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A Modelica Toolbox for the Simulation of Borehole Thermal Energy Storage Systems

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  • Julian Formhals

    (Geothermal Science and Technology, Technical University of Darmstadt, Schnittspahnstraße 9, 64287 Darmstadt, Germany
    Graduate School of Excellence Energy Science and Engineering, Technical University of Darmstadt, Otto-Berndt-Str. 3, 64287 Darmstadt, Germany)

  • Hoofar Hemmatabady

    (Geothermal Science and Technology, Technical University of Darmstadt, Schnittspahnstraße 9, 64287 Darmstadt, Germany
    Graduate School of Excellence Energy Science and Engineering, Technical University of Darmstadt, Otto-Berndt-Str. 3, 64287 Darmstadt, Germany)

  • Bastian Welsch

    (Geothermal Science and Technology, Technical University of Darmstadt, Schnittspahnstraße 9, 64287 Darmstadt, Germany
    Graduate School of Excellence Energy Science and Engineering, Technical University of Darmstadt, Otto-Berndt-Str. 3, 64287 Darmstadt, Germany)

  • Daniel Otto Schulte

    (Geothermal Science and Technology, Technical University of Darmstadt, Schnittspahnstraße 9, 64287 Darmstadt, Germany)

  • Ingo Sass

    (Geothermal Science and Technology, Technical University of Darmstadt, Schnittspahnstraße 9, 64287 Darmstadt, Germany
    Graduate School of Excellence Energy Science and Engineering, Technical University of Darmstadt, Otto-Berndt-Str. 3, 64287 Darmstadt, Germany)

Abstract

Borehole thermal energy storage (BTES) systems facilitate the subsurface seasonal storage of thermal energy on district heating scales. These systems’ performances are strongly dependent on operational conditions like temperature levels or hydraulic circuitry. Preliminary numerical system simulations improve comprehension of the storage performance and its interdependencies with other system components, but require both accurate and computationally efficient models. This study presents a toolbox for the simulation of borehole thermal energy storage systems in Modelica . The storage model is divided into a borehole heat exchanger (BHE), a local, and a global sub-model. For each sub-model, different modeling approaches can be deployed. To assess the overall performance of the model, two studies are carried out: One compares the model results to those of 3D finite element method (FEM) models to investigate the model’s validity over a large range of parameters. In a second study, the accuracies of the implemented model variants are assessed by comparing their results to monitoring data from an existing BTES system. Both studies prove the validity of the modeling approaches under investigation. Although the differences in accuracy for the compared variants are small, the proper model choice can significantly reduce the computational effort.

Suggested Citation

  • Julian Formhals & Hoofar Hemmatabady & Bastian Welsch & Daniel Otto Schulte & Ingo Sass, 2020. "A Modelica Toolbox for the Simulation of Borehole Thermal Energy Storage Systems," Energies, MDPI, vol. 13(9), pages 1-23, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:9:p:2327-:d:355049
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    References listed on IDEAS

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    1. Schweiger, Gerald & Larsson, Per-Ola & Magnusson, Fredrik & Lauenburg, Patrick & Velut, Stéphane, 2017. "District heating and cooling systems – Framework for Modelica-based simulation and dynamic optimization," Energy, Elsevier, vol. 137(C), pages 566-578.
    2. Michael Lanahan & Paulo Cesar Tabares-Velasco, 2017. "Seasonal Thermal-Energy Storage: A Critical Review on BTES Systems, Modeling, and System Design for Higher System Efficiency," Energies, MDPI, vol. 10(6), pages 1-24, May.
    3. Claesson, Johan & Eskilson, Per, 1988. "Conductive heat extraction to a deep borehole: Thermal analyses and dimensioning rules," Energy, Elsevier, vol. 13(6), pages 509-527.
    4. Welsch, Bastian & Göllner-Völker, Laura & Schulte, Daniel O. & Bär, Kristian & Sass, Ingo & Schebek, Liselotte, 2018. "Environmental and economic assessment of borehole thermal energy storage in district heating systems," Applied Energy, Elsevier, vol. 216(C), pages 73-90.
    5. Tordrup, K.W. & Poulsen, S.E. & Bjørn, H., 2017. "An improved method for upscaling borehole thermal energy storage using inverse finite element modelling," Renewable Energy, Elsevier, vol. 105(C), pages 13-21.
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    Citations

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    Cited by:

    1. Lyden, A. & Brown, C.S. & Kolo, I. & Falcone, G. & Friedrich, D., 2022. "Seasonal thermal energy storage in smart energy systems: District-level applications and modelling approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    2. Hoofar Hemmatabady & Julian Formhals & Bastian Welsch & Daniel Otto Schulte & Ingo Sass, 2020. "Optimized Layouts of Borehole Thermal Energy Storage Systems in 4th Generation Grids," Energies, MDPI, vol. 13(17), pages 1-26, August.
    3. Claire Bossennec & Lukas Seib & Matthis Frey & Jeroen van der Vaart & Ingo Sass, 2022. "Structural Architecture and Permeability Patterns of Crystalline Reservoir Rocks in the Northern Upper Rhine Graben: Insights from Surface Analogues of the Odenwald," Energies, MDPI, vol. 15(4), pages 1-30, February.
    4. Hemmatabady, Hoofar & Welsch, Bastian & Formhals, Julian & Sass, Ingo, 2022. "AI-based enviro-economic optimization of solar-coupled and standalone geothermal systems for heating and cooling," Applied Energy, Elsevier, vol. 311(C).
    5. Antonio Rosato & Antonio Ciervo & Giovanni Ciampi & Michelangelo Scorpio & Sergio Sibilio, 2020. "Integration of Micro-Cogeneration Units and Electric Storages into a Micro-Scale Residential Solar District Heating System Operating with a Seasonal Thermal Storage," Energies, MDPI, vol. 13(20), pages 1-40, October.
    6. Formhals, Julian & Feike, Frederik & Hemmatabady, Hoofar & Welsch, Bastian & Sass, Ingo, 2021. "Strategies for a transition towards a solar district heating grid with integrated seasonal geothermal energy storage," Energy, Elsevier, vol. 228(C).

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