基于实时电价的电动汽车智能充电导航

大量电动汽车（electric vehicle，EV）并网将影响电力系统规划与运行，对电动汽车的充电行为进行有序引导具有重要意义。针对这类问题，提出一种基于实时电价的EV智能充电导航策略，采用粒子群-遗传算法求解最优方案，旨在应用实时电价机制，引导EV有序充电。根据用户的不同需求，设计3种不同导航目标的充电导航策略，分别使用户的时间成本、费用成本以及综合成本最小化。最后在Matlab中建立IEEE-33节点系统模型，以地图上某一真实交通区域为背景，运用蒙特卡罗法模拟路况和EV时空分布信息，验证该方法的有效性。仿真结果表明，该方法可以降低EV用户的出行成本，同时提高配电系统的节点电压质量。

Intelligent Charging Navigation for Electric Vehicles Based on Real-time Electricity Price

A large number of electric vehicles (EVs) connected to the grid will affect the planning and operation of the power system. Hence it is of great significance to guide the charging behavior of electric vehicles in an orderly manner. Regarding this kind of problem, an electric vehicle intelligent charging navigation strategy based on real-time electricity price is proposed, where the PSO-GA algorithm is used to solve the optimal scheme. The motivation is to guide the EV charging toward an orderly way by virtue of the real-time electricity price mechanism. Furthermore, according to the different requirements of users, three charging navigation strategies are designed for different objectives, i.e., the minimization of the user's time cost, expense cost or overall cost respectively. Finally, the IEEE-33 bus system model is established in Matlab. By utilizing the Monte Carlo method, the road condition and EV space-time distribution information is simulated in a real traffic area on the map so as to verify the effectiveness of the method. The simulation results show that the method can reduce the travel cost of EV users and improve the node voltage quality of the power distribution system.