The increasing popularity of electric vehicles (EVs) has made electric transportation a popular research topic. The demand for EV charging resources has significantly reshaped the net demand profile of power distribution systems. This paper proposes an online optimal charging strategy for multiple EV charging stations in distribution systems with power flow and bus voltage constraints satisfied. First, we formulate the online optimal charging problem as an optimal power flow problem that minimizes the total system energy cost based on short-term predictive models and operates in a time-receding manner with the latest system information. Then, the problem is convexified by a modified convex relaxation technique based on the bus injection model, so that the globally optimal solution can be obtained with high efficiency. Moreover, a distributed model predictive control based scheme is designed to solve the optimization problem per concerns regarding data privacy, individual economic interests, and EV uncertainties. The obtained optimal schedules are dispatched to the EVs parked at each charging station according to a fuzzy rule, which guarantees full charging at the departure time for each vehicle. The effectiveness of the proposed method is demonstrated via simulations on a modified IEEE 15-bus distribution system with charging stations located in both residential and commercial areas.

中文翻译：

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基于MPC在线分布式的EV充电站优化调度

电动汽车（EV）的日益普及已使电动交通成为热门的研究主题。对电动汽车充电资源的需求已大大改变了配电系统的净需求状况。本文提出了一种满足潮流和母线电压约束的配电系统中多个EV充电站的在线最优充电策略。首先，我们基于短期预测模型将在线最优充电问题公式化为最优潮流问题，该问题可最大程度地降低系统总能源成本，并利用最新的系统信息以递减的方式运行。然后，通过基于总线注入模型的改进的凸松弛技术将问题凸化，从而可以高效地获得全局最优解。而且，设计了一种基于分布式模型预测控制的方案，以解决有关数据隐私，个人经济利益和电动汽车不确定性的优化问题。根据模糊规则，将获得的最佳时间表调度到停在每个充电站的电动汽车，从而确保每个车辆在出发时间进行完全充电。通过在改进的IEEE 15总线配电系统上进行仿真，证明了该方法的有效性，该配电系统在住宅区和商业区均设有充电站。这样可以保证每辆车在出发时都充满电。通过在改进的IEEE 15总线配电系统上进行仿真，证明了该方法的有效性，该配电系统在住宅区和商业区均设有充电站。这样可以保证每辆车在出发时都充满电。通过在改进的IEEE 15总线配电系统上进行仿真，证明了该方法的有效性，该配电系统在住宅区和商业区均设有充电站。