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Pollution control

Carbon-Aware Dynamic Pricing Framework for Electric Vehicle Charging Stations Reduces Emissions by 24%

Strategic Insights

— 1 min read

Research on energy economics has led to a novel carbon-aware dynamic pricing framework for electric vehicle charging stations. According to a study from INESC, this framework optimizes costs and reduces emissions by considering the uncertainties in renewable energy generation, load, and grid carbon intensity. The study found that the framework can reduce emissions by 24% compared to deterministic models. The framework uses a chance-constrained optimization problem to generate day-ahead dynamic tariffs for EV drivers and a surrogate machine learning model to approximate the outcomes of stochastic optimization.

Key Takeaways:

  • A carbon-aware dynamic pricing framework for EV charging stations was developed to optimize costs and reduce emissions.
  • The framework uses a chance-constrained optimization problem to consider the uncertainties in renewable energy generation, load, and grid carbon intensity.
  • The study found that the framework can reduce emissions by 24% compared to deterministic models.
  • The framework generates day-ahead dynamic tariffs for EV drivers, taking into account their elastic behavior and the carbon emissions budget.
  • The framework uses a surrogate machine learning model to approximate the outcomes of stochastic optimization, enabling the application of explainability techniques to enhance understanding and communication of dynamic pricing decisions.
  • The most important feature in determining tarffs was found to be the hour of the day.
  • The research has been peer-reviewed and published in the journal Applied Energy.

Statistics:

  • 24% reduction in emissions per feasible day of optimization compared to deterministic models.
  • 125 London Wall, London, England, is the contact address for Elsevier Sci Ltd, the publisher of Applied Energy.
  • 4200465 is the postal code for the Ctr Power & Energy Syst, Rua Dr Roberto Frias, P-Porto, Portugal, the location of INESC.

Sources:

  • Applied Energy, 2025; 389.
  • INESC research paper: "Carbon-aware Dynamic Tariff Design for Electric Vehicle Charging Stations With Explainable Stochastic Optimization."
  • NewsRx, "Reports on Renewable Energy Findings from INESC Provide New Insights (Carbon-aware Dynamic Tariff Design for Electric Vehicle Charging Stations With Explainable Stochastic Optimization)."
  • Elsevier Sci Ltd.
  • INESC, Ctr Power & Energy Syst, Rua Dr Roberto Frias, P-4200465 Porto, Portugal.

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