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Coordinating Cross-Country Congestion Management

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  • Friedrich Kunz
  • Alexander Zerrahn

Abstract

We employ a detailed two-stage model to simulate the operation of the Central Eastern European electricity market and network. Implementing different cases of coordination in congestion management between national transmission system operators, numerical results show the beneficial impact of closer cooperation. Specific steps comprise the sharing of network and dispatch information, cross-border counter-trading, and multilateral redispatch in a flow-based congestion management framework. Efficiency gains are accompanied by distributional effects. Closer economic cooperation becomes especially relevant against the background of changing spatial generation patterns, deeper international integration ofnational systems, and spillovers of national developments to adjacent systems.

Suggested Citation

  • Friedrich Kunz & Alexander Zerrahn, 2016. "Coordinating Cross-Country Congestion Management," Discussion Papers of DIW Berlin 1551, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp1551
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    References listed on IDEAS

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    1. Jonas Egerer & Jens Weibezahn & Hauke Hermann, 2015. "Two Price Zones for the German Electricity Market: Market Implications and Distributional Effects," Discussion Papers of DIW Berlin 1451, DIW Berlin, German Institute for Economic Research.
    2. Jan Abrell & Friedrich Kunz, 2015. "Integrating Intermittent Renewable Wind Generation - A Stochastic Multi-Market Electricity Model for the European Electricity Market," Networks and Spatial Economics, Springer, vol. 15(1), pages 117-147, March.
    3. Jonas Egerer & Clemens Gerbaulet & Richard Ihlenburg & Friedrich Kunz & Benjamin Reinhard & Christian von Hirschhausen & Alexander Weber & Jens Weibezahn, 2014. "Electricity Sector Data for Policy-Relevant Modeling: Data Documentation and Applications to the German and European Electricity Markets," Data Documentation 72, DIW Berlin, German Institute for Economic Research.
    4. Giorgia Oggioni & Yves Smeers & Elisabetta Allevi & Siegfried Schaible, 2012. "A Generalized Nash Equilibrium Model of Market Coupling in the European Power System," Networks and Spatial Economics, Springer, vol. 12(4), pages 503-560, December.
    5. OGGIONI, Giorgia & SMEERS, Yves, 2013. "Market failures of market coupling and counter-trading in Europe: an illustrative model based discussion," LIDAM Reprints CORE 2553, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    Citations

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    Cited by:

    1. Bjørndal, Endre & Bjørndal, Mette & Cai, Hong & Panos, Evangelos, 2018. "Hybrid pricing in a coupled European power market with more wind power," European Journal of Operational Research, Elsevier, vol. 264(3), pages 919-931.
    2. Kunz, Friedrich, 2018. "Quo Vadis? (Un)scheduled electricity flows under market splitting and network extension in central Europe," Energy Policy, Elsevier, vol. 116(C), pages 198-209.
    3. Jan Málek & Lukáš Recka & Karel Janda, 2017. "Impact of German Energiewende on transmission lines in the Central European region," CAMA Working Papers 2017-72, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    4. Zepter, Jan Martin & Weibezahn, Jens, 2019. "Unit commitment under imperfect foresight – The impact of stochastic photovoltaic generation," Applied Energy, Elsevier, vol. 243(C), pages 336-349.
    5. Karel Janda & Jan Malek & Lukas Recka, 2017. "Influence of Renewable Energy Sources on Electricity Transmission Networks in Central Europe," Working Papers IES 2017/05, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Feb 2017.
    6. Xiong, Bobby & Predel, Johannes & Crespo del Granado, Pedro & Egging-Bratseth, Ruud, 2021. "Spatial flexibility in redispatch: Supporting low carbon energy systems with Power-to-Gas," Applied Energy, Elsevier, vol. 283(C).
    7. Zerrahn, Alexander, 2017. "Wind Power and Externalities," Ecological Economics, Elsevier, vol. 141(C), pages 245-260.
    8. Horn, Henrik & Tangerås, Thomas, 2021. "National Transmission System Operators in an International Electricity Market," CEPR Discussion Papers 16289, C.E.P.R. Discussion Papers.
    9. Abadie, Luis María & Chamorro, José Manuel, 2021. "Evaluation of a cross-border electricity interconnection: The case of Spain-France," Energy, Elsevier, vol. 233(C).
    10. Janda, Karel & Málek, Jan & Rečka, Lukáš, 2017. "Influence of renewable energy sources on transmission networks in Central Europe," Energy Policy, Elsevier, vol. 108(C), pages 524-537.
    11. Pearson, Simon & Wellnitz, Sonja & Crespo del Granado, Pedro & Hashemipour, Naser, 2022. "The value of TSO-DSO coordination in re-dispatch with flexible decentralized energy sources: Insights for Germany in 2030," Applied Energy, Elsevier, vol. 326(C).
    12. Geoffrey J. Blanford & Christoph Weissbart, 2019. "A Framework for Modeling the Dynamics of Power Markets – The EU-REGEN Model," ifo Working Paper Series 307, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    13. Karel Janda & Jan Málek & Lukáš Rečka, 2017. "Vliv obnovitelných zdrojů na českou soustavu přenosu elektřiny [The Impact of Renewable Energy Sources on the Czech Electricity Transmission System]," Politická ekonomie, Prague University of Economics and Business, vol. 2017(6), pages 728-750.
    14. Karel Janda & Jan Malek & Lukas Recka, 2017. "The Influence of Renewable Energy Sources on the Czech Electricity Transmission System," Working Papers IES 2017/06, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Mar 2017.
    15. Ringkjøb, Hans-Kristian & Haugan, Peter M. & Solbrekke, Ida Marie, 2018. "A review of modelling tools for energy and electricity systems with large shares of variable renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 440-459.
    16. Yue Pu & Yunting Li & Yingzi Wang, 2021. "Structure Characteristics and Influencing Factors of Cross-Border Electricity Trade: A Complex Network Perspective," Sustainability, MDPI, vol. 13(11), pages 1-25, May.

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    More about this item

    Keywords

    Electricity; congestion management; network modeling; Europe;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • L51 - Industrial Organization - - Regulation and Industrial Policy - - - Economics of Regulation
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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