Bounded Rationality Based Multi-VPP Trading in Local Energy Markets:A Dynamic Game Approach with Different Trading Targets
作者机构：College of Electrical EngineeringSichuan UniversityChengdu 610065China Global Energy Interconnection Research Institute North AmericaSan JoseCA 95134USA State Grid Sichuan Economic Research InstituteChengdu 610041SichuanChina
出 版 物：《CSEE Journal of Power and Energy Systems》 (中国电机工程学会电力与能源系统学报（英文）)
年 卷 期：2023年第9卷第1期
页 面：221-234页
核心收录：
学科分类：07[理学] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：This work was supported by the National Key R&D Program of China(Grant No.2019YFE0123600) National Science Foundation of China(Grant No.52077146) Young Elite Scientists Sponsorship Program by CSEE(Grant No.CESS-YESS-2019027).
主 题：Bounded rationality different trading targets dynamic game local energy market virtual power plant
摘 要：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 multiVPPs 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 largescale 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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