Optimized demand-side management for large power consumers using PSO and MLIP algorithms: A case study of the Western Cape municipality

Abstract

The increasing power demand, transmission line congestion, and increasing electricity traffic necessitate the effective implementation of demand-side management (DSM) strategies to improve energy efficiency and sustainability. This research presents an optimized DSM framework for large power consumers in the Western Cape municipality, utilizing particle swarm optimization (PSO) integrated with machine learning improved prediction algorithms to achieve peak clipping and reduce peak load power demand under real-time pricing conditions. The developed algorithms were validated using actual energy consumption data from large industrial customers in the Western Cape province. Simulation results indicate that the PSO-driven DSM framework significantly reduces peak demand, improves the load factor, and offers substantial cost savings compared to conventional load management techniques. This study highlights the potential of intelligent optimization methods to support municipalities and major energy users in adopting more flexible, affordable, and sustainable energy consumption practices.

Keywords

Demand side management / Energy efficiency / Large power user / Load management / Machine learning improved prediction technique / Particle swarm optimization / Smart grids

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