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Comparison of the Economic Impact of Different Wind Power Forecast Systems for Producers : Volume 11, Issue 1 (19/05/2014)

By Alessandrini, S.

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Book Id: WPLBN0003982530
Format Type: PDF Article :
File Size: Pages 5
Reproduction Date: 2015

Title: Comparison of the Economic Impact of Different Wind Power Forecast Systems for Producers : Volume 11, Issue 1 (19/05/2014)  
Author: Alessandrini, S.
Volume: Vol. 11, Issue 1
Language: English
Subject: Science, Advances, Science
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2014
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Monache, L. D., Sperati, S., Benini, M., Davò, F., & Alessandrini, S. (2014). Comparison of the Economic Impact of Different Wind Power Forecast Systems for Producers : Volume 11, Issue 1 (19/05/2014). Retrieved from http://www.worldebookfair.org/


Description
Description: RSE, Ricerca Sistema Energetico, Milano, Italy. Deterministic forecasts of wind production for the next 72 h at a single wind farm or at the regional level are among the main end-users requirement. However, for an optimal management of wind power production and distribution it is important to provide, together with a deterministic prediction, a probabilistic one. A deterministic forecast consists of a single value for each time in the future for the variable to be predicted, while probabilistic forecasting informs on probabilities for potential future events. This means providing information about uncertainty (i.e. a forecast of the PDF of power) in addition to the commonly provided single-valued power prediction. A significant probabilistic application is related to the trading of energy in day-ahead electricity markets. It has been shown that, when trading future wind energy production, using probabilistic wind power predictions can lead to higher benefits than those obtained by using deterministic forecasts alone. In fact, by using probabilistic forecasting it is possible to solve economic model equations trying to optimize the revenue for the producer depending, for example, on the specific penalties for forecast errors valid in that market. In this work we have applied a probabilistic wind power forecast systems based on the analog ensemble method for bidding wind energy during the day-ahead market in the case of a wind farm located in Italy. The actual hourly income for the plant is computed considering the actual selling energy prices and penalties proportional to the unbalancing, defined as the difference between the day-ahead offered energy and the actual production. The economic benefit of using a probabilistic approach for the day-ahead energy bidding are evaluated, resulting in an increase of 23% of the annual income for a wind farm owner in the case of knowing a priori the future energy prices. The uncertainty on price forecasting partly reduces the economic benefit gained by using a probabilistic energy forecast system.

Summary
Comparison of the economic impact of different wind power forecast systems for producers

Excerpt
Alessandrini, S., Sperati, S., Pinson, and P. A: comparison between the ECMWF and COSMO Ensemble Prediction Systems applied to short-term wind power forecasting on real data, Appl. Energ., 107, 271–280, 2013.; Bremnes, J. B.: Probabilistic wind power forecasts using local quantile regression, Wind Energy, 7, 47–54, 2004.; Delle Monache, L., Nipen, T., Liu, Y., Roux, G., and Stull, R.: Kalman filter and analog schemes to post-process numerical weather predictions, Mon. Weather Rev., 139, 3554–3570, 2011.; Delle Monache, L., Eckel, A., Rife, D., and Searight, K.: Probabilistic weather predictions with an analog ensemble, Mon. Weather Rev., 141, 3498–3516, 2013.; Gneiting, T.: Quantiles as optimal point forecasts, Int. J. Forecasting, 27, 197–207, 2011.; Linnet, U.: Tools supporting wind energy trade in deregulated markets, Master's Thesis, Technical University of Denmark, 2005.; Pielke, R. A., Cotton, W. R., Walko, R. L., Tremback, C. J., Lyons, W. A., Grasso, L. D., Nicholls, M. E., Moran, M. D., Wesley, D. A., Lee, T. J., and Copeland, J. H.: A Comprehensive Meteorological Modeling System – RAMS, Meteorol. Atmos. Phys., 49, 69–91, 1992.; Pinson, P., Chevalier, C., and Kariniotakis, G.: Trading wind generation from short-term probabilistic forecasts of wind power, IEEE T. Power Syst., 22, 1148–1156, 2007.; Roulston, M. S., Kaplan, D. T., Hardenberg, J., and Smith, L. A.: Using medium-range weather forecasts to improve the value of wind power production, Renew. Energ., 28, 585–602, 2003.; Zugno, M., Pinson, P., and Jonsson, T.: Trading wind energy based on probabilistic forecasts of wind generation and market quantities, Wind Energy, 16, 909–926, 2013.

 

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