A Load-following Particle Swarm Optimization-based Energy Management Technique for Integrating Electric Vehicles into a Renewable Microgrid System
DOI:
https://doi.org/10.12928/biste.v8i4.16850Keywords:
Renewable Energy, Load Following, Electrical Vehicles, Energy Management Strategy, Particle Swarm OptimizationAbstract
The growing incorporation of Electric Vehicles (EVs) and Renewable Energy Sources (RES) into power systems presents new challenges and opportunities for the operation of microgrid (MG). To solve these limitations, this article presents a novel load-following particle swarm optimization (LF-PSO)-based energy management strategy (EMS) for on-grid renewable microgrids with electric vehicle (EV) integration. The proposed EMS optimally coordinates bidirectional power flow between the photovoltaic (PV) system, EV and MG thereby ensuring stable DC-link voltage regulation, improved power quality, efficient battery management, and enhanced overall system energy efficiency. The studied microgrid in this paper is composed of a 21 kW photovoltaic array, a 355 V lithium-ion battery (60 Ah), a 750 V DC bus, a 400 V utility grid, and an electric vehicle load. The assessment of the suggested EMS has been conducted in MATLAB/Simulink across diverse irradiation and load scenarios. The findings demonstrate that the suggested EMS shows improved efficacy, thus guaranteeing the management of the EV and PV system during all atypical circumstances. The obtained results are compared with the results obtained by the classical EMS methods such as proportional-integral (PI) and artificial intelligence (AI) techniques. The proposed LF-PSO strategy reduces the DC-link voltage overshoot from about 30% to less than 5%, which is a reduction of 83.3%, and improves the transient response, power quality, and renewable energy utilization compared with the conventional PI controller.
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Copyright (c) 2026 Zaid KH. Sadane, Mustafa Naozad Taifor , Arwa Amer Abdulkareem, Naseer T. Alwan
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