Wave Energy Potential in the Sea of Iroise Nicolas Guillou, Georges Chapalain
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Wave Energy Potential in the Sea of Iroise Nicolas Guillou, Georges Chapalain To cite this version: Nicolas Guillou, Georges Chapalain. Wave Energy Potential in the Sea of Iroise. 11th European Wave and Tidal Energy Conference Series, Sep 2015, Nantes, France. hal-01865260 HAL Id: hal-01865260 https://hal.archives-ouvertes.fr/hal-01865260 Submitted on 31 Aug 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Wave Energy Potential in the Sea of Iroise Nicolas Guillou and Georges Chapalain Laboratory of Coastal Engineering and Environment, Cerema/DTEMF/DS Technopoleˆ Brest-Iroise BP 5, 29280 Plouzane,´ France E-mails: [email protected]; [email protected] Abstract—The wave energy resource in coastal areas of the With a total resource amount estimated over 230 GW ( [2], Sea of Iroise (western Europe) is evaluated with an unstructured [7]), particular attention has been given to European shelf version of the phase-averaged wave model SWAN (Simulating seas with numerical applications at spatial scales ranging WAves Nearshore) for a eight-year period between 2004 and 2011. Numerical predictions are calibrated and evaluated against from regional domains (e.g., [8]–[10]) to shallow-water coastal available measurements of significant wave height and peak areas with increased computational resolutions (e.g., [11]– period at nine wave buoys in offshore and nearshore waters. [13]). A detailed review of these modelling has recently been In spite of strong energy dissipation in shallow water, up established by Guedes Soares et al. [5]. to 60 % of wave power, the medium-term evaluation of the resource reveals major coastal energetic patterns, between 20 Whereas coastal applications, mostly based on structured and 35 kWm−1, off the isles of Ushant and Sein and in the regular or curvilinear computational meshes, identify generally nearshore areas of the Crozon Peninsula, the bay of Audierne and well the distribution of nearshore energetic patterns, unstruc- the northern coastline. The variability of wave power production tured grid computation offers attractive options (1) increasing is estimated revealing, in accordance with previous numerical spatial resolution at the coast, (2) solving numerics and physics estimations over European shelf seas, significant inter-annual and inter-seasonal evolutions at the scale of the Sea of Iroise. mismatches boundaries problems of embedded domains and Changes are particularly noticeable during the winter period with (3) optimising CPU performance with a reduced number of opposite situations in the distribution of monthly averaged wave grid nodes. energy flux. In the perspective of implementation of wave energy The present study extends these coastal numerical eval- converters, the local distributions of energy flux against periods and directions are finally investigated in areas of maximum mean uations relying on an unstructured version of the phase- wave power. averaged wave model SWAN (Simulating WAves Nearshore) Index Terms—marine renewable energy, wave power, SWAN, [14] implemented in the Sea of Iroise at the western extend of unstructured grid, Brittany, western Europe. Brittany (Fig. 1). The site of application is considered as one I. INTRODUCTION m Marine renewable energy (MRE) is recognised by many 2460000 #1 countries as a promising alternative to mitigate the effects of 180 180 climate change induced by human activities and achieve future 2440000 energy security [1]. Among the different MREs, wave energy 150 constitutes an abundant resource with (1) a global worldwide 02902 #4 150 2420000 power estimated around 2 TW [2] and (2) high power den- Ushant sity in the nearshore areas [3]. Numerous technologies are #2 120 currently in development to improve extraction performance 2400000 Brest Le Conquet Bay of for electricity generation [4]. Accurate energy assessments are 02911 Brest SEA OF #5 #6 Crozon thus requested by potential developers to optimise location and Y (m) 90 2380000 IROISE E3 90 E1 design of wave energy converters (WEC). Bay of T3 T1 Douarnenez #7 The available resource is commonly evaluated with the wave T2 Sein Douarnenez 2360000 Raz E2 60 energy flux (also denominated the wave power or potential) Audierne 60 characterising the transport of energy per unit length of wave #8 −1 Bay of front and expressed in kW m . Among the different methods #3 Audierne 2340000 3030 implemented to assess wave power (e.g., buoys records, ad- vanced combination of measurements and large-scale numer- Penmarc’h 2320000 02914 ical simulations) [5], third-generation spectral wave models 0 0 are traditionally implemented to extrapolate the resource at 0 20000 40000 60000 80000 100000 120000 extended time scales and locations approaching, in particu- X (m) lar, the associated variability in coastal areas [6]. Numerous Fig. 1. Bathymetry of the Sea of Iroise with locations of available mea- numerical studies have thus been conducted to refine wave- surements points (red circles for wave buoys and black squares for currents power assessments in the most energetic regions of the world. stations). of the most energetic region along the French coasts with a where t denotes times. The right-hand side of this equation mean offshore wave energy flux estimated around 50 kW m−1 contains the source and sink terms of physical processes which [8]. generate, dissipate or redistribute wave energy: The approach retained here relies on a comparison between S = S 4 +S 3 +S +S +S +S +S . (2) numerical predictions and available observations of the signifi- tot nl nl in wc bot brk wc,cur cant wave height and the peak period. Field measurements are Parameterisations here adopted for each terms are briefly collected at nine offshore and nearshore locations including detailed hereafter. The redistribution of energy by nonlinear archived data of long-term observation systems and field data quadruplet wave-wave interactions Snl4 is computed with the acquired during short-term campaigns in coastal areas (section Discrete Interaction Approximation of Hasselmann et al. [17]. II-A). SWAN (version 40.91), modified to integrate an en- The non-linear triad re-distribution of wave energy Snl3 is hanced dissipation term for current-induced whitecapping [15] approached with the Lumped Triad Approximation derived (section II-B), includes heterogeneous parameterisations of by Eldeberky [18]. The transfer of energy from the wind bottom roughness and variations of tidal free-surface elevation to the waves Sin and the dissipation of wave energy due and depth-averaged currents (section II-D). A medium-term to whitecapping Swc are approached with the saturation- evaluation of the wave energy resource for a eight-year period, based model of van der Whesthuysen [19] combined with between 2004 and 2011 (section III-A), reveals major coastal the wind input formulation proposed by Yan [20]. The sink energetic patterns exhibiting significant inter-annual and inter- term of energy dissipation by bottom friction Sbot is computed seasonal variabilities (section III-B). In the perspective of according to the formulation proposed by Madsen et al. [21]. WEC implementation, the local distributions of energy flux Energy dissipation in random waves due to depth-induced against periods and directions is finally investigated in areas breaking Sbrk is quantified according to Battjes and Janssen of maximum mean wave power (section III-C). [22]. An additional dissipation term Swc,cur recently proposed by van der Westhuysen [15] is finally included to limit the II. MATERIALS AND METHODS overprediction of wave height on negative current gradients (accelerating opposing currents or decelerating following cur- A. Measurements Description rents). Its implementation is conducted following previous recent calibrations of this dissipation term in SWAN (e.g., [15], Wave buoy data here used consist of the archived measure- [23]). ments of the French CANDHIS database (”Centre d’Archivage In SWAN, wave power is approximated with default quan- National de Donnees´ de Houle In Situ”, Cerema, France) tities outputs of energy transport components along (x) and (points 02902, 02911 and 02914) complemented by obser- (y) directions: vations acquired during campaigns conducted off and in the bay of Douarnenez (points E1 to E3 and T1 to T3) (Fig. 1). 2 2 1/2 PSWAN = PSWAN,x + PSWAN,y . (3) Long-term measurements are only available at the offshore with wave buoys 02902 and 02911. Whereas the associated data 2π ∞ may lack during periods over three months in relation to PSWAN,x = ρg cxEdσdθ (4) measuring-system malfunction, it covers globally well the Z0 Z0 period of interest between 2004 and 2011. Medium term and 2π ∞ observations are available at the wave buoy 02914 off Pen- PSWAN,y = ρg cyEdσdθ (5) marc’h headland over the period November 2009-February Z0 Z0 2010. Complementary measurements were acquired at six where ρ is the water density, g is the acceleration of gravity, θ offshore and nearshore wave buoys (E1-E3, T1-T3) during is the wave direction and cx and cy are the propagation veloc- three short-term campaigns: in 15-22 April 2005, in 13-23 ities of wave energy in spatial space [24]. This computation of September 2005 and in 10 April-10 May 2006 [16]. The wave power based on the summation of squared (x) and (y) associated instrumentation network is deployed in water depths components neglects additional terms included when directly ranging from 10-15 m off beaches of the bay of Douarnenez estimating wave potential from the amplitude of wave energy: (points E3, T2 and T3) to 110 m at the wave buoy 02902 off 2π ∞ the isle of Ushant.