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

arXiv: https://arxiv.org/abs/2608.20914 · HTML: https://mlsanity.com/paper/2608.20914/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 5 of 5)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [A Closer Look at Invalid Action Masking in Policy Gradient Algorithms](https://mlsanity.com/paper/2006.14171/) (2020)
- [Learning to run a Power Network Challenge: a Retrospective Analysis](https://mlsanity.com/paper/2103.03104/) (2021)
- [Transient Stability-Constrained OPF: Neural Network Surrogate Models and Pricing Stability](https://mlsanity.com/paper/2502.01844/) (2025)
- [Learning to Optimize meets Neural-ODE: Real-Time, Stability-Constrained AC OPF](https://mlsanity.com/paper/2410.19157/) (2024)

## 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.