A Novel Prairie Dog Optimization for Energy Management of Multi-Microgrid System Considering Uncertainty and Load Management

This study introduces a design-oriented framework for an intelligent Energy Management System (EMS) in a Multi-Microgrid (MMG) environment to achieve efficient, reliable, and sustainable power operation. The proposed EMS is systematically designed to coordinate three interconnected microgrids with the main grid, optimizing Distributed Energy Resource (DER) utilization under uncertain weather, load, and market conditions. A novel Prairie Dog Optimization (PDO) algorithm is developed as a key algorithmic design innovation to enhance decision-making in day-ahead scheduling and load management. Through an optimization-based design approach, the EMS minimizes Energy Generation Cost (EGC) and Probability of Power Supply Deficit (PPSD). Simulation studies on a modified 33-bus system validate the design’s effectiveness, showing that PDO reduces operational cost by 5% and carbon emissions by 20% compared to Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). A better system performance is indicated by the optimal EGC of 0.1567 $/kWh and PPSD of 0.155%. Comprehensively, the PDO-based EMS is an important addition to the design engineering field by offering scalable, adaptive, and sustainable energy system design to the design of resilient and zero-emission MMG architectures to be used in the future in smart grids.