The grey wolf optimization-based demand side management strategy for peak clipping and load shifting

Abstract

Demand Side Management (DSM) plays a critical role in modern smart grids by optimizing electricity consumption patterns to improve energy efficiency and system stability. This paper proposes a Grey Wolf Optimization (GWO)-based DSM strategy to achieve peak clipping and load shifting. This study focuses on a real-world case study 1 using the Paarl, South Africa user load profile, while case study 2 applies GWO to residential, commercial, and industrial load profiles for peak clipping and load shifting under time-varying electricity prices. The GWO algorithm is used to determine the optimal load scheduling that minimizes peak demand while maintaining consumer comfort. For comparative analysis, the widely used Particle Swarm Optimization (PSO) algorithm is implemented as a benchmark. Hypothetical yet realistic simulation results demonstrate that the proposed GWO approach reduces peak demand by 18.5% compared to 14.2% using PSO and achieves a 9.7% improvement in overall energy cost savings. GWO’s convergence behavior indicates that optimal solutions for the DSM objective are obtained more quickly than those for PSO. The results confirm the GWO algorithm's potential for scalable and effective DSM implementation in upcoming smart grids.