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The role of predictability in the investment phase of wind farms

Abstract : This chapter shows some test cases from the EU project SAFEWIND on different ways that the planning phase of wind farms can benefit from early knowledge of wind power predictability. The interest is focused on day-ahead short-term forecasting, as the most relevant range for TSOs and wind energy traders. We shall use the term predictability assessment when we quantify wind predictability during the planning phase of a wind farm. This can be associated to the prediction of the hub-height wind speed (the meteorological wind resource), the gross wind power from a specified turbine power-curve model, or the net wind power output, which includes wind farm losses due to wake effects and other environmental aspects that are site and wind farm layout specific. Within the SAFEWIND project, wind power predictability is evaluated to suggest potential benefits during the planning phase of wind farm deployment. Predictability assessment can be useful, for instance, to anticipate operational costs due to market imbalance for energy traders, costs related to prediction of offshore weather windows for turbine installation or maintenance, or to benefit from portfolio effects when aggregating wind farms that operate in uncorrelated wind climates. While the weight of predictability for a wind farm developer is low in the decision-making process compared to the capacity factor, it remains a useful indicator of the quality of wind for grid integration purposes. A series of case studies are presented in various European regions to present tools for wind power predictability assessment at different scales.
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Contributor : Magalie Prudon Connect in order to contact the contributor
Submitted on : Thursday, June 22, 2017 - 10:15:18 AM
Last modification on : Friday, May 13, 2022 - 3:36:42 AM



Sanz Rodrigo Javier, Laura Frías Paredes, Robin Girard, Georges Kariniotakis, Kevin Laquaine, et al.. The role of predictability in the investment phase of wind farms. Renewable Energy Forecasting: From Models to Applications , Elsevier - Woodhead Publishing, Chapter 14 - p. 341-357, 2017, Woodhead Publishing Series in Energy, 978-0-0810-0504-0 ⟨10.1016/B978-0-08-100504-0.00014-7⟩. ⟨hal-01544744⟩



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