Optimizing Economic Dispatch for Microgrid Clusters Using Improved Grey Wolf Optimization

With the rapid development of renewable energy generation in recent years, microgrid technology has increasingly emerged as an effective means to facilitate the integration of renewable energy. To efficiently achieve optimal scheduling for microgrid cluster (MGC) systems while guaranteeing the safe and stable operation of a power grid, this study, drawing on actual electricity-consumption patterns and renewable energy generation in low-latitude coastal areas, proposes an integrated multi-objective coordinated optimization strategy. The objective function includes not only operational costs, environmental costs, and energy storage losses but also introduces penalty terms to comprehensively reflect the operation of the MGC system. To further enhance the efficiency of solving the economic dispatch model, this study combines chaotic mapping and dynamic opposition-based learning with the traditional Grey Wolf Optimization (GWO) algorithm, using the improved GWO (CDGWO) algorithm for optimization. Comparative experiments comprehensively validate the significant advantages of the proposed optimization algorithm in terms of economic benefits and scheduling efficiency. The results indicate that the proposed scheduling strategy, objective model, and solution algorithm can efficiently and effectively achieve multi-objective coordinated optimization scheduling for MGC systems, significantly enhancing the overall economic benefits of the MGC while ensuring a reliable power supply.