Master–Slave Game Optimization Scheduling of Multi-Microgrid Integrated Energy System Considering Comprehensive Demand Response and Wind and Storage Combination
This paper addresses the critical challenge of scheduling optimization in regional integrated energy systems, characterized by the coupling of multiple physical energy streams (electricity, heat, and cooling) and the participation of various stakeholders. To tackle this, a novel multi-load and multi...
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MDPI AG
2024-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/22/5762 |
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| _version_ | 1846153635188178944 |
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| author | Hongbin Sun Hongyu Zou Jianfeng Jia Qiuzhen Shen Zhenyu Duan Xi Tang |
| author_facet | Hongbin Sun Hongyu Zou Jianfeng Jia Qiuzhen Shen Zhenyu Duan Xi Tang |
| author_sort | Hongbin Sun |
| collection | DOAJ |
| description | This paper addresses the critical challenge of scheduling optimization in regional integrated energy systems, characterized by the coupling of multiple physical energy streams (electricity, heat, and cooling) and the participation of various stakeholders. To tackle this, a novel multi-load and multi-type integrated demand response model is proposed, which fully accounts for the heterogeneous characteristics of energy demands in different campus environments. A leader–follower two-layer game equilibrium model is introduced, where the system operator acts as the leader, and campus load aggregators, energy storage plants, and wind farm operators serve as followers. The layer employs an enhanced particle swarm optimization (PSO) algorithm to iteratively adjust energy sales prices and response compensation unit prices, influencing the user response plan through the demand response model. In the lower layer, the charging and discharging schedules of energy storage plants, wind farm energy supply, and outputs of energy conversion devices are optimized to guide system operation. The novelty of this approach lies in the integration of a game-theoretic framework with advanced optimization techniques to balance the interests of all participants and enhance system coordination. A case study is conducted to evaluate the effectiveness of the proposed strategy, demonstrating significant economic benefits. The results show that the model encourages stakeholders to invest in energy infrastructure and actively participate in coordinated dispatch, leading to improved overall system efficiency and comprehensive revenue enhancement for the multi-agent energy system. |
| format | Article |
| id | doaj-art-15274757eefe44e38a9a20baa8634e5c |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-15274757eefe44e38a9a20baa8634e5c2024-11-26T18:02:40ZengMDPI AGEnergies1996-10732024-11-011722576210.3390/en17225762Master–Slave Game Optimization Scheduling of Multi-Microgrid Integrated Energy System Considering Comprehensive Demand Response and Wind and Storage CombinationHongbin Sun0Hongyu Zou1Jianfeng Jia2Qiuzhen Shen3Zhenyu Duan4Xi Tang5School of Electrical Engineering, Changchun Institute of Technology, Changchun 130012, ChinaSchool of Electrical Engineering, Changchun Institute of Technology, Changchun 130012, ChinaSchool of Electrical Engineering, Changchun Institute of Technology, Changchun 130012, ChinaSchool of Electrical Engineering, Changchun Institute of Technology, Changchun 130012, ChinaSchool of Electrical Engineering, Changchun Institute of Technology, Changchun 130012, ChinaSchool of Electrical Engineering, Changchun Institute of Technology, Changchun 130012, ChinaThis paper addresses the critical challenge of scheduling optimization in regional integrated energy systems, characterized by the coupling of multiple physical energy streams (electricity, heat, and cooling) and the participation of various stakeholders. To tackle this, a novel multi-load and multi-type integrated demand response model is proposed, which fully accounts for the heterogeneous characteristics of energy demands in different campus environments. A leader–follower two-layer game equilibrium model is introduced, where the system operator acts as the leader, and campus load aggregators, energy storage plants, and wind farm operators serve as followers. The layer employs an enhanced particle swarm optimization (PSO) algorithm to iteratively adjust energy sales prices and response compensation unit prices, influencing the user response plan through the demand response model. In the lower layer, the charging and discharging schedules of energy storage plants, wind farm energy supply, and outputs of energy conversion devices are optimized to guide system operation. The novelty of this approach lies in the integration of a game-theoretic framework with advanced optimization techniques to balance the interests of all participants and enhance system coordination. A case study is conducted to evaluate the effectiveness of the proposed strategy, demonstrating significant economic benefits. The results show that the model encourages stakeholders to invest in energy infrastructure and actively participate in coordinated dispatch, leading to improved overall system efficiency and comprehensive revenue enhancement for the multi-agent energy system.https://www.mdpi.com/1996-1073/17/22/5762multi-entity integrated energy systemtwo-tier optimizationimproved particle swarmintegrated demand responsemaster-slave game |
| spellingShingle | Hongbin Sun Hongyu Zou Jianfeng Jia Qiuzhen Shen Zhenyu Duan Xi Tang Master–Slave Game Optimization Scheduling of Multi-Microgrid Integrated Energy System Considering Comprehensive Demand Response and Wind and Storage Combination Energies multi-entity integrated energy system two-tier optimization improved particle swarm integrated demand response master-slave game |
| title | Master–Slave Game Optimization Scheduling of Multi-Microgrid Integrated Energy System Considering Comprehensive Demand Response and Wind and Storage Combination |
| title_full | Master–Slave Game Optimization Scheduling of Multi-Microgrid Integrated Energy System Considering Comprehensive Demand Response and Wind and Storage Combination |
| title_fullStr | Master–Slave Game Optimization Scheduling of Multi-Microgrid Integrated Energy System Considering Comprehensive Demand Response and Wind and Storage Combination |
| title_full_unstemmed | Master–Slave Game Optimization Scheduling of Multi-Microgrid Integrated Energy System Considering Comprehensive Demand Response and Wind and Storage Combination |
| title_short | Master–Slave Game Optimization Scheduling of Multi-Microgrid Integrated Energy System Considering Comprehensive Demand Response and Wind and Storage Combination |
| title_sort | master slave game optimization scheduling of multi microgrid integrated energy system considering comprehensive demand response and wind and storage combination |
| topic | multi-entity integrated energy system two-tier optimization improved particle swarm integrated demand response master-slave game |
| url | https://www.mdpi.com/1996-1073/17/22/5762 |
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