPs have LV network models available; however, only one DNSPs use the models for network operation and planning and eight use the models only for planning. Also, five DNSPs have only a fraction of models of the existing LV networks.

28 The meaning of the superscripts in Figure 13 are: (*) Business-as-usual and short-term plans, (1) available for most of the HV networks, (2)

available for most of the distribution transformers, (3) available for a few large distribution transformers, (4) models are not necessarily fully validated and (5) only a fraction of the existing LV networks.

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These preliminary results confirm the lack of electrical models of LV networks of most DNSPs, which may raise challenges to estimating DOE at household location levels.

Figure 12. Availability spectrum of electrical models. Source: (Ochoa, 2022)

Figure 17 shows the availability spectrum of network monitoring, which is important as the DOE calculation method needs to measure the dynamic and real conditions of LV networks (Final Report RACE for Networks Program Research Theme N2: Low voltage network visibility and optimising DER hosting capacity, 2021). The available capacity of an electricity network varies over time as it depends on households' electricity demand and local generation from DER in the network.

Therefore, monitoring equipment in LV networks is necessary to track the network's fluctuating capacity and use the estimated network capacity to calculate DOE. Figure 17 shows that network monitoring equipment is generally available at the zone substation point, but access to data at the distribution transformer or LV network is limited. 29 This study confirms that DOE implementation still faces some challenges around network data.

29 The meaning of the superscripts in Figure 14 are: (*) Business-as-usual and short-term plans, (1) available for most of the high voltage head of the

feeder, (2) available for a fraction of the distribution transformers, and (3) available at the end of few long LV feeders and/or next to few customers’ connection point.

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Figure 13. Availability spectrum of network monitoring. Source: (Ochoa, 2022)

Potential solutions for these data availability challenges could be to develop new DOE calculation methods that require fewer network data and monitoring equipment. For example, the research in Bassi et al., 2022 has developed a free electrical-model method to estimate DOE using data from smart meters without accurate network models.

Findings Although the rollout of DOE is expected to increase in the next five years, DNSPs are still facing technical and data access challenges to speed up the process and improve the accuracy of their calculations. These challenges have given rise to the following research questions: • Given the existing challenges in network models and monitoring equipment availability, how many DNSPs will be able to offer DOE by 2025, and 2030?

• Regarding the DOE service offerings: o How does DNSP plan to manage the phasing-out transition to more customers with DOE? o How will DNSP incentivise/engage people to opt in for DOE? o How new tariff structures and DOE can be aligned? And how may this impact households' benefits? o How is the effect on DOE impact factors if customers pay for greater access or less curtailment?

• Regarding the DOE technical characteristics, o What is the most common method used by DNSPs for DOE capacity allocation? o Is it possible to have a common agreement on some principles to calculate DOE? o Would national standardisation be beneficial for households and DNSPs? • Regarding DOE regulatory compliance, o How could DNSP report on the output of DOE? What metrics and tools are required for reporting DOE performance? o What parties are responsible for guaranteeing compliance with DOE? o What kind of penalties could be used to enforce compliance? What effect could penalties have on household decisions to participate in DOE?

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3.2.10 Academic relevant literature This section provides a summary of the academic literature on DOE, based on a review of 19 research papers. It should be noted that the review only included papers that specifically mention or use the concept of DOE or import and export limits, as this is the focus of this RT. The purpose of the literature review was to address the following questions: • What topics does the academic literature on DOE cover? Is there any research on the social impact of DOE? • Is there an examination of import and export DOE in the research?

• What types of DER are studied in the literature? Do they consider flexible loads?

• Do researchers investigate the factors that influence the impact of DOE in their studies?

• What other relevant aspects do academic studies of DOE consider?

Findings Table 26 presents an overview of existing academic literature about DOE. The majority of the literature centres on defining and explaining the concept of DOE, as well as evaluating various calculation methods, such as optimisation algorithms, state estimation techniques and data-driven methods. Indeed, some of these methods are being used by existing trial DOE projects, as noted in Section 3.2.5.

In contrast to the paucity of evidence in grey literature and existing trials on import limits, the formulation and calculation of both export and import limits are well-established in academic literature. Therefore, if a method for calculating export limits is already in place, calculating import limits may not pose a significant challenge for DNSPs. However, most research to date has focused on rooftop solar and battery systems rather than flexible loads, providing an opportunity for future research. Additionally, the study of DOE impact factors and the underlying trade-offs has been mostly avoided or restricted to the analysis of fairness only.

Most recent work is also exploring new functionalities of DOE, including the use of DOE for reactive power, network planning applications, market participation and integration with DER aggregation models. Another key consideration is the application of DOE in unbalance distribution networks. Preliminary findings from (B. Liu & Braslavsky, 2022) indicate that overvoltage can still occur when all customers are within their DOE. Hence, further research is needed to test DOE under different operating conditions (e.g., unbalance distribution networks and different network topologies) and new potential functionalities, such as DOE for reactive power and network planning applications.

Based on these findings and future research suggested in the literature, the following research questions are proposed for future work: • There is a lack of academic research on the social implications of DOE. What are the social implications of DOE? Why has there been so little social research on DOEs to date? And what are the reasons for this?

• Some research has started to explore additional functionalities of DOE, such as reactive power and market participation. What are the challenges of implementing these new functionalities in real-world distribution networks?

• Many methods (optimisation, state estimation, data-driven) have been proposed for calculating DOE. What are the advantages and disadvantages of each method? Which methods are more suitable for implementation in realworld distribution networks?

• Existing research mainly focuses on current DER penetration levels and does not examine future scenarios with higher penetration. How will DOE be affected by an increase in DER in the network? How might the factors that influence the impact of DOE change in these future scenarios?

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•

• •

Most methods for calculating DOE rely on full network visibility. How many of the proposed methods in the literature can be practically applied? Are there potential methods that do not require full visibility? And can these methods calculate DOE with reasonable accuracy with only limited visibility?

To what extent can DOE be applied to unbalanced distribution networks? What additional considerations are needed for DOE to be applied in unbalanced distribution networks?

What are the opportunities for future research on flexible loads in the context of DOE given that most previous research has been focused on rooftop solar and battery systems?

Table 17. Overview of existing academic literature about DOEs

Paper

Main scope

Ricciardi et al., 2019

Methodologies to define DOEs to minimise curtailment and investigate trade-offs between the effects of DOE in networks capacity and household’s exports

(Petrou et al.,

2020) (Michael Z. Liu

et al., 2021)

Type of DOE

DER types

Trade-off analysis

Export and Import DOE

Solar, Battery

Yes. It proposes two performance metrics to assess the effects on energy exports and curtailment on households.

Uses a weighted proportional fairness factor to define DOEs

Export and Import DOE

Solar, Battery

Yes. It compares two methods that prioritise effective and fair capacity allocation.

Explains the operating envelope concept

Export and Import DOE

Not applicable

No

Explains the need for OE

Export and Import DOE

Solar, Battery

No

(M. Z. Liu,

Ochoa, Ting, & Theunissen, 2021)

A framework for DNSPs to ensure the integrity of MV-LV networks by using DOEs

Export and Import DOE

Solar, Battery

Yes. It proposes two performance metrics to assess the effects on energy exports and curtailment on households.

Krause, 2021)

Explores the implementation of state estimation and DOEs

Export and Import DOE

Solar, but battery implications are discussed

No

(B. Liu & Braslavsky, 2022)

Analyses phase voltage sensitivities around DOEs in unbalance LV networks

Export

Solar

No

(Mahmoodi &

Proposes DOEs which contains lower and upper bounds of unbalance LV network

Export and Import DOE

Solar

Yes. It compares equal allocation and individual allocation methods.

(Petrou et al.,

2021)

(Milford &

Blackhall, 2021)

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(Riaz &

Proposes nodal operating envelopes for DER flexibility modelling and characterisation

Export and Import DOE

Solar and battery

No

DOEs to facilitate the participation of DER in energy and reserve markets

Export and Import DOE

Solar and battery

No

Proposes a framework around DOEs to quantify and integrate the supply capacity of VPPs in the operation of microgrids

Export and Import DOE

Solar

No

Explores the impact of capacity limitation services on network state estimation and forecasting

Export and Import DOE

EV

No

Proposes a framework that enables DOE aggregatoragnostic approach for DNSPs

Export and Import DOE

Solar and battery

No

Gerdroodbari, Khorasany, & Razzaghi, 2022)

Proposes a framework with dynamic active and reactive OEs

Export and Import DOE

Solar and battery

No

(Yi & Verbič,

Bi-level framework to calculate DOEs under uncertainty

Export and Import DOE

Solar and battery

Yes. It proposes a metric to measure fairness

& Alpcan, 2021; Bassi, Ochoa, Alpcan, & Leckie, 2022)

Proposes an electrical modelfree voltage calculation approach that can be applied to estimate DOEs

Export and Import DOE

NA

No

(McLaren,

Presents an analytical methodology to identify DOEs to manage interconnection costs and explores their economic feasibility

Export and import DOE

Solar and battery

No

Mancarella, 2022) (Attarha,

Mahdi Noori R. A, Scott, & Thiébaux, 2022) (Mohy-ud-din,

Muttaqi, & Sutanto, 2022) (Chen, Ziras, &

Bindner, 2022) (Michael Z.

Liu, Ochoa, Wong, & Theunissen, 2022) (Zabihinia

2022) (Bassi, Ochoa,

Abraham, Darghouth, & Forrester, 2022)

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3.2.11 International precedents While the research on DOE is timely and systematic in Australia, DOEs have received scant attention in the research and industry institutions from overseas. As of the writing of this document, there is only one relevant international precedent to review. Electric Power Research Institute (EPRI) published a report highlighting the importance of DOEs for managing flexibility in distribution systems (Electric Power Research Institute (EPRI), 2022). Also, EPRI presents examples of use cases and data requirements for each application. As DOE have the potential to become a powerful mechanism for grid operation, EPRI notes the following considerations to improve their design and implementation: • Promote a framework to improve network data models and data management.

• Improve DER and load forecasting methods.

• Review hosting capacity analytics.

• Create intuitive displays, dashboards, and other visualisation tools to communicate current and forecast conditions clearly.

• Document the flow of data and signals related to DOE between households, aggregators, and networks.

Additionally, there is one practical example of operating envelopes in California, US (Electric Power Research Institute (EPRI), 2022). The California Public Utilities Commission introduced the term limited generation profile (LGP) in late 2020 through changes in the rules for DER interconnections. This change allows using DOE for DER interconnections, traditionally under fixed-capacity connection agreements. However, its implementation has experienced some delays due to technical hurdles, including certification of inverter power control systems that comply with the use and modification of LGPs, and the need for tools and processes for integrating these changes into grid planning and operation.

Findings Australia is leading the work and research in DOE around the world. The considerations suggested by EPRI are aligned with the outcomes of DOE grey and academic literature in Australia. Additionally, it is evident that the practical implementation of DOE in the US is facing challenges previously experienced in Australian trial projects. For example, the test of LGP in the US has experienced delays due to equipment compatibility issues, similar to the challenges identified through the Flexible Export Limits project in SA.

3.2.12 Research questions The research questions of this RT are given in Table 27. The research questions have been classified based on the sections presented in this RT.

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Table 18. Research questions for RT2

Category

Section

Research question How do different DOE allocation methods and location scale levels, technologies and household behaviour synergistically interact at the individual household level, and how does this translate into system-wide impacts and therefore impact other households?

How different DOE allocation methods should be compared? What are the metrics to compare the methods? What are the advantages and disadvantages of each method?

What are the consequences, benefits, and other impacts for households of the proposed DOE allocation principles by the DEIP DOE WG?

DOE allocation principles

3.2.4

How do different DOE allocation methods and location scale levels impact household decisions regarding DER investment and operation?

To what extent can the proposed DOE allocation principles also be applied for import DOE? How do DOEs allocation principles can affect EVs charging behaviour?

How do allocation methods and location scale level affect the strategy of HEMS and aggregators?

In which conditions are households willing to trade transparency and fairness vs efficiency?

Although standardising a common approach for DOE calculation and implementation is a complex task (Australian Renewable Energy Agency (ARENA), 2021), a comprehensive comparative analysis between the different DOE calculation approaches could reveal commonalities that can be used to establish a guideline for DOE implementations and support the DOE allocation principles. What are the main similarities and differences between the DOE calculation approaches? How do the DOE calculation approaches perform against the DOE impact factors? Why is the Evolve DOE calculation approach the most adopted approach?

Trial DOE projects

3.2.5

What are the barriers to incorporating social research in the DOE trial projects, and how can they be overcome to ensure the social study of DOE? Although the DOE calculation approaches can calculate import and export DOE, most projects do not consider import DOE. What are the implications of neglecting import DOE for deploying DOE? As the most recent projects are testing new applications of DOE to deliver market and network services, how do the new applications differ from the initial DOE calculation approach in terms of the DOE impact factors?

Regulatory framework

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3.2.6

Current connection agreements are appropriate to set out conditions for export DOEs, but the AER recognises that some potential changes must be reviewed to protect the customer. What are the risks and benefits of changing connection agreements to recognise DOE?

Some potential changes in the regulation regarding DOE performance and consumers' compliance may be included in the future regulatory framework. How DOE performance and compliance can be measured and monitored? Should households be exposed to penalties in case of no response to DOEs signals? How should no-response situations be managed? What is a reasonable time frame to receive and respond to a DOE signal?

The AER follows the DOE allocation principles proposed by the DEIP DOE WG. Regarding how DOE should be offered, relevant emerging questions are: What is the impact of opt-in versus opt-out DOE on (i) household engagement and trust in DNSPs, and (ii) household electricity use and load profiles?

The implementation of DOEs requires the involvement of multiple parties, including households, installers, retailers, networks, aggregators. What is the role and responsibilities of each party for successfully deploying DOEs? Although the AER recognises the role of import limits, the initial assessment only considers export limits. How does the regulatory framework need to change to incorporate import DOE? What type of information do households need to know to make informed decisions to optin DOE? How can the information be explained or communicated to all households simply and transparently, considering that they have different motivations, attitudes, and expectations?

What are the best methods for creating visual aids and measuring progress to effectively communicate the performance and benefits of DOE to customers and the entire system?

Household Considerations

3.2.7

Existing research has also highlighted the need to engage with relevant parties, in addition to households, to upskill and explain the emerging DOE concept and implementation. This includes retailers and installers as the first point of contact with households. What is the most effective way to engage and educate retailers and installers, so they are able to communicate the DOE concept to households?

Create educational tools to communicate the adopted allocation levels and capacity allocation approaches in a simple and transparent way to households. This may include visualisation tools, metrics, and communication channels to better interact with households.

Technical considerations

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3.2.8

Equipment compatibility represents a challenge for deploying DOEs. Households may need to invest in new equipment or upgrades, which not all households are willing to accept.

How many households currently have equipment that is compatible with DOEs? To what extent are households willing to accept new upfront costs to enable their DER to respond to DOE signals?

Most of the existing work has been focused on facilitating the compatibility of generation assets or their associated inverters. However, it is unclear how other assets will interact with DOE signals. What is the technical compatibility of flexible loads to respond to DOE signals? What do flexible loads need to respond to DOE signals? What potential additional costs may the household need to face to adopt DOE?

If communication is lost, it has been proposed that DER be set to predefined limits by DNSPs. What are the potential consequences for customers? What alternative solutions should be considered?

Stakeholders have emphasised the need for a nationally consistent approach to DOE across DNSPs, including consistent communication and publishing specifications to avoid additional costs. What would a national approach for communication and publishing DOE involve?

Given the existing challenges in network models and monitoring equipment availability, how many DNSPs will be able to offer DOE by 2025, and 2030?

Implementation pathway

3.2.9

Regarding the DOE service offerings: • How does DNSP plan to manage the phasing-out transition to more customers with DOE? • How will DNSP incentivise/engage people to opt in for DOE? • How new tariff structures and DOE can be aligned? And how may this impact households' benefits?

• How is the effect on DOE impact factors if customers pay for greater access or less curtailment?

Regarding the DOE technical characteristics, • What is the most common method used by DNSPs for DOE capacity allocation?

• Is it possible to have a common agreement on some principles to calculate DOE? • Would national standardisation be beneficial for households and DNSPs? Regarding DOE regulatory compliance, • How could DNSP report on the output of DOE? What metrics and tools are required for reporting DOE performance?

• What parties are responsible for guaranteeing compliance with DOE? • What kind of penalties could be used to enforce compliance? What effect could penalties have on household decisions to participate in DOE? There is a lack of academic research on the social implications of DOE. What are the social implications of DOE? Why has there been so little social research on DOEs to date? And what are the reasons for this?

Some research has started to explore additional functionalities of DOE, such as reactive power and market participation. What are the challenges of implementing these new functionalities in real-world distribution networks?

Academic literature

3.2.10

Many methods (optimisation, state estimation, data-driven) have been proposed for calculating DOE. What are the advantages and disadvantages of each method? Which methods are more suitable for implementation in real-world distribution networks?

Existing research mainly focuses on current DER penetration levels and does not examine future scenarios with higher penetration. How will DOE be affected by an increase in DER in the network? How might the factors that influence the impact of DOE change in these future scenarios?

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Most methods for calculating DOE rely on full network visibility. How many of the proposed methods in the literature can be practically applied? Are there potential methods that do not require full visibility? And can these methods calculate DOE with reasonable accuracy with only limited visibility?

To what extent can DOE be applied to unbalanced distribution networks? What additional considerations are needed for DOE to be applied in unbalanced distribution networks?

What are the opportunities for future research on flexible loads in the context of DOE given that most previous research has been focused on rooftop solar and battery systems?

3.3 RT.3 Household level appliance/PV integration model comparison 3.3.1 Introduction The integration of behind-the-meter DER has the potential to provide significant benefits. Households can use the inherent flexibility of DER to manage their electricity usage and production in response to price and dispatch signals, potentially reducing their electricity costs, increasing their independence from the grid, and maximising the value of their DER. DER flexibility can also be seen as a valuable resource for the power system, offering opportunities to avoid costly network investments and facilitate the adoption of renewable energy sources. For instance, in Australia, the orchestration of DER has the potential to avoid $16 billion in network infrastructure investment by 2050 (CSIRO and Energy Networks Australia, 2017). The Australian Energy Market Commission (AEMC) has also recognised the potential of DER to provide ancillary services, such as frequency control ancillary services, through the use of batteries connected at the distribution level (Australian Energy Market Operator (AEMO), 2019).

As a consequence of the breadth of potential benefits of DER, different approaches for integrating DER into power systems have been developed. This RT aims to review and examine the implications of these approaches for households. In doing so, this RT will compare outcomes (savings to prosumers, benefits to DNPSs, user/prosumer preferences) between different aggregation models. The emphasis will be on models which include using solar and flexible loads. This RT will study the outcomes for the different models and modes of operation in terms of usage profiles, cost effectiveness, and value propositions to both the owners of the DER and the network.

This RT is organised as follows. Section 3.3.2 summarises relevant work done in previous OAs about integration of household level appliance and PV systems into the network. Section 3.3.3 briefly presents potential benefits and opportunities of DER integration that are considered in this RT to compare and assess DER integration models. In Section 3.3.4, the adopted DER integration models are described and compared. The market status of the DER integration is reviewed in Section 3.3.5. Finally, this RT concludes by overviewing the identified research questions through this RT.

3.3.2 Review of work done in previous OAs The RACE OA H1 reviews relevant elements of residential solar pre-cooling (and heating) models and technologies. The work in the H1 OA is relevant to this H3H5 OA as the SPC/H is one example of DER integration. Hence, this OA expands the study presented in the H1 OA by considering additional models of DER integration.

The work in the H4 OA is focused on rewarding, flexible demand, which is also relevant for this work. In particular, the H4 OA provides an overview of models that customers can use to respond to various incentive mechanisms. Hence, some of the models described in the H4 OA are also considered in this report. However, it is worth noting that the type of analysis

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is different as this OA mainly focuses on comparing outcomes of various DER integration models rather than rewarding mechanisms.

Similarly, the N2 OA summarises the value that DER can provide in relation to network support services. Nonetheless, the N2 OA does not compare different types of models in terms of usage profiles, cost-effectiveness, and value proposition for the DER owners. This is the gap that the H3/H5 OA will cover.

Finally, the RACE FT project named 'business models and the energy transition' reviews business models in the electricity system that can accelerate the energy transition. Among the highest value models identified, some business models can be associated with the DER integration models presented in this OA, such as virtual power plants (VPPs). The analysis of each DER integration model's business models is out of this OA's scope. Instead, this OA focuses on reviewing different DER integration models, which complements the work presented in the RACE FT, (Mouritz et al., 2022).

3.3.3 DER integration potential benefits and opportunities Before comparing the outcome of different DER integration models, it is worth briefly summarising the value opportunities of DER integration, considering the potential DER benefits and services. Note that the scope of this OA considers the following DERs: PV systems, community batteries, and shiftable 30, curtailable 31 and interruptible 32 flexible loads 33.

DERs can provide a range of services across the power supply chain and understanding these services can help to unlock the value of DER and facilitate higher levels of integration. This understanding can support the analysis of different DER integration models. Figure 18 describes possible DER services across the power system (AEMO and Energy Networks Australia, 2019; Australian Energy Market Operator (AEMO), 2019; DER Roadmap, 2019):

30 Shiftable loads refer to flexible loads which operate at a specified interval with a certain amount of energy consumption and their operation can

be transferred to another period of time, such as washing machines and dishwashers.

31 Curtailable loads refer to flexible loads which can be reduced in peak periods (e.g., high energy prices and high peak network demand), such as

dimmer lights.

32 Interruptible loads refer to flexible loads which can be temporarily disconnected and connected to another time. For example, air conditioning systems that can be cycled on and off to reduce energy consumption during peak demand periods.

33 The term flexible loads are applied to electrical loads on the network that households may be able to shift or adjust their operation at different times in response to generation, network or market signals and considering household preferences.

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Customer

Network services

System services

Other values

• Reduce energy costs of electricity bills • Retailer buyback payments • Reduced carbon footprint • Achieve some level of control and independency

• Network reliability services: • Providing power in the event of a network disruption • Netork capacity and support: • Defer or avoid costly network investment • Support during peak events • Support voltage management by injecting or absorbing reactive power

• Provide flexibility value to the market (upwards/downward s contingency reserve, frequency up/down and voltage regulation services) • Provide demand reduction or generation capacity services • Wholesale electricity market participation • Reduce losses on the transmission and distribution systems due to local balancing

• Hedge customers against market extreme events using local generation and energy arbitrage services • Opportunities for innovation and new product offerings

Figure 14. Possible DER services across the power system

However, unlocking these DER values create new challenges for the energy system. Multiple elements in the energy system need to be integrated and synchronised effectively to maximise the potential benefits (AEMO and Energy Networks Australia, 2019). The growing uptake of DER connected to distribution networks has led to increased variability and unpredictability in network flows, requiring new considerations. The study of DER integration models is crucial to ensure that DER provide benefits to both households and the network, and do not worsen existing issues or create new ones. For example, a distribution network with high penetration of PV systems may experience overvoltage issues if they are not coordinated with the network operating conditions, leading to PV inverters shutting down. This may even prevent them from restarting. Therefore, it is important to find effective models to better integrate DER, while maximising the value for the DER owner and the system.

3.3.4 DER integration models There is a plethora of research and work on DER integration models (Charbonnier, Morstyn, & McCulloch, 2022; Guerrero, Gebbran, Mhanna, Chapman, & Verbič, 2020; Khorasany, Razzaghi, Dorri, Jurdak, & Siano, 2020). They may differ substantially from each other in terms of DER capabilities (e.g., generation, storage, flexibility), associated programs (e.g., demand response, virtual power plants, peer-to-peer energy trading), and algorithms, or in terms of services that DER can deliver (e.g., market ancillary service and network services). Hence, a comprehensive framework of DER integration models based on the work presented in (Charbonnier et al., 2022) is adopted to conduct the review presented in this RT. Specifically, the structure of the adopted framework is presented in Figure 19. This framework categorises DER integration models into two groups based on the aggregation of DER: those that aggregate DER at a household level, and those that aggregate DER from multiple households. The latter group can be further divided based on the method of control and coordination of the DER. In more detail, a brief description of the DER integration models is presented below.

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Household level integration DER integration

Direct control Mediated coordination

Aggregated Indirect control

Bilateral coordination

Figure 15. Adopted classification of DER integration models. Adapted from (Charbonnier et al., 2022)

• • •

Household-level integration: This is the status quo model and considers individual households with DERs, whose operation depends on the sole benefit of the DER owners without any aggregation or coordination.

Aggregated direct control integration: This model considers aggregated DER which are directly controlled and coordinated by a third-party, such as an aggregator, retailer or DNSP. Aggregated indirect control integration: This model can be broken down into two models: o Mediated coordination, which considers the intervention of a third-party (e.g., aggregator, retailer, DNSPs) agent to facilitate the DER coordination process, but households make their own decisions based on the information provided by the third-party. o Bilateral coordination, which relays on the exchange of information between households to achieve an aggregated outcome.

Additionally, these DER integration models can be described by three common system characteristics, such as: i.

Customer participation, which refers to how households participate in the DER integration model and whether they can satisfy their preferences and maintain autonomy in the control of their devices. ii.

Network awareness, which refers to the capacity of the DER integration models to consider the electricity network constraints. iii.

Computation and communication, which refers to the collection and management of data to control the households DERs. Table 28 briefly describes these common system characteristics for each DER integration model, along with some representative examples and relevant academic research references.

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Table 19. Summary of key elements of DER integration models

Customer participation

Household level integration

Aggregated direct control

Aggregated indirect control, mediated coordination

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Households are autonomous in controlling their DERs and to satisfy their individual preferences.

Tariffs are used to engage household participation.

A third party has a certain level of control over some DER. Incentives are used to encourage household participation Households interact with an aggregator to achieve aggregated goals.

Incentives are used to encourage household participation.

Network awareness

Network operating constraints are not considered (beyond the initial and fixed limits established in the connection agreement)

Network constraints may be considered depending on the interaction of aggregators and network operators

Network constraints may be considered depending on the interaction of aggregators and network operators

Computation and communication

Examples

Relevant references

All information is collected and managed behind the meter.

Households with HEMS and time-varying tariffs (e.g., ToU, real-time price, etc). Examples of households' objectives include minimising electricity costs while maintaining acceptable comfort levels.

(Mahapatra & Nayyar, 2022) explains the HEMS concept, technical considerations, and common issues and challenges faced by HEMS.

Direct load control mechanisms.

For example, controlling A/C systems or thermostats. Or curtailing/interrupting specific devices in response to signals (e.g., DOEs provided by DNSPs).

(Callaway & Hiskens, 2011) presents an overview of direct control objectives, architectures, potential benefits, and challenges of direct control models.

Virtual power plants. Also, community batteries. Examples of aggregator's goals include minimising system and household costs and providing system services. An example of a VPP aggregator is a retailer.

(Molzahn et al., 2017) surveys the literature on distributed mechanisms to optimise and control power systems, including DER.

Information is managed and collected by the aggregator.

Calculations are conducted centrally by the aggregator.

Information is managed and collected by the aggregator, but the calculation to achieve consensus is done by the aggregator and local computations of each household.

Household exchange information among themselves.

Aggregated indirect control, bilateral coordination

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Households have autonomy to participate to satisfy their preferences and achieve benefits.

Network constraints may be considered, but requires intervention in the negotiation/iteration process

Information is managed and collected by households.

Calculations are conducted by each household.

Decentralised peer-to-peer (P2P) energy trading. Households with DER trade their energy surplus with other households willing to buy in a local market.

(Tushar, Saha, Yuen, Smith, & Poor, 2020) provides a comprehensive review of the state-of-theart in research on P2P energy trading techniques.

Comparison of DER integration models As described in the introduction of this RT, this OA compares the DER integration models in terms of savings to households, benefits to DNSPs, household preferences, cost effectiveness, computation and communication burden and value preposition. In doing so, this section starts with a review of each model based on these items, and Table 29 summarises the findings.

Household level integration Household level integration refers to approaches that consider self-interested consumers with PV systems or flexible loads. This model is the status quo scenario as households are autonomous in executing their DER control strategy that satisfies their individual preferences. These DERs can be controlled or coordinated BTM through a HEMS, but the management mechanism does not directly consider external interest from other households or agents in the electricity system (Mahapatra & Nayyar, 2022). Note that under this model, households can incorporate external signals to inform management and control over appliances, such as market prices, weather data or public historical system information. In this model, household savings are defined by the tariff structures and how the devices respond to these tariffs, such as time-of-use or real-time pricing. Potential benefits to DNSPs may include reducing or shifting load peaks that alleviate network congestion (Ruth et al.; Tuomela, de Castro Tomé, Iivari, & Svento, 2021).

Aggregated DER Integration As earlier noted, the cluster of individual DERs and their coordination can unlock major benefits to power systems.

Common examples of aggregated DER integration models include demand response mechanisms, VPPs, and peer-to-peer energy trading. Existing trials have tested VPP market participation and value staking. For example, the AEMO VPP Demonstration project considered 8 VPPs across the NEM with a total registered capacity of 31 MW and around 7150 consumers participated in the project (Australian Energy Market Operator (AEMO), 2021). A quarter of these consumers have battery systems. VPPs supported the power system during numerous major contingency events and the VPPs reached 3% of the market share in the contingency FCAS market.

Now, extensive research has been conducted to propose multiple coordination mechanisms to orchestrate DER and align the objectives of customers, network, and market. A typical aggregated DER integration model comprises an aggregator that coordinates DER participants through advanced information and communication technologies. Alternatively, DER coordination can also be achieved through the interaction between households to work in their bests interest, such as peer-to-peer markets. There are many diverse methodologies and approaches to aggregate and coordinate DER. In order to conduct a systematic review, this section follows the classification presented in Figure 19. Hence, aggregated DER integration models can be broken down into direct control, mediated, bilateral and implicit coordination as presented below. a) Aggregated direct control.

This category groups other terms such as, direct load control and centralised approaches. The household devices and appliances can be centrally controlled directly by the aggregator agent. This control can be executed through setting operation points, limiting loads or generation or adjusting cycling operation modes (Borsche & Andersson, 2014). This model requires that households provide a certain level of control over their devices to the aggregator agent. Additionally, new monitoring equipment and advanced communication infrastructure are likely needed to allow coordinated active management of household devices. However, collecting and managing data from a large number of agents can become unwieldy (Sleiman Mhanna, Chapman, & Verbič, 2018). Hence, the other DER aggregation models have adopted distributed and decentralised approaches to alleviate these barriers and increase the value for households and DNSPs, as noted below.

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b) Aggregated mediated coordination.

In mediated coordination models, the aggregator exchange information with households (or automated HEMS acting on households' behalf), such as price signals or variables that household computation devices need to coordinate their response with the rest of the aggregated DERs. The outcome of this approach relies on the information shared by households; therefore, inaccurate information or strategic behaviour (e.g., do not respond to aggregator's signals) may lead to an inefficient outcome, such as a reduction in billing savings (Wooldridge & Jennings, 1995; S. Xu et al., 2021).

Typical methodologies to achieve coordination includes auction and optimisation mechanisms that aim at minimising the cost of the aggregator and household participants and maximising the value that the aggregated DERs can deliver to the system. Hence, households can also obtain new financial benefits from providing network and market services (Attarha, Scott, & Thiébaux, 2020). c) Aggregated bilateral coordination.

This model considers the exchange of information between households to achieve individual goals. The DER coordination is achieved through the exchange of information between households and considers limited participation of a third-party in the consensus among the households. Some examples of these models are peer-to-peer markets in which households can trade energy among themselves with limited participation of a third-party entity (Sousa et al., 2019; Tushar et al., 2020).

Findings The findings and research questions in this section are presented by comparing the DER integration models in terms of savings to households, benefits to DNSPs, household preferences, cost effectiveness, computation and communication burden and value proposition. In doing so, Table 29 presents an overview of each DER integration model against these elements. The information in Table 29 is used to compare the DER integration models and provide the findings below.

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Table 20. Comparison of DER integration models

DER integration model

Household level integration

Aggregated direct control

Savings to households

Benefits to DNSPs

Household preferences

Computation and communication burden

Cost effectiveness

Value propositions

Limited to tariff rates

Easy to satisfy household preferences (e.g., Reduce/shift load peaks. comfort levels, environmental goals)

No need to exchange signals and requires local computation

It may be an affordable option, but it may not be able to unlock the full value of DERs

Easy implementation, and allow household autonomy and satisfaction of individual preferences

It can be complex to manage if all data and calculation is carried out centrally, especially for large clusters of DERs

It may require additional equipment to allow control, but the associated costs may be covered by the incentives

Offers additional financial benefits without the need of new complex and costly systems

Additional financial benefits through incentives for allowing certain level of control and energy arbitrage

Direct control may allow some management of network capacity.

Defer/avoid costly network investment.

Households may cede autonomy and comfort to allow a certain level of control

It can be used to address some contingencies.

Manage network capacity.

Aggregated indirect control, mediated coordination

Additional financial benefits through network and market services

Aggregated indirect control, bilateral coordination

Additional financial benefits through negotiation with other households and deliver

Defer/avoid costly network investment.

It can be used to address some contingencies.

Manage network capacity.

Defer/avoid costly network investment.

Households may cede autonomy and comfort to allow a certain level of control

Easy to satisfy household preferences (e.g., comfort levels, environmental goals), but it can also require

It requires control and communication layers to allow two-way channels of exchange of signals Scalability can be achieved through distributed algorithms It requires control and communication layers to allow two-way channels of exchange of signals. It may also need emerging ICT

Requires new investment in ICT infrastructure to

Requires new investment in ICT infrastructure

Enable new benefits to the whole systems as it allows DER coordination

Democratisation of energy Empower households by allowing the trade among themselves

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network and market services

It can be used to address some contingencies.

Network local balancing

trade-off between comfort and financial benefits

such as, distributed ledger technologies to allow exchange of financial flows Scalability can be achieved through decentralised algorithms

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Overall findings The conducted review revealed that no studies or projects compare all DER integration models under similar operating conditions or case studies. This is an important gap because the assessment and comparison of each DER integration model's benefits, challenges and drawbacks can vary depending on the specific operating conditions. Existing literature, such as the work in (Khorasany et al., 2020) and (Charbonnier et al., 2022), considers all DER integration models, but focuses their work on reviewing relevant literature to clarify concepts, terminology and classifications. That is, they do not explicitly compare their outcomes. The work in (Guerrero et al., 2020) technically compares different DER integration models simulating a case study of high penetration of PV systems and battery storage in a LV network. Specifically, the analysis is mainly focused on the potential economic benefits for DER owners under different DER integration models, and how each model may impact voltage levels and thermal congestion. The results showed that the aggregated indirect DER integration models are the most profitable models for households and the coordination through the VPP and P2P market ensures that the network constraints are respected. In contrast, the individual and aggregated direct control model (i.e., HEMS and HEMS with OE in (Guerrero et al., 2020)) have the lowest benefits for households and they may lead to overvoltage and overlading issues. Note that this study did not consider market and network services, which may increase the financial benefits of households with the aggregated indirect DER integration models.

Savings to households Aggregated models are more likely to achieve more household savings than individual household level integration models.

Savings in household level integration models are limited to tariff rates. For example, households can avoid peak prices by shifting loads and receive incentives by exporting power to the grid. In contrast, aggregated levels are likely to unlock more DER values and savings for households. As noted earlier in this RT, aggregated DER can provide network support and market services, bringing new savings for households. Additionally, emerging aggregated models, such as P2P energy trading, would create new savings for households by allowing energy trading among them.

• [RQ] If aggregated models would enable more household savings, what barriers prevent the number of household participants in aggregation programs from increasing?

• [RQ] How would new tariff reforms affect savings for household-level integration models 34?

Benefits to DNSPs DER integration models can benefit DNSPs, especially for managing network congestion and deferring or avoiding costly network investment 35 . However, some DER integration models do not explicitly consider technical and operating constraints associated with the electricity network, which may lead to network problems such as overvoltage and overloading. Hence, household level DER integration models are suited to networks with low DER penetration, but when the number of DER increases, the aggregated DER integration models are likely to be more appropriate. In this context, aggregated mediated and bilateral coordination models may bring more benefits to DNSPs by respecting network constraints, offering network support (e.g., generation and load peak management, and balance of households' generation and demand), and alleviating network investment in costly solutions.

• [RQ] Although the potential benefits of DNSPs have been identified in the literature, the explicit value and association between DNSPs benefits and customer incentives are still unclear. What is the explicit value between DNSP benefits and household incentives?

• [RQ] How can network services provided by DER help households recover their investment in DER and the relevant equipment to respond to coordination aggregation models?

34 For example, the imposition of export tariffs for rooftop PV to reduce network congestion and support grid upgrades as determined in the

AEMC Final determination, ‘Access, pricing and incentive arrangements for distributed energy resources’, 2021.

35 Note that the scope of this review is on the benefits of DER integration models. Hence, the challenges associated.

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•

[RQ] What regulatory standards can be implemented to avoid DER integration models exacerbating or creating new network issues?

Household preferences DER integration models have different ways of considering and satisfying household energy consumption preferences, and they may experience a trade-off between financial benefits and household preferences. Household level integration models can directly meet household preferences as the control of the DERs is conducted by households. Although households may need to interrupt or shift the operation of DERs to achieve more savings (e.g., schedule the operation of a washing machine to avoid peak price tariffs), households have the autonomy to control DERs based on the comfort levels that they are willing to accept. In contrast, households in aggregated models likely need to cede a certain level of control in exchange for rewards since the aggregator needs to align the preferences and goals of all participants. Note that mediated and bilateral integration models may need to achieve agreement between household participants to satisfy their preferences and unlock more value. For example, a VPP may need that the majority of households reduce their consumption at a specific time. Another example is the negotiation process in P2P transactions, which requires an agreement between buyers and sellers on a transaction price and amount of energy.

Computation and communication burden The computation and communication requirements increase with the number of DERs to control and household participants in aggregation programs. Local computation capabilities are required to effectively operate their DERs and satisfy their preferences subject to relevant conditions such as, weather and tariff structures. Household-level integration models carry out the schedule of DERs locally based on their individual preferences. In contrast, the schedule in the aggregated models requires the exchange of information between households and aggregators through two-way communication channels with adequate latency and bandwidth to dynamically receive and send signals. One example of control signals are DOEs sent by DNSPs as they set the export and import limits for DER owners. Now, the communication and communication burden can become unwieldy in large-scale systems. Hence, mediated, and bilateral coordination models consider distributed and decentralised approaches that distribute the computation burden among the participants to alleviate these barriers. For example, some P2P trading approaches consider self-interested households, and each household can develop their own bidding strategy to trade energy, so there is no need to collect and manage data by a central agent.

• [RQ] What are the minimum ICT requirements to effectively adopt each DER integration model?

• [RQ] How issues related to ICT (e.g., communication lost) impact the potential savings and benefits of the DER integration models?

Cost-effectiveness The expenses of implementing DER integration models vary based on the type of DER technology used and the necessary ICT to support its operation. For instance, HEMS can generate household savings, and blockchain can facilitate peer-topeer market transactions. Implementing integration models at the household level may not be costly, but models that involve coordination with an aggregator or other households may require more ICT resources, such as controllers, smart meters, smart inverters, etc.

• [RQ] What is the economic value released by each DER integration models and how does it compare across different models when using cost, incomes and investment data?

Value propositions Each DER integration model has unique benefits or services to attract households. Household level integration models can offer effective control and operation of DERs without costly ICT. Hence, the value proposition of household level integration models is to allow households to effectively schedule their DER to reduce electricity bills while satisfying their 111

preferences, such as comfort levels or environmental goals. The value proposition of aggregated models is to unlock the value of DERs by delivering network and market services through the control and coordination of a large number of DERs. Specifically, the value proposition of aggregated direct control is to enable managing peak demand and generation by remotely controlling DERs during peak events, resulting in cost savings for households and networks. The value proposition of aggregated indirect mediated control is to enable the coordination of DERs to improve grid reliability and stability and allow households to provide network and market services. And the value proposition of aggregated indirect bilateral control model, such as P2P trading, is to allow households and communities to trade excess energy generated by their renewable energy sources directly with their neighbours, instead of selling it to the grid. This can lead to greater energy independence, cost savings, and increased use of renewable energy sources. Additionally, P2P trading can lead to greater engagement and awareness of energy usage and foster a sense of community among neighbours.

3.3.5 Market Status This section reviews the current technology and market status of DER integration models in Australia. In particular, this section reviews the available offers for DER coordination, the maturity of DER integration models, and examples of relevant Australian projects.

Available offers for DER orchestration in Australia.

The work in (Ochoa, Goncalves, Jaglal, Liu, & Macmanson, 2021) summarises the commercial DER integration products that more than 20 companies in Australia offer. In doing so, the products are reviewed considering the companies that are conducting the DER coordination and providing the equipment or software for DER orchestration. Also, the review considers the type of services that the products are offering, including: • System level services, such as participating in the wholesale energy market and frequency control ancillary services.

• LV network services, balancing supply, and demand; and • Consumer energy management services, such as the control and management of DER behind-the-meter.

Table 30 presents the review presented in (Ochoa et al., 2021), summarising the type of service and DER technology offered by each company and product.

Table 21. Available products in Australia for residential DER coordination. Source: (Ochoa et al., 2021)

Coordinator

Technology provider

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Company

System level services

AGL



Origin



Simply Energy



Mondo



Jet Charge GreenSync

LV network services

Consumer Energy management

DER technology



PV, BESS PV, BESS



PV, BESS





PV, BESS, DR, Others







DR, Others







PV, BESS, DR, Others

Solar analytics



PV, DR

PowerLedger



PV, BESS, DR, Others

Enphase



PV, BESS, DR

Coordinator and technology provider

Evergen







PV, BESS, DR

SwitchDin







PV, BESS, DR

SolarEdge







PV, BESS, DR

Tesla







PV, BESS

Sonnen





PV, BESS

Findings Around a third of the companies available offers some level of DER coordination (i.e., AGL, Origin, Simply Energy, Tesla and Sonnen). The rest of the companies are enablers of DER coordination and are mainly used for consumer energy management (which fits in the household level integration model described in section 3.3.4), but they do not orchestrate DERs as such.

Currently, many DER coordination products are mainly market-based services for the transmission level. However, there is potential for DER coordination to provide network services at the distribution level, which is yet to be fully explored.

While some companies are experimenting with DER coordination, there is a lack of established standards, protocols for connecting DER, and control strategies (Ochoa et al., 2021). Further research is needed, particularly through collaboration with industry partners, to accelerate the development process and uncover new possibilities or requirements for DER coordination.

Maturity assessment of DER technical integration The work in (Farrier Swier Consulting and GridWise Energy Solutions, 2020b) elaborated a DER technology integration functional framework that can be used to track progress towards effective DER integration across Australia. Building on this functional framework, the report in (Farrier Swier Consulting and GridWise Energy Solutions, 2020a) assesses DER integration projects summarised in (Australian Renewable Energy Agency (ARENA), 2020), considering the stage of relevant projects, standards and specifications, costs, ease of implementation. A brief summary of their findings is provided in Table 31. 36 The maturity assessment was conducted using the following definitions: • Mature, if the functional area is being used in all parts of the NEM by most of the DERs, and is used commercially in the marketplace in Australia, and potentially internationally.

• Partially mature, desirable capabilities and main technology options for delivering most of those capabilities are well-defined and understood, although there may be multiple potential options and no current agreement on the optimal approach and end-state.

• Trial stage, if trial/pilots/demonstration projects are underway to test desirable capabilities. Some of the desirable capabilities and main technology options for delivering those capabilities are well-defined and understood, but for a significant number of capabilities, those issues are not well-defined or there is significant disagreement amongst relevant stakeholders.

• Research stage, may be some trials, but significant research still required to determine the desirable capabilities, sequencing and or technology required to deliver those capabilities.

36 Note that we have only considered the relevant functional areas for this OA, that is (i) DER respond to power system disturbances and grid support,

(ii) integration of DER within market and operator system, (iii) ability of DER to participate in electricity markets, (iv) and provision of network services. Interested readers in other functional areas such as cybersecurity and network visibility, are referred to the main report in (Farrier Swier Consulting and GridWise Energy Solutions, 2020a).

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Table 22. Summary of maturity assessment, relative projects and initiatives and gaps of DER functional areas

Relevant Functional Area

Maturity Assessment

Grid support: The ability of DER devices to respond to power system disturbances in a manner that limits or prevents any adverse impacts caused by the DER themselves and provides additional grid support that benefits overall grid security and reliability

Partially mature. The aggregated effect of DER and their associated grid services is becoming increasingly important, including frequency control, voltage control, and participation and coordination with existing and new emergency frequency control schemes to maintain system security. Existing and past projects have proven the relationship of DER capabilities and grid support.

Communication and interoperability: The ability of all parties who need to interact with DER devices to communicate and exchange information with those devices and with each other. This includes a common understanding between parties regarding what information will be shared, the format for the information and how it is to be used

Relevant projects and initiatives • • • •

• Trial stage. Improving DER interoperability is key for effective DER integration. Existing initiatives and projects are advancing in testing ways to communicate and exchange data between and other relevant parties.

• • • •

Integration of DER into market and operators' systems: The system and ICT requirements necessary for AEMO and network service providers' systems to utilise the capabilities provided by DER and manage the potential impacts of DER in a coordinated and integrated manner

Trial stage. There are several initiatives focussed on DER integration into the bulk power system, but they are relatively small-scale trials or being undertaken in a sandbox environment.

Integration with energy and system security market: The ability for DER to participate in current and future markets for wholesale energy and system security services

Trial stage. There are many trials testing this DER capability, but most of them are in progress or have not yet progressed beyond trials.

• • • • • • •

Gaps

AEMO's DER impact on bulk power system operations initiative (link) CONSORT project (ID#5) Community battery initiatives in QLD and WA (ID#15, #27) Standards technical revisions of AS/NZS 4777 (link)

Regulatory framework in relation to arrangements, technical standards, compliance, and adoption of new DER capabilities.

DER Visibility and Monitoring Best Practice Guide (link) DEIP Interoperability steering committee (link) DER Interoperability Assessment Framework (link) ESB Post 2025 DER Implementation Plan – interoperability policy framework (link)

Standards and common agreement on how DER information need to be shared, including type of data, format, and method for sharing.

SAPN advanced VPP grid integration project (ID#2) Evoenergy's DER integration and automation project (ID#6) Evolve project (ID#8) The Townsville community scale battery storage project (ID#27) VPP demonstration project (ID#29)

Cover a wider range of DNSPs and AEMO systems.

VPP demonstrations project (ID#29) WA community battery project (ID#15) Horizon Power's Project Highgarden (ID#10)

Potential market reforms (e.g., twosided markets by ESB, wholesale demand response mechanism by AEMC, etc.) are being assessed by

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•

Provision of localised network services: The ability of DER to provide services to network service providers to enable them to operate their networks more efficiently and/or maintain or improve reliability and power quality

Trial stage. Several projects have provided a good • understanding of the capabilities and technology • • options for using DERs to provide a range of network support services.

Demand response programs (ID#7)

market bodies that can change the current suite of market services, incentives and the ability of DER to provide services.

WA community batteries project (ID#15) CONSORT project (ID#5) Optimal DER scheduling for frequency stability (ID#13)

Move from small-scale projects to broader commercial deployments covering a variety of different types of DER network locations and network support services.

Findings Most of the considered DER functional areas are in a trial stage, which means that they are being tested in existing trial projects. The emerging outcomes from the listed project trials may help establish consensus between key stakeholders to improve the maturity of these areas and promote commercial deployments. It can be seen that there are still many gaps in relation to regulatory reforms that are required to clarify DER market services, incentives, DER capabilities, communication standards and interoperability.

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3.3.6 Findings This section summarises the findings and potential research questions of this RT. These research questions have been classified based on the sections presented in this RT, as shown in Table 32.

Table 23. Summary of findings

Category

Overall findings

Savings to households

Section

Research question

3.3.4

The conducted review revealed that no studies or projects compare all DER integration models under similar operating conditions or case studies. This is an important gap because the assessment and comparison of each DER integration model's benefits, challenges and drawbacks can vary depending on the specific operating conditions. Existing literature, such as the work in (Khorasany et al., 2020) and (Charbonnier et al., 2022), considers all DER integration models, but focuses their work on reviewing relevant literature to clarify concepts, terminology and classifications. That is, they do not explicitly compare their outcomes. The work in (Guerrero et al., 2020) technically compares different DER integration models simulating a case study of high penetration of PV systems and battery storage in a LV network. Specifically, the analysis is mainly focused on the potential economic benefits for DER owners under different DER integration models, and how each model may impact voltage levels and thermal congestion. The results showed that the aggregated indirect DER integration models are the most profitable models for households and the coordination through the VPP and P2P market ensures that the network constraints are respected. In contrast, the individual and aggregated direct control model (i.e., HEMS and HEMS with OE in (Guerrero et al., 2020)) have the lowest benefits for households and they may lead to overvoltage and overlading issues. Note that this study did not consider market and network services, which may increase the financial benefits of households with the aggregated indirect DER integration models.

3.3.4

Aggregated models are more likely to achieve more household savings than individual household level integration models. Savings in household level integration models are limited to tariff rates. For example, households can avoid peak prices by shifting loads and receive incentives by exporting power to the grid. In contrast, aggregated levels are likely to unlock more DER values and savings for households. As noted earlier in this RT, aggregated DER can provide network support and market services, bringing new savings for households. Additionally, emerging aggregated models, such as P2P energy trading, would create new savings for households by allowing energy trading among them.

[RQ] If aggregated models would enable more household savings, what barriers prevent the number of household participants in aggregation programs from increasing?

[RQ] How would new tariff reforms affect savings for household-level integration models?

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Benefits to DNSPs

3.3.4

DER integration models can benefit DNSPs, especially for managing network congestion and deferring or avoiding costly network investment 37.

However, some DER integration models do not explicitly consider technical and operating constraints associated with the electricity network, which may lead to network problems such as overvoltage and overloading. Hence, household level DER integration models are suited to networks with low DER penetration, but when the number of DER increases, the aggregated DER integration models are likely to be more appropriate. In this context, aggregated mediated and bilateral coordination models may bring more benefits to DNSPs by respecting network constraints, offering network support (e.g., generation and load peak management, and balance of households' generation and demand), and alleviating network investment in costly solutions.

[RQ] Although the potential benefits of DNSPs have been identified in the literature, the explicit value and association between DNSPs benefits and customer incentives are still unclear. What is the explicit value between DNSP benefits and household incentives?

[RQ] How can network services provided by DER help households recover their investment in DER and the relevant equipment to respond to coordination aggregation models?

[RQ] What regulatory standards can be implemented to avoid DER integration models exacerbating or creating new network issues?

Household preferences

Computation and communication

3.3.4

DER integration models have different ways of considering and satisfying household energy consumption preferences, and they may experience a trade-off between financial benefits and household preferences. Household level integration models can directly meet household preferences as the control of the DERs is conducted by households. Although households may need to interrupt or shift the operation of DERs to achieve more savings (e.g., schedule the operation of a washing machine to avoid peak price tariffs), households have the autonomy to control DERs based on the comfort levels that they are willing to accept. In contrast, households in aggregated models likely need to cede a certain level of control in exchange for rewards since the aggregator needs to align the preferences and goals of all participants. Note that mediated and bilateral integration models may need to achieve agreement between household participants to satisfy their preferences and unlock more value. For example, a VPP may need that the majority of households reduce their consumption at a specific time. Another example is the negotiation process in P2P transactions, which requires an agreement between buyers and sellers on a transaction price and amount of energy.

3.3.4

The computation and communication requirements increase with the number of DERs to control and household participants in aggregation programs.

Local computation capabilities are required to effectively operate their DERs and satisfy their preferences subject to relevant conditions such as, weather and tariff structures. Household-level integration models carry out the schedule of DERs locally based on their individual preferences. In contrast, the schedule in the aggregated models requires the exchange of information between households and aggregators through two-way communication channels with adequate latency and bandwidth to dynamically receive and send signals. One example of control signals are DOEs sent

by DNSPs as they set the export and import limits for DER owners. Now, the communication and communication burden can become unwieldy in large-scale systems. Hence, mediated, and bilateral coordination models consider distributed and decentralised approaches that distribute the

37 Note that the scope of this review is on the benefits of DER integration models. Hence, the challenges associated.

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computation burden among the participants to alleviate these barriers. For example, some P2P trading approaches consider self-interested households and each household can develop their own bidding strategy to trade energy, so there is no need to collect and manage data by a central agent.

[RQ] What are the minimum ICT requirements to effectively adopt each DER integration model?

[RQ] How issues related to ICT (e.g., communication lost) impact the potential savings and benefits of the DER integration models?

Cost-effectiveness

3.3.4

The expenses of implementing DER integration models vary based on the type of DER technology used and the necessary ICT to support its operation.

For instance, HEMS can generate household savings, and blockchain can facilitate peer-to-peer market transactions. Implementing integration models at the household level may not be costly, but models that involve coordination with an aggregator or other households may require more ICT resources, such as controllers, smart meters, smart inverters, etc.

[RQ] What is the economic value released by each DER integration models and how does it compare across different models when using cost, incomes, and investment data?

Value propositions

Available offers for DER orchestration

3.3.4

Each DER integration model has unique benefits or services to attract households. Household level integration models can offer effective control and operation of DERs without costly ICT. Hence, the value proposition of household level integration models is to allow households to effectively schedule their DER to reduce electricity bills while satisfying their preferences, such as comfort levels or environmental goals. The value proposition of aggregated models is to unlock the value of DERs by delivering network and market services through the control and coordination of a large number of DERs. Specifically, the value proposition of aggregated direct control is to enable managing peak demand and generation by remotely controlling DERs during peak events, resulting in cost savings for households and networks. The value proposition of aggregated indirect mediated control is to enable the coordination of DERs to improve grid reliability and stability and allow households to provide network and market services. And the value proposition of aggregated indirect bilateral control model, such as P2P trading, is to allow households and communities to trade excess energy generated by their renewable energy sources directly with their neighbours, instead of selling it to the grid. This can lead to greater energy independence, cost savings, and increased use of renewable energy sources. Additionally, P2P trading can lead to greater engagement and awareness of energy usage and foster a sense of community among neighbours.

3.3.5

Each DER integration model has unique benefits or services to attract households. Household level integration models can offer effective control and operation of DERs without costly ICT. Hence, the value proposition of household level integration models is to allow households to effectively schedule their DER to reduce electricity bills while satisfying their preferences, such as comfort levels or environmental goals. The value proposition of aggregated models is to unlock the value of DERs by delivering network and market services through the control and coordination of a large number of DERs. Specifically, the value proposition of aggregated direct control is to enable managing peak demand and generation by remotely controlling DERs during peak events, resulting in cost savings for households and networks. The value proposition of aggregated indirect mediated control is to enable the coordination of DERs to improve grid reliability and stability and allow households to provide network and market services. And the value proposition of aggregated indirect bilateral control model, such as P2P trading, is to allow households and communities to trade excess energy generated by their renewable energy sources directly with their neighbours, instead of selling it to the grid. This can lead to greater energy

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independence, cost savings, and increased use of renewable energy sources. Additionally, P2P trading can lead to greater engagement and awareness of energy usage and foster a sense of community among neighbours.

Maturity assessment of DER integration

3.3.5

DER integration projects in Australia

Most of the considered DER functional areas are in a trial stage, which means that they are being tested in existing trial projects. The emerging outcomes from the listed project trials may help establish consensus between key stakeholders to improve the maturity of these areas and promote commercial deployments. It can be seen that there are still many gaps in relation to regulatory reforms that are required to clarify DER market services, incentives, DER capabilities, communication standards and interoperability.

It can be seen that all DER integration models are being tested across Australia. The most common model is the aggregated mediated coordination since VPPs and community batteries. A potential reason for this is that households have a positive experience with VPP due to the opportunity to save money and help the environment or community (Customer Service Benchmarking Australia, 2021). Another reason is that they can be incorporated into the existing market without changes in the regulatory framework. Moreover, Table 33 shows that many projects are also considering aggregated direct control models, through demand response and DOE programs.

Table 33. List of DER projects in Australia

ID

Project name

Aim / Objectives

Location

DER integration model

Status

1

Addressing Barriers to Efficient Renewable Integration

This project focused on frequency control, ancillary services, and electricity market rules/operating procedures. By using bench testing, this project directly tested the response of a range of photovoltaics (PV) and storage inverters to disturbances of different kinds on the network.

NSW

Household level integration

Completed

2

Advanced VPP Grid integration

Tested the value that dynamic export limits can create for customers and VPP operators.

SA

VPP/ Aggregated mediated coordination

Completed

3

AGL Virtual Power Plant

The aim of this project was to demonstrate the value that grid-connected batteries can create for a range of stakeholders when managed as part of a coordinated virtual power plant. The functionalities include solar selfconsumption, network support through peak demand management and frequency control services, and a physical hedge or arbitrage opportunity in wholesale energy market.

SA

VPP/ Aggregated mediated coordination

Completed

4

Broome Smart Sun – Horizon Power

The aim of the project was to investigate and test an integrated energy solution and work towards solving key grid and land development challenges for projects of the future.

WA

VPP/ Aggregated mediated coordination

Completed

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CONSORT project

The CONSORT Bruny Island Battery Trial successfully developed and demonstrated an innovative automated control platform that enables consumers with battery systems to provide support to a constrained electricity network. It continues to do so in a way that is of maximum benefit to both the consumer and the network.

TAS

Aggregated mediated coordination

Completed

DER Integration and Automation Project

This project demonstrated how collaboration between a Distributed Energy Resources Management System (DERMS) and the GreenSync Decentralised Energy Exchange (deX) platform can unlock existing network hosting capacity to enable consumers to gain more value from their energy assets (such as solar, batteries and electric vehicles).

ACT

VPP and P2P / Aggregated mediated coordination

Completed

7

Enel X Demand Response Project

The combined 50 MW demand response portfolio consists of commercial and industrial energy users who have agreed to safely reduce their electricity consumption during demand response events dispatched by AEMO.

NSW, VIC

Demand management / Aggregated direct control

Completed

8

Evolve project

Test a marketplace for DER to provide wholesale and local network services within the network constraints (i.e., DOEs).

ACT, NSW, QLD

DOEs / Aggregated direct control

Completed

9

GreenSync - SA Demand Management Trials

The aim of the project is to allow South Australian residential customers to find and accept offers from energy service providers which value the services of their DDERR.

SA

Demand Management/ Aggregated mediated coordination

In progress

10

Horizon Power Business Model Pilot Phase 1 (Project Highgarden)

The aim of the project is to understand the impact of cloud events on customer DER generation and network operation, and to test the monitor and control of DER systems to achieve orchestration for network optimisation.

WA

Demand Management/ Aggregated mediated coordination

Completed

11

Macedon Ranges Battery Initiative Groundwork (MR-BIG)

The aim of this project is undertaking a feasibility study and community engagement program with the objective of developing a business case and implementation project for a community battery in the Macedon Ranges.

VIC

Community battery/ Aggregated mediated coordination

Announced

12

Neighbourhood Battery Initiative – Tarneit

The aim of this project is to install a community battery in Tarneit to enable additional solar exports in the area.

VIC

Community battery/ Aggregated mediated coordination

Announced

5

6

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13

Optimal DER Scheduling for Frequency Stability

This project investigated how to optimally schedule distributed energy resources (DER) to provide low-cost frequency stability services within distribution network constraints and while respecting the motivations and primary functionality desired by DER owners (i.e., reduced cost, independence etc.).

14

Pooled Energy Demonstration Project

The aim of the project was to manage a discretionary virtual load of swimming pools providing flexibility in the electricity grid to support the increasing share of renewable energy.

NSW

Demand Management/ Aggregated direct control

Completed

15

PowerBank Community Battery Trial

The aim of the project is to provide valuable insights such as customer behaviour and appetite for a front-of-the-meter battery solution along with the profitability of this offer.

WA

Community battery/ Aggregated mediated coordination

In progress

16

Powershop Australia Demand Response Program

The aim of this project was to save more energy during peak-demand events.

VIC

Demand management / Aggregated mediated coordination

Completed

Project Converge

The project is testing tools designed to increase the benefits created by DER without breaching the physical and operational limits of the distribution. Two of the tools they are testing are shaped operating envelopes (SOEs), which prioritises allocating network capacity to sites that have the lowest cost of energy, and a 'real-time system that can more efficiently procure network support at a lower cost to existing processes.

ACT

Aggregated mediated coordination

In progress

18

Project EDGE

This project is testing a DER marketplace where aggregators or traders can offer a range of local and system level services facilitated via a common data exchange hub. This data hub caters for traders bidding into energy markets, receiving dynamic operating envelopes, and offer network support capacity through a local services exchange.

VIC

VPP and P2P / Aggregated mediated coordination

In progress

19

Project Edith

The project is testing tools for managing power flows on the distribution network, as well as how that can co-exist with market participation from DER.

NSW

Aggregated mediated coordination

In progress

17

TAS

Aggregated mediated coordination

Completed

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20

Project Symphony

The Western Australia Distributed Energy Resources Orchestration Pilot is piloting the orchestration of customer-owned distributed energy resources (DER) such as rooftop solar, batteries and major appliances to participate in a future energy market.

21

Rheem Active Hot Water Control

The aim of the project is to demonstrate active control over 2,400 residential hot water systems in South Australia.

SA

Demand Management/ Aggregated direct control

In progress

22

SA Power Networks Flexible Exports for Solar PV Trial

Test flexible export limits with solar PV customers.

SA

DOEs / Aggregated direct control

In progress

23

Schools VPP Pilot Project

The aim of the project is to transform WA schools into smart, green, and flexible VPPs.

WA

VPP / Aggregated mediated coordination

In progress

24

Simply Energy Virtual Power Plant

The aim of the project is to demonstrate that by integrating in a VPP the open-sourced distributed energy market platform software, deX Platform, value can be generated for customers.

SA

VPP/ Aggregated mediated coordination

In progress

25

Solar and Storage Trial at Alkimos Beach Residential Development

The aim of the project was to demonstrate the benefits of community scale BESS, in combination with a virtual storage retail offering and to understand customer usage and behaviour changes under a time-of-use tariff structure. It also tested direct load control devices on air conditioners.

WA

Community battery/ Aggregated mediated coordination

Completed

26

The Beehive Project Shared Community Battery Scheme

The aim of the project is to implement peer-to-peer trading of the stored energy across 500 Enova customers and aims to reduce electricity bills for Enova customers.

NSW

Community battery/ Aggregated bilateral coordination

In progress

27

The Townsville community scale battery storage project

A community scale 4MW/8MWh battery will provide network support throughout hot summer months and allow potential defer investment in network infrastructure.

QLD

Community battery/ Aggregated mediated coordination

In progress

28

Village Power Neighbourhood Battery Project

The aim of the project is to study and design a safe connection to the network; legal arrangements for the underlying business entity and refine

VIC

Community battery/ Aggregated mediated coordination

Announced

WA

Aggregated mediated coordination

In progress

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the commercial model to maximise value to the community of subscribers.

29

VPP demonstrations project

The demonstrations were a first step in a broad program of work designed to inform changes to regulatory frameworks and operational processes so Distributed Energy Resources (DER) can be effectively integrated into the National Electricity Market (NEM), maximising value to consumers while also supporting power system security.

30

Yarra Energy Storage Service Trial Fitzroy North

This project aims to construct a neighbourhood solar sponge battery in Fitzroy North and demonstrate community support, operational, and technical viability of community batteries.

NSW, QLD, SA, VIC

VPP/ Aggregated mediated coordination

Completed

VIC

Community battery/ Aggregated mediated coordination

In progress

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3.4 RT.4 Standards review 3.4.1 Introduction RT4 is focussed on residential DER, with a focus on flexible loads (air conditioners, pool pumps and electric storage water heaters). It reviews the progress of standard AS/NZS 4755 and alternative standards such as IEEE 2030.5 alongside updates to the inverter standard AS4777.2 and a recent AEMC determination which disallows curtailing of PV export. All of these have the potential to impact on the operation and uptake of rooftop PV installations.

Desktop research conducted for this RT reveals two issues relevant to the above paragraph. Firstly, the standards committee tasked with updating AS/NZS 4755 operates under a committee-in-confidence principle, thus it is not possible to access current drafts of the standards or the current approach that the committee is taking.

Secondly, regarding the 2021 reforms introduced by the AEMC, which are aimed at increasing the utilisation of rooftop PV 38 - the AEMC has disallowed blanket PV export bans and required that tariffs be offered that encourage PV owners to limit wasted output. Disallowing PV power export bans will only strengthen the urgency for expanding the demand response capability of flexible loads.

General summary of findings/RQs/recommendations This RT made several findings, and posed associated research questions, as follows: (A) There is a lack of standardisation for flexible loads.

(B) AS/NZS 4755 is in the process of being modernised. This standard, in its current form, is considered by some stakeholders to be too prescriptive, one-way, out of date and designed to reduce peak demand rather than address minimum demand. However, the new draft of AS/NZS 4755.2 aims to address these shortcomings and provide a modernised framework for residential DERs, as well as mapping functions to international standards OpenADR and IEEE 2030.5. Research questions from this finding are as follows: • What can we learn from the historical development of AS/NZS 4755?

• What demand response capabilities are required from (each type of) flexible load?

• Should more than one standard be mandated for flexible loads (allowing manufacturers to choose from a list)?

Regarding the upcoming version of 4755.2: • How will it compare to / map against OpenADR and IEEE 2030.5?

• Will it adequately cover the functions required?

• Will it adequately address the shortcomings of AS/NZS 4755.1?

• How will it perform in computer simulations? In the laboratory? In the field?

• What research projects could be undertaken by RACE to support further development and uptake of AS/NZS 4755? What input is there from the standards committee, regulators, and stakeholders?

(C) International standards are used elsewhere. Standards such as OpenADR, IEEE 2030.5 and CTA 20245 have gained some traction overseas.

38 https://www.aemc.gov.au/news-centre/media-releases/smart-solar-reforms-will-inject-more-clean-energy-grid

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(D) International standards are used to control PV inverters and energy storage devices. These may be the obvious choice for flexible loads. Research questions from this finding are as follows: • Should all residential DERs (loads, inverters, and energy storage) adopt the same demand response standard?

(E) Few jurisdictions have mandated standards. As summarised in section 3.4.4, despite recent advances in standards, few jurisdictions have mandated standards for flexible loads. Some jurisdictions have opted to commence with broader approaches which outline the architecture and communications required for residential DERs to participate in demand response.

Research questions from this finding are as follows: • Should Australia wait until another jurisdiction has mandated standards for flexible loads?

• Should an international consensus be developed for flexible load standards?

• Is there a ‘first mover advantage’ in adopting flexible load standards?

• Should the first step be to adopt a (more basic) publicly available standard (PAS)?

• Should the first step be to adopt a code of conduct, such as the EU are proposing?

• Should the first step be to mandate looser requirements such as scheduling capability, internet connection, cyber security requirements and consumer over-ride?

(F) Standardisation carries risk. There is an inherent tension between the benefits and risks of standardisation. Research questions from this finding are as follows: • Given the risks associated with mandating standards, should a standard be mandated at this time?

• What are the pros and cons of mandating demand response standards for flexible loads?

• Can regulation and standards keep up with the rapid development of smart flexible loads?

• At what point in the development cycle of smart devices should standards be mandated?

• Do mandatory standards help or hinder product development?

• What is the case for and against mandating demand response capabilities for flexible loads?

• What is the case for and against interoperability? (e.g., hindering of product development versus open interoperability) • How can standards be made more adaptable?

(G) Wireless/internet communications can be unstable. Research questions from this finding are as follows: • Are wireless/internet communications reliable/fast enough for flexible loads?

• Should we rely on device manufacturers’ servers to control flexible loads?

(H) Standardisation cannot be applied to existing devices. As discussed in section 3.4.4, standardisation can only be applied to new devices that are sold. The area of existing devices is, understandably, often neglected by government policy makers, who can’t retrospectively apply these regulations to devices already in people’s homes. There are numerous retrofit gadgets available in the marketplace today, however, there remains a question as to what intervention (if any) is required to optimise this segment.

Research questions from this finding are as follows: • What is the state of the market for retrofit solutions (and HEMS) in Australia?

• What communications protocols are used for retrofit solutions (and HEMS)?

• Do retrofit solutions (and HEMS) suffer from a lack of interoperability and is market intervention required?

• Is market intervention required to accelerate the uptake of demand response for existing devices?

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•

Should standardisation apply to retrofit solutions (and HEMS) in order to increase their interoperability and suitability for demand response?

Description of sections Section 3.4.2 summarises the recommended research from this RT and section 3.4.2 extracts relevant findings from previous OAs (which are expanded upon in this H3H5 OA). The investigative work of this RT begins at section 3.4.3, which provides an overview of the relevant standards. Section 3.4.4, and summarises how these have been used in Australia and overseas as well as discussing the use of ‘non-standards’ and the use of standards for inverters and energy storage devices.

Section 3.4.5 summarises the available research on this topic, section 3.4.6 presents the findings and associated research questions.

3.4.2 Review of work done in previous OAs There are two RACE opportunity assessments which have relevance to this OA: N2 - Low Voltage Network Visibility and Optimising DER Hosting Capacity; and H4 – Rewarding Flexible Demand: Customer Friendly cost reflective tariffs and incentives. The following relevant findings were made in these two OAs, and these are expanded upon in this H3H5 OA: • AS/NZS 4755 is outdated and should be updated to align better with the international standards IEEE 2030.5 and OpenADR. • AS/NZS 4755 demand response modes (DRMs) should be reviewed to determine whether they remain valid if IEEE 2030.5 and/or OpenADR communication standards are included.

• AS/NZS 4755 DRMs lack granularity, have no local override capability and support only one-way communication with DERs. • During field trials, the incompatibility of air conditioners with AS/NZS 4755 was the main impediment to successful recruitment.

• AS/NZS 4755 is only being considered for adoption in Australia.

• API (application programming interface) connectivity may replace ‘outdated’ DRMs, however there are latency issues arising from communications via the cloud.

• IEEE 2030.5 is primarily designed for direct control of DERs whilst OpenADR relies on a gateway device, Smart HEMS, or aggregator to translate flexibility requirements from a utility into specific DER behaviours.

3.4.3 Overview of relevant standards This section provides an overview of the relevant standards used to control flexible loads. A major challenge is that standards are typically developed within each technology domain and without a complete vision for how the parts must interact from one end to another to deliver grid services (GMLC, 2020).

AS/NZS 4755 (current) The AS/NZS 4755 series of standards were written to enable demand response in Australia, and these have been used in various programs and trials. The standards create a framework aimed at allowing flexible loads to be used for demand response. They specify the minimum and optional functionalities of a demand response enabling device (DRED) as well as the means of communication. Figure 20 is a graphical representation of a remand response system (from AS/NZS 4755.12017). It shows the flexible load (‘electrical product’), the DRED itself and the remote agent, i.e., a third party which can send demand response signals.

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Figure 16. Graphical representation of a demand response system (source: AS/NZS 4755.1-2017)

AS/NZS 4755 defines demand response modes (DRMs). DRMs are modes of operation for flexible loads, which can be requested by a third party during a demand response event. The nine specified DRMs are as follows: • DRM 0 - Disconnect • DRM 1 - Do not consume power.

• DRM 2 - Do not consume at more than 50% of a reference value.

• DRM 3 - Do not consume at more than 75% of a reference value.

• DRM 4 - Start or increase load.

• DRM 5 - Do not export power to the grid.

• DRM 6 - Do not export at more than 50% of a reference value.

• DRM 7 - Do not export at more than 75% of a reference value.

• DRM 8 - Start or increase export to grid.

AS/NZS 4755 also describe time delays, resetting and randomisation of start/stop times, as well as DER labelling and verification/testing. The standards forbid user over-ride (however over-ride can be enabled via the DRED owner/controller communicating directly to a customer interface) and have optional requirements for recording the type of DER connected to the interface of the DRED, as well as optional requirements for bi-directional communications between the DRED and a remote agent (these are not specified).

AS/NZS 4755 currently has a part written for each of these DERs: air conditioners, pool pumps, water heaters and batteries (parts 3.1, 3.2, 3.3 and 3.5 respectively). These parts primarily specify the response of each DER to commands from a DRED (including testing/verification) as well as the physical connection to the DRED. The DRED itself is described in part 1 of the standard.

AS/NZS 4755 (draft of new part 2) A new part for AS/NZS 4755 has been drafted, 4755.2:2019 - Demand response capabilities and supporting technologies for electrical products, Part 2: Demand response framework and requirements for Communications between Remote Agents and Electrical Products. This part is currently in draft form, awaiting publication following a public consultation process that was held in 2019. Its purpose is to allow an alternative framework (to AS/NZS 4755.1) for residential DERs to

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comply with the standard. This is illustrated in Figure 21 which shows the 4755.1 and alternative 4755.2 frameworks for product compliance.

Figure 17. Proposed pathways for residential DERs to comply with AS/NZS 4755 (E3, 2019)

As can be seen in Figure 21, AS/NZS 4755.2 would allow residential DERs to provide demand response services to the grid, but without using a physical DRED. The draft of 4755.2 also addresses a number of issues that have been raised, such as cybersecurity, customer override and internet protocol communications. It is a comprehensive standard (~150 pages) however any proposed changes to the draft, since the public consultation in 2019, are not yet available in the public domain. It is also understood that Standards Australia has established a working group to investigate whether OpenADR could serve as one of the options for realising the requirements of AS/NZS 4755.2 (E3, 2019).

OpenADR OpenADR was developed by the nor-for-profit OpenADR Alliance ‘to standardise, automate, and simplify demand response and distributed energy resources to enable utilities and aggregators to cost-effectively manage growing energy demand and decentralised energy production, and customers to control their electrical loads’ (Coote, 2022). In November 2018, OpenADR 2.0 was published as an IEC standard, IEC 62746-10-1 - Systems Interface Between Customer Energy Management System and the Power Management System - Part 10-1: Open Automated Demand Response.

OpenADR is relevant to this OA as it a purpose-written standard for demand response that is being trialled or adopted by several jurisdictions (refer section 3.4.4). It is a comprehensive, highly flexible standard that includes many features such as bi-directional communications between the DER and a remote agent, and support for dynamic pricing. It also has a strong focus on data security and is based on the internet protocol (Gabrielle Kuiper).

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OpenADR defines a communications data model and a wide variety of services for demand response and continuous dynamic price signals. An OpenADR message can be simple (e.g., shed load now) or complex (e.g., shed 200 kW for 3 hours starting at a particular time). It is the ability to create highly flexible messages that sets OpenADR apart from AZ/NZS 4755, which is more prescriptive (Coote, 2022).

OpenADR interacts with building and industrial control systems that have been pre-programmed to respond based on a demand response or dynamic price signal. Such systems include HVAC control systems, lighting control systems, EV chargers, and industrial processes controllers. Currently, there are no Open-ADR certified air conditioners, water heaters, or pool pump controllers 39.

Figure 22 illustrates the potential point of interoperability between AS/NZS 4755 and OpenADR. It shows where each of these standards would fit within the communications path between a remote agent and the DER (product). As can be seen in the figure, OpenADR is not implemented at the DER (product) level.

Figure 18. Diagrammatic representation of AS/NZS 4755, Echonet and OpenADR (E3, 2019)

IEEE 2030.5 IEEE 2030.5 was developed by the IEEE as part of the IEEE2030 suite of standards for smart grids, starting from the earlier ZigBee Smart Energy Profile 2.0. It was designed to manage DERs directly, however this may change in later versions to include functionality similar to the Open ADR approach of using a site EMS (energy management system) or aggregator to translate demand management requirements into specific DER commands (GMLC, 2020).

Similar to OpenADR, IEEE 2030.5 is relevant to this OA as it a purpose-written standard for demand response that is being trialled or adopted by several jurisdictions (refer section 3.4..4) and includes many features not currently incorporated in AS/NZS 4755.

39 https://products.openadr.org

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IEEE 2030.5 is an internet-protocol based standard which supports pricing, DER management, load control, discovery, metering, and others. It is commonly used to curtail PV inverters, however broader deployment to manage other DERs at a district level has also been trialled. Cybersecurity is a strong focus, and the standard is designed to meet NIST cybersecurity standards (Coote, 2022) and (GMLC, 2020).

IEEE 2030.5 defines several function sets, the most notable of which are (Tylor Slay, 2021): • Time: Establishes a means for the client to synchronise with the IEEE 2030.5 server clock to ensure controls are executed at the correct time.

• Demand Response and Load Control: allows IEEE 2030.5 servers a means of shifting loads or adjusting control set points to reduce energy consumption during peak usage times.

• Flow Reservation: allows the client a means of requesting a specified amount of energy or a specified amount of power for a duration of time from the IEEE 2030.5 server within a future time window. The server responds with the start time for the specified amount of energy or power with the ability to supersede the control any time within the time window.

• Distributed Energy Resources: Allows the server to control the active/reactive power settings of inverter-based systems as well as autonomous Volt-VAR, Volt-Watt, and Frequency-Watt control curves.

Note that OpenADR typically relies on a gateway device, building Energy Management System (GMLC, 2020), or aggregator to translate utility DR/DER requirements into specific DER behaviours, whereas IEEE 2030.5 can connect and directly control DERs. CTA 2045 Similar to AS/NZS 4755, the CTA 2045 standard (where CTA is the Consumer Technology Association 40) was developed with the objective of describing an interface which can be built into DERs. This modular communications interface provides a standard interface for energy management signals and messages to reach residential DERs (GMLC, 2020). The MCI protocol supports pass-through of other standards including OpenADR and IEEE 2030.5 41. Described as a ‘USB for appliances’, the MCI plugs-in to an CTA-2045 compliant appliance and allows two-way communications between the DER and a utility. CTA-2045 devices can include energy management controllers, gateways, sensors, thermostats, appliances, and other consumer products. The CTA-2045 specification creates a communication interface that enables translation between the physical and data link layers of a device 42.

Comparison of key standards Table 34 provides a quick overview of the attributes and functions of the four key standards used for residential DER. From this we can see that OpenADR, IEEE 2030.5 and CTA 2045 have considerably more functionality than AS/NZS 4755 (current) and that the draft of AS/NZS 4755.2 attempts to address this.

40 https://www.cta.tech 41 https://www.openadr.org/assets/OADR_CTA2045_Overview%20Webinar.pdf 42 https://shop.cta.tech/products/modular-communications-interface-for-generic-display-message-set-amendment-

1?_ga=2.204589008.1363048089.16741803131466984414.1674180313&_gl=1%2Ajs4zvx%2A_ga%2AMTQ2Njk4NDQxNC4xNjc0MTgwMzEz%2A_ga_V8XKN0510H%2AMTY3NDE4MDMxMy4xLjEuM TY3NDE4MDM1MC4wLjAuMA..

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Table 34. Comparison of functionality between key standards

Standard

Implementation point

AS/NZS 4755 (current)

DER

AS/NZS 4755.2 (draft)

DER

OpenADR

IEEE 2030.5

CTA 2045

Third-party, DER control system, Third-party, DER control system, DER DER

Bi-directional communication s

Customer over-ride

Security

Based on internet protocol

No

No

No

No

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Power down/up, delay, Dynamic pricing

Yes

Yes

Yes

Yes

Power down/up, delay, dynamic pricing

Yes

Yes

N/A

N/A

Functions supported Power down/up, randomisation Power down/up, randomisation, delay Power down/up, delay, dynamic pricing

3.4.4 State of the Market/Technology This section provides an overview as to how relevant standards have been used in Australia and in a number of important overseas jurisdictions. This is relevant as the use of standards in the field provides many insights into their efficacy and allows Australia to assess harmonisation with overseas jurisdictions. The section Utilisation of proprietary and other protocols discusses the use of ‘non-standards’ (e.g., proprietary communications protocols). Further sections examine the use of standards for inverters and energy storage devices, which is relevant as it may be wise to adopt the same standards for residential DERs and existing devices (to which it is not possible to apple a standard) and is concluded with a summary of the above.

Utilisation of standards in Australia In Australia and most of the world, use of flexible loads for grid support is at an embryonic stage, meaning that primarily only pilot projects have taken place (Guidehouse, 2020). Table 35 lists the known Australian programs and pilot projects along with details of the flexible loads controlled and the types of communications used.

Table 35. List of demand response (DR) trials in Australia

Name Off peak hot water Off peak hot water Peaksmart

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Comms from Organisation

Device Comms / Control

Various

Ripple control

240v relay

Various

Local timer

240v relay

Ripple control + SMS

AS4755 DRED

Organisation

Energy Queensland (EQLD)

Date

Current

Device Controlled

A/C

CoolSaver Curb your power Behavioural Economics Team of Australia Zen Ecosystems Peak Energy Rewards Air conditioner program Power Response PeakSaver – Rooty Hill CoolSaver – Rooty Hill PeakSaver – Glenmore Park CoolSaver – Glenmore Park Energy Partners Summer Saver

Ausgrid

2013-17

A/C

Ripple control + 3G

AS4755 DRED

Powershop

2017-18

A/C

SMS / human-in-loop

N/A

A/C Zen Ecosystems, RACV

2017-18

A/C

SMS / human-in-loop

N/A

AGL

2018

A/C

SMS / human-in-loop

N/A

AGL

2018-20

A/C

Internet / API

AS4755 DRED + IR remote

Energy Australia

2017-19

Various

SMS / human-in-loop

N/A

Endeavour

2010

A/C

SMS / human-in-loop

N/A

Endeavour

2010

A/C

SMS / human-in-loop

N/A

Endeavour

2010

A/C

SMS / human-in-loop

N/A

Endeavour

2010

A/C

AS4755 DRED IR remote

Powercor, CitiPower, Senseco, RACV United Energy

2018-2019

A/C

2022-23

A/C

SMS / human-in-loop

AusNet

2017-18

A/C

SMS / human-in-loop

Jemena

2017-18

A/C

Human-in-loop

Origin

Current

Various

SMS / human-in-loop

Peak Partners Power Changers Spike Embertec A/C load control Solahart

Solahart

Pooled Energy

Pooled Energy

2018-2020

Tariff 33 Hot Water Load Control Mallacoota Reverse Flow Management Active Hot Water Control Project Symphony Parkes NSW Solar Soak Trial Off peak Plus Program

Energex

Ongoing

Ausgrid (ARENA)

2016

AusNet Services (ARENA)

2019

Rheem (ARENA)

2021-ongoing

Western Power (ARENA)

2021-2023

Essential Energy (ARENA)

2021-ongoing

PowerSaver

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Embertect

Endeavour Energy (ARENA) Endeavour Energy (ARENA)

A/C

From 2021 Until 2025

Pool pump Water heater Water heater Water heater Water heater Water heater Water heater Water heater

IR remote ?

Internet, proprietary control protocol Ripple control

Solahart HEMS 240v relay 240v relay

SolarShift

UNSW (ARENA)

2022-2024

Water heater

From 35 we can see that dozens of flexible load projects have taken= audio placefrequency in Australia over thesent past few years. Many Notes:Table 240v relay = device de-energised by simple relay at power source; ripple control control signal over distribution system; = text message; human-in-loop = occupant device manually.and many have used a relay to de-energise equipment of theseSMS have used SMS communications with acontrols human-in-the-loop, at times of peak load (e.g., pool pumps). There is a smaller number of projects that have employed the AS/NZS 4755 DRED (mostly for air conditioners). Data was not available for all projects, however the numbers of participating customers in each project ranged from dozens to around 9000 (Peaksmart Queensland). Typically, projects involved several hundred homes.

South Australia has undertaken a number of pilot projects and is considering regulation for control of flexible loads. The South Australian Demand Management Trials Program allocated $11m for funding of demand response projects and distributed energy resources 43. The relevant residential DER projects funded were: • Automating DR of flexible loads (including batteries) in response to wholesale prices from Amber Electric using proprietary technology.

• Efficient targeting and automated control of residential A/C loads by Embertec Pty Ltd. This project uses an analysis tool to inform consumers of the best (cheapest) time to use electricity, as well as allowing A/C control.

• Bringing South Australia's hot water load under active control - Rheem in partnership with Combined Energy Technologies, Simply Energy, SA Power Networks and Marchment Hill Consulting. The project used a HEMS to control DERs such as water heaters, batteries, A/C systems, Electric Vehicle chargers, pool pumps and resistive heaters. The HEMS used the existing building electrical wiring to communicate with load control adapters that were retrofitted to existing devices.

As well as regulations for curtailment of inverters 44 South Australia have proposed to mandate DR requirements for A/C units, EV chargers, PP controllers and EHW heaters 45. The original proposal 46 (March 2021) was to mandate AS/NZS 4755 for air conditioners, pool pump controllers, electric resistive storage water heaters and electric vehicle chargers. However, a large number of submissions 47 were received which did not support this mandate, for the following reasons: • National regulations for flexible loads are preferred to state-based.

• AS/NZS 4755 is out of date with respect to international standards.

• AS/NZS 4755 is designed to assist with reducing peak demand not addressing minimum demand.

• Regulations should wait for AS/NZS 4755.2 - Demand response framework and requirements for communication between remote agents and electrical products – to be completed.

• AS/NZS 4755 supports only one-way communication.

• Mandating AS/NZS 4755 is too prescriptive – other deemed-to-comply solutions should be allowed.

• The proposed timetable is too short.

43 https://www.energymining.sa.gov.au/industry/modern-energy/solar-batteries-and-smarter-homes/south-australian-demand-management-trials-

program 44 https://www.energymining.sa.gov.au/industry/modern-energy/solar-batteries-and-smarter-homes/regulatory-changes-for-smarter-homes 45 https://www.energymining.sa.gov.au/community/consultation/proposed-local-demand-response-requirements-for-selected-appliances-andproposed-amendments-to-local-energy-requirements-for-water-heaters 46

https://www.energymining.sa.gov.au/__data/assets/pdf_file/0009/673758/Consultation_Paper_Proposed_DR_Capabilities_and_Amendments_to_Wat er_Heater_Requirements.pdf 47 https://www.energymining.sa.gov.au/public-consultations/consultation/proposed-local-demand-response-requirements-for-selected-appliancesand-proposed-amendments-to-local-energy-requirements-for-water-heaters

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• • • •

Many electric storage water heaters operate on an off peak (night-time) tariff and thus do not offer demand response benefits (daytime corresponding with PV generation) Mandating compliance with AS/NZS 4755 is too prescriptive.

Customer response to tariff or market signals is preferred to third party control of DERs. Customers prefer to have an override control.

Utilisation of standards internationally California Energy policy in the US is enacted at the state level, and California is a leading state in terms of clean energy policy. The California Energy Commission (CEC) is the state's primary energy policy and planning agency, along with the California Public Utilities Commission (CPUC). These agencies are currently in the process of aligning flexible loads standards with demand response programs 48.

For pool pumps, the current draft CEC proposal 49 is that new pool pump controllers sold would be required to ship with a pre-programmed default operating schedule (e.g., 9am-3pm), be capable of connecting to the internet, and meet cybersecurity and consumer consent requirements. An earlier CEC draft pre-rulemaking draft 50 proposed requirements for DERs (approximately 51) along these lines: • Incorporate a delay timer or scheduling capability (for pool pumps, incorporate a minimum of five time-of-use schedules) • Be capable of connecting to the internet.

• Meet reliability and cybersecurity requirements.

• Incorporate customer consent.

Neither of the above proposals appears to propose that a communications standard be mandated.

For PV inverters, the California Public Utilities Commission 52 references IEEE 2030 as the default protocol in its Rule 21 and Common Smart Inverter Profile (CSIP). CPUC Rule 21 mandates that generators such as wind turbines and PV systems using inverters must support an application layer protocol that can be used by utilities to manage smart inverter functions and send status information to the utility. 2030.5 supports the inverter messaging functionality required by CSIP but also supports other DER functionality.

US Northwest Region The US the Bonneville Power Administration is a hydropower generator in the Columbia River Basin area of the US Northwest region. It recently used CTA 2045 in a large-scale water heater demonstration project and the state of Washington has passed legislation requiring CTA 2045 (or similar) to be included on all water heaters sold in that state (Tylor Slay, 2021).

48 https://www.energy.ca.gov/proceedings/energy-commission-proceedings/flexible-demand-appliances 49 https://efiling.energy.ca.gov/GetDocument.aspx?tn=243783 50 https://efiling.energy.ca.gov/GetDocument.aspx?tn=239896&DocumentContentId=73341 51 Refer to the reference for detailed requirements.

52 https://www.solarplaza.com/resource/12008/sunspec-ca-rule-21-pki-primer/

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US Energy Star The US Energy Star program is a certification program for efficient appliances and equipment. In recent years the program has included optional ‘connected criteria’ for the following devices 53: connected thermostats, HEMS, lighting, room air purifiers, refrigerators and freezers, clothes washers, clothes dryers, dishwashers, room air-conditioners, electric vehicle supply equipment, pool pumps, water heaters, central air-conditioners and heat pumps and ice makers. These criteria typically 54 include requirements for energy consumption reporting, operational status reporting, remote management, and demand response. For example, for pool pumps 55 the criteria for demand response include the following: • Communication Protocols: The device shall meet the communication and equipment performance standards for CTA-2045- A, CTA-2045-B, or OpenADR 2.0b (Virtual End Node) or any combination thereof.

• Override: the device shall provide for both manual and automated (pre-set) override.

• Loss of connectivity: procedure for device response if connectivity is lost.

• DR requests and responses: the device shall be capable of responding to Consumer Authorised Third Parties by providing the following three responses: General Curtailment, Grid Emergency and Load Up. • DR information and messaging: the device shall support specified upstream messaging from the device when available (refer to the specification for more detail).

Europe The European Commission is in the process of developing policy proposals for ‘energy smart appliances’ 56. At the heart of this is a technical report entitled ‘Energy Smart Appliances Interoperability: Analysis on Data Exchange from State-ofthe-Art Use Cases’ (Papaioannou, 2022). This report states that ‘The problem is that, whereas full interoperability among different products from various manufacturers is important, the use of regulatory instruments might not be able to ensure it. It would require compliance with a multitude of standards, some of them not yet (fully) developed. In addition, any such regulation would heavily rely on the standards (references to which might need to be included in the regulatory text) while the standards themselves are in a very rapid evolution (much faster than the regulatory cycles). Thus, regulation seems inappropriate’. For these reasons the EU are drafting a code of conduct rather than a regulation.

The European standards organisation have also published 57 (and are currently refining) CSN EN 50631-1 - Household appliances network and grid connectivity - Part 1: General Requirements, Generic Data Modelling and Neutral Messages.

This standard defines data models for interoperable connected residential DERs. The data model is derived from a logical decomposition of use cases into ‘functional blocks’.

United Kingdom The UK government has launched the Interoperable Demand Side Response Programme 58 which aims to support the development and demonstration of energy smart appliances for the delivery of interoperable demand side response. So

53 https://www.energystar.gov/products/smart_home_tips/about_products_connected_functionality/connected_criteria_partners 54 Criteria vary by device type.

55

https://www.energystar.gov/sites/default/files/asset/document/ENERGY%20STAR%20Version%203.1%20Pool%20Pumps%20Final%20Specification.p df 56 https://ses.jrc.ec.europa.eu/development-of-policy-proposals-for-energy-smart-appliances 57 https://www.en-standard.eu/csn-en-50631-1-household-appliances-network-and-grid-connectivity-part-1-general-requirements-generic-datamodelling-and-neutral-messages/ 58 https://www.gov.uk/government/collections/interoperable-demand-side-response-programme

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far, the UK has developed a publicly available standard (PAS) - PAS 1878:2021 - Energy Smart Appliances – System Functionality and Architecture – Specification. This PAS describes the actors, architecture, communications connections, and messaging required for flexible loads to support the grid.

New Zealand New Zealand have elected not to proceed with mandating AS/NZS 4755 59 and have elected to trial both OpenADR and IEEE 2030.5 in demand response pilots 60, 61. The Demand Flexibility Common Communication Protocols Project 62 is an industry collaboration between the electricity industry and EECA (the New Zealand Energy Efficiency & Conservation Authority) to evaluate the processes that need to be put in place to apply the open ADR protocol to electric vehicle charging equipment.

Similar to the UK, New Zealand have also published a PAS (publicly available standard) known as SNZ PAS 6012 – Smart Home Guidelines. This PAS is a high-level document that explains what a smart (grid interactive) home is, what DERs might participate, and the role of a HEMS. It also outlines the importance of issues such as data security privacy.

Utilisation of proprietary and other protocols There are numerous energy-related products and platforms that currently do not utilise an open demand response standard such as AS/NZS 4755, IEEE 2030.5 or Open ADR. Many are using proprietary protocols, in-house protocols or non-energy-specific protocols such as Modbus (GMLC, 2020). For many, the communications technology used is not stated publicly. Examples of hardware that utilise these approaches include energy diverters (divert PV power to a water heater using a relay), energy monitors, smart power plugs, smart power boards and a range of other gadgets. Relevant ‘platforms’ include Apple HomeKit, Home Connect, Intesis, Swtichedin, DNA Energy, Home Assistant, Open Energy Monitor, IFTTT and Gswitch, to name a few. The degree of interoperability of these products and platforms is varied. In some cases, the objective is to be interoperable, and in some cases, for commercial reasons the opposite is the case (EDNA, 2022).

Worthy of mention are IoT (internet of things) protocols that aim to increase interoperability between devices, such as Matter. Matter has 277 members 63 including Amazon, apple, Google, LG, Samsung, etc. and is billed as ‘one protocol to connect compatible devices and systems with one another’. It supports direct communications between devices and mesh networking (Gill, 2022).

Utilisation of standards for inverters and energy storage The use of standards and communications protocols is often opaque, particularly for commercially-oriented virtual power plants (VPPs) involving control of residential batteries (GMLC, 2020). However, there are a number of open-source standards that used for controlling inverters and energy storage, and these have some relevance because, if they become widespread in this application, they may also become the obvious choice for residential DERs.

59 https://www.eeca.govt.nz/regulations/regulatory-requirements-under-review/smart-appliances/ 60 https://www.eea.co.nz/Site/news-events/upcoming-eea-events/2022/demand-flexibility-common-communication-protocols-project.aspx 61 https://www.araake.co.nz/assets/Uploads/FlexForum-Output-for-Feedback.pdf 62 https://www.eea.co.nz/Site/news-events/past-event-presentations/asset-management-events-and-forums/demand-flexibility-communication-

protocols---why-openadr-.aspx 63 https://csa-iot.org/members/

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Australian inverter standard AS 4777.2 The AS4777.2 standard specifies device specifications, functionality, testing and compliance requirements for electrical safety and performance for PV inverters. It has some relevance to this OA as it references AS/NZS 4755. In addition, a key objective for demand response is the ability of flexible loads to respond to net power generation levels from rooftop PV. AS/NZS 4777.2 includes the demand response modes from AS/NZS 4555 (see section 3.4.3) however only DRM 1 (disconnect) is mandatory – all other modes are optional. However, this requirement is not relevant to this OA – disconnection or curtailment of PV generation is not considered demand response. AS/NZS 4777.2 does not appear to have a requirement for inverters to communicate their level of PV power generation, which would be useful for demand response. Note however that the total residential load is also required to be monitored in order to calculate the net oversupply of PV power. This is potentially where data from a smart meter would also be useful, i.e. to communicate the level of PV export, voltage, etc. (Gill).

Common smart inverter profile The Common Smart Inverter Profile (CSIP) for Australia 64 was developed ‘to assist organisations involved in the Australian electricity network specifically with the deployment, monitoring and orchestration/active management of DER, via the creation of a standardised minimum communication protocol to assist in their operation in Australia’. It’s authors, the DER Integration API Technical Working Group, determined that the most appropriate starting point to promote interoperability amongst DER and DNSPs in Australia was to leverage existing standards, namely IEEE 2030.5 and the Sunspec Common Smart Inverter Profile 65. These standards were chosen principally due to their coverage of relevant data communications, and uptake in related international jurisdictions 66. Note that the Australian CSIP also maps AS/NZS 4755 DRMs to IEEE 2030.5.

Standards for energy storage devices OpenADR, IEEE2030.5 and EEBUS support stationary battery and electric vehicle charging (Coote, 2022). The Open Charge Point Protocol (OCPP) and Open Smart Charging Protocol (OSCP) are dedicated to electric vehicle charging 67.

IEEE 2030.5 is also being used for virtual power plants (Gabrielle Kuiper, 2021).

Existing devices Standardisation (of inbuilt demand response capability) can only be applied to new devices that are sold. Existing devices can last for decades before they wear out and are replaced by new (demand response capable) devices. Standards (and the associated regulations) for new devices can take years to develop and enact. Thus, the retrofit of demand response capability to existing devices is an important segment that needs to be addressed. To a certain extent, demand response capability can however be retrofitted to existing devices by adding gadgets such as relays, smart thermostats, water heating controls, smart plugs/boards, infra-red communications, etc. (EDNA, 2021).

64 https://arena.gov.au/assets/2021/09/common-smart-inverter-profile-australia.pdf 65 https://sunspec.org/common-smart-inverter-profile-csip/ 66 https://arena.gov.au/assets/2021/09/common-smart-inverter-profile-australia.pdf 67 https://www.openchargealliance.org/

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Summary of current standards and their utilisation The Australian demand response standard AS/NZS 4755 (in its current form) has been used in a small number of pilot projects involving air conditioners as well as in the much larger Queensland PeakSmart A/C program. South Australia have also proposed to mandate this standard for air conditioners, electric vehicle chargers, pool pump controllers and electric storage water heaters. However, a number of submissions to the South Australian regulatory process (and elsewhere) have criticised this single-state approach, as well as making claims about the existing standard being too prescriptive, oneway, out of date and designed to reduce peak demand rather than address minimum demand.

A new part of the standard, 4755.2 (currently in draft form) proposes an alternative pathway for product compliance, allowing flexible loads to provide demand response services to the grid, but without using a DRED. It also addresses issues such as cybersecurity, customer override and internet protocol communications. Note that any proposed changes to the draft (since public consultation in 2019) are not in yet in the public domain. A Standards Australia working group is also investigating whether OpenADR and IEEE 2030.5 will be referenced by AS/NZS 4755.2.

Standards such as OpenADR, IEEE 2030.5 and CTA 20245 have gained some traction overseas. OpenADR typically relies on a gateway device, building EMS, or aggregator to translate utility demand response requirements into specific DER behaviours, while IEEE 2030.5 can connect and directly control DERs. OpenADR, IEEE2030.5 and EEBUS all support stationary battery and electric vehicle charging and have been used for this purpose. The Australian Common Smart Inverter Profile (CSIP) for Australia supports both AS/NZS 4755 and IEEE 2030.5 CTA 20245 provides for a standard energy management interface which can be built into any device. The US Energy Star certification program’s optional ‘connected criteria’ specifies CTA 2045 and/or OpenADR. California appears to be taking a broad approach, requiring that DERs have delay/scheduling capability, be internet connected, incorporate customer consent and meet reliability/cybersecurity requirements.

Europe are proposing to adopt a code of conduct rather than regulations and the UK have developed a publicly available standard (PAS) which describes the architecture and communications required for residential appliances to participate in demand response. Similarly, the UK has developed PAS 1878:2021 which describes the actors, architecture, communications connections and messaging required for residential appliances to participate in demand response. New Zealand have also developed a high level PAS and are trialling Open ADR and IEEE 2030.5 for electric vehicle charging.

Many energy-related products and platforms currently do not utilise an open demand response standard, and many are using proprietary, in-house or opaque protocols.

3.4.5 State of Research This section summarises relevant information and research, and these are grouped together. Note that useful Information relating to standards for residential DERs comes overwhelmingly from pilot projects, government and other reports and industry submissions. This is because the issues involved are practical in nature – these kinds of communications standards are designed to operate effectively, but typically this cannot be confirmed until they are tested in practice and market players have had the chance to analyse them and test them on their own products.

The available academic research seems to be focussed on the potential for demand response, hypothetical implementations of demand response, and cybersecurity, and there is much written about communications standards being used for inverters and energy storage. There is very little on the practical issues associated with implementing demand response standards for flexible loads.

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Lack of standardisation (GMLC, 2020) finds that there is a lack of standardisation in this area, and currently there are multiple, usually proprietary and potentially unique methods of controlling the power consumption of residential DERs. Risks associated with standardisation.

There is an inherent tension between the benefits and risks of standardisation. Without careful consideration and management, standardisation can stifle innovation by locking in approaches that inhibit future developments. It is therefore important that any approach balances unlocking benefits today, whilst enabling innovation in future (ESB, 2022).

Adequacy of current standard AS/NZS 4755 The standard, in its current form, was found to be difficult to use (and in some cases unviable) during air conditioner trials (ARENA, 2019), (AGL, 2021), (Ausgrid, 2022).

Stability of wireless/internet communications.

Control that relies on wireless/internet communications can be problematic for example when customers change their Wi-Fi password or when a device drops off the Wi-Fi network (which can go unnoticed). To support more stable and resilient communication, some demand response installation models require a physical ethernet or dedicated cellular connection (ESB, 2022).

Reliance on device manufacturers’ servers.

Where flexible load control is routed through the device manufacturer’s server, if the manufacturer decides to discontinue support for their server the connected DER will lose its controllability (Gill, 2022).

Two-way communications are essential.

(Tylor Slay, 2021) finds that demand response participation of a flexible load must be validated, although this can be challenging (e.g. provision of high resolution data for frequency-based responses). Probabilistic modelling is suggested as a means of addressing this.

3.4.6 Findings and Research Questions The findings of this RT are as follows, together with associated research questions.

(A) There is a lack of standardisation for flexible loads. As discussed in section 3.4.4, there is a lack of standardisation for flexible loads, and currently there are multiple, often proprietary, and potentially unique methods of controlling the power consumption of flexible loads.

(B) AS/NZS 4755 is in the process of being modernised. As summarised in section 3.4.6, this standard, in its current form, is considered by some stakeholders to be too prescriptive, one-way, out of date and designed to reduce peak demand rather than address minimum demand. Pilot projects (section 3.4.5) found that it does not appear to support interoperability and in practice was found to be difficult to use (and in some cases unviable) during air conditioner trials.

It is also specific to Australia (noting that New Zealand have opted not to adopt this standard). However, the new draft of AS/NZS 4755.2 aims to address these shortcomings and provide a modernised framework for residential DERs, as well as mapping functions to OpenADR and IEEE 2030.5.

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Research questions from this finding are as follows: • What can we learn from the historical development of AS/NZS 4755?

• What demand response capabilities are required from (each type of) flexible load?

• Should more than one standard be mandated for flexible loads (allowing manufacturers to choose from a list)?

Regarding the upcoming version of 4755.2: • How will it compare to / map against OpenADR and IEEE 2030.5?

• Will it adequately cover the functions required?

• Will it adequately address the shortcomings of AS/NZS 4755.1?

• How will it perform in computer simulations? In the laboratory? In the field?

• What research projects could be undertaken by RACE to support further development and uptake of AS/NZS 4755? What input is there from the standards committee, regulators, and stakeholders?

(C) International standards are used elsewhere. As summarised in section 3.4.4, standards such as OpenADR, IEEE 2030.5 and CTA 20245 have gained some traction overseas.

(D) International standards are used to control PV inverters and energy storage devices. As mentioned in section 3.4.4, there are a number of open-source standards used for controlling inverters and energy storage, and if they become widespread in this application, they may be the obvious choice for flexible loads.

Research questions from this finding are as follows: • Should all residential DERs (loads, inverters, and energy storage) adopt the same demand response standard?

(E) Few jurisdictions have mandated standards. As summarised in section 3.4.4, despite recent advances in standards, few jurisdictions have mandated standards for flexible loads. Some jurisdictions have opted to commence with broader approaches which outline the architecture and communications required for residential DERs to participate in demand response.

Research questions from this finding are as follows: • Should Australia wait until another jurisdiction has mandated standards for flexible loads?

• Should an international consensus be developed for flexible load standards?

• Is there a ‘first mover advantage’ in adopting flexible load standards?

• Should the first step be to adopt a (more basic) publicly available standard (PAS)?

• Should the first step be to adopt a code of conduct, such as the EU are proposing?

• Should the first step be to mandate looser requirements such as scheduling capability, internet connection, cyber security requirements and consumer over-ride?

(F) Standardisation carries risk. As mentioned earlier, there is an inherent tension between the benefits and risks of standardisation. Without careful consideration and management, standardisation can stifle innovation by locking in approaches that inhibit future developments. It is therefore important that any approach balances unlocking benefits today, whilst enabling innovation in future.

Research questions from this finding are as follows: • Given the risks associated with mandating standards, should a standard be mandated at this time?

• What are the pros and cons of mandating demand response standards for flexible loads?

• Can regulation and standards keep up with the rapid development of smart flexible loads?

• At what point in the development cycle of smart devices should standards be mandated?

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• • • •

Do mandatory standards help or hinder product development?

What is the case for and against mandating demand response capabilities for flexible loads?

What is the case for and against interoperability? (e.g., hindering of product development versus open interoperability) How can standards be made more adaptable?

(G) Wireless/internet communications can be unstable. As mentioned in earlier sections, control that relies on wireless/internet communications and device manufacturers’ servers can be problematic.

Research questions from this finding are as follows: • Are wireless/internet communications reliable/fast enough for flexible loads?

• Should we rely on device manufacturers’ servers to control flexible loads?

(H) Standardisation cannot be applied to existing devices. As discussed in section 3.4.4, standardisation can only be applied to new devices that are sold. The area of existing devices is, understandably, often neglected by government policy makers, who can only apply regulations to new devices sold. There are numerous retrofit gadgets available in the marketplace today, however, there remains a question as to what intervention (if any) is required to optimise this segment.

Research questions from this finding are as follows: • What is the state of the market for retrofit solutions (and HEMS) in Australia?

• What communications protocols are used for retrofit solutions (and HEMS)?

• Do retrofit solutions (and HEMS) suffer from a lack of interoperability and is market intervention required?

• Is market intervention required to accelerate the uptake of demand response for existing devices?

• Should standardisation apply to retrofit solutions (and HEMS) in order to increase their interoperability and suitability for demand response?

3.5 RT.5 Algorithms for the control and optimisation of major electric household appliances 3.5.1 Introduction This RT has reviewed the scope for algorithms of home energy management systems (HEMSs) adopting the flexible loads with high power ratings and solar photovoltaics (PVs). HEMS algorithms can provide optimal or real-time decision making on scheduling or controlling flexible loads, bringing economic and/or technical benefits. Mainstream algorithms for HEMS are rule-based control and centralised optimisation algorithms, while emerging ones include distributed control/optimisation, hierarchical coordination, and machine learning (ML) based algorithms.

Various HEMSs, control and optimisation algorithms will be introduced in section 3.5.3. The HEMS algorithms are generally embedded on cloud-based platforms with essential controlling devices completing the scheduling or controlling tasks.

The market status of the HEMS, the cloud-based platforms where the HEMS algorithms are designed and applied in the backend and the controlling devices required by HEMS has been assessed by this RT. The findings of HEMS development status and industry practice, including performance, economic/technical benefits, as well as implementation issues, have been summarised. The market status of HEMSs, cloud-based platforms and controlling devices will be introduced in section 3.5.4. Australian pilot studies using HEMSs have also been reviewed. The findings from these projects and the development direction of HEMS will be introduced in section 3.5.5. By investigating the academic literature, this RT has

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reviewed the state-of-the-art research outcomes on various HEMS algorithms. It is an imperative aspect for this RT to indicate how effectively and efficiently the HEMS algorithms contribute to expected outcomes such as minimising household energy bills, minimising thermal discomfort and provision of network support services. The HEMS algorithms have been comprehensively analysed, by assessing their ability to achieve the aforementioned outcomes, as well as comparing the pros and cons of each, in terms of computation and communication requirements, optimum scheduling, response speed, etc. Moreover, research problems concerning user preference, thermodynamics, uncertainty, and price/tariff, have been identified. Potential solutions provided by advanced HEMS algorithms have also been given. The various HEMS algorithms in the academic literature, the research problems and solutions will be introduced in section 3.5.6. This study has identified the technical barriers to implementing the advanced HEMS algorithms from the academic literature, and also provided imperative research questions (RQs). Based on the market status, pilot studies and state of research, the key findings and technical barriers associated with implementing the various HEMS algorithms, along with the imperative RQs, are summarised in section 3.5.7.

Recommended research activities for future development of HEMS algorithms are also proposed, intended to resolve the identified RQs and the outcomes of which will provide insight into the extent to which the HEMS algorithms can support a reliable, affordable, and clean energy transition in Australia.

Section 3.5.2 reviews the works done in the previous OAs, Section 3.5.3 introduce fundamentals of HEMS and algorithms, Section 3.5.4 presents the market status of HEMSs, Section 3.5.5 reviews the pilot studies, Section 3.5.6 presents the state of research, and Section 3.5.7 summarises the key findings.

3.5.2 Review of work done in previous OAs Themes H1, H4 and N2 also include the research on flexible loads and HEMSs. The work done in the previous OAs of these themes has been reviewed to avoid replication. Overlaps between this RT and the previous OAs have been identified and summarised in this section. These overlaps won’t be covered in this RT. OA of H1 The OA of H1 focused on the use of A/C and PV systems that could effectively be pre-cooled or pre-heated as a demand response approach.

The following areas were covered by this OA and therefore won't be covered in this RT. • Manual activation and automation with various communication pathways.

• Pre-cooling optimisation algorithm with considering a specific set of conditions.

The algorithms for HEMS were only partially covered by this OA, i.e., only the optimisation algorithm for A/C and PV systems to achieve the objective of pre-cooling was introduced. Comprehensive control/optimisation algorithms for HEMS with various objectives will be covered in detail in this RT. OA of H4 The OA of H4 explained how different tariff schemes impact the household energy consumption and system-wide operation. The following areas were covered by this OA and therefore won't be covered in this RT. • Various tariffs and incentives for customers.

• The energy use optimisation based on tariff signals.

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The various tariffs as one characteristic of HEMS algorithms were covered by this OA, and the other characteristics, including objective, user preference and uncertainty, will be covered in detail in this RT. OA of N2 OA of N2 discussed the opportunities for mainstreaming network support services from flexible loads in combination with PV and battery systems. The control algorithms of flexible loads were only partially covered by this OA and will be covered with more details in this RT. In addition, only the objective of providing network support services was covered by this OA, and the other objectives such as reducing user electricity bills and improving thermal comfort will be covered in detail in this RT.

3.5.3 Home Energy Management System and Algorithms A HEMS can schedule or control flexible loads, using essential management software with algorithms and controlling hardware, to achieve specific objectives such as improving household economic benefits and providing network support services. The general framework of HEMS will be introduced first in this section, followed by the control mechanism of flexible loads which is the fundamental for designing HEMS algorithms in practice. Last, the categorisation and explanation, as well as implementation requirements of HEMS algorithms will be introduced in this section.

Home Energy Management System A HEMS is typically composed of a HEMS centre, communication, and networking system, DERs (including flexible loads, PVs, batteries, electric vehicles (EVs)), non-flexible loads, metering and controlling devices. Flexible loads can be categorised in terms of power rating, thermal/non-thermal, controllability, etc. Considering the high-power ratings, A/C systems, EHW systems and PPs are three expected flexible loads in a HEMS. A HEMS can have functionalities including monitoring, logging, control, alarm, and management. These functionalities are described below. A HEMS can also be designed for a single household, or an aggregation of multiple households to form a virtual power plant (VPP). The functionalities of HEMS are introduced below.

• Monitoring: access to real-time measurement on energy consumption and display operating modes/status of devices.

• Logging: collect, save, manage, and analyse data of HEMS. • Control: directly or remotely control devices, by turning devices on/off or changing operating set-points.

• Alarm: generate and send alarm messages of faults or warning to HEMS users.

• Management: conduct computation of control or optimisation algorithms, and management services on devices, to improve electricity utilisation efficiency of HEMS. A general architecture of HEMS is demonstrated in Figure 23

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HEMS Centre Monitoring

Flexible Loads A/C EHW

Controllin g Devices

communicatio PP

Logging Control

PVs

Alarm

Batteries

Management

EVs

Metering Devices

power flow

Power grid

Non-Flexible Loads Figure 19. Architecture of HEMS

The HEMS centre provides the aforementioned functionalities, and it generally has communication links with controlling and metering devices, as well as DERs. The centre directly monitors and controls the smart flexible loads which are enabled with communication and control functions, while it uses necessary controlling devices to communicate and control the conventional flexible loads. For PVs, batteries and EVs have the smart inverters with high communication capabilities, the centre directly collects data from and send control signals to the smart inverters. The centre also collects real-time measurement data from metering devices including smart meters.

On the other hand, all DERs and non-flexible loads are connected to the power grid (low-voltage distribution networks) through the metering devices, such that the metering devices can measure the total power generation/consumption of the household. In practice, the HEMS centre is generally a cloud-based platform where control and optimisation algorithms are applied in the backend that usually handles data storage and computational logic. For communication, the cloud-based platforms may require the gateway devices which collect and upload local real-time information from controlling and metering devices, as well as DERs. The controlling devices are necessary to implement the control/schedule decisions given by the HEMS algorithms on the flexible loads. It is noted the controlling devices may also have simple control and optimisation algorithms enabled. The controlling devices include timers, smart plugs/sockets, smart relays, infra-red controllers, smart thermostats specifically for A/C systems and diverters specifically for electric hot water systems. The emerging smart thermostats and diverters are introduced below.

• Smart thermostats: sense room/water temperature and perform actions to maintain the temperature, with features of remote setting, energy consumption optimisation and intelligent automation.

• Diverters: sense PV power generation and utilise excess PV power for EHW systems throughout the day to enhance solar self-consumption and reduce cost of hot water.

In practice, HEMSs has three major types: building installed HEMS, third-party generalised HEMS, and brand owned HEMS. They are introduced below.

• Building installed HEMS: work well for the building installed appliances including A/C systems, lighting systems, door intercoms and shutters. They can provide real-time control on each appliance, automation with essential sensing interface, and user preference learning.

• Third-party generalised HEMS: generally, consist of gateway hardware and cloud-based software. They can easily be installed by users, and usually have web/phone applications.

• brand owned HEMS: only communicate and control the brands’ own designed appliances, and usually have web/phone applications.

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HEMS algorithms HEMS algorithms can provide optimal or real-time decision making on scheduling or controlling flexible loads, bringing economic and/or technical benefits to the household and the network. These algorithms can be used for either a single household HEMS or an aggregated HEMS of multiple households.

Categorisation HEMS algorithms can be categorised into rule-based control, centralised optimisation, distributed control/optimisation, hierarchical coordination, and ML based algorithms. The rule-based control and centralised optimisation algorithms are the most common with a low requirement of computing capability in comparison with other algorithms. They have been implemented in commercial products of HEMS. The emerging distributed control/optimisation, hierarchical coordination and ML based algorithms have been well developed in the academic literature. They require the higher computing capability than the conventional algorithms for practical implementation. It is unknown if these emerging algorithms are currently implemented in commercial products.

Rule-based control algorithm: According to a set of predefined rules, a HEMS will automatically change set points of flexible load operation when real-time input data, such as local measurements and external signals, reaches a threshold.

Only logic operation and addition/subtraction calculations are required for rule-based control algorithms. The rule-based control algorithm is commonly used for controlling local solar self-consumption. The total household power consumption and the solar PV power generation measured in real time are the inputs, and control decisions are made immediately based on the comparison between the consumption and the generation. A HEMS starts flexible loads or increase their power consumption if the total household consumption is less than the solar PV generation; otherwise, the HEMS stops flexible loads or decrease their power consumption. The local rule-based control algorithms have naturally fast response capability, as the major advantage, but then fail to provide optimum operating results.

Centralised optimisation algorithm: With the system-wide information collected to the HEMS control centre as the input data, a constrained optimisation problem can be formulated and solved at a constant time interval to obtain an optimal schedule over a specific time horizon, or to determine control decisions, for flexible loads. The system-wide information data can be categorised into household data and external data. The household data, which is given by the user, includes flexible load parameters and operating characteristics, user preferences, building envelope parameters, historical data of energy generation and consumption, etc. The external data, which is obtained from third-party systems, includes varying network operating conditions, dynamic electricity prices/tariffs, service requests from network operators, weather information, etc. The household data is generally pre-set and constant during a time interval, while the external data may be uncertain and varying so that uncertainty modelling is required. The centralised optimisation algorithms are conducted daily for most cases, considering that PV generation and load profiles are typically diurnal, or as short as minutes depending on HEMS computing capability and household appliance response speed. HEMS centralised optimisation algorithms generally utilise constrained optimisation models. Common constraints include preferred appliance operating power and time, indoor temperature setpoint or range, and maximum power consumption/generation. Some constraints like power limits are the hard constraints which must be satisfied, while others such as indoor temperature are the soft constraints which allow temporary or slight violations under specific assumptions. Flexible load operating decisions, including turning-on/off, temperature setpoints/range, and operating levels, can be optimised and then dispatched to each load at the scheduled time spots. The centralised optimisation algorithms for scheduling flexible loads can be conducted by cloud-based platforms with satisfactory computing capability for optimisation. The optimisation models are generally simple (e.g., linear) at the current implementation stage considering existing computing capability. The widely used

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centralised optimisation algorithms aim to minimise household electricity bills by scheduling flexible loads into time slots of high PV power generation or low electricity price.

Distributed control/optimisation algorithm: With shared and updated information, agents can control or optimise local operation of flexible loads, and gradually reach a consensus of a global objective. In comparison with the centralised algorithms, the distributed algorithms do not require a control centre to collect the system-wide information and dispatch decisions. Distributed control/optimisation algorithms can protect users’ privacy to some extent, since only necessary information is shared with certain counterparts, rather than all information being shared across a whole HEMS. Hierarchical coordination algorithm: To make use of the advantages of both local control and the centralised optimisation algorithms, hierarchical coordination algorithms of HEMS have been developed. For a hierarchical coordination algorithm, a centralised schedule and a set of local control rules are simultaneously designed over a specific period with full consideration of mutual impacts between scheduling and controlling results.

ML based algorithm: Utilising a ML method to obtain a set of well-trained neural networks, a HEMS is enabled to make decisions of controlling flexible loads in real time by the neural networks. Advanced ML methods include deep learning, reinforcement learning and deep reinforcement learning. all ML methods can be used for control and schedule of flexible loads in a HEMS. Implementation requirements The basic rule-based control algorithms, which use HEMS internal information such as user preference and measured temperature as inputs, can be embedded in local controlling devices. The advanced rule-based control algorithms, which use external information such as varying electricity prices and network support services requests as inputs, need to be implemented on cloud-based platforms with effective communication links. The centralised optimisation algorithms require multiple functions such as data collection and processing, optimisation, and signal dispatch, and they can be implemented on cloud-based platforms with effective communication links.

Comparison of HEMS algorithms The major features of the HEMS algorithms are summarised in Table 36 for a comprehensive technical comparison.

Table 36. Comparison of HEMS algorithms

Algorithm

Logic Function

Optimisation Model

Computation Requirement

Communication Requirement

Data Storage Requirement

Response Speed

Rule-based Control

Yes

No

Low

Low

Low

High

Centralised Optimisation

No

Yes

High

Medium

High

Low

Distributed Control

Yes

No

Medium

High

Low

Medium

Distributed Optimisation

No

Yes

High

High

Medium

Low

Hierarchical Coordination

Yes

Yes

High

High

High

High

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Machine Learning based

No

Depends*

High

High

Depends#

High

*Optimisation models may be required for the training processes of reinforcement learning and DRL algorithms, but not for that of deep learning algorithms. #

ML based algorithms require high data storage space in the training process but require medium data storage space for neural networks.

Control mechanism of flexible loads This section describes the general mechanism used by HEMSs to control A/C systems, EHW systems and PPs, respectively.

How to consider and use the mechanism in developing HEMS algorithms will also be introduced.

Air-conditioning systems A/C systems are high-power thermal flexible household appliances. They are also regarded as thermostatically controlled loads for aggregation applications. The mechanism of A/C systems has two control stages, i.e., manual setting of indoor temperature setpoint or range, and then automation to fulfill the temperature setpoint or range. While the latter is generally designed and fixed by the manufacturers, the former can be determined by optimisation or additional control rules. Thus, most control and optimisation algorithms for A/C systems aim to obtain the manual setting of the temperature setpoint or range and control the indoor temperature. Whether a temperature setpoint or a range is used, a corresponding thermal performance threshold or constraint can be formulated in a HEMS algorithm to ensure the dynamic indoor temperature reaches setpoint or falls within the range. Real-time measurement of indoor temperature is required as input into the algorithm for checking if a threshold holds and implementing rule-based control algorithms, and a thermal model of the thermal zone (the area for which temperature is being controlled, a bedroom for example) is also required as a thermodynamics constraint for optimisation algorithms.

Electric hot water systems EHW systems including water heaters and heat pumps are also high-power thermal flexible appliances. As per A/C systems, the mechanism is also a two-stage control for EHW systems, setting of the water temperature setpoint, which can be controlled to achieve any optimisation objective or through rule-based control, and the automatic control of water temperature in real-time to maintain a temperature setpoint designed by the manufacturers. Similar to A/C systems, with the setpoint of the water temperature, a thermal performance threshold or constraint can be formulated and used in HEMS algorithms.

Pool pump PPs are high-power non-thermal flexible appliances, which are normally interruptible and deferrable. According to AS4755.3.2, PPs can be enabled with three demand response modes, including DRM1 - No load or minimal load, DRM2 Restrict load to no more than 50% of reference value, and DRM 4 - Commence operation or increase load. Generally, discrete operating decisions are expected to be determined for a control purpose. For example, off-operation, 50% load and 100% load are three operating modes for demand response on PPs (also meeting AS4755.3.2), and a binary decision variable for each mode is defined and used in HEMS algorithms to present the corresponding power consumption. There are also operating thresholds or constraints for PP operation in HEMSs. Since the pumping rates are predefined, with a given water volume target, operating time length limits are set for the pumping modes.

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3.5.4 Market status This section provides the market status of the HEMSs, the cloud-based platforms, and the controlling devices. Findings from the market status, including pros and cons, as well as barriers to improving uptake of HEMSs and algorithms, are identified, and given in this section. The HEMS algorithms of the commercial products have been identified from website introduction or official brochures. However, given the confidential nature of the commercial products, some features may be missed, and the details of the HEMS algorithms in market are unknown.

Home energy management systems Commercial examples of HEMS have been found in market. According to the three major types categorised in section 14.3.1, the commercial HEMSs are introduced below.

First, building installed HEMSs with customised solutions, such as ABB-free@home (Global), MyPlace (QLD) and Ivoryegg (NSW), have been found in market. They can provide smart home automation and energy self-consumption optimisation.

Whether these building installed HEMSs use local or cloud-based computing resources for the optimisation algorithms is unknown.

Second, third-party generalised HEMSs, such as Google Home and Amazon Alexa, have been found in market. They currently have a wide range of compatible smart appliances. Users can personalise settings of automation and combined operation, via well-designed web/phone applications.

Third, brand owned HEMSs, such as LG ThinQ (South Korea) and Xiaomi Mi Home (China), have been found in market.

They usually have manual scheduling functions and support simple rule-based control algorithms such as one appliance operation starting after completion of another.

Based on the information given by the websites/brochures of these HEMSs, this RT has the following findings and RQ. Findings: •

•

•

The HEMS in market have low compatibility with the flexible loads which usually have specific and confidential communication protocols. The HEMS developers require the agreements with manufacturers to obtain the protocols. It is a barrier to the applicability of HEMS when various flexible loads are used in households.

For the building installed HEMS, installing the local HEMS centre, wired communication system, and controlling devices requires qualified professionals, and may impact the building construction in terms of wire and device mounting. It is expensive to adopt a building installed HEMS in an established building.

Customisation on HEMS is highly expected by household users, considering various types and brands of flexible loads, building configurations, user preferences and so on. For the building installed HEMS, professional consultants can provide customised solutions, but with a cost. For the well-designed third-party generalised and brand owned HEMS, customisation on the web/phone applications is difficult.

RQs: •

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Is it possible to standardise the communication protocols of flexible loads? Who can and will lead such standardisation of the protocols to enhance the HEMS compatibility?

Cloud-based platforms The conventional rule-based control and centralised optimisation algorithms have been implemented in commercial products, typically in cloud-based platforms. In market, there are a number of commercial cloud-based platforms with gateway for scheduling or controlling flexible loads in a HEMS. These cloud-based platforms are fully capable to provide the functionalities introduced in Section 3.5.3. The aforementioned conventional HEMS algorithms have been identified in the cloud-based platforms which are introduced in Table 37, and the key features are summarised.

Table 37. Summary of cloud-based platforms

Platform

Country

Applicable

Algorithm

Features

Friendly Technologies

Israel

Household

Rule-based control

IoT application (security and automation) and energy analysis

Household

Rule-based Device local control, energy analysis and control and optimisation (electricity bills reduction with Centralised maximised solar self-consumption) optimisation

Household VPP

Rule-based Device local control, energy analysis and or control and optimisation (considering information of Centralised market, network, and weather). optimisation

CarbonTrack

SwitchDin

AutoGrid

Next Kraftwerke

Australia

Australia

USA

Germany

VPP

Centralised optimisation

Comprehensive energy management system (analysis, management, forecasting, optimisation, notification, control, etc.)

VPP

Centralised optimisation

Schedule optimisation and price-based dispatch of DERs (considering energy trading and network support services).

Herein, Friendly Technologies, CarbonTrack and SwitchDin have their own gateway devices to communicate with the cloud-based platforms. Users need to buy the gateway devices and then set up their accounts in the cloud-based platforms. These gateway devices have high communication compatibility with PV/battery/EV inverters, but their communication compatibility with various controlling devices and flexible loads is to be investigated and provided by these platform development companies. The optimisation capability required by the cloud-based platforms can be provided by third-party commercial cloud computing services such as Amazon Web Services. Whether these cloud-based platforms use their own or the third-party computing resources is unknown, which is confidential. Based on the information given by the websites/brochures of these cloud-based platforms, this RT has the following findings and RQs.

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Findings: •

•

•

Details of the rule-based control and centralised optimisation algorithms used in the cloud-based platforms are not available, due to confidential information of commercial products. Thus, the performance and efficiency of these algorithms may not be validated, and essential information is lack for further research and development on improving the practical algorithms.

Households need to buy corresponding gateway devices, and prices need to be quoted. It may be a concern about cost-effectiveness of cloud-based platforms which impedes the increasing uptake of cloud-based platforms.

The compatibility of the cloud-based platforms with various controlling devices and flexible loads is not available to public and may be low. It is difficult for households to choose platforms considering the existing and future flexible loads.

RQ: • •

Who can access the cloud-based platforms to validate the algorithms in the backend?

What less sensitive information of the HEMS algorithms can be published for researchers to fill the gaps between industrial development and academic research?

Controlling devices The controlling devices found in market are summarised with major characteristics, including the manufacturers, control capability and limitation, in Table 38.

Table 38. Summary of controlling devices

Technology

Appliance

Manufacturers (examples)

Control Capability

Limitation

Timer

Any appliances

Appliance manufacturers

On/off with countdown

Directly installed in appliances

Smart plug/socket

Any appliances with low power rating

IoT manufacturers, including TP-link, Philips.

On/off in real time

Australian household rating 10 A

Smart relay

Any appliances

Electronics manufacturers, including Schneider, Omron.

On/off in real time

Licenced electricians for installation

Infra-red controller

AC, PP

Sensibo (Israel), AmbiClimate (Hong Kong), Tado (Germany)

All functional control in real time

unknown

Smart thermostat

AC, heat pump

ZenEcosystems (US, div.

VIC), Tado (Germany), Ecobee (US)

Replacing wall-mounted 24 V thermostat

Compatibility, may require qualified installers

Diverter

EHW

Powerdiverter (SA), CatchPower (NSW)

Using solar power for water heating

Licenced electricians required for installation

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With the DRM interfaces required by Australian Standard AS4755, A/C systems, EHW systems and PPs can be enabled to provide demand response capabilities. Details of AS4755 can be checked in RT4. Based on the information given by the websites/brochures of these controlling devices, this RT has the following findings.

Findings: •

•

•

As the limitations summarised in Table 19, the smart relay, smart thermostat, and diverter require qualified professionals for installation. The installation cost may be high, taking considerable proportion of the HEMS cost, impeding the use of these controlling devices.

Advised by the commercial product websites, the smart thermostat may have a compatibility issue concerning replacing an old wall-mounted thermostat. This means that for some households, they cannot adopt the smart thermostats.

The controlling devices may have local computing capability for logic operation only. The local data storage capability is usually low. Thus, they hardly work for distributed optimisation or ML based algorithms. It is a barrier to applications of advanced distributed optimisation and ML based algorithms in practice.

3.5.5 Pilot Studies and Projects A number of pilot studies and trial projects on demand side management have been completed by researchers/consultants and electricity retailers across Australia in recent years. However, it is found that most of them focuses on the behavioural demand response where households are incentivised to change their energy consumption behaviour to meet grid’s operational needs, and the aggregated power reserve for network support services. This RT reviewed one completed pilot study and one pilot project in process, which focus on HEMSs generally, not on HEMS algorithms. The study and project have been summarised with the key findings in this section.

Pilot study “Smarter homes for distributed energy” The pilot study “Smarter homes for distributed energy” (Smarter homes for distributed energy, 2022) focusing on HEMSs has been completed by Enea Consulting under an Australian Renewable Energy Agency (ARENA) funding. This study studied and summarised the HEMS development consisting of infrastructure, technology, and benefits. This study also analysed two commercial products, i.e., SwitchDin Droplet (gateway device) and Solaredge Energy Hub Inverter. The former is a local controller, while the latter is a smart inverter embedded with energy management capabilities. Both can provide remote control on DERs, improved device management, as well as scheduling and controlling a growing range of flexible loads. This study focused on demonstration of HEMS infrastructure and the capability for network support services via dynamic operating envelope. The details of HEMS algorithms were not given by the study.

This study made a conclusion that HEMS technologies are still in market infancy, with the barriers to HEMS deployment identified as below.

• The value proposition of HEMS products and services can be relatively low for partial residential customers.

• Proprietary protocols may not be openly published, and access permissions can be denied.

• There are no requirements that promote open device standards for flexible loads.

• The regulatory requirements for cyber security are limited.

• Customer understanding of HEMS is low, and customer trust in the energy industry is low.

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Pilot project “Net Zero Energy Homes” Recently, a project led by Mirvac (Net Zero Energy Homes – Milestone 1 Knowledge Sharing Report) was conducted in Victoria to demonstrate the feasibility of building net zero energy homes, with smart energy monitoring system as well as energy-efficient A/C and EHW systems. The project commenced in November 2019 and will be completed in September 2024. The first milestone report was published in May 2021, and the knowledge sharing from this report is summarised below.

This project aims to supply energy for all appliances (including flexible loads) by only utilising solar PVs and batteries with a HEMS. The PV systems were sized as 5kWp for 3- and 4-bedroom homes, and 3.8kWp for 2-bedroom homes. Highefficiency reverse cycle ducted A/C systems and heat pumps were selected for installation. The customer insights from the surveys, including knowing or learning the meaning of energy rating, and willing to purchase an Evs, have been considered in the building design. The HEMS to be used can minimise energy usage and costs.

This project identified the key challenges, which are summarised below.

• Energy modelling of fully electric home to achieve the required energy rating.

• Restricted roof space of townhouses for the PVs required by the HEMS. Findings Besides the findings provided by the reports (Smarter homes for distributed energy, 2022) and (Net Zero Energy Homes – Milestone 1 Knowledge Sharing Report), this RT also has the following findings and RQs according to the mentioned pilot study and project.

Findings: •

•

Besides the HEMSs, the algorithms should also be explained to households, thus promoting the HEMSs and algorithms. However, the media release on the HEMSs and algorithms is lack or inefficient, and it is a long way for households to fully understand the benefits from the HEMSs and algorithms.

The HEMS algorithm design should be considered in the DER sizing process. The detailed information of the HEMS algorithm design procedure in practice is unavailable, and it is to be investigated. It is still difficult for researchers or industrial developers to validate and improve the existing HEMS algorithm design procedure.

RQs: • •

Who can and will make the information of practical HEMSs and algorithms transparent and more understandable to the public?

When a HEMS is required, what is the general HEMS algorithm design procedure in practice? What factors/conditions should be considered and addressed in the procedure?

3.5.6 State of research Systematic literature review has been conducted to search for the research and review articles that are closely coupled with this RT. The articles focusing on HEMS, demand-side management, appliance scheduling, flexible loads, thermal loads, as well as control and optimisation algorithms have been selected and studied. This literature review has informed the

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analysis of the technical barriers and potential solutions to improving uptake of HEMSs and developing efficient algorithms. The cutting-edge research works of various HEMS algorithms and the key findings are discussed and summarised in this section.

Rule-based control algorithms Rule-based (also regarded as logic-based) control algorithms have been widely embedded in the local controllers of flexible loads which are able to do logic operation. For example, local temperature controllers in A/C and EHW systems keep the temperature to the setpoint or within the satisfactory range (D. Wang et al., 2014). The conventional local rulebased control algorithms check local measurement of temperature, trigger the control actions, and keep thermal comfort only without an economic or technical objective.

HEMSs can have rule-based control algorithms with system-wide information data and more complicated control rules.

The system-wide information includes household data and external data such as network operating conditions, electricity prices and weather information. The research work (Heo et al., 2017) developed control rules for a HEMS to turn on and off smart plugs considering grid connection and PV generation situations. Further, in (Cheng et al., 2017), with real-time measurement of power system frequency and designed droop control functions, controllers could modify the power consumption of flexible loads to support grid frequency control services. With available large amount of data, the heat pump power regarding both steady-stage and transient response to power system frequency can be calculated and adjusted with designed control rules (Youngjin, 2016). These advanced rule-based control algorithms can allow HEMSs to provide network support services, thus promoting the uptake of HEMSs. However, HEMS operating cost during the rulebased control is yet to be investigated, and incentives are required for attracting household to apply rule-based control algorithms for network support services.

From the literature, it is found that rule-based control algorithms are straightforward and efficient for practical implementation, for no optimisation computing capability is required. They work according to real-time measurement, but not for a scheduling period like daily or hourly. They can provide economic or technical benefits if the external data is available. Naturally, the rule-based control results are not optimal, so that control rules are expected to be properly designed for expected performances. The barriers to applying the rule-based control algorithms in practice are summarised as findings below. The RQ has also been proposed.

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Findings: •

•

•

According to (D. Wang et al., 2014), the conventional local rule-based control algorithms for keeping thermal comfort cannot contribute to an economic schedule or provide network support services. On the other hand, the advanced rule-based control algorithms (Heo, Park, & Lee, 2017) (Cheng, Sami, & Wu, 2017) (Youngjin, 2016) require system-wide information data to achieve economic and technical benefits, and access to the data with additional cost may be a concern. The computing and data storage capabilities are also to be enhanced for the advanced logic functions and complex calculations, which may impair the original advantage of the rule-based control algorithms on the low requirement of the capabilities.

The advanced rule-based control algorithms for network support services like the works (Cheng et al., 2017) (Youngjin, 2016) cannot consider economic compensation for households, indicating the nature of the logic functions which focus on the technical benefits only. No payment is a barrier to attracting households to enable such rule-based control algorithms for network support services.

The rule-based control algorithms do not have the optimisation capability of scheduling flexible loads over a given period. Moreover, the rule-based control algorithms for controlling the flexible loads in real time cannot provide solution optimality. Thus, like what works (Heo et al., 2017) (Cheng et al., 2017) (Youngjin, 2016) demonstrate, the control rules should be properly designed and customised with various parameters of HEMS. It takes time to design and apply control rules/logic functions for various households.

RQ: •

How can the incentives for households to provide network support services be designed and involved in the in the practical rule-based control algorithms?

Centralised optimisation algorithms Centralised optimisation algorithms generally aim to minimise or maximise an objective by optimise the schedule or control of flexible loads. Other than the most common objective used in market, i.e., minimisation of household electricity bill/energy cost, various objectives can be found in the academic literature. The research work on centralised optimisation algorithms (A. Anvari-Moghaddam et al., 2016) aims to maximise household thermal comfort level, and the work (Aguilar et al., 2019) aims to maximise of flexible load performance coefficient. For providing network support services, maximisation of demand response reward payment was given by the work (Hu et al., 2018), and minimisation of network operating constraint violation was proposed by (Hughes et al., 2016). These objectives require the higher computing capability and the more accurate system-wide information data, than the widely used economic objectives. Some required system-wide information data are not available in practice.

Multiple objectives can be applied and optimised simultaneously, and a Pareto front can be obtained as a solution pool of decision making. In (Jin et al., 2017), five objectives were developed to be simultaneously minimised, including thermal discomfort, energy cost, carbon emission, user inconvenience and equipment degradation. To address multi-objective optimisation problems, various methods (C. Zhang et al., 2020), including advanced adaptive weighted-sum and normal boundary intersection, can effectively transform multi-objective optimisation models into single-objective equivalent models and suggest solutions with fairness among objectives. It is noted that seeking for a Pareto front requires the higher data storage capability, due to using multiple sets of parameters and obtaining multiple sets of decisions.

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In the academic literature, various optimisation solution methods have been applied for HEMSs, from conventional programming methods and intelligent algorithms to ML applications. With acceptable high computing capability, (mixed integer) linear programming (H. T. Nguyen et al., 2015), (mixed integer) quadratic programming models (Hu et al., 2018) and (mixed integer) non-linear programming models with relaxation and linearisation ((Baniasadi et al., 2019), (Z. Xu et al., 2012)) are mainstream. These programming optimisation methods require corresponding solvers, which are generally commercial, such as Cplex, GUROBI and Mosek. Although the programming methods are generalised and applicable to various household settings, such model-based methods require detailed modelling instructions for wide implementation.

The limited applicability of a specific optimisation model is a major barrier.

Advanced intelligent algorithms for HEMS centralised optimisation including gradient-based particle swarm optimisation algorithm (Huang, Tian, & Wang, 2015) and improved butterfly optimisation algorithm (X. Wang, Mao, & Khodaei, 2021) have been developed for flexible load scheduling. These algorithms work well for a HEMS with significantly complicated optimisation models, but they may be of low computing efficiency for the practical HEMSs with only approximated and simplified models. Thus, these intelligent algorithms are not suggested in practice.

The ML applications, including those for HEMS optimisation methods, will be introduced in section 3.5.6.5. To achieve fast and stable data collection and signal dispatch for the centralised optimisation algorithms, advanced IoT infrastructure (Ghiasi et al., 2019) and communication protocols (e.g., Zigbee (Han & Lim, 2010)) have been developed in the academic literature. Standardisation and possible high costs impede implementing these communication upgrade suggestions. From the literature, various objectives have been proposed and they require high computation and data storage capabilities in HEMSs. The limited applicability of optimisation models are the major barriers to applying the programming optimisation methods and intelligent algorithms. The barriers to applying the centralised optimisation algorithms in practice are summarised as findings below. The RQ and recommendations have also been proposed.

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Findings: •

•

•

•

•

The high computing capability, which can handle the programming models and solvers suggested by the (Hu, Li, Fang, & Bai, 2018), (H. T. Nguyen, Nguyen, & Le, 2015), (Baniasadi, Habibi, Bass, & Masoum, 2019), (Z. Xu et al., 2012), is required to solve the complex optimisation problems with the advanced objectives in terms of household technical benefits and network support services. If multi-objective problems such as the one in (Jin, Baker, Christensen, & Isley, 2017)are applied, the required computing capability should be even higher. Additional cost for the high computing capability is usually expensive, and it leads to a barrier to applying the centralised optimisation algorithms in practice.

High data storage capability is required to save the solution pool of decision making for multi-objective optimisation problems (C. Zhang, Xu, Dong, & Zhang, 2020). It is also a barrier to achieving multiple objectives by the centralised optimisation algorithms, due to the more cost for the more data storage space.

Although the programming optimisation methods are generalised, a specific optimisation model to be solved has the limited applicability. The models provided by (Hu et al., 2018), (A. Anvari-Moghaddam, Monsef, & Rahimi-Kian, 2016), (Aguilar et al., 2019), (Hughes, Domínguez-García, & Poolla, 2016) require modelling specification. It is the nature of model-based optimisation algorithms, and it takes significant time if a centralised optimisation algorithm is applied for various households which have quite HEMS parameters.

The intelligent algorithms for solving complicated optimisation models are not suggested in practice, since the HEMS optimisation models are generally simplified or approximated. Thus, the practicability of the intelligent algorithms is low for the HEMS problems.

Advanced communication infrastructure (Ghiasi, Akbary, Pourkheranjani, Alipour, & Ghadimi, 2019) and protocols (Han & Lim, 2010)have been developed and validated. However, national standardisation and possible high cost impede the communication upgrade for the advanced centralised optimisation algorithms.

RQs: •

Are there any efficient methods to deal with modelling specification in practice? Can the characterisation and classification on HEMSs work to allow fast modelling specification?

Recommendations: • •

It is necessary to investigate and validate the cost-effectiveness of enhancing the computing and data storage capabilities for the advance centralised optimisation algorithms developed in the academic literature.

It is necessary to investigate and validate the cost-effectiveness of communication infrastructure and protocol upgrade for the centralised optimisation algorithms.

Distributed control/optimisation algorithms Household level aggregated flexible loads can also be controlled in a distributed manner, where they share local information with other equivalent entities via advanced communication infrastructure. In the academic literature, distributed optimisation algorithms have been developed for efficient information sharing among multiple homes and

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decision making for local flexible loads at each home. With an aggregator, multiple agents for different homes can communicate and optimise their flexible load schedules to minimise the cost and/or comfort dissatisfaction. Widely used distributed optimisation algorithms include Lagrangian multiplier based decomposition (S. Mhanna et al., 2016), and alternating direction method of multipliers (ADMM) (Kou et al., 2020). On the other hand, in (Chavali et al., 2014), with a game theory method, the retailer could provide electricity prices and users could optimise their flexible load scheduling, via sharing prices and power amounts to converge to a globally optimised decision with the maximised social welfare.

However, as a barrier to implementing such distributed optimisation algorithms in practice, the high distributed computing and communication capabilities are required.

On the other hand, rule-based control can also be achieved with a distributed structure. In practice, advanced metering infrastructure and programmable logic controllers are required to implement distributed control algorithms (Chavali et al., 2014). They could be applied for a household level aggregated HEMS, i.e., working for a VPP, but difficultly for a single household HEMS. It is also why no distributed control algorithms are found in market for flexible load coordination within a single building. A new distributed communication structure with the high communication capability among HEMSs is required for the distributed control/optimisation algorithms. Since it is difficult to make direct and fast communication among flexible loads without a data collection centre, the distributed control/optimisation algorithms cannot be used for individual flexible loads in a single household building. The distributed control/optimisation algorithms have been intensively developed in the academic literature in recent years. However, the high distributed computing capability is required. Besides, they are suitable for VPPs, rather than the individual flexible loads in a single household building. The barriers to applying the distributed control/optimisation algorithms in practice are summarised as findings below. The RQ and recommendation have also been proposed.

Findings: •

•

Because of the required high computing and communication capabilities as indicated in (S. Mhanna, Chapman, & Verbič, 2016) (Kou et al., 2020) (Chavali, Yang, & Nehorai, 2014), the distributed optimisation/control algorithms do not work for individual flexible loads of a household HEMS in practice. Currently, they can only be applied for the HEMS of multiple households, i.e., in a form of VPP. The required new distributed communication structure may lead to the high investment cost which is also a barrier to applying the distributed algorithms.

RQ: •

How can the computing and communication capabilities of individual flexible loads be enhanced? Is it possible to design additional interface devices to provide the high computing and communication capabilities for the distributed optimisation/control algorithms?

Recommendation: •

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It is necessary to investigate and validate the cost-effectiveness of the distributed communication structure required by the distributed optimisation/control algorithms.

Hierarchical coordination algorithms The hierarchical coordination algorithms for HEMS, which can provide both optimal scheduling and real-time control, have recently been developed in the academic literature. In research works (Pilloni et al., 2018)and (Vedullapalli et al., 2019), via estimating power consumption amounts and variations of thermostatically controlled loads, the impacts of local controllers embedded in appliances on HEMS operation can be projected in centralised optimisation models. The recent work (Saberi et al., 2021) further modelled local controllers in a centralised optimisation model with the whole HEMS information, such that the set-points of local controllers can be optimised with dynamic real-time impacts addressed. This is advanced hierarchical coordination of local rule-based control and centralised optimisation. It is expected as a tendency to coordinate the local control and the centralised optimisation in practice, thus maximising the respective benefits and mitigating the limitations. The high performance of an algorithm generally means high requirements, leading to a barrier to applying the hierarchical coordination algorithms for HEMS in practice. This barrier is described as a finding below and the RQ has been proposed.

Finding: •

Considering the significantly complex models given by works (Pilloni, Floris, Meloni, & Atzori, 2018) (Vedullapalli, Hadidi, & Schroeder, 2019) (Saberi, Zhang, & Dong, 2021), the hierarchical coordination algorithms require the high capabilities of computation, communication and data storage. The cost of building such a HEMS with the high capabilities is expensive, which is the major barrier for practical implementation of the hierarchical coordination algorithms.

•

Is it possible to develop ML based algorithms, which can avoid the complex models, to achieve the hierarchical coordination of the flexible loads in a HEMS?

RQ:

Machine learning-based algorithms ML methods can be applied for monitoring, analysis and decision making for HEMS, and an academic introduction can be found in (Aguilar, Garces-Jimenez, R-Moreno, & García, 2021). Focusing on the HEMS algorithms, the ML based decisionmaking applications include control schemes, optimisation models and scheduling models. For example, the research work (Gao, Li, & Wen, 2020) applied a ML based algorithm to control an A/C system to achieve thermal comfort with minimisation of energy cost. In (Kim & Lim, 2018) a ML based algorithm was adopted for a smart building to reduce energy cost under unknown future information. The work (Ye, Qiu, Wu, Strbac, & Ward, 2020) applied a ML based algorithm to schedule various flexible loads to minimise energy costs for household users. The work (Lissa, 2020) developed a ML based algorithm for HEMSs to minimise energy consumption for household users by optimising heat pump operation.

The ML based algorithms require relatively high computing capability, and even higher if the training process is also made locally. The ML based algorithms for HEMS are attractive, due to significant applicability, low dependency on models and high computing efficiency in optimisation. They can provide fast responding solutions or optimised scheduling solutions for various flexible loads. However, for implementation, a barrier is described as a finding below. The RQ and recommendation have also been proposed.

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Finding: •

The ML based algorithms require the high capabilities of computation, communication, and data storage. The cost of building such a HEMS with the high capabilities is expensive, which is the major barrier for practical implementation of the ML-based algorithms.

•

How can the ML-based algorithms work in a practical HEMS, informing requirements of hardware and software?

RQ:

Recommendations: •

It is necessary to investigate and validate the cost-effectiveness of enhancing the computing, communication and data storage capabilities for the ML-based algorithms developed in the academic literature.

Research problems There are research problems related to the HEMS algorithms, which researchers have been making great efforts to solve.

These research problems have been identified in terms of user preference, thermodynamics, uncertainty, as well as price and tariff. These research problems and the suggested solutions from the academic literature are introduced in this section.

User preference Users have different preferences of operating times and modes for different flexible loads, depending on various factors including workday/holiday, season, weather, and special activity. Especially for A/C and EHW systems, users have quite different thermal comfort requirements on room/water temperature. The work (Pilloni et al., 2018) investigated the user preferences via a means of survey and made categorisation. These thermal comfort requirements are generally used as inputs in the HEMS control/optimisation algorithms, thus forming updated thresholds or constraints as a solution. The work (Yang, Liu, Fang, & Liu, 2018) provided the typical user preferences on appliance scheduling for HEMS optimisation modelling, and the work (Althaher, Mancarella, & Mutale, 2015) defined a satisfaction factor for HEMSs considering various appliance controller inputs.

Thermodynamics The thermodynamics of A/C and EHW systems impacts their power consumption, and it should be considered and addressed in optimisation models. The power consumption of A/C systems depends on the corresponding thermal zone, i.e., the thermal environment determined by a number of factors such as heat conduction through the building envelope, solar radiation from windows, heat convention within rooms, heat dissipation from user activities and appliances, as well as thermal inertia of the building. Similarly, the power consumption of EHW systems depends on the water thermal zone which is impacted by the heat conduction through the water tank. Thus, the thermal zone models which can present the thermodynamics are required. The resistance and capacitance network model has been widely used for thermal zone modelling, and examples can be found in (Amjad Anvari-Moghaddam, Rahimi-Kian, Mirian, & Guerrero, 2017). On the other hand, the work (Hu et al., 2018) adopted detailed simulation of Acs to formulate an indoor temperature estimation model.

Despite all efforts in thermal zone modelling, the assumptions, estimated parameters and simplified models still lead to

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nonnegligible errors in the power consumption calculation. In the academic literature, ML methods have been applied to thermal zone modelling and AC power consumption calculation in (Qin, Lysecky, & Sprinkle, 2015) and (D. Zhang, Li, Sun, & O’Neill, 2016), respectively.

Uncertainty Uncertainty refers to the situations involving imperfect or unknown information, and it commonly exists in complex optimisation problems including those for HEMS. The uncertainty parameters in HEMSs include random use of some appliances, PV generation, and ambient temperature. To deal with the uncertainty in HEMSs, two kinds of methods have been widely used in the academic literature, one by uncertainty modelling and the other by the frequent update of uncertainty prediction/measurement. The uncertainty modelling methods include stochastic programming methods (Shafie-Khah & Siano, 2018) which aim to obtain an expected objective result under representative scenarios of possible uncertainty realisation, and robust optimisation methods (Cuo Zhang, Yang Dong, & Yin, 2020) which optimise decisions under the worst case of possible uncertainty realisation. Further, advanced model based and data-driven distributionally robust optimisation methods, which aim to obtain robust decisions under the worst distribution of possible uncertainty realisation, have been applied to HEMSs in (Du, Jiang, Duan, Li, & Smith, 2018) and (Saberi, Zhang, & Dong, 2022), respectively. The optimisation methods with the frequent update of uncertainty prediction/measurement include model predictive control (Jin et al., 2017), rolling horizon-based optimisation (Althaher et al., 2015) and dynamic programming methods (Nistor & Antunes, 2018).

Price and tariff Electricity price and tariff parameters can significantly impact the HEMS operating decisions and objective results. Widely used price/tariff schemes could be categorised into three major types, i.e., constant (flat), time of use, and real-time variable. New price and tariff schemes are also expected in practice to better incentivise HEMS development. For example, the work on HEMS optimisation algorithm (Luo, Kong, Ranzi, & Dong, 2020) considered real-time pricing and proposed a new type of tariff – demand charge tariff which was a one-off charge based on the maximum demand. The findings identified from the research problems and the recommendations are summarised below.

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Findings: •

•

•

Most research works developed the HEMS algorithms based on the assumptions of full availability of data, full compatibility of HEMS with flexible loads as well as high computation, communication, and data storage capabilities. However, these assumptions may not be held in practice, leading to a barrier to application of the advanced HEMS algorithms.

Most research problems are still open to address, and a wide range of solutions from the academic literature are available. However, most research articles are not public, and the HEMS developers need to pay for access. It is a barrier for the HEMS developers to learning the timely and efficient solutions.

No research articles show the advanced HEMS algorithms have been implemented in commercial products to solve these problems. The effectiveness and efficiency of the HEMS algorithms should be validated on commercial products, i.e., the commercial cloud-based platforms with the controlling devices.

Recommendations: • • •

It is necessary to investigate what general capabilities the existing HMESs have. Thus, it is expected to rethink and modify the assumptions when developing the advanced HEMS algorithms.

The research works on advancing HEMS algorithms are also expected to be published in the open access research journals and magazines.

Research and industry collaboration projects focusing on applying the advanced algorithms in practical HEMSs are highly expected.

3.5.7 Summary of Findings and RQs The key findings, including the barriers to improving uptake of HEMS and applying advanced HEMS algorithms, are summarised in Table 39. The RQs proposed by this RT are summarised in Table 40.

Table 39. Summary of key findings for RT5

Category

Section

Key Finding The HEMSs in market have low compatibility with the flexible loads which usually have specific and confidential communication protocols.

The building installed HEMSs require qualified professionals for installation, and it is expensive.

HEMS

3.5.4.1

Customisation on HEMSs is required. It is expensive for the building installed HEMSs, and it is difficult for the third-party generalised and brand owned HEMSs. Details of the algorithms used in the cloud-based platforms are not available, and the performance and efficiency of these algorithms may not be validated.

Cloudbased platforms

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3.5.4.2

Households need to buy gateway devices and there is a concern about cost-effectiveness when using the cloud-based platforms.

The compatibility of the cloud-based platforms with various controlling devices and flexible loads is not available to public and may be low.

Some controlling devices need qualified professionals for installation with the cost which may be high.

Controlling devices

3.5.4.3

The smart thermostat may have a compatibility issue concerning replacing an old wall-mounted thermostat.

The controlling devices hardly work for distributed optimisation or ML based algorithms.

Pilot studies and Projects

3.5.5.3

The algorithms should also be explained to households. The media release on the HEMSs and algorithms is lack or inefficient.

The detailed information of the HEMS algorithm design procedure in practice is unavailable.

The advanced rule-based control algorithms require system-wide information data, and access to the data with additional cost may be a concern.

Rule-based algorithms

3.5.6.1

No payment for attracting households to enable the advanced rule-based control algorithms for network support services.

It takes time to design and apply control rules/logic functions for various households.

The additional cost for the high computing capability required by the advanced centralised optimisation algorithms is expensive.

More cost is required for the higher data storage capability required by the solution pool of decision making.

Centralised algorithms

3.5.6.2

A specific optimisation model to be solved in an advanced centralised optimisation algorithms has the limited applicability.

The practicability of the intelligent algorithms for solving complicated optimisation models is low for the HEMS problems.

National standardisation and possible high cost impede the communication upgrade for the advanced centralised optimisation algorithms The distributed optimisation/control algorithms do not work for individual flexible loads of a household HEMS in practice.

Distributed algorithms

3.5.6.3

Hierarchical algorithms

3.5.6.4

Building a HEMS with the required high capabilities for the hierarchical coordination algorithms is expensive.

Machine learning algorithms

3.5.6.5

Building a HEMS with the required high capabilities for the ML-based algorithms is expensive.

Research problems

3.5.6.6

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The required new distributed communication structure may lead to the high investment cost which is also a barrier to applying the distributed algorithms.

The assumptions used in the academic research may not be held in practice.

Most research articles are not public, and the HEMS developers need to pay for access

No research articles show the advanced HEMS algorithms have been implemented in commercial products.

Table 40. Summary of RQs for RT5

Category

Section

HEMS

3.5.4.1

Cloud-based platforms

3.5.4.2

Pilot studies and projects

3.5.5.3

Rule based algorithms

3.56.1

Centralised algorithms

3.5.6.2

Distributed algorithms

3.5.6.3

Hierarchical algorithms

3.5.6.4

Machine Learning algorithms

3.5.6.5

RQ Is it possible to standardise the communication protocols of flexible loads? Who can and will lead such standardisation of the protocols to enhance the HEMS compatibility?

Who can access the cloud-based platforms to validate the algorithms in the backend?

What less sensitive information of the HEMS algorithms can be published for researchers to fill the gaps between industrial development and academic research?

Who can and will make the information of practical HEMSs and algorithms transparent and more understandable to the public?

When a HEMS is required, what is the general HEMS algorithm design procedure in practice? What factors/conditions should be considered and addressed in the procedure?

How can the incentives for households to provide network support services be designed and involved in the in the practical rule-based control algorithms?

Are there any efficient methods to deal with modelling specification in practice? Can the characterisation and classification on HEMSs work to allow fast modelling specification?

It is necessary to investigate and validate the cost-effectiveness of communication infrastructure and protocol upgrade for the centralised optimisation algorithms.

How can the computing and communication capabilities of individual flexible loads be enhanced? Is it possible to design additional interface devices to provide the high computing and communication capabilities for the distributed optimisation/control algorithms?

Is it possible to develop ML based algorithms, which can avoid the complex models, to achieve the hierarchical coordination of the flexible loads in a HEMS? How can the ML-based algorithms work in a practical HEMS, informing requirements of hardware and software?

3.6 RT.6 Methods to make maximum use of data 3.6.1 Introduction Energy awareness is an effective way to drive behaviour change in consumers. Traditional energy bills inform households about their usage on a monthly seasonal basis. In most cases, bills contain a minimum of information - an overview of energy consumption for the current billing period, a few reference statistics over the past period, and payments due.

Information is too coarse to identify when and where energy was consumed, where waste took place, and how it could have been avoided. These shortcomings have prompted smart meters and other smart monitoring devices to be introduced, resulting in a revolution in the energy industry. A smart meter measures energy consumption dynamically (usually fractions of a second to minutes) and reports the data to a utility. “Smart meters turn power into knowledge, and knowledge is power. Consumers can make informed choices which in turn open the way to greater retail options that suit their family or business usage patterns,” AEMC Chair Ms Collyer stated on 03 November 2022. This technology allows households (and businesses) to track precisely how much energy they consume, correlate it with their lifestyles, habits, appliance inventory, and other factors, and, based on this insight, change their behaviour, so they are more responsible energy consumers.

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As reported by AMEC in October 2020, smart meter penetration is about 17.4% (1.04 million) across the NEM (excluding Victoria)(magazine, 4 December 2020). AEMC recommends that smart meters be universally adopted in 5 jurisdictions across the NEM by 2030. There are four states affected most by this recommendation: New South Wales, the Australian Capital Territory, Queensland, and South Australia. Tasmania, however, already has a program in place to deploy smart meters more quickly, which would apply to this recommendation as well. In Victoria, smart meters have already been adopted almost universally AEMC, 3 November 2022 #654}. The NEM, including NSW, ACT, Queensland, and South Australia, would benefit by $507 million if smart meters were fully adopted (AEMC, 03 November 2022). Using gross appliance level information in real time resulted in a 12% savings, which increased by 35% when combined with 'Smart Program Design' such as emailing or texting consumers to suggest voluntary responses (AEMO, February 2022) Smart meters will certainly be useful, but without user-friendly in-home displays or apps that show useful graphs and visualisations that are simple and informative for consumers, smart meters may not be as effective at encouraging customer engagement. In this regard, ARENA supports the nationwide distribution of Wattwatchers energy monitoring hardware and software packages to thousands of homes and small businesses and hundreds of schools. The project led

by Wattwatchers is called My Energy Marketplace (MEM) project, which is an approximately $9.6 million project, with $2.7 million in grant funding from ARENA being focused mainly on subsidising participation in the project by homes, small businesses, and schools. As of November 2021, nearly 30% (1391) of the planned 5000 residential and small business smart energy management packages had been installed and deployed Australia-wide (Murray Hogarth, 19 November 2021). As another incentive tool for households, the Power Pal energy monitor is freely available through the Victorian government's energy upgrade program, and now has been installed in over 95,000 households.

Against this background, this RT looks at methods to maximise the use of both synthetic and measured data from HEMS devices (smart plugs, for example), behind-the-meter DER monitoring devices, and smart meters. The aim of this research is to answer how maximise the use of this data to inform, advise, and empower customers to reduce their energy usage in their homes and reduce their utility bills. To accomplish this, we took two steps: (i) review existing literature on load disaggregation algorithms that can assist customers in gaining insight into the electricity usage of their appliances and their consumption habits, without extra cost of having individual smart plugs, sensors or CT clamps installed on their devices. (2) review current customer-centric apps and technologies offered in Australia and internationally, identifying any gaps in their implementation and their usefulness.

3.6.2 Review of work done in previous OAs Aside from the H4 opportunity assessment entitled "Rewarding flexible demand: Customer-friendly Tariffs and Incentives," published in November 2021(Roberts, November 2021), previous OAs don’t cover reviews of the most popular apps and tools in Australia. In the H4, the review explores ways to promote households to use flexibility in electricity. This is done by reviewing access to the market for residential electricity flexibility and different network and retail incentives that can be used to encourage electricity flexibility. As well, it outlines the tools that support household decision-making by informing consumers about different retailers and responding to market signals. The tools are categorised according to design type, social support framework, and the passive-interactive-proactive (PIP) typology. The review covers tools that support households for (i) retailer comparison, (ii) technology assessment such as assessing the size of solar and battery systems, (iii) monitoring the electricity use and (iv) smart meters. In this RT, we expand on the current apps and technologies available to households to inform them about their energy usage. Furthermore, we discuss the algorithms the apps and tools rely on as well as their effectiveness, which is not covered in H4.

3.6.3 Current research and technology status This section presents the current state of research and technology status of customer centric apps and tools in Australia and international level. The current state of research and technology of the tools and apps can be analysed by looking at: (i) the underlying algorithms used, (ii) existing apps and tools focusing on making the most of the data available in smart meters to inform households about their consumption patterns and their appliances electricity usage.

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Algorithms A smart meter and the Internet of Things (IoT) have opened up new possibilities ranging from monitoring electrical energy to extracting information about household individual appliances' energy usage, frequency usage and household consumption patterns.

I. Extracting this information can be performed by installing monitoring sensors or smart plugs for each appliance, which is known as intrusive load monitoring (ILM). It allows accurate detection of the operating state of each appliance.

II.

Using artificial intelligence and machine learning methods to extract the usage of each device in the house by using the total aggregated power signals collected from a smart meter. This is called non-intrusive load monitoring (NILM).

For two reasons, our main focus here is on reviewing the current state of research on NILM methods because ILM deployment is costly as it requires installation of a number of sensors, and also the cost of wiring issues, retrofitting, data acquisition, and wired/wireless communication. More importantly as the RT title implies, we are looking at methods to make maximum use of available household data without adding extra cost or enforcing the installation of smart plugs.

Non-intrusive load monitoring NILM was proposed nearly two decades ago by Hart (HART, 1992) for disaggregating electrical loads by examining only the appliance-specific power consumption signatures within aggregated load data. Due to the fact that the data is acquired from the entry point of the meter in the building, the method is considered non-intrusive since it does not require any equipment to be installed inside the residence. In order to achieve disaggregated energy sensing, the NILM decomposes aggregated electricity signals from active appliances into appliance-specific power signals.

The energy consumed by each appliance has a unique energy consumption pattern named ‘Power signature’. Various techniques have been proposed in the literature owing to the diverse nature of appliances. The identification of appliances is highly dependent on load signatures, which are further classified based on appliance category. According to (HART, 1992), consumer appliances can be classified into the following categories based on their operational states: Type I: These are the two-state appliances and can also be referred to as on/off devices such as light, table lamp, toaster, etc.

Type II: These appliances consume different power during different states of their operation. Microwave, washing machine, etc. are examples of such appliances.

Type III: These appliances exhibit improper power characteristics due to continuously variable power consumption.

Examples of such appliances are dimmer light, power drills, etc. The varying and sometimes random energy consumption of these devices makes it difficult to model their behaviour. The reason is that they require waveforms to be sampled at a much higher frequency in the range of 10 to 100MHz. It is important to note, however, that these high-frequency energy meters are usually custom-built and expensive because they depend on sophisticated hardware and are customised for the type of information they need to collect.

Type IV: These are the appliances that remain active throughout the week and thus are permanent consumer appliances.

Such appliances include telephone set, router, etc.

Some appliances in a household use a lot of energy, such as stoves, air conditioners, and refrigerators. These are known as the top consumers. Additionally, there are appliances that consume very little energy, such as laptops, DVD players, and

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routers. It is hard to track the power signature of small consumers because the top consumers are so dominant. Therefore, the NILM methods must therefore focus on identifying the top consumers.

Figure 24 illustrates a general framework for the NILM system that consists of three main modules: data acquisition, feature extraction, and inference and learning. There is a plethora of research and work on different NMI algorithms (Ahmad Zoha; Hasan Rafiq; Mporas ) (Mporas, January 2023; Zoha, Gluhak, Imran, & Rajasegarar, 2012) (Rafiq, Shi, Zhang, Li, & Ochani, 2020). They may differ substantially from each other in terms of the methods used for appliance feature extraction and inference and learning algorithms.

Figure 20. General framework of NILM approach. Adopted from (Ahmad Zoha, 2012)

Here is a brief discussion of each component of the general NILM framework: I. Data acquisition: the data acquisition module captures aggregated load measurements at an appropriate sampling frequency from the meters in order to determine distinct load patterns.

II. Appliance feature extraction: following the acquisition of data, it is necessary to process the raw data (i.e., voltage and current waveforms) in order to compute power metrics (e.g., active and reactive power). Following the raw data processing, a subsequent step is to detect events such as appliance state transitions (e.g., on to off) from the power measurements. By analysing the changes in power levels, event detection modules detect the on/off transition of appliances. Accordingly, these events can be further classified as steady-state or transient changes.

For characterising the detected events, two feature extraction methods are generally used: (i) steady-state and (ii) transient event-based. The steady-state method identifies devices based on variations in the steady-state signatures of the devices. For example, changing the value of the steady-state active power measurement from a high to a low level can identify whether the appliance is being turned on or off. In contrast, transient methods extract features from transient waveforms like shape, size, duration, and harmonics in order to identify appliance state transitions. A sampling rate above 1,000 samples per second, however, is necessary for detecting distinctive

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III.

transient signatures. As both steady-state and transient-based feature extraction methods have their advantages and disadvantages, there has been a debate regarding which is better for load disaggregation. Taking into consideration the cost of the solution, steady state methods seem to be a more practical option since they require low-cost hardware. In contrast, load disaggregation algorithms can use transient features to better segregate appliances that possess overlapping steady-state features.

Inference and learning: In order to identify appliance-specific states from aggregated measurements, the extracted appliance features are further analysed by load identification algorithms. In NILM methods, most research is focused on supervised machine learning techniques that require labelled data to train classifiers. There are, however, two major drawbacks. As the method attempts to provide a solution based on a combination of known appliances, the presence of unknown aggregate loads complicates the problem. In other words, there is no guarantee for the performance of supervised learning methods on any other unseen house or appliances.

Another requirement is the availability of large training data which is expensive and time-consuming to collect enough labelled data. Recently, researchers have shown a growing interest in unsupervised methods for load disaggregation to eliminate the need for data annotation. Unsupervised methods have no event-based classification like supervised methods that rely on event detection. The methods involve unsupervised learning techniques and attempt to disaggregate the aggregated load measurements directly without detecting events. In general, deep neural network (DNN) -based disaggregation algorithms outperform other methods in terms of learning agent signatures featuring rich features and scalability. The DNN model can be generalised and highly accurate if a large amount of data is used to train it. Using a large amount of data and many features don't guarantee the best performance due to some misleading and irrelevant features. We refer to steady-state active power, reactive power, or line current, line voltage, neutral current, and load angle as features. It is, therefore, necessary to identify a comprehensive set of features capable of disaggregating all types of appliances with the highest estimation accuracy and the lowest generalisation error (Rafiq et al., 2020).

Deep transfer learning A NILM model requires extensive computational and memory resources, along with vast quantities of labelled data. It is not always possible to access large datasets. Furthermore, creating large datasets can be laborious and expensive, and monitoring many facilities may not be enough to create a large dataset. Transfer learning (TL) has been proposed as an alternative to fully training the NILM models. In TL knowledge from other fields (e.g., another building or another task) can be applied to solve a targeted building disaggregation problem. As a result of TL, (i) new NILM models can be pretrained offline on large-scale datasets and then fine-tuned on small datasets, thereby saving computing resources and improving efficiency; (ii) NILM models can be trained on annotated datasets before they can be validated on unlabeled datasets. It is of the utmost importance, keeping in mind that labelling data is a challenging task that takes time and effort and requires the intervention of experts; (iii) train NILM models using synthetic data instead of real-world environments, and (iv) utilising the knowledge acquired from previous tasks to improve generalisations about others. Traditionally, machine learning models are deployed to perform multiple classification/learning tasks independently, without interacting with one another. On the other hand, TL is considered, which refers to using knowledge learned on one task to accomplish an entirely different but related task. As a result, knowledge is shared between different tasks. It is expected that transfer learning will have a significant impact on NILM because it will play a crucial role in its real-world implementation. By extracting appliance invariant features, learning can be transferred from a trained data set to a new data set or from a customer with the same characteristics to another customer. Indeed, quantitative measurement is needed to determine whether performance improvements for a transferability approach are actually associated with improved capture of invariant appliance signatures or with general improvements (Mporas, January 2023).

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Training data It is difficult to record the load monitoring of devices and appliances; therefore, various benchmarking datasets have been proposed in recent years. The author (Iqbal et al., 2021) has conducted a comprehensive review of 42 NILM datasets that have been generated to highlight the diversity of features in the datasets. Their study reviewed 42 publicly available real and synthetic NILM datasets that are being used in the research community to benchmark algorithms for load identification and energy disaggregation. They compared the datasets in terms of their geographical location, resolution, duration, and type of appliance. Generally, the datasets contain appliance load measurement data for a small number of countries. Major datasets are released by the US, Germany, the UK, Canada, India, France, Austria, Singapore, Switzerland, Holland, and Portugal. Unfortunately, Australia lacks its own country-specific energy disaggregation datasets.

Findings By using the NILM application, households can learn more about which appliances account for the largest share of their total electricity consumption and how to respond to price signals during peak periods. Additionally, households can access more customised home energy management systems, which are more likely to maximise their benefits and satisfaction. Still, there are some challenges that need further research to be addressed. Our findings are as follows: •

•

•

Current HEMS products provide customers with real-time consumption information, but this information is not accessible at the appliance level. Or, if it is available, it will cost more as it will require additional CT clamps, sensors, or plugs.

For the evaluation of different NILM methods, as well as for app development and assessment, there are several publicly available national and international data sets(Shubhankar Kapoor). It is difficult for researchers to develop new algorithms and evaluate them due to the lack of publicly available datasets representing household usage in Australia.

Some appliances in a household use a lot of energy, such as stoves, air conditioners, and refrigerators. These are known as the top consumers. Additionally, there are appliances that consume very little energy, such as laptops, DVD players, and routers. It is hard to track the power signature of small consumers because the top consumers are so dominant. Therefore, the NILM methods must therefore focus on identifying the top consumers.

Current apps, and available technologies status Mobile applications (apps) and external monitoring hardware make it easier and more accessible to track household energy consumption and generation. There are several apps and tools that inform households about their energy consumption, particularly at the appliance level, offer different electricity tariff plans, and automate controllable devices.

Through this, households can reduce their energy bills by cutting energy waste, improving consumer understanding of electricity retail packages, using energy efficiently at the right times, optimising rooftop solar (or right-sizing solar investment), and enabling energy-rewarding programs.

Table 41 compares Australian mobile apps based on their cost, whether or not they offer different electricity tariff plans, whether or not NILM algorithms are used to determine individual appliance usage patterns, whether or not automation is available through the app, and the type of connection required. In line with this RT goal, an important feature is whether the apps can provide NILM-based load disaggregation information to households. As we can see only Sense and Smappee offer individual appliance usage based on NILM. The other apps either do not support individual appliance usage or do so by installing individual CT clamps for each appliance.

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Table 41. Mobile apps for tracking and managing energy are mostly used in Australia (emberpulse; IoTaWatt; Sense; smappee)

Apps name

Cost (AUD)

Suggest different electricity tariff plan

Visualise energy usage of appliances

Automation

Connection type

PowerPal

no cost to Victorians.

N

Y, using individual smart plugs for each appliance.

N

Bluetooth

Emerald EMS

no cost to Victorian’s. Other states ~$129

N

N

N

Bluetooth

Fronius meter

$330 – $550 (single phase)

N

N

N

Wi-Fi

Solar Analytics

A lifetime subscription for $500, plus cost of Wattwatchers hardware.

Y

Y, using maximum 6 CT clamps.

N

3G / 4G

AGL

Free for AGL customers.

N

N

N

Wattwatchers

700$ plus Solar Analytics subscription fee.

-

Y, using 6-channel WattWatcher monitoring device with 6 CT clamps attached to various circuits.

-

-

Wattcost

~$200

N

N

N

Wi-Fi

Amber

~$15 monthly subscription.

Y, wholesale prices are available through the app.

N

N

Y

N

N

Wi-Fi

Discover Energy

Wi-Fi

Wi-Fi

Powershop

Among expensive electricity retailers.

Y

N

N

Bluetooth

Enphase

Comes with PV system.

N

N

N

Wi-Fi

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Wi-Fi

Origin

Free for Origin customers.

N

N

N

IoTaWatt

No subscription fees.

-

Y, using 12 channels to monitor all house circuits.

-

-

Shelly EM

~$95-105

N

Y, by adding individual clamp for each appliance.

Y

Wi-Fi

Sense

~$300, Free app with the purchase of Sense sensor.

N

Y, based on NILM algorithms.

-

Wi-Fi

Smappee

~$1,300

Y, Based on NILM algorithms.

Y

Wi-Fi

Emberpulse

Free for SA.

Y with additional cost for smart plugs.

Y

Wi-Fi

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Y

Y, shows the best electricity plan.

Unfortunately, there has not been a survey conducted to gather feedback from Australian households on these apps.

Furthermore, there are no guidelines to help them choose the right app from the many available on the market. Due to a lack of these resources, we refer you to the Facebook group called "My Efficient Electric Home (MEEH)". This group was formed in June 2015 in order to establish a DATABASE OF INFORMATION to improve home comfort and energy efficiency across Australia. As of now, there are 73.9 members in the group. There are a significant number of posts about different apps, along with user experiences. Due to privacy restrictions, we cannot directly mention those comments and feedback, but it is worth knowing what people's general concerns are about using the apps and their applicability.

Findings The following are some feedback from households using the apps listed in Table 41: • Bluetooth communication is used by some apps, which allows them to communicate only within a short distance of the receiving device.

• There are some apps that don't show all the information that households are interested in. As an example, only grid consumption is shown, not PV consumption.

• There are some apps that require users to buy power packages every month. Those who fail to do so miss out on the discount.

• Apps that use NILM require a week or two of exposure to many on/off cycles for a model to be built up to detect appliance signatures. Several users reported significant noise when identifying appliances due to variations in power quality and the types of devices across homes. A long period of time can be required for different appliances to be detected.

• Users need the current app to be able to accommodate future additions of EVs and batteries easily and without extra costs.

• Some parts of Australia don't have local installers, making installing apps expensive.

• One of the concerns that customers have is switching between different apps and having their historical consumption transferred and compatible with new apps.

3.6.4 Findings This section summarises the findings and potential research questions of this RT. These research questions have been classified based on the sections presented in this RT, as shown in Table 42.

Table 42. Summary of findings

Category

Non-intrusive load monitoring

Non-intrusive load monitoring

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Section

Finding

3.6.3

Using the most popular machine learning methods such as DNN, it is still challenging to develop an algorithm that can identify all types of appliances within a reasonable computational time, with a high level of accuracy, and without requiring a great deal of training data.

3.6.3

Some appliances in a household use a lot of energy, such as stoves, air conditioners, and refrigerators. These are known as the top consumers. Additionally, there are appliances that consume very little energy, such as laptops, DVD players, and routers. It is hard to track the power signature of small consumers because the top consumers are so dominant. Therefore, the NILM methods must therefore focus on identifying the top consumers.

Training data

Current apps, and available technologies status

3.3.6

For the evaluation of different NILM methods, as well as for app development and assessment, there are several publicly available national and international data sets (Shubhankar Kapoor). It is difficult for researchers to develop new algorithms and evaluate them due to the lack of publicly available datasets representing household usage in Australia.

3.6.3

In spite of considerable research on NILM algorithms, there are only limited apps (Sens, and Samppee) that use NILM methods. However, apps that use NILM require a week or two of exposure to many on/off cycles before a model can be built up to detect appliance signatures. Several users reported significant noise when identifying appliances because of differences in power quality and the types of devices. Different appliances may take a long time to be detected.

Apps are still not quite satisfying households as they expect: Improved communication between the app and smart meters. For example, Bluetooth-enabled apps' short working distance may limit the comfort of the household and their applicability.

Current apps, and available technologies status

3.6.3

It is important that customers are given complete information about the apps, not just information on generation or consumption. Additionally, apps need to be able to accommodate the addition of EVs and batteries in the future without incurring additional charges.

In order to switch between different apps, historical data must be transferred and compatible with the newly installed app.

Households seeking convenience would benefit from an appliance control automation feature that is lacking in most of the available apps. Additionally, some apps require customers to purchase energy packages frequently (monthly), which if automated would allow busy households to save time and money.

3.7 RT.7 Behavioural aspects of energy use and energy savings 3.7.1 Introduction Domestic energy users can be characterised as heterogeneous and therefore domestic energy consumption is affected by different factors such as end user habits, gender, education, income, and age. Hence, understanding the behavioural aspects underlying how domestic customers use energy and what makes them switch providers and different ways to reduce their expenditure on energy is of significant importance to better operate the grid. Residential customers’ behaviours about key past technology and service purchases and their future intentions and willingness to pay for technology and service alternatives are vital to identify the different drivers and barriers in reducing energy consumption to enable flexible load management and to implement necessary infrastructures to support home homes and smart grids.

3.7.2 Methodology Going beyond an expert review which would be limited by an ad hoc literature selection a systematic literature review (SLR) was chosen to address the research questions. This SLR process ensures bias is reduced by the use of a systematic method for selecting and analysing studies, and it provides the highest rigor in research quality, delivering enhanced transparency and validity. This systematic review was conducted in both scientific and generic outlets to ensure maximum

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quality and transparency of the methods which includes formulation of research questions, location of relevant studies, study selection and evaluation, analysis, and synthesis, and reporting of the findings. This approach was inspired by ‘Preferred Items for Systematic Review Recommendations’ (PRISMA) described by (Moher et al., 2015). How this method was used in this study is explained in more detail below.

SLR Method As the initial step, two key terms were identified (residential and energy) for developing search strings for the literature search. These keywords together with synonyms and closely related terms were identified and were connected using different Boolean operators such as ‘OR’, ‘AND’, and ‘NOT’ to create Boolean search strings. In addition to this, a third search string which consitutes both behavioural and techological solutions to reduce domestic energy consumption were also developed. The search parameters were refined following consultation with relevant experts and the final search strings used in this SLR comprised the following statements: (residen* OR household* OR domestic OR home* OR “home user*” OR “domestic user*” OR “residential consumer*” OR “residential customer*” OR “residential user*”) AND (“energy us*” OR “energy consumption” OR “energy reduction” OR “energy saving*” OR “energy manag*” OR “energy regulation*” OR “demand management”) AND (behaviour* OR “behaviour* change” OR “decision making” OR “Smart device*” OR “Smart appliance*” OR “smart meter*” OR “home energy technolog*” OR “home energy product*”) Databases were selected according to their relevance and coverage of the field and seven databases were systematically searched by two researchers for relevant records in October and November 2022. Scopus, Web of Science, ProQuest, Ebsco, Informit, Taylor and Francis, and Embase were the selected databases for data collection. A preliminary search for publications in the last five years using the identified key search terms in these databases resulted in approximately 3900 publications.

A heading and abstract-level review of a random sample of approximately 300 of these papers found that most were not central to energy behaviour of residential energy consumers and among the rest majority were not directly relevant to the aims of this SLR. To filter the most relevant papers, several filters were used such as limiting the search fields to article title and abstract and limiting to peer reviewed academic articles. Table 43 below shows the various filters used in the process and final results.

Table 43. Search results after using figures

Databases

Search fields

Scopus

Article Title, Abstract, Keywords

173

English language, Peer reviewed articles only, 2018-2022 (after applying subject filters)

Subject area/ Research area excluded/ limited to (along with dates)

845

Excluded subject areas: Medicine, Agriculture and Biological Sciences, Biochemistry, Chemistry, Material Science, Chemical Engineering, Genetics and Molecular Biology, Physics and astronomy, Nursing, Earth and Planetary Sciences, Psychology,

Neuroscience, Pharmacology, Toxicology and Pharmaceutics, Health Professions, Immunology and Microbiology, Veterinary Keywords excluded: Machine learning, Architectural design, Population statistics, Electric Vehicles, Neural Networks, Article, Office Buildings, European Union, Developing countries, United States, Economics, Economic Analysis, United Kingdom, Genetic Algorithm, China, Regression Analysis, Deep learning, Numerical Model

Web of Science

ProQuest

Abstract

Abstract

492

Limited to research areas: Engineering, Energy Fuels, Computer Science, Science Tech other topics, Business Economics, Automation Control Systems, Communication, Mathematics, Operations Research Management, Physics, Urban studies, Public administration, Education Educational Research, Instruments Instrumentation, Social Sciences Other Topics, Social Issues

213

Excluded subjects: machine learning, literature reviews, life cycles, emissions, electric vehicles, water demand, construction, cooking, poverty, income, carbon dioxide, artificial neural networks, cloud computing Databases chosen: Alternative Press Index Archive Applied Science & Business Periodicals Retrospective: 19131983 (H.W. Wilson) Applied Science & Technology Index Retrospective: 1913-1983 (H.W. Wilson) Australia/New Zealand Reference Centre

EBSCO

Abstract

255

Business Periodicals Index Retrospective: 1913-1982 (H.W. Wilson) EconLit with Full Text GreenFILE Humanities & Social Sciences Index Retrospective: 1907-1984 (H.W. Wilson) Humanities Index Retrospective: 1907-1984 (H.W. Wilson) Library, Information Science & Technology Abstracts Social Sciences Index Retrospective: 1907-1983 (H.W. Wilson) Business Source Ultimate

174

Subjects excluded: climate, natural disasters, global warming, United Kingdom, China, carbon dioxide mitigation, older people, caloric expenditure, ethnicity Taylor and Francis

Abstract

61

Informit

Abstract

2

Embase

Title, Abstract, Author keywords

77

TOTAL

Excluded: Sports and Leisure, Law, Politics and International Relations, Museum and heritage studies, Bioscience, Geography, Earth Sciences

Excluded study types: animal experiment, major clinical study, animal model, animal tissue, biological model, clinical article, digital twin, systematic review, virtual reality

1945

The combined total of records downloaded from all databases was 1945. All downloaded records were imported into EndNote 20. From the initial records collected, 465 duplicate records were removed by EndNote, leaving 1480 unique sources. After duplications were removed, two reviewers reviewed titles and abstracts of the remaining papers for eligibility according to the inclusion and exclusion criteria developed which is shown in the Table 44 below.

Table 44. Inclusion and Exclusion criteria

Inclusion Criteria

Exclusion Criteria

Only peer reviewed academic articles

Grey literature, review papers

Published between 2018-2022

Published prior to 2018

Papers that focus on residential/domestic energy users and/or energy consumption and/or energy management

Papers that focus on energy consumption and management of other sectors such as business, industries, commercial sites, schools etc.

Papers that focus on energy behaviours of residential users

Papers that focus on infrastructure or construction related technologies

Papers that focus on solutions – behavioural solutions and technologies (e.g., smart devices, demand modelling, solar, refrigerators etc. that can be used by consumers and implemented in the house)

Papers that focus on energy efficiency technologies during the design and construction phase of the building – materials, insulation, façade etc.

Papers that focus on better understanding of residential energy utilisation and/or reducing energy load from grid for residential consumers.

Papers on smart devices that can forecast energy usage.

Papers focus on energy retrofits in houses if it includes renewable energy retrofits.

175

Papers that focus improving security/privacy of smart devices/grids

Papers published only in English

Papers published in other languages

Papers with studies in the OECD countries

Papers with studies not in the OECD countries

For the generic outlets, the following search terms and websites including google search in Table 45 were used.

Table 45. Grey literature search

Site

Search Terms ‘Behavioural data’ ‘Behaviour’

Australian Energy Council (AEC)

‘Behaviour data’ ‘Consumer data’ ‘Consumer’ ‘Behaviour’ ‘Behaviour data’

AIE

‘Consumer data’ ‘Consumer’ ‘Consumer behaviour’ ‘Behaviour data’ ‘Consumer data’

ARENA (Knowledge bank category)

‘Consumer behaviour’ ‘Energy behaviour’ ‘Energy efficiency’ ‘Consumer behaviour data’ ‘Behaviour data’

Government websites (NSW/QLD/SA/WA/NT/VIC)

‘Consumer data’ ‘Consumer behaviour’ Energy behaviour data

State/territory energy ministry sites

Behaviour data Consumer data

Department of Climate change, energy, the environment, and water

176

Energy data

Energy Ministers publications

Data ‘behaviour data’

Energy Company Websites

‘Energy behaviour data’ ‘behaviour report’ ‘consumer behaviour’

Google

‘Australia consumer behaviour’ ‘energy consumer barriers’ ‘energy consumer drivers’

Each eligible study will go through a full text screening and will be systematically reviewed and analysed. A codebook will be developed to ensure strong validity and reliability of data extraction and analysis. Findings will be extracted and synthesised with a focus on the forementioned research topics.

3.7.3 Review This review aims to provide an overview of the state of the residential energy market, by collating various energy reduction strategies and energy user behaviour transition interventions that are in practice. To guide the review, the author explored three questions: 1. What is the extent of knowledge about energy use and energy reduction among residential consumers?

2. What are the drivers and barriers that encourage residential consumers to reduce their energy consumption and to take away energy load from the grid?

3. What are the available effective strategies for reducing energy consumption and energy load from the grid for residential consumers?

Findings were synthesised from the identified literature. Note that little research was conducted to shed lights on the current status of the market and information was scattered with no consistent measure of frameworks in present. The review brought in both Australian and international literature to provide a comprehensive overview of what has been investigated and reported. Conclusions were drawn mainly from the Australian context while international studies were used to serve as comparisons or examples.

What is the extend of knowledge about energy use and energy reduction among residential consumers?

Household energy consumptions are continuing to rise and there are several studies that are focused on different aspects of residential energy consumption such as renewable energy sources, transport, emissions, and technological solutions such as smart home management and improving product and appliance efficiency, whereas several other studies focused on household consumption practices and related consumer behaviours. The review of the selected literature resulted in drawing some general observations about domestic energy usage and consumer behaviours.

Firstly, household characteristics such as tenure period, household income, education, way of living and demographic conditions such as number of persons in the dwelling, age, and gender, are found to be the most significant factors that affect residential energy consumption patterns. For instance, women in a Swedish study show higher levels of recognition, behaviour and willingness to learn and obtain information about energy reduction and management (Azizi, Nair, & Olofsson, 2019). A study in Queensland confirms this illustrating that women and younger respondents have higher

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willingness to pay for renewable energy (Ivanova, 2013). Similarly, when it comes to energy retrofits, Swedish homeowners who are below 45 years old are more likely to implement energy retrofits in their houses (Azizi et al., 2019). Looking at Australian homeowners, it is clear that adding solar hot water and PV are popular, with little data defining age rage interests (Judson, Iyer-Raniga, & Horne, 2014). It is also important to note that within the residential sector, energy can be perceived as a necessity good, and a household that has already restricted its energy usage may no longer be able to do so, while houses with overconsumption are likely to have more flexibility in their energy usage as shown in a French study as data on this in Australia is limited (Bakaloglou & Charlier, 2021).

Moreover, the way of living and current consumption habits and the context of what the participants had already been doing is relevant in success of new energy reduction initiatives. UK consumers who have environmental concerns and had acted environmentally before (e.g.: giving up the car) were likely to have more energy reduction target effects than those that did not (Al-Chalabi, Banister, & Brand, 2018). Besides, the occupants’ daily activity profiles and occupancy patterns significantly affect the magnitude and timing of energy demand in households. Daily energy profiles are different during weekdays and weekends found in a study in Denmark; however, peak values of hourly electric load profiles were observed during the same hours of the day (Barthelmes et al., 2018). Generally, there are mainly three peak timings in a day: early morning hours, lunch time and dinner time, and this could be directly related to cooking and eating activities. For example, an Italian household represents three peaks during a day: the evening hours between 19:30 and 22:30 reaching a maximum of 580 W of absorbed power, early morning from 7:30 to 9:30 reaching 600 W of absorbed power and lunch time from 11:30 to 13:30 reaching 480 W of absorbed power whereas the energy consumption is minimum at night-time hours around 2:30 to 5:30 am (Besagni, Vilà, & Borgarello, 2020). According to Australian Power and Gas (APG), peak times are 4 to 8 pm during the week, while 7 am to 4 pm and 8 to 10 pm on weekdays, and 7 to 10 am on weekends are considered shoulder times (Riga, 2022).

Moreover, domestic electricity consumption is sensitive to outdoor temperatures and therefore location and environmental conditions such as the number of heating degree days and the preferences for comfort over economy also plays an important factor in residential energy sector. According to a Canadian study, a 1°C decrease in indoor temperature may provide significant energy as well as carbon savings (Abdeen, Kharvari, O'Brien, & Gunay, 2021). The Australian Department of Climate Change, Energy, Environment and Water state that an extra degree of utilising heating or cooling systems increase between 5% and 10% highlighting that Australians too can save energy by reducing the indoor temperature (Department of Climate Change, 2023a).

What are the drivers and barriers that encourage residential consumers to reduce their energy consumption and to take away energy load from the grid?

There are several drivers and barriers that effect energy behaviours of residential consumers. Among the many drivers noted in the literature, environmental identity and ecological behaviour are more likely to encourage consumers to reduce their energy consumption. Environmental identity is one part of the way in which people form their self-image and people with high environmental self-identity are likely to involve in more pro-environmental behaviours. Similarly, self-reported ecological behaviour was significantly inversely related to household electricity consumption even when the income is controlled. German customers who are conscious of environmental concerns are more ecologically engaged and consumed one third less electricity per year than did regular customers (Arnold, Kibbe, Hartig, & Kaiser, 2018). For example, German houseowners with the intention to become less dependent on fossil fuels are more interested in energy reduction related initiatives (Baumhof, Decker, Röder, & Menrad, 2018). The Finder Green Consumer Report 2022 highlighted that 88% of Australians have reduced their environmental impact and 42% are utilising energy efficient appliances, thus reducing their energy consumption (Finder Green, 2022). Improving the indoor environment to improve

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health and comfort was found to be another significant driver for the homeowners to adopt energy measures such as energy retrofits and smart appliances as shown in a Swedish study (Azizi et al., 2019).

Certain demographic qualities are drivers of energy usage in households stated in a UK study (Taneja & Mandys, 2022).

Household size, location, and the number of rooms all impact the amount of energy used, with homeowners being more sensitive to energy usage (Taneja & Mandys, 2022). In relation to adapting homes to energy technologies, high heating bills and low heat comfort in households is a driver to renovate the household energy technologies In Lithuania (Streimikiene & Balezentis, 2020). In a Sydney based study, researchers discovered that household demographics and behaviours had an impact on household energy usage (Fan, MacGill, & Sproul, 2015).

In a US study, utilising pro-environmental investment strategies were found to increase consumer trust in relation to ways to reduce their energy consumption (Przepiorka & Horne, 2020). Financial reasons are a main driver when encouraging Canadian consumers to reduce their energy usage (Lazowski, Parker, & Rowlands, 2018). It was found that cash based incentives regarding demand response programs are a driver for consumers to reduce their energy usage (Z. Wang, Zhao, Deng, Zhang, & Wang, 2021). In a study of European residential energy users, 62% indicated that financial reward is the main driver to energy consumption reduction (Morton, Reeves, Bull, & Preston, 2020). Sweetnam, Spataru, Barrett, and Carter (2019) also found that British consumers would be more inclined to reduce their energy consumption for a financial reward. Rodrigues et al. (2020) also found that financial savings was an important driver for UK residential energy usage.

In the Finder Green Consumer Report 2022, going sustainable was established as not being expensive but switching to a sustainable super, or investing in an electric car or solar panels.

Increasing information awareness of energy consumption is also a driver to help Canadian consumers reduce energy consumption (Lazowski et al., 2018). Creating informational awareness leads to developing more pro-environmental moral obligations in consumers to better understand and utilise certain tariffs (ex. time of use) (Lazowski et al., 2018).

Visualisation of energy information for Dutch consumers is an important step in increasing awareness of their energy usage (Obinna, Joore, Wauben, & Reinders, 2018). Another concept of increasing information, is the perceived ease of programming heating schedules of energy technologies in households as seen in a British study (Miu et al., 2019). Utilising a smart home installation intervention, perceived ease of heating schedule programming increased among respondents (Miu et al., 2019). Personalising energy use information is a main incentive for consumers to reduce their energy consumption in a study through three EU countries (Morton et al., 2020). Ease of use is a key driver for consumers when it comes to energy conservation (Obinna et al., 2018). The Finder Green Consumer Report 2022 found numerous drivers for Australian consumers consisting of increased natural disaster events and social influence.

When it comes to barriers, lack of information is the most significant area of concern. The lack of direct feedback potentially leads to mistakes in households' decision-making. For example, usually utility bills received by households contain all electricity use during a fixed period is lumped together illustrated in Lithuania (Asmare, Jaraitė, & Kažukauskas, 2021). IMPROVE project in Switzerland found that more than 90% of residential consumers currently receive paper bills and half of the consumers spend less than five minutes to read their energy bill. This project also pointed out that only around 14% of the consumers receive a qualitative or quantitative information to position their energy consumption.

While almost 75% of the remaining consumers in Switzerland are interested to receive such additional information as two thirds of them wish to compare their energy consumption with equivalent households of the neighbourhood and are willing to put into place energy-saving actions if their consumption is above average (Bichsel et al., 2019). Therefore, the availability of reliable and user-specific information when supporting households in changing their consumption habits is vital as illustrated in a Finnish study (Ahvenniemi & Häkkinen, 2020). For Australians, one major barrier to energy efficiency is the information available surrounding future costs, both financial and environmental (Finder Green, 2022). Barriers

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found in Australian consumers also consist of lack of awareness, limited resources or funds, and limited metering (Bertone et al., 2018).

Capturing the attention of the owners is required to improve the energy reduction and if the information given is most effective if it is directly related to the problems and wishes of the residents. If the level of recognition is high, the level of behaviour and the willingness to acquire information are also high. Also, even if the level of recognition is low, but behavioural motivation is high, then the motivation to acquire information is high as seen in a Japanese study (Abe, Rijal, Bogaki, Shukuya, & Mikami, 2019). In a study conducted across 6 countries, it is also important to provide technical information that is relevant to the users, such as costs, savings, funding opportunities and how and when to carry out the measures in order to get best outcomes (Alex Gonzalez, 2018). Therefore, energy reduction interventions should be designed in such a way that it will reach the homeowners with tailor-made information and strategies to successfully motivate diverse households with different characteristics. Similarly, lack of accuracy and trust in energy reduction measures are a huge turn on for consumers to adopt energy reduction measures. Across the 6 countries of study, the lack of a clear procedure and ready-to-use measures negatively influence energy consumers in adopting new ways (Alex Gonzalez, 2018).

The other major barrier to encourage energy reduction Is financial constraints. Difficulty to find incentives or low interest loan were found to widely obstruct implementation of energy reduction strategies. Lack of incentives or low-interest bank loans was found to be a determinative barrier particularly in low-income households (Azizi et al., 2019). Another major barrier is tenancy type. Users are generally less likely to install new technology in or on properties where they do not intend to live for an extended period of time. In general, uncertainties about what is going to happen in the future and issues related to family conditions represent an important limiting factor when considering energy reduction measures.

These behaviours are also influenced by contextual elements from within the UK household such as relationship with other family members and degree of control over energy consumption as well as those outside of the household (Ambrosio-Albala, Upham, & Bale, 2019).

What are the available effective strategies for reducing energy consumption and energy load from grid for residential consumers?

Motivation in the form of incentives and monetary savings is an effective strategy to reduce energy consumption by residential consumers. According to the Austrian study conducted by (Azarova, Cohen, Kollmann, & Reichl, 2020), households which received a monetary incentive showed the strongest behavioural change effects while only limited evidence of changes showed by the non-monetary treatment groups. This study analysed consumption data of 1,257 households in Austria and results showed that monetary remuneration was the strongest of the tested incentives for changing household behaviour (Azarova et al., 2020).

Conducting home visits that target behaviour changes and knowledge is another effective strategy to improve household energy efficiency. Along with improving household energy efficiency, home visits can also bring co-benefits to the householders such as improving health, well-being and knowledge outcomes as they relate to home energy consumption, particularly when households are facing asset-related barriers, and not just reducing energy bills identified in an Australian study (Áron et al., 2019).

In addition, energy efficiency of buildings is a major part of domestic energy consumption. Domestic energy retrofits have significant potential in reducing energy demand and associated carbon emissions. Carefully chosen retrofit measures that are tailored to each household archetype can considerably improve the house’s energy saving potential. Retrofits and

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improvements in UK building systems especially heating system upgrades are the most effective retrofit options across all archetypes, followed by internal solid wall insulation (Ben & Steemers, 2020).

Another important strategy to energy reduction in residential sector is to provide efficient feedback that makes energy visible, comparable as well as promote competition with other energy users like neighbours. Feedback can be provided through different ways. Displaying energy consumption information in monetary terms rather than in physical units is very effective as this can make positively influences on the customers and motivate them to conduct an investment analysis which in turn increases the probability to choose the most cost efficient appliance shown in a Swiss study (Blasch, Filippini, & Kumar, 2019). In an EU study, feedback was found to be provided through the use of smart meters which resulted in energy savings up to 4.5% (Bauer, Hoeltl, & Brandtweiner, 2018).

Real-time feedback provides an effective platform for household’s customers to experiment and see the effects of their behaviours and actions. If the feedback is provided continuously, effectively and in an easily understandable manner to end consumers it can lead to persisting energy savings. These savings can also act as a proof that households that effectively engaged in behaviour changes can reduce their environmental impacts and can achieve significant cost savings.

Another advantage of providing effective feedback is that it represents electricity consumption in a way which addresses different motivations of diverse consumers. For example, the given feedback can be used by a cost sensitive person to learn more about electricity costs, whereas a person interested in environmental impacts receive knowledge of the environmental impact different appliances may have (Bauer et al., 2018).

Another effective strategy is educating and increasing awareness of customers. Customers who possess energy-related knowledge and high cognitive abilities, captured by high levels of energy and investment literacy, were more likely to opt for optimisation as the decision-making strategy which in turn positively influences the probability to identify the most cost-efficient appliance (Blasch et al., 2019). It can therefore be concluded, that enhancing an individua's energy-related knowledge and the ability to make complex (investment) calculations seems to be one important prerequisite to empower consumers to make rational and informed energy-related choices (Blasch et al., 2019).

3.7.4 Findings The study findings which include the recognition of the relationships between actors across the multiple layers in energy use behaviours enables the industry to design and implement solutions (such as infrastructural improvement, developing energy use apps, and promoting smart devices) more customer-oriented and therefore more likely to be upscaled and mass adopted. Through this, this review sets a solid foundation for future research in this field.

Key findings include: • Australian households on average utilise 40% of household energy usage on heating and cooling, with hot water and appliances following at 25% and 30% respectively (Department of Climate Change, 2023b) • Solar PV is gaining its popularity in Australia. It is reported that around 30% of Australian households have installed rooftop solar PV. more than 3 million rooftops had solar PV systems installed by 31 January 2022 (Department of Climate Change, 2023b) • Little interest was shown in the adaptation of new technologies, with only 19% stated they were interested in new technologies that would help manage household energy bill pricings (Energy Consumers Australia, 2022) • Consumer sentiment towards TOU tariffs appears to be negative, with 43% of households indicating that the tasks need to be done at a convenient time for the consumer, not when the tariff pricing is different (Energy Consumers Australia, 2022)

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• •

Smart devices were perceived as too complicated and expensive with only 22% stating smart appliances in their home can help save money (Statista, 2022) Effectiveness of financial incentives is mediocre with only 35% of households indicating their willingness to reduce their energy usage during a hot period only if they were offered a financial incentive, with 8% saying that nothing could be done to have them reduce their energy usage. (Energy Consumers Australia, 2022)

Table 46 below lists the findings from the review in more detail.

Table 46. Detailed findings

Category

Findings An increasing number of Australian households (more than three million) have adopted rooftop solar PVs as of 2022.

The average Australian household utilises 40% of its energy usage on heating and cooling, 30% on appliances, and 25% on hot water.

State of the market

Around 20% of Australian households surveyed illustrated interest in learning about new ways to generate and distribute energy.

Almost half of respondents (44%) indicated that smart home devices are either too complicated to use or too expensive to invest in.

COVID-19 saw a decline in Australian energy consumption in Commercial and Service sectors while there was an increase in the Agriculture and Residential sectors.

There is a lack of knowledge among consumers in relation to the proper knowledge about energy decisions and smart grids, its benefits, and impacts.

Consumers have a desire to learn about smart energy technology such as solar panels, smart meters, eVs, and household heating systems.

What is the extent of knowledge about energy use and energy saving among residential consumers?

Consumers are inclined to install smart technology in the future with an estimated 1 in 3 wanting to install in the next 5 years.

Households need different avenues of engagement from stakeholders to learn more about the energy sector and policy in Australia relating to personal energy usage of the consumer.

Electricity is viewed as a value for money among many consumers, equalising its importance with other necessities such as telecommunications, banking, and water

What are the drivers and barriers that encourage residential consumers to reduce their energy consumption and to take away energy load from the grid?

Housing tenure and a house not needing improvements are two key barriers to invest in smart technology due to the limitations of what tenants can do in certain housing situations.

Lack of motivation, knowledge, or priority are all prohibitors for consumers to reduce their energy consumption.

Having past bad experiences with energy companies can create distrust and impact consumer views of the companies.

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Consumers are aware of the declining costs of energy efficient technology and are driven to participate due to the declining price.

Community engagement, environmental values, and the desire to become self-sufficient are a driver for consumers to invest in energy efficient technologies to reduce their energy consumption.

An increase of consumer knowledge and educations relating to energy efficient technologies are key drivers in consumers entering the market.

Loans like the NSW Empowering Homes Solar Battery Loan, Solar PV and Battery System Interest-Free Loan, and Solar Victoria Virtual Power Plant Pilot Program are one type of strategy to help consumers reduce their grid energy load.

What are the available effective strategies for reducing energy load from grid for residential consumers?

Pricings such as the Victorian Default Offer and smart energy devices like smart meter devices are two additional examples of effective strategies for assisting consumers with their energy reduction.

Incentives like gift cards is an effective way to enable consumers to reduce their energy consumption.

Tariffs such as the fixed volume-based, seasonal time-of-use volume-based, critical peak, and single rate tariffs are all effective strategies to help consumers reduce their energy load.

Little research on the effectiveness of strategies in market such as tariff, consumer trust campaigns, as well as policy implementation.

Research was heavily focused on understanding the product efficacy (through product trials and algorithm simulations etc.) and customer acceptance and utility, whereas little practical recommendations were made and tested.

General findings

Most of the consumers self-reported they believe to have done everything to reduce their energy usage, which indicate the existence of a strong knowledge gap, presenting opportunities for awareness/education campaigns.

The largest factor in play seems to be lack of easy access to data on how they utilise their energy currently and skepticism towards services providers and networks. Consumers need the ability to override automated settings and personalisation abilities so consumers can understand how to utilise smart technology, all of which should be considered in campaign strategy designs.

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