mlsanityData to 14 Oct 2026

A Reinforcement Learning-based Transmission Expansion Framework Considering Strategic Bidding in Electricity Markets

arXiv 2602.19421 ↗First posted 23 Feb 2026eess.SY cs.SYcited by 0

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

Abstract (arXiv)

Transmission expansion planning in electricity markets is tightly coupled with the strategic bidding behaviors of generation companies. This paper proposes a Reinforcement Learning (RL)-based co-optimization framework that simultaneously learns transmission investment decisions and generator bidding strategies within a unified training process. Based on a multiagent RL framework for market simulation, the proposed method newly introduces a design policy layer that jointly optimizes continuous/discrete transmission expansion decisions together with strategic bidding policies. Through iterative interaction between market clearing and investment design, the framework effectively captures their mutual influence and achieves consistent co-optimization of expansion and bidding decisions. Case studies on the IEEE 30-bus system are provided for proof-of-concept validation of the proposed co-optimization framework.

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 3 of its 19 references that are in the map. First posted on arXiv 23 Feb 2026.

Builds on

3 of 19 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Reinforcement Learning for Efficient Design and Control Co-optimisation of Energy Systems28 Jun

Other references · 2

  1. 2018Jointly Learning to Construct and Control Agents using Deep Reinforcement Learningcited by 55
  2. 2020Hardware as Policy: Mechanical and Computational Co-Optimization using Deep Reinforcement Learningcited by 32

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Reinforcement Learning versus Optimization for Optimal Transmission Switching: A Comparative Study
  2. 2026A Dual-Positive Monotone Parameterization for Multi-Segment Bids and a Validity Assessment Framework for Reinforcement Learning Agent-based Simulation of Electricity Markets
  3. 2025Assessing Long-Term Electricity Market Design for Ambitious Decarbonization Targets using Multi-Agent Reinforcement Learning
  4. 2025Arbitrage Tactics in the Local Markets via Hierarchical Multi-agent Reinforcement Learning ↗
  5. 2025Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges
  6. 2026Profit Maximization for Electric Vehicle Charging Stations Using Multiagent Reinforcement Learning ↗
  7. 2026Optimal design of solar-battery hybrid resources considering multi-market participation under weather and price uncertainty ↗
  8. 2024Multi-Agent Reinforcement Learning for Energy Networks: Computational Challenges, Progress and Open Problems