Smart Bidding Strategy of the Demand-side Loads Based on the Reinforcement Learning

被引:0
|
作者
Zhou, Jing [1 ]
Wang, Ke [1 ]
Mao, Wenbo [1 ]
Wang, Yong [1 ]
Huang, Pengxiang [2 ]
机构
[1] China Elect Power Res Inst, Power Automat Dept, Nan Jing, Peoples R China
[2] State Grid Anhui Elect Power Co, Distribut Network Dispatch Dept, He Fei, Peoples R China
基金
中国国家自然科学基金;
关键词
Electricity market; Demand-side bidding; Reinforcement learning; Bidding acceptance probability; MARKETS;
D O I
暂无
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
With the rapid reform of the electricity market in China, a more practical and smart bidding strategy is needed for the demand-side loads when they become the market participants. Based on the different regulation characteristics, a piecewise regulation cost model is established for continuous or discrete regulation demand loads. Meanwhile, considering the factors besides market demand and historical data, the paper proposes the intelligent bidding strategy based on an adaptive reinforcement learning model. The flowcharts for the process of bidding strategy was also proposed. Finally, the simulation results show that the bidding strategy is simple and easy to operate, which will further improve the decision-making ability of the demand side loads.
引用
收藏
页码:447 / 451
页数:5
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