Trading wind energy on the basis of probabilistic forecasts both of wind generation and of market quantities

Wind power is not easily predictable and non-dispatchable. Nevertheless, wind power producers are increasingly urged to participate in electricity market auctions in the same manner as conventional power producers. The aim of this paper is to propose an operational strategy for trading wind energy in liberalized electricity markets and to assess its performance. At first, the so-called optimal quantile strategy is revisited. It is proved that without market power, i.e. under the price-taker assumption, this strategy maximizes expected market revenues. Forecasts of wind power production, of day-ahead and real-time market prices and of the system imbalance are inputs to this strategy. Subsequently, constraining of the bid that maximizes the expected revenues is proposed as a way to overcome the strategy's disregard of practical limitations and, at the same time, of risk. Two constraining techniques are introduced: constraining in the decision space and in the probability space. Finally, the trade of a wind power producer is simulated in a test case for the Eastern Danish (DK-2) price area of the Nordic Power Exchange (Nord Pool) during a 10 month period in 2008. The results of the test case show the financial benefits of the aforementioned strategy as well as the consequent interaction with the electricity market. This study will support a demonstration in the framework of the EU project ANEMOS.plus. Copyright © 2012 John Wiley & Sons, Ltd.

[1]  Alexander Boogert,et al.  On the effectiveness of the anti-gaming policy between the day-ahead and real-time electricity markets in The Netherlands , 2005 .

[2]  Henrik Madsen,et al.  A review on the young history of the wind power short-term prediction , 2008 .

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

[4]  劉有飛.  Network and temporal effects on strategic bidding in electricity markets , 2006 .

[5]  H. Raiffa,et al.  Applied Statistical Decision Theory. , 1961 .

[6]  Tryggvi Jonsson,et al.  Forecasting of Electricity Prices Accounting for Wind Power Predictions , 2008 .

[7]  F. Alvarado.  The stability of power system markets , 1999 .

[8]  Vladimiro Miranda,et al.  ‘Good’ or ‘bad’ wind power forecasts: a relative concept , 2011 .

[9]  Julio Usaola,et al.  Combined hydro-wind generation bids in a pool-based electricity market , 2009 .

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

[11]  Tryggvi Jónsson,et al.  Forecasting and decision-making in electricity markets with focus on wind energy , 2012 .

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

[13]  Gregor Giebel,et al.  The State-Of-The-Art in Short-Term Prediction of Wind Power. A Literature Overview , 2003 .

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

[15]  A. Conejo,et al.  Short-Term Trading for a Wind Power Producer , 2010, IEEE Transactions on Power Systems.

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

[17]  Wil L. Kling,et al.  Economic evaluation of offshore wind power in the liberalized Dutch power market , 2009 .

[18]  Howard Raiffa,et al.  Applied Statistical Decision Theory. , 1961 .

[19]  Julio Usaola-García,et al.  Analysis of a wind farm's revenue in the British and Spanish markets , 2007 .

[20]  Ken Binmore,et al.  Game theory - a very short introduction , 2007 .

[21]  H. Madsen,et al.  On the Market Impact of Wind Power (Forecasts) - An Overview of the Effects of Large-scale Integration of Wind Power on the Electricity Market , 2010 .

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

[23]  K. Binmore.  2. Game Theory , 2008 .

[24]  Torben Skov Nielsen,et al.  Online prediction and control in nonlinear stochastic systems , 2002 .

[25]  E. Zwet,et al.  Bidding and regulating strategies in a dual imbalance pricing system : case study for a Dutch wind producer , 2008 .

[26]  Christoph Weber,et al.  Adequate intraday market design to enable the integration of wind energy into the European power systems , 2010 .

[27]  Vladimiro Miranda,et al.  Wind power forecasting in U.S. Electricity markets , 2010 .

[28]  Vladimiro Miranda,et al.  Wind power forecasting : state-of-the-art 2009. , 2009 .

[29]  Henrik Madsen,et al.  Feedback, competition and stochasticity in a day ahead electricity market , 2010 .

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

[31]  T. Gneiting.  Quantiles as optimal point forecasts , 2011 .

[32]  P Pinson,et al.  Conditional Prediction Intervals of Wind Power Generation , 2010, IEEE Transactions on Power Systems.

[33]  R. Barthelmie,et al.  The economic benefit of short-term forecasting for wind energy in the UK electricity market , 2008 .

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

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

[36]  S. Galloway,et al.  Managing the risk of trading wind energy in a competitive market , 2006 .