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The increasing penetration of Electric Vehicles (EVs) and renewable energy sources is placing significant stress on existing power grid infrastructure. This work investigates the application of vehicle-to-grid (V2G)-enabled smart charging in workplace environments from the perspective of EV aggregators, using real-world charging data from Dutch business parking lots. To address the limitations of conventional deep Reinforcement Learning (RL) methods in enforcing operational constraints, we propose a Safe RL method using the Constrained Variational Policy Optimization (CVPO) algorithm, specifically designed to reduce constraint violations and enhance reliability. Empirical results show that CVPO outperforms classic RL baselines and rule-based policies, closely approximating the performance of an optimal offline benchmark while exhibiting strong generalization to unseen scenarios.

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Safe Reinforcement Learning for V2G-Enabled Electric Vehicle Aggregators

  • 会议论文

  • 发表日期：

    2026年

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  • Ruben Eland,
  • Stavros Orfanoudakis,
  • Pedro P. Vergara

摘要

The increasing penetration of Electric Vehicles (EVs) and renewable energy sources is placing significant stress on existing power grid infrastructure. This work investigates the application of vehicle-to-grid (V2G)-enabled smart charging in workplace environments from the perspective of EV aggregators, using real-world charging data from Dutch business parking lots. To address the limitations of conventional deep Reinforcement Learning (RL) methods in enforcing operational constraints, we propose a Safe RL method using the Constrained Variational Policy Optimization (CVPO) algorithm, specifically designed to reduce constraint violations and enhance reliability. Empirical results show that CVPO outperforms classic RL baselines and rule-based policies, closely approximating the performance of an optimal offline benchmark while exhibiting strong generalization to unseen scenarios.