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Energy Transition Model (ETM)

website·Indexed 33 days ago

https://docs.energytransitionmodel.com/

Documentation for the Energy Transition Model, covering scenario development, economic viability, and CO2 emission modeling. Includes a user manual for interface navigation and scenario management, as well as API references for Scenario and SavedScenario objects, interpolation, and data sources for electricity, hydrogen, and building stock.

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What's inside ETM

  1. Overview of the Energy Transition Model (ETM) Architecture

    The Energy Transition Model (ETM) is composed of a centrally hosted calculation engine accessible via an API and three primary web interfaces: The Energy Game, The Professional interface, and The Mobile Game (which uses pre-calculated, cached data rather than a direct connection to the engine).

  2. Overview of liquid fuel and feedstock production processes in ETM

    The Energy Transition Model (ETM) simulates the conversion of various feedstocks (crude oil, biomass, waste, hydrogen, and CO2) into transport fuels and chemical feedstocks. These processes are configured in the 'Liquid fuels and feedstock' section of the ETM. Depending on the process, different feedstock routes can be selected, which in turn affect the resulting fuel output.

  3. Overview of CCUS sources in ETM

    The Energy Transition Model (ETM) supports five primary sources for the supply of CO2: capture in industry, capture in the energy sector, capture in hydrogen production, Direct Air Capture (DAC), and the import of CO2. Users can configure the extent of application for each capture technology and set the amount of baseload CO2 to be imported. Any surplus of captured CO2 not allocated to utilization or storage is exported; any deficit is imported.

  4. Overview of the Energy Transition Model (ETM)

    The Energy Transition Model (ETM) is an open-source, interactive energy modelling tool developed by Quintel. It is designed to help users build and explore energy system scenarios for countries, regions, or cities to generate fact-based insights for energy transitions.

  5. Overview of the ETM Merit Order module

    The Merit Order module calculates the hourly electricity mix by matching electricity demand against the installed capacities and marginal costs of producing technologies. Its primary outputs are the full load hours for producers and the amount of excess electricity utilized by flexibility technologies. These results impact the 'future' year scenario's CO2 emissions, costs, imports/exports, total energy use, and renewable percentages, as well as producer financial metrics like profit and profitability.

  6. Overview of PyETM capabilities

    PyETM is a modular Python package designed for advanced users to interact with the Energy Transition Model's API. It allows for the automation of scenario workflows, including creating and updating scenarios, running bulk GQueries across multiple scenarios, managing custom hourly profiles, and exporting data. It can be integrated into scripts, Jupyter Notebooks, or used to process scenario data via Excel files.

  7. Overview of Load Shifting for Demand Side Response

    Load shifting enables demand side response in the electricity merit order. It acts as 'storage in reverse': the load shifting node outputs energy immediately at a pre-set price point (creating a deficit) and attempts to recover that energy later in the year when prices fall. Load shifting is energy-neutral over the year. If the simulation reaches the end of the year, the component will place an inflexible consumption load to reclaim any remaining deficit regardless of market price.

  8. Understand and calculate Useful Demand for energy modeling

    Useful demand (e.g., 'useful heat demand') represents the actual energy service required by an end-user, independent of the technology used to provide it. This value is used to simulate the impact of switching to alternative technologies with different efficiencies.

    Because useful demand is rarely available in literature, it is typically derived by analyzing the shares of technologies used and their respective efficiencies. The model kernel uses this useful demand, combined with converter shares and efficiencies, to calculate the total consumption of energy carriers (e.g., gas, electricity). The accuracy of the useful demand value is verified by checking if the resulting calculated final demand matches verified data from sources like the CBS (Central Bureau of Statistics).

  9. Understand Loss Of Load Expectation (LOLE) in the ETM

    The Loss Of Load Expectation (LOLE) in the Energy Transition Model (ETM) represents the expected number of hours per year that a country's electricity production park cannot meet its demand. It serves as a metric to determine if there is sufficient dispatchable electricity production to meet expected demand throughout the year, highlighting the need for backup capacity in scenarios with high shares of volatile energy technologies.

  10. Use the ETM API without authentication

    The API can be used without authentication, but any scenarios created will be considered "unowned." Unowned scenarios can be viewed and modified by any user. To control access and prevent unauthorized changes, create an ETM account and use personal access tokens.

  11. Understand and use Gqueries in ETEngine

    Gqueries are stored Graph Query Language (GQL) procedures identified by a unique key. They allow users to request complex data (e.g., total_co2_emissions) from ETEngine without needing to understand the underlying GQL intricacies. All available gqueries are stored in the etsource GitHub repository.

  12. Retrieve a list of available gqueries

    Use the GET /api/v3/gqueries endpoint to fetch a detailed listing of all available gqueries. This endpoint provides metadata about the queries but does not return values for specific scenarios. You can filter the results by providing a list of labels in the request body; if no labels are specified, all gqueries are returned.
    GET /api/v3/gqueries HTTP/2
Host: engine.energytransitionmodel.com
Accept: application/json

{
  "labels": [
    "area"
  ]
}