Use the Delete All button below the resources list to remove all current resources, then add resources from your desired ATB data year.
Create complete settings files for capacity expansion modeling
Upload the complete settings ZIP or its workflow_state.yml manifest
to restore your editable defaults.
This web application helps you design your energy system model and create PowerGenome settings files. It guides you through three key tasks:
Capacity expansion models help answer questions like "What's the least-cost way to meet future electricity demand while meeting policy goals?" These models need several key inputs:
resource_modifiers).
Use numeric values for absolute settings or prefix with operator
(add:, mul:, truediv:, sub:)
for relative changes (e.g., add:1000 or mul:1.1).
This default interconnection cost applies to resources like natural gas, batteries, or nuclear that don't have specific geospatial locations. Resources with specific locations (wind and solar) have their interconnection costs calculated dynamically based on how regions are aggregated. Learn more about advanced configuration →
For wind and solar resources with specific geospatial locations and generation profiles, interconnection costs are calculated dynamically. These costs are included in parquet files along with an approximate LCOE (levelized cost of energy), which is based on ATB resource costs, interconnection costs, and average capacity factors. The parquet files and accompanying JSON files are referred to as "Resource Group" files. The LCOE values from these files are used in the "Renewables" tab for selecting the lowest-cost sites to include as resources in each model region.
resource_groups_2r_eastern-western_nercr)
inside the downloaded settings ZIP, and data.yml's
RESOURCE_GROUPS lists that folder as its first entry. Download here only
if you want the files on their own.
weather_year in data.yml
picks the weather year used for renewable profiles and demand; adding
more years increases memory use and processing time.
clustering_n_jobs in data.yml parallelizes new-build
renewable profile clustering — more jobs run faster but use more memory.