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Wave energy resource assessment in Menorca (Spain)
Renewable Energy 71 (2014) 51e60
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Wave energy resource assessment in Menorca (Spain) € sso a, b, D. Gonza lez-Marco a, b J.P. Sierra a, b, *, C. Mo a dul D1, Campus Nord, Laboratori d'Enginyeria Marítima, Universitat Polit ecnica de Catalunya e BarcelonaTech, Jordi Girona 1-3, Mo 08034 Barcelona, Spain b dels Recursos Costaners, Jordi Girona 1-3, Mo dul D1, Campus Nord, 08034 Barcelona, Spain Centre Internacional d'Investigacio
a r t i c l e i n f o
a b s t r a c t
Article history: Received 7 November 2013 Accepted 6 May 2014 Available online
Menorca (Balearic Islands) only covers 2% of its electricity needs with renewable energy sources, which is far below the European Union's objective of obtaining 20% of its energy from these sources. This study analyses the island's wave energy resources using a 17-year series of data obtained from numerical modeling (forecast). The spatial distribution of wave power is analyzed using data from 12 points around the island. The obtained resources (average wave power, around 8.9 kW/m; average annual wave energy, about 78 MW h/m) are relatively modest but among the largest found in the Mediterranean Sea. The northeast and east of the island are the most productive areas. Considerable seasonal variability is found, with winters being rather energetic and summers quite mild. The power matrices of three wave energy converters (WECs) are considered to assess the average power output at all of the points. Four places are identified as the best candidates for WEC deployment, with non-negligible productivity that can be exploited to supply energy to small villages. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Wave energy Wave power Forecasting Menorca Island Balearic Islands Wave energy converter
1. Introduction Wave energy appears to be one of the most promising alternatives to fossil fuels, which need to be substituted for many reasons, including economic (lack of production in many countries and the consequent need for imports), practical (potential future depletion) and environmental (the need to reduce greenhouse gas emissions) reasons. It is estimated that the use of wave energy will increase significantly over the next few decades as wave energy converter (WEC) technology matures [1]. While ocean wave energy conversion is still unproven on a commercial scale, significant advances in research, design and testing continue to be made [2] and a number of wave energy projects, almost all still in the pilot stage, are currently underway around the world [3]. Research on wave energy resource assessment is particularly advanced in countries bordering large oceans, where the greatest wave energy potential is found [1]. Thus, marine energy assessments have been carried out on Spain's Atlantic coasts [4e6], in Portugal [7,8], in Australia [3,9], in Canada [10], in the United States
cnica * Corresponding author. Laboratori d'Enginyeria Marítima, Universitat Polite dul D1, Campus Nord, 08034 de Catalunya e BarcelonaTech, Jordi Girona 1-3, Mo Barcelona, Spain. Tel.: þ34 934016467; fax: þ34 934011861. E-mail address: [email protected] (J.P. Sierra). http://dx.doi.org/10.1016/j.renene.2014.05.017 0960-1481/© 2014 Elsevier Ltd. All rights reserved.
[2,11], and in Asia [12,13]. Europe-wide [14,15] and worldwide [16e18] marine energy assessments have also been made. Wave energy is particularly appropriate for islands, which receive a large amount of this resource and are usually highly dependent on external energy sources. For this reason, wave energy potential has been assessed on various islands in the Atlantic Ocean, including the Canary Islands [19e21], Madeira [22] and the Azores [23]; in the Pacific Ocean, on islands such as Hawaii [24,25] and Taiwan [26]; and in the Caribbean Sea [27]. In general, research on wave energy production is focused on locations that, like the aforementioned places, have high energy potential. As pointed out in Ref. [1], this high energy potential usually implies extreme events, posing serious engineering challenges and increasing the costs of design, production, deployment and maintenance of WECs. In calmer seas, where a lower amount of energy is available, many technical issues related to extreme sea climate probably could be solved more easily and wave energy production could still be economically viable [1]. Taking this into account, several studies have recently assessed the wave energy potential of areas such as the Mediterranean Sea [1,28,29], the Black Sea [30,31] and the Caspian Sea [32]. This paper focuses on the assessment of wave power potential around Menorca, an island in the Spanish Mediterranean Sea. Section 2 briefly describes the study area. Section 3 presents the available data and the methodology used. The island's wave energy resource, including its spatial distribution and seasonal variations,
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J.P. Sierra et al. / Renewable Energy 71 (2014) 51e60
is assessed in Section 4. In Section 5, the best locations for WEC deployment are identified and their feasibility discussed. Finally, the conclusions of the paper are presented in Section 6. 2. Study area The study area is located in the northwestern Mediterranean Sea (39.81e40.09 N, 3.79e4.32 E) and forms part of the Balearic Islands (Fig. 1, left). Menorca has a surface area of 701.8 km2 and a population of about 100,000 inhabitants, most of them living along the coast. The island was declared a Biosphere Reserve in 1993. At present, 80% of the electric energy needed on the island is supplied by a thermal power station (gas and fuel) with a capacity of 169.5 Mw, 18% is imported from the neighboring island of Mallorca and just 2% is extracted from renewable sources (two solar facilities and one wind farm). Taking into account the European Union's objective of obtaining 20% of its energy from renewable sources by 2020, wave energy is a good candidate for contributing to this goal, overcoming the island's present energy deficit and, in particular, its deficit of renewable energy sources. Moreover, according to a recent study [1], the most productive area in the Mediterranean Sea in terms of wave energy potential is located between the Balearic Islands and the western coast of Sardinia. As in other mid-latitude areas, the Mediterranean basin climate is dominated by extra-tropical cyclones [33] formed via baroclinic instability, which is highest during the winter season [34]. Moreover, it is affected by moving depressions generated either in the Atlantic Ocean or in northwestern Europe [35]. According to [36], Mediterranean storms are normally shorter and less intense than those in northern Europe, with many subregional and mesoscale effects taking place and producing large spatial and seasonal variability [37]. The reduced scale, along with the peculiar features of the basin (complex orography and the moisture of a relatively large mass of water) makes the Mediterranean climate more difficult to predict than climates in other places [33]. Examples of local features are the Tramontana and Mistral winds, which consist of intense, persistent N and NW winds often caused by a cyclone over the Gulf of Genoa, which is then channeled and intensified through the valleys between mountain ranges on the north side of the northwestern Mediterranean. During the summer season, thermal and orographic effects play a greater role in the genesis and maintenance of cyclones. In the northwestern Mediterranean, the main cyclone centers are again located in the Gulf of Genoa and over the Iberian Peninsula, the
latter caused by temperature contrasts between land and sea [37]. During the warm period, the Mediterranean is also exposed to tropical systems [38] as a result of its location in a transitional zone between humid mountains in the north and arid regions in the south. Finally, spring and autumn can be considered transitional periods between the contrasting winter and summer patterns [38].
3. Data and methods 3.1. Available wave data Two sources of data are available for Menorca. The first is a 17year hindcast wave climate database (1996e2013) generated by the Spanish Port Authority (Organismo Público Puertos del Estado). This wave data set was obtained using the WAM model [39] and forced by the wind output of the HIRLAM [40] regional atmospheric model (developed cooperatively by several meteorological institutes and run by the Spanish State Meteorological Agency). Data are from 12 points surrounding the island (Fig. 1, right) with a spatial resolution of 0.25 0.25 (although resolution is 0.125 close to the coast) and a time interval of 1 h. The second data source is a wave buoy located close to the island (39.72 N, 4.42 E). Placed at a depth of 300 m, this buoy is in deep water and its position practically coincides with one of the points where the numerical simulation data were obtained. The time series includes hourly directional information from 1993 to 2013 and is used to validate the numerical wave data.
3.2. Methodology With the exception of point 4, all points considered are located in deep water and thus are not affected by propagation processes such as refraction and diffraction. Wave power can be obtained using the following deep-water expression:
P¼
rg2 2 H Te x0:491Hs2 Te 64p s
(1)
where P is the is the wave power per unit of crest length (kW/m), Hs is the significant wave height, Te is the energy period, r is the density of seawater (assumed to be 1025 kg/m3) and g is the gravitational acceleration. Te is computed as a function of spectral moments:
Fig. 1. Location of Menorca in the northwestern Mediterranean Sea (left). Location of the analyzed points and buoy (right).
J.P. Sierra et al. / Renewable Energy 71 (2014) 51e60
Te ¼
m1 m0
(2)
As pointed out in Ref. [16], measured sea states are often specified in terms of significant wave height Hs and either peak period Tp or mean period Tz. The energy period Te is rarely specified and must be estimated from other variables when the spectral shape or the spectral moments are unknown, as in this case. One approach when Tp is known is to assume the following:
Te ¼ aTp
(3)
where a is a coefficient whose value depends on the shape of the wave spectrum (0.86 for a PiersoneMoskowitz spectrum and increasing towards unity with decreasing spectral width) [16]. Taking into account the predominance of the wind-sea waves together with mixed sea states in this areadwhich is reasonable considering the short fetches of the area [41]dthe wave spectra are rather wide. Therefore, as suggested by Refs. [16,42], a value of Te ¼ 0.9Tp was used to assess the wave energy resource. With Equations (1) and (3), the total wave energy resource at a point can be assessed, allowing the computation of the power average at each point. This assessment can also be done for various directions at each point in order to obtain a directional distribution of wave power. As mentioned above, this assessment of wave power uses available data from 17-year numerical simulations. To verify the suitability of these data, model data at point 7 (the closest to the eave buoy) have been compared with those measured at the buoy during the same time interval. With this aim, the relative mean absolute error (RMAE) was computed for both significant wave height and peak period, as follows: Wave height:
RMAE ¼
〈jH H H j〉 m
b
(4)
b
Peak period:
RMAE ¼
〈jT
m
Tb j Tb
〉
(5)
in which Hm and Hb are, respectively, the significant wave height given by the model and measured at the buoy; Tm and Tb are,
53
respectively, the peak period given by the model and measured at the buoy; j…j indicates absolute value; and 〈:::〉 means average over the time series. The RMAE value obtained is 0.25 for the wave height, which is fair, and 0.14 for the wave period, which is good. Fig. 2 compares the values of Hs and Tp measured at the buoy and with those values obtained by the model and shows fair agreement between the two data sources. Moreover, it appears that the two types of values compensate each other because they are spread above and below the line that indicates the exact fit between the two data types, without showing a clear bias. Thus, the correlation coefficient between the measured and modeled values is 0.91 for Hs and 0.81 for Tp. Nevertheless, in general, the model slightly underpredicts the buoy measurements, in particular wave heights and, as a consequence, wave power. In Fig. 3, where the average monthly wave power computed at the buoy and at point 7 (data from the numerical model) are shown, this underprediction is clearly seen. In all months (except in April, month 4), the wave power given by the model is lower than that measured at the buoy. Moreover, the average annual power measured at the buoy is 9.08 kW/m, while that obtained with the numerical data at point 7 is 8.62 kW/m, confirming this slight underprediction (5%). These small differences support the assumption that the numerical data are suitable for assessing wave energy in this area, with the obtained results acting as a lower bound on the energy potential in the area.
4. Analysis of the wave energy resource 4.1. Spatial distribution Fig. 4 shows the average wave power at the 12 locations. In this figure, three areas with different wave energy distributions can be distinguished: a higher-energy area, encompassing the north and east of the island (points 1e7) with annual average wave powers greater than 8 kW/h (except point 4, due to its nearshore location) and annual wave energies ranging from 71.6 to 78 MW h/m; a lowenergy area comprising the south of the island (points 8e11) with annual average wave powers ranging from 2.22 to 4.16 kW/h and annual wave energies ranging from 19.4 to 36.4 MW h/m; an intermediate-energy area along the west side of the island (point 12) with annual average wave powers around 6 kW/h and annual wave energies of 54.8 MW h/m. The variations in average power
Fig. 2. Comparison of buoy data and forecasting data: Hs (left) and Tp (right).
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J.P. Sierra et al. / Renewable Energy 71 (2014) 51e60
Fig. 3. Average monthly wave power at the buoy and at point 7 (given by the model).
around the island are plotted in Fig. 5, where the three areas can be clearly discerned. Another factor considered is the direction of the waves and how the study points are exposed to them. The directional distribution of wave energy is important for non-point absorber devices. Fig. 6 and Table 1 show the average annual power at the study points distributed in directions according to the waves that arrive at each point. At most of the points, in particular those located in the north, east and west, the bulk of the energy comes from the N, followed by NE and NW. At the points located in the south, the sheltering effect provided by the island itself prevents the arrival of waves from northern directions, significantly reducing the amount of energy received by these points and making them less suitable for WEC deployment. At these points (8e11), SW waves provide an important amount of the total energy.
4.2. Temporal variability This section analyses the monthly and seasonal wave power fluctuations around Menorca. Table 2 shows the average seasonal wave power at the 12 study sites. These data show the strong seasonal character of Menorca's wave energy, with a considerable range of variation among seasons. The wave energy starts to increase in autumn, reaching its peak in winter, decreasing in spring and hitting its minimum in summer. About 42% of Menorca's annual wave power corresponds to winter, 26% to autumn, 24% to spring, and less than 8% to summer, such that the island's wave
energy resource is more than five times greater in winter than in summer. Table 3 shows the average monthly wave power at the 12 study sites. A clear seasonal trend can be observed, with energy levels reaching their highest values in the winter months (DecembereFebruary) and peaking in December. After the winter, wave power drops to its lowest values in the summer (JuneeAugust), with minimum values in August. On average, wave power in December is 6.6 times that of August. Fig. 7 shows the monthly wave power averaged at the 12 points, which is representative of the energy level in Menorca and which follows the same seasonal trend. As in many water wave analyses, two different seasons can be defined: a stormy period from October to March and a calmer period from April to September. Data from Table 3 show a concentration of wave energy during the stormy season, with wave energy values ranging from 72% to 77% of the average annual energy at the 12 points. In the next section, further analyses of temporal variability are carried out on the basis of two variability coefficients. The aim is to determine the suitability of the various sites for WEC installation. 5. Identification of the best locations for WEC deployment The primary consideration for a WEC site is the amount of wave power and total energy that can potentially be obtained at the site, taking into account its distribution over wave heights, periods and directions (see Fig. 6 and Table 1). At points 1 to 7 and 12, the prevailing direction is N, followed by NE and NW (except at points 6 and 7, where wave energy from the SW is also considerable). At points 8 to 11, the main wave energy direction is SW (although waves from the N provide the same amount of energy at these points). Another factor to take into account in choosing a WEC site is temporal variability at different time scales (daily, monthly and seasonal). Sites with a steady, moderate wave energy flux may be more attractive than sites where energy is higher but is also unsteady and thus less reliable because WEC efficiency may decrease significantly under more variable wave conditions [16]. Different coefficients have been proposed to describe the temporal variability in wave power at a particular site. In this study, two of the coefficients proposed by Ref. [16] are used: the seasonal variability index (SV) and the monthly variability index (MV). The SV is defined as follows:
SV ¼
Ps1 Ps4 Pyear
(6)
where Ps1 is the mean wave power for the highest-energy season (usually winter) and Ps4 is the mean wave power for the lowestenergy season (usually summer), and Pyear is the annual mean wave power. The greater the value of SV the larger the seasonal variability, with values lower than 1 indicating moderate seasonal variability. MV is defined as follows:
MV ¼
Fig. 4. Annual average wave power at the study points.
PM1 PM12 Pyear
(7)
where PM1 is the mean wave power for the highest-energy month and PM12 is the mean wave power for the lowest-energy month. Obviously, the values of MV are greater than those of SV. Table 4 shows the values of these variability coefficients and the mean annual wave power at the 12 study points. The SV and MV values are similar for the highest-energy points (SV between 1.38 and 1.44 and MV between 1.52 and 1.56), which suggests that
J.P. Sierra et al. / Renewable Energy 71 (2014) 51e60
55
Fig. 5. Annual average wave power around Menorca.
waves show certain variability at all of these locations (points 1e3 and 5e7) as shown in Section 4.2. On the contrary, points 8e11 have lower SV and MV but they can be ruled out as WEC sites because the wave energy resource is too small to be exploited. Points 5, 3 and 7 offer the highest-energy levels and similar SV and MV values, so they can be considered the most promising areas along the Menorcan cost for wave energy production taking into account both wave energy potential and inter-annual variability. Another interesting location from the point of view of wave energy
production is point 6 because, although the potential wave power is slightly lower than at the aforementioned points, as discussed below, its power output is similar to that of those points and its variability coefficients are slightly lower. Figs. 8 and 9 show scatter diagrams of Hs and Te for the four preselected sites (3, 5, 6 and 7). The figures show the total annual energy that could be extracted from each sea state (with intervals of 1 s for the period and 0.5 m for the wave height). At each point, there is a zone with significant energy potential: the highest-
Fig. 6. Directional average annual power at the study points.
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J.P. Sierra et al. / Renewable Energy 71 (2014) 51e60
Table 1 Directional average annual power at the study points.
10.21
N
NE
E
SE
S
SW
W
NW
1 2 3 4 5 6 7 8 9 10 11 12
15.81 16.13 16.85 11.78 16.82 15.20 15.73 6.06 3.14 1.70 4.07 11.61
5.40 5.53 5.84 4.75 5.86 5.61 5.71 3.31 2.31 0.62 0.97 2.72
2.03 2.31 2.84 2.42 3.50 3.51 3.52 2.80 2.42 0.87 1.29 1.48
0.83 0.90 1.28 1.04 2.42 2.35 2.39 2.15 2.03 1.11 1.44 0.97
0.99 0.94 0.97 0.70 2.08 2.13 2.38 2.07 1.87 1.23 1.44 1.25
2.21 1.76 1.73 0.70 4.19 5.30 7.70 6.05 5.36 4.01 4.06 3.56
4.21 3.90 3.87 1.13 3.84 3.90 5.41 4.06 3.88 2.95 3.08 4.54
5.14 4.89 4.81 2.10 4.33 3.78 5.59 3.53 3.28 2.14 3.37 5.68
10.01 8.29
6.00
5.94
P (kW/h)
Point
11.53
10.82
3.48
3.18
2.22
J
F
M
A
M
J
2.10
1.81
J
A
S
O
N
D
Fig. 7. Average monthly wave power (kW/h) at the 12 points.
Table 2 Average seasonal wave power (kW/h) at the study sites. Point
1 2 3 4 5 6 7 8 9 10 11 12
Table 4 Mean annual wave power and temporal variability coefficients at the 12 study sites.
Season Spring
Summer
Autumn
Winter
7.17 7.38 7.74 5.8 8.07 7.53 8.06 4.24 3.49 2.31 2.88 5.54
2.57 2.7 2.86 2.35 2.99 2.74 2.78 1.23 0.94 0.6 0.8 1.95
8.54 8.66 8.94 6.76 9.01 8.28 8.79 4.39 3.52 2.43 3.02 6.62
14.33 14.7 15.31 11.12 15.44 14.06 14.75 6.69 5.06 3.49 4.46 10.82
Point
Pyear (kW/h)
SV
MV
1 2 3 4 5 6 7 8 9 10 11 12
8.17 8.38 8.73 6.52 8.90 8.18 8.62 4.16 3.27 2.22 2.80 6.25
1.44 1.43 1.43 1.35 1.40 1.38 1.39 1.31 1.26 1.30 1.31 1.42
1.56 1.55 1.54 1.53 1.53 1.52 1.55 1.53 1.46 1.49 1.44 1.52
lower-energy sea states. In addition, because of their low frequency, their contribution to the annual wave energy is not very large. The amount of electric energy delivered by a WEC depends on the average wave energy available at the location of the device but is also highly dependent on the way in which this energy is scattered along the energy bins, defined by intervals of significant wave height and wave energy period [22]. This is because each WEC has its own power matrix, indicating the power output for each energy bin. In this study, three WECs (whose power matrices are available) were considered: Pelamis [43], Aqua Buoy [44] and Wave Dragon [45]. The power matrices for the three devices were obtained from Ref. [22]. With these matrices and the bin distribution of wave heights and periods, the power output for the 12 analyzed points and the 3 devices was computed (Table 5).
energy area corresponds to periods of 6e10 s and wave heights of 1.5e5 m. Obviously, the highest waves are associated with the largest periods and, for example, wave heights between 3.5 and 5 m are associated with periods between 8 and 10 s while wave heights between 1.5 and 3.5 m have periods of 6e8 s. Sea states with Hs between 1 and 5 m account for a considerable amount of the total energy (77%e84%, depending on the point) while Te between 6 and 9 s is the prevailing range from an energy point of view (74%e76%, depending on the point). Sea states with Hs between 1 and 5 m and Te between 6 and 9 s account for 62%e69% of the total energy (depending on the point). As pointed out by Ref. [1], wave power associated with the less-frequent and highest-energy states cannot be taken into account since its exploitation requires oversized infrastructure and the use of WECs that are probably not able to perform well in
Table 3 Average monthly wave power (kW/h) at the study sites. Point
1 2 3 4 5 6 7 8 9 10 11 12
Month Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
13.29 13.61 14.11 10.22 14.18 13.02 13.84 6.61 5.22 3.69 4.49 10.21
14.60 15.04 15.75 11.06 15.83 14.24 14.65 6.07 4.42 3.03 4.17 11.02
10.79 11.06 11.57 8.46 11.81 10.81 11.24 5.26 4.05 2.71 3.62 8.12
6.92 7.13 7.46 5.73 7.94 7.59 8.33 4.75 4.07 2.68 3.17 5.50
3.57 3.73 3.98 3.06 4.24 4.01 4.41 2.66 2.33 1.54 1.81 2.85
2.69 2.82 2.97 2.45 3.14 2.92 3.06 1.51 1.20 0.81 0.98 2.10
2.69 2.84 3.02 2.48 3.15 2.86 2.85 1.17 0.86 0.53 0.75 2.00
2.33 2.44 2.58 2.12 2.69 2.45 2.44 1.03 0.76 0.46 0.68 1.77
4.55 4.70 4.90 3.77 5.02 4.57 4.66 2.10 1.61 1.03 1.37 3.43
7.56 7.78 8.15 6.39 8.47 7.85 8.22 4.06 3.17 2.07 2.61 5.72
13.20 13.19 13.44 9.87 13.22 12.13 13.17 6.84 5.62 4.08 4.95 10.46
15.10 15.46 16.06 12.09 16.30 14.92 15.77 7.39 5.54 3.76 4.70 11.24
J.P. Sierra et al. / Renewable Energy 71 (2014) 51e60
57
Fig. 8. Scatter diagrams showing the various sea states' contribution to total annual energy at points 3 (top) and 5 (bottom). Numbers indicate total energy (MW h/m).
These values indicate that, in addition to the three points with the largest average wave power (3, 5 and 7), there is another point (6) with a high power output that is comparable to the others and therefore should be taken into account. This confirms that the northeast and the east of island are the areas with the greatest potential for wave energy production. By analyzing the values of Table 5 and those shown in Ref. [29], the average power output around Menorca can be compared with
that of other areas. For the three devices, the power output (at the most productive points) is about half of that estimated at the islands of Madeira or Canada (west coast) but about twice that computed in Lebanon. In the case of the Pelamis device, the average power output in relation to that assessed in other places is around 25% (Ireland), 40% (Azores and the west coast of the United States), 65% (Portugal) or very similar (east coast of the United States). Therefore, although the power output that can be obtained in
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J.P. Sierra et al. / Renewable Energy 71 (2014) 51e60
Fig. 9. Scatter diagrams showing the various sea states' contribution to total annual energy at points 6 (top) and 7 (bottom). Numbers indicate total energy (MW h/m).
Menorca is significantly lower than in other high-energy areas, it is on the same order as other places and among the largest available in the Mediterranean Sea. Another important parameter in assessing the feasibility of a WEC is the capacity factor, which is the actual output of the WEC over a period of time and the energy produced if the WEC had been operating the entire time at the rated output. This factor was computed for the 4 preselected points (3, 5, 6 and 7), as shown in Table 6.
These values indicate that the capacity factors at the selected points are around 10% for the Pelamis device, between 8 and 9% for the Aqua Buoy and between 10 and 11% for the Wave Dragon, the latter being the most productive and efficient of the three analyzed WECs. These values are low compared to those obtained in Canada, which ranged from 21.3% to 32.1% for the Wave Dragon, from 9.8% to 18.4% for the Aqua Buoy and from 17.1% to 26.2% for the Pelamis [10]. Nevertheless they are greater than those obtained in other
J.P. Sierra et al. / Renewable Energy 71 (2014) 51e60 Table 5 Average power output (in GW.h per annum) from the three selected devices at the 12 study points. Point
Pelamis
Aqua Buoy
Wave Dragon
1 2 3 4 5 6 7 8 9 10 11 12
0.62 0.63 0.65 0.51 0.67 0.64 0.68 0.35 0.28 0.18 0.24 0.50
0.17 0.18 0.18 0.15 0.19 0.18 0.20 0.09 0.07 0.04 0.06 0.14
6.03 6.11 6.27 5.34 6.47 6.32 6.63 4.29 3.60 2.88 3.34 5.22
59
Compared to other Spanish or European areas, in particular those facing the Atlantic Ocean, the prospective wave energy around Menorca is relatively low, as expected. However, this site offers one of the best areas in the Mediterranean Sea from the point of view of wave energy potential. The wave power potential of 8.9 kW/h is on the lower bound of feasibilitydfrom a technical and economic point of viewdgiven the current state of technology, although the annual average power outputs of 0.68 GW.h (Pelamis) and 6.63 GW.h (Wave Dragon) are not negligible. In the near future, it might be interesting for a WEC farm to supply energy to one of Menorca's six small villages (<10,000 inhabitants), thereby contributing to the island's energy self-sufficiency. Acknowledgments
Table 6 Capacity factor (in %) for the four selected points and the three considered devices. Point
Pelamis
Aqua Buoy
Wave Dragon
3 5 6 7
9.9 10.2 9.7 10.4
8.2 8.7 8.2 9.1
10.2 10.6 10.3 10.8
This study was funded by the research project “Desarrollo de n como soporte al disen ~ o, una herramienta de alta resolucio n y explotacio n de instalaciones para energías marinas colocacio (DARDO)” funded by the Spanish Ministry of Economy and Competitineness (ref. ENE2012-38772-C02-02). The authors are also grateful to the Spanish Port Authority (Organismo Público Puertos del Estado) for providing the wave data. References
areas of the Mediterranean Sea, such as Lebanon, where the capacity factors were 4%, 5% and 5% for the Wave Dragon, Aqua Buoy and Pelamis, respectively [29]. 6. Conclusions Nowadays, just 2% of the electric energy needed on the island of Menorca is extracted from renewable sources, which is far from the European Union's objective of obtaining 20% of its energy from these sources by 2020. In order to contribute to this aim, in this study the wave energy resource at 12 sites around the island was assessed using a 17-year series of data obtained from numerical modeling (forecast). The average wave power obtained is considerably lower than the values obtained by Ref. [1], also from numerical modeling (8.9 vs. 10.9 kW/h). The comparison to data recorded at a buoy also gives lower values of the average wave power (by about 5%). This and the assumptions made to compute Te, introducing uncertainty, make it possible to consider the values presented here as a lower bound on the wave energy potential at Menorca. The spatial distribution of Menorca's wave energy resource shows great variability, with a high-energy area to the north and east of the island, an intermediate-energy area to the west, and a low-energy area to the south and west. The highest-energy area has an average power of up to 8.9 kW/h with a total annual energy of 78 MW h/m. Regarding the wave directionality, waves from the N are the most energetic at all points except those located to the south of the island, which are mainly affected by SW waves due to the sheltering effect of the island. The temporal variability of Menorca's wave energy resource shows a clear seasonal pattern, with a high-energy winter, a mild summer, and an intermediate-energy spring and autumn (with autumn being slightly higher-energy than spring). Winter and autumn account for 68% of total annual energy. There is also significant monthly variability, with December being the highestenergy month and August the mildest one. Taking into account the power matrices of three WECs, the four points with the most productive output were identified. Located to the northeast and east of the island, these points are the best places to deploy WECs.
[1] Liberti L, Carillo A, Sannino G. Wave energy resource assessment in the Mediterranean, the Italian perspective. Renew Energy 2013;50:938e49. € [2] Lenee-Bluhm P, Paasch R, Ozkan-Haller HT. Characterizing the wave energy resource of the US Pacific Northwest. Renew Energy 2011;36: 2106e19. [3] Hughes MG, Heap AD. National-scale wave energy resource assessment for Australia. Renew Energy 2010;35:1783e91. pez M, Carballo R, Castro A, Fraguela JA, Frigaard P. Wave energy [4] Iglesias G, Lo potential in Galicia (NW Spain). Renew Energy 2009;34:1517e24. [5] Iglesias G, Carballo R. Wave energy resource in the Estaca de Bares area (Spain). Renew Energy 2010;35:1574e84. [6] Iglesias G, Carballo R. Offshore and inshore wave energy assessment: Asturias (N Spain). Energy 2010;35:1964e72. [7] Mollison D, Pontes MT. Assessing the Portuguese wave-power resource. Energy 1992;17:255e68. [8] Rusu E, Guedes Soares C. Numerical modelling to estimate the spatial distribution of the wave energy in the Portuguese nearshore. Renew Energy 2009;34:1501e16. [9] Behrens S, Hayward J, Hemer M, Osman P. Assessing the wave energy converter potential for Australian coastal regions. Renew Energy 2012;43: 210e7. [10] Dunnett D, Wallace JS. Electricity generation from wave power in Canada. Renew Energy 2009;34:179e95. [11] Beyene A, Wilson JH. Comparison of wave energy flux for northern, central, and southern coast of California based on long-term statistical wave data. Energy 2006;31:1520e33. [12] Kim G, Jeong WM, Lee KS, Jun K, Lee ME. Offshore and nearshore wave energy assessment around the Korean peninsula. Energy 2011;36:1460e9. [13] Liang B, Fan F, Yin Z, Shi H, Lee D. Numerical modelling of the nearshore wave energy resources of Shandong peninsula, China. Renew Energy 2013;57: 330e8. [14] Pontes MT. Assessing the European wave energy resource. J Offshore Mech Arct Eng 1998;120:226e31. ment A, McCullen P, Falca ~o A, Fiorentino A, Gardner F, Hammarlund K, [15] Cle et al. Wave energy in Europe: current status and perspectives. Renew Sustain Energy Rev 2002;6:405e31. [16] Cornett AM. A global wave energy resource assessment. In: International Offshore and Polar Engineering Conference, Vancouver, Canada; 2008. pp. 318e26. [17] Arinaga RA, Cheung KF. Atlas of global wave energy from 10 years of reanalysis and hindcast data. Renew Energy 2012;39:49e64. [18] Gunn K, Stock-Williams C. Quantifying the global wave power resource. Renew Energy 2012;44:296e304. [19] Iglesias G, Carballo R. Wave resource in El Hierro e an island towards energy self-sufficiency. Renew Energy 2011;36:689e98. €sso C, S [20] Sierra JP, Gonz alez-Marco D, Sospedra J, Gironella X, Mo anchezArcilla A. Wave energy resource assessment in Lanzarote (Spain). Renew Energy 2013;55:480e9. [21] Iglesias G, Carballo R. Wave power for La Isla Bonita. Energy 2010;35:513e21. [22] Rusu E, Guedes Soares C. Wave energy pattern around the Madeira Islands. Energy 2012;45:771e85.
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[23] Rusu L, Guedes Soares C. Wave energy assessments in the Azores Islands. Renew Energy 2012;45:183e96. [24] Stopa JE, Cheung KF, Chen Y-L. Assessment of wave energy resources in Hawaii. Renew Energy 2011;36:554e67. [25] Stopa JE, Filipot J-F, Li N, Cheung KF, Chen Y-L, Vega L. Wave energy resources along the Hawaiian Island chain. Renew Energy 2013;55:305e21. [26] Chiu F-C, Wuang W-Y, Tiao W-C. The spatial and temporal characteristics of the wave energy resources around Taiwan. Renew Energy 2013;52:218e21. [27] Ortega S, Osorio AF, Agudelo P. Estimation of the wave power resource in the Caribbean Sea in areas with scarce instrumentation. Case study: Isla Fuerte, Colombia. Renew Energy 2013;57:240e8. [28] Vicinanza D, Contestabile P, Ferrante V. Wave energy potential in the northwest of Sardinia (Italy). Renew Energy 2013;50:506e21. [29] Aoun NS, Harajali HA, Queffeulou P. Preliminary appraisal of wave power prospects in Libanon. Renew Energy 2013;53:165e73. [30] Aydogan B, Ayat B, Yüksel Y. Black Sea eave energy atlas from 13 years hindcasted wave data. Renew Energy 2013;57:436e47. €mürkü MI. Wave energy potential along the south-east coasts of [31] Akpinar A, Ko Black Sea. Energy 2012;42:289e302. [32] Rusu E, Onea F. Evaluation of the wind and wave energy along the Caspian Sea. Energy 2013;50:1e14. [33] Ulbrich U, Leckebusch GC, Pinto JG. Extra-tropical cyclones in the present and future climate: a review. Theor Appl Climatol 2009;96:117e31. [34] Bengtsson L, Hodges KI, Keenlyside N. Will extratropical storms intensify in a warmer climate? J Clim 2009;22:2276e301.
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Wave energy resource assessment in Menorca (Spain) € sso a, b, D. Gonza lez-Marco a, b J.P. Sierra a, b, *, C. Mo a dul D1, Campus Nord, Laboratori d'Enginyeria Marítima, Universitat Polit ecnica de Catalunya e BarcelonaTech, Jordi Girona 1-3, Mo 08034 Barcelona, Spain b dels Recursos Costaners, Jordi Girona 1-3, Mo dul D1, Campus Nord, 08034 Barcelona, Spain Centre Internacional d'Investigacio
a r t i c l e i n f o
a b s t r a c t
Article history: Received 7 November 2013 Accepted 6 May 2014 Available online
Menorca (Balearic Islands) only covers 2% of its electricity needs with renewable energy sources, which is far below the European Union's objective of obtaining 20% of its energy from these sources. This study analyses the island's wave energy resources using a 17-year series of data obtained from numerical modeling (forecast). The spatial distribution of wave power is analyzed using data from 12 points around the island. The obtained resources (average wave power, around 8.9 kW/m; average annual wave energy, about 78 MW h/m) are relatively modest but among the largest found in the Mediterranean Sea. The northeast and east of the island are the most productive areas. Considerable seasonal variability is found, with winters being rather energetic and summers quite mild. The power matrices of three wave energy converters (WECs) are considered to assess the average power output at all of the points. Four places are identified as the best candidates for WEC deployment, with non-negligible productivity that can be exploited to supply energy to small villages. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Wave energy Wave power Forecasting Menorca Island Balearic Islands Wave energy converter
1. Introduction Wave energy appears to be one of the most promising alternatives to fossil fuels, which need to be substituted for many reasons, including economic (lack of production in many countries and the consequent need for imports), practical (potential future depletion) and environmental (the need to reduce greenhouse gas emissions) reasons. It is estimated that the use of wave energy will increase significantly over the next few decades as wave energy converter (WEC) technology matures [1]. While ocean wave energy conversion is still unproven on a commercial scale, significant advances in research, design and testing continue to be made [2] and a number of wave energy projects, almost all still in the pilot stage, are currently underway around the world [3]. Research on wave energy resource assessment is particularly advanced in countries bordering large oceans, where the greatest wave energy potential is found [1]. Thus, marine energy assessments have been carried out on Spain's Atlantic coasts [4e6], in Portugal [7,8], in Australia [3,9], in Canada [10], in the United States
cnica * Corresponding author. Laboratori d'Enginyeria Marítima, Universitat Polite dul D1, Campus Nord, 08034 de Catalunya e BarcelonaTech, Jordi Girona 1-3, Mo Barcelona, Spain. Tel.: þ34 934016467; fax: þ34 934011861. E-mail address: [email protected] (J.P. Sierra). http://dx.doi.org/10.1016/j.renene.2014.05.017 0960-1481/© 2014 Elsevier Ltd. All rights reserved.
[2,11], and in Asia [12,13]. Europe-wide [14,15] and worldwide [16e18] marine energy assessments have also been made. Wave energy is particularly appropriate for islands, which receive a large amount of this resource and are usually highly dependent on external energy sources. For this reason, wave energy potential has been assessed on various islands in the Atlantic Ocean, including the Canary Islands [19e21], Madeira [22] and the Azores [23]; in the Pacific Ocean, on islands such as Hawaii [24,25] and Taiwan [26]; and in the Caribbean Sea [27]. In general, research on wave energy production is focused on locations that, like the aforementioned places, have high energy potential. As pointed out in Ref. [1], this high energy potential usually implies extreme events, posing serious engineering challenges and increasing the costs of design, production, deployment and maintenance of WECs. In calmer seas, where a lower amount of energy is available, many technical issues related to extreme sea climate probably could be solved more easily and wave energy production could still be economically viable [1]. Taking this into account, several studies have recently assessed the wave energy potential of areas such as the Mediterranean Sea [1,28,29], the Black Sea [30,31] and the Caspian Sea [32]. This paper focuses on the assessment of wave power potential around Menorca, an island in the Spanish Mediterranean Sea. Section 2 briefly describes the study area. Section 3 presents the available data and the methodology used. The island's wave energy resource, including its spatial distribution and seasonal variations,
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J.P. Sierra et al. / Renewable Energy 71 (2014) 51e60
is assessed in Section 4. In Section 5, the best locations for WEC deployment are identified and their feasibility discussed. Finally, the conclusions of the paper are presented in Section 6. 2. Study area The study area is located in the northwestern Mediterranean Sea (39.81e40.09 N, 3.79e4.32 E) and forms part of the Balearic Islands (Fig. 1, left). Menorca has a surface area of 701.8 km2 and a population of about 100,000 inhabitants, most of them living along the coast. The island was declared a Biosphere Reserve in 1993. At present, 80% of the electric energy needed on the island is supplied by a thermal power station (gas and fuel) with a capacity of 169.5 Mw, 18% is imported from the neighboring island of Mallorca and just 2% is extracted from renewable sources (two solar facilities and one wind farm). Taking into account the European Union's objective of obtaining 20% of its energy from renewable sources by 2020, wave energy is a good candidate for contributing to this goal, overcoming the island's present energy deficit and, in particular, its deficit of renewable energy sources. Moreover, according to a recent study [1], the most productive area in the Mediterranean Sea in terms of wave energy potential is located between the Balearic Islands and the western coast of Sardinia. As in other mid-latitude areas, the Mediterranean basin climate is dominated by extra-tropical cyclones [33] formed via baroclinic instability, which is highest during the winter season [34]. Moreover, it is affected by moving depressions generated either in the Atlantic Ocean or in northwestern Europe [35]. According to [36], Mediterranean storms are normally shorter and less intense than those in northern Europe, with many subregional and mesoscale effects taking place and producing large spatial and seasonal variability [37]. The reduced scale, along with the peculiar features of the basin (complex orography and the moisture of a relatively large mass of water) makes the Mediterranean climate more difficult to predict than climates in other places [33]. Examples of local features are the Tramontana and Mistral winds, which consist of intense, persistent N and NW winds often caused by a cyclone over the Gulf of Genoa, which is then channeled and intensified through the valleys between mountain ranges on the north side of the northwestern Mediterranean. During the summer season, thermal and orographic effects play a greater role in the genesis and maintenance of cyclones. In the northwestern Mediterranean, the main cyclone centers are again located in the Gulf of Genoa and over the Iberian Peninsula, the
latter caused by temperature contrasts between land and sea [37]. During the warm period, the Mediterranean is also exposed to tropical systems [38] as a result of its location in a transitional zone between humid mountains in the north and arid regions in the south. Finally, spring and autumn can be considered transitional periods between the contrasting winter and summer patterns [38].
3. Data and methods 3.1. Available wave data Two sources of data are available for Menorca. The first is a 17year hindcast wave climate database (1996e2013) generated by the Spanish Port Authority (Organismo Público Puertos del Estado). This wave data set was obtained using the WAM model [39] and forced by the wind output of the HIRLAM [40] regional atmospheric model (developed cooperatively by several meteorological institutes and run by the Spanish State Meteorological Agency). Data are from 12 points surrounding the island (Fig. 1, right) with a spatial resolution of 0.25 0.25 (although resolution is 0.125 close to the coast) and a time interval of 1 h. The second data source is a wave buoy located close to the island (39.72 N, 4.42 E). Placed at a depth of 300 m, this buoy is in deep water and its position practically coincides with one of the points where the numerical simulation data were obtained. The time series includes hourly directional information from 1993 to 2013 and is used to validate the numerical wave data.
3.2. Methodology With the exception of point 4, all points considered are located in deep water and thus are not affected by propagation processes such as refraction and diffraction. Wave power can be obtained using the following deep-water expression:
P¼
rg2 2 H Te x0:491Hs2 Te 64p s
(1)
where P is the is the wave power per unit of crest length (kW/m), Hs is the significant wave height, Te is the energy period, r is the density of seawater (assumed to be 1025 kg/m3) and g is the gravitational acceleration. Te is computed as a function of spectral moments:
Fig. 1. Location of Menorca in the northwestern Mediterranean Sea (left). Location of the analyzed points and buoy (right).
J.P. Sierra et al. / Renewable Energy 71 (2014) 51e60
Te ¼
m1 m0
(2)
As pointed out in Ref. [16], measured sea states are often specified in terms of significant wave height Hs and either peak period Tp or mean period Tz. The energy period Te is rarely specified and must be estimated from other variables when the spectral shape or the spectral moments are unknown, as in this case. One approach when Tp is known is to assume the following:
Te ¼ aTp
(3)
where a is a coefficient whose value depends on the shape of the wave spectrum (0.86 for a PiersoneMoskowitz spectrum and increasing towards unity with decreasing spectral width) [16]. Taking into account the predominance of the wind-sea waves together with mixed sea states in this areadwhich is reasonable considering the short fetches of the area [41]dthe wave spectra are rather wide. Therefore, as suggested by Refs. [16,42], a value of Te ¼ 0.9Tp was used to assess the wave energy resource. With Equations (1) and (3), the total wave energy resource at a point can be assessed, allowing the computation of the power average at each point. This assessment can also be done for various directions at each point in order to obtain a directional distribution of wave power. As mentioned above, this assessment of wave power uses available data from 17-year numerical simulations. To verify the suitability of these data, model data at point 7 (the closest to the eave buoy) have been compared with those measured at the buoy during the same time interval. With this aim, the relative mean absolute error (RMAE) was computed for both significant wave height and peak period, as follows: Wave height:
RMAE ¼
〈jH H H j〉 m
b
(4)
b
Peak period:
RMAE ¼
〈jT
m
Tb j Tb
〉
(5)
in which Hm and Hb are, respectively, the significant wave height given by the model and measured at the buoy; Tm and Tb are,
53
respectively, the peak period given by the model and measured at the buoy; j…j indicates absolute value; and 〈:::〉 means average over the time series. The RMAE value obtained is 0.25 for the wave height, which is fair, and 0.14 for the wave period, which is good. Fig. 2 compares the values of Hs and Tp measured at the buoy and with those values obtained by the model and shows fair agreement between the two data sources. Moreover, it appears that the two types of values compensate each other because they are spread above and below the line that indicates the exact fit between the two data types, without showing a clear bias. Thus, the correlation coefficient between the measured and modeled values is 0.91 for Hs and 0.81 for Tp. Nevertheless, in general, the model slightly underpredicts the buoy measurements, in particular wave heights and, as a consequence, wave power. In Fig. 3, where the average monthly wave power computed at the buoy and at point 7 (data from the numerical model) are shown, this underprediction is clearly seen. In all months (except in April, month 4), the wave power given by the model is lower than that measured at the buoy. Moreover, the average annual power measured at the buoy is 9.08 kW/m, while that obtained with the numerical data at point 7 is 8.62 kW/m, confirming this slight underprediction (5%). These small differences support the assumption that the numerical data are suitable for assessing wave energy in this area, with the obtained results acting as a lower bound on the energy potential in the area.
4. Analysis of the wave energy resource 4.1. Spatial distribution Fig. 4 shows the average wave power at the 12 locations. In this figure, three areas with different wave energy distributions can be distinguished: a higher-energy area, encompassing the north and east of the island (points 1e7) with annual average wave powers greater than 8 kW/h (except point 4, due to its nearshore location) and annual wave energies ranging from 71.6 to 78 MW h/m; a lowenergy area comprising the south of the island (points 8e11) with annual average wave powers ranging from 2.22 to 4.16 kW/h and annual wave energies ranging from 19.4 to 36.4 MW h/m; an intermediate-energy area along the west side of the island (point 12) with annual average wave powers around 6 kW/h and annual wave energies of 54.8 MW h/m. The variations in average power
Fig. 2. Comparison of buoy data and forecasting data: Hs (left) and Tp (right).
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J.P. Sierra et al. / Renewable Energy 71 (2014) 51e60
Fig. 3. Average monthly wave power at the buoy and at point 7 (given by the model).
around the island are plotted in Fig. 5, where the three areas can be clearly discerned. Another factor considered is the direction of the waves and how the study points are exposed to them. The directional distribution of wave energy is important for non-point absorber devices. Fig. 6 and Table 1 show the average annual power at the study points distributed in directions according to the waves that arrive at each point. At most of the points, in particular those located in the north, east and west, the bulk of the energy comes from the N, followed by NE and NW. At the points located in the south, the sheltering effect provided by the island itself prevents the arrival of waves from northern directions, significantly reducing the amount of energy received by these points and making them less suitable for WEC deployment. At these points (8e11), SW waves provide an important amount of the total energy.
4.2. Temporal variability This section analyses the monthly and seasonal wave power fluctuations around Menorca. Table 2 shows the average seasonal wave power at the 12 study sites. These data show the strong seasonal character of Menorca's wave energy, with a considerable range of variation among seasons. The wave energy starts to increase in autumn, reaching its peak in winter, decreasing in spring and hitting its minimum in summer. About 42% of Menorca's annual wave power corresponds to winter, 26% to autumn, 24% to spring, and less than 8% to summer, such that the island's wave
energy resource is more than five times greater in winter than in summer. Table 3 shows the average monthly wave power at the 12 study sites. A clear seasonal trend can be observed, with energy levels reaching their highest values in the winter months (DecembereFebruary) and peaking in December. After the winter, wave power drops to its lowest values in the summer (JuneeAugust), with minimum values in August. On average, wave power in December is 6.6 times that of August. Fig. 7 shows the monthly wave power averaged at the 12 points, which is representative of the energy level in Menorca and which follows the same seasonal trend. As in many water wave analyses, two different seasons can be defined: a stormy period from October to March and a calmer period from April to September. Data from Table 3 show a concentration of wave energy during the stormy season, with wave energy values ranging from 72% to 77% of the average annual energy at the 12 points. In the next section, further analyses of temporal variability are carried out on the basis of two variability coefficients. The aim is to determine the suitability of the various sites for WEC installation. 5. Identification of the best locations for WEC deployment The primary consideration for a WEC site is the amount of wave power and total energy that can potentially be obtained at the site, taking into account its distribution over wave heights, periods and directions (see Fig. 6 and Table 1). At points 1 to 7 and 12, the prevailing direction is N, followed by NE and NW (except at points 6 and 7, where wave energy from the SW is also considerable). At points 8 to 11, the main wave energy direction is SW (although waves from the N provide the same amount of energy at these points). Another factor to take into account in choosing a WEC site is temporal variability at different time scales (daily, monthly and seasonal). Sites with a steady, moderate wave energy flux may be more attractive than sites where energy is higher but is also unsteady and thus less reliable because WEC efficiency may decrease significantly under more variable wave conditions [16]. Different coefficients have been proposed to describe the temporal variability in wave power at a particular site. In this study, two of the coefficients proposed by Ref. [16] are used: the seasonal variability index (SV) and the monthly variability index (MV). The SV is defined as follows:
SV ¼
Ps1 Ps4 Pyear
(6)
where Ps1 is the mean wave power for the highest-energy season (usually winter) and Ps4 is the mean wave power for the lowestenergy season (usually summer), and Pyear is the annual mean wave power. The greater the value of SV the larger the seasonal variability, with values lower than 1 indicating moderate seasonal variability. MV is defined as follows:
MV ¼
Fig. 4. Annual average wave power at the study points.
PM1 PM12 Pyear
(7)
where PM1 is the mean wave power for the highest-energy month and PM12 is the mean wave power for the lowest-energy month. Obviously, the values of MV are greater than those of SV. Table 4 shows the values of these variability coefficients and the mean annual wave power at the 12 study points. The SV and MV values are similar for the highest-energy points (SV between 1.38 and 1.44 and MV between 1.52 and 1.56), which suggests that
J.P. Sierra et al. / Renewable Energy 71 (2014) 51e60
55
Fig. 5. Annual average wave power around Menorca.
waves show certain variability at all of these locations (points 1e3 and 5e7) as shown in Section 4.2. On the contrary, points 8e11 have lower SV and MV but they can be ruled out as WEC sites because the wave energy resource is too small to be exploited. Points 5, 3 and 7 offer the highest-energy levels and similar SV and MV values, so they can be considered the most promising areas along the Menorcan cost for wave energy production taking into account both wave energy potential and inter-annual variability. Another interesting location from the point of view of wave energy
production is point 6 because, although the potential wave power is slightly lower than at the aforementioned points, as discussed below, its power output is similar to that of those points and its variability coefficients are slightly lower. Figs. 8 and 9 show scatter diagrams of Hs and Te for the four preselected sites (3, 5, 6 and 7). The figures show the total annual energy that could be extracted from each sea state (with intervals of 1 s for the period and 0.5 m for the wave height). At each point, there is a zone with significant energy potential: the highest-
Fig. 6. Directional average annual power at the study points.
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J.P. Sierra et al. / Renewable Energy 71 (2014) 51e60
Table 1 Directional average annual power at the study points.
10.21
N
NE
E
SE
S
SW
W
NW
1 2 3 4 5 6 7 8 9 10 11 12
15.81 16.13 16.85 11.78 16.82 15.20 15.73 6.06 3.14 1.70 4.07 11.61
5.40 5.53 5.84 4.75 5.86 5.61 5.71 3.31 2.31 0.62 0.97 2.72
2.03 2.31 2.84 2.42 3.50 3.51 3.52 2.80 2.42 0.87 1.29 1.48
0.83 0.90 1.28 1.04 2.42 2.35 2.39 2.15 2.03 1.11 1.44 0.97
0.99 0.94 0.97 0.70 2.08 2.13 2.38 2.07 1.87 1.23 1.44 1.25
2.21 1.76 1.73 0.70 4.19 5.30 7.70 6.05 5.36 4.01 4.06 3.56
4.21 3.90 3.87 1.13 3.84 3.90 5.41 4.06 3.88 2.95 3.08 4.54
5.14 4.89 4.81 2.10 4.33 3.78 5.59 3.53 3.28 2.14 3.37 5.68
10.01 8.29
6.00
5.94
P (kW/h)
Point
11.53
10.82
3.48
3.18
2.22
J
F
M
A
M
J
2.10
1.81
J
A
S
O
N
D
Fig. 7. Average monthly wave power (kW/h) at the 12 points.
Table 2 Average seasonal wave power (kW/h) at the study sites. Point
1 2 3 4 5 6 7 8 9 10 11 12
Table 4 Mean annual wave power and temporal variability coefficients at the 12 study sites.
Season Spring
Summer
Autumn
Winter
7.17 7.38 7.74 5.8 8.07 7.53 8.06 4.24 3.49 2.31 2.88 5.54
2.57 2.7 2.86 2.35 2.99 2.74 2.78 1.23 0.94 0.6 0.8 1.95
8.54 8.66 8.94 6.76 9.01 8.28 8.79 4.39 3.52 2.43 3.02 6.62
14.33 14.7 15.31 11.12 15.44 14.06 14.75 6.69 5.06 3.49 4.46 10.82
Point
Pyear (kW/h)
SV
MV
1 2 3 4 5 6 7 8 9 10 11 12
8.17 8.38 8.73 6.52 8.90 8.18 8.62 4.16 3.27 2.22 2.80 6.25
1.44 1.43 1.43 1.35 1.40 1.38 1.39 1.31 1.26 1.30 1.31 1.42
1.56 1.55 1.54 1.53 1.53 1.52 1.55 1.53 1.46 1.49 1.44 1.52
lower-energy sea states. In addition, because of their low frequency, their contribution to the annual wave energy is not very large. The amount of electric energy delivered by a WEC depends on the average wave energy available at the location of the device but is also highly dependent on the way in which this energy is scattered along the energy bins, defined by intervals of significant wave height and wave energy period [22]. This is because each WEC has its own power matrix, indicating the power output for each energy bin. In this study, three WECs (whose power matrices are available) were considered: Pelamis [43], Aqua Buoy [44] and Wave Dragon [45]. The power matrices for the three devices were obtained from Ref. [22]. With these matrices and the bin distribution of wave heights and periods, the power output for the 12 analyzed points and the 3 devices was computed (Table 5).
energy area corresponds to periods of 6e10 s and wave heights of 1.5e5 m. Obviously, the highest waves are associated with the largest periods and, for example, wave heights between 3.5 and 5 m are associated with periods between 8 and 10 s while wave heights between 1.5 and 3.5 m have periods of 6e8 s. Sea states with Hs between 1 and 5 m account for a considerable amount of the total energy (77%e84%, depending on the point) while Te between 6 and 9 s is the prevailing range from an energy point of view (74%e76%, depending on the point). Sea states with Hs between 1 and 5 m and Te between 6 and 9 s account for 62%e69% of the total energy (depending on the point). As pointed out by Ref. [1], wave power associated with the less-frequent and highest-energy states cannot be taken into account since its exploitation requires oversized infrastructure and the use of WECs that are probably not able to perform well in
Table 3 Average monthly wave power (kW/h) at the study sites. Point
1 2 3 4 5 6 7 8 9 10 11 12
Month Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
13.29 13.61 14.11 10.22 14.18 13.02 13.84 6.61 5.22 3.69 4.49 10.21
14.60 15.04 15.75 11.06 15.83 14.24 14.65 6.07 4.42 3.03 4.17 11.02
10.79 11.06 11.57 8.46 11.81 10.81 11.24 5.26 4.05 2.71 3.62 8.12
6.92 7.13 7.46 5.73 7.94 7.59 8.33 4.75 4.07 2.68 3.17 5.50
3.57 3.73 3.98 3.06 4.24 4.01 4.41 2.66 2.33 1.54 1.81 2.85
2.69 2.82 2.97 2.45 3.14 2.92 3.06 1.51 1.20 0.81 0.98 2.10
2.69 2.84 3.02 2.48 3.15 2.86 2.85 1.17 0.86 0.53 0.75 2.00
2.33 2.44 2.58 2.12 2.69 2.45 2.44 1.03 0.76 0.46 0.68 1.77
4.55 4.70 4.90 3.77 5.02 4.57 4.66 2.10 1.61 1.03 1.37 3.43
7.56 7.78 8.15 6.39 8.47 7.85 8.22 4.06 3.17 2.07 2.61 5.72
13.20 13.19 13.44 9.87 13.22 12.13 13.17 6.84 5.62 4.08 4.95 10.46
15.10 15.46 16.06 12.09 16.30 14.92 15.77 7.39 5.54 3.76 4.70 11.24
J.P. Sierra et al. / Renewable Energy 71 (2014) 51e60
57
Fig. 8. Scatter diagrams showing the various sea states' contribution to total annual energy at points 3 (top) and 5 (bottom). Numbers indicate total energy (MW h/m).
These values indicate that, in addition to the three points with the largest average wave power (3, 5 and 7), there is another point (6) with a high power output that is comparable to the others and therefore should be taken into account. This confirms that the northeast and the east of island are the areas with the greatest potential for wave energy production. By analyzing the values of Table 5 and those shown in Ref. [29], the average power output around Menorca can be compared with
that of other areas. For the three devices, the power output (at the most productive points) is about half of that estimated at the islands of Madeira or Canada (west coast) but about twice that computed in Lebanon. In the case of the Pelamis device, the average power output in relation to that assessed in other places is around 25% (Ireland), 40% (Azores and the west coast of the United States), 65% (Portugal) or very similar (east coast of the United States). Therefore, although the power output that can be obtained in
58
J.P. Sierra et al. / Renewable Energy 71 (2014) 51e60
Fig. 9. Scatter diagrams showing the various sea states' contribution to total annual energy at points 6 (top) and 7 (bottom). Numbers indicate total energy (MW h/m).
Menorca is significantly lower than in other high-energy areas, it is on the same order as other places and among the largest available in the Mediterranean Sea. Another important parameter in assessing the feasibility of a WEC is the capacity factor, which is the actual output of the WEC over a period of time and the energy produced if the WEC had been operating the entire time at the rated output. This factor was computed for the 4 preselected points (3, 5, 6 and 7), as shown in Table 6.
These values indicate that the capacity factors at the selected points are around 10% for the Pelamis device, between 8 and 9% for the Aqua Buoy and between 10 and 11% for the Wave Dragon, the latter being the most productive and efficient of the three analyzed WECs. These values are low compared to those obtained in Canada, which ranged from 21.3% to 32.1% for the Wave Dragon, from 9.8% to 18.4% for the Aqua Buoy and from 17.1% to 26.2% for the Pelamis [10]. Nevertheless they are greater than those obtained in other
J.P. Sierra et al. / Renewable Energy 71 (2014) 51e60 Table 5 Average power output (in GW.h per annum) from the three selected devices at the 12 study points. Point
Pelamis
Aqua Buoy
Wave Dragon
1 2 3 4 5 6 7 8 9 10 11 12
0.62 0.63 0.65 0.51 0.67 0.64 0.68 0.35 0.28 0.18 0.24 0.50
0.17 0.18 0.18 0.15 0.19 0.18 0.20 0.09 0.07 0.04 0.06 0.14
6.03 6.11 6.27 5.34 6.47 6.32 6.63 4.29 3.60 2.88 3.34 5.22
59
Compared to other Spanish or European areas, in particular those facing the Atlantic Ocean, the prospective wave energy around Menorca is relatively low, as expected. However, this site offers one of the best areas in the Mediterranean Sea from the point of view of wave energy potential. The wave power potential of 8.9 kW/h is on the lower bound of feasibilitydfrom a technical and economic point of viewdgiven the current state of technology, although the annual average power outputs of 0.68 GW.h (Pelamis) and 6.63 GW.h (Wave Dragon) are not negligible. In the near future, it might be interesting for a WEC farm to supply energy to one of Menorca's six small villages (<10,000 inhabitants), thereby contributing to the island's energy self-sufficiency. Acknowledgments
Table 6 Capacity factor (in %) for the four selected points and the three considered devices. Point
Pelamis
Aqua Buoy
Wave Dragon
3 5 6 7
9.9 10.2 9.7 10.4
8.2 8.7 8.2 9.1
10.2 10.6 10.3 10.8
This study was funded by the research project “Desarrollo de n como soporte al disen ~ o, una herramienta de alta resolucio n y explotacio n de instalaciones para energías marinas colocacio (DARDO)” funded by the Spanish Ministry of Economy and Competitineness (ref. ENE2012-38772-C02-02). The authors are also grateful to the Spanish Port Authority (Organismo Público Puertos del Estado) for providing the wave data. References
areas of the Mediterranean Sea, such as Lebanon, where the capacity factors were 4%, 5% and 5% for the Wave Dragon, Aqua Buoy and Pelamis, respectively [29]. 6. Conclusions Nowadays, just 2% of the electric energy needed on the island of Menorca is extracted from renewable sources, which is far from the European Union's objective of obtaining 20% of its energy from these sources by 2020. In order to contribute to this aim, in this study the wave energy resource at 12 sites around the island was assessed using a 17-year series of data obtained from numerical modeling (forecast). The average wave power obtained is considerably lower than the values obtained by Ref. [1], also from numerical modeling (8.9 vs. 10.9 kW/h). The comparison to data recorded at a buoy also gives lower values of the average wave power (by about 5%). This and the assumptions made to compute Te, introducing uncertainty, make it possible to consider the values presented here as a lower bound on the wave energy potential at Menorca. The spatial distribution of Menorca's wave energy resource shows great variability, with a high-energy area to the north and east of the island, an intermediate-energy area to the west, and a low-energy area to the south and west. The highest-energy area has an average power of up to 8.9 kW/h with a total annual energy of 78 MW h/m. Regarding the wave directionality, waves from the N are the most energetic at all points except those located to the south of the island, which are mainly affected by SW waves due to the sheltering effect of the island. The temporal variability of Menorca's wave energy resource shows a clear seasonal pattern, with a high-energy winter, a mild summer, and an intermediate-energy spring and autumn (with autumn being slightly higher-energy than spring). Winter and autumn account for 68% of total annual energy. There is also significant monthly variability, with December being the highestenergy month and August the mildest one. Taking into account the power matrices of three WECs, the four points with the most productive output were identified. Located to the northeast and east of the island, these points are the best places to deploy WECs.
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