Master-Slave Game Based Optimal Pricing Strategy for Load Aggregator in Day-ahead Electricity Market

被引：0
|
作者：
Sun W. [1 ]
Liu X. [1 ]
Xiang W. [2 ]
Li H. [3 ]
机构：
[1] School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai
[2] Shanghai Shenergy New Power Storage R&D Co., Ltd., Shanghai
[3] School of Electrical Engineering, Shanghai University of Electric Power, Shanghai
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2021年 / 45卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Demand response; Generalized demand side resource; Master-slave game; Optimal pricing; Response preference;
D O I：
10.7500/AEPS20200519003
中图分类号：
学科分类号：
摘要：
A load aggregator (LA) provides load smoothing services to the power grid and obtains revenue by integrating user-side resources through demand response. Therefore, the response pricing strategy of LA and users' response preference will affect the accuracy of users' response directly, and then affect the revenue of LA. The load resources involved in the demand response are regarded as generalized demand side resources (GDSRs), and demand response mechanism based on price incentives is proposed. Then, a user utility model considering user preference and an aggregator revenue model are constructed. Furthermore, aiming at maximizing the interests of both users and LA, a master-slave game model is established, which is calculated to obtain the optimal compensation pricing strategy of LA and analyze the users' electricity elasticity to optimize users' response. Finally, the data of American PJM market are used for simulation. The simulation results verify that the optimal pricing strategy based on the master-slave game can reduce users' comprehensive cost effectively through full consideration of the differences in users' response preference, and increase the market revenue of LA by smoothing the load fluctuation. © 2021 Automation of Electric Power Systems Press.
引用
收藏
页码：159 / 167
页数：8
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