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

TL;DR

This paper introduces PSML, a comprehensive multi-scale time-series dataset generated through co-simulation, designed to advance machine learning applications for reliable and sustainable electric grid operation in the context of decarbonization.

Contribution

It provides the first open-access dataset capturing grid dynamics at multiple scales, along with baseline ML models for disturbance detection, forecasting, and synthetic data generation.

Findings

State-of-the-art ML baselines established for disturbance detection.

Effective hierarchical load and renewable energy forecasting demonstrated.

Synthetic data generation constrained by physical laws achieved.

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 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.