An Open-source Model for Simulation and Corrective Measure Assessment of the 2021 Texas Power Outage

TL;DR

This paper introduces an open-source, realistic synthetic model of the Texas power grid, enabling policy analysis and assessment of corrective measures to mitigate extreme weather-induced blackouts.

Contribution

It provides a novel, calibrated open-source synthetic grid model for Texas, along with data sets, to support policy evaluation and outage mitigation strategies.

Findings

Regional disparity in load shedding identified

Sensitivity analysis of corrective measures conducted

Model can be applied to other regions' energy transition challenges

Abstract

Unprecedented winter storms that hit across Texas in February 2021 have caused at least 69 deaths and 4.5 million customer interruptions due to the wide-ranging generation capacity outage and record-breaking electricity demand. While much remains to be investigated on what, how, and why such wide-spread power outages occurred across Texas, it is imperative for the broader macro energy community to develop insights for policy making based on a coherent electric grid model and data set. In this paper, we collaboratively release an open-source extendable model that is synthetic but nevertheless provides a realistic representation of the actual energy grid, accompanied by open-source cross-domain data sets. This simplified synthetic model is calibrated to the best of our knowledge based on published data resources. Building upon this open-source synthetic grid model, researchers could quantitatively assess the impact of various policies on mitigating the impact of such extreme events. As an example, in this paper we critically assess several corrective measures that could have mitigated the blackout under such extreme weather conditions. We uncover the regional disparity of load shedding. The analysis also quantifies the sensitivity of several corrective measures with respect to mitigating the severity of the power outage, as measured in Energy-not-Served (ENS). This approach and methodology are generalizable for other regions experiencing significant energy portfolio transitions.