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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Main Authors: Tianle Li, Yifei Li, Fang Wang, Cheng Gong, Jingrui Zhang, Hao Ma
Format: Article
Language:English
Published: MDPI AG 2025-07-01
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.
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issn 1996-1073
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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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AT chenggong improvedparalleldifferentialevolutionalgorithmwithsmallpopulationformultiperiodoptimaldispatchproblemofmicrogrids
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