Household Energy Demand Management Strategy Based on Operating Power by Genetic Algorithm

Abstract

Effective and adaptable household energy management system needs to be established to promote and implement demand response projects in smart grids. The current household energy demand management strategy cannot provide users with a choice to ensure user comfort, its time sampling accuracy is not high enough, and the operation using the rated power results in a large deviation from the actual cost. In order to solve these problems, this paper proposes an optimization control strategy to achieve the minimum electricity cost based on the user response, equipment operating power, and dynamic pricing. The genetic algorithm is used for calculating the optimal operating parameters of each equipment by using the operating power. The correctness and the high accuracy of the algorithm are verified by comparing with the loop search optimization algorithm. The results show that the daily electricity cost is reduced by 29.0%, and the peak-to-average ratio is reduced by 36.2% after adopting the proposed strategy.