Forecasting intrahourly variability of wind generation

Intrahourly fluctuations in wind power generation cause various technical and economic challenges for wind farm integration. With increasing levels of wind energy uptake, managing these fluctuations is likely to require further attention. Intrahourly fluctuations in wind power are caused by fluctuations in wind speed that can be linked with a range of atmospheric phenomena. Explicitly forecasting intrahourly wind variability requires both statistical and physical modelling. Predictors of wind and power variability include large-scale weather patterns, the wind speed timeseries itself, and information about approaching weather regimes. Numerical weather prediction models have been shown to be a useful tool for day-ahead forecasts of intrahourly variability. A promising application of intrahourly variability forecasts is the management of variable wind farm output using a flexible dispatch margin.

[1]  C. Lindsay Anderson,et al.  A Flexible Dispatch Margin for Wind Integration , 2015, IEEE Transactions on Power Systems.

[2]  S. Sethuraman A case of persistent breaking of internal gravity waves in the atmospheric surface layer over the ocean , 1980 .

[3]  P. Pinson,et al.  Very‐short‐term probabilistic forecasting of wind power with generalized logit–normal distributions , 2012 .

[4]  C. J. Russell,et al.  Simulation of Wind Power at Several Locations Using a Measured Time-Series of Wind Speed , 2013, IEEE Transactions on Power Systems.

[5]  Merete Badger,et al.  A case‐study of mesoscale spectra of wind and temperature, observed and simulated , 2011 .

[6]  T. Davies,et al.  A mass restoration scheme for limited‐area models with semi‐Lagrangian advection , 2015 .

[7]  Stephen Dorling,et al.  Modelling sea‐breeze climatologies and interactions on coasts in the southern North Sea: implications for offshore wind energy , 2015 .

[8]  Henrik Madsen,et al.  Adaptive modelling and forecasting of offshore wind power fluctuations with Markov‐switching autoregressive models , 2012 .

[9]  Henrik Madsen,et al.  Automatic Classification of Offshore Wind Regimes With Weather Radar Observations , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[10]  R. Davy,et al.  Cross-Spectrum of Wind Speed for Meso-Gamma Scales in the Upper Surface Layer over South-Eastern Australia , 2011 .

[11]  Charlotte Bay Hasager,et al.  Wind Class Sampling of Satellite SAR Imagery for Offshore Wind Resource Mapping , 2010 .

[12]  W. Skamarock Evaluating Mesoscale NWP Models Using Kinetic Energy Spectra , 2004 .

[13]  Mark C. Kelly,et al.  Idealized Mesoscale Model Simulations of Open Cellular Convection Over the Sea , 2011, Boundary-Layer Meteorology.

[14]  Antonio Vigueras-Rodríguez,et al.  Modelling of power fluctuations from large offshore wind farms , 2008 .

[15]  Sukanta Basu,et al.  Mesoscale modeling of coastal low‐level jets: implications for offshore wind resource estimation , 2014 .

[16]  Margaret A. LeMone,et al.  The Structure and Dynamics of Horizontal Roll Vortices in the Planetary Boundary Layer , 1973 .

[17]  Murray Thomson,et al.  Going with the wind: temporal characteristics of potential wind curtailment in Ireland in 2020 and opportunities for demand response , 2015 .

[18]  S. Larsen,et al.  Cross-Spectra Over the Sea from Observations and Mesoscale Modelling , 2013, Boundary-Layer Meteorology.

[19]  G. D. Nastrom,et al.  Kinetic energy spectrum of large-and mesoscale atmospheric processes , 1984, Nature.

[20]  Ana Estanqueiro,et al.  Impacts of large amounts of wind power on design and operation of power systems, results of IEA collaboration , 2008 .

[21]  R. Davy,et al.  Statistical Downscaling of Wind Variability from Meteorological Fields , 2010 .

[22]  Xiaoli Guo Larsén,et al.  Spectral structure of mesoscale winds over the water , 2013 .

[23]  H. Madsen,et al.  Resolving Nonstationary Spectral Information in Wind Speed Time Series Using the Hilbert-Huang Transform , 2010 .

[24]  H. Madsen,et al.  Influence of local wind speed and direction on wind power dynamics - Application to offshore very short-term forecasting , 2011 .

[25]  V. Akhmatov Influence of Wind Direction on Intense Power Fluctuations in Large Offshore Windfarms in the North Sea , 2007 .

[26]  Cameron W. Potter,et al.  Potential benefits of a dedicated probabilistic rapid ramp event forecast tool , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[27]  Henrik Madsen,et al.  Regime-switching modelling of the fluctuations of offshore wind generation , 2008 .

[28]  Pierre Pinson,et al.  Wind fluctuations over the North Sea , 2011 .

[29]  Filip Johnsson,et al.  Dampening variations in wind power generation—the effect of optimizing geographic location of generating sites , 2014 .

[30]  Georgios A. Skrimpas,et al.  Advantages on monitoring wind turbine nacelle oscillation. , 2015 .

[31]  Nick Ellis,et al.  Predicting wind power variability events using different statistical methods driven by regional atmospheric model output , 2015 .

[32]  Henrik Madsen,et al.  Weather radars - the new eyes for offshore wind farms? , 2014 .

[33]  Alexandre Costa,et al.  A wavelet-based approach for large wind power ramp characterisation , 2013 .

[34]  M. Handschy,et al.  Variability of interconnected wind plants: correlation length and its dependence on variability time scale , 2015 .