A Physics-informed data reconciliation framework for real-time electricity and emissions tracking

TL;DR

This paper introduces a physics-informed optimization framework that automates and accelerates the processing of real-time electricity data, ensuring physical consistency and enabling near real-time emissions tracking for the US electricity system.

Contribution

It presents a novel, automated data reconciliation method based on physical principles, improving data quality and timeliness for emissions monitoring.

Findings

Successfully applied to US electricity data

Produces physically consistent, real-time data updates

Facilitates near real-time emissions tracking

Abstract

To encourage and guide decarbonization efforts, better tools are needed to monitor real-time CO2 and criteria air pollutant emissions from electricity consumption, production, imports, and exports. Using real-time data from the electricity system is especially challenging for quantitative applications requiring high quality and physically consistent data. Until now, time-intensive, ad-hoc and manual data verification and cleaning strategies have been used to prepare the data for quantitative analysis. As an alternative to existing techniques, here we provide a physics-informed framework to greatly accelerate and automate data processing to enable internally consistent electric system consumption, production, import, and export data in near real-time. A key component of this framework is an optimization program to minimize the data adjustments required to satisfy energy conservation equations. The effectiveness of this method is demonstrated by applying it to the continental United States electricity network. The resulting publicly-available data set, which provides in near-real time, hourly updates on electricity generation, consumption, imports, exports and associated emissions, is the first of this nature.