LAPSE:2024.1300
Published Article
LAPSE:2024.1300
Optimization Strategy for Shared Energy Storage Operators-Multiple Microgrids with Hybrid Game-Theoretic Energy Trading
Yi Chen, Shan He, Weiqing Wang, Zhi Yuan, Jing Cheng, Zhijiang Cheng, Xiaochao Fan
June 21, 2024
To address the issue of low utilization rates, constrained operational modes, and the underutilization of flexible energy storage resources at the end-user level, this research paper introduces a collaborative operational approach for shared energy storage operators in a multiple microgrids (ESO-MGs) system. This approach takes into account the relation of electricity generated by MGs and the integration of diverse energy storage resources managed by ESO. A hybrid game-theoretic energy trading strategy is employed to address the challenges associated with energy trading and revenue distribution in this joint operational mode. Firstly, a multi-objective master−slave game optimization model is developed with the objective of maximizing the revenue earned by shared energy storage operators while simultaneously minimizing the operational costs of multiple microgrids. Secondly, acknowledging the peer-to-peer (P2P) energy sharing dynamics inherent in the multiple microgrid system, a non-co-operative game model is formulated. This model seeks to establish a multi-microgrid Nash equilibrium and equitable income allocation. Finally, leveraging the Karush−Kuhn−Tucker (KKT) conditions and drawing upon the principles of strong duality theory, precise dimensionality reduction is executed on the master−slave game model. The non-co-operative income is iteratively determined using the alternating direction multiplier algorithm. The empirical findings of this study indicate that the integration of electric vehicle clusters contributes to flexible storage resources for shared energy storage operators. Moreover, the proposed hybrid game optimization strategy enhances the overall benefits for shared energy storage operators and multiple microgrids, thereby affirming the economic viability and reliability of this innovative strategy.
Keywords
electric vehicle clusters, hybrid game theory, KKT conditions, multiple microgrids, shared energy storage operators, strong duality theory
Suggested Citation
Chen Y, He S, Wang W, Yuan Z, Cheng J, Cheng Z, Fan X. Optimization Strategy for Shared Energy Storage Operators-Multiple Microgrids with Hybrid Game-Theoretic Energy Trading. (2024). LAPSE:2024.1300
Author Affiliations
Chen Y: Key Laboratory of Renewable Energy Power Generation and Grid-Connected Technology in the Autonomous Region, Xinjiang University, Urumqi 830017, China
He S: Key Laboratory of Renewable Energy Power Generation and Grid-Connected Technology in the Autonomous Region, Xinjiang University, Urumqi 830017, China; Engineering Research Center of Renewable Energy Power Generation and Grid-Connected Control, Ministry of
Wang W: Key Laboratory of Renewable Energy Power Generation and Grid-Connected Technology in the Autonomous Region, Xinjiang University, Urumqi 830017, China; Engineering Research Center of Renewable Energy Power Generation and Grid-Connected Control, Ministry of
Yuan Z: Key Laboratory of Renewable Energy Power Generation and Grid-Connected Technology in the Autonomous Region, Xinjiang University, Urumqi 830017, China; Engineering Research Center of Renewable Energy Power Generation and Grid-Connected Control, Ministry of
Cheng J: Key Laboratory of Renewable Energy Power Generation and Grid-Connected Technology in the Autonomous Region, Xinjiang University, Urumqi 830017, China; Engineering Research Center of Renewable Energy Power Generation and Grid-Connected Control, Ministry of
Cheng Z: Key Laboratory of Renewable Energy Power Generation and Grid-Connected Technology in the Autonomous Region, Xinjiang University, Urumqi 830017, China; Engineering Research Center of Renewable Energy Power Generation and Grid-Connected Control, Ministry of
Fan X: School of Energy Engineering, Xinjiang Institute of Engineering, Urumqi 830023, China
Journal Name
Processes
Volume
12
Issue
1
First Page
218
Year
2024
Publication Date
2024-01-18
Published Version
ISSN
2227-9717
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PII: pr12010218, Publication Type: Journal Article
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doi:10.3390/pr12010218
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