Renewable Energy 36 (2011) 689e698
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Wave resource in El Hierrodan island towards energy self-sufficiency G. Iglesias*, R. Carballo Univ. of Santiago de Compostela, Hydraulic Eng., EPS, Campus Univ. s/n, 27002 Lugo, Spain
a r t i c l e i n f o
a b s t r a c t
Article history: Received 18 March 2010 Accepted 13 August 2010 Available online 15 September 2010
The island of El Hierro (Spain), a UNESCO Biosphere Reserve in the Atlantic Ocean, aims to become the first 100% renewable energy island in the world. With a V54 million wind project already under way, the present research looks at the island’s wave resource using a 44-year hindcast dataset obtained through numerical modelling. The geographical distribution of wave energy is examined on the basis of eight study sites around the island. A substantial resource is found west and north of El Hierro, with average wave power in the order of 25 kW m1 and total annual energy in excess 200 MW h m1; the resource is less abundant east and south of the island. In addition to these geographical variations, wave energy in El Hierro presents seasonal variations, with energetic winters and mild summers. After analysing the total resource and its spatial and seasonal variations, its composition in terms of sea states (significant wave heights and energy periods) is examined, and how this composition affects the selection of the Wave Energy Converters to be installed is discussed. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Wave energy Wave power Wave model Numerical model El Hierro Canary Islands
1. Introduction El Hierro is the smallest (278 km2) and least inhabited (10753 inhabitants) of the Canary Islands (Spain), a volcanic archipelago in the Atlantic Ocean (27.5 Ne29.5 N, 013 We018.5 W) (Fig. 1). Although the influence of Saharan air masses results in an arid landscape in the eastern islands, rainfall increases towards the westdso much so that El Hierro, the westernmost island, is famed for its lushness. A UNESCO Biosphere Reserve, El Hierro aims to become the first 100% carbon-free island. Perhaps nowhere is the case for renewable energy easier to present than in El Hierro. First, the surrounding water depths are too large to allow any submarine connection, so its energy needs must be provided for locally. Second, the island is endowed with two natural commodities of the greatest interestdwaves and wind. Third, its mountainous nature (with a 1501 m high peak) is an excellent basis for energy storage by means of water reservoirs. Last, but not least, there is considerable consensus among its population and policy makers in support of renewable energy. These favourable conditions have been recognised both locally and nationally, and a V54 million project combining a 10 MW wind farm with two water reservoirs is under way [1]. The present research looks at El Hierro’s wave resource. Although there are no (prior) assessments in the Canary Islands, marine energy was assessed in other regions of Spain, notably the
* Corresponding author. Tel.: þ34 982 285 900; fax: þ34 982 285 926. E-mail address:
[email protected] (G. Iglesias). 0960-1481/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.renene.2010.08.021
north-western and northern coastlines of Iberia [2e7]. Wave energy assessments in other European regions include the Baltic Sea, Denmark and Sweden [8e10], among others; Europe-wide and global assessments may be found in [11e15]. As regards other Atlantic islands, wave energy was evaluated in Madeira (Portugal) [16] and, as part of a wider study, in Azores (Portugal) [17]. The combination of wave and wind power was studied by [18e20], and a 100% renewable energy system was discussed by Lund et al. [21]. This article is structured as follows. Section 2 presents the wave data and how they were obtained. Section 3 deals with the geographical and seasonal variations of wave energy; the area with the highest potential for a wave farm is determined. Section 4 looks at the composition of the resource in terms of sea states (wave heights and periods) and how this composition affects the selection of the Wave Energy Converters to be installed. Finally, Section 5 presents the conclusions.
2. Wave data With no wave buoys in the vicinity of El Hierro, hindcast wave data obtained through numerical modelling were used in this research. The basis for the hindcast dataset was the global atmospheric reanalysis carried out by the U.S. National Center for Environmental Prediction, Washington, D.C., USA (NCEP) and the National Center for Atmospheric Research, Boulder, Colorado, USA (NCAR) integrating instrumental observations and satellite data [22]. Data from this reanalysis were used to force a regional atmospheric model, REMO [23e25]; the model grid covered the
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G. Iglesias, R. Carballo / Renewable Energy 36 (2011) 689e698 29.5
Lanzarote
Latitude (º)
29
La Palma
Fuerteventura
Tenerife
28.5
La Gomera
Gran Canaria
El HIERRO 7
28
1 8
6
Morocco
2 4
27.5
3
5
-19
Western Sahara
-18
-16
-17
-14
-15
-13
Longitude (º) Fig. 1. Map of the Canary Islands showing the location of the eight study sites around El Hierro.
North Atlantic with a resolution of 300 300 , enhanced near the coastline to 150 150 . The effects of the interaction between the larger-scale (atmospheric) flow and smaller-scale features (topography, land uses) were included using a spectral nudging technique [23]. The high-resolution atmospheric data thus obtained were used to force the third generation wave model WAM (WAve prediction Model) cycle 4 run on the same grid (So-called third generation models are those in which the wave spectrum is free to
develop without any shape imposed a priori). After running the WAM model, the hindcast wave database was obtained; it covers a 44-year period (from 1.1.1958 to 31.12.2001) with a three-hourly frequency. Only a brief summary of the WAM model is included hered further details may be found in [26e28]. The model’s formulation is based on the spectral action density rather than the spectral energy density because wave action is conserved in the presence of
28.1 7
8
1
28
27.9
Latitude (º)
Pt. de Salmora
6
27.8
Pt. de la Sal
Pt. Norte Pt. de la Caleta 2
Ensenada el Golfo
EL HIERRO 27.7 Pt. de la Palometa
1000m 2000m
500m
27.6 5
Pt. de la Restinga
4
3
3000m
27.5
27.4 −18.4
−18.3
−18.2
−18.1
−18
−17.9
−17.8
−17.7
−17.6
Longitude (º) Fig. 2. Map of El Hierro with the annual wave roses for the eight study sites (the centre of each rose is located at the corresponding site).
G. Iglesias, R. Carballo / Renewable Energy 36 (2011) 689e698 Table 1 Wave height, power and energy statistics at the study sites [Hm0, significant wave height; J, power per metre of wave front; (E)annual, total annual energy per metre of wave front]. Jmax (E)annual (Hm0)mean (Hm0)max Jmean (kW/m) (kW/m) (MWh/m) std. dev. (m) (m)
Site Location No. 1 2 3 4 5 6 7 8
17.75 W, 28.0 N 17.75 W, 27.75 N 17.75 W, 27.5 N 18.0 W, 27.5 N 18.25 W, 27.5 N 18.25 W, 27.75 N 18.25 W, 28.0 N 18.0 W, 28.0 N
1.84 1.63 1.63 1.52 1.93 1.88 2.05 1.91
0.70 0.59 0.58 0.58 0.80 0.80 0.82 0.81
8.5 6.5 8.6 8.8 9.1 9.4 9.7 8.7
19.59 14.34 13.59 12.59 24.20 23.51 26.52 22.91
488.1 304.1 486.6 512.9 560.8 581.3 619.0 576.4
171.7 125.7 119.2 110.37 212.1 206.1 232.5 200.8
currents, whereas wave energy is not [29]. The spectral action density is defined by
Nðs; qÞ ¼
Eðs; qÞ ;
s
691
frequency in a system moving with the current). The WAM model solves the spectral action balance equation:
v v v v v S ðNÞ þ ðcx NÞ þ cy N þ ðcq NÞ þ ðcs NÞ ¼ ; s vt vx vy vs vq
(2)
where cx and cy are the propagation velocity components in the x- and y-space, respectively; cq is the propagation velocity in the q-space; cs is the propagation velocity in the s-space; and S ¼ S(s, q) represents sources or sinks of wave energy. The first term on the left-hand side of Equation (2) represents the local rate of change of action density in time, the second and third terms represent propagation of action in geographic space, the fourth term represents depth-induced and current-induced refraction, and the fifth term represents shifting of the relative frequency due to variations in depth and currents.
3. Geographical and seasonal variations of the wave resource
(1)
where E ¼ E(s, q) is the spectral energy density, with q the propagation direction and s the relative radian frequency (i.e. the radian
The assessment of wave energy in El Hierro was based on data from the eight nodes in the model grid closest to the island (Figs. 1 and 2). The inexistence of a continental shelf (a consequence of the
Fig. 3. Annual wave power roses at study sites 1e4 (low-energy area) [ J, power per metre of wave front].
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Fig. 4. Annual wave power roses at study sites 5e8 (high-energy area) [ J, power per metre of wave front].
island’s volcanic origin) means that the eight study sites are located in deepwater; therefore, unlike at sites with intermediate and shallow water depths [3e5,30e33], wave power is unaffected by refraction or shoaling and can be computed directly from the WAM model’s results using the following deepwater expression [10]:
J ¼
rg2 2 Te Hm0 ; 64p
(3)
where J is wave power, Te is energy period, Hm0 is significant wave height, r is seawater density, and g is gravitational acceleration. The significant wave height and energy period are defined as a function of the spectral moments as follows. If the n-th spectral moment is given by
Z2p ZN mn ¼ 0
f n Eðf ; qÞdf dq;
(4)
0
where f is wave frequency and E ¼ E(f, q) is energy density, the significant wave height is defined as 1
Hm0 ¼ 4ðm0 Þ2 ;
(5)
where m0 is the zeroth moment (the variance) of the wave spectrum. The energy period is given by
Te ¼
m1 : m0
(6)
Although there are other representative wave period measures, the energy period is favoured for wave energy studies as it weights waves according to their spectral energy content [34,35]; moreover, its physical interpretation is straightforwarddit is the period of a sinusoidal wave with the same energy as the sea state. The wave power and energy statistics for the eight study sites are summarised in Table 1. Annual figures refer to an average year (the average of the 44-year period). There are two well-defined groups in the table: a high-energy and a low-energy group. In the high-energy group (sites 5 to 7), average wave power is in the order of 25 kW m1 and annual wave energy is well above 200 MW h m1; in the low-energy group (sites 2 to 4), average power is in the order of 13 kW m1 and annual wave energy is well below 150 MW h m1. Although intermediate between the two, sites 1 and 8 will be associated hereafter with the low-energy and high-energy groups, respectively, for practical reasons (e.g. presentation of figures). The variations in average power and
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693
Fig. 5. Mean wave power around El Hierro.
annual energy around the island are presented in Figs. 5 and 6, respectively, using a colour scale; the colours at the positions between the actual study sites were obtained by interpolating the values at the sites. The division between a high-energy NW and a low-energy SE is clearly delineated.
To make sense of these geographical variations, two main elements must be considereddthe wave directions and the degree of exposure to them at different points around the island. Waves come from the I and IV quadrants, mainly from the NWeNE arc (Fig. 2); of these, the most energetic direction is NNW, followed by
Fig. 6. Annual wave energy around El Hierro.
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Fig. 7. Seasonal wave height roses at study site 7 [Hm0, significant wave height].
NW and N (Figs. 3 and 4). The high-energy group is composed of the sites west and north of El Hierro, fully exposed to these directions. The low-energy group is composed of the sites east and south of the island, sheltered by the island itselfdwaves from the most energetic directions only reach this area through diffraction, with the consequent decrease in energy. Finally, the sites with intermediate energy levels (#1 and #8) are partially sheltered from northerly waves by the neighbouring island of La Palma. In a more detailed analysis, the largest resource within the high-energy group is found at site 7 because sites 6 and 5 are sheltered from I quadrant waves by El Hierro; this is sufficient to reduce the total annual energy by approx. 10% relative to site 7. Thus, the area with the highest potential for a wave farm is the north-west of the island. Having analysed the geographical distribution of wave energy, its seasonal variations are considered next. The first wave power plant should arguably be installed in the most energetic area, so site 7 is used as a reference. Seasonal wave roses in terms of significant wave height and wave power are shown in Figs. 7 and 8, respectively, and the main statistics are presented in Table 2 (The seasons for this analysis were defined using whole months, e.g. winter encompasses January to March). Three facts stand out. First, wave heights exhibit a pronounced seasonalitydsea states with
a significant wave height above 3 m, relatively common in winter and (to a lesser extent) autumn, are rare in spring and very rare in summer; the mean significant wave height in winter is 59% larger than in summer. Second, seasonal variations, already substantial in terms of wave height, are exacerbated when it comes to wave powerdmean power in winter is 295% larger than in summer. The reason is that wave power varies with the square of the significant wave height, Equation (3). The consequence with respect to wave energy is that 73% of the annual resource corresponds to winter and autumn. This seasonal variability, along with the smaller-term variability inherent to the wave resource [28], calls for energy storage. In this respect El Hierro offers the advantage of its rugged orography, with high altitudes (up to 1501 m) for its small extension. The wind energy project under development [1] comprises two water reservoirs, and a similar scheme should be included alongside the wave farm. Third, the seasonality is also well marked in the wave directions. The prevailing direction veers from NW in winter to NNE in summer, with intermediate directions in spring and autumn. These seasonal variations are related to the general dynamics of the Atlantic, which is governed by two centres of action: the Azores High and the Icelandic Low [36e38]. In summer the Azores High strengthens at the expense of the Icelandic Low
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695
Fig. 8. Seasonal wave power roses at study site 7 [ J, power per metre of wave front].
and moves north, causing I quadrant winds in El Hierro. In winter it is the Icelandic Low that reinforces at the expense of the Azores High, which retreats south; as a result, IV quadrant winds prevail in El Hierro. 4. Composition of the wave resource and WEC selection In Section 3 it emerged that El Hierro is endowed with a substantial wave resource, in particular in its north-western area. In addition to the total amount of wave energy, its composition in terms of sea states is also important at least for two reasons. First,
Table 2 Seasonal wave height and power statistics at site 7 [Hm0, significant wave height; J, power per metre of wave front]. Season
(Hm0)mean (m)
Hm0 > 3.0 m (% time)
Jmean (kW/m)
J > 50 kW/m (% time)
Winter Spring Summer Autumn
2.56 1.80 1.61 2.24
27.06 4.81 0.87 17.16
44.20 17.33 11.20 33.52
27.22 4.47 0.78 17.83
the exploitation of this resource is to be carried out by means of Wave Energy Converters (WECs), and these do not operate in all sea states; for example, WECs will typically cease to function in storm conditions. The probability of occurrence of the different sea states at the reference site (#7) is shown in Table 3, expressed in number of hours in the average year. Of course, there are many different technologies (e.g. [39e45]) and the operational ranges are specific to each of them. Assuming, for illustration, that a WEC with operational ranges of 1e5 m (significant wave height) and 6e16 s (energy period) is selected, it will function 85% of the time in an average year; if the ranges are, instead, 1e4 m and 8e16 s, the WEC will be operational 69% of the time. The second reason is that, even within its operational ranges, the efficiency of a WEC varies depending on the waves’ height and period, e.g. [46]. It follows that the selection of the WECs to be installed at a site should always be based on a characterisation of the resource in terms of sea statesdceteris paribus, the technology that guarantees maximum efficiency in the ranges of wave heights and periods providing the bulk of the energy at the site should be favoured. The contribution of the different sea states to the total annual energy at the eight study sites is presented in visual form in Figs. 9 and 10. The area in each graph is divided into energy bins of 0.5 s
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Table 3 Composition of the wave energy resource at site 7 expressed in number of hours per year [Hm0, significant wave height; Te, energy period]. Hm0 (m)
Te (s) 2
2e4
4e6
6e8
8e10
10e12
12e14
14e16
16e18
18e20
>20
Total
1 1e2 2e3 3e4 4e5 5e6 6e7 7e8 8e9 9e10 >10 Total
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2.5 1.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.8
18.5 753.8 61.7 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 834.1
142.0 813.1 397.8 40.0 0.6 0.0 0.0 0.0 0.0 0.0 0.0 1393.6
109.0 1552.3 302.8 67.8 14.3 2.0 0.0 0.0 0.0 0.0 0.0 2048.1
47.9 1353.2 1017.1 131.0 25.6 7.0 1.4 0.1 0.0 0.0 0.0 2583.1
16.0 360.9 624.7 281.7 53.7 13.6 3.8 1.0 0.3 0.1 0.0 1355.8
2.2 79.7 133.5 128.1 59.7 15.9 4.2 1.4 0.5 0.0 0.0 425.2
0.4 12.5 25.4 33.6 23.3 10.1 4.4 1.3 0.7 0.0 0.0 111.7
0.0 1.2 3.3 3.8 1.8 0.5 0.1 0.0 0.0 0.0 0.0 10.8
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
338.4 4928.0 2566.2 686.1 179.0 49.1 13.7 3.8 1.4 0.1 0.0 8766.0
(DTe) 0.5 m (DHm0), the colour of each bin representing its contribution (in MW h per metre of wave front). Wave power isolines computed with Equation (3) are also depicted in the graphs. It may be observed that most of the energy is provided by sea states with a not-so-large wave power; this is because large
wave power values are associated with large significant wave heights and energy periods, and these have low probabilities of occurrence (e.g. Table 3). At the high-energy sites the bulk of the energy is provided by sea states with energy periods between 10.0 s and 13.5 s and significant wave heights between 1.5 m and 3 m
Fig. 9. Contribution to the total annual energy of the different sea states at study sites 1e4 (low-energy area) [Te, energy period; Hm0, significant wave height].
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Fig. 10. Contribution to the total annual energy of the different sea states at study sites 5e8 (high-energy area) [Te, energy period; Hm0, significant wave height].
(Fig. 10) (Incidentally, the long periods indicate that this energy corresponds to swells generated over the long oceanic fetch). At the low-energy sites the energy is split between two clusters of sea states (Fig. 9). The first corresponds to the same Atlantic swells, hence it has similar periods (between 10.0 s and 13.5 s); wave heights are somewhat lower (between 1 m and 2.5 m) because I quadrant swells undergo diffraction before reaching the lowenergy sites, as explained in Section 3. The second cluster of sea states occurs between 5.5 s and 7 s (energy period) and 1.5 m and 2.5 m (significant wave height); its lower wave periods are indicative of locally generated seas. In any case, given that the wave farm should be installed in the high-energy area, the selection of its WECs should aim for maximum efficiency in the following ranges: energy periods between 10.0 s and 13.5 s and significant wave heights between 1.5 m and 3 m. 5. Conclusions El Hierro, the westernmost of the Canary Islands and a UNESCO Biosphere Reserve, aims to become the first 100% renewable energy island in the world. In this research its wave resource was investigated using a 44-year hindcast dataset obtained through
numerical modelling. First, the geographical distribution of the resource was examined based on eight sites around the island. Significant variations were found, with a high-energy area west and north of El Hierro and a low-energy area east and south. The highenergy area has over 200 MW h me1 of total annual energy, with average wave power of approx. 25 kW me1. The low-energy area has less than 150 MW h m1 of total annual energy, with average wave power in the order of 13 kW m1. The reason for these differences is that the west and north of El Hierro are directly exposed to the powerful swells generated by I quadrant winds over the Atlantic fetch, whereas the south and east of the island are sheltered from these swells by the island itself. From this it may be concluded that the best location for the wave farm is the north-west of the island. Second, the seasonal variations of the wave energy resource were examined. Well-delineated seasonal patterns were found, consisting essentially of energetic winters and mild summers. Although both autumn and spring present intermediate levels of wave energy, autumn is more winter-like, spring is more summerlike. The result is that winter and autumn account for 73% of the annual energy. This, together with the shorter-scale variability inherent to the wave resource, highlights the importance of energy
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G. Iglesias, R. Carballo / Renewable Energy 36 (2011) 689e698
storagedwhich can be implemented by means of water reservoirs, taking advantage of the mountainous nature of El Hierro. Finally, the composition of the wave resource in terms of sea states, and how this composition should influence the selection of the WECs to be installed, was discussed. It was found that the bulk of the energy is provided by sea states between 10.0 s and 13.5 s (energy period) and 1.5 m and 3.0 m (significant wave height) in the high-energy area, or 1.0 m and 2.5 m in the low-energy area; the long periods indicate that this energy corresponds to oceanic swells. There is a second cluster of sea states, of some importance in the low-energy sites, between 5.5 s and 7 s and 1.5 m and 2.5 m; the smaller periods indicate that this energy corresponds to locally generated seas. Given that the wave farm should be located in the high-energy area, the selection of WECs should aim for maximum efficiency in the ranges 10.0 se13.5 s (energy period) and 1.5 me3.0 m (significant wave height). In sum, El Hierro was shown to be endowed with a substantial wave resource, which can contribute to make it the first 100% renewable energy island. The geographical distribution of the resource was examined, and the area with the highest potential for a wave farm was determined; the seasonal variations of the resource and its composition in terms of sea states were examined; finally, how this composition should influence the selection of the WECs to be installed was discussed. Acknowledgements This research is part of the project “Assessment of Renewable Energy Resources” (DPI2009-14546-C02-02) supported by Spain’s Ministry of Science and Innovation (Ministerio de Ciencia e Innovación). Its authors are indebted to Spain’s State Ports (Puertos del Estado), in particular to Dr. I. Rodríguez-Arévalo, Dr. E. Fanjul, Ms. P. Gil and Ms. S. Pérez. References [1] Bueno C, Carta JA. Technicaleeconomic analysis of wind-powered pumped hydrostorage systems. Part II: model application to the island of El Hierro. Solar Energy 2005;78:396e405. [2] Iglesias G, Carballo R. Offshore and inshore wave energy assessment: Asturias (N Spain). Energy 2010;35:1964e72. [3] Iglesias G, Carballo R. Wave energy resource in the Estaca de Bares area (Spain). Renewable Energy 2010;35:1574e84. [4] Iglesias G, Carballo R. Wave energy potential along the Death cCoast (Spain). Energy 2009;34:1963e75. [5] Iglesias G, López M, Carballo R, Castro A, Fraguela JA, Frigaard P. Wave energy potential in Galicia (NW Spain). Renewable Energy 2009;34:2323e33. [6] Carballo R, Iglesias G, Castro A. Numerical model evaluation of tidal stream energy resources in the Ría de Muros (NW Spain). Renewable Energy 2009;34:1517e24. [7] Vidal Pascual C. Análisis de la energía del oleaje en las costas españolas. Revista de Obras Públicas 1986;133:95e108 [in Spanish]. [8] 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. [9] Henfridsson U, Neimane V, Strand K, Kapper R, Bernhoff H, Danielsson O, et al. Wave energy potential in the Baltic Sea and the Danish part of the North Sea, with reflections on the Skagerrak. Renewable Energy 2007;32:2069e84. [10] Waters R, Engström J, Isberg J, Leijon M. Wave climate off the Swedish west coast. Renewable Energy 2009;34:1600e6. [11] 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. [12] CRES. Ocean Energy Conversion in Europe e recent advancements and prospects. Published in the framework of the “Coordinated action on ocean energy” EU project under FP6 priority: 6.1.3.2.3. Renewable Energy Technologies with the support of the European Commission directorate-General for Research under contract SES6-CT2004-502701; 2006. [13] Cornett AM. A global wave energy resource assessment. In: International offshore and polar engineering conference Vancouver, Canada; 2008. p. 318e26.
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