akdemir-etal_2025_advances_in_applied_energy

Grid Stress and Reliability in Future Electricity Grids: Impacts of Generation Mix, Weather, and Demand Growth

Kerem Ziya Akdemir^1*, Kendall Mongird¹, Cameron Bracken¹, Casey D. Burleyson¹, Jordan D. Kern², Konstantinos Oikonomou¹, Travis B. Thurber¹, Chris R. Vernon¹, Nathalie Voisin^1,3, Mengqi Zhao¹, and Jennie S. Rice¹

¹ Pacific Northwest National Laboratory, Richland, WA, USA
² North Carolina State University, Raleigh, NC, USA
³ University of Washington, Seattle, WA, USA

* corresponding author: keremziya.akdemir@pnnl.gov

Abstract

The reliability of power grids in the future will depend on how system planners account for the uncertainties in demand growth from increased electrification and data centers, integration of renewable energy, and extreme weather events. This study introduces an open-source multisectoral, multiscale modeling framework that projects grid stress and reliability trends between 2020-2055 in the Western Interconnection of the United States. The framework integrates global to national energy-water-land dynamics with power plant siting and hourly grid operations modeling. We analyze future wholesale electricity price shocks and unserved energy events across eight scenarios spanning a wide but plausible range of greenhouse gas emissions constraints, generation mixes, extreme weather events, and socioeconomic changes. Our results show that future grids with a high percentage of renewable resources have lower median but more volatile wholesale electricity prices as well as more frequent and severe unserved energy events compared to scenarios with less renewables which rely on more dispatchable generators. These events occur because the higher proportion of solar and wind causes net demand curves to deepen during midday (i.e., duck curves get progressively severe), exacerbating the challenge of meeting demand during evening peaks in summer months. Scenarios with higher technological and economic growth are characterized by higher reliability and lower wholesale electricity prices than lower growth scenarios because of more reliance on dispatchable generators and lower fossil fuel extraction costs. Robust and co-optimized transmission and energy storage planning are recommended to maintain low wholesale electricity prices and high reliability levels in future electricity grids.

Journal reference

To be updated with appropriate reference information once the paper is published.

Code reference

Akdemir, K. Z., Mongird, K., Bracken, C., Burleyson, C. D., Kern, J. D., Oikonomou, K., Thurber, T. B., Vernon, C. R., Voisin, N., Zhao, M., & Rice, J. S. (2025). Meta-repository for data and code associated with the Akdemir et al. 2025 submission to Advances in Applied Energy (Version v1.0.0) [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.15530297

Data reference

Input data

Dataset	Repository Link	DOI
GO and TEP Inputs	https://data.msdlive.org/records/7art3-45280	https://doi.org/10.57931/2497839

Output data

Dataset	Repository Link	DOI
GO and TEP Outputs	https://data.msdlive.org/records/7art3-45280	https://doi.org/10.57931/2497839
CERF Outputs	https://data.msdlive.org/records/62fpt-0jr75	https://doi.org/10.57931/2479527

Supplementary data

All supplementary data can be found in the supplementary_data directory.

Dataset	Description	Reference
BA_Topology_Files/10k_Load.csv	Nodal information including number IDs, names, area names, voltages, angles, locations, and loads within 10000-nodal topology of the U.S. Western Interconnection	ACTIVSg10k
BA_Topology_Files/BAs	Names and abbreviations of 28 balancing authorities considered in the U.S. Western Interconnection	Created by authors
BA_Topology_Files/line_params_125.csv	Names, reactances and thermal limits of transmission lines within reduced 125-nodal topology of the U.S. Western Interconnection	Created by authors
BA_Topology_Files/Nodal_information.csv	Number IDs, names, area names, locations, transmission planning regions, and load weights of individual nodes within reduced 125-nodal topology of the U.S. Western Interconnection	Created by authors
BA_Topology_Files/nodes_to_BA_state.csv	Nodal information including number IDs, names, area names, voltages, angles, locations, loads, geometries, balancing authority and state information within 10000-nodal topology of the U.S. Western Interconnection	ACTIVSg10k (Modified by authors)
BA_Topology_Files/selected_nodes_125.csv	Number IDs of the selected nodes within reduced 125-nodal topology of the U.S. Western Interconnection	Created by authors
Shapefiles/NERC_regions	Folder including shapefile of North American Electric Reliability Corporation (NERC) regions	HIFLD
Shapefiles/US_states	Folder including shapefile of U.S. census states	U.S. EIA

Contributing modeling software

Model	Version	Model Repository Link	DOI of Specific Version
GO	v0.1.0	https://github.com/IMMM-SFA/go	https://doi.org/10.5281/zenodo.15399795
TEP	v1.1.0	https://github.com/keremakdemir/Transmission_Expansion_Planner	https://doi.org/10.5281/zenodo.15413081
GCAM-USA	v5.3	https://github.com/JGCRI/gcam-core	https://doi.org/10.5281/zenodo.3908600
CERF	v2.4.0	https://github.com/IMMM-SFA/cerf	https://doi.org/10.5281/zenodo.13830460
TELL	v1.1.0	https://github.com/IMMM-SFA/tell	https://doi.org/10.5281/zenodo.8264217
reV	v0.7.0	https://github.com/NREL/reV	https://doi.org/10.5281/zenodo.7301491

Reproduce my experiment

Use the scripts/files found in the workflow directory to reproduce the experiment presented in this publication.

• Please check and make sure that all the necessary packages listed in requirements.txt are installed in your local Python environment.
• Please download input/output/supplementary datasets.
• Please update all the paths in the configuration files and scripts so that they point to the local paths of the downloaded input/output/supplementary datasets.
• By default, transmission network optimization outputs from TEP is fed into GO and used as an input.

Script/File Name	Description
GO_config.yml	Configuration file containing paths to the input/output files of GO
GO_simulation.py	Script that creates GO model input database and starts GO model simulation
TEP_config.yml	Configuration file containing paths to the input/output files and model settings of TEP
TEP_setup.py	Script that prepares TEP model input database
TEP_simulation.py	Script that starts TEP model simulation

Steps of running GO

1. Example GO_config.yml file includes paths to the inputs/outputs for scenario rcp45cooler_ssp3 and year 2050. Determine which scenario/year you would like to run and alter the paths in GO_config.yml so that they point to the specific input/output/supplementary datasets.
2. Make sure my_config_file_path parameter in GO_simulation.py script points to the path of GO_config.yml file.
3. my_simulation_days parameter in GO_simulation.py script defaults to a full-year. If you need to simulate only a certain part of the year, adjust my_simulation_days accordingly.
4. Change my_solver_name parameter in GO_simulation.py script so that it matches the solver you would like to use. Make sure that the solver you would like to use can be accessed via pyomo package.
5. Run GO_simulation.py and analyze/compare the outputs.
6. Restart from step 1 for every different scenario/year you would like to simulate.

Steps of running TEP

1. Example TEP_config.yml file includes paths to the inputs/outputs for scenario rcp45cooler_ssp3 and year 2050. Determine which scenario/year you would like to run and alter the paths in TEP_config.yml so that they point to the specific input/output/supplementary datasets. Note that existing_line_param_file and existing_line_param_output_file point to year t-5 to reflect transmission network in previous timestep.
2. Please do not change the settings (i.e., last three parameters in TEP_config.yml), if you would like to get the same results presented in this paper.
3. Make sure my_config_file_path parameter in TEP_setup.py script points to the path of TEP_config.yml file.
4. Run TEP_setup.py to create TEP model input database.
5. Make sure my_config_file_path parameter in TEP_simulation.py script points to the path of TEP_config.yml file.
6. Change my_solver_name parameter in TEP_simulation.py script so that it matches the solver you would like to use. Make sure that the solver you would like to use can be accessed via pyomo package.
7. Run TEP_simulation.py and analyze/compare the outputs.
8. Restart from step 1 for every different scenario/year you would like to simulate.

Reproduce my figures

Use the scripts found in the figures directory to reproduce the figures used in this publication.

• Please check and make sure that all the necessary packages listed in requirements.txt are installed in your local Python environment.
• Please download input/output/supplementary datasets.
• Please update all the paths in the scripts so that they point to the local paths of the downloaded input/output/supplementary datasets.
• Setting t_scenario = cooler would produce the figures in the main body of the manuscript, whereas setting t_scenario = hotter would produce the figures in the supplementary information.

Figure Number	Script/File Name	Description
1	Experiment_flowchart.pptx	Shows the flowchart of the modeling chain to simulate grid stress and reliability between 2020 and 2055
2	Nodal_topology.py	Plots the 125-nodal topology of GO model and three transmission planning regions of the U.S. Western Interconnection
3	Grid_futures.py	Plots the changes in dispatchable generation capacity, renewable generation capacity, storage discharge capacity, intraregional transmission capacity, interregional transmission capacity, and average hourly demand between 2020-2055
4	Generation_mix.py	Plots the annual generation mix in U.S. Western Interconnection between 2020-2055
5	LMP_demand_boxplots.py	Plots the distributions of daily average LMP and daily average demand for the U.S. Western Interconnection between 2020-2055
6	LMP_LOL_heatmaps.py	Plots the yearly average LMP and yearly unserved energy to demand ratio for each U.S. Western Interconnection balancing authority and for each simulation year between 2020-2055
7	Reasons_for_grid_stress.py	Plots the demand, available renewable and storage capacities, intraregional and interregional transmission line usage, day of year and hour of day distributions of high LMP and unserved energy events considering all simulation years between 2020-2055
8	Storage_net_demand_trends.py	Plots the average hour of day trends of storage capacity utilization and net demand for each simulation year between 2020-2055