A Robust Optimization Model for Cost-Efficient and Fast Electric Vehicle Charging with L2-norm Uncertainty

TL;DR

The paper addresses robust EV charging optimization by jointly minimizing total cost and enabling fast charging under $\|\pi-\hat{\pi}\|_2 \le \rho$ uncertainty. It formulates a nominal bi-criteria objective $y = y^C(r) + \alpha y^F(r)$ with constraints on per-vehicle charging, timing, and station capacity, and derives a convex robust counterpart that adds a penalty term $\rho \sum_i \sqrt{\sum_t r_{i,t}^2}$. Using real-world ACN charging data and Vietnam TOU pricing, the simulations demonstrate a clear cost–time trade-off and identify a practical balance near $\alpha \approx 1$. The approach offers operators a tunable framework to meet cost and service objectives under price fluctuations, contributing toward flexible, resilient EV charging management.

Abstract

In this paper, we propose a robust optimization model that addresses both the cost-efficiency and fast charging requirements for electric vehicles (EVs) at charging stations. By combining elements from traditional cost-minimization models and a fast charging objective, we construct an optimization model that balances user costs with rapid power allocation. Additionally, we incorporate L2-norm uncertainty into the charging cost, ensuring that the model remains resilient under cost fluctuations. The proposed model is tested under real-world scenarios and demonstrates its potential for efficient and flexible EV charging solutions.

Figures (8)

• Figure 1: Power Allocation to EVs
• Figure 2: The number of charging sessions over 30 days.
• Figure 3: Total parking time over 24 hours.
• Figure 4: Total power allocation over 24 hours when $\alpha = 0.1$.
• Figure 5: Total power allocation over 24 hours when $\alpha = 10$.