Bidding Wind Energy Exploiting Wind Speed Forecasts

In this paper, we address the problem of determining the optimal day-ahead generation profile for a wind power producer by exploiting wind speed forecasts provided by a meteorological service. In the considered framework, the wind power producer is called to take part in the responsibility of system operation by providing day-ahead generation profiles and undergoing penalties in case of deviations from the schedule. Penalties are applied only if the delivered hourly energy deviates from the schedule more than a given relative tolerance. The optimal solution is obtained analytically by formulating and solving a stochastic optimization problem aiming at maximizing the expected profit. The proposed approach consists in exploiting wind speed forecasts to classify the next day into one of several predetermined classes, and then selecting the optimal solution derived for each class. The performance of the bidding strategy is demonstrated using real data from an Italian wind plant and weather forecasts provided by a commercial meteorological service.

[1]  John Bjørnar Bremnes,et al.  Probabilistic wind power forecasts using local quantile regression , 2004 .

[2]  H. Holttinen Optimal electricity market for wind power , 2004 .

[3]  H. Madsen,et al.  On the market impact of wind energy forecasts , 2010 .

[4]  Antonio J. Conejo,et al.  Economic Valuation of Reserves in Power Systems With High Penetration of Wind Power , 2009, IEEE Transactions on Power Systems.

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

[6]  M. O'Malley,et al.  Wind generation, power system operation, and emissions reduction , 2006, IEEE Transactions on Power Systems.

[7]  Antonio Vicino,et al.  Wind power bidding in a soft penalty market , 2013, 52nd IEEE Conference on Decision and Control.

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

[9]  B. F. Hobbs,et al.  Opportunity Cost Bidding by Wind Generators in Forward Markets: Analytical Results , 2011, IEEE Transactions on Power Systems.

[10]  Antonio Vicino,et al.  Bidding strategies for renewable energy generation with non stationary statistics , 2014 .

[11]  Zhang Yan,et al.  A review on the forecasting of wind speed and generated power , 2009 .

[12]  Vladimiro Miranda,et al.  Risk management and optimal bidding for a wind power producer , 2010, IEEE PES General Meeting.

[13]  S. Boucheron,et al.  Theory of classification : a survey of some recent advances , 2005 .

[14]  Jianxue Wang,et al.  Review on probabilistic forecasting of wind power generation , 2014 .

[15]  K. Poolla,et al.  The role of co-located storage for wind power producers in conventional electricity markets , 2011, Proceedings of the 2011 American Control Conference.

[16]  J. Usaola,et al.  Benefits for Wind Energy in Electricity Markets from Using Short Term Wind Power Prediction Tools; A Simulation Study , 2004 .

[17]  Antonio Vicino,et al.  Exploiting weather forecasts for sizing photovoltaic energy bids , 2013, IEEE PES ISGT Europe 2013.

[18]  A. Fabbri,et al.  Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market , 2005, IEEE Transactions on Power Systems.

[19]  N.D. Hatziargyriou,et al.  An Advanced Statistical Method for Wind Power Forecasting , 2007, IEEE Transactions on Power Systems.

[20]  Antonio J. Conejo,et al.  Short-Term Trading for a Wind Power Producer , 2010 .

[21]  P. Varaiya,et al.  Bringing Wind Energy to Market , 2012, IEEE Transactions on Power Systems.

[22]  A. Conejo,et al.  Economic Valuation of Reserves in Power Systems With High Penetration of Wind Power , 2009 .

[23]  Christoph Weber,et al.  Distribution of costs induced by the integration of RES-E power , 2008 .

[24]  O. Mangasarian,et al.  Multicategory discrimination via linear programming , 1994 .