Optimized Load Scheduling Approach for Residential Consumers Using Metaheuristic Algorithms Under Time of Use Tariff Schemes

Abstract

This project focuses on developing an advanced load scheduling strategy as part of a demand-side management (DSM) approach tailored for residential consumers in Dhaka City, Bangladesh. The primary objective is to optimize electricity consumption patterns to reduce costs and alleviate peak demand pressures on the grid. The proposed methodology employs sophisticated optimization algorithms, including Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and a Hybrid Grey Wolf and Particle Swarm Optimization (HGWOPSO) algorithm. By strategically rescheduling the operation times of household appliances from peak to off-peak hours, the method aims to lower electricity bills and the peak-to-average ratio (PAR), while maximizing user comfort. Additionally, the integration of renewable energy sources, such as photovoltaic (PV) systems, enhances the overall efficiency and sustainability of the load scheduling approach. This research demonstrates the potential of combining ToU pricing schemes with advanced optimization techniques to create a more balanced and cost-effective power system.