Title: Demand Management Aggregator Framework Abstract · D7.3 – Demand Management Aggregator...

Title:

This report represents Deliverable D7.3, Demand Management Aggregator Framework. The purpose of this report is to

document the framework developed (early in Stage 2), and the analysis undertaken with Trial data and its conclusions (after

the Consumer Charging Trials).

Context:The objective of the Consumers. Vehicles and Energy Integration (CVEI) project is to inform UK Government and European

policy and to help shape energy and automotive industry products, propositions and investment strategies. In addition to

developing new knowledge and understanding, the project aims to develop an integrated set of analytical tools that can be

used to model future market scenarios in order to test the impact of future policy, industry and societal choices. The project

is made up of two stages:

• Stage 1 aims to characterize market and policy frameworks, business propositions, and the integrated vehicle and

energy infrastructure system and technologies best suited to enabling a cost-effective UK energy system for low-carbon

vehicles, using the amalgamated analytical toolset.

• Stage 2 aims to fill knowledge gaps and validate assumptions from Stage 1 through scientifically robust research,

including real world trials with private vehicle consumers and case studies with business fleets. A mainstream consumer

uptake trial will be carried out to measure attitudes to PiVs after direct experience of them, and consumer charging trials will

measure mainstream consumer PiV charging behaviours and responses to managed charging options.

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Programme Area: Energy Storage and Distribution

Project: Consumers, Vehicles and Energy Integration (CVEI)

Demand Management Aggregator Framework

Abstract:

PROJECT REPORT

Consumers, Vehicles and Energy Integration Project

Deliverable D7.3 Demand Management Aggregator Framework

Report details

Report prepared for: Energy Technologies Institute

Project/customer reference: TR1006_D7.3

Copyright:

Report date: 27/08/2019

Report status/version: 1.0

Quality approval:

James Greenleaf Technical Lead Stephen Skippon Technical Reviewer

Disclaimer

This document is provided to the ETI under, and is subject to the terms of, the Energy Technologies Institute’s Agreement for the Consumers, Vehicles and Energy Integration (CVEI) Project – Stage 2.

Authors

The following individuals have contributed to the development of this report:

James Greenleaf and Natalie Bird, Baringa Partners, Ltd.

Name: [email protected]

Name: [email protected]

Contents amendment record

This report has been amended and issued as follows:

Version Date Description Editor Technical Reviewer

D1.0 18/01/2019 First version delivered to client JG, RN, NB, OR DS, SS (TRL)

D2.0 15/03/2019 Second delivered to client JG, RN, NB, OR DS, SS (TRL)

D3.0 20/06/2019 Third version delivered to client JG, RN, NB, OR DS, SS (TRL)

1.0 20/08/2019 Final approved version for publication JG, NB DS

Document last saved on: 27/08/2019

Document last saved by: Deborah Stubbs

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mailto:[email protected]

D7.3 – Demand Management Aggregator Framework

ETI ESD Consumers, Vehicles and Energy Integration Project

i

Table of Contents

Table of Contents ........................................................................................................................ i

List of Figures .............................................................................................................................. ii

List of Tables .............................................................................................................................. iii

Abbreviations ............................................................................................................................ iv

D7.3 Summary ............................................................................................................................ v

Executive Summary .................................................................................................................... 1

1. Introduction ........................................................................................................................ 7

1.1 Background and context ............................................................................................ 7

1.2 Demand Management of PiVs ................................................................................... 8

1.3 Structure of this report .............................................................................................. 9

2. Approach and key assumptions ....................................................................................... 10

2.1 Overview .................................................................................................................. 10

2.2 Consumer behaviour ................................................................................................ 11

2.3 Market data .............................................................................................................. 16

2.4 Other key assumptions ............................................................................................ 23

2.5 Aggregator actions ................................................................................................... 24

3. Analysis ............................................................................................................................. 28

3.1 Scenarios .................................................................................................................. 28

3.2 Example of gross margin build up ............................................................................ 29

3.3 Summary of flexibility payment results ................................................................... 32

3.4 Focus on commercial strategy ................................................................................. 35

3.5 Alternative consumer payment structures .............................................................. 38

4. Conclusions ....................................................................................................................... 41

4.1 Potential value of flexibility ...................................................................................... 41

4.2 Commercial strategy and accessible value .............................................................. 41

4.3 Potential policy and regulatory changes .................................................................. 43

4.4 Areas for further work.............................................................................................. 45

Appendix A Use of trial data for sampling ........................................................................... 46



ii

List of Figures

Figure 1: Overview of model design ........................................................................................ 11

Figure 2: Distribution of PiV owners (BEVs left / PHEVs right) plugging in to charge per day 12

Figure 3: Share of BEVs plugging in by hour: average and +/- two standard deviations ........ 13

Figure 4: Share of PHEVs plugging in by hour: average and +/- two standard deviations ..... 13

Figure 5: Population average duration plugged from start hour for BEVs: average and +/- two standard deviations .................................................................................................................. 14

Figure 6: Population average duration plugged from start hour for PHEVs: average and +/- two standard deviations ........................................................................................................... 15

Figure 7: Delta State of Charge (SOC) requested by BEVs at plug-in hour: average and +/- two standard deviations .................................................................................................................. 16

Figure 8: Delta State of Charge (SOC) requested by PHEVs at plug-in hour: average and +/- two standard deviations .................................................................................................................. 16

Figure 9: Electricity installed capacity ..................................................................................... 17

Figure 10: Commodity Prices .................................................................................................. 18

Figure 11: Winter weekday intraday wholesale price: average and +/- two standard deviations .................................................................................................................................................. 19

Figure 12: Winter weekday imbalance prices: average and +/- two standard deviations ..... 19

Figure 13: Summer weekend intraday wholesale price: average and +/- two standard deviations ................................................................................................................................. 20

Figure 14: Summer weekend imbalance prices: average and +/- two standard deviations .. 20

Figure 15: Average intraday wholesale and retail prices for winter weekday ....................... 21

Figure 16: Average intraday wholesale and retail prices for summer weekend .................... 22

Figure 17: Examples of forward prices for characteristic days ............................................... 22

Figure 18: Value of pre-contracted services ........................................................................... 23

Figure 19: Illustrative example considering intra-day prices and a pre-contracted service ... 25

Figure 20: Illustrative results (revenues are positive) ............................................................. 29

Figure 21: Distribution of charging profile under counterfactual static time of use tariff ..... 31

Figure 22: Illustration of limited charge volume assuming 7kW charger and 14 kWh requirement ............................................................................................................................. 33

Figure 23: Simulated distribution of energy costs in 2030 relative to average daily cost ...... 34

Figure 24: Scenario 7 - Decarbonisation Case prices, higher risk, 100k PiVs .......................... 36

Figure 25: Scenario 8 - Decarbonisation case prices, lower risk, 100k PiVs ........................... 37

Figure 26: Delta in cashflow components between Scenario 7 and Scenario 8 ..................... 38

Figure 27: Spread of consumer payments based on flexibility provided in Scenario 7 .......... 39

Figure 28: Overview of approach for simulating variation in share of PiVs plugging in by hour .................................................................................................................................................. 47



iii

List of Tables

Table 1: Summary of key revenue areas modelled ................................................................... 3

Table 2: Maximum per car flexibility payment as proportion of annual vehicle charging cost 4

Table 3: BM impacts on supplier/aggregator cashflow .......................................................... 26

Table 4: Overview of scenarios ............................................................................................... 28

Table 5: Components of gross margin .................................................................................... 30

Table 6: Maximum £/car/year flexibility payment by scenario cf. a time of use counterfactual .................................................................................................................................................. 32

Table 7: Maximum £/car/year flexibility payment by scenario cf. flat average tariff ............ 35

Table 8: Maximum per car flexibility payment as proportion of annual vehicle charging cost .................................................................................................................................................. 41



iv

Abbreviations

BEV Battery Electric Vehicle

BM Balancing Mechanism

BS Balancing Services

CM Capacity Market

CVEI Consumers Vehicles and Energy Integration (project)

DM Demand Management

ICEV Internal Combustion Engine Vehicle

FCV Fuel Cell Vehicle

FFR Firm Frequency Response

NMC Non-Managed Charging

PHEV Plug-in Hybrid Electric Vehicle

PiVP Plug-in vehicle

SOC State of Charge

SMC Supplier Managed Charging

STOR Short Term Operating Reserve

UMC User Managed Charging

V2G Vehicle 2 Grid



v

D7.3 Summary

Background

The Consumers, Vehicles and Energy Integration (CVEI) project investigated challenges and opportunities involved in transitioning to a secure and sustainable low carbon vehicle fleet. The project explored how integration of vehicles with the energy supply system can benefit vehicle users, manufacturers and those involved in the supply of energy. The project’s objective was to inform policy, and to help shape energy and automotive industry products, propositions and investment strategies. In addition to developing new knowledge and understanding, the project developed an integrated set of analytical tools that can be used to model future market scenarios in order to test the impact of future policy, industry and societal choices.

Project scope

The CVEI project consisted of two stages: Stage 1 aimed to characterise market and policy frameworks, business propositions, and the integrated vehicle and energy infrastructure system and technologies best suited to enabling a cost-effective UK energy system for low-carbon vehicles. Stage 2 aimed to fill knowledge gaps and validate assumptions from Stage 1 through scientifically robust research, including real-world trials with private vehicle consumers and case studies with business fleets.

Purpose and scope of this deliverable

The purpose of this deliverable is to undertake more detailed analysis of the monetary value that could be obtained in different parts of the electricity market via flexibility in direct charging and Demand Management (DM) of Plug-in Vehicles (PiVs). The analysis explores the value that be achieved by monetising the flexibility of consumers charging their PiVs under Supplier-Managed Charging (SMC) schemes, using data from the Consumer Charging Trials carried out in this project. One of these trials imitated a 3rd-party supplier/ aggregator Managed Charging scheme whereby the consumer is rewarded based on the level of flexibility they provide (i.e. the length of the plug-in window provided relative to the time needed to achieve a desired level of charge) with the aggregator managing the charging profile. It should be noted that the structure of the CVEI trial did not allow the relationship between consumer reward for flexibility and the scale of flexibility provision to be explored directly as it was designed to test consumer response rather than optimal product design. For this analysis it is therefore assumed that consumers managed by the aggregator are relatively insensitive to the level of reward, with the provision of flexibility more a function of the aggregate response from the consumers’ fundamental travel patterns, as based on data from the CVEI trial.

The value streams available to the aggregator in this analysis were wholesale energy (intra-day), avoidance of certain retail tariff components such as use of system charges (assuming these are passed through to domestic consumers under future half-hourly settlement), Balancing Mechanism and pre-contracted services such as frequency, reserve and provision into the Capacity Market. The analysis is focused in particular on the constraints on flexibility value imposed by uncertainty in consumer behaviour. Vehicle to Grid charging was excluded from the CVEI project and hence the trial represents flexibility from a DM perspective only. The analysis is not trying to represent the minutiae of the specific electricity market arrangements that exist today, as these are likely to evolve over time (e.g. given limitations that exist such as the lack of cost reflectivity in current use of system charges for avoiding distribution network reinforcement), but a more stylised representation of the revenue sources that the aggregator could access given current market arrangements.

Method

A bespoke model was developed for this study, which combines a number of key functions:

1. A Monte Carlo generator that simulates a range of inputs reflecting uncertainty in:

Consumer PiV behaviour (e.g. plug-in times or energy requirements) based on the results from the CVEI Consumer Charging Trials. Where relevant, consumers are separated into Battery Electric Vehicles and Plug-in Hybrid Electric Vehicle owners.

Market prices (e.g. intraday wholesale or imbalance prices) and market events (e.g. where the aggregator has committed to providing a reserve or Capacity Market service, the number of times an event happens requiring the aggregator to reduce the charging of its customers)

2. An optimisation engine that decides how to manage the charging profile of the PiVs across a number of characteristic days to maximise revenue for the aggregator, subject to a user defined ‘commercial strategy’ for each scenario, for example, the extent to which the aggregator is trying to monetise pre-contracted services versus trying to extract value from the wholesale or balancing markets.

The ability to manage the charging profile is subject to a number of constraints, for example the need to inject the required energy into the PiVs across the charging window provided to meet the customer’s State of Charge requirements.

3. A cashflow model that calculates the revenues and costs of the supplier/aggregator given the results of the optimisation. From this, an estimate is made of the potential pot of flexibility payments that would be available to consumers under a SMC scheme broadly reflecting current market incentives.

The analysis has been undertaken for three spot years, 2020, 2025 and 2030. Within each year, eight characteristic days are modelled, four seasons by weekday and weekend. Finally, for each characteristic day 30 simulations are undertaken to explore the range of consumer behaviour and electricity market conditions, which affect the ability of the aggregator to maximise its revenue.

Key Findings and Conclusions

The analysis has shown that the value that the DM aggregator could achieve by monetising PiV charging flexibility is relatively modest in absolute terms. This is the value that could be provided by more sophisticated charging by an aggregator, over and above simple load shifting against a static time of use tariff. This is in part because a sizeable portion of monetary value from shifting PiV load can be achieved via avoiding peak costs, albeit noting this does not account for future system implications of potential cliff-edges or localised peaks on distribution networks that could arise from a response to such tariffs. The highest monetary values in 2030 equate to around 5% and 7% of the annual charging cost of a BEV or PHEV, respectively. Under tariffs where consumers are rewarded in proportion to the flexibility they provide, this could increase to around 15% of annual charging costs given the behaviour observed in the CVEI charging trial results. The table below shows the results under the 8 scenarios explored, in terms of the maximum per car flexibility payment as proportion of annual vehicle charging cost.

BEV PHEV

Scenario Description 2020 2025 2030 2020 2025 2030

Scenario 1 Reference case prices, higher risk, 50 PiVs 1% 2% 2% 2% 3% 3%

Scenario 2 Reference case prices, lower risk, 50 PiVs 1% 2% 2% 2% 2% 2%

Scenario 3 Reference case prices, higher risk, 100k PiVs 1% 2% 2% 2% 4% 3%

Scenario 4 Reference case prices, lower risk, 100k PiVs 1% 2% 2% 2% 3% 2%

Scenario 5 Decarbonisation case prices, higher risk, 50 PiVs 2% 3% 5% 4% 5% 6%

Scenario 6 Decarbonisation case prices, lower risk, 50 PiVs 2% 2% 3% 3% 3% 4%

Scenario 7 Decarbonisation case prices, higher risk, 100k PiVs 2% 3% 5% 4% 5% 7%

Scenario 8 Decarbonisation case prices, lower risk, 100k PiVs 2% 2% 3% 3% 4% 4%



1

Executive Summary

The Consumers, Vehicles and Energy Integration (CVEI) Project was established to examine how mass deployment and use of Ultra-Low Emissions Vehicles in the UK could be delivered. Part of the project involved a trial with mass-market consumers to monitor the home charging behaviour of their electric vehicles, under various simulated electricity pricing structures.

One of these imitated a potential 3rd-party supplier/aggregator managed structure whereby the consumer is rewarded based on the level of flexibility they provide (i.e. the length of the plug-in window provided relative to the time needed to achieve a desired level of charge) with the aggregator managing the charging profile. It should be noted that the trial was designed to test broad consumer response rather than to develop an optimised product design, in practice the price may only be one aspect of a compelling consumer offering alongside convenience, simplicity, certainty of cost, etc. Vehicle to Grid (V2G) charging was excluded from the CVEI project and hence the trial represents flexibility from a Demand Management (DM) perspective only.

This report explores the revenue that can be achieved by monetising this flexibility in different parts of the electricity market including wholesale energy (intra-day), avoidance of certain retail tariff components such as use of system charges, the Balancing Mechanism (BM) and pre-contracted services such as frequency, reserve and provision into the Capacity Market1. The analysis is broadly representative of the high-level market structures that exist today, with a small number of changes. For example, it is assumed that current use of system charges are passed through to consumers under the proposed moves to half-hourly settlement. However, it is not yet clear what the changes will look like to make these charges more cost-reflective, particularly for avoided network reinforcement costs at distribution level. In addition, there are elements of underlying system value that are underestimated in the current market structure such as the higher marginal cost of carbon for charging at peak periods.

The analysis is focused in particular on the constraints on flexibility imposed by uncertainty in consumer behaviour. In addition, the analysis has focused on the incremental revenue that an aggregator could achieve - via more sophisticated charging management - over and above that which can be realised by a consumer minimising their costs in response to a simple Static Time of Use tariff.

The level and structure of reward in the consumer trial appeared sufficient to create a material consumer response in terms of the provision of flexibility, however, the structure of the trial did not allow for alternative consumer offerings to be tested. For this analysis it has therefore been explicitly assumed that consumers are relatively insensitive to the level of reward, with the provision of flexibility more a function of consumers’ fundamental travel patterns.

A bespoke model was developed to assess the monetary value from the flexibility associated with managing PiV charging. This undertakes a Monte Carlo simulation of both consumer behaviour parameters (using the trial results to consider variability in plug-in times, plug-in durations, energy requirements, etc) and variability in market prices. These simulated sets of inputs are provided to an optimisation engine, which maximises the revenue to the aggregator

1 Flexibility services to Distribution System Operators were not explored quantitatively in the analysis, given the limited data on the likely value and form these will take in future and that the value is likely to be highly location specific.



2

under various constraints (e.g. the battery must be charged to the consumers’ requirements) under the assumption that the aggregator is effectively a ‘price taker’. I.e. a single aggregator’s actions when shifting electric vehicle charging do not affect market prices, however, this is unlikely to hold when very large numbers of electric vehicles are being actively managed in a similar way. Finally, the outputs from the optimisation are passed to a cashflow model to assess the maximum ‘pot of flexibility value’ that could be paid to the PiV owners after accounting for the aggregator’s costs and margin.

A number of scenarios were explored for the spot years 2020, 2025 and 2030, considering differences in:

Electricity market prices under a ‘Reference Case’ (reflecting a central or business as usual evolution of the electricity market given current policy and regulatory arrangement) and ‘Decarbonisation Case’ (i.e. with lower electricity system emissions and increased price volatility due to more intermittent renewables in the system).

The Reference Case assumes approximately 5M PiVs by 2030 split evenly between BEVs and PHEVs and the Decarbonisation Case around 9M PiVs. In both cases a degree of managed charging is assumed, such that the unmanaged evening peak demand that would be incurred from charging immediately at the end of a journey is reduced by half through load shifting into the late evening and overnight periods.

The number of electric vehicles being managed by an individual aggregator, given diversity implications and the reduction in volatility around charging behaviour with more vehicles.

The broad commercial strategy pursued by the aggregator. The first strategy focuses on a lower risk approach, locking in as much value as possible in advance through pre-contracted services. The second strategy represents a higher risk approach, focusing on the response to near term price volatility in the intra-day market and BM.

Risk in this sense is focused around the potential to generate flexibility value for the consumer. The risk to the aggregator will ultimately depend on how any tariff structure is set up and the extent to which the value is passed through ex-post (i.e. after the total pool of flexibility value is known) versus ex-ante.

Table 1 provides a summary of the key areas of revenue covered and not covered by the modelling, as well as an indication of whether the size of the value pool presents a potential limit for a large number of PiVs being managed by aggregators.



3

Table 1: Summary of key revenue areas modelled

Value Area Description Modelled Limited value pool for PiVs

Minimising cost of energy supply in intra-day and BM markets via cash-out.

Shifting PiV load into the cheapest hours of the day in the intraday wholesale market, or by increasing load in the BM relative to the contracted position at Gate Closure when the system as a whole is long (and therefore imbalance prices are lower than prices in the intraday market).

✓ No – but mass market management of PiVs may flatten prices and cannibalise value available.

Minimising cost of energy retail adders

Shifting PiV load across the day to avoid shaped use of system charges (distribution and transmission) and the Capacity Market Supplier Charge.

✓ No – but shaping of charges and value available is subject to regulatory reform and linked to fundamentals such as the shape of demand which will be influenced by mass market management of PiVs

BM cash-out balancing payments

Payments for reducing PiV load in the BM relative to their contracted position at Gate Closure when the system is short.

✓ No – but mass market management of PiVs may alter prices and value available

Active participation in the BM

Submitting priced offers and bids to turn down and up, respectively to help balance the system.

X – Alternative route to cash-out to accessing value in BM. Value broadly equivalent to cash-out, but is not additive and with different operational challenges to realise this value.

Yes - Historically ca. around half of net imbalance volumes within +/- 0.5 GWh, although this is expected to grow with increasing intermittent renewables

Pre-contracted balancing services

Availability payments for providing given holding volumes to services analogous to Frequency Response and Short Term Operating Reserve (STOR). For STOR also considering utilisation payments when volume is called.

✓ Yes - More lucrative frequency response and fast reserve represent ca. 2.5-3 GW of requirement and this is not expected to grow significantly in future. Lower value STOR requirement is ca. 3 GW and expected to grow with increasing intermittent renewables.

Capacity Market Availability payments for providing given holding volume in the CM

✓ No - Currently ca. 50GW requirement, but not clear how significant a contribution demand management can make to this total.

Flexibility services to Distribution System Operators

Potentially analogous to pre-contracted balancing services provided to the overall electricity system operator

X - Limited data on the likely value and form these will take in future and that the value is likely to be highly location specific. Operation to provide these services may also then preclude accessing other value streams at the same time.

Not explored directly – value and volume procured likely to be highly location specific.

Given the revenues made by the aggregator from managing the PiV flexibility, Table 2 summarises the payment per car that can be made as a proportion of the total annual cost of



4

vehicle charging. Importantly, the revenues are estimated relative to consumers who would have already shifted their load in response to a simple time of use tariff. I.e. this illustrates the incremental monetary value that the more sophisticated aggregator management could provide over and above a simple consumer response.

The highest values generated by the supplier/aggregator in 2030, which could be passed through as a flexibility payment to the consumer, equate to around 5% and 7% of the annual average charging cost of a BEV or PHEV, respectively, as shown in Table 1. Under alternative tariffs where consumers are rewarded in proportion to the flexibility they provide, this could increase to around 15% annual charging costs based on the behaviour observed in the CVEI trial.

Table 2: Maximum per car flexibility payment as proportion of annual vehicle charging cost

BEV PHEV










Of the two commercial strategies (i.e. high and low risk), the lower risk strategy produces a materially lower value than the higher risk strategy, which is evident when comparing like-for-like scenarios (e.g. Scenario 1 with 2, 7 with 8, etc).

The primary reason for the lower expected value is due to the very low volumes (relative to the maximum charging rate across the pool of PiVs being managed) that could be offered into the pre-contracted services, whilst still being able to deliver them effectively. This is due to the ‘relatively’ low volume of charging energy required on a daily basis across the pool of PiVs. The need to position the charging profile to provide these pre-contracted services across a given time window rapidly fills the batteries and points to the need for V2G to provide higher volumes of services, as this would provide additional optionality for the aggregator through direct dispatch back into the grid. However, even if this proves effective it should be noted that many of the more lucrative pre-contracted services markets are relatively small and would quickly become saturated with the flexibility associated with millions of electric vehicles.

A higher risk strategy focused on minimising energy costs and generating value through the BM appears more lucrative overall. This exploits the ability of the aggregator to respond rapidly to price variability across the day, which is expected to increase over time due to increasing levels of intermittent renewables, in particular where electricity system decarbonisation is happening more rapidly. Whilst this appears a higher value strategy it is important to emphasise that there are real world challenges to realising this value in practice,



5

for example due to the ability to forecast price variations and system conditions reliably in the intra-day and BM markets.

In like-for-like scenarios the average value per vehicle appears similar where the aggregator is managing 50 PiVs or 100,000 PiVs despite the significant reduction in the variability of consumer behaviour with the larger pool of consumers (e.g. comparing scenario 5 with 7 or 6 with 8). However, the average value in the 50 PiV scenarios masks the far more substantial swings in daily value that can be achieved. In some cases this could be well above the potential value in the 100,000 PiVs scenario and in others well below. A smaller pool of PiVs therefore increases the risk of achieving an expected level of value and makes it far harder to achieve this via routes which require certainty of consumer response, such as pre-contracted balancing services.

Overall, the analysis shows that the value the aggregator could achieve by monetising PiV charging flexibility through demand management is relatively modest in the period to 20302, at least under market arrangements which are broadly reflective of those currently in place. This is partly because a material portion of “value” from shifting of Plug in Vehicle (PiV) load, particularly in terms of managing wholesale energy costs and additional shaped supplier costs such as use of system charges, can be achieved via a simple time of use tariff. To explore this, a separate set of scenario runs were undertaken with a flat tariff counterfactual, whereby consumers would have started charging straight away after plugging in at the end of their journey. In the flat tariff counterfactual case the revenues made by the aggregator were around 2-4 times higher than the results shown in Table 2, largely due to the additional value from moving charging away from peak periods, which has already happened to a significant degree in the core Time of Use tariff counterfactual.

Dynamic time of use tariffs, coupled with in-home optimisation systems, may be able to capture more of this flexibility value without the need for an aggregator (particularly with respect to minimising energy costs). However, the feasibility of implementing this needs further investigation, particularly with respect to the level of coordination and certainty required to provide pre-contracted services. If it were to prove feasible, the distinctions between this and the aggregator model may well be quite nuanced.

It is clear that the monetary value that the aggregator can make is dependent on the underlying market and policy structure, which is subject to significant and ongoing changes. For example, the most recently proposed changes from Ofgem’s Targeted Charging Review3 indirectly imply that behind the meter generation and demand side response (including electric vehicle charging management) could lose some of the existing monetary value achieved by shifting demand to avoid certain use of system charges.

2 The modelling for the separate D7.4 Market Design and System Integration deliverable contained a simpler representation of the Demand Management aggregator but this indicated that some components of value - such as helping to balance the electricity system and manage peak demand - continue to grow steadily from 2030-2050 as further decarbonisation is needed.

3 https://www.ofgem.gov.uk/publications-and-updates/targeted-charging-review-minded-decision-and-draft-

impact-assessment

https://www.ofgem.gov.uk/publications-and-updates/targeted-charging-review-minded-decision-and-draft-impact-assessment




6

Other changes may be more favourable, for example, the forward-looking component of network charges is also under review4. Part of this aims to explore a more temporally and spatially dynamic set of price signals along with more focus on capacity-based charging, particularly at distribution level, which are more cost-reflective of the avoided cost of avoided reinforcement. These could better reflect conditions across different parts of the distribution network, which an aggregator of PiV charging could monetise, subject to the challenges noted above about routes to market that require the aggregator to provide a holding volume over an extended period to offer flexible capacity.

Importantly, these changes point at the potential disconnect in monetary value accessible by the aggregator and the underlying ‘system value’ of managing PiV flexibility. In some cases, the current market structures provide a poor proxy for this system value, but are a focus of reform. For example, existing static distribution use of system charges (assuming these would be passed through to domestic consumers under half-hourly settlement) would not adequately account for the system impacts of load shifting under time of use tariffs that creates new localised peaks in parts of the distribution system at different times of day. This inadvertently reduces the advantage of more sophisticated aggregator managed charging from a system perspective. However, the policy reforms mentioned above, such as capacity based charging, aim to better account for the link between system value and monetary value.

4 https://www.ofgem.gov.uk/publications-and-updates/electricity-network-access-and-forward-looking-charging-review-significant-code-review-launch-and-wider-decision

https://www.ofgem.gov.uk/publications-and-updates/electricity-network-access-and-forward-looking-charging-review-significant-code-review-launch-and-wider-decision




7

1. Introduction

1.1 Background and context

The Consumers, Vehicles and Energy Integration (CVEI) Project, commissioned and funded by the Energy Technologies Institute (ETI), has been established to examine how to deliver mass deployment and use of Ultra-Low Emissions Vehicles (ULEVs) in the UK. It is focused on cars and light vans including Plug-in-Vehicles (PiVs) - both Plug-in Hybrids (PHEVs) and full Battery Electric Vehicles (BEVs), hydrogen Fuel Cell Vehicles (FCVs) and traditional Internal Combustion Engine Vehicles (ICEVs). It addresses the challenges and opportunities of ULEV integration with the full energy system over the period from 2015 to 2050.

The CVEI project comprises two main parts:

A mass-market trial5 with real users, which has explored:

Key factors of consumer purchase decisions for PHEVs and BEVs compared to ICEVs.

Home charging behaviour of consumers with PHEVs and BEVs under three different simulated electricity tariffs:

Non-Managed Charging (NMC) reflecting a flat average electricity price.

User Managed Charging (UMC) reflecting a simple time of use tariff where the consumer decides when to charge based on a known set of electricity tariffs, which vary by season and time of day.

Supplier Managed Charging (SMC) reflecting a supplier or aggregator directly managing the charging on behalf of the consumer to minimise the costs of charging and/or generate additional revenue. The consumer plugs the vehicle in / out times and specifies a minimum state of battery charge that they require by a particular time. Where consumers provide more ‘flexibility’ to the aggregator,6 they receive a higher ‘reward’, all else being equal. This reward may vary by season and time of day depending on the conditions on the wider electricity system. For example, flexibility is more valuable when the system is already under ‘stress’, with limited spare capacity.

A number of case studies exploring the factors influencing commercial fleet owners’ decisions to transition their fleet to ULEVs.

A suite of broader analyses - informed by the trial results where relevant - exploring Market and Policy Frameworks, business propositions, and the integrated vehicle and infrastructure system and technologies best suited to enabling a cost-effective UK energy system for low-carbon vehicles.

5 Full details on the real-world trials and the resulting insights are provided in separate deliverables D5.3 - Consumer Uptake Trials - Summary Report and D5.3 - Consumer Charging Trials - Summary Report.

6 I.e. where they provide increasingly longer plug-in windows relative to the minimum time necessary to charge the vehicle to its desired level.



8

1.2 Demand Management of PiVs

This report is focused on the role of Supplier Managed Charging, undertaking more detailed analysis of the revenue that could be obtained in different parts of the electricity market via flexibility in direct charging and Demand Management (DM) of PiVs. This comprises:

Shifting load to different times of day to minimise the cost of wholesale electricity and/or avoid time dependent retail price adders, such as use of system charges.

Driving incremental value through the Balancing Mechanism (BM) via direct offers (to turn down charging) and bids (to turn up charging) or managing exposure to imbalance prices through cash-out7, based on the overarching system requirements.

Providing pre-contracted peak load reduction into the Capacity Market (CM), committing to down charging during times of system stress when a CM Event is called.

Providing pre-contracted Balancing Services (BS) such as Frequency Response or Reserve to the electricity system operator, by rapidly managing the charging rate (both up and down) across a pool of PiVs.

The analysis is not trying to represent the minutiae of the specific electricity market arrangements that exist today, as these are likely to evolve over time, but a more stylised representation of the main value pools that the aggregator could access across the wholesale market, capacity, balancing or avoidance of other system charges. For example, the Balancing Services are only broadly representative of the current Firm Frequency Response (FFR) and Short Term Operating Reserve (STOR) products. In addition, it has been assumed that domestic consumers would be exposed to the underlying shape of network use of system charges (e.g. red, amber and green bands for distribution networks at different times of day) as part of a move to half-hourly settlement8, whereas this is not the case the present.

There are a number of key factors that must be considered as part of this analysis, including:

There is significant variability in the nature of the load that could be managed, for example, with respect to how many vehicles are plugged in at any one time or how long they will be plugged in for. This creates risks for the aggregator which need to be managed when trying to monetise the value of DM. This is further compounded by the variability that exists in different parts of the electricity market (e.g. in hourly electricity prices).

7 The process to settle the difference between contracted generation, or consumption, and the amount that was actually generated, or consumed, in each half hour trading period. The imbalance prices calculated in each settlement period by the System Operator are used as part of the cash-out process to determine the many paid to or by each party. The process also helps to provide a financial incentive to parties to help balance the electricity system. This is because being out of balance in the opposite direction to the system (e.g. supplying more energy than contracted when the system is short overall) is more favourable compared to being penalised for being out of balance in the same direction.

8 https://www.ofgem.gov.uk/electricity/retail-market/market-review-and-reform/smarter-markets-programme/electricity-settlement



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This analysis only explores DM of electric vehicles and not Vehicle to Grid (V2G), as no data was provided on this area from the CVEI trial.

The CVEI trial only considered one potential form of a consumer SMC offering. Therefore, this analysis is focused on how the flexibility seen in the trial could be monetised and not how this flexibility may change given different types of tariff and levels of consumer reward. This implicitly assumes that consumers are relatively insensitive to the level of reward, with the provision of flexibility more a function of consumers’ fundamental travel patterns.

The resulting commercial cashflow analysis is undertaken from the perspective of a supplier with an additional aggregator function to manage and monetise the flexibility of PiV charging. In Great Britain, an aggregator without a supply licence cannot trade electricity directly in the wholesale market, but does have direct market access in some areas such as Balancing Services.

1.3 Structure of this report

This report is structured as follows:

Section 2 provides an overview of the approach and key assumptions

Section 3 presents the results of the analysis

Section 4 discusses the conclusions and implications for the aggregator



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2. Approach and key assumptions

2.1 Overview

A bespoke model was developed for this study, which combines a number of key functions:

A Monte Carlo generator that simulates a range of inputs reflecting uncertainty in:

Consumer PiV behaviour (e.g. plug-in times or energy requirements) based on the results from the CVEI trial. Where relevant, consumers are separated into BEV and PHEV owners.

Market prices (e.g. intraday wholesale or imbalance prices) and market events (e.g. where the aggregator has committed to providing a reserve or CM service, the number of times an event happens requiring the aggregator to reduce the charging of its customers)

An optimisation engine that decides how to manage the charging profile of the PiVs across a number of characteristic days to maximise revenue for the aggregator, subject to a user defined ‘commercial strategy’ for each scenario (this is described further in Section 3.1), for example, the extent to which the aggregator is trying to monetise pre-contracted services versus trying to extract value from the wholesale or BM markets.

The ability to manage the charging profile is subject to a number of constraints, for example the need to inject the required energy into the PiVs across the charging window provided to meet the customer’s transport requirements.

Each characteristic day is assumed to be treated independently with the aggregator managing the charging requirements across the day, but with the level of plug-in across participants, as well as the plug-in times, durations and charging requirements varying.

The optimisation assumes perfect foresight across each characteristic day for each given Monte Carlo simulation. In practice there will be a degree of forecast uncertainty in consumer behaviour and some components of market prices within day, which mean that the optimisation is likely to represent more of an upper bound of revenue for the aggregator.

A cashflow model that calculates the revenues and costs of the supplier/aggregator given the results of the optimisation. From this, an estimate is made of the potential pot of flexibility payments that could be available to consumers under an SMC-type tariff.

The analysis has been undertaken for three spot years, 2020, 2025 and 2030. Within each year, eight characteristic days are modelled, four seasons by weekday and weekend. Finally, for each characteristic day 120 simulations9 are undertaken to explore the range of consumer

9 Tests were undertaken with a number of different sample sizes. 120 simulations provided stabilisation in key metrics of the sampled distributions (mean, standard deviation) without leading to excessively long model run-times.



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behaviour and electricity market conditions, which affect the ability of the aggregator to maximise its revenue.

An overview of the model design is shown in Figure 1. Sections 2.2 to 2.4 describe the key input data in more detail whilst Section 2.5 provides examples of how the aggregator acts to maximise its gross margin in response to the conditions across different parts of the electricity market.

Figure 1: Overview of model design

2.2 Consumer behaviour

Distributions of consumer behaviour that form an input to the aggregator model have been parameterised from the SMC CVEI trial results for each characteristic day. Illustrative examples for a winter weekday and winter weekend are shown below, with very similar results for participants covered in the summer periods of the trial.

The data presented below is the direct outcome of the trial and is provided for the purpose of enabling the reader to understand the nature of the modelling inputs, however, the focus of this analysis is to understand how the flexibility observed can be monetised and its associated value in the various parts of the current electricity market.

The trial results are reflective of individuals within a group of 50 PiV owners, subject to the simulated SMC offering, which shows more volatility in behaviour than for larger groups, which an aggregator would be managing in reality. For each of the sets of inputs, we have used the trial results to simulate a group of 50 consumers being managed by an aggregator as well as a group of 100,000 consumers. The latter is broadly the point at which the diversity benefits of a larger group stabilise.

The use of the trial data to create Monte Carlo simulated inputs for the model is described in Appendix A.

Consumers(based on trial data)

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unaffected by change in payments)

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• Range of value for consumers providing high/low flexibility

• Charging profile• Penalties• Revenues• Costs of electricity

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• Revenues mix of direct and indirect given assumed counterfactual



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Simulated consumer behaviour inputs

Figure 2 shows the proportion of PiV owners plugging in each day. The aggregate behaviour of consumers implies that they only tend to plug-in on average once every few days and that this is slightly higher in the case of PHEVs drivers. There is also significant reduction in variability of this behaviour around the expected mean profile as you move from 50 to 100,000 consumers.

However, an important caveat is that broader analysis of the trial data has indicated that there was significant variation in the way PHEV drivers utilised the vehicles during the trial. Whilst many drove a large number of electric miles, effectively trying to maximise the battery component of the PHEV, a number appeared to drive the vehicle incorrectly in ‘charge sustain’ mode, using the conventional engine to keep the battery topped up. As noted previously, this analysis has used the flexibility provided by the trial participants at ‘face value’. However, the results may therefore slightly underestimate the scale of flexibility and resulting aggregator value given this PHEV driving behaviour, as if future PHEV participants maximised the use of the battery (e.g. following educational or technical measures) they would have been expected to plug in more frequently.

Figure 2: Distribution of PiV owners (BEVs left / PHEVs right) plugging in to charge per day

Figure 3 and Figure 4 illustrate the proportion of consumers plugging in during any given hour of a characteristic day. The majority of consumers plug-in during early evening on both weekdays and weekends, but this can vary significantly due to underlying travel patterns, by comparing the average to the values +/- two standard deviations away from this. The chart also illustrates the substantial reduction in variation in of plug-in time as you move from 50 to 100,000 consumers, shown by the spread in the dotted lines.

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Figure 3: Share of BEVs plugging in by hour: average and +/- two standard deviations

Figure 4: Share of PHEVs plugging in by hour: average and +/- two standard deviations

Figure 5 and Figure 6 illustrate the duration of time the vehicles remain plugged in, once they are plugged in, for BEVs and PHEVs respectively. This tends to provide a 10-15 hour window given typical journey patterns, for example, involving a commute home in the early evening and the need for the vehicle the following morning. However, the data shows there is potential for an individual consumer to vary their travel requirements significantly on a day-to-day basis. It should be noted that due to limitations in the number of data points available gap filling has been undertaken for a small number of hours within each characteristic day.

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The value used is the average plug-in duration calculated across the hours of each characteristic day for which sufficient data is available10.

Figure 5: Population average duration plugged from start hour for BEVs: average and +/- two standard deviations

10 The threshold was a minimum of 5 data points to create a parameterised distribution for a given hour of the characteristic day structure used in the modelling, to balance use of the trial data and likely robustness of the results.

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Figure 6: Population average duration plugged from start hour for PHEVs: average and +/- two standard deviations

The maximum duration of plug-in is combined with the energy requirement for the vehicles to provide a view of the charging flexibility that the aggregator has available. The energy requirement is illustrated in Figure 7 and Figure 8 for BEVs and PHEVs respectively, as the Delta State of Charge (SOC) requested. In a similar manner to the plug-in window, gap filling has been undertaken where data is insufficient. From the trial data, consumers usually requested the battery to be fully charged at the end of the plug-in period11. The Monte Carlo generation of these inputs also reflects a positive correlation between requested delta SOC and plug-in duration.

As noted above, the behaviour of some PHEV trial participants who did not act to maximise the use of the battery component of the PHEV may mean that the requested SoC in Figure 8 is slightly underestimated.

11 Two-thirds of all charging requests asked for ≥ 90% SOC at the end of the plug-in window.

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Figure 7: Delta State of Charge (SOC) requested by BEVs at plug-in hour: average and +/- two standard deviations

Figure 8: Delta State of Charge (SOC) requested by PHEVs at plug-in hour: average and +/- two standard deviations

2.3 Market data

Electricity market data is based on Baringa’s internal electricity market modelling. This provides an internally consistent view of the prices, capacities, and generation output across different parts of the electricity market. We have used data from two main cases as inputs for this analysis:

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A ‘Reference Case’ broadly based on central expectations of policy and underlying fundamentals. This case assumes approximately 5M PiVs by 2030 split evenly between BEVs and PHEVs. A degree of managed charging is assumed, such that the unmanaged evening peak electricity demand that would be incurred from charging immediately at the end of a journey is reduced by half through load shifting into the late evening and overnight periods.

A ‘Decarbonisation Case’ that achieves a greater level of decarbonisation to 2030, primarily through greater uptake of renewables (90 gCO2/kWh compared to 113 gCO2/kWh in the Reference Case). This case assumes approximately 9M PiVs by 2030 split evenly between BEVs and PHEVs. The split of PiVs and assumptions around managed charging are consistent with the reference case.

Figure 9 illustrates the underlying electricity capacity mix in both cases, whilst Figure 10 shows the difference in commodity prices. By 2030 the Decarbonisation Case has around 5 GW more intermittent renewable generation and materially higher gas, oil and carbon prices. The combined impact leads to both higher average prices, but more importantly increased price volatility. This accentuates the potential upward and downward swings in intraday wholesale and imbalance prices, as shown in section 2.3.1. This volatility creates additional potential risks and benefits for the aggregator’s management of PiV charging.

Figure 9: Electricity installed capacity

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Note: CIF ARA = Cost Insurance Freight Amsterdam-Rotterdam-Antwerp, NBP = National Balancing Point

Figure 10: Commodity Prices

2.3.1 Wholesale and imbalance prices

Figure 11 provides an illustration of average wholesale prices in the intraday electricity market on the characteristic winter weekday and the volatility around this. It shows an increase in average prices and volatility (particularly around the point of evening peak demand) as you move from 2020 to 2030, with higher overall prices and volatility in the Decarbonisation Case.

Figure 12 shows the outturn imbalance prices within the Balancing Mechanism in the corresponding periods, consistent with the underlying system conditions in the wholesale market. It illustrates the higher volatility in imbalance prices (both positive and negative), particularly across the day compared to the intraday market, where this is focused primarily around the evening peak. This creates the potential for both higher risk and reward as a possible route to market for the aggregator’s PiV flexibility and monetising this is described further in Section 2.5.

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Figure 11: Winter weekday intraday wholesale price: average and +/- two standard deviations

Figure 12: Winter weekday imbalance prices: average and +/- two standard deviations

Figure 13 and Figure 14 present the same information for the summer weekend, illustrating the lower absolute prices and volatility compared to the winter weekend, but the same relative differences between the Reference Case / Decarbonisation and the intraday / imbalance prices.

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Figure 13: Summer weekend intraday wholesale price: average and +/- two standard deviations

Figure 14: Summer weekend imbalance prices: average and +/- two standard deviations

2.3.2 Retail price adders

In addition to shifting the time of PiV charging across the day to minimise the wholesale cost of electricity, a number of additional components of supplier costs are also shaped at different times of year and/or day and can potentially be minimised by shifting PiV charging. These include use of system charges such as transmission, distribution and the Capacity Market Supplier Charge. These are added on top of the underlying wholesale prices shown in the previous section.

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Figure 15 and Figure 16 illustrate the difference between the average intraday wholesale price compared to the implied final retail price for the winter weekday and summer weekend, respectively. This assumes the full shaped supplier costs are passed through to the consumer under a future move to half-hourly settlement. The additional retail price adders are not simulated, as they are not subject to the same near-term uncertainty as wholesale/imbalance prices12, but do change over time from 2020 to 2030; i.e. across simulations whilst the wholesale electricity price may be varying substantially the absolute value of the retail adders does not, hence volatility in the final retail price is being driven by volatility in the wholesale price component. The retail adder values are based on Baringa’s internal analysis and separate work for the ETI under the 2030 Electricity Price Time Series project13.

It should be noted that a number of use of system charges are currently subject to reviews by Ofgem and their structure might change significantly going forwards; this is discussed further in section 4.3.

Figure 15: Average intraday wholesale and retail prices for winter weekday

12 E.g. Use of System charges defined in advance.

13 https://www.eti.co.uk/programmes/energy-storage-distribution/2030-electricity-price-time-series

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Figure 16: Average intraday wholesale and retail prices for summer weekend

2.3.3 Forward prices

Forward prices are based on the average of the intra-day wholesale prices for the relevant period assumed to align with the Static Time of Use tariff tested within the CVEI trial for simplicity. This assumed prices are averaged within 4 windows (as per Figure 17 below) for specific characteristic days by season and split by weekday / weekend. These are used to help define a likely contracted position that the supplier/aggregator would have at Gate Closure, against which actions in the BM may drive addition cost or revenue (see section 2.5).

Figure 17: Examples of forward prices for characteristic days

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2.3.4 Pre-contracted services

Figure 18 shows how the value of pre-contracted CM and our illustrative frequency response and reserve services evolve over time. Committing to provide these services requires the aggregator to manage the PiV charging profile in a specific way at certain times of day, which is discussed further in Section 2.5.

All services have a capacity-based (or availability-based) component of value. For reserve, however, this capacity-component is assumed to be minimal and the vast majority of the value is when the service is called to be utilised (i.e. to turn the PiV charging down).

The value of these services is not simulated. However, as noted previously, the occurrence of an event for either reserve utilisation or the CM is simulated for each characteristic day. For the CM this is aligned to National Grid’s stated loss of load expectation for a given de-rated margin on the system (which is expected to be a rare event), whilst for reserve utilisation it is consistent with historically observed utilisation levels of several hundred hours across the year.

Note: Reserve utilisation values are £/MWh all others are £/kW.

Figure 18: Value of pre-contracted services14

2.4 Other key assumptions

Other key modelling assumptions include:

All consumers have a maximum home charging rate of 7kW, this is consistent with the typical home charging rate in the analysis in the main D7.4 Market Design and System Integration report.

14 The analysis was undertaken prior to the recent suspension of the CM by the European Court of Justice, however, it is assumed that a compliant equivalent will be found.

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Over the period modelled, 2020-2030, average battery sizes are assumed to increase from 9 to 10 kWh for PHEVs and 37 to 40 kWh for BEVs. As per the above assumption, these values are consistent with the assumptions book supporting the D7.4 report, based on expectations of what manufacturers will build during this period.

Findings from the uptake trial described in the separate project deliverable (D5.2 Consumer Uptake Trial – Summary Report) indicated that for over 90% of consumers to consider PHEVs and BEVs as their main vehicle, necessary to achieve mass market update in the longer term to 2040/2050, 16 kWh and 65 kWh sizes might be necessary. However, in the nearer term to 2030 the battery sizes assumed are broadly consistent with the scale of underlying market uptake assumed, circa 15-30% of consumers.

Separate analysis within the CVEI project15 on the impact of PiV demand management on battery life has estimated that the likely effect is negligible and therefore it is not considered further as part of this report.

2.5 Aggregator actions

To maximise the revenue from PiV flexibility the aggregator has to decide how to set the charging profile across each characteristic day, considering trade-offs due to variations in consumer behaviour, market prices/events and various constraints. For example, consumer energy demands and pre-contracted service volumes must be met.

A number of illustrative examples for the choices facing the aggregator are shown below. Figure 19 highlights a simple example considering intra-day retail prices (with no hedging in place) and a single pre-contracted service. It is important to note that the individual blocks of consumers plugging in each hour are treated as having their own individual sets of energy requirements and windows of flexibility that must each be respected16.

In the absence of the pre-contracted service, the aggregator would simply focus on charging in the cheapest hours available, subject to meeting consumer requirements, taking into account both wholesale and retail prices components. With the pre-contracted service in place, the aggregator must by default position its charging at the contracted volume (for example 1 MW) during the relevant commitment window. Where an event is simulated (e.g. the system operator calling on the reserve service) the charging profile must then be turned down.

These service actions reduce the degrees of freedom of the aggregator to minimise the retail energy costs both directly (i.e. during the commitment window), but also indirectly as some portion of the battery charging energy is being met through this commitment period.

15 See deliverable TRL1006_D7.2 State of Health Model

16 For example, 100 PiVs from “block 1” plugging in during hour 1 may offer a window of 10 hours and need 2 of these at a minimum to achieve the desired state of charge, similarly 200 PiVs “block 2” may then plug in hour 2 and offer 10 hours and require 5 to charge. Whilst the aggregator can utilise the overall pool of flexibility across both blocks, it must ensure the windows/energy requirements for each individual block are respected (e.g. no charging associated with block 1 could occur in hour 11).



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Figure 19: Illustrative example considering intra-day prices and a pre-contracted service

The next example considers the trade-offs associated with implicitly managing a portion of charging energy through the BM and trying to maximise the value from this relative to the wholesale market. There are two main routes for extracting value via the BM in Great Britain with different advantages and disadvantages for the aggregator:

The first is by managing the supplier/aggregator’s imbalance exposure and influencing payments that are received/made through the cash-out process. The starting point for this is an assumed counterfactual charging profile for the PiVs based on minimising energy costs against forward prices – i.e. this is a proxy for their contracted demand position at Gate Closure, prior to the BM17. Where the aggregator shifts the charging profile away from their contracted position after Gate Closure in a manner that helps to resolve a system imbalance (e.g. turn charging down/up when the system as a whole is short/long) they benefit. Conversely, if they shift the charging in the same direction as the system there is a dis-benefit. The key challenge is therefore being able to predict successfully the direction of a system imbalance, given that Gate Closure occurs 1 hour before the settlement period and the aggregator will not know the overall system position and imbalance prices until after settlement.

The second route is via active participation in the BM. Here the aggregator would submit offers (to turn down charging of a specified volume at a price) or bids (to turn up charging) to the System Operator18. If their offer/bid is accepted (depending on the price of competing offers/bids) they would adjust their charging and receive the corresponding payment. Unlike the above example, the decision to adjust the charging profile is at the direct request of the System Operator and the supplier/aggregator is not subject to any imbalance exposure because of this. Key

17 This is based on average consumer behaviour requirements against forward prices.

18 Currently subject to a minimum 1 MW threshold.

Market commitment window and volume MW (e.g. for CM, Frequency, Reserve) requires aggregate charging profile across blocks to be committed appropriately to receive revenue (or hit with penalties)

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challenges in this route involve being able to set prices for offers/bids that are consistently competitive and that the size of the value pool is volume limited19

The stylised modelling for this analysis is reflective of the first route, but the value would be broadly equivalent to the second if the aggregator was always able to set the prices of their offers/bids at the value of the accepted marginal 1 MWh offer/bid and their full volumes were always accepted. However, a broader issue is that given the real-world challenges of monetising value via either of these BM routes, the modelling is likely to represent a more optimistic upper bound of the value that can be achieved in practice20.

The outturn cost implications for the supplier aggregator under the first route are summarised in Table 2. As shown in section 2.3.1, there is significantly more volatility in imbalance prices compared to intraday prices and hence there is both significant potential for upside value as well as downside cost. Note that the aggregator also still needs to provide the necessary PiV charging energy across the day even when it is focused on maximising value through the BM. However, it is faced with different trade-offs to maximise value via the BM compared to the previous examples for maximising value via the wholesale market only or pre-contracted services.

Table 3: BM impacts on supplier/aggregator cashflow

PiV Charging vs contracted position at Gate Closure

System Imbalance

Increase Decrease

Short Pay outturn imbalance price (> intraday price) for extra energy

I.e. it would have been cheaper to purchase energy in intra-day market

Paid for reduction in volume at imbalance price (> intraday price)

Hence benefit is the delta between the two (given energy contracted for and not used), but note that the aggregator still needs to charge elsewhere in day to provide consumer requirements

Long Pay imbalance price (< intraday price) for extra energy

I.e. it is cheaper to purchase energy via BM than in the intra-day market. Note that the imbalance price could be negative in this case, which becomes an incoming payment and makes it more beneficial overall.

Paid for reduction in volume at imbalance price (< intraday price).

Hence, the cost is the delta between two (given energy contracted for but not used) and aggregator still needs to charge elsewhere in day to provide consumer requirements. Note that the imbalance price could be negative in this case, which becomes an outgoing payment and makes it more negative overall.

Forward commodity hedging

Many energy suppliers also undertake some form of commodity hedging strategy via forward purchase of electricity, to help build in greater certainty around the costs of supply to meet their demand, including from PiVs. However, there are potential volume risks (e.g. a mismatch

19 E.g. over half of all net imbalance volumes have historically been within +/- 1 GW, although this is expected to increase in future due primarily to the increase in intermittent renewable generation.

20 Where the aggregator struggles to maximise the monetary value of the PiV flexibility given these challenges this will reduce the pool of value that can be passed back to the consumer.



27

in actual demand versus volume hedged) and price risks (the forward price paid21 versus the outturn intra-day price).

Most suppliers look to hedge a proportion (but not 100%) of their energy requirements and trade in near-term markets to manage any residual risks. In some cases, very high intra-day wholesale prices may not matter as the supplier has already contracted for the energy at the lower forward price and so maintains its charging in these periods. There is an additional degree of optionality in that the aggregator could potentially sell back power into the intra-day market and capture the higher price. If it sells back the power (i.e. is not charging in this period) it still needs to charge at some other point across the day to ensure the PiV consumer requirements are met. In some forms of future SMC tariff there may also exist the potential for anticipatory charging where the supplier/aggregator charges the vehicle to 100% even if the consumer has requested less than this across the plug-in window. For example, where the supplier can provide this additional charge at lower cost now compared to a later date.

However, it is important to note that activity by the aggregator to optimise its intraday PiV charging choices (by focusing charging in the cheapest hours) is unaffected by the hedging strategy. I.e. by charging in the cheapest intra-day hours, this by definition still maximises the value by potentially re-selling forward purchased energy. Whilst the commodity hedging strategy is a valuable tool to reduce risk and potentially lower costs associated with PiV charging (if outturn prices turn out to be higher than the forward purchased energy), this is separate to the notion of the supplier/aggregator maximising the value of the flexibility of PiV charging, which is the focus of this report.

Although the modelling framework can technically layer in assumptions around a forward hedging strategy and look at the implications on resulting cashflows, we have purposefully excluded this in the scenario analysis given the desire to focus principally on the value of flexibility.

21 This is not a single price, but dependent on the availability of different traded contracts ahead of real time – e.g. Day-Ahead, Month-Ahead, Season-Ahead. This may include the ability to contract for at different prices for different within-day periods, however, this generally becomes more limited the further ahead you are looking to hedge.



28

3. Analysis

3.1 Scenarios

To explore the potential value the DM aggregator can generate from the PiV charging flexibility eight scenarios were explored, which vary the following elements:

The underlying price of electricity, using the Baringa Reference or Decarbonisation Case.

The number of PiVs that the aggregator is managing to understand the impact of diversity on charging requirements. In all cases, it is assumed the PiVs are split equally between BEVs and PHEVs. Whilst this is a simplifying assumption it is broadly consistent with PiV uptake modelling results seen across the spread of Narratives in the main D7.4 Market Design and System Integration report.

The commercial contracting strategy pursued by the aggregator.

Table 4: Overview of scenarios

Scenario Prices # PiVs managed

Risk Volume PiV energy managed via BM

CM contracted vol. (kW)

Reserve contracted vol. (kW)

Freq. contracted vol. (kW)

1 Ref 50 High 50% 0 0 0

2 Ref 50 Low 10% 3 ~0-0.5 ~1.5-5

3 Ref 100,000 High 50% 0 0 0

4 Ref 100,000 Low 10% 2.5k ~0-2k ~4k-6k

5 Decarb 50 High 50% 0 0 0

6 Decarb 50 Low 10% 3 ~0-0.5 ~1.5-5

7 Decarb 100,000 High 50% 0 0 0

8 Decarb 100,000 Low 10% 2.5k ~0-2k ~4k-6k

The commercial strategy is the most complicated aspect, with a large number of possible permutations. With reference to Baringa’s work on contracting strategies for developers of batteries and distributed generation, and the need to illustrate differences within a small number of scenarios, we have focused on two main strategies:

[1] A lower risk strategy, locking in as much value as possible in advance by focusing on the provision of pre-contracted services in the CM, reserve and frequency response markets. To complement this, volume of energy managed through the BM is very low. The overall aim is to fix the revenues from the PiV charging flexibility ahead of time to the extent possible, with limited repositioning due to changing intra-day and BM prices. The aggregator then focuses on positioning the charging profile to provide the pre-contracted services.

The provision of different pre-contracted services can also vary by time of year and day. Within this strategy the CM provision is focused on the winter weekday from 4-7pm, frequency response is provided in overnight periods and reserve is focused in both the morning and evening peak windows (excluding winter weekdays).



29

[2] A higher risk strategy, trying to maximise value by responding to near term fluctuations in prices in the intra-day market and BM, with no pre-contracted services. The corresponding level of BM volume is significantly higher providing more degrees of freedom for the aggregator to change the charging profiles to maximise value within these markets.

As noted in section 2.4, some iteration has been undertaken to fine-tune the volume of pre-contracted services under the lower risk strategy. In particular, to avoid any penalties for non-provision of these and to avoid non-provision of consumer energy. As a result, the volumes offered are very small compared to the maximum instantaneous charging rates of 350kW and 700MW for the low and high numbers of PiVs. This is a key finding that is discussed further in the sections below.

3.2 Example of gross margin build up

The results from the model are converted into illustrative costs and revenues from the perspective of the supplier (with the aggregator capability) using the mean from across the simulations. An example is shown in Figure 20 the sign convention shows revenues as positive and costs as negative. These are summed to work out the net cashflow (after the aggregator’s operating costs and margin are accounted for) and the maximum amount of money that could be paid to consumers for their provision of flexibility.

Figure 20: Illustrative results (revenues are positive)

There are a number of components to the analysis, which are explained below.

20

20

-150

-100

-50

0

50

100

150

£/P

iV/y

ear



30

Table 5: Components of gross margin

Item Description Direction

Administrative Costs For operating the business, set to the equivalent of ca. £28/MWh of PiV charging energy based on Ofgem’s most recent analysis of energy bills and supplier costs22. This is assumed to be passed through to consumers in their tariff (see “Energy revenue” below)

Cost

Retail Tariff Costs Use of system and other charges paid by the supplier on top of the wholesale cost of energy (see below)

Cost

Energy Costs (intra-day prices)

Wholesale energy cost paid by retail supplier for energy in the intra-day market.

Cost

BM cash-out (energy purchased in BM)

Where the supplier/aggregator increases its customers’ charging profile from its pre-contracted position they pay the prevailing imbalance price for the additional energy. Whether this is positive or negative overall for the aggregator depends on the direction of system imbalance in the period (see Table 2)

Cost

Aggregator margin Set by default to ca. £8/MWh of PiV charging which as a proxy is equivalent to a 5% pre-tax margin from Ofgem’s most recent analysis of electricity supplier bills and supplier costs22 (noting that the current value is only ca. 0.4%). This margin is assumed to be passed through to consumers in their tariff (see “Energy revenue” below).

Cost

Energy revenue Revenue from the sale of retail electricity to the PiV owners for charging their vehicles. This is assumed to be based on a Static Time of Use tariff equivalent to the wholesale forward prices shown in Figure 17, which has 4 fixed price blocks per characteristic day, with the retail prices adders and supplier operating cost/margin passed through on top. It is assumed that consumers have minimised their costs by shifting charging under this tariff23 reducing the revenue to the supplier compared to charging straight away at the end of a journey. The rationale for this is discussed further below.

Revenue

BM cash-out payments Where the aggregator decreases its customers’ charging profile from its pre-contracted position they are paid the prevailing imbalance price for the reduction in energy. Whether this is positive or negative overall for the aggregator depends on the direction of system imbalance in the period (see Table 2)

Revenue

RE utilisation fee The revenue for turning charging down when the reserve service is called.

Revenue

RE / FR / CM availability fee The revenue for providing the pre-contracted “holding volumes” for each service.

Revenue

Max flexibility payments The maximum flexibility payment possible to consumers is the sum of revenues minus sum of costs (including the supplier/aggregator’s assumed margin) and is shown as a cost item, as it would be an outgoing payment.

Cost

The flexibility value achieved by the supplier/aggregator in the cashflow therefore comes from two main areas:

Direct revenues as payments for services in the CM, frequency, reserve markets and via the BM.

22 https://www.ofgem.gov.uk/publications-and-updates/infographic-bills-prices-and-profits

23 This is calculated via a simple optimisation sub-module within the overarching aggregator model.

https://www.ofgem.gov.uk/publications-and-updates/infographic-bills-prices-and-profits



31

Indirect cost savings value by minimising the total costs of energy purchased (across the intra-day and BM markets) and use of system / other charges relative to the revenue from the consumer’s PiV retail electricity tariff.

The choice of retail tariff counterfactual is therefore important in determining the additional value of flexibility that can monetised by the aggregator and passed to consumers, in particular that which is over and above cost savings that consumers could make themselves.

The broad direction of travel in the retail electricity market in the nearer term is expected to be towards a Static Time of Use tariff and this has been used as the counterfactual. Under this PiV owners can manage their charging relatively easily via timers - i.e. delaying charging after plugging in until the block of cheapest overnight electricity, subject to constraints imposed by their underlying travel patterns (the assumed charging profile is illustrated in Figure 21).

Figure 21: Distribution of charging profile under counterfactual static time of use tariff

This means that the additional monetary value of the aggregator’s actions via more sophisticated PiV charging management presented in the following sections is more limited under a comparison to a Static Time of Use counterfactual than if the comparison was against fully unmanaged charging (e.g. if consumers are already avoiding the bulk of high evening peak prices).

As described previously, the modelling assumes that the aggregator is a price taker and its load shifting does not influence market prices. The overarching market assumptions and prices described in section 2.3 reflect a sizeable degree of PiV uptake combined with basic Time of Use load shifting. However, the current market structure and monetary value available to the aggregator does not necessarily reflect the underlying system value of flexibility, particularly with respect to distribution networks. For example, the current DUoS charges are currently being revised (see section 4.3) and are unlikely to reflect the value of avoiding new localised peaks on the distribution system caused by similar shifting of load by consumers under Time of Use tariffs.

5 9

13 17 21

1 7

11 15 19 23

3 5 9

13 17 21

1 7

11 15 19 23

3

Summer Weekday Summer Weekend Winter Weekday Winter Weekend

0%

10%

20%

30%

2020 - Reference 2030 - Reference

2020 - Decarbonisation 2030 - Decarbonisation



32

3.3 Summary of flexibility payment results

A comparison of the potential value of the aggregator’s management of PiV charging across the scenarios is shown in Table 5. This illustrates the maximum flexibility payment available on a simple £/vehicle/year basis after accounting for all the aggregator’s revenues net of all costs and margin (alternative approaches to distributing this value amongst the aggregator’s consumers are discussed in Section 3.4).

Table 6: Maximum £/car/year flexibility payment by scenario cf. a time of use counterfactual

Scenario Description 2020 2025 2030

Scenario 1 Reference case prices, higher risk, 50 PiVs 5 8 7

Scenario 2 Reference case prices, lower risk, 50 PiVs 5 5 6

Scenario 3 Reference case prices, higher risk, 100k PiVs 5 9 8

Scenario 4 Reference case prices, lower risk, 100k PiVs 5 7 6

Scenario 5 Decarbonisation case prices, higher risk, 50 PiVs 9 13 18

Scenario 6 Decarbonisation case prices, lower risk, 50 PiVs 7 8 11

Scenario 7 Decarbonisation case prices, higher risk, 100k PiVs 9 13 19

Scenario 8 Decarbonisation case prices, lower risk, 100k PiVs 7 9 13

A number of insights can be drawn from this:

The higher risk strategy (focusing on nearer term value from the intra-day and BM markets) appears more valuable than the lower risk, pre-contracted service approach on a like for like basis (i.e. comparing S1 with S2, S3 with S4, etc).

This is primarily a function of limited PiV charging energy requirements, which prevent significant volumes being offered into pre-contracted services. I.e. charging at a high level to provide a large holding volume (that can be turned down if needed) leads to the batteries quickly being charged to full.

A pure DM-only aggregator is only able to maintain a given charging rate for a short period (usually insufficient to qualify for a pre-contracted service) or provide a significantly lower charging rate for an extended period. Undertaking the latter leads to lower revenues due to both smaller availability payments for most pre-contracted services and limited flexibility to minimise broader energy costs.

For example Figure 22 assumes a 14 kWh energy charging requirement across the 4-7pm period. With a 7kW charger this would be met within the first 2 hours and hence the 7kW holding volume cannot be maintained for the full 4 hours, only a lower charging rate and holding volume of 3.5 kW.



33

Figure 22: Illustration of limited charge volume assuming 7kW charger and 14 kWh requirement

The value of flexibility increases most significantly over time in the scenarios with Decarbonisation Case market prices and focusing on a higher risk strategy. This is due to the aggregator’s ability to re-profile quickly its charging in the face of greater volatility in both wholesale and BM prices, when decarbonisation in electricity is being driven by increasing levels of intermittent renewables. Only modest increases in value are seen over time in the Reference Case where prices are less volatile.

Diversity benefits moving from 50 to 100,000 PiVs appear very limited when translated into an average flexibility value per vehicle including under a lower and higher risk commercial strategy. However, the average values in the 50 PiV scenarios mask the far more substantial swings in daily value that can be achieved, in some cases being well above the 100,000 PiV scenario and in others well below. This is illustrated in Figure 20. A smaller pool of PiVs therefore increases the risk of achieving an expected level of value and makes it far harder to achieve this via routes which require certainty of response such as pre-contracted balancing services.

13 14 15 16 17 18

0

10

20

Hour of day

kW /

kW

h

Max charging rate

Avg. charging rate

Volume @max rate

Volume @avg. rate



34

Note: values less than 100% indicated that outturn energy costs are lower than the average daily cost. Negative values indicate the case where the supplier/aggregator is being paid for part of its consumption due to negative electricity prices in either the intraday wholesale market or BM.

Figure 23: Simulated distribution of energy costs in 2030 relative to average daily cost

Comparing the revenue value of the aggregator’s actions to the unmanaged charging case

As a comparison to the results shown in Table 6, all of the scenarios have also been re-run assuming the supplier/aggregator revenues are based on a flat average retail tariff and that consumers would have started charging as soon as they plug-in, prior to the actions of the aggregator (i.e. unmanaged charging). The maximum £/car/year flexibility payment in Table 6 therefore better reflects the full system value of moving from fully unmanaged to aggregator managed charging. However, it should be noted that the focus of the analysis is on the revenues that can be made by the aggregator given a broad view of current market structure. A number of these structures are currently being reviewed (see section 4.3), in particular use of system charges at distribution level, which are viewed as not particularly cost-reflective of the system value of avoid reinforcement.

-50

%

-25

%

0%

25

%

50

%

75

%

10

0%

12

5%

15

0%

17

5%

20

0%

22

5%

25

0%

27

5%

30

0%

32

5%

0%

10%

20%

30%

40%

50%

60%

Daily outturn cost of electricity for PiV charging relative to average daily cost

Fre

qu

ency

Scenario 5 - 50 PiVs - Winter Weekday

Scenario 7 - 100k PiVs - Winter Weekday

Scenario 5 - 50 PiVs - Summer Weekend

Scenario 7 - 100k PiVs - Summer Weekend



35

Table 7: Maximum £/car/year flexibility payment by scenario cf. flat average tariff

Scenario Description 2020 2025 2030

Scenario 1 Reference case prices, higher risk, 50 PiVs 17 22 22

Scenario 2 Reference case prices, lower risk, 50 PiVs 17 20 20

Scenario 3 Reference case prices, higher risk, 100k PiVs 18 23 23

Scenario 4 Reference case prices, lower risk, 100k PiVs 18 21 21

Scenario 5 Decarbonisation case prices, higher risk, 50 PiVs 25 30 44

Scenario 6 Decarbonisation case prices, lower risk, 50 PiVs 23 25 37

Scenario 7 Decarbonisation case prices, higher risk, 100k PiVs 25 30 44

Scenario 8 Decarbonisation case prices, lower risk, 100k PiVs 24 27 38

In a similar manner to Table 5, the value of flexibility when using the flat average tariff counterfactual increases more substantially over time in scenarios where more volatile Decarbonisation Prices are used and only more gradually with Reference Case Prices, along with broadly similar results for scenarios which provide a like-for-like comparison between 50 and 100,000 PiVs under management.

The delta in values between Table 5 and Table 6 provides an indication of the extent of monetary flexibility value that could be accessed easily via the consumer versus charging management by an aggregator. Taking the example of Scenario 7, which has the highest absolute flexibility values on an incremental basis compared to a static time of use tariff, it can be seen that around one-half of the total value is derived from the more sophisticated charging management by the aggregator, in particular additional value derived through the BM. By contrast, Scenario 8 derives around a quarter of this value as it is less focused on nearer to real-time optimisation of charging against changing intra-day and imbalance prices.

The evolution of the retail market to manage PiV charging flexibility is still highly uncertain and may encompass a spectrum of alternatives beyond the explored above for modelling purposes. For example. it is possible that more dynamic time of use tariffs under proposed half-hourly settlement of domestic consumers24, coupled with in-home optimisation systems, could potentially capture more of this flexibility value without the need for an aggregator (particularly with respect to minimising energy costs). However, it is likely to be difficult to do this for pre-contracted services (either existing or new services at distribution level) due to the certainty of response that is required. An aggregator/supplier is better placed to actively coordinate this charging across a pool of consumers such that the required volume can be provided, rather than relying on a set of individual uncoordinated responses.

3.4 Focus on commercial strategy

The following section explores the impact of the commercial strategy on the value of flexibility in more detail. Scenario 7 is used as the point of focus at it shows the highest potential for consumer flexibility payments in the medium and longer term (given the aggregator’s actions above simple time of use shifting) and the detailed underlying cashflow is shown in Figure 20. This scenario reflects management of 100,000 PiVs and is based on the electricity market

24 https://www.ofgem.gov.uk/system/files/docs/2019/02/call_for_evidence.pdf



36

Decarbonisation Case prices with high and increasing volatility over time. The aggregator’s strategy is to maximise value by rapidly adjusting to changing conditions in the intra-day and BM markets. The two main sources of value in the results for this scenario are:

Minimising the costs of energy purchased versus the retail tariff charged to consumers (i.e. the sum of energy revenue net of all energy costs in the intra-day and BM markets and retail tariff components in Figure 20). This value equates to just under £30/customer/year in 2020 rising to slightly over £35 2030.

It should be noted that some of the energy value materialises through charging actions in the BM as the aggregator can capture the benefits of only paying ‘spill priced energy’ (i.e. based on imbalance prices when the system is long) that is materially cheaper than the intra-day wholesale price.

BM revenues from direct payments via cash-out where the supplier/aggregator decreases its PiV charging relative to its contracted position when the system is also short25. However, the value of this is much smaller overall, representing less than £10/customer/year in 2030 as shown in Figure 20.

Figure 24: Scenario 7 - Decarbonisation Case prices, higher risk, 100k PiVs

Scenario 7 assumes that 50% of the PiV charging requirements are managed with a view to maximising value via BM. The share of direct BM payments within the total value of flexibility achieved rises from around 15% in 2020 to 20% in 2030. This might suggest that more effort should be expended trying to maximise value through the BM, but as noted in section 2.5 there are a number of real-world complications to realising this value in practice.

25 Although the supplier also technically receives a payment for turning down when the system is long, the imbalance price this is based on is lower than the prevailing intra-day price and hence represents a worse situation as the supplier has lost money on pre-contracted energy it has not used. It still also needs to charge elsewhere in the day to meet its customers’ PiV energy requirements.

20

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30

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The Scenario 8 results, shown in Figure 25 provide a like-for-like comparison with Scenario 7 (i.e. Decarbonisation Case electricity prices and 100,000 PiVs under management) with the exception of a different trading strategy, since Scenario 8 is focused on locking in value from pre-contracted services. The overall value is materially lower in Scenario 8 compared to Scenario 7, particularly over the longer term.

However, as noted in Section 3.1, the level of pre-contracted services that can be provided without incurring penalties is very small and the total value across the CM, frequency and reserve services equates to only around £2/customer/year in each year to 2030, based on managing PiV charging across 100,000 customers. The bigger factor driving the difference in values between the scenarios is that by focusing the charging profile on the provision of these services it limits the ability of the aggregator to monetise the PiV charging flexibility through the wholesale and BM markets. In Scenario 8, the value of these routes is circa £35/customer/year 2030, compared to almost £45 in Scenario 7. The additional revenue from the pre-contracted services is not sufficient to make up for this gap, but is more certain, given that it is based on contracts agreed in advance.

Figure 25: Scenario 8 - Decarbonisation case prices, lower risk, 100k PiVs

The difference in values between Scenario 7 and Scenario 8 is shown more clearly in Figure 26. Positive values indicate revenues, costs and flexibility payments were higher in Scenario 7. The maximum pool of flexibility payments is around £0.2M higher in 2020 rising to £0.6M in 2030.

This increase in value is driven by higher BM cash-out payments (given the higher proportion of charging energy being actively managed through the BM in Scenario 7) and slightly lower net overall costs of energy supplier (taking the net of changes from purchasing energy in the intraday wholesale and BM markets). This increase in value is, however, offset to some extent by the small loss of value from the pre-contracted services.

20

20

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20

30

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iV\y

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38

Note: Positive values indicate revenues / costs / flexibility payments were higher in Scenario 7.

Figure 26: Delta in cashflow components between Scenario 7 and Scenario 8

3.5 Alternative consumer payment structures

The £/vehicle/year flexibility payment values shown in Table 5 represent the incremental system value that the supplier/aggregator could provide on average to their customers, over that achieved by simple static time of use load shifting. However, this does not reward consumers based on the flexibility they are actually providing. Using data from the CVEI trial and a simple flexibility metric, based on the window of charging provided relative to the minimum window needed to charge the vehicle26, it is possible to estimate the range of flexibility being contributed by the individual trial participants. For 1 kWh of charging energy required within a fixed plug-in window the aggregator is agnostic to whether this flexibility is provided by a PHEV or a BEV27.

Figure 27 illustrates how the amount of flexibility value in Scenario 7 might be distributed based on trial participants’ observed behaviour. This shows average per BEV and per PHEV participant behaviour as well as the highest and lowest individual participants in each category. At the highest end a consumer providing substantial levels of flexibility could be rewarded at a level three times that seen on a £/vehicle basis.

From the trial results, PHEV owners were observed to offer more flexibility under the simulated SMC tariff than BEV owners. A more detailed assessment of consumer behaviour

26 Flexibility units per charging event = [ Duration of plug-in window / minimum hours needed to charge (at maximum charge rate ] – 1. Maximum charge rate is the same for all participants.

27 Under a more sophisticated tariff arrangement whereby the aggregator could provide less than the requested state of charge for a penalty payment this may be different as the penalty for a PHEV owner is likely to be significantly lower given their ability to use conventional liquid fuels.

Energy revenue

CM payment

FR availability fee

RE availability fee

RE utilisation fee

BM cash-out payments

Energy cost (intra-day prices)

Retail tariffs/costs

BM cash-out (energy purchased in BM)

Administrative costs

Aggregator margin

Max flexibility payments

Re

ven

ue

s.

Co

sts

.

-4 -2 0 2 4 6 8

Delta £/PiV/year

2020

2025

2030



39

in this part of the trial is explored in the separate project report28. In general, if underlying travel and plug-in patterns are broadly the same for both BEV and PHEV owners, the latter benefit from lower typical charging requirement due to the smaller batteries. Therefore, for a given PHEV plug-in window there is generally more flexibility over the hours within which the charging could take place compared to a BEV which may require more of the overall window to achieve the desired State of Charge.

However, as noted in 2.2 the interpretation of results in Figure 27 is complicated by the fact that the PHEV drivers in the trial exhibited a range of driving behaviour, from those trying to maximise the number of electric miles driven to those inefficiently using the conventional engine to keep the battery charged. This is likely to mean that the average per PHEV value is slightly higher than is realistic if drivers maximised electric miles and needed more energy at each plug-in, thus reducing the typical flexibility across a plug-in window. At the high end, the value for PHEV flexibility is considered to be a function of genuine consumer flexibility offered. The individual participant this is based on was observed to require an average delta SoC requirement of over 60% during the course of the trial, indicating that they were driving a significant number of electric miles.

Note: solid lines represent average values, dotted lines represent upper and lower bounds of flexibility being offered based on individual CVEI trial participants.

Figure 27: Spread of consumer payments based on flexibility provided in Scenario 7

The above assumes for illustrative purposes that the consumer is being rewarded ex-post, i.e. once the maximum pool of flexibility value is known by the aggregator to minimise its own financial risks. In practice, tariffs may be created that consider variations in:

What the price per unit of flexibility is (i.e. whether that is set in advance or there is some kind of ex-post profit share).

28 D5.3 - Consumer Charging Trials - Summary Report.

20

20

20

25

20

30

0

10

20

30

40

50

60

£/y

ear

Per PHEV (High Flex)

Per PHEV

Per BEV (High Flex)

Per Vehicle

Per BEV

Per PHEV (Low Flex)

Per BEV (Low Flex)



40

The absolute level of payment (which could be dependent on the volume of flexibility provided).

When the payment is made (i.e. does the consumer get a discount from the tariff throughout or some re-payment afterwards).

In tariff designs that (at least partially) reward the consumer ex-ante, there is an additional risk that these payment turns out to be greater than the outturn value of flexibility achieved by the aggregators.

It is important to reiterate that although the additional value of flexibility provided back to consumers that is suggested by the modelling is modest, it is delivered in a way that does not affect their underlying travel patterns (i.e. the state of charge being requested in each plug-in window is achieved). Therefore, from a consumer perspective there would be minimal downside to a tariff structured in a simple way that shares the value ex-post, assuming this value is actually passed through by the supplier/aggregator.



41

4. Conclusions

4.1 Potential value of flexibility

The analysis has shown that the value that the DM aggregator could achieve by monetising PiV charging flexibility is relatively modest in absolute terms. This is in part because a significant portion of the underlying “system value” from shifting of PiV load can be achieved via a simple static time of use tariff given market scenarios that assume material uptake of PiVs. However, as noted previously current market structures, particularly use of system charges for distribution, do not provide a sufficiently cost-reflective signal of the value of avoided network reinforcement and are currently being reviewed (see section 4.3).

The highest values in 2030 only equate to around 5% and 7% of the annual charging cost of a BEV or PHEV, respectively, as shown in Table 7. This is the value that could be provided by more sophisticated charging by an aggregator, over and above simple load shifting against a static time of use tariff. Under tariffs where consumers are rewarded in proportion to the flexibility they provide, this could increase to around 15% of annual charging costs given the behaviour observed in the CVEI charging trial results.

Table 8: Maximum per car flexibility payment as proportion of annual vehicle charging cost

BEV PHEV










4.2 Commercial strategy and accessible value

Two broad commercial strategies have been explored in the analysis:

A lower risk strategy, locking in as much value as possible in advance through pre-contracted CM, frequency and reserve services.

A higher risk strategy responding to nearer term price volatility in the intra-day and BM markets.

The first strategy produced materially lower results. However, this is largely a function of the very low volumes that could be offered into these services (relative to the maximum charging rate) whilst still being able to deliver them. The primary reason is the relatively low volume of charging energy required on a daily basis across the pool of managed PiVs. The potential



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reasons for this are explored in the separate CVEI project report29 - i.e. the extent to which this was driven by underlying journey patterns versus potential disengagement with the trial.

To provide these services the charging profile needs to be positioned at the contracted volume so that it is ready to turn down if called (as well as up in the case of frequency services). Where these services are rarely called the batteries are quickly filled and only low volumes can be offered across the service windows. This points to a need for V2G capability to provide the aggregator with more optionality to offer larger volumes into these services – i.e. also being able to dispatch the batteries directly into the grid as opposed to simply turning down charging demand.

Whilst V2G may allow higher volumes, it is important to note that the size of many of the pre-contracted service markets is relatively small. As a point of comparison, the max charge/discharge rate across the 100k pool of PiVs is around 700MW30. The more lucrative balancing services markets for frequency response and fast reserve together only represent ca. 2.5-3 GW31 of requirement in Great Britain and this is not expected to grow significantly in future.

Reserve (STOR) is larger by itself at around 3 GW31 and is likely to increase in line with growth in intermittent renewables such as wind and solar but is materially less valuable than the other Balancing Services. Finally, the CM has the most significant volume (ca. 50GW), but the market is highly competitive, which has depressed prices in recent years (potential implications of the recent European Court of Justice (ECJ) ruling that has led to the suspension of the CM are noted briefly in section 4.3). In addition, whilst demand management more generally can contribute to the CM – and part of the recent legal challenge revolved around the lack of level playing field for this compared to generation – it is not yet clear how significant a contribution it can make practically to the total CM requirement.

A higher risk strategy focused on minimising energy costs and generating value through the BM appears more lucrative overall. This exploits the ability of the aggregator to respond rapidly to price volatility across the day, which is expected to increase over time due to increasing levels of intermittent renewables. Whilst this appears to be a significantly higher value strategy, there are challenges to achieving this value in the real world for a number of reasons:

• The modelling simulates uncertainty in both consumer behaviour and market prices (in both the intra-day wholesale and BM markets), but the charging optimisation implicitly assumes perfect forecasting of outturn prices on a daily basis in the intra-day and BM markets and this is particularly challenging in the BM.

- Whilst the key for the aggregator is to forecast the relative price differences rather than absolute levels (i.e. they still have to charge the vehicle but can choose which hours to do this in), there will be times when they get this wrong earlier in the day leading to unfavourable outcomes later in the day. For

29 D5.3 - Consumer Charging Trials - Summary Report.

30 Even with V2G it is still unlikely that the aggregator could offer close to these volumes in practice.

31 National Grid Balancing Services data https://www.nationalgrideso.com/balancing-data/system-balancing-reports?order=field_publication_date&page=0%2C9%2C22%2C0%2C3%2C1%2C0&sort=asc

https://www.nationalgrideso.com/balancing-data/system-balancing-reports?order=field_publication_date&page=0%2C9%2C22%2C0%2C3%2C1%2C0&sort=asc

https://www.nationalgrideso.com/balancing-data/system-balancing-reports?order=field_publication_date&page=0%2C9%2C22%2C0%2C3%2C1%2C0&sort=asc



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example, where charging is delayed in anticipation of lower prices later in the day that do not transpire.

- This issue is compounded in the BM due to the potential for the overall system imbalance to flip from short to long or vice versa in quick succession, which significantly alters the financial incentives for the aggregator.

• The amount of flexibility value that can be provided to the PiV consumers by managing their charging is estimated after operating costs (using energy suppliers as a proxy) and the aggregator margin (assumed to be 5%) are accounted for. Any increase in these costs or desired margin would decrease the remaining flexibility value that could be passed back to consumers under a given tariff design.

In addition, although the analysis indicates that providing flexibility in the BM, subject to the caveats above, becomes increasingly valuable over time, the size of the market is still relatively small. For example, just over half of all BM actions are currently within +/- 1 GW, although this is expected to increase gradually over time as the level of intermittent renewables increases.

4.3 Potential policy and regulatory changes

The analysis has used two views for electricity market prices (a Reference Case and a Decarbonisation Case) where policy and regulation are broadly as per today, but the underlying fundamentals are changing, particularly the share of wind and solar in the electricity mix.

The policy and regulatory landscape covering the different parts of the electricity market is highly complex and continually evolving, but there are potential changes that present both risks and opportunities for the PiV DM aggregator. The first two relate to network charges and are important given that PiV flexibility can help to contribute to network operation and avoid reinforcement from the lowest voltage levels upwards:

Ofgem is currently consulting on potential changes under its Targeted Charging Review32, which focuses on how residual network charges should be set to recover the costs of the existing network (as distinct from forward-looking charges to signal the potential cost of new investments). These form part of the costs the aggregator can potentially avoid by shifting charging to different times of day. The proposed changes are complex and affect demand and generation at different levels of the network in different ways, but indirectly imply that the value of load shifting to reduce use of system costs via demand side response (including PiV DM) could be reduced, depending on how forward looking charges (next bullet) are also reformed.

Changes to forward-looking Use of System Charges are being developed under a separate review33. Current distribution charges are shaped but have limited capability to reflect temporal and spatial differences in conditions across different parts of the network. The review is intended to be wide ranging and look at

32 https://www.ofgem.gov.uk/publications-and-updates/targeted-charging-review-minded-decision-and-draft-

impact-assessment 33 https://www.ofgem.gov.uk/publications-and-updates/electricity-network-access-and-forward-looking-charging-review-significant-code-review-launch-and-wider-decision







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alternative capacity and usage based charges. These would potentially provide more dynamic price signals, which distributed generation or DM could monetise.

As highlighted by the analysis, highly specific locational signals, such as for an individual Low Voltage Feeder may be harder for the DM PiV aggregator to manage, given volatility in charging patterns and flexibility offered across few consumers. This may be particularly problematic if the route to market for flexibility is via provision of pre-specified capacity / holding volumes as the more volatile range of charging behaviour raises the risk of the aggregator not being able to provide this volume when it is required. This issue may be mitigated somewhat by the use of V2G.

Other potential changes of note include:

The move to half-hourly settlement for domestic consumers34 will allow the costs of supply to be attributed more accurately across the day. This may in turn incentivise suppliers to offer new products and services - such as more dynamic and granular time-based price signals - which consumers could respond to reduce their energy bills. More responsive behaviour coming directly from consumers (potentially facilitated via in-home energy management systems) could reduce the incremental value of an aggregator managing the PiV charging on the consumer’s behalf.

In addition to an aggregator being able to respond to more dynamic, locational price signals at the distribution level associated with network charging reform many Distribution Network Operators35 are exploring direct provision of flexibility products and services to support their transition to a Distribution System Operator. This entails greater responsibility for managing supply and demand within their own networks (rather than this being the domain of the overall electricity system operator) and provides another route to market for a DM aggregator managing the flexibility associated with PiV charging.

The ongoing evolution and reform of products that National Grid procures to provide its Balancing Services36, which may affect the value and/or ability of PiV DM to contribute to the provision of frequency and reserve. A general theme is increasing competition in provision of these services, leading to downward pressure on prices although in same cases the volumes required are likely to increase.

Wider access to balancing markets facilitated by Project TERRE (Trans-European Replacement Reserves Exchange)37 will provide an easier route for aggregators and other operators of flexible generation to sell their flexibility to the system operator, without the need to become a licensed supplier. However, the bigger change is to facilitate market coupling of balancing markets across European countries, analogous to that which has already happened for wholesale electricity markets.

34 https://www.ofgem.gov.uk/system/files/docs/2019/02/call_for_evidence.pdf

35 http://futuresmart.ukpowernetworks.co.uk/wp-content/themes/ukpnfuturesmart/assets/pdf/futuresmart-flexibility-roadmap.pdf

36 https://www.nationalgrideso.com/insights/future-balancing-services

37 https://www.elexon.co.uk/mod-proposal/p344/

https://www.ofgem.gov.uk/system/files/docs/2019/02/call_for_evidence.pdf

http://futuresmart.ukpowernetworks.co.uk/wp-content/themes/ukpnfuturesmart/assets/pdf/futuresmart-flexibility-roadmap.pdf

http://futuresmart.ukpowernetworks.co.uk/wp-content/themes/ukpnfuturesmart/assets/pdf/futuresmart-flexibility-roadmap.pdf

https://www.nationalgrideso.com/insights/future-balancing-services



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This may provide a bigger accessible market for competitive providers of flexibility, but conversely a deeper and more efficient pool of providers is likely to reduce outturns prices and the value of such flexibility.

Potential changes to the CM following its recent suspension by the European Court of Justice. Whilst the suspension was invoked on procedural grounds related to State Aid assessments, the original challenge was brought by a DSR provider arguing that the current CM does not provide a level playing field for different forms of capacity provision such as DSR/DM. Potential future changes to the rules (e.g. extending the availability of longer contracts to DSR) as a result of this may benefit PiV DM aggregators.

4.4 Areas for further work

The analysis has provided valuable insight into the potential monetary value of PiV flexibility, using direct consumer behaviour data from the CVEI trial and Baringa’s detailed view of the evolution of the electricity market. However, it has also raised a number of areas that may benefit from further investigation in future:

The inclusion of V2G to understand the extent to which this alters the potential value that can be achieved (primarily for pre-contracted services, but potentially also in terms of energy and BM markets) and to what extent this alters the aggregator’s preferred commercial strategy.

Refinements to the modelling approach to better reflect forecasting uncertainty across the day and the potential for the aggregator to make imperfect decisions earlier on in the charging flexibility windows that are hard or impossible to compensate for later in. This would help provide a more accurate view on value to go alongside what could be considered more of an upper bound.

The implications of very large numbers of PiVs as well as other forms of residential flexibility, being managed by numerous aggregators. It is clear that many of the pre-contracted services are relatively small compared to the potential flexibility that can be provided by millions of PiVs. However, it is not clear at what level this will start to materially reduce the value of further flexibility in the wholesale energy market.

Further analysis exploring the relationship between the levels of flexibility provided by mass-market consumers in response to changing levels of reward under different tariff structures.

The potential implications of V2G in addition to pure DM of PiV charging and the additional value that could be extracted as an aggregator with this.

The impact of changes in consumer behaviour with higher uptake of PiVs and mass market consumer awareness and what this means for available flexibility and the ability to monetise this.

Consideration of future policy changes such as reforms to Use of System Charges.



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Appendix A Use of trial data for sampling

For the model inputs based around the ‘distribution of PiVs plugging in per day’, the ‘plug-in duration’ and the ‘delta State of Charge (SOC) required’, the sampling methodology was relatively straightforward for the 50 PiV consumer case:

The trial results were mapped directly to the model timeslices - 8 characteristic days by seasons, weekend / weekday and hours within each day as relevant.

Distribution fitting was undertaken on each timeslice to determine the most representative distribution (e.g. normal, triangular) and the associated parameters.

A fixed number of samples (120) was generated from the result distributions to simulate variability in the consumer behaviour inputs.

Correlation in the different inputs was explored, but only found to be material between plug-in duration and delta SOC. A positive correlation factor (i.e. higher delta SOC is associated with longer plug-in duration) was included as part of the sampling of these two sets of inputs.

For the model input on ‘share of PiVs plugging in by hour’ for a given characteristic day the sampling methodology in the 50 PiV consumer case was slightly more complex (note that this is in addition to the above modelling of the likelihood of plug-in on a given day). This was due to the need to maintain consistency of the distribution of consumers plugging in each hour of the day such that this always equalled 100%38. For example, if in one simulation a higher proportion of consumers plug in at 6pm this must be offset by a lower proportion plugging in across the remainder of the day.

The trial data was first used to characterise all the plug-in events flagged by each individual trial participant, by characteristic timeslices, vehicle type, etc. This assumes that behaviour is not correlated between drivers.

From this it is possible to infer the distribution of the plug in hours for each individual participant – in particular their earliest, latest, and average plug time.

By then aggregating the earliest/latest plug-in times across all 50 participants (assuming behaviour is independent) it was then possible to create internally consistent ‘bookends’ or extremes for the plug-in profiles across each characteristic day which reflecting the cases where i) all participants plugged in their earliest observed hours and ii) plugged in their latest observed hours. The average aggregate profile was also calculated reflecting the most likely plug-in profile across the day.

For each of the earliest, latest and average profiles the peak hour (where the highest proportion of PiVs were first plugged in across the day) was used to define the min, max and mode of a triangular distribution.

A fixed number of samples (120) was generated from this triangular distribution and the resulting sample used to linearly interpolate between the earliest, latest and average daily profile. For example, a sample from the triangular distribution that

38 This is not to say that all consumers plug-in each day, but that for the given number of consumers who plug-in their distribution of plug-in times across the hours of the day must always equal 100%.



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was halfway between the min and mode would then take the 50% of the earliest and 50% of the average daily profiles to create the outturn simulated plug-in profile for that characteristic day.

Figure 28: Overview of approach for simulating variation in share of PiVs plugging in by hour

For the 100k PiV owners case the underlying sampling methodologies described above for each input from the 50 PiV are essentially re-used. 2000 ‘groups’ of 50 PiVs were each simulated independently for 30 sets of samples and then combined to create 30 final sets of simulated inputs which are reflective of a combined fleet of 100,000 PiVs. This captures the reduction in variability observed in the aggregate profile given a much larger number of consumers.

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profile where sum

of hours = 100%

across day

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