A Multi-scale Time-series Dataset with Benchmark for Machine Learning in Decarbonized Energy Grids

Abstract

The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change. With deepening penetration of renewable energy resources and electrified transportation, the reliable and secure operation of the electric grid becomes increasingly challenging. In this paper, we present PSML, a first-of-its-kind open-access multi-scale time-series dataset, to aid in the development of data-driven machine learning (ML) based approaches towards reliable operation of future electric grids. The dataset is generated through a novel transmission + distribution (T+D) co-simulation designed to capture the increasingly important interactions and uncertainties of the grid dynamics, containing electric load, renewable generation, weather, voltage and current measurements at multiple spatio-temporal scales. Using PSML, we provide state-of-the-art ML baselines on three challenging use cases of critical importance to achieve: (i) early detection, accurate classification and localization of dynamic disturbance events; (ii) robust hierarchical forecasting of load and renewable energy with the presence of uncertainties and extreme events; and (iii) realistic synthetic generation of physical-law-constrained measurement time series. We envision that this dataset will enable advances for ML in dynamic systems, while simultaneously allowing ML researchers to contribute towards carbon-neutral electricity and mobility.

Massively Digitized Power Grid: Opportunities and Challenges of Use-Inspired AI

“…Third, the value of creating comprehensive and trustworthy benchmark power datasets has been overlooked by the power system community. There have been few opensource datasets [220], [221] and online contests dedicated to topics such as forced oscillation localization [222] and power system operation [223], [224]. However, far more will be needed to build a standard library of opensource benchmark datasets along with critical tasks in a clear mathematical formulation that can be used to train, calibrate, test, and benchmark data-driven models.…”