Publication: A bidding strategy for minimizing the imbalances costs for renewable generators in Spanish power markets
Loading...
Files
Official URL
Full text at PDC
Publication Date
2016
Authors
Advisors (or tutors)
Editors
Journal Title
Journal ISSN
Volume Title
Publisher
Facultad de Ciencias Económicas y Empresariales. Instituto Complutense de Análisis Económico (ICAE)
Citations
Exportar
Abstract
The aim of this paper is to suggest a simple methodology to be used by renewable power generators to bid in Spanish markets in order to minimize the cost of their imbalances. As it is known, the optimal bid depends on the probability distribution function of the energy to produce, of the probability distribution function of the future system imbalance and of its expected cost. We assume simple methods for estimating any of these parameters and, using actual data of 2014, we test the potential economic benefit for a wind generator from using our optimal bid instead of just the expected power generation. We find evidence that Spanish wind generators savings would be from 7% to 26%.
Description
UCM subjects
Unesco subjects
Keywords
Citation
[1] "Forecasting output", Wind Power Monthly, pp 37-46, Dec. 2003.
[2] G. Giebel, R. Brownword, and G. Kariniotakis, “State of the art on short term wind power prediction,” ANEMOS. Project Deliverable D1.1, June 2003, available online: http://anemos.cma.fr.
[3] I. Marti, G. N. Kariniotakis, P. Pinson, and I. Sanchez, “Evaluation of advanced wind power forecasting models - the results of the anemos project,” in In Proc. of the European Wind Energy Conference 2006, vol. 27/2-2/3, Athens, Greece, 2006.
[4] A. Fabbri, et al. “Assessment of the Cost Associated with Wind Generation Prediction Errors in a Liberalized Electricity Market”, IEEE Transactions on Power Systems, Vol. 20, No. 3, August 2005.
[5] P. Pinson, C. Chevallier, G. N. Kariniotakis, “Trading wind generation from short-term probabilistic forecasts of wind power,” IEEE Transactions on Power Systems, vol. 22, no. 3, pp. 1148–1156, 2007.
[6] J. Matevosyan and L. Söder, “Minimization of imbalance cost trading wind power on the short-term power market,” IEEE Transactions on Power Systems, vol. 21, no. 3, pp. 1396–1404, Aug. 2006.
[7] T. Jonsson, “Forecasting of electricity prices accounting for wind power predictions”, Masters Thesis, IMM-DTU, Kongens Lyngby, 2008.
[8] Bessa, R.J., “Methodologies for the participation of an electric vehicles’ aggregator in the electricity market”, PhD Thesis, FEUP, Porto, 2013.
[9] J. Juban, L. Fugon, and G. N. Kariniotakis, “Probabilistic short-term wind power forecasting based on kernel density estimators,” in In Proc. of the European Wind Energy Conference, Milan, Italy, May 2007.
[10] J. Juban, N. Siebert, and G. N. Kariniotakis, “Probabilistic short-term wind power forecasting for the optimal management of wind generation,” in In Proc. of the IEEE PowerTech Conference, Lausanne, Switzerland, July 2007.
[11] H. A. Nielsen, H. Madsen, and T. S. Nielsen, “Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts,” Wind Energy, vol. 9, no. 1-2, pp. 95–108, 2006.
[12] A. Botterud, et al., “Wind power trading under uncertainty in LMP markets”, IEEE Transactions on Power Systems, vol. 27, no. 2, pp. 894–903, May 2012.
[13] Zugno, M.; Jónsson, T.; Pinson, P., “Trading wind energy on the basis of probabilistic forecasts both of wind generation and of market quantities”, Wind Energy, vol. 16, no. 6, pp. 909–926, Sept. 2013.
[14] Bremnes, J. B. “Probabilistic wind power forecasts using local quantile regression,” Wind Energy, vol. 7, no. 1, pp. 47–54, Jan.–Mar. 2004.
[15] Angarita J.L, Hernandez C.A., Usaola, J. “Analysis of a wind farm’s revenue in the British and Spanish markets”, Energy Policy, Vol. 35, Issue 10, oct 2007, pp. 5051-5059.
[16] Holttinen H. “Optimal electricity market for wind power”, Energy Policy, Vol. 33, Issue 16, nov. 2005, pp. 2052-2063.
[17] U. Linnet, “Tools supporting wind energy trade in deregulated markets,” Master’s thesis, Technical Univ. Denmark, Kgs. Lyngby, Denmark, 2005, IMM-Thesis-2005-56.
[18] Chaves-Ávila, J. P., Hakvoort, R. A., Ramos, A., “Short-term strategies for Dutch wind power producers to reduce imbalance costs”, Energy Policy, vol. 52, pp. 573–582, January 2013.
[19] Bourry, F.; Kariniotakis, G., “Strategies for wind power trading in sequential shortterm electricity markets”, European Wind Energy Conference & Exhibition EWEC, Marseille, April 2009.