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Matching supply and demand of electricity network-supportive flexibility: A case study with three comprehensible matching algorithms

Author

Listed:
  • Erik Heilmann

    (University of Kassel)

  • Andreas Zeiselmair

    (Technical University of Munich)

  • Thomas Estermann

    (Technical University of Munich)

Abstract

Due to an ongoing energy transition, electricity networks are increasingly challenged by situations where local electrical power demands are high but local generation is low and vice versa. This finally leads to a growing number of technical problems. To solve these problems in the short-term, the electrical power of load and generation must be adjusted as available flexibility. In zonal electricity systems, one often discussed concept to utilize flexibility is local flexibility markets. Based on auction theory, we provide a comprehensible framework for the use of network-supportive flexibility in general. In this context, we discuss the problem of matching supply and demand. We introduce three matching approaches that can be applied and adapted for different network situations. In addition to a qualitative description of the three approaches, we present a case study of an exemplary distribution network and explore different scenarios to demonstrate the utility of the algorithms. We compare the three approaches on a qualitative level with quantitative inputs from the case study. The comparison considers the specific cost, flexible energy, ensured demand coverage, data minimization, computational effort and the transferability of the three approaches.

Suggested Citation

  • Erik Heilmann & Andreas Zeiselmair & Thomas Estermann, 2021. "Matching supply and demand of electricity network-supportive flexibility: A case study with three comprehensible matching algorithms," MAGKS Papers on Economics 202110, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
  • Handle: RePEc:mar:magkse:202110
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    File URL: https://www.uni-marburg.de/en/fb02/research-groups/economics/macroeconomics/research/magks-joint-discussion-papers-in-economics/papers/2021-papers/10-2021_heilmann.pdf
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    Citations

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    Cited by:

    1. Andreas Zeiselmair & Simon Köppl, 2021. "Constrained Optimization as the Allocation Method in Local Flexibility Markets," Energies, MDPI, vol. 14(13), pages 1-21, June.
    2. Heilmann, Erik, 2023. "The impact of transparency policies on local flexibility markets in electric distribution networks," Utilities Policy, Elsevier, vol. 83(C).
    3. Erik Heilmann, 2021. "The impact of transparency policies on local flexibility markets in electrical distribution networks: A case study with artificial neural network forecasts," MAGKS Papers on Economics 202141, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    4. Erik Heilmann & Nikolai Klempp & Kai Hufendiek & Heike Wetzel, 2022. "Long-term Contracts for Network-supportive Flexibility in Local Flexibility Markets," MAGKS Papers on Economics 202224, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    5. Ajla Mehinovic & Matej Zajc & Nermin Suljanovic, 2023. "Interpretation and Quantification of the Flexibility Sources Location on the Flexibility Service in the Distribution Grid," Energies, MDPI, vol. 16(2), pages 1-18, January.

    More about this item

    Keywords

    local flexibility markets; matching; multi-dimensional winner determination; electricity network operation;
    All these keywords.

    JEL classification:

    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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