# Reinforcement Learning Optimizes Power Dispatch in Decentralized Power Grid

arXiv: https://arxiv.org/abs/2407.15165 · HTML: https://mlsanity.com/paper/2407.15165/

## 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 12 of its 60 references that are in the map.
- First posted on arXiv 21 Jul 2024.

## 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, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [POMO: Policy Optimization with Multiple Optima for Reinforcement Learning](https://mlsanity.com/paper/2010.16011/) (2020)
- [Graph Convolutional Reinforcement Learning](https://mlsanity.com/paper/1810.09202/) (2018)
- [Predicting Basin Stability of Power Grids using Graph Neural Networks](https://mlsanity.com/paper/2108.08230/) (2021)
- [Power-grid stability predictions using transferable machine learning](https://mlsanity.com/paper/2105.07562/) (2021)
- [Impact of Low Rotational Inertia on Power System Stability and Operation](https://mlsanity.com/paper/1312.6435/) (2013)
- [Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural Networks](https://mlsanity.com/paper/2206.06369/) (2022)
- [Global performance metrics for synchronization of heterogeneously rated power systems: The role of machine models and inertia](https://mlsanity.com/paper/1710.07195/) (2017)
- [Optimal placement of inertia and primary control : a matrix perturbation theory approach](https://mlsanity.com/paper/1906.06922/) (2019)
- [Primary Control Effort under Fluctuating Power Generation in Realistic High-Voltage Power Networks](https://mlsanity.com/paper/2006.16888/) (2020)
- [Understanding Braess' Paradox in power grids](https://mlsanity.com/paper/2209.13278/) (2022)

## Abstract (arXiv)

Effective frequency control in power grids has become increasingly important with the increasing demand for renewable energy sources. Here, we propose a novel strategy for resolving this challenge using graph convolutional proximal policy optimization (GC-PPO). The GC-PPO method can optimally determine how much power individual buses dispatch to reduce frequency fluctuations across a power grid. We demonstrate its efficacy in controlling disturbances by applying the GC-PPO to the power grid of the UK. The performance of GC-PPO is outstanding compared to the classical methods. This result highlights the promising role of GC-PPO in enhancing the stability and reliability of power systems by switching lines or decentralizing grid topology.