III. STUDY ON THE RESIDENTIAL AND TERTIARY FLEXIBILITY BY DELTA-EE

This appendix is a summary of the study performed by DELTA-EE on the residential and tertiary flexibility. The study is also published on Elia’s website [ELI-18]. The study assessed the potential flexibility from different types of electric loads with a focus on new electrified loads such as heating, via heat pumps (HPs) or direct electric heater, and transportation, via electric vehicles (EVs). Several barriers to unlock potential flexibility were identified and quantified in order to assess the amount of devices that could be made flexible in the future.

Many uncertainties remain to know (i) which technologies are interesting to be made flexible (meaning the one consuming most energy, or with the greatest installed power), (ii) the enablers needed to unlock flexibility, (iii) the future forecast of unlocked flexibility. For this reason, Elia hired DELTA-EE (a consulting company) to study the challenges stated here above and cipher future evolution of flexibility in the residential and tertiary sector, to take these into account in this AdeqFlex’23. The study was discussed and presented to stakeholders on 13th and 28th October 2022 and was part of the public consultation on scenarios and methodology in November 2022.

The overall approach is described in Figure III-1. The process of identifying and implementing technologies to deliver flexibility involves several key steps. Here is an overview of these steps:

• Identification of Technologies: The first step is to identify the technologies that have the potential to deliver flexibility on the residential and tertiary consumption side. These also included energy storage systems (such as home batteries), heat pumps, electric vehicles, etc.

• Forecasting Flexibility Capacity: Once the technologies are identified, it is essential to forecast the amount of flexibility capacity they can unlock. This involves assessing the capability of each technology to adjust their output or consumption patterns in response to system needs. For example, batteries can provide rapid response and short-duration

III.1. TECHNOLOGY OF INTEREST

Not all technologies can provide large amounts of flexibility. A few characteristics are needed to make a technology interesting to flexibilise: (i) a large installed capacity relative to the load in the future and (ii) a relatively large capability for flexibility. The latter could be defined as the technical capability to variate its power output, as well as a minimal impact on the comfort of consumers.

Several technologies were reviewed, as summarised in Table III-1. It turns out from DELTA-EE analysis that the most relevant technologies to focus on would be the (i) electric vehicles, (ii) heating loads and (iii) energy storage technolo- gies. The reader should bear in mind that heating loads are defined by both hot water and space heating. flexibility but are bounded by the amount of energy they can store, while other types of loads can have longer reaction times or other constraints (amount of activations, comfort level...); a global system/market perspective. It is described with the subscript 1H

The amount of electric vehicles and heat pumps installed in the coming years will represent an increasing share of the load in the residential sector, which makes them interesting to flexibilise. It is also worth noting that it is easier to flexibilise new assets being installed, rather than retrofitting existing ones. Then, electric vehicles and energy storage have high capability for flexibility, linked to the technology ramping characteristics as well as their limited impact on the comfort of consumers.

• Enablers for Flexibility: Each technology requires specific enablers to deliver flexibility effectively. These enablers can include advanced control systems, communication infrastructure, data analytics, forecasting tools, and regulatory frameworks that incentivise flexibility services. For example, electric vehicles may require sophisticated control algorithms and grid integration capabilities to optimise their operation.

• Integration and Optimisation: The final step involves integrating and optimising the different flexibility assets within the electricity system. This includes developing advanced modelling and optimisation tools to coordinate the dispatch and operation of these assets in a way that maximises system flexibility and efficiency. It also involves market mechanisms that incentivise the provision of flexibility services. By following these steps, it is possible to identify the most promising technologies for delivering flexibility, forecast the amount of flexible capacity they can provide, and determine the necessary enablers to unlock their full potential. This process plays a crucial role in designing and implementing flexible electricity systems that can adapt to the changing needs of the grid and support the integration of renewable energy sources.

Some technologies will not deliver flexibility themselves but rather enable the deployment of flexibility from other technologies. The enabling technologies and the framework developed by DELTA-EE will be covered in following sections. Notably, smart meters allowing more granular metering for consumption are one of the technologies required to unlock flexibility.

Other loads were disregarded from the scope of the study. These include: (i) cooling loads and (ii) miscellaneous loads. The former covers air conditioning systems, and the latter concerns house appliances such as freezer, refrigerator, and lightning.

For cooling loads, the main argument was the lack of data to analyse, as well as the relevance for adequacy. The adequacy issues happening in winter when the air-conditioning is not used. Those could be included in future studies.

Then for miscellaneous, it is important to note that not all loads from a household can be considered flexible. For instance, all appliances related to cooking, lighting, audiovisual, IT cannot be considered flexible without impacting the comfort of the user. The appliances to be considered as flexible are cold appliances (refrigerator), dishwasher and water heating. However, with the electrification of heating and transport, the power that this installed capacity represents is rather small. This is represented on Figure III-2, where it is observed that out of the total peak load of the house, the share that this potential flexibility from miscellaneous loadsfalls to an average of 6% while combining several appliances.

• Smart load profile – 1M: With electricity dispatch changing every day, the most optimal way to operate any asset would be to have it adapting its load depending on the market prices or even in real-time to the RES production. This results in a dispatch guided by market signals noted as the subscript 1M

• Optimised bi-directional exchange of energy – 2H: For certain assets like batteries or certain EVs, energy can be

III.2. OPERATION MODE OF THE DIFFERENT ASSETS

Having identified the technologies relevant to provide flexibility, defining how these assets can be operated is key. A flexible operation of an asset can only happen if the asset receives a control signal. This control signal could have different origin, and DELTA-EE summarised these in two categories: (i) a Local (or House signal) and (ii) a Global (or Market) signal. In the first case, the asset is operated flexibly based on a signal related to the home energy management. The signal could be linked to PV production, or network time-of-use tar- iffs, and the goal would be to minimise costs. Whereas in the second case, the signal comes from an aggregator or system operator whose purpose are to balance the grid. e.g. : time-of-use network charges, or capacity tariffs: system operators incentivise consumers to alter the timing of their consumption in order to reduce grid congestion

The parallel can be made with ‘implicit’ and ‘explicit’ flexibility (these are shown in Figure III-3). The control signal for implicit flexibility is based on avoiding costs (like network or capacity tariff), which could be linked to the local control signal. Whereas control signal for explicit flexibility means to generate revenue, similar to what is described as market signal.

The technical characteristics of the asset could also be impactful on the definition of the operation mode, for instance the ability to inject energy back to the grid (such is the case for EVs and batteries).

Overall, the following categories for all assets were defined:

• Natural load – 0: This load profile represents the current operation of the asset without considering optimisation for grid management, price clearing, or renewable energy inte- e.g.: ancillary services: system operators pay for active frequency control Tomorrow? gration. It serves as a baseline and is denoted by the subscript 0

• Optimised load profile – 1H: With relevant network tariffs or appropriate market reforms, virtuous behaviours in regards to the grid could be incentivised (e.g. flatten the load by minimising peak load, consuming outside of peak hours, maximising self-consumption of PV's…). It aims to flatten the load and is guided by a local signal. However, it may not represent the most optimal way to operate the asset from exchanged in two directions. This has the potential to not only move the load outside of peak hours, but also inject electricity back into the grid during these peak hours. When this operation happens after following a local signal, it is defined by the subscript 2H

• Smart dispatch of virtual power plant – 2M: With proper market reforms and infrastructure, the bi-directional assets could be dispatched by the market. Similarly to the 1M subscript, this is described as a 2M subscript with the only difference residing in the ability to inject electricity back into the grid, on top of moving the load outside of peak hours.

TABLE II-2 — SUMMARY OF OPERATION MODES AS DEFINED BY DELTA-EE

CONTROL SIGNALS

H - House Signal

Operation of asset based on a Local signal from the household

E.g.: Static & Dynamic time of use tariffs, capacity tariffs, PV optimisation

Heat-Pumps HP1H Flexible operation - implicit flexibility

V1H Smart charging - implicit flexibility

Electric Vehicles

V2H

Bi-directional smart charging - implicit flexibility

Residential Batteries B2H

Flexible operation - implicit flexibility

The modelling of these operation modes takes into account the technical characteristics of the asset, such as its ramp rate, and also considers the impact on the consumer’s comfort. It recognises that certain assets, like a heat pump, may have the technical capability to shut down, but if it results in an uncomfortable living environment for the owner, it would not be realistic to model this behaviour. Therefore, the model ensures that the operation of the asset remains realistic and within acceptable comfort ranges for the energy end-uses.

These end-user technologies do not only contribute to adequacy through the flexible operation, they also contribute to short-term flexibility in the AdeqFlex’23 study. DELTA-EE covered the capabilities of end-user technology to deliver short-term flexibility. The latter is defined with different timeframes (ramp, fast and slow flexibility). For each timeframe, a different share of end-user appliances can react: less appliances will react to short-term flexibility than for the long-term flexibility. The main underlying constraints lie in (i)

M - Market Signal

Formal contract with the market to provide flexibility

E.g: Ancillary services, Interval balancing, Trading, DSO services

HP1M

Flexible operation - implicit & explicit flexibility

V1M

Smart charging - implicit & explicit flexibility

V2M

Bi-directional smart charging - implicit & explicit flexibility

B2M

Flexible operation - implicit & explicit flexibility communication with the aggregator running smoothly, (ii) technical constraints of flexibility (e.g. if the compressor of a heat-pump is running) and (iii) the current operation of the appliance (e.g. whether the appliance is on or off). All in all, the share of end-user appliances expected to deliver shortterm flexibility respectively for ramp, fast and slow flex are 50%, 70% and 100%.

Moving forward, the study focuses on identifying the elements required to unlock flexibility, which are referred to as ‘enablers of flexibility’. These enablers play a crucial role in facilitating the delivery of flexibility services by the relevant technologies. They encompass various factors, such as market mechanisms, regulatory frameworks, grid infrastructure, communication systems, and advanced control algorithms, among others. The analysis of these enablers is essential to understand the requirements and conditions necessary to fully utilise the flexibility potential of the identified technologies.

III.3. ENABLERS OF FLEXIBILITY

It is not because an asset is technically able to deliver flexibility, that it will do so. Different barriers need to be overcome before the asset is operated flexibly. These key enablers are summarised Figure III-4.

FIGURE III-4 — SCHEMATIC DEPICTION OF FLEXIBILITY ENABLERS

The key factors are described hereunder:

• Asset Volume: This defines the total installed base and its rated capacity. Factors that incentivise technology deployment or that require technology deployment are the primary enablers. This is detailed in the scenario assumptions of the AdeqFlex’23 study.

• Asset flexibility: Assets have a primary function and demand profile that defines the capability of that asset to provide flexibility in coordination with any inherent energy storage. The demand profile defines the temporal limitations to flexibility and also when increased load is required to compensate for reductions in demand.

- EV: cars being parked most of the time, the main factor impacting flexibility will be the charger availability: whether chargers are available where EVs are parked (at home, at the office, in public parking). Also, not all segments were evaluated fit to deliver flexibility by DELTA-EE. Notably, public charging due to the limited time connection at these locations. Additionally, in the coming years, new cars will be compatible with bi-directional exchange of energy. Now, to know if chargers installed will be compatible, or needs retrofitting, is also another question that needs to be assessed in order to define the flexibility potential to come.

- HP: the flexibility of heat pumps depends on the margin available to pre-heat the house before it is occupied, and let it cool down. This varies depending on the building insulation as well as on each given day where the outside temperature (and heat losses) varies as well.

• Control signal: A local or market signal is necessary to drive the flexible operation of assets. This signal is responsible to adjust the consumption of the asset and variate over time according to the system’s need.

• Control capability: Depending on the required control signal, the asset(s) must have the necessary capability to respond appropriately and optimise performance to meet the necessary flexibility requirements. Note also that communication protocols and standards need to be established. Indeed, standardising exchange of information between devices and ability to control them is also key (e.g. an electric vehicle from a certain brand and using a smart charger from another brand should be allowed to know when is the most appropriate moment to charge based on the home PV generation from another brand).

- HPs: For all assets to deliver flexibility, they will need to be able to receive signals and be automated. It is expected that not many HPs are retrofitted to meet this requirement, but that HPs need to be made smart at conception.

- EV: For EVs, it is the charger itself that needs to be made smart in order to enable smart charging, whether uni- or bi-directional. However, for the latter, a special charger needs to be installed to allow flow of energy in both directions, and these are expected to come only when the appropriate EVs will be sold on the market. In Figure III-5, the reader can see the assumed evolution in the chargers installed in coming years. The vast majority of installed chargers are expected to be made smart (meaning having the capability to charge based on a signal), and also unidirectional as these are linked to uni-directional charging cars. The projection of bi-directional chargers is based on the forecast of EVs sold with the bi-directional functionality. Note that the forecast is built under the assumption to keep a ratio of 1 charger per 2.17 EVs.

FIGURE III-5 — ASSUMED FUTURE EVOLUTION OF EV CHARGERS PER TYPE

Roll-out of bi-directional chargers as new car sales implement the technology, after 2027

The majority of chargers are expected to be installed with smart features

• Appropriate metering: Monetising flexibility requires measuring the dynamic electricity load from the household with sufficient accuracy for the control signal and flexibility service provided. These systems must also be appropriately connected such that services can be effectively measured and billed by the correct parties. All regions in Belgium have made their plans to roll-out smart meters. The global deployment of smart meters is depicted in Figure III-6. installing 40k smart meters per year. Wallonia targeted 90k smart meters by the end of 2022. A constant rate is assumed after of Flemish household covered with smart meters by 2024.

• Customer barriers: With control signals and assets available, the complexity of the service offering and the impact on the customer comfort will be the key final barriers to participation in flexibility services before consideration of the scale of customer benefit. The customer barrier will also not be the same for out-of-market flexibility, or in-the-market. In the former, the customer retains more control on their appliances. For this reason, DELTA-EE assumed that the uptake of the latter to be lower (see Table III-3). This is where initiatives like Customer Centricity Market Design of Elia aims to (i) help consumers to engage with flexibility providers and (ii) simplify measurement behind the meter, as well as ease the settlement and financial valuation of flexibility services provided by consumers.

- EV: the main barrier for customer will be regarding their comfort. For EVs, this can take the form of a guaranteed State of Charge that the user can impose for a certain time he or she will need to use their cars.

- HP: the main barrier for customer will be regarding their comfort. This is especially challenging for heating as consumers expect a certain temperature inside their homes. The common way to answer this is to make assumptions on the temperature setpoint of the house. Regarding space heating, the appliance can become flexible only if a range of temperature is considered instead of a setpoint (e.g. heating +- 2°C around a setpoint, instead of a constant setpoint). For hot water, it is assumed that water can be heated with marginal heat losses and no impact on the consumer’s comfort. While the capability of heating technologies is highly sensitive to the season and local assets, the enabling technologies and customer barriers will be critical, with impact on consumer and complexity being the most important barriers to overcome for widespread uptake.

With DELTA-EE’s enablers framework made explicit, the three next sections explain how this has been translated into uptake of the different operation modes for each asset.