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

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  • 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
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    References listed on IDEAS

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    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, 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.
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    Keywords

    Energy system model; rolling-horizon optimization; linear programming;

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