IDEAS home Printed from https://ideas.repec.org/a/eee/juipol/v83y2023ics0957178723001042.html
   My bibliography  Save this article

The impact of transparency policies on local flexibility markets in electric distribution networks

Author

Listed:
  • Heilmann, Erik

Abstract

The energy transition brings various technical, economic, and organizational challenges. One major topic, especially in zonal electricity markets, is the organization of future congestion management. Local flexibility market (LFM) is an often discussed concept of market-based congestion management. Like the whole energy system, the market transparency of LFMs can influence individual bidders' behavior. In this context, the predictability of the network status of distribution networks and an LFM's outcome, depending on a given transparency policy, is investigated in this paper. For this, forecast models based on artificial neural networks (ANN) are implemented on synthetic distribution network and LFM data. Three defined transparency policies determine the amount of input data used for the models. The results suggest that the transparency policy can influence the distribution network status and LFM outcome predictability, but appropriate forecasts are generally feasible. Therefore, the transparency policy should not conceal information but provide a level playing field for all parties involved. Providing semi-disaggregated data on the network area level can be suitable for bidders' decision-making and reduces transaction costs.

Suggested Citation

  • Heilmann, Erik, 2023. "The impact of transparency policies on local flexibility markets in electric distribution networks," Utilities Policy, Elsevier, vol. 83(C).
    • Handle: RePEc:eee:juipol:v:83:y:2023:i:c:s0957178723001042
    DOI: 10.1016/j.jup.2023.101592
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0957178723001042
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jup.2023.101592?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Buchmann, Marius, 2020. "How decentralization drives a change of the institutional framework on the distribution grid level in the electricity sector – The case of local congestion markets," Energy Policy, Elsevier, vol. 145(C).
    2. Rosen, Christiane & Madlener, Reinhard, 2012. "Auction Design for Local Reserve Energy Markets," FCN Working Papers 7/2012, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN), revised Mar 2013.
    3. Radecke, Julia & Hefele, Joseph & Hirth, Lion, 2019. "Markets for Local Flexibility in Distribution Networks," EconStor Preprints 204559, ZBW - Leibniz Information Centre for Economics.
    4. 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).
    5. Nils-Henrik M. von der Fehr, 2013. "Transparency in Electricity Markets," Economics of Energy & Environmental Policy, International Association for Energy Economics, vol. 0(Number 2).
    6. Keles, Dogan & Scelle, Jonathan & Paraschiv, Florentina & Fichtner, Wolf, 2016. "Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks," Applied Energy, Elsevier, vol. 162(C), pages 218-230.
    7. Buchmann, Marius, 2017. "Governance of data and information management in smart distribution grids: Increase efficiency by balancing coordination and competition," Utilities Policy, Elsevier, vol. 44(C), pages 63-72.
    8. Emil Kraft & Dogan Keles & Wolf Fichtner, 2020. "Modeling of frequency containment reserve prices with econometrics and artificial intelligence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1179-1197, December.
    9. Schittekatte, Tim & Meeus, Leonardo, 2020. "Flexibility markets: Q&A with project pioneers," Utilities Policy, Elsevier, vol. 63(C).
    10. Lago, Jesus & De Ridder, Fjo & De Schutter, Bart, 2018. "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms," Applied Energy, Elsevier, vol. 221(C), pages 386-405.
    11. McAfee, R Preston & McMillan, John, 1987. "Auctions and Bidding," Journal of Economic Literature, American Economic Association, vol. 25(2), pages 699-738, June.
    12. Ward, K.R. & Green, R. & Staffell, I., 2019. "Getting prices right in structural electricity market models," Energy Policy, Elsevier, vol. 129(C), pages 1190-1206.
    13. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    14. Ramos, Ariana & De Jonghe, Cedric & Gómez, Virginia & Belmans, Ronnie, 2016. "Realizing the smart grid's potential: Defining local markets for flexibility," Utilities Policy, Elsevier, vol. 40(C), pages 26-35.
    15. Jin, Xiaolong & Wu, Qiuwei & Jia, Hongjie, 2020. "Local flexibility markets: Literature review on concepts, models and clearing methods," Applied Energy, Elsevier, vol. 261(C).
    16. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    17. Fraunholz, Christoph & Kraft, Emil & Keles, Dogan & Fichtner, Wolf, 2021. "Advanced price forecasting in agent-based electricity market simulation," Applied Energy, Elsevier, vol. 290(C).
    18. Heilmann, Erik & Klempp, Nikolai & Wetzel, Heike, 2020. "Design of regional flexibility markets for electricity: A product classification framework for and application to German pilot projects," Utilities Policy, Elsevier, vol. 67(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. 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).
    3. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    4. Rebenaque, Olivier & Schmitt, Carlo & Schumann, Klemens & Dronne, Théo & Roques, Fabien, 2023. "Success of local flexibility market implementation: A review of current projects," Utilities Policy, Elsevier, vol. 80(C).
    5. Heilmann, Erik & Klempp, Nikolai & Wetzel, Heike, 2020. "Design of regional flexibility markets for electricity: A product classification framework for and application to German pilot projects," Utilities Policy, Elsevier, vol. 67(C).
    6. 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.
    7. Bellenbaum, Julia & Höckner, Jonas & Weber, Christoph, 2022. "Designing flexibility procurement markets for congestion management – investigating two-stage procurement auctions," Energy Economics, Elsevier, vol. 106(C).
    8. Lago, Jesus & Poplavskaya, Ksenia & Suryanarayana, Gowri & De Schutter, Bart, 2021. "A market framework for grid balancing support through imbalances trading," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    9. Marcjasz, Grzegorz & Narajewski, Michał & Weron, Rafał & Ziel, Florian, 2023. "Distributional neural networks for electricity price forecasting," Energy Economics, Elsevier, vol. 125(C).
    10. Potenciano Menci, Sergio & Valarezo, Orlando, 2024. "Decoding design characteristics of local flexibility markets for congestion management with a multi-layered taxonomy," Applied Energy, Elsevier, vol. 357(C).
    11. Fraunholz, Christoph & Kraft, Emil & Keles, Dogan & Fichtner, Wolf, 2021. "Advanced price forecasting in agent-based electricity market simulation," Applied Energy, Elsevier, vol. 290(C).
    12. Aikaterini Forouli & Emmanouil A. Bakirtzis & Georgios Papazoglou & Konstantinos Oureilidis & Vasileios Gkountis & Luisa Candido & Eloi Delgado Ferrer & Pandelis Biskas, 2021. "Assessment of Demand Side Flexibility in European Electricity Markets: A Country Level Review," Energies, MDPI, vol. 14(8), pages 1-23, April.
    13. Andreas Zeiselmair & Simon Köppl, 2021. "Constrained Optimization as the Allocation Method in Local Flexibility Markets," Energies, MDPI, vol. 14(13), pages 1-21, June.
    14. Karim L. Anaya & Michael G. Pollitt, 2021. "How to Procure Flexibility Services within the Electricity Distribution System: Lessons from an International Review of Innovation Projects," Energies, MDPI, vol. 14(15), pages 1-26, July.
    15. Umut Ugurlu & Ilkay Oksuz & Oktay Tas, 2018. "Electricity Price Forecasting Using Recurrent Neural Networks," Energies, MDPI, vol. 11(5), pages 1-23, May.
    16. Avci, Ezgi & Ketter, Wolfgang & van Heck, Eric, 2018. "Managing electricity price modeling risk via ensemble forecasting: The case of Turkey," Energy Policy, Elsevier, vol. 123(C), pages 390-403.
    17. Emil Kraft & Dogan Keles & Wolf Fichtner, 2020. "Modeling of frequency containment reserve prices with econometrics and artificial intelligence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1179-1197, December.
    18. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression," Applied Energy, Elsevier, vol. 239(C), pages 610-625.
    19. 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.
    20. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).

    More about this item

    Keywords

    Local flexibility markets; Electricity market transparency; Transparency policy; Electricity distribution networks;
    All these keywords.

    JEL classification:

    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • L98 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Government Policy
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:juipol:v:83:y:2023:i:c:s0957178723001042. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.sciencedirect.com/journal/utilities-policy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.