Inputs Documentation

This page aggregates documentation from each folder README under the inputs directory.

Table of Contents

Canadian Input Files

  • can_exports.csv: Annual exports [MWh] to Canada by county

  • can_exports_szn_frac.csv: Fraction of annual exports [fraction] to Canada by season.

  • can_imports.csv: Annual imports [MWh] from Canada by county

  • can_imports_quarter_frac.csv: Fraction of annual imports [fraction] from Canada by season

Capacity Exogenous Input Files

  • cappayments.csv:

  • cappayments_ba.csv:

  • demonstration_plants.csv: Nuclear-smr demonstration plants [MW]

  • exog_cap_geohydro_allkm_reference.csv:

  • exog_cap_geohydro_reference.csv:

  • exog_cap_upv_*.csv:

    • exog_cap_upv_limited.csv:

    • exog_cap_upv_open.csv:

    • exog_cap_upv_reference.csv:

  • exog_cap_wind-ons_*.csv:

    • exog_cap_wind-ons_limited.csv:

    • exog_cap_wind-ons_open.csv:

    • exog_cap_wind-ons_reference.csv:

  • interconnection_queues.csv:

  • prescribed_builds_wind-ofs_meshed_*.csv:

    • prescribed_builds_wind-ofs_meshed_limited.csv:

    • prescribed_builds_wind-ofs_meshed_open.csv:

    • prescribed_builds_wind-ofs_meshed_reference.csv:

  • prescribed_builds_wind-ofs_radial_*.csv:

    • prescribed_builds_wind-ofs_radial_limited.csv:

    • prescribed_builds_wind-ofs_radial_open.csv:

    • prescribed_builds_wind-ofs_radial_reference.csv:

  • prescribed_builds_wind-ons_*.csv:

    • prescribed_builds_wind-ons_limited.csv:

    • prescribed_builds_wind-ons_open.csv:

    • prescribed_builds_wind-ons_reference.csv:

  • ReEDS_generator_database_final_EIA-NEMS.csv: ReEDS generator database derived from the EIA NEMS plant file and EIA 860M. See https://github.com/ReEDS-Model/ReEDS_Input_Processing/tree/main/nems_database_processing.

Climate Input Files

  • climate_heuristics_finalyear.csv:

  • climate_heuristics_yearfrac.csv:

Consume Input Files

  • consume_char_*.csv: Cost (capex, FOM, VOM) and efficiency (gas and electrical) as well as storage and transmission adder (stortran_adder) inputs for various H2 producing technologies. Units vary by parameter; refer to b_inputs.gms

    • consume_char_low.csv: Conservative assumptions

    • consume_char_ref.csv: Reference assumptions

  • dac_elec_BVRE_2021_*.csv: DAC costs (capex [\(/(metric ton CO2/hr)], FOM [\)/(metric ton CO2/hr)/yr], VOM [$/metric ton CO2]) and conversion rate, over time.

    • dac_elec_BVRE_2021_high.csv: High assumptions

    • dac_elec_BVRE_2021_low.csv: Low assumptions

    • dac_elec_BVRE_2021_mid.csv: Mid assumptions

    • Citation: J. Valentine and A. Zoelle, “Direct Air Capture Case Studies: Sorbent System,” National Energy Technology Laboratory, Pittsburgh, PA, July 8, 2022. https://doi.org/10.2172/1879535

  • dac_gas_BVRE_2021_*.csv: DAC costs (capex [\(/(metric ton CO2/hr)], FOM [\)/(metric ton CO2/hr)/yr], VOM [$/metric ton CO2]) and conversion rate, over time.

    • dac_gas_BVRE_2021_high.csv: High assumptions

    • dac_gas_BVRE_2021_low.csv: Low assumptions

    • dac_gas_BVRE_2021_mid.csv: Mid assumptions

    • Citation: J. Valentine and A. Zoelle, “Direct Air Capture Case Studies: Sorbent System,” National Energy Technology Laboratory, Pittsburgh, PA, July 8, 2022. https://doi.org/10.2172/1879535

  • dollaryear.csv: Dollar year for DAC costs

  • h2_demand_county_share.csv: The fraction of national hydrogen demand in that year that corresponds to each county

    • Demand estimates come from https://data.openei.org/submissions/5655

    • 2021 demand shares correspond to the “Reference” scenario with light-duty vehicles / biofuels / methanol demand removed and 2050 shares correspond to the “Low Cost Electrolysis” scenario

  • h2_exogenous_demand.csv: Exogenous hydrogen demand by industries other than the power sector per year

  • h2_transport_and_storage_costs.csv: Transport and storage costs of hydrogen per year (in $2004)

Ctus Input Files

  • co2_site_char.csv:

  • cs.csv:

  • dollaryear.csv:

Degradation Input Files

  • degradation_annual_default.csv:

Demand Response Input Files

  • dr_shed_avail_scalar.csv:

  • dr_shed_capacity_scalar_demo_data_January_2025.csv:

dGen Input Files

  • stscen2023_electrification/distpvcap_stscen2023_electrification.csv:

  • stscen2023_highng/distpvcap_stscen2023_highng.csv: Setting for distpv scenario capacity - from standard scenarios 2023 with high NG (including distpv) costs

  • stscen2023_highre/distpvcap_stscen2023_highre.csv: Setting for distpv scenario capacity - from standard scenarios 2023 with high RE (including distpv) costs

  • stscen2023_lowng/distpvcap_stscen2023_lowng.csv: Setting for distpv scenario capacity - from standard scenarios 2023 with low NG (including distpv) costs

  • stscen2023_lowre/distpvcap_stscen2023_lowre.csv: Setting for distpv scenario capacity - from standard scenarios 2023 with low RE (including distpv) costs

  • stscen2023_mid_case/distpvcap_stscen2023_mid_case.csv:

  • stscen2023_mid_case_95_by_2035/distpvcap_stscen2023_mid_case_95_by_2035.csv:

  • stscen2023_mid_case_95_by_2050/distpvcap_stscen2023_mid_case_95_by_2050.csv:

  • stscen2023_taxcredit_extended2050/distpvcap_stscen2023_taxcredit_extended2050.csv:

Disaggregation Input Files

  • county_population.csv: The population of each county, relative values are used as multipliers for downselecting data

  • county_state_lpf.csv:

  • disagg_hydroexist.csv: The hydropower capacity fraction of each county within a given ReEDS BA, used as multipliers for downselecting data

Emission Constraints Input Files

CO2 and CO2e Caps

CO2 and CO2e emissions caps are defined in co2_cap.csv, which includes a range of different emission cap trajectories until 2050. CO2 tax for varying scenarios are defined in co2_tax.csv.

Emission Rates

Upstream and process emission rates by technology and pollutant used in ReEDS are defined in emitrate.csv. These emission rates are taken from Table A-9 in Appendix A.5 of NLR’s Standard Scenarios 2024 (https://www.nlr.gov/docs/fy25osti/92256.pdf), which details the multiple sources that these emission rates are obtained from such as U.S. Life Cycle Inventory Database, EPA’s Emissions & Generation Resource Integrated Database, California Air Resources Board, etc.

Note that CH4 upstream emission rate for natural gas is zero in emitrate.csv as we use CH4 methane leakage defined using the GSw_MethaneLeakageScen switch to calculate it.

Global Warming Potentials

A range of values for global warming potentials (GWP) of CH4 and N2O are taken from the most recent IPCC assessment reports (AR4 to AR6). Summary of the ranges of GWPs for these pollutants can be found in Table 7.15, page 1017 of the IPCC AR6 report (https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_FullReport.pdf).

Natural Gas Capital Recovery Factor (CRF) Penalties

ng_crf_penalty.csv contains a cost adjustment for NG techs in scenarios with national decarbonization targets. For more information on how these were calculated, refer to PR #1220.

The Regional Greenhouse Gas Initiative (RGGI)

Other files

  • co2_tax.csv: Annual CO2 tax

Power Sector Employment Data

Data input options

  • employment_factor_plant_jedi.csv: Employment factor data for power plants of different technologies, taken from the JEDI/WIRED model. Sources for employment data of individual technologies in JEDI/WIRED are shown in the table below.

Technology

Source

Biopower

JEDI Biofuels Model (B12.23.16)

Battery

WIRED Battery Storage Model (BESS.2025.09.30) based on Ramasamy et al. (2022).

Coal-IGCC

WIRED Coal Model (COAL.2025.09.30) based on NETL’s studies - Schmitt et al. (2022), Turner et al. (2023), and Buchheit et al. (2023) .

Coal-PC

WIRED Coal Model (COAL.2025.09.30) based on NETL’s studies - Schmitt et al. (2022), Turner et al. (2023), and Buchheit et al. (2023) .

Coal-CCS RT

WIRED Coal Model (COAL.2025.09.30) based on NETL’s studies - Schmitt et al. (2022), Turner et al. (2023), and Buchheit et al. (2023) .

Coal-CCS GF

WIRED Coal Model (COAL.2025.09.30) based on NETL’s studies - Schmitt et al. (2022), Turner et al. (2023), and Buchheit et al. (2023) .

DPV

JEDI Photovoltaics Model (PV05.20.21)

Geothermal-Hydrothermal

WIRED Geothermal Model (CPG.2025.09.30) based on NLR’s SAM-GETEM

Geothermal-EGS

WIRED Geothermal Model (CPG.2025.09.30) based on NLR’s SAM-GETEM

Hydropower

JEDI Conventional Hydro Model (CH12.23.16)

Land-based Wind

JEDI Onshore Wind Model (W2000)

NG-CC

WIRED Natural Gas Model (NG.2025.09.30) based on NETL’s studies - Schmitt et al. (2022), Oakes et al. (2023), and Schmitt and Homsy (2023)

NG-CC-CCS RT

WIRED Natural Gas Model (NG.2025.09.30) based on NETL’s studies - Schmitt et al. (2022), Oakes et al. (2023), and Schmitt and Homsy (2023)

NG-CC-CCS GF

WIRED Natural Gas Model (NG.2025.09.30) based on NETL’s studies - Schmitt et al. (2022), Oakes et al. (2023), and Schmitt and Homsy (2023)

NG-CT

WIRED Natural Gas Model (NG.2025.09.30) based on NETL’s studies - Schmitt et al. (2022), Oakes et al. (2023), and Schmitt and Homsy (2023)

Nuclear Conventional

Abou-Jaoude et al. (2023)

Nuclear SMR

Asuega et al. (2023)

Offshore Wind

Hammond and Cooperman (2022) and Nunemaker et al. (2020)

Transmission-500kW AC

WIRED Transmission Line Model (TL.12.23.16) based on JEDI Transmission Line Model

Transmission-230kW AC

WIRED Transmission Line Model (TL.12.23.16) based on JEDI Transmission Line Model

UPV

JEDI Photovoltaics Model (PV05.20.21)

Employment factor units

  • Power plants:

    • Construction: [job-years/MW]

    • FOM: [job-years/MW-year]

    • VOM: [job-years/MWh]

  • Transmission lines:

    • Construction: [job-years/(2004$)]

Financials Input Files

  • cap_penalty.csv:

  • construction_schedules_default.csv:

  • construction_times_default.csv:

  • currency_incentives.csv:

  • deflator.csv: Dollar year deflator to convert values to 2004$

  • depreciation_schedules_default.csv:

  • energy_communities.csv:

  • financials_hydrogen.csv:

  • financials_sys_ATB2023.csv:

  • financials_sys_ATB2024.csv:

  • financials_tech_ATB2023_CRP20.csv:

  • financials_tech_ATB2023.csv:

  • financials_tech_ATB2024.csv:

  • financials_transmission_30ITC_0pen_2022_2031.csv:

  • financials_transmission_default.csv:

  • incentives_*.csv:

    • incentives_annual.csv:

    • incentives_biennial.csv:

    • incentives_ira_45q_45v_extension.csv:

    • incentives_ira.csv:

    • incentives_noira.csv:

    • incentives_none.csv:

    • incentives_obbba_conservative.csv:

    • incentives_obbba.csv:

  • inflation_default.csv: Annual inflation factors from 1914 through 2200

  • nuclear_energy_communities.csv: Counties belonging to metropolitan statistical areas (MSAs) for which at least 0.17% of direct employment has been related to nuclear power at any point since 2010

    • These are determined partly by following the process described in Section 2.6 of https://home.treasury.gov/system/files/8861/EnergyCommunities_Data_Documentation.pdf and substituting in the NAICS code for nuclear electric power generation (221113) and partly by determining counties that belong to MSAs where the number of people employed by national labs engaged in nuclear research and development (PNNL, INL, ORNL, SNL, LLNL, Argonne, and LANL) has been at least 0.17 percent of the MSA’s total employment at any point since 2010

  • reg_cap_cost_diff_default.csv: Region-specific differences for capital cost of all resources

    • Add 1 to produce a multiplier

  • retire_penalty.csv:

  • supply_chain_adjust.csv:

  • tc_phaseout_schedule_ira2022.csv:

Fuel Prices Input Files

  • alpha_AEO_{YYYY}_*.csv: census division alpha values, used in the calculation of natural gas demand curves ($2004 dollar year)

    • alpha_AEO_{YYYY}_HOG.csv: High Oil and Gas Resource and Technology scenario

    • alpha_AEO_{YYYY}_LOG.csv: Low Oil and Gas Resource and Technology scenario

    • alpha_AEO_{YYYY}_reference.csv: Reference scenario

  • cd_beta0.csv: Reference census division beta levels electric sector ($2004 dollar year)

  • cd_beta0_allsector.csv: Reference census division beta levels all sectors ($2004 dollar year)

  • coal_AEO_2025_reference.csv: AEO2025 Reference case census division fuel price [$/MMBtu] of coal with missing values forward-filled from earlier years and missing New England values set to Mid Atlantic

  • coal_AEO_2026_altelec.csv: AEO2026 Alternative Electricity case census division fuel price [$/MMBtu] of coal with missing New England values set to Mid Atlantic

  • coal_AEO_2026_baseline.csv: AEO2026 Counterfactual Baseline case census division fuel price [$/MMBtu] of coal with missing values forward-filled from earlier years and missing New England values set to Mid Atlantic

  • dollaryear.csv: Dollar year mapping for each fuel price scenario

  • h2-combustion_*.csv: price of hydrogen for combustion technologies (h2-ct and cc) at $X/MMBtu for all years

    • h2-combustion_10.csv: $10/MMBtu

    • h2-combustion_30.csv: $30/MMBtu

    • h2-combustion_reference.csv: $20/MMBtu

  • ng_AEO_{YYYY}_*.csv: census division fuel price [\(/MMBtu] of natural gas (\)2004 dollar year)

    • ng_AEO_{YYYY}_HOG.csv: High Oil and Gas Resource and Technology scenario

    • ng_AEO_{YYYY}_LOG.csv: Low Oil and Gas Resource and Technology scenario

    • ng_AEO_{YYYY}_reference.csv: Reference scenario

    • ng_AEO_{YYYY}_baseline.csv:

  • ng_demand_AEO_{YYYY}_*.csv: census division natural gas demand [Quads] for the electric sector, used in the calculation of natural gas demand curves ($2004 dollar year)

    • ng_demand_AEO_{YYYY}_HOG.csv: High Oil and Gas Resource and Technology census division natural gas demand

    • ng_demand_AEO_{YYYY}_LOG.csv: Low Oil and Gas Resource and Technology census division natural gas demand

    • ng_demand_AEO_{YYYY}_reference.csv: Reference census division natural gas demand

    • ng_demand_AEO_{YYYY}_baseline.csv:

  • ng_tot_demand_AEO_{YYYY}_*.csv: census division natural gas demand [Quads] across all sectors, used in the calculation of natural gas demand curves ($2004 dollar year)

    • ng_tot_demand_AEO_{YYYY}_HOG.csv: High Oil and Gas Resource and Technology census division natural gas demand

    • ng_tot_demand_AEO_{YYYY}_LOG.csv: Low Oil and Gas Resource and Technology census division natural gas demand

    • ng_tot_demand_AEO_{YYYY}_reference.csv: Reference census division natural gas demand

    • ng_tot_demand_AEO_{YYYY}_baseline.csv:

  • uranium_AEO_2025_*.csv:

    • uranium_AEO_2025_reference.csv:

    • uranium_AEO_2026_baseline.csv:

Geothermal Input Files

  • geo_discovery_BAU.csv:

  • geo_discovery_factor_ATB_2023.csv:

  • geo_discovery_factor_reV.csv:

  • geo_discovery_TI.csv:

  • geo_rsc_ATB_2023.csv:

Growth Constraints Input Files

  • gbin_min.csv:

  • growth_bin_size_mult.csv:

  • growth_limit_absolute.csv: Maximum expected annual builds [MW/year] for wind, batteries, and UPV from 2024-2026 using observed record builds

  • growth_penalty.csv:

Hydro Input Files

  • cap_existing_hydro.csv: Annual capacities [MW] for hydro plants spanning 2007-2022, which come from ORNL’s Existing Hydropower Assets dataset

  • hyd_fom.csv: Regional FOM costs for hydro

  • hydcf_fixed.csv: Fixed monthly zonal hydro capacity factor data partially created by ORNL and partially derived from ORNL’s Existing Hydropower Assets dataset

  • hydro_mingen.csv:

  • net_gen_existing_hydro.csv: Monthly net generation values [MWh] for hydro plants spanning 2007-2022, which come from ORNL’s Existing Hydropower Assets dataset

  • SeaCapAdj_hy.csv:

Load Input Files

Load Growth Projections

  • demand_AEO_2025_high.csv: Load growth projection from the AEO2025 High Economic Growth scenario

  • demand_AEO_2025_low.csv: Load growth projection from the AEO2025 Low Economic Growth scenario

  • demand_AEO_2025_reference.csv: Load growth projection from the AEO2025 Reference scenario

  • demand_AEO_2026_baseline.csv: Load growth projection from the AEO2026 Counterfactual Baseline scenario

  • demand_AEO_2026_high.csv: Load growth projection from the AEO2026 High Economic Growth scenario

  • demand_AEO_2026_low.csv: Load growth projection from the AEO2026 Low Economic Growth scenario

Load Growth Multipliers

  • cangrowth.csv: Canada load growth multiplier

  • mex_growth_rate.csv: Mexico load growth multiplier

Other

  • EIA_loadbystate.csv:

  • loadsite_country_test.csv:

National Generation Input Files

Clean Air Act, Section 111

The regulation itself

The Clean Air Act, Section 111 is a federal regulation from the Environmental Protection Agency (EPA), which features carbon pollution standards to reduce greenhouse gas emissions from power plants. These regulations are called Section 111 for the section of the tax code which it is implemented in. The regulations stipulate a couple of compliance mechanisms for plants to comply with these regulations.

The first compliance mechanism, and the strictest, is a best system of emissions reduction (BSER) for every technology and vintage (existing vs new plants). This is enforced at the plant level i.e. each plant must meet its BSER. The BSERs are listed below:

  • Existing coal plants:

    • If the plant will permanently cease operations before Jan. 1, 2032: they are not subject to these standards

    • If the plant is operating on or after Jan. 1, 2032, and demonstrate that they plan to permanently cease operation before Jan. 1, 2039: they must cofire with 40% natural gas from 2030 through 2039

    • If the plant plant to operate on or after Jan. 1, 2039: they must upgrade with CCS with 90% capture by Jan. 1, 2032

  • New coal plants:

    • No regulations since no new coal plants are being built these days

  • Existing gas-CCs and gas-CTs:

    • No regulations

  • New gas-CCs and gas-CTs:

    • If the plant is operating at <= 40% capacity factor: they are unregulated

    • If a plant is operating above 40% CF: they must upgrade with CCS by 2032.

The second compliance mechanism, which is slightly more lenient, is a emissions rate-based mechanism. This is enforced at the state level. If a state opts into this compliance mechanism, the emissions rate (tons CO2/MWh) of their coal fleet must be less than or equal to the emissions rate of a 90% coal-CCS plant. This in theory enables some unabated coal plants to remain online after 2032, even though they won’t be able to generate much. This is only possible if that state also has coal-CCS plants with high capture rates that stay online and generate, to average out the emissions rate to below the threshold.

Other resources

Implementation in ReEDS

In ReEDS, new gas plants must adhere to their BSER and existing coal plants adhere to an emissions rate-based standard.

For new gas plants, this is the code implementation:

  1. inputs/scalars.csv

    • caa_gas_max_cf = 0.40. This is the maximum capacity factor that new gas plants (CCs or CTs) can operate at without being regulated under Clean Air Act, Section 111, expressed as a fraction.

  2. c_model.gms - eq_caa_max_cf enforces the maximum capacity factor for new gas plants.

For existing coal plants, this is the code implementation:

  1. inputs/scalars.csv

    • caa_coal_retire_year = 2032. This is the year in which coal capacity is forced to either retire or upgrade with CCS to meet the emissions requirements under Clean Air Act, Section 111.

    • caa_first_year = 2024. This is the year in which the emissions requirements under Clean Air Act, Section 111 are first active.

    • caa_capture_rate_standard = 0.90. This is the CO2 capture rate that a state’s coal fleet must match on average. It is applied to each unit’s own uncontrolled emissions rate, taken as the sum of the emit_rate and capture_rate parameters, so a unit that captures 90% of its CO2 complies regardless of its heat rate. A unit that captures more than 90%, such as a new coal-CCS plant at 95%, emits less than its own allowance and so creates headroom for other coal in that state.

  2. reeds/input_processing/WriteHintage.py

    • Coal plants are binned at the unit level if GSw_Clean_Air_Act=1 so that each coal unit can independently choose to retire or upgrade.

    • Coal plants maintain their exogenous retirement assumption, except after 2032, when the Clean Air Act regulations begin and coal can retire endogenously. For example, if the NEMS data states that a plant will retire in 2029, we maintain that assumption and that plant will retire in 2029. However, if the NEMS data previously stated that a plant will retire in 2040, they are now subject to the Clean Air Act regulations and may retire sooner than their previously stated retirement date.

  3. b_inputs.gms

    • numhintage is set to 300, so that we can accommodate a large number of coal bins since they are binned at the unit level.

    • Revise m_capacity_exog so that it matches with which coal capacity is allowed.

    • if caa_coal_retire_year is not in the set of years being modeled for this run, then set it to the first year that is modeled after caa_coal_retire_year. For example, if running 5 year solves, then instead of enforcing coal retirement in 2032, it will be enforced in 2035.

  4. c_model.gms

    • eq_caa_rate_standard(st,t) - this constraint enforces the rate-based emissions standard by setting the maximum coal emissions per state under Clean Air Act Section 111. Generation on both sides is weighted by hours over representative periods, so the standard is applied on an MWh-weighted basis.

Assumptions

  • We do not include the compliance mechanism for coal plants to cofire with natural gas in the model. We have modeled this in ReEDS previously in our analysis of these regulations and found that coal plants almost never choose this compliance mechanism. They would prefer to upgrade with CCS or retire.

Plant Characteristics Input Files

  • battery_ATB_2024_*.csv:

    • battery_ATB_2024_advanced.csv:

    • battery_ATB_2024_conservative.csv:

    • battery_ATB_2024_moderate.csv:

  • beccs_BVRE_2021_*.csv:

    • beccs_BVRE_2021_high.csv:

    • beccs_BVRE_2021_low.csv:

    • beccs_BVRE_2021_mid.csv:

  • beccs_lowcost.csv:

  • beccs_reference.csv:

  • biopower_ATB_2024_moderate.csv:

  • ccsflex_ATB_2020_*.csv:

    • ccsflex_ATB_2020_cost.csv:

    • ccsflex_ATB_2020_perf.csv:

  • coal-ccs_ATB_2024_*.csv:

    • coal-ccs_ATB_2024_advanced.csv:

    • coal-ccs_ATB_2024_conservative.csv:

    • coal-ccs_ATB_2024_moderate.csv:

  • coal_ATB_2024_moderate.csv:

  • cost_opres_*.csv:

    • cost_opres_default.csv:

    • cost_opres_market.csv:

  • csp_ATB_2023_*.csv:

    • csp_ATB_2023_advanced.csv:

    • csp_ATB_2023_conservative.csv:

    • csp_ATB_2023_moderate.csv:

  • csp_ATB_2024_*.csv:

    • csp_ATB_2024_advanced.csv:

    • csp_ATB_2024_conservative.csv:

    • csp_ATB_2024_moderate.csv:

  • csp_SunShot2030.csv: CSP costs from the SunShot2030 cost scenario

  • dollaryear.csv: Dollar year mapping for each plant cost scenario

  • dr_shed_*_demo_data_January_2025.csv:

    • dr_shed_capcost_scalars_demo_data_January_2025.csv:

    • dr_shed_fom_demo_data_January_2025.csv:

    • dr_shed_vom_demo_data_January_2025.csv:

  • evmc_*_Baseline.csv:

    • evmc_shape_Baseline.csv:

    • evmc_storage_Baseline.csv:

  • fuelcell_ATB_2024_*.csv:

    • fuelcell_ATB_2024_advanced.csv:

    • fuelcell_ATB_2024_moderate.csv:

  • gas-ccs_ATB_2024_*.csv:

    • gas-ccs_ATB_2024_advanced.csv:

    • gas-ccs_ATB_2024_conservative.csv:

    • gas-ccs_ATB_2024_moderate.csv:

  • gas_ATB_2024_moderate.csv:

  • geo_ATB_2023_*.csv:

    • geo_ATB_2023_advanced.csv:

    • geo_ATB_2023_conservative.csv:

    • geo_ATB_2023_moderate.csv:

  • geo_ATB_2024_*.csv:

    • geo_ATB_2024_advanced.csv:

    • geo_ATB_2024_conservative.csv:

    • geo_ATB_2024_moderate.csv:

  • h2-combustion_ATB_202*.csv:

    • h2-combustion_ATB_2023.csv:

    • h2-combustion_ATB_2024.csv: Hydrogen CT and CC plant costs generated in preprocessing from moderate case NREL ATB 2024 data

  • heat_rate_adj.csv: Heat rate adjustment multiplier by technology

  • heat_rate_penalty_spin.csv:

  • hydro_ATB_2019_*.csv:

    • hydro_ATB_2019_constant.csv: Hydro costs from the 2019 ATB constant cost scenario

    • hydro_ATB_2019_low.csv: Hydro costs from the 2019 ATB low cost scenario

    • hydro_ATB_2019_mid.csv: Hydro costs from the 2019 ATB mid cost scenario

  • maxage.csv: Maximum age allowed for each technology

  • maxdailycf.csv: Maximum daily capacity factor - dr_shed input supply curves are based on one 4-hour event per day

  • minCF.csv: Minimum annual capacity factor for each tech fleet - applied to i-rto

  • min_retire_age.csv: Minimum retirement age for given technology

  • mingen_fixed.csv:

  • minloadfrac0.csv: Characteristics/minloadfrac0 database of minloadbed generator cs

  • mttr.csv:

  • nuclear-smr_ATB_2024_*.csv:

    • nuclear-smr_ATB_2024_advanced.csv:

    • nuclear-smr_ATB_2024_conservative.csv:

    • nuclear-smr_ATB_2024_moderate.csv:

  • nuclear_ATB_2024_*.csv:

    • nuclear_ATB_2024_advanced.csv:

    • nuclear_ATB_2024_conservative.csv:

    • nuclear_ATB_2024_moderate.csv:

  • ofs-wind_ATB_2023_*.csv:

    • ofs-wind_ATB_2023_advanced.csv: 2023 advanced ofs-wind capital, fixed O&M, var O&M costs and rsc_mult (SC cost reduction mult) by class and year

    • ofs-wind_ATB_2023_conservative.csv: 2023 conservative ofs-wind capital, fixed O&M, var O&M costs and rsc_mult (SC cost reduction mult) by class and year

    • ofs-wind_ATB_2023_moderate.csv: 2023 moderate ofs-wind capital, fixed O&M, var O&M costs and rsc_mult (SC cost reduction mult) by class and year

    • ofs-wind_ATB_2023_moderate_noFloating.csv: 2023 moderate_noFloating ofs-wind capital (5x floating capital cost), fixed O&M, var O&M costs and rsc_mult (SC cost reduction mult) by class and year

  • ofs-wind_ATB_2024_*.csv:

    • ofs-wind_ATB_2024_advanced.csv:

    • ofs-wind_ATB_2024_conservative.csv: 2024 conservative ofs-wind capital, fixed O&M, var O&M costs and rsc_mult (SC cost reduction mult) by class and year

    • ofs-wind_ATB_2024_moderate.csv: 2024 moderate ofs-wind capital, fixed O&M, var O&M costs and rsc_mult (SC cost reduction mult) by class and year

    • ofs-wind_ATB_2024_moderate_noFloating.csv: 2024 moderate_noFloating ofs-wind capital (5x floating capital cost), fixed O&M, var O&M costs and rsc_mult (SC cost reduction mult) by class and year

  • ons-wind_ATB_2023_*.csv:

    • ons-wind_ATB_2023_advanced.csv: Advanced cost and performance inputs from the 2023 Annual Technology Baseline for land-based wind

    • ons-wind_ATB_2023_conservative.csv: Conservative cost and performance inputs from the 2023 Annual Technology Baseline for land-based wind

    • ons-wind_ATB_2023_moderate.csv: Moderate cost and performance inputs from the 2023 Annual Technology Baseline for land-based wind

  • ons-wind_ATB_2024_*.csv:

    • ons-wind_ATB_2024_advanced.csv: Advanced cost and performance inputs from the 2024 Annual Technology Baseline for land-based wind

    • ons-wind_ATB_2024_conservative.csv: Conservative cost and performance inputs from the 2024 Annual Technology Baseline for land-based wind

    • ons-wind_ATB_2024_moderate.csv: Moderate cost and performance inputs from the 2024 Annual Technology Baseline for land-based wind

  • other_plantchar.csv:

  • outage_forced_*.csv:

    • outage_forced_static.csv: Forced outage rates by technology

    • outage_forced_temperature_murphy2019.csv:

  • outage_scheduled_*.csv:

    • outage_scheduled_monthly.csv:

    • outage_scheduled_static.csv: Scheduled outage rate by technology

  • pcm_defaults.json:

  • pvb_benchmark2020.csv:

  • ramprate.csv: Generator ramp rates by technology

  • startcost.csv:

    • Most startup costs are taken from Lew et al 2013 - Western Wind and Solar Integration Study Phase 2 (NREL/TP-5500-55588)

    • The original data, reported in $2011/MW in Table 7 are:

      • Coal: 124

      • Gas-CC: 81

      • Gas-CT: 67

      • Steam: 86

      • Nuclear: 155

    • RPM uses 466 $2011/MW for nuclear, but we don’t have a citable source for that number

    • CCS startup costs are assumed to be the startup cost for the non-CCS version multiplied by the ratio of 2035 VOM costs between gas-cc-ccs and gas-cc in ATB 2023

  • unitsize_atb.csv:

  • upv_ATB_2023_*.csv:

    • upv_ATB_2023_advanced.csv:

    • upv_ATB_2023_conservative.csv:

    • upv_ATB_2023_moderate.csv:

  • upv_ATB_2024_*.csv:

    • upv_ATB_2024_advanced.csv:

    • upv_ATB_2024_conservative.csv:

    • upv_ATB_2024_moderate.csv:

  • years_until_endogenous.csv:

Reserves Input Files

  • ccseason_dates.csv: Defines the time resolution (ccseason) on which the capacity market is cleared when using the capacity credit resource adequacy method. New ccseason definitions may be added as new columns (and added to the choices for the GSw_PRM_CapCreditSeasons switch in cases.csv), but each column should include a hot and cold season, since the nameplate capacity is adjusted in those seasons based on summer/winter capacities from EIA and projected climate impacts (if activated via the GSw_ClimateHeuristics switch).

  • peak_net_imports.csv: 99.9th percentile coincident transfers (BA-level, aggregated to NERC region-level) between 2019-2023 from EIA Hourly Grid Monitor used as an estimate of current interregional transfer capabilities, as tabulated in ESIG 2024. Values for SERC-E/SE/C calculated using ESIG 2024 methodology. The same 0.1% definition has been used elsewhere, e.g. https://www.congress.gov/bill/118th-congress/senate-bill/2827/text#id6543a3657b784ecbb9362b629a5290ea. Values for MW_TotalDemand are for 2024 from LTRA 2023 and represent on-peak projections (same as used in NERC planning reserve margin calculations).

  • prm_annual.csv: Annual planning reserve margin by NERC region

    • Taken from the 2023 NERC LTRA (specifically the “Reference Margin Level (%)” reported for each reliability region)

  • opres_periods.csv:

  • orperc.csv:

  • ramptime.csv:

Sets

Formatting guidelines

  • Primary sets (those that define elements that are not subsets of other sets):

    • No header column

    • One element per line

    • No element-wise comments; each line should contain only the element

  • Subsets (groups of elements from other sets, either 1-dimensional or multidimensional):

    • Include a header column specifying the relevant primary sets

    • The header column should start with a *

      • Even 1-dimensional subsets should have a header column. So if the set food has elements [apple, banana, cauliflower], the subset fruit(food) (specified by fruit.csv) has the following lines:

        • *food

        • apple

        • banana

  • Don’t use * or # for element expansion in GAMS

  • Don’t use * for full-line comments; only use it for the first (header) row in subset definitions

Set-defining files

  • ctt.csv: cooling technology types

    • o: once through

    • r: recirculating

    • d: dry cooled

    • p: pond cooled

    • n: no cooling (or generic placeholder)

  • sc_cat.csv: resource supply curve data categories

    • cap: power capacity available [MW]

    • cost: total supply curve cost [$/MW]

    • cost_trans: transmission (spur, point-of-interconnection, and reinforcement) component of supply curve cost [$/MW]

    • cost_cap: economies of scale, land cost, and other modifier components of supply curve cost [$/MW]

  • wst.csv: water source type

    • fsu: fresh surface water that is unappropriated

    • fsa: fresh surface water that is appropriated

    • fsl: fresh surface lake

    • fg: fresh groundwater

    • sg: brackish or saline groundwater

    • ss: saline surface water

    • ww: wastewater effluent

Special-case files

  • _aliases.csv: aliases (extra names for the same set) used in GAMS

    • Aliases of primary sets should be added here

    • Aliases of sets defined in b_inputs.gms (e.g., h→hh) should instead be defined in GAMS after the set definition

Additional files

  • RPSCat.csv: set of RPS constraint categories, including clean energy standards

  • aclike.csv: set of AC transmission capacity types

  • allt.csv: set of all potential years

  • bioclass.csv: set of bio tech classes

  • c.csv: set of renewable resource classes

  • ccsflex_cat.csv:set of flexible ccs performance parameter categories

  • climate_param.csv: set of parameters defined in climate_heuristics_finalyear

  • consumecat.csv: set of categories for consuming facility characteristics

  • csapr_cat.csv: set of CSAPR regulation categories

  • csapr_group.csv: set of CSAPR trading groups

  • e.csv: set of emission categories used in model

  • eall.csv: set of emission categories used in reporting

  • etype.csv: set of emission types (process or upstream)

  • f.csv: set of fuel types

  • flex_type.csv: set of demand flexibility types

  • fuel2tech.csv: mapping between fuel types and generations

  • fuelbin.csv: set of gas usage brackets

  • gb.csv: set of gas price bins

  • gbin.csv: set of growth bins

  • geotech.csv: set of geothermal technology categories

  • h2_st.csv: defines investments needed to store and transport H2

  • h2_stor.csv: set of H2 storage options

  • hintage_char.csv: set of characteristics available in hintage_data

  • i.csv: set of technologies

  • i_c.csv: map from technologies to their resource class (c.csv)

  • i_geotech.csv: crosswalk between an individual geothermal technology and its category

  • i_h2_ptc_gen.csv: set of technologies which can produce energy for electrolyzers claiming the hydrogen production tax credit due to their low lifecycle carbon emissions

  • i_p.csv: mapping from technologies to the products they produce

  • i_subtech.csv: set of categories for subtechs

  • i_water_nocooling.csv: set of technologies that use water, but are not differentiated by cooling tech and water source

  • lcclike.csv: set of transmission capacity types where lines are bundled with AC/DC converters

  • month.csv:

  • noretire.csv: set of technologies that will never be retired

  • notvsc.csv: set of transmission capacity types that are not VSC

  • ofstype.csv: set of offshore types used in offshore requirement constraint (eq_RPS_OFSWind)

  • ofstype_i.csv: crosswalk between ofstype and i

  • orcat.csv: set of operating reserve categories

  • ortype.csv: set of types of operating reserve constraints

  • p.csv: set of products produced

  • plantcat.csv: set of categories for plant characteristics

  • prepost.csv:

  • prescriptivelink0.csv: initial set of prescribed categories and their technologies - used in assigning prescribed builds

  • pvb_agg.csv: crosswalk between hybrid pv+battery configurations and technology options

  • pvb_config.csv: set of hybrid pv+battery configurations

  • quarter.csv:

  • sdbin.csv: set of storage durage bins

  • tg.csv: set of technology groups

  • tg_rsc_cspagg.csv: set of csp technologies that belong to the same class

  • tg_rsc_upvagg.csv: set of pv and pvb technologies that belong to the same class

  • trancap_fut_cat.csv: set of categories of near-term transmission projects that describe the likelihood of being completed

  • trtype.csv: set of transmission capacity types

  • unitspec_upgrades.csv: set of upgraded technologies that get unit-specific characteristics

  • upgrade_hintage_char.csv: set to operate over in extension of hintage_data characteristics when sw_upgrades = 1

  • w.csv: set of water withdrawal or consumption options for water techs

  • wst_climate.csv: set of water sources affected by climate change

  • wst_surface.csv:

  • yearafter.csv: set to loop over for the final year calculation

Shapefiles Input Files

  • ctus_cs_polygons.gpkg:

  • greatlakes.gpkg:

  • h2_storage_sites.gpkg:

  • offshore_zones.gpkg:

  • state_fips_codes.csv: Mapping of states to FIPS codes and postal code abbreviations

  • timezones.gpkg:

State Policies Input Files

  • acp_disallowed.csv: List of states which do not allow alternative compliance payments in place of meeting RPS or CES requirements

  • acp_prices.csv:

  • ces_fraction.csv: Annual compliance for states with a CES policy

  • forced_retirements.csv: List of regions with mandatory retirement policies for certain technologies

  • hydrofrac_policy.csv:

  • ng_crf_penalty_st.csv: Cost adjustment for NG techs in states where all NG techs must be retired by a certain year

  • nuclear_subsidies.csv:

  • offshore_req_default.csv: Default state mandates of offshore wind capacity [MW], updated in November 2025

  • oosfrac.csv: Defines the fraction of renewable and clean energy credits can be purchased from out of state (oos). Applied for RPS and CES.

  • recstyle.csv: Indication for how to apply state requirement (0 = end-use sales, 1 = bus-bar sales, 2 = generation). Default is 0.

  • rectable.csv: Table defining which states are allowed to trade RECs

  • rps_fraction.csv: Indicates what fraction of sales or generation (based on recstyle.csv) must be from renewable energy

  • storage_mandates.csv: Energy storage mandates by region

  • techs_banned_ces.csv: Indicates which technologies are not eligible to contribute to CES

  • techs_banned_imports_rps.csv:

  • techs_banned_rps.csv: Indicates which technologies are not eligible to contribute to RPS

  • techs_banned.yaml:

  • unbundled_limit_ces.csv: Limit on fraction of credits towards CES which can be purchased unbundled from other states

  • unbundled_limit_rps.csv: Limit on fraction of credits towards RPS which can be purchased unbundled from other states

Storage Input Files

Renewable Energy Supply Curve Input Files

  • CSP (concentrated solar thermal power):

    • The CSP resource classes are defined as follows:

      • 1: CF < 0.23

      • 2: 0.23 ≤ CF <0.26

      • 3: 0.26 ≤ CF

    • Site-level CSP supply curve costs are copied from the site-level supply curve costs for utility-scale photovoltaics (UPV). The mapping code is available on the ReEDS input-processing repo.

  • bio_supplycurve.csv: Regional biomass supply and costs by resource class

    • Dollar year: 2015

  • dollaryear.csv:

  • dr_shed_cap_demo_data_January_2025.csv:

  • dr_shed_cost_demo_data_January_2025.csv:

  • hyd_add_upg_cap.csv:

  • hydcap.csv:

  • hydcost.csv:

  • interconnection_land.h5:

  • interconnection_offshore.h5:

  • PSH_supply_curves_capacity_*.csv: Pumped storage hydropower supply curve capacity as used in 2025 Annual Technology Baseline. Citation: https://www.nlr.gov/gis/psh-supply-curves

    • PSH_supply_curves_capacity_10hr_ref_apr2025.csv: supply curve capacity assuming 10 hour duration and reference exclusions

    • PSH_supply_curves_capacity_10hr_wEph_apr2025.csv: supply curve capacity assuming 10 hour duration and allowing sites on ephemeral streams

    • PSH_supply_curves_capacity_10hr_wExist_apr2025.csv: supply curve capacity assuming 10 hour duration and allowing sites using existing reservoirs

    • PSH_supply_curves_capacity_10hr_wExist_wEph_apr2025.csv: supply curve capacity assuming 10 hour duration and allowing sites using existing reservoirs and on ephemeral streams

    • PSH_supply_curves_capacity_12hr_ref_apr2025.csv: supply curve capacity assuming 12 hour duration and reference exclusions

    • PSH_supply_curves_capacity_12hr_wEph_apr2025.csv: supply curve capacity assuming 12 hour duration and allowing sites on ephemeral streams

    • PSH_supply_curves_capacity_12hr_wExist_apr2025.csv: supply curve capacity assuming 12 hour duration and allowing sites using existing reservoirs

    • PSH_supply_curves_capacity_12hr_wExist_wEph_apr2025.csv: supply curve capacity assuming 12 hour duration and allowing sites using existing reservoirs and on ephemeral streams

    • PSH_supply_curves_capacity_8hr_ref_apr2025.csv: supply curve capacity assuming 8 hour duration and reference exclusions

    • PSH_supply_curves_capacity_8hr_wEph_apr2025.csv: supply curve capacity assuming 8 hour duration and allowing sites on ephemeral streams

    • PSH_supply_curves_capacity_8hr_wExist_apr2025.csv: supply curve capacity assuming 8 hour duration and allowing sites using existing reservoirs

    • PSH_supply_curves_capacity_8hr_wExist_wEph_apr2025.csv: supply curve capacity assuming 8 hour duration and allowing sites using existing reservoirs and on ephemeral streams

  • PSH_supply_curves_cost_*.csv: Pumped storage hydropower supply curve cost as used in 2025 Annual Technology Baseline. Citation: https://www.nlr.gov/gis/psh-supply-curves

    • PSH_supply_curves_cost_10hr_ref_apr2025.csv: assuming 10 hour duration and reference exclusions

    • PSH_supply_curves_cost_10hr_wEph_apr2025.csv: assuming 10 hour duration and allowing sites on ephemeral streams

    • PSH_supply_curves_cost_10hr_wExist_apr2025.csv: assuming 10 hour duration and allowing sites using existing reservoirs

    • PSH_supply_curves_cost_10hr_wExist_wEph_apr2025.csv: assuming 10 hour duration and allowing sites using existing reservoirs and on ephemeral streams

    • PSH_supply_curves_cost_12hr_ref_apr2025.csv: assuming 12 hour duration and reference exclusions

    • PSH_supply_curves_cost_12hr_wEph_apr2025.csv: assuming 12 hour duration and allowing sites on ephemeral streams

    • PSH_supply_curves_cost_12hr_wExist_apr2025.csv: assuming 12 hour duration and allowing sites using existing reservoirs

    • PSH_supply_curves_cost_12hr_wExist_wEph_apr2025.csv: assuming 12 hour duration and allowing sites using existing reservoirs and on ephemeral streams

    • PSH_supply_curves_cost_8hr_ref_apr2025.csv: assuming 8 hour duration and reference exclusions

    • PSH_supply_curves_cost_8hr_wEph_apr2025.csv: assuming 8 hour duration and allowing sites on ephemeral streams

    • PSH_supply_curves_cost_8hr_wExist_apr2025.csv: assuming 8 hour duration and allowing sites using existing reservoirs

    • PSH_supply_curves_cost_8hr_wExist_wEph_apr2025.csv: assuming 8 hour duration and allowing sites using existing reservoirs and on ephemeral streams

  • rev_paths.csv:

  • sc_point_gid_old2new.csv:

  • sitemap.h5:

  • supplycurve_egs-reference.csv:

  • supplycurve_upv-*.csv:: UPV supply curve from reV. Capacity numbers are in MW_DC and cost numbers are in $/MW_AC. Citation: https://docs.nlr.gov/docs/fy25osti/91900.pdf

    • supplycurve_upv-limited.csv: limited siting scenario

    • supplycurve_upv-open.csv: open siting scenario

    • supplycurve_upv-reference.csv: reference siting scenario

  • supplycurve_wind-ofs-*.csv: Offshore wind supply curve from reV. Citation: https://docs.nlr.gov/docs/fy25osti/91900.pdf

    • supplycurve_wind-ofs-limited.csv: limited siting scenario

    • supplycurve_wind-ofs-open.csv: open siting scenario

    • supplycurve_wind-ofs-reference.csv: reference siting scenario

  • supplycurve_wind-ons-*.csv: Land-based wind supply curve. Citation: https://docs.nlr.gov/docs/fy25osti/91900.pdf

    • supplycurve_wind-ons-limited.csv: limited siting scenario

    • supplycurve_wind-ons-open.csv: open siting scenario

    • supplycurve_wind-ons-reference.csv: reference siting scenario

  • trans_intra_cost_adder.csv:

Techs

  • tech_resourceclass.csv:

  • techs_default.csv: List of technologies to be used in the model

  • techs_subsetForTesting.csv: Short list of technologies for testing

Temporal Input Files

  • month2quarter.csv:

  • period_szn_user.csv:

  • stressperiods_user.csv:

Transmission Input Files

  • b2b_converters.csv: Power capacity and location of back-to-back (B2B) AC/DC/AC converters in the USA.

    • Power capacities are from Brinkman et al. 2020 for converters at the eastern/western interface and ERCOT 2020 for converters at the eastern/texas interface.

    • Locations are from the Open Infrastructure Map. The osm_id column gives the OpenStreetMap ID of the converter; for example, the “Miles City, MT” converter (with osm_id = 137835349) can be found at https://www.openstreetmap.org/way/137835349. This file is used to validate the interface-level B2B capacity for different spatial resolutions stored at inputs/zones/{GSw_ZoneSet}/b2b.csv.

  • conductor_(ac|dc)*.csv: Conductor and power rating assumptions for AC/DC transmission lines as a function of voltage from the MISO 2025 Transmission Cost Estimation Guide (parent page, description, data workbook)

    • conductor_ac_acss.csv and conductor_dc.csv use the conductors specified by the MISO guide. AC lines use either ACSR or ACSS conductors depending on the voltage.

    • conductor_ac_acsr.csv instead uses ACSR for all AC voltages. Ampacities for 477 kcmil (Flicker) and 795 kcmil (Drake) ACSR conductors are taken from Southwire.

  • cost_hurdle_country.csv: Hurdle rate for transmission flows [$/MWh] between USA/Canada and USA/Mexico.

  • cost_hurdle_intra.csv: Hurdle rate for transmission flows [$/MWh] between ReEDS spatial hierarchy levels.

  • dollaryear.scv: U.S. dollar year for cost-related input files

  • hvdc_existing.csv: Power capacity and start/end locations of high-voltage direct current (HVDC) lines in the USA. These lines are mapped to ReEDS zone interfaces during input processing.

  • hvdc_planned-*.csv: Individual planned transmission projects

    • Files:

      • hvdc_planned-baseline: Included in all runs

      • hvdc_planned-NTP_MT: Lines used in the “MT” scenario of the NTP Study

      • hvdc_planned-NTP_P2P: Lines used in the “P2P” scenario of the NTP Study

    • Columns:

      • year_online: If set to 0, determined from this_year and years_until_trans_longterm in inputs/scalars.csv

      • trtype: LCC or VSC (also accepts AC, but it is better to handle AC additions via the ITL calculation than to add their rated capacity directly)

      • certain: If 1, the line MUST be built at the provided MW capacity in year_online; if 0, the line MAY be built at up to the provided MW capacity starting in year_online

  • itl_config.yaml: Configuration file for interface transfer limit (ITL) calculations using the TSC model. Metadata only; not used directly in ReEDS.

  • itl_NARIS.csv: Database of initial forward/reverse AC ITLs [MW] between pairs of connected ReEDS model zones for all supported zone resolutions. Calculated using the TSC model as described by Brown et al. using nodal network data from NARIS.

    • The zone identifier is the md5 hash of the ‘,’-delimited sorted list of 5-digit FIPS codes for the counties that define the zone.

      • For example, Delaware is 3 counties, with FIPS codes 10001, 10003, and 10005. Its delimited string is 10001,10003,10005, and the md5 hash of that string is a182e260da3f30b54260bf499f0db584. (If on mac you can check it on the terminal with $ echo -n 10001,10003,10005 | md5sum.)

      • That hash is the same whether we call the zone DE, Delaware, p125, or something else.

      • The hash can be determined on the fly, but to make it easier to inspect, we record it for each supported spatial resolution in the inputs/zones/{GSw_ZoneSet}/zonehash.csv files.

    • The itl_NARIS.csv is indexed by the hashes of the two zones that define the interface (md5_from and md5_to).

      • So even though the DE and MD zones are used in many of the supported region resolutions, we only store the ITL for the DE/MD interface once, with md5_from = a182e260da3f30b54260bf499f0db584 and md5_to = f8644441280e76e07363ed18c744f98e.

      • The interfaces to expect values for are listed in the inputs/zones/{GSw_ZoneSet}/interfaces_{level}.csv files, where level can be r or transgrp.

    • The most straightforward way to read all the ITLs for a given region resolution is to run the following commands from the root of the ReEDS repo with the reeds conda environment activated:

      import reeds
## GSw_ZoneSet can be any of the supported zone resolutions listed in the `GSw_ZoneSet` row of `cases.csv`
GSw_ZoneSet = 'z90'
reeds.inputs.get_itls(GSw_ZoneSet=GSw_ZoneSet)

  • newlinks_offshore_backbone.csv: Candidate connections between offshore zones

    • Similarly formatted files for candidate connections between offshore and coastal land-based zones are found at inputs/zones/{GSw_ZoneSet}/newlinks_offshore_radial.csv

  • transmission_cost_ac_500kv_z134.h5: Example file illustrating the required format when using the transmission upgrade supply curve (TSC) method for GSw_ZoneSet = z134

    • The full method is not yet supported; when implemented, it will only be supported for a limited number of GSw_ZoneSet definitions

  • transmission_cost_distance.csv: Cost [USD2024] and distance [miles] for greenfield interzonal single-circuit transmission lines of the specified polarity (AC or DC) and voltage [kV] between nodes of the specified zone hashes

    • Node locations are described in inputs/zones/README.md and found in the inputs/zones/{GSw_ZoneSet}/zonehash.csv files

    • Least-cost paths between nodes (and integrated land/terrain-dependent costs along those paths) are determined using the reV Routing (reVRt) model

    • Technical assumptions:

      • The underlying cost model is built using the MISO 2025 Transmission Cost Estimation Guide (parent page, description, data workbook)

      • Costs for DC connections assume a 500 kV bipole architecture

      • Costs for AC connections use interface-dependent voltage assumptions. The voltage is given by the maximum voltage of an existing transmission line between the pair of zones defining the interface (using the same NARIS dataset described above), with a floor of 138 kV.

    • The MISO Transmission Cost Estimation Guide applies a 30% length adder to the straight-line distance between the endpoints of a candidate line to account for the “squiggliness” of line routes in practice. Many of the least-cost routes from the reV model have a smaller squiggliness factor. In reeds/input_processing/transmission.py (which is run at the beginning of each ReEDS run), if the representative route between two zones has a squiggliness factor less than the user-provided GSw_TransSquigglinessMin switch (with a default of 1.3, matching the MISO guide), the cost and distance of that route are scaled up by the ratio of (GSw_TransSquigglinessMin / (squiggliness of the least-cost route)), such that every interzonal interface is represented by a line at least as squiggly as GSw_TransSquigglinessMin.

  • transmission_cost_distance_lines.csv: Similar to transmission_cost_distance.csv, but for individual existing and planned/possible HVDC lines, with latitude/longitude for start/end points instead of zone hashes

    • Voltages are in kilovolts [kV]

    • Costs and lengths (and underlying routes, not included in the file) are from the reV Routing (reVRt) model and are not expected to exactly match the actual costs, lengths, or routes of existing lines

Upgrades Input Files

  • i_coolingtech_watersource_upgrades.csv: List of cooling technologies for water sources that can be upgraded.

  • i_coolingtech_watersource_upgrades_link.csv: List of cooling technologies for water sources that can be upgraded + their to, from, ctt (cooling technology type) and wst (water source type)

  • upgrade_costs_ccs_coal.csv:

  • upgrade_costs_ccs_gas.csv:

  • upgrade_link.csv: Techs that can be upgraded including the original technology, the technology it is upgrading to, and the delta.

  • upgrade_mult_atb23_ccs_*.csv: Cost adjustment over various years for upgrade technologies

    • upgrade_mult_atb23_ccs_adv.csv: advanced

    • upgrade_mult_atb23_ccs_con.csv: conservative

    • upgrade_mult_atb23_ccs_mid.csv: Mid

  • upgradelink_water.csv: Water techs that can be upgraded including the original technology, the technology it is upgrading to, and the delta

User Input Files

  • futurefiles.csv:

  • ivt_default.csv:

  • ivt_small.csv:

  • ivt_step.csv: ivt steps for endyears beyond 2050

  • mcs_distribution_rules.yaml:

  • mcs_distributions_default.yaml:

  • modeled_regions.csv: Sets of BA regions that a user can model in a run

    • Each column is a different region option and can be specified in cases using GSw_Region

  • windows_2100.csv: Window size for using window solve method to 2100

  • windows_default.csv: Window size for using window solve method

  • windows_step10.csv: Window size for beyond2050step10

  • windows_step5.csv: Window size for beyond2050step5

Valuestreams Input Files

  • var_map.csv:

Waterclimate Input Files

  • cost_cap_mult.csv:

  • cost_vom_mult.csv:

  • heat_rate_mult.csv:

  • i_coolingtech_watersource.csv:

  • i_coolingtech_watersource_link.csv:

  • tg_rsc_cspagg_tmp.csv:

  • unapp_water_sea_distr.csv:

  • wat_access_cap_cost.csv:

  • water_req_psh_10h_1_51.csv:

  • water_with_cons_rate.csv:

Model Zone Definitions

Model zones are defined by the following user-generated files:

  • county2zone.csv: Maps from counties (FIPS column) to ReEDS zones (ba column)

  • hierarchy.csv: Maps from ReEDS zones (r column) to the larger region hierarchy levels

  • b2b.csv: Back-to-back converter (B2B) capacity [MW] between model zones

  • newlinks_offshore_radial.csv: Candiate connections between offshore and coastal land-based zones

And the following automatically generated files:

  • interfaces_r.csv: Lists the pairs of zones that are connected by alternating current (AC) transmission lines

    • Specific to a nodal dataset (e.g., NARIS); changing the nodal dataset (which may entail adding lines or shifting the estimated substation locations) can change the set of connected interfaces, particularly for small or sparsely connected zones

  • interfaces_transgrp.csv: The same as interfaces_r.csv but for the larger transgrp regions defined in hierarchy.csv

  • zonehash.csv: The string identifier and node lat/lon for each model zone. The lat/lon is used for plotting, representative least-cost paths and greenfield costs, and interzonal distances for losses and TW-mile calculations.

    • If the centroid is within the polygon defining a zone, the centroid is used as the node location.

    • If the centroid is NOT within the zone polygon, the node location is the “most interior” point in the polygon (determined by iteratively inward-buffering the polygon until it disappears, then keeping the centroid of the penultimate iteration).

Notes on zone options

  • z3109: 1:1 mapping from counties to zones

    • Not all counties have high-voltage load buses, so if GSw_LoadAllocationMethod = state_lpf, some counties may have zero load.

Creating a new set of model zones

Start by copying the county2zone.csv and hierarchy.csv file for an existing set of zones to a new folder (named with a memorable name for your new set of zones) in the ReEDS_Input_Processing/zones directory.

Deciding on the zones

The ReEDS_Input_Processing/zones/make_maps.py script creates a collection of static and interactive maps based on the user-supplied county2zone.csv and hierarchy.csv files, intended to help decide on the new zone boundaries. These maps show the new zones alongside existing grid features (transmission lines and planning area boundaries from various sources) and geographic features (mountain ranges). Maps are also created to show the average load and sum of existing generation capacity by zone. The resulting static maps are saved to a .pptx file, and an interactive/zoomable map is saved to a .html file.

Generating the rest of the inputs

Once you’re happy with your zone and hierarchy level definitions, run the following processing steps:

  1. Run the TSC/analysis/make_zone_shapefiles.py script.

    • This script creates:

      • Zone shapefiles at the r and transgrp resolutions

      • Lists of interfaces at the r and transgrp resolutions that are connected by AC transmission lines

      • Maps of existing back-to-back (B2B) converters, to help decide which zonal interfaces to assign their capacity to. (Because some zone definitions put the interconnection seams quite far from their actual locations, this step is not automated and relies on user judgment.)

    • It also checks to make sure the user-specified interconnect for each zone matches the interconnection for the majority of network buses located within that zone.

  2. Calculate the AC interface transfer limits (ITLs) by running TSC/interfacemax.py

    • Include the --dbpath={path/to/ITL folder} argument and point it to the existing directory of ITLs to avoid recalculating ITLs for interfaces that already have data

  3. Calculate the length and cost of new greenfield transmission lines between zones

    • Write the new interzonal endpoint pairs using TSC/analysis/zone_links.py

      • Write pairs that require offshore routes (the links between land-based and offshore zones, new backbone links between offshore zones, and/or new exogenous offshore HVDC lines) into their own file, separate from routes that should be land-only

    • Send these files (with a note on which should allow offshore paths and which should not) to a member of the reV team who can calculate the interzonal least-cost paths using the reVRt tool

  4. Write the resulting files to the ReEDS repo by running TSC/analysis/write_for_reeds.py. This script:

    • Copies the interface_r.csv and interface_transgrp.csv files

    • Creates the zonehash.csv file

    • Rewrites the itl_NARIS.csv, transmission_cost_distance.csv, and transmission_cost_distance_lines.csv files (existing data in the file are preserved, so you should only see new rows added, typically at the bottom of the file)

  5. If the new zone set requires special processing in ReEDS, add it to the appropriate sections of inputs/zones/zoneset_config.yaml (see below)

  6. Add the new zone definition to the choices for the GSw_ZoneSet switch in cases.csv

  7. To make sure it worked (or just to read the ITLs in general), you can run import reeds and then reeds.inputs.get_itls(GSw_ZoneSet='your new zoneset name') in Python with the reeds conda environment activated.

  8. Try a ReEDS run.

    • The following checks will be performed; if any of them fail, the run will stop.

      • b2b.csv, county2zone.csv, hierarchy.csv, zonehash.csv, interfaces_r.csv, and interfaces_transgrp.csv should all be preset in the inputs/zones/{GSw_ZoneSet} folder

      • All the interfaces specified by interfaces_r.csv, and interfaces_transgrp.csv should have data in itl_NARIS.csv

      • hierarchy.csv should have all the required columns (st, interconnect, transreg, transgrp, and nercr)

Additional input files

  • county_state.csv: Mapping from 5-digit county FIPS codes to county names and 2-letter state abbreviations.

  • hierarchy_offshore.csv: Spatial hierarchy levels for offshore zones. Only used when GSw_OffshoreZones = 1. The offshore zones are not user-adjustable.

  • state_groups.csv: Spatial hierarchy levels defined by groups of states.

  • zoneset_config.yaml: Settings for zoneset-specific processing. If GSw_ZoneSet is listed beneath one of the following switch names, the described behavior is applied during input processing at the beginning of each ReEDS run using that GSw_ZoneSet.

    • drop_single_county_reinforcement_cost: Drop the network reinforcement cost (a component of the interconnection cost) for new wind/solar capacity in single-county zones

    • drop_interfaces_missing_cost: If an AC transmission interface has existing capacity but no expansion cost or representative distance, remove its existing capacity and do not allow it to be expanded. (Turned off for most zone sets because we’d rather stop the run and add the missing data. Turned on for some zone sets with single-county zones that have no high-voltage transmission lines crossing the single-county zone boundaries.)

    • reeds2pras_unitsize_unconstrain_counties: Do not specify max unit sizes using the planning reserve margin (PRM) for single-county zones during ReEDS2PRAS unit disaggregation