Optimal allocation of electric vehicle charging stations and renewable energy resources using a modified version of walrus optimizer
Abstract
Decarbonization policies aim to electrify the transportation sector by increasing the use of Electric Vehicles (EVs); consequently, Electric Vehicle charging stations (EVCSs) are widely integrated into distribution systems (DSs). EVCSs can be considered a significant additional electrical load on DSs, leading to negative effects such as increased system power losses and reduced voltage profile and voltage stability. In this regard, the negative effects of adding EVCSs can be mitigated through optimal integration of EVCSs and renewable energy resources (RERs), such as wind turbines (WTs), photovoltaics (PVs), and biomass units. This paper aims to optimize the planning of integrating the EVCSs and RERs into multi-microgrid-based DSs (IEEE 33-bus, 69-bus, and 94-bus) to reduce the total voltage deviations (VDs), the total costs, and improve the total voltage stability index (VSI). A modified version of Walrus Optimizer (MWO) is proposed and applied to solve this problem. MWO is based on enhancing the search capability of the conventional WO algorithm and avoiding its stagnation at local optima using three approaches, including a hybrid chaotic mutation (HCM), Fitness Distance Balance (FDB), and Quasi-opposition-based learning (QOBL). The performance and capability of the MWO are tested and verified using standard benchmark functions, and the obtained results are compared to other metaheuristic optimization methods. The simulation results reveal that the proposed MWO is an effective optimizer for optimal planning solutions in which the overall cost was reduced by 21.36%, 28.25%, and 29.9% for the IEEE 33-bus, 69-bus, and 94-bus systems, respectively, while the system total VDs were reduced by 70.93%, 65.26%, and 82.3% for the IEEE 33-bus, 69-bus, and 94-bus systems, respectively. In addition, the total VSI was improved by 21.41%, 8.47%, and 58.34% for the IEEE 33-bus, 69-bus, and 94-bus systems, respectively.
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BibTeX
@article{elela2026optimal,
title = {{Optimal allocation of electric vehicle charging stations and renewable energy resources using a modified version of walrus optimizer}},
author = {Adel A. Abou El-Ela and Said Elbanna and Mohamed Ebeed and Nadia A. Nagem},
journal = {Scientific Reports},
year = {2026},
month = nov,
doi = {10.1038/s41598-026-70273-3},
url = {https://www.nature.com/articles/s41598-026-70273-3},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Optimal allocation of electric vehicle charging stations and renewable energy resources using a modified version of walrus optimizer
AU - Adel A. Abou El-Ela
AU - Said Elbanna
AU - Mohamed Ebeed
AU - Nadia A. Nagem
PY - 2026
DA - 2026/11/04
JO - Scientific Reports
DO - 10.1038/s41598-026-70273-3
UR - https://www.nature.com/articles/s41598-026-70273-3
AB - Decarbonization policies aim to electrify the transportation sector by increasing the use of Electric Vehicles (EVs); consequently, Electric Vehicle charging stations (EVCSs) are widely integrated into distribution systems (DSs). EVCSs can be considered a significant additional electrical load on DSs, leading to negative effects such as increased system power losses and reduced voltage profile and voltage stability. In this regard, the negative effects of adding EVCSs can be mitigated through optimal integration of EVCSs and renewable energy resources (RERs), such as wind turbines (WTs), photovoltaics (PVs), and biomass units. This paper aims to optimize the planning of integrating the EVCSs and RERs into multi-microgrid-based DSs (IEEE 33-bus, 69-bus, and 94-bus) to reduce the total voltage deviations (VDs), the total costs, and improve the total voltage stability index (VSI). A modified version of Walrus Optimizer (MWO) is proposed and applied to solve this problem. MWO is based on enhancing the search capability of the conventional WO algorithm and avoiding its stagnation at local optima using three approaches, including a hybrid chaotic mutation (HCM), Fitness Distance Balance (FDB), and Quasi-opposition-based learning (QOBL). The performance and capability of the MWO are tested and verified using standard benchmark functions, and the obtained results are compared to other metaheuristic optimization methods. The simulation results reveal that the proposed MWO is an effective optimizer for optimal planning solutions in which the overall cost was reduced by 21.36%, 28.25%, and 29.9% for the IEEE 33-bus, 69-bus, and 94-bus systems, respectively, while the system total VDs were reduced by 70.93%, 65.26%, and 82.3% for the IEEE 33-bus, 69-bus, and 94-bus systems, respectively. In addition, the total VSI was improved by 21.41%, 8.47%, and 58.34% for the IEEE 33-bus, 69-bus, and 94-bus systems, respectively.
SN - 2045-2322
ER -