Existing Stock Characterization

Comprehensive power plant inventory through multi-source data integration

VerveStacks transforms fragmented global energy data into complete, spatially-resolved power plant inventories for each ISO. Each country receives its own unique existing stock characterization, combining plant-level precision from the Global Energy Monitor with statistical completeness from IRENA and EMBER datasets. Rather than relying on generic capacity assumptions, we integrate individual power plant specifications with gap-filling algorithms that preserve spatial intelligence and operational realism. This centralised process replaces what has traditionally been repeated in parallel by many research teams: each group cleaning the same source data, defining its own thresholds and mappings, and building bespoke stock representations that are difficult for others to reuse. By doing this once and documenting it fully, VerveStacks provides a common, auditable foundation that many studies and frameworks can share.

ISO-Specific Characterization: Every country’s existing stock reflects its unique energy infrastructure, technology mix, and operational constraints. No universal template could capture both Germany’s complex renewable transition and India’s coal-dominated system with rapid solar expansion.

Methodology Overview

The existing stock characterization follows a systematic three-stage process applied individually to each ISO:

1. Plant-Level Foundation: Process individual power plant data from Global Energy Monitor database

2. Statistical Reconciliation: Fill capacity gaps using IRENA/EMBER national statistics with spatial distribution

3. Techno-Economic Enhancement: Assign technology costs, efficiencies, performance parameters, and operational constraints

Stage 1: Plant-Level Foundation

Individual Power Plant Processing

VerveStacks begins with the most comprehensive global power plant database available - the Global Energy Monitor’s integrated power facilities dataset containing 151,000+ individual power plants worldwide with precise locations, capacities, fuel types, commissioning years, and operational status.

Core Data Processing

Dynamic Capacity Threshold System

VerveStacks implements a sophisticated capacity threshold system that adapts to each country’s power system scale and complexity. Rather than using a static 100 MW threshold, the system employs fuel-specific thresholds optimized for model performance and appropriate detail.

Threshold Strategy by Fuel Type

Fuel Group	Threshold Logic	Rationale
Fossil Fuels (Coal, Gas, Oil)	Combined threshold targeting ≤200 units	Fossil plants create ~3x more model variables due to CCS retrofit options
Nuclear	Always 0.0 (track all)	Critical infrastructure requiring individual modeling
Hydro + Geothermal	Combined threshold targeting ≤100 units	Grid flexibility and storage characteristics
Solar/Wind	Individual thresholds, minimum 200 MW	Utility-scale focus with economic viability considerations
Other Renewables	Default 50 MW threshold	Standard renewable technology handling

System-Scale Examples:

Threshold Variations by Country Scale

Country	Fossil (MW)	Hydro (MW)	Solar (MW)	Wind (MW)
China (Large)	1000	1000	500	360
USA (Large)	860	190	200	260
Germany (Medium)	110	10	200	200
Small Countries	10	10	200	200

Configuration Source:

Thresholds are automatically generated from the Global Energy Monitor database using the count_power_plants_by_iso.py script, which analyzes each country’s power plant distribution and calculates optimal thresholds to maintain model complexity targets while preserving appropriate spatial and technical detail.

Note

The complete threshold configuration is stored in assumptions/iso_fuel_capacity_thresholds.csv with metadata in assumptions/iso_fuel_capacity_thresholds_metadata.yaml. This system ensures that small island nations receive detailed modeling while large continental systems maintain computational efficiency.

Technology Mapping Logic

The system uses a sophisticated fuel type mapping that handles the complexity of real-world power plant classifications:

def custom_fuel_mapping(plant_record):
    """Transform GEM fuel types to standardized model categories"""
    if plant_record['Type'] != 'oil/gas':
        return 'hydro' if plant_record['Type'] == 'hydropower' else plant_record['Type']
    else:
        fuel_detail = str(plant_record['Fuel']) if not_null(plant_record['Fuel']) else ''
        return 'oil' if fuel_detail.lower().startswith('fossil liquids:') else 'gas'

Plant Aggregation Strategy

Plants are intelligently aggregated based on capacity thresholds and spatial proximity:

• Large Plants (Above Fuel-Specific Threshold): Tracked individually with full spatial and technical detail

• Small Plants (Below Fuel-Specific Threshold): Aggregated by technology and region to reduce model complexity

• Mothballed Plants: Tracked separately with ‘ep_m_’ prefix for potential reactivation scenarios

• Dynamic Thresholds: Automatically calculated per country and fuel type to optimize model performance

Threshold Generation Methodology

The capacity thresholds are generated through systematic analysis of each country’s power plant portfolio:

Generation Process

Step	Process
Data Analysis	Load operational plants ≥10 MW from GEM database
Fuel Grouping	Separate plants into fossil, hydro-geo, solar-wind, and other categories
Threshold Calculation	For each fuel group, find capacity of Nth largest plant (N = target count)
Rounding	Round thresholds to nearest 10 MW for cleaner values
Validation	Ensure thresholds meet model complexity targets
Export	Generate CSV configuration file with metadata

Target Complexity Limits: - Fossil Units: ≤200 per country (due to CCS retrofit complexity) - Hydro+Geothermal: ≤100 per country (grid flexibility modeling) - Solar/Wind: ≤100 each per country (utility-scale focus) - Minimum Solar/Wind: 200 MW (economic viability threshold)

Quality Assurance: - All thresholds validated against actual plant counts - Metadata includes generation parameters and methodology - Notes column indicates if targets were exceeded (e.g., F207_HG30)

Stage 2: Data Reconciliation

Gap-Filling Methodology

Even the comprehensive GEM database has coverage gaps, particularly for smaller renewable installations and distributed generation. VerveStacks reconciles plant-level data with authoritative national statistics to ensure complete capacity accounting.

IRENA Renewable Capacity Reconciliation

IRENA Gap-Filling Process

Step	Methodology
Baseline Comparison	Compare IRENA 2022 capacity vs. cumulative GEM capacity (≤2022)
Gap Identification	Calculate missing capacity: IRENA_2022 - GEM_cumulative
Spatial Distribution	Distribute gaps across REZ zones weighted by resource quality (70%) and potential (30%)
Plant Creation	Generate realistic “Aggregated Plant - IRENA Gap” records with spatial attributes

EMBER Thermal Capacity Reconciliation

Spatial Intelligence Preservation

Critical to VerveStacks’ spatial modeling capability is ensuring that gap-filled capacity maintains geographic precision:

• Renewable Gaps: Assigned to specific REZ grid cells based on resource quality rankings

• Thermal Gaps: Assigned to transmission buses with existing thermal plant clusters

• Commodity Mapping: All gap plants receive proper spatial commodities (e.g., elc_spv-DEU_0042 not elc_spv-DEU)

Stage 3: Techno-Economic Enhancement

Technology Cost Assignment

Every power plant receives comprehensive techno-economic parameters sourced from VerveStacks’ curated technology database, with regional adjustments and size-based multipliers.

Key Innovation: VerveStacks implements two sophisticated systems: 1) Dynamic fuel-specific capacity thresholds that adapt to each country’s power system scale (10-1000 MW), ensuring appropriate model detail from small islands to continental grids, and 2) WEO region-level inheritance system for technology costs and efficiencies. This dual approach optimizes both model complexity and technical accuracy.

Cost Parameter Integration

Techno-Economic Parameter Sources

Parameter	Source File	Application Logic
CAPEX ($/kW)	ep_technoeconomic_assumptions.xlsx	Technology + size class + regional multiplier
Fixed O&M ($/kW-yr)	ep_technoeconomic_assumptions.xlsx	Annual fixed operating costs
Variable O&M ($/MWh)	ep_technoeconomic_assumptions.xlsx	Per-MWh operating costs
Thermal Efficiency (%)	VS_mappings.xlsx (thermal_eff sheet)	Fuel-to-electricity conversion efficiency
Capacity Factor (%)	Resource data or historical utilization	Annual availability factor

Regional Cost Adjustments

Technology costs are adjusted for local economic conditions using regional multipliers:

SELECT
    base_capex * regional_multiplier * size_multiplier AS adjusted_capex,
    base_fixom * regional_multiplier AS adjusted_fixom,
    base_varom * regional_multiplier AS adjusted_varom
FROM technology_costs T1
JOIN regional_multipliers T2 ON T1.region = T2.region
JOIN size_multipliers T3 ON T1.size_class = T3.size_class

Technology Cost Inheritance Formula

SELECT
    weo_base_cost * regional_multiplier * size_multiplier * vintage_factor AS final_cost,
    weo_base_efficiency * regional_efficiency_factor * vintage_degradation AS final_efficiency
FROM weo_technology_assumptions W
JOIN ep_regionmap R ON W.region = R.region
JOIN regional_multipliers M ON R.region = M.region
WHERE R.iso = '{country_iso_code}'

Life Extension Cost Integration

VerveStacks incorporates sophisticated life extension cost modeling that captures the economic reality of aging thermal infrastructure. As power plants approach the end of their design life, operators face critical investment decisions: retire the facility or invest in major refurbishments to extend operational life.

The life extension cost methodology follows the approach established in EPA’s Integrated Planning Model (IPM) version 6, which recognizes that thermal power plants can operate beyond their original design life through substantial capital investments. These costs reflect the reality that aging infrastructure requires major overhauls of critical components including boilers, turbines, generators, and environmental control systems.

Life Extension Cost Assumptions (IPM v6 Methodology)

Technology Type	Design Life (years)	Life Extension Cost ($/kW)
Biomass Steam	40	$253
Coal Steam	40	$203
Combined Cycle Gas Turbine	30	$82
Combustion Turbine (Peaker)	30	$242
Internal Combustion Engine	30	$226
Oil/Gas Steam	40	$174
Integrated Gasification Combined Cycle	40	$258
Landfill Gas	20	$135

Economic Logic and Implementation

The life extension cost framework operates on the principle that thermal power plants face discrete investment decisions at the end of their design life. Plants that have reached their original lifespan can continue operating only if operators invest in major refurbishments that essentially reset the plant’s operational capability.

Key characteristics of the life extension cost methodology:

• Threshold-Based Application: Life extension costs are triggered only when plants operate beyond their technology-specific design life

• Technology-Specific Costs: Reflect the varying complexity and capital intensity of different thermal technologies

• One-Time Investment: Life extension costs represent a discrete capital investment, not ongoing maintenance

• Operational Realism: Captures the actual decision-making process utilities face with aging assets

Technology-Specific Considerations

The variation in life extension costs across technologies reflects fundamental differences in plant complexity and refurbishment requirements:

• Combined Cycle Gas Turbines ($82/kW): Lower costs reflect modular design and standardized components

• Coal Steam Plants ($203/kW): Moderate costs for mature technology with established refurbishment practices

• IGCC Plants ($258/kW): Highest costs due to complex gasification systems and limited operational experience

• Combustion Turbines ($242/kW): High costs despite simple design due to intensive operational duty cycles

This approach ensures that fossil plant retirement decisions reflect realistic economics rather than arbitrary assumptions, enabling more accurate modeling of energy transition pathways and stranded asset risks.

Unit Commitment Parameter Integration

Thermal power plants also receive detailed operational flexibility parameters that capture their constraints - critical for high-renewable energy system modeling:

Unit Commitment Parameters Added to Thermal Plants

Parameter	Units	Description
Min Stable Factor	% of capacity	Minimum operating level when online
Min Up Time	Hours	Minimum continuous operation period
Min Down Time	Hours	Minimum offline period between starts
Max Ramp Up Rate	%/hour	Maximum power increase rate
Max Ramp Down Rate	%/hour	Maximum power decrease rate
Startup Time	Hours	Time required to reach minimum stable level
Startup Cost	$/MW	Cost per MW of capacity started
Shutdown Cost	$/MW	Cost per MW of capacity shut down

The system uses sophisticated pattern matching to assign appropriate unit commitment characteristics based on technology type (coal, gas CCGT, gas OCGT, nuclear) and plant size class (Small/Medium/Large/XLarge).

Complete Assumption Tables

VS_mappings.xlsx Reference Tables

The following tables provide complete transparency about all data transformations and assumptions used in existing stock characterization:

Data Sources Documentation

VerveStacks maintains comprehensive data source documentation within the VS_mappings.xlsx file to ensure complete transparency and reproducibility:

Primary Data Sources and Update Frequencies

Data Source	Content & Coverage	Update Frequency	Quality
Global Energy Monitor (GEM)	Individual power plants worldwide (151,000+ facilities)	Monthly updates	⭐⭐⭐⭐⭐
IRENA Statistics	National renewable capacity & generation (2000-2022)	Annual updates	⭐⭐⭐⭐⭐
EMBER Climate	Global electricity data by country & fuel (2000-2022)	Annual updates	⭐⭐⭐⭐⭐
World Energy Outlook (WEO)	Technology costs & performance by region	Annual updates	⭐⭐⭐⭐⭐
UNSD Energy Statistics	UN energy balances & trade flows	Annual updates	⭐⭐⭐⭐
REZoning Database	50x50km renewable resource potential grid	Static analysis	⭐⭐⭐⭐
SARAH/ERA5 Weather	Hourly solar/wind profiles (2013 reference year)	Historical data	⭐⭐⭐⭐⭐
EPA CCS Retrofit	Carbon capture retrofit potential & costs	Periodic updates	⭐⭐⭐⭐

Data Integration Methodology

Source Integration Hierarchy

Priority	Data Source	Usage Logic
1st Priority	GEM Plant Database	Individual plant specifications for capacity ≥100 MW
2nd Priority	IRENA Statistics	Fill renewable capacity gaps vs. GEM cumulative
3rd Priority	EMBER Statistics	Fill thermal capacity gaps vs. GEM cumulative
4th Priority	WEO Assumptions	Technology costs & performance parameters
5th Priority	Default Values	Conservative fallbacks for missing parameters

GEM Technology Mapping (gem_techmap)

Technology Standardization Rules

GEM Type	GEM Technology	Model Fuel	Model Name
coal	subcritical	coal	coal_sub
coal	supercritical	coal	coal_super
coal	ultra-supercritical	coal	coal_ultra
gas	combined cycle	gas	gas_ccgt
gas	gas turbine	gas	gas_ocgt
nuclear	pwr	nuclear	nuclear_pwr
nuclear	bwr	nuclear	nuclear_bwr
solar	PV	solar	solar_pv_fix
wind	Onshore	windon	wind_on
wind	Offshore	windoff	wind_off
hydropower	run-of-river	hydro	hydro_ror
hydropower	conventional storage	hydro	hydro_res

IRENA-EMBER Type Mapping (irena_ember_typemap)

Technology Classification Tables

WEO Power Generation Technologies (weo_pg_techs)

Thermal Technology Definitions

Technology	Include	Description
coal_sub	Y	Subcritical coal steam plants
coal_super	Y	Supercritical coal steam plants
coal_ultra	Y	Ultra-supercritical coal steam plants
gas_ccgt	Y	Natural gas combined cycle gas turbines
gas_ocgt	Y	Natural gas open cycle gas turbines
nuclear_pwr	Y	Pressurized water reactor nuclear plants
nuclear_bwr	Y	Boiling water reactor nuclear plants
oil_st	Y	Oil-fired steam turbines
bio_st	Y	Biomass steam turbines

Storage Technologies (storage_techs)

Demand Technologies (dem_techs)

Demand Sector Definitions

Technology	Description
dem_res	Residential electricity demand
dem_com	Commercial electricity demand
dem_ind	Industrial electricity demand
dem_tra	Transport electrification demand

Techno-Economic Assumptions Reference

Technology Assumptions Database Structure

VerveStacks maintains comprehensive technology assumption gleaned from the world’s leading energy institutions:

Core Technology Parameters

Techno-Economic Parameter Categories

Parameter Type	Content & Data Sources
Base Technology Costs	Base CAPEX, FIXOM, VAROM by technology | Sources: IEA WEO 2024, NREL Annual Technology Baseline, IRENA Global Energy Transformation studies
Scale Economy Factors	Plant size adjustment multipliers | Sources: Engineering cost curves, industry benchmarks, EIA capital cost studies
Regional Cost Adjustments	Labor, materials, market condition multipliers | Sources: World Bank construction cost indices, ILO wage statistics, regional energy market analysis
Regional Mapping	ISO-to-WEO region parameter inheritance | Sources: IEA World Energy Outlook regional classifications, economic development indicators
Efficiency by Vintage	Technology efficiency with degradation curves | Sources: EPRI power plant performance database, manufacturer specifications, operational data
Technology Lifetimes	Asset lifetime assumptions by fuel and vintage | Sources: IEA technology roadmaps, EPRI technical assessments, regulatory depreciation schedules

Advanced Operational Parameters

Unit Commitment Parameter Categories

Parameter Type	Content & Data Sources
Unit Commitment Data	Min up/down times, ramp rates, startup costs by technology and size class | Sources: NERC generator performance standards, utility operational data, IEEE power system flexibility studies
Technology Mapping	Pattern matching for UC parameter assignment | Sources: Power plant classification standards, VEDA/TIMES technology definitions, operational constraint databases
Documentation	Data source methodology and validation notes | Sources: Compilation methodology, data quality assessments, validation procedures

WEO Technology Cost Integration

WEO 2024 Power Generation Assumptions Integration

WEO Sheet	VerveStacks Application
Renewables	Solar PV, onshore/offshore wind cost projections by region
Nuclear	Nuclear power plant costs and performance parameters
Gas	CCGT, OCGT technology costs and efficiency assumptions
Coal	Coal plant costs with subcritical/supercritical/ultra-supercritical variants
Fossil fuels equipped with CCUS	CCS retrofit costs and performance penalties

Authoritative Data Sources Summary

The technology assumption files represent a curated compilation from the world’s leading energy institutions:

Primary Institutional Sources

Institution	Contribution to VerveStacks Technology Database
International Energy Agency (IEA)	WEO 2024 regional technology costs, efficiency assumptions, market projections
National Renewable Energy Laboratory (NREL)	Annual Technology Baseline (ATB) cost projections, performance parameters
International Renewable Energy Agency (IRENA)	Global Energy Transformation cost studies, renewable technology benchmarks
Electric Power Research Institute (EPRI)	Power plant performance databases, operational constraint parameters, flexibility studies
North American Electric Reliability Corporation (NERC)	Generator performance standards, grid reliability requirements, operational limits
World Bank Group	Construction cost indices, regional economic multipliers, infrastructure cost benchmarks
International Labour Organization (ILO)	Regional wage statistics, labor cost adjustments, economic development indicators
U.S. Energy Information Administration (EIA)	Capital cost studies, technology performance data, market analysis
Institute of Electrical and Electronics Engineers (IEEE)	Power system flexibility studies, technical standards, operational best practices

Data Quality and Validation Standards

Complete Technology Assumption Tables

The following tables provide the complete parameter values used in VerveStacks existing stock characterization:

Base Technology Efficiency

Technology Efficiency by Commissioning Year (by size class)

size	model_name	2020	2010	2000	1990	…	1920
> 300MW	ep_bioenergy	0.35	0.33	0.31	0.285		0.2
> 300MW	ep_coal_CFB	0.385	0.375	0.365	0.35		0.28
> 300MW	ep_coal_IGCC	0.435	0.42	0.41	0.4		0.32
> 300MW	ep_coal_subcritical	0.36	0.36	0.36	0.36		0.225
> 300MW	ep_coal_subcritical_CCS	0.305	0.3	0.3	0.3		0.24
> 300MW	ep_coal_supercritical	0.425	0.415	0.395	0.39		0.3
> 300MW	ep_coal_supercritical_CCS	0.355	0.35	0.34	0.33		0.24
> 300MW	ep_coal_ultra-supercritical	0.445	0.435	0.42	0.41		0.31
> 300MW	ep_coal_ultra-supercritical_CCS	0.385	0.375	0.36	0.35		0.25
> 300MW	ep_gas_internal_combustion	0.36	0.355	0.345	0.33		0.265
> 300MW	ep_oil_internal_combustion	0.355	0.35	0.34	0.325		0.26
> 300MW	ep_gas_combined_cycle	0.6	0.58	0.545	0.525		0.36
> 300MW	ep_oil_combined_cycle	0.58	0.56	0.52	0.5		0.34
> 300MW	ep_gas_gas_turbine	0.33	0.32	0.3	0.275		0.2
> 300MW	ep_oil_gas_turbine	0.32	0.31	0.29	0.265		0.19
> 300MW	ep_gas_steam_turbine	0.365	0.365	0.36	0.35		0.25
> 300MW	ep_oil_steam_turbine	0.355	0.355	0.35	0.34		0.24
50-300MW	ep_bioenergy	0.33	0.31	0.29	0.265		0.19
50-300MW	ep_coal_CFB	0.365	0.355	0.345	0.33		0.26
50-300MW	ep_coal_IGCC	0.415	0.4	0.39	0.38		0.3
50-300MW	ep_coal_subcritical	0.34	0.34	0.34	0.34		0.215
50-300MW	ep_coal_subcritical_CCS	0.285	0.28	0.28	0.28		0.22
50-300MW	ep_coal_supercritical	0.405	0.395	0.375	0.37		0.28
50-300MW	ep_coal_supercritical_CCS	0.335	0.33	0.32	0.31		0.22
50-300MW	ep_coal_ultra-supercritical	0.425	0.415	0.4	0.39		0.29
50-300MW	ep_coal_ultra-supercritical_CCS	0.365	0.355	0.34	0.33		0.23
50-300MW	ep_gas_internal_combustion	0.36	0.355	0.345	0.33		0.265
50-300MW	ep_oil_internal_combustion	0.355	0.35	0.34	0.325		0.26
50-300MW	ep_gas_combined_cycle	0.58	0.56	0.52	0.5		0.34
50-300MW	ep_oil_combined_cycle	0.56	0.54	0.5	0.48		0.32
50-300MW	ep_gas_gas_turbine	0.32	0.31	0.29	0.265		0.19
50-300MW	ep_oil_gas_turbine	0.31	0.3	0.28	0.255		0.18
50-300MW	ep_gas_steam_turbine	0.345	0.345	0.34	0.33		0.24
50-300MW	ep_oil_steam_turbine	0.335	0.335	0.33	0.32		0.23
<50MW	ep_bioenergy	0.32	0.3	0.28	0.255		0.18
<50MW	ep_coal_CFB	0.355	0.345	0.335	0.32		0.25
<50MW	ep_coal_IGCC	0.395	0.38	0.37	0.36		0.28
<50MW	ep_coal_subcritical	0.33	0.33	0.33	0.33		0.205
<50MW	ep_coal_subcritical_CCS	0.265	0.26	0.26	0.26		0.2
<50MW	ep_coal_supercritical	0.385	0.375	0.355	0.35		0.26
<50MW	ep_coal_supercritical_CCS	0.315	0.31	0.3	0.29		0.2
<50MW	ep_coal_ultra-supercritical	0.405	0.395	0.38	0.37		0.27
<50MW	ep_coal_ultra-supercritical_CCS	0.345	0.335	0.32	0.31		0.21
<50MW	ep_gas_internal_combustion	0.36	0.355	0.345	0.33		0.265
<50MW	ep_oil_internal_combustion	0.355	0.35	0.34	0.325		0.26
<50MW	ep_gas_combined_cycle	0.56	0.54	0.5	0.48		0.32
<50MW	ep_oil_combined_cycle	0.54	0.52	0.48	0.46		0.3
<50MW	ep_gas_gas_turbine	0.31	0.3	0.28	0.255		0.18
<50MW	ep_oil_gas_turbine	0.3	0.29	0.27	0.245		0.17
<50MW	ep_gas_steam_turbine	0.335	0.335	0.33	0.32		0.23
<50MW	ep_oil_steam_turbine	0.325	0.325	0.32	0.31		0.22

Base Technology Costs

Technology Cost Parameters (CAPEX $/kW, FIXOM $/kW-yr, VAROM $/MWh)

model_name	capex	fixom	varom
ep_bioenergy	3500	130	8.0
ep_coal_CFB	2300	65	6.0
ep_coal_IGCC	4200	120	6.0
ep_coal_subcritical	2000	60	5.0
ep_coal_subcritical_CCS	3600	110	7.0
ep_coal_supercritical	2200	65	5.5
ep_coal_supercritical_CCS	3700	115	7.5
ep_coal_ultra-supercritical	2400	70	5.5
ep_coal_ultra-supercritical_CCS	3900	120	7.5
ep_geothermal	4200	130	5.0
ep_hydro_dam	2500	50	2.0
ep_hydro_ror	2200	55	2.5
ep_hydro_ps	1800	40	1.5
ep_nuclear	6500	150	4.0
ep_nuclear_smr	8500	180	4.0
ep_gas_internal_combustion	1100	45	6.5
ep_oil_internal_combustion	1000	40	6.5
ep_gas_combined_cycle	1000	30	3.0
ep_oil_combined_cycle	1100	35	3.5
ep_gas_gas_turbine	800	20	4.0
ep_oil_gas_turbine	900	22	4.5
ep_gas_steam_turbine	1200	40	4.5
ep_oil_steam_turbine	1300	45	5.0
ep_solar_PV	900	15	0.0
ep_solar_thermal	5500	65	2.0
ep_wind_offshore	4000	100	3.0
ep_wind_onshore	1600	40	2.0

Size-Based Cost Multipliers

Scale Economy Adjustments by Plant Size

size	capex	fixom	varom
>300MW	1	1	1
50-300MW	1.15	1.1	1.1
<50MW	1.35	1.3	1.2

Regional Cost Multipliers

Regional Economic Adjustments by WEO Region

region	efficiency	capex	fixom	varom	Notes
European Union	1.03	1.1	1.1	1.05	High-cost, high-tech region; strict efficiency standards
United States	1.0	1.0	1.0	1.0	Baseline for many international benchmarks
Japan	1.02	1.2	1.15	1.05	High labor costs; small plant sites, advanced tech
Russia	0.92	0.8	0.85	0.9	Legacy stock + low labor costs; older designs
China	1.0	0.75	0.8	0.8	Local EPC firms dominate; ultra-supercritical widespread
India	0.96	0.7	0.75	0.8	Domestic tech, moderate efficiency gains post-2000
Middle East	0.93	0.9	0.95	0.95	Harsh ambient conditions lower GT efficiency
Africa	0.9	0.85	0.9	0.9	Smaller plants, older tech, fewer advanced designs
Brazil	0.96	0.85	0.9	0.9	CCGT fleet newer, other tech mid-efficiency

Technology Lifetimes

Asset Lifetime Assumptions by Technology

model_name	life
ep_bioenergy	50
ep_coal_CFB	50
ep_coal_IGCC	50
ep_coal_subcritical	50
ep_coal_subcritical_CCS	50
ep_coal_supercritical	50
ep_coal_supercritical_CCS	50
ep_coal_ultra-supercritical	50
ep_coal_ultra-supercritical_CCS	50
ep_geothermal	50
ep_hydro_dam	100
ep_hydro_ror	100
ep_hydro_ps	100
ep_nuclear	50
ep_nuclear_smr	50
ep_gas_internal_combustion	40
ep_oil_internal_combustion	40
ep_gas_combined_cycle	50
ep_oil_combined_cycle	50
ep_gas_gas_turbine	40
ep_oil_gas_turbine	40
ep_gas_steam_turbine	50
ep_oil_steam_turbine	50
ep_solar_PV	30
ep_solar_thermal	30
ep_wind_offshore	30
ep_wind_onshore	30

How Plant Lifetimes Are Assigned

Each existing plant is assigned a lifetime using a two-priority rule:

1. Known retirement date (highest priority). When the source data records a specific retirement year for an individually-tracked plant, the lifetime is set to exactly that span — commissioning year to retirement year. This reflects scheduled closures, regulatory mandates, or operator decisions already captured in the underlying plant database.

2. No known retirement date (default). The lifetime is set to whichever is longer: the technology design life from the table above, or the number of years remaining between the plant’s commissioning year and 2055. The 2055 floor is a deliberate modeling convention — it ensures that no existing plant drops out of the model before the end of the planning horizon, avoiding artificial capacity cliffs in mid-period years.

The practical effect of the 2055 floor is most visible for legacy plants commissioned in the 1960s and 1970s: a coal unit from 1971 would have exhausted its 50-year design life well before 2055, so it receives a lifetime of 84 years (2055 − 1971) instead. Modern plants commissioned after 2005 are typically governed by the design life rather than the floor.

Illustrative Lifetime Outcomes

Plant	COD	Design life	2055 − COD	Assigned lifetime
Medupi U6 (supercritical coal)	2015	50 yrs	40 yrs	50 yrs (design life)
Kusile U3 (supercritical coal)	2021	50 yrs	34 yrs	50 yrs (design life)
Legacy subcritical unit	1971	50 yrs	84 yrs	84 yrs (2055 floor)
Plant with known closure in 2035	2000	—	—	35 yrs (explicit retirement)

Note

If a coal phase-out policy or early retirement scenario is being modeled, the correct mechanism is to record a retirement year against the affected plants in the source database. This takes priority over both the design life and the 2055 floor, and is the only way to force retirement before the horizon ends.

CCS Retrofit Lifetime

Carbon capture retrofit options are treated as discrete 20-year investments, regardless of the age or technology type of the host plant. This follows the approach used in the EPA Integrated Planning Model (IPM v6), which frames post-combustion capture additions as a bounded capital commitment rather than a permanent life extension of the underlying asset.

Unit Commitment Parameters

Operational Constraints by Technology and Size Class

technology	Size Class	Min Stable Factor (%)	Min Up Time (h)	Min Down Time (h)	Max Ramp Up (%/h)	Max Ramp Down (%/h)	Startup Time (h)	Startup Cost ($/MW)	Shutdown Cost ($/MW)	decomissioning_cost
OCGT (Peaker)	<50 MW	25	0.5	0.5	120	120	0.5	10	2	50
OCGT (Peaker)	50-200 MW	30	1.0	1.0	100	100	0.75	15	4	40
OCGT (Peaker)	>200 MW	35	1.5	1.5	80	80	1.25	20	6	30
CCGT	<300 MW	50	2.0	2.0	60	60	1.75	25	6	60
CCGT	>300 MW	55	4.0	4.0	45	45	3.0	40	9	50
Gas/Oil Steam	<200 MW	35	3.0	3.0	40	40	2.0	25	5	70
Gas/Oil Steam	>200 MW	40	5.0	5.0	30	30	3.0	35	7	60
Diesel	<20 MW	30	1.0	1.0	120	120	0.4	10	2	40
Diesel	>20 MW	35	2.0	2.0	60	60	1.5	20	6	30
Subcritical Coal	<300 MW	38	5.0	5.0	30	30	5.0	40	10	100
Subcritical Coal	>300 MW	42	7.0	7.0	20	20	8.0	60	14	90
Supercritical Coal	<500 MW	45	6.0	6.0	25	25	6.0	50	12	110
Supercritical Coal	>500 MW	48	8.0	8.0	15	15	9.0	70	16	100
Nuclear	All	70	24.0	24.0	5	5	48.0	100	50	500

Unit Commitment Technology Mapping

Pattern Matching Rules for UC Parameter Assignment

technology	model_name
CCGT	ep_gas_combined_cycle,ep_oil_combined_cycle
Diesel	ep_gas_internal_combustion,ep_oil_internal_combustion
Gas/Oil Steam	ep_gas_steam_turbine,ep_oil_steam_turbine
Nuclear	ep_nuclear,ep_nuclear_smr
OCGT (Peaker)	ep_gas_gas_turbine,ep_oil_gas_turbine
Subcritical Coal	ep_bioenergy,ep_coal_CFB,ep_coal_IGCC,ep_coal_subcritical,ep_coal_subcritical_CCS
Supercritical Coal	ep_coal_supercritical,ep_coal_supercritical_CCS,ep_coal_ultra-supercritical,ep_coal_ultra-supercritical_CCS

Technology Classification Tables

GEM Technology Mapping

GEM Fuel-Technology Combinations to VerveStacks Model Names

Type	Technology	model_fuel	model_tech	model_name
bioenergy	bioenergy	bioenergy		ep_bioenergy
coal	CFB	coal	CFB	ep_coal_CFB
coal	IGCC	coal	IGCC	ep_coal_IGCC
coal	subcritical	coal	subcritical	ep_coal_subcritical
coal	subcritical/CCS	coal	subcritical_CCS	ep_coal_subcritical_CCS
coal	supercritical	coal	supercritical	ep_coal_supercritical
coal	supercritical/CCS	coal	supercritical_CCS	ep_coal_supercritical_CCS
coal	ultra-supercritical	coal	ultra-supercritical	ep_coal_ultra-supercritical
coal	ultra-supercritical/CCS	coal	ultra-supercritical_CCS	ep_coal_ultra-supercritical_CCS
coal	unknown	coal	subcritical	ep_coal_subcritical
coal	unknown/CCS	coal	supercritical_CCS	ep_coal_supercritical_CCS
geothermal	advanced geothermal systems (AGS)	geothermal		ep_geothermal
geothermal	binary cycle	geothermal		ep_geothermal
geothermal	dry steam	geothermal		ep_geothermal
geothermal	enhanced geothermal systems (EGS)	geothermal		ep_geothermal
geothermal	flash steam - double	geothermal		ep_geothermal
geothermal	flash steam - single	geothermal		ep_geothermal
geothermal	flash steam - triple	geothermal		ep_geothermal
geothermal	flash steam - unknown	geothermal		ep_geothermal
geothermal	geopressured geothermal system (GGS)	geothermal		ep_geothermal
geothermal	unknown type	geothermal		ep_geothermal
hydropower	conventional and pumped storage	hydro	dam	ep_hydro_dam
hydropower	conventional and run-of-river	hydro	ror	ep_hydro_ror
hydropower	conventional storage	hydro	dam	ep_hydro_dam
hydropower	pumped storage	hydro	ps	ep_hydro_ps
hydropower	run-of-river	hydro	ror	ep_hydro_ror
hydropower	unknown	hydro	ror	ep_hydro_ror
nuclear	boiling water reactor	nuclear		ep_nuclear
nuclear	fast breeder reactor	nuclear		ep_nuclear
nuclear	fast neutron reactor	nuclear		ep_nuclear
nuclear	gas-cooled reactor	nuclear		ep_nuclear
nuclear	high temperature gas reactor	nuclear		ep_nuclear
nuclear	light water graphite reactor	nuclear		ep_nuclear
nuclear	liquid-metal-cooled fast reactor	nuclear		ep_nuclear
nuclear	molten salt reactor	nuclear		ep_nuclear
nuclear	pressurized heavy water reactor	nuclear		ep_nuclear
nuclear	pressurized water reactor	nuclear		ep_nuclear
nuclear	small modular reactor	nuclear	smr	ep_nuclear_smr
nuclear	unknown	nuclear		ep_nuclear
oil/gas	ICCC	gas	internal_combustion	ep_gas_internal_combustion
oil/gas	ICCC	oil	internal_combustion	ep_oil_internal_combustion
oil/gas	IGCC	gas	combined_cycle	ep_gas_combined_cycle
oil/gas	ISCC	gas	combined_cycle	ep_gas_combined_cycle
oil/gas	combined cycle	gas	combined_cycle	ep_gas_combined_cycle
oil/gas	combined cycle	oil	combined_cycle	ep_oil_combined_cycle
oil/gas	gas turbine	gas	gas_turbine	ep_gas_gas_turbine
oil/gas	gas turbine	oil	gas_turbine	ep_oil_gas_turbine
oil/gas	internal combustion	gas	internal_combustion	ep_gas_internal_combustion
oil/gas	internal combustion	oil	internal_combustion	ep_oil_internal_combustion
oil/gas	steam turbine	gas	steam_turbine	ep_gas_steam_turbine
oil/gas	steam turbine	oil	steam_turbine	ep_oil_steam_turbine
oil/gas	unknown	gas	steam_turbine	ep_gas_steam_turbine
oil/gas	unknown	oil	steam_turbine	ep_oil_steam_turbine
solar	Assumed PV	solar	pv	ep_solar_pv
solar	PV	solar	pv	ep_solar_pv
solar	Solar Thermal	solar	thermal	ep_solar_thermal
wind	Offshore floating	windoff	wind	ep_windoff_wind
wind	Offshore hard mount	windoff	wind	ep_windoff_wind
wind	Offshore mount unknown	windoff	wind	ep_windoff_wind
wind	Onshore	windon	wind	ep_windon_wind
wind	Unknown	windon	wind	ep_windon_wind

IRENA-EMBER Type Mapping

Statistical Data Source Harmonization

Source	Type	model_fuel	model_tech
EMBER	Bioenergy	bioenergy	bioenergy
EMBER	Other Renewables	bioenergy	bioenergy
EMBER	Coal	coal	coal
EMBER	Gas	gas	gas
EMBER	Other Fossil	oil	oil
EMBER	Hydro	hydro	hydro
EMBER	Nuclear	nuclear	nuclear
EMBER	Solar	solar	solar
EMBER	Wind	windon	windon
IRENA	Biogas	bioenergy	bioenergy
IRENA	Liquid biofuels	bioenergy	bioenergy
IRENA	Renewable municipal waste	bioenergy	bioenergy
IRENA	Solid biofuels	bioenergy	bioenergy
IRENA	Coal and peat	coal	coal
IRENA	Fossil fuels n.e.s.	oil	oil
IRENA	Natural gas	gas	gas
IRENA	Oil	oil	oil
IRENA	Other non-renewable energy	oil	oil
IRENA	Geothermal energy	geothermal	geothermal
IRENA	Mixed Hydro Plants	hydro	hydro
IRENA	Pumped storage	hydro	ps
IRENA	Renewable hydropower	hydro	hydro
IRENA	Nuclear	nuclear	nuclear
IRENA	Solar photovoltaic	solar	solar pv
IRENA	Solar thermal energy	solar	csp
IRENA	Offshore wind energy	windoff	offshore
IRENA	Onshore wind energy	windon	onshore

WEO Power Generation Technologies

IEA WEO Technology Classifications

tech
Bioenergy + CCUS
Bioenergy - Large scale unit
CCGT
CCGT + CCS
Coal + CCS
Gas turbine
IGCC
IGCC + CCS
Nuclear large
Oxyfuel + CCS
Steam Coal - SUBCRITICAL
Steam Coal - SUPERCRITICAL
Steam Coal - ULTRASUPERCRITICAL

Storage Technologies

Storage Technology Definitions

tech	description
EN_STG8hbNREL	8-hour utility storage battery - NREL
EN_STG4hbNREL	4-hour utility storage battery - NREL

Data Quality and Validation

Coverage Metrics

VerveStacks provides complete transparency about data coverage and gap-filling:

Validation Rules

Error Handling

Data Quality Exceptions

Exception Type	Resolution Strategy
Missing Efficiency	Default to technology-typical efficiency (e.g., 35% for coal)
Invalid Start Year	Construction plants: 2028, Others: 2015 (conservative estimate)
Unmapped Technology	Flag for manual review, exclude from automated processing
Missing Spatial Data	Assign to country-level commodity with quality warning

This comprehensive methodology ensures that every ISO receives a complete, spatially-intelligent, and operationally-realistic characterization of its existing power generation fleet - the foundation for all subsequent energy system modeling and scenario analysis.

Note

This methodology has been validated across 100+ countries and territories, from small island systems to continental grids, consistently delivering plant-level precision with statistical completeness for robust energy system optimization modeling.