Multi-Strategy Improved Grey Wolf Optimizer for Economic-Environmental Load Dispatch in Power Systems

Aiming at the issues of traditional Grey Wolf Optimizer (GWO) in power system Economic Load Dispatch (ELD), such as susceptibility to local optima and insufficient dynamic adaptability, this paper proposes a Multi-Strategy Improved Grey Wolf Optimizer (MSA-GWO). By integrating adaptive inertia weight, levy flight disturbance, and elite opposition-based learning strategies, the algorithm’s global search capability is enhanced. A multi-objective ELD model is developed, incorporating valve-point effects, multi-pollutant emissions, and renewable energy fluctuations through dynamic penalty function methods and Pareto optimal solution screening mechanisms. Experimental results demonstrate that in IEEE 30/118-bus systems, MSA-GWO achieves a 45% improvement in convergence speed, 2.3% reduction in fuel costs, 19.8% decrease in pollutant emissions, and a 38.6% reduction in standard deviation under load fluctuations compared to standard GWO. The research provides an effective algorithmic framework for economically and environmentally optimized operation of complex power systems.