Supplementary: Model Coupling

This supplementary material provides resources supporting the implementation of the refined DataDesc metadata schema and the ioProc workflow manager, described in the working title, “Model coupling through reproducible adapter workflows based on shared transformation functions.”

It aims to facilitate data and software integration, as well as data processing, by formalizing interface and data model descriptions and automating the identification of data transformation requirements. Using the ioProc and Snakemake workflow tools, these resources support reproducible, transparent adapter workflows with reusable transformation functions. The DataDesc schema is applied to create machine-interpretable descriptions of transformations, ensuring transparency in data handling across complex, coupled workflows.

Organization

The project is organized into sections for DataDesc Annotations, Data Models, Workflows, and Shared Data.

• DataDesc Annotations

  • Data: Contains JSON files with metadata annotations for energy technologies and renewable resources:
    • PEM electrolysis (86 PEMEC 100 MW.json)
    • Solar PV (ninja_pv_country_DE_merra-2_corrected.json)
    • Wind onshore (ninja_wind_country_DE_current-merra-2_corrected.json)
    • Technology data for electricity and district heating (technology_data_for_el_and_dh.json)
    • Technology data for energy storage (technology_datasheet_for_energy_storage.json)
    • Specific technologies, including gas turbines, heat pumps, and lithium-ion batteries.
  • Data Models: JSON files representing data models for different workflows:
    • FINE framework models (fine_dd.json, fine_merra_comparison.json)
    • REMix framework models (REMix_dd.json, remix_readRemixCsv.json)

• Workflows

  • FINE Workflow and REMix Workflow: Workflow configuration files for each framework, implementing the transformations and data processing steps.

• Shared Data

  • Contains .xlsx and .csv datasets shared across workflows:
    • Renewable fuels data (data_sheets_for_renewable_fuels.xlsx)
    • Technology data for electricity, heat, and storage (technology_data_for_el_and_dh.xlsx, technology_datasheet_for_energy_storage.xlsx)
    • High-resolution generation data for Germany from renewables.ninja, including solar PV and wind (ninja_pv_country_DE_merra-2_corrected.csv, ninja_wind_country_DE_current-merra-2_corrected.csv).

• README.md: General project instructions and usage guidelines.

Use Cases and Frameworks

Two frameworks, REMix and FINE, demonstrate this methodology’s general applicability. REMix, exemplified by the FlexMex2 Use Case 4a (11 nodes, including DE, with sector coupling and capacity expansion), and FINE, with an EnergyLand model (1 node, DE, with sector coupling and capacity expansion), showcase practical implementations.

Data Sets and Technologies

The supplementary material includes links to datasets used in the analysis, covering both coarse (e.g., DEA technology data for electricity, district heat, and energy storage) and high-resolution data (e.g., renewables.ninja PV and Wind Onshore generation for DE from 2016). Descriptions of key technologies used (e.g., Lithium-ion NMC batteries, gas turbines, heat pumps, and PEM electrolysis) along with essential parameters such as investment costs, O&M costs, efficiencies, and energy losses during storage are included for reference.

This material supports the reproducibility and transparency of workflows, empowering researchers to apply the FAIR principles in data-driven scientific analyses.

Input Data

• Danish Energy Agency (Excel)
• Renewables Ninja Wind (CSV, Zeitreihen)
• Renewables Ninja Solar (CSV, Zeitreihen)

FINE Example Model: Energy Land

• Process raw data with fine/01_data_processing.ipynb
• Run the model with fine/02_model_calculation.ipynb

License

All source code is licensed under the BSD 3-Clause License, including the jupyter notebooks. All input data from the Danish Energy Agency and Renewables Ninja have their respective licenses specified in accompanying license files. All other data is licensed under CC-BY 4.0 Attribution 4.0 International.

Copyright (C) 2023-2024 FZJ-ICE-2 and Deutsches Zentrum für Luft- und Raumfahrt

About Us

Institute of Climate and Energy Systems (ICE) - Jülich Systems Analysis

We are the Institute of Climate and Energy Systems (ICE) - Jülich Systems Analysis belonging to the Forschungszentrum Jülich. Our interdisciplinary department's research is focusing on energy-related process and systems analyses. Data searches and system simulations are used to determine energy and mass balances, as well as to evaluate performance, emissions and costs of energy systems. The results are used for performing comparative assessment studies between the various systems. Our current priorities include the development of energy strategies, in accordance with the German Federal Government’s greenhouse gas reduction targets, by designing new infrastructures for sustainable and secure energy supply chains and by conducting cost analysis studies for integrating new technologies into future energy market frameworks.

DLR Institute of Networked Energy Systems, VE

At the Institute of Networked Energy Systems, we pursue an overarching goal: We want to make energy transition in the economy and society successful through our research. We develop transformation strategies and technical solutions to link the electricity, heating, transport and industrial sectors efficiently. To this end, we work together with our partners from industry and research to develop a holistic understanding of the system and effective system solutions.

Our research teams consider all levels of the energy system in their work - from centralised large-scale infrastructures such as storage caverns to the electricity and gas grids and the technical equipment of buildings. We take into account technical, economic, political, ecological and social framework conditions and create valuable synergy effects through co-operation between our technical and systems analysis research groups. As a result, we are able to address two key transfer levels: Technology transfer through our technical solutions and knowledge transfer with the results of our internally developed modelling and analysis tools.

At our Oldenburg and Stuttgart sites, we focus on the following four fields of action, which we consider to be key challenges for the continued success of energy transition:

• Strategies for sustainable energy systems Technology assessment, resources, energy scenarios, system modelling

• Market design and business cases Agent-based simulation of markets, business scenarios

• Balancing energy supply and demand Load and generation forecasts, energy management through sector integration and storage

• Grid management: electricity, gas and heating grids Integrated grid operation management, system services

Acknowledgement

The authors would like to thank the Federal Ministry for Economic Affairs and Climate Action of Germany (BMWK) for supporting this work with a grant for the project LOD-GEOSS (03EI1005A-G). Furthermore, the authors are grateful to the German federal government, the German state governments, and the Joint Science Conference (GWK) for their funding and support as part of the NFDI4Ing consortium, managed by the German Research Foundation (DFG) – 442146713. This work was also supported by the Helmholtz Association as part of the program "Energy System Design".