This paper presents a particle swarm optimization (PSO) method to optimize the operation of a hybrid renewable energy microgrid system, considering reserve margins for critical loads. The microgrid consists of wind, photovoltaic (PV) systems, battery energy storage systems (BESS), and electric vehicles (EVs). The PSO algorithm is adapted to handle the specific constraints and objectives of the microgrid optimization problem. The study simulates the microgrid's operation under different weather conditions (clear and partly cloudy days) and evaluates the impact of BESS on grid interaction costs. The results show that the PSO algorithm effectively minimizes grid interaction costs, with a 58% reduction on clear days and a 153% reduction on partly cloudy days. The BESS ensures that the microgrid can meet critical load demands even when renewable energy sources are insufficient or unavailable, maintaining high operational efficiency and economic benefits. The paper also discusses the importance of choosing appropriate PSO parameters to ensure efficient convergence and optimal performance. Future work will involve real-time EV simulations and a performance evaluation of alternative heuristic optimization techniques.This paper presents a particle swarm optimization (PSO) method to optimize the operation of a hybrid renewable energy microgrid system, considering reserve margins for critical loads. The microgrid consists of wind, photovoltaic (PV) systems, battery energy storage systems (BESS), and electric vehicles (EVs). The PSO algorithm is adapted to handle the specific constraints and objectives of the microgrid optimization problem. The study simulates the microgrid's operation under different weather conditions (clear and partly cloudy days) and evaluates the impact of BESS on grid interaction costs. The results show that the PSO algorithm effectively minimizes grid interaction costs, with a 58% reduction on clear days and a 153% reduction on partly cloudy days. The BESS ensures that the microgrid can meet critical load demands even when renewable energy sources are insufficient or unavailable, maintaining high operational efficiency and economic benefits. The paper also discusses the importance of choosing appropriate PSO parameters to ensure efficient convergence and optimal performance. Future work will involve real-time EV simulations and a performance evaluation of alternative heuristic optimization techniques.