Emission-Aware Reinforcement Learning for Sustainable Electric Vehicle Charging and Carbon Dioxide Reduction Under Varying Renewable Penetration

2026-05-23

AI Summary

This study addresses the challenges of peak grid load, voltage instability, and transformer overload caused by uncoordinated electric vehicle (EV) charging, while accounting for the impact of real-time carbon intensity and renewable energy variability on decarbonization potential. The authors propose an emissions-aware Soft Actor-Critic reinforcement learning strategy that, for the first time, integrates real-time carbon intensity forecasts into both the state space and a multi-objective reward function to jointly optimize carbon emissions, curtailment of wind and solar generation, and user charging satisfaction. Evaluated on the EV2Gym platform using EirGrid carbon data and distributed wind–solar models under 50% wind penetration, the approach reduces system carbon intensity to 23.96 gCO₂/kWh—achieving an 87% reduction compared to an uncontrolled baseline—keeps transformer overload below 7 kWh, and attains a combined wind–solar self-consumption rate of 52%.

Abstract

The rapid growth of Electric Vehicle (EV) adoption challenges power distribution networks through peak load spikes, voltage instability, and transformer overloads from uncoordinated charging. While Model Predictive Control (MPC) and standard Reinforcement Learning (RL) methods have addressed these issues, existing approaches rarely treat real-time carbon intensity or fluctuating renewable energy (RE) availability as primary scheduling objectives, leaving substantial decarbonisation potential unrealised. This paper proposes an emission-aware RL strategy based on the Soft Actor Critic (SAC) algorithm, with a multi-objective reward that penalises carbon emissions, curtailed on-site renewables, and unmet user demand. The agent is trained within a unified benchmarking framework on the EV2Gym platform, incorporating behind-the-meter solar and wind profiles, time-varying EirGrid carbon intensity data, and realistic workplace EV behaviour across 25 Electric Vehicle Supply Equipment (EVSE) units. Nine control strategies, including heuristics, emission-aware MPC variants, and the proposed RL agent, are compared under five renewable penetration scenarios (0%-50%) over ten independent runs each. The RL agent achieves a carbon intensity as low as 23.96 grams of carbon dioxide per kilowatt-hour under 50% wind penetration, representing up to 87% emission reduction versus the uncontrolled baseline, and outperforms the external graph-based Power Distribution Network (PDN) benchmark. Transformer overload remains below 7 kWh across scenarios, against up to 1093 kWh for the As Fast As Possible (AFAP) heuristic, and renewable self-consumption reaches 52% under combined wind and solar supply. Embedding carbon intensity forecasts into the RL state and reward aligns charging with low-emission periods while preserving grid compliance and user satisfaction.

Problem

• electric vehicle charging
• carbon intensity
• renewable energy penetration
• emission reduction
• grid stability

Innovation

• emission-aware reinforcement learning
• carbon intensity forecasting
• multi-objective reward design
• renewable energy integration
• EV charging scheduling