Intelligent Residential Demand Response Optimization under Real-time Pricing: A Comparison of Heuristics

This paper proposes a framework to compare meta-heuristic optimization techniques for household energy management while taking into account the operational and power constraints. It considers real-time price (RTP) to evaluate the proposed algorithms. In addition, it uses 60-minute operational time intervals to estimate the end-users demand and behavior for the proposed scheme. We evaluate the performance of four algorithms, including Harris hawks optimization (HHO), grey wolf optimization (GWO), arithmetic optimization algorithm (AOA), and ant lion optimization (ALO), based on electricity cost, peak-to-average ratio (PAR), and waiting time. The goal is to identify how these schemes work to the benefit of the user. Simulation results reveal that the proposed evaluation scheme compares these heuristics how, for various appliances, they benefit in lowering the electricity charges and peak minimization without jeopardizing users' comfort.