A novel demand side management program using water heaters and particle swarm optimization

Abstract

Power systems' operators have the task of maintaining the balance between the demand and generation of electric power. Much research and attention is being given to find more environmental friendly sources of power generation. Naturally, more power is required when the load is at its peak value, and this tends to be when the most non environmentally friendly sources of power generation are used. This paper proposes a new controller for peak load shaving by intelligently scheduling power consumption of domestic electric water heater using binary particle swarm optimization. Past studies show that similar demand side management programs were not successful because the impact that the load control has on the end users' comfort. In this study, Binary Particle Swarm Optimization (BPSO) finds the optimal load demand schedule for minimizing the peak load demand while maximizing customer comfort level. A simulation in Matlab is used to test the performance of the demand response program using field data gathered by smart meters from 200 households. The direct load control is shown to be an effective tool for peak shaving of load demand, shifting the loads to valleys and reducing the aggregated load of electricity without compromising customer satisfaction.