Optimal energy management of multi-energy multi-microgrid networks using mountain gazelle optimizer for cost and emission reduction

Multi-energy multi-microgrid networks have emerged as an effective solution for integrating various energy sources and improving energy efficiency, particularly as carbon emissions regulations in energy management become increasingly important. This article proposes an optimal energy management approach for a multimicrogrid network to minimize operational costs and environmental impacts, within a framework that considers operational constraints and carbon emissions. Day-ahead scheduling and real-time updates are incorporated into the developed optimal energy management strategy. A Mountain Gazelle Optimizer (MGO) is developed based on fuzzy theory and a sorting algorithm, combined with Pareto optimality methodology, to enhance search efficiency and solution accuracy. The proposed energy management strategy enables a decentralized multi-microgrid network, allowing each microgrid to operate independently, preserve privacy, and improve the efficiency of energy resource allocation. Simulation results demonstrate significant improvements across all microgrids. In Case 2, wind curtailment decreases by 30.7 % in MG1, 26.8 % in MG2, and 26.9 % in MG3. Carbon emissions are reduced by 5.6 %, 5.1 %, and 3.1 %, while operational costs decrease by approximately 3.8 % in all microgrids. In Case 3, wind curtailment is eliminated, emissions are reduced by up to 37.5 % in MG1, and costs are reduced by 12.3 %. Further optimization in Case 4 leads to additional reductions in both emissions and costs across all microgrids.