# Multi-Stage Transmission Line Flow Control Using Centralized and Decentralized Reinforcement Learning Agents

arXiv: https://arxiv.org/abs/2102.08430 · HTML: https://mlsanity.com/paper/2102.08430/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 16 Feb 2021.

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

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Adaptive Power System Emergency Control using Deep Reinforcement Learning](https://mlsanity.com/paper/1903.03712/) (2019)
- [Smart-PGSim: Using Neural Network to Accelerate AC-OPF Power Grid Simulation](https://mlsanity.com/paper/2008.11827/) (2020)
- [Reinforcement Learning and Deep Learning based Lateral Control for Autonomous Driving](https://mlsanity.com/paper/1810.12778/) (2018)
- [Optimal Tap Setting of Voltage Regulation Transformers Using Batch Reinforcement Learning](https://mlsanity.com/paper/1807.10997/) (2018)

## Abstract (arXiv)

Planning future operational scenarios of bulk power systems that meet security and economic constraints typically requires intensive labor efforts in performing massive simulations. To automate this process and relieve engineers' burden, a novel multi-stage control approach is presented in this paper to train centralized and decentralized reinforcement learning agents that can automatically adjust grid controllers for regulating transmission line flows at normal condition and under contingencies. The power grid flow control problem is formulated as Markov Decision Process (MDP). At stage one, centralized soft actor-critic (SAC) agent is trained to control generator active power outputs in a wide area to control transmission line flows against specified security limits. If line overloading issues remain unresolved, stage two is used to train decentralized SAC agent via load throw-over at local substations. The effectiveness of the proposed approach is verified on a series of actual planning cases used for operating the power grid of SGCC Zhejiang Electric Power Company.