ssembatya-etal_2024_earths_future

Dual Impacts of Space Heating Electrification and Climate Change Increase Uncertainties in Peak Load Behavior and Grid Capacity Requirements in Texas

Henry Ssembatya^1*, Jordan D. Kern¹, Konstantinos Oikonomou², Nathalie Voisin ², Casey D. Burleyson², and Kerem Z. Akdemir¹

¹ North Carolina State University, Raleigh, NC, USA
² Pacific Northwest National Laboratory, Richland, WA, USA

* corresponding author: [email protected]

Abstract

Around 60% of households in Texas currently rely on electricity for space heating. Asdecarbonization efforts increase, non‐electrified households could adopt electric heat pumps, significantlyincreasing peak (highest) electricity demand in winter. Simultaneously, anthropogenic climate change isexpected to increase temperatures, the potential for summer heat waves, and associated electricity demand forcooling. Uncertainty regarding the timing and magnitude of these concurrent changes raises questions abouthow they will jointly affect the seasonality of peak demand, firm capacity requirements, and grid reliability.This study investigates the net effects of residential space heating electrification and climate change on long‐term demand patterns and load shedding potential, using climate change projections, a predictive load model,and a direct current optimal power flow (DCOPF) model of the Texas grid. Results show that full electrificationof residential space heating by replacing existing fossil fuel use with higher efficiency heat pumps couldsignificantly improve reliability under hotter futures. Less efficient heat pumps may result in more severe winterpeaking events and increased reliability risks. As heating electrification intensifies, system planners will need tobalance the potential for greater resource adequacy risk caused by shifts in seasonal peaking behavior alongsidethe benefits (improved efficiency and reductions in emissions).

Journal reference

Ssembatya, H., Kern, J. D., Oikonomou, K., Voisin, N., Burleyson, C. D., & Akdemir, K. Z. (2024). Dual impacts of space heating electrification and climate change increase uncertainties in peak load behavior and grid capacity requirements in Texas. Earth's Future, 12(6), e2024EF004443. https://doi.org/10.1029/2024EF004443

Code reference

Ssembatya, H., Burleyson, C., Akdemir, K. Z., Konstantinos, O., Kern, J., & Voisin, N. (2024). Supporting code for Ssembatya et al. 2024 - Earth's Future (v1.0.0) [Code]. Zenodo. https://doi.org/10.5281/zenodo.10934193

Data references

Input data

Dataset	Repository Link	DOI
White et al., 2021 model output	https://data.mendeley.com/datasets/v8mt9d3v6h/1	https://doi.org/10.17632/v8mt9d3v6h.1
Jones et al., 2022 IM3/HyperFACETS Thermodynamic Global Warming (TGW) simulations	https://tgw-data.msdlive.org	https://doi.org/10.57931/1885756
Burleyson et al., 2023 meteorology datasets	https://data.msdlive.org/records/cnsy6-0y610	https://doi.org/10.57931/1960530
ERCOT historical reported load	https://www.ercot.com/gridinfo/load/load_hist	-

Output data

Dataset	Repository Link	DOI
ML models load output & GO ERCOT simulations	https://data.msdlive.org/records/nth03-3ta28	https://doi.org/10.57931/2331202

Contributing modeling software

Model	Version	Repository Link	DOI
Ssembatya et al., 2024 Grid Operations (GO) ERCOT model version used	v1.0.0	https://zenodo.org/records/10475965	https://doi.org/10.5281/zenodo.10475965

Reproduce my experiment

Clone this repository to get access to the scripts used in parameretizing the Machine Learning (ML) models used to predict residential and total load under different scenarios. Download the version of the GO ERCOT model version used in this experiment (https://doi.org/10.5281/zenodo.10475841). The accompanying output data contains all the output datasets from these model runs. Run the following scripts in the workflow directory to process the raw data used in this experiment:

Script Number	Script Name	Purpose
1	texas_ht_pred_3_mlp_github.py	Parameterize the ML model, generate datasets (predictions) of residential load under different scenarios and the non-residential load
2	peaking_results_peak_hourly_total.py	Combines the residential and non-residential load to obtain the total load datasets

Run the following scripts for the GO ERCOT model.

Script Number	Script Name	Purpose
1	reduced_network_data_allocation_hecc.py	Create different subfolders (using a 150 node reduced topology) each containing a scenario year of the model parameterization
2	ERCOTDataSetup.py	Creates the ERCOT_data.dat file under each subfolder
3	ERCOT_simple.py	Runs the DC OPF model as an LP

Reproduce my figures

Use the following scripts to reproduce figures used in this publication.

Figure Numbers	Script Name	Description
1	minmax_temp_withhistoricals_from1980_paper.py	Minimum and maximum hourly annual temperature under historical and climate scenarios
3	nodal_topology_with_lines_ERCOT_paper.py	Reduced topology framework of the selected GO ERCOT version showing nodes and transmission lines
4	pk_seas_hrly_res_results_visuals_ssp3_paper.py	Season of peak hourly residential load for all future scenario simulations
5	pk_seas_hrly_tot_results_visuals_ssp3_paper.py	Season of peak hourly total load for all future scenario simulations
6	plot_year_examples_paper.py	Comparing weather and load for two selected years
7	peak_totload_ssp3_paper.py	Peak hourly total load for all future scenario simulations
8a	lol_distribution_rcp85hotterssp3base_paper.py	Nodal location of loss of load on simulation day rcp85hotterssp3_base_3rd_aug_2091
8c	lol_distribution_rcp45coolerssp3stdd_paper.py	Nodal location of loss of load on simulation day rcp45coolerssp3_stdd_23rd_dec_2069
8b,d	ercot_temperature_maps_paper.ipynb	Max and min hourly temperature distribution on selected simulation days
9	lol_visuals_manuscript_SSP3_paper.py	Cumulative loss of load for all scenarios