Data-Driven Time Series Reconstruction for Modern Power Systems Research

Understand

A critical aspect of power systems research is the availability of suitable data, access to which is limited by privacy concerns and the sensitive nature of energy infrastructure.

• This lack of data, in turn, hinders the development of modern research avenues such as machine learning approaches or stochastic formulations.
• To overcome this challenge, this paper proposes a systematic, data-driven framework for reconstructing high-fidelity time series, using publicly-available grid snapshots and historical data published by transmission system operators.
• The proposed approach, from geo-spatial data and generation capacity reconstruction, to time series disaggregation, is applied to the French transmission grid.