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Electricity, Heat and Gas Sector Data for Modelling the German System

Author

Listed:
  • Friedrich Kunz
  • Mario Kendziorski
  • Wolf-Peter Schill
  • Jens Weibezahn
  • Jan Zepter
  • Christian von Hirschhausen
  • Philipp Hauser
  • Matthias Zech
  • Dominik Möst
  • Sina Heidari
  • Björn Felten
  • Christoph Weber

Abstract

This data documentation describes a data set of the German electricity, heat, and natural gas sectors compiled within the research project ‘LKD-EU’ (Long-term planning and short-term optimization of the German electricity system within the European framework: Further development of methods and models to analyze the electricity system including the heat and gas sector). The project is a joined effort by the German Institute for Economic Research (DIW Berlin), the Workgroup for Infrastructure Policy (WIP) at Technische Universität Berlin (TUB), the Chair of Energy Economics (EE2) at Technische Universität Dresden (TUD), and the House of Energy Markets & Finance at University of Duisburg-Essen. The project was funded by the German Federal Ministry for Economic Affairs and Energy through the grant ‘LKD-EU’, FKZ 03ET4028A. The objective of this paper is to document a reference data set representing the status quo of the German energy sector. We also update and extend parts of the previous DIW Data Documentation 75 (Egerer et al. 2014). While the focus is on the electricity sector, the heat and natural gas sectors are covered as well. With this reference data set, we aim to increase the transparency of energy infrastructure data in Germany. On the one hand, this documentation presents sources of original data and information used for the data set. On the other hand, it elaborates on the methodologies which have been applied to derive the data from respective sources in order to make it useful for modeling purposes and to promote a discussion about the underlying assumptions. Furthermore, we briefly discuss the underlying regulations with regard to data transparency in the energy sector. Where not otherwise stated, the data included in this report is given with reference to the year 2015 for Germany.

Suggested Citation

  • Friedrich Kunz & Mario Kendziorski & Wolf-Peter Schill & Jens Weibezahn & Jan Zepter & Christian von Hirschhausen & Philipp Hauser & Matthias Zech & Dominik Möst & Sina Heidari & Björn Felten & Christ, 2017. "Electricity, Heat and Gas Sector Data for Modelling the German System," Data Documentation 92, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwddc:dd92
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    Cited by:

    1. Kendziorski, Mario & Göke, Leonard & von Hirschhausen, Christian & Kemfert, Claudia & Zozmann, Elmar, 2022. "Centralized and decentral approaches to succeed the 100% energiewende in Germany in the European context – A model-based analysis of generation, network, and storage investments," Energy Policy, Elsevier, vol. 167(C).
    2. Arjuna Nebel & Christine Krüger & Tomke Janßen & Mathieu Saurat & Sebastian Kiefer & Karin Arnold, 2020. "Comparison of the Effects of Industrial Demand Side Management and Other Flexibilities on the Performance of the Energy System," Energies, MDPI, vol. 13(17), pages 1-20, August.
    3. Jan Dasenbrock & Adam Pluta & Matthias Zech & Wided Medjroubi, 2021. "Detecting Pipeline Pathways in Landsat 5 Satellite Images with Deep Learning," Energies, MDPI, vol. 14(18), pages 1-13, September.
    4. Pedro Durán & Herena Torio & Patrik Schönfeldt & Peter Klement & Benedikt Hanke & Karsten von Maydell & Carsten Agert, 2021. "Technology Pathways and Economic Analysis for Transforming High Temperature to Low Temperature District Heating Systems," Energies, MDPI, vol. 14(11), pages 1-24, May.
    5. Jens Weibezahn & Mario Kendziorski, 2019. "Illustrating the Benefits of Openness: A Large-Scale Spatial Economic Dispatch Model Using the Julia Language," Energies, MDPI, vol. 12(6), pages 1-21, March.
    6. Bucksteeg, Michael & Wiedmann, Michael & Pöstges, Arne & Haller, Markus & Böttger, Diana & Ruhnau, Oliver & Schmitz, Richard, 2022. "The transformation of integrated electricity and heat systems—Assessing mid-term policies using a model comparison approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    7. Göke, Leonard & Kendziorski, Mario & Kemfert, Claudia & Hirschhausen, Christian von, 2022. "Accounting for spatiality of renewables and storage in transmission planning," Energy Economics, Elsevier, vol. 113(C).
    8. Paul Neetzow & Roman Mendelevitch & Sauleh Siddiqui, 2018. "Modeling Coordination between Renewables and Grid: Policies to Mitigate Distribution Grid Constraints Using Residential PV-Battery Systems," Discussion Papers of DIW Berlin 1766, DIW Berlin, German Institute for Economic Research.
    9. Zepter, Jan Martin & Weibezahn, Jens, 2019. "Unit commitment under imperfect foresight – The impact of stochastic photovoltaic generation," Applied Energy, Elsevier, vol. 243(C), pages 336-349.
    10. Schönheit, David & Weinhold, Richard & Dierstein, Constantin, 2020. "The impact of different strategies for generation shift keys (GSKs) on the flow-based market coupling domain: A model-based analysis of Central Western Europe," Applied Energy, Elsevier, vol. 258(C).
    11. Sina Heidari, 2020. "How Strategic Behavior of Natural Gas Exporters Can Affect the Sectors of Electricity, Heating, and Emission Trading during the European Energy Transition," Energies, MDPI, vol. 13(19), pages 1-20, September.
    12. Bloess, Andreas, 2020. "Modeling of combined heat and power generation in the context of increasing renewable energy penetration," Applied Energy, Elsevier, vol. 267(C).
    13. Sadeghian, Omid & Mohammadpour Shotorbani, Amin & Mohammadi-Ivatloo, Behnam & Sadiq, Rehan & Hewage, Kasun, 2021. "Risk-averse maintenance scheduling of generation units in combined heat and power systems with demand response," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    14. Pearson, Simon & Wellnitz, Sonja & Crespo del Granado, Pedro & Hashemipour, Naser, 2022. "The value of TSO-DSO coordination in re-dispatch with flexible decentralized energy sources: Insights for Germany in 2030," Applied Energy, Elsevier, vol. 326(C).
    15. Göke, Leonard & Kendziorski, Mario, 2022. "Adequacy of time-series reduction for renewable energy systems," Energy, Elsevier, vol. 238(PA).
    16. Meya, Jasper N. & Neetzow, Paul, 2021. "Renewable energy policies in federal government systems," Energy Economics, Elsevier, vol. 101(C).
    17. Neetzow, Paul & Mendelevitch, Roman & Siddiqui, Sauleh, 2019. "Modeling coordination between renewables and grid: Policies to mitigate distribution grid constraints using residential PV-battery systems," Energy Policy, Elsevier, vol. 132(C), pages 1017-1033.
    18. Xiong, Bobby & Predel, Johannes & Crespo del Granado, Pedro & Egging-Bratseth, Ruud, 2021. "Spatial flexibility in redispatch: Supporting low carbon energy systems with Power-to-Gas," Applied Energy, Elsevier, vol. 283(C).
    19. Jasper Meya & Paul Neetzow, 2019. "Renewable energy policies in federal government systems," Working Papers V-423-19, University of Oldenburg, Department of Economics, revised Jul 2019.
    20. Lovis Anderson & Mark Turner & Thorsten Koch, 2022. "Generative deep learning for decision making in gas networks," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 95(3), pages 503-532, June.
    21. Mira Watermeyer & Thomas Mobius & Oliver Grothe & Felix Musgens, 2023. "A hybrid model for day-ahead electricity price forecasting: Combining fundamental and stochastic modelling," Papers 2304.09336, arXiv.org.

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