U.S. Department of Energy Office of Biological and Environmental Research

BER Research Highlights


Evaluating Penalized Logistic Regression Models to Predict Heat-Related, Environmentally-Induced Electric Grid Stress?
Published: September 22, 2017
Posted: January 19, 2018

Regression model performs well in predicting grid stress based on regional weather conditions.

The Science
Understanding the environmental conditions associated with stress on the electric grid has important practical considerations, but also represents a complex scientific and modeling challenge. A research team led by scientists at Pacific Northwest National Laboratory explored how well statistical models could predict grid stress based on weather conditions in a particular region. Scientists found one type of statistical model provided predictive value and was easy to interpret.

The Impact
The electricity sector develops contingency plans so that the grid is reliable even during periods when it is stressed by extreme weather events such as heat waves. Industry planning and operations teams could use the novel statistical techniques developed through this research to better understand and predict grid stress in the context of evolving electricity infrastructure configurations and environmental conditions. The results also provide insight into the development of next-generation modeling and analysis tools to represent interactions between energy and Earth systems.

Summary
Researchers constructed statistical models based on the weather variables that tend to give rise to grid stress. They used 10 years of high time-resolution electricity load and pricing data from 16 zones in the PJM (Pennsylvania-New Jersey-Maryland) Interconnection (a regional transmission organization), along with observed weather data from the same time period. After testing several model types, researchers found that a penalized logistic regression model performed well in predicting grid stress when fit to a specific operational zone. It also revealed the weather variables most important for predicting grid stress in each zone. In addition to daily maximum temperature—typically the only variable that the electric power industry considers when making load forecasts—researchers found that other predictors of grid stress included humidity, precipitation, and lagged variables that account for persistent stresses on the grid over multiple days. In some zones, model performance was improved by including weather information from other zones, which may reflect the grid’s interconnected nature. Assuming that data are available, the methods presented in this work could be extended to other regions or used to project potential changes in grid stress associated with future climate and infrastructure scenarios.

Contacts (BER PM)
Bob Vallario
Integrated Assessment Research
Bob.Vallario@science.doe.gov

(PI Contact)
Ian Kraucunas
Pacific Northwest National Laboratory
ian.kraucunas@pnnl.gov

Funding
The U.S. Department of Energy Office of Science, Biological and Environmental Research supported this research as part of the Integrated Assessment Research program through the Integrated Multi-sector, Multi-scale Modeling (IM3) Scientific Focus Area.

Publications
L.M. Bramer, J. Rounds, C.D. Burleyson, D. Fortin, J. Hathaway, J. Rice, I. Kraucunas. “Evaluating Penalized Logistic Regression Models to Predict Heat-Related Electric Grid Stress Days.” Applied Energy 205:1408-1418. (2017). [DOI: 10.1016/j.apenergy.2017.09.087]

Related Links
Paper: Reference Link
Supplemental Material (docx file)

Topic Areas:

  • Research Area: Multisector Dynamics (formerly Integrated Assessment)

Division: SC-23.2 Biological Systems Science Division, BER

 

BER supports basic research and scientific user facilities to advance DOE missions in energy and environment. More about BER

Recent Highlights

May 10, 2019
Quantifying Decision Uncertainty in Water Management via a Coupled Agent-Based Model
Considering risk perception can improve the representation of human decision-making processes in age [more...]

May 09, 2019
Projecting Global Urban Area Growth Through 2100 Based on Historical Time Series Data and Future Scenarios
Study provides country-specific urban area growth models and the first dataset on country-level urba [more...]

May 05, 2019
Calibrating Building Energy Demand Models to Refine Long-Term Energy Planning
A new, flexible calibration approach improved model accuracy in capturing year-to-year changes in bu [more...]

May 03, 2019
Calibration and Uncertainty Analysis of Demeter for Better Downscaling of Global Land Use and Land Cover Projections
Researchers improved the Demeter model’s performance by calibrating key parameters and establi [more...]

Apr 22, 2019
Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles
Representation of warm temperature events varies considerably among global climate models, which has [more...]

List all highlights (possible long download time)