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SimBench—A Benchmark Dataset of Electric Power Systems to Compare Innovative Solutions Based on Power Flow Analysis

Author

Listed:
  • Steffen Meinecke

    (Department of Energy Management and Power System Operation (e 2 n), University Kassel, 34121 Kassel, Germany)

  • Džanan Sarajlić

    (Institute for Energy Systems, Energy Efficiency and Energy Economy (ie 3 ), TU Dortmund University, 44227 Dortmund, Germany)

  • Simon Ruben Drauz

    (Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), 34119 Kassel, Germany)

  • Annika Klettke

    (Institute of Power Systems and Power Economics (IAEW), RWTH Aachen University, 52062 Aachen, Germany)

  • Lars-Peter Lauven

    (Department of Energy Management and Power System Operation (e 2 n), University Kassel, 34121 Kassel, Germany)

  • Christian Rehtanz

    (Institute for Energy Systems, Energy Efficiency and Energy Economy (ie 3 ), TU Dortmund University, 44227 Dortmund, Germany)

  • Albert Moser

    (Institute of Power Systems and Power Economics (IAEW), RWTH Aachen University, 52062 Aachen, Germany)

  • Martin Braun

    (Department of Energy Management and Power System Operation (e 2 n), University Kassel, 34121 Kassel, Germany
    Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), 34119 Kassel, Germany)

Abstract

Publicly accessible, elaborated grid datasets, i.e., benchmark grids, are well suited to publish and compare methods or study results. Similarly, developing innovative tools and algorithms in the fields of grid planning and grid operation is based on grid datasets. Therefore, a general methodology to generate benchmark datasets and its voltage level dependent implementation is described in this paper. As a result, SimBench, a comprehensive dataset for the low, medium, high and extra-high voltage level, is presented. Besides grids that can be combined across several voltage levels, the dataset offers an added value by providing time series for a whole year as well as future scenarios. In this way, SimBench is applicable for many use cases and simplifies reproducing study results. As proof, different automated algorithms for grid planning are compared to show how to apply SimBench and make use of it as a simulation benchmark.

Suggested Citation

  • Steffen Meinecke & Džanan Sarajlić & Simon Ruben Drauz & Annika Klettke & Lars-Peter Lauven & Christian Rehtanz & Albert Moser & Martin Braun, 2020. "SimBench—A Benchmark Dataset of Electric Power Systems to Compare Innovative Solutions Based on Power Flow Analysis," Energies, MDPI, vol. 13(12), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3290-:d:376724
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    References listed on IDEAS

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    1. Steffen Meinecke & Leon Thurner & Martin Braun, 2020. "Review of Steady-State Electric Power Distribution System Datasets," Energies, MDPI, vol. 13(18), pages 1-17, September.
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    Cited by:

    1. Stute, Judith & Klobasa, Marian, 2024. "How do dynamic electricity tariffs and different grid charge designs interact? - Implications for residential consumers and grid reinforcement requirements," Energy Policy, Elsevier, vol. 189(C).
    2. Kuntuarova, Saltanat & Licklederer, Thomas & Huynh, Thanh & Zinsmeister, Daniel & Hamacher, Thomas & Perić, Vedran, 2024. "Design and simulation of district heating networks: A review of modeling approaches and tools," Energy, Elsevier, vol. 305(C).
    3. Cheng, Xiaoyuan & Yao, Ruiqiu & Postnikov, Andrey & Hu, Yukun & Varga, Liz, 2024. "Decentralized intelligent multi-party competitive aggregation framework for electricity prosumers," Applied Energy, Elsevier, vol. 373(C).
    4. Eva Buchta & Mathias Duckheim & Michael Metzger & Paul Stursberg & Stefan Niessen, 2023. "Leveraging Behavioral Correlation in Distribution System State Estimation for the Recognition of Critical System States," Energies, MDPI, vol. 16(20), pages 1-21, October.
    5. Sarah A. Steinbach & Maximilian J. Blaschke, 2024. "How grid reinforcement costs differ by the income of electric vehicle users," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    6. Sebastian Schreck & Robin Sudhoff & Sebastian Thiem & Stefan Niessen, 2022. "On the Importance of Grid Tariff Designs in Local Energy Markets," Energies, MDPI, vol. 15(17), pages 1-25, August.
    7. Schedler, Steffen & Meilinger, Stefanie & Clees, Tanja, 2024. "A new bottom-up method for classifying a building portfolio by building type, self-sufficiency rate, and access to local grid infrastructure for storage demand analysis," Applied Energy, Elsevier, vol. 371(C).
    8. Zhang, Haoyang & Zhan, Sen & Kok, Koen & Paterakis, Nikolaos G., 2024. "Establishing a hierarchical local market structure using multi-cut Benders decomposition," Applied Energy, Elsevier, vol. 363(C).
    9. Carlo Schmitt & Felix Gaumnitz & Andreas Blank & Olivier Rebenaque & Théo Dronne & Arnault Martin & Philippe Vassilopoulos & Albert Moser & Fabien Roques, 2021. "Framework for Deterministic Assessment of Risk-Averse Participation in Local Flexibility Markets †," Energies, MDPI, vol. 14(11), pages 1-34, May.
    10. Steffen Meinecke & Leon Thurner & Martin Braun, 2020. "Review of Steady-State Electric Power Distribution System Datasets," Energies, MDPI, vol. 13(18), pages 1-17, September.
    11. Steinbach, Sarah A. & Blaschke, Maximilian J., 2024. "Enabling electric mobility: Can photovoltaic and home battery systems significantly reduce grid reinforcement costs?," Applied Energy, Elsevier, vol. 375(C).
    12. Florian Schäfer & Martin Braun, 2020. "Multi-Year High-Voltage Power System Planning Considering Active Power Curtailment," Energies, MDPI, vol. 13(18), pages 1-15, September.
    13. Robin Sudhoff & Sebastian Schreck & Sebastian Thiem & Stefan Niessen, 2022. "Operating Renewable Energy Communities to Reduce Power Peaks in the Distribution Grid: An Analysis on Grid-Friendliness, Different Shares of Participants, and Economic Benefits," Energies, MDPI, vol. 15(15), pages 1-18, July.

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