A Cost-Optimization Model for EV Charging Stations Utilizing Solar Energy and Variable Pricing

Abstract

Managing electric vehicle (EV) charging at stations with on-site solar (PV) generation is a complex task, made difficult by volatile electricity prices and the need to guarantee services for drivers. This paper proposes a robust optimization (RO) framework to schedule EV charging, minimizing electricity costs while explicitly hedging against price uncertainty. The model is formulated as a tractable linear program (LP) using the Bertsimas–Sim reformulation and is implemented in an online, adaptive manner through a model predictive control (MPC) scheme. Evaluated on extensive real-world charging data, the proposed controller demonstrates significant cost reductions, outperforming a PV-aware Greedy heuristic by 17.5% and a deep reinforcement learning (DRL) agent by 12.2%. Furthermore, the framework exhibits lower cost volatility and is proven to be computationally efficient, with solving times under five seconds even during peak loads, confirming its feasibility for real-time deployment. The results validate our framework as a practical, reliable, and economically superior solution for the operational management of modern EV charging infrastructure.