基于实时电价的电动汽车充放电优化策略和经济调度模型

实时电价为优化电动汽车(EV)充放电负荷提供了手段,从而实现经济调度。首先建立用户最优充放电策略模型:以计及EV电池退化成本的用户成本最小为目标,以满足EV行驶荷电状态和充放电荷电状态等为约束。在此基础上建立电动汽车用户实时电价响应模型,通过实时电价计算用户充电成本,使电动汽车充放电负荷与电价联动调整,并将该模型嵌入电动汽车充放电策略优化目标函数。求解过程中,用"停泊时长"确定单车一日可多次充放电的时段和行驶时段,从而在EV可充放电时长范围内优化每时段充放电负荷。最后建立经济调度模型:目标中计及机组阀点效应、约束中考虑EV充放电负荷以及机组爬坡速率等限制的多目标经济调度模型,提出一种改进模式搜索算法求解该时间耦合、非线性、非凸模型。以IEEE 39节点为例,验证了所建立模型和求解算法的有效性。

Optimal Scheduling of Charging and Discharging of Electric Vehicle Based on Real Time Price and Economic Dispatch Model

Real time pricing makes it possible to optimize the charging load and discharging load of electric vehicles (Evs). First the real time price model based on time-of-use pricing is built; the minimization of the charging cost in consideration of battery degradation cost as well as constraints including mobility requirements is taken as objective function. The charging price response from EV users aims to interact the charging and discharging load with charging price, therefore the real time pricing model is embedded into the EV charging optimization model mentioned above. A new method, which limits the EV charging and discharging time slot according to the parking periods, is used to solve the EV charging optimization problem. Having obtained the charging load profile, the security-constrained economic dispatch (SCED) problem with V2G model is studied,in which two stage optimization model including user side and grid side is established. To obtain a credible result from the high time coupling, non-linear and non-convex SCED problem an improved Pattern Search method combined with Genetic Algorithm and augmented Lagrange approach is developed. The simulation and numerical results are based on an IEEE 39 bus system, showing that the model is suitable and robust to analyze the impact of charging load on the grid and the correlation between the scheduling of EVs and output power of generators.