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<p>The increasing penetration of Electric Vehicles (EVs), including Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs), poses major challenges to existing power grids, such as load imbalance, voltage instability, and inefficient energy scheduling<b>,</b> especially in renewable-based microgrids. The objective of this study is to develop an optimized Energy Management System (EMS) that ensures efficient power distribution, minimizes energy losses, and maintains grid stability in hybrid renewable microgrids integrated with EVs. To address these issues, a Coati Optimisation Algorithm (COA)-based EMS is proposed, integrating a battery storage system, wind turbines, and solar photovoltaic (PV). The COA, inspired by the cooperative foraging behaviour of coatis, effectively balances exploration and exploitation to achieve real-time optimization in nonlinear, multi-source energy systems. The proposed EMS is modelled and simulated in MATLAB and benchmarked against two types of neural networks are artificial (ANN), convolutional (CNN), and the Bald Eagle Search Optimizer (BESO). Simulation results indicate that the COA-based EMS achieves 99.7% efficiency, outperforming existing methods in stability, convergence speed, and cost-effectiveness. The findings highlight COA’s potential as a scalable and intelligent optimization frameworkfor sustainable, EV-integrated renewable microgrids.</p>

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Optimized a hybrid sustainable energy source-based microgrids with coati optimization algorithm for enhanced efficiency and grid stability

  • 期刊论文

  • 发表日期：

    2025年12月20日

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  • M. Panneer Selvam,
  • Gurinder Pal Singh,
  • Kodeeswara kumaran,
  • N. Manoj Kumar

摘要

The increasing penetration of Electric Vehicles (EVs), including Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs), poses major challenges to existing power grids, such as load imbalance, voltage instability, and inefficient energy scheduling, especially in renewable-based microgrids. The objective of this study is to develop an optimized Energy Management System (EMS) that ensures efficient power distribution, minimizes energy losses, and maintains grid stability in hybrid renewable microgrids integrated with EVs. To address these issues, a Coati Optimisation Algorithm (COA)-based EMS is proposed, integrating a battery storage system, wind turbines, and solar photovoltaic (PV). The COA, inspired by the cooperative foraging behaviour of coatis, effectively balances exploration and exploitation to achieve real-time optimization in nonlinear, multi-source energy systems. The proposed EMS is modelled and simulated in MATLAB and benchmarked against two types of neural networks are artificial (ANN), convolutional (CNN), and the Bald Eagle Search Optimizer (BESO). Simulation results indicate that the COA-based EMS achieves 99.7% efficiency, outperforming existing methods in stability, convergence speed, and cost-effectiveness. The findings highlight COA’s potential as a scalable and intelligent optimization frameworkfor sustainable, EV-integrated renewable microgrids.