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Integrating Intermittent Renewable Wind Generation: A Stochastic Multi-Market Electricity Model for the European Electricity Market

  • Jan Abrell
  • Friedrich Kunz

In northern Europe wind energy has become a dominating renewable energy source due to natural conditions and national support schemes. However, the uncertainty about wind generation affects existing network infrastructure and power production planning of generators and cannot not be fully diminished by wind forecasts. In this paper we develop a stochastic electricity market model to analyze the impact of uncertain wind generation on the different electricity markets as well as network congestion management. Stochastic programming techniques are used to incorporate uncertain wind generation. The technical characteristics of transporting electrical energy as well as power plants are explicitly taken into account. The consecutive clearing of the electricity markets is incorporated by a rolling planning procedure reflecting the market regime of European markets. The model is applied to the German electricity system covering an exemplary week. Three different cases of considering uncertain wind generation are analyzed. The results reveal that the flexibility of the generation dispatch is increased either by using more flexible generation technologies or by flexibilizing the generation pattern of rather inflexible technologies.

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Paper provided by DIW Berlin, German Institute for Economic Research in its series Discussion Papers of DIW Berlin with number 1301.

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Length: 27 p.
Date of creation: 2013
Date of revision:
Handle: RePEc:diw:diwwpp:dp1301
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  1. Jan Abrell & Hannes Weigt, 2012. "Combining Energy Networks," Networks and Spatial Economics, Springer, vol. 12(3), pages 377-401, September.
  2. Delarue, Erik & D'haeseleer, William, 2008. "Adaptive mixed-integer programming unit commitment strategy for determining the value of forecasting," Applied Energy, Elsevier, vol. 85(4), pages 171-181, April.
  3. Weigt, Hannes & Jeske, Till & Leuthold, Florian & von Hirschhausen, Christian, 2010. ""Take the long way down": Integration of large-scale North Sea wind using HVDC transmission," Energy Policy, Elsevier, vol. 38(7), pages 3164-3173, July.
  4. Stein W. Wallace & Stein-Erik Fleten, 2002. "Stochastic programming in energy," GE, Growth, Math methods 0201001, EconWPA, revised 13 Nov 2003.
  5. Möst, Dominik & Keles, Dogan, 2010. "A survey of stochastic modelling approaches for liberalised electricity markets," European Journal of Operational Research, Elsevier, vol. 207(2), pages 543-556, December.
  6. Thure Traber & Claudia Kemfert, 2009. "Gone with the Wind?: Electricity Market Prices and Incentives to Invest in Thermal Power Plants under Increasing Wind Energy Supply," Discussion Papers of DIW Berlin 852, DIW Berlin, German Institute for Economic Research.
  7. Helga Habis & Dávid Csercsik, 2015. "Cooperation with Externalities and Uncertainty," Networks and Spatial Economics, Springer, vol. 15(1), pages 1-16, March.
  8. Friedrich Kunz, 2013. "Improving Congestion Management: How to Facilitate the Integration of Renewable Generation in Germany," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
  9. Neuhoff, Karsten & Barquin, Julian & Bialek, Janusz W. & Boyd, Rodney & Dent, Chris J. & Echavarren, Francisco & Grau, Thilo & von Hirschhausen, Christian & Hobbs, Benjamin F. & Kunz, Friedrich & Nabe, 2013. "Renewable electric energy integration: Quantifying the value of design of markets for international transmission capacity," Energy Economics, Elsevier, vol. 40(C), pages 760-772.
  10. Wang, J. & Botterud, A. & Bessa, R. & Keko, H. & Carvalho, L. & Issicaba, D. & Sumaili, J. & Miranda, V., 2011. "Wind power forecasting uncertainty and unit commitment," Applied Energy, Elsevier, vol. 88(11), pages 4014-4023.
  11. Tuohy, Aidan & Meibom, Peter & Denny, Eleanor & O'Malley, Mark, 2009. "Unit commitment for systems with significant wind penetration," MPRA Paper 34849, University Library of Munich, Germany.
  12. Bahmani-Firouzi, Bahman & Farjah, Ebrahim & Azizipanah-Abarghooee, Rasoul, 2013. "An efficient scenario-based and fuzzy self-adaptive learning particle swarm optimization approach for dynamic economic emission dispatch considering load and wind power uncertainties," Energy, Elsevier, vol. 50(C), pages 232-244.
  13. Leuthold, Florian & Jeske, Till & Weigt, Hannes & von Hirschhausen, Christian, 2009. "When the Wind Blows Over Europe: A Simulation Analysis and the Impact of Grid Extensions," MPRA Paper 65655, University Library of Munich, Germany.
  14. Mel Devine & James Gleeson & John Kinsella & David Ramsey, 2014. "A Rolling Optimisation Model of the UK Natural Gas Market," Networks and Spatial Economics, Springer, vol. 14(2), pages 209-244, June.
  15. 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.
  16. Hannes Weigt & Jan Abrell, 2012. "Storage and Investments in a Combined Energy Network Model," EcoMod2012 4319, EcoMod.
  17. Spiecker, Stephan & Weber, Christoph, 2014. "The future of the European electricity system and the impact of fluctuating renewable energy – A scenario analysis," Energy Policy, Elsevier, vol. 65(C), pages 185-197.
  18. van der Weijde, Adriaan Hendrik & Hobbs, Benjamin F., 2012. "The economics of planning electricity transmission to accommodate renewables: Using two-stage optimisation to evaluate flexibility and the cost of disregarding uncertainty," Energy Economics, Elsevier, vol. 34(6), pages 2089-2101.
  19. 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.
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