Cooperative Multi-Objective Optimization of DC Multi-Microgrid Systems in Distribution Networks
By constructing a DC multi-microgrid system (MMGS) including renewable energy sources (RESs) and electric vehicles (EVs) to coordinate with the distribution network, the utilization rate of RESs can be effectively improved and carbon emissions can be reduced. To improve the economy of MMGS and reduc...
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MDPI AG
2021-09-01
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author | Zhiwen Xu Changsong Chen Mingyang Dong Jingyue Zhang Dongtong Han Haowen Chen |
author_facet | Zhiwen Xu Changsong Chen Mingyang Dong Jingyue Zhang Dongtong Han Haowen Chen |
author_sort | Zhiwen Xu |
collection | DOAJ |
description | By constructing a DC multi-microgrid system (MMGS) including renewable energy sources (RESs) and electric vehicles (EVs) to coordinate with the distribution network, the utilization rate of RESs can be effectively improved and carbon emissions can be reduced. To improve the economy of MMGS and reduce the network loss of the distribution network, a cooperative double-loop optimization strategy is proposed. The inner-loop economic dispatching reduces the daily operating cost of MMGS by optimizing the active power output of RESs, EVs, and DC/AC converters in MMGS. The outer-loop reactive power optimization reduces the network loss of the distribution network by optimizing the reactive power of the bidirectional DC/AC converters. The double-loop, which synergistically optimizes the economic cost and carbon emissions of MMGS, not only improves the economy of MMGS and operational effectiveness of the distribution network but also realizes the low-carbon emissions. The Across-time-and-space energy transmission (ATSET) of the EVs is considered, whose impact on economic dispatching is analyzed. Particle Swarm Optimization (PSO) is applied to iterative solutions. Finally, the rationality and feasibility of the cooperative multi-objective optimization model are proved by a revised IEEE 33-node system. |
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language | English |
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spelling | doaj.art-c08b242b28f24a4bae1c2b2ccc5ba0dc2023-11-22T15:44:56ZengMDPI AGApplied Sciences2076-34172021-09-011119891610.3390/app11198916Cooperative Multi-Objective Optimization of DC Multi-Microgrid Systems in Distribution NetworksZhiwen Xu0Changsong Chen1Mingyang Dong2Jingyue Zhang3Dongtong Han4Haowen Chen5State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaBy constructing a DC multi-microgrid system (MMGS) including renewable energy sources (RESs) and electric vehicles (EVs) to coordinate with the distribution network, the utilization rate of RESs can be effectively improved and carbon emissions can be reduced. To improve the economy of MMGS and reduce the network loss of the distribution network, a cooperative double-loop optimization strategy is proposed. The inner-loop economic dispatching reduces the daily operating cost of MMGS by optimizing the active power output of RESs, EVs, and DC/AC converters in MMGS. The outer-loop reactive power optimization reduces the network loss of the distribution network by optimizing the reactive power of the bidirectional DC/AC converters. The double-loop, which synergistically optimizes the economic cost and carbon emissions of MMGS, not only improves the economy of MMGS and operational effectiveness of the distribution network but also realizes the low-carbon emissions. The Across-time-and-space energy transmission (ATSET) of the EVs is considered, whose impact on economic dispatching is analyzed. Particle Swarm Optimization (PSO) is applied to iterative solutions. Finally, the rationality and feasibility of the cooperative multi-objective optimization model are proved by a revised IEEE 33-node system.https://www.mdpi.com/2076-3417/11/19/8916DC multi-microgrid systemcarbon emissionseconomic dispatchacross-time-and-space energy transmissioncooperative multi-objective optimization |
spellingShingle | Zhiwen Xu Changsong Chen Mingyang Dong Jingyue Zhang Dongtong Han Haowen Chen Cooperative Multi-Objective Optimization of DC Multi-Microgrid Systems in Distribution Networks Applied Sciences DC multi-microgrid system carbon emissions economic dispatch across-time-and-space energy transmission cooperative multi-objective optimization |
title | Cooperative Multi-Objective Optimization of DC Multi-Microgrid Systems in Distribution Networks |
title_full | Cooperative Multi-Objective Optimization of DC Multi-Microgrid Systems in Distribution Networks |
title_fullStr | Cooperative Multi-Objective Optimization of DC Multi-Microgrid Systems in Distribution Networks |
title_full_unstemmed | Cooperative Multi-Objective Optimization of DC Multi-Microgrid Systems in Distribution Networks |
title_short | Cooperative Multi-Objective Optimization of DC Multi-Microgrid Systems in Distribution Networks |
title_sort | cooperative multi objective optimization of dc multi microgrid systems in distribution networks |
topic | DC multi-microgrid system carbon emissions economic dispatch across-time-and-space energy transmission cooperative multi-objective optimization |
url | https://www.mdpi.com/2076-3417/11/19/8916 |
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