Optimal Scheduling of Hybrid Games Considering Renewable Energy Uncertainty
As the integration of renewable energy sources into microgrid operations deepens, their inherent uncertainty poses significant challenges for dispatch scheduling. This paper proposes a hybrid game-theoretic optimization strategy to address the uncertainty of renewable energy in microgrid scheduling....
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/16/7/401 |
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| author | Haihong Bian Kai Ji Yifan Zhang Xin Tang Yongqing Xie Cheng Chen |
| author_facet | Haihong Bian Kai Ji Yifan Zhang Xin Tang Yongqing Xie Cheng Chen |
| author_sort | Haihong Bian |
| collection | DOAJ |
| description | As the integration of renewable energy sources into microgrid operations deepens, their inherent uncertainty poses significant challenges for dispatch scheduling. This paper proposes a hybrid game-theoretic optimization strategy to address the uncertainty of renewable energy in microgrid scheduling. An energy trading framework is developed, involving integrated energy microgrids (IEMS), shared energy storage operators (ESOS), and user aggregators (UAS). A mixed game model combining master–slave and cooperative game theory is constructed in which the ESO acts as the leader by setting electricity prices to maximize its own profit, while guiding the IEMs and UAs—as followers—to optimize their respective operations. Cooperative decisions within the IEM coalition are coordinated using Nash bargaining theory. To enhance the generality of the user aggregator model, both electric vehicle (EV) users and demand response (DR) users are considered. Additionally, the model incorporates renewable energy output uncertainty through distributionally robust chance constraints (DRCCs). The resulting two-level optimization problem is solved using Karush–Kuhn–Tucker (KKT) conditions and the Alternating Direction Method of Multipliers (ADMM). Simulation results verify the effectiveness and robustness of the proposed model in enhancing operational efficiency under conditions of uncertainty. |
| format | Article |
| id | doaj-art-1abcc274a2bc46599155affcbf6baff9 |
| institution | Kabale University |
| issn | 2032-6653 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-1abcc274a2bc46599155affcbf6baff92025-08-20T03:56:47ZengMDPI AGWorld Electric Vehicle Journal2032-66532025-07-0116740110.3390/wevj16070401Optimal Scheduling of Hybrid Games Considering Renewable Energy UncertaintyHaihong Bian0Kai Ji1Yifan Zhang2Xin Tang3Yongqing Xie4Cheng Chen5School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaAs the integration of renewable energy sources into microgrid operations deepens, their inherent uncertainty poses significant challenges for dispatch scheduling. This paper proposes a hybrid game-theoretic optimization strategy to address the uncertainty of renewable energy in microgrid scheduling. An energy trading framework is developed, involving integrated energy microgrids (IEMS), shared energy storage operators (ESOS), and user aggregators (UAS). A mixed game model combining master–slave and cooperative game theory is constructed in which the ESO acts as the leader by setting electricity prices to maximize its own profit, while guiding the IEMs and UAs—as followers—to optimize their respective operations. Cooperative decisions within the IEM coalition are coordinated using Nash bargaining theory. To enhance the generality of the user aggregator model, both electric vehicle (EV) users and demand response (DR) users are considered. Additionally, the model incorporates renewable energy output uncertainty through distributionally robust chance constraints (DRCCs). The resulting two-level optimization problem is solved using Karush–Kuhn–Tucker (KKT) conditions and the Alternating Direction Method of Multipliers (ADMM). Simulation results verify the effectiveness and robustness of the proposed model in enhancing operational efficiency under conditions of uncertainty.https://www.mdpi.com/2032-6653/16/7/401shared energy storageintegrated energy microgridsmixed gamesrobust chance constraintsNash negotiation |
| spellingShingle | Haihong Bian Kai Ji Yifan Zhang Xin Tang Yongqing Xie Cheng Chen Optimal Scheduling of Hybrid Games Considering Renewable Energy Uncertainty World Electric Vehicle Journal shared energy storage integrated energy microgrids mixed games robust chance constraints Nash negotiation |
| title | Optimal Scheduling of Hybrid Games Considering Renewable Energy Uncertainty |
| title_full | Optimal Scheduling of Hybrid Games Considering Renewable Energy Uncertainty |
| title_fullStr | Optimal Scheduling of Hybrid Games Considering Renewable Energy Uncertainty |
| title_full_unstemmed | Optimal Scheduling of Hybrid Games Considering Renewable Energy Uncertainty |
| title_short | Optimal Scheduling of Hybrid Games Considering Renewable Energy Uncertainty |
| title_sort | optimal scheduling of hybrid games considering renewable energy uncertainty |
| topic | shared energy storage integrated energy microgrids mixed games robust chance constraints Nash negotiation |
| url | https://www.mdpi.com/2032-6653/16/7/401 |
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