Multi-Objective Optimization Framework for Sustainable Operation of Grid-Connected Microgrids

This paper proposes an optimal operational framework for enhancing the economic, technical, and environmental performance of a renewable energy-based microgrid. The proposed system integrates photovoltaic (PV) generation, wind turbines (WTs), battery energy storage systems (BESSs), diesel generators (DGs), and utility grid interaction. Three multi-objective optimization algorithms, namely Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Genetic Algorithm (MOGA), and Multi-Objective Celestial Orbit Optimization (MOCOO), are employed to minimize the total operating cost and grid dependency. The obtained results demonstrate that MOPSO achieves the best techno-economic performance with a minimum operating microgrid cost of 2.2 M$/year and a low grid dependency ratio of 0.0333. The operational analysis confirms that the proposed renewable-priority scheduling strategy significantly reduces operational emissions and reliance on the utility grid through coordinated BESS charging/discharging and efficiency-aware DG dispatch. The microgrid (MG) achieves zero-emission operation during operating periods dominated by renewable generation. Furthermore, the DG operates within an efficiency range of 36.8–39.3%, improving fuel utilization and reducing unnecessary emissions. The battery degradation analysis indicates high lifetime cycle capability under shallow depth-of-discharge operation, demonstrating improved long-term operational sustainability. Overall, the proposed framework provides a reliable and economically balanced solution for sustainable microgrid energy management.