burleyson-etal_2024_applied_energy

When Do Different Scenarios of Projected Electricity Demand Start to Meaningfully Diverge?

Casey D. Burleyson^1*, Zarrar Khan^1,2, Misha Kulshresta^1,3,4, Nathalie Voisin^1,4, and Jennie S. Rice¹

¹ Pacific Northwest National Laboratory, Richland, WA, USA
² Joint Global Change Research Institute, College Park, MD, USA
³ University of California - Santa Barbara, Santa Barbara, CA, USA
⁴ University of Washington, Seattle, WA, USA

* corresponding author: casey.burleyson@pnnl.gov

Abstract

Resource adequacy studies look at balancing electricity supply and demand on 10- to 15-year time horizons while asset investment planning typically evaluates returns on 20- to 40-year time horizons. Projections of electricity demand are factored into the decision-making in both cases. Climate, energy policy, and socioeconomic changes are key uncertainties known to influence electricity demands, but their relative importance for demands over the next 10-40 years is unclear. The power sector would benefit from a better understanding of the need to characterize these uncertainties for resource adequacy and investment planning. In this study, we quantify when projected United States (U.S.) electricity demands start to meaningfully diverge in response to a range of climate, energy policy, and socioeconomic drivers. We use a wide yet plausible range of 21st century scenarios for the U.S. The projections span two population/economic growth scenarios (Shared Socioeconomic Pathways 3 and 5) and two climate/energy policy scenarios, one including climate mitigation policies and one without (Representative Concentration Pathways 4.5 and 8.5). Each climate/energy policy scenario has two warming levels to reflect a range of climate model uncertainty. We show that the socioeconomic scenario matters almost immediately – within the next 10 years, the climate/policy scenario matters within 25-30 years, and the climate model uncertainty matters only after 50+ years. This work can inform the power sector working to integrate climate change uncertainties into their decision-making.

Journal reference

Burleyson, C.D., Z. Khan, M. Kulshresta, N. Voisin, and J.S. Rice (2025). When do different scenarios of projected electricity demand start to meaningfully diverge? Applied Energy, 380, 124948, https://doi.org/10.1016/j.apenergy.2024.124948.

Code reference

Burleyson, C.D., Z. Khan, M. Kulshresta, N. Voisin, and J.S. Rice (2024). Supporting code for Burleyson et al. 2024 - Applied Energy [Code]. Zenodo. https://doi.org/10.5281/zenodo.13952373.

Data references

Input data

Dataset	Repository Link	DOI
GCAM-USA Output	https://data.msdlive.org/records/43sy2-n8y47	https://doi.org/10.57931/1989373
TGW Weather Forcing	https://data.msdlive.org/records/cnsy6-0y610	https://doi.org/10.57931/1960530

Output data

The output of the TELL model is stored in the data repository linked below. The post-processed files (resulting from the analysis scripts itemized below) are stored in the /data directory in this meta-repository.

Dataset	Repository Link	DOI
TELL Output	https://data.msdlive.org/records/r0rvc-kjw89	https://doi.org/10.57931/2228460
Post-Processed Data	https://github.com/IMMM-SFA/burleyson-etal_2023_applied_energy/tree/main/data	https://doi.org/10.5281/zenodo.10278502

Contributing modeling software

Model	Version	Repository Link	DOI
GCAM-USA	v5.3	https://data.msdlive.org/records/r52tb-hez28	https://doi.org/10.57931/1960381
TELL	v1.1	https://github.com/IMMM-SFA/tell	https://doi.org/10.5281/zenodo.8264217

Reproduce my experiment

Clone this repository to get access to the notebooks used to execute the TELL runs for this experiment. You'll also need to download the input files from the accompanying data repository (https://doi.org/10.57931/2228460) and the weather forcing data (https://doi.org/10.57931/1960530). Once you have the input datasets downloaded you can use the following notebooks to rerun the TELL model and produce the post-processed data used in this analysis. For the TELL runs you should only have to adjust the input directories to reflect the paths to wherever you choose to store the input files. The accompanying data repository already contains the output from the TELL model so you can skip rerunning the TELL model if you want to save time.

Script Name	Description
tell_runs_exp_group_b.ipynb	Runs the TELL model based on the GCAM-USA outputs and TGW weather forcing
interconnection_time_series_analysis.ipynb	Processes the time series of annual and total loads by interconnection
ba_time_series_analysis.ipynb	Processes the time series of annual and total loads by Balancing Authority
state_time_series_analysis.ipynb	Processes the time series of annual and total loads by state
ba_divergence_analysis.ipynb	Processes the pairwise scenario differences by Balancing Authority
state_divergence_analysis.ipynb	Processes the pairwise scenario differences by state
ba_peakiness_analysis.ipynb	Processes the change in load peakiness by Balancing Authority

Reproduce my figures

Use the following notebooks to reproduce the main and supplementary figures used in this publication.

Figure Numbers	Script Name	Description
2	difference_calculation.ipynb	Shows how the mean and peak differences are calculated
3	interconnection_time_series_analysis.ipynb	Analyzes the time series of annual total and peak loads by interconnection
4	ba_time_series_analysis.ipynb	Analyzes the time series of annual total and peak loads by Balancing Authority
Supplemental	state_time_series_analysis.ipynb	Analyzes the time series of annual total and peak loads by state
5, 6	ba_divergence_analysis.ipynb	Analyzes the pairwise scenario differences by Balancing Authority
7	state_divergence_analysis.ipynb	Analyzes the pairwise scenario differences by state
8	peakiness_calculation.ipynb	Shows how the change in load peakiness is calculated
9	ba_peakiness_analysis.ipynb	Analyzes the change in load peakiness by Balancing Authority
Supplemental	plot_ba_service_territory.ipynb	Plots the service territory for each Balancing Authority

Supplemental figures

These landing pages show the complete results for each state and Balancing Authority (BA).

State-Level Analyses
BA-Level Analyses

Random other figures that might be useful:

Description	Figure
Approximate interconnection boundaries
Normalized interconnection time series
State load change box plots
State divergence box plots
Comparison of peak load definition methods