Hybrid GWOPSO Algorithm Based Load Scheduling Approach for PV Integrated Households

The rapid advancement in technology and economic growth over recent decades has led to a substantial rise in electricity consumption within residential sectors. This surge presents a challenge in balancing the supply and demand of electric power. This paper proposes an innovative Load Scheduling method aimed at optimizing this balance. The primary objectives of this method are to decrease electricity costs and the peak-to-average ratio (PAR) while maximizing user comfort (UC). In this approach, electricity is procured from both utility providers and renewable energy sources (RESs). To achieve these objectives, Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and a Hybrid Grey Wolf and Particle Swarm Optimization (HGWOPSO) algorithms are employed within a Time of Use (ToU) tariff framework, focusing on the context of Bangladesh. This approach strategically shifts household loads from peak to off-peak hours, capitalizing on the tariff structure as well as the power generated by PV units. Simulation results validate the balanced and optimal performance of HGWOPSO over PSO and GWO. Notably, HGWOPSO achieved a reduction of 12.1% in electricity costs and 47.4% in PAR without RES and a reduction of 15.6% in electricity costs and 52.3% in PAR with RES.