Design of an Approximate Dynamic Programming based neural controller for Smart Home Energy Management

Demand Side Management (DSM) is the control of consumer demand for energy via different techniques such as financial incentives. This technology has become inevitable in the new smart grid infrastructure. In this study, a DSM scheme, a novel smart home energy management system, is proposed. The goal, defined in terms of cost, is to manage the home energy system according to time-varying prices in a way that energy demand from grid is reduced as much as possible or it is moved to off-peak times. The proposed scheme takes advantage of local energy generation, energy storage unit and schedulable load. Our offline scheme uses an Adaptive Dynamic Programming (ADP) based algorithm to solve the energy management problem and optimally schedule the battery and load operations in a given time horizon. We also use PSO method to solve the mentioned problem. The results obtained by PSO are used as an element of comparison. Simulation results show that the ADP algorithm can reduce costs with respect to PSO due to better decision making ability.