Multi-Objective Reinforcement Learning for Power Grid Topology Control
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
Transmission grid congestion increases as the electrification of various sectors requires transmitting more power. Topology control, through substation reconfiguration, can reduce congestion but its potential remains under-exploited in operations. A challenge is modeling the topology control problem to align well with the objectives and constraints of operators. Addressing this challenge, this paper investigates the application of multi-objective reinforcement learning (MORL) to integrate multiple conflicting objectives for power grid topology control. We develop a MORL approach using deep optimistic linear support (DOL) and multi-objective proximal policy optimization (MOPPO) to generate a set of Pareto-optimal policies that balance objectives such as minimizing line loading, topological deviation, and switching frequency. Initial case studies show that the MORL approach can provide valuable insights into objective trade-offs and improve Pareto front approximation compared to a random search baseline. The generated multi-objective RL policies are 30% more successful in preventing grid failure under contingencies and 20% more effective when training budget is reduced - compared to the common single objective RL policy.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 14 of its 28 references that are in the map. First posted on arXiv 27 Jan 2025.
Builds on
14 of 28 references.
References · 14
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2021A Practical Guide to Multi-Objective Reinforcement Learning and Planningcited by 256
- 2016Multi-Objective Deep Reinforcement Learningcited by 83
- 2021Learning to run a Power Network Challenge: a Retrospective Analysiscited by 55
- 2019Learning to run a power network challenge for training topology controllerscited by 34
- 2020Reinforcement Learning for Electricity Network Operationcited by 20
- 2022Power Grid Congestion Management via Topology Optimization with AlphaZerocited by 20
- 2022PowRL: A Reinforcement Learning Framework for Robust Management of Power Networkscited by 18
- 2021Learning to run a power network with trustcited by 17
- 2023Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agentscited by 16
- 2019AI-Based Autonomous Line Flow Control via Topology Adjustment for Maximizing Time-Series ATCscited by 15
- 2020Exploring grid topology reconfiguration using a simple deep reinforcement learning approachcited by 15
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Citing papers per year: 2026: 1.
1 paper.
- 2026PRISM: Parallel Reward Integration with Symmetry for MORL20 Feb 2026
Similar, not cited
Close in wording, no citation either way
- 2025Towards Efficient Multi-Objective Optimisation for Real-World Power Grid Topology Control
- 2025Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges
- 2026Interpretable Policy Distillation for Power Grid Topology Control
- 2025A Deep Reinforcement Learning Method for Multi-objective Transmission Switching
- 2026Learning to Run Power Networks: Effective AlphaZero-inspired Topological Control
- 2024Reinforcement Learning Optimizes Power Dispatch in Decentralized Power Grid
- 2024Implementing Deep Reinforcement Learning-Based Grid Voltage Control in Real-World Power Systems: Challenges and Insights
- 2026LLM-Guided Safe Reinforcement Learning for Energy System Topology Reconfiguration ↗