基于需求侧管理的电动汽车有序充放电策略

Coordinated Charging and Discharging Strategy for Electric Vehicles Based on Demand Side Management

针对电动汽车有序充放电策略的研究,本文通过采用蒙特卡洛随机抽样建立了反映家用车辆用户行为的统计学结果,得到充电规律与充电功率需求期望,在此基础上,建立峰谷分时充放电电价策略的数学模型,分别研究了自然充电与有序充放电策略下规模化电动汽车充放电行为对电网负荷变化规律的影响,并采用遗传算法对分时时段的制定方案进行寻优求解,通过需求侧管理的方法对电动汽车进行有效引导,达到了电动汽车充放电行为有序优化控制的目的。

In this study, Monte Carlo random sampling statistics reflecting the family vehicles is established by using results of user behavior, charging regularity and charging power demand expectations. On this basis, the peak-valley gap charging model of electricity price strategies is established to respectively study the natural and coordinated strategy with a large-scale electric vehicle charging penetration. The genetic algorithm is adopted to solve this optimization model of time-span based scheme. By means of the strategy of demand side management for effective guidance, peak-valley gap of the load curve is reduced due to the oriented charging load. In this sense, coordinated electric vehicle charging optimization control is achieved.