计及预测负荷和用户需求差异的电动汽车实时调度优化

Real-time Scheduling Optimization of Electric Vehicles Considering the Predicted Load and the Difference Between Users’Demand

针对电动汽车(electric vehicle,EV)大规模接入电网充电造成的过载风险问题,提出了计及预测负荷和用户需求差异的电动汽车实时优化策略。首先,根据签约与否将入网EV分为签约用户和非签约用户,通过分析实际场景将非签约用户纳入为需求响应对象,并根据用户的充电需求差异将其分为不同类别并建立相应的模型。其次,引入非签约用户响应概率与补偿电价的关系模型,建立聚合商和用户的补贴机制,根据电网需求和各类用户潜力制定各时段的实时调度方案。然后以配电网控制目标功率为约束,以聚合商盈利增比和用户平均收益增比为综合优化目标,采用粒子群算法分时段求解参与需求响应的EV充电功率。最后,经过多组仿真分析,证明了所提优化策略在削减负荷峰值的同时可以兼顾聚合商和用户的收益,验证了所提实时调度策略的有效性以及适用性。

Aiming at the overload risk caused by large-scale charging of electric vehicles(EV)connected to the power grid,a real-time optimization strategy of EV considering the difference between predicted load and user demand is proposed.First of all,according to the signing or not,the EV connected to the grid is divided into signed users and non-signed users.By analyzing the actual situation,the non-signed users are included as demand response objects,and they are divided into different categories according to the difference of users’charging requirements and the corresponding models are established.Secondly,the relationship model between the response probability of noncontracted users and the compensation electricity price is introduced,the subsidy mechanism of aggregators and users is established,and the real-time dispatching scheme of each period is formulated according to the demand of power grid and the potential of various users.Then,with the control target power of distribution grid as the constraint,and the profit increase ratio of aggregate quotient and the average profit increase ratio of users as the comprehensive optimization objectives,the particle swarm optimization algorithm is used to solve the EV charging power participating in demand response in different periods.Finally,through several groups of simulation analysis,it is proved that the proposed optimization strategy can not only reduce the peak load,but also take into account the benefits of aggregators and users,which verifies the effectiveness and applicability of the proposed real-time scheduling strategy.