Price-responsive early charging control based on data mining for electric vehicle online scheduling

Abstract The uncertainty of electric vehicle (EV) behavior is deemed as a major challenge in online charging scheduling. It may lead to charging congestion to compromise the whole benefits of EV owners and aggregators. Early charging is the most efficient way to tackle the dynamic problem. However, it is very challenging for early charging to achieve the adaptive control and minimize electricity bill. In this paper, a price-responsive early charging adaptive control (PRECC) is proposed. The speedup factor is designed as a subtotal of charging demand categorized by electricity price, and it can be determined with only one offline charging optimization through a data-mining method. Due to the strong correlation with electricity price, PRECC can help online scheduling algorithms minimize early charging cost. Since it is not limited by the states of EVs, it can rapidly respond to the variations of base load and electricity price. Besides, with the independent design, it can well match online scheduling algorithms. Computer simulations are made to verify the proposed control. The results show that PRECC can improve the optimality of online scheduling by an average of 5.4%. Compared with the traditional early charging strategies, it has obvious advantages in terms of optimality, power capacity utilization, and profitability.