Multi-Objective Optimization Scheduling of Microgrids Using an Improved Crow Search Algorithm with Levy Flight Strategy

Abstract

With the depletion of traditional energy sources and the rising significance of renewable energy systems, optimizing the scheduling of microgrids has emerged as a critical area of research to ensure safe, stable, and cost-effective operation. This paper presents a multi-objective optimization scheduling model for microgrids, addressing both economic cost minimization and environmental benefits. To overcome the limitations of traditional optimization algorithms, an improved crow search algorithm (PCSA) integrating dynamic sensing probability and the Levy flight strategy is proposed. The enhancements improve the parameter and position update mechanisms of the standard CSA, resulting in increased global search efficiency, faster convergence, and avoidance of local optima. Simulation results demonstrate that the proposed method significantly enhances the global optimization performance, reduces load peak-valley differences, and achieves superior economic and environmental outcomes compared to traditional methods.