Back to the programme printer.gif Print




Delegates are invited to meet and discuss with the poster presenters in this topic directly after the session 'How does the wind blow behind wind turbines and in wind farms?' taking place on Tuesday, 11 March 2014 at 16:30-18:00. The meet-the-authors will take place in the poster area.

Fernando del Jesus University of Cantabria, Spain
Co-authors:
Fernando del Jesus (1) F P Raúl Guanche (1) Melisa Menéndez (1) Iñigo J. Losada (1)
(1) University of Cantabria, Santander, Spain

Printer friendly version: printer.gif Print

Abstract

Coastal topography influence over offshore wind energy production

Introduction

Some coastal locations are characterized by a great occurrence of winds flowing from earth to sea; consequently wind energy potential shows reductions due to coastal topography. On the present paper, high-resolution wind reanalysis database (SeaWind HR) is used to analyze offshore wind resource (Sempreviva et al., 2008). SeaWind HR has been validated with instrumental data (buoys and floating met masts (Guanche et al., 2011)) and used to address the coastal topography influence; as well as the turbine adaptation to wind energy potential. The main goal is to develop a methodology suitable to determine the best location under coastal topography influence.

Approach

SeaWind HR is a 500 day re-forecast set for the northern Spanish coast with 1.8 km resolution, providing wind-related variables with hourly frequency. This database is produced by a mesoscale limited-area atmospheric model (WRF) nested into NCEP-NCAR reanalysis data (Skamarock et al., 2008). Five hundred cases have been selected using the surface pressure data from reanalysis databases. The enhanced resolution enables the model to represent fine details of the coastline as bays and capes, which influence the behavior of the wind and the waves in sensible areas, such as inlets or capes. SeaWind HR has been validated based on instrumental data from meteorological buoys and floating meteorological masts: two buoys, one at La Virgen del Mar location and, one at Santoña location (eastern coast of Cantabria, Spain); two floating meteorological masts at La Virgen del Mar and the one at Ubiarco (western coast of Cantabria, Spain). These masts register wind speed using cup anemometers and sonic anemometers from 45 to 90 meters depending on the mast .
Topography influence has been analyzed taking into account wind behavior and wind energy potential. Firstly, wind speed reduction and wind direction changes have been studied. These two phenomena should be considered in order to understand wind behavior and wind energy spatial patterns. Wind direction (as the mean wind direction of all offshore nodes) has been used to separate the situations where wind is flowing from earth to sea (when it comes from the range 110ᵒ - 250ᵒ). Wind speed cubed determines wind energy potential. Yearly wind mean power is used for valuing the influence of the topography.
In addition, wind turbine power curve adaptation is included in this work trying to find the way to make every site attractive for exploitation (EL-Shimy, 2010; Chowdhury et al., 2013). Two different wind turbines have been selected: one from Gamesa (4.5MW) and one theoretical (Jonkman et al., 2009) NREL 5 MW. Theoretical production for every turbine has been evaluated at each node of the reanalysis database in offshore locations evaluating the influence of the topography over the energy production.


Main body of abstract

High resolution wind data base calibration
SeaWind HR database has been validated comparing numerical versus instrumental wind speed time series, as well as the intensity wind roses against the meteorological buoys and the floating meteorological masts measurements. From comparisons between SeaWind HR and Idermar Meteo (3.5 km north from the shoreline), it can be concluded that SeaWind HR is able to simulate daily wind variability. Idermar Meteo I lowest wind measurements are at 20 m, therefore wind measurements has been extrapolated to 10m based on the measured wind profile . Finally, wind direction has been also compared between the floating met mast and the high-resolution numerical database with good results .
Figure 4 shows a comparison between SeaWind HR and Santoña meteorological buoy. In this particular case, SeaWind HR shows a good prediction of wind variability. In this area, the influence of the topography is less than in La Virgen del Mar because Cantabrian Range decreases its height at the East part of the region.
Wind assessment
Once the database is has been validated, it has been used for analyzing wind resource along Cantabrian coast. The main objective is to determine the influence of the coastal topography, so southerly wind is analyzed separately from the rest because is the main direction affected. Figure 5 shows one example of a wind situation flowing from south to north. It can be seen how the European Peaks diverge wind creating a low intensity area after them and a high intensity area at east and west due to the convergence that takes place at both sides of these great peaks. It can be seen that this phenomenon occurs due to the topographic structure of Cantabria, formed by valleys and ranges that are almost perpendicular to coast. The influence of the European Peaks over wind energy potential is shown on figure 6 . It has been calculated for southerly winds and for the total amount of data. Based on both plots, both climate scenarios can be compare and determine the real affection of coastal geographic elements.
Wind turbine power curve analysis
As it has been noticed, the coast of Cantabria, like every site, should be studied in detail in order to reach a balance between the energy production and the costs of construction and O&M of an offshore wind farm. Therefore, it is important to take into account the power curve of the turbine. Depending on it, the capacity factor may change, increasing the margin but reducing the amount of energy produced. Figure 7 shows two maps of capacity factor for two different wind turbines (2.5 MW and 5 MW). It can be noticed that the capacity factor increases if the power of the turbine decreases. The 2.5 MW wind turbine reaches a 50% approximately along the coast of Cantabria. It is lower in the area affected by European Peaks (West of the region). Moreover, the NREL 5 MW has a capacity factor of 40-45% approximately, between five and ten points lower than the 2.5 MW wind turbine. The influence of the European Peaks can noticed better in this particular case.



Conclusion

This paper developed a methodology for offshore wind assessment and optimal offshore wind farm site selection. This methodology provides optimal decision-making regarding the influence of the topography and the wind turbine selection to reach greater capacity factors.
The accuracy of reanalysis databases should be addressed in order to reduce the uncertainty in wind assessment. Firstly, the implementation of a well dimensioned monitoring system (meteorological masts and new technologies, such as LIDAR and SODAR) along the coast is essential for this purpose as a base of numerical databases validation. Secondly, a global validation, and not only local, should be developed to be used in first steps of decision process. In this paper, it has been shown that SeaWind HR is able to reproduce wind intensity and wind direction accurately; moreover, it reproduces wind variability too.
The methodology to obtain SeaWind HR can be applied to every part of the world and the results are good to the study of wind resource as it has been demonstrated in this work.
The influence of the coastal topography has been shown to be a key parameter. In this paper, the coastal topography influence is studied at Cantabria, Spain. This region is characterized by great ranges near to the shoreline. Due to the influence of the topography, wind energy potential is reduced at some areas of the region studied. In order to find the more profitable option in every site, the turbine power curve should be included in the study. It has been shown that a greater wind turbine is not always the best solution and an economical study should be done in order to find the optimal power curve that implies the greater capacity factor.
Support from the Fundación Iberdrola’s program: Grants for Energy and Environmental Research is gratefully acknowledge.



Learning objectives
A methodology to take into account the influence of the coastal topography is developed, based on validated high resolution reanalysis wind database.
The study of the power curve adaptation could increase the profitability of offshore sites.



References
Chouwdhury S., Zhang J., Messac A., Castillo L., 2013. Optimizing the arrangement and the selection of turbines for wind farms subject to varying wind conditions. Renewable Energy, Volume 52, 273-282
EL-Shimy M., 2010. Optimal site matching of wind turbine generator: Case study of the Gulf of Suez region in Egypt. Renewable Energy, Volume 35, 1870-1878.
Guanche R, Vidal C, Piedra A, Losada I. «IDERMAR METEO. Offshore wind assessment at high and very high water depths.» OCEANS, IEEE-Spain (OCEANS, 2011 IEEE-Spain, 1-8), 2011: 1-8.
Jonkman J., Butterfield S., Musial W., Scott G., 2009. Definition of a 5MW reference wind turbine for offshore system development. NREL.
Pérez B., Mínguez R., Guanche R., 2013. Offshore wind farm layout optimization using mathematical programming techniques. Renewable Energy, Volume 53, 389-399.
Scamarock W., Klemp J.B., Dudhia J., Gill D.O., Barker D.M., Duda M.G., Huang X.-Y., Wang W., Powers J.G. 2008. A Description of the Advanced Research WRF Version 3. NCAR Technical Note NCAR/TN-475+STR.