Abstract：As for the reserve of system,demand-side resource can be used to balance the power fluctuation caused by renewable energy through demand response.Because of the huge and disperse load resource,load is hard to be dispatched by the system directly.A demand response scheduling strategy based on multi-agent system is presented in the paper.The model of agents demand response reserve reporting based on game theory considering load possibility of breach and the model of capacity distribution of system dispatch based on the agents′ trust degree are built to realize the demand response resource distribution properly,dispatch cost saving and the highest income of load agents on the condition of system constrains.Study case shows that,the model can find the equilibrium solution of the game around the multi agents to realize the purpose of demand response resource distribution to the agents properly.
张鹏,李春燕,张谦. 基于需求响应调度容量上报策略博弈的电网多代理系统调度模式[J]. 电工技术学报, 2017, 32(19): 170-179.
Zhang Peng, Li Chunyan, Zhang Qian. A Power System Dispatch Model Based on Game-Theoretic Multi-Agent System Demand Response Scheduling Capacity Reporting. Transactions of China Electrotechnical Society, 2017, 32(19): 170-179.
[1] 王赵宾.中国弃风限电报告[J].能源,2014(7):42-48. Wang Zhaobin.China wind power abandoned report[J].Energy,2014(7):42-48. [2] 王蓓蓓,李义荣,李扬,等.考虑响应不确定性的可中断负荷参与系统备用配置的协调优化[J].电力自动化设备,2015,35(11):82-89. Wang Beibei,Li Yirong,Li Yang,et al.Optimal coordination between system reserve and interruptible loads with response uncertainty[J].Electric Power Automation Equipment,2015,35(11):82-89. [3] 刘萌,褚晓东,张文,等.计及网络约束的源-荷协同频率控制策略[J].电工技术学报,2016,31(6):195-205. Liu Meng,Chu Xiaodong,Zhang Wen,et al.Cooperative generation-load frequency control strategy accounting for power network constraints[J].Transactions of China Electrotechnical Society,2016,31(6):195-205. [4] 刘文颖,文晶,谢昶,等.考虑风电消纳的电力系统源荷协调多目标优化方法[J].中国电机工程学报,2015,35(5):1079-1088. Liu Wenying,Wen Jing,Xie Chang,et al.Multi-objective optimal method considering wind power accommodation based on source-load coordination[J].Proceedings of the CSEE,2015,35(5):1079-1088. [5] 文晶,刘文颖,谢昶,等.计及风电消纳效益的电力系统源荷协调二层优化模型[J].电工技术学报,2015,30(8):247-256. Wen Jing,Liu Wenying,Xie Chang,et al.Source-load coordination optimal model considering wind power consumptive benefits based on bi-level programming[J].Transactions of China Electrotechnical Society,2015,30(8):247-256. [6] 鞠立伟,秦超,吴鸿亮,等.计及多类型需求响应的风电消纳随机优化调度模型[J].电网技术,2015,39(7):1839-1846. Ju Liwei,Qin Chao,Wu Hongliang,et al.Wind power accommodation stochastic optimization model with multi-type demand response[J].Power System Technology,2015,39(7):1839-1846. [7] Zakariazadeh A,Jadid S,Siano P.Smart microgrid energy and reserve scheduling with demand response using stochastic optimization[J].International Journal of Electrical Power and Energy Systems,2014,63:523-533. [8] 王蓓蓓,刘小聪,李扬.面向大容量风电接入考虑用户侧互动的系统日前调度和运行模拟研究[J].中国电机工程学报,2013,33(22):35-44. Wang Beibei,Liu Xiaocong,Li Yang.Day-ahead generation scheduling and operation simulation considering demand response in large-capacity wind power integrated systems[J].Proceedings of the CSEE,2013,33(22):35-44. [9] 刘小聪,王蓓蓓,李扬,等.计及需求侧资源的大规模风电消纳随机机组组合模型[J].中国电机工程学报,2015,35(14):3714-3723. Liu Xiaocong,Wang Beibei,Li Yang,et al.Stochastic unit commitment model for high wind power integration considering demand side resources[J].Proceedings of the CSEE,2015,35(14):3714-3723. [10]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. [11]Guo Fanghong,Wen Changyun,Mao J,et al.Distributed economic dispatch for smart grids with random wind power[J].IEEE Transactions on Smart Grid,2016,7(3):1572-1583. [12]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 System,2014,38(9):85-92. [13]刘壮志,许柏婷,牛东晓.智能电网需求响应与均衡分析发展趋势[J].电网技术,2013,37(6):1555-1561. Liu Zhuangzhi,Xu Baiting,Niu Dongxiao,et al.Development tendency of equilibrium analysis and demand response for smart grid[J].Power System Technology,2013,37(6):1555-1561. [14]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. [15]王珂,刘建涛,姚建国,等.基于多代理技术的需求响应互动调度模型[J].电力系统自动化,2014,38(13):121-127. Wang Ke,Liu Jiantao,Yao Jianguo,et al.A muti-agent based interactive scheduling model considering demand response[J].Automation of Electric Power System,2014,38(13):121-127. [16]张开宇,宋依群,严正.考虑用户违约可能的负荷聚合商储能配置策略[J].电力系统自动化,2015,39(17):127-133. Zhang Kaiyu,Song Yiqun,Yan Zheng.Energy storage capacity optimization for load aggregators considering probability of demand response resources′ breach[J].Automation of Electric Power System,2015,39(17):127-133. [17]赵敏,沈沉,刘锋,等.基于博弈论的多微电网系统交易模式研究[J].中国电机工程学报,2015,35(4):848-857. Zhao Min,Shen Chen,Liu Feng,et al.A game-theoretic approach to analyzing power trading possibilities in multi-microgrids[J].Proceedings of the CSEE,2015,35(4):848-857. [18]江润洲,邱晓燕,李丹.基于多代理的多微网智能配电网动态博弈模型[J].电网技术,2014,38(12):3321-3327. Jiang Runzhou,Qiu Xiaoyan,Li Dan.Multi-agent system based dynamic game model of smart distribution network containing multi-microgrid[J].Power System Technology,2014,38(12):3321-3327. [19]贾星蓓,窦春霞,岳东,等.基于多代理系统的微电网多尺度能量管理[J].电工技术学报,2016,31(17):63-73. Jia Xingbei,Dou Chunxia,Yue Dong,et al.Multiple-time-scales optimal energy management in microgrid system based on multi-agent-system[J].Transactions of China Electrotechnical Society,2016,31(17):63-73. [20]罗云峰.博弈论教程[M].北京:北京交通大学出版社,2007. [21]Gibbons R.Game theory for applied economist[M].Princeton:Princeton University Press,1992. [22]江岳文,温步瀛.结合风电功率超短期预测值偏差的实时市场调度[J].电力自动化设备,2015,35(3):12-17. Jiang Yuewen,Wen Buying.Real-time market dispatch based on ultra-short-term forecast error[J].Electric Power Automation Equipment,2015,35(3):12-17.
2017, Vol. 32 
