1 Research Updates
1.1 Customer road map for hot water
The customer road map aims to create an analysis of all hot water technology options available to households around Australia. It focuses on the development of a financial calculator for households to compare water heating options. This analysis takes into consideration several parameters and variables with the goal of providing insight into energy usage, efficiency, financial analysis, and emission considerations. The goal of this road map is to allow households around Australia to better understand all the options available regarding water heating. A breakdown and description of all these variables and parameters, as well as how they are used in this evaluation can be found within the tables below.
Table 1 Hot water roadmap considered variables
Location Sydney, Melbourne, Brisbane, Adelaide, Canberra, Hobart
Hot water draw profile (HWDP)
Household number of people
1, 2, 3, 4, 5, 6 (see Figure 1 below)
1, 2, 3, 4, 5+
PV ownership Yes (5kW system), No
Water heater Electric resistive, heat pump, solar thermal, gas storage, gas instantaneous
Control strategies
Controlled load, general supply, timer, diverter (see Table 3. below)
Table 2 Hot water draw profile (HWDP) definitions
Figure 1 Graphical representation of the six HWDPs considered in this analysis found through clustering typical HWDPs taken from previous research 1
Below Table 3 summarizes the details for the hot water control
Table 3 Control type details
Type of Control
Description
General Supply (GS) with flat and Time of Use (tou) Always connected
CL1
CL2
CL3
Timer flat (Solar Soak)
Timer tou (Off-peak)
Diverter tou (Off-peak)
Diverter + CL1
Overnight – at least 6-hour period between 10 pm and 7 am
24/7 except seasonal peak demand periods - more than 6 hours between 8 pm and 7 am and more than 4 hours between 7 am and 5 pm
CL1 with Solar Soak
Timer control for sunny hours aiming for selfconsumption
Timer control aiming for off-peak tariff
Diverter for self-consumption with night-time top up
Hypothetical case (currently not allowed by DNSPs) using diverter for self-consumption with CL1 tariff 1 Yildiz
B, Bilbao JI, Roberts M, Heslop S, Dore J, Bruce A, et al.
For this customer roadmap analysis and calculations, the following Table 4 outlines the water heaters, associated tariffs, and modelling assumptions.
Table 4 Technologies and modelling conditions and assumptions
Technology Control Additional Equipment Assumptions
Resistive
Heat Pump
Solar Thermal
• GS_flat
• GS_tou
• CL1
• CL2
• CL3
• Timer_SS_flat
• Timer_OP_tou
• Diverter_OP_tou
• Diverter + CL1
• GS_flat
• GS_tou
• CL1
• CL2
• CL3
• Timer_SS_flat
• Timer_OP_tou
• GS_flat
• GS_tou
• PV system
• Timer
• Diverter
• Diverter on CL1 assumes that the hot water can switch between general supply and CL1 to make use of both controls.
• PV system
• Timer
• Timer
• Auxiliary heater: Resistive
• Household won’t have a PV system and solar thermal water heating system
Gas Instantaneous
Gas Storage
• Gas (continuos)
• Gas (continuos)
The outlined parameters are used to develop case scenarios for this road map. The following list includes all the essential components analysed in this roadmap analysis
• Electricity and gas tariffs
• Energy and connection costs
• Thermal hot water simulation modelling results (presented in previous Milestone Report)
• Annual bill for water heating
• Capital costs
• Installation costs
• Operation and maintenance costs
• Incentives and rebates
• Disconnection and/or removal costs
• Lifespan and warranties
The information used to determine these components reflect the parameters outlined previously. For example, the location or household size would impact the available rebates or selected water heater size.
Utilizing all these determined values and costs, the financial analysis can be undertaken. Appendix A displays the detailed capital cost and installation breakdown for all technologies considered in this financial analysis.
The financial calculations are expected to include annual bills, net present costs over 10-year, payback periods, levelized cost of hot water, emissions and more. Currently, the roadmap determines the annual bill and net present costs over a 10-year period. For the net present cost calculations, there is an assumed discount rate of 8%. Additionally, all the energy price information used for these calculations reflect the published 2024 energy prices.
Figure 2 below demonstrates the summary of the process for this financial analysis and indicates how the information is found and used.
At this stage the road map provides insights for households within Sydney region with different number of people and water usage patterns including different water heating technologies.
The figures below display preliminary results of the net present cost and annual bill finances for different water heater technology and tariff options. The following table explains the abbreviations used within the figure legends
Figure 2 Customer Road map financial analysis process
3 Financial output for NPC over 10 for a household of 4 in Sydney with HWDP
Figure 4 Financial output for the annual water heating bill over 10 for a household of 4 in Sydney with HWDP 1
Figure
Legend
RS_nPV
RS_PV
HP_nPV
HP_PV
STC
GI
Table 5 Abbreviations for
Water Heating System
Electric Resistive with no PV system
Electric Resistive with PV system
Heat Pump with no PV system
Heat Pump with PV system
Solar Thermal
Gas Instantaneous
GS Gas storage
IC
OC
Initial costs - capital costs and rebates
Operational costs
MC Maintenance costs
CC Connection costs
The results displayed in Figure 3 and Figure 4 provides detailed cost break down including capital costs, operational and maintenance costs and connections costs. For this analysis the following assumptions have been made:
• For CL2 and CL3 solar self-consumption is assumed to be feasible for households with PV systems. In reality, this is not possible as hot water system is connected to a separate electrical circuit. This assumption allows a potential future scenario where switching between the control load and general supply is possible
• Connection costs, the daily supply charges, are not included in the analysis for systems with general supply as the cost would be incurred by the household regardless
• The gas operation costs are taken from the Rheem website, a more detailed insight into this assumption can be found in Appendix A
• Figure 3 presents the net present costs for a household installing an entirely new water heating system, not upgrading to a new system. As a result, only STC rebates are considered to be relevant. It should be noted that when upgrading to a heat pump from an old electric system, further rebates would be available which would reduce the initial costs of the system. The available rebates will be assessed in further analysis of switching scenarios of different technologies and reporting the relevant pay-back times
Future work will include correcting some bugs within the TRNSYS model to provide more accurate results for solar thermal system. Similarly, a more accurate model for predicting gas operations will be investigated Additionally, in the future the model will continue to perform financial calculations and analysis for all capital cities around Australia including different number of occupants and water usage behaviour. Furthermore, the above mentioned other financial parameters, such as payback periods and emissions, will be calculated and considered in the analysis and roadmap recommendations.
1.2 Field trial analysis results
Several studies have been carried out on the market and power quality data of Endeavour Energy (EE) customers in Albion Park, NSW. These customers are categorized into Origin Energy (OE) customers and
those that are with other retailers. In this section, first, some major challenges and issues regarding these datasets are identified. Then, several results are reported.
It is noted that, due to these issues, UNSW has requested EE a new dataset to improve the accuracy and reliability of the conducted analyses.
1.2.1 Dataset
The market data includes active energy consumption recorded by different channels of a smart meter: general supply net import, general supply net export, and controlled load circuit. Depending on the meter configuration, one customer might have different channels.
Power quality data is not consistent for all customers and might include instantaneous or total active or reactive energy import and export, instantaneous/average/minimum/maximum voltage or current on different phases, average THD and average phase angle
While the market data are available for all customers, power quality data are recorded only if customers request it.
Table 6 presents an overview of key statistics concerning these datasets. Here, OE customers are referred as “VPP” and the rest of the fleet are grouped as “CL” customers. According to Table 6, around 20% of CL customers and 12% of VP customers do not have an active HW system on their controlled circuit. Respectively, around 33% and 26% of CL and VP customers own a PV system.
Table 6 Summary of EE dataset
• Only 51% of CL customers and 69% of VP customers have at least one form of voltage measurements and a significant proportion of the customers don’t have voltage data.
• Only 77% of these customers have some form of market data in 5-min resolution. The rest of market data are mainly in 30-min or 15-min resolution. It is noted that resolution of market data for each customer is not usually consistent over time, varying from 30-min to 15-min and 5-min which creates difficulties for analysis.
• Power quality data are sampled at different and non-consistent resolutions such as 1-min, 3-min, 5-min, 7-min, 11-min, 13-min, 15-min, etc for each customer which creates additional difficulties
• In addition to the above issues, the market data includes several gaps, as illustrated by Figure 5
Figure 5 Total half-hourly-sampled HW load vs number of available customers
Figure 6 demonstrates the number of customers based on the availability of voltage measurements on four days in Jan 2023 as an example. As seen, the data gaps among power quality data are more severe and volatile.
1.2.2 Hot water (HW) system rating
The power rating of each hot water (HW) system is estimated based on the distribution of their maximum power consumption Figure 7 shows the obtained results for all customers to categorize HW system based on their rater power. According to Figure 7, the two most common systems are 3.6kW and 4.8kW system making up of 75.7% and 17.4% of customers, respectively. It is noted that HW systems with a resistive element are voltage sensitive, making their power consumption affected by the voltage. Upon receiving a fresh dataset and resolving current power quality data gaps, these estimates can be improved.
Figure 6 Number of customers based on voltage data availability
1.2.3 Trial operations
Daily operation of HW loads for the two groups of VP and CL customers is analysed. Figure 8 presents results for two days of operation in Nov. 2022. Here, the traditional night-time window, i.e., 10:00pm-07:00am, is depicted with a grey background.
As seen, most HW loads operate during the night-time with a major peak at midnight followed by a smaller peak at the morning.
By calculating the daytime and night-time loads, respectively, the proportion of HW load that is shifted to daytime can be found as shown in Figure 9 As seen, the peak shifted loads appear on the weekends for both CL and VPP groups, where controlled load circuit is always energized (except the peak evening times). The proportion of shifted load is greater for VPP group of OE compared to other CL group. During the weekdays, VPP group has shifted around 10% more HW load to the daytime period.
Figure 7 Albion Park HW system rating
Figure 8 Daily operation of HW loads
1.2.4 Power quality analysis
Due to voltage-to-power sensitivity of network at each node, operation of HW is accompanied with a local voltage drop. This phenomenon can be seen in Figure 10, where spikes in blue plot, i.e., HW load, is accompanied with a drop in voltage demonstrated by the red for a sample customer
10
It is noted that voltage is an electrical phenomenon which is affected by all electrical dynamics within the entire network. However, it is mainly affected locally. Furthermore, a concurrent load in the general supply circuit has the similar effect of a HW load on the voltages. Nevertheless, an average voltage drop for each onoff operation of HW system is calculated. Figure 11 demonstrates the results for all qualified samples of on-off operation of HW loads. According to Figure 11, the median voltage drop for the whole fleet is 1.7V while the 95th percentile value is 4.9V. It is noted that voltage-to-power sensitivity is a measure of the network strength, where higher voltage drop values with similar HW rated power indicate weaker connection points.
Through this analysis, customers experiencing higher voltages can be identified. As illustrated by Figure 12, these higher voltages occur during night-time rather than daytime where there is abundance of solar generation. The reason lies in the fact that EE intentionally lowers the zone substation voltages during the daytime considering voltage rise caused by solar generation to prevent curtailment and other power quality isssues
Figure 9 Proportion of daily HW load shifted to daytime for solar-soaking
Figure
Voltage-to-power sensitivity in HW operation
1.3.1 Overall update
SolarShift consumer tool developed by Solar Analytics has had 104,000 users up to date. A follow-up survey was sent to users at least 3 months after their first use of the tool which revealed that:
• 28% had purchased or upgraded their solar system after using the Tool
• 32% were still considering it
• 40% had done nothing and weren’t planning to
Analysis of user trends in the Tool over time indicate that since late 2023, there has been increasing interest in batteries and heat pumps.
The Tool continues to be developed to offer users more customisation and information about solar, hot water, and other CER.
Figure 11 Distribution of voltage drop due to HW operation
Figure 12 One-day operation of a customer experiencing higher voltages
1.3 Solar Analytics SolarShift Consumer Tool
• The ability for users to share energy data from their smart meter via CDR (Consumer Data Rights) was added to the Tool
• Support for controlled load tariffs was introduced (which was particularly important for the financial analysis of hot water systems)
• An option to run a heat pump on a timer was introduced to the Tool
• All relevant rebate information was updated in the Tool
1.3.2 Heat pump optimisation
Optimising the daily operation of heat pumps based on a household’s energy usage, solar generation, and energy plan does not have significant financial benefit over using a simple timer, but is significantly more complex in terms of software and hardware to implement.
• Solar Analytics has finished developing and testing an optimisation control algorithm for heat pump operation - which orchestrates operation based on household load, PV generation and energy plan structure to maximise savings (Note that the algorithm requires forecasts for load and PV generation to orchestrate control ahead of time)
• On Solar Analytics’ existing fleet of >150 customers with heat pumps, the control algorithm could save on average $61 per year (around 5-10% of their energy bill).
• However, additional experiments were run to simulate running heat pumps on a simple timer (e.g., between 11am - 3pm) and these simulations could produce up to 85% of the savings from the control algorithm ($51 on average), but without the overhead of complex control algorithms, the need to forecast load and PV data, or the need to purchase expensive hardware.
• In timer simulations it was found that the optimum timer period differed for summer and winter:
o Summer: 11 am - 3pm
o Winter: 9am - 4pm
The average heat pump energy usage across the fleet monitored by Solar Analytics was 2.3kWh per day.
1.4 Knowledge sharing with International Energy Agency Task 69
SolarShift project is partnering with International Energy Agency through Task 69 (PV water heating). As part of the knowledge sharing activities, the project has contributed to the Solar Heat Worldwide Report and shared results to be published later in 2024.
The project lead CI, Dr. Baran Yildiz also co-hosted the Task meeting in December 2023 as part of Asia Pacific Research Conference (APSRC 2023) in Melbourne and presented project updates and results. Project team has also participated in two other online task meetings in 2023 and 2024 where project results were shared amongst international community of experts.
1.5 Next steps
The next steps for the project can be summarized as below:
• Apart from real power quality data, the low-voltage network of Albion Park will be studied to better understand the effect of hot water operation on the voltage profile of different customers. In other words, the HW load operation as a potential voltage control mechanism will be studied.
• DigSILENT power flow modelling software will be considered to model the impact of hot water control operations for different parts of networks (feeder, substation, zone substation etc.)
• Customer roadmap will expand on to other capital cities of Australia and hot water technology switching scenarios will be presented.
• Team has been working on developing hot water load forecasting models. There has been promising preliminary results and the research will further progress and more detailed results will be shared in the next Milestone report.
• UNSW and EE team will have another meeting with Origin to discuss a possible collaboration to try different control methods within Albion Park.
