IDEAS home Printed from https://ideas.repec.org/p/dui/wpaper/2006.html
   My bibliography  Save this paper

Rolling-horizon optimization as a speed-up method - assessment using the electricity system model JMM

Author

Listed:
  • Thomas Kallabis

    (House of Energy Markets and Finance, University of Duisburg-Essen)

Abstract

Energy system models are limited in their scope and level of disaggregation by the availability offast computing hardware. While improvements in hardware and solver developments have led toan increasing size of solvable models, problems with high temporal and geographical resolutionremain difficult to solve in one loop. In this paper, we evaluate the use of rolling planning as aspeed-up method for energy system models. In a stylized model, we highlight potential issuesthat occur at the boundary of optimization horizons, especially regarding time-linking constraintssuch as energy storage balances. In multiple configurations of the energy system model WILMAR-JMM, we investigate the tradeoff between solution quality and problem size / solution time thatcharacterize the use of rolling planning.

Suggested Citation

  • Thomas Kallabis, "undated". "Rolling-horizon optimization as a speed-up method - assessment using the electricity system model JMM," EWL Working Papers 2006, University of Duisburg-Essen, Chair for Management Science and Energy Economics.
  • Handle: RePEc:dui:wpaper:2006
    as

    Download full text from publisher

    File URL: https://www.wiwi.uni-due.de/fileadmin/fileupload/BWL-ENERGIE/Arbeitspapiere/RePEc/pdf/wp2006_RollingHorizonOptimizationAs_aSpeedUpMethod.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pfenninger, Stefan & Hirth, Lion & Schlecht, Ingmar & Schmid, Eva & Wiese, Frauke & Brown, Tom & Davis, Chris & Gidden, Matthew & Heinrichs, Heidi & Heuberger, Clara & Hilpert, Simon & Krien, Uwe & Ma, 2018. "Opening the black box of energy modelling: Strategies and lessons learned," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, pages 63-71.
    2. Florian Leuthold & Hannes Weigt & Christian Hirschhausen, 2012. "A Large-Scale Spatial Optimization Model of the European Electricity Market," Networks and Spatial Economics, Springer, vol. 12(1), pages 75-107, March.
    3. Ventosa, Mariano & Baillo, Alvaro & Ramos, Andres & Rivier, Michel, 2005. "Electricity market modeling trends," Energy Policy, Elsevier, vol. 33(7), pages 897-913, May.
    4. Trepper, Katrin & Bucksteeg, Michael & Weber, Christoph, 2015. "Market splitting in Germany – New evidence from a three-stage numerical model of Europe," Energy Policy, Elsevier, vol. 87(C), pages 199-215.
    5. Guigues, Vincent & Sagastizábal, Claudia, 2012. "The value of rolling-horizon policies for risk-averse hydro-thermal planning," European Journal of Operational Research, Elsevier, vol. 217(1), pages 129-140.
    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. Savvidis, Georgios & Siala, Kais & Weissbart, Christoph & Schmidt, Lukas & Borggrefe, Frieder & Kumar, Subhash & Pittel, Karen & Madlener, Reinhard & Hufendiek, Kai, 2019. "The gap between energy policy challenges and model capabilities," Energy Policy, Elsevier, vol. 125(C), pages 503-520.
    2. Pałka, Piotr, 2017. "Derivatives of the nodal prices in market power screening," Energy Economics, Elsevier, vol. 64(C), pages 149-157.
    3. Ruhnau, O. & Bucksteeg, M. & Ritter, D. & Schmitz, R. & Böttger, D. & Koch, M. & Pöstges, A. & Wiedmann, M. & Hirth, L., 2022. "Why electricity market models yield different results: Carbon pricing in a model-comparison experiment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    4. Karl-Kiên Cao & Kai von Krbek & Manuel Wetzel & Felix Cebulla & Sebastian Schreck, 2019. "Classification and Evaluation of Concepts for Improving the Performance of Applied Energy System Optimization Models," Energies, MDPI, vol. 12(24), pages 1-51, December.
    5. Makpal Assembayeva & Jonas Egerer & Roman Mendelevitch & Nurkhat Zhakiyev, 2017. "A Spatial Electricity Market Model for the Power System of Kazakhstan," Discussion Papers of DIW Berlin 1659, DIW Berlin, German Institute for Economic Research.
    6. Syranidis, Konstantinos & Robinius, Martin & Stolten, Detlef, 2018. "Control techniques and the modeling of electrical power flow across transmission networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3452-3467.
    7. Assembayeva, Makpal & Egerer, Jonas & Mendelevitch, Roman & Zhakiyev, Nurkhat, 2018. "A spatial electricity market model for the power system: The Kazakhstan case study," Energy, Elsevier, vol. 149(C), pages 762-778.
    8. Lang, Lukas Maximilian & Dallinger, Bettina & Lettner, Georg, 2020. "The meaning of flow-based market coupling on redispatch measures in Austria," Energy Policy, Elsevier, vol. 136(C).
    9. Md. Nasimul Islam Maruf, 2019. "Sector Coupling in the North Sea Region—A Review on the Energy System Modelling Perspective," Energies, MDPI, vol. 12(22), pages 1-35, November.
    10. Sander Claeys & Marta Vanin & Frederik Geth & Geert Deconinck, 2021. "Applications of optimization models for electricity distribution networks," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 10(5), September.
    11. Dávid Csercsik & László Á. Kóczy, 2017. "Efficiency and Stability in Electrical Power Transmission Networks: a Partition Function Form Approach," Networks and Spatial Economics, Springer, vol. 17(4), pages 1161-1184, December.
    12. Neuhoff, Karsten & Barquin, Julian & Boots, Maroeska G. & Ehrenmann, Andreas & Hobbs, Benjamin F. & Rijkers, Fieke A.M. & Vazquez, Miguel, 2005. "Network-constrained Cournot models of liberalized electricity markets: the devil is in the details," Energy Economics, Elsevier, vol. 27(3), pages 495-525, May.
    13. Fürsch, Michaela & Hagspiel, Simeon & Jägemann, Cosima & Nagl, Stephan & Lindenberger, Dietmar & Tröster, Eckehard, 2013. "The role of grid extensions in a cost-efficient transformation of the European electricity system until 2050," Applied Energy, Elsevier, vol. 104(C), pages 642-652.
    14. Rafal Weron & Adam Misiorek, 2006. "Short-term electricity price forecasting with time series models: A review and evaluation," HSC Research Reports HSC/06/01, Hugo Steinhaus Center, Wroclaw University of Technology.
    15. Francesco Bandarin & Enrico Ciciotti & Marco Cremaschi & Giovanna Madera & Paolo Perulli & Diana Shendrikova, 2020. "Which Future for Cities after COVID-19 An international Survey," Reports, Fondazione Eni Enrico Mattei, October.
    16. Schönheit, David & Hladik, Dirk & Hobbie, Hannes & Möst, Dominik, 2020. "ELMOD documentation: Modeling of flow-based market coupling and congestion management," EconStor Preprints 217278, ZBW - Leibniz Information Centre for Economics.
    17. Vangelis Marinakis & Themistoklis Koutsellis & Alexandros Nikas & Haris Doukas, 2021. "AI and Data Democratisation for Intelligent Energy Management," Energies, MDPI, vol. 14(14), pages 1-14, July.
    18. Leonard Goke & Jens Weibezahn & Christian von Hirschhausen, 2021. "Fictional expectations in energy scenarios and implications for bottom-up planning models," Papers 2112.04821, arXiv.org.
    19. Takashima, Ryuta & Goto, Makoto & Kimura, Hiroshi & Madarame, Haruki, 2008. "Entry into the electricity market: Uncertainty, competition, and mothballing options," Energy Economics, Elsevier, vol. 30(4), pages 1809-1830, July.
    20. Kunz, Friedrich & Neuhoff, Karsten & Rosellón, Juan, 2016. "FTR allocations to ease transition to nodal pricing: An application to the German power system," Energy Economics, Elsevier, vol. 60(C), pages 176-185.

    More about this item

    Keywords

    Energy system model; rolling-horizon optimization; linear programming;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:dui:wpaper:2006. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/fwessde.html .

    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: Andreas Fritz (email available below). General contact details of provider: https://edirc.repec.org/data/fwessde.html .

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.