Trading Wind Generation From Short-Term Probabilistic Forecasts of Wind Power

By Pierre Pinson, C. Chevallier, G. Kariniotakis. IEEE Transactions on Power Systems, 2007

623 citations recorded by the source corpus.

Due to the fluctuating nature of the wind resource, a wind power producer participating in a liberalized electricity market is subject to penalties related to regulation costs. Accurate forecasts of wind generation are therefore paramount for reducing such penalties and thus maximizing revenue. Despite the fact that increasing accuracy in spot forecasts may reduce penalties, this paper shows that, if such forecasts are accompanied with information on their uncertainty, i.e., in the form of predictive distributions, then this can be the basis for defining advanced strategies for market participation. Such strategies permit to further increase revenues and thus enhance competitiveness of wind generation compared to other forms of dispatchable generation. This paper formulates a general methodology for deriving optimal bidding strategies based on probabilistic forecasts of wind generation, as well as on modeling of the sensitivity a wind power producer may have to regulation costs. The benefits resulting from the application of these strategies are clearly demonstrated on the test case of the participation of a multi-MW wind farm in the Dutch electricity market over a year.

Subfields: Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/a86fa4b76d3766c4dadd088d9700eb786d8e40af

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Citations in the corpus

A Statistical Model for Wind Power Forecast Error and its Application to the Estimation of Penalties in Liberalized Markets (2011, background): “Optimal bidding strategies based on forecast uncertainty have been formulated in [20] and storage sizing based on an energy rejection limit has been described in [21].”
Strategic Offering for a Wind Power Producer (2013, background): “In [10], probabilistic forecasts of wind production and regulation costs are the basis to develop an offering strategy.”
Very‐short‐term probabilistic forecasting of wind power with generalized logit–normal distributions (2012, background): “…(Tay and Wallis, 2000; Timmermann, 2000), earthquake prediction (Kagan and Jackson, 2000) and meteorology (Leutbecher and Palmer, 2008), as well as for weather-related processes like renewable energy production (Bacher et al., 2009; Pinson et al., 2007) or floods (Cloke and Pappenberger, 2009).”
Pricing Electricity in Pools With Wind Producers (2012, background): “Note that nowadays this offering strategy constitutes a common practice for wind producers participating in electricity markets [24], [25].”
Opportunity Cost Bidding by Wind Generators in Forward Markets: Analytical Results (2011, methodology): “Reference [6] then introduced a closed-form expression for the optimal contract volume in the forward market given the forward and real-time prices; the same optimization approach was used in [7], which compares strategies based on point prediction and probabilistic wind forecasts, and also discusse”
Wind Power Trading Under Uncertainty in LMP Markets (2012, background): “[7] also focus on minimizing the imbalance cost.”
Powering Up With Space-Time Wind Forecasting (2010, background): “underestimates more strongly than overestimates ( Pinson, Chevallier, Kariniotakis 2007 ), which”
On the market impact of wind energy forecasts (2010, background): “Some efforts have been made to optimise the bidding of wind energy into deregulated electricity markets based on these forecasts, and in some cases on information about their situation-dependent uncertainty as well (Pinson et al., 2007; Matevosyan and Söder, 2006; Bathurst et al., 2002).”
Rolling Optimization of Wind Farm and Energy Storage System in Electricity Markets (2015, background): “So intraday rebidding is beneficial to both WFs and system operation [11], [12].”
A Convex Model of Risk-Based Unit Commitment for Day-Ahead Market Clearing Considering Wind Power Uncertainty (2015, background): “The PDFs can either be obtained from a probabilistic forecast [30], [31] or from historical statistics [32].”
Bringing Wind Energy to Market (2012, background): “[32] show that the optimal forward contract can be analytically expressed as a probabilistic quantile on prices—a well-established result in the field of inventory theory [33].”