Swarm Intelligence-driven Multi-objective Optimization for Microgrid Energy Management and Trading considering DERs and EVs integration: Case Studies from Green Energy Park, Morocco

The objective of this study is to develop and validate a comprehensive multi-objective optimization approach for energy management and trading in microgrids, with a particular focus on the integration of Distributed Energy Resources (DERs) and Electric Vehicles (EVs). As the demand for sustainable and smart energy solutions increases, the development of robust Energy Management Systems (EMS) that optimize energy flows while ensuring efficiency, reliability, cost-effectiveness, and sustainability becomes crucial. In this work, we propose an advanced EMS that employs an enhanced Particle Swarm Optimization (PSO) technique to address the complexities of optimal energy scheduling, cost minimization, revenue maximization, battery health preservation, and EV users satisfaction. Additionally, our EMS incorporates demand response (DR) mechanisms while considering dynamic pricing strategies to enhance operational efficiency and adaptability. This methodology is rigorously validated through a case study at the Green Energy Park (GEP) in Morocco, serving as a practical testbed for real-world applications. The results of this study demonstrate that the proposed EMS strategy can reduce net costs by up to 42 % compared to a baseline scenario while simultaneously optimizing renewable energy utilization and enhancing EV users’ satisfaction. The findings elucidate significant trade-offs and provide insights into the multi-dimensional decision-making processes essential for effective microgrid management. This research contributes to advancing the development of sustainable energy systems and offers a robust framework for future investigations focused on microgrid optimization.