This article proposes a new stochastic day-ahead residential charging model for a vehicle-to-grid (V2G)-capable plug-in electric vehicle (PEV). The aim is to minimize the expected customer's charging cost, including energy cost and battery aging cost while satisfying the customer service quality constraints. The proposed model integrates a detailed PEV lithium-ion battery aging model as a function of average battery's cell surface temperature, average current rate, average state of charge (SoC), and depth of discharge (DoD). Customer service quality constraints are mathematically modeled using Kano's dissatisfaction model as an exponential function of the customer's waiting time and charging level. Given the uncertain behavior of a PEV owner, the charging scheduling problem is formulated as a two-stage stochastic programming problem. In summary, this article contributes to the technical literature by developing a two-stage stochastic optimization framework for optimal charge scheduling of PEVs, which integrate a comprehensive battery aging cost model, and models customer dissatisfaction as Kano's model-based function of the customer's waiting time and charging level. Comparing the results in various deterministic, Monte Carlo simulation-based and the two-stage stochastic studies show that the proposed scheme can lead to low dissatisfaction for the customer, without a significant increment in costs.

中文翻译：

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支持 V2G 的 PEV 的随机充电优化：电池老化和客户服务质量的综合模型


本文为具有车辆到电网 (V2G) 功能的插电式电动汽车 (PEV) 提出了一种新的随机日前住宅充电模型。目的是在满足客户服务质量约束的同时，最小化预期客户的充电成本，包括能源成本和电池老化成本。所提出的模型集成了详细的 PEV 锂离子电池老化模型，该模型是平均电池表面温度、平均电流速率、平均充电状态 (SoC) 和放电深度 (DoD) 的函数。使用卡诺不满意模型对客户服务质量约束进行数学建模，将其作为客户等待时间和收费水平的指数函数。考虑到 PEV 车主的不确定行为，充电调度问题被表述为两阶段随机规划问题。总之，本文通过开发用于 PEV 最佳充电调度的两阶段随机优化框架为技术文献做出了贡献，该框架集成了全面的电池老化成本模型，并将客户不满意程度建模为 Kano 基于模型的客户等待时间函数和充电水平。比较各种确定性、基于蒙特卡罗模拟和两阶段随机研究的结果表明，所提出的方案可以降低客户的满意度，而不会显着增加成本。