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:
- Representing the current energy system: Understanding the existing power plants, grids, and energy demand as it is today.
- 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.