LAUSR

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.