Improved Parallel Differential Evolution Algorithm with Small Population for Multi-Period Optimal Dispatch Problem of Microgrids
Microgrids have drawn attention due to their helpfulness in the development of renewable energy. It is necessary to make an optimal power dispatch scheme for each micro-source in a microgrid in order to make the best use of fluctuating and unpredictable renewable energy. However, the computational t...
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MDPI AG
2025-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/14/3852 |
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| author | Tianle Li Yifei Li Fang Wang Cheng Gong Jingrui Zhang Hao Ma |
| author_facet | Tianle Li Yifei Li Fang Wang Cheng Gong Jingrui Zhang Hao Ma |
| author_sort | Tianle Li |
| collection | DOAJ |
| description | Microgrids have drawn attention due to their helpfulness in the development of renewable energy. It is necessary to make an optimal power dispatch scheme for each micro-source in a microgrid in order to make the best use of fluctuating and unpredictable renewable energy. However, the computational time of solving the optimal dispatch problem increases greatly when the grid’s structure is more complex. An improved parallel differential evolution (PDE) approach based on a message-passing interface (MPI) is proposed, aiming at the solution of the optimal dispatch problem of a microgrid (MG), reducing the consumed time effectively but not destroying the quality of the obtained solution. In the new approach, the main population of the parallel algorithm is divided into several small populations, and each performs the original operators of a differential evolution algorithm, i.e., mutation, crossover, and selection, in different processes concurrently. The gather and scatter operations are employed after several iterations to enhance population diversity. Some improvements on mutation, adaptive parameters, and the introduction of migration operation are also proposed in the approach. Two test systems are employed to verify and evaluate the proposed approach, and the comparisons with traditional differential evolution are also reported. The results show that the proposed PDE algorithm can reduce the consumed time on the premise of obtaining no worse solutions. |
| format | Article |
| id | doaj-art-18b5ba0000b240988a9c8743a03479d1 |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Energies |
| spelling | doaj-art-18b5ba0000b240988a9c8743a03479d12025-08-20T02:45:55ZengMDPI AGEnergies1996-10732025-07-011814385210.3390/en18143852Improved Parallel Differential Evolution Algorithm with Small Population for Multi-Period Optimal Dispatch Problem of MicrogridsTianle Li0Yifei Li1Fang Wang2Cheng Gong3Jingrui Zhang4Hao Ma5Beijing Dingcheng Hongan Technology Development Co., Ltd., Beijing 101399, ChinaBeijing Dingcheng Hongan Technology Development Co., Ltd., Beijing 101399, ChinaState Grid Beijing Electric Power Research Institute, Beijing 100031, ChinaBeijing Dingcheng Hongan Technology Development Co., Ltd., Beijing 101399, ChinaDepartment of Instrumental & Electrical Engineering, Xiamen University, Xiamen 361005, ChinaBeijing Dingcheng Hongan Technology Development Co., Ltd., Beijing 101399, ChinaMicrogrids have drawn attention due to their helpfulness in the development of renewable energy. It is necessary to make an optimal power dispatch scheme for each micro-source in a microgrid in order to make the best use of fluctuating and unpredictable renewable energy. However, the computational time of solving the optimal dispatch problem increases greatly when the grid’s structure is more complex. An improved parallel differential evolution (PDE) approach based on a message-passing interface (MPI) is proposed, aiming at the solution of the optimal dispatch problem of a microgrid (MG), reducing the consumed time effectively but not destroying the quality of the obtained solution. In the new approach, the main population of the parallel algorithm is divided into several small populations, and each performs the original operators of a differential evolution algorithm, i.e., mutation, crossover, and selection, in different processes concurrently. The gather and scatter operations are employed after several iterations to enhance population diversity. Some improvements on mutation, adaptive parameters, and the introduction of migration operation are also proposed in the approach. Two test systems are employed to verify and evaluate the proposed approach, and the comparisons with traditional differential evolution are also reported. The results show that the proposed PDE algorithm can reduce the consumed time on the premise of obtaining no worse solutions.https://www.mdpi.com/1996-1073/18/14/3852parallel DEmicrogridoptimal power dispatchpower flow constraintsmall population |
| spellingShingle | Tianle Li Yifei Li Fang Wang Cheng Gong Jingrui Zhang Hao Ma Improved Parallel Differential Evolution Algorithm with Small Population for Multi-Period Optimal Dispatch Problem of Microgrids Energies parallel DE microgrid optimal power dispatch power flow constraint small population |
| title | Improved Parallel Differential Evolution Algorithm with Small Population for Multi-Period Optimal Dispatch Problem of Microgrids |
| title_full | Improved Parallel Differential Evolution Algorithm with Small Population for Multi-Period Optimal Dispatch Problem of Microgrids |
| title_fullStr | Improved Parallel Differential Evolution Algorithm with Small Population for Multi-Period Optimal Dispatch Problem of Microgrids |
| title_full_unstemmed | Improved Parallel Differential Evolution Algorithm with Small Population for Multi-Period Optimal Dispatch Problem of Microgrids |
| title_short | Improved Parallel Differential Evolution Algorithm with Small Population for Multi-Period Optimal Dispatch Problem of Microgrids |
| title_sort | improved parallel differential evolution algorithm with small population for multi period optimal dispatch problem of microgrids |
| topic | parallel DE microgrid optimal power dispatch power flow constraint small population |
| url | https://www.mdpi.com/1996-1073/18/14/3852 |
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