Robust dynamic charging price in PV-assisted charging stations

Marcos Tostado-Véliz, Hany M. Hasanien, Paul Arévalo and Francisco Jurado

Applied Energy, 2025, vol. 395, issue C, No S030626192500981X

Abstract: With the increasing number of electric vehicles on road, the deployment of sufficient public charging infrastructures has become critical. To encourage the installation of new public charging points, such infrastructures need to be economically viable and profitable. In this regard, exploring economic activities within charging infrastructures has become a key topic to ensure the long-term financial sustainability of charging installations. In line with this objective, this paper develops a new robust methodology to setting dynamic charging prices in charging stations. Unlike to conventional charging prices based on flat tariffs, dynamic pricing strategies can follow wholesale electricity prices, potentially setting low prices and therefore displacing the fleet from domestic to public charging. The new proposal renders as a game-theoretical max-min bi-level optimization problem. To address the initial complexity of the formulation, a tailored solution algorithm is developed, which allows accessing to robust solutions efficiently. An adaptive robust modelling of uncertainties is proposed, based on intervals, which allows representing uncertainties as box-constrained variables. Moreover, this paper contributes with a new data-driven approach to determine limits on uncertainties based on bootstrapping. The new solution strategy is validated on a benchmark large-scale charging station installing a photovoltaic facility. Additionally, the effect of the risk level and photovoltaic size on final results is evaluated. In addition, the effectiveness of the charging pricing strategy is assessed, along with the influence of uncertainties on the final results.

Keywords: Bootstrapping; Fast charging; Electric vehicles; Robust optimization (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.126251

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