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

An Nguyen (), Hung Pham and Cuong Do ()
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An Nguyen: College of Engineering and Computer Science, VinUniversity, Hanoi 131000, Vietnam
Hung Pham: College of Engineering and Computer Science, VinUniversity, Hanoi 131000, Vietnam
Cuong Do: College of Engineering and Computer Science, VinUniversity, Hanoi 131000, Vietnam

Energies, 2025, vol. 18, issue 20, 1-16

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.

Keywords: electric vehicle charging; robust optimization; model predictive control; solar energy; smart grid; linear programming (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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