Abstract
•An enhanced MOPSO algorithm is proposed for tri-objective energy management in grid-connected hybrid microgrids.•The framework minimizes operational cost and emissions while maximizing renewable energy utilization.•Hybrid PV–WT–MT–DG–BESS coordination is optimized under realistic system constraints.•Simulation results show faster convergence and superior Pareto-optimal solutions compared to GA and DE.•The method demonstrates robustness, scalability, and suitability for real-time microgrid operation.
This study presents an approach to optimizing energy management (EM) in a microgrid (MG) equipped with a battery energy storage system (BESS) and distributed generation. The methodology addresses the dual challenges of minimizing operational costs and emissions while maximizing renewable energy utilization, ensuring both economic and environmental sustainability. A Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is employed to solve this complex optimization problem by efficiently distributing generated power from various sources within the microgrid. The algorithm's objectives include minimizing operational and generation costs, reducing pollution emissions, and maximizing the use of renewable energy resources. The performance of MOPSO is compared to Genetic Algorithm (GA) and Differential Evolution (DE) across three case scenarios. MOPSO consistently demonstrates superior results, achieving significant savings in operational costs, greater reductions in emissions, and higher renewable energy utilization. In Case 1, MOPSO achieves 72.1% cost savings, a 22.4% reduction in emissions, and a 46.6% increase in renewable energy usage. Similarly, in Case 2, it delivers 65.1% cost savings, a 19.6% reduction in emissions, and a 42.9% increase in renewable energy utilization. Case 3 highlights MOPSO’s remarkable ability to reduce costs by 55.0%, emissions by 15.1%, and increase renewable energy utilization by 39.2%. Across all cases, MOPSO outperforms GA and DE, especially in renewable energy utilization and cost reductions, while also achieving substantial emission reductions. These results underscore the robustness and efficiency of MOPSO as a comprehensive tool for microgrid energy management. By effectively balancing economic and environmental objectives, MOPSO demonstrates its potential as a scalable solution for optimizing power distribution in complex energy systems, contributing to sustainable and cost-effective microgrid operations.