基于Stackelberg博弈的智能电网完全分布式需求响应策略
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摘要
智能电网中的电力用户主动参与电网的运行,极大方便了电力负荷的管理,同时也对电网的稳定运行提供保障和带来更多的社会效益。结合现有的技术,首先提出Stackelberg博弈模型,以上层零售商为领导者,下层电力用户为跟随者,模拟双方博弈过程。下层的每个电力用户作为1个求解终端构成完全分布式求解。通过双方的博弈进行负荷控制和实时定价。然后采用逆向归纳法简化求解模型,将双层多目标动态博弈问题转化为单目标优化问题,验证模型纳什均衡解的存在性。最后利用Matlab产生电力用户数据、数字化仿真求得纳什均衡解。通过算例求解固定电价和实时电价下双方的效益,验证、对比、分析所了提模型的有效性和经济性。
Power users in smart grid actively participate in the operation of the grid, greatly facilitating the management of power load and providing not only the guarantee of the stable operation of the grid but also more social benefits. Firstly, combined with the existing technology, a Stackelberg game model is proposed to simulate the game process between the upper retailer as the leader and the lower user as the follower. As a solution terminal, each power user in the lower layer is regarded as a completely distributed solution. Through the game of both sides, load control and real-time pricing strategy are obtained. Then backward induction is used to simplify and solve the model, converting a two-level multi-objective dynamic game problem into a single-objective optimization problem to verify the existence of the Nash equilibrium solution of the model. Finally, the Nash equilibrium solution is obtained by using Matlab to generate power user data and digital simulation, and the effectiveness and economy of the proposed model is verified by calculating the benefits of both sides with fixed and real-time electricity price.
Power users in smart grid actively participate in the operation of the grid, greatly facilitating the management of power load and providing not only the guarantee of the stable operation of the grid but also more social benefits. Firstly, combined with the existing technology, a Stackelberg game model is proposed to simulate the game process between the upper retailer as the leader and the lower user as the follower. As a solution terminal, each power user in the lower layer is regarded as a completely distributed solution. Through the game of both sides, load control and real-time pricing strategy are obtained. Then backward induction is used to simplify and solve the model, converting a two-level multi-objective dynamic game problem into a single-objective optimization problem to verify the existence of the Nash equilibrium solution of the model. Finally, the Nash equilibrium solution is obtained by using Matlab to generate power user data and digital simulation, and the effectiveness and economy of the proposed model is verified by calculating the benefits of both sides with fixed and real-time electricity price.
引文
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[16] LATIFI M, KHALILI A, RASTEGARNIA A, et al.Fully distributed demand response using the adaptive diffusionstackelberg algorithm[J].IEEE transactionson Industrial Informatics,2017,13(5):2291-2301.
[17]黄伟,李玟萱,车文学.基于Stackelberg模型的主动配电网动态电价需求响应设计[J].电力系统自动化,2017,41(14):70-77.HUANG Wei, LI Wenxuan, JU Whenxue. Dynamic price demand response design of active distribution network based on stackelberg model[J]. Automation of Electric Power System, 2017,41(14):70-77.
[18] NASH J. Equilbrium points in n-person games[J].Proceedings of the National Academy of Sciences of the United States of America, 1950,36(1):48-49
[19] University of Strathclyde.[Online].ailable:http://www.esru.strath.ac.andE/Web_sites/12-13/Demand_supply_orchestration/electrical.html
[20] MOHSENIAN-RAD A, WONGV W, JATS KEVICH J, et al.Autonomous demand-side management based on gametheoric energy consumption scheduling for the future smart grid[J].IEEE Transactions on Smart Grid,2010,1(3):320-331.
[2]何德卫,杨威,陈皓勇等.电力市场环境下需求侧响应相关问题的探讨[J].智慧电力,2018,46(6):41-48.HE Dewei, YANG Wei, CHEN Haoyong, et al.Discussion on demand response under electricity market environment[J]. Smart Power,2018,46(6):41-48.
[3]曾鸣,武赓,李冉,等.能源互联网中综合需求侧响应的关键问题及展望[J].电网技术,2016, 40(11):3391-3398.ZENG Ming, WU Geng, LI Ran, et al. Key problems and prospects of integrated demand response in energy Internet[J]. Power System Technology, 2016,40(11):3391-3398.
[4] MOHSENIAN-Rad A, WONG V W, JATSKEVICH J,et al.Autonomous demand-side management based on gametheoretic energy consumption scheduling for the future smart grid[J].IEEE Transactions on Smart Grid, 2010, 1(3):320-331.
[5] BAHARLOUEI Z, HASHEMI M, NARIMANI H, et al.Achieving optimality and fairness in autonomous demand response:benchmarks and billing mechanisms[J]. IEEE Transactions on Smart Grid,2013,4(2):968-975.
[6]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. Customer response behavior in time-of-use price[J]. Power System Technology, 2012, 36(7):86-93.
[7]谈金晶,王蓓蓓,李扬.基于多智能体的用户分时电价响应模型[J].电网技术,2012,36(2):257-263.TAN Jinjing, WANG Beibei, LI Yang.Modeling of user response to time-of-use price based on multi-agent technology[J].Power System Technology, 2012, 36(2):257-263.
[8]毛颖兔,王盛,邵常政等.基于双层联合优化的电-气综合能源生态系统需求响应模型[J].智慧电力,2018,46(10):18-25.MAO Yingtu, WANG Sheng,SHAO Changzheng,et al.Bilevel joint optimization based multi-energy demand response model for integrated electricity and gas system[J].Smart Power,2018,46(10):18-25.
[9]戚建文.考虑需求响应的含风电场电力系统经济调度[D].北京:华北电力大学,2016.
[10] WEI W, LIU F, MEI S, Energy pricing and dispatch for smart grid retailers under demand response and market price uncertainty[J].IEEE Transactions on Smart Grid,2015,6(3):1364-1374
[11]唐学军,韩佶,苗世洪,等.基于需求侧响应的多类型负荷协调控制模型[J].电力系统保护与控制,2017,45(16):116-123.TANG Xuejun, HAN Ji, MIAO Shihong, et al. Coordinated control model of multi-type load based on demand response[J].Power System Protection and Control, 2017, 45(16):116-123.
[12]董军,张晓虎,李春雪,等.自动需求响应背景下考虑用户满意度的分时电价最优制定策略[J].电力自动化设备,2016,36(7):67-73.DONG Jun, ZHANG Xiaohu, LI Chunxue, et al. Optimum TOU pricing strategy considering user satisfaction in automated demand response background[J].Electric Power Automation Equipment, 2016,36(7):67-73.
[13]方燕琼,甘霖,艾芊,等.基于主从博弈的虚拟电厂双层竞标策略[J].电力系统自动化,2017.41(14):61-69.FANG Yanqiong, GAN lin, AI Qian, et al. Stackelberg game based bi-level bidding strategy for virtual power plant[J]. Automation of Electric Power System, 2017, 41(14):61-69.
[14] DONG Q, YU L, SONG W, et al.Fast distributed demand response algorithm on smart grid[J].IEEE/CAA Journal of Automatica Sinica,2017,4(2):280-296.
[15] DONG Y,XIE X, SHI W,et al.Demand response based distributed preventive control to improve short-term voltage stability[J].IEEE Transactions on Smart Grid, 2018,9(5):4785-4795.
[16] LATIFI M, KHALILI A, RASTEGARNIA A, et al.Fully distributed demand response using the adaptive diffusionstackelberg algorithm[J].IEEE transactionson Industrial Informatics,2017,13(5):2291-2301.
[17]黄伟,李玟萱,车文学.基于Stackelberg模型的主动配电网动态电价需求响应设计[J].电力系统自动化,2017,41(14):70-77.HUANG Wei, LI Wenxuan, JU Whenxue. Dynamic price demand response design of active distribution network based on stackelberg model[J]. Automation of Electric Power System, 2017,41(14):70-77.
[18] NASH J. Equilbrium points in n-person games[J].Proceedings of the National Academy of Sciences of the United States of America, 1950,36(1):48-49
[19] University of Strathclyde.[Online].ailable:http://www.esru.strath.ac.andE/Web_sites/12-13/Demand_supply_orchestration/electrical.html
[20] MOHSENIAN-RAD A, WONGV W, JATS KEVICH J, et al.Autonomous demand-side management based on gametheoric energy consumption scheduling for the future smart grid[J].IEEE Transactions on Smart Grid,2010,1(3):320-331.