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Open Access

Bounded Rationality Based Multi-VPP Trading in Local Energy Markets: A Dynamic Game Approach with Different Trading Targets

Hongjun Gao1 ( )Fan Zhang1Yingmeng Xiang2Shengyong Ye3Xuna Liu3Junyong Liu1
College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Global Energy Interconnection Research Institute North America, San Jose, CA 95134, USA
State Grid Sichuan Economic Research Institute, Chengdu 610041, Sichuan, China
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Abstract

It is expected that multiple virtual power plants (multi-VPPs) will join and participate in the future local energy market (LEM). The trading behaviors of these VPPs needs to be carefully studied in order to maximize the benefits brought to the local energy market operator (LEMO) and each VPP. We propose a bounded rationality-based trading model of multi-VPPs in the local energy market by using a dynamic game approach with different trading targets. Three types of power bidding models for VPPs are first set up with different trading targets. In the dynamic game process, VPPs can also improve the degree of rationality and then find the most suitable target for different requirements by evolutionary learning after considering the opponents' bidding strategies and its own clustered resources. LEMO would decide the electricity buying/selling price in the LEM. Furthermore, the proposed dynamic game model is solved by a hybrid method consisting of an improved particle swarm optimization (IPSO) algorithm and conventional large-scale optimization. Finally, case studies are conducted to show the performance of the proposed model and solution approach, which may provide some insights for VPPs to participate in the LEM in real-world complex scenarios.

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CSEE Journal of Power and Energy Systems
Pages 221-234
Cite this article:
Gao H, Zhang F, Xiang Y, et al. Bounded Rationality Based Multi-VPP Trading in Local Energy Markets: A Dynamic Game Approach with Different Trading Targets. CSEE Journal of Power and Energy Systems, 2023, 9(1): 221-234. https://doi.org/10.17775/CSEEJPES.2021.01600

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Received: 06 March 2021
Revised: 24 June 2021
Accepted: 02 August 2021
Published: 10 September 2021
© 2021 CSEE
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