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

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  • Jan Abrell
  • Friedrich Kunz

Abstract

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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File URL: http://www.diw.de/documents/publikationen/73/diw_01.c.421477.de/dp1301.pdf
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Bibliographic Info

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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Keywords: Electricity; Unit Commitment; Stochasticity; Renewable Energy;

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  1. 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.
  2. 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.
  3. Stein W. Wallace & Stein-Erik Fleten, 2002. "Stochastic programming in energy," GE, Growth, Math methods 0201001, EconWPA, revised 13 Nov 2003.
  4. 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.
  5. 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.
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