Renewable Energy Consumption Strategies for Electric Vehicle Aggregators Based on a Two-Layer Game
Rapid advances in renewable energy technologies offer significant opportunities for the global energy transition and environmental protection. However, due to the fluctuating and intermittent nature of their power generation, which leads to the phenomenon of power abandonment, it has become a key ch...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/1/80 |
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| author | Xiu Ji Mingge Li Zheyu Yue Haifeng Zhang Yizhu Wang |
| author_facet | Xiu Ji Mingge Li Zheyu Yue Haifeng Zhang Yizhu Wang |
| author_sort | Xiu Ji |
| collection | DOAJ |
| description | Rapid advances in renewable energy technologies offer significant opportunities for the global energy transition and environmental protection. However, due to the fluctuating and intermittent nature of their power generation, which leads to the phenomenon of power abandonment, it has become a key challenge to efficiently consume renewable energy sources and guarantee the reliable operation of the power system. In order to address the above problems, this paper proposes an electric vehicle aggregator (EVA) scheduling strategy based on a two-layer game by constructing a two-layer game model between renewable energy generators (REG) and EVA, where the REG formulates time-sharing tariff strategies in the upper layer to guide the charging and discharging behaviors of electric vehicles, and the EVA respond to the price signals in the lower layer to optimize the large-scale electric vehicle scheduling. For the complexity of large-scale scheduling, this paper introduces the A2C (Advantage Actor-Critic) reinforcement learning algorithm, which combines the value network and the strategy network synergistically to optimize the real-time scheduling process. Based on the case study of wind power, photovoltaic, and wind–solar complementary data in Jilin Province, the results show that the strategy significantly improves the rate of renewable energy consumption (up to 97.88%) and reduces the cost of power purchase by EVA (an average saving of RMB 0.04/kWh), realizing a win–win situation for all parties. The study provides theoretical support for the synergistic optimization of the power system and renewable energy and is of great practical significance for the large-scale application of electric vehicles and new energy consumption. |
| format | Article |
| id | doaj-art-678cfcefb40c4a35a5266c92b2d49ac4 |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-678cfcefb40c4a35a5266c92b2d49ac42025-08-20T02:36:12ZengMDPI AGEnergies1996-10732024-12-011818010.3390/en18010080Renewable Energy Consumption Strategies for Electric Vehicle Aggregators Based on a Two-Layer GameXiu Ji0Mingge Li1Zheyu Yue2Haifeng Zhang3Yizhu Wang4Future Industrial Technology Innovation Institute, Changchun Institute of Technology, Changchun 130000, ChinaFuture Industrial Technology Innovation Institute, Changchun Institute of Technology, Changchun 130000, ChinaFuture Industrial Technology Innovation Institute, Changchun Institute of Technology, Changchun 130000, ChinaPower Science Research Institute of State Grid Jilin Electric Power Co., Changchun 130000, ChinaFuture Industrial Technology Innovation Institute, Changchun Institute of Technology, Changchun 130000, ChinaRapid advances in renewable energy technologies offer significant opportunities for the global energy transition and environmental protection. However, due to the fluctuating and intermittent nature of their power generation, which leads to the phenomenon of power abandonment, it has become a key challenge to efficiently consume renewable energy sources and guarantee the reliable operation of the power system. In order to address the above problems, this paper proposes an electric vehicle aggregator (EVA) scheduling strategy based on a two-layer game by constructing a two-layer game model between renewable energy generators (REG) and EVA, where the REG formulates time-sharing tariff strategies in the upper layer to guide the charging and discharging behaviors of electric vehicles, and the EVA respond to the price signals in the lower layer to optimize the large-scale electric vehicle scheduling. For the complexity of large-scale scheduling, this paper introduces the A2C (Advantage Actor-Critic) reinforcement learning algorithm, which combines the value network and the strategy network synergistically to optimize the real-time scheduling process. Based on the case study of wind power, photovoltaic, and wind–solar complementary data in Jilin Province, the results show that the strategy significantly improves the rate of renewable energy consumption (up to 97.88%) and reduces the cost of power purchase by EVA (an average saving of RMB 0.04/kWh), realizing a win–win situation for all parties. The study provides theoretical support for the synergistic optimization of the power system and renewable energy and is of great practical significance for the large-scale application of electric vehicles and new energy consumption.https://www.mdpi.com/1996-1073/18/1/80renewable energy consumptionelectric vehiclesdouble layer gameA2C algorithmelectric vehicle scheduling |
| spellingShingle | Xiu Ji Mingge Li Zheyu Yue Haifeng Zhang Yizhu Wang Renewable Energy Consumption Strategies for Electric Vehicle Aggregators Based on a Two-Layer Game Energies renewable energy consumption electric vehicles double layer game A2C algorithm electric vehicle scheduling |
| title | Renewable Energy Consumption Strategies for Electric Vehicle Aggregators Based on a Two-Layer Game |
| title_full | Renewable Energy Consumption Strategies for Electric Vehicle Aggregators Based on a Two-Layer Game |
| title_fullStr | Renewable Energy Consumption Strategies for Electric Vehicle Aggregators Based on a Two-Layer Game |
| title_full_unstemmed | Renewable Energy Consumption Strategies for Electric Vehicle Aggregators Based on a Two-Layer Game |
| title_short | Renewable Energy Consumption Strategies for Electric Vehicle Aggregators Based on a Two-Layer Game |
| title_sort | renewable energy consumption strategies for electric vehicle aggregators based on a two layer game |
| topic | renewable energy consumption electric vehicles double layer game A2C algorithm electric vehicle scheduling |
| url | https://www.mdpi.com/1996-1073/18/1/80 |
| work_keys_str_mv | AT xiuji renewableenergyconsumptionstrategiesforelectricvehicleaggregatorsbasedonatwolayergame AT minggeli renewableenergyconsumptionstrategiesforelectricvehicleaggregatorsbasedonatwolayergame AT zheyuyue renewableenergyconsumptionstrategiesforelectricvehicleaggregatorsbasedonatwolayergame AT haifengzhang renewableenergyconsumptionstrategiesforelectricvehicleaggregatorsbasedonatwolayergame AT yizhuwang renewableenergyconsumptionstrategiesforelectricvehicleaggregatorsbasedonatwolayergame |