A large synthetic dataset for machine learning applications in power transmission grids

TL;DR

This paper introduces a large, validated synthetic dataset of power grid data for machine learning applications, addressing data scarcity issues in power transmission grid analysis.

Contribution

It presents a novel algorithm to generate extensive synthetic power injection time series for European transmission grids, enabling ML research without real operational data.

Findings

Generated datasets are statistically validated against real data.

The method allows scalable creation of diverse grid scenarios.

Supports real-time safety and stability assessments.

Abstract

With the ongoing energy transition, power grids are evolving fast. They operate more and more often close to their technical limit, under more and more volatile conditions. Fast, essentially real-time computational approaches to evaluate their operational safety, stability and reliability are therefore highly desirable. Machine Learning methods have been advocated to solve this challenge, however they are heavy consumers of training and testing data, while historical operational data for real-world power grids are hard if not impossible to access. This manuscript presents a large synthetic dataset of power injections in an electric transmission grid model of continental Europe, and describes the algorithm developed for its generation. The method allows one to generate arbitrarily large time series from the knowledge of the grid -- the admittance of its lines as well as the location, type and capacity of its power generators -- and aggregated power consumption data, such as the national load data given by ENTSO-E. The obtained datasets are statistically validated against real-world data.