基于移动社交网络平台的电动汽车充放电行为预测

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。

Charging and Discharging Behavior Prediction of Electric Vehicles Based on Mobile Social Network Platform

Uncontrolled charging and discharging process for a large number of electric vehicles(EVs)could have significant negative impacts on the secure and economic operation of a power system concerned. On the other hand, the vehicle-to-grid(V2G)technology can support the EVs to participate in ancillary service provision, but the effects on the behavior of the interactive power systems for a different scheduling policy are different. Users can make decisions assisted by mobile social networking to select the scheduling strategy with beneficial effects. Based on the mobile social network analysis, a charging and discharging behavior prediction method of EVs with time-of-use(TOU)constraints is studied, which views per EV as a single individual in a social network. Subjected to the overall influence, its spatial and temporal characteristics are similar to the cross in the elementary particle group. Combined with the influence factor of mobile social networks, a predictive model of EVs charging and discharging behavior with constrains of safe operation for power system and the best interests for EVs users is established. Simulation results show that the method is valid. This work is supported by National Natural Science Foundation of China(No. 51477151, No. 51407076), Natural Science Foundation of Hebei Province (No. F2014502050), and the Fundamental Research Funds for the Central Universities (No. 2015ZD28).