Demand Response Optimization for Smart Home Scheduling Using Genetic Algorithm

For mitigating the urgency of constructing power plant, alleviating supply pressure of power company, electricity scheduling of customers is a very important issue, wherein to promote demand response is a key factor. The demand response is mainly through electricity price publicized by utility company to guide customers in electricity scheduling, and by use of price negotiating mechanism, to reach mutual benefits for both sides of demand and supply. The paper proposes a method of minimizing tariff for customers through changing elastic load use time intervals where customers' electricity use time is divided into inelastic and elastic intervals by electricity use characteristics. In the paper, customer's one day electricity used is assumed to conduct simulation, by genetic algorithm, comparing variations among scheduling and tariff under different electricity use limitation situations. As shown in the results, it is found that through elastic load use time interval changes, minimum tariff objective can be reached, and feasibility of the proposed method is verified.