Optimizing sustainable energy management in grid connected microgrids using quantum particle swarm optimization for cost and emission reduction
Abstract
The global shift towards decentralized energy systems, driven by the integration of distributed generation technologies and renewable energy sources, underscores the critical need for effective energy management strategies in microgrids. This study proposes a novel multi-objective optimization framework for grid-connected microgrids using quantum particle swarm optimization (QPSO) to address the dual challenges of minimizing operational costs and reducing environmental emissions. The microgrid configuration analyzed includes renewable energy sources like photovoltaic panels and wind turbines, along with conventional energy sources and battery storage. By incorporating quantum-inspired mechanics, QPSO overcomes limitations such as premature convergence and solution stagnation, often seen in traditional methods. Simulation results demonstrate that QPSO achieves a 9.67% reduction in operational costs, equating to savings of €158.87, and a 13.23% reduction in carbon emissions, lowering emissions to 513.70 kg of CO2 equivalent in the economic scheduling scenario. In the environmentally constrained economic scheduling scenario, the method delivers a balanced solution with operational costs of €174.11 and emissions of 401.63 kg of CO2. The algorithm’s performance is validated across various microgrid configurations, including standard low-voltage setups. These results highlight QPSO’s potential as an efficient tool for optimizing microgrid energy management, promoting both economic…
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