OPTIMIZATION OF MICROGRID PERFORMANCE VIA RENEWABLE ENERGY AND ELECTRIC VEHICLE INTEGRATION

Abstract

The increasing global demand for sustainable and reliable energy has accelerated the development of microgrids that integrate renewable energy sources (RES) and electric vehicles (EVs). This study focuses on optimizing microgrid performance through the coordinated operation of renewable energy generation, energy storage systems, and electric vehicle charging infrastructure. By employing advanced optimization techniques such as mixed-integer linear programming (MILP) and artificial intelligence (AI)-based algorithms, the proposed model aims to minimize operational costs, reduce power losses, and enhance energy reliability. The integration of EVs is treated both as dynamic loads and as distributed storage units, enabling bidirectional energy flow that supports grid stability. Simulation results demonstrate that effective scheduling of renewable resources and EV charging can significantly improve energy efficiency, reduce carbon emissions, and increase the resilience of the microgrid under varying demand and generation conditions. The optimized framework provides a scalable solution for future smart grid applications, promoting sustainable and cost-effective energy management.