  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
over 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.
