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大量电动汽车的自由充放电会给电力系统的安全与经济运行带来较大影响。另一方面,电动汽车向电力系统反向送电(V2G)技术的发展可支持电动汽车参与电力系统的调频等辅助服务,但采用不同充放电调度策略时,对V2G和电力系统的交互行为的效果会有不同的影响。移动社交网络(MSN)作为车联网的一个有益补充,在电动汽车用户进行充放电决策上具有一定的互动性,进而会影响电动汽车充放电的调度策略。考虑到MSN的影响力,研究了在分时电价约束下的电动汽车充放电行为预测方法,将每辆参与调度的电动汽车看成是MSN平台中的单一个体,受整体影响力的制约,其时空特征类似于交叉粒子群中的基本粒子,在保持电力系统安全运行与电动汽车用户利益最优的目标约束下,结合MSN的影响力因子,建立了电动汽车充放电行为的预测模型。仿真实验结果表明,该方法可以在不同的调度策略下,预测出电动汽车的充放电行为。
A large number of electric vehicles free charging and discharging will have a greater impact on the safety and economic operation of the power system. On the other hand, the development of V2G technology for electric vehicles to support the participation of electric vehicles in the power system FM and other auxiliary services, but with different charge-discharge scheduling strategy, V2G and power system interaction Effects have different effects. Mobile social network (MSN) is a useful supplement to the car networking. It has some interactivity in charging and discharging decision-making of electric vehicle users, and further affects the scheduling strategy of electric vehicle charging and discharging. Taking into account the influence of MSN, this paper studies the EV charging and discharging behavior prediction method under the constraint of time-of-use price. Each EV participating in the scheduling is regarded as a single entity in the MSN platform and is constrained by the overall influence. The spatio-temporal features are similar to the basic particles in the crossover particle swarm, and the prediction model of the charge-discharge behavior of the electric vehicle is established based on the influence factor of the MSN, while keeping the power system safe operation and the target of the electric vehicle user’s interests optimal. Simulation results show that this method can predict the charge and discharge behavior of EV under different scheduling strategies.