Optimal residence energy management with time and device-based preferences using an enhanced binary grey wolf optimization algorithm

Abstract

In residential energy management (REM), time of use (TOU) of appliances scheduling based on user-defined preferences is an essential task performed by the home energy management controller. This paper devised a robust REM technique capable of monitoring and controlling residential loads within a smart home. The method is based on an improved binary grey wolf accretive satisfaction algorithm (GWASA), which is developed based on four hypotheses that allow time-varying preferences to be quantifiable in terms of time and device-dependent features. Based on household appliances TOU, the absolute satisfaction derived from the preferences of appliance and power ratings, the GWASA can produce optimum energy consumption pattern that will give the customer maximum satisfaction at the predefined user budget. A cost per unit satisfaction index is also established to relate daily consumer expenses with the achieved satisfaction. Simulation results on three peak budgets from $1.5/day to $2.5/day with a step size of $0.5 are carried out to analyze the efficacy of GWASA. Accordingly, the result of each of the scenario is compared with the result obtained from three other different algorithms, namely, BPSO, BGA, BGWO. The simulation results reveal that the proposed demand side residential management based on GWASA offers the least cost per unit satisfaction and maximum percentage satisfaction in each scenario.