肖云鹏：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 电气工程学院肖云鹏 2023年9月 目 录CONTES 2/30 标题01PART容量市场的作用及问保障灵活调节资源充裕性的容量市场出清模型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 结论与展望 Part1 作用与问题ü容量市场的建设意义Ø主要目是保障系统充裕度。•新型电力系统不确定性极强可再生能源波动、高峰期电力需求或突发情况威胁系统供电可靠性•火机组投资成本回收困难利用小时数较低的传无法在能量市场中获得持久稳定的收益 •市场多样性主体增，电源/负荷结构变化较快，导致多样化能源需求•竞争定价电由供需关系决统容量不足时电价高涨用户电成本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创造长期价格信号引导资源投电力市场特征 提供更稳的机制首标拥有足够的能来满足力需求，在高峰期或突发情况下保障系统安全运行。3/0 Part1 作用与问题üPJM容量市场的发展 20719 2015 可靠性定价市场模式（RPM）容量信用市场模式（CM）PJM容量市场建立容量表现市场阶段容量义务分配模式改革前•LSE承担责任 通过自供给或双边协商方实现•LSE承担责任 通过内集中、自供给边协商方实现 对原有资源做了进一步改善基本容量Base表现CP•PJ通过拍卖购买后分配LSEJ从或、双边协商方实 l历程 4/30 Part1 作用与问题üPJM容量市场的发展lR架构供给侧资源电 规划中的资源聚合能效需求输电升级项目 P JM负荷供应商LSE12LSE3…双边交易拍卖市场基础（BRA）追加（IA）双边合同合同出售容量购买容量容量购买费用分摊自给在BR中申报5/30 Part1 作用与问题üPJM容量市场的发展lR交易时序PJM市场交易时序 六月九月 基本拍卖场五月七月二月容量交付年三年 次20个月13第一次追加拍卖第二次追加拍卖三条件采购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 6/30 Part1 作用与问题üPJM容量市场的发展lR模式需求曲线制定—可变容量需求曲线（Varible Rsource Rqiremnt ,VR）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 NetCon0.75 NetCon IRM-0.2%IR+2.9%IRM+8.%价格上限:联合循环燃气轮机新进入成本净额的0% A(0.98 IRM, 1.5Net Con）需求曲线与价格上限的交叉点B1207)C(. I,) 于存在区域输电约束的地区,每个域（LDA可以有单独的需求曲线。 根据十年一遇失负荷期望(LOE)要求计算得出。 容量需求—资源充裕性目标设定,即峰值负荷加所的装机备用度(IR) 7/30 Part1 作用与问题üPJM容量市场的发展lR出清流程：供给容量资源和报价需求基本拍卖市场中各LDA的VR求解优化算法出清结果•区域容量价格输送权 （CTR）约束区域限制出清容量 8/30 持续时间Part1 作用与问题üPJM容量市场的发展信用（CM模式）l不同模式对比： 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需求曲线提前3年的瞻容量拍卖市场采用倾斜需求曲线 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、聚合能效以及规划中的参与市场竞争考虑传约束的分区定价全区域统一定价不考虑间传输约束仅限在役发电机组所有价格下都固资源充裕性目标上，导致剧烈波动内部受地产生可靠性问题资源利充分开展日、月度和多 9/30 10/3 Part1 作用与问题ü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。能源、储等新兴市场主体的有效容量评估困难。 问题 Energy Conversion and Economics DOI: 10.1049/enc2.12050 ORIGINAL RESEARCH PAPER Distributed control strategy for transactive energy prosumers in real-time markets Chen Yin 1 Ran Ding 2 Haixiang Xu 2 Gengyin Li 1 Xiupeng Chen 3 Ming Zhou 1 1 State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of Electrical and Electronic Engineering, North China Electric Power University, Beijing, China 2 State Grid Jibei Electric Power Co., Ltd., Beijing, China 3 Engineering and Technology Institute Groningen, University of Groningen, Groningen, The Netherlands Abstract The increasing penetration of distributed energy resources (DERs) has led to increasing research interest in the cooperative control of multi-prosumers in a transactive energy (TE) paradigm. While the existing literature shows that TE offers significant grid flexibility and economic benefits, few studies have addressed the incorporation of security constraints in TE. 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 time intervals and incorporate the following generation and consumption predictions. Owing to the computational burden and individual privacy issues, an efficient distributed algorithm is developed to solve the optimal power flow problem. The strong coupling between pro- sumers through power networks is removed by introducing auxiliary variables to acquire locational marginal prices (LMPs) covering energy, congestion, and loss components. Case studies based on the IEEE 33-bus system demonstrated the efficiency and effectiveness of the proposed method and model. 1 INTRODUCTION Driven by growing environmental and climate concerns, dis- tributed energy resources are increasing in the penetration rate of distribution networks, and distribution power networks are undergoing a fundamental transition. In traditional power grids, users only have load characteristics, but with the rapid develop- ment of distributed power generation technology and Internet technology, users can gradually manage internal power genera- tion and storage resources, and deliver electrical energy, namely prosumers.Prosumersareend-useconsumerswithlocalgenera- tion sources, for example, photovoltaic (PV) panels and/or bat- tery, and are able to manage their consumption and production of energy actively. Under the promotion of the market-based trading, these prosumers are held as independent stakeholders toparticipateinpowermarketoperation[1].Traditionally,distri- bution power networks are kept stable and secure by centralized This 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 is properly cited. © 2022 The Authors. Energy Conversion and Economics published by John Wiley 3:1–10. wileyonlinelibrary.com/iet-ece 1 26341581, 2022, 1, Downloaded from https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/enc2.12050 by CochraneChina, Wiley Online Library on [13/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Promotional Article added by the ECE, not included in the original slides 目 录CONTES 标题01PART容量市场的作用及问02T保障灵活调节资源充裕性的容量市场出清模型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结论与展望 ü容量市场出清模型构建根据各类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，分考虑长期有效和煤电深调容量的市场出清模型。 , ,,max SW= ( ) d g g w w s s e e l n l n i i h h k k m ml n i h k md P c P c P c P c P        l目标函数：社会福利最大化负荷火电风光伏储能传统只考虑保障负荷峰值时段，未来在高比例新能源接入的型电力系统场景下，新能源波动性和不确定将对电力的调能力灵活爬坡调节力提出了更要求。价格/(元(MW·天) 出清价格 容量/MW 容量市场需求曲线资源供应曲线Ø前考虑保障负荷峰值时段充裕度灵活爬坡能力充裕度。下一步计划将似调峰 Part2 出清模型 12/30 Part2 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的市场出清模型。 ,= : , n n n n n g w s e i h k mi h k m CIO d Cap sn l n ns l P P P P P P n                 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平衡约束区域向传输的容量•火电机组、储能提供采用嵌入式优化考虑新源不确定性波动 , , min : , D wD sD n n nn h k g e CIO i m s n ni m u sD P n F P R R p n F F F F            系统灵活爬坡调节容量需求负荷出力的不确定性偏差 , , ( ( ))min 0: , D wD sD n n nn h k g e CIO R n i m sn ni FR mP P dn sD n F F F F              ü市场出清模构建l约束条件：•机组中标需求 13/0 l约束条件：•供需平衡各类型机组中标容量约束、需求约束火电 ,max ,min ,max ,max ,min ,max ,max ,min ,max ,max ,min ,max 0 : , , 0 : , , 0 : , , 0 : , , e e e e m m m m e e e fce fce m m m m m e e e ep ep m m m m m e e e en en m m m m m P CP m F R P m P F P m P F P m                      新能源 ,max ,min ,max ,max ,min ,max 0 : , , 0 : , , w w w w h h h h s s s s k k k k P CP h P CP k           ,max ,min ,max ,max ,min ,max ,max ,min ,max ,max ,min ,max 0 : , , 0 : , , 0 : , , 0 : , , g g g g i i i i g g g fcg fcg i i i i i g g g gp gp i i i i i g g g gn gn i i i i i P CP i F R P i P F P i P F P i                       由边际带负荷能力的有效容量评估方法得到，火电资源参与市场可提供所最大灵活爬坡调节容量需求 ,max , , ,min ,max , , 0 : , , , d d l n l n d d l n l n P P l n     区域间传输容量 max max ,min ,max max max ,min ,max max max ,min ,max : , 0: : , 0: : , , , CIO CIO CIO sn sn sn sn sn CIO CIO CIO sn ns sn CIO FCIO FCIO sn sn sn sn sn CIO CIO FCIO sn ns sn CIO CIO L L sn sn sn sn sn sn n L P L P P L F L F F L P F L n s                          Part2 出清模型ü容量市场出清模型构建 储能 14/30 目 录CONTES标题01PART容量市场的作用及问题02T保障灵活调节资源充裕性的容量市场出清模型03PART 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满足系统灵活爬坡调节需求的容 量价格Capn 坡调节预测需求价格不确定性偏差需求EFRn 灵活爬坡调节向上差需求价格下 UFRDN n UFRIN n ü与规则设计 16/30 Part3 定价与结算机制②灵活爬坡调节不确定性偏差需求价格 偏差 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 向上偏差 价格 ( ) ( ),min ( ) ( ),min (2( ) )) (2(,max ( ) ( )( ) ( ) ( )( ) ( ) ( )( ) n n FRup FRdn n n F H Rup FRdn n n u UFRIN FRup FRdn n n h n hwD h FRup FRdn n H k n H ksD k FRup FRdFR p FRdn n H K N n nD n n n n L P L P L D                           u u u u u u ) )) , K N n n   灵活爬坡调节需求 向下偏差 价格 ( ) ( ),max (2 ) (2 ),max ( ),min ( ) ( ) ( ) ( ) ( )( ) n n UFRDN FRup FRdn n n H K N h n H K N hwD h FRup FRdn n H K N k n H K N ksD k FRup FRd FRup FRdn n n FRup FRdn n n FRup nn H K n nD n L P L P L D                                    u u u u u ( )( ) , n H KFRd nnn n  u p保障系统灵活性的容量价格①预测需求ü容量市场机制与规则设计l定： 17/30 Part3 定价与结算机制l容量市场结算机制：火电机组、储能站•保障负荷峰值时段系统充裕性的容量收益 +灵活 : : :( ) , n n n g Cap g FRup FRdn g i ni i ni ni iP F i         : : :( ) , n n n e Cap e FRup FRdn e m nm m nm nm mP F m        风电场、光伏站•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灵活调节需求成本 ,exp ,max : : : ,min : ( ) , ( ) n n n n EFR wD UFRDN wD nh h nh hw Cap w j nh h UFRIN wD nh h P PP h P                    ,exp ,max : : : ,min : ( ) , ( ) n n n n EFR sD UFRDN sD nk k nk ks Cap s k nk k UFRIN sD nk k P PP k P                    •给出火电、新能源储等不同类型资相应的规则。•有效区分不对于保障负荷峰值时段系统充裕度、灵活爬坡调节力有容量贡献与引起灵活爬坡调需求的责任。ü机制与规则设计 18/30 Part3 定价与结算机制l容量市场结算机制：•给出火电、新能源储等不同类型资相应的规则。•有效区分不对于保障负荷峰值时段系统充裕度、灵活爬坡调节力有容量贡献与引起灵活爬坡调 需求的责任。 负荷•向容量市场支付保障峰值时段系统充裕性+系统灵活性的费用 ,exp ,min , ,max ( ) ,EFR D UFRDN Dn n n nd Cap d n n l n UFRIN Dl n n D DP n D               区域间传输容量•考虑了之的价格差异，当通道发生阻塞时会产生盈余应分配给对电权所有者。 ( )Cap CIO FRup FRdn CIOsn n sn n n snP F      ü机制与规则设计 19/30 20/3 Part3 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，激励市场主体动参，促进资源优化配置。 社会效率(Social Eficeny) 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。 ü容量性质验证 21/30 Part3 定价与结算机制收支平衡(Budget alnce)•市场运营机构应为非盈利机构，市场的流入和出资金应相等，即收支平衡。容量流入资金： IN Cap d EFR exp UFRDN D,min UFRIN D,max , EFR wD,exp UFRDN wD,max UFRIN wD,min ( ( ) ) ( ) ( ( )) n l n n n n n n n n l n n n h n h n hh h P D D D P P P                                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 EFR sD,exp UFRDN sD,max UFRIN sD,min ( ) ( ( )) n k n k n kk kP P P           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： Cap g FRup FRdn g OT Cap e FRup FRdn e Cap w Cap s Cap CIO ( ( ) ) ( ( ) ) ( n i n n ii n m n n mm n h n kh k n sn P F P F P P P                          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 FRup FRdn CIO( ) ) n n snsn F   区域传输容量阻塞盈余 根据供需平衡约束和KT条件，可以推导出IN OT  ü容量市场机制性质验证 2/30 Part3 定价与结算机制个体理性(Indivual Rtionality)•指市场成员愿意主动参与市场，即各成员的净利润非负。 以火电机组为例利润： : : : : : : ,max ,min ,max ,min ,max ,min ,max ,min , ( ) ( ) ( ) ( ) ( n n n n n n g cap g FRup FRdn g g g i ni i ni ni i i i cap g g FRup FRdn g ni i i ni ni i g g gp gp gn gn g i i i i i i i gp gp gn i i i P F c P c P F P                                        max ,min ,max ,min ,max ,max ,max ,max ,max ,max ) ( ) 0 gn fcg fcg g i i i i g g gp gn g g fcg i i i i i i i F P R P                根据KT条件，可以推导出ü容量市场机制质验证 Part3 定价与结算机制激励相容(Incetiv Compatibly)•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，即市场成员根据出清价格计算使得化出力计划根据成员报价出清力计划。•量出清模型 Tmin s.t. : , ( , , ) , n n n r Cap FR UFR r r r r r r n nr r r r r r r n r                 r rx,y,z c x A x A y A z B τ x y z Χ •市场成员根据出清价格以自身利润为目标进行优模型 max T : : : max s.t. ( , , ) , n n n r Cap FR UFR nr r nr r nr r r r r r r R r          r r r rx ,y ,z ρ x ρ y ρ z c x x y z Χ max * * * T * : : : : : : T * * * ( , , ) ( ) ( ) ( )min ( , , ) , n n n n n n n n n n Cap FR UFR Cap FR UFR r nr nr nr nr r nr r nr r rr r Cap FR UFR n r r r r r r nn r r r r r r r R r                             r τ 0 ρ ρ ρ ρ x ρ y ρ z c x τ A x A y A z B x y z Χ 对偶转换 max * * * * * * * * * T * : : : : : :[ ( , , ) ( )] 0 n n n n n n Cap FR UFR Cap FR UFR r nr nr nr nr r nr r nr r rr R            rρ ρ ρ ρ x ρ y ρ z c x由KT可得，目标函数满足当 上式等号成立，即市场员使得自身利润最大化的容量策略与市场出清的中容量一致 * * *, , r r r r r r  x x y y z z ü容量市场机制性质验证 23/0 Received: 16 December 2020 Revised: 11 April 2021 Accepted: 17 April 2021 Energy Conversion and Economics DOI: 10.1049/enc2.12036 ORIGINAL RESEARCH PAPER Option-based portfolio risk hedging strategy for gas generator based on mean-variance utility model Shuying Lai Jing Qiu Yuechuan Tao School of Electrical and Information Engineering, The University of Sydney, Sydney, Australia Abstract Natural gas generators are promising devices for reducing greenhouse gas emissions. How- ever, gas generators encounter difficulties in the bid-to-sell process based on a relatively high levelised cost of energy for power generation. Therefore, a novel risk hedging strategy is developed based on the mean-variance portfolio theory to reduce the operational risks of 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, and short call option. Two types of energy storage devices are used to facilitate the risk hedging process, namely power-to-gas and battery devices. Simulation results demonstrate that the proposed risk hedging model can ensure higher profits for gas generators with reduced risk compared to the traditional risk hedging model and a model using only one type of option. Additionally, the varied risk preferences of gas generators lead to varied portfolio combinations. The more risk averse a gas generator, the more likely the long-put option will be utilised. In contrast, the less risk averse a gas generator, the more likely that short calls will be utilised. 1 INTRODUCTION 1.1 Background and motivation Based on an increased focus on the reduction of greenhouse gases and detrimental gas emissions, as well as on the fast response ability of natural gas generators, the use of natural gas to generate electricity has become pervasive [1–5]. In some countries such as China and Australia, coal power generation serves the baseload (i.e. customers), while natural gas genera- tors are used primarily for peak hours, when electricity prices are high or fast response regulation is required. This opera- tion process incurs high risks because gas generators generate electricity only when electricity prices are high. Therefore, as a type of thermal power generation (i.e. power generation process in which heat energy is converted into electricity), natural-gas- fired power generation requires a relatively high levelised cost of energy (LCOE) compared to coal-fired power generation. LCOE is defined as the average price per unit output required for a plant to break even over its operating lifetime [6]. There- fore, in the bid-to-sell process, gas generators will bid at prices This 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 is properly cited. © 2021 The Authors. Energy Conversion and Economics published by John Wiley 3:20–30. Promotional Article added by the ECE, not included in the original slides 目 录CONTES标题0