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1、 May 2024 OIES Paper:EL55 Reforming Capacity Markets:How to Incorporate the Flexibility of Residential Consumers?Dimitra Apostolopoulou,Research Fellow,OIES Rahmatallah Poudineh,Head of Electricity Research Programme,OIES i The contents of this paper are the authors sole responsibility.They do not n

2、ecessarily represent the views of the Oxford Institute for Energy Studies or any of its Members.The contents of this paper are the authors sole responsibility.They do not necessarily represent the views of the Oxford Institute for Energy Studies or any of its members.Copyright 2024 Oxford Institute

3、for Energy Studies(Registered Charity,No.286084)This publication may be reproduced in part for educational or non-profit purposes without special permission from the copyright holder,provided acknowledgement of the source is made.No use of this publication may be made for resale or for any other com

4、mercial purpose whatsoever without prior permission in writing from the Oxford Institute for Energy Studies.ISBN 978-1-78467-245-4 ii The contents of this paper are the authors sole responsibility.They do not necessarily represent the views of the Oxford Institute for Energy Studies or any of its Me

5、mbers.Abstract Capacity markets as a mechanism of capacity remuneration,have been widely applied in the United States,Australia,Europe,and United Kingdom.In these markets,generators and other resources make offers based on their unavoidable costs to keep them available,and the transmission system op

6、erator determines the demand based on forecasted peak load.However,capacity markets have limitations,mainly because they are one-sided since the system operator is the only buyer,with the government deciding on the required reliability levels,and not consumers who are the ones actually experiencing

7、the risk of outage.Moreover,capacity markets have been tailored for conventional resources whereas renewables,storage,and demand response resources have different characteristics.Demand response is specifically very important because it constitutes a potential alternative to building new generation

8、capacity.At times of generation scarcity there are consumers who might be willing to reduce their demand in return for financial compensation.This paper analyses the capacity market and proposes an approach to incorporate the flexibility of residential consumers in this market.We introduce and asses

9、s the potential of non-linear pricing schemes,specifically priority pricing contracts,as mechanisms to enhance the implicit residential demand response.We suggest a model to integrate priority pricing contracts via an aggregator as explicit demand response in capacity markets,advocating for particip

10、ation on the demand side rather than the supply side to prevent market distortion and better align with consumer reliability preferences.To incorporate these contracts into the capacity demand curve,we examine the correlation between capacity and reliability,establishing that under ideal conditions,

11、the marginal cost of outages aligns with the marginal cost of capacity,thus linking capacity to the value of lost load and loss of load expectations.This connection informs the design of the capacity demand curve using data from priority pricing contracts.We demonstrated this approach using the 2027

12、-28 UK capacity market,and also introduced a refined capacity product for trading in capacity markets,designed to encourage investment in demand response resources and incorporating specifics about location,flexibility,and adjustments to the calculation of firm capacity profile.Lastly,we propose a p

13、otential business model for the aggregator that leverages residential demand response that does not create distortions in the retail market.iii The contents of this paper are the authors sole responsibility.They do not necessarily represent the views of the Oxford Institute for Energy Studies or any

14、 of its Members.Contents Abstract.ii Contents.iii Figures.iii Tables.iv 1.Introduction.1 2.Overview of Capacity Markets.3 2.1 Capacity Remuneration Mechanisms.3 2.2 Shortcomings in Capacity Market Design.5 3.Residential Demand Participation in Capacity Markets.10 3.1 Overview of Demand Response Part

15、icipation in Wholesale Electricity Markets.10 3.2 Barriers faced by Residential Demand Participation.12 3.3 Priority Pricing Contracts for Residential Demand Response.14 4.Proposed Capacity Market Reform.19 4.1 Demand Response as a Demand Resource.19 4.2 Capacity Demand Curve Construction with Prior

16、ity Pricing Contracts.21 4.3 Refined Capacity Product for Enabling Demand Response.26 5.Aggregator Business Model.27 5.1 Aggregator Relationship with Consumers.27 5.2 Aggregator Relationship with other Market Entities.28 6.Concluding Remarks.30 References.32 Figures Figure 1:Capacity remuneration me

17、chanisms taxonomy.3 Figure 2:Load duration curve and approximation of investment and production costs for different technologies.6 Figure 3:Missing money problem.7 Figure 4:Capacity market demand curve.8 Figure 5:Impacts of DR on the market equilibrium.10 Figure 6:Impacts of the DR on the cleared po

18、wer demand over a one day period.11 Figure 7:DR participation in energy markets under three different ways.11 Figure 8:Reliability strips of a daily load curve.15 Figure 9:Example of priority pricing contracts.16 Figure 10:Optimal investment decision problem.21 Figure 11:Modification in EEU with a c

19、apacity increase of c.21 Figure 12:Inverted equivalent load duration curve and expected energy unserved.22 Figure 13:Reliability levels as a function of consumer valuation.23 Figure 14:Capacity demand curve of T-4 capacity market for delivery in 2027-28.24 Figure 15:Updated capacity demand curve of

20、T-4 capacity market for delivery in 2027-28 taking into account VOLL of consumers.25 Figure 17:Aggregator and market entities interaction model.29 Figure 18:Demand resource revenue stacking.30 iv The contents of this paper are the authors sole responsibility.They do not necessarily represent the vie

21、ws of the Oxford Institute for Energy Studies or any of its Members.Tables Table 1:Types of CRMs around the world and participation of technologies.5 Table 2:Barriers categorization for DR resources.12 Table 3:Priority pricing contracts with 70%,90%and 100%reliability levels.16 Table 4:Consumer type

22、s definition.17 Table 5:Examples of priority pricing contracts for each consumers type.17 Table 6:Comparison of different types of tariffs.18 Table 7:Priority pricing contracts with 70%,99.7%and 100%reliability levels.22 Table 8:Priority pricing contracts with 10 reliability levels.23 Table 9:Consum

23、er types and their valuation for reliability.23 Table 10:Value of capacity demand for different reliability levels.24 Table 11:Great Britain VOLL as a function of consumer groups.25 1 The contents of this paper are the authors sole responsibility.They do not necessarily represent the views of the Ox

24、ford Institute for Energy Studies or any of its Members.1.Introduction The decarbonization of power systems has led to the integration of a high number of renewable resources thus changing significantly the generation portfolio in many countries.For example,in the United Kingdom(UK)solar photovoltai

25、c(PV)capacity totals 15 GW,and generated a record of 5.5 TWh of energy between April to June 2023(DESNZ 2023).However,renewable resources are highly intermittent and variable,and lead to net load variations of increased uncertainty.To maintain system reliability under peak load as well as in conting

26、ency cases like unexpected outages,there is a need for sufficient energy,sufficient capacity,and flexibility.To further explain the three points above,electric devices consume energy that is supplied to them by the electricity system,and is measured in Watt-hour(Wh).The electricity system needs the

27、required potential to meet demand under peak conditions;this is what we call capacity that is measured in Watts(W).A newer concept in power system operations is flexibility or ramp rate which is the capacity amount that may be provided in a given timeframe,measured in Watt per hour(W/h).The latter h

28、as attracted more attention due to operational issues when renewable generation increases and decreases its output in a short time.An example of such a situation is the duck curve phenomenon in California(CAISO 2016).When the Sun sets,the contribution of PV resources drops suddenly and there is an i

29、ncreased need for the quick ramp-up of energy production,or ramp down of demand.Many researchers have studied the adequacy of energy,capacity,and flexibility,collectively referred to as resource adequacy in the context of increasingly frequent and severe extreme weather events and the ongoing transf

30、ormation of power systems around the world.Resource adequacy has become more challenging in the energy transition because of market failures that do not promote adequate investments.Electricity markets are currently designed to dispatch generation based on economic merit order.In this regard,under c

31、urrent market designs zero carbon resources lead to low future electricity prices,as these will be cleared first.Moreover,units with high marginal costs,like combined cycle gas turbine(CCGT)units,will most likely not be cleared(or cleared very infrequently)in the energy market(Papavasiliou 2020).Fur

32、thermore,regulators impose price caps in energy markets to address potential market power issues and this reduces payments for all types of generation,the so-called missing money problem(Neuhoff&De Vries 2004).As a result,generators are faced with insufficient market revenues to support adequate new

33、 investments,which in turn affects system resource adequacy.The refinement of scarcity pricing in the energy-only markets alleviates this situation since it uplifts balancing prices when the system is tight,and thus rewards flexible resources for providing much-needed energy to the system when it is

34、 under stress(Hogan 2013).Another solution to the missing money problem is the introduction of capacity remuneration mechanisms(CRMs).These mechanisms make payments to installed capacity based upon various factors,including location,availability during peaks,and the total capacity relative to the ne

35、ed,based on reliability criteria.CRMs compensate resources for making generation capacity available for utilization,regardless of the extent to which it is actually operated.This provides an additional revenue stream to generators and incentivizes investment in additional generation capacity,thereby

36、 ensuring resource adequacy.There are several types of CRMs;they can be technology neutral or not,centralized or decentralized,volume-or price-based.The commonality of all CRM designs is that they reduce the risks for new investments by offering resource providers supplementary income on top of thei

37、r earnings from selling electricity in the market,thus ensuring there are no system adequacy concerns at times of stress.In the US,the earliest such mechanisms date back to the late 1990s.In recent years,several European countries have also started implementing different kinds of CRMs(Bublitz et al.

38、2019).In this paper we focus on capacity markets as a form of CRM,with such mechanisms widely used in many jurisdictions like the US,Australia,Europe,and the UK.However,capacity markets have several shortcomings,mainly that a central agency makes decisions on behalf of consumers relating to the reli

39、ability needs and capacity margins of the system,whereas consumers who are actually experiencing outage risks cannot affect this decision,since reliability is considered to be a public good.Moreover,historically,capacity markets have typically targeted more conventional resources and have received c

40、riticism that even though they are supposed to be technology-neutral they fail to do so due to their regulatory framework.For example,participation rules do not favour the participation of distributed energy resources,such as electricity storage or demand 2 The contents of this paper are the authors

41、 sole responsibility.They do not necessarily represent the views of the Oxford Institute for Energy Studies or any of its Members.response(DR)and in fact provide hidden subsidies to operators of conventional power plants.For example,in the UK,the four year ahead capacity auction with delivery year o

42、f 2026-27 cleared 19 GW of gas-fuelled generation out of 43 GW total cleared capacity1.However,an intuitive alternative to building more capacity would be to encourage the development and use of DR resources.Indeed,in times of scarcity a transmission system operator(TSO)can identify consumers who ar

43、e willing to reduce their demand for financial compensation(Lambin 2020).The European Network Transmission System Operator for Electricity(ENTSO-E)stresses that DR often has a high capacity value relative to its energy value in many countries(ENTSO-E 2015).In this regard,the European Commission(EC)h

44、as officially required full technology neutrality in all types of CRMs in Europe(EC 2013).In the same vein,in the US the Federal Energy Regulatory Commission(FERC)with Order No.2222 enables distributed energy resources participation in electricity markets(including capacity markets)(FERC 2021).This

45、is in line with other policy mandates for encouraging DR participation at the wholesale market level.The Energy Policy Act of 2005 states,It is the policy of the United States that unnecessary barriers to DR participation in energy,capacity,and ancillary service markets shall be eliminated(US 2005).

46、In the UK,the minimum capacity threshold was reduced from 2 MW to 1 MW,allowing smaller entities to participate in the capacity market.The primary rationale behind this was to align capacity markets with the other energy markets;however,this also benefited smaller providers who have indicated their

47、desire to operate independently of aggregators(ESO 2021).However,while most CRMs in Europe and the US generally allow the participation of DR units,the rules applied to each mechanism differ substantially.This is related to the fact that unlike conventional power plants DR cannot provide full power

48、output throughout scarcity periods of whatever length due to their technical characteristics.The rules defined for DR participation in a given CRM have a strong impact on the competitiveness of these technologies.In this paper we discuss how DR resources can participate in wholesale markets as well

49、as the associated system benefits.Even though the systems benefits,as well as those for consumers,from DR have been broadly demonstrated and documented(see for example,OConnell et al.2014,Dupuy&Linvill 2019),we notice that residential DR with great potential with the use of heating,refrigerators,air

50、 conditioning,electric vehicles(EVs)and others has not yet been fully developed.This is due to the several barriers,sourcing from market,political,social and technological factors,to name a few,that residential consumers need to overcome.On the bright side,conditions today favour the development of

51、DR in the sense that some of these barriers can be addressed with the appropriate mechanisms.In particular,we propose the use of non-linear pricing and more specifically priority pricing contracts in the residential sector to incentivize its participation in DR.Priority pricing contracts offer consu

52、mers a menu of reliability levels with different prices.For example,let us consider three pairs:(i)cheap power that can be interrupted frequently;(ii)power that can be interrupted in emergency situations;and(iii)expensive power that cannot be interrupted.One main advantage of priority pricing contra

53、cts are their simplicity that makes them appropriate for residential DR programs.These contracts are between consumers and an aggregator who then participates in capacity markets.We propose the participation of the aggregator in capacity markets as a demand resource so that consumers express their w

54、illingness to pay for capacity.As such,one of the key shortcomings of a capacity market is addressed.To this end,we analyze how reliability concepts,say,the value of lost load(VOLL)and Expected Energy Unserved(EEU),are related with capacity and how optimal capacity investments may be determined.In o

55、rder though for capacity markets to welcome such initiatives there is a need for some changes in the capacity product definition.In this regard,we propose a refined capacity market product that is expanded to include location as well as flexibility requirements.Last,we develop a business model for a

56、n aggregator and analyze how priority pricing contracts interact with the retail market so that potential market distortion issues are resolved.To summarise,the contributions of this paper are outlined as follows:1 It has also the rule that resources which are currently receiving,or will receive,sup

57、port under Contracts for Difference(CfDs),Final Investment Decision Enabling Regime(FIDeR),Feed in Tariffs(FiT),and Renewables Obligation(RO)are not eligible for participation in the capacity market(ESO 2023b)3 The contents of this paper are the authors sole responsibility.They do not necessarily re

58、present the views of the Oxford Institute for Energy Studies or any of its Members.1)Integration of priority pricing contracts via an aggregator as explicit demand response in capacity markets,participation on the demand side and construction of the capacity demand curve 2)Refinement of capacity mar

59、ket products to encourage investment in demand response resources and incorporate specifics about location,flexibility,and adjustments to the calculation of firm capacity profile 3)End-to-end business model for an aggregator incentivising residential demand response participation in capacity markets

60、 The remainder of the paper is organized as follows.In Section 2 we provide an overview of CRMs around the world and their basic principles;we then focus on capacity markets and their shortcomings.Next,in Section 3 we provide an overview of the benefits of DR participation in wholesale markets,comme

61、nt on the barriers that DR face,and how some of these can be lifted with appropriate pricing schemes like priority pricing contracts.In Section 4,we discuss how capacity markets need to be adapted to incorporate residential DR on the demand side and how the capacity product exchanged can be refined

62、so that it represents the needs of low-carbon future power systems paradigm with increased need for flexibility coming from DR.In Section 5,we propose an end-to-end business model for an aggregator implementing the participation of DR in capacity markets through priority pricing contracts.Lastly,in

63、Section 6,we summarise the results and provide concluding remarks.2.Overview of Capacity Markets In this section,we categorize the CRMs and discuss their basic principles of operation.We then focus on capacity markets and discuss their shortcomings in terms of incentivizing new investments in approp

64、riate locations and with specific characteristics.2.1 Capacity Remuneration Mechanisms In Section 1 we discussed the need for CRMs to guarantee new investment targets and ensure that all generators could recover their capital costs.Initially,some countries including some European countries did not s

65、ee the value in CRMs;however,this has changed in recent years since short-term,marginal cost-based prices usually cover the short-term costs rather than the capital costs(Keay&Robinson 2019).In this regard,a number of studies have investigated how markets can be redesigned to incentivise a socially

66、optimal generation portfolio.A taxonomy of different CRMs that have been introduced to address this missing money problem is depicted in Figure 1(ACER 2018).Figure 1:Capacity remuneration mechanisms taxonomy Source:adapted from ACER 2018 These are broadly categorized into volume-based mechanisms,whe

67、re a specific capacity sufficient to guarantee the desired level of system adequacy is set and price-based mechanisms,where the amount of the procured capacity is steered by setting a target price.Both categories can also be subdivided into market-wide and targeted approaches.Whereas market-wide mec

68、hanisms provide support to all capacity in the market,targeted mechanisms aim at supporting only a subset,for instance,newly built 4 The contents of this paper are the authors sole responsibility.They do not necessarily represent the views of the Oxford Institute for Energy Studies or any of its Mem

69、bers.capacity or capacity expected to be required additionally to that already provided by the market.More specifically,six different types of mechanisms can be differentiated:1)Capacity markets:The total amount of required capacity is set by a central body and procured through a central bidding pro

70、cess so that the market determines the price.Two common variants of the central buyer mechanism include the forward capacity market(see Cramton&Stoft 2005)and reliability options2(Oren 2005).Such mechanisms are found in the UK,Ireland,Italy,Poland,ISO New England(ISONE),MISO,NYISO,and PJM Interconne

71、ction(PJM).2)De-central obligation:This refers to the obligation of each load-serving entity to secure the total capacity it needs to meet its consumers demand.In contrast to the central buyer model,there is no central bidding process.Instead,individual contracts between load serving entities and ca

72、pacity providers are negotiated.Such mechanisms are found in France,Australia,CAISO,and SPP.3)Tenders for new capacity:Financial support is provided to capacity providers to build the required additional capacity,for example by financing the construction of new capacity,or offer long-term power purc

73、hase agreements(PPAs).Such mechanisms are found in Bulgaria and Croatia.4)Strategic reserve:A certain amount of additional capacity is contracted and held in reserve outside the energy-only market.The reserve capacity is only operated if specific conditions are met,as in a shortage of capacity in th

74、e spot market,or a price settlement above a certain threshold.Mechanisms like these are found in Germany,Belgium,Sweden,and Finland.5)Market-wide capacity payment:A capacity price is determined centrally based on estimates of the level of capacity required to meet reliability criteria.This is then p

75、aid to all capacity providers in the market.6)Targeted capacity payment:A central body sets a fixed price paid only to selected technology-type resources.Such mechanisms are found in Spain and Portugal.CRMs are typically designed to maintain generation adequacy and ultimately avoid shortage situatio

76、ns by offering capacity providers income on top of their earnings in energy and ancillary services markets.Although mechanisms may vary substantially in the way the required capacity and the corresponding capacity prices are determined,all types of CRMs should in theory lead to similar outcomes.Pric

77、e-based mechanisms,such as the targeted capacity payment approach used in Spain,face the problem that if payments are too low there is no guarantee that investments will occur,while if payments are too high,excess investments can happen;both scenarios are equally inefficient.CRM approaches that only

78、 support new plants may create distortions in the energy market since they depress pricing,thus exacerbating the need to pay existing plants to avoid their closure.In Europe the most popular CRMs are strategic reserves and capacity markets,as seen in Table 1 where an overview of implemented CRMs is

79、presented(adapted from Bublitz et al.2019).2 Reliability options can be introduced in capacity markets by requiring every generator that receives a payment for capacity to sell a reliability option for the same amount of capacity.The buyer of the option contract has the right to buy equivalent elect

80、ricity on the wholesale market at a predefined strike price.Reliability options are in essence risk sharing arrangements between load serving entities and capacity providers.Reliability options penalise generators that remain unavailable during a period when the spot price is above the strike price.

81、5 The contents of this paper are the authors sole responsibility.They do not necessarily represent the views of the Oxford Institute for Energy Studies or any of its Members.Table 1:Types of CRMs around the world and participation of technologies Type Market area Thermal power plants Variable renewa

82、ble resources Demand side management Interconnections Capacity market Colombia X X Ireland X X X X Italy X X X Poland X X X X Belgium X X X X UK X X X X US-ISONE X X X X US MISO X X X X US NYISO X X X X US-PJM X X X X De-central obligation Australia SWIS X X X France X X X X US CAISO X X X X US SPP

83、X X X X Strategic reserve Germany X X Sweden X X Targeted capacity payment Spain X Source:Bublitz et al.2019 The main advantage of capacity markets,when well designed,is that they almost guarantee that the desired level of reliability is achieved.Furthermore,competitive bidding reduces prices and ca

84、n encourage innovation,especially when bidding is open to new and existing capacity,both from the demand and the supply sides.One measure of the success of capacity markets is their increasingly widespread use.For instance,they have been central to almost all North American liberalized markets(with

85、the exception of Texas),many Latin American countries(notably Colombia,Brazil,Peru,Chile),and a growing number of European countries,including the UK,Belgium,Italy,Ireland and Poland.2.2 Shortcomings in Capacity Market Design Capacity markets are designed to create the investment incentives that wil

86、l provide revenue sufficiency to a least-cost set of generators or demand-side resources to meet resource adequacy goals.In contrast with a forward contract for energy(say,through a PPA),capacity markets only require the availability to deliver energy,but they do not specify the price at which the e

87、nergy will be purchased.Capacity awards,therefore,provide an option-like payment to generators,and in return,consumers are less exposed to high energy prices due to the additional capacity that enters the market.Energy and capacity prices are linked because the implementation of capacity markets wil

88、l tend to suppress the price spikes in the energy market that would have otherwise served as price signals for additional capacity.Unlike energy,capacity is inherently procured ahead of time,so the capacity resources will be able to contribute to system reliability during critical periods.6 The cont

89、ents of this paper are the authors sole responsibility.They do not necessarily represent the views of the Oxford Institute for Energy Studies or any of its Members.All capacity markets have some similar elements.Demand is based on a peak demand forecast that determines the need for future-installed

90、capacity.Capacity market participants who are willing to supply power submit capacity offers.The market is cleared with a competitive auction where TSOs order capacity offers from the lowest to the highest,then use the capacity demand curve to determine the market clearing price of capacity.Capacity

91、 market supply is determined by resources offering into the capacity market.Capacity offers are based on the unavoidable costs to keep each resource available,for example,ongoing maintenance costs of existing resources,or the capital investment costs of new resources or planned improvements.Since mo

92、st resources will recover a significant portion of their capital costs through the energy market,capacity market offers are also reduced based on the resources expected energy and ancillary services revenues.Peaking plants will tend to offer the highest costs in the capacity market since they are on

93、ly expected to operate a limited number of hours per year,and will not receive as much energy market revenue as baseload resources do.An example of such a case is depicted in Figure 2;hydroelectric,nuclear,wind and solar resources are used the majority of the time and gas units only for a small frac

94、tion,as seen in the load duration curve.However,as seen in the curve depicting a linear approximation of their investment and production costs,the gas units are the most expensive.Figure 2:Load duration curve and approximation of investment and production costs for different technologies Source:auth

95、ors This is exacerbated by the introduction of price caps to avoid the exercise of market power as seen in Figure 3 that mainly affects the peaking units.7 The contents of this paper are the authors sole responsibility.They do not necessarily represent the views of the Oxford Institute for Energy St

96、udies or any of its Members.Figure 3:Missing money problem Source:authors Capital and operational costs can also be affected by state subsidies(for example,a subsidized plant can offer capacity at near zero-cost to clear the market,which has led to price suppression and some rule changes within capa

97、city markets).To make sure resources are able in practice to produce power or reduce demand when needed,several markets have added various pay-for-performance capacity market mechanisms.For example,some markets(PJM,ISONE,NYISO)either pay resources a bonus or charge resources a penalty for each hour

98、they do not meet their compliance obligation during certain compliance hours(like when the system is under shortage of capacity).Most markets employ capacity zones to reflect regional or more granular capacity requirements.Conventional generators participate in capacity markets with an outage-adjust

99、ed capacity factor,the so-called equivalent forced outage rate on demand(EFORd)3 multiplied by the resources nameplate capacity(Cramton&Stoft 2005).Like conventional generators,renewable generator capacities are also derated of power capacity for participation in capacity markets.Based on the expect

100、ed availability during system peak,wind is usually credited at about 20 percent and solar about 60 percent of their nameplate capacity.However,many TSOs have implemented enhanced mechanisms for renewable capacity market participation because,unlike conventional generators,unforced capacity(UCAP)calc

101、ulations do not accurately capture the amount of system capacity provided by resources with correlated output.That is,UCAP will overestimate the capacity provided by renewable power generation since all solar or wind generation will have low output at the same time,and in addition,the typical change

102、 in renewable power output throughout the day can shift the timing of critical periods when the system is short on capacity.Most markets have implemented or are considering an approach called effective load-carrying capability(ELCC)to quantify the incremental contribution of renewables to capacity a

103、nd resource adequacy needs.ELCC methods broadly consist of performing Monte Carlo simulations to estimate the marginal contribution of additional capacity given typical weather patterns and the markets existing resource portfolio.ELCC calculations are typically performed for each individual resource

104、 and,therefore,will vary by location.The effects of implementing ELCCs over previous heuristic methods can have a huge impact on investment decisions(see Dent et al.2010).Capacity market demand is different from energy market demand because it is determined administratively by the TSO.As such,capaci

105、ty markets are essentially one-sided since they do not bring the buyers and sellers together;instead it is the TSO which is the single buyer(see Keay&Robinson 2019,Billimoria&Poudineh 2019).In this regard,reliability is seen as a public good,the desired value of which is set by the TSO or government

106、,and not a private responsibility which creates capacity market distortions.The framework proposed in this paper tries to address this problem through the 3 EFORd refers to a metric used to gauge the likelihood that a power generation unit will be unavailable because of unexpected breakdowns or redu

107、ctions in its power output when it is needed to produce electricity.8 The contents of this paper are the authors sole responsibility.They do not necessarily represent the views of the Oxford Institute for Energy Studies or any of its Members.construction of the capacity demand curve where informatio

108、n from the consumers value of reliability is used,as further explained in Section 4.The TSO calculates the required installed capacity,which is the capacity needed to meet forecasted peak demand plus a capacity reserve margin.Next,the TSO calculates a price cap to anchor demand that is based on the

109、cost of new entry(CONE)for a typical peaking plant,usually the cost of a new gas-fired power plant.The CONE represents how much investors are willing to pay to add new capacity.The TSO also calculates net CONE,which is the CONE minus energy and ancillary services market revenues.Net CONE estimates t

110、he missing money for the representative plant in the market.The TSO then uses a methodology to determine the downward sloping demand curve for capacity,which determines how much capacity the TSO will procure at each price point.The demand curve is the key element in the capacity market design.A prop

111、er demand curve should reflect the true requirement of generation supply and incentivize sufficient capacity investments.Originally,the implementation of capacity markets relied on inelastic demand curves for capacity,which resulted in a bipolar behaviour of capacity prices depending on whether the

112、system was temporarily short or long of the desired capacity target.The idea of a downward-sloping demand curve,which is nowadays typically employed in capacity markets,is to reduce the volatility of the capacity payments while aiming to cover,on average,the cost of building capacity and also allowi

113、ng for a certain degree of mitigation on the exercise of market power in the capacity auction.The downward slope is based on the fact that people are willing to pay less for capacity once they have reached a desired reliability level.A prototypical design of a demand curve for capacity is discussed

114、in(Cramton&Stoft 2005),and is presented in Figure 4.Figure 4:Capacity market demand curve Source:Cramton&Stoft 2005 In this figure three capacity levels are determined and associated with a price and reliability level,these are:minimum capacity(Cmin),capacity at the kink in the demand curve(CK),and

115、maximum capacity(Cmax).The minimum capacity corresponds to the amount of capacity needed to keep loss-of-load events to,for example,3 hours per year.More details on how these points are determined may be found in(Cramton&Stoft 2005).Among countries that implement a centralised capacity market,there

116、is a consensus in utilizing a downward sloping demand curve,and the net CONE and target capacity level are key parameters in the definition of the demand curve.The fine balance that one attempts to strike when calibrating these demand curves is to secure adequate investment in capacity while ensurin

117、g that the procured capacity is not excessive,especially given the uncertain conditions that unfold in the energy market after the capacity market is concluded.The intersection of supply and demand determines the market clearing price.If the shape of the demand curve is wrong,the TSO will procure to

118、o much or too little capacity.A first question is whether capacity markets are well-suited for incentivizing investments in renewable or carbon-free resources such as hydroelectric or nuclear.Various generation technologies depend on capacity revenues to varying degrees based on their competitivenes

119、s in the energy market.For example,some generators may require a large capital investment cost in order to produce electricity more efficiently and at a lower marginal cost.These resources can rely on the energy market to recover most of their investment costs.Conversely,other resources may have ver

120、y low investment costs,but are accordingly less efficient and have more expensive marginal operating costs.These resources would not be very profitable in the energy market due to their high costs,but their low capital investment costs may make them attractive for meeting resource adequacy needs.In(

121、Mays et al.2019)the authors 9 The contents of this paper are the authors sole responsibility.They do not necessarily represent the views of the Oxford Institute for Energy Studies or any of its Members.conclude that capacity markets,although nominally technology neutral,favour investment in resource

122、s with high marginal costs and low capital costs due to differences in the risks associated with energy and capacity market revenues.Capacity market incentives may,therefore,work against investment in resources with low marginal costs and high capital costs,such as renewable wind and solar resources

123、.Capacity market reforms have the potential to correct the issues discussed above and to better align investment incentives in renewable resources with system capacity needs.For example,very recently on January 8th,2024 Ofgem sought to obtain views from people with an interest in the functioning of

124、the capacity market as part of the Ten-Year Review of the Capacity Market Rules(Ofgem 2024a).The proposed changes focus on enabling battery operators to address issues around degradation,greater flexibility to allow low-carbon projects with longer-build times to access the scheme,new longer-term agr

125、eement options for low-carbon technology and measures to support the growth of the residential demand response sector.The locational contribution of renewable capacity is a major area for potential reforms.Because renewable power generation is correlated with geography and can affect the timing of p

126、eak net load,additional capacity investments can better improve resource adequacy if they are less correlated,or even negatively correlated with the existing renewable capacity mix.The authors in(Bothwell&Hobbs 2017)show how capacity markets can support more efficient investment incentives by using

127、the location and type of renewable resources to calculate each capacity resources marginal contribution to system resource adequacy.Todays capacity markets include zonal definitions that reduce capacity price signals to a rough approximation.However,the zonal definitions often follow preexisting reg

128、ulatory or service area boundaries that do not reflect differences in resource variability.How to design zonal and system demand curves that appropriately capture the interaction between zonal and system capacities while keeping the curves in a simple form becomes the major technical challenge.Usual

129、ly assuming statistical independence of generator outages and load,the desired reserve margin would be calculated by convolving generator outages and loads,considering unit nameplate capacities,forced outage rates,and load distributions.However,these simple probabilistic methods do not capture the i

130、ncreased uncertainty introduced by intermittent renewable generators whose outputs,like load,depend on weather patterns and cannot be modelled as independent.Conventional generators remain available essentially year-round,with the exception of planned maintenance or(typically rare)unplanned forced o

131、utages,and this corresponds well with annual capacity payments that compensate the reliability benefit provided by the resources consistent availability.In contrast,renewable resources are weather-dependent,with geographic and temporal correlations among separate resources.Demand-side resources also

132、 vary in their availability throughout the day.The capacity contribution of renewable,storage and demand-side resources is,therefore,not as straightforward as calculating an outage-adjusted capacity factor times the resources nameplate capacity.For example,capacity markets in Colombia due to their r

133、egulation favour conventional thermal and hydroelectric power plants instead of DR resources;and faced adequacy problems in the el Nio hydro shortages in 2015-16.In the UK a parallel auction was held to enable DR resources as a transitional arrangement in the two years preceding full capacity market

134、 delivery in 2018-19.However,still in UK capacity markets only a small percentage of DR participates;namely less than 7 per cent in the T-1 auction for the delivery year 2023/24.One notable debate is on whether DR cleared in the capacity market should be required to offer into wholesale energy marke

135、ts like generation(i.e.,must-offer requirement).For generation,the must-offer requirement aims to mitigate the risk of resources withholding to drive up energy prices.These examples show,that there still exists no consensus about the role of such resources in capacity markets.While it is generally a

136、greed that these technologies have some kind of capacity value,the specific rules of participation in capacity markets may hinder them from being competitive against conventional resources.Secondary trading in capacity markets is necessary when for example a capacity market unit is down for maintena

137、nce or,in the case of a new build asset,there are construction delays which means it will not be operational when required.It can also provide real-time information about how much capacity is worth;as such they might discourage unnecessary future investment in generation.This refers to the trade in

138、capacity contractual obligations that owners no longer want,or are unable to fulfil,to other suitable providers.Currently,secondary trading in the capacity market is performed on a one-to-one basis,meaning capacity market providers have no way of gaining exposure to the entire market and finding a b

139、uyer can be time-consuming and complex.Full transparency into who would like to buy and sell contracts,and streamlining the trading process,would promote economic efficiency.Over the past two delivery years,the majority of secondary trades were made by DR resources.10 The contents of this paper are

140、the authors sole responsibility.They do not necessarily represent the views of the Oxford Institute for Energy Studies or any of its Members.3.Residential Demand Participation in Capacity Markets In this section,we provide an overview of the ways DR can participate in wholesale markets,consisting of

141、 energy,ancillary services and capacity markets,and investigate the barriers that residential consumers face when participating in DR programs with a focus on capacity markets.This allows us to understand which barriers need to be lifted to enable residential DR resources.Next,we discuss how dynamic

142、 pricing can lead to increasing DR participation,and in particular focus on priority pricing contracts which have several advantages compared with other dynamic tariff designs.3.1 Overview of Demand Response Participation in Wholesale Electricity Markets The literature provides various definitions o

143、f DR,but a clear common theme is that DR reflects electricity demand that changes based on a signal(Warren 2014).For example,FERC defines DR as changes in electric usage by demand-side resources from their normal consumption patterns in response to changes in the price of electricity over time,or to

144、 incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized(Murthy Balijepalli et al.2011).Information sent to consumers in terms of,say,their consumption patterns,electricity prices,or the consequences of their cons

145、umption for the power system,can contribute to consumers adjusting their demand.More specifically,DR is usually based on the following mechanisms:price-based programs(or implicit demand response),which use price signals and tariffs to incentivize consumers to shift consumption;and incentive-based pr

146、ograms(or explicit demand response),which make direct payments to consumers who shift demand as part of a demand-side response programs.We therefore might say that reducing costs is the main reason for implicit DR(reducing or shifting demand)and earning revenues is the main reason for explicit DR(sa

147、les into markets).Advances in modelling and information technology capabilities have made DR an attractive option to increase power system flexibility.This will consequently allow a more efficient use of system assets and resources.The flexibility provided by DR can be used to meet the fluctuations

148、of renewable generation and facilitate a higher penetration than could be achieved by relying on conventional generation alone(OConnell et al.2014).This is most effective in systems operating with market-based DR mechanisms as even a relatively minor DR will tend to displace the most expensive peaki

149、ng units,reducing the system marginal cost and resulting in substantial welfare gains,as depicted in Figure 5.As it is seen in the Figure,the market clearing price without DR(*)is higher or equal than the market clearing price with DR(*),i.e.,*.The market cleared quantity without DR(q*)is higher tha

150、n the market cleared quantity with DR(q*),i.e.,q*1 since Consumer 2 chooses a higher portion of the highest reliability contracts.In other words,consumers with higher valuation select more reliable plans and pay more.By the consumers selection of contracts and quantities their VOLL can be inferred.T

151、he relationship between contract prices and consumer valuation is given by:pi=v0 r0+vi(ri ri1)ik=1,23 The contents of this paper are the authors sole responsibility.They do not necessarily represent the views of the Oxford Institute for Energy Studies or any of its Members.where pi is the priority c

152、harge of contract with reliability level ri and vi is the valuation of consumer that buys a contract with the corresponding reliability level.In Table 8,we provide an example of priority pricing contracts with 10 reliability levels and plot in Figure 13 the valuation of consumers for each reliabilit

153、y level.Table 8:Priority pricing contracts with 10 reliability levels Reliability%Priority Charge/MWh 70 21 80 30 85 35 87 37 90 42 95 54 97 60 99 67 99.7 70 100 72 Figure 13:Reliability levels as a function of consumer valuation When consumers select priority pricing contracts they demonstrate thei

154、r valuation for each reliability level.By considering the reliability levels of Table 7 the consumer valuation may be divided into three components that correspond to the three reliability levels:100%,99.7%,and 70%.In Table 9 we provide an example of priority pricing contracts for the four Type of c

155、onsumers of Section 3.3.Table 9:Consumer types and their valuation for reliability Consumer Type 1 2 3 4 Priority Pricing Contract Reliability level 70%Price/MWh 21 Consumer valuation 30 0.1 0.1 0.15 0.15 99.7%154 450 0.05 0.15 0.05 0.03 100%157 600 0.2 0.01 0 0 Average Consumer VOLL/MWh 416 294 135

156、 100 24 The contents of this paper are the authors sole responsibility.They do not necessarily represent the views of the Oxford Institute for Energy Studies or any of its Members.The average consumer VOLL for Type is calculated as follows:Average Consumer VOLL=300.1+4500.05+6000.20.1+0.05+0.2=416/.

157、The others are calculated in a similar manner.The three reliability levels of Table 7 correspond to different levels of loss of load expectations 100%=3h/y10,99.7%=29h/y and 70%=2630h/y.Each LOLE corresponds to a capacity level,for example,for the UK system 100%corresponds to 44 GW of capacity and 9

158、9.7%to 40 GW,respectively(ESO 2022).These values are calculated based on Monte Carlo simulation studies.Let us assume we have data for the priority pricing contracts of customers.Then we can construct the capacity demand curve as follows:the value of capacity demand is=,where is the reliability reli

159、ability level and takes values in our example of 100 per cent,99.7 per cent,and 70 per cent,in Table 10.Table 10:Value of capacity demand for different reliability levels Reliability Level()%100 99.7 70 h/y 3 29 2630/MWh 600 450 30 /kw/y 1.8 13.2 79 Percentage of Load%51 28 21 We apply the aforement

160、ioned procedure in the recent T-4 capacity market for delivery in 2027-28 in the UK.The actual capacity demand curve used is depicted in Figure 14 based on the data available in the auction report(ESO 2024).The market cleared at 65/kW/y with a total of 43 GW capacity procured,less than the 44 GW tar

161、get capacity.Indicatively,out of the 43 GW less than 3 per cent corresponds to DR resources.The Net CONE used as seen in Figure 14 was 49/kW/y with a reliability standard of 3 hour per year LOLE.Figure 14:Capacity demand curve of T-4 capacity market for delivery in 2027-28 Source:authors 10 The loss

162、 of load expectation of 100 per cent reliability is considered to be 3h/y instead of zero as per industry standards.25 The contents of this paper are the authors sole responsibility.They do not necessarily represent the views of the Oxford Institute for Energy Studies or any of its Members.By using

163、the relationship derived above,i.e.,=,we calculate the implied VOLL in the UK capacity market,which is =49/3/=16,333/.However,if we consider that residential consumers have indicated their VOLL with priority pricing contracts then we have a modified capacity demand curve.An example of such a curve i

164、s depicted in Figure 15.This example is based on the assumption that the UK power system comprises small and medium-sized enterprises(SMEs)and residential consumers whose respective VOLL are based on(London Economics,2013)for Winter weekend peak(shown in Table 11).Under the assumption of 24 per cent

165、 and 76 per cent split between residential and SMEs the load-share weighted average across domestic and SME users for winter,peak,weekday is 16,333/MWh.This number coincides with the implied VOLL in the UK capacity market for delivery in 2027-28.Table 11:Great Britain VOLL as a function of consumer

166、groups Consumer VOLL/MWh Residential 10,289 SMEs 35,488 Source:London Economics,2013 If we go back to the example of Table 9 there are consumers that are willing to pay less than what the TSO assumes,as shown in Table 11.In particular,the four Type of consumers have an average VOLL of 416,294,135,an

167、d 100 expressed in/MWh,respectively(as seen in Table 9).If we assume that these consumers comprise 1.8 per cent of the residential population,then consumers might be willing to pay less that what the TSO assumes for desired capacity for LOLE of 3h/y,that is to say,48.5/kW/y,compared with 49/kW/y11.M

168、oreover,the minimum capacity assumed by the TSO is 42.5 GW.However,consumers are willing to experience reduced reliability levels as indicated by the priority pricing contracts which correspond to lower capacity levels.For example,LOLE99.7%=29h/y corresponds to 40 GW of capacity(ESO 2022).If more re

169、liability levels are included in priority pricing contracts(as in Table 8)then the capacity demand curve would have more breakpoints corresponding to the different LOLEs.Figure 15:Updated capacity demand curve of T-4 capacity market for delivery in 2027-28 taking into account VOLL of consumers The c

170、apacity cost will be distributed to all consumers since the long-term security of supply benefits all consumers.Any other arrangement would create an evident situation of free-riding,because some users 11 The new implied VOLL is 48.5/3/=16,167/=0.24 35,488+0.742 10,289+0.018 (30+450+600).26 The cont

171、ents of this paper are the authors sole responsibility.They do not necessarily represent the views of the Oxford Institute for Energy Studies or any of its Members.will be taking advantage of the system adequacy without paying the associated costs.Consumers that value capacity less,as indicated by t

172、he VOLL and capacity demand curves,benefit from the existing capacity but pay less for the energy they consume as well as they receive benefit for the capacity value of their demand response resources.4.3 Refined Capacity Product for Enabling Demand Response Drawing from the conclusions of Section 2

173、.2 with respect to the shortcomings of capacity markets;a refined capacity product is defined as follows:Capacity product=Location,Capacity profile,Flexibility;we elaborate on each element below.Location:In capacity market literature,reliability is often used interchangeably with resource adequacy.T

174、hese two terms,however,are different.Resource adequacy is the ability of generation resources to meet the aggregated demand.The term originated from the conventional planning process of the vertically integrated industry.In such an environment,a utility company carries out the resource adequacy proc

175、ess on the aggregated system level without taking into account transmission,because any transmission security issues are handled in the subsequent transmission planning process.This can be done because the company owns both generation and transmission.On the other hand,reliability refers to a system

176、s ability to serve electricity on a nearly continuous basis.Such a definition necessarily encapsulates both resource adequacy and transmission security.In summary,the capacity market should address the reliability requirement,which is a broader concept than just resource adequacy.The differentiation

177、 of these two concepts is important since the reliability objective justifies the need for the location attribute of capacity product.The location attribute is captured through the definition of capacity zones by the TSO.Ideally,the location of a capacity resource should be specified at the more gra

178、nular level to reflect the different reliability impacts of different locations.However,such granularity brings computational challenges to the solution of a capacity market.In addition,an aggregate DR resource may spread across several locations,but only reveals itself as a single capacity profile

179、without specifying its actual distribution among buses.This led to the adoption of zonal models in some existing capacity markets where each zone is an aggregation of buses without considering internal transmission.In practice,the definition of zones is often a compromise of many factors,for example

180、,geographic and regulatory boundaries,problem scalability,and similar electric features of buses in a zone.The introduction of capacity zones can be viewed as an approximation to the location attribute of a resources reliability impact.For example,in PJM,locational constraints that represent transmi

181、ssion facility limitations or voltage limitations are included in their capacity market to quantify the locational value of capacity within their region.Capacity Profile:Capacity profiles are probabilistic in nature.As a result,they are inherently difficult to measure and price.To overcome this diff

182、iculty,the TSO designates a deterministic capacity value to a resource to represent its capacity profile.Depending on the size,technology and maintenance of generators,resources may have differing abilities in providing a certain amount of power when called on,as in a 100 MW thermal resource with a

183、0.05 EFORd,or a wind resource with a varying output from 0 MW to 100 MW.For instance,a wind resource may be qualified at 50 MW based on its evenly distributed capacity profile between 0 and 100 MW.Note that such representation is only approximate since it does not distinguish the above resource from

184、 a thermal resource with a 50 MW unforced capacity.Such ability,or capacity profile,obviously affects a resources contribution to the system reliability.The use of the ELCC by many TSOs is promising.However,outage risks might not be uniform throughout the year so,for example,DR resources based on ai

185、r conditioning load control are better supported by capacity obligations defined for shorter time intervals.In this regard,capacity market products that split into seasons,months and times of day would send more granular signals about when capacity is needed,and would also provide incentives for res

186、ources like DR to contribute when they are needed.The clearing of these individual capacity products would be co-optimized to ensure that the total capacity procurement is cost-effective while meeting reliability standards.Flexibility:In present capacity market models,the flexibility requirement is

187、not included explicitly,which leads to fewer incentives for flexible resource investment as flexible generation is generally more expensive than less flexible generation.In the UK,the underlying reason for redesigning the existing 27 The contents of this paper are the authors sole responsibility.The

188、y do not necessarily represent the views of the Oxford Institute for Energy Studies or any of its Members.capacity market mechanism is to encourage investment in and the deployment of flexible resources.For instance,in February 2018,the capacity market auctions witnessed record low prices due to a v

189、ery high participation by conventional generators,which were seeking additional revenue,while a very small amount of the more flexible resources was contracted.Although the clearing price of the capacity market was low,the need to incentivize flexibility was high.Another potential element to be incl

190、uded in the capacity product definition is resource carbon intensity.However,it is not clear how this will interact with other subsidies offered to renewable resources,for instance,contracts for difference,and it is beyond the scope of this paper to review.Capacity markets have carbon emissions limi

191、ts that resources must meet to enter the auction.Yet,there are certain gaps in carbon policies that allow,for example,a high percentage of capacity market agreements to be awarded to carbon-emitting gas generators smaller than 20MW that are exempt from the UK emissions trading scheme.There have been

192、 efforts to enforce new,lower emissions limits in the capacity market which will kick in for new plants from 1 October,2034.This means all new oil and gas plants receiving long-term agreements through the capacity market will be obliged to lower emissions,through decarbonizing their capacity by intr

193、oducing carbon capture,hydrogen and other low-carbon methods into their generation mix,and by reducing running hours(Department for Business&Strategy 2023).If the level of renewable energy support is defined centrally,for example in the form of feed-in tariffs or premiums,the aim is to ensure suffic

194、ient profitability for renewable energy investments.Any extra revenues will therefore result in windfall profits for such investments and an unnecessary burden on end-users.Reducing the level of remuneration for renewables in accordance with the amount of revenues received from capacity mechanisms,a

195、s in the French,Irish,and Italian policy mixes,appears to be a viable solution.In such cases,system reliability requirements can potentially determine the technological mix of renewables in the system(Kozlova&Overland 2022).5.Aggregator Business Model In this section,we discuss the relationship of t

196、he aggregator,providing priority pricing contracts to consumers and participating in capacity markets,with the consumers as well as with the other market entities.To this end,we present an analytical framework for a possible aggregator business model.5.1 Aggregator Relationship with Consumers With p

197、riority service,the aggregator can always provide the qualified capacity with a considerable level of certainty,since consumers engage in capacity subscription.A remaining risk for the aggregator is to match the offered reliability to the that of the priority service menu.Once a household is subscri

198、bed,a home energy router allocates particular devices within the house to different colours by ensuring that the mean power over time does not exceed the subscribed amount of kilowatts for that particular colour.In offering priority service contracts to residential consumers,the aggregator commits t

199、o a certain level of reliability for each service option.This level of reliability must be respected on average over an extended period(say,annually)even if certain periods may be characterised by fluctuations around this average.Another challenge for the aggregator is to design the menu of reliabil

200、ity and price pairs to offer.If reliability levels are priced too low,then all consumers would select the highest value reliability.This would be undesirable since the aggregator will not be able to provide such reliability at the selected price.If reliability levels are priced too high,then consume

201、rs will not enrol.Aggregate statistical information about the population is needed to design the menu to avoid the aforementioned problems(Mou et al.2021).Then the priority and service charges can be tuned accordingly to various reliability levels.We propose a business model and economic paradigm fo

202、r a utility or aggregator that bridges the gap between wholesale markets and retail service.Aggregators incentivise participation of residential consumers through the priority service contracts and then participate in wholesale markets.In this paper,we show how they can participate in capacity marke

203、ts but a similar approach can be followed for energy and ancillary services markets.(Strauss and Oren 1993)propose the use of priority pricing contracts in the context of pooling them together with intermittent generation resources to transfer the risk from generators to load.In order to analyze the

204、 viability of such a business model we look at three aspects:value proposition,value creation and delivery and value capture(Hamwi et al.2021).28 The contents of this paper are the authors sole responsibility.They do not necessarily represent the views of the Oxford Institute for Energy Studies or a

205、ny of its Members.In our case the value proposition is the capacity value of DR.For value creation a key mechanism is aggregation,which offers the opportunity for small-scale energy customers to exploit their potential.The isolated contribution of an individual residential DR to the power system is

206、negligible,and the effects of small-sized consumers in the electricity market are inefficient.Therefore,using a mechanism that increases the efficiency of the resources is a prerequisite.The aggregator,as a market intermediary is responsible for many activities,such as participant registration and c

207、ommunication infrastructure,load-data transfer,data security,and participants remunerations.In terms of resource availability,this is associated with behavioural patterns of residential consumers.Value capture comprises cost structure and revenue model.The cost considered is mainly the initial parti

208、cipant cost.This consists of the activation cost,and includes the costs of investing in the enabling technology and establishing a response plan.This main cost can be categorised into two types:transaction cost,and intervention cost.The market transaction cost includes the costs of collecting inform

209、ation regarding products and customers,managing contracts,and procedures for external transactions.The DR transaction cost represents the costs of spending time identifying potential resources that can adjust their electricity consumption,understanding their electricity consumption patterns,assessin

210、g their suitability for participation,selecting the appropriate flexibility product,and evaluating the cost and benefits of each customer.These are affected by customers preferences and the physical characteristics of devices deployed(as explained in Section 3.3)with the various preferences in prior

211、ity pricing contracts and available resources.The information interaction between aggregators and customers is carried out in an automated manner through smart grid infrastructures,including control and communication devices,like smart meters with load control capabilities.The installation and maint

212、enance of these devices are the obligation of aggregators.The transaction cost is high when the aggregator manages and aggregates a large number of customers.The intervention cost is related to behavioural adaptation,and is based on the fact that consumers are traditionally unpredictable,and are not

213、 used to dynamically and temporally adapting their consumption processes.The main economic challenge in DR operations is in generating a sufficient income to cover the expenses.In DR,the captured value is shared by both the aggregator and customers.The aggregator generates revenue,and customers rece

214、ive remuneration for their participation.Aggregators provide financial incentives to customers to encourage them to actively participate in DR.Economic incentives come in many forms,such as extra compensation or discounts on electricity charges.This revenue model is based on electricity bill savings

215、 and capacity payment,or payment reductions as is the case of implicit demand response.5.2 Aggregator Relationship with other Market Entities Retailers are independent entities that,besides bridging the gap between the wholesale market and customers,are responsible for load forecasting;risk assessme

216、nt from load and price fluctuation;and designing tariffs(Lu et al.2020).Retailers are the best candidates to be aggregators due to their existing strong interaction with consumers as they have energy supply contracts with them.However,retailers are not responsible for the installation of control and

217、 communication infrastructures for the customers.In the current power system under EU regulations,the retailer acquires the amount of energy the consumer is expected to consume and informs the TSO about the consumers projected consumption for the coming hours or next day.This is usually referred to

218、as scheduled demand or the firm program.Moreover,the retailer takes responsibility for imbalances between the retailers forecast of the flexible consumers demand and the actual consumption that occurs in real time with its balancing responsible party(BRP).When the aggregator is an independent entity

219、 it has its own BRP.The problem in the case of independent aggregator is that the forecasted load and purchased energy by the retailer will be different due to the priority pricing contracts offered to the prosumers by the aggregator.This might create retail market distortion and financial penalties

220、 to retailers if not addressed appropriately.More specifically,the retailer bears the full risk of any imbalances in its supply portfolio.In addition to this,the consumers consumption falls short of the amount of energy procured by the retailer.In the absence of any further arrangements,the retailer

221、 may not invoice this difference and thus,may not recover the full electricity procurement cost.In this regard,we propose as a possible solution using the separated win-win model as discussed in(Alba et al.2021).Just to note there is extended literature on how independent aggregators can participate

222、 in wholesale markets and their responsibilities to other market participants(see Carreiro et al.2017,He et al.2013).In the business model where the aggregator is a 29 The contents of this paper are the authors sole responsibility.They do not necessarily represent the views of the Oxford Institute f

223、or Energy Studies or any of its Members.different entity to the retailer,the consumer has two contracts,one with his retailer for supplying energy,and one with his aggregator for the priority pricing contracts.Aggregator and retailer are different companies,each with different balancing responsibili

224、ties.In the win-win model,the balancing responsibility is transferred from the retailer to the independent aggregator before day-ahead gate closure.Thus,the independent aggregator takes responsibility for its selected consumers and for their imbalances over the next 24 hours,allowing it to adjust th

225、e portfolio in all energy markets.Before day-ahead gate closure,the independent aggregator communicates to the TSO the aggregated load curve of its customers who at the same time have an energy contract with any retailer.The independent aggregator has its own BRP and is responsible for the imbalance

226、 of the program communicated to the TSO.The retailer purchases the energy of all its customers in the day-ahead market.The retailer remains responsible for procuring the energy for all customers,including those that will be managed by an independent aggregator.The TSO transfers the aggregated load p

227、rofile previously defined by the aggregator at the day-ahead market price.This transfer is not a real one,but an accounting one for the upcoming settlement through which the independent aggregator pays for the energy at the market price before selling it in the markets.The retailer,therefore,is alre

228、ady compensated for such energy.The aggregator offers demand services in the other markets where it detects an opportunity.The aggregator receives the income for the services provided to the system.Measurement deviations from its program are settled by the aggregator BRP.Since the retailer has to bi

229、ll consumers,the revenue for the energy consumed,as measured by smart meters each hour,is transferred by the aggregator to the retailer at the day-head market price,in addition to the average cost of imbalances incurred by the retailer for the energy sold to customers for which the aggregator is res

230、ponsible.This addition is important since retailers usually internalize in their offers any imbalances their customers may have.A retailers customers,therefore,pay an amount of money to their retailer for system imbalances.In this model,the aggregator is responsible for the customers imbalances and

231、therefore they must pay the system for such imbalances.However,since the retailer is billing such customers,retailers(instead of aggregators who bear the cost)are compensated for customers imbalances.These benefits received by the retailer should somehow be transferred to the aggregator since they a

232、re the agents assuming the whole imbalances of these customers.The aggregator only buys the energy in those hours where it activates the demand management service.However,it is responsible for the imbalances for the DR customers in other hours,solving the rebound effect and,thus,ensuring that imbala

233、nces do not penalize retailers.In this model,each agent is responsible for his own participation in the market.On the one hand,the retailer takes responsibility for the imbalances between the flexible consumers forecast and actual consumption that may occur in real-time only for those customers who

234、are not also managed by an independent aggregator.On the other hand,the independent aggregator is responsible for the imbalances between the flexible consumers forecast and actual consumption only for those customers previously communicated to the TSO.This procedure is depicted in Figure 17.Figure 1

235、7:Aggregator and market entities interaction model Source:authors As explained above,the TSO acts as the buyer of capacity in the form of DR offered by the aggregators,whose operation rationality needs to be verified by the TSO,including the activation,control and 30 The contents of this paper are t

236、he authors sole responsibility.They do not necessarily represent the views of the Oxford Institute for Energy Studies or any of its Members.scheduling scheme.The verification can be more competently done by the distribution system operator(DSO),who has direct access to the authentication demand from

237、 customers within its jurisdiction area.The DSO operates the network to ensure the efficient and reliable power delivery to customers.It has access to the consumption data of the customers measured by the advanced metering infrastructure,which could also be fetched by the aggregators who will utilis

238、e it for settlement.In this regard,aggregators do not need to invest in separate communication infrastructure and could use the one belonging to the DSO.As such,aggregator and DSOs are linked in this manner.Using the framework presented here an aggregator does not have to limit its activity to the c

239、apacity market but can stack its revenues by participating in energy and ancillary services markets see Figure 18.Figure 18:Demand resource revenue stacking Source:authors In revenue stacking there are still some challenges,for example,DR is not allowed to participate in wholesale energy markets in

240、the UK(this will change at the end of 2024 with the adoption of new rules).It is also interesting to investigate the relative importance of DR in the various markets from an aggregators perspective as well as from a system perspective,and from the view that some DR products may be more suitable for

241、capacity markets,and others for energy and ancillary markets.6.Concluding Remarks Energy-only markets rely on high price signals during periods of scarcity to incentivize investment.However,these signals can be uncertain and insufficient to cover the fixed costs of new generation.In this regard,capa

242、city remuneration mechanisms have been introduced in many countries to compensate generators for making generation capacity available for utilization.In this paper,we focused on capacity markets as a form of remuneration mechanism and analyzed their shortcomings.This was principally that a central a

243、gency decides on the reliability level that needs to be met by the capacity cleared in capacity markets.However,the consumers are the ones that are actually experiencing outage risk.Moreover,we demonstrated that the current capacity market regulatory framework is more tailored for conventional gener

244、ating resources;thus,leaves less room for storage and demand response resources.However,instead of building new generating units to meet reliability criteria,demand response resources could be used.During times of generation scarcity there exist consumers who are willing to reduce their demand for m

245、onetary compensation.Although commercial demand response has been more prominent,there has not been great engagement from residential demand response.In this regard,we discussed the benefits of demand response in wholesale markets;in terms of social welfare maximisation,economic use of interconnecti

246、on,reductions in generation capacity requirements,31 The contents of this paper are the authors sole responsibility.They do not necessarily represent the views of the Oxford Institute for Energy Studies or any of its Members.transmission and distribution network congestion management,and increased e

247、conomic efficiency.Next,we analysed the barriers that prohibit greater demand response participation.These span from economic,social,technological,political,and structural.The main barriers include the market product traded and consumers perception.Residential consumers tend to withdraw from demand

248、response programs if the inconvenience of participating becomes too high.However,consumers may attach different value to the guarantee of continuity of supply.This is especially the case when considering consumption for non-essential uses in excess of what is necessary to cover basic needs(lighting,

249、cooking and heating).In this regard,we proposed in this paper the use of priority pricing contracts so that consumers can demonstrate their value for reliability by subscribing in specific contracts.We analysed the use of priority pricing contracts under different consumer types and how much each on

250、e can benefit from such contracts.We compared priority pricing contracts to other implicit demand response contracts and showed that their benefits include:privacy,control over consumption,simplicity and modularity.Thus it might be able to address some of the barriers faced by residential demand res

251、ponse in terms of privacy concerns,consumer engagement,product definition,and complexity.We also provided guidelines on the actions that need to be taken so that consumers are more receptive to priority pricing contracts through education campaigns and bill protection for sensitive or disadvantaged

252、consumers.We then proposed a framework to incorporate priority pricing contracts through an aggregator as explicit demand response in capacity markets.We argued that the aggregator should participate through the demand instead of the supply side in the capacity market to avoid market distortion and

253、be able to reflect desired consumers reliability levels.In order to input priority pricing contracts in the capacity demand curve we studied the relationship of capacity and reliability.Under optimal conditions the marginal cost of outages is equal to the marginal cost of capacity.This can be transl

254、ated to a relationship connecting capacity with the value of lost load and the loss of load expectations.The information that can be inferred by priority pricing contracts,in terms of value of lost load and loss of load expectations can be incorporated in the construction of the capacity demand curv

255、e.We illustrated the proposed framework in the 2027-28 delivery UK capacity market.Moreover,we proposed a refined capacity product to be traded in capacity markets so that it incentivizes investments in demand response resources.The refined product included information about location,flexibility and

256、 modification of the firm capacity profile calculation.Last,we proposed a business model for an aggregator that provides priority pricing contracts to consumers and participates in capacity markets.We analysed their relationship with consumers and how they can build their value proposition,value cre

257、ation and delivery and value capture.We also studied the aggregator relationship with other market entities.We focused on their relationship with retailers and what information-responsibilities need to be exchanged so that penalties due to inconsistencies in actual and projected load are avoided.32

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