Real-time plug-in electric vehicles charging control for V2G frequency regulation

In this paper, a real-time energy management algorithm for charging a plug-in electric vehicles (PEVs) network in a large urban area with renewable energy resources is proposed. In this system, the PEVs charging rates are controlled by a central aggregator through wireless communication. A statistical forecasting model of the energy requirement of the PEVs network at different times during the day is developed based on statistical US drivers´ driving habits. With historical solar irradiance, and wind speed in this area, genetic algorithm (GA) is used to find the optimal scale of the renewable farm that can feed proper power for the PEVs network. Meanwhile, this urban area has a certain amount of local load that follows a daily pattern. Through vehicle to grid (V2G) and vehicle to vehicle (V2V) services, the proposed power optimization algorithm based on fuzzy logic control is used to minimize the impact of charging PEVs to the power grid, maximize the utilization of renewable energy and help the power grid to regulate the utility frequency, which will benefit the utility AC grid, PEVs network and its customers. The simulation results based on a large PEVs network demonstrate that the proposed smart charging algorithm can effectively limit the PEVs charging impact and help the grid regulate the frequency.