Exploring grid topology reconfiguration using a simple deep reinforcement learning approach

11/26/2020
by   Medha Subramanian, et al.
0

System operators are faced with increasingly volatile operating conditions. In order to manage system reliability in a cost-effective manner, control room operators are turning to computerised decision support tools based on AI and machine learning. Specifically, Reinforcement Learning (RL) is a promising technique to train agents that suggest grid control actions to operators. In this paper, a simple baseline approach is presented using RL to represent an artificial control room operator that can operate a IEEE 14-bus test case for a duration of 1 week. This agent takes topological switching actions to control power flows on the grid, and is trained on only a single well-chosen scenario. The behaviour of this agent is tested on different time-series of generation and demand, demonstrating its ability to operate the grid successfully in 965 out of 1000 scenarios. The type and variability of topologies suggested by the agent are analysed across the test scenarios, demonstrating efficient and diverse agent behaviour.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

12/18/2021

Curriculum Based Reinforcement Learning of Grid Topology Controllers to Prevent Thermal Cascading

This paper describes how domain knowledge of power system operators can ...
04/24/2019

Autonomous Voltage Control for Grid Operation Using Deep Reinforcement Learning

Modern power grids are experiencing grand challenges caused by the stoch...
12/23/2020

Rethink AI-based Power Grid Control: Diving Into Algorithm Design

Recently, deep reinforcement learning (DRL)-based approach has shown pro...
03/03/2019

Hacking Google reCAPTCHA v3 using Reinforcement Learning

We present a Reinforcement Learning (RL) methodology to bypass Google re...
05/09/2020

Reinforcement Learning for Thermostatically Controlled Loads Control using Modelica and Python

The aim of the project is to investigate and assess opportunities for ap...
10/21/2021

Learning to run a power network with trust

Artificial agents are promising for realtime power system operations, pa...
07/12/2021

Reinforcement Learning based Proactive Control for Transmission Grid Resilience to Wildfire

Power grid operation subject to an extreme event requires decision-makin...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.