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RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operations
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Reinforcement learning (RL) can provide adaptive and scalable controllers essential for power grid decarbonization. However, RL methods struggle with power grids' complex dynamics, long-horizon goals, and hard physical constraints. For these reasons, we present RL2Grid, a benchmark designed in collaboration with power system operators to accelerate progress in grid control and foster RL maturity. Built on RTE France's power simulation framework, RL2Grid standardizes tasks, state and action spaces, and reward structures for a systematic evaluation and comparison of RL algorithms. Moreover, we integrate operational heuristics and design safety constraints based on human expertise to ensure alignment with physical requirements. By establishing reference performance metrics for classic RL baselines on RL2Grid's tasks, we highlight the need for novel methods capable of handling real systems and discuss future directions for RL-based grid control.
Forward citations
Cited by 4 Pith papers
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OpenG2G: A Simulation Platform for AI Datacenter-Grid Runtime Coordination
OpenG2G is a new extensible simulation platform that lets users implement and compare classic, optimization, and learning-based controllers for AI datacenter power flexibility coordinated with the grid.
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MARS-DA: A Hierarchical Reinforcement Learning Framework for Risk-Aware Multi-Agent Bidding in Power Grids
MARS-DA uses a top-level meta-controller to blend safe day-ahead allocation and real-time arbitrage sub-policies, delivering better risk-adjusted returns than baselines in a PJM-grounded two-settlement market simulator.
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Probabilistic Verification of Recurrent Neural Networks for Single and Multi-Agent Reinforcement Learning
RNN-ProVe uses policy-driven sampling and statistical error bounds to produce high-confidence probabilistic estimates of behavioral violations in RNN policies for single- and multi-agent POMDPs.
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Interpretable Policy Distillation for Power Grid Topology Control
PPO policy for grid topology control is distilled into decision trees and random forests that outperform the teacher on reward and survival time with lower inference cost and high interpretability.
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