Optimal placement of distribution network‐connected microgrids on multi‐objective energy management with uncertainty using the modified Harris Hawk optimization algorithm

Abstract Considering the importance of the renewable energy sector in the distribution systems, energy operation, and management which are connected to the distribution network (DN) in the form of multiple microgrids (MMGs) is crucial in reducing cost and pollution. Hence, this paper aims to propose...

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Main Authors: Marzieh Poshtyafteh, Hassan Barati, Ali Darvish Falehi
Format: Article
Language:English
Published: Wiley 2024-02-01
Series:IET Generation, Transmission & Distribution
Subjects:
Online Access:https://doi.org/10.1049/gtd2.13116
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author Marzieh Poshtyafteh
Hassan Barati
Ali Darvish Falehi
author_facet Marzieh Poshtyafteh
Hassan Barati
Ali Darvish Falehi
author_sort Marzieh Poshtyafteh
collection DOAJ
description Abstract Considering the importance of the renewable energy sector in the distribution systems, energy operation, and management which are connected to the distribution network (DN) in the form of multiple microgrids (MMGs) is crucial in reducing cost and pollution. Hence, this paper aims to propose optimal energy management for MMGs in the DN. Different objective functions have been taken into account in this optimization, including network cost, pollution reduction, and distribution network power losses. To design the multi‐objective optimization problem, a fuzzy method has been adopted for simultaneous multi‐objective calculations. Furthermore, the effect of the placement of distributed generations (DGs) and microgrids (MGs) is considered to reduce the distribution network power losses. Information gap decision theory (IGDT) has formulated uncertainties about renewable sources and consumers. To solve this optimization problem, a new method of the modified Harris Hawk optimization (MHHO) algorithm has been implemented, compared with the original HHO and genetic algorithm (GA). Finally, the proposed method has been analysed under the IEEE 33‐bus distribution network for a 24‐hour time horizon, including three MGs considering different renewable energy sources (RESs). The simulation results have demonstrated the high performance of the allocated network with the MHHO algorithm compared to the other scenarios.
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spelling doaj.art-203db0aaa629411988aaf1b36a6170522024-02-21T10:51:22ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952024-02-0118480983310.1049/gtd2.13116Optimal placement of distribution network‐connected microgrids on multi‐objective energy management with uncertainty using the modified Harris Hawk optimization algorithmMarzieh Poshtyafteh0Hassan Barati1Ali Darvish Falehi2Department of Electrical Engineering, Dezful Branch Islamic Azad University Dezful IranDepartment of Electrical Engineering, Dezful Branch Islamic Azad University Dezful IranDepartment of Electrical Engineering, Shadegan Branch Islamic Azad University Shadegan IranAbstract Considering the importance of the renewable energy sector in the distribution systems, energy operation, and management which are connected to the distribution network (DN) in the form of multiple microgrids (MMGs) is crucial in reducing cost and pollution. Hence, this paper aims to propose optimal energy management for MMGs in the DN. Different objective functions have been taken into account in this optimization, including network cost, pollution reduction, and distribution network power losses. To design the multi‐objective optimization problem, a fuzzy method has been adopted for simultaneous multi‐objective calculations. Furthermore, the effect of the placement of distributed generations (DGs) and microgrids (MGs) is considered to reduce the distribution network power losses. Information gap decision theory (IGDT) has formulated uncertainties about renewable sources and consumers. To solve this optimization problem, a new method of the modified Harris Hawk optimization (MHHO) algorithm has been implemented, compared with the original HHO and genetic algorithm (GA). Finally, the proposed method has been analysed under the IEEE 33‐bus distribution network for a 24‐hour time horizon, including three MGs considering different renewable energy sources (RESs). The simulation results have demonstrated the high performance of the allocated network with the MHHO algorithm compared to the other scenarios.https://doi.org/10.1049/gtd2.13116distribution networksenergy management systemsmicrogridsrenewable energy sourcesuncertain systems
spellingShingle Marzieh Poshtyafteh
Hassan Barati
Ali Darvish Falehi
Optimal placement of distribution network‐connected microgrids on multi‐objective energy management with uncertainty using the modified Harris Hawk optimization algorithm
IET Generation, Transmission & Distribution
distribution networks
energy management systems
microgrids
renewable energy sources
uncertain systems
title Optimal placement of distribution network‐connected microgrids on multi‐objective energy management with uncertainty using the modified Harris Hawk optimization algorithm
title_full Optimal placement of distribution network‐connected microgrids on multi‐objective energy management with uncertainty using the modified Harris Hawk optimization algorithm
title_fullStr Optimal placement of distribution network‐connected microgrids on multi‐objective energy management with uncertainty using the modified Harris Hawk optimization algorithm
title_full_unstemmed Optimal placement of distribution network‐connected microgrids on multi‐objective energy management with uncertainty using the modified Harris Hawk optimization algorithm
title_short Optimal placement of distribution network‐connected microgrids on multi‐objective energy management with uncertainty using the modified Harris Hawk optimization algorithm
title_sort optimal placement of distribution network connected microgrids on multi objective energy management with uncertainty using the modified harris hawk optimization algorithm
topic distribution networks
energy management systems
microgrids
renewable energy sources
uncertain systems
url https://doi.org/10.1049/gtd2.13116
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