Wave Energy assessment around the Aegadian Islands

Transcription

1 Wave Energy assessment around the Aegadian Islands C. Lo Re a, M. Monteforte a,, G. Besio b, C. Aricò a, G.B. Ferreri a a Department of Civil, Environmental, Aerospace, Material Engineering, University of Palermo, Viale delle Scienze ed. 8, Palermo, Italy b Department of Civil, Chemical and Environmental Engineering (DICCA), University of Genoa, Genoa, Italy Abstract In this work we present the estimation of the wave energy potential in the Aegadian Islands (Italy), carried out starting from reanalysis wave data. After an analysis of the offshore wave climate, we carry out a detailed wave propagation around the Aegadian Islands finding the most energetic areas, where we try the installation of several Wave Energy Converters (WECs), appropriately fixed and scaled for the limited wave energy available in the Mediterranean Sea. Keywords: Wave energy assessment, Aegadian Islands, Wave farm, SWAN. Introduction Wave energy is the renewable source that arouses the more and more increasing interest of the Researchers and the Companies, especially for the advancement of energy conversion technologies. Actually, in recent years we are witnessing a race to develop more and more wave energy converters (WEC) [citare qualcuno](e.g., AcquaBuOY, Pelamis, Wave Dragon, etc.) but, obviously, these devices must be designed and sized for the various installation sites and, therefore, they can not disregard from the energy resource availability of sites themselves. Numerous studies on wave resource assessment have been carried out for many world areas. Although most of wave power is available in the oceans, Mørk et al. () assessed the gross wave power resource to be GW in the Mediterranean Sea. Liberti et al. () and Besio et al. (6) carried out an assessment of the wave energy resources in the Mediterranean Sea using a third generation wave model, showing the area between Sardinia and Balearic Islands as the most promising of entire Mediterranean Sea. Their results, furthermore, affirmed the western Sardinia and southern and western Sicily as the most promising Italian areas for wave energy production. Particularly, as the most productive area of Sicily identified the coastal stretch lying north of Mazara del Vallo with average power equal to about 6 kw/m, reaching Corresponding author addresses: carlo.lore@unipa.it (C. Lo Re), massimiliano.monteforte@unipa.it (M. Monteforte), giovanni.besio@unige.it (G. Besio), costanza.aric@unipa.it (C. Aricò), giovannibattista.ferreri@unipa.it (G.B. Ferreri) Preprint submitted to Renewable Energy September 8, 6

2 values around kw/m near Favignana Island. Monteforte et al. () confirmed the results of Liberti et al. () and detected the western coast of Sicily, in front of Aegadian Islands, as the most energetically promising, with average wave power reaching about kw/m. In particular in the island areas the renewable resources are fundamental for ensuring long-term socio-ecological sustainability and resilience. Indeed the small islands of the Mediterranean sea have limited access to the national or European Network grid and the local authorities frequently are obliged to use stand-alone power systems (SAPSs). Moreover these islands are locations which receive protection because of their recognized natural, ecological and/or cultural values; their territories are in several cases National Parks or Marine Protected Areas. For these reasons we have to look for new sustainable alternatives in the field of energy production and the wave power could prove to be a sound alternative among other renewable resources (Rusu and Onea, 6). Moreover shallow water WECs array can also protect coast from erosion (Iglesias and Carballo, ) attenuating significant wave heights and inducing also variations in near-shore currents (Rusu and Soares, ; Zanopol et al., ). The greatest limitation for wave energy production in the Mediterranean sites is the available energy, rather low if compared with that estimated for oceanic coasts (Veigas and Iglesias, ). As demonstrate by Bozzi et al. (), however, the abovementioned limitation does not preclude the WEC installation, because these devices can be scaled and fixed to low energetic availability of the Mediterranean Sea. In this framework we present a detailed analysis on the wave energy resource near the coasts of the Aegadian Islands, first performing a wave energy assessment in deep water then studying the resource in shallower water by mean of a fine numerical propagation. [? the existence of possible hot spots was investigated?] [? the performance of N WECs were estimated?]. Study area The area of study is a Marine Protected Area located off the Sicilian north-western coast surrounding the archipelago named Egadi or Aegadian, which includes the islands of Favignana, Levanzo and Marettimo as well as the islets of Formica and Maraone. Egadi is the largest marine reserve in Europe, encompassing about, hectares or 8. square miles and exhibits excellent solar radiation and wind resources, which provide favorable RES (Renewable Energy Systems) utilization conditions. Against this background, the study of wave energy assessment is a crucial aspect of this geographic area being the fossilized fuels (used by SAPs) the most important source of energy in the Archipelago.. Reanalysis dataset In the present work the wave climate was determined using a -year hindcast dataset developed by Mentaschi et al. (a,b, ) covering the years from 99 and. The reanalysis was carried out using the Weather Research and Forecast (WRF) model and the WAVEWATCH III R, while both the source term of wave

3 . x 6.. x Northing [m] ITALY Northing [m] Easting [m] Easting [m] Wave Reanalysis point point surroundig the SWAN domain reference point SWAN propagation zone (Aegadian Islands) Figure : Map of Italy and Aegadian Islands showing the reanalysis data points and the SWAN propagation zone (ED-UTMN). growth-dissipation proposed by Ardhuin et al. () and a new parametrization (labeled as DICCA by Mentaschi et al. ()) were employed. The hindcast was validated by Mentaschi et al. () using the entire set of records from Rete Ondametrica Nazionale (RON), RedExterior (REDEXT) buoys and a set of buoys from the Greek Poseidon network. The positions of the wave reanalysis points are shown in Fig., the points used for the nearshore wave propagation are marked with a green square or red triangle. The points marked with a red triangle were used also as reference points to characterize the offshore wave clima surrounding the numerical model domain.. Offshore analysis In order to characterize the wave climate in the study area a directional analysis of wave power was performed for three representative points for all the years of hindcast data. In Figs., and the wave power roses are shown for the three reference points. For deep-water, the approximate relationship for wave power per unit of wavefront length is then estimated by: P = ρg 6π T eh m () Where ρ is the water density, H s is the and T e is the energy period better defined as the spectral period:

4 (a) [EG] Power [kw/m] W NW N NE >= % % SW SE S E Power [kw/m] Jan May Aug Dec H s [m] T e [s] P [kw/m] Annual energy [kwh/m] Figure : Characterisation of mean yearly wave energy and wave power climate at point # ; in plot H s e T e the colour scale indicates annual energy per metre of wave front (kwhm ), the numbers within the bins indicate the occurrence of sea states (hours/year) and the isolines specify the wave power; the histograms refer to the monthly mean power. T e = T m, = m m = π π f S ( f, θ) d f dθ S ( f, θ) d f dθ The Point # is the more energetic, being characterized by large numbers of significant wave heights higher than m, the point # presents the lowest values. In all the points, the waves coming from the W-NW sector can be considered as dominant, being % of the total and being the most energetic one. It should be emphasized that the direction SW it is characteristic of this area of Tyrrhenian sea, this direction is associated with Scirocco, a wind coming from the Sahara, that often can be associated with storms and heavy rain lasting about four days. As regards the points # and #, it can be observed that has a very similar energetic pattern but the # it is slightly more energetic because of more numerous and more energetic wave from West direction. The point # is less energetic than other points but has a more uniform distribution of energy among directions. In Figs., and are also reported the power matrix or scatter energy diagram. The area within each diagram was divided into bins of. m. s ( H s T e ). The number inside each bin indicates the mean occurrence (hours per year) of sea states having H s and T e falling within the respective ranges relating to the bin itself. The colour of each bin indicates the mean annual energy per metre of the wave front (kwh m ); the grey lines represent wave power isolines calculated by Eq. (). The before mentioned figures include also the monthly mean power for the three reference points; the bulk of wave power is available in January, February, March, November, and December, May and September have a Energy flux between - kw m, while during the months of June, July and August the flux of energy is very low. ()

5 (a) [EG] Power [kw/m] W NW N NE >= % % SW SE S E Power [kw/m] Jan May Aug Dec H s [m] T e [s] 9 P [kw/m] Annual energy [kwh/m] Figure : Characterisation of mean yearly wave energy and wave power climate at Point #. The symbols are the same of Fig. (a) [EG] Power [kw/m] W NW N NE >= % % SW SE S E Power [kw/m] Jan May Aug Dec H s [m] T e [s] 8 P [kw/m] Annual energy [kwh/m] Figure : Characterisation of mean yearly wave energy and wave power climate at Point #. The symbols are the same of Fig.

6 6 P [kw/m] 8 6 EG EG6 EG EG8 EG EG6 EG EG EG9 EG6 EG6 EG EG EG8 Jan Feb Mar Apr Mar Jun Jul Aug Sep Oct Nov Dec Figure : Monthly average wave energy flux per unit of crest on reanalysis points corresponding to the -year time interval considered (99-). Fig. shows the monthly distribution of wave power of the nodes surrounding the nearshore propagation zone (green triangle fig. ). From the energy flux monthly evolution,it can be noticed that the point # presents a maximum value of kw m- during January and December. Table : Statistical Energy parameters at each reference point; COV is the Coefficient of Variation, S V is the Seasonality Variability index, MV is the Monthly Variability index, P the mean wave power, and E y the total wave energy in the average year. Point Depth [m] COV S V MV P[kW m ] P y [MWh m ] # # # Figs. 6,, 8 and 9, show the distribution of average wave power flux per unit of crest (P med ) respectively for winter, spring, summer and autumn. These distributions were obtained interpolating the values of the wave reanalysis data during the period [inser. periodo]. In winter (Fig. 6) P med varies from kw/m in stretch between Sicily and the Aegadian Islands group, up to kw/m off Marettimo island. In the spring months (Fig. ), P med ranges from kw/m to 9 kw/m, while in summer P med has a small variation (.- kw/m). In autumn the distribution is similar to spring one, but the P med values are 8% lower than spring. This analysis shows that area off the northwest coast of Marettimo is the most stressed one, and in particular.. Nearshore analysis For increasing the details, especially between the islands and nearshore, a wave propagation modelling was carried out by using SWAN (Simulating WAves Nearshore) 6

7 Figure 6: Distribution of winter average wave power flux per unit of crest on Aegadian Islands coastline (ED-UTMN). Figure : Distribution of spring average wave power flux per unit of crest on Aegadian Islands coastline (ED-UTMN).

8 Figure 8: Distribution of summer average wave power flux per unit of crest on Aegadian Islands coastline (ED-UTMN). Figure 9: Distribution of autumn average wave power flux per unit of crest on Aegadian Islands coastline (ED-UTMN). 8

9 Figure : Da scrivere. (Booij et al., 999). The computational domain consists of an D unstructured grid reported in fig..... Validation of SWAN propagation The propagation model was validated by comparing HS estimated by the model on 6 nodes with reanalysis data of ERA-INTERIM project (European Centre for Mediumrange Weather Forecasts). The indices for estimating the goodness of validation (bias, RMSE, SI and slope) are reported in Table.. for each of 6 ECMWF nodes inside the our propagation domain. In particular the SWAN simulation was run for the yea r every minutes, while the comparison was carried out considering the values every 6 hours, compatibly with the data availability in-time of ECMWF. Furthermore, the indices were estimated for all of 6 point globally, obtaining the following values:.69 m (bias),.8 m (RMSE),.88 (SI) and.9 (slope). This analysis demonstrated an adequate agreement of two HS data-sets and then the model validity is confirmed.. Theory/calculation AquaBuoy (Weinstein et al., ). Pelamis (Henderson, 6). Wave Dragon (Kofoed et al., 6). Oyster (Whittaker et al., ). Seawave Slot-Cone Generator (Vicinanza et al., ). 9

10 Table : Boundary conditions of each side of the numerical domain for the stationary simulation. side case case case # H s [m] T p [m] θ [ ] H s [m] T p [m] θ [ ] H s [m] T p [m] θ [ ] H s [m] case# case# case# T p [s] case# case# case# θ [degree] case# case# case# 9Nov8 6Dec8 Dec8 Figure : The selected cases in the record of wave data for the reanalysis point # (that falls within the side#).

11 Table : Comparison of the ECMWF and model H S in the selected points of simulation domain (coordinates in ED-UTMN); particularly the indices are the bias, the root mean square error (RMSE), the scatter index (SI) and the regression line slope. Point Easting [m] Northing [m] Bias [m] RMSE [m] SI Slope ECMWF ECMWF ECMWF ECMWF ECMWF ECMWF Results. Discussions Bibliography Ardhuin, F., Rogers, E., Babanin, A., Filipot, J.-F., Magne, R., Roland, A., van der Westhuysen, A., Queffeulou, P., Lefevre, J.-M., Aouf, L., Collard, F.,. Semiempirical dissipation source functions for ocean waves. part i: Definition, calibration, and validation. Journal of Physical Oceanography (9), 9 9. Besio, G., Mentaschi, L., Mazzino, A., 6. Wave energy resource assessment in the mediterranean sea on the basis of a -year hindcast. Energy 9, 6. Booij, N., Ris, R., Holthuijsen, L. H., 999. A third-generation wave model for coastal regions:. model description and validation. Journal of Geophysical Research: Oceans (98 ) (C), Bozzi, S., Archetti, R., Passoni, G.,. Wave electricity production in Italian offshore: A preliminary investigation. Renewable Energy 6, 6. Henderson, R., 6. Design, simulation, and testing of a novel hydraulic power takeoff system for the pelamis wave energy converter. Renewable energy (), 8. Iglesias, G., Carballo, R.,. Wave farm impact: The role of farm-to-coast distance. Renewable Energy 69, 8. Kofoed, J. P., Frigaard, P., Friis-Madsen, E., Sørensen, H. C., 6. Prototype testing of the wave energy converter wave dragon. Renewable energy (), Liberti, L., Carillo, A., Sannino, G.,. Wave energy resource assessment in the Mediterranean, the Italian perspective. Renewable Energy, Mentaschi, L., Besio, G., Cassola, F., Mazzino, A., a. Developing and validating a forecast/hindcast system for the Mediterranean Sea. Journal of Coastal Research, SI 6, 6.

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