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The electric grid is a key enabling infrastructure for the ambitioustransition towards carbon neutrality as we grapple with climate change. Withdeepening penetration of renewable energy resources and electrifiedtransportation, the reliable and secure operation of the electric grid becomesincreasingly challenging. In this paper, we present PSML, a first-of-its-kindopen-access multi-scale time-series dataset, to aid in the development ofdata-driven machine learning (ML) based approaches towards reliable operationof future electric grids. The dataset is generated through a novel transmission+ distribution (T+D) co-simulation designed to capture the increasinglyimportant interactions and uncertainties of the grid dynamics, containingelectric load, renewable generation, weather, voltage and current measurementsover multiple spatio-temporal scales. Using PSML, we provide state-of-the-artML baselines on three challenging use cases of critical importance to achieve:(i) early detection, accurate classification and localization of dynamicdisturbance events; (ii) robust hierarchical forecasting of load and renewableenergy with the presence of uncertainties and extreme events; and (iii)realistic synthetic generation of physical-law-constrained measurement timeseries. We envision that this dataset will enable advances for ML in dynamicsystems, while simultaneously allowing ML researchers to contribute towardscarbon-neutral electricity and mobility.

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