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

Electric vehicle (EV) is an ideal solution to resolve the carbon emission issue and the fossil fuels scarcity problem in the future. However, a large number of EVs will be concentrated on charging during the valley hours leading to new load peaks under the guidance of static time-of-use tariff. Therefore, this paper proposes a dynamic time-of-use tariff mechanism, which redefines the peak and valley time periods according to the predicted loads using the fuzzy C-mean (FCM) clustering algorithm, and then dynamically adjusts the peak and valley tariffs according to the actual load of each time period. Based on the proposed tariff mechanism, an EV charging optimization model with the lowest cost to the users and the lowest variance of the grid-side load as the objective function is established. Then, a weight selection principle with an equal loss rate of the two objectives is proposed to transform the multi-objective optimization problem into a single-objective optimization problem. Finally, the EV charging load optimization model under three tariff strategies is set up and solved with the mathematical solver GROUBI. The results show that the EV charging load optimization strategy based on the dynamic time-of-use tariff can better balance the benefits between charging stations and users under different numbers and proportions of EVs connected to the grid, and can effectively reduce the grid load variance and improve the grid load curve. [ABSTRACT FROM AUTHOR]