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

1.School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;2.Shanghai Shenergy New Power Storage R&D Co., Ltd., Shanghai 201419, China;3.School of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China

Abstract:

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.

Keywords:

Foundation:

This work is supported by National Natural Science Foundation of China (No. 51777126).

Get Citation
[1]SUN Weiqing, LIU Xiaonan, XIANG Wei, et al. Master-Slave Game Based Optimal Pricing Strategy for Load Aggregator in Day-ahead Electricity Market[J]. Automation of Electric Power Systems,2021,45(1):159-167. DOI:10.7500/AEPS20200519003
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History
  • Received:May 19,2020
  • Revised:September 15,2020
  • Adopted:
  • Online: January 05,2021
  • Published: