Optimal economic dispatching of multi‐microgrids by an improved genetic algorithm

Abstract A multi‐microgrid economic dispatching strategy based on adaptive mutation genetic algorithm is proposed for multi‐microgrid systems with different load types and power demands. Based on the analysis of industrial, residential and commercial loads, considering the synergy and complementarit...

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Main Authors: Haoyu Jiang, Shiyuan Ning, Quanbo Ge, Wang Yun, JinQiang Xu, Yu Bin
Format: Article
Language:English
Published: Wiley 2021-03-01
Series:IET Cyber-systems and Robotics
Subjects:
Online Access:https://doi.org/10.1049/csy2.12008
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author Haoyu Jiang
Shiyuan Ning
Quanbo Ge
Wang Yun
JinQiang Xu
Yu Bin
author_facet Haoyu Jiang
Shiyuan Ning
Quanbo Ge
Wang Yun
JinQiang Xu
Yu Bin
author_sort Haoyu Jiang
collection DOAJ
description Abstract A multi‐microgrid economic dispatching strategy based on adaptive mutation genetic algorithm is proposed for multi‐microgrid systems with different load types and power demands. Based on the analysis of industrial, residential and commercial loads, considering the synergy and complementarity between multi‐microgrids, an optimal dispatching model of multi‐microgrids based on the minimum operation cost and environmental protection cost of multi‐microgrids is established from the point of view of environmental protection and economy. At the same time, an adaptive mutation genetic algorithm is proposed to optimize the system model and find the optimal economic dispatching scheme.
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spelling doaj.art-dc9e55c1522e487bb59af7507cf2fedc2022-12-22T02:40:30ZengWileyIET Cyber-systems and Robotics2631-63152021-03-0131687610.1049/csy2.12008Optimal economic dispatching of multi‐microgrids by an improved genetic algorithmHaoyu Jiang0Shiyuan Ning1Quanbo Ge2Wang Yun3JinQiang Xu4Yu Bin5School of Electronics and Information Engineering Guangdong Ocean University Guangdong ChinaSchool of Automation Hangzhou Dianzi University Hangzhou ChinaSchool of Electronics and Information Engineering Guangdong Ocean University Guangdong ChinaSchool of Logistics Engineering Shanghai Maritime University Shanghai ChinaSchool of Electronics and Information Engineering Guangdong Ocean University Guangdong ChinaSchool of Automation Hangzhou Dianzi University Hangzhou ChinaAbstract A multi‐microgrid economic dispatching strategy based on adaptive mutation genetic algorithm is proposed for multi‐microgrid systems with different load types and power demands. Based on the analysis of industrial, residential and commercial loads, considering the synergy and complementarity between multi‐microgrids, an optimal dispatching model of multi‐microgrids based on the minimum operation cost and environmental protection cost of multi‐microgrids is established from the point of view of environmental protection and economy. At the same time, an adaptive mutation genetic algorithm is proposed to optimize the system model and find the optimal economic dispatching scheme.https://doi.org/10.1049/csy2.12008distributed power generationgenetic algorithmspower generation dispatchpower generation economics
spellingShingle Haoyu Jiang
Shiyuan Ning
Quanbo Ge
Wang Yun
JinQiang Xu
Yu Bin
Optimal economic dispatching of multi‐microgrids by an improved genetic algorithm
IET Cyber-systems and Robotics
distributed power generation
genetic algorithms
power generation dispatch
power generation economics
title Optimal economic dispatching of multi‐microgrids by an improved genetic algorithm
title_full Optimal economic dispatching of multi‐microgrids by an improved genetic algorithm
title_fullStr Optimal economic dispatching of multi‐microgrids by an improved genetic algorithm
title_full_unstemmed Optimal economic dispatching of multi‐microgrids by an improved genetic algorithm
title_short Optimal economic dispatching of multi‐microgrids by an improved genetic algorithm
title_sort optimal economic dispatching of multi microgrids by an improved genetic algorithm
topic distributed power generation
genetic algorithms
power generation dispatch
power generation economics
url https://doi.org/10.1049/csy2.12008
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