Analyzing At-Scale Distribution Grid Response to Extreme Temperatures

TL;DR

This paper develops a simulation platform for electric distribution grids to estimate power demand during extreme weather events, demonstrating its effectiveness with real and hypothetical scenarios including DER integration and weatherization.

Contribution

The paper introduces a novel modeling and simulation platform capable of accurately estimating distribution grid demand under extreme weather conditions and various future scenarios.

Findings

Estimated a 34 GW peak capacity deficit during winter storm Uri

Projected a 124% increase in peak demand with future electrification of heating

Reduced unmet energy by 31-40% through solar PV and storage integration

Abstract

Threats against power grids continue to increase, as extreme weather conditions and natural disasters (extreme events) become more frequent. Hence, there is a need for the simulation and modeling of power grids to reflect realistic conditions during extreme events conditions, especially distribution systems. This paper presents a modeling and simulation platform for electric distribution grids which can estimate overall power demand during extreme weather conditions. The presented platform's efficacy is shown by demonstrating estimation of electrical demand for 1) Electricity Reliability Council of Texas (ERCOT) during winter storm Uri in 2021, and 2) alternative hypothetical scenarios of integrating Distributed Energy Resources (DERs), weatherization, and load electrification. In comparing to the actual demand served by ERCOT during the winter storm Uri of 2021, the proposed platform estimates approximately 34 GW of peak capacity deficit1. For the case of the future electrification of heating loads, peak capacity of 78 GW (124% increase) is estimated, which would be reduced to 47 GW (38% increase) with the adoption of efficient heating appliances and improved thermal insulation. Integrating distributed solar PV and storage into the grid causes improvement in the local energy utilization and hence reduces the potential unmet energy by 31% and 40%, respectively.