# Indonesia Source: https://docs.transitionzero.org/countries/indonesia This section provides specific details about Scenario Builder's Indonesia model # Model scope * Base year: 2023 * End year: 2050 * Spatial resolution: * National (1-node) * Regional (7-nodes island-wise): * Java (IDN-JW) * Sumatra (ISN-SM) * Bali and Nusa Tenggara (IDN-NU) * Kalimantan (IDN-KA) * Sulawesi (IDN-SL) * Maluku (IDN-ML) * Papua (IDN-PP) * Temporal resolution: * Low resolution (24-hourly and yearly) - 1 Timeslice * Medium resolution (3-hourly and 3-monthly) - 32 Timeslices # Data sourcing table | **Input variable** | **Data source** | **Data standard** | | :------------------------------------------------------------------------------------------ | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :---------------- | | Demand profiles - current | Shape of the demand at the national-level synthetically generated from a historical year (2015) | Silver | | Demand profiles - future | Shape of the demand profile does not change in the future. | Bronze | | Annual demand - **current** | CASE Indonesia ([https://caseforsea.org/indonesia/](https://caseforsea.org/indonesia/)) | Gold | | Annual demand - **future** | CASE Indonesia ([https://caseforsea.org/indonesia/](https://caseforsea.org/indonesia/) | Gold | | Renewable profiles | TransitionZero in-house methodology ([https://www.transitionzero.org/insights/from-vision-to-voltage-with-tz-apg](https://www.transitionzero.org/insights/from-vision-to-voltage-with-tz-apg)) | Bronze | | Renewable potentials | TransitionZero in-house methodology ([https://www.transitionzero.org/insights/from-vision-to-voltage-with-tz-apg](https://www.transitionzero.org/insights/from-vision-to-voltage-with-tz-apg)) | Bronze | | Power plant data - **current** (location, capacity, fuel, technology, start year, end year) | Data served as initial and planned capacities from 2023 to 2050. This data is validated and updated based on the Global Energy Monitor assets. | Gold | | Power plant data - future | Data served as initial and planned capacities from 2023 to 2050. This data is validated and updated based on the Global Energy Monitor assets. | Gold | | Technology costs - **current** | [Danish Energy Agency and The Ministry of Energy and Mineral Resources of Indonesia](https://gatrik.esdm.go.id/assets/uploads/download_index/files/c4d42-technology-data-for-the-indonesian-power-sector-2024-annoteret-af-kb-.pdf) | Gold | | Technology costs - **future** | [Danish Energy Agency and The Ministry of Energy and Mineral Resources of Indonesia](https://gatrik.esdm.go.id/assets/uploads/download_index/files/c4d42-technology-data-for-the-indonesian-power-sector-2024-annoteret-af-kb-.pdf) | Gold | | Commodity costs - **current** | [TransitionZero in-house methodology](https://www.transitionzero.org/insights/from-vision-to-voltage-with-tz-apg) | Silver | | Commodity costs - **future** | [TransitionZero in-house methodology](https://www.transitionzero.org/insights/from-vision-to-voltage-with-tz-apg) | Silver | | Interconnector data - current (location, capacity, technology, start year, end year) | [Government of Indonesia (RUKN 2025-2060)](https://gatrik.esdm.go.id/assets/uploads/download_index/files/28dd4-rukn.pdf) | Gold | | Interconnector data - future | [Government of Indonesia (RUKN 2025-2060)](https://gatrik.esdm.go.id/assets/uploads/download_index/files/28dd4-rukn.pdf) | Gold | | Efficiency / Losses | [Danish Energy Agency and The Ministry of Energy and Mineral Resources of Indonesia](https://gatrik.esdm.go.id/assets/uploads/download_index/files/c4d42-technology-data-for-the-indonesian-power-sector-2024-annoteret-af-kb-.pdf) | Gold | | Minimum generation level | Default, technology-specific | Bronze | | Planned outages / Availability | [NREL ATB 2024](https://atb.nrel.gov/electricity/2024/index) | Bronze | | Discount rate | Default (5%) | Bronze | | WACC | IEA Cost of Capital Observatory | Bronze | # Example country data CSVs Coming soon! # Maldives Source: https://docs.transitionzero.org/countries/maldives This section provides specific details about Scenario Builder's model of the Maldives. # Model scope * Base year: 2024 * End year: 2050 * Spatial resolution: Regionalised (3 nodes) * Greater Male' Region (ML) * Other inhabited islands (OT) * Resorts/industrial/agricultural islands (RI) * Temporal resolution: * Low resolution (24-hourly and yearly) - 1 Timeslice * Medium resolution (3-hourly and 3-monthly) - 32 Timeslices # Data sourcing table | **Input variable** | **Data source** | **Data standard** | | :------------------------------------------------------------------------------------------ | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :---------------- | | Demand profiles - current | Profile is generated using a profile from a proxy country (Indonesia) | Bronze | | Demand profiles - future | Shape of the demand profile does not change in the future. | Bronze | | Annual demand - **current** | Annual data from [Roadmap for the Energy Sector 2024-2033](https://www.environment.gov.mv/v2/wp-content/files/publications/20241107-pub-energy-roadmap-maldives-2024-2033-.pdf) zoned location in the highest resolution. Available for base year (2024) | Gold | | Annual demand - **future** | Annual data from [Roadmap for the Energy Sector 2024-2033](https://www.environment.gov.mv/v2/wp-content/files/publications/20241107-pub-energy-roadmap-maldives-2024-2033-.pdf) zoned location in the highest resolution. Available for base year (2024) | Gold | | Renewable profiles | [renewables.ninja](http://renewables.ninja) | Bronze | | Renewable potentials | [REZoning](https://rezoning.energydata.info/) | Bronze | | Power plant data - **current** (location, capacity, fuel, technology, start year, end year) | Top-down data at the regional level from [Roadmap for the Energy Sector 2024-2033](https://www.environment.gov.mv/v2/wp-content/files/publications/20241107-pub-energy-roadmap-maldives-2024-2033-.pdf) | Silver | | Power plant data - future | Top-down data at the regional level from [Roadmap for the Energy Sector 2024-2033](https://www.environment.gov.mv/v2/wp-content/files/publications/20241107-pub-energy-roadmap-maldives-2024-2033-.pdf) | Silver | | Technology costs - **current** | [NREL ATB 2024](https://atb.nrel.gov/electricity/2024/index) | Bronze | | Technology costs - **future** | [NREL ATB 2024](https://atb.nrel.gov/electricity/2024/index) | Bronze | | Commodity costs - **current** | Cost of electricity generation by power plant type from [Roadmap for the Energy Sector 2024-2033](https://www.environment.gov.mv/v2/wp-content/files/publications/20241107-pub-energy-roadmap-maldives-2024-2033-.pdf) | Gold | | Commodity costs - **future** | Commodity costs assumed to stay constant | Bronze | | Fuel reserves | \[N/A] | \[N/A] | | Fuel processing capacity - **current** | \[N/A] | \[N/A] | | Emissions targets | \[N/A] | \[N/A] | | RE targets | [Roadmap for the Energy Sector 2024-2033](https://www.environment.gov.mv/v2/wp-content/files/publications/20241107-pub-energy-roadmap-maldives-2024-2033-.pdf) (33% by 2028) | Gold | | Energy efficiency targets | \[N/A] | \[N/A] | | Interconnector data - current (location, capacity, technology, start year, end year) | \[N/A] | \[N/A] | | Interconnector data - future | \[N/A] | \[N/A] | | Efficiency / Losses | [NREL ATB 2024](https://atb.nrel.gov/electricity/2024/index) | Bronze | | Minimum generation level | Default, technology-specific | Bronze | | Planned outages / Availability | [NREL ATB 2024](https://atb.nrel.gov/electricity/2024/index) | Bronze | | Discount rate | Default (5%) | Bronze | | WACC | IEA Cost of Capital Observatory | Bronze | # Example country data CSVs Coming soon! # Samoa Source: https://docs.transitionzero.org/countries/samoa This section provides specific details about Scenario Builder's model of Samoa. # Model scope * Base year: 2024 * End year: 2050 * Spatial resolution: Regionalised (2 nodes) * Upolu (UP) * Savai’i (SA) * Temporal resolution: * Low resolution (24-hourly and yearly) - 1 Timeslice * Medium resolution (3-hourly and 3-monthly) - 32 Timeslices # Data sourcing table | **Input variable** | **Data source** | **Data standard** | | :------------------------------------------------------------------------------------------ | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :---------------- | | Demand profiles - current | Profile is generated using a profile from a proxy country (Indonesia). | Bronze | | Demand profiles - future | Shape of the demand profile does not change in the future. | Bronze | | Annual demand - **current** | Annual data from [Samoa Energy Sector Plan FY2023/24-FY2027/28](https://cdn.prod.website-files.com/67a155f272e2c5aeb2caf892/67f728e7cacda1d4c3bdb90a_Samoa-Energy-Sector-Plan.pdf) zoned location in the highest resolution. Available for base year (2024) | Gold | | Annual demand - **future** | Annual data from [Samoa Energy Sector Plan FY2023/24-FY2027/28](https://cdn.prod.website-files.com/67a155f272e2c5aeb2caf892/67f728e7cacda1d4c3bdb90a_Samoa-Energy-Sector-Plan.pdf) zoned location in the highest resolution. Available for base year (2024) | Gold | | Renewable profiles | [renewables.ninja](http://renewables.ninja) | Bronze | | Renewable potentials | [REZoning](https://rezoning.energydata.info/) | Bronze | | Power plant data - **current** (location, capacity, fuel, technology, start year, end year) | Top-down data at the regional level from [Samoa Energy Sector Plan FY2023/24-FY2027/28](https://cdn.prod.website-files.com/67a155f272e2c5aeb2caf892/67f728e7cacda1d4c3bdb90a_Samoa-Energy-Sector-Plan.pdf) | Silver | | Power plant data - future | Top-down data at the regional level from [Samoa Energy Sector Plan FY2023/24-FY2027/28](https://cdn.prod.website-files.com/67a155f272e2c5aeb2caf892/67f728e7cacda1d4c3bdb90a_Samoa-Energy-Sector-Plan.pdf) | Silver | | Technology costs - **current** | [NREL ATB 2024](https://atb.nrel.gov/electricity/2024/index) | Bronze | | Technology costs - **future** | [NREL ATB 2024](https://atb.nrel.gov/electricity/2024/index) | Bronze | | Commodity costs - **current** | Cost of electricity generation by power plant type from [Samoa Energy Sector Plan FY2023/24-FY2027/28](https://cdn.prod.website-files.com/67a155f272e2c5aeb2caf892/67f728e7cacda1d4c3bdb90a_Samoa-Energy-Sector-Plan.pdf) | Gold | | Commodity costs - **future** | Constant prices | Bronze | | Fuel reserves | \[N/A] | \[N/A] | | Fuel processing capacity - **current** | \[N/A] | \[N/A] | | Emissions targets | \[N/A] | \[N/A] | | RE targets | [Samoa Energy Sector Plan FY2023/24-FY2027/28](https://cdn.prod.website-files.com/67a155f272e2c5aeb2caf892/67f728e7cacda1d4c3bdb90a_Samoa-Energy-Sector-Plan.pdf) (70% by 2031) | Gold | | Energy efficiency targets | \[N/A] | \[N/A] | | Interconnector data - current (location, capacity, technology, start year, end year) | \[N/A] | \[N/A] | | Interconnector data - future | \[N/A] | \[N/A] | | Efficiency / Losses | [NREL ATB 2024](https://atb.nrel.gov/electricity/2024/index) | Bronze | | Minimum generation level | Default, technology-specific | Bronze | | Planned outages / Availability | [NREL ATB 2024](https://atb.nrel.gov/electricity/2024/index) | Bronze | | Discount rate | Default (5%) | Bronze | | WACC | IEA Cost of Capital Observatory | Bronze | # Example country data CSVs Coming soon! # Get in touch with TransitionZero Source: https://docs.transitionzero.org/getintouch If you have any questions, please reach out to support@transitionzero.org To submit feedback about Scenario Builder, please use the in-app form. # Commodity (fuel) prices Source: https://docs.transitionzero.org/methodology/commodity-prices This page discusses commodity prices, an essential data input for energy system models. The cost of fuels used for energy production - primarily coal, natural gas, and crude oil for oil-fired power plants - is a key driver of electricity generation costs. Commodity prices are influenced by global supply and demand, geopolitical events, transportation costs, regional infrastructure, mining costs, and environmental regulations. # Typical Value Ranges These values are illustrative, and can vary significantly | **Fuel** | **Typical Value Range (USD, 2023 Constant)** | **Key Factors** | | :---------- | :------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------- | | Coal | \$50 - \$150 / ton | Quality, mining/transportation costs, regional demand, environmental regulations. | | Natural Gas | \$3 - \$15 / MMBtu | Regional pipeline infrastructure, LNG trade, production costs, geopolitical events, demand from heating/cooling/electricity generation. | | Crude Oil | \$50 - \$150 / barrel | Global supply/demand, OPEC policies, geopolitical events, economic growth, technological advancements, pace of energy transition. | | Biomass | Highly variable (e.g., \$30 - \$100 / ton) | Type of biomass, local availability, processing, transportation, sustainability certification. | Data sourcing standards for commodity prices are detailed below. # Data sourcing standards – commodity prices | **Input Variable** | **Model Type** | **Gold Standard ('Best in Class')** | **Silver Standard ('Good')** | **Bronze Standard ('Publishable')** | | :------------------------ | :------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------ | | Commodity costs - current | CE & UD | Data owner data at the commodity exchange or asset level (e.g., mine-mouth coal contracts). Regionally specific import prices. | IEA commodity prices (e.g., from WEO) by region, by year. | World Bank Commodity Outlook (includes recent past): global value per year per commodity. | | Commodity costs - future | CE | Detailed, scenario-specific projections from specialized energy agencies or robust internal analysis considering resource depletion, technology, and policy. | Projections from reputable public sources (e.g., IEA WEO scenarios). | Constant prices from the present, or simple trend extrapolation. *(Note: Future price methodologies are continuously refined.)* | # Demand profiles Source: https://docs.transitionzero.org/methodology/demand-profiles What are electricity demand profiles, and how do they impact model results? Electricity demand profiles represent how electricity consumption varies over time. Accurate profiles are critical for capturing the operational behaviour of the system and ensuring a reliable, cost-effective energy supply. Key characteristics include: * **Temporal resolution:** Profiles may be hourly, sub-hourly (e.g. every 15 or 5 minutes), daily, weekly, or seasonal * **Shape:** * Daily peaks: typically in the morning and evening. * Seasonal variation: higher demand in summer (cooling) or winter (heating). * Base load: the steady, minimum level of demand. Factors influencing demand: * **Weather conditions** (temperature, humidity, solar irradiance). * **Economic activity** (industrial and commercial usage). * **Human behaviour** (residential patterns). * **Daylight hours** (affecting lighting needs). * **Holidays and weekends.** Data sourcing standards for current and future electricity demand profiles are detailed below. # Data sourcing standards – demand profiles | **Input variable** | **Model type** | **Gold** **standard**
(’best in class’) | **Silver** **standard**
(‘Good’) | \*\*Bronze standard \*\*
(‘Publishable’) | | :------------------------ | :------------- | :-------------------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------- | | Demand profiles - current | CE & UD | Hourly data from data owner by sector and zoned location in the highest resolution. Available for reference year e.g. (2023) | Shape of the demand at the national-level synthetically generated from any year (e.g. 2015 when our reference is 2023) | Profile is generated using a profile from a proxy country. | | Demand profiles - future | CE & UD | Shape of the demand profile changes based on weather (linked) and degree of electrification. Synthetic methodology applied (TBD). | Shape of the demand profile changes based on weather year only (linked). | Shape of the demand profile does not change in the future. | # Demand Source: https://docs.transitionzero.org/methodology/demand-projections This section outlines the methodology and input data used to develop demand projections within Scenario Builder. The primary focus is on projecting power consumption (demand) at each model node. Projections include national-level data for 153 countries and sub-national data for 10 additional countries. All data and results are presented on an annual timescale. For countries represented as national nodes, the methodology begins with a trend analysis of **Gross Domestic Product (GDP)** growth and population growth data. This analysis identifies the growth line's shape (linear, exponential, or polynomial), which is then used for regression. Datasets are analysed to quantify their influence on historical demand data. This forms the basis for assigning weighting factors to each dataset, which are applied in regression. A similar method is applied to countries with sub-national zones. The focus shifts to regional data, specifically Gross Regional Domestic Product (GRDP), regional population statistics, and regional electricity demand. Where granular power demand data is unavailable, a proportional scaling approach is used based on the ratio of GRDP to GDP within each node. Each node is assessed individually, considering power usage and characteristics within the context of its unique macroeconomic conditions. The top-down approach used in this demand analysis may not capture granular ground-level data with precision. This is due to constraints in obtaining comprehensive socio-economic data for in-depth behavioural analysis that impacts power utilisation. However, this top-down approach is considered an effective way to illustrate power demand growth at the national level. Power demand projection relies on three primary input variables: GDP, population, and historical electricity demand data. ## GDP and population data The World Bank provides data from 1990 to 2022 on GDP (Purchasing Power Parity in 2017 international USD) and population. For future growth projections (2025-2100), IIASA’s Shared Socioeconomic Pathways (SSP2) dataset is used. The IMF’s GDP growth forecast up to 2025 bridges the gap between the World Bank and SSP2 data. ## Historical electricity demand data Data is sourced from EMBER’s open dataset and validated against IEA’s energy statistics data. The table below summarises our data sources for GDP, population, and historical electricity demand. | **Input Variable** | **Data Source** | **Data Period** | **Unit of Measurement** | | :------------------------------- | :------------------------------------------------------------------------- | :-------------- | :----------------------------------- | | GDP | [World Bank](https://data.worldbank.org/indicator/) | 1990 - 2022 | PPP in USD 2017 | | Population | [World Bank](https://data.worldbank.org/indicator/) | 1990 - 2022 | Total population | | Short-term GDP growth projection | [IMF](https://www.imf.org/external/datamapper/datasets/WEO) | 2022 - 2024 | Annual percent change | | Long-term GDP projection | [IIASA SSP2](https://data.ece.iiasa.ac.at/ssp/) | 2025 - 2100 | PPP in USD 2017 | | Historical electricity demand | [EMBER](https://ember-climate.org/data-catalogue/yearly-electricity-data/) | 2000-2022 | Terawatt hours of electricity demand | For countries represented at the sub-national level, data is collected from the official websites and documents of each country as listed in the table below: | **Country/Region** | **Sources** | | :----------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Canada | [National Statistical Agency](https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=3610022201) | | USA | [The Bureau of Economic Analysis](https://apps.bea.gov/regional/downloadzip.cfm), [Energy Information Administration](https://www.eia.gov/state/seds/seds-data-complete.php) | | Russia | [Federal State Statistic Service](https://eng.rosstat.gov.ru/) | | India | [Ministry of Statistics and Program Implementation](https://www.mospi.gov.in/), [Central Electricity Authority](https://cea.nic.in/dashboard/?lang=en) | | China | [National Bureau of Statistics of China](http://www.stats.gov.cn/english/) | | Indonesia | [RUPTL 2021-2030](https://web.pln.co.id/statics/uploads/2021/10/ruptl-2021-2030.pdf), [Visi Indonesia 2045](https://perpustakaan.bappenas.go.id/e-library/file_upload/koleksi/migrasi-data-publikasi/file/Policy_Paper/Ringkasan%20Eksekutif%20Visi%20Indonesia%202045_Final.pdf) | | Vietnam | [Vietnam Statistical Yearbook](https://www.gso.gov.vn/en/data-and-statistics/2023/06/statistical-yearbook-of-2022/), [Eight National Power Development Plan (PDP8) 2021-2030](https://vanban.chinhphu.vn/?pageid=27160) | | Malaysia | [The Department of Statistics Malaysia](https://www.dosm.gov.my/portal-main/landingv2), [Malaysia Energy Statistics Handbook](https://www.st.gov.my/en/contents/files/download/116/Malaysia_Energy_Statistics_Handbook_20201.pdf) | | Philippines | [Philippine Statistics Authority](https://openstat.psa.gov.ph/), [Philippine Energy Plan 2020-2040](https://www.doe.gov.ph/sites/default/files/pdf/pep/PEP%202022-2040%20Final%20eCopy_20220819.pdf) | | Thailand | [Office of The National Economic and Social Development Council](https://www.nesdc.go.th/nesdb_en/main.php?filename=national_account), [Electricity Statistic of Energy Policy and Planning Office](https://www.eppo.go.th/index.php/en/en-energystatistics/electricity-statistic) | # Existing assets Source: https://docs.transitionzero.org/methodology/existing-assets This page discusses how Scenario Builder uses existing asset data, an essential starting input for energy system modelling. Every model begins with a snapshot of the current energy system, including all existing generation (power plants), storage (e.g. batteries), and transmission (major power lines) assets. For each asset, the following information is required: * **Capacity (MW):** The capacity of operating assets in the model’s start year, referred to as ‘Residual Capacity’ in the TZ-OSeMOSYS framework. Assets that are under construction or committed are included in the future system. * **Location:** Latitude and longitude coordinates. * **Year of construction:** The commissioning year of the asset. * **Operational life:** The expected number of years the asset remains in service. Data sourcing standards for existing assets is detailed below. # Data sourcing standards – existing assets | **Input variable** | **Model type** | **Gold Standard ('Best in Class')** | **Silver Standard ('Good')** | **Bronze Standard ('Publishable')** | | :---------------------------------------------------------------------------------------------- | :------------- | :------------------------------------------------------------------------------ | :------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------- | | Power plant data - current (location, capacity, fuel, technology, start year, operational life) | CE & UD | Asset-by-asset validation and gap-filling by TransitionZero’s country analysts. | Asset-by-asset validation. | Global datasets. | | Interconnector data - current (location, capacity, technology, start year, operational life) | CE & UD | Asset-by-asset validation and gap-filling by TransitionZero’s country analysts. | [Global Transmission database](https://zenodo.org/records/10870602) with subnational or grid zone gap-filling. | Country-level copper plate from [Global Transmission database](https://zenodo.org/records/10870602), national or subnational level (grid-zone). | # Financial inputs Source: https://docs.transitionzero.org/methodology/financial-inputs This page discusses how financial data inputs influence a model Financial parameters significantly influence investment decisions in the model. # Discount rate (Social Discount Rate - SDR) The discount rate used here is the social discount rate (SDR), which reflects society’s valuation of costs and benefits over time. Unlike private discount rates such as the weighted average cost of capital (WACC), which reflect investor returns, the SDR takes a broader view, incorporating long-term social and environmental impacts. It is used to convert future costs and benefits into present-day values, enabling consistent comparison of scenarios with different timelines. The choice of SDR can strongly influence model results: a lower SDR places more value on future outcomes and tends to favour long-term investments, while a higher SDR gives more weight to short-term benefits. Data sourcing standards for discount rates are detailed below. ## Data sourcing standards – discount rates | **Input Variable** | **Model Type** | **Gold Standard ('Best in Class')** | **Silver Standard ('Good')** | **Bronze Standard ('Publishable')** | | :----------------- | :------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------- | :----------------------------------------------------------------------------- | :------------------------------------------------------------------ | | Discount rate | CE & UD | Country-specific SDR based on government guidelines or extensive literature review, subjected to sensitivity analysis for transparency (e.g., below 3%). | Domestic "Overton window" (common range in literature for the country/region). | Typical range for region/scenario (e.g., 3-5%), or a default value. | # Weighted Average Cost of Capital (WACC) WACC represents the average rate of return a project must offer to satisfy its investors, covering both debt and equity, and reflects the perceived investment risk for a given technology or project. It is used as the discount rate when calculating the net present value (NPV) of lifetime project costs. Differences in WACC between technologies can significantly affect their relative competitiveness. It is expressed as a percentage and is often held constant over the model period for simplicity, though in reality it may vary by technology and over time. Data sourcing standards for WACC are detailed below. ## Data sourcing standards – WACC | **Input Variable** | **Model Type** | **Gold Standard ('Best in Class')** | **Silver Standard ('Good')** | **Bronze Standard ('Publishable')** | | :----------------- | :------------- | :--------------------------------------------------------------------- | :--------------------------- | :-------------------------------------------------------------------------------- | | WACC | CE & UD | Country-level, technology-specific data from investors and developers. | Central bank discount rate. | IEA Cost of Capital Observatory (regional/country averages), or generic defaults. | # Future assets Source: https://docs.transitionzero.org/methodology/future-assets This page describes input data for future assets, or what could be built in the future. Models also consider new assets that could be built in the future, including: # Committed / Under Construction Assets Projects already underway or officially planned, which are typically forced into the model under certain scenarios (e.g. “Current Policies” or “Net Zero”). # Potential Assets Candidate power plants, storage, or transmission lines that the model may choose to build if they are economically optimal. Data for planned and candidate future assets is similar to existing assets: * **Capacity (MW)** * **Location (potential sites or region/node)** * **Planned year of operation (for committed assets)** * **Operational life (Years)** * **Technology type** * **Fuel type** Data sourcing standards for future assets is detailed below. # Data sourcing standards – future assets | **Input Variable** | **Model Type** | **Gold Standard ('Best in Class')** | **Silver Standard ('Good')** | **Bronze Standard ('Publishable')** | | :--------------------------- | :------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------ | :------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------- | | Power plant data - future | CE & UD | Project-by-project validation and gap-filling by TransitionZero’s country analysts for committed projects. Detailed resource assessment for candidates. | Project-by-project validation. | Open access data trackers based on plant status. Generalised technology lifetimes. Average data for candidates. | | Interconnector data - future | CE & UD | Asset-by-asset validation from government data with gap-filling and market adjustments by by TransitionZero’s country analysts. | [Global Transmission database](https://zenodo.org/records/10870602) with subnational or grid zone gap-filling. | Country-level copper plate from [Global Transmission database](https://zenodo.org/records/10870602), national or subnational level (grid-zone). | # Model methodology Source: https://docs.transitionzero.org/methodology/model-methodology This section outlines the modelling methodology behind Scenario Builder. Our goal is to make energy system modelling accessible by being transparent about the data, assumptions, and calculations we use - even for those without a technical background. This document is a key part of your learning journey. It's a living resource, updated as the platform evolves and shaped by valuable user feedback. # What is Scenario Builder for? Scenario Builder is a no-code energy system modelling tool designed to help you understand and explore potential future electricity systems. You can use it to: * Analyze different energy scenarios and pathways. * Assess the impact of various technology choices and policy directions. * Understand the costs and benefits of different energy futures. * Explore how existing energy infrastructure might evolve. Our models focus on the electricity sector, aiming to meet demand in the most cost-effective way while accounting for technical, economic, and policy constraints. # How do the models work? Building an energy system model involves two main aspects: 1. **Representing the current energy system:** Understanding the existing power plants, grids, and energy demand as it is today. 2. **Projecting the future energy system:** Making informed assumptions about how technology costs, demand, and policies might change, and how the energy system could evolve in response. The models use optimisation techniques to identify the least-cost way to meet electricity demand—either over several decades for long-term planning (known as Capacity Expansion or CE modelling), or in high temporal detail for a specific year (known as Unit Dispatch or UD modelling). The objective is typically to minimise the Net Present Value (NPV) of total system costs, including investment in new infrastructure and ongoing operating costs. Scenario Builder currently supports building Capacity Expansion (CE) models only, with Unit Dispatch (UD) features and functionality scheduled for release in Q4. # Key components of model methodology This document outlines the data and assumptions that underpin our energy system models: * **Spatial, temporal, and sectoral scope**: Defines the geography, time horizon, and energy types covered. * **Data inputs** * Existing and future assets: Describes current and potential power plants, storage, and transmission infrastructure. * Techno-economic inputs: Includes costs, efficiencies, and operational characteristics of each technology. * Commodity (fuel) prices: Covers prices for fuels such as natural gas and coal. * Financial inputs: Includes discount rates and other parameters affecting investment decisions. * Renewable energy profiles and potentials: Captures the availability and characteristics of solar, wind, and other renewable resources. * Demand data: Projects future electricity demand and its variation over time. * Policy information: Reflects regulations, targets, and other policy considerations. # Data quality standards (medallion system) Throughout this document, we use a Gold, Silver, and Bronze medallion system to indicate the source and quality of data, ensuring transparency in how models are built. The choice of data standard can impact the precision and granularity of the model results. Higher standards generally lead to more robust and reliable outputs. Bronze is considered the minimum standard for publication. Each input variable is assigned a medallion level based on the quality of its source. | **Medallion** | **Description** | | :----------------------- | :---------------------------------------------------------------------------------------------------------------------------------------- | | Gold (“*Best in Class*”) | Typically uses highly verified, country-specific, and often asset-level data. Provides the highest accuracy. | | Silver (“*Good*”) | Uses good quality national or regional estimates, often from reputable international sources or validated datasets. | | Bronze (“*Publishable*”) | Uses more generalised or readily available global/regional data, which is acceptable for initial assessments but may have less precision. | We also apply the following key to each input: * **Fact:** A historical or current value with a single source of truth, typically from an asset owner. * **Assumption:** A value based on accepted benchmarks or norms (e.g. cost of capital). * **Projection:** A future value derived from calculations, models, or scenario assumptions. The **‘Country Models**’ section clearly indicates the data standard used for all inputs in each modelled country. # Model resolution Source: https://docs.transitionzero.org/methodology/model-resolution This page explains how spatial (geographic) and temporal (time) resolution impacts modelling results. # Spatial resolution (geographical detail) Models can represent different geographical areas. ## **National level** Models the entire country as a single point or “node.” ## **Sub-national or nodal level** Divides the country or region into multiple interconnected zones or “nodes” (e.g., states, islands, or distinct grid areas), enabling the modelling of electricity flows between them. Scenario Builder currently lets you select from predefined regions or countries, with the level of spatial detail clearly described for each.
# Temporal resolution (time detail) The models consider how energy supply and demand change over time. ## Modelling horizon Typically spans from a base year (e.g., 2023 or 2024) to a future year (e.g., 2050 or 2060). ## Time slices To manage computational complexity - especially in long-term Capacity Expansion models - a year is often represented using a set of “representative days” or “time slices.” For example, a typical setup might use 32 time slices: 4 seasons × 2 day types (weekday/weekend) × 4 parts of the day (morning peak, midday, evening peak, overnight). ## Hourly detail For Unit Dispatch models focusing on a single year's operation, full hourly (or even sub-hourly) resolution is often used to capture variability more accurately. Scenario Builder currently allows you to choose between low (1 time slice) and medium (32 time slices) temporal resolution, depending on your analysis needs. # Policies and targets Source: https://docs.transitionzero.org/methodology/policies-information Coming soon! # Renewable energy potentials Source: https://docs.transitionzero.org/methodology/renewable-potentials What are RE potentials and how do they impact modelling results? Renewable potentials for solar photovoltaic (PV), onshore wind, and offshore wind were calculated across 201 model nodes using an area-based approach. For each node, the total available area for installing each technology was estimated, and this area was multiplied by an assumed installable capacity per unit area to determine the technical potential. For solar PV and onshore wind, the area analysis excluded unsuitable land types such as protected areas defined by the [World Database on Protected Areas (WDPA)](https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA). Usable land was then estimated based on assumed percentages of each land type within the node. For example, 3% of cropland was assumed usable for onshore wind, based on a European area analysis by Scholz (2012). These assumptions may not be universally applicable due to differing socio-political contexts across regions. Protected area data was sourced from the WDPA, and land cover data was drawn from the [Copernicus Global Land Service (CGLS)](https://land.copernicus.eu/global/products/lcv), which provides a global 100 m resolution land cover map with 23 discrete classes. The table below maps CGLS land class codes to specific land types used in the analysis. | **Land Type** | **Solar Usable Fraction (%)** | **Onshore Wind Usable Fraction (%)** | **CGLS Land Classes** | | :---------------------------- | :---------------------------- | :----------------------------------- | :---------------------------------------------------- | | Protected Areas | 0 | 0 | N/A | | Urban | 2.4 | 0 | 50 | | Cropland | 0.03 | 3 | 40 | | Forest | 0 | 3 | 111, 112, 113, 114, 115, 116, 121, 122, 124, 125, 126 | | Shrubs and vegetation | 0.03 | 3 | 20, 30 | | Bare | 33 | 33 | 60 | | Water, wetland, moss and ice. | 0 | 0 | 70, 80, 90, 100, 200 | For offshore wind, assumptions were applied to restrict turbine siting within each node’s exclusive economic zone (EEZ). Turbines were only considered feasible if located at least 5 km offshore and in waters shallower than 300 m. EEZ boundaries were obtained from the [Marine Regions](https://www.marineregions.org/) World EEZ v12 dataset, and bathymetry data from  [GEBCO](https://www.gebco.net/data_and_products/gridded_bathymetry_data/). Installable capacity assumptions were based on [Scholz et al. (2012)](https://elib.uni-stuttgart.de/server/api/core/bitstreams/4c31d528-3f6e-496c-bcd2-23b8aee870fc/content): onshore wind – 10.42 MW/km², offshore wind – 10.42 MW/km², and solar PV – 141.9 MW/km². Hydropower potentials were sourced from [Hoes et al. (2017)](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0171844), an online database of potential hydropower locations. These were aggregated at the node level, excluding any sites located within protected areas. Data sourcing standards for RE potentials are detailed below. # Data sourcing standards – RE potentials | **Input Variable** | **Model Type** | **Gold Standard ('Best in Class')** | **Silver Standard ('Good')** | **Bronze Standard ('Publishable')** | | :-------------------------- | :------------- | :----------------------------------------------------------------------------------------------------------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------- | :-------------------------------------------------------------------- | | Renewable energy potentials | CE & UD | Land and policy constraints (e.g. local, state and national land use regulations). Based on a peer reviewed methodology. | Land and policy constraints (e.g. local, state and national land use regulations) bands broken up by RE quality. Based on a peer reviewed methodology. | Uniform global methodology which is not customised for every country. | # Renewable energy profiles Source: https://docs.transitionzero.org/methodology/renewable-profiles What are RE profiles, and how do they impact modelling results? Profiles for onshore wind, offshore wind, and solar PV were sourced from [renewables ninja](https://www.renewables.ninja/). This platform uses the VWF model to convert wind speed data from NASA MERRA reanalysis into power output, and the [GSEE model (Global Solar Energy Estimator)](https://gsee.readthedocs.io/en/latest/) to generate solar PV profiles from solar radiation data. For each model node, a representative latitude and longitude were selected, and the 2013 profile at that point was used as the node’s generation profile. Hydropower profiles were obtained from the [PLEXOS World model data](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/CBYXBY), which consolidates monthly capacity factors for 7,155 hydro power plants using data from the [Global Reservoir and Dam Database (GRAND)](https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1890/100125) and a study by [Gernaat et al (2017)](https://www.nature.com/articles/s41560-017-0006-y). The study identified over 60,000 potential new hydropower sites and developed monthly discharge profiles for both new and existing sites, based on 30 years of runoff data. Data sourcing standards for RE profiles are detailed below # Data sourcing standards – RE profiles | **Input Variable** | **Model Type** | **Gold Standard ('Best in Class')** | **Silver Standard ('Good')** | **Bronze Standard ('Publishable')** | | :----------------- | :------------- | :---------------------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------- | :----------------------------------------------------- | | Renewable profiles | CE & UD | Sub-Admin- polygons with weighted averages. Size of the polygon based on a peer-reviewed methodology. | Synthetically generate a profile based on available historical data—best case from actual Admin-1, worst case from some other reference region. | Renewable profiles are weighted averages over Admin 1. | # Techno-economic inputs Source: https://docs.transitionzero.org/methodology/techno-economic-inputs This page discusses the different types of tecno-economic input data used in energy system modelling # Summary An energy system model includes a set of ‘technologies’ – a broad term that encompasses all forms of energy infrastructure, including power plants, transmission lines, and storage systems. Techno-economic inputs describe the characteristics of these technologies Below is a representative list of technology groups and the specific technologies they include. The full set of technologies available in Scenario Builder is detailed below. # Technology set ``` thermal coal coal-subcritical coal-supercritical coal-ultrasupercritical coal-circulating-fluidized-bed coal-integrated-gasification-combined-cycle coal-unspecified gas gas-internal-combustion-combined-cycle gas-combined-cycle gas-turbine gas-open-cycle-gas-turbine gas-steam-turbine gas-integrated-solar-combined-cycle gas-allum-fetvedt-cycle gas-unspecified oil petroleum-products-internal-combustion-engine oil-unspecified cofiring gas-coal-cofiring gas-oil-cofiring coal-bio-cofiring gas-bio-cofiring cofiring-unspecified gas-ammonia-cofiring cogeneration gas-cogeneration coal-cogeneration bio-cogeneration cogeneration-unspecified waste thermal-unspecified bioenergy biomass biogas bioenergy-unspecified storage battery utility-scale domestic-scale battery-unspecified battery-energy-storage-system carbon-capture-and-storage coal-ccs gas-ccs ccs-unspecified renewables marine wave tidal marine-unspecified solar solar-unspecified solar-thermal concentrated-solar-thermal solar-thermal-unspecified photovoltaic concentrated-photovoltaic photovoltaic-unspecified wind wind-nearshore-intertidal wind-onshore wind-offshore wind-offshore-unspecified wind-offshore-hard-mount wind-offshore-floating wind-unspecified geothermal geothermal-unspecified geothermal-flash-steam geothermal-flash-steam-unspecified geothermal-flash-steam-single geothermal-flash-steam-double geothermal-flash-steam-triple geothermal-dry-steam geothermal-binary-cycle enhanced-geothermal-system low-carbon nuclear hydro-reservoir-storage hydro-reservoir hydro-reservoir-and-run-of-river hydro-pumped-storage hydro-pumped-storage-unspecified hydro-reservoir-and-pumped-storage hydro-run-of-river ammonia interconnection transmission ``` # Technology costs ## Capital costs (CAPEX) CAPEX refers to the upfront investment needed to build new energy infrastructure. These are one-time costs for purchasing and installing technologies. The costs applied here are overnight costs - they do not include the interest during construction. This could lead to an underestimation of capital costs, especially in cases with high upfront costs, construction times, and interest rates. * **Includes:** equipment, engineering, procurement, construction (EPC), land, grid connection, permitting, environmental impact assessments. * **Units:** typically expressed as currency per unit of capacity (e.g. \$/kW for power plants and transmission, \$/kWh for storage). ## Operating expenditures (OPEX) OPEX refers to the ongoing costs to operate and maintain energy infrastructure over its lifetime, after the initial CAPEX. * **Variable O\&M costs**: proportional to the amount of electricity generated or activity level * Includes: fuel costs (though sometimes treated separately, Commodity (Fuel) Price section), consumables. * Units: \$/MWh of electricity generated. * **Fixed O\&M costs:** incurred regardless of energy production level, typically time-based * Includes: salaries, insurance, routine maintenance, property taxes. * Units: \$/MW/year (or \$/kW/year). Data sourcing standards for technology costs are detailed below. ## Data sourcing standards – technology costs | **Input Variable** | **Model Type** | **Gold Standard ('Best in Class')** | **Silver Standard ('Good')** | **Bronze Standard ('Publishable')** | | :-------------------------------------------------------- | :------------- | :------------------------------------------------------------------------------------------------------------------------------------------------ | :---------------------------------------------------------------------- | :------------------------------------------------------------------------------ | | Technology costs - current (CAPEX, Fixed & Variable OPEX) | CE & UD | For deregulated or liberalised markets: Auction results. Analysis based on equipment manufacturers, project developers, country-specific studies. | National-level estimates from data owner or specific national reports. | IEA region-level data, or global averages, applied to the country/region. | | Technology costs - future projections | CE | Detailed, country-specific cost projection studies incorporating learning curves, R\&D impact, and local manufacturing potential. | IEA scenarios (e.g., WEO) or other reputable international projections. | Extrapolation of current costs or application of generic global learning rates. | # Efficiencies Efficiency represents the ratio of useful energy output to energy input, expressed as a percentage. * Power plant efficiency: Electricity output / fuel energy input (e.g. 40% for a coal plant, 60% for a CCGT gas plant). * Battery efficiency (round-trip efficiency): Energy discharged / energy used to charge (e.g. 85%). Can be broken down into charging and discharging efficiencies. * Transmission and distribution (T\&D) efficiency/losses: Energy delivered to end-user / energy entering the network. Losses occur over power lines. Data sourcing standards for technology costs are detailed below. ## Data sourcing standards – efficiencies | **Input Variable** | **Model Type** | **Gold Standard ('Best in Class')** | **Silver Standard ('Good')** | **Bronze Standard ('Publishable')** | | :------------------ | :------------- | :---------------------------------------------------------------------------------------- | :----------------------------------------- | :---------------------------------- | | Efficiency / Losses | CE & UD | Observed data by asset or specific technology from data owner, adjusted by age/retrofits. | Regional/country-level technology studies. | Global technology catalogues. | # Maximum utilisation rates The highest level at which a power plant can operate, limited by technical, economic, or regulatory factors. * **Maintenance:** Planned and unplanned maintenance reduces annual output. * **Resource availability:** Technologies such as solar and wind are limited by nature. * **Regulatory constraints:** Policy limits or permits can reduce operating hours. | **Power Plant Type** | **Suggested Default Maximum Utilisation Rate** | **Reference** | | :------------------- | :--------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Nuclear | 91% | [‘Electricity Annual Technology Baseline (ATB) 2024: Technologies and Data Overview’, National Renewable Energy Laboratory (NREL), 2024](https://atb.nrel.gov/electricity/2024/index) | | Coal | 80% | [‘Electricity Annual Technology Baseline (ATB) 2024: Technologies and Data Overview’, National Renewable Energy Laboratory (NREL), 2024](https://atb.nrel.gov/electricity/2024/index) | | Natural Gas (CCGT) | 88% | [‘Electricity Annual Technology Baseline (ATB) 2024: Technologies and Data Overview’, National Renewable Energy Laboratory (NREL), 2024](https://atb.nrel.gov/electricity/2024/index) | | Natural Gas (OCGT) | 88% | [‘Electricity Annual Technology Baseline (ATB) 2024: Technologies and Data Overview’, National Renewable Energy Laboratory (NREL), 2024](https://atb.nrel.gov/electricity/2024/index) | | Biomass | 83% | [‘Electricity Annual Technology Baseline (ATB) 2024: Technologies and Data Overview’, National Renewable Energy Laboratory (NREL), 2024](https://atb.nrel.gov/electricity/2024/index) | | Geothermal | 90% | [‘Electricity Annual Technology Baseline (ATB) 2024: Technologies and Data Overview’, National Renewable Energy Laboratory (NREL), 2024](https://atb.nrel.gov/electricity/2024/index) | # Operational life (Lifetime) The expected number of years a technology can operate before needing replacement. This is a key input for investment decisions. Sourced similarly to technology costs. The following table consolidates the operational lifespan data for various power plant technologies as identified from the referenced sources. | **Power Plant Type** | **Operational Lifespan (Years)** | **Reference** | | :------------------- | :------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Coal | 50 | [‘Mineral requirements for electricity generation’, 2021, World Nuclear Association (citing IEA)](https://world-nuclear.org/information-library/energy-and-the-environment/mineral-requirements-for-electricity-generation) | | Natural Gas (CCGT) | 30 | [‘Mineral requirements for electricity generation’, 2021, World Nuclear Association (citing IEA)](https://world-nuclear.org/information-library/energy-and-the-environment/mineral-requirements-for-electricity-generation) | | Natural Gas (OCGT) | 30 | [‘Mineral requirements for electricity generation’, 2021, World Nuclear Association (citing IEA)](https://world-nuclear.org/information-library/energy-and-the-environment/mineral-requirements-for-electricity-generation) | | Nuclear | 60 | [‘Mineral requirements for electricity generation’, 2021, World Nuclear Association (citing IEA)](https://world-nuclear.org/information-library/energy-and-the-environment/mineral-requirements-for-electricity-generation) | | Solar PV | 25 | [‘Mineral requirements for electricity generation’, 2021, World Nuclear Association (citing IEA)](https://world-nuclear.org/information-library/energy-and-the-environment/mineral-requirements-for-electricity-generation) | | Wind-Onshore | 25 | [‘Mineral requirements for electricity generation’, World Nuclear Association (citing IEA), 2021](https://world-nuclear.org/information-library/energy-and-the-environment/mineral-requirements-for-electricity-generation) | | Wind-Offshore | 25 | [‘Mineral requirements for electricity generation’, World Nuclear Association (citing IEA), 2021](https://world-nuclear.org/information-library/energy-and-the-environment/mineral-requirements-for-electricity-generation) | | Hydropower | 100 | [‘Hydropower Explained: Hydropower and the environment’, U.S. Energy Information Administration (EIA), 2023](https://www.eia.gov/energyexplained/hydropower/hydropower-and-the-environment.php) | | Geothermal | 30 | [‘FAQ (Geothermal Energy)’, Enel Green Power](https://www.enelgreenpower.com/learning-hub/renewable-energies/geothermal-energy/faq) | | Biomass | 25 | [‘Biomass CCS Study’, Global CCS Institute, 2015](https://www.globalccsinstitute.com/archive/hub/publications/98606/biomass-ccs-study.pdf) | # Growth or Build Rates (Capacity addition constraints) The maximum rate at which new capacity of a given technology can be built and brought online in a given year or period. This reflects real-world limitations like supply chain capacity, skilled labor, and planning/permitting timelines. These limits are set to whichever is greater: 2% of the base year capacity (measured in MW) or a 20% increase from the previous year. # Emission factors The rate at which a technology emits pollutants, especially greenhouse gases (CO₂), per unit of energy produced or fuel consumed (e.g. tonnes CO₂/MWh or tonnes CO₂/TJ). Sources include IPCC guidelines, national emissions inventories, and specific studies. These factors are critical for calculating total emissions and assessing alignment with climate targets. The following table summarizes the life-cycle GHG emission factors for various electricity generation technologies based on the IPCC AR6 WGIII. | **Technology** | **Median (gCO2eq/kWh)** | | :------------------- | :---------------------- | | Coal | 980 | | Natural Gas (CCGT) | 490 | | Natural Gas (OCGT) | 680 | | Oil (Heavy Fuel Oil) | 740 | Data sourcing standards for emission factors are detailed below. Scenario Builder currently evaluates only CO₂ emissions and does not yet account for other regulated air pollutants such as NOₓ and SOₓ. ## Data sourcing standards – emission factors | **Input Variable** | **Model Type** | **Gold Standard ('Best in Class')** | **Silver Standard ('Good')** | **Bronze Standard ('Publishable')** | | :--------------------- | :------------- | :------------------------------------------------------------------------------------------------------------------------ | :------------------------------------------------------------------------------------------------ | :----------------------------------- | | Emission factors (CO2) | CE & UD | Plant-specific or country-specific, fuel-specific, technology-specific data from official national reporting (e.g. EUTL). | Default factors from IPCC or reputable regional databases, differentiated by technology and fuel. | Global average IPCC default factors. | # Get Started Source: https://docs.transitionzero.org/platform/get-started If you have any questions, please reach out to support@transitionzero.org # Accessing Scenario Builder To access Scenario Builder, navigate to [**builder.transitionzero.org/workspace**](http://builder.transitionzero.org/workspace) # Platform demonstration In this video, we do a short demonstration of Scenario Builder and its key features.