保障灵活调节资源充裕性的容量市场机制.pdf

保障灵活调节资源充裕性的容量市场机制西安交通大学 电气工程学院西安交通大学 电气工程学院肖云鹏肖云鹏2023年9月2023年9月目 录CONTENTS2/30标题0101P A R T容量市场的作用及问题保障灵活调节资源充裕性的容量市场出清模型保障灵活调节资源充裕性的容量市场定价与结算机制保障灵活调节资源充裕性的容量市场仿真测算结论与展望Part1 作用与问题容量市场的建设意义容量市场的建设意义 容量市场的主要目的是保障系统充裕度。新型电力系统不确定性极强可再生能源波动性、高峰期电力需求或突发情况威胁系统供电可靠性火电机组投资成本回收困难利用小时数较低的传统机组无法在电能量市场中获得持久稳定的收益市场多样性市场主体增多，电源/负荷结构变化较快，导致多样化的能源需求竞争性定价电价由市场供需关系决定，系统容量不足时电价高涨，用户用电成本大大提高可靠性容量保障火电固定成本回收容量市场建设意义容量市场建设意义创造长期价格信号引导资源投资电力市场特征电力市场特征提供更稳定的定价机制首要目标是确保电力系统拥有足够的发电能力来满足电力需求，在高峰期或突发情况下保障系统安全运行。3/30Part1 作用与问题PJM容量市场的发展PJM容量市场的发展200719992015可靠性定价市场模式可靠性定价市场模式（RPM）（RPM）容量信用市场模式容量信用市场模式（CCM）（CCM）PJM容量市场建立PJM容量市场建立容量表现市场阶段容量表现市场阶段容量义务分配模式容量义务分配模式改革前LSE承担容量责任 LSE通过自供给或双边协商方式实现LSE承担容量责任 LSE通过场内集中、自供给、双边协商方式实现对原有容量市场资源做了进一步改善基本容量Base容量表现CPPJM通过拍卖市场购买后分配LSE通过PJM从拍卖市场分配或自供给、双边协商方式实现l 容量市场发展历程4/30Part1 作用与问题PJM容量市场的发展PJM容量市场的发展l RPM市场架构供给侧资源发电资源规划中的资源聚合资源能效资源需求侧资源输电升级项目P J M负荷供应商LSE1负荷供应商LSE2负荷供应商LSE3双边交易拍卖市场基础拍卖（BRA）追加拍卖（IA）双边合同双边合同出售容量购买容量容量购买费用分摊自供给在BRA中申报5/30Part1 作用与问题PJM容量市场的发展PJM容量市场的发展l RPM市场交易时序PJM市场交易时序六月九月基本拍卖市场五月七月二月容量交付年三年次年六月20个月10个月3个月第一次追加拍卖第二次追加拍卖第三次追加拍卖条件追加拍卖采购LDA的额外容量，以解决由骨干传输线延迟引起的可靠性问题持续开展的双边市场6/30Part1 作用与问题PJM容量市场的发展PJM容量市场的发展l RPM模式需求曲线制定可变容量需求曲线（Variable Resource Requirement,VRR）曲线取决于系统可靠性需求和新建机组的净成本,对市场出清价格有重要影响。曲线取决于系统可靠性需求和新建机组的净成本,对市场出清价格有重要影响。1.5 Net Cone0.75 Net ConeIRM-0.2%IRM+2.9%IRM+8.8%价格上限:联合循环燃气轮机新进入成本净额的150%A(0.998 IRM,1.5 Net Cone）需求曲线与价格上限的交叉点B(1.029 IRM,0.75 Net Cone)C(1.088 IRM,0)IRM对于存在区域输电约束的地区,每个区域（LDA）可以有单独的需求曲线。根据十年一遇失负荷期望(LOLE)要求计算得出。容量需求根据资源充裕性目标设定,即峰值负荷加上所需的装机备用裕度(IRM)7/30Part1 作用与问题PJM容量市场的发展PJM容量市场的发展l RPM市场出清流程：供给容量资源供给容量和报价需求基本拍卖市场中各LDA的VRR求解优化算法出清结果区域出清容量区域容量价格容量输送权（CTR）价格约束区域限制约束出清容量约束8/30持续时间Part1 作用与问题PJM容量市场的发展PJM容量市场的发展容量信用市场（CCM模式）l 不同市场模式对比：可靠性定价市场（RPM模式）提前1年的容量拍卖市场采用垂直的容量需求曲线提前3年的前瞻性容量拍卖市场采用倾斜的容量需求曲线 需求曲线制定供给侧资源定价模式允许需求侧资源、输电升级项目、聚合资源、能效资源以及规划中的资源参与市场竞争考虑传输约束的分区定价全区域统一定价不考虑区域间传输约束仅限在役发电机组所有价格下的容量需求都固定在资源充裕性目标上，导致价格剧烈波动区域内部受约束地区产生可靠性问题资源利用不充分开展日前、月度和多月容量市场9/3010/30Part1 作用与问题当前容量市场存在的问题当前容量市场存在的问题l 新型电力系统对充裕性需求多样化。l 新能源、储能等新兴市场主体的有效容量评估困难。问问题题Energy Conversion and EconomicsDOI:10.1049/enc2.12050ORIGINAL RESEARCH PAPERDistributed control strategy for transactive energy prosumers inreal-time marketsChen Yin1Ran Ding2Haixiang Xu2Gengyin Li1Xiupeng Chen3Ming Zhou11 State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources,School of Electrical and Electronic Engineering,North China Electric Power University,Beijing,China2 State Grid Jibei Electric Power Co.,Ltd.,Beijing,China3 Engineering and Technology Institute Groningen,University of Groningen,Groningen,The NetherlandsAbstractThe increasing penetration of distributed energy resources(DERs)has led to increasingresearchinterestin thecooperativecontrolofmulti-prosumers inatransactiveenergy(TE)paradigm.While the existing literature shows that TE offers significant grid flexibility andeconomic benefits,few studies have addressed the incorporation of security constraints inTE.Herein,a market-based control mechanism in real-time markets is proposed to eco-nomically coordinate the TE among prosumers while ensuring secure system operation.Considering the dynamic characteristics of batteries and responsive demands,a model pre-dictive control(MPC)method is used to handle the constraints between different timeintervals and incorporate the following generation and consumption predictions.Owing tothe computational burden and individual privacy issues,an efficient distributed algorithmis developed to solve the optimal power flow problem.The strong coupling between pro-sumers through power networks is removed by introducing auxiliary variables to acquirelocational marginal prices(LMPs)covering energy,congestion,and loss components.Casestudies based on the IEEE 33-bus system demonstrated the efficiency and effectiveness ofthe proposed method and model.1INTRODUCTIONDriven by growing environmental and climate concerns,dis-tributed energy resources are increasing in the penetration rateof distribution networks,and distribution power networks areundergoing a fundamental transition.In traditional power grids,users only have load characteristics,but with the rapid develop-ment of distributed power generation technology and Internettechnology,users can gradually manage internal power genera-tion and storage resources,and deliver electrical energy,namelyprosumers.Prosumersareend-useconsumerswithlocalgenera-tion sources,for example,photovoltaic(PV)panels and/or bat-tery,and are able to manage their consumption and productionof energy actively.Under the promotion of the market-basedtrading,these prosumers are held as independent stakeholderstoparticipateinpowermarketoperation1.Traditionally,distri-bution power networks are kept stable and secure by centralizedThis is an open access article under the terms of the Creative Commons Attribution License,which permits use,distribution and reproduction in any medium,provided the original work isproperly cited.2022 The Authors.Energy Conversion and Economics published by John Wiley&Sons Ltd on behalf of The Institution of Engineering and Technology and the State Grid Economic&Technological Research Institute Co.,Ltd.control actions.However,this centralized network architectureis of great concern,because sending all this information to asystem operator introduces scalability,complexity and privacyissues 2.Consequently,more decentralized network controland optimization techniques are required to support the energyamong large numbers of prosumers 3.It is necessaryto coordinatethemarket and controland man-age the system through economic value to ensure that pro-sumers participate in market transactions and the safe and flex-ible operation of the system,the existing research about mech-anism design for prosumers can be classified into two cate-gories:distributed optimization-based method 4 and gametheory based method 5.In the former approach,all prosumersare willing to collaborate to achieve a certain goal,for example,maximizing social welfare.A non-profit agent,for example,sys-tem operator(SO),is programmed to set prices and individualprosumers choose their corresponding strategies as price takes.Energy Convers.Econ.2022;3: 26341581,2022,1,Downloaded from https:/ by CochraneChina,Wiley Online Library on 13/11/2022.See the Terms and Conditions(https:/ Wiley Online Library for rules of use;OA articles are governed by the applicable Creative Commons LicensePromotional Article added by the ECE,not included in the original slides目 录CONTENTS标题0101P A R T容量市场的作用及问题0202P A R T保障灵活调节资源充裕性的容量市场出清模型保障灵活调节资源充裕性的容量市场定价与结算机制保障灵活调节资源充裕性的容量市场仿真测算结论与展望容量市场出清模型构建容量市场出清模型构建根据各类型资源有效容量评估方法、系统容量充裕度评估方法、关键断面约束辨识技术，构建充分考虑长期有效容量和煤电深调容量的容量市场出清模型。,max SW=()dggwwsseel nl niihhkkmml nihkmdPc Pc Pc Pc Pl 目标函数：社会福利最大化负荷火电风电光伏储能传统容量市场只考虑保障负荷峰值时段系统充裕度，未来在高比例新能源接入的新型电力系统场景下，新能源的波动性和不确定性将对电力系统的调峰能力、灵活爬坡调节能力提出了更高的要求。价格价格/(元(元/(MW天)天)出清价格出清价格容量容量/MW容量市场需求曲线容量市场需求曲线资源供应曲线资源供应曲线 目前考虑保障负荷峰值时段系统充裕度、灵活爬坡能力充裕度。下一步计划将类似考虑调峰能力充裕度。Part2 出清模型12/30Part2 出清模型 根据各类型资源有效容量评估方法、系统容量充裕度评估方法、关键断面约束辨识技术，构建充分考虑长期有效容量和煤电深调容量的容量市场出清模型。,=:,nnnnngwseihkmihkmCIOdCapsnl nnslPPPPPPn保障负荷峰值时段系统充裕度的容量供需平衡约束满足系统灵活爬坡调节需求的容量供需平衡约束区域s向区域n传输的容量火电机组、储能提供灵活爬坡调节容量采用嵌入式优化考虑新能源不确定性波动,min :,DwDsDnnnnhkgeCIOimsnnimusDPnFPRR pnFFFF系统灵活爬坡调节容量需求考虑负荷、新能源出力的波动量的不确定性偏差,()min 0:,DwDsDnnnnhkgeCIORnimsnniFRmPPdnsDnFFFF 容量市场出清模型构建容量市场出清模型构建l 约束条件：供需平衡约束各类型机组中标容量约束、容量需求约束13/30l 约束条件：供需平衡约束各类型机组中标容量约束、容量需求约束火电,max,min,max,max,min,max,max,min,max,max,min,max0:,0:,0:,0:,eeeemmmmeeefcefcemmmmmeeeepepmmmmmeeeenenmmmmmPCPmFR PmPFPmPFPm新能源,max,min,max,max,min,max0:,0:,wwwwhhhhsssskkkkPCPhPCPk,max,min,max,max,min,max,max,min,max,max,min,max0:,0:,0:,0:,ggggiiiigggfcgfcgiiiiiggggpgpiiiiiggggngniiiiiPCPiFR PiPFPiPFPi由边际带负荷能力的有效容量评估方法得到的，火电资源参与容量市场可提供的有效容量火电资源所能提供的最大灵活爬坡调节容量容量需求,max,min,max,0:,ddl nl nddl nl nPPl n区域间传输容量maxmax,min,maxmaxmax,min,maxmaxmax,min,max:,0:,0:,CIOCIOCIOsnsnsnsnsnCIOCIOCIOsnnssnCIOFCIOFCIOsnsnsnsnsnCIOCIOFCIOsnnssnCIOCIOLLsnsnsnsnsnsnnLPLPPLFLFFLPFLns Part2 出清模型容量市场出清模型构建容量市场出清模型构建储能14/30目 录CONTENTS标题0101P A R T容量市场的作用及问题0202P A R T保障灵活调节资源充裕性的容量市场出清模型0303P A R T保障灵活调节资源充裕性的容量市场定价与结算机制保障灵活调节资源充裕性的容量市场仿真测算结论与展望Part3 定价与结算机制l 容量市场定价机制：容量市场出清价格保障负荷峰值时段系统容量充裕度的容量价格满足系统灵活爬坡调节需求的容量价格Capn灵活爬坡调节预测需求价格灵活爬坡调节不确定性偏差需求价格EFRn灵活爬坡调节向上偏差需求价格灵活爬坡调节向下偏差需求价格UFRDNnUFRINn容量市场机制与规则设计容量市场机制与规则设计16/30Part3 定价与结算机制灵活爬坡调节不确定性偏差需求价格 灵活爬坡调节不确定性偏差需求价格与负荷、风电、光伏出力波动量的不确定性偏差值有关。灵活爬坡调节需求向上向上偏差偏差价格价格()(),min()(),min(2()(2(,max()()()()()()()()()nnFRupFRdnnnFHRupFRdnnnuUFRINFRupFRdnnnhnhwDhFRupFRdnnH knH ksDkFRupFRdFR pFRdnnH KN nnDnnnnLPLPLD uuuuuu),KN nn灵活爬坡调节需求向下向下偏差偏差价格价格()(),max(2)(2),max(),min()()()()()()nnUFRDNFRupFRdnnnHKN hnHKN hwDhFRupFRdnnHKN knHKN ksDkFRupFRdFRupFRdnnnFRupFRdnnnFRupnnHK nnDnLPLPLD uuuuu()(),nHKFRdnnnnup保障系统灵活性的容量价格保障系统灵活性的容量价格灵活爬坡调节预测需求价格容量市场机制与规则设计容量市场机制与规则设计l 容量市场定价机制：17/30Part3 定价与结算机制l 容量市场结算机制：火电机组、储能电站保障负荷峰值时段系统充裕性的容量收益 +保障系统灵活性的容量收益:(),nnngCapgFRupFRdngin iin in iiPFi:(),nnneCapeFRupFRdnemn mmn mn mmPFm风电场、光伏电站提供容量保障负荷峰值时段系统充裕性的收益 -分摊由于自身出力波动造成的灵活调节需求成本,exp,max:,min:(),()nnnnEFRwDUFRDNwDn hhn hhwCapwjn hhUFRINwDn hhPPPhP,exp,max:,min:(),()nnnnEFRsDUFRDNsDn kkn kksCapskn kkUFRINsDn kkPPPkP给出火电、新能源、储能等不同类型资源相应的结算规则。有效区分不同类型资源的对于保障负荷峰值时段系统充裕度、灵活爬坡调节能力充裕度的有效容量贡献与引起灵活爬坡调节需求的责任。容量市场机制与规则设计容量市场机制与规则设计18/30Part3 定价与结算机制l 容量市场结算机制：给出火电、新能源、储能等不同类型资源相应的结算规则。有效区分不同类型资源的对于保障负荷峰值时段系统充裕度、灵活爬坡调节能力充裕度的有效容量贡献与引起灵活爬坡调节需求的责任。负荷向容量市场支付保障负荷峰值时段系统充裕性+保障系统灵活性的容量费用,exp,min,max(),EFRDUFRDNDnnnndCapdnnl nUFRINDlnnDDPnD 区域间传输容量考虑了区域之间的价格差异，当区域间传输通道发生阻塞时会产生阻塞盈余，应分配给对应输电权所有者。()CapCIOFRupFRdnCIOsnnsnnnsnPF容量市场机制与规则设计容量市场机制与规则设计19/3020/30Part3 定价与结算机制良好的市场机制应满足社会效率、收支平衡、个体理性和激励相容等性质，激励市场主体主动参与，促进资源优化配置。社会效率社会效率(Social Efficiency)所提出的容量市场鲁棒优化出清模型的目标函数为最大化社会福利，即出清结果能够在应对负荷、风电、光伏的任何不确定波动情况下实现尽可能大的社会福利，因此可以满足社会效率性质。容量市场机制性质验证容量市场机制性质验证21/30Part3 定价与结算机制收支平衡(Budget Balance)市场运营机构应为非盈利机构，市场的流入和流出资金应相等，即收支平衡。容量市场流入资金：INCapdEFRexpUFRDND,minUFRIND,max,EFRwD,expUFRDNwD,maxUFRINwD,min()()()nl nnnnnnnnlnnnhnhnhhhPDDDPPP 负荷为引起峰值时段需求、引起灵活调节需求所支付的费用风EFRsD,expUFRDNsD,maxUFRINsD,min ()()nknknkkkPPP 电为引起灵活调节需求所支付的费用光伏为引起灵活调节需求所支付的费用容量市场流出资金：CapgFRupFRdngOTCapeFRupFRdneCapwCapsCapCIO()()(ninniinmnnmmnhnkhknsnPFPFPPP 支付给火电、储能保障负荷峰值时段系统充裕度、满足系统灵活调节需求的费用支付给风电和光伏保障负荷峰值时段系统充裕度的费用FRupFRdnCIO()nnsnsnF 区域传输容量阻塞盈余根据供需平衡约束和KKT条件，可以推导出INOT容量市场机制性质验证容量市场机制性质验证22/30Part3 定价与结算机制个体理性(Individual Rationality)个体理性指市场成员愿意主动参与市场，即各市场成员的净利润非负。以火电机组为例火电机组利润为：:,max,min,max,min,max,min,max,min,()()()()(nnnnnngcapgFRupFRdngggin iin in iiiicapggFRupFRdngn iiin in iigggpgpgngngiiiiiiigpgpgniiiPFc PcPFPmax,min,max,min,max,max,max,max,max,max)()0gnfcgfcggiiiigggpgnggfcgiiiiiiiFPR P根据KKT条件，可以推导出容量市场机制性质验证容量市场机制性质验证Part3 定价与结算机制激励相容(Incentive Compatibility)激励相容是指市场成员追求自身利润最大的结果与市场整体实现社会福利最大化的结果一致，即市场成员根据市场出清价格计算使得自身利润最大化的出力计划与市场根据成员报价出清的出力计划一致。容量市场出清模型Tmins.t.:,(,),nnnrCapFRUFRrrrrrrnnrrrrrrrnrrrx,y,zc xAxAyAzBxy z市场成员根据市场出清价格以自身利润最大化为目标进行优化的模型maxT:maxs.t.(,),nnnrCapFRUFRn rrn rrn rrrrrrrRrrrrrx,y,zxyzc xxy zmax*T*:T*(,)()()()min(,),nnnnnnnnnnCapFRUFRCapFRUFRrn rn rn rn rrn rrn rrrrrCapFRUFRnrrrrrrnnrrrrrrrRrr0 xyzc xAxAyAzBxy z对偶转换max*T*:(,)()0nnnnnnCapFRUFRCapFRUFRrn rn rn rn rrn rrn rrrrRrxyzc x由KKT可得，目标函数满足当 上式等号成立，即市场成员使得自身利润最大化的容量策略与容量市场出清的中标容量一致*,rrrrrrxxyy zz容量市场机制性质验证容量市场机制性质验证23/30Received:16 December 2020Revised:11 April 2021Accepted:17 April 2021Energy Conversion and EconomicsDOI:10.1049/enc2.12036ORIGINAL RESEARCH PAPEROption-based portfolio risk hedging strategy for gas generatorbased on mean-variance utility modelShuying LaiJing QiuYuechuan TaoSchool of Electrical and Information Engineering,The University of Sydney,Sydney,AustraliaAbstractNaturalgasgeneratorsarepromisingdevicesforreducinggreenhousegasemissions.How-ever,gas generators encounter difficulties in the bid-to-sell process based on a relativelyhigh levelised cost of energy for power generation.Therefore,a novel risk hedging strategyis developed based on the mean-variance portfolio theory to reduce the operational risksof gas generators and enhance their profits.Three types of options are utilised and com-bined to form a portfolio of financial hedges:the short put option,long put option,andshort call option.Two types of energy storage devices are used to facilitate the risk hedgingprocess,namely power-to-gas and battery devices.Simulation results demonstrate that theproposed risk hedging model can ensure higher profits for gas generators with reducedrisk compared to the traditional risk hedging model and a model using only one type ofoption.Additionally,the varied risk preferences of gas generators lead to varied portfoliocombinations.The more risk averse a gas generator,the more likely the long-put optionwill be utilised.In contrast,the less risk averse a gas generator,the more likely that shortcalls will be utilised.1INTRODUCTION1.1Background and motivationBased on an increased focus on the reduction of greenhousegases and detrimental gas emissions,as well as on the fastresponse ability of natural gas generators,the use of naturalgas to generate electricity has become pervasive 15.In somecountries such as China and Australia,coal power generationserves the baseload(i.e.customers),while natural gas genera-tors are used primarily for peak hours,when electricity pricesare high or fast response regulation is required.This opera-tion process incurs high risks because gas generators generateelectricity only when electricity prices are high.Therefore,as atype of thermal power generation(i.e.power generation processin which heat energy is converted into electricity),natural-gas-fired power generation requires a relatively high levelised costof energy(LCOE)compared to coal-fired power generation.LCOE is defined as the average price per unit output requiredfor a plant to break even over its operating lifetime 6.There-fore,in the bid-to-sell process,gas generators will bid at pricesThis is an open access article under the terms of the Creative Commons Attribution License,which permits use,distribution and reproduction in any medium,provided the original work isproperly cited.2021 The Authors.Energy Conversion and Economics published by John Wiley&Sons Ltd on behalf of The Institution of Engineering and Technology and the State Grid Economic&Technological Research Institute Co.,Ltd.higher than their LCOEs,making them much more likely to failin bidding compared to coal-fired generators.As a result,gasgenerators cannot sell sufficient electricity and significant risksare incurred.Althoughgasgeneratorsincurtheriskofbeingunabletosuc-ceedinthebid,theystillhaveapromisingfuture.Thisisbecausenatural-gas-fired power generation plays a critical role in con-verting gas back into electricity and selling energy in electric-ity markets.Based on emerging power-to-gas(P2G)technology,renewable energy could be stored economically on a large scalein the form of natural gas 7.Because gas generators play animportant role in power systems,it is necessary to implementcertain hedging tools to reduce the risks associated with thesegenerators.1.2Literature reviewPrevious studies have utilise