Electric Vehicle Charging Load Optimization Strategy Based on Dynamic Time-of-Use Tariff

Abstract A new electricity price mechanism is proposed in this paper, namely dynamic time-of-use tariff, to address the problem that the static time-of-use tariff cannot be dynamically adjusted with the actual operating state of the grid and is prone to generate new load peaks by concentrated charging of electric vehicles (EVs) in the valley hours. Dynamic time-of-use tariff reclassifies the peak -valley hours with the fuzzy C-mean (FCM) clustering algorithm, and then dynamically adjusts the peak and valley tariff according to the actual load of each hour. Based on the dynamic time-of-use tariff, an EV charging optimization model is established with the objective function of minimum EV users charging cost and minimum grid load variance, and its constraints include user-side constraints and charging station-side constraints. In order to solve the multi-objective optimization problem, a weight selection principle with equal loss rate of the two objectives is proposed to transform the multi-objective optimization problem into a single-objective optimization problem, and finally the mathematical solver GUROBI is invoked to solve the model. The results show that the EV charging load optimization strategy based on dynamic time-of-use tariff can better balance the interests between charging stations and EV users under different number and ratio of EVs connected to the grid, and can effectively reduce the grid load variance.