Preprints
https://doi.org/10.5194/angeo-2019-88
https://doi.org/10.5194/angeo-2019-88
02 Jul 2019
 | 02 Jul 2019
Status: this discussion paper is a preprint. It has been under review for the journal Annales Geophysicae (ANGEO). The manuscript was not accepted for further review after discussion.

Air Density Induced Error on Wind Energy Estimation

Aurore Dupré, Philippe Drobinski, Jordi Badosa, Christian Briard, and Riwal Plougonven

Abstract. In recent years, environmental concerns have encouraged the use of wind power as a renewable energy resource. However, high penetration of the wind power in the electricity system is a challenge due to the uncertainty of wind energy forecast. Estimation of the wind energy production requires a forecast for the wind (the main source of uncertainty) but also of density, often overlooked. Measure of air density is a key for more accurate wind energy prediction. Wind farms often lack instrumentations of temperature and pressure, needed for accurate air density estimation at hub height to be used for locally debiasing air density forecast. In this study, the error budget of air density estimate is computed distinguishing temperature and pressure contributions. The analysis uses measurements for in-depth local analysis as well as meteorological reanalysis to investigate the added-value of a model-based value when measurement is missing. Meteorological reanalysis is also used to study spatial pattern of error budgets (mountainous area, coastal regions, plains, ...). The effect of altitude is carefully accounted for. Temperature is by far the variable inducing the largest errors when it is missing in the air density correction, and replaced by the standard atmosphere value (i.e. 15 °C, used as reference in power curves). It is particularly true for very cold or warm conditions (i.e. far from the standard value), for which the error on wind energy production is nearly halved when an accurate correction of temperature is performed.

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Aurore Dupré, Philippe Drobinski, Jordi Badosa, Christian Briard, and Riwal Plougonven
 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Aurore Dupré, Philippe Drobinski, Jordi Badosa, Christian Briard, and Riwal Plougonven
Aurore Dupré, Philippe Drobinski, Jordi Badosa, Christian Briard, and Riwal Plougonven

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Short summary
In a context of climate change, the wind energy sector has seen a very sharp growth requiring accurate forecasts. Air density is a key variable in the wind energy modeling as it can make the power output varies by almost 20 %. In this paper, a numerically low-cost method is evaluated. This method improves the wind energy modeling by more than 15 % and by almost 40 % when the atmospheric conditions are far from the standards atmospheric conditions used to produce the wind turbine power curve.