mlsanityData to 14 Oct 2026

Multi-Objective Deep Reinforcement Learning for Secure and Stable Power System Operation

arXiv 2608.20914 ↗First posted 21 Aug 2026eess.SY cs.SYcited by 0

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

Abstract (arXiv)

The ongoing energy transition challenges the stable operation of power systems and increases the need for rapid decision-making under uncertainty. While reinforcement learning has emerged as a promising framework for power system control and operation, existing applications typically focus on a single operational criterion, such as thermal security or small-signal stability. However, power system operation is inherently multi-objective and may involve trade-offs between objectives. This paper develops a unified-control deep reinforcement learning agent that maintains thermal security under stochastic load variations while steering the system toward operating points with improved damping of the most critical mode. Compared to a thermal-security-only agent and a business-as-usual policy, the proposed agent achieves a better balance among the operational objectives considered, with notably improved damping and negligible thermal-security violations. Finally, the operational value of increased critical damping is demonstrated under small- and large-signal disturbances, where operating points with higher damping lead to faster oscillation decay and improved critical clearing times.

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 5 of its 16 references that are in the map. First posted on arXiv 21 Aug 2026.

Builds on

5 of 16 references.

References · 5

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2020A Closer Look at Invalid Action Masking in Policy Gradient Algorithmscited by 171
  3. 2021Learning to run a Power Network Challenge: a Retrospective Analysiscited by 55
  4. 2025Transient Stability-Constrained OPF: Neural Network Surrogate Models and Pricing Stabilitycited by 2
  5. 2024Learning to Optimize meets Neural-ODE: Real-Time, Stability-Constrained AC OPFcited by 1

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  2. 2024A Review of Safe Reinforcement Learning Methods for Modern Power Systems
  3. 2026Interpretable Policy Distillation for Power Grid Topology Control
  4. 2020Rethink AI-based Power Grid Control: Diving Into Algorithm Design
  5. 2026Safe Deep Reinforcement Learning for Building Heating Control and Demand-side Flexibility
  6. 2025Towards Efficient Multi-Objective Optimisation for Real-World Power Grid Topology Control
  7. 2024Make Safe Decisions in Power System: Safe Reinforcement Learning Based Pre-decision Making for Voltage Stability Emergency Control
  8. 2024A Critical Review of Safe Reinforcement Learning Techniques in Smart Grid Applications