An optimal decentralized scheduling strategy for charging one Electric Vehicle (EV) is proposed to minimize the customer charging cost. Moreover, the EVs can offers more profit when considering the vehicle to grid feature, by discharging the EV in the grid at high peak demand the EV’ owner can earn money and reduce his charging bill. Compared to existing methods, the main advantages of the proposed strategy is the considerations of an optimized time step. By doing so, the optimization problem uses a minimum number of decision variables and constraints. Then, the problem can be solved by all optimization method to reach the global optimum in reduced time. To formulate and solve a non-linear constrained optimization problem, the scheduling process takes into consideration: the time of arrival and time departure of the EV, the daily energy prices, the initial State of Charge (SoC) and the final SoC desired by the customer, the power limitations, and the temperature. The results obtained show a high impact of the optimal scheduling strategy and significant charging cost reduction compared to the uncontrolled charging and fixed time step algorithms. Moreover, the charging strategy only requires that each EV solves its optimization problem locally, therefore, its deployment requires a low computing capacity.

提出了一种为电动汽车（EV）充电的最佳分散调度策略，以最大程度地减少客户的充电成本。此外，当考虑到车辆到电网的功能时，电动汽车可以提供更多的利润，通过在高峰值需求下将电动汽车放电到电网中，电动汽车的所有者可以赚钱并减少其充电费用。与现有方法相比，提出的策略的主要优点是考虑了优化的时间步长。这样，优化问题将使用最少数量的决策变量和约束。然后，可以通过所有优化方法解决该问题，从而在较短的时间内达到全局最优。为了制定和解决非线性约束优化问题，调度过程要考虑以下因素：电动汽车的到达和离开时间，每日能源价格，客户所需的初始充电状态（SoC）和最终SoC，功率限制和温度。与不受控制的充电和固定时间步长算法相比，获得的结果显示出最佳调度策略的显着影响，并且显着降低了充电成本。此外，充电策略仅要求每个EV在本地解决其优化问题，因此，其部署需要较低的计算能力。