mlsanityData to 7 Oct 2026

Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges

arXiv 2102.01168 ↗First posted 27 Jan 2021cs.LG cs.AI cs.SYcited by 40

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Abstract (arXiv)

With large-scale integration of renewable generation and distributed energy resources, modern power systems are confronted with new operational challenges, such as growing complexity, increasing uncertainty, and aggravating volatility. Meanwhile, more and more data are becoming available owing to the widespread deployment of smart meters, smart sensors, and upgraded communication networks. As a result, data-driven control techniques, especially reinforcement learning (RL), have attracted surging attention in recent years. This paper provides a comprehensive review of various RL techniques and how they can be applied to decision-making and control in power systems. In particular, we select three key applications, i.e., frequency regulation, voltage control, and energy management, as examples to illustrate RL-based models and solutions. We then present the critical issues in the application of RL, i.e., safety, robustness, scalability, and data. Several potential future directions are discussed as well.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). Builds on 64 of its 205 references that are in the map. First posted on arXiv 27 Jan 2021.

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64 of 205 references.

References · 64

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  35. 2020Online Multi-agent Reinforcement Learning for Decentralized Inverter-based Volt-VAR Controlcited by 15
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Citing papers per year: 2022: 7; 2023: 7; 2024: 11; 2025: 9; 2026: 6.

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  1. 2022Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learningcited by 18
  2. 2025Robust Gymnasium: A Unified Modular Benchmark for Robust Reinforcement Learningcited by 12
  3. 2024Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical Behaviors in Deep Off-Policy RLcited by 10
  4. 2025X-GridAgent: An LLM-Powered Agentic AI System for Assisting Power Grid Analysiscited by 10
  5. 2024PDE Control Gym: A Benchmark for Data-Driven Boundary Control of Partial Differential Equationscited by 8
  6. 2024Enhancing Efficiency of Safe Reinforcement Learning via Sample Manipulationcited by 8
  7. 2025RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operationscited by 8
  8. 2022Winning the CityLearn Challenge: Adaptive Optimization with Evolutionary Search under Trajectory-based Guidancecited by 6
  9. 2024Contextual Restless Multi-Armed Bandits with Application to Demand Response Decision-Makingcited by 6
  10. 2024Transmission Interface Power Flow Adjustment: A Deep Reinforcement Learning Approach based on Multi-task Attribution Mapcited by 6
  11. 2022Contingency-constrained economic dispatch with safe reinforcement learningcited by 5
  12. 2022Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Controlcited by 4
28 more
  1. 2025A Survey of Reinforcement Learning for Optimization in Automationcited by 4
  2. 2024CommonPower: A Framework for Safe Data-Driven Smart Grid Controlcited by 3
  3. 2025Reinforcement Learning with Imperfect Transition Predictions: A Bellman-Jensen Approachcited by 3
  4. 2022Data-Driven Linear Koopman Embedding for Networked Systems: Model-Predictive Grid Controlcited by 2
  5. 2023Control invariant set enhanced safe reinforcement learning: improved sampling efficiency, guaranteed stability and robustnesscited by 2
  6. 2024Safety Constrained Multi-Agent Reinforcement Learning for Active Voltage Controlcited by 2
  7. 2024Online Event-Triggered Switching for Frequency Control in Power Grids with Variable Inertiacited by 2
  8. 2023Control invariant set enhanced reinforcement learning for process control: improved sampling efficiency and guaranteed stabilitycited by 1
  9. 2023A Multiobjective Reinforcement Learning Framework for Microgrid Energy Managementcited by 1
  10. 2023Out-of-Distribution-Aware Electric Vehicle Chargingcited by 1
  11. 2024Imitation Game: A Model-based and Imitation Learning Deep Reinforcement Learning Hybridcited by 1
  12. 2025Data-Driven Policy Mapping for Safe RL-based Energy Management Systemscited by 1
  13. 2026PseudoAct: Leveraging Pseudocode Synthesis for Flexible Planning and Action Control in Large Language Model Agentscited by 1
  14. 2022Reducing Action Space: Reference-Model-Assisted Deep Reinforcement Learning for Inverter-based Volt-Var Controlcited by 0
  15. 2022Efficient Learning of Voltage Control Strategies via Model-based Deep Reinforcement Learningcited by 0
  16. 2023Bayesian Reinforcement Learning for Automatic Voltage Control under Cyber-Induced Uncertaintycited by 0
  17. 2023Distributionally Robust Safety Filter for Learning-Based Control in Active Distribution Systemscited by 0
  18. 2023Resilient Control of Networked Microgrids using Vertical Federated Reinforcement Learning: Designs and Real-Time Test-Bed Validationscited by 0
  19. 2024Distributed Policy Gradient for Linear Quadratic Networked Control with Limited Communication Rangecited by 0
  20. 2024Robust Deep Reinforcement Learning for Inverter-based Volt-Var Control in Partially Observable Distribution Networkscited by 0
  21. 2025Position: Emergent Machina Sapiens Urge Rethinking Multi-Agent Paradigmscited by 0
  22. 2025Near Optimal Convergence to Coarse Correlated Equilibrium in General-Sum Markov Gamescited by 0
  23. 2025EcoNet: Multiagent Planning and Control Of Household Energy Resources Using Active Inferencecited by 0
  24. 2026Meta-RL with Shared Representations Enables Fast Adaptation in Energy Systemscited by 0
  25. 2026Reinforcement Learning for Speculative Trading under Exploratory Frameworkcited by 0
  26. 2026Bridging Artificial Intelligence and Power Systems Education Using a Hands-On Executable Frameworkcited by 0
  27. 2026Risk and Anomaly Identification for Distribution Network Optimal Operation Based on Reinforcement Learning and Uncertainty Quantificationcited by 0
  28. 2026PowerZooJax: A JAX-based Power System Benchmark for Reinforcement Learningcited by 0