Summary
Wind power is highly variable and partly unpredictable and therefore energy systems of the future have to cope with increased variability and stochasticity. The paper describes the use of a novel stochastic programming model to assess the impact of increased wind power generation on electricity systems. This WILMAR model takes explicitly the stochastic behavior of wind generation and the forecast errors into account. Also a detailed modeling of power plant, grid and market characteristics is performed. WILMAR thus allows to assess the impact of increased wind generation on reserve needs and usage, power plant operation and system cost.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
H. Auer et al. Cost and Technical Constraints of RES-E Grid Integration. Technical report, EU-project GreenNet, 2004. http://www.greennet.at/downloads/WP2%20Report%20GreenNet.pdf.
R. Barth, H. Brand, and C. Weber. Transmission restrictions and wind power extension — case studies for Germany using stochastic modelling. In Proceedings of the European Wind Energy Conference, London, 2004.
R. Barth, H. Brand, D. J. Swider, C. Weber, and P. Meibom. Regional electricity price differences due to intermittent wind power in Germany — Impact of extended transmission and storage capacities. International Journal of Global Energy Issues, Special issue “Integrating intermittent renewable energy technologies, limits to growth?”:276–297, 2006.
J. Birge and F. Louveaux. Introduction to stochastic programming. Springer, New York, 2000.
C. S. Buchanan, K. I. M. McKinnon, and G. K. Skondras. The recourse definition of stochastic linear programming problems within an algebraic modelling Language. Annals of operations research, 104:15–32, 2001.
J. Coughlan, P. Smith, A. Mullane, and M. O'Malley. Wind turbine modelling for power system stability analysis. A system operator perspective. IEEE Transactions on Power Systems, 22:929–936, 2007.
J. Dupacova, N. Gröwe-Kuska, and W. Römisch. Scenario reduction in stochastic programming — an approach using probability metrics. Mathematical Programming Series A, 95:493–511, 2003.
R. Elsässer. Kosten der Windenergienutzung in Deutschland. In Präsentation im Rahmen der Sitzung des Wirtschafts-beirates der Union, 2002. Berlin, 23 July 2002.
ETSO (ed.). European Wind Integration Study (EWIS) - towards a successful integration of wind power into European electricity grids. Technical report, Final Report Phase I, 2007. available from http://www.etso-net.org/upload/documents/Final-report-EWIS-phase-I-approved.pdf.
M. Fuchs. Windpower in Germany. Present situation and outlook. In Presentation Brussels, 2003. 23 January 2003.
N. Gröwe-Kuska, H. Heitsch, and W. Römisch. Scenario reduction and scenario tree construction for power management problems. In IEEE Bologna Power Tech Proceedings, Bologna, 2001. Downloadable at http://www.mathematik.hu-berlin.de/~heitsch/ieee03ghr.pdf.
M. J. Grubb. Value of variable sources on power systems. IEE Proceedings-C, 138:149165, 1991.
IEA, editor. Integration of wind power into electricity grids. economic and reliability impacts. Workshop Paris, 2004. 25 May 2004.
ILEX Energy Consulting. Quantifying the system costs of additional renewables in 2020. A report of ILEX Energy Consulting in association with Manchester Centre for Electrical Energy (UMIST) for the Department of Trade and Industry (DTI), 2002.
P. Meibom, R. Barth, H. Brand, and C. Weber. Impacts of wind power in the Nordic electricity system in 2010. In Technologies for sustainable energy development in the long term. Proceedings Risø international energy conference, 2005. Risø (DK), 23–25 May 2005.
P. Meibom, J. Kiviluoma, R. Barth, H. Brand, C. Weber, and H. Larsen. Value of electrical heat boilers and heat pumps for wind power integration. Wind Energy, 10:321–337, 2007.
P. Meibom, C. Weber, R. Barth, and H. Brand. Operational costs induced by fluctuating wind power production in Germany and Scandinavia. Working Paper, 2008.
L. Söder. Simulation of wind speed forecast errors for operation planning of multi-area power systems. In 8th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Iowa, 2004.
D. J. Swider and C. Weber. The Costs of Wind's Intermittency in Germany: Application of a Stochastic Electricity Market Model. European Transactions on Electrical Power, 17:151–172, 2007.
C. Weber. Uncertainty in the power industry: methods and models for decision support. Springer, New York, 2005.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Weber, C., Meibom, P., Barth, R., Brand, H. (2009). WILMAR: A Stochastic Programming Tool to Analyze the Large-Scale Integration of Wind Energy. In: Kallrath, J., Pardalos, P.M., Rebennack, S., Scheidt, M. (eds) Optimization in the Energy Industry. Energy Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88965-6_19
Download citation
DOI: https://doi.org/10.1007/978-3-540-88965-6_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88964-9
Online ISBN: 978-3-540-88965-6
eBook Packages: EngineeringEngineering (R0)