A Cost-Optimization Model for EV Charging Stations Utilizing Solar Energy and Variable Pricing
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| Format: | Preprint |
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2025
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| _version_ | 1866912588432932864 |
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| author | Nguyen, An Pham, Hung Do, Cuong |
| author_facet | Nguyen, An Pham, Hung Do, Cuong |
| contents | This paper presents a cost optimization framework for electric vehicle (EV) charging stations that leverages on-site photovoltaic (PV) generation and explicitly accounts for electricity price uncertainty through a Bertsimas--Sim robust formulation. The model is formulated as a linear program that satisfies vehicle energy demands, respects charging and grid capacity constraints, and minimizes procurement cost. Evaluations on real charging data from the Caltech ACN dataset show average savings of about 12\% compared to a first-come--first-served baseline, with peak monthly reductions up to 19.2\%. A lightweight sensitivity analysis indicates that a modest $\sim$5\% increase in nominal cost can reduce worst-case exposure by 14\%. Computational tests confirm real-time feasibility, with instances of up to 50 concurrent EVs solved in under 5 seconds on a standard laptop. The proposed method provides a practical, grid-friendly, and scalable solution for future EV charging operations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12214 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Cost-Optimization Model for EV Charging Stations Utilizing Solar Energy and Variable Pricing Nguyen, An Pham, Hung Do, Cuong Systems and Control Computational Engineering, Finance, and Science This paper presents a cost optimization framework for electric vehicle (EV) charging stations that leverages on-site photovoltaic (PV) generation and explicitly accounts for electricity price uncertainty through a Bertsimas--Sim robust formulation. The model is formulated as a linear program that satisfies vehicle energy demands, respects charging and grid capacity constraints, and minimizes procurement cost. Evaluations on real charging data from the Caltech ACN dataset show average savings of about 12\% compared to a first-come--first-served baseline, with peak monthly reductions up to 19.2\%. A lightweight sensitivity analysis indicates that a modest $\sim$5\% increase in nominal cost can reduce worst-case exposure by 14\%. Computational tests confirm real-time feasibility, with instances of up to 50 concurrent EVs solved in under 5 seconds on a standard laptop. The proposed method provides a practical, grid-friendly, and scalable solution for future EV charging operations. |
| title | A Cost-Optimization Model for EV Charging Stations Utilizing Solar Energy and Variable Pricing |
| topic | Systems and Control Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2509.12214 |