mlsanityData to 14 Oct 2026

Physics-Informed Reinforcement Learning for Large-Scale EV Smart Charging Considering Distribution Network Voltage Constraints

arXiv 2510.12335 ↗First posted 14 Oct 2025eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Electric Vehicles (EVs) offer substantial flexibility for grid services, yet large-scale, uncoordinated charging can threaten voltage stability in distribution networks. Existing Reinforcement Learning (RL) approaches for smart charging often disregard physical grid constraints or have limited performance for complex large-scale tasks, limiting their scalability and real-world applicability. This paper introduces a physics-informed (PI) RL algorithm that integrates a differentiable power flow model and voltage-based reward design into the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, enabling EVs to deliver real-time voltage support while meeting user demands. The resulting PI-TD3 algorithm achieves faster convergence, improved sample efficiency, and reliable voltage magnitude regulation under uncertain and overloaded conditions. Benchmarks on the IEEE 34-bus and 123-bus networks show that the proposed PI-TD3 outperforms both model-free RL and optimization-based baselines in grid constraint management, user satisfaction, and economic metrics, even as the system scales to hundreds of EVs. These advances enable robust, scalable, and practical EV charging strategies that enhance grid resilience and support distribution networks operation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 39 references that are in the map. First posted on arXiv 14 Oct 2025.

Builds on

8 of 39 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024RL-ADN: A High-Performance Deep Reinforcement Learning Environment for Optimal Energy Storage Systems Dispatch in Active Distribution Networks7 Aug
  2. 2024Safety-Aware Reinforcement Learning for Electric Vehicle Charging Station Management in Distribution Network20 Mar

Other references · 6

  1. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  2. 2019Two-Timescale Voltage Control in Distribution Grids Using Deep Reinforcement Learningcited by 20
  3. 2024Stabilizing Reinforcement Learning in Differentiable Multiphysics Simulationcited by 20
  4. 2024EV2Gym: A Flexible V2G Simulator for EV Smart Charging Research and Benchmarkingcited by 8
  5. 2024EV-PINN: A Physics-Informed Neural Network for Predicting Electric Vehicle Dynamicscited by 4
  6. 2024Adaptive Informed Deep Neural Networks for Power Flow Analysiscited by 1

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Close in wording, no citation either way

  1. 2024Advancements in Electric Vehicle Charging Optimization: A Survey of Reinforcement Learning Approaches
  2. 2025A Hybrid Physics-Based and Reinforcement Learning Framework for Electric Vehicle Charging Time Prediction
  3. 2026Reinforcement Learning for Vehicle-to-Grid Voltage Regulation: Single-Hub to Multi-Hub Coordination with Battery-Aware Constraints
  4. 2026Smart charging of large fleets of Electric Vehicles: Independent Multi-Agent Reinforcement Learning approaches
  5. 2025Reinforcement Learning-based Approach for Vehicle-to-Building Charging with Heterogeneous Agents and Long Term Rewards
  6. 2025Agent-Based Decentralized Energy Management of EV Charging Station with Solar Photovoltaics via Multi-Agent Reinforcement Learning
  7. 2022Optimized cost function for demand response coordination of multiple EV charging stations using reinforcement learning
  8. 2025Dynamic Electric Vehicle Charging Pricing for Load Balancing in Power Distribution Networks based on Collaborative DDPG Agents ↗