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Renewable Energy Sources—Modeling and Forecasting

  • Juan M. MoralesEmail author
  • Antonio J. Conejo
  • Henrik Madsen
  • Pierre Pinson
  • Marco Zugno
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 205)

Abstract

Short-term forecasts of renewable power generation are a necessary input to nearly all operational problems in electricity markets. For instance, both market and system operators may use them for the clearing of day-ahead and real-time electricity markets. In addition, market participants rely on forecasts for determining their optimal offering strategies in view of uncertainties brought in by renewable energy production. The various forms of renewable power predictions are introduced here based on real-world examples. Special emphasis is placed on probabilistic forecasts in their general form and to scenarios mimicking spatial and temporal dependencies, as well as potential dependencies among different types of renewable energy sources. The way forecasts are issued and subsequently evaluated is also covered.

Keywords

Renewable Energy Lead Time Renewable Energy Source Forecast Error Wind Farm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Bacher, P., Madsen, H., Nielsen, H.A.: Online short-term solar power forecasting. Sol. Energy 83(10), 1772–1783 (2009)CrossRefGoogle Scholar
  2. 2.
    Gneiting, T.: Quantiles as optimal point forecasts. Int. J. Forecasting 27(2), 197–207 (2011)CrossRefGoogle Scholar
  3. 3.
    Gneiting, T., Balabdaoui, F., Raftery, A.E.: Probabilistic forecasts, calibration and sharpness. J. Royal Stat. Soc. B 69(2), 243–268 (2007)CrossRefGoogle Scholar
  4. 4.
    Gneiting, T., Stanberry, L.I., Grimit, E.P., Held, L., Johnson, N.A.: Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds. Test 17(2), 211–235 (2008)CrossRefGoogle Scholar
  5. 5.
    Hyndman, R.J.: Highest-density forecast regions for nonlinear and non-normal time series models. J. Forecasting 14(5), 431–441 (1995)CrossRefGoogle Scholar
  6. 6.
    Jolliffe, I.T., Stephenson, D.B.: Forecast Verification - A Practitioner’s Guide in Atmospheric Science, 2nd edn. Wiley, New York (2012)Google Scholar
  7. 7.
    Lange, M., Focken, U.: Physical Approach to Short-term Wind Power Prediction. Springer, Berlin (2005)Google Scholar
  8. 8.
    Lorenz, E., Hurka, J., Heinemann, D., Beyer, H.G.: Irradiance forecasting for the power prediction of grid-connected photovoltaic systems. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2(1), 2–10 (2009)CrossRefGoogle Scholar
  9. 9.
    Madsen, H., Pinson, P., Kariniotakis, G., Nielsen, H.A., Nielsen, T.S.: Standardizing the performance evaluation of short-term wind power prediction models. Wind Eng. 29(6), 475–489 (2005)CrossRefGoogle Scholar
  10. 10.
    Madsen, H., Thyregod, P.: Introduction to General and Generalized Linear Models. Chapman & Hall/CRC, Boca Raton (2011)Google Scholar
  11. 11.
    Matos, M.A., Bessa, R.J.: Setting the operating reserve using probabilistic wind power forecasts. IEEE T. Power Syst. 26(2), 594–603 (2011)CrossRefGoogle Scholar
  12. 12.
    Morales, J.M., Minguez, R., Conejo, A.J.: A methodology to generate statistically dependent wind speed scenarios. Appl. Energy 87(3), 843–855 (2010)CrossRefGoogle Scholar
  13. 13.
    Nelsen, R.B.: An Introduction to Copulas. Springer, New York (1999)CrossRefGoogle Scholar
  14. 14.
    Pinson, P., Girard, R.: Evaluating the quality of scenarios of short-term wind power generation. Appl. Energy 96, 12–20 (2012)CrossRefGoogle Scholar
  15. 15.
    Pinson, P., Madsen, H., Nielsen, H.A., Papaefthymiou, G., Klöckl, B.: From probabilistic forecasts to statistical scenarios of short-term wind power production. Wind Energy 12(1), 51–62 (2009)CrossRefGoogle Scholar
  16. 16.
    Pinson, P., Nielsen, H.A., Møller, J.K., Madsen, H., Kariniotakis, G.N.: Non-parametric probabilistic forecasts of wind power: Required properties and evaluation. Wind Energy 10(6), 497–516 (2007)CrossRefGoogle Scholar
  17. 17.
    Pinson, P., Reikard, G., Bidlot, J.R.: Probabilistic forecasting of the wave energy flux. Appl. Energy 93, 364–370 (2012)CrossRefGoogle Scholar
  18. 18.
    Reikard, G., Pinson, P., Bidlot, J.R.: Forecasting ocean wave energy: The ECMWF wave model and time series methods. Ocean Eng. 38(10), 1089–1099 (2011)CrossRefGoogle Scholar
  19. 19.
    Wilks, D.S.: The minimum spanning tree histogram as a verification tool for multidimensional ensemble forecasts. Mon. Weather Rev. 132(6), 1329–1340 (2004)Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Juan M. Morales
    • 1
    Email author
  • Antonio J. Conejo
    • 2
  • Henrik Madsen
    • 1
  • Pierre Pinson
    • 3
  • Marco Zugno
    • 1
  1. 1.DTU ComputeTechnical University of DenmarkLyngbyDenmark
  2. 2.University of Castilla – La ManchaCiudad RealSpain
  3. 3.DTU ElektroTechnical University of DenmarkLyngbyDenmark

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