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Impact of Tree Growth on AEP Estimates & Uncertainty
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Carlos Silva Santos Megajoule Inovação Lda, Portugal IMPACT OF TREE GROWTH ON AEP ESTIMATES & UNCERTAINTY Abstract ID: 289 Poster code: PO.247 | Download poster: ![]() |
Presenter's biography
Biographies are supplied directly by presenters at WindEurope 2016 and are published here uneditedWith a PhD in Computational Fluid Dynamics from the University of Bath, in 2004, Dr. Silva Santos was a Senior Research Fellow at the University of Oporto.
In 2009, Dr. Silva Santos joined fellow researchers to create Megajoule Inovação, where he is a managing partner. Its aim is to develop innovative solutions for the renewable energy market. In the process, it deployed WINDIE™ CFD to wind energy applications, used in over 3.5 GW of installed wind power. He is also a lecturer at the Polytechnic Institute of Porto. He is author of over 20 peer-reviewed publications on renewable energy.
Abstract
Impact of Tree Growth on AEP Estimates & Uncertainty
Introduction
The modelling of the effect of forested areas on wind resource and site assessment quantities has become increasingly sophisticated. This refinement in technological approach has come from different areas: a) increase in accuracy of computational forest modelling in CFD tools, b) use of increasingly detailed forest data, including vertical descriptions of leaf density distributions, and c) incorporation of the variation of forest characteristics throughout the year, namely loss of foliage.
Whilst all these advancements are welcome, the impact of forest growth in the long-term wind farm production is often treated parametrically as a linear loss subtracted ad-hoc from final AEP values.
The authors believe that such losses are better assessed by incorporating tree growth in CFD AEP calculations on a case-by-case basis.
Approach
WINDIE™ CFD wind assessment studies consist of simulations describing the wind flow from a number of directions at a given site.
In the case of forested areas, the forest characteristics are described explicitly in the model through additional terms in momentum and turbulence equations, in terms of tree height, leaf area index (LAI) and the species-dependent vertical variation of leaf density.
Boundary conditions are idealized profiles of wind speed and turbulence quantities or, instead, boundary conditions extracted from mesoscale simulations which incorporate regional wind patterns and thermal stratification signatures. Upon completion of the simulation sets, these are used as transfer functions to transport measured data to wind turbine locations, i.e. to extrapolate the wind conditions in space.
Main body of abstract
To incorporate the effect of tree growth, an additional set of simulations is performed using the predicted forest characteristics halfway through the wind farm operation life. Due to the use of identical boundary conditions for the two forest conditions, a transfer function can be constructed to allow for extrapolation of the wind data, not only in space but also in time.
This approach is used in three wind farms in southern France, with significant forest cover.
Measurements were collected at 4 masts in total, under observed forest characteristics. However, due to the young age of the vegetation, tree growth as large as 10m is expected in some areas, with more moderate growth in others.
Simulations are carried out using the forest conditions present during measurement campaigns, and results validated using measured wind profiles at the various masts. Results showed good agreement between numerical and measured data.
After this, the forest characteristics were changed in the WINDIE CFD model to those forecast for a horizon of 15 years in the future, expected to be representative of the average operational conditions of the wind farm. This was performed for hub heights of 80m and 120m agl.
Conclusion
WINDIE CFD simulations predicted a reduction of up to -13.6% in the first wind farm to a minimum reduction of -3.4% in the third wind farm in terms of gross AEP.
Based on these results, the additional contribution to overall gross AEP uncertainty was estimated for various degrees of confidence in the future forest scenario. Results show that even for a 90% confidence in future forest growth the additional uncertainty can reach nearly 7% even for hub heights of 120m agl.
Learning objectives
The results to be presented are relevant in the context of increasing number of wind farms being installed in forested areas. In a considerable number of these cases, AEP and site assessment results can be considerably affected if forest characteristics change significantly during the wind farm lifetime.
The results presented here show that these changes can be significant in terms of the magnitude of AEP values. They also show that these changes vary significantly from site to site.
Furthermore, this work quantifies the increase in uncertainty for 1-year and 20-year AEP estimates due to forest growth during the wind farm operation life.
It is the authors opinion, that in some cases the impact of tree growth should be carefully investigated.
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