Wind resource assessment method for floating deep offshore wind turbines

The floating wind turbine industry is still in its infancy both in terms of technology and the associated methodologies.

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Jul 21, 2017
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Authors: Ana Estanqueiro; António Couto; Luis Rodrigues Jr.; Raquel Marujo 

Abstract

This study presents a new methodology for the assessment of the wind energy resource at deep offshore locations where the use of floating wind turbines is foreseen. The wind resource assessment methodology developed follows the principles used by IEC 61400-12-1 standard in general and proposes the use of experimental data from a floating light detection and ranging (LIDAR) system on a deep offshore region – that assumes the role of the ‘temporary mast’ – and a coastal meteorological mast installed onshore acting as the ‘permanent met mast’. The methodology takes into consideration the time shift between the locations of the two measurement points and the wind direction associated with different atmospheric phenomena. The results obtained conclude that the methodology increases the accuracy of the wind resource assessment campaign while reducing the involved costs and technical risks. An added advantage is the possibility of extending this methodology for the evaluation of the power performance of floating wind turbines during the operational phase of the power plant.

Introduction

In the actual framework of sustainable energy systems, the development of floating offshore wind plants [1] presents several potential advantages when compared with onshore or even the conventional offshore wind technologies using foundations fixed to the sea's floor: (i) a higher wind potential, when compared with onshore or near shore locations; (ii) lower turbulence levels originated by geographical constraints than near shore turbines; (iii) a low social impact and consequently low resistance from the coastal populations and tourism industry (e.g. reduced impact on coastal fisherman) and practically inexistent visual impact from shore; and last, but not the least; (iv) the capacity of each wind power plant is theoretically unlimited and may be located (and strategically electrically connected to; (v) serve major primary load centres [24] thus reducing costs, losses and the loading of the power transmission system.

On the other hand, a few negative aspects associated with conventional offshore wind turbines may be overcome with the floating technology. The installation and maintenance of floating turbines is currently being demonstrated and is found to be far simpler than for fixed to the sea-bottom technologies [5]. As a consequence the availability of the few existing floating wind turbines tends to be noteably higher than the average availability of fixed to the bottom offshore technologies [6].

 Known best practices widely accepted for onshore and near shore purposes are currently being adapted for the deep offshore environment [7] and know-how from oil and gas platforms is being strongly embedded in these systems but these turbines still face a number of challenges. One of the most immediate and critical challenge consists of the development of methodologies to perform the wind resource evaluation at the site where the installation of floating wind turbines is foreseen. These methodologies need to grant acceptance both from the researchers of the area and wind energy consultancy companies, but also be recognised as valid, accurate and in accordance with the wind sector best practises during the offshore wind power plant due diligence process.

The difficulty of assessing the wind resource at the deep offshore location of floating wind turbines starts with the challenging task of installing met masts fixed to the sea-bottom at such high bathymetries [8]. Thus the actual approach for deep offshore wind resource evaluation is based on the use of long term simulations using high resolution numerical weather prediction models [2], floating meteorological ocean buoys [4] or the combination of both [9]. However, these approaches present serious limitations in what concerns their reduced accuracy. The recent deployment and commercial availability of floating LIDARs and their installation in ocean buoys brought positive expectations for the deep offshore wind resource area. Nevertheless, the recent experience of using floating LIDARs in maritime environments [10] does not enable them to overcome the operational ocean conditions during the periods of the year when severe sea storms are expected to occur (e.g. winter in the European coasts of the Atlantic and hurricane season in the American coast).

Owing to the technical challenges and the high costs to obtain wind observations at sea, the wind resource assessment process in deep offshore regions needs to be revised and improved. In that sense and using a case-study, this paper aims to present the development of a new methodology for wind resource assessment in deep offshore areas. The method is inspired by the site calibration procedures presented in the IEC 61400-12-1 Ed.1 [11] with the use of a meteorological mast installed on the onshore coast and the installation and operation of floating remote sensors, for example, LIDARs during periods when sea storms that may affect their integrity are not expected to occur.

The site calibration procedure as described in the actual standard [11] is performed by using two anemometric masts (one permanent and one temporary) placed at a distance between them of 2 D to 4 D, where D represents the diameter of the wind turbine to be installed at the site. This method was developed for onshore applications at sites where, because of the complexity of the terrain, the wind speed monitored at a nearby meteorological mast requires a spatial calibration in order to represent the atmospheric flow driving a specific wind turbine.

In the section ‘Wind Resource Assessment Using a Spatial Calibration Function’ of the paper provides a brief background of the spatial calibration methodology developed. In the section ‘Wind Resource Assessment Results’ presents the results obtained with the methodology proposed for deep offshore wind assessment and the section ‘Power Performance Evaluation’ is presents a brief description of the potential added value of this methodology for power performance evaluation according to IEC 61400-12-1. Finally, in the last section some conclusions are drawn.

Wind Resource Assessment Using a Spatial Calibration Function

Obtaining the in-situ data necessary for an accurate offshore wind resource evaluation is a very expensive process. Most of the initial offshore wind parks were deployed without such a local wind resource assessment, hence deviations between observed data and estimates of wind power production were identified during the operational phase of those parks [6]. Reliable wind measurements in deep offshore applications are extremely important considering the total amount of investment and the high technical and financial risks associated with the challenging ocean environment [12].

The methodology presented in this study aims to make the best use of the homogeneous characteristics of the flow over the ocean and is based on the use of the well tested and widely accepted ‘site calibration’ procedure of meteorological masts [6]. The measurement setup uses a temporary floating LIDAR measurement system, thus reducing the experimental campaign costs and the risk of loss/damage associated to severe conditions in an open ocean environment. In this sense, the campaign time frame can cover all wind regimes in the region to be characterised and avoiding, at the same time, exposing the very expensive equipment to harsh storm conditions. Moreover, the experience so far on floating wind turbines shows that during the stormy conditions the wind turbine is not expected to be operating (for safety reasons) hence the method proposed covers adequately the range of operation of floating wind turbines.

Two steps are followed for a spatial calibration on a deep offshore region using a floating LIDAR as a temporary met mast and one permanent anemometric mast onshore. The temporary LIDAR is installed at a deep offshore region – characterised by bathymetries that prevent the use of meteorological masts with fixed foundations – and the permanent mast is placed at a coastal onshore location. In the first step a spatial calibration based on a linear regression is conducted. The second step introduces a time shift (lead/lag) between the two measurement points. This second step presents an improvement in the correct characterisation of the wind resource on deep offshore regions taking into account the wind direction associated with different atmospheric phenomena.

Through the application of this methodology it is possible to assess the spatial variability of the atmospheric flow in the deep offshore region as well as to determine the frequency distributions of wind directions and speeds correction factors.

Spatial calibration – step A

The spatial calibration takes place for each direction sector, with the wind speed correction factors applied as [8]

where, U  is the 10 min average wind speed at the coastal onshore permanent mast [m/s], U  is the 10 min average wind speed at floating remote sensing (e.g. LIDAR) temporary mast location [m/s],  is the time variable [s], θ  is the direction bin referenced to the North as 0° with an angular dimension of 10° (clockwise) [°], α  is the linear correction factor for the ith direction bin [adim.].

Spatial calibration using temporal shifts – step B

On deep offshore regions where floating wind turbines are to be installed, the large distance between the measurements points will result in a noteable time shift between the events registered. For instance, in a deep offshore location the distance D between the point of interest to be measured and the permanent mast located onshore is typically superior to 5 km (Fig 1).

Fig 1: Experimental campaign setup

The spatial calibration as described in (1) is only suitable to adjust two measurements made relatively close to each other (between 2 D to 4 D). That methodology does not take into account the time a registered phenomenon will take to propagate from one measurement point to another.

A calibration equation more suitable to extrapolate registered phenomena to another location is obtained by adding the time shift variable τ

with

where, ϕ represents the angle formed by a line segment connecting the two measurement points and the north [°], τ is the time advance/delay variable [s] and  is the mean wind speed in bin j, within an interval of 4 m/s, according to the U  [m/s].

It is implied that τ can assume either positive or negative values depending on the relative position of the measurement points in relation to the direction of the wind. The most notable consequence in comparison with the procedure established by step A is that average measures taken synchronously are no longer directly related. Instead, depending on the direction and propagation speed of a phenomenon, the relatively large distance between the measurement points will require to relate a measurement to another measurement taken at a different instant (before or after) to be determined in each case (Fig 2). For a 10 min average wind time series, the τ values are rounded to the nearest interval, for example, for a time shift of 16 min the τ value will be 20 min.

Fig 2: Time association between measurements in step A of the methodology (left) and step B (right)

Fig shows the results of the methodology application for the time shift calculation (3) in the case study under analysis. Further information of the case study configuration and data is provided in the section ‘Wind Resource Assessment Results’.

Fig 3: Time lag (in minutes) for the case study calculated through eq. 3

The results were obtained for each wind direction sector (θ ), with distance (D) equal to 15 km and ϕ equal to 0°

The results depicted enable us to understand the temporal shift correction needed to adjust the data measured in the onshore mast, according to the different wind direction and wind speed associated to each meteorological phenomenon. As expected the minimum values of time shift (advance/delay) are observed when the phenomena propagated perpendicular to the two met masts, and on the other hand, the maximum values are found when it propagated parallel to the monitoring systems.

Methodology evaluation

The quality of the wind data series obtained by the methodology application can be inferred through the evaluation of the following statistical parameters bias error (BE), mean square error (MSE), root mean square error (RMSE) and Pearson Correlation (r), which are mathematically defined as

where N is the total number of wind observations.

Wind Resource Assessment Results

As part of the development of this methodology a case study was designed considering one year of data from an experimental setup composed by an onshore coastal permanent mast (OCPM) and a temporary offshore LIDAR (TOL) sited 15 km away from Portuguese coast, (Table and Fig 4). The offshore data (for 80 m a.g.l.) used for this case study was obtained from a LIDAR system installed in the Berlenga Islet after applying orography correction factors obtained in wind tunnel scale model [13].

Fig 4: Location of measurement points at the Portuguese coast

MONITORING SYSTEMWGS84 POLAR COORDINATESCAMPAIGN PERIOD
LONGITUDELONGITUDE
OCPM39.36°N−9.41°W01 June 2011–31 May 2012
TOL39.36°N−9.58°W

(1) 

Table 1: Experimental setup data

Following the methodology presented and according to (1) and (2), the wind flow correction factors (α) were obtained with the wind data (direction and average wind speed) from the LIDAR installed at the Berlenga Islet acting as a (temporary) floating LIDAR and permanent mast during a short synchronised period between 01 June 2011 and 30 September 2011 (summer period). The remaining period was used for methodology validation. In Fig.  , are presented a short period of the wind speed data from the temporary mast (TS), permanent mast (PS) and both wind speed estimates (step A – WSE-A; step B – WSE-B) with the correction factors application.

Fig 5: Example of the methodology application for two different periods

Black solid line represents the data from the TS; the dashed green line represents the WSE using the step A; the dashed red line represents the WSE using the step B; and the blue dashed line represents the data from the PS

Gray dashed represents the wind direction observed in the PS

Table  presents the statistical results obtained for the evaluation period (1 October 2011–31 May 2012) of each method (WSE-A and WSE-B) based on the data from floating system for the wind speed estimates and permanent mast measures.

DATABE, M/SMSE, M/SRMSE, M/SR, >#/TH###
PS−0.16811.5551.24793.6
WSE – A−0.16491.4721.21394.1
WSE – B−0.06881.0591.02994.8

Table 2: Statistics results - steps A&B

Results depicted in Table indicate that the application of the proposed method can effectively reduce the wind assessment errors and improve the wind speed estimations for a deep offshore area. The characterisation and implementation of the time shift step in the methodology decreases approximately 50% the estimate wind speed error.

The evaluation of the economic viability of a wind power plant is performed through parameters such as the annual energy production (AEP) and the equivalent number of hours at full capacity (NEPs). In this sense, based on the previous wind speed data the AEP and NEPs values for a generic offshore wind turbine (with 6 MW nominal power) were calculated to determine the deviation for the wind resource assessment and the wind production estimates over the deep offshore area under analysis (Table  3). The deviation results presented in Table  were obtained by calculating the difference from the temporary system data and the estimate wind speed results.

DATAAEP, GWHNEPS, H/YEARAEP DEVIATION, GWHNEPS DEVIATION, H/YEAR
TS21.3183553
WSE – A21.5863598−268−45
WSE – B21.5523592−234−39

Table 3

Table 3: AEP and deviation results

As expected, for deep offshore locations far from the coast, the results show that using step B of the method for calculating AEP produces more accurate results than using just the step A of the method, that is only suitable for very short distances.

The small difference found between the two steps can be partly explained by: (a) the intrinsic conditions of the case study since the distance for deep offshore regions is usually higher than 15 km considered in the present case study; and (b) the main characteristics of atmospheric circulation in the area under study. The atmospheric circulation is influenced by seasonal migration of the mid-latitudes weather circulation systems [14] with prevailing winds from the North/Northwest sectors. Therefore, the high frequency of occurrences (40% from the North sector during the validation period) observed from the wind direction sectors that does not require the time shift correction (see Fig 3), can statistically mitigate the added value of the step B methodology.

Power Performance Evaluation

The need for evaluating the power curve of wind turbines at their site of operation is usually mandatory for conventional wind turbine technologies, a procedure that is expected to be replicated for floating wind systems. The power performance's characterisation of a wind turbine in the real operating environment is essential both for the wind industry and for the power plants investors since it is the basis for the confirmation of the wind turbine efficiency characteristics, hence on the fulfilment of mutual contractual obligations. In this sense, the experimental setup foreseen and the spatial calibration methodology proposed may be firstly used during the wind resource assessment as described in this paper and later extended for the (simpler) application of IEC 61400-12-1 standard, eventually with the removal of the floating LIDAR during stormy periods occurring between the two different phases of the deep offshore floating power plant deployment.

Conclusion

This paper presents a new methodology for wind resource assessment that enables, based on data gathering from an onshore mast (permanent mast) and a temporary LIDAR, to obtain the required correction factors in order to be representative of the wind flow in deep offshore regions where floating wind turbines are expected to be installed. The method builds on and develops the concept of ‘site calibration’ of the IEC 61400-12-1 standard widely used for power performance assessment of wind turbines.

To interpret and characterise the spatial quality of the new methodology presented, a statistical evaluation was performed using the common statistical parameters, for example, BIAS. Results show that for an offshore wind resource analysis the time shift introduction on the correction factor calculation enables a remarkable reduction on the wind speed estimates errors against the simple spatial calibration method.

Although further research is requested to enable its validation, for different weather regimes and coastal characteristics the new methodology presents a promising improvement in the accuracy of the wind energy resource assessment that allows to increase the precision of the resource assessment and the validation of AEP estimates during the due diligences procedures of deep offshore floating wind power plants. Moreover the correction factors achieved during this wind assessment experimental campaign can be used for an accurate power performance evaluation at a later phase, enabling also to reduce the costs associated with additional experimental campaigns.

Acknowledgements

The authors acknowledge the FP7-ENERGY-296050 DemoWFloat project for the partial financial support for this study.

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Go to the profile of Ana Estanqueiro

Ana Estanqueiro

Senior Researcher , National Laboratory of Energy and Geology

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