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Computer Science 7

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Multi-sector coupled energy system modeling on a regional level

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Multi-sector coupled energy system modeling on a regional level

Logo ESM Regio

Project Description

Reducing primary energy use and greenhouse gases are central goals of the energy transition. However, switching from fossil to regenerative energy sources is not enough to achieve them. An overarching view and optimization of the different sectors of the energy system - electricity, gas, heat and transport - can significantly advance the further development of the energy system in Germany. Potential exists above all on a regional level.

The goal of the ESM-Regio project - short for "Multisectoral Coupled Energy System Modeling on a regional level" - is to create a temporally high-resolution energy system model on a county level that takes into account the four sectors of electricity, gas, heat and transport as well as the required interface technologies. A key feature of the project is a cross-sector model logic. Suitable simulation methods enable a holistic analysis and optimization of the system operation under consideration of the four relevant sectors of the energy system.

Project Period

    2021-05-01 – 2024-04-30

Project leader

    Reinhard German

Project Participants

    Alexander Martin, Jürgen Karl, Marco Pruckner, Daniel Scharrer, Natalia Luna-Jaspe Roa, Kevin-Martin Aigner

Contact

    Daniel Scharrer

Sponsored by

    Bundesministerium für Wirtschaft und Technologie (BMWi)

External Partners

  • Institut für Hoch­spannungs­technik, Energiesystem- und Anlagendiagnose – Hochschule für angewandte Wissenschaften Coburg
  • Energieagentur Nordbayern GmbH
  • Stadtwerke Bayreuth Holding GmbH
  • Bayern Innovativ Bayerische Gesellschaft für Innovation und Wissenstransfer mbH

Related Publications

  • Aigner KM., Schaumann P., von Loeper F., Martin A., Schmidt V., Liers F.:
    Robust DC Optimal Power Flow with Modeling of Solar Power Supply Uncertainty via R-Vine Copulas
    In: Optimization and Engineering (2022)
    ISSN: 1389-4420
    DOI: 10.1007/s11081-022-09761-0
    URL: https://link.springer.com/article/10.1007/s11081-022-09761-0
    BibTeX: Download
  • Aigner KM., Bärmann A., Braun K., Liers F., Pokutta S., Schneider O., Sharma K., Tschuppik S.:
    Distributionally Robust Optimization over Time
    (2023)
    URL: https://opus4.kobv.de/opus4-trr154/frontdoor/index/index/docId/496
    BibTeX: Download

Links

    https://www.esm-regio.de
    https://cris.fau.de/converis/portal/Project/261431764
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