计及EV和电池储能的能源调度策略

Energy dispatch strategies considering EV and battery storage

为了降低家庭用电成本,将两阶段随机规划应用于家庭能源管理系统,优化电动汽车和电池储能设备的调度策略。随机规划的决策变量是电动汽车和电池储能设备的充放电功率。为了创建随机场景,使用人工神经网络随机生成光伏系统的发电量、家庭用电负载、实时电价等随机变量。通过不同调度方案的实验,深入分析了退化成本、充放电率、电池成本对调度结果的影响,通过随机解度量值(VSS)来分析随机规划方法的效率,最后证明了考虑电动汽车和电池储能设备能源调度策略能够有效降低用电总成本。

In order to reduce the cost of household electricity, two-stage stochastic programming is applied to the household energy management system to optimize the scheduling strategy of electric vehicles and battery energy storage devices.The decision variables of stochastic programming are the charging and discharging power of electric vehicles and battery energy storage equipments.In order to create random scenarios, artificial neural networks are used to randomly generate random variables such as photovoltaic system power generation, household electricity load, real-time electricity prices, etc.Through the experiments of different scheduling schemes, the influence of degradation cost, charge-discharge rate, and battery cost on the scheduling result is deeply analyzed.The efficiency of the random planning method is analyzed by the value of the random solution measure(VSS).Finally, it is proved that the energy scheduling strategy of electric vehicles and battery energy storage equipment can effectively reduce the total cost of electricity consumption.