Optimal Electricity Charging for Smart Grid

Abstract

Abstract. The amount of energy consumption has been increasing over the years and the peak usage of energy is approaching to limited capacity of a power grid system. To prevent large scale black-out power outage in worst case, the consumer can control their own energy consumption, and electric vehicles are good means to control the demand of electricity. The time when the users charge the electric vehicles is one of the important factors to avoid peak usage of energy. Even though the electric rates are decided depending on the demand which is decided according to the weather, temperature, and a variety of regional factors, there are regular patterns in the past and current rates, which means the current electric rates can be decided by referring the past data. Therefore, we proposed a method to optimally charge electric vehicles based on past real-time price information with Genetic Algorithm. For this, the past date to use and the time zone to charge electric vehicles are encoded as a binary string in a gene. With a real data set collected by an electricity power company, we tested and evaluated the proposed approach. We could effectively charge electric vehicles at a low cost with Genetic Algorithm, which means we can effectively avoid the peak usage of energy.