Optimal energy scheduling using genetic algorithm approach with consideration of consumer preferences in a residential smart home.

In this paper, an optimization approach is brought forward for optimal energy scheduling in a modern smart home. After the development of smart devices and their integration with smart grid, the end consumers have the chance to schedule their home appliances in order to decrease the overall electricity price and allaying the power Peak to Average ratio (PAR). The application pattern of home devices is observed at first for optimal energy scheduling. The home gateway (HG) obtains the demand response (DR) showing the real-time electricity price (RTEP) and later sent to energy management controller (EMC). With the Demand Response, the EMC results in the essential energy scheduling that can be sent to each electric appliance by the Home Gateway. The next feature is integrating RTEP with IBR, because if only RTEP is considered the appliances tend to be scheduled at lowest electricity price slot, this may lead to blackout during that time period. By executing this optimization method, our proposed approach could gradually reduce boththe electricity price and PAR, thereby, strengthening the stability of the entire Grid system. The optimization technique we used to solve this problem was Genetic Algorithm (GA) as the optimization problem was nonlinear. [ABSTRACT FROM AUTHOR]