Electric vehicles (EVs) are rapidly replacing internal combustion cars in urban transportation markets. Smart charging technology and vehicle-to-grid systems enable bi-directional charging, allowing for load shifting and shaping, which reduces both charging costs and grid stress. However, EV fleets and charging infrastructure operators face the challenge of ensuring fair energy distribution, as not all EVs can charge or discharge at their preferred times or rates. Additionally, uncoordinated charging can place significant stress on sections of the grid that are near capacity. To address this, we propose a 2-stage optimization model to ensure no EV is unfairly disadvantaged by charging constraints. In the first stage, we use a mixed-integer linear programming formulation to determine the optimal charging strategy for the entire fleet without considering grid limitations. This stage aims to minimize charging costs while meeting EV charging demands, producing optimal target values for the second stage. In the second stage, we focus on minimizing unfair energy allocation compared to the unconstrained optimal schedule while accounting for grid limitations. Among the methods we propose, the fairest method achieves near-perfect fairness with an almost zero cost in terms of the overall utility, outperforming baseline models by 19% to 99.9% across all fairness metrics. • Formulate an optimization problem for bidirectional EV charging with spatiotemporal energy constraints. • Develop a mixed-Śinteger linear programming base model. • Propose the 2-stage model for fair bidirectional energy management. • Incorporate real-time prices, charging demands, and diverse driving routines. • Evaluate fairness using Jain’s index, standard deviation, min-max metrics, and heatmap results.
Lee et al. (Fri,) studied this question.