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Wave energy assessments in the Azores islands
Renewable Energy 45 (2012) 183e196
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Renewable Energy journal homepage: www.elsevier.com/locate/renene
Wave energy assessments in the Azores islands Liliana Rusu, C. Guedes Soares* Centre for Marine Technology and Engineering (CENTEC), Instituto Superior Técnico, Technical University of Lisbon, Portugal
a r t i c l e i n f o
a b s t r a c t
Article history: Received 18 October 2011 Accepted 27 February 2012 Available online 17 March 2012
Motivated by the fact that in isolated island environments the extraction of the renewable energy becomes an issue of increasing importance, the objective of the present work is to evaluate the wave energy patterns in the Archipelago of Azores. An analysis of the wave climate in the target area is first carried out considering both remotely sensed and historical data. As a further step, a wave prediction system based on spectral wave models is implemented and validated against satellite data in the coastal environment of the archipelago. Using the above wave modelling system, the spatial distribution of the wave energy is evaluated considering relevant wave patterns for both winter and summer seasons. The results show some significant peaks of wave energy that usually occur at the western edges of the islands. Scatter diagrams are developed for some of these locations found richer in wave energy. Using these diagrams, an evaluation is made for the average energy that would be provided in the nearshore targeted locations by a PELAMIS installation. The results show that the Archipelago of Azores has considerable resources of wave energy, some of them located in the immediate vicinity of the shore. Ó 2012 Elsevier Ltd. All rights reserved.
Keywords: Wave energy Azores islands SWAN model Hot spots
1. Introduction Extraction of the wave energy represents one of the greatest challenges of present days. Wave energy is abundant and could provide considerably clean energy. On the other hand, although hundreds of devices for extracting the wave energy have been designed and tested with varying success, most of the technologies still need to be improved. This especially concerns the resistance of the converters to the strong environmental conditions that often occur in many ocean areas. Assessments of the wave energy resources have been carried out so far at global scale (e.g. [1].) or in various regions of Europe (e.g. [2e12]. in North and South of America (e.g. [13e15].) and Asia (e.g. [16].), and some advances in the statistics behind the assessment have been also made (e.g. [17,18].). In island environment, the importance of implementing such energy extraction systems is enhanced by the fact that they can contribute to the energetic autonomy of the local communities in places where the price of the conventional energy can be high. Moreover, these coastal areas have often the particularity that they are characterized by high bathymetric gradients so that the transition from deep to shallow water is very sharp and as a consequence the waves propagate close to the coast without losing energy but, on the other hand, the bathymetry may induce relevant wave energy
* Corresponding author. E-mail address: [email protected] (C. Guedes Soares). 0960-1481/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.renene.2012.02.027
peaks. These areas, when relatively small changes of the geographical position may lead to large modifications in the available wave power, denoted also as ‘hot spots’, have the highest potential as prospective energy farm sites. Moreover, as the main conclusions of the study carried out by Folley and Whittaker [19] also indicate, for many exposed coasts nearshore sites offer similar potential for exploitation of the wave energy resource as offshore sites. In this context, the objective of the present work is to evaluate the wave climate and energy patterns in the Azores islands using both satellite data and numerical models. This environment holds a great potential of wave energy and is also drastically bounded by physical boundaries that prevent the exchange of resources and livelihoods with other regions. As a consequence, the islands are dependent on a set of resources, not only as vital nature, but also all those who support the economy (transport, fisheries, and tourism) as well as those that determine its safety. All these factors are dependent on weather climate, which include the sea state conditions. Azores is a group of nine islands located in the North Atlantic Ocean about 1200 km west of Portugal. The entire archipelago is oriented west-northwest to east-southeast (Fig. 1). The islands are volcanic in origin and are the largest group of peaks of the MidAtlantic Ridge to form islands. These nine islands are divided in three groups: the Oriental group (Santa Maria and São Miguel), the Central group (Terceira, Graciosa, São Jorge, Pico and Faial) and the Occidental group (Flores and Corvo). The islands of the Occidental group are relatively distant from the others (about 234 km of Faial) and they are the most occidental territory of both Portugal and
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Fig. 1. The Azores islands, the positions of eighteen reference points considered for the analysis of the remotely sensed data are illustrated together with the geographical spaces of the three medium resolution SWAN computational domains, in background the bathymetry is presented.
Europe. The Archipelago of Azores, including the bathymetric map, is presented in Fig. 1. From geographical reasons the Atlantic islands are exposed to various wave systems that propagate across the North Atlantic Ocean. When the waves come from the west, southwest, or northwest, a sheltering effect occurs and an area protected from the high waves towards Portugal continental appears [20]. The archipelago is subjected to both direct approach of the swell coming from distant storms as well as the sea waves generated by local winds that create a local wave system. When the swell system crosses the archipelago the wave directions are significantly modified and this induces the occurrence of various wave systems with different directions in the coastal environment of the archipelago. In this region, characterized by important maritime activities, few in-situ measurements were performed to allow relevant studies concerning the wave conditions. The most recent measurements became available with the implementation of the project CLIMAAT [21], but they are not widely available. Therefore to be able to conduct a validation of the numerical model developed resource had to be made to remote sensed data, and two years of data have been collected and are analyzed here including the production of statistics of wave conditions. The paper deals then with the implementation of the wave prediction system and its validation. The spatial distribution of energy is studied and the hot spots in the coats are identified. Finally a comparison is made with results from studies by other authors. 2. Wave climate in the target area To evaluate the medium and long term wave climate in the Archipelago of Azores two different data sources were considered and analyzed. A two-year analysis of some remotely sensed data was first performed. The main source considered at this step are the altimeter products provided by Ssalto/Duacs and distributed by AVISO (Archiving, Validation and Interpretation of Satellite Oceanographic data), with support from CNES (Centre National d’Etudes Spaciales). A node of the gridded data gives for each day at 0 h near real time
multi-mission merged non interpolated values of the significant wave height (Hs). These are time (for the last 48 h) and space (for 1 squares centred in the node) averaged data sets. In order to provide the most recent picture of the characteristics and dynamics of the wave conditions in the environment neighbouring the Azores islands, some synthetic results coming from the analysis of the altimeter data, corresponding to the period September 2009eAugust 2011, are presented here. Eighteen reference points covering the entire archipelago are defined and their locations are indicated in Fig. 1. They are denoted as P1 to P18 and they are counted from west to east and from north to south, respectively. Table 1 presents the overall Hs statistics for the above remotely sensed data registered in the eighteen reference points considered. The above data are structured in winter and summer time, respectively. As in most climatologic analyses concerning the wave data, in the present work the winter time is considered the period from 1st of October to 31st of March while the summer time is the rest. The parameters evaluated at each point are mean and median values, minimum, maximum and range and also standard deviation, kurtosis and skewness, computed according to the standard definitions of these quantities. Kurtosis is a measure of the peakedness while skewness is a measure of the asymmetry of the distribution of a real-valued random variable. The skewness value can be positive or negative. Qualitatively, a negative skew indicates that the tail on the left side of probability density function is longer than the right side and the bulk of the values (including the median) lie to the right of the mean. A positive skew indicates a reversed tendency. An analysis of the data presented in Table 1 shows that the average significant wave height is in all reference points greater than 1.7 m for summer time and than 3.2 m for winter time. Greater values for the maximum significant wave heights are encountered in the north-western part of the archipelago (about 8.5 m in P1), while lower values in the south-eastern part (about 6.3 m in P16). It has to be mentioned however the fact that these values do not represent maximum significant wave heights but, according to the mediation rule used for these data, space and time average values.
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Table 1 Overall statistics for the remotely sensed significant wave height (Hs) registered in the archipelago of Azores in eighteen reference points for the two-year time interval September 2009eAugust 2011 structured in summer time (the period from 1st of April to 30th September) and winter time (the period from 1st of October to 31st March). Point long ( )/Lat ( )
Season
Mean (m)
Median (m)
Min (m)
Max (m)
Range (m)
Std Dev (m)
Kurtosis
Skew
P1 (32W,40N)
Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter
1.98 3.76 1.96 3.73 1.94 3.69 1.93 3.67 1.92 3.64 1.91 3.63 1.89 3.59 1.87 3.56 1.85 3.52 1.85 3.49 1.84 3.47 1.78 3.38 1.77 3.35 1.78 3.33 1.78 3.32 1.79 3.30 1.73 3.20 1.75 3.20
1.80 3.59 1.78 3.56 1.80 3.52 1.82 3.48 1.85 3.45 1.72 3.50 1.70 3.45 1.70 3.40 1.73 3.36 1.74 3.31 1.74 3.30 1.62 3.25 1.64 3.19 1.65 3.18 1.66 3.18 1.67 3.17 3.81 5.24 1.62 3.05
0.67 1.54 0.63 1.53 0.61 1.54 0.61 1.56 0.57 1.59 0.66 1.50 0.62 1.49 0.59 1.51 0.59 1.54 0.57 1.59 0.57 1.60 0.57 1.52 0.55 1.50 0.55 1.45 0.56 1.37 0.59 1.30 0.55 1.18 0.60 1.13
4.71 8.46 4.61 8.21 4.54 7.96 4.49 7.59 4.34 7.27 4.89 7.91 4.74 7.72 4.68 7.48 4.50 7.20 4.24 6.90 4.22 6.87 4.43 6.95 4.12 6.74 4.25 6.55 4.35 6.37 4.44 6.33 4.36 6.42 4.47 6.39
4.04 6.92 3.98 6.68 3.93 6.42 3.88 6.03 3.77 5.68 4.23 6.41 4.12 6.23 4.09 5.97 3.91 5.66 3.67 5.31 3.65 5.27 3.86 5.43 3.57 5.24 3.70 5.10 3.79 5.00 3.85 5.03 1.59 3.08 3.87 5.26
0.81 1.24 0.78 1.21 0.76 1.19 0.74 1.17 0.73 1.15 0.80 1.18 0.76 1.15 0.74 1.12 0.71 1.09 0.68 1.08 0.67 1.08 0.68 1.04 0.65 1.02 0.64 1.01 0.63 1.02 0.62 1.02 0.60 0.99 0.60 0.99
3.14 3.32 3.08 3.39 3.03 3.36 3.06 3.21 3.21 3.13 3.86 3.10 3.75 3.16 3.60 3.14 3.52 3.05 3.63 2.92 3.90 3.00 4.04 2.88 4.17 2.80 4.39 2.75 4.85 2.83 5.49 2.91 5.45 2.85 6.20 2.88
0.76 0.68 0.71 0.70 0.66 0.70 0.64 0.70 0.66 0.71 1.00 0.58 0.96 0.60 0.90 0.62 0.85 0.61 0.85 0.60 0.90 0.64 1.05 0.53 1.05 0.52 1.08 0.52 1.16 0.54 1.27 0.56 1.34 0.52 1.47 0.53
P2 (31W,40N) P3 (30W,40N) P4 (29W,40N) P5 (28W,40N) P6 (32W,39N) P7 (31W,39N) P8 (30W,39N) P9 (29W,39N) P10 (28W,39N) P11 (27W,39N) P12 (29W,38N) P13 (28W,38N) P14 (27W,38N) P15 (26W,38N) P16 (25W,38N) P17 (26W,37N) P18 (25W,37N)
This means that the maximum values of this parameter may be considerably higher. Fig. 2 shows the Hs histograms for three categories of points. These are the points P1 and P2, characterized by greatest average values of the maximum significant wave height (greater than 8 m), the points P10 and P11 having average values of the maximum significant wave height of about 6.9 m and the points P15, P16 having the lowest average values of the maximum significant wave height (about 6.3 m). From the above histograms, it can be observed also that in the summer time the average Hs values between 1 and 3 m represent about 75% from the total for the points P1, P2, 85% for P10, P11 and 90% for P15, P16. For the winter time the percents corresponding to the Hs interval 1e4 m are 60e65% from the total for the points P1, P2 and about 70% for all the other points. A first conclusion, coming from this analysis of the time and space averaged satellite data, is that the Azores islands are an environment very rich in wave energy resources but, on the other hand, is subjected periodically to severe weather conditions that might affect the functionality of a wave energy farm operating in that area. Nevertheless, in relation with this aspect, an important observation is that the southern part of the archipelago, namely the oriental group that includes the islands São Miguel and Santa Maria, although having almost the same average significant wave heights is subjected to considerable lower extreme conditions. Thus, whether the differences with respect to the northern part of the archipelago (the occidental group) in terms of the mean Hs values are of about 0.2 m for summer time and 0.4 m for winter time, the maximum averaged Hs values are lower in the southern part with about 0.5 m in summer time and with more than 2 m in winter time.
For a longer term analysis of the wave climate in the Azores islands, including also directional information, the KNMI/ERA-40 Wave Atlas [22] was considered. The wave climate information provided in the above atlas is based on 30 years from 1971 to 2000. Tabular significant wave height (corrected ERA-40) and mean wave period bivariate histograms are provided for areas with the lengtHs of 9 in both longitude and latitude, the directional space being structured in sectors of 45 . The data related with two such areas that cover the Azores islands were processed extracting only the information related with the significant wave heights and the wave directions and they are presented in Tables 2 and 3. The above two areas are denoted as A1 (longitude 36 W27 W and latitude 36 N-45 N) and A2 (longitude 27 W-18 W and latitude 36 N-45 N). Tables 2 and 3 give in their first column the percents (%) from the total wave data of the waves having the directions in each directional sector of 45 , while the other columns provide the distribution of the significant wave heights from the respective sector with a resolution of 1 m, in percents 1/10000. The results presented in Tables 2 and 3 show that, for the areas A1 and A2, 74% and 79%, respectively of waves come from W-NW. On the other hand, from the waves arriving from these directions in the two areas about 76% have significant wave heights between 1 and 4 m. It can be concluded from the two analyses carried out above, that the Azores islands appear to be in general an environment rich in wave energy. Moreover, since about 75% of waves come from W-NW it can be assumed also that the waves are in general directionally focused. Nevertheless, it has to be also mentioned that
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Fig. 2. Hs histograms for the satellite measurements corresponding to the period September 2009eJuly 2011 in the Archipelago of Azores. P1(32 E,40 N), P2(31 E,40 N), P10(28 E,39 N), P11(27 E,39 N) P15(26 E,38 N), P16(25 E,38 N).
there are relevant differences in terms of significant wave heights between summer and winter seasons with average Hs values in summer around 1.8 m and in winter about 3.5 m. It can be also assumed that the area is subjected sometimes to extreme wave conditions since significant wave heights greater than 14 m occur almost every year in the archipelago.
3. Implementation and validation of a wave prediction system The methodology proposed herewith to model the wave conditions in the Archipelago of Azores is based on the two state ofthe-art spectral phase averaged wave models. These are WAM [23]
Table 2 Wave climate in the 9 rectangular zone denoted as A1 (longitude 36W-27W and latitude 36 N-45 N) processed from the results of KNMI/ERA-40 wave Atlas. The first column gives the percents (%) from the total wave data of the waves having the directions in each directional sector of 45 , while the other columns provide the distribution of the significant wave heights from the respective sector with a resolution of 1 m, in percents 1/10000. Dir ( )/Hs (m)
0e1
1e2
2e3
3e4
4e5
5e6
6e7
7e8
8e9
9e10
10e11
11e12
12e13
13e14
14e33
sum
0e45(7%) 45e90(5%) 90e135(3%) 135e180(4%) 180e225(8%) 225e270(24%) 270e315(31%) 315e360(19%) Dir all
432 541 642 471 294 250 284 302 320
3972 4598 4400 3162 2383 2510 2719 3259 2984
3466 3069 2920 3032 2736 2520 2653 3218 2832
1464 1342 1265 1943 2236 2005 1896 1839 1865
455 351 542 861 1301 1280 1123 793 1003
146 79 163 338 597 725 614 326 504
46 15 49 152 295 402 369 155 275
13 4 13 32 115 180 184 64 12
5 0 5 8 32 72 85 27 52
2 0 0 0 9 32 39 11 23
0 0 0 0 3 12 19 4 10
0 0 0 0 0 6 10 1 5
0 0 0 0 0 3 4 0 2
0 0 0 0 0 2 2 0 1
0 0 0 0 0 1 1 0 0
10000 10000 10000 10000 10000 10000 10000 10000 10000
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187
Table 3 Wave climate in the 9 rectangular zone denoted as A2 (longitude 27W-18W and latitude 36N-45N) processed from the results of KNMI/ERA-40 wave Atlas. The first column gives the percents (%) from the total wave data of the waves having the directions in each directional sector of 45 , while the other columns provide the distribution of the significant wave heights from the respective sector with a resolution of 1 m, in percents 1/10000. Dir ( )/Hs (m)
0e1
1e2
2e3
3e4
4e5
5e6
6e7
7e8
8e9
9e10
10e11
11e12
12e13
13e14
14e33
sum
0e45(10%) 45e90(5%) 90e135(1%) 135e180(1%) 180e225(4%) 225e270(17%) 270e315(39%) 315e360(23%) Dir all
400 302 351 224 171 211 324 369 314
4444 4530 3725 2700 1963 2493 3204 3743 3341
3327 3332 3203 3166 2569 2505 2742 3143 2883
1256 1408 1748 2279 2500 2053 1757 1595 1741
402 347 661 1162 1609 1314 973 700 907
132 68 251 354 728 749 516 273 444
31 11 40 90 292 380 254 103 204
8 1 17 21 112 177 123 40 93
1 0 5 4 36 71 60 18 41
0 0 0 0 14 27 27 9 18
0 0 0 0 4 10 12 5 8
0 0 0 0 0 5 6 2 3
0 0 0 0 0 2 2 1 1
0 0 0 0 0 1 1 0 1
0 0 0 0 0 1 0 0 0
10000 10000 10000 10000 10000 10000 10000 10000 10000
for wave generation, applied at ocean-scale in a two way nested version [24], and SWAN (Simulating Waves Nearshore [25],) applied at regional and local scales. Ocean-scale simulations, covering almost the entire North Atlantic basin with WAM [26], provide the boundary conditions for SWAN implemented first in a large area that covers the Azores islands. Medium resolution computational domains were also defined inside the large SWAN area for each group of islands (Oriental, Central and Occidental). The same computational scheme was applied with good results in Madeira Archipelago as presented in [27], and also on the Portuguese Continental coast [28]. Some details about the computational domains used for the SWAN simulations are presented in Table 4 and Fig. 1. The implementation of the SWAN model was made for 36 directions and 30 frequencies logarithmically spaced from 0.0418 Hz to 0.6 Hz at intervals of Df/f ¼ 0.1, and the simulations were performed in the non-stationary mode. The reanalyzed wind fields from the North Atlantic basin, that were determined hourly with a resolution of 0.5 in the HIPOCAS project [29], were used as input for the wave modelling system. More details as regards the wind data fields are presented in [30]. In accordance with most of the data sources, it was assumed that there are no relevant currents in the coastal environment of the archipelago, which implies that refraction is only due to spatial variations of the water depth. The input fields considered and the physical processes activated in the simulations with the SWAN modelling system are presented in Table 5. Model simulations were performed for the three-month period JanuaryeMarch 2001. This time interval is characterized by both extreme and average energetic conditions. For example, at the beginning of January wave groups with significant wave heights of about 13 m approached the northern part of the archipelago, and in 6th of February the North-Northwest part of the archipelago was affected by a strong storm when the wind velocity was sometimes greater that 25 m/s. To quantify the prediction skill, the wave model performance was evaluated against altimeter measurements over the entire region of the archipelago. For the period analyzed, global altimeter significant wave height data set provided from the four altimeter missions ERS-2, TOPEX, Poseidon and GEOSAT Follow-On (GFO) are used.
Table 4 Computational grids for the SWAN simulations. Lx, Ly grid lengtHs and Dx, Dy grid resolutions in the geographical space. SWAN grids
Lx ( )
Ly ( )
Dx Dy ( )
Large area Occidental area Central area Oriental area
8.5 0.8 2.5 2.0
5.0 0.8 1.5 2.0
0.05 0.01 0.01 0.01
0.05 0.01 0.01 0.01
The altimeter footprints along the satellite tracks provide a large spatial coverage that cannot be accomplished by in-situ observations at fixed stations. The ground tracks for January 2001 over the geographical space of the Azores islands are illustrated in Fig. 3. As the above figure shows, the combined coverage from tracks of the all satellites is extensive and relatively uniform over the area. The results of the wave modelling system were interpolated in both space and time, with bilinear interpolation schemes to collocate with the altimeter data. To do this, hourly model Hs within 60 min of the altimeter collection time were spatially interpolated from the grid points to the locations of the altimeter measurements along the satellite tracks, and in time to fit the time of the satellite pass, which is very similar to the method used by Cavaleri and Sclavo [31]. In all cases, collocation files were used to compute the statistics and produce scatter diagrams that present the measured against the simulated values. The comparisons with buoy data carried out by Queffeulou [32] showed that the altimeter Hs is in general in agreement with the insitu data, with standard deviations of differences of the order of 0.30 m, but tends to slightly overestimate low Hs and to underestimate high Hs. He has established corrections to Hs that are in general linear and correspond to a few percents of Hs. These corrections were considered in relationship with the altimeter data used in the present work. As an example, Fig. 4 shows the spatial variation of Hs measured by GFO altimeter along the track that passed in 24th of January 2001 at 12 h over the Azores islands and simulated by the model results. The GFO altimeter data provide point estimates at about 7 km intervals, along the ground track of the satellite. It can be observed that the satellite data present a greater variability in comparison with the SWAN results, despite the fact that the spatial resolutions of the wave model and of the satellite measurements are sensibly equal. This is probable due to the low resolution of the wind field used (0.5 , which means about 50 km), resolution that cannot represent with accuracy the local phenomena. Nevertheless, in general the model represents well the enhancements and decreases of Hs along the track. In the southern part Hs is underestimated by the model, effect that is accentuated in the points that correspond to the zone located between the islands of the central group (about 38.5 latitude). As the satellite goes out the environment of the islands a slight overestimation of Hs occurs. The Hs validation obtained from the SWAN model (in the large area) against satellite measurements is presented in Table 6 in terms of the main statistical parameters. The comparisons are structured by month and all satellites. The parameters considered are the mean error computed as the difference between the simulated and observed values, divided by the number of observations N (Bias), the root mean square error (RMSE), the scatter index (SI) defined as the ratio of standard deviation of error to the mean observed Hs, the linear correlation coefficient (R) and the
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Table 5 Input fields considered and physical processes activated in the SWAN model simulations. Wave e wave forcing, Tide e tide forcing, wind e wind forcing, Curr e current field input, Gen e generation by wind. Wcap - whitecapping dissipation, Quad e quadruplet nonlinear interactions, Triad e triad nonlinear interactions, Diff e diffraction, Bfric e bottom friction, set up e wave induced set up, Br e depth induced wave breaking. Input/Process
Wave
Wind
Tide
Curr
Gen
Wcap
Quad
Triad
Diffr
Bfr
Set up
Br
SWAN large SWANmedium
X X
X X
0 0
0 0
X X
X X
X X
0 X
0 X
X X
0 0
X X
Fig. 3. ERS-2, TOPEX, Poseidon and GEOSAT Follow-On (GFO) satellite tracks over the Azores Archipelago area in January 2001.
symmetric slope (S), that is the coefficient of the regression line through the origin (model against measured data). Model values are over-predicted for S > 1 and under-predicted for S < 1. The wave statistics presented in Table 6 shows that the SWAN models results are in agreement with the satellite measurements, with slopes close to 1, low values of the scatter indexes and high correlation coefficients. The fits are better in January that is characterized by higher waves and well defined meteorological situations. Statistical analyses of the Hs corresponding to each satellite that passed over the Azores islands in the three-month period were also carried out and the results are presented in Table 7. According to this table, the model results fit the best with the measurements performed by GEOSAT Follow-On. Fig. 5 shows the scatter plots of Hs (altimeter against wave model data) for January, February and March 2001. The blue lines
denote the perfect fit between the model and the observed values. A linear regression (green line) was adjusted to each data set as well and it is observed that in January the model slightly underestimates the extreme significant wave heights, while in February overestimates them. By increasing the resolution of the SWAN simulations in the geographical space and nesting new areas inside the initial computational domain it is expected also an improvement of the model results. From this reason new SWAN domains with increased spatial resolution were defined (see Table 4). These areas cover the Oriental, Central and Occidental groups of islands, respectively. In order to assess the influence of this enhancement of the spatial resolution, simulations in the above three areas were carried out for January 2001. This represents also the time range when the statistical results from the large area are better, as presented in Table 6. The quality evaluation of the results in these three medium resolution areas was made by means of the same statistical parameters previously defined, the collocation files being obtained by applying the methodology mentioned above. In order to compare the results obtained in the large SWAN area that covers the entire archipelago with those from the medium resolution computational domains only the data covering these last three areas (both simulations and measurements) were extracted from the initial collocations files. Thus for each area, either: Oriental, Central or Occidental, statistical analyses were performed both considering the results with medium and lower spatial resolution. Table 8 presents such statistical results, were LR (low resolution) indicates the results from the larger area while MR (medium resolution) indicates the results obtained in the Central area for the simulations performed with higher resolution in the geographical space. The results presented in Table 8 indicate that in the Central area the increasing of the bathymetric resolution induces also a slight improvement of the statistical results, especially in terms of Bias and RMSE. In the Oriental and Occidental areas no relevant enhancement of accuracy was observed but it is also true that most of the measurements correspond to deep water where the influence of the spatial resolution is not as important as in intermediate or shallow water. The global Hs statistics, SWAN simulations against altimeter data in the large area, for all altimeters and for the entire period JanuaryeMarch 2001 are presented in Table 7, while the corresponding scatter plot is illustrated in Fig. 6. The results presented in Tables 6e8 and in Figs. 5 and 6 show that the wave prediction system based on spectral wave models developed herewith provide in general reliable information concerning the wave conditions in the target area. As a further step, the
Table 6 Hs statistics, SWAN simulations against altimeter data in the large area, results presented for each month separately and for all altimeters.
Fig. 4. Comparison of GFO altimeter Hs data along the track (cycle 59, in 2001/01/24h12), against SWAN interpolated Hs. The x-axis represents the latitude of the altimeter footprints.
Month
Bias
RMSE
SI
R
S
N
2001/01 2001/02 2001/03
0.07 0.08 0.02
0.71 0.75 0.53
0.17 0.23 0.15
0.90 0.96 0.89
0.99 1.06 0.99
4433 3856 3993
L. Rusu, C. Guedes Soares / Renewable Energy 45 (2012) 183e196 Table 7 Hs statistics, SWAN simulations against altimeter data in the large area, results presented for each altimeter separately and for all altimeters, the entire period JanuaryeMarch 2001. Satellite
Bias
RMSE
SI
R
S
N
ERS-2 TOPEX Poseidon GFO All satellites
0.05 0.07 0.04 0.06 0.06
0.68 0.71 0.68 0.63 0.67
0.21 0.19 0.21 0.16 0.18
0.82 0.95 0.93 0.94 0.93
0.99 1.03 1.04 1.01 1.01
3233 4277 473 4299 12282
above wave prediction system is used to make assessments of the wave energy in both time and space frames. For the three-month period of model simulations (JanuaryeMarch 2001), Table 9 presents, the statistics of the significant wave height and energy period in all the eighteen reference points defined in the previous section (where the analysis of the remotely sensed data was performed). The wave energy period Te is defined as:
RR Te ¼ 2p
u1 Eðu; qÞdudq ; Eðu; qÞdudq
RR
189
of 3 h, is focused especially on the spatial distribution of the significant wave height scalar fields coupled with the wave vectors and of the energy transport scalar fields coupled with the energy transport vectors. In SWAN, the energy transport components (expressed in W/m, i.e., energy transport per unit length of wave front), are computed with the relationships:
RR ETRx ¼ rg RR cx Eðs; qÞdsdq ETRy ¼ rg cy Eðs; qÞdsdq;
(2)
where: x, y are the problem coordinate system (for the spherical coordinates x-axis corresponds to longitude and y axis to latitude), Eðs; qÞ the wave energy spectrum and cx, cy are the propagation velocities of the wave energy in the geographical space. Hence the absolute value of the energy transport (denoted also as wave power) will be:
ETR ¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 þ E2 : ETRx TRy
(3)
The non dimensional normalized wave power is expressed as:
(1)
with Eðu; qÞ the is the variance density spectrum, u the absolute radian frequency determined by the Doppler shifted dispersion relation and q the wave direction. According to the results from Table 9, the average significant wave heights are in the same range with those from Table 1 in the Central and the Oriental parts of the archipelago while in the Occidental part the model simulations give average Hs values greater with about 0.3e0.4 m than the corresponding satellite data. This means also that the winter time reference interval considered for the model simulations (JanuaryeMarch 2001) gives a realistic picture of the average winter time wave conditions in the Central and Oriental SWAN computational domains. For the same reference points analyzed in Fig. 2 (P1, P2, P10, P11, P15, P16), Fig. 7 illustrates the directional distribution of the significant wave height from which it can be noticed that especially in the points P10, P11 and P16 the variation of the wave direction is in a quite narrow range. The dominant wave direction is from W-NW as the ERA40 data indicated. 4. Spatial distribution of wave energy The results of the model simulations carried out in the threemonth period JanuaryeMarch 2001 are analyzed also in the spatial domain. This analysis, which is performed with a time step
ETRn ¼
ETR : ETRmax
(4)
In the present work ETRmax is defined separately for each individual case study and approximates the maximum wave power corresponding to each computational domain. A first case study (denoted as CS1 - time frame 2001/01/05/h15) is illustrated in Fig. 8a and reflects a typical winter time average energetic pattern for the coastal environment considered. For the simulations performed in the large SWAN computational domain, the figure presents in background the significant wave height scalar fields while in foreground the wave vectors are represented with black arrows. Significant wave heights with values between 3 and 4 m are common to such winter average energetic situation. In order to provide also an image of the high wave conditions in the Azores islands, Fig. 8b presents the Hs fields for such an enhanced energetic situation (denoted as CS2 - time frame 2001/ 02/04/h18). Significant wave heights greater than 14 m may occur in such cases in the area of the archipelago. It has to be also underlined that significant wave heights with the same order of magnitude can be encountered almost every year so they do not represent a very rare extreme event. The same wave prediction system considered in the present work for providing the ocean forcing is used for operational forecast and focused on the Portuguese continental nearshore, as described in [33]. Using the information given by this system
Fig. 5. Hs scatter plots corresponding to the data registered at all satellites for each month; (a) January, (b) February and (c) March, 2001.
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Table 8 Hs statistics, SWAN simulations against altimeter data in the large and medium areas, respectively. Results presented for January 2001 and for all altimeters. Grid
Bias
RMSE
SI
R
S
N
LR MR
0.12 0.08
0.67 0.65
0.17 0.16
0.90 0.91
0.99 0.99
411 411
simulations with the SWAN modelling system were also performed in the Azores islands for May 2011. In this way, some summer wave patterns were also defined. Two such average summer patterns are illustrated in Fig. 8c and d. Thus Fig. 8c presents CS3 (time frame 2001/05/17/h12) when the mean wave direction is from North West while Fig. 8d presents CS4 (time frame 2011/05/24/h03) when the mean wave is from west. Significant wave heights around 2 m are common to such summer average energetic situations. The maximum values of the wave energy transport and their locations in the field are also represented in Fig. 8 and these are: 84 kW/m for CS1, 1508 kW/m for CS2, 19 kW/m for CS3 and 23 kW/m for CS4, respectively. Even from the simulations performed in the large SWAN computational domain it can be noticed that while the maximum values of the significant wave height are encountered usually in the offshore, concentrations of the wave energy frequently occur at the edges of the islands. As a consequence, in this coastal environment, higher values of the wave energy occur very often in the nearshore. This observation is even more obvious in the simulations performed in the medium resolution computational domains. In this connection, Fig. 9 illustrates such energetic
Fig. 6. Hs scatter plot corresponding to the data registered at all satellites for the entire period JanuaryeMarch, 2001.
patterns in the Central and Oriental areas, respectively that represent not an exception, but a rule, and that can be defined as hot energetic spots. For the medium resolution domain corresponding to the Central part of the archipelago, CS5 (time frame 2001/03/25/h09), illustrated in Fig. 9a, shows an energetic peak of about 90 kW/m in the western nearshore of the island São Jorge, while in CS6 (time frame
Table 9 Statistics for the wave parameters Hs and Te in the same eighteen reference points defined in Table 1. Results of the SWAN simulations for the period JanuaryeMarch 2001. Point
Season
Mean (m)
Median (m)
Min (m)
Max (m)
Range (m)
Std Dev (m)
Kurtosis
Skew
P1 (32W,40N)
Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s)
4.29 9.94 4.20 9.94 4.06 9.84 4.08 9.90 4.00 9.91 4.08 9.89 3.83 9.71 3.79 9.69 3.78 9.73 1.91 8.22 3.46 9.76 3.57 9.63 3.10 9.11 3.13 9.22 3.37 9.58 3.16 9.63 3.21 9.59 1.55 8.33
3.81 9.88 3.66 9.85 3.49 9.76 3.51 9.88 3.44 9.84 3.6 9.78 9.85 8.71 10.82 9.34 11.54 9.88 4.62 8.63 11.68 11.24 10.47 9.37 7.36 7.46 7.72 8.25 9.84 10.17 10.27 11.43 8.30 9.25 2.90 10.58
1.71 5.75 1.59 5.54 1.47 5.29 1.36 5.34 1.17 5.2 1.64 6.06 3.49 9.64 1.39 5.68 1.21 5.48 0.55 4.17 1.02 4.87 1.13 5.88 1.18 5.82 1.15 6.04 1.04 6.10 0.86 5.22 0.95 6.11 0.56 4.64
13.82 15.2 13.89 15.17 13.88 15.29 13.9 15.38 13.84 15.64 12.86 14.94 1.59 5.91 12.21 15.02 12.75 15.36 5.17 12.80 12.70 16.11 11.60 15.25 8.54 13.28 8.87 14.29 10.88 16.27 11.13 16.65 9.25 15.36 3.46 15.22
12.11 9.27 12.3 9.63 12.41 10.0 12.54 10.04 12.67 10.44 11.22 8.88 11.44 14.62 3.35 9.62 3.29 9.68 1.75 7.98 2.91 9.66 3.12 9.50 2.89 9.04 2.89 9.17 3.01 9.51 2.67 9.40 2.91 9.47 1.44 7.67
1.97 1.78 1.98 1.84 1.99 1.88 2.04 1.89 2.06 1.91 1.87 1.78 1.67 1.66 1.75 1.83 1.87 1.89 0.90 1.90 1.88 2.08 1.70 1.87 1.34 1.52 1.37 1.59 1.65 1.89 1.71 2.14 1.46 1.76 0.56 2.19
7.80 3.34 8.31 3.34 8.67 3.39 8.19 3.46 8.07 3.52 7.36 3.24 6.82 3.03 7.63 3.38 7.64 3.46 3.76 2.42 8.45 3.29 7.35 3.47 4.94 2.44 5.37 2.94 6.01 3.74 7.65 3.45 5.19 3.13 3.32 3.19
1.80 0.25 1.92 0.25 2.02 0.36 1.93 0.35 1.92 0.41 1.72 0.33 1.64 0.13 1.77 0.44 1.81 0.48 0.99 0.23 2.07 0.54 1.73 0.56 1.28 0.14 1.30 0.38 1.47 0.78 1.96 0.79 1.27 0.49 0.82 0.86
P2 (31W,40N) P3 (30W,40N) P4 (29W,40N) P5 (28W,40N) P6 (32W,39N) P7 (31W,39N) P8 (30W,39N) P9 (29W,39N) P10 (28W,39N) P11 (27W,39N) P12 (29W,38N) P13 (28W,38N) P14 (27W,38N) P15 (26W,38N) P16 (25W,38N) P17 (26W,37N) P18 (25W,37N)
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2001/03/06/h18) presented in Fig. 9b the energetic peak (75 kW/m) is close to the island Graciosa. As regards the medium resolution domain corresponding to the Oriental part of the archipelago, CS7 (time frame 2001/03/02/h21), illustrated in Fig. 9c, shows an energetic peak of about 101 kW/m in the western nearshore of the island Santa Maria, while in CS8 (2001/03/26/h21) presented in Fig. 9d the energetic peak (112 kW/m) is close to the island São Miguel. For all the above cases the incoming waves in the targeted areas have significant wave heights of about 3 m. Since the dominant wave pattern is west-northwest, these energetic peaks usually occur in the west side of the islands as illustrated by all the examples presented in Figs. 8 and 9.
191
5. Evaluation of the expected electric power in some coastal hot spots Starting from the observation made at the end of the previous section, according to which significant concentrations of wave energy usually occur in the Azores islands in the western edges of the islands, a study on the mean wave energy was also made for some nearshore locations. The time period considered is JanuaryeMarch 2001 in which the model system simulations are performed for all computational domains defined. Thus, for the central medium resolution computational domain the main locations considered are those that correspond to the
Fig. 7. Directional distribution, classes of Hs for the reference points P1(32 E,40 N), P2(31 E,40 N), P10(28 E,39 N), P11(27 E,39 N) P15(26 E,38 N), P16(25 E,38 N). Results of the simulations with the wave modelling system in the period JanuaryeMarch 2001.
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Fig. 8. Hs scalar fields and wave vectors in the SWAN large domain covering the entire Archipelago of Azores. The maximum values of Hs and wave power are also indicated. a) CS1 average winter wave conditions (time frame 2001/01/05/h15); b) CS2 high wave conditions (time frame 2001/02/04/h18); c) CS3 average summer wave conditions (time frame 2001/05/17/h12, with waves arriving from northwest); d) CS4 average summer wave conditions (time frame 2011/05/24/h03, with waves arriving from west).
Fig. 9. Normalized wave energy maps corresponding to winter average energetic conditions. a) CS5 (time frame 2001/03/25/h09), Central medium resolution computational domain; b) CS6 (time frame 2001/03/06/h18), Central medium resolution computational domain; c) CS7 (time frame 2001/03/02/h21), Oriental medium resolution computational domain; d) CS8 average winter wave conditions (time frame 2001/03/26/h21), Oriental medium resolution computational domain.
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energetic peaks presented in CS4, CS5 and CS6, illustrated in the Figs. 8d, 9a and 9b, respectively. Figs. 8d and 9b are pointing Graciosa island while Fig. 9a is pointing the island São Jorge as the locations of the peaks. Nevertheless, as Fig. 9a and b show, energetic peaks also occur in the coastal environment of the island Terceira and from this reason a location in the neighbourhood of this island is considered as well. Taking into account the results presented in the Figs. 8 and 9 but also the general wave patterns as reflected by Tables 2 and 3, all sites analyzed are situated in northwesten sides of the respective islands. Following the same pattern, and considering also the results presented in the Figs. 8a, 9c and 9d that correspond to the case studies CS1, CS7 and CS8, two other locations are defined in the Oriental medium resolution computational domain. The first is situated in the coastal environment of the island Santa Maria and the second close to the island São Miguel. Table 10 presents for each of the five considered locations the average wave energy computed for the reference interval, in three points that go from deep to intermediate water deptHs following the direction of a cross shore line. Taking also into account the high bathymetric gradients that are characteristic to the archipelago, the nearshore locations are no farther than a few hundreds of metres away from shore. The results from the above table show that for the considered coastal sectors the average wave energy in intermediate water deptHs is in general at the same level, or even higher, than in deep water. For the period JanuaryeMarch 2001, scatter diagrams of the HsTe joint distribution are generated using the 3-h consecutive significant wave height and energy wave period time sequences resulted from the simulations with the SWAN model. The bivariate distributions of HsTe are determined for all five coastal sectors considered in Table 10. Such a diagram presents the probability of occurrence of the different sea states expressed in percentage from the total number of occurrences. The sea states are structured into bins of 0.5 s 0.5 m (DTe DHs) and the colour of each bin represents the percentage according to a colour-map, which was designed the same for all diagrams and is illustrated in the colourbar of the figures. The wave power isolines are also represented in each diagram. These have been calculated using the deep water energy flux approximation of equation (see [7]):
ETR ¼
rg2 Te Hs2 64p
193
gravity, Te is the energy period, and Hs is the significant wave height. Fig. 10 illustrates the above scatter diagrams for the coastal sector in the neighbourhood of São Jorge island. The diagrams correspond to the locations defined in Table 10 and denoted as SJ1, SJ2 and SJ3 and reflect the evolution of the sea states from offshore to intermediate water depth. The analysis of the scatter diagrams presented in Fig. 10 (a, b and c) shows that in the coastal
(5)
where ETR is the energy flux in watts per metre of crest length, r ¼ 1025 kg/m3 is the density of sea water, g is the acceleration of Table 10 Variation of the average mean energy from deep to shallow water for the targeted locations. Results of the SWAN simulations for the period JanuaryeMarch 2001. Island/(comp. domain)
Point
Long ( )/Lat ( )
Depth (m)
Average energy (kW/m)
S. Jorge (Central)
SJ1 SJ 2 SJ 3 G1 G2 G3 T1 T2 T3 SMI1 SMI2 SMI3 SMA1 SMA2 SMA3
28.3079/38.7628 28.3184/38.7618 28.3263/38.7539 28.0432/39.1077 28.0249/39.1051 28.0359/39.1034 27.3829/38.7775 27.1889/38.8116 27.3830/38.7627 25.8537/37.8836 25.8686/37.8611 25.3425/37.8593 25.1875/36.9761 25.1839/36.9869 25.1851/36.9821
53.0 43.0 29.0 77.0 46.5 40.0 91.0 65.0 46.8 82.0 36.0 28.5 66.0 42.0 27.6
58.0 73.0 71.0 78.0 71.0 75.5 64.0 63.0 51.5 57.8 62.5 56.6 44.2 38.3 37.4
Graciosa (Central)
Terceira (Central)
S. Miguel (Oriental)
S. Maria (Oriental)
Fig. 10. Scatter diagrams (Hs against Te) for São Jorge coastal sector in the period JanuaryeMarch 2001 corresponding to the three locations defined in Table 11. The coloured rectangles represent the number of occurrences, in percents from the total, which correspond to each rectangular sector with the lengths of 0.5s in x-direction and 0.5m in y-direction. The wave power isolines are also represented. a) Location denoted as SJ1, b) Location denoted as SJ2, c) Location denoted as SJ3.
194
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environment of São Jorge island the probability of occurrence of the more energetic sea states increases from offshore to nearshore. For the other four coastal sectors, Fig. 11 presents the scatter diagrams that correspond to the location that is closest to the shore. From Figs. 10 and 11 it results also that in the coastal environment under consideration the sea states with most occurrences are concentrated between 7 s and 12 s (in terms of energy period) and between 1 m and 5 m (in terms of significant wave height), being located between the power isolines of 5 kW/m and 100 kW/m. The wave models give the theoretical wave power available and the characteristics of the wave resources in terms of sea states (i.e. the characteristics of the waves providing the power), but the actual wave power yield will depend on the particular Wave Energy Converter (WEC) device rated by their wave power generation. Because, each technology has different operational ranges and also different efficiencies under various sea states (e.g. [34].), the more efficient WECs for a specific area are those that have the maximum efficiency in the ranges of Hs and Te that provide the bulk of occurrences. WEC manufacturers provide performance data on their products as a function of Hs and a wave period, which can be Te or any other wave period as: the peak period Tp or the zero crossing period Tz (depending on the manufacturer). The performance table provides the expected power output for each significant wave height and wave period. A distinct pair of significant wave height and wave period is referred to as an energy bin. One way of calculating the electricity production of a WEC is to create a similar table with the same indices, but providing the expected number of hours that each energy bin occurs instead of
power output. Electricity production (in kWh) is simply the expected power output (in kW) for an energy bin multiplied by the expected occurrence (in hours). The sum of these products over all energy bins gives the total electricity produced by the WEC at that location for the period analyzed. The PELAMIS wave energy converter (http://www.pelamiswave. com) is used in the present work as a reference for making an initial assessment of the electric power that it can deliver in the locations considered. The Power Matrix for this device is illustrated in Fig. 12. This figure shows the power generated by the PELAMIS device in a range of the sea spectra defined by significant wave height and energy period (Te) in the same manner as the diagrams from the Figs. 10 and 11 are designed. Using the information related with the joint frequency distribution of Hs and Te generated in the point denoted as SJ2 (see Table 10) the wave activity in hours is computed and is presented in Table 11. For the winter time period analyzed using the joint data from Table 11 and Fig. 12, results a daily average electric energy produced by PELAMIS of about 4.6 MWh. In the same manner, for the points SJ1 and SJ3 the daily average electric energy results 3.4 and 4.5 MWh, respectively. The same computational procedure is applied for the nearshore points of the archipelago denoted as G3, T3, SMI3 and SMA3, for which the scatter diagrams are illustrated in Fig. 11. The corresponding daily average electric energy produced by the PELAMIS device in the above locations, resulted 3.7 MWh in G3, 3.1 MWh in T3, 4.1 MWh in SMI3 and 3.8 MWh in SMA3. Very similar values for the same conversion system (PELAMIS) with a daily average electric energy about 4 MWh were obtained on the west coast of Canada as they were presented in the study performed by Dunnett and Wallace [34].
Fig. 11. Scatter diagrams (Hs against Te) for four nearshore locations in the coastal environment of the Azores islands in the period JanuaryeMarch 2001. The coloured rectangles represent the number of occurrences, in percents from the total, which correspond to each rectangular sector with the lengths of 0.5s in x-direction and 0.5 m in y-direction. The wave power isolines are also represented. a) Graciosa island (location denoted as G3), b) Terceira island (location denoted as T3), c) São Miguel island (location denoted as SMi3), d) Santa Maria island (location denoted as SMa3).
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195
Fig. 12. Pelamis Power Matrix (adapted from http://www.pelamiswave.com).
6. Comparisons with other studies If comparing the present results with the west Iberian nearshore results presented by Rusu and Guedes Soares [8] it appears that in the north of the Portuguese coastal environment, which is considered also rich in such wave energy resources, the average wintertime wave conditions are characterized by significant wave heights of about 2.5 m while in the Azores islands the average winter time significant wave height is about 3.8 m and the average Hs for the total year is of about 2.7 m. This means that the wave climate is in general much more energetic in Azores than in the Portuguese continental nearshore. The results provided by [35] related with the wave energy resources in the Archipelago of Hawaii show that there are some similar features between the two archipelagos as regards the average energetic conditions. For the winter time, they found average values of the wave power of about 60k W/m on the north facing shores of the Hawaiian Islands. This represents the same order of magnitude with the results presented in Table 10. In another similar environment, El Hierro island from the Canary Archipelago, Iglesias and Carballo [9] by designing similar
scatter diagrams with those presented in Figs. 10 and 11 analyzed the composition of the wave resource in terms of sea states, and how this composition influence the selection of the WECs to be installed. The results found by them are also coherent with those from the present work although by comparing the results it becomes obvious that the wave energy resources in the Azores islands are considerably higher than those from the Canary Islands. There are however also two aspects that have to be properly accounted when evaluating the wave energy in the Azores islands. The first is related with the fact that in winter the coastal environment of the archipelago is often subjected to very severe conditions characterized some times by significant wave heights greater than 14 m and maximum wave heights greater than 20 m. Such conditions may destroy most of the existing systems for wave energy conversion, making survivability studies of equipment very important. The second issue is related to the considerable difference that occurs in terms of average significant wave heights between the winter and summer seasons. While in the Portuguese nearshore for example, this average difference is of about 0.9 m [36], in the Azores islands the same difference is of about 1.9 m. Thus, if the Portuguese
Table 11 Wave activity during the period JanuaryeMarch 2001. São Jorge coastal environment, the location denoted as SJ2 (see Table 10). Activity (h)
Te (s)
Hs (m)
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0
0 18 3 0 0 0 0 0 0 0 0 0 0 0 0 0
0 9 0 3 0 0 0 0 0 0 0 0 0 0 0 0
0 3 0 0 3 0 0 0 0 0 0 0 0 0 0 0
0 15 45 3 3 0 0 0 0 0 0 0 0 0 0 0
6 6 39 27 6 6 0 0 0 0 0 0 0 0 0 0
12 15 18 21 12 15 0 0 0 0 0 0 0 0 0 0
9 30 48 39 27 18 12 3 0 0 0 0 0 0 0 0
0 36 42 66 60 27 12 9 0 3 0 0 0 0 0 0
0 6 45 27 84 12 12 15 3 3 0 0 0 0 0 0
0 6 45 54 57 12 15 24 9 3 0 3 0 0 0 0
0 3 24 39 72 12 15 21 24 6 0 0 0 0 0 0
0 0 24 33 60 18 18 9 12 21 0 0 0 0 0 0
0 0 6 27 42 9 15 24 21 15 0 3 3 0 0 0
0 0 3 12 24 3 27 9 12 9 6 9 6 0 0 0
0 0 0 0 0 21 6 18 6 6 6 3 6 0 0 0
0 0 0 0 0 6 0 0 12 0 6 3 6 6 0 0
0 0 0 0 0 0 0 0 0 9 0 6 0 6 0 0
196
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continental nearshore is considered as a reference, for the summer time the significant wave heights are on the same order of magnitude with that from Azores while in the winter time average Hs are about 1.2 m greater in the Azores islands. Finally it can be also highlighted that the objective of the present work was to give a better perspective on the wave energy potential in the Azores islands and according to the results obtained this area appears to be an environment rich in such resources where the implementation of the wave energy farms might bring significant economic benefits and energetic autonomy. 7. Conclusions The results of the present work show that the Azores islands are a coastal environment very rich in wave energy. As regards the wave directions, although in island environments they usually are more variable than close to the continental coasts, in the Azores islands they are focused since about 75% of the waves come from west-northwest. The validation of the wave modelling system in some case studies representative of extreme and typical winter and summer wave conditions have illustrated its effectiveness in describing the wave conditions in the Azores islands. The evaluation of the spatial distribution of the wave energy shows that, due to the particular features of the archipelago, relevant energy concentrations usually occur in the west side corners of the islands. Furthermore, the analysis of the variation of the wave energy towards the shore, which was performed for wintertime conditions, show that the wave energy propagates without significant dissipation close to the shore and sometimes the average wave energy becomes even higher in intermediate and shallow water. The evaluation performed for the PELAMIS wave energy converter shows that at least as regards the winter season the energy extraction in the targeted coastal areas might have high efficiency. Moreover, using the results from Figs. 10 and 11 and Table 11 the above analysis can be extended from the PELAMIS devices to any other type of wave energy converters. However the severe winter conditions require careful analysis of the survivability of such systems to the environment. Acknowledgment This work has been performed with the project MAREN e Marine Renewable Energy e Energy Extraction and Hydroenvironmental sustainability, which is partially funded by the Atlantic Area Programme. The first author have been funded by Fundação para a Ciência e Tecnologia (Portuguese Foundation for Science and Technology) under pos-doctoral grant SFRH/BPD/65553/2009. References [1] Cornett AM. A global wave energy resource assessment. In: International offshore and polar engineering conference, Vancouver, Canada; 2008. p. 318e23. [2] Mollison D, Pontes MT. Assessing the Portuguese wave-power resource. Energy 1992;17:255e68. [3] Pontes MT. Assessing the European wave energy resource. Journal of Offshore Mechanics and Arctic Engineering 1998;120:226e31. [4] Clément A, McCullen P, Falcão A, Fiorentino A, Gardner F, Hammarlund K, et al. Wave energy in Europe: current status and perspectives. Renewable and Sustainable Energy Reviews 2002;6:405e31. [5] Bernhoff H, Sjöstedt E, Leijon M. Wave energy resources in sheltered sea areas: a case study of the Baltic Sea. Renewable Energy 2006;31:2164e70.
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International Journal of Climatology 2005;25:963e77. [23] WAMDI Group, Hasselmann S, Hasselmann K, Bauer E, Janssen PAEM, Komen GJ, et al. The WAM Model - A third generation ocean wave prediction model. Journal of Physical Oceanography 1998;18:1775e810. [24] Gómez M, Carretero JC. A two-way nesting procedure for the WAM model: application to the Spanish coast. Journal of Offshore Mechanics and Arctic Engineering 1997;119:20e4. [25] Booij N, Ris RC, Holthuijsen LH. A third-generation wave model for coastal regions, 1, Model description and validation. Journal of Geophysical Research 1999;104:7649e66. [26] Pilar P, Guedes Soares C, Carretero JC. 44-year wave hindcast for the north east Atlantic European coast. Coastal Engineering 2008;55:861e71. [27] Rusu E, Guedes Soares C, Pilar P. Evaluation of the wave conditions in Madeira Archipelago with spectral models. Ocean Engineering 2008;35:1357e71. [28] Rusu L, Pilar P, Guedes Soares C. Hindcast of the wave conditions along the west Iberian coast. Coastal Engineering 2008;55:906e19. [29] Guedes Soares C. Hindcast of dynamic processes of the ocean and coastal areas of Europe. Coastal Engineering 2008;55:825e6. [30] Weisse R, Feser F. Evaluation of a method to reduce uncertainty in wind hindcasts performed with regional atmosphere model. Coastal Engineering 2003;48:211e25. [31] Cavaleri L, Sclavo M. The calibration of wind and wave model data in the Mediterranean Sea. Coastal Engineering 2006;53:613e27. [32] Queffeulou P. Long term validation of wave height measurements from altimeters. Marine Geodesy 2004;27:495e510. [33] Guedes Soares C, Rusu L, Bernardino M, Pilar P. An operational wave forecasting system for the Portuguese continental coastal area. Journal of Operational Oceanography 2011;4:17e27. [34] Dunnett D, Wallace JS. Electricity generation from wave power in Canada. Renewable Energy 2009;34:179e95. [35] Stopa JE, Cheung KF, Chen YL. Assessment of wave energy resources in Hawaii. Renewable Energy 2011;36:554e67. [36] Rusu E, Guedes Soares C. Wave energy assessments in the coastal environment of Portugal Continental. In: Proceedings of the 27th International Conference on offshore mechanics and Arctic engineering, June 15-20, Estoril, Portugal; 2008. p. 761e72.
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Renewable Energy journal homepage: www.elsevier.com/locate/renene
Wave energy assessments in the Azores islands Liliana Rusu, C. Guedes Soares* Centre for Marine Technology and Engineering (CENTEC), Instituto Superior Técnico, Technical University of Lisbon, Portugal
a r t i c l e i n f o
a b s t r a c t
Article history: Received 18 October 2011 Accepted 27 February 2012 Available online 17 March 2012
Motivated by the fact that in isolated island environments the extraction of the renewable energy becomes an issue of increasing importance, the objective of the present work is to evaluate the wave energy patterns in the Archipelago of Azores. An analysis of the wave climate in the target area is first carried out considering both remotely sensed and historical data. As a further step, a wave prediction system based on spectral wave models is implemented and validated against satellite data in the coastal environment of the archipelago. Using the above wave modelling system, the spatial distribution of the wave energy is evaluated considering relevant wave patterns for both winter and summer seasons. The results show some significant peaks of wave energy that usually occur at the western edges of the islands. Scatter diagrams are developed for some of these locations found richer in wave energy. Using these diagrams, an evaluation is made for the average energy that would be provided in the nearshore targeted locations by a PELAMIS installation. The results show that the Archipelago of Azores has considerable resources of wave energy, some of them located in the immediate vicinity of the shore. Ó 2012 Elsevier Ltd. All rights reserved.
Keywords: Wave energy Azores islands SWAN model Hot spots
1. Introduction Extraction of the wave energy represents one of the greatest challenges of present days. Wave energy is abundant and could provide considerably clean energy. On the other hand, although hundreds of devices for extracting the wave energy have been designed and tested with varying success, most of the technologies still need to be improved. This especially concerns the resistance of the converters to the strong environmental conditions that often occur in many ocean areas. Assessments of the wave energy resources have been carried out so far at global scale (e.g. [1].) or in various regions of Europe (e.g. [2e12]. in North and South of America (e.g. [13e15].) and Asia (e.g. [16].), and some advances in the statistics behind the assessment have been also made (e.g. [17,18].). In island environment, the importance of implementing such energy extraction systems is enhanced by the fact that they can contribute to the energetic autonomy of the local communities in places where the price of the conventional energy can be high. Moreover, these coastal areas have often the particularity that they are characterized by high bathymetric gradients so that the transition from deep to shallow water is very sharp and as a consequence the waves propagate close to the coast without losing energy but, on the other hand, the bathymetry may induce relevant wave energy
* Corresponding author. E-mail address: [email protected] (C. Guedes Soares). 0960-1481/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.renene.2012.02.027
peaks. These areas, when relatively small changes of the geographical position may lead to large modifications in the available wave power, denoted also as ‘hot spots’, have the highest potential as prospective energy farm sites. Moreover, as the main conclusions of the study carried out by Folley and Whittaker [19] also indicate, for many exposed coasts nearshore sites offer similar potential for exploitation of the wave energy resource as offshore sites. In this context, the objective of the present work is to evaluate the wave climate and energy patterns in the Azores islands using both satellite data and numerical models. This environment holds a great potential of wave energy and is also drastically bounded by physical boundaries that prevent the exchange of resources and livelihoods with other regions. As a consequence, the islands are dependent on a set of resources, not only as vital nature, but also all those who support the economy (transport, fisheries, and tourism) as well as those that determine its safety. All these factors are dependent on weather climate, which include the sea state conditions. Azores is a group of nine islands located in the North Atlantic Ocean about 1200 km west of Portugal. The entire archipelago is oriented west-northwest to east-southeast (Fig. 1). The islands are volcanic in origin and are the largest group of peaks of the MidAtlantic Ridge to form islands. These nine islands are divided in three groups: the Oriental group (Santa Maria and São Miguel), the Central group (Terceira, Graciosa, São Jorge, Pico and Faial) and the Occidental group (Flores and Corvo). The islands of the Occidental group are relatively distant from the others (about 234 km of Faial) and they are the most occidental territory of both Portugal and
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Fig. 1. The Azores islands, the positions of eighteen reference points considered for the analysis of the remotely sensed data are illustrated together with the geographical spaces of the three medium resolution SWAN computational domains, in background the bathymetry is presented.
Europe. The Archipelago of Azores, including the bathymetric map, is presented in Fig. 1. From geographical reasons the Atlantic islands are exposed to various wave systems that propagate across the North Atlantic Ocean. When the waves come from the west, southwest, or northwest, a sheltering effect occurs and an area protected from the high waves towards Portugal continental appears [20]. The archipelago is subjected to both direct approach of the swell coming from distant storms as well as the sea waves generated by local winds that create a local wave system. When the swell system crosses the archipelago the wave directions are significantly modified and this induces the occurrence of various wave systems with different directions in the coastal environment of the archipelago. In this region, characterized by important maritime activities, few in-situ measurements were performed to allow relevant studies concerning the wave conditions. The most recent measurements became available with the implementation of the project CLIMAAT [21], but they are not widely available. Therefore to be able to conduct a validation of the numerical model developed resource had to be made to remote sensed data, and two years of data have been collected and are analyzed here including the production of statistics of wave conditions. The paper deals then with the implementation of the wave prediction system and its validation. The spatial distribution of energy is studied and the hot spots in the coats are identified. Finally a comparison is made with results from studies by other authors. 2. Wave climate in the target area To evaluate the medium and long term wave climate in the Archipelago of Azores two different data sources were considered and analyzed. A two-year analysis of some remotely sensed data was first performed. The main source considered at this step are the altimeter products provided by Ssalto/Duacs and distributed by AVISO (Archiving, Validation and Interpretation of Satellite Oceanographic data), with support from CNES (Centre National d’Etudes Spaciales). A node of the gridded data gives for each day at 0 h near real time
multi-mission merged non interpolated values of the significant wave height (Hs). These are time (for the last 48 h) and space (for 1 squares centred in the node) averaged data sets. In order to provide the most recent picture of the characteristics and dynamics of the wave conditions in the environment neighbouring the Azores islands, some synthetic results coming from the analysis of the altimeter data, corresponding to the period September 2009eAugust 2011, are presented here. Eighteen reference points covering the entire archipelago are defined and their locations are indicated in Fig. 1. They are denoted as P1 to P18 and they are counted from west to east and from north to south, respectively. Table 1 presents the overall Hs statistics for the above remotely sensed data registered in the eighteen reference points considered. The above data are structured in winter and summer time, respectively. As in most climatologic analyses concerning the wave data, in the present work the winter time is considered the period from 1st of October to 31st of March while the summer time is the rest. The parameters evaluated at each point are mean and median values, minimum, maximum and range and also standard deviation, kurtosis and skewness, computed according to the standard definitions of these quantities. Kurtosis is a measure of the peakedness while skewness is a measure of the asymmetry of the distribution of a real-valued random variable. The skewness value can be positive or negative. Qualitatively, a negative skew indicates that the tail on the left side of probability density function is longer than the right side and the bulk of the values (including the median) lie to the right of the mean. A positive skew indicates a reversed tendency. An analysis of the data presented in Table 1 shows that the average significant wave height is in all reference points greater than 1.7 m for summer time and than 3.2 m for winter time. Greater values for the maximum significant wave heights are encountered in the north-western part of the archipelago (about 8.5 m in P1), while lower values in the south-eastern part (about 6.3 m in P16). It has to be mentioned however the fact that these values do not represent maximum significant wave heights but, according to the mediation rule used for these data, space and time average values.
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Table 1 Overall statistics for the remotely sensed significant wave height (Hs) registered in the archipelago of Azores in eighteen reference points for the two-year time interval September 2009eAugust 2011 structured in summer time (the period from 1st of April to 30th September) and winter time (the period from 1st of October to 31st March). Point long ( )/Lat ( )
Season
Mean (m)
Median (m)
Min (m)
Max (m)
Range (m)
Std Dev (m)
Kurtosis
Skew
P1 (32W,40N)
Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter Summer Winter
1.98 3.76 1.96 3.73 1.94 3.69 1.93 3.67 1.92 3.64 1.91 3.63 1.89 3.59 1.87 3.56 1.85 3.52 1.85 3.49 1.84 3.47 1.78 3.38 1.77 3.35 1.78 3.33 1.78 3.32 1.79 3.30 1.73 3.20 1.75 3.20
1.80 3.59 1.78 3.56 1.80 3.52 1.82 3.48 1.85 3.45 1.72 3.50 1.70 3.45 1.70 3.40 1.73 3.36 1.74 3.31 1.74 3.30 1.62 3.25 1.64 3.19 1.65 3.18 1.66 3.18 1.67 3.17 3.81 5.24 1.62 3.05
0.67 1.54 0.63 1.53 0.61 1.54 0.61 1.56 0.57 1.59 0.66 1.50 0.62 1.49 0.59 1.51 0.59 1.54 0.57 1.59 0.57 1.60 0.57 1.52 0.55 1.50 0.55 1.45 0.56 1.37 0.59 1.30 0.55 1.18 0.60 1.13
4.71 8.46 4.61 8.21 4.54 7.96 4.49 7.59 4.34 7.27 4.89 7.91 4.74 7.72 4.68 7.48 4.50 7.20 4.24 6.90 4.22 6.87 4.43 6.95 4.12 6.74 4.25 6.55 4.35 6.37 4.44 6.33 4.36 6.42 4.47 6.39
4.04 6.92 3.98 6.68 3.93 6.42 3.88 6.03 3.77 5.68 4.23 6.41 4.12 6.23 4.09 5.97 3.91 5.66 3.67 5.31 3.65 5.27 3.86 5.43 3.57 5.24 3.70 5.10 3.79 5.00 3.85 5.03 1.59 3.08 3.87 5.26
0.81 1.24 0.78 1.21 0.76 1.19 0.74 1.17 0.73 1.15 0.80 1.18 0.76 1.15 0.74 1.12 0.71 1.09 0.68 1.08 0.67 1.08 0.68 1.04 0.65 1.02 0.64 1.01 0.63 1.02 0.62 1.02 0.60 0.99 0.60 0.99
3.14 3.32 3.08 3.39 3.03 3.36 3.06 3.21 3.21 3.13 3.86 3.10 3.75 3.16 3.60 3.14 3.52 3.05 3.63 2.92 3.90 3.00 4.04 2.88 4.17 2.80 4.39 2.75 4.85 2.83 5.49 2.91 5.45 2.85 6.20 2.88
0.76 0.68 0.71 0.70 0.66 0.70 0.64 0.70 0.66 0.71 1.00 0.58 0.96 0.60 0.90 0.62 0.85 0.61 0.85 0.60 0.90 0.64 1.05 0.53 1.05 0.52 1.08 0.52 1.16 0.54 1.27 0.56 1.34 0.52 1.47 0.53
P2 (31W,40N) P3 (30W,40N) P4 (29W,40N) P5 (28W,40N) P6 (32W,39N) P7 (31W,39N) P8 (30W,39N) P9 (29W,39N) P10 (28W,39N) P11 (27W,39N) P12 (29W,38N) P13 (28W,38N) P14 (27W,38N) P15 (26W,38N) P16 (25W,38N) P17 (26W,37N) P18 (25W,37N)
This means that the maximum values of this parameter may be considerably higher. Fig. 2 shows the Hs histograms for three categories of points. These are the points P1 and P2, characterized by greatest average values of the maximum significant wave height (greater than 8 m), the points P10 and P11 having average values of the maximum significant wave height of about 6.9 m and the points P15, P16 having the lowest average values of the maximum significant wave height (about 6.3 m). From the above histograms, it can be observed also that in the summer time the average Hs values between 1 and 3 m represent about 75% from the total for the points P1, P2, 85% for P10, P11 and 90% for P15, P16. For the winter time the percents corresponding to the Hs interval 1e4 m are 60e65% from the total for the points P1, P2 and about 70% for all the other points. A first conclusion, coming from this analysis of the time and space averaged satellite data, is that the Azores islands are an environment very rich in wave energy resources but, on the other hand, is subjected periodically to severe weather conditions that might affect the functionality of a wave energy farm operating in that area. Nevertheless, in relation with this aspect, an important observation is that the southern part of the archipelago, namely the oriental group that includes the islands São Miguel and Santa Maria, although having almost the same average significant wave heights is subjected to considerable lower extreme conditions. Thus, whether the differences with respect to the northern part of the archipelago (the occidental group) in terms of the mean Hs values are of about 0.2 m for summer time and 0.4 m for winter time, the maximum averaged Hs values are lower in the southern part with about 0.5 m in summer time and with more than 2 m in winter time.
For a longer term analysis of the wave climate in the Azores islands, including also directional information, the KNMI/ERA-40 Wave Atlas [22] was considered. The wave climate information provided in the above atlas is based on 30 years from 1971 to 2000. Tabular significant wave height (corrected ERA-40) and mean wave period bivariate histograms are provided for areas with the lengtHs of 9 in both longitude and latitude, the directional space being structured in sectors of 45 . The data related with two such areas that cover the Azores islands were processed extracting only the information related with the significant wave heights and the wave directions and they are presented in Tables 2 and 3. The above two areas are denoted as A1 (longitude 36 W27 W and latitude 36 N-45 N) and A2 (longitude 27 W-18 W and latitude 36 N-45 N). Tables 2 and 3 give in their first column the percents (%) from the total wave data of the waves having the directions in each directional sector of 45 , while the other columns provide the distribution of the significant wave heights from the respective sector with a resolution of 1 m, in percents 1/10000. The results presented in Tables 2 and 3 show that, for the areas A1 and A2, 74% and 79%, respectively of waves come from W-NW. On the other hand, from the waves arriving from these directions in the two areas about 76% have significant wave heights between 1 and 4 m. It can be concluded from the two analyses carried out above, that the Azores islands appear to be in general an environment rich in wave energy. Moreover, since about 75% of waves come from W-NW it can be assumed also that the waves are in general directionally focused. Nevertheless, it has to be also mentioned that
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Fig. 2. Hs histograms for the satellite measurements corresponding to the period September 2009eJuly 2011 in the Archipelago of Azores. P1(32 E,40 N), P2(31 E,40 N), P10(28 E,39 N), P11(27 E,39 N) P15(26 E,38 N), P16(25 E,38 N).
there are relevant differences in terms of significant wave heights between summer and winter seasons with average Hs values in summer around 1.8 m and in winter about 3.5 m. It can be also assumed that the area is subjected sometimes to extreme wave conditions since significant wave heights greater than 14 m occur almost every year in the archipelago.
3. Implementation and validation of a wave prediction system The methodology proposed herewith to model the wave conditions in the Archipelago of Azores is based on the two state ofthe-art spectral phase averaged wave models. These are WAM [23]
Table 2 Wave climate in the 9 rectangular zone denoted as A1 (longitude 36W-27W and latitude 36 N-45 N) processed from the results of KNMI/ERA-40 wave Atlas. The first column gives the percents (%) from the total wave data of the waves having the directions in each directional sector of 45 , while the other columns provide the distribution of the significant wave heights from the respective sector with a resolution of 1 m, in percents 1/10000. Dir ( )/Hs (m)
0e1
1e2
2e3
3e4
4e5
5e6
6e7
7e8
8e9
9e10
10e11
11e12
12e13
13e14
14e33
sum
0e45(7%) 45e90(5%) 90e135(3%) 135e180(4%) 180e225(8%) 225e270(24%) 270e315(31%) 315e360(19%) Dir all
432 541 642 471 294 250 284 302 320
3972 4598 4400 3162 2383 2510 2719 3259 2984
3466 3069 2920 3032 2736 2520 2653 3218 2832
1464 1342 1265 1943 2236 2005 1896 1839 1865
455 351 542 861 1301 1280 1123 793 1003
146 79 163 338 597 725 614 326 504
46 15 49 152 295 402 369 155 275
13 4 13 32 115 180 184 64 12
5 0 5 8 32 72 85 27 52
2 0 0 0 9 32 39 11 23
0 0 0 0 3 12 19 4 10
0 0 0 0 0 6 10 1 5
0 0 0 0 0 3 4 0 2
0 0 0 0 0 2 2 0 1
0 0 0 0 0 1 1 0 0
10000 10000 10000 10000 10000 10000 10000 10000 10000
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187
Table 3 Wave climate in the 9 rectangular zone denoted as A2 (longitude 27W-18W and latitude 36N-45N) processed from the results of KNMI/ERA-40 wave Atlas. The first column gives the percents (%) from the total wave data of the waves having the directions in each directional sector of 45 , while the other columns provide the distribution of the significant wave heights from the respective sector with a resolution of 1 m, in percents 1/10000. Dir ( )/Hs (m)
0e1
1e2
2e3
3e4
4e5
5e6
6e7
7e8
8e9
9e10
10e11
11e12
12e13
13e14
14e33
sum
0e45(10%) 45e90(5%) 90e135(1%) 135e180(1%) 180e225(4%) 225e270(17%) 270e315(39%) 315e360(23%) Dir all
400 302 351 224 171 211 324 369 314
4444 4530 3725 2700 1963 2493 3204 3743 3341
3327 3332 3203 3166 2569 2505 2742 3143 2883
1256 1408 1748 2279 2500 2053 1757 1595 1741
402 347 661 1162 1609 1314 973 700 907
132 68 251 354 728 749 516 273 444
31 11 40 90 292 380 254 103 204
8 1 17 21 112 177 123 40 93
1 0 5 4 36 71 60 18 41
0 0 0 0 14 27 27 9 18
0 0 0 0 4 10 12 5 8
0 0 0 0 0 5 6 2 3
0 0 0 0 0 2 2 1 1
0 0 0 0 0 1 1 0 1
0 0 0 0 0 1 0 0 0
10000 10000 10000 10000 10000 10000 10000 10000 10000
for wave generation, applied at ocean-scale in a two way nested version [24], and SWAN (Simulating Waves Nearshore [25],) applied at regional and local scales. Ocean-scale simulations, covering almost the entire North Atlantic basin with WAM [26], provide the boundary conditions for SWAN implemented first in a large area that covers the Azores islands. Medium resolution computational domains were also defined inside the large SWAN area for each group of islands (Oriental, Central and Occidental). The same computational scheme was applied with good results in Madeira Archipelago as presented in [27], and also on the Portuguese Continental coast [28]. Some details about the computational domains used for the SWAN simulations are presented in Table 4 and Fig. 1. The implementation of the SWAN model was made for 36 directions and 30 frequencies logarithmically spaced from 0.0418 Hz to 0.6 Hz at intervals of Df/f ¼ 0.1, and the simulations were performed in the non-stationary mode. The reanalyzed wind fields from the North Atlantic basin, that were determined hourly with a resolution of 0.5 in the HIPOCAS project [29], were used as input for the wave modelling system. More details as regards the wind data fields are presented in [30]. In accordance with most of the data sources, it was assumed that there are no relevant currents in the coastal environment of the archipelago, which implies that refraction is only due to spatial variations of the water depth. The input fields considered and the physical processes activated in the simulations with the SWAN modelling system are presented in Table 5. Model simulations were performed for the three-month period JanuaryeMarch 2001. This time interval is characterized by both extreme and average energetic conditions. For example, at the beginning of January wave groups with significant wave heights of about 13 m approached the northern part of the archipelago, and in 6th of February the North-Northwest part of the archipelago was affected by a strong storm when the wind velocity was sometimes greater that 25 m/s. To quantify the prediction skill, the wave model performance was evaluated against altimeter measurements over the entire region of the archipelago. For the period analyzed, global altimeter significant wave height data set provided from the four altimeter missions ERS-2, TOPEX, Poseidon and GEOSAT Follow-On (GFO) are used.
Table 4 Computational grids for the SWAN simulations. Lx, Ly grid lengtHs and Dx, Dy grid resolutions in the geographical space. SWAN grids
Lx ( )
Ly ( )
Dx Dy ( )
Large area Occidental area Central area Oriental area
8.5 0.8 2.5 2.0
5.0 0.8 1.5 2.0
0.05 0.01 0.01 0.01
0.05 0.01 0.01 0.01
The altimeter footprints along the satellite tracks provide a large spatial coverage that cannot be accomplished by in-situ observations at fixed stations. The ground tracks for January 2001 over the geographical space of the Azores islands are illustrated in Fig. 3. As the above figure shows, the combined coverage from tracks of the all satellites is extensive and relatively uniform over the area. The results of the wave modelling system were interpolated in both space and time, with bilinear interpolation schemes to collocate with the altimeter data. To do this, hourly model Hs within 60 min of the altimeter collection time were spatially interpolated from the grid points to the locations of the altimeter measurements along the satellite tracks, and in time to fit the time of the satellite pass, which is very similar to the method used by Cavaleri and Sclavo [31]. In all cases, collocation files were used to compute the statistics and produce scatter diagrams that present the measured against the simulated values. The comparisons with buoy data carried out by Queffeulou [32] showed that the altimeter Hs is in general in agreement with the insitu data, with standard deviations of differences of the order of 0.30 m, but tends to slightly overestimate low Hs and to underestimate high Hs. He has established corrections to Hs that are in general linear and correspond to a few percents of Hs. These corrections were considered in relationship with the altimeter data used in the present work. As an example, Fig. 4 shows the spatial variation of Hs measured by GFO altimeter along the track that passed in 24th of January 2001 at 12 h over the Azores islands and simulated by the model results. The GFO altimeter data provide point estimates at about 7 km intervals, along the ground track of the satellite. It can be observed that the satellite data present a greater variability in comparison with the SWAN results, despite the fact that the spatial resolutions of the wave model and of the satellite measurements are sensibly equal. This is probable due to the low resolution of the wind field used (0.5 , which means about 50 km), resolution that cannot represent with accuracy the local phenomena. Nevertheless, in general the model represents well the enhancements and decreases of Hs along the track. In the southern part Hs is underestimated by the model, effect that is accentuated in the points that correspond to the zone located between the islands of the central group (about 38.5 latitude). As the satellite goes out the environment of the islands a slight overestimation of Hs occurs. The Hs validation obtained from the SWAN model (in the large area) against satellite measurements is presented in Table 6 in terms of the main statistical parameters. The comparisons are structured by month and all satellites. The parameters considered are the mean error computed as the difference between the simulated and observed values, divided by the number of observations N (Bias), the root mean square error (RMSE), the scatter index (SI) defined as the ratio of standard deviation of error to the mean observed Hs, the linear correlation coefficient (R) and the
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Table 5 Input fields considered and physical processes activated in the SWAN model simulations. Wave e wave forcing, Tide e tide forcing, wind e wind forcing, Curr e current field input, Gen e generation by wind. Wcap - whitecapping dissipation, Quad e quadruplet nonlinear interactions, Triad e triad nonlinear interactions, Diff e diffraction, Bfric e bottom friction, set up e wave induced set up, Br e depth induced wave breaking. Input/Process
Wave
Wind
Tide
Curr
Gen
Wcap
Quad
Triad
Diffr
Bfr
Set up
Br
SWAN large SWANmedium
X X
X X
0 0
0 0
X X
X X
X X
0 X
0 X
X X
0 0
X X
Fig. 3. ERS-2, TOPEX, Poseidon and GEOSAT Follow-On (GFO) satellite tracks over the Azores Archipelago area in January 2001.
symmetric slope (S), that is the coefficient of the regression line through the origin (model against measured data). Model values are over-predicted for S > 1 and under-predicted for S < 1. The wave statistics presented in Table 6 shows that the SWAN models results are in agreement with the satellite measurements, with slopes close to 1, low values of the scatter indexes and high correlation coefficients. The fits are better in January that is characterized by higher waves and well defined meteorological situations. Statistical analyses of the Hs corresponding to each satellite that passed over the Azores islands in the three-month period were also carried out and the results are presented in Table 7. According to this table, the model results fit the best with the measurements performed by GEOSAT Follow-On. Fig. 5 shows the scatter plots of Hs (altimeter against wave model data) for January, February and March 2001. The blue lines
denote the perfect fit between the model and the observed values. A linear regression (green line) was adjusted to each data set as well and it is observed that in January the model slightly underestimates the extreme significant wave heights, while in February overestimates them. By increasing the resolution of the SWAN simulations in the geographical space and nesting new areas inside the initial computational domain it is expected also an improvement of the model results. From this reason new SWAN domains with increased spatial resolution were defined (see Table 4). These areas cover the Oriental, Central and Occidental groups of islands, respectively. In order to assess the influence of this enhancement of the spatial resolution, simulations in the above three areas were carried out for January 2001. This represents also the time range when the statistical results from the large area are better, as presented in Table 6. The quality evaluation of the results in these three medium resolution areas was made by means of the same statistical parameters previously defined, the collocation files being obtained by applying the methodology mentioned above. In order to compare the results obtained in the large SWAN area that covers the entire archipelago with those from the medium resolution computational domains only the data covering these last three areas (both simulations and measurements) were extracted from the initial collocations files. Thus for each area, either: Oriental, Central or Occidental, statistical analyses were performed both considering the results with medium and lower spatial resolution. Table 8 presents such statistical results, were LR (low resolution) indicates the results from the larger area while MR (medium resolution) indicates the results obtained in the Central area for the simulations performed with higher resolution in the geographical space. The results presented in Table 8 indicate that in the Central area the increasing of the bathymetric resolution induces also a slight improvement of the statistical results, especially in terms of Bias and RMSE. In the Oriental and Occidental areas no relevant enhancement of accuracy was observed but it is also true that most of the measurements correspond to deep water where the influence of the spatial resolution is not as important as in intermediate or shallow water. The global Hs statistics, SWAN simulations against altimeter data in the large area, for all altimeters and for the entire period JanuaryeMarch 2001 are presented in Table 7, while the corresponding scatter plot is illustrated in Fig. 6. The results presented in Tables 6e8 and in Figs. 5 and 6 show that the wave prediction system based on spectral wave models developed herewith provide in general reliable information concerning the wave conditions in the target area. As a further step, the
Table 6 Hs statistics, SWAN simulations against altimeter data in the large area, results presented for each month separately and for all altimeters.
Fig. 4. Comparison of GFO altimeter Hs data along the track (cycle 59, in 2001/01/24h12), against SWAN interpolated Hs. The x-axis represents the latitude of the altimeter footprints.
Month
Bias
RMSE
SI
R
S
N
2001/01 2001/02 2001/03
0.07 0.08 0.02
0.71 0.75 0.53
0.17 0.23 0.15
0.90 0.96 0.89
0.99 1.06 0.99
4433 3856 3993
L. Rusu, C. Guedes Soares / Renewable Energy 45 (2012) 183e196 Table 7 Hs statistics, SWAN simulations against altimeter data in the large area, results presented for each altimeter separately and for all altimeters, the entire period JanuaryeMarch 2001. Satellite
Bias
RMSE
SI
R
S
N
ERS-2 TOPEX Poseidon GFO All satellites
0.05 0.07 0.04 0.06 0.06
0.68 0.71 0.68 0.63 0.67
0.21 0.19 0.21 0.16 0.18
0.82 0.95 0.93 0.94 0.93
0.99 1.03 1.04 1.01 1.01
3233 4277 473 4299 12282
above wave prediction system is used to make assessments of the wave energy in both time and space frames. For the three-month period of model simulations (JanuaryeMarch 2001), Table 9 presents, the statistics of the significant wave height and energy period in all the eighteen reference points defined in the previous section (where the analysis of the remotely sensed data was performed). The wave energy period Te is defined as:
RR Te ¼ 2p
u1 Eðu; qÞdudq ; Eðu; qÞdudq
RR
189
of 3 h, is focused especially on the spatial distribution of the significant wave height scalar fields coupled with the wave vectors and of the energy transport scalar fields coupled with the energy transport vectors. In SWAN, the energy transport components (expressed in W/m, i.e., energy transport per unit length of wave front), are computed with the relationships:
RR ETRx ¼ rg RR cx Eðs; qÞdsdq ETRy ¼ rg cy Eðs; qÞdsdq;
(2)
where: x, y are the problem coordinate system (for the spherical coordinates x-axis corresponds to longitude and y axis to latitude), Eðs; qÞ the wave energy spectrum and cx, cy are the propagation velocities of the wave energy in the geographical space. Hence the absolute value of the energy transport (denoted also as wave power) will be:
ETR ¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 þ E2 : ETRx TRy
(3)
The non dimensional normalized wave power is expressed as:
(1)
with Eðu; qÞ the is the variance density spectrum, u the absolute radian frequency determined by the Doppler shifted dispersion relation and q the wave direction. According to the results from Table 9, the average significant wave heights are in the same range with those from Table 1 in the Central and the Oriental parts of the archipelago while in the Occidental part the model simulations give average Hs values greater with about 0.3e0.4 m than the corresponding satellite data. This means also that the winter time reference interval considered for the model simulations (JanuaryeMarch 2001) gives a realistic picture of the average winter time wave conditions in the Central and Oriental SWAN computational domains. For the same reference points analyzed in Fig. 2 (P1, P2, P10, P11, P15, P16), Fig. 7 illustrates the directional distribution of the significant wave height from which it can be noticed that especially in the points P10, P11 and P16 the variation of the wave direction is in a quite narrow range. The dominant wave direction is from W-NW as the ERA40 data indicated. 4. Spatial distribution of wave energy The results of the model simulations carried out in the threemonth period JanuaryeMarch 2001 are analyzed also in the spatial domain. This analysis, which is performed with a time step
ETRn ¼
ETR : ETRmax
(4)
In the present work ETRmax is defined separately for each individual case study and approximates the maximum wave power corresponding to each computational domain. A first case study (denoted as CS1 - time frame 2001/01/05/h15) is illustrated in Fig. 8a and reflects a typical winter time average energetic pattern for the coastal environment considered. For the simulations performed in the large SWAN computational domain, the figure presents in background the significant wave height scalar fields while in foreground the wave vectors are represented with black arrows. Significant wave heights with values between 3 and 4 m are common to such winter average energetic situation. In order to provide also an image of the high wave conditions in the Azores islands, Fig. 8b presents the Hs fields for such an enhanced energetic situation (denoted as CS2 - time frame 2001/ 02/04/h18). Significant wave heights greater than 14 m may occur in such cases in the area of the archipelago. It has to be also underlined that significant wave heights with the same order of magnitude can be encountered almost every year so they do not represent a very rare extreme event. The same wave prediction system considered in the present work for providing the ocean forcing is used for operational forecast and focused on the Portuguese continental nearshore, as described in [33]. Using the information given by this system
Fig. 5. Hs scatter plots corresponding to the data registered at all satellites for each month; (a) January, (b) February and (c) March, 2001.
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Table 8 Hs statistics, SWAN simulations against altimeter data in the large and medium areas, respectively. Results presented for January 2001 and for all altimeters. Grid
Bias
RMSE
SI
R
S
N
LR MR
0.12 0.08
0.67 0.65
0.17 0.16
0.90 0.91
0.99 0.99
411 411
simulations with the SWAN modelling system were also performed in the Azores islands for May 2011. In this way, some summer wave patterns were also defined. Two such average summer patterns are illustrated in Fig. 8c and d. Thus Fig. 8c presents CS3 (time frame 2001/05/17/h12) when the mean wave direction is from North West while Fig. 8d presents CS4 (time frame 2011/05/24/h03) when the mean wave is from west. Significant wave heights around 2 m are common to such summer average energetic situations. The maximum values of the wave energy transport and their locations in the field are also represented in Fig. 8 and these are: 84 kW/m for CS1, 1508 kW/m for CS2, 19 kW/m for CS3 and 23 kW/m for CS4, respectively. Even from the simulations performed in the large SWAN computational domain it can be noticed that while the maximum values of the significant wave height are encountered usually in the offshore, concentrations of the wave energy frequently occur at the edges of the islands. As a consequence, in this coastal environment, higher values of the wave energy occur very often in the nearshore. This observation is even more obvious in the simulations performed in the medium resolution computational domains. In this connection, Fig. 9 illustrates such energetic
Fig. 6. Hs scatter plot corresponding to the data registered at all satellites for the entire period JanuaryeMarch, 2001.
patterns in the Central and Oriental areas, respectively that represent not an exception, but a rule, and that can be defined as hot energetic spots. For the medium resolution domain corresponding to the Central part of the archipelago, CS5 (time frame 2001/03/25/h09), illustrated in Fig. 9a, shows an energetic peak of about 90 kW/m in the western nearshore of the island São Jorge, while in CS6 (time frame
Table 9 Statistics for the wave parameters Hs and Te in the same eighteen reference points defined in Table 1. Results of the SWAN simulations for the period JanuaryeMarch 2001. Point
Season
Mean (m)
Median (m)
Min (m)
Max (m)
Range (m)
Std Dev (m)
Kurtosis
Skew
P1 (32W,40N)
Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s) Hs (m) Te (s)
4.29 9.94 4.20 9.94 4.06 9.84 4.08 9.90 4.00 9.91 4.08 9.89 3.83 9.71 3.79 9.69 3.78 9.73 1.91 8.22 3.46 9.76 3.57 9.63 3.10 9.11 3.13 9.22 3.37 9.58 3.16 9.63 3.21 9.59 1.55 8.33
3.81 9.88 3.66 9.85 3.49 9.76 3.51 9.88 3.44 9.84 3.6 9.78 9.85 8.71 10.82 9.34 11.54 9.88 4.62 8.63 11.68 11.24 10.47 9.37 7.36 7.46 7.72 8.25 9.84 10.17 10.27 11.43 8.30 9.25 2.90 10.58
1.71 5.75 1.59 5.54 1.47 5.29 1.36 5.34 1.17 5.2 1.64 6.06 3.49 9.64 1.39 5.68 1.21 5.48 0.55 4.17 1.02 4.87 1.13 5.88 1.18 5.82 1.15 6.04 1.04 6.10 0.86 5.22 0.95 6.11 0.56 4.64
13.82 15.2 13.89 15.17 13.88 15.29 13.9 15.38 13.84 15.64 12.86 14.94 1.59 5.91 12.21 15.02 12.75 15.36 5.17 12.80 12.70 16.11 11.60 15.25 8.54 13.28 8.87 14.29 10.88 16.27 11.13 16.65 9.25 15.36 3.46 15.22
12.11 9.27 12.3 9.63 12.41 10.0 12.54 10.04 12.67 10.44 11.22 8.88 11.44 14.62 3.35 9.62 3.29 9.68 1.75 7.98 2.91 9.66 3.12 9.50 2.89 9.04 2.89 9.17 3.01 9.51 2.67 9.40 2.91 9.47 1.44 7.67
1.97 1.78 1.98 1.84 1.99 1.88 2.04 1.89 2.06 1.91 1.87 1.78 1.67 1.66 1.75 1.83 1.87 1.89 0.90 1.90 1.88 2.08 1.70 1.87 1.34 1.52 1.37 1.59 1.65 1.89 1.71 2.14 1.46 1.76 0.56 2.19
7.80 3.34 8.31 3.34 8.67 3.39 8.19 3.46 8.07 3.52 7.36 3.24 6.82 3.03 7.63 3.38 7.64 3.46 3.76 2.42 8.45 3.29 7.35 3.47 4.94 2.44 5.37 2.94 6.01 3.74 7.65 3.45 5.19 3.13 3.32 3.19
1.80 0.25 1.92 0.25 2.02 0.36 1.93 0.35 1.92 0.41 1.72 0.33 1.64 0.13 1.77 0.44 1.81 0.48 0.99 0.23 2.07 0.54 1.73 0.56 1.28 0.14 1.30 0.38 1.47 0.78 1.96 0.79 1.27 0.49 0.82 0.86
P2 (31W,40N) P3 (30W,40N) P4 (29W,40N) P5 (28W,40N) P6 (32W,39N) P7 (31W,39N) P8 (30W,39N) P9 (29W,39N) P10 (28W,39N) P11 (27W,39N) P12 (29W,38N) P13 (28W,38N) P14 (27W,38N) P15 (26W,38N) P16 (25W,38N) P17 (26W,37N) P18 (25W,37N)
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2001/03/06/h18) presented in Fig. 9b the energetic peak (75 kW/m) is close to the island Graciosa. As regards the medium resolution domain corresponding to the Oriental part of the archipelago, CS7 (time frame 2001/03/02/h21), illustrated in Fig. 9c, shows an energetic peak of about 101 kW/m in the western nearshore of the island Santa Maria, while in CS8 (2001/03/26/h21) presented in Fig. 9d the energetic peak (112 kW/m) is close to the island São Miguel. For all the above cases the incoming waves in the targeted areas have significant wave heights of about 3 m. Since the dominant wave pattern is west-northwest, these energetic peaks usually occur in the west side of the islands as illustrated by all the examples presented in Figs. 8 and 9.
191
5. Evaluation of the expected electric power in some coastal hot spots Starting from the observation made at the end of the previous section, according to which significant concentrations of wave energy usually occur in the Azores islands in the western edges of the islands, a study on the mean wave energy was also made for some nearshore locations. The time period considered is JanuaryeMarch 2001 in which the model system simulations are performed for all computational domains defined. Thus, for the central medium resolution computational domain the main locations considered are those that correspond to the
Fig. 7. Directional distribution, classes of Hs for the reference points P1(32 E,40 N), P2(31 E,40 N), P10(28 E,39 N), P11(27 E,39 N) P15(26 E,38 N), P16(25 E,38 N). Results of the simulations with the wave modelling system in the period JanuaryeMarch 2001.
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Fig. 8. Hs scalar fields and wave vectors in the SWAN large domain covering the entire Archipelago of Azores. The maximum values of Hs and wave power are also indicated. a) CS1 average winter wave conditions (time frame 2001/01/05/h15); b) CS2 high wave conditions (time frame 2001/02/04/h18); c) CS3 average summer wave conditions (time frame 2001/05/17/h12, with waves arriving from northwest); d) CS4 average summer wave conditions (time frame 2011/05/24/h03, with waves arriving from west).
Fig. 9. Normalized wave energy maps corresponding to winter average energetic conditions. a) CS5 (time frame 2001/03/25/h09), Central medium resolution computational domain; b) CS6 (time frame 2001/03/06/h18), Central medium resolution computational domain; c) CS7 (time frame 2001/03/02/h21), Oriental medium resolution computational domain; d) CS8 average winter wave conditions (time frame 2001/03/26/h21), Oriental medium resolution computational domain.
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energetic peaks presented in CS4, CS5 and CS6, illustrated in the Figs. 8d, 9a and 9b, respectively. Figs. 8d and 9b are pointing Graciosa island while Fig. 9a is pointing the island São Jorge as the locations of the peaks. Nevertheless, as Fig. 9a and b show, energetic peaks also occur in the coastal environment of the island Terceira and from this reason a location in the neighbourhood of this island is considered as well. Taking into account the results presented in the Figs. 8 and 9 but also the general wave patterns as reflected by Tables 2 and 3, all sites analyzed are situated in northwesten sides of the respective islands. Following the same pattern, and considering also the results presented in the Figs. 8a, 9c and 9d that correspond to the case studies CS1, CS7 and CS8, two other locations are defined in the Oriental medium resolution computational domain. The first is situated in the coastal environment of the island Santa Maria and the second close to the island São Miguel. Table 10 presents for each of the five considered locations the average wave energy computed for the reference interval, in three points that go from deep to intermediate water deptHs following the direction of a cross shore line. Taking also into account the high bathymetric gradients that are characteristic to the archipelago, the nearshore locations are no farther than a few hundreds of metres away from shore. The results from the above table show that for the considered coastal sectors the average wave energy in intermediate water deptHs is in general at the same level, or even higher, than in deep water. For the period JanuaryeMarch 2001, scatter diagrams of the HsTe joint distribution are generated using the 3-h consecutive significant wave height and energy wave period time sequences resulted from the simulations with the SWAN model. The bivariate distributions of HsTe are determined for all five coastal sectors considered in Table 10. Such a diagram presents the probability of occurrence of the different sea states expressed in percentage from the total number of occurrences. The sea states are structured into bins of 0.5 s 0.5 m (DTe DHs) and the colour of each bin represents the percentage according to a colour-map, which was designed the same for all diagrams and is illustrated in the colourbar of the figures. The wave power isolines are also represented in each diagram. These have been calculated using the deep water energy flux approximation of equation (see [7]):
ETR ¼
rg2 Te Hs2 64p
193
gravity, Te is the energy period, and Hs is the significant wave height. Fig. 10 illustrates the above scatter diagrams for the coastal sector in the neighbourhood of São Jorge island. The diagrams correspond to the locations defined in Table 10 and denoted as SJ1, SJ2 and SJ3 and reflect the evolution of the sea states from offshore to intermediate water depth. The analysis of the scatter diagrams presented in Fig. 10 (a, b and c) shows that in the coastal
(5)
where ETR is the energy flux in watts per metre of crest length, r ¼ 1025 kg/m3 is the density of sea water, g is the acceleration of Table 10 Variation of the average mean energy from deep to shallow water for the targeted locations. Results of the SWAN simulations for the period JanuaryeMarch 2001. Island/(comp. domain)
Point
Long ( )/Lat ( )
Depth (m)
Average energy (kW/m)
S. Jorge (Central)
SJ1 SJ 2 SJ 3 G1 G2 G3 T1 T2 T3 SMI1 SMI2 SMI3 SMA1 SMA2 SMA3
28.3079/38.7628 28.3184/38.7618 28.3263/38.7539 28.0432/39.1077 28.0249/39.1051 28.0359/39.1034 27.3829/38.7775 27.1889/38.8116 27.3830/38.7627 25.8537/37.8836 25.8686/37.8611 25.3425/37.8593 25.1875/36.9761 25.1839/36.9869 25.1851/36.9821
53.0 43.0 29.0 77.0 46.5 40.0 91.0 65.0 46.8 82.0 36.0 28.5 66.0 42.0 27.6
58.0 73.0 71.0 78.0 71.0 75.5 64.0 63.0 51.5 57.8 62.5 56.6 44.2 38.3 37.4
Graciosa (Central)
Terceira (Central)
S. Miguel (Oriental)
S. Maria (Oriental)
Fig. 10. Scatter diagrams (Hs against Te) for São Jorge coastal sector in the period JanuaryeMarch 2001 corresponding to the three locations defined in Table 11. The coloured rectangles represent the number of occurrences, in percents from the total, which correspond to each rectangular sector with the lengths of 0.5s in x-direction and 0.5m in y-direction. The wave power isolines are also represented. a) Location denoted as SJ1, b) Location denoted as SJ2, c) Location denoted as SJ3.
194
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environment of São Jorge island the probability of occurrence of the more energetic sea states increases from offshore to nearshore. For the other four coastal sectors, Fig. 11 presents the scatter diagrams that correspond to the location that is closest to the shore. From Figs. 10 and 11 it results also that in the coastal environment under consideration the sea states with most occurrences are concentrated between 7 s and 12 s (in terms of energy period) and between 1 m and 5 m (in terms of significant wave height), being located between the power isolines of 5 kW/m and 100 kW/m. The wave models give the theoretical wave power available and the characteristics of the wave resources in terms of sea states (i.e. the characteristics of the waves providing the power), but the actual wave power yield will depend on the particular Wave Energy Converter (WEC) device rated by their wave power generation. Because, each technology has different operational ranges and also different efficiencies under various sea states (e.g. [34].), the more efficient WECs for a specific area are those that have the maximum efficiency in the ranges of Hs and Te that provide the bulk of occurrences. WEC manufacturers provide performance data on their products as a function of Hs and a wave period, which can be Te or any other wave period as: the peak period Tp or the zero crossing period Tz (depending on the manufacturer). The performance table provides the expected power output for each significant wave height and wave period. A distinct pair of significant wave height and wave period is referred to as an energy bin. One way of calculating the electricity production of a WEC is to create a similar table with the same indices, but providing the expected number of hours that each energy bin occurs instead of
power output. Electricity production (in kWh) is simply the expected power output (in kW) for an energy bin multiplied by the expected occurrence (in hours). The sum of these products over all energy bins gives the total electricity produced by the WEC at that location for the period analyzed. The PELAMIS wave energy converter (http://www.pelamiswave. com) is used in the present work as a reference for making an initial assessment of the electric power that it can deliver in the locations considered. The Power Matrix for this device is illustrated in Fig. 12. This figure shows the power generated by the PELAMIS device in a range of the sea spectra defined by significant wave height and energy period (Te) in the same manner as the diagrams from the Figs. 10 and 11 are designed. Using the information related with the joint frequency distribution of Hs and Te generated in the point denoted as SJ2 (see Table 10) the wave activity in hours is computed and is presented in Table 11. For the winter time period analyzed using the joint data from Table 11 and Fig. 12, results a daily average electric energy produced by PELAMIS of about 4.6 MWh. In the same manner, for the points SJ1 and SJ3 the daily average electric energy results 3.4 and 4.5 MWh, respectively. The same computational procedure is applied for the nearshore points of the archipelago denoted as G3, T3, SMI3 and SMA3, for which the scatter diagrams are illustrated in Fig. 11. The corresponding daily average electric energy produced by the PELAMIS device in the above locations, resulted 3.7 MWh in G3, 3.1 MWh in T3, 4.1 MWh in SMI3 and 3.8 MWh in SMA3. Very similar values for the same conversion system (PELAMIS) with a daily average electric energy about 4 MWh were obtained on the west coast of Canada as they were presented in the study performed by Dunnett and Wallace [34].
Fig. 11. Scatter diagrams (Hs against Te) for four nearshore locations in the coastal environment of the Azores islands in the period JanuaryeMarch 2001. The coloured rectangles represent the number of occurrences, in percents from the total, which correspond to each rectangular sector with the lengths of 0.5s in x-direction and 0.5 m in y-direction. The wave power isolines are also represented. a) Graciosa island (location denoted as G3), b) Terceira island (location denoted as T3), c) São Miguel island (location denoted as SMi3), d) Santa Maria island (location denoted as SMa3).
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195
Fig. 12. Pelamis Power Matrix (adapted from http://www.pelamiswave.com).
6. Comparisons with other studies If comparing the present results with the west Iberian nearshore results presented by Rusu and Guedes Soares [8] it appears that in the north of the Portuguese coastal environment, which is considered also rich in such wave energy resources, the average wintertime wave conditions are characterized by significant wave heights of about 2.5 m while in the Azores islands the average winter time significant wave height is about 3.8 m and the average Hs for the total year is of about 2.7 m. This means that the wave climate is in general much more energetic in Azores than in the Portuguese continental nearshore. The results provided by [35] related with the wave energy resources in the Archipelago of Hawaii show that there are some similar features between the two archipelagos as regards the average energetic conditions. For the winter time, they found average values of the wave power of about 60k W/m on the north facing shores of the Hawaiian Islands. This represents the same order of magnitude with the results presented in Table 10. In another similar environment, El Hierro island from the Canary Archipelago, Iglesias and Carballo [9] by designing similar
scatter diagrams with those presented in Figs. 10 and 11 analyzed the composition of the wave resource in terms of sea states, and how this composition influence the selection of the WECs to be installed. The results found by them are also coherent with those from the present work although by comparing the results it becomes obvious that the wave energy resources in the Azores islands are considerably higher than those from the Canary Islands. There are however also two aspects that have to be properly accounted when evaluating the wave energy in the Azores islands. The first is related with the fact that in winter the coastal environment of the archipelago is often subjected to very severe conditions characterized some times by significant wave heights greater than 14 m and maximum wave heights greater than 20 m. Such conditions may destroy most of the existing systems for wave energy conversion, making survivability studies of equipment very important. The second issue is related to the considerable difference that occurs in terms of average significant wave heights between the winter and summer seasons. While in the Portuguese nearshore for example, this average difference is of about 0.9 m [36], in the Azores islands the same difference is of about 1.9 m. Thus, if the Portuguese
Table 11 Wave activity during the period JanuaryeMarch 2001. São Jorge coastal environment, the location denoted as SJ2 (see Table 10). Activity (h)
Te (s)
Hs (m)
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0
0 18 3 0 0 0 0 0 0 0 0 0 0 0 0 0
0 9 0 3 0 0 0 0 0 0 0 0 0 0 0 0
0 3 0 0 3 0 0 0 0 0 0 0 0 0 0 0
0 15 45 3 3 0 0 0 0 0 0 0 0 0 0 0
6 6 39 27 6 6 0 0 0 0 0 0 0 0 0 0
12 15 18 21 12 15 0 0 0 0 0 0 0 0 0 0
9 30 48 39 27 18 12 3 0 0 0 0 0 0 0 0
0 36 42 66 60 27 12 9 0 3 0 0 0 0 0 0
0 6 45 27 84 12 12 15 3 3 0 0 0 0 0 0
0 6 45 54 57 12 15 24 9 3 0 3 0 0 0 0
0 3 24 39 72 12 15 21 24 6 0 0 0 0 0 0
0 0 24 33 60 18 18 9 12 21 0 0 0 0 0 0
0 0 6 27 42 9 15 24 21 15 0 3 3 0 0 0
0 0 3 12 24 3 27 9 12 9 6 9 6 0 0 0
0 0 0 0 0 21 6 18 6 6 6 3 6 0 0 0
0 0 0 0 0 6 0 0 12 0 6 3 6 6 0 0
0 0 0 0 0 0 0 0 0 9 0 6 0 6 0 0
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continental nearshore is considered as a reference, for the summer time the significant wave heights are on the same order of magnitude with that from Azores while in the winter time average Hs are about 1.2 m greater in the Azores islands. Finally it can be also highlighted that the objective of the present work was to give a better perspective on the wave energy potential in the Azores islands and according to the results obtained this area appears to be an environment rich in such resources where the implementation of the wave energy farms might bring significant economic benefits and energetic autonomy. 7. Conclusions The results of the present work show that the Azores islands are a coastal environment very rich in wave energy. As regards the wave directions, although in island environments they usually are more variable than close to the continental coasts, in the Azores islands they are focused since about 75% of the waves come from west-northwest. The validation of the wave modelling system in some case studies representative of extreme and typical winter and summer wave conditions have illustrated its effectiveness in describing the wave conditions in the Azores islands. The evaluation of the spatial distribution of the wave energy shows that, due to the particular features of the archipelago, relevant energy concentrations usually occur in the west side corners of the islands. Furthermore, the analysis of the variation of the wave energy towards the shore, which was performed for wintertime conditions, show that the wave energy propagates without significant dissipation close to the shore and sometimes the average wave energy becomes even higher in intermediate and shallow water. The evaluation performed for the PELAMIS wave energy converter shows that at least as regards the winter season the energy extraction in the targeted coastal areas might have high efficiency. Moreover, using the results from Figs. 10 and 11 and Table 11 the above analysis can be extended from the PELAMIS devices to any other type of wave energy converters. However the severe winter conditions require careful analysis of the survivability of such systems to the environment. Acknowledgment This work has been performed with the project MAREN e Marine Renewable Energy e Energy Extraction and Hydroenvironmental sustainability, which is partially funded by the Atlantic Area Programme. The first author have been funded by Fundação para a Ciência e Tecnologia (Portuguese Foundation for Science and Technology) under pos-doctoral grant SFRH/BPD/65553/2009. References [1] Cornett AM. A global wave energy resource assessment. In: International offshore and polar engineering conference, Vancouver, Canada; 2008. p. 318e23. [2] Mollison D, Pontes MT. Assessing the Portuguese wave-power resource. Energy 1992;17:255e68. [3] Pontes MT. Assessing the European wave energy resource. Journal of Offshore Mechanics and Arctic Engineering 1998;120:226e31. [4] Clément A, McCullen P, Falcão A, Fiorentino A, Gardner F, Hammarlund K, et al. Wave energy in Europe: current status and perspectives. Renewable and Sustainable Energy Reviews 2002;6:405e31. [5] Bernhoff H, Sjöstedt E, Leijon M. Wave energy resources in sheltered sea areas: a case study of the Baltic Sea. Renewable Energy 2006;31:2164e70.
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