This study implements two-stage stochastic programming in a smart home application to reduce the electricity procurement cost of an ordinary household. In this concern, vehicle to home (V2H) capability of the available electric vehicle (EV) is used in coordination with battery energy storage system (BESS) under control of a home energy management system. The stochastic decision variables are the charge-discharge power of these components. The uncertainties derived from the power production of the roof-mounted solar photovoltaic panels, household’s load demand, real-time electricity price are assimilated into the problem. Besides, to create the stochastic process, an artificial neural network (ANN) is trained using historical time series. Furthermore, as one of the main contributions, a proper analytical battery degradation cost model is integrated into the problem. Hence, different schemes such as with and without degradation cost, with and without BESS and uncoordinated charging are investigated under various charging rates. Also, the sensitivity of the problem for different charging rates of the EV and BESS is analyzed. Furthermore, the influence of probable future battery storage cost reductions on the home energy management system is investigated. Eventually, the efficiency of the stochastic programming method is analyzed by the value of stochastic solution (VSS) metric.

这项研究在智能家居应用中实现了两阶段随机编程，以降低普通家庭的购电成本。在这种情况下，在家庭能量管理系统的控制下，将可用电动汽车（EV）的车辆到家庭（V2H）能力与电池储能系统（BESS）配合使用。随机决策变量是这些组件的充放电功率。问题来自屋顶太阳能光伏板的发电量，家庭的负荷需求，实时电价等不确定性。此外，为了创建随机过程，使用历史时间序列来训练人工神经网络（ANN）。此外，作为主要贡献之一，正确的分析电池退化成本模型已集成到问题中。因此，在各种充电速率下研究了不同的方案，例如有和没有降级成本，有和没有BESS以及不协调的充电。此外，分析了问题对于EV和BESS不同充电率的敏感性。此外，研究了将来可能减少的电池存储成本对家庭能源管理系统的影响。最终，通过随机解（VSS）度量值来分析随机编程方法的效率。分析了问题对于EV和BESS不同充电率的敏感性。此外，研究了将来可能减少的电池存储成本对家庭能源管理系统的影响。最终，通过随机解（VSS）度量值来分析随机编程方法的效率。分析了问题对于EV和BESS不同充电率的敏感性。此外，研究了将来可能减少的电池存储成本对家庭能源管理系统的影响。最终，通过随机解（VSS）度量值来分析随机编程方法的效率。