电动汽车行驶里程短、充电时间长是影响驾驶体验的关键。通过对电网、充电设备进行大规模升级的方法减少充电时间，成本昂贵，因此充分利用现有路网、电网资源，制定智能充电调度策略成为提高驾驶体验的重要手段。考虑到驾驶者对充电时间敏感度的异质性，提出具有差异化的调度策略以满足不同优先级驾驶者的需求。首先，为均衡不同优先级驾驶者的利益，提出一种基于动态截断机制的两优先级队列模型;其次，定义充电站的准入原则，保证高优先级驾驶者对预留桩的使用权及对空闲桩的优先抢占权;然后，提出基于截断机制的双层优化模型CCPQ(charging with cut-off priority queue)，在顶层高优先级车辆与充电桩最优匹配的基础上，设计底层低优先级车辆的分配策略优化模型，将最小化低优先级驾驶者的总等待时间构建为凸优化问题;最后，通过仿真验证策略的有效性及优越性。
Electric vehicle charging scheduling strategy considering differentiated demand
Limited drivable range and long charging duration are the key hurdles for electric vehicles(EVs)to provide an acceptable driving experience.The solution for reducing charging duration is a significant upgrade in power networks to accommodate a widespread installation of chargers.Such an upgrade can be too expensive,hence,formulating intelligent charging scheduling strategy becomes an important means to improve driving experience after making sure that existing road network resources and power networks resources are efficiently utilized.Considering the heterogeneity of drivers'sensitivity to charging duration,this paper proposes a differentiated scheduling strategy for drivers with different priorities.Firstly,to balance the benefits of drivers with different priorities,a two-priority queuing model based on dynamic cut-off mechanism is proposed.Secondly,the admission principle of charging station is defined to ensure that high-priority drivers have the right to use the reserved chargers or to preempt the idle chargers.Then,a two-tier optimization model CCPQ(charging with cut-off priority queue)is proposed.Based on the optimal matching model for high-priority vehicles and charging chargers on the top tier,an optimal allocation model for low-priority vehicles on bottom tier is designed.Minimizing the total waiting time of low-priority drivers is constructed as a convex optimization problem.Finally,the effectiveness and superiority of the strategy are verified by simulation.
electric vehiclescharging strategydifferentiated servicesreservationcut-off priority queuingconvex optimization
万方数据
维普
