Optimizing Microgrid Energy Management via DE-HHO Hybrid Metaheuristics

In response to the increasing global energy demand and environmental pollution, microgrids have emerged as an innovative solution by integrating distributed energy resources (DERs), energy storage systems, and loads to improve energy efficiency and reliability. This study proposes a novel hybrid optimization algorithm, DE-HHO, combining differential evolution (DE) and Harris Hawks optimization (HHO) to address microgrid scheduling issues. The proposed method adopts a multi-objective optimization framework that simultaneously minimizes operational costs and environmental impacts. The DE-HHO algorithm demonstrates significant advantages in convergence speed and global search capability through the analysis of wind, solar, micro-gas turbine, and battery models. Comprehensive simulation tests show that DE-HHO converges rapidly within 10 iterations and achieves a 4.5% reduction in total cost compared to PSO and a 5.4% reduction compared to HHO. Specifically, DE-HHO attains an optimal total cost of $20,221.37, outperforming PSO ($21,184.45) and HHO ($21,372.24). The maximum cost obtained by DE-HHO is $23,420.55, with a mean of $21,615.77, indicating stability and cost control capabilities. These results highlight the effectiveness of DE-HHO in reducing operational costs and enhancing system stability for efficient and sustainable microgrid operation.