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

TLDR

Wind power producers in liberalized markets face regulation‑cost penalties due to the resource’s fluctuating nature. The study aims to develop optimal bidding strategies that use probabilistic wind forecasts to reduce regulation penalties and maximize revenue. The authors formulate a methodology that derives optimal bidding strategies from probabilistic forecasts and a model of regulation‑cost sensitivity. The paper demonstrates that probabilistic forecasts enable advanced market‑participation strategies that increase revenues and competitiveness, with benefits shown in a Dutch market case study of a multi‑MW wind farm over a year.

Abstract

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.