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Wave energy assessment based on a 33-year hindcast for the Canary Islands
Journal Pre-proof Wave energy assessment based on a 33-year hindcast for the Canary Islands
Marta Gonçalves, Paulo Martinho, C. Guedes Soares PII:
S0960-1481(20)30012-4
DOI:
https://doi.org/10.1016/j.renene.2020.01.011
Reference:
RENE 12873
To appear in:
Renewable Energy
Received Date:
20 April 2018
Accepted Date:
03 January 2020
Please cite this article as: Marta Gonçalves, Paulo Martinho, C. Guedes Soares, Wave energy assessment based on a 33-year hindcast for the Canary Islands, Renewable Energy (2020), https://doi.org/10.1016/j.renene.2020.01.011
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.
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Wave energy assessment based on a 33-year hindcast for the Canary Islands Marta Gonçalves, Paulo Martinho and C. Guedes Soares*
Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Portugal *Corresponding author: [email protected] ABSTRACT: A wave energy assessment is performed in the Canary Islands, based on a 33-year hindcast, between 1979 and 2011. The third-generation wave models WAVEWATCH III and SWAN are used to study the generation of the waves in the North Atlantic basin and the transformation of the waves in the Canary Islands, respectively. The hindcast system was validated in a prior study, showing a relevant wave energy resource distribution with an average annual energy availability of 2530kW/m. This study intends to offer a detail description of the wave climate, combining the previous results with the new studies. The results show that the seasonal mean distribution of wave energy varies between 15-20kW/m, in the spring and 25-30kW/m, in the winter, while in the less energetic areas the seasonal average varies between 1015 kW/m, in the spring, and 15-20kW/m, in the winter. Also, the temporal variability indexes suggest that the East coast of the islands presents less variability and that the North/Northwest coast of the islands exhibits greater energy availability.
Keywords: Wave energy; Spectral models; WAVEWATCH III; SWAN; Canary Islands, WEC performance.
1. INTRODUCTION The continuous request for clean energy resources, with low impact on the environment, has increased the progress of new technologies to convert wave energy for the future generations. Numerical models can reflect the physics of wave generation and propagation from deep to shallow waters, and in consequence, they can make more accurate assessments of the wave energy potential in coastal areas, in which renewable energy developments are being planned. Extensive research on the wave energy resources and assessment has already been made providing indexes of monthly and seasonal variability at a global scale. Cornet [1] 1
Journal Pre-proof studied the spatial and temporal variations of the global wave energy resource. Barstow et al. [2] show that the most energetic area is located in the Southern Ocean’s Roaring Forties, with an annual average greater than 140kw/m. Arinaga et al. [3] showed that the monthly wave power in the Pacific and Atlantic Oceans, above 30°N, has at least 35kW/m during the winter months. The 61-year hindcast provided by [4] shows the seasonal, inter-annual and long-term variability of the global wave power. Also in [5] a review the global wave energy resource according to the most recent datasets available was made, identifying the geographical regions with the maximum wave power. Wei et al, [6] provided an overview of long-term climatic trends and medium- to long-term predictions of the global wave energy. At a European level, Neil et al, [7] showed the temporal variation of the wave resource over seasonal and inter-annual timescales. Also in Guedes Soares et al, [8][9] an assessment of the wave energy was performed for the Atlantic European Coast. Over the past years, considerable amount of hindcast studies have been performed with state-of-the-art numerical models, around the world, revealing the most effective areas for renewable energy developments. Along the European coast various studies were made. A 10-year hindcast study on the wave climate was performed for the Portuguese coast [10]. The wave energy around the Portuguese Islands of Azores [11] and Madeira [12] has also been studied (with a potential average of 60kW/m and 14kW/m respectively). In [13] the wave energy resource was evaluated in Menorca considering a 17-year hindcast wave climate database, presenting a significant seasonal variability, with an average wave power, around 8.9 kW/m and average annual wave energy of about 78 MW h/m. Vicinanza et al. [14] showed an analysis based on wave measurements carried out along a 20-year period in the north west of Sardinia (Italy), presenting an annual offshore wave power e between 8.9 kW/m and 10.3 kW/m and [15] presented a 10-year evaluation for the western Sardinia coast and the Sicily Channel, as this are considered to be most profitable areas in the Mediterranean. Neill et al, [16] presented a 10-year hindcast showing a considerable variability of the wave resource surrounding Orkney, with the winter mean wave power varying from 10-25kW/m. In [17] the wave energy potential over the Eastern Mediterranean Sea shows that the most energetic offshore areas of the Levantine Basin are the western coastline of Cyprus, the sea around Israel and Lebanon and the coastline of Alexandria in Egypt (with a potential of about 2.5 kW/m). 2
Journal Pre-proof Around the world many studies have been performed. For example, for the Cape Verde Islands, Bernardino et al. [18] studied the energy profile, showing an average potential above 7kW/m and identifying areas with considerable energy. In [19] a 10year hindcast for the Hawaiian Islands is presented, showing that the northwest swells during winter months produce 35-50 kW/m. Appendidi et al, [20] produced a 30-year wave hindcast to evaluate the wave energy in the Caribbean Sea, suggesting a 814kW/m extractable energy in the Caribbean Low-Level Jet region. Moreover, other studies where performed by Morin et al, [21] for the Southeast coast of Australia estimating a wave energy resource between 4-16kW/m; for the Aegean Sea, Lavidas and Venugopal [22] showed a 35-year hindcast study on the assessment of the wave characteristics and estimate the wave energy resource available in coastal areas; also Khojasteh et al, [23] evaluated the wave energy for Iran, showing the energy resource around the Persian Gulf islands and the Gulf of Oman coastline with 16.6kW/m and 12.6kW/m of average wave power, respectively. Based on the HIPOCAS project [24], and as part of the European projects MAREN and MAREN II, several studies have been performed using WAVEWATCH III [25] and SWAN [26] models, for the West coast of Ireland [27], the Southwest of UK [28], Canary Islands [29], Galway Bay [30], the North of Spain, [31], Portugal [32], France [33], assessing the wave energy resource and showing its availability and variability. The hindcast validation results prove that the system provides data that is in agreement with wave measurements. Wave energy resource around the Canary Islands has been estimated, showing that the main marine resources are offshore wind and waves. These resources are mostly set in the northern coast of the Islands, which has available 25-30kW/m [34]. Other studies have been made showing the geographical distribution of the wave energy in different islands of the Canary archipelago. Iglesias e Carbalho conducted a study on wave energy for La Palma (La Isla Bonita) [36] and El Hierro [37], showing a substantial resource in the north and northwest coasts of La Palma and in the west and north coast of El Hierro. The implementation of wave energy plants requests a preceding study of the sea wave conditions, as well as the evaluation of the accessible energy in a given area. It is estimated that the installation of offshore wave energy plants is the most suitable, when compared with coastal and nearshore installations. However, is worth to highlight that, mechanisms triggered by wave-bottom interaction occurring in the nearshore zones can 3
Journal Pre-proof induce nearshore hot-spots where the wave energy potential is greater than in the offshore zone, as mentioned by Vannucchi and Cappietti [41], among others. This exploration has had a significant growth in the next decades due to the technological advances [42][43]. There are a few aspects that should be taken into account when selecting a wave power device, such as: easy access to the device at any time, energy storage capacity, among others, should be considered, [44]-[46]. This study combines and extends an earlier 10-years study, [47], by presenting the results of a 33-year hindcast performed for the Canary Islands. Being the installation of wave energy test sites an attractive investment and being the Canary Island been identified as a good candidate for WEC deployment [36]-[40], this study aims to be a support for those offshore renewable energy investors, as the activity of the wave converters is directly associated with the wave energy available. Even though the evaluation of the energetic potential in the Canary Islands is the central motivation of this work, 6 points are selected and the wave power at each point is evaluated. Also, the performance of 6 wave energy converters on these locations will be studied.
2. HINDCAST SYSTEM DESCRIPTION AND VALIDATION 2.1.
Theoretical formulations
WAVEWATCH III is a full spectral third-generation wind-wave model developed by the National Centers for Environmental Prediction (NCEP). The model solves an advection type wave action equation [48]: DN S Dt
(1)
where N is the action density spectrum, σ is the relative frequency and S describes the source terms. The right-hand side of equation defines the kinematics of the model, while the left side-hand characterises the physical processes that generate, dissipate and distribute the wave energy. The source terms include: wind-wave interactions, quadruplet wave-wave interactions, and dissipation through whitecapping and bottom friction.
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N kN N S x xN t k
(2)
x c g U
(3)
d U k k d s s
(4)
1 d U k k d m m
(5)
where,
being k the wave number vector, σ the relative frequency, U the current velocity. cg is the group velocity given by cg and θ, s is the coordinate in the direction θ and m is a coordinate perpendicular to s. 2.2.
Hindcast system methodology
State-of-the-art third-generation wave models WAVEWATCH III and SWAN are used to study the generation of the waves in the North Atlantic basin and the transformation of the waves in the Canary Islands. The main model used was SWAN which is a more flexible, easy learning model. It simplicity makes it a good option for simulations all the way from intermediate waters up to the nearshore. The WWIII model considers an energy spectrum with 24 frequencies, logarithmically spaced, from 0.040Hz, with increments of 1.12Hz and 24 directions spaced 15 degrees and a JONSWAP-spectra with peak enhancement factor of γ = 3.3. SWAN model assumes a spectral grid of 30 frequencies, logarithmic spaced, between 0.050Hz e 0.6Hz, with Δf/f =0.1 intervals and 36 directions with 5ᵒ spacing. Computations were implemented with a 20min time step, in the non-stationary mode. In this study no currents are incorporated. For both models the bathymetry derive from GEBCO database and the wind input fields used are from ERA-Interim [51] database, the characteristics can be seen in Table 1. 5
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Figure 1 – Hindcast study area (a-North Atlantic; b-Canary Islands). Table 1– Computational grid information
Wind
Area
Latitude/Longitude
Resolution
North Atlantic
10ºN-75ºS/70ºW-30ºE
1ºx1º
6h / 1.5ºx1.5º
Canary Islands
27ºN-30.5ºN/20ºW-13ºW
0.05ºx0.1º
6h / 0.5ºx0.5
ERA Interim
The system is validated considering 15-years of measurements from the Gran Canaria buoy (28.20ºN, 15.78ºW), comparing Significant Wave Height (Hs), Mean Period (Tm) and Peak Period (Tp) data. The statistical results, as shown in Figure 2, show a good agreement, with correlation values above 85% for Hs. The scatter plots (on the left) show that the higher concentrations are between 1-2m for Hs, 5-6s for Tm and 6-8s for Tp. The Q-Q plots (on the right), compare the two probability distributions by plotting their quantiles against each other, showing that the two datasets follow a similar statistic up to 2m for Hs and 6s for both Tm and Tp. Also, it is visible that overestimations of the model for wave heights higher then 2m and 6s, which might mislead to a wrong evaluation of the wave energy resource available, as the wave power is proportional to the square of the Hs. Regarding the wave periods; one should note a wide deviation of the mean period for values above 6s. A possible reason for such a
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Figure 2 – Statistical results for Hs, Tm and Tp.
3. CHARACTERIZATION OF THE WAVE RESOURCES
3.1.
Significant Wave Height
The wave climate in the area of the Canary Island is evaluated, showing mean values of significant wave height of 2m for the 33-year period, with a general average variability of 0.6m, being this variability larger in the north coast of the islands (Figure 3).
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Figure 3 - Annual average and standard deviation distributions of the significant wave height.
With the purpose of studying the temporal variability of the wave resources in the study area, two coefficients are calculated: The Annual Variability index (AV) and the Coefficient of Variation (COV). The first quantifies the annual variability of the wave resource: AV
PA1 PA2
(6)
Pyear
where PA1 is the mean wave power of the most energetic year, PA2 is the least energetic year and Pyear is the yearly mean. The second coefficient calculates the amount of variability in relation to the mean and is obtained dividing the standard deviation (σ) by the mean (μ): COV
(7)
Figure 4 – Significant wave height Annual Variability Index (on the left) and Coefficient of Variation (on the right)
Figure 4 illustrates the distribution of the variability and, as it can be seen, both Annual Variability and Coefficient of Variation present low values. Low values are desirable since it would mean that each year has the similar amount of Hs.
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Wave Energy
The hindcast results also give information about the wave energy resource. In SWAN, the energy transport components are computed according: Px g
c E , dd
(8)
Py g
c
, dd
(9)
x
yE
where x and y are the problem coordinate system (for the spherical coordinates x axis corresponds to longitude and y axis to latitude), and cx, cy are the propagation velocities of wave energy in the spatial domain. As a result, the absolute value of the energy transport is given by: Pot Px2 Py2
(10)
The Canary Islands are in the Atlantic Ocean, having a short continental shelf. The energy of the waves is therefore not affected much by the refraction or shoaling of the waves and can be obtained by: J
g 2 Hs 2Te 64
(11)
where J is the wave power per unit of crest length (kW/m), Hs is the significant wave height, Te is the energy period, ρ is the density of seawater (assumed to be 1.025 kg/m3) and g is the gravitational acceleration. According to [52], it is assumed that Te≈0.90Tp, which means to assume a typical JONSWAP spectrum with a peak enhancement factor of γ=3.3. The statistical evaluation is presented in Figure 5, showing the results for Bias, root mean square error (RMSE), scatter index (SI) and Pearson’s Correlation Coefficient (r) on the left side and the Q-Q plots on the right side. A good agreement can be seen between buoy measurements and simulations. Also, it is visible that the higher concentrations of energy are found below 25kW/m. Also, the Q-Q plots show that the two datasets follow a similar statistic up to 50kW/m. The geographical distribution of the seasonal wave energy resource accessible during the year is shown in Figure 6. The average energy varies between 8-18kW/m in the summer and spring and 16-30kW/m in the autumn and winter, respectively. It is verified that the most energetic zones are to the North and West of the island of Lanzarote.
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Figure 5 - Statistical results for wave energy
Figure 6 – Seasonal variability of wave energy resource, for the 33-year period.
The inter-annual variability evaluation is calculated by considering the box-whisker analysis. This describes a set of numerical data through their quartiles. On each box, the central mark is the median (2nd quartile), the edges of the box are the 25th and 75th percentiles (1st and 3rd quartiles, respectively), the whiskers (the vertical lines outside the box) extend to the most extreme dataset (highest and lowest values). Also exhibited are the outliers (an observation point that is distant from other observations). In Figure 7a, it is visible, in most cases, the great difference between the lowest and the highest extreme points. Nevertheless, though these variations can be seen, the interannual variability is not very significant and so no trends can be noticed. The smallest wave energy doesn’t show great changes through the years, on the other hand, the largest wave energy presents a higher variability. Figure 7b shows the monthly mean wave power, it is visible that the median ranges between 10-30kW/m, in summer and winter months, respectively. 10
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Figure 7 – Box-whisker plots of the mean annual (on top) and monthly (at the bottom) wave power, for the 33-year period.
Regarding the variability of the waver energy, additional variability indexes are also calculated: the seasonal variability index (SV) and the monthly variability index (MV). They quantify the variability of the wave power resource and are given by: SV
MV
PS1 PS 2 Pyear PM 1 PM 2 Pyear
(12) (13)
where PS1 is the mean wave power of the most energetic season (winter), PS2 is the least energetic season (summer) and Pyear is the yearly mean, for the seasonal variability index. And for monthly variability index, PM1 is the mean wave power of the most energetic month (January), PM2 is the least energetic month (August). The distribution of wave energy variability can be seen in Figure 8. Small values of SV and MV are indicators of low variability. As can be seen the eastern coast of the islands is where this variability is much less pronounced, especially in Fuerteventura and Lanzarote, but also in Tenerife, Gran-Canaria and La Palma. As can be seen the annual variability is generally low, with values around 0.5.
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Figure 8 - Wave power temporal variability indexes (the coefficient of variation (COV), seasonal variability index (SV), monthly variability index (MV) and annual variability index (AV)
As a word of caution, it should be noted that despite the fact that GEBCO data is good in the offshore region, it is not so accurate near the coast suggesting that better bathymetry close to the coast could improve the results locally. The accuracy of the present results can be further discussed in light of published studies. Campos and Guedes Soares [53] have compared the two long-term wave hindcasts over the North Atlantic. One was produced within the ERA40 project [55] and the other within the HIPOCAS project [24]. Both have been produced in European funded projects in the same framework programme (EVK2-CT-1999-00027 and EVK2CT-1999-00038, respectively). The first one was coordinated by the European ECMWF consisting of a global atmospheric reanalysis, while the second was coordinated by CENTEC-IST and aimed at reanalysing wind, sea level and waves in the coastal waters around Europe [56]. Both covered the North Atlantic and both used the WAM model. The predictions of ocean waves are very sensitive to the quality of the forcing wind fields used as has been demonstrated by several authors [57]-[60]. This is the reason why Campos and Guedes Soares [61] have conducted a comparison of the wind fields used in the known wave hindcast databases, as part of the differences can be ascribed to the forcing wind fields. They concluded that “the reanalyses HIPOCAS and specially NOAA/CFSR are overestimated while ERA-Interim has underestimated wind intensities, which confirm results of Stopa and Cheung [62]”.
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Journal Pre-proof Campos and Guedes Soares [63] have extended their study to compare the wave hindcasts also with the CFSR [64] which is a global reanalysis developed by the National Centers for Environmental Prediction / National Oceanic and Atmospheric Administration (NCEP/NOAA) using WAVEWATCH3 instead of the WAM model used in the previous analysis. This allows a comparison of the performance of WAM and WAVEWATCH3, which were also compared in [62]”. The evaluation of CFSR, ERA-Interim and HIPOCAS used as reference the GlobWave Satellite data. The GlobWave Project is an initiative funded by European Space Agency (and subsidised by CNES) through the Data User Element (DUE), which is a programmatic element of the 3rd period of the Earth Observation Envelope Programme (EOEP-3), an optional programme of the European Space Agency. Therefore, this new satellite database represents a standard and reliable source of public data, in accordance with the state-of-the art in Satellite Altimetry, following the same procedure and using the same data as the most operational agencies, research centres and companies. It is well known that satellite can also show inaccuracies, however errors are small under non extreme conditions in offshore areas as described by the GlobWave Product User Guide [65] and discussed by Janssen et al. [66]. Campos and Guedes Soares [62] have concluded that “the wave reanalyses assessment using satellite data suggests that NOAA/CFSR presents the largest waves among hindcasts (0.2 meters higher, in average), followed by HIPOCAS and ERAInterim. The larger waves of NOAA/CFSR compared to ERA-Interim confirms results of [61]”. The inter-comparison indicated HIPOCAS to be more similar to ERA-Interim than to NOAA/CFSR for overall non-extreme conditions. Under extreme conditions it was found important differences among hindcasts at mid-high latitudes above 40º North, reaching 2.5 meters and a wide area is seen with differences above 2 meters.” This confirms the results of [53] who found an increasing underestimation by WAVEWATCH in respect to WAM when moving to extreme conditions. The conclusions in [63] are also that for overall non-extreme conditions, the three wave hindcasts are very similar, differences are less than 0.5 meters at most grid points, corroborating with most of comparison papers. The energy produced by different wave energy devices depend on their transfer function and the global energy values are dominated by the operation in non-extreme wave conditions, a most devices stop production and go in survival mode when the waves become large than moderate seas [44]. Therefore, the different comparison and 13
Journal Pre-proof calibration studies indicate that the values that have been produced in this study may be fairly representative of the wave conditions appropriate for wave energy conversion. The conditions for extreme conditions and assessment of survival conditions may need a more careful study
3.3.
Wave Energy Converters Performance
As mentioned, the implementation of WEC devices demands a prior evaluation of the sea wave conditions, along with the estimation of the accessible energy in a given area. Even though the focus of this work is the evaluation of the energy potential in the Canary Islands, a study on the performance of some energy converters is also carried out to provide a feeling for the amount of energy really being able to be captured by different devices. To estimate the potential of wave energy converters, 6 points are selected to evaluate the wave energy resource available (Figure 9). The selected points are found between 43-90m water depth and their characteristics are specified in Table 2. As can be seen, the mean values of Hs vary between 1.24m (P1) and 1.92m (P2). Furthermore, the mean wave energy has its lowest values for P4, with 4.96kW/m, and its highest values for P2, with 15.71kW/m. The distribution of wave energy variability oscillates between 1.03 (for P3 and P6) and 1.56 (P1).
Figure 9 - Location of the selected points.
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Table 2 – Characteristics of the selected points.
Lat (º)
Lon (º)
Depth (m)
Hsmax (m)
Hsmean (m)
Pmax (kW/m)
Pmean (kW/m)
COV
P1
28.70
-18.00
90.00
7.04
1.24
212.97
8.33
1.56
P2
28.60
-16.30
82.00
6.33
1.95
264.42
15.71
1.14
P3
28.20
-15.50
53.00
5.58
1.75
166.81
11.33
1.03
P4
27.99
-15.37
43.00
4.91
1.27
100.55
4.96
1.06
P5
28.20
-14.35
73.00
5.50
1.78
239.54
13.98
1.14
P6
29.30
-13.35
49.00
5.47
1.63
128.09
9.28
1.03
The seasonal wave power presented in Figure 10 shows that the most energetic points are P2 and P5 with mean winter values above 20kW/m and with total mean values above 13kW/m. Point P4 is the least energetic with mean values generally below 5kW/m for all seasons. The monthly wave power shows that the energy found during the winter months is practically twice that found during the summer months (Figure 11). It is worth mentioning that points P2, P5 present the highest energy values. Moreover, it is visible, for point P4, that the average values stay considerably constant throughout the year, with an average value of 5kW/m.
Figure 10 – Total and seasonal mean wave power for the selected points, for the 33-year period.
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Figure 11 - Monthly wave power for the selected points, for the 33-year period.
To evaluate the performance of the wave energy converters, 6 devices are considered; their characteristics can be seen in Table 3. They have different characteristics and different rated capacities aiming to show the differences that they induce. The power matrix for each device can be found in [44]. The method to estimate the electricity production of a WEC, in a specific site, is to associate the energy converter power matrix with the wave activity at that specific site, which is calculated considering the joint distribution diagrams of Hs-Te of each point. The electricity production is given by: Pe
1 100
N
N
i 1
j 1
pij Pij
(14)
where pij is the energy percentage at a given point and Pij is the WEC electric power.
Table 3 - Characteristics of the different WEC’s selected Device
Rated Capacity (kW)
Depth (m)
Wave Dragon
7000
>30
Wavebob
1000
50-100
Pelamis
750
>50
Oceantec
500
50-100
Aqua Buoy
250
>50
Seabased AB
15
>50
Average electrical power expected for each WEC, at each location can be seen in Figure 12. The results show that in this case, the Wave Dragon device has the highest 16
Journal Pre-proof values, reaching the 400kW/m for all points during the total period, and the 500kW/m for almost all points during winter. The Oceantec device also show good results for all points, with values above 100kW for almost all points, for both in the total and winter periods.
Figure 12 - Average electrical power expected for each device, at each site, for total and winter periods.
The normalized electric power is also calculated for each device. This allows to relate the results, as all values are expressed in the same range. The normalized electric power is given by: Pe n
Pe Pe max
(15)
where Pe is the estimated electric power in each point, for each WEC and Pemax is the highest value at each device, for the total period. As can be seen in Figure 13, the most energetic points are those found in the north coast of the islands (P2, P5), as they are the most expose to the North Atlantic swells. P6 also presents good results for all devices. It is visible that during the winter periods points P2 and P5. The biggest variations between the total and winter periods occur for Aqua Buoy and Wavebob devices with values around 1.37 and 1.39, respectively, while for SeabasedAB and Oceantec these variations are not very pronounced and almost all points do not exceed the 1.
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Figure 13 - Normalized electrical power for each device, at each site, for total and winter periods.
4. CONCLUSIONS Wave models deliver an understanding of the wave power and the characteristics of the available wave energy resource. Given the increasing need for clean renewable energies, a good description of the wave resources is needed. This study aims to deliver a general description of the resources available by characterizing the wave climate. The present study is focused on a 33-year hindcast system, over the Canary Islands. For this study two state-of-the-art spectral models are used to assess the wave energy. The hindcast system is validated with the Gran Canary buoy and the statistical analyses show an overall good relation between measurements and simulations. The wave climate resource was evaluated showing the low variability in the eastern coast of the islands, being the north coast more intense as it is more exposed to the direct swell from the Atlantic Ocean. As mentioned before, low levels of variability are desirable, as this indicates that the amount of wave energy resource would be the same each year. The geographical distributions of the wave power show the high values in the north of the La Palma, Tenerife, Fuerteventura e Lanzarote contrast with the rest of the island. It is visible the shadow effect in the northern coast of the Gran Canary and La Gomera islands. 18
Journal Pre-proof The installation of wave energy converter devices is an appealing investment; therefore, a good description of the wave conditions, along with the amount of accessible energy in a given location is required. Though the focus of this work is the assessment of the energy potential in the Canary Islands, a study on the performance of some energy converters was made. The electrical power was calculated showing that the Wave Dragon device has the highest values, ranging between 430-820kW/h, followed by the Oceantec device, ranging between 8-180kW/h. Moreover; it is visible that the most energetic points are P2 and P5. The normalized electrical power allows relating the results, as all values are expressed in the same range. The biggest differences between the total and winter periods occur for Aqua Buoy and Wavebob devices. These seem to be a good solution for this location. Considering the results obtained and comparing them with other studies on wave energy, for instance: in the Azores Island [11] with a potential average of 60kW/m; in the Madeira Islands [12] with average of 14kW/m; in the Cape Verde Islands [18], where the energy is around 7kW/m; or on the west coast of France [33] with an average potential of 15kW/m, it can be concluded that the Canary Islands offers an appealing wave energy resources supply to the implementation of wave energy devices in certain areas, where average values are around 15-20kW/m.
ACKNOWLEDGEMENTS This present work has started within the project “MAREN2-Hydro-environmental modelling of multi-purpose marine renewable energy platforms” funded by the Atlantic Area Transnational Programme (European Regional Development Fund) under contract 2013-1/225. This study was completed within the project ARCWIND - Adaptation and implementation of floating wind energy conversion technology for the Atlantic region, which is co-financed by the European Regional Development Fund through the Interreg Atlantic Area Programme under contract EAPA 344/2016.
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Journal Pre-proof [17] Zodiatis, G., Galanis, G., Nikolaidis, A., Kalogeri, C., Hayes, D., Georgiou, G. C., Chu,P. C. and Kallos, G. 2014. Wave energy potential in the Eastern Mediterranean Levantine Basin. An integrated 10-year study. Renewable Energy, 69, 311–323 [18] Bernardino, M., Rusu, L., Guedes Soares C., 2017. Evaluation of the wave energy resources in the Cape Verde Islands. Renewable Energy. 101:316-326. [19] Stopa, J. E., Filipot, J. F., Li, N., Cheung, K. F., Chen, Y. L. and Vega, L. 2013. Wave energy resources along the Hawaiian Island chain. Renewable Energy, 55, 305–321. [20] Appendini, C. M., Urbano-Latorre, C. P., Figueroa, B., Dagua-Paz, C. J., TorresFreyermuth, A. and Salles, P. 2015. Wave energy potential assessment in the Caribbean Low-Level Jet using wave hindcast information. Applied Energy, 137, [21] Morim, J., Cartwright, N., Etemad-shahidi, A., Strauss, D., and Hemer, M. 2016. Wave energy resource assessment along the Southeast coast of Australia on the basis of a 31-year hindcast. Applied Energy, 184, pp. 276–297. [22] Lavidas, G., and Venugopal, V. 2017. A 35-year high-resolution wave atlas for nearshore energy production and economics at the Aegean Sea. Renewable Energy, 103, pp. 401–417. [23] Khojasteh, D., Khojasteh, D., Kamali, R., Beyene, A., and Iglesias, G. 2017. Assessment of renewable energy resources in Iran; with a focus on wave and tidal energy. Renewable and Sustainable Energy Reviews, 81(2), pp.2992-3005 [24] Guedes Soares C. 2008. Hindcast of Dynamic Processes of the Ocean and Coastal Areas of Europe. Coastal Engineering. 55:825-826. [25] Tolman H. 1991. A third-generation model for wind waves on slowly varying, unsteady, and inhomogeneous depths and currents. Journal of Physical Oceanography; 21(6):782-797. [26] Booij N., Ris R.C. and Holthuijsen L.H. 1999. A third-generation wave model for coastal regions, 1, Model description and validation. Journal of Geophysical Research. 104: 7649–7666. [27] Bento, A. R., Martinho, P., Campos, R. and Guedes Soares, C. 2011. Modelling Wave Energy Resources in the Irish West Coast. Proceedings of the 30th International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2011); Volume 5: Ocean Renewable Energy, 945–953. [28] Bento, A. R., Martinho, P. and Guedes Soares, C. 2011. Modelling Wave Energy Resources for UK’s Southwest Coast, Proceedings of the IEEE OCEANS2011, 6-9 June, Santander, Spain. [29] Gonçalves, M.; Martinho, P. and Guedes Soares, C. 2014. Assessment of Wave Energy in the Canary Islands. Renewable Energy. 68, 774-784. [30] Bento, A. R., Martinho, P. and Guedes Soares C., 2015. Numerical modelling of the wave energy in Galway Bay. Renewable Energy, 78, 457-466. [31] Bento, A. R., Martinho, P. and Guedes Soares, C. 2018. Wave energy assessment for Northern Spain from a 33-year hindcast. Renewable Energy. 127, 322-333. [32] Silva, D., Martinho, P. and Guedes Soares, C. 2018. Wave energy distribution along the Portuguese continental coast based on a thirty three years hindcast Renewable Energy. 127, 1064-1075. 21
Journal Pre-proof [33] Gonçalves, M.; Martinho, P. and Guedes Soares, C. 2018. A 33-year hindcast on wave energy assessment in the western French coast. Energy. 165, 790-801 [34] Cortadellas. A., Rodríguez. B., Pereda C. and Moreno. D. 2011. Preliminary study for the implementation of the “Wave Dragon” in Grã-Canária. Canary Islands. Spain. In proceedings of International Conference on Renewable Energies and Power Quality. Las Palmas de Grã-Canária. Spain. April de 2011. [35] Carballo R. and Iglesias G. 2012. A methodology to determine the power performance of wave energy converters at a particular coastal location. Energy Conversion and Management. 61:8–18. [36] Iglesias G, Carballo R. 2010. Wave power for La Isla Bonita. Energy 35 pp. 5013-5021. [37] Iglesias G, Carballo R. 2011. Wave resource in El Hierro - an island towards energy self-sufficiency. Renewable Energy 36, pp. 689-698. [38] Sierra JP, González-Marco D, Sospedra J, Gironella X, Mösso C, SánchezArcilla A. 2013. Wave energy resource assessment in Lanzarote (Spain). Renew Energy 55, pp. 480-489. [39] Chiri, H., Pacheco, M., Rodriguez., G. 2013. Spatial variability of wave energy resources around the Canary Island. WIT Transactions on Ecology and the Environment, Vol169, 15-26. [40] Hernández-Brito, J.J., Monagas, V., González, J., Schallenberg, J., Llinás, O., 2012. Vision for marine renewables in the Canary Islands. In Proceedings of 4th international conference on ocean energy, Dublin. [41] Vannucchi V. and Cappietti L. (2016), Wave Energy Assessment and Performance Estimation of State-of-the-Art Wave Energy Converters in Italian Hotspots, Sustainability 2016, 8, 1300 [42] Falcão A.F.O. 2015. “Developments in oscillating water column wave energy converters and air turbines”, in: Guedes Soares, C. (Ed.). Renewable Energies Offshore, London, UK: Taylor & Francis Group; pp. 3-11. [43] Guedes Soares. C.; Bhattacharjee. J.; Tello. M. and Pietra. L. 2012. Review and classification of Wave Energy Converters. C. Guedes Soares. Y. Garbatov S. Sutulo T. A. Santos. (Eds.). Maritime Engineering and Technology, Taylor & Francis; pp. 585-594. [44] Silva. D.; Rusu. E., and Guedes Soares. C. 2013 Evaluation of Various Technologies for Wave Energy Extraction in the Portuguese Nearshore. Energies. 6: 1344-1364 [45] Andrés, O. M., Ruiz, F. C., and Rusu. L., 2014. Efficiency assessments for different WEC types in the Canary Islands, Developments in Maritime Transportation and Exploitation of Sea Resources, Guedes Soares, C. and López Peña, F. (Eds.), Francis & Taylor Group, London, UK, pp. 879-887 [46] Rusu E. 2014. Evaluation of the Wave Energy Conversion Efficiency in Various Coastal Environments. Energies 7, 4002-4018 [47] Gonçalves, M., Martinho, P. e Guedes Soares, C. (2015), Wave Energy Assessment in the Canary Islands from a 10-year Hindcast, Renewable Energies Offshore, Guedes Soares, C. (Ed.), Taylor & Francis Group, London, UK, pp. 8590 [48] Tolman, H.L., 2009: User manual and system documentation of WAVEWATCH III version 3.14. NOAA / NWS / NCEP / MMAB Technical Note 276, 194 pp 22
Journal Pre-proof [49] Hasselmann K, Barnett TP, Bouws E, Carlson H, Cartwright DE, Enke K, et al,et al, 1973. Measurements of wind-wave growth and swell decay during the Joint North Sea Wave Project (JONSWAP). Deutsche Hydrographische Zeitscheift;.p. 95. A8(12). [50] Holthuijsen LH, Herman A. and Booij N. 2003 Phase-coupled refraction and diffraction for spectral wave models. Coast Eng, 49(4):291-305. [51] Dee, DP, Uppala, S, Simmons, A, Berrisford, P, Poli, P, Kobayashi, S, Andrae, U, M. Balmaseda, A, Balsamo, G, Bauer, P, Bechtold, P, Beljaars, ACM, van de Berg, L, Bidlot, J-R, Bormann, N, Delsol, C, Dragani, R, Fuentes, M, Geer, A, Haimberger, L, Healy, S, Hersbach, H, Hólm, EV, Isaksen, L, Kållberg, PW, Köhler, M, Matricardi, M, McNally, A, Monge-Sanz, BM, Morcrette, J-J, Peubey, C, De Rosnay, P, Tavolato, C, Thepaut, J-J, Vitart, F. 2011. The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. R. Meteorol. Soc. 137 (2011) 553-597. [52] Boronowski S., Wild P., Rowe A., 2010. Kooten G.C. Integration of wave power in Haida Gwaii. Renewable Energy; 35: 2415-2421. [53] Ardhuin, F., Bertotti, L., Bidlot, J., Cavaleri, L., Filipetto, V., Lefevre, J., Wittmann, P., 2007. Comparison of wind and wave measurements and models in the Western Mediterranean Sea. Ocean Eng., 34, 526-541. [54] Campos, R. and Guedes Soares C. Comparison of HIPOCAS and ERA wind and wave reanalysis in the North Atlantic ocean. Ocean Engineering. 2016; 112:320-334. [55] Uppala, S.M., Kållberg, P.W., Simmons, A.J., Andrae, U., da Costa Bechtold, V., Fiorino, M., Gibson, J.K., Haseler, J., Hernandez, A., Kelly, G.A., Li, X., Onogi, K., Saarinen, S., Sokka, N., Allan, R.P., Andersson, E., Arpe, K., Balmaseda, M.A., Beljaars, A.C.M., van de Berg, L., Bidlot, J., Bormann, N., Caires, S., Chevallier, F., Dethof, A., Dragosavac, M., Fisher, M., Fuentes, M., Hagemann, S., Hólm, E., Hoskins, B.J., Isaksen, L., Janssen, P.A.E.M., Jenne, R., McNally, A.P., Mahfouf, J.-F., Morcrette, J.-J., Rayner, N.A., Saunders, R.W., Simon, P., Sterl, A., Trenberth, K.E., Untch, A., Vasiljevic, D., Viterbo, P., and Woollen, J., 2005. The ERA-40 re-analysis. Q. J. R. Meteorol. Soc., 131, 29613012. [56] Guedes Soares, C., Weisse, R., Carretero, J.C., Alvarez, E., 2002. A 40 years hindcast of wind, sea level and waves in European waters. Proceedings of the 21st International Conference on Offshore Mechanics and Arctic Engineering, (OMAE 2002). ASME, New York. Paper OMAE2002-28604. [57] Teixeira, J.C., Abreu, M.P., Guedes Soares, C.,1995. Uncertainty of Ocean Wave Hindcasts due to Wind Modelling. Journal of Offshore Mechanics and Arctic Engineering 117, 294–297. [58] Holthuijsen, L.H., Booji, N., Bertotti, L., 1996. The Propagation of Wind Errors Through Ocean Wave Hindcasts. Journal of Offshore Mechanics and Arctic Engineering 118, 184–189. [59] Cavaleri, L., Bertotti, L., 2006. The improvement of modelled wind and wave fields with increasing resolution. Ocean Engineering 33, 553–565. [60] Ponce de León, S., Guedes Soares, C., 2008. Sensitivity of wave model predictions to wind fields in the Western Mediterranean sea. Coast. Eng. 55, 920– 929. 23
Journal Pre-proof [61] Campos, R. and Guedes Soares C. Assessment of Three Wind Reanalysis in the North Atlantic Ocean. Journal of Operational Oceanography. 2017; 10(1):30-44. [62] Stopa, J. E., Cheung, K. F., 2014. Intercomparison of wind and wave data from the ECMWF Reanalysis Interim and the NCEP Climate Forecast System Reanalysis. Ocean Modelling 75, 65–83. [63] Campos, R. and Guedes Soares C. Comparison and Assessment of Three Wave Hindcasts in the North Atlantic Ocean. Journal of Operational Oceanography. 2016; 9(1):26-44. [64] Saha, S., Moorthi, S., Pan, H., Wu, X., Wang, J., Nadiga, S., Tripp, P., Kistler, R., Wollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R., Gayno, G., Wang, J., Hou, Y., Chuang, H., Juang, H., Sela, J., Iredell, M., Treadon, R., Kleist, D., VanDelst, P., Keyser, D., Derber, J., Ek, M., Meng, J., Wei, H., Yang, R., Lord, S., van den Dool, H., Kumar, A., Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J., Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou, C., Liu, Q., Chen,Y., Han, Y., Cucurull, L., Reynolds, R., Rutledge, G., Goldberg, M., 2010. The NCEP climate forecast system reanalysis. Bull. Am. Meteorol. Soc. 91, 1015–1057. [65] GlobWave/DD/PUG. GLOBWAVE Product User Guide. A/I/P. SatOC, Ifremer, NOC, CLS; 2013. Report No.: 21891/08/I-EC. [66] Janssen, P. A. E. M., Abdalla, S., Hersbach, H., Bidlot, J.R. 2007. Error Estimation of Buoy, Satellite, and Model Wave Height Data. J. Atmos. Oceanic Technol., 24, 1665–1677.
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Journal Pre-proof Conflicts of Interest
There are no Conflicts of Interest
Journal Pre-proof 2533 - CRediT author statement Marta Gonçalves: Methodology, Software, Formal analysis, Writing - Original Draft, Visualization Paulo Martinho: Methodology, Software C Guedes Soares: Conceptualization, Methodology, Writing - Review & Editing, Supervision, Project administration, Funding acquisition
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Wave energy assessment based on a 33-year hindcast for the Canary Islands Marta Gonçalves, Paulo Martinho and C. Guedes Soares*
Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Portugal
Highlights A wave energy assessment is performed in the Canary Islands, based on a 33-year hindcast The third-generation wave models WW III and SWAN are used to study the evolution of the waves The results show an average annual energy availability of 25-30kW/m. The results also suggest that the East coast of the islands presents less variability The results also suggest that the N/NW coast of the islands exhibits greater energy availability
Marta Gonçalves, Paulo Martinho, C. Guedes Soares PII:
S0960-1481(20)30012-4
DOI:
https://doi.org/10.1016/j.renene.2020.01.011
Reference:
RENE 12873
To appear in:
Renewable Energy
Received Date:
20 April 2018
Accepted Date:
03 January 2020
Please cite this article as: Marta Gonçalves, Paulo Martinho, C. Guedes Soares, Wave energy assessment based on a 33-year hindcast for the Canary Islands, Renewable Energy (2020), https://doi.org/10.1016/j.renene.2020.01.011
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.
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Wave energy assessment based on a 33-year hindcast for the Canary Islands Marta Gonçalves, Paulo Martinho and C. Guedes Soares*
Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Portugal *Corresponding author: [email protected] ABSTRACT: A wave energy assessment is performed in the Canary Islands, based on a 33-year hindcast, between 1979 and 2011. The third-generation wave models WAVEWATCH III and SWAN are used to study the generation of the waves in the North Atlantic basin and the transformation of the waves in the Canary Islands, respectively. The hindcast system was validated in a prior study, showing a relevant wave energy resource distribution with an average annual energy availability of 2530kW/m. This study intends to offer a detail description of the wave climate, combining the previous results with the new studies. The results show that the seasonal mean distribution of wave energy varies between 15-20kW/m, in the spring and 25-30kW/m, in the winter, while in the less energetic areas the seasonal average varies between 1015 kW/m, in the spring, and 15-20kW/m, in the winter. Also, the temporal variability indexes suggest that the East coast of the islands presents less variability and that the North/Northwest coast of the islands exhibits greater energy availability.
Keywords: Wave energy; Spectral models; WAVEWATCH III; SWAN; Canary Islands, WEC performance.
1. INTRODUCTION The continuous request for clean energy resources, with low impact on the environment, has increased the progress of new technologies to convert wave energy for the future generations. Numerical models can reflect the physics of wave generation and propagation from deep to shallow waters, and in consequence, they can make more accurate assessments of the wave energy potential in coastal areas, in which renewable energy developments are being planned. Extensive research on the wave energy resources and assessment has already been made providing indexes of monthly and seasonal variability at a global scale. Cornet [1] 1
Journal Pre-proof studied the spatial and temporal variations of the global wave energy resource. Barstow et al. [2] show that the most energetic area is located in the Southern Ocean’s Roaring Forties, with an annual average greater than 140kw/m. Arinaga et al. [3] showed that the monthly wave power in the Pacific and Atlantic Oceans, above 30°N, has at least 35kW/m during the winter months. The 61-year hindcast provided by [4] shows the seasonal, inter-annual and long-term variability of the global wave power. Also in [5] a review the global wave energy resource according to the most recent datasets available was made, identifying the geographical regions with the maximum wave power. Wei et al, [6] provided an overview of long-term climatic trends and medium- to long-term predictions of the global wave energy. At a European level, Neil et al, [7] showed the temporal variation of the wave resource over seasonal and inter-annual timescales. Also in Guedes Soares et al, [8][9] an assessment of the wave energy was performed for the Atlantic European Coast. Over the past years, considerable amount of hindcast studies have been performed with state-of-the-art numerical models, around the world, revealing the most effective areas for renewable energy developments. Along the European coast various studies were made. A 10-year hindcast study on the wave climate was performed for the Portuguese coast [10]. The wave energy around the Portuguese Islands of Azores [11] and Madeira [12] has also been studied (with a potential average of 60kW/m and 14kW/m respectively). In [13] the wave energy resource was evaluated in Menorca considering a 17-year hindcast wave climate database, presenting a significant seasonal variability, with an average wave power, around 8.9 kW/m and average annual wave energy of about 78 MW h/m. Vicinanza et al. [14] showed an analysis based on wave measurements carried out along a 20-year period in the north west of Sardinia (Italy), presenting an annual offshore wave power e between 8.9 kW/m and 10.3 kW/m and [15] presented a 10-year evaluation for the western Sardinia coast and the Sicily Channel, as this are considered to be most profitable areas in the Mediterranean. Neill et al, [16] presented a 10-year hindcast showing a considerable variability of the wave resource surrounding Orkney, with the winter mean wave power varying from 10-25kW/m. In [17] the wave energy potential over the Eastern Mediterranean Sea shows that the most energetic offshore areas of the Levantine Basin are the western coastline of Cyprus, the sea around Israel and Lebanon and the coastline of Alexandria in Egypt (with a potential of about 2.5 kW/m). 2
Journal Pre-proof Around the world many studies have been performed. For example, for the Cape Verde Islands, Bernardino et al. [18] studied the energy profile, showing an average potential above 7kW/m and identifying areas with considerable energy. In [19] a 10year hindcast for the Hawaiian Islands is presented, showing that the northwest swells during winter months produce 35-50 kW/m. Appendidi et al, [20] produced a 30-year wave hindcast to evaluate the wave energy in the Caribbean Sea, suggesting a 814kW/m extractable energy in the Caribbean Low-Level Jet region. Moreover, other studies where performed by Morin et al, [21] for the Southeast coast of Australia estimating a wave energy resource between 4-16kW/m; for the Aegean Sea, Lavidas and Venugopal [22] showed a 35-year hindcast study on the assessment of the wave characteristics and estimate the wave energy resource available in coastal areas; also Khojasteh et al, [23] evaluated the wave energy for Iran, showing the energy resource around the Persian Gulf islands and the Gulf of Oman coastline with 16.6kW/m and 12.6kW/m of average wave power, respectively. Based on the HIPOCAS project [24], and as part of the European projects MAREN and MAREN II, several studies have been performed using WAVEWATCH III [25] and SWAN [26] models, for the West coast of Ireland [27], the Southwest of UK [28], Canary Islands [29], Galway Bay [30], the North of Spain, [31], Portugal [32], France [33], assessing the wave energy resource and showing its availability and variability. The hindcast validation results prove that the system provides data that is in agreement with wave measurements. Wave energy resource around the Canary Islands has been estimated, showing that the main marine resources are offshore wind and waves. These resources are mostly set in the northern coast of the Islands, which has available 25-30kW/m [34]. Other studies have been made showing the geographical distribution of the wave energy in different islands of the Canary archipelago. Iglesias e Carbalho conducted a study on wave energy for La Palma (La Isla Bonita) [36] and El Hierro [37], showing a substantial resource in the north and northwest coasts of La Palma and in the west and north coast of El Hierro. The implementation of wave energy plants requests a preceding study of the sea wave conditions, as well as the evaluation of the accessible energy in a given area. It is estimated that the installation of offshore wave energy plants is the most suitable, when compared with coastal and nearshore installations. However, is worth to highlight that, mechanisms triggered by wave-bottom interaction occurring in the nearshore zones can 3
Journal Pre-proof induce nearshore hot-spots where the wave energy potential is greater than in the offshore zone, as mentioned by Vannucchi and Cappietti [41], among others. This exploration has had a significant growth in the next decades due to the technological advances [42][43]. There are a few aspects that should be taken into account when selecting a wave power device, such as: easy access to the device at any time, energy storage capacity, among others, should be considered, [44]-[46]. This study combines and extends an earlier 10-years study, [47], by presenting the results of a 33-year hindcast performed for the Canary Islands. Being the installation of wave energy test sites an attractive investment and being the Canary Island been identified as a good candidate for WEC deployment [36]-[40], this study aims to be a support for those offshore renewable energy investors, as the activity of the wave converters is directly associated with the wave energy available. Even though the evaluation of the energetic potential in the Canary Islands is the central motivation of this work, 6 points are selected and the wave power at each point is evaluated. Also, the performance of 6 wave energy converters on these locations will be studied.
2. HINDCAST SYSTEM DESCRIPTION AND VALIDATION 2.1.
Theoretical formulations
WAVEWATCH III is a full spectral third-generation wind-wave model developed by the National Centers for Environmental Prediction (NCEP). The model solves an advection type wave action equation [48]: DN S Dt
(1)
where N is the action density spectrum, σ is the relative frequency and S describes the source terms. The right-hand side of equation defines the kinematics of the model, while the left side-hand characterises the physical processes that generate, dissipate and distribute the wave energy. The source terms include: wind-wave interactions, quadruplet wave-wave interactions, and dissipation through whitecapping and bottom friction.
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N kN N S x xN t k
(2)
x c g U
(3)
d U k k d s s
(4)
1 d U k k d m m
(5)
where,
being k the wave number vector, σ the relative frequency, U the current velocity. cg is the group velocity given by cg and θ, s is the coordinate in the direction θ and m is a coordinate perpendicular to s. 2.2.
Hindcast system methodology
State-of-the-art third-generation wave models WAVEWATCH III and SWAN are used to study the generation of the waves in the North Atlantic basin and the transformation of the waves in the Canary Islands. The main model used was SWAN which is a more flexible, easy learning model. It simplicity makes it a good option for simulations all the way from intermediate waters up to the nearshore. The WWIII model considers an energy spectrum with 24 frequencies, logarithmically spaced, from 0.040Hz, with increments of 1.12Hz and 24 directions spaced 15 degrees and a JONSWAP-spectra with peak enhancement factor of γ = 3.3. SWAN model assumes a spectral grid of 30 frequencies, logarithmic spaced, between 0.050Hz e 0.6Hz, with Δf/f =0.1 intervals and 36 directions with 5ᵒ spacing. Computations were implemented with a 20min time step, in the non-stationary mode. In this study no currents are incorporated. For both models the bathymetry derive from GEBCO database and the wind input fields used are from ERA-Interim [51] database, the characteristics can be seen in Table 1. 5
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Figure 1 – Hindcast study area (a-North Atlantic; b-Canary Islands). Table 1– Computational grid information
Wind
Area
Latitude/Longitude
Resolution
North Atlantic
10ºN-75ºS/70ºW-30ºE
1ºx1º
6h / 1.5ºx1.5º
Canary Islands
27ºN-30.5ºN/20ºW-13ºW
0.05ºx0.1º
6h / 0.5ºx0.5
ERA Interim
The system is validated considering 15-years of measurements from the Gran Canaria buoy (28.20ºN, 15.78ºW), comparing Significant Wave Height (Hs), Mean Period (Tm) and Peak Period (Tp) data. The statistical results, as shown in Figure 2, show a good agreement, with correlation values above 85% for Hs. The scatter plots (on the left) show that the higher concentrations are between 1-2m for Hs, 5-6s for Tm and 6-8s for Tp. The Q-Q plots (on the right), compare the two probability distributions by plotting their quantiles against each other, showing that the two datasets follow a similar statistic up to 2m for Hs and 6s for both Tm and Tp. Also, it is visible that overestimations of the model for wave heights higher then 2m and 6s, which might mislead to a wrong evaluation of the wave energy resource available, as the wave power is proportional to the square of the Hs. Regarding the wave periods; one should note a wide deviation of the mean period for values above 6s. A possible reason for such a
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Journal Pre-proof performance may well be attributed to an insufficient accuracy in the calculation of the 2D-shape of the wave spectrum.
Figure 2 – Statistical results for Hs, Tm and Tp.
3. CHARACTERIZATION OF THE WAVE RESOURCES
3.1.
Significant Wave Height
The wave climate in the area of the Canary Island is evaluated, showing mean values of significant wave height of 2m for the 33-year period, with a general average variability of 0.6m, being this variability larger in the north coast of the islands (Figure 3).
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Figure 3 - Annual average and standard deviation distributions of the significant wave height.
With the purpose of studying the temporal variability of the wave resources in the study area, two coefficients are calculated: The Annual Variability index (AV) and the Coefficient of Variation (COV). The first quantifies the annual variability of the wave resource: AV
PA1 PA2
(6)
Pyear
where PA1 is the mean wave power of the most energetic year, PA2 is the least energetic year and Pyear is the yearly mean. The second coefficient calculates the amount of variability in relation to the mean and is obtained dividing the standard deviation (σ) by the mean (μ): COV
(7)
Figure 4 – Significant wave height Annual Variability Index (on the left) and Coefficient of Variation (on the right)
Figure 4 illustrates the distribution of the variability and, as it can be seen, both Annual Variability and Coefficient of Variation present low values. Low values are desirable since it would mean that each year has the similar amount of Hs.
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Wave Energy
The hindcast results also give information about the wave energy resource. In SWAN, the energy transport components are computed according: Px g
c E , dd
(8)
Py g
c
, dd
(9)
x
yE
where x and y are the problem coordinate system (for the spherical coordinates x axis corresponds to longitude and y axis to latitude), and cx, cy are the propagation velocities of wave energy in the spatial domain. As a result, the absolute value of the energy transport is given by: Pot Px2 Py2
(10)
The Canary Islands are in the Atlantic Ocean, having a short continental shelf. The energy of the waves is therefore not affected much by the refraction or shoaling of the waves and can be obtained by: J
g 2 Hs 2Te 64
(11)
where J is the wave power per unit of crest length (kW/m), Hs is the significant wave height, Te is the energy period, ρ is the density of seawater (assumed to be 1.025 kg/m3) and g is the gravitational acceleration. According to [52], it is assumed that Te≈0.90Tp, which means to assume a typical JONSWAP spectrum with a peak enhancement factor of γ=3.3. The statistical evaluation is presented in Figure 5, showing the results for Bias, root mean square error (RMSE), scatter index (SI) and Pearson’s Correlation Coefficient (r) on the left side and the Q-Q plots on the right side. A good agreement can be seen between buoy measurements and simulations. Also, it is visible that the higher concentrations of energy are found below 25kW/m. Also, the Q-Q plots show that the two datasets follow a similar statistic up to 50kW/m. The geographical distribution of the seasonal wave energy resource accessible during the year is shown in Figure 6. The average energy varies between 8-18kW/m in the summer and spring and 16-30kW/m in the autumn and winter, respectively. It is verified that the most energetic zones are to the North and West of the island of Lanzarote.
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Figure 5 - Statistical results for wave energy
Figure 6 – Seasonal variability of wave energy resource, for the 33-year period.
The inter-annual variability evaluation is calculated by considering the box-whisker analysis. This describes a set of numerical data through their quartiles. On each box, the central mark is the median (2nd quartile), the edges of the box are the 25th and 75th percentiles (1st and 3rd quartiles, respectively), the whiskers (the vertical lines outside the box) extend to the most extreme dataset (highest and lowest values). Also exhibited are the outliers (an observation point that is distant from other observations). In Figure 7a, it is visible, in most cases, the great difference between the lowest and the highest extreme points. Nevertheless, though these variations can be seen, the interannual variability is not very significant and so no trends can be noticed. The smallest wave energy doesn’t show great changes through the years, on the other hand, the largest wave energy presents a higher variability. Figure 7b shows the monthly mean wave power, it is visible that the median ranges between 10-30kW/m, in summer and winter months, respectively. 10
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Figure 7 – Box-whisker plots of the mean annual (on top) and monthly (at the bottom) wave power, for the 33-year period.
Regarding the variability of the waver energy, additional variability indexes are also calculated: the seasonal variability index (SV) and the monthly variability index (MV). They quantify the variability of the wave power resource and are given by: SV
MV
PS1 PS 2 Pyear PM 1 PM 2 Pyear
(12) (13)
where PS1 is the mean wave power of the most energetic season (winter), PS2 is the least energetic season (summer) and Pyear is the yearly mean, for the seasonal variability index. And for monthly variability index, PM1 is the mean wave power of the most energetic month (January), PM2 is the least energetic month (August). The distribution of wave energy variability can be seen in Figure 8. Small values of SV and MV are indicators of low variability. As can be seen the eastern coast of the islands is where this variability is much less pronounced, especially in Fuerteventura and Lanzarote, but also in Tenerife, Gran-Canaria and La Palma. As can be seen the annual variability is generally low, with values around 0.5.
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Figure 8 - Wave power temporal variability indexes (the coefficient of variation (COV), seasonal variability index (SV), monthly variability index (MV) and annual variability index (AV)
As a word of caution, it should be noted that despite the fact that GEBCO data is good in the offshore region, it is not so accurate near the coast suggesting that better bathymetry close to the coast could improve the results locally. The accuracy of the present results can be further discussed in light of published studies. Campos and Guedes Soares [53] have compared the two long-term wave hindcasts over the North Atlantic. One was produced within the ERA40 project [55] and the other within the HIPOCAS project [24]. Both have been produced in European funded projects in the same framework programme (EVK2-CT-1999-00027 and EVK2CT-1999-00038, respectively). The first one was coordinated by the European ECMWF consisting of a global atmospheric reanalysis, while the second was coordinated by CENTEC-IST and aimed at reanalysing wind, sea level and waves in the coastal waters around Europe [56]. Both covered the North Atlantic and both used the WAM model. The predictions of ocean waves are very sensitive to the quality of the forcing wind fields used as has been demonstrated by several authors [57]-[60]. This is the reason why Campos and Guedes Soares [61] have conducted a comparison of the wind fields used in the known wave hindcast databases, as part of the differences can be ascribed to the forcing wind fields. They concluded that “the reanalyses HIPOCAS and specially NOAA/CFSR are overestimated while ERA-Interim has underestimated wind intensities, which confirm results of Stopa and Cheung [62]”.
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Journal Pre-proof Campos and Guedes Soares [63] have extended their study to compare the wave hindcasts also with the CFSR [64] which is a global reanalysis developed by the National Centers for Environmental Prediction / National Oceanic and Atmospheric Administration (NCEP/NOAA) using WAVEWATCH3 instead of the WAM model used in the previous analysis. This allows a comparison of the performance of WAM and WAVEWATCH3, which were also compared in [62]”. The evaluation of CFSR, ERA-Interim and HIPOCAS used as reference the GlobWave Satellite data. The GlobWave Project is an initiative funded by European Space Agency (and subsidised by CNES) through the Data User Element (DUE), which is a programmatic element of the 3rd period of the Earth Observation Envelope Programme (EOEP-3), an optional programme of the European Space Agency. Therefore, this new satellite database represents a standard and reliable source of public data, in accordance with the state-of-the art in Satellite Altimetry, following the same procedure and using the same data as the most operational agencies, research centres and companies. It is well known that satellite can also show inaccuracies, however errors are small under non extreme conditions in offshore areas as described by the GlobWave Product User Guide [65] and discussed by Janssen et al. [66]. Campos and Guedes Soares [62] have concluded that “the wave reanalyses assessment using satellite data suggests that NOAA/CFSR presents the largest waves among hindcasts (0.2 meters higher, in average), followed by HIPOCAS and ERAInterim. The larger waves of NOAA/CFSR compared to ERA-Interim confirms results of [61]”. The inter-comparison indicated HIPOCAS to be more similar to ERA-Interim than to NOAA/CFSR for overall non-extreme conditions. Under extreme conditions it was found important differences among hindcasts at mid-high latitudes above 40º North, reaching 2.5 meters and a wide area is seen with differences above 2 meters.” This confirms the results of [53] who found an increasing underestimation by WAVEWATCH in respect to WAM when moving to extreme conditions. The conclusions in [63] are also that for overall non-extreme conditions, the three wave hindcasts are very similar, differences are less than 0.5 meters at most grid points, corroborating with most of comparison papers. The energy produced by different wave energy devices depend on their transfer function and the global energy values are dominated by the operation in non-extreme wave conditions, a most devices stop production and go in survival mode when the waves become large than moderate seas [44]. Therefore, the different comparison and 13
Journal Pre-proof calibration studies indicate that the values that have been produced in this study may be fairly representative of the wave conditions appropriate for wave energy conversion. The conditions for extreme conditions and assessment of survival conditions may need a more careful study
3.3.
Wave Energy Converters Performance
As mentioned, the implementation of WEC devices demands a prior evaluation of the sea wave conditions, along with the estimation of the accessible energy in a given area. Even though the focus of this work is the evaluation of the energy potential in the Canary Islands, a study on the performance of some energy converters is also carried out to provide a feeling for the amount of energy really being able to be captured by different devices. To estimate the potential of wave energy converters, 6 points are selected to evaluate the wave energy resource available (Figure 9). The selected points are found between 43-90m water depth and their characteristics are specified in Table 2. As can be seen, the mean values of Hs vary between 1.24m (P1) and 1.92m (P2). Furthermore, the mean wave energy has its lowest values for P4, with 4.96kW/m, and its highest values for P2, with 15.71kW/m. The distribution of wave energy variability oscillates between 1.03 (for P3 and P6) and 1.56 (P1).
Figure 9 - Location of the selected points.
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Table 2 – Characteristics of the selected points.
Lat (º)
Lon (º)
Depth (m)
Hsmax (m)
Hsmean (m)
Pmax (kW/m)
Pmean (kW/m)
COV
P1
28.70
-18.00
90.00
7.04
1.24
212.97
8.33
1.56
P2
28.60
-16.30
82.00
6.33
1.95
264.42
15.71
1.14
P3
28.20
-15.50
53.00
5.58
1.75
166.81
11.33
1.03
P4
27.99
-15.37
43.00
4.91
1.27
100.55
4.96
1.06
P5
28.20
-14.35
73.00
5.50
1.78
239.54
13.98
1.14
P6
29.30
-13.35
49.00
5.47
1.63
128.09
9.28
1.03
The seasonal wave power presented in Figure 10 shows that the most energetic points are P2 and P5 with mean winter values above 20kW/m and with total mean values above 13kW/m. Point P4 is the least energetic with mean values generally below 5kW/m for all seasons. The monthly wave power shows that the energy found during the winter months is practically twice that found during the summer months (Figure 11). It is worth mentioning that points P2, P5 present the highest energy values. Moreover, it is visible, for point P4, that the average values stay considerably constant throughout the year, with an average value of 5kW/m.
Figure 10 – Total and seasonal mean wave power for the selected points, for the 33-year period.
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Figure 11 - Monthly wave power for the selected points, for the 33-year period.
To evaluate the performance of the wave energy converters, 6 devices are considered; their characteristics can be seen in Table 3. They have different characteristics and different rated capacities aiming to show the differences that they induce. The power matrix for each device can be found in [44]. The method to estimate the electricity production of a WEC, in a specific site, is to associate the energy converter power matrix with the wave activity at that specific site, which is calculated considering the joint distribution diagrams of Hs-Te of each point. The electricity production is given by: Pe
1 100
N
N
i 1
j 1
pij Pij
(14)
where pij is the energy percentage at a given point and Pij is the WEC electric power.
Table 3 - Characteristics of the different WEC’s selected Device
Rated Capacity (kW)
Depth (m)
Wave Dragon
7000
>30
Wavebob
1000
50-100
Pelamis
750
>50
Oceantec
500
50-100
Aqua Buoy
250
>50
Seabased AB
15
>50
Average electrical power expected for each WEC, at each location can be seen in Figure 12. The results show that in this case, the Wave Dragon device has the highest 16
Journal Pre-proof values, reaching the 400kW/m for all points during the total period, and the 500kW/m for almost all points during winter. The Oceantec device also show good results for all points, with values above 100kW for almost all points, for both in the total and winter periods.
Figure 12 - Average electrical power expected for each device, at each site, for total and winter periods.
The normalized electric power is also calculated for each device. This allows to relate the results, as all values are expressed in the same range. The normalized electric power is given by: Pe n
Pe Pe max
(15)
where Pe is the estimated electric power in each point, for each WEC and Pemax is the highest value at each device, for the total period. As can be seen in Figure 13, the most energetic points are those found in the north coast of the islands (P2, P5), as they are the most expose to the North Atlantic swells. P6 also presents good results for all devices. It is visible that during the winter periods points P2 and P5. The biggest variations between the total and winter periods occur for Aqua Buoy and Wavebob devices with values around 1.37 and 1.39, respectively, while for SeabasedAB and Oceantec these variations are not very pronounced and almost all points do not exceed the 1.
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Figure 13 - Normalized electrical power for each device, at each site, for total and winter periods.
4. CONCLUSIONS Wave models deliver an understanding of the wave power and the characteristics of the available wave energy resource. Given the increasing need for clean renewable energies, a good description of the wave resources is needed. This study aims to deliver a general description of the resources available by characterizing the wave climate. The present study is focused on a 33-year hindcast system, over the Canary Islands. For this study two state-of-the-art spectral models are used to assess the wave energy. The hindcast system is validated with the Gran Canary buoy and the statistical analyses show an overall good relation between measurements and simulations. The wave climate resource was evaluated showing the low variability in the eastern coast of the islands, being the north coast more intense as it is more exposed to the direct swell from the Atlantic Ocean. As mentioned before, low levels of variability are desirable, as this indicates that the amount of wave energy resource would be the same each year. The geographical distributions of the wave power show the high values in the north of the La Palma, Tenerife, Fuerteventura e Lanzarote contrast with the rest of the island. It is visible the shadow effect in the northern coast of the Gran Canary and La Gomera islands. 18
Journal Pre-proof The installation of wave energy converter devices is an appealing investment; therefore, a good description of the wave conditions, along with the amount of accessible energy in a given location is required. Though the focus of this work is the assessment of the energy potential in the Canary Islands, a study on the performance of some energy converters was made. The electrical power was calculated showing that the Wave Dragon device has the highest values, ranging between 430-820kW/h, followed by the Oceantec device, ranging between 8-180kW/h. Moreover; it is visible that the most energetic points are P2 and P5. The normalized electrical power allows relating the results, as all values are expressed in the same range. The biggest differences between the total and winter periods occur for Aqua Buoy and Wavebob devices. These seem to be a good solution for this location. Considering the results obtained and comparing them with other studies on wave energy, for instance: in the Azores Island [11] with a potential average of 60kW/m; in the Madeira Islands [12] with average of 14kW/m; in the Cape Verde Islands [18], where the energy is around 7kW/m; or on the west coast of France [33] with an average potential of 15kW/m, it can be concluded that the Canary Islands offers an appealing wave energy resources supply to the implementation of wave energy devices in certain areas, where average values are around 15-20kW/m.
ACKNOWLEDGEMENTS This present work has started within the project “MAREN2-Hydro-environmental modelling of multi-purpose marine renewable energy platforms” funded by the Atlantic Area Transnational Programme (European Regional Development Fund) under contract 2013-1/225. This study was completed within the project ARCWIND - Adaptation and implementation of floating wind energy conversion technology for the Atlantic region, which is co-financed by the European Regional Development Fund through the Interreg Atlantic Area Programme under contract EAPA 344/2016.
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Journal Pre-proof Conflicts of Interest
There are no Conflicts of Interest
Journal Pre-proof 2533 - CRediT author statement Marta Gonçalves: Methodology, Software, Formal analysis, Writing - Original Draft, Visualization Paulo Martinho: Methodology, Software C Guedes Soares: Conceptualization, Methodology, Writing - Review & Editing, Supervision, Project administration, Funding acquisition
Journal Pre-proof
Wave energy assessment based on a 33-year hindcast for the Canary Islands Marta Gonçalves, Paulo Martinho and C. Guedes Soares*
Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Portugal
Highlights A wave energy assessment is performed in the Canary Islands, based on a 33-year hindcast The third-generation wave models WW III and SWAN are used to study the evolution of the waves The results show an average annual energy availability of 25-30kW/m. The results also suggest that the East coast of the islands presents less variability The results also suggest that the N/NW coast of the islands exhibits greater energy availability