A microgrid (MG) topology combines various kinds of resources like solar photovoltaic (PV) systems, wind turbines (WTs), energy storage systems, and the conventional utility grid. These different resources need to be coordinated in an optimal way to keep the power balanced, reduce the operational cost, and make the system resilient to any kind of failures. Therefore, an efficient energy management system (EMS) is essential in an MG system to provide suitable and reliable operation under different weather and demand load conditions. In this paper, a new EMS-based multi-objective Nizar Optimization Algorithm (NOA) is proposed. The suggested EMS aims to improve the power quality problem caused by the unpredictable nature of renewable energy sources and then minimize the grid power and battery degradation costs. By leveraging the adaptability of the NOA, the applied EMS method simply optimizes the allocation and energy sharing of the resources in a grid-connected MG. The proposed EMS was verified in simulation using MATLAB software. The performance of the proposed EMS was tested under different weather conditions, and the obtained results have been compared with those obtained in the existing methods. The obtained results indicate that the proposed EMS based on the NOA is capable of adjusting the multi-source energy allocation with minimal grid costs and the battery degradation issue. The proposed NOA indicates robust performance with total cost savings varying from USD 17 to USD 34 compared to other optimizers, as well as a great reduction in degradation cost, up to 27% improvement over the conventional methods. Finally, the proposed EMS offers several advantages over the conventional methods, including the improved dynamic system, faster convergence, lower operational costs, and higher energy efficiency.

Introduction: The rapid growth of electric vehicles (EVs) has created significant challenges for the modernization of power systems, particularly in energy management, grid stability, and infrastructure readiness. Methods: This article presents a sophisticated energy management system (EMS) intended for a photovoltaic (PV)-battery-electric vehicle (EV) charging station, employing a hybrid Particle Swarm Optimization-Salp Swarm Algorithm (PSO-SSA). It depicts a complex setup that combines PV electricity production, battery storage, EV charging demand, and building consumption under actual operating conditions. The suggested method focuses on improving battery operational behavior (stress, peak power, and smoothness) while maintaining acceptable system reliability. The proposed algorithm aims to enhance the balance between exploration and exploitation, thus allowing for more effective power load coordination. The proposed EMS is evaluated under deterministic operating conditions, and the simulation results are obtained via MATLAB software. Results: The simulation results show that the hybrid PSO-SSA improves battery operation considerably, while full PV utilization (100%) and reliability are kept at the same level. The proposed PSOSSA method reduces battery throughput by 3% and peak discharge power by 29% compared to the baseline strategy. Conclusion: These findings demonstrate that the proposed hybrid PSO-SSA is a robust and effective optimization technique for PV-powered EV charging systems, particularly by mitigating battery degradation and improving long-term operational efficiency.