Learning to run a power network challenge for training topology controllers

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Reinforcement Learning: An Introduction

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Dueling Network Architectures for Deep Reinforcement Learning

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Machine behaviour

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Optimal Transmission Switching

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Reinforcement Learning for Electric Power System Decision and Control: Past Considerations and Perspectives

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Network Topology Optimization with Security Constraints

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Safe and Nested Subgame Solving for Imperfect-Information GamesPreprint

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Learning to Run Challenge: Synthesizing Physiologically Accurate Motion Using Deep Reinforcement Learning

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Behaviour Suite for Reinforcement LearningPreprint

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Hierarchical Decision Making In Electricity Grid ManagementPreprint

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Expert System for topological remedial action discovery in smart grids

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Anticipating contingengies in power grids using fast neural net screening

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Tackling climate change with machine learning

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Vulnerability of Machine Learning Approaches Applied in IoT-Based Smart Grid: A Review

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Perspectives on Future Power System Control Centers for Energy Transition

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Machine Learning for Sustainable Energy Systems

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A multi-scale time-series dataset with benchmark for machine learning in decarbonized energy grids

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Learning to run a Power Network Challenge: a Retrospective AnalysisPreprint

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Vulnerability Assessment of Deep Reinforcement Learning Models for Power System Topology Optimization

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Reinforcement learning in deregulated energy market: A comprehensive review

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Applying reinforcement learning and tree search to the unit commitment problem

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Massively Digitized Power Grid: Opportunities and Challenges of Use-Inspired AI

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Integrating Reinforcement Learning and Optimal Power Dispatch to Enhance Power Grid Resilience

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Optimal Power Flow With Physics-Informed Typed Graph Neural Networks

López-García, T. B., & Dominguez-Navarro, J. A. (2024) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2024.3394371 · Cited by 47 · see citing works

An integrated modeling framework for cascading failure study and robustness assessment of cyber-coupled power grids

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Recent Developments in Security-Constrained AC Optimal Power Flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition

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Learning to run a power network with trust

Marot, A., Donnot, B., Chaouache, K., Kelly, A., Huang, Q., Hossain, R. R., & Cremer, J. L. (2022) · Electric Power Systems Research · doi:10.1016/j.epsr.2022.108487 · Cited by 34 · see citing works

Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents

Lehna, M., Viebahn, J., Marot, A., Tomforde, S., & Scholz, C. (2023) · Energy and AI · doi:10.1016/j.egyai.2023.100276 · Cited by 28 · see citing works

Deep Reinforcement Learning-Based Approach for Autonomous Power Flow Control Using Only Topology Changes

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Exploring grid topology reconfiguration using a simple deep reinforcement learning approach

Subramanian, M., Viebahn, J., Tindemans, S. H., Donnot, B., & Marot, A. (2021) · IEEE PowerTech Conference · doi:10.1109/powertech46648.2021.9494879 · Cited by 27 · see citing works

Curriculum Based Reinforcement Learning of Grid Topology Controllers to Prevent Thermal Cascading

Matavalam, A. R. R., Guddanti, K. P., Weng, Y., & Ajjarapu, V. (2022) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2022.3213487 · Cited by 25 · see citing works

Online Preventive Control for Transmission Overload Relief Using Safe Reinforcement Learning With Enhanced Spatial-Temporal Awareness

Cui, H., Ye, Y., Hu, J., Tang, Y. P., Lin, Z., & Štrbac, G. (2023) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2023.3257259 · Cited by 22 · see citing works

PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution SystemsPreprint

Fan, T.-H., Lee, X. Y., & Wang, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.03970 · Cited by 23 · see citing works

Reinforcement Learning for Electricity Network OperationPreprint

Kelly, A., O'Sullivan, A., de Mars, P., & Marot, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.07339 · Cited by 20 · see citing works

Towards an AI Assistant for Power Grid Operators

Marot, A., Rozier, A., Dussartre, M., Crochepierre, L., & Donnot, B. (2022) · Frontiers in artificial intelligence and applications · doi:10.3233/faia220191 · Cited by 19 · see citing works

Reinforcement Learning Environment for Cyber-Resilient Power Distribution System

Sahu, A., Venkatraman, V., & Macwan, R. (2023) · IEEE Access · doi:10.1109/access.2023.3282182 · Cited by 16 · see citing works

Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic

Yoon, D., Hong, S., Lee, B.-J., & Kim, K.-E. (2021) · Cited by 16 · see citing works

Action Set Based Policy Optimization for Safe Power Grid Management

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PowRL: A Reinforcement Learning Framework for Robust Management of Power Networks

Chauhan, A., Baranwal, M., & Basumatary, A. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i12.26724 · Cited by 13 · see citing works

Adversarial Training for a Continuous Robustness Control Problem in Power Systems

Omnes, L., Marot, A., & Donnot, B. (2021) · IEEE PowerTech Conference · doi:10.1109/powertech46648.2021.9494982 · Cited by 13 · see citing works

High Performance Computing Reinforcement Learning Framework for Power System Control

Damjanović, I., Pavić, I., Brčić, M., & Jerčić, R. (2023) · IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) · doi:10.1109/isgt51731.2023.10066416 · Cited by 12 · see citing works

Progressive decision-making framework for power system topology control

Liu, S., Zhou, Y., Song, M., Bu, G., Guo, J., & Chen, C. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121070 · Cited by 12 · see citing works

DRAGON: Deep Reinforcement Learning for Autonomous Grid Operation and Attack Detection

Landen, M., Chung, K., Ike, M., Mackay, S., Watson, J., & Wenke Lee, B. (2022) · Annual Computer Security Applications Conference · doi:10.1145/3564625.3567969 · Cited by 12 · see citing works

Reward design for intelligent deep reinforcement learning based power flow control using topology optimization

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Real‐time pricing response attack in smart grid

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Large-Scale Grid Optimization: the Workhorse of Future Grid Computations

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Power grid segmentation for local topological controllers

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HUGO – Highlighting Unseen Grid Options: Combining deep reinforcement learning with a heuristic target topology approach

Lehna, M., Holzhüter, C., Tomforde, S., & Scholz, C. (2024) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2024.101510 · Cited by 8 · see citing works

CEM: Constrained Entropy Maximization for Task-Agnostic Safe Exploration

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An Intelligent Algorithm for Solving Unit Commitments Based on Deep Reinforcement Learning

Huang, G., Mao, T., Zhang, B., Cheng, R., & Ou, M. (2023) · Sustainability · doi:10.3390/su151411084 · Cited by 6 · see citing works

Substation reconfiguration selection algorithm based on PTDFs for congestion management and RL approach

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Understanding the Safety Requirements for Learning-based Power Systems OperationsPreprint

Chen, Y., Arnold, D., Shi, Y., & Peisert, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.04983 · Cited by 6 · see citing works

Blackout Mitigation via Physics-Guided RL

Dwivedi, A., Paternain, S., & Tajer, A. (2024) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2024.3472570 · Cited by 5 · see citing works

Toward Fair Power Grid Control: A Hierarchical Multiobjective Reinforcement Learning Approach

Hu, X., Zhang, Y., Xia, H., Wei, W., Dai, Q., & Li, J. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3314522 · Cited by 5 · see citing works

Deep statistical solvers & power systems applications

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A hybrid curriculum learning and tree search approach for network topology control

Meppelink, G. J., Rajaei, A., & Cremer, J. L. (2025) · Electric Power Systems Research · doi:10.1016/j.epsr.2025.111455 · Cited by 4 · see citing works

A Fast Converged Voltage Control Method based on Deep Reinforcement Learning

Wang, X., Liu, S., & Wang, B. Y. (2021) · Power System and Green Energy Conference (PSGEC) · doi:10.1109/psgec51302.2021.9541922 · Cited by 4 · see citing works

Risk Evaluation of AI Systems in the Energy Sector - Three Case Studies from TSO Business

Heymann, F., Marot, A., Subramanian, M., & Galus, M. D. (2024) · IEEE International Forum on Research and Technology for Society and Industry (RTSI) · doi:10.1109/rtsi61910.2024.10761769 · Cited by 3 · see citing works

Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approach

Hassouna, M., Holzhüter, C., Lehna, M., de Jong, M., Viebahn, J., Sick, B., & Scholz, C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-06129-4_8 · Cited by 3 · see citing works

Distribution Network Topology Control Using Attention Mechanism-Based Deep Reinforcement Learning

Qiu, Z., Zhao, Y., Shi, W., Su, F., & Zhu, Z. (2022) · International Conference on Electrical Engineering and Control Technologies (CEECT) · doi:10.1109/ceect55960.2022.10030642 · Cited by 3 · see citing works

Learning-Based Optimization of the Exchange Price in a Heterogeneous Market

Chérot, G., Latimier, R. L. G., Cajna, B., & Ahmed, H. B. (2024) · International Conference on Intelligent System Applications to Power Systems · doi:10.1109/isap63260.2024.10744279 · Cited by 2 · see citing works

Option-Based Deep Reinforcement Learning for Topology Control of Power Systems

Zhang, H., Wang, C., Lee, M. H., & Moon, J. (2025) · IEEE Access · doi:10.1109/access.2025.3539770 · Cited by 2 · see citing works

Control method of power grid topology structure based on reinforcement learning

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Multiplex Community Detection for Resilient Electrical Segmentation Enabling Management of an Increasingly Complex Power Grid

Henka, N., Tazi, S., & Assaad, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70381-2_16 · Cited by 1 · see citing works

Vulnerability and Adaption of Power Grids for Global Warming Induced Energy DemandsPreprint

Guddanti, K. P., Weng, Y., & Yu, Y. (2021) · Research Square · doi:10.21203/rs.3.rs-956921/v1

Redes neuronales artificiales para la predicción de flujos de carga aplicadas al sistema de trasmisión de Uruguay

Garabedian, S., Porteiro, R., & Pena, P. (2021) · Revista Tecnología en Marcha · doi:10.18845/tm.v34i7.6040

Data-driven operation and control for power systems with high-level renewable energy resources

Yan, Z. (2021) · doi:10.32657/10356/161731

A cascading policy learning framework for enhancing power grid resilience

Bensalah, A. S., Marir, T., Yaich, R., & Chebout, M. S. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.3358

Offline Reinforcement Learning Approaches for Safe and Effective Smart Grid Control

Peredo, A., Lugo, H., III, Narcia-Macias, C. I., Espinoza, J., Masamba, D., Gandarilla, A., Enriquez, E., & Kim, D. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-6429-0_38

Weakly Supervised Segmentation and Context-Aware Classification in Computational Pathology

Lerousseau, M. (2022) · doi:10.70675/8d10f89dz0f91z4e9dzb3a8zb5228b1172c1

Methodology for Design and Analysis of Machine Learning Competitions

Pavão, A. (2023) · theses.fr (ABES) · doi:10.70675/9234d2f0zebc5z4c1czaf3bzb0ad7a481e04

Participatory design to support power grid operators in control rooms

Sahel, W. (2024) · doi:10.70675/171925daz12e3z4706z96fez5b636f2347a6

Enhancing Grid Resilience with Line-Aware Reinforcement Learning under High Renewable Generation Uncertainty

Minh, N. Q., Hung, T. T., Hao, N. I., Minh, L. T. N., Anh, N. S. N., & Hoang, N. M. (2025) · Asia Meeting on Environment and Electrical Engineering (EEE-AM) · doi:10.1109/eee-am66675.2025.11473368

VPP-Sim: A Modular Open-Source Framework for Developing and Deploying ML-Driven Strategies in Virtual Power Plants

Paldino, G. M., & Bontempi, G. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-19102-1_8

PPTopoGym: Towards an RL Environment for Topology Actions on Power Grids

Köhler, D., Hassouna, M., Degtyar, D., Krauß, J., Brendlinger, K., & Scholz, C. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-19102-1_6

RL-Based Corrective Control Actions by VSC Converters in Hybrid AC/DC Power Systems

Raghuwanshi, Y., Jat, C., & Dubey, A. (2026) · IEEE Texas Power and Energy Conference (TPEC) · doi:10.1109/tpec67884.2026.11513175

AI systems in the energy sector: risk evaluation for business operations and regulatory oversight

Heymann, F., Huang, R., Marot, A., Subramanian, M., & McKenna, R. (2026) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2026.111961

Exact and Evolutionary Algorithms for Sequential Multi-Objective Transmission Topology PlanningPreprint

Groeneveld, J., Muñoz, M., Viebahn, J., & Zocca, A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6899791

DSA-SafetyBench: A safety-aware benchmark for artificial-intelligence-based dynamic security assessment in energy systems

Shamseldein, M. (2026) · Energy and AI · doi:10.1016/j.egyai.2026.100811

Global Explainability for Trustworthy RL in Renewable-Integrated Grids: A Framework for Substation Topology Control

Hung, T. T., & Minh, N. Q. (2026) · International Conference on Green Energy and Applications (ICGEA) · doi:10.1109/icgea69553.2026.11642804

AI-Driven Energy Management and Optimal Coordination of Distributed Generation and Electric Vehicles in Smart Distribution Systems: A Systematic and Critical Review

Mandal, S., Pal, P., & Singh, U. (2026) · International Journal of Artificial Intelligence and Machine Learning · doi:10.51483/ijaiml.6.10s.2026.222-251

Artificial-Intelligence-Enabled Wind-Grid Intelligence Systems: A Systematic Review of AI Evolution, Functional Intelligence Layers, and Deployment Readiness

Asad, M. U., & Sánchez, J. A. (2026) · Sustainability · doi:10.3390/su18199921

Exploring grid topology reconfiguration using a simple deep reinforcement learning approach

Subramanian, M., Viebahn, J., Tindemans, S. H., Donnot, B., & Marot, A. (2021) · IEEE PowerTech Conference · doi:10.1109/powertech46648.2021.9494879 · Cited by 27 · see citing works

PPTopoGym: Towards an RL Environment for Topology Actions on Power Grids

Köhler, D., Hassouna, M., Degtyar, D., Krauß, J., Brendlinger, K., & Scholz, C. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-19102-1_6

Learning to Run a Power Network under Varying Grid Topology

Taha, S., Poland, J., Knezović, K., & Shchetinin, D. (2022) · IEEE International Energy Conference (ENERGYCON) · doi:10.1109/energycon53164.2022.9830198 · Cited by 5 · see citing works

Learning to Run Power Networks: Effective AlphaZero-inspired Topological ControlPreprint

Zetto, L., Schäfer, B., & Huang, Q. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2608.14114

Curriculum Based Reinforcement Learning of Grid Topology Controllers to Prevent Thermal Cascading

Matavalam, A. R. R., Guddanti, K. P., Weng, Y., & Ajjarapu, V. (2022) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2022.3213487 · Cited by 25 · see citing works