区域综合能源系统多主体非完全信息下的双层博弈策略
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摘要
针对区域综合能源系统多主体高耦合的特点,提出一种由供能商、配电网和用户组成的多主体双层博弈互动策略。博弈互动策略包括调度和竞价两个方面。调度部门协调各方可调资源,根据供能商的报价和配电网的分时电价预测多能负荷,以系统用能费用最小为目标协调优化,真正实现合作博弈下的多能互补。文中在非完全信息和有限理性的假设下设计供能商报价和系统结算策略。在此基础上,根据历史调度结果和自身机组特性,以追求自身最大利益为目标,模拟供能商代理的冷热电日前市场非合作竞价策略后上报调度部门,并采用Q-Learning算法优化多代理竞价策略。应用实际算例研究区域综合能源系统调度—竞价双层博弈过程的演化规律,并分析了所提策略的局部Nash均衡性。
In view of the characteristics of multi-agent and strong coupling of regional integrated energy system(RIES),a multi-agent bi-level game interaction strategy consisting of energy suppliers,distribution networks,and users is proposed.The game strategy includes two aspects:dispatch and bidding.According to energy supplier's bidding electricity price,multi-energy loads are forecasted.With the aim of minimizing system costs,the dispatch department coordinates all adjustable resources which belong to different participants.The cooperative game realizes the purpose of multi-energy complementary.Under the assumption of incomplete information and bounded rationality,this paper designs the energy supplier bidding strategy and corresponding clearing mechanism.On this basis,according to the history of scheduling results and unit characteristics,the non-cooperative bidding strategy of day-ahead energy markets with cold,thermal,and electric energies is simulated to pursue their own profits.And the Q-Learning algorithm is used to optimize the multi-agent bidding strategy which is then reported to the dispatch department.The evolutionary law of dispatch-bidding bi-level game of RIES is studied using a case study,and the local Nash equilibrium of the strategy is analyzed.
In view of the characteristics of multi-agent and strong coupling of regional integrated energy system(RIES),a multi-agent bi-level game interaction strategy consisting of energy suppliers,distribution networks,and users is proposed.The game strategy includes two aspects:dispatch and bidding.According to energy supplier's bidding electricity price,multi-energy loads are forecasted.With the aim of minimizing system costs,the dispatch department coordinates all adjustable resources which belong to different participants.The cooperative game realizes the purpose of multi-energy complementary.Under the assumption of incomplete information and bounded rationality,this paper designs the energy supplier bidding strategy and corresponding clearing mechanism.On this basis,according to the history of scheduling results and unit characteristics,the non-cooperative bidding strategy of day-ahead energy markets with cold,thermal,and electric energies is simulated to pursue their own profits.And the Q-Learning algorithm is used to optimize the multi-agent bidding strategy which is then reported to the dispatch department.The evolutionary law of dispatch-bidding bi-level game of RIES is studied using a case study,and the local Nash equilibrium of the strategy is analyzed.
引文
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[19]ZHOU Zhi,ZHAO Fei,WANG Jianhui.Agent-based electricity market simulation with demand response from commercial buildings[J].IEEE Transactions on Smart Grid,2011,2(4):580-588.
[20]TANG W,JAIN R.Dynamic economic dispatch game:the value of storage[J].IEEE Transactions on Smart Grid,2016,7(5):2350-2358.
[21]阮文骏,王蓓蓓,李扬,等.峰谷分时电价下的用户响应行为研究[J].电网技术,2012,36(7):86-93.RUAN Wenjun,WANG Beibei,LI Yang,et al.Study on user response behavior under TOU price[J].Power System Technology,2012,36(7):86-93.
[22]GAJJAR G R,KHAPARDE S A,NAGARAJU P.Application of actor-critic learning algorithm for optimal bidding problem of a Genco[J].IEEE Transaction on Power Systems,2003,18(1):11-18.
[23]RAGUPATHI R,DAS T K.A stochastic game approach for modeling wholesale energy bidding in deregulated power markets[J].IEEE Transactions on Power Systems,2004,19(2):849-856.
[2]吴建中.欧洲综合能源系统发展的驱动与现状[J].电力系统自动化,2016,40(5):1-7.DOI:10.7500/AEPS20150512001.WU Jianzhong.Drivers and state-of-the-art of integrated energy systems in Europe[J].Automation of Electric Power Systems,2016,40(5):1-7.DOI:10.7500/AEPS20150512001.
[3]余晓丹,徐宪东,陈硕翼,等.综合能源系统与能源互联网简述[J].电工技术学报,2016,31(1):1-13.YU Xiaodan,XU Xiandong,CHEN Shuoyi,et al.A brief review to integrated energy system and energy internet[J].Transactions of China Electrotechnical Society,2016,31(1):1-13.
[4]王伟亮,王丹,贾宏杰,等.能源互联网背景下的典型区域综合能源系统稳态分析研究综述[J].中国电机工程学报,2016,36(12):3292-3305.WANG Weiliang,WANG Dan,JIA Hongjie,et al.Overview of steady state analysis of typical regional comprehensive energy system under the background of energy internet[J].Proceedings of the CSEE,2016,36(12):3292-3305.
[5]RASHEDI N,TAJEDDINI M A,KEBRIAEI H.Markov game approach for multi-agent competitive bidding strategies in electricity market[J].IET Generation Transmission&Distribution,2016,10(15):3756-3763.
[6]王帅.发电商基于Q-Learning算法的日前市场竞价策略[J].能源技术经济,2010,22(3):34-39.WANG Shuai.Generators’bidding strategies in the day-ahead market based on Q-Learning algorithm[J].Electric Power Technologic Economics,2010,22(3):34-39.
[7]卢强,陈来军,梅生伟.博弈论在电力系统中典型应用及若干展望[J].中国电机工程学报,2014,34(29):5009-5017.LU Qiang,CHEN Laijun,MEI Shengwei.Typical applications and prospects of game theory in power system[J].Proceedings of the CSEE,2014,34(29):5009-5017.
[8]梅生伟.工程博弈论基础及电力系统应用[M].北京:科学出版社,2016:178-191.
[9]MOGHADDAM I G,SANIEI M,MASHHOUR E.Acomprehensive model for self-scheduling an energy hub to supply cooling,heating and electrical demands of a building[J].Energy,2016,94:157-170.
[10]周任军,冉晓洪,毛发龙,等.分布式冷热电三联供系统节能协调优化调度[J].电网技术,2012,36(6):8-14.ZHOU Renjun,RAN Xiaohong,MAO Falong,et al.Coordinated optimization of energy saving for distributed CCHP system[J].Power System Technology,2012,36(6):8-14.
[11]顾泽鹏,康重庆,陈新宇,等.考虑热网约束的电热能源集成系统运行优化及其风电消纳效益分析[J].中国电机工程学报,2015,35(14):3596-3604.GU Zepeng,KANG Chongqing,CHEN Xinyu,et al.Operation optimization of integrated power and heat energy systems and the benefit on wind power accommodation considering heating network constraints[J].Proceedings of the CSEE,2015,35(14):3596-3604.
[12]GU Wei,WANG Jun,LU Shuai,et al.Optimal operation for integrated energy system considering thermal inertia of district heating network and buildings[J].Applied Energy,2017,199:234-246.
[13]HERRANDO M,MARKIDES C N,HELLGARDT K.A UK-based assessment of hybrid PV and solar-thermal systems for domestic heating and power:system performance[J].Applied Energy,2014,122:288-309.
[14]段炜,胡兆光,吴思竹,等.基于智能体响应均衡模型的经济政策-电力需求动态模拟[J].中国电机工程学报,2014,34(7):1206-1212.DUAN Wei,HU Zhaoguang,WU Sizhu,et al.Dynamic simulation of economic policy and electricity demand by agents response equilibrium model[J].Proceedings of the CSEE,2014,34(7):1206-1212.
[15]田建伟,胡兆光,吴俊勇.基于多智能体建模的经济-电力动态模拟系统[J].中国电机工程学报,2010,30(7):85-91.TIAN Jianwei,HU Zhaoguang,WU Junyong,et al.Dynamic economy and power simulation system based on multi-agent modeling[J].Proceedings of the CSEE,2010,30(7):85-91.
[16]黄仙,郭睿.一种电力市场环境下的电源规划多智能体模型[J].电力系统保护与控制,2016,44(24):1-8.HUANG Xian,GUO Rui.A multi-agent model of generation expansion planning in electricity market[J].Power System Protection and Control,2016,44(24):1-8.
[17]DEHGHANPOUR K,NEHRIR M H,SHEPPARD J W.Agent-based modeling in electrical energy markets using dynamic bayesian networks[J].IEEE Transactions on Power Systems,2016,31(6):4744-4754.
[18]NESHAT N,AMIN-NASERI M R.Cleaner power generation through market-driven generation expansion planning:an agent-based hybrid framework of game theory and Particle Swarm Optimization[J].Journal of Cleaner Production,2015,105:206-217.
[19]ZHOU Zhi,ZHAO Fei,WANG Jianhui.Agent-based electricity market simulation with demand response from commercial buildings[J].IEEE Transactions on Smart Grid,2011,2(4):580-588.
[20]TANG W,JAIN R.Dynamic economic dispatch game:the value of storage[J].IEEE Transactions on Smart Grid,2016,7(5):2350-2358.
[21]阮文骏,王蓓蓓,李扬,等.峰谷分时电价下的用户响应行为研究[J].电网技术,2012,36(7):86-93.RUAN Wenjun,WANG Beibei,LI Yang,et al.Study on user response behavior under TOU price[J].Power System Technology,2012,36(7):86-93.
[22]GAJJAR G R,KHAPARDE S A,NAGARAJU P.Application of actor-critic learning algorithm for optimal bidding problem of a Genco[J].IEEE Transaction on Power Systems,2003,18(1):11-18.
[23]RAGUPATHI R,DAS T K.A stochastic game approach for modeling wholesale energy bidding in deregulated power markets[J].IEEE Transactions on Power Systems,2004,19(2):849-856.