# A Reinforcement Learning Approach for Fast Frequency Control in Low-Inertia Power Systems

arXiv: https://arxiv.org/abs/2007.05474 · HTML: https://mlsanity.com/paper/2007.05474/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [Bi-level Off-policy Reinforcement Learning for Volt/VAR Control Involving Continuous and Discrete Devices](https://mlsanity.com/paper/2104.05902/) (2021)
- [Safe Reinforcement Learning for Strategic Bidding of Virtual Power Plants in Day-Ahead Markets](https://mlsanity.com/paper/2307.05812/) (2023)
- [Reinforcement Learning for Sustainable Energy: A Survey](https://mlsanity.com/paper/2407.18597/) (2024)
- [Supporting Future Electrical Utilities: Using Deep Learning Methods in EMS and DMS Algorithms](https://mlsanity.com/paper/2303.00428/) (2023)

## Abstract (arXiv)

The electric grid is undergoing a major transition from fossil fuel-based power generation to renewable energy sources, typically interfaced to the grid via power electronics. The future power systems are thus expected to face increased control complexity and challenges pertaining to frequency stability due to lower levels of inertia and damping. As a result, the frequency control and development of novel ancillary services is becoming imperative. This paper proposes a data-driven control scheme, based on Reinforcement Learning (RL), for grid-forming Voltage Source Converters (VSCs), with the goal of exploiting their fast response capabilities to provide fast frequency control to the system. A centralized RL-based controller collects generator frequencies and adjusts the VSC power output, in response to a disturbance, to prevent frequency threshold violations. The proposed control scheme is analyzed and its performance evaluated through detailed time-domain simulations of the IEEE 14-bus test system.