mlsanityData to 14 Oct 2026

Reinforcement Learning versus Optimization for Optimal Transmission Switching: A Comparative Study

arXiv 2607.10948 ↗First posted 12 Jul 2026eess.SY cs.SYcited by 0

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

Abstract (arXiv)

Optimal Transmission Switching (OTS) reduces generation cost by strategically opening transmission lines, but its mixed-integer linear program (MILP) formulation scales poorly for large-scale transmission networks. Reinforcement learning (RL) offers a computationally efficient alternative, but existing RL-based OTS approaches rely on soft penalties that permit physical constraint violations. This paper presents a comparison between an RL framework and an MILP-based optimization method for OTS. Case studies were carried out on the IEEE RTS-96 24-bus system; results show that the agent was able to produce near-optimal solutions at low switching budgets and tended to yield suboptimal solutions at high switching budgets. However, the RL agent was able to generate feasible solutions two-to-three orders of magnitude faster than the optimization solver.

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 10 of its 20 references that are in the map. First posted on arXiv 12 Jul 2026.

Builds on

10 of 20 references.

Closest ancestors · 2

Same direction, newest first

  1. 2026Physics Informed Reinforcement Learning with Gibbs Priors for Topology Control in Power Grids2 Apr
  2. 2025A Deep Reinforcement Learning Method for Multi-objective Transmission Switching15 Jul

Other references · 8

  1. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  2. 2021Action Set Based Policy Optimization for Safe Power Grid Managementcited by 21
  3. 2022Contingency-constrained economic dispatch with safe reinforcement learningcited by 8
  4. 2025Flow-Aware GNN for Transmission Network Reconfiguration via Substation Breaker Optimizationcited by 3
  5. 2017A Bound Strengthening Method for Optimal Transmission Switching in Power Systemscited by 2
  6. 2021Optimal Transmission Switching: Improving Solver Performance Using Heuristicscited by 1
  7. 2023Optimal Transmission Switching with Uncertainties from both Renewable Energy and N-k Contingenciescited by 1
  8. 2024AC-Informed DC Optimal Transmission Switching Problems via Parameter Optimizationcited by 1

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Close in wording, no citation either way

  1. 2026A Reinforcement Learning-based Transmission Expansion Framework Considering Strategic Bidding in Electricity Markets
  2. 2025Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges
  3. 2025Multi-Objective Reinforcement Learning for Power Grid Topology Control
  4. 2024Blackout Mitigation via Physics-guided RL
  5. 2025Towards Efficient Multi-Objective Optimisation for Real-World Power Grid Topology Control
  6. 2018Optimal Tap Setting of Voltage Regulation Transformers Using Batch Reinforcement Learning
  7. 2023Stable Relay Learning Optimization Approach for Fast Power System Production Cost Minimization Simulation ↗
  8. 2024Optimizing Load Scheduling in Power Grids Using Reinforcement Learning and Markov Decision Processes