Probabilistic Ramp Detection and Forecasting for Wind Power Prediction
This chapter proposes a new way to detect and represent the probability of ramping events in short-term wind power forecasting. Ramping is one notable characteristic in a time series associated with a drastic change in value in a set of consecutive time steps. Two properties of a ramp event forecast, that is, slope and phase error, are important from the point of view of the system operator (SO): they have important implications in the decisions associated with unit commitment or generation scheduling, especially if there is thermal generation dominance in the power system. Unit commitment decisions, generally taken some 12–48 h in advance, must prepare the generation schedule in order to smoothly accommodate forecasted drastic changes in wind power availability.
This manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (‘Argonne’). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up non-exclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. The authors acknowledge EDP Renewables North America for providing the wind farm data used in the analysis.
The general fundamental work at INESC TEC is partially funded by the ERDF from the EU through the Programme COMPETE and by the Portuguese Government through FCT—Foundation for Science and Technology, namely under PEST-C/EEI/LA 0014/2011 and project ref. LASCA PTDC/EEA-EEL/104278/2008 and GEMS PTDC/EEA-EEL/105261/2008.
- 1.Ferreira C, Gama J, Miranda V, Botterud A (2010) A survey on wind power ramp forecasting. Report ANL/DIS 10–13, Argonne national laboratory, DecGoogle Scholar
- 2.Greaves B et al (2009) Temporal forecast uncertainty for ramp events. In: Proceedings of EWEC’09, Marseille, FranceGoogle Scholar
- 3.Potter CW, Grimit E, Nijssen B (2009) Potential benefits of a dedicated probabilistic rapid ramp event forecast tool. In: Proceedings of PSCE’09, Seattle, USAGoogle Scholar
- 4.Freedman J, Markus M, Penc R (2008) Analysis of west texas wind plant ramp-up and ramp-down events. AWS Truewind, LLC, NYGoogle Scholar
- 5.Kamath C (2010) Understanding wind ramp events through analysis of historical data. In: Proceedings of IEEE PES transmission and distribution conference and exposition, LA, United StatesGoogle Scholar
- 6.Zheng H, Kusiak A (2009) Prediction of wind farm power ramp rates: a data-mining approach. J Solar Energ Eng, vol 131Google Scholar
- 7.Jørgensen JU, Mörlen C (2008) AESO wind power forecasting pilot project. Technical report, Ebberup, DenmarkGoogle Scholar
- 8.Truewind-LLC AWS (2008) AWS Truewind’s final report for the alberta forecasting pilot project. Alberta, CanadaGoogle Scholar
- 10.Bossavy A, Girard R, Kariniotakis G (2010) Forecasting uncertainty related to ramps of wind power production. In: Proceedings of EWEC’10, Warsaw, PolandGoogle Scholar
- 12.Bessa RJ, Miranda V, Gama J (2009) Entropy and correntropy against minimum square error in offline and online three-day ahead wind power forecasting. IEEE Trans Power Sys 24(4):1657–1666, NovGoogle Scholar
- 13.Juban J, Siebert N, Kariniotakis GN (2007) Probabilistic short-term wind power forecasting for the optimal management of wind generation. In: Proceedings IEEE PowerTech, Lausanne, France, pp 683–688Google Scholar
- 14.Provost F, Fawcett T (1997) Analysis and visualization of classifier performance: comparison under imprecise class and cost distributions, KDD’97, USA, pp 43–48Google Scholar