Metaheuristic Method for a Wind-Integrated Distribution Network to Support Voltage Stabilisation Employing Electric Vehicle Loads
Distributed generation (DG) has been incorporated into the distribution networks and, despite the rising prevalence of electric vehicle (EV) loads that are uncertain and cause substantial challenges in their operation, it is necessary to enhance the voltage profile, reduce power losses, and conseque...
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ftmdpi:oai:mdpi.com:/2076-3417/13/4/2254/ 2023-08-20T04:05:34+02:00 Metaheuristic Method for a Wind-Integrated Distribution Network to Support Voltage Stabilisation Employing Electric Vehicle Loads Nasir Rehman Mairaj-Ud Din Mufti Neeraj Gupta agris 2023-02-09 application/pdf https://doi.org/10.3390/app13042254 EN eng Multidisciplinary Digital Publishing Institute Energy Science and Technology https://dx.doi.org/10.3390/app13042254 https://creativecommons.org/licenses/by/4.0/ Applied Sciences; Volume 13; Issue 4; Pages: 2254 distributed generations electric vehicle beluga whale optimisation optimal location wind turbine generating system Text 2023 ftmdpi https://doi.org/10.3390/app13042254 2023-08-01T08:44:02Z Distributed generation (DG) has been incorporated into the distribution networks and, despite the rising prevalence of electric vehicle (EV) loads that are uncertain and cause substantial challenges in their operation, it is necessary to enhance the voltage profile, reduce power losses, and consequently improve the stability of whole networks. The recently proposed beluga whale optimisation algorithm is explored in the optimisation framework to determine the most suitable size of wind turbine generating systems (WTGS), while the optimum placements are determined by minimising the placement index (P-Index) using the distribution load flow (DLF) method. The voltage stability factor (VSF) is employed to formulate the P-Index to enhance voltage sensitivity in distribution systems. The main purpose of this article is to assess the influence of voltage-dependent, uncertain ZIP-form EV loads in order to analyse their potential in the active and reactive power operations of the distribution network while retaining the system voltage within a specified limit by significantly reducing system losses and taking distribution network-level constraints into account. The efficacy of the methodology is validated on the standard IEEE-33 test system by formulating two performance indices to determine a significant enhancement in convergence characteristics and a reduction in system losses. Text Beluga Beluga whale Beluga* MDPI Open Access Publishing Applied Sciences 13 4 2254 |
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MDPI Open Access Publishing |
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English |
topic |
distributed generations electric vehicle beluga whale optimisation optimal location wind turbine generating system |
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distributed generations electric vehicle beluga whale optimisation optimal location wind turbine generating system Nasir Rehman Mairaj-Ud Din Mufti Neeraj Gupta Metaheuristic Method for a Wind-Integrated Distribution Network to Support Voltage Stabilisation Employing Electric Vehicle Loads |
topic_facet |
distributed generations electric vehicle beluga whale optimisation optimal location wind turbine generating system |
description |
Distributed generation (DG) has been incorporated into the distribution networks and, despite the rising prevalence of electric vehicle (EV) loads that are uncertain and cause substantial challenges in their operation, it is necessary to enhance the voltage profile, reduce power losses, and consequently improve the stability of whole networks. The recently proposed beluga whale optimisation algorithm is explored in the optimisation framework to determine the most suitable size of wind turbine generating systems (WTGS), while the optimum placements are determined by minimising the placement index (P-Index) using the distribution load flow (DLF) method. The voltage stability factor (VSF) is employed to formulate the P-Index to enhance voltage sensitivity in distribution systems. The main purpose of this article is to assess the influence of voltage-dependent, uncertain ZIP-form EV loads in order to analyse their potential in the active and reactive power operations of the distribution network while retaining the system voltage within a specified limit by significantly reducing system losses and taking distribution network-level constraints into account. The efficacy of the methodology is validated on the standard IEEE-33 test system by formulating two performance indices to determine a significant enhancement in convergence characteristics and a reduction in system losses. |
format |
Text |
author |
Nasir Rehman Mairaj-Ud Din Mufti Neeraj Gupta |
author_facet |
Nasir Rehman Mairaj-Ud Din Mufti Neeraj Gupta |
author_sort |
Nasir Rehman |
title |
Metaheuristic Method for a Wind-Integrated Distribution Network to Support Voltage Stabilisation Employing Electric Vehicle Loads |
title_short |
Metaheuristic Method for a Wind-Integrated Distribution Network to Support Voltage Stabilisation Employing Electric Vehicle Loads |
title_full |
Metaheuristic Method for a Wind-Integrated Distribution Network to Support Voltage Stabilisation Employing Electric Vehicle Loads |
title_fullStr |
Metaheuristic Method for a Wind-Integrated Distribution Network to Support Voltage Stabilisation Employing Electric Vehicle Loads |
title_full_unstemmed |
Metaheuristic Method for a Wind-Integrated Distribution Network to Support Voltage Stabilisation Employing Electric Vehicle Loads |
title_sort |
metaheuristic method for a wind-integrated distribution network to support voltage stabilisation employing electric vehicle loads |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2023 |
url |
https://doi.org/10.3390/app13042254 |
op_coverage |
agris |
genre |
Beluga Beluga whale Beluga* |
genre_facet |
Beluga Beluga whale Beluga* |
op_source |
Applied Sciences; Volume 13; Issue 4; Pages: 2254 |
op_relation |
Energy Science and Technology https://dx.doi.org/10.3390/app13042254 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/app13042254 |
container_title |
Applied Sciences |
container_volume |
13 |
container_issue |
4 |
container_start_page |
2254 |
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1774716117549318144 |