APPLICATION OF COST FUNCTIONS FOR LARGE SCALE INTEGRATION OF WIND POWER USING A MULTI-SCHEME ENSEMBLE PREDICTION TECHNIQUE - Energy Research Developments: Tidal Energy, Energy Efficiency and Solar Energy - page 360

“cocktail” of pooled energy, weather uncertainty and advances trading according to the
ensemble minimum. The more degrees of freedom, the higher the price for the intermittent
energy, which is critical for the success rate in the future energy markets, because it can not
be expected that the required investments in renewable energy will increase or even be kept
at today's rate without an economic incentive in addition to the environmental benefits.

References

[1] Global Wind Energy Council (www.gwec.net), Jan. 2008.

[2] B. Lange, M. Wolff, R. Mackensen, R. Jursa, K. Rohrig, D. Braams, B. Valov, L.
Hofmann, C. Scholz, and K. Biermann, ”Operational control of wind farm clusters
for transmission system operators,” in Prod. European Offshore Conference , Berlin,
2007.

[3] IEA, ”Variability of wind power and other renewables: Management options and

strategies”, IEA/OECD, Paris, 2005.

[4] IEA, ”World energy outlook 2004”, IEA/OECD, Paris, 2004.

[5] B. Ernst, B. Oakleaf, M. L. Ahlstrom, M. Lange, C. M ohrlen, B. Lange, U. Focken,
and K. Rohrig, ”Predicting the wind”, IEEE Power and Energy Magazine , vol. 5, no.
6, pp. 78-89, 2007.

[6] Europressedienst (www.europressedienst.com/news/?newsdate=2-2007), Feb. 2007.

[7] T. Acker, ”Characterization of wind and hydropower integration in the USA,” in Proc.
AWEA Conference Windpower , Denver, May 2005.

[8] E. D. Castronuovo and J. A. P. Lopes, ”Optimal operation and hydro storage sizing of
a wind-hydro power plant,” El. Power Energy Sys. , vol. 26, pp. 771-778, 2004.

[9] M. Pahlow, L.-E. Langhans, C. M ohrlen, and J. U. Jørgensen, ”On the potential of cou-
pling renewables into energy pools,” Z. Energiewirtschaft , vol. 31, pp. 35-46, 2007.

[10] www.bundesnetzagentur.de, Nov. 2007.

[11] G. Giebel, G. Kariniotakis, and R. Brownsword, ”The state- of-the-art in short-term
forecasting of wind power - a literature overview,” Position paper ANEMOS Project ,
38 pp., 2005.

[12] G. Hassan, ”Short-term wind energy forecasting: Technology and policy,” Report for
the Canadian Wind Energy Assocoation , 2006.

[13] T. P. Legg, K. R. Mylne, and C. Woolcock, ”Use of medium-range ensembles at the
Met Office I: PREVIN - a system for the production of probabilistic forecast informa-
tion from the ECMWF EPS,” Met. Appl. , vol. 9, pp. 255-271, 2002.