Emission-Aware Reinforcement Learning for Sustainable Electric Vehicle Charging and Carbon Dioxide Reduction Under Varying Renewable Penetration
A new study proposes an emission-aware reinforcement learning strategy for electric vehicle charging. This approach aims to reduce carbon emissions while managing the challenges posed by increased EV adoption and renewable energy variability. The proposed method significantly outperforms traditional strategies in terms of emission reduction and grid compliance.
- ▪The study introduces a reinforcement learning strategy based on the Soft Actor Critic algorithm to optimize electric vehicle charging.
- ▪The RL agent achieved a carbon intensity as low as 23.96 grams of CO2 per kWh under 50% wind penetration, representing an 87% reduction compared to uncontrolled charging.
- ▪The research compares nine control strategies across five renewable penetration scenarios, demonstrating the effectiveness of the proposed RL approach.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.24543 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
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Computer Science > Artificial Intelligence arXiv:2605.24543 (cs) [Submitted on 23 May 2026] Title:Emission-Aware Reinforcement Learning for Sustainable Electric Vehicle Charging and Carbon Dioxide Reduction Under Varying Renewable Penetration Authors:Ninglin Ou, Mohammad A. Razzaque, Iftekher Islam Shovon, Shafkat Khan Siam, Shafiuzzaman K Khadem, Krishnendu Guha, Mayeen U Khandaker, Md. Noor-A-Rahim View a PDF of the paper titled Emission-Aware Reinforcement Learning for Sustainable Electric Vehicle Charging and Carbon Dioxide Reduction Under Varying Renewable Penetration, by Ninglin Ou and 7 other authors View PDF HTML (experimental) Abstract:The rapid growth of Electric Vehicle (EV) adoption challenges power distribution networks through peak load spikes, voltage instability, and…
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