基于实时电价的产消者综合响应模型
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摘要
产消者作为新兴的特殊电力消费者,拥有电力供给和消费的双重角色,具有较强参与实时市场的响应潜力。为提高分布式发电利用率和用户供用电收益,提出基于实时电价的产消者的多阶段综合响应模型。前期优化阶段,综合考虑用户用电经济性、舒适度和电价激励作用,提出用户最优小时需求出价模型;以用户供电收益最大为目标,考虑储能容量和功率约束、储能闭锁和动作死区等因素,提出用户最优小时供给报价模型。出清响应阶段,基于实时电价,结合用户最优小时供需出报价信息,提出用户可控负荷需求响应和分布式发电—储能系统供给响应交替的综合响应模型。算例分析表明,所提综合响应方法能及时响应实时电价的波动,不仅灵活削减和转移了用户在电价高峰区的负荷,而且实现了分布式发电供给从低电价区域向高电价区域的转移,提高产消者供用电的经济性。
Prosumers, a group of special end-users emerging in recent years, have the dual roles of power supply and consumption with high potential in responding to the real-time market. In order to improve the efficiency of distributed generation and profits of energy supply and consumption, a multi-stage integrated response model is proposed for prosumers based on real-time electricity price. In the stage of pre-optimization, an hourly optimal demand bidding model is established with a comprehensive goal considering the incentive effect on economic, comfort and price of power consumption. With the goal of maximizing the profits of energy supply and considering the factors e.g. storage capacity and charge/discharge power constraints, storage blocking and dead zone, an hourly optimal supply offering model is presented. In the stage of response, an integrated response model is developed based on real-time electricity price and using hourly optimal demand bidding and supply offering information. It is essentially a model alternated by demand response for controllable load and supply response for distributed generation-storage system. Case study shows that the proposed integrated response approach not only notably reduces and shifts load at a high real-time price, but also successfully transfers power supply from low electricity price zones to high electricity price zones, which significantly improves the economics of power supply and consumption.
Prosumers, a group of special end-users emerging in recent years, have the dual roles of power supply and consumption with high potential in responding to the real-time market. In order to improve the efficiency of distributed generation and profits of energy supply and consumption, a multi-stage integrated response model is proposed for prosumers based on real-time electricity price. In the stage of pre-optimization, an hourly optimal demand bidding model is established with a comprehensive goal considering the incentive effect on economic, comfort and price of power consumption. With the goal of maximizing the profits of energy supply and considering the factors e.g. storage capacity and charge/discharge power constraints, storage blocking and dead zone, an hourly optimal supply offering model is presented. In the stage of response, an integrated response model is developed based on real-time electricity price and using hourly optimal demand bidding and supply offering information. It is essentially a model alternated by demand response for controllable load and supply response for distributed generation-storage system. Case study shows that the proposed integrated response approach not only notably reduces and shifts load at a high real-time price, but also successfully transfers power supply from low electricity price zones to high electricity price zones, which significantly improves the economics of power supply and consumption.
引文
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[8]赵波,包侃侃,徐志成,等.考虑需求侧响应的光储并网型微电网优化配置[J].中国电机工程学报,2015,35(21):5465-5474.ZHAO Bo,BAO Kankan,XU Zhicheng,et al.Optimal sizing for grid-connected PV-and-storage microgrid considering demand response[J].Proceedings of the CSEE,2015,35(21):5465-5474.
[9]赵波,肖传亮,徐琛,等.基于渗透率的区域配电网分布式光伏并网消纳能力分析[J].电力系统自动化,2017,41(21):105-111.DOI:10.7500/AEPS20170306016.ZHAO Bo,XIAO Chuanliang,XU Chen,et al.Penetration based accommodation capacity on distributed photovoltaic connection in regional distribution network[J].Automation of Electric Power Systems,2017,41(21):105-111.DOI:10.7500/AEPS20170306016.
[10]孙宇军,王岩,王蓓蓓,等.考虑需求响应不确定性的多时间尺度源荷互动决策方法[J].电力系统自动化,2018,42(2):106-113.DOI:10.7500/AEPS20170416004.SUN Yujun,WANG Yan,WANG Beibei,et al.Multi-time scale decision method for source-load interaction considering demand response uncertainty[J].Automation of Electric Power Systems,2018,42(2):106-113.DOI:10.7500/AEPS20170416004.
[11]武赓,隆竹寒,曾博,等.计及用户行为的需求响应对分布式发电系统充裕度的影响[J].电力系统自动化,2018,42(8):119-126.DOI:10.7500/AEPS20170515006.WU Geng,LONG Zhuhan,ZENG Bo,et al.Influence of demand response on supply adequacy of distributed generation system considering behaviors of users[J].Automation of Electric Power Systems,2018,42(8):119-126.DOI:10.7500/AEPS20170515006.
[12]代业明,高红伟,高岩,等.具有电力需求预测更新的智能电网实时定价机制[J].电力系统自动化,2018,42(12):58-63.DOI:10.7500/AEPS20170614008.DAI Yeming,GAO Hongwei,GAO Yan,et al.Real-time pricing mechanism in smart grid with forecasting update of power demand[J].Automation of Electric Power Systems,2018,42(12):58-63.DOI:10.7500/AEPS20170614008.
[13]WANG Qi,ZHANG Chunyu,DING Yi,et al.Review of realtime electricity markets for integrating distributed energy resources and demand response[J].Applied Energy,2015,138:695-706.
[14]SILVA P G D,ILI C'D,KARNOUSKOS S.The impact of smart grid prosumer grouping on forecasting accuracy and its benefits for local electricity market trading[J].IEEETransactions on Smart Grid,2014,5(1):402-410.
[15]CAI Y,HUANG T,BOMPARD E,et al.Self-sustainable community of electricity prosumers in the emerging distribution system[J].IEEE Transactions on Smart Grid,2017,8(5):2207-2216.
[16]MA L,LIU N,ZHANG J,et al.Energy management for joint operation of CHP and PV prosumers inside a gridconnected microgrid:agame theoretic approach[J].IEEETransactions on Industrial Informatics,2016,12(5):1930-1942.
[17]CUI S,WANG Y,LIU N.Distributed game-based pricing strategy for energy sharing in microgrid with PV prosumers[J].IET Renewable Power Generation,2018,12(3):380-388.
[18]刘念,王程,雷金勇.市场模式下光伏用户群的电能共享与需求响应模型[J].电力系统自动化,2016,40(16):49-55.LIU Nian,WANG Cheng,LEI Jinyong.Power energy sharing and demand response model for photovoltaic prosumer cluster under market environment[J].Automation of Electric Power Systems,2016,40(16):49-55.
[19]NAZIR M S,HISKENS I A.A dynamical systems approach to modeling and analysis of transactive energy coordination[EB/OL].[2018-07-30].http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8357509&isnumber=4374138.
[20]艾欣,周志宇,魏妍萍,等.基于自回归积分滑动平均模型的可转移负荷竞价策略[J].电力系统自动化,2017,41(20):26-31.DOI:10.7500/AEPS20170119009.AI Xin,ZHOU Zhiyu,WEI Yanping,et al.Bidding strategy for time-shiftable load based on autoregressive integrated moving average model[J].Automation of Electric Power Systems,2017,41(20):26-31.DOI:10.7500/AEPS20170119009.
[21]CHOI S,MIN S.Optimal scheduling and operation of the ESSfor prosumer market environment in grid-connected industrial complex[J].IEEE Transactions on Industry Applications,2018,54(3):1949-1957.
[22]QIU J,ZHAO J,YANG H,et al.Optimal scheduling for prosumers in coupled transactive power and gas systems[J].IEEE Transactions on Power Systems,2018,33(2):1970-1980.
[23]WAN C,XU Z,PINSON P,et al.Probabilistic forecasting of wind power generation using extreme learning machine[J].IEEE Transactions on Power Systems,2014,29(3):1033-1044.
[24]SANJARI M J,GOOI H B.Probabilistic forecast of PV power generation based on higher order Markov chain[J].IEEETransactions on Power Systems,2017,32(4):2942-2952.
[25]WAN C,NIU M,SONG Y,et al.Pareto optimal prediction intervals of electricity price[J].IEEE Transactions on Power Systems,2017,32(1):817-819.
[26]FAN S,HYNDMAN R J.Short-term load forecasting based on a Semi-Parametric additive model[J].IEEE Transactions on Power Systems,2012,27(1):134-141.
[27]CERNE G,DOVAN D,KRJANC I.Short-term load forecasting by separating daily profiles and using a single fuzzy model across the entire domain[J].IEEE Transactions on Industrial Electronics,2018,65(9):7406-7415.
[28]姜琳.锂离子电池荷电状态估计与寿命预测技术研究[D].成都:电子科技大学,2013.JIANG Lin.Research on Lithium-ion battery SOC estimation and RUL prediction[D].Chengdu:University of Electronic Science and Technology of China,2013.
[29]PJM.Real-time five minute LMPs[EB/OL].[2018-07-30].http://dataminer2.pjm.com/feed/rt_fivemin_hrl_lmps/definition.
[2]EFTEKHARNEJAD S,VITTAL V,HEYDT G T,et al.Impact of increased penetration of photovoltaic generation on power systems[J].IEEE Transactions on Power Systems,2013,28(2):893-901.
[3]WAN C,LIN J,WANG J,et al.Direct quintile regression for nonparametric probabilistic forecasting of wind power generation[J].IEEE Transactions on Power Systems,2017,32(4):2767-2778.
[4]WAN C,XU Z,PINSON P,et al.Optimal prediction intervals of wind power generation[J].IEEE Transactions on Power Systems,2014,29(3):1166-1174.
[5]姚建国,杨胜春,王珂,等.平衡风功率波动的需求响应调度框架与策略设计[J].电力系统自动化,2014,38(9):85-92.YAO Jianguo,YANG Shengchun,WANG Ke,et al.Framework and strategy design of demand response scheduling for balancing wind power fluctuation[J].Automation of Electric Power Systems,2014,38(9):85-92.
[6]王蓓蓓,唐楠,赵盛楠,等.需求响应参与风电消纳的随机&可调节鲁棒混合日前调度模型[J].中国电机工程学报,2017,37(21):6339-6346.WANG Beibei,TANG Nan,ZHAO Shengnan,et al.Stochastic&adjustable robust hybrid scheduling model of power system considering demand response’s participation in large-scale wind power consumption[J].Proceedings of the CSEE,2017,37(21):6339-6346.
[7]鞠立伟,于超,谭忠富.计及需求响应的风电储能两阶段调度优化模型及求解算法[J].电网技术,2015,39(5):1287-1293.JU Liwei,YU Chao,TAN Zhongfu.A two-stage scheduling optimization model and corresponding solving algorithm for power grid containing wind farm and energy storage system considering demand response[J].Power System Technology,2015,39(5):1287-1293.
[8]赵波,包侃侃,徐志成,等.考虑需求侧响应的光储并网型微电网优化配置[J].中国电机工程学报,2015,35(21):5465-5474.ZHAO Bo,BAO Kankan,XU Zhicheng,et al.Optimal sizing for grid-connected PV-and-storage microgrid considering demand response[J].Proceedings of the CSEE,2015,35(21):5465-5474.
[9]赵波,肖传亮,徐琛,等.基于渗透率的区域配电网分布式光伏并网消纳能力分析[J].电力系统自动化,2017,41(21):105-111.DOI:10.7500/AEPS20170306016.ZHAO Bo,XIAO Chuanliang,XU Chen,et al.Penetration based accommodation capacity on distributed photovoltaic connection in regional distribution network[J].Automation of Electric Power Systems,2017,41(21):105-111.DOI:10.7500/AEPS20170306016.
[10]孙宇军,王岩,王蓓蓓,等.考虑需求响应不确定性的多时间尺度源荷互动决策方法[J].电力系统自动化,2018,42(2):106-113.DOI:10.7500/AEPS20170416004.SUN Yujun,WANG Yan,WANG Beibei,et al.Multi-time scale decision method for source-load interaction considering demand response uncertainty[J].Automation of Electric Power Systems,2018,42(2):106-113.DOI:10.7500/AEPS20170416004.
[11]武赓,隆竹寒,曾博,等.计及用户行为的需求响应对分布式发电系统充裕度的影响[J].电力系统自动化,2018,42(8):119-126.DOI:10.7500/AEPS20170515006.WU Geng,LONG Zhuhan,ZENG Bo,et al.Influence of demand response on supply adequacy of distributed generation system considering behaviors of users[J].Automation of Electric Power Systems,2018,42(8):119-126.DOI:10.7500/AEPS20170515006.
[12]代业明,高红伟,高岩,等.具有电力需求预测更新的智能电网实时定价机制[J].电力系统自动化,2018,42(12):58-63.DOI:10.7500/AEPS20170614008.DAI Yeming,GAO Hongwei,GAO Yan,et al.Real-time pricing mechanism in smart grid with forecasting update of power demand[J].Automation of Electric Power Systems,2018,42(12):58-63.DOI:10.7500/AEPS20170614008.
[13]WANG Qi,ZHANG Chunyu,DING Yi,et al.Review of realtime electricity markets for integrating distributed energy resources and demand response[J].Applied Energy,2015,138:695-706.
[14]SILVA P G D,ILI C'D,KARNOUSKOS S.The impact of smart grid prosumer grouping on forecasting accuracy and its benefits for local electricity market trading[J].IEEETransactions on Smart Grid,2014,5(1):402-410.
[15]CAI Y,HUANG T,BOMPARD E,et al.Self-sustainable community of electricity prosumers in the emerging distribution system[J].IEEE Transactions on Smart Grid,2017,8(5):2207-2216.
[16]MA L,LIU N,ZHANG J,et al.Energy management for joint operation of CHP and PV prosumers inside a gridconnected microgrid:agame theoretic approach[J].IEEETransactions on Industrial Informatics,2016,12(5):1930-1942.
[17]CUI S,WANG Y,LIU N.Distributed game-based pricing strategy for energy sharing in microgrid with PV prosumers[J].IET Renewable Power Generation,2018,12(3):380-388.
[18]刘念,王程,雷金勇.市场模式下光伏用户群的电能共享与需求响应模型[J].电力系统自动化,2016,40(16):49-55.LIU Nian,WANG Cheng,LEI Jinyong.Power energy sharing and demand response model for photovoltaic prosumer cluster under market environment[J].Automation of Electric Power Systems,2016,40(16):49-55.
[19]NAZIR M S,HISKENS I A.A dynamical systems approach to modeling and analysis of transactive energy coordination[EB/OL].[2018-07-30].http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8357509&isnumber=4374138.
[20]艾欣,周志宇,魏妍萍,等.基于自回归积分滑动平均模型的可转移负荷竞价策略[J].电力系统自动化,2017,41(20):26-31.DOI:10.7500/AEPS20170119009.AI Xin,ZHOU Zhiyu,WEI Yanping,et al.Bidding strategy for time-shiftable load based on autoregressive integrated moving average model[J].Automation of Electric Power Systems,2017,41(20):26-31.DOI:10.7500/AEPS20170119009.
[21]CHOI S,MIN S.Optimal scheduling and operation of the ESSfor prosumer market environment in grid-connected industrial complex[J].IEEE Transactions on Industry Applications,2018,54(3):1949-1957.
[22]QIU J,ZHAO J,YANG H,et al.Optimal scheduling for prosumers in coupled transactive power and gas systems[J].IEEE Transactions on Power Systems,2018,33(2):1970-1980.
[23]WAN C,XU Z,PINSON P,et al.Probabilistic forecasting of wind power generation using extreme learning machine[J].IEEE Transactions on Power Systems,2014,29(3):1033-1044.
[24]SANJARI M J,GOOI H B.Probabilistic forecast of PV power generation based on higher order Markov chain[J].IEEETransactions on Power Systems,2017,32(4):2942-2952.
[25]WAN C,NIU M,SONG Y,et al.Pareto optimal prediction intervals of electricity price[J].IEEE Transactions on Power Systems,2017,32(1):817-819.
[26]FAN S,HYNDMAN R J.Short-term load forecasting based on a Semi-Parametric additive model[J].IEEE Transactions on Power Systems,2012,27(1):134-141.
[27]CERNE G,DOVAN D,KRJANC I.Short-term load forecasting by separating daily profiles and using a single fuzzy model across the entire domain[J].IEEE Transactions on Industrial Electronics,2018,65(9):7406-7415.
[28]姜琳.锂离子电池荷电状态估计与寿命预测技术研究[D].成都:电子科技大学,2013.JIANG Lin.Research on Lithium-ion battery SOC estimation and RUL prediction[D].Chengdu:University of Electronic Science and Technology of China,2013.
[29]PJM.Real-time five minute LMPs[EB/OL].[2018-07-30].http://dataminer2.pjm.com/feed/rt_fivemin_hrl_lmps/definition.