Enhancing cost-effectiveness in residential microgrids: an optimization for energy management with proactive electric vehicle charging - Khan, Abdul Moeed; Hekmati, Arsalan; Bagheri, Mehdi (Frontiers in Energy Research, 2025)
This article presents a novel particle swarm optimization (PSO)-based energy management system (EMS) designed for microgrids (MGs) to enhance operational efficiency, reduce dependency on the utility grid, and minimize electric vehicle (EV) charging costs. The proposed EMS integrates distributed energy sources, including photovoltaics (PVs), wind turbines (WTs), and battery energy storage systems (BESSs), alongside several EVs with variable loads to evaluate energy optimization. A comprehensive MG model is developed to assess multiple operational scenarios, including variations in EV demand, load, renewable energy production, and dynamic grid power prices. The study demonstrates the effectiveness of the PSO-based EMS in optimizing energy exchanges with the grid, leading to cost reductions of 14% and 21% with and without EV integration, respectively. Furthermore, the EMS facilitates efficient BESS charging/discharging schedules and implements proactive EV charging strategies to ensure uninterrupted electricity supply during peak and off-peak hours. These findings underscore the potential of PSO-based EMSs to provide a sustainable and cost-effective energy management solution in MGs by leveraging renewable resources and addressing the challenges posed by fluctuating energy demand and grid prices. Hence, the proposed model provides an optimized EMS and a proactive EV charging system.