Dynamic sizing of energy storage for hedging wind power forecast uncertainty

In market conditions where program responsible parties are penalized for deviations from proposed bids, energy storage can be used for compensating the energy imbalances induced by limited predictability of wind power. The energy storage capacity necessary for performing this task will differ between delivery periods, according to the magnitude and the evolution of forecast errors in each delivery period. A methodology is presented for the assessment of the necessary storage capacity for each delivery period, based on the degree of risk that the power producer accepts to be exposed to. This approach leads to a dynamic assessment of the energy storage capacity for different delivery periods. In such a context, energy storage is used as a means of risk hedging against penalties from the regulation market. The application of the algorithm on real data (both measurements and forecasts) of the yearly output of a wind farm shows that the application of a dynamic daily sizing of the necessary storage leads to a significant reduction of the storage capacity used, without affecting the producer's profit significantly. The method proposed here may provide the basis for the introduction of storage as an independent market entity, where each producer may rent the necessary daily storage capacity for hedging the risk of the wind power limited predictability.

[1]  John P. Barton,et al.  A probabilistic method for calculating the usefulness of a store with finite energy capacity for smoothing electricity generation from wind and solar power , 2006 .

[2]  G. Strbac,et al.  Value of combining energy storage and wind in short-term energy and balancing markets , 2003 .

[3]  P. Pinson,et al.  Trading Wind Generation From Short-Term Probabilistic Forecasts of Wind Power , 2007, IEEE Transactions on Power Systems.

[4]  Bernd Klöckl.  Impacts of energy storage on power systems with stochastic generation , 2007 .

[5]  Pierre Pinson,et al.  Estimation of the uncertainty in wind power forecasting , 2006 .

[6]  Dorota Kurowicka,et al.  Integration of stochastic generation in power systems , 2006 .

[7]  M. Lange.  On the Uncertainty of Wind Power Predictions—Analysis of the Forecast Accuracy and Statistical Distribution of Errors , 2005 .

[8]  J.P. Barton,et al.  Energy storage and its use with intermittent renewable energy , 2004, IEEE Transactions on Energy Conversion.

[9]  L. Soder,et al.  Minimization of imbalance cost trading wind power on the short term power market , 2005, 2005 IEEE Russia Power Tech.

[10]  G. Strbac,et al.  Trading Wind Generation in Short-Term Energy Markets , 2002, IEEE Power Engineering Review.

[11]  H. Aa,et al.  PREDICTION OF WIND POWER USING TIME-VARYING COEFFICIENT-FUNCTIONS , 2002 .

[12]  H. Madsen,et al.  From probabilistic forecasts to statistical scenarios of short-term wind power production , 2009 .

[13]  Klaus Skytte,et al.  The regulating power market on the Nordic power exchange Nord Pool: an econometric analysis , 1999 .

[14]  Henrik Madsen,et al.  Ensemble-based Probabilistic Forecasting at Horns Rev , 2009 .

[15]  Magnus Korpås,et al.  Improving the network infeed accuracy of non-dispatchable generators with energy storage devices ! , 2008 .

[16]  G. Strbac,et al.  Value of Bulk Energy Storage for Managing Wind Power Fluctuations , 2007, IEEE Transactions on Energy Conversion.

[17]  Pierre Pinson,et al.  Standardizing the Performance Evaluation of Short-Term Wind Power Prediction Models , 2005 .

[18]  J.A.P. Lopes,et al.  On the optimization of the daily operation of a wind-hydro power plant , 2004, IEEE Transactions on Power Systems.

[19]  Jan Kloppenborg Møller,et al.  ARTICLE IN PRESS Computational Statistics & Data Analysis ( ) – Time-adaptive quantile regression , 2022 .

[20]  Ulfar Linnet,et al.  Tools supporting wind energy trade in deregulated markets , 2005 .

[21]  John S. Anagnostopoulos,et al.  Simulation and size optimization of a pumped–storage power plant for the recovery of wind-farms rejected energy , 2008 .

[22]  Magnus Korpaas,et al.  Operation and sizing of energy storage for wind power plants in a market system , 2003 .

[23]  Pierre Pinson,et al.  Non‐parametric probabilistic forecasts of wind power: required properties and evaluation , 2007 .

[24]  X.Y. Wang,et al.  Determination of Battery Storage Capacity in Energy Buffer for Wind Farm , 2008, IEEE Transactions on Energy Conversion.

[25]  H. Madsen,et al.  From wind ensembles to probabilistic information about future wind power production -- results from an actual application , 2006, 2006 International Conference on Probabilistic Methods Applied to Power Systems.