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Numerical modelling of wave energy resources and assessment of wave energy extraction by large scale wave farms
Ocean & Coastal Management xxx (2017) 1e12
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Ocean & Coastal Management journal homepage: www.elsevier.com/locate/ocecoaman
Numerical modelling of wave energy resources and assessment of wave energy extraction by large scale wave farms € gler b Vengatesan Venugopal a, *, Reddy Nemalidinne a, Arne Vo a
Institute for Energy Systems, School of Engineering, The University of Edinburgh, Faraday Building, King's Buildings, Colin Maclaurin Road, Edinburgh, EH9 3DW, UK b Marine Energy Research Group, Lews Castle College, University of the Highlands and Islands, Stornoway, Isle of Lewis, Scotland, GB-HS2 0XR, UK
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 June 2016 Received in revised form 2 January 2017 Accepted 9 March 2017 Available online xxx
This study reports the practicalities of applying a numerical model to assess the wave energy extraction at proposed development sites in Orkney Waters, Scotland. A state-of-the-art phase averaged spectral wave model, MIKE 21 Spectral Wave, in association with the wave-structure interaction software tool WAMIT, has been employed to study the impact of energy extraction by large arrays of Wave Energy Converters (WECs) on the wave height alteration in the neighbourhood of WEC arrays. Two generic types of WEC, one representing surface attenuators (deployed in deep water) and other representing Oscillating Wave Surge Converters (deployed in shallow waters) are used for numerical modelling. The power extraction performance of the WECs are initially modelled using WAMIT and validated with data from literature. As MIKE 21 SW has limited ability in modelling complete dynamics of a moving structure, each WEC has been modelled as a generic structure but with appropriate reflection, transmission and energy absorption properties derived from WAMIT, and this methodology is found to have worked well. In total there are 198 attenuators and 120 Oscillating Wave Surge Converters are constructed within the numerical model at potential energy locations in Orkney Waters, and the wave-WECs array interactions for the year 2010 have been simulated. The results suggest that the wave farm will have an impact on the wave climate immediately in the lee of the array, although the magnitude of these effects decreases monotonically with distance from the farm. © 2017 Elsevier Ltd. All rights reserved.
Keywords: Wave energy converter Wave energy extraction Wave farms Spectral wave modelling
1. Introduction and overview Scotland is geographically well placed on the globe where large energetic waves from the North Atlantic Ocean provide a high level of sustainable wave power resources. The Scottish Government has committed to the development of a successful marine renewable energy industry in Scotland as the country has an estimated 25% of Europe's tidal potential and 10% of its wave potential (http://www. gov.scot/Topics/Business-Industry/Energy/Facts). However, harvesting this energetic resource increases the number of challenges associated with it. One of such challenges is to understand how nearshore coastal processes may be modified by energy extraction, as the installed capacity of wave farms increases to hundreds of MW in capacity. The extraction of wave energy from a wave farm
* Corresponding author. E-mail addresses: [email protected] (V. Venugopal), reddy.nemalidinne@ € gler). gmail.com (R. Nemalidinne), [email protected] (A. Vo
produces a wave energy deficit or shadow down region which in turn may affect the downstream sediment transport and may thus result in beach erosion/deposition. The quantification of the wave energy reduction and identification of induced wave height gradients is desired for an accurate assessment of the environmental impact of a Wave Energy Converter (WEC) array on the nearby marine environment. The ability to predict the wave shadow is of topical interest due to significant stakeholder concerns about potential impacts from wave shadowing arising from wave energy device installations. The primary aim of this work is to assess the consequences wave energy extraction by large scale wave arrays in the areas of the Crown Estate Round 1 lease sites in the Pentland Firth and Orkney Waters (PFOW). Based on guidance from commercial stakeholders two state-of-the-art modelling suites were initially selected for the TeraWatt project, namely MIKE by Danish Hydraulic Institute and Delft3D by Deltares to predict the physical effects of wave energy extraction and compare the advantages and disadvantages of each
http://dx.doi.org/10.1016/j.ocecoaman.2017.03.012 0964-5691/© 2017 Elsevier Ltd. All rights reserved.
Please cite this article in press as: Venugopal, V., et al., Numerical modelling of wave energy resources and assessment of wave energy extraction by large scale wave farms, Ocean & Coastal Management (2017), http://dx.doi.org/10.1016/j.ocecoaman.2017.03.012
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modelling suite. Both models have produced good results of comparative quality. The results included in the present paper are based on MIKE suite only as due to unforeseen circumstances data from Delft3D model is not available for demonstration. Although much of the existing literature on wave energy is related to device development, a small number of studies have been carried out to investigate the wave height reduction in the lee of WEC arrays. Millar et al. (2007) used the third generation phase averaged spectral wave model, SWAN, to investigate the effects of the scale of energy extraction and incident wave parameters on the far field wave energy deficit. Venugopal and Smith (2007) used the MIKE 21 Boussinesq wave (BW), which is a phase resolving model, to assess the resulting wave field around a single row of overtopping type WECs which partially reflect energy to achieve a wave energy deficit in the down wave region rather than by specifically extracting it. Palha et al. (2010) used the parabolic mild slope wave model REF/DIF to assess the wave shadow in the lee of a series of large energy sinks that represent clusters of devices. Beels et al. (2010) used the time dependent mild slope wave model MILDWave to assess the wave shadow in the lee of a 2D array of Wave dragon overtopping type WEC devices. Nørgaard and Andersen (2012) investigated potential benefits to shore protection by simulating an array of Wave Dragon overtopping type WECs in the relatively near-shore region using the MIKE 21 Boussinesq wave model. Monk et al. (2013) developed an approximate analytical solution to assess the distributed wave field about a single row array of overtopping WEC devices, represented as a series of partially absorbing and transmitting breakwater segments. In the named studies WECs were implemented through different means, e.g. as small islands or obstructions with defined coefficients for transmission and absorption, and where possible reflection. A common problem to all previous, and also to the model discussed in this paper, is the lack of validation data from field deployments. Another challenge is related to the performance parameters of different WEC systems, as these are often not available to the scientific community for reasons of confidentiality. In this paper the MIKE 21 Spectral Wave model (SW) (MIKE 21, 2012) is used to simulate large scale WEC array based on the planned developments in Orkney waters. Energy extraction is represented through arrays of surface attenuators in deep or intermediate water depth, together with oscillating wave surge energy converters in the shallow waters and results are compared against a baseline scenario without WEC deployments. Modelling a realistic wave energy device with energy extraction in MIKE 21 SW module is not a direct process as this tool does not have the ability to model moving bodies dynamics, however, the hydrodynamic coefficients describing energy loss, wave scattering, wave diffraction and wave transmission can be implemented if these coefficients are known for a particular energy device. In order to generate the hydrodynamic coefficients the state of art wavestructure interaction software tool WAMIT (http://www.wamit. com/), which is capable of analysing wave interactions with fixed and floating offshore structures has been used, and the wave absorption, reflection and transmission characteristics of the devices are obtained. These hydrodynamic parameters were then input to MIKE 21 SW to model the wave device. The initial part of this paper provides a detailed description of the model and this is followed by an overview of results obtained for the year 2010 without the implementation of energy extraction (baseline). Following on from that baseline scenario details are given on the approach used in this paper for the representation of WECs, and the simulated consequences of the large scale energy extraction on the surrounding wave field are presented and compared against the baseline.
2. Study area Exposed to energetic seas from the Atlantic and strong tidal currents driven by a semidiurnal ebb/flood cycle that combines the Atlantic with the North Sea, the waters surrounding the Orkney Islands, together with the Pentland Firth at the north of Scotland are a core area for wave and tidal energy extraction targeted by government and licensing authorities. In 2010 lease agreements for site developments were in place between project developers and The Crown Estate for a total rated capacity of 1,600 MW shared between six wave and five tidal energy extraction projects (see Fig. 1). A number of prototype device deployments have been completed at the European Marine Energy Centre (EMEC), and as part of previous and ongoing research and site development projects considerable baseline data has been gathered in terms of bathymetry and wave observations in the area. The combination of real sites with planned developments and access to relevant datasets required for setting up and running resource models made the area around Orkney an obvious candidate for use as model domain in this project. 3. Model overview MIKE 21 SW is a third generation spectral wind-wave model utilising unstructured meshes (MIKE 21, 2012) to simulate the growth, decay and transformation of wind-generated sea and ocean swells in offshore and coastal areas. The wind waves are expressed by the wave action density spectrum. The MIKE 21 spectral wave model includes two methods of wave simulation, namely, (i) the directional decoupled parametric formulation and (ii) the fully spectral formulation, both based on the wave action conservation equations in either Cartesian (for small scale applications) or spherical (for large scale applications) co-ordinate systems (Komen et al., 1994; Young et al., 1999). The model accounts for the physical phenomenon of wave growth from wind, energy transfer due to non-linear quadruplet or triad waveewave interaction, and includes energy dissipation terms for white-capping, bottom friction and depth-induced breaking. The wind input is based on Janssen's (1989, 1991) quasi-linear theory. A cell-centered finite volume method is applied in the discretization of the governing equations in geographical and spectral space and a multisequence explicit method is applied for the wave propagation with the time integration carried out using a fractional step approach. The model generates phase averaged wave parameters as output for either the total computational domain, or selected parts thereof. Further details can be found in the MIKE 21 wave modelling user guide (MIKE 21, 2012). 3.1. Model set-up An unstructured computational mesh (see Fig. 2) was constructed for the North Atlantic region (10oE e 75oW and 10oN70oN) using bathymetry data compiled from General Bathymetric Chart for Oceans (GEBCO) and Marine Scotland Science (Marine Scotland). GEBCO bathymetry data with a spatial resolution of 30 arc seconds was used for most parts of the model domain and only the area around Orkney, Pentland Firth, Shetland and northwest of the Isle of Lewis was covered by bathymetry datasets obtained from Marine Scotland Science. The unstructured mesh of the computational domain visible in Fig. 1 was triangulated using the natural neighbour interpolation method (MIKE 21, 2012). Finer mesh resolutions were produced for Pentland Firth and Orkney Waters with a mesh area of 0.0005 square degrees (approx. 1700 m2), for the Hebrides and northwest Scotland of 0.001 square degrees and 0.75 square degrees (approx. 2.5 km2)
Please cite this article in press as: Venugopal, V., et al., Numerical modelling of wave energy resources and assessment of wave energy extraction by large scale wave farms, Ocean & Coastal Management (2017), http://dx.doi.org/10.1016/j.ocecoaman.2017.03.012
V. Venugopal et al. / Ocean & Coastal Management xxx (2017) 1e12
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Fig. 1. Location of Pentland Firth showing wave and tidal energy leasing sites (http://www.thecrownestate.co.uk/energy-and-infrastructure/wave-and-tidal/the-resources-andtechnologies/3).
Fig. 2. Computational domain for North Atlantic wave model.
Please cite this article in press as: Venugopal, V., et al., Numerical modelling of wave energy resources and assessment of wave energy extraction by large scale wave farms, Ocean & Coastal Management (2017), http://dx.doi.org/10.1016/j.ocecoaman.2017.03.012
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Fig. 3. Comparison of significant wave height, between measurements and MIKE21 model: (a) Blackstone - top, (b) Orkney-E middle (c) Firth of Forth e bottom.
Table 1 Performance parameters for Significant wave height, Hm0, for the year 2010. Site
Mean
Bias
RMSE
SI
R
Blackstone Orkney-E Firth of Forth
2.3 m 1.67 m 1.15 m
1.23 m 1.3 m 0.10 m
1.45 m 1.31 m 1.25 m
0.22 0.19 0.22
0.94 0.95 0.96
for the North Atlantic Ocean. Further details on the model domain and setup are available in Venugopal and Nemalidinne (2015). 3.2. Model forcing and physical processes The model was forced with wind data obtained from the operational model of the European Centre for Medium-Range Weather Forecasts (ECMWF) at 6 hourly intervals with a spatial resolution of 0.125 x 0.125 . To ensure fetch unlimited wave growth, decay and
transformation of wind sea and swells, the model was run in ‘fully spectral’ mode with ‘Instationary formulation’. The number of frequencies used for the model was 25 with fmin ¼ 0.04 Hz and a logarithmic frequency distribution with a frequency factor of 1.1. The directional discretization had 24 directional bins, each with 15 resolution. A low order fast algorithm has been chosen as the solution technique with the ‘maximum number of levels in the transport calculation’ set as 32. A quadruplet-wave interaction has been applied. No current, ice coverage and diffraction were included into the model. Dissipation due to whitecapping based on the theory of Hasselmann (1974) and Komen et al. (1994), bottom friction according to Nikuradse roughness (Weber, 1991) and depth-induced wave breaking based on the specified gamma model (Nelson, 1987; Nelson, 1994; Ruessink et al., 2003), were considered in the simulations and the energy transfer was activated. For a detailed description of the above source terms and basis for selection of the appropriate values the reader is referred to the MIKE 21 SW user guide (MIKE 21, 2012) and (Venugopal and Nemalidinne, 2015).
Fig. 4. Wave rose diagram of the MIKE21 model data at Orkney location: The left hand plot is for Hm0 (m) and the right hand plot is for Tp(s).
Please cite this article in press as: Venugopal, V., et al., Numerical modelling of wave energy resources and assessment of wave energy extraction by large scale wave farms, Ocean & Coastal Management (2017), http://dx.doi.org/10.1016/j.ocecoaman.2017.03.012
V. Venugopal et al. / Ocean & Coastal Management xxx (2017) 1e12
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Fig. 5. Comparison between measurements and MIKE21 model at Orkney location: Shown is Hm0 (m) on the right, and Tp (s) to the left.
3.3. Validation of the wave model Measured wave data from scientific buoys deployed around Scotland was used to calibrate and validate the model. The successful calibration of the model is described in detail in (Venugopal and Nemalidinne, 2015) and for this paper a further validation was carried out for the year 2010 for Scottish waters, based on available measured wave data for Blackstone (56.062 N 007.056 W, depth ¼ 97 m), Orkney-E (58.970 N 003.390 W, depth ¼ 53 m), and Firth of Forth (56.188 N 002.503 W, depth ¼ 65 m) and the results are presented in Fig. 3. The results have shown good comparison with measurements for most of the time period for all three sites and this was particularly true for the significant wave height. As the hindcasting data covers a full calendar year including both summer and winter months, it is evident that the model is able to resolve wave conditions for different sea states throughout all seasons. Although the wind input used here has a temporal resolution of 6 h, the impact of varying the resolution to 1 h and 3 h has also been examined in another publication by the authors (see Venugopal V and Nemalidinne, 2015), however, it was noticed that the temporal variation of wind speed has less impact on the resulting wave parameters when large scale modelling is considered. The performance indices (or quality parameters) for the significant wave height for the model period of 2010 were calculated for the three buoy locations Blackstone, Orkney-E, and Firth of Forth and are shown in Table 1. A good agreement between model and buoy data is observed for all three locations and this is particularly remarkable for the Firth of Forth location, where the significant wave height for the majority of the time period in the year 2010 is less than 2 m and yet the model was still able to predict this accurately. Alternative ways to inspect model results or to compare against measured data are to represent the data as wave rose and scatter plots as shown in Figs. 4 and 5. For the Orkney-E site, both the data for significant wave height and peak wave period from the model and buoy measurements are found to be in close proximity to or on the equality line illustrating that the significant wave height was highly accurately resolved, and this is also indicated by low values of Bias, RMSE, Scatter Index (SI) and high Correlation Coefficient (R) values as shown in Table 1.
Matrices are a standard way of expressing WEC performance against particular sea states and the selection of the most appropriate site and technology for wave farm operation in a specific region should be based on a thorough analysis of the energy production of different combinations of different types of WECs at a range of sites. To be able to undertake such an assessment an accurate and detailed resource characterisation at each location of interest has to be conducted. The approach taken here is to run the MIKE 21 Spectral Wave model for the year 2010, with and without WECs, and to subtract the results from each other to produce maps of the differences in wave parameters following the inclusion of WECs. The evolution of mean significant wave height (Hm0) for the pre-device model, for the study area around Orkney is shown in Fig. 6 for the period January to December 2010, which is extracted from the validated model described in section (3) above. Note that
4. Predictions without energy extraction The efficiency of Wave Energy Converters (WEC) depends on the characteristics of the site specific resource, and key parameters are the wave height and period of the different sea states. Power
Fig. 6. Mean significant wave height for the period January to December 2010 without energy extraction.
Please cite this article in press as: Venugopal, V., et al., Numerical modelling of wave energy resources and assessment of wave energy extraction by large scale wave farms, Ocean & Coastal Management (2017), http://dx.doi.org/10.1016/j.ocecoaman.2017.03.012
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V. Venugopal et al. / Ocean & Coastal Management xxx (2017) 1e12
relative to one another as waves pass along the length of the machine and the wave induced motion causes hydraulic cylinders to pump high pressure oil through electric generators. Based on the power matrix of an early prototype the Pelamis machine attains its maximum power of 750 kW for a range of values of the wave height and period, generally with Hm0 >5 m and periods around 6e12 s. The second device, an OWSC, used in this study is based on the Oyster 800 by Aquamarine Power, an Oscillating Wave Surge Converter that is fixed to the seabed with a hinged flap protruding the surface, where it is pushed backwards and forwards in an oscillating motion through the wave action. For the first generation Oyster prototypes power is produced onshore by pumping water through a closed loop towards a Pelton wheel. The maximum power of this device is rated as 800 kW. The WEC arrays proposed by the developers are: i) Marwick Head: An array of 66 Pelamis type devices with a 350 400 m (cross stream x down stream) staggered spacing across 4 rows was considered. ii) West Orkney: Arrays of 132 Pelamis devices with a 400 400 m distance between the WECs in two staggered rows, and 10 times the device length between the arrays (1800 m) were considered. iii) Brough Head: Single row of 120 Oyster devices (26 m wide and 45 m spacing) was distributed as 4 arrays along the 12.5 m depth contour. For the detailed methodology used for the array layouts see O'Hara Murray et al., (this issue). 5.2. Implementation of energy removal in MIKE 21 spectral wave
Fig. 7. Map of the Orkney showing the layout of 300 WEC devices.
this is one of the strategic deployment zone identified by the Crown Estate in Fig. 1. As expected the wave height decreases as the wave progresses from a westerly direction across the domain into reduced water depths. The annual average wave height varies from about 1.6 m to about 2.5 m at the sites where nearshore arrays are proposed (refer to Fig. 1).
5. Implementation of energy extraction 5.1. Wave energy converters and its array layouts Marine Scotland Science developed possible array layout scenarios from Environmental Statements submitted by developers. WECs were considered as arrays at the following three sites identified within the TeraWatt project to enhance the understanding of the impact of removing wave energy on physical processes within the region (see Fig. 7). Two generic types of WEC are used, a surface based line attenuator and an Oscillating Wave Surge Converter (OWSC). The first device is based on the Pelamis Wave Energy Converter, which has been in operation for over a decade with considerable sea trial experience, and is an attenuator designed to operate in deep water. This converter type consists of semi-submerged cylindrical sections, moored perpendicular to the wave front. The segments move
This study explores a new method of removing energy from the model domain as the MIKE 21 SW model has no built-in algorithm for simulating WECs. By including individual WECs or WEC arrays as additional source terms representing the energy extracted and redistributed by the WECs in spectral wave models as used in this study, it is possible to assess the hydrodynamic behaviour and power performance of WEC array. Such an approach in the representation of WECs in the most commonly used coastal modelling packages has significant practical implications on the development of software tools for the planning of wave farms. Since the horizontal dimensions of WECs are usually smaller than the mesh resolution used in the computational grid, the generic wave energy device in the MIKE 21 SW are modelled using a sub-grid scaling technique. The location of a WEC is given by a number of geo-referenced points which together make up a polyline. The location and geometry of these polyline structures are included when the mesh is created. The values of the wave energy transmission factors are still not completely understood (due to a lack of installed systems for validation) or openly disclosed by the WEC developers. Moreover, these values depend not only on the farm geometry, but on a wider range of factors, and they often appear to have a dynamic behaviour relationship with the impacting wave conditions. Given that this work is concerned with surface attenuator and Oscillating Wave Surge Converter type technology as represented by Pelamis and Oyster devices respectively, the values of the reflection, transmission and energy loss coefficients were obtained from a proper wave-structure interaction prediction tool, WAMIT, to represent various scenarios. Although the MIKE21 SW model does not have the ability to model the entire dynamics of a floating wave energy device, it can model wave propagation accurately over varying complex bathymetry in coastal to ocean scale regions, thus it makes a highly
Please cite this article in press as: Venugopal, V., et al., Numerical modelling of wave energy resources and assessment of wave energy extraction by large scale wave farms, Ocean & Coastal Management (2017), http://dx.doi.org/10.1016/j.ocecoaman.2017.03.012
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7
parameters, using Eqns (3) and (4),
Pzrg
Hm02 Cg ðTe ; dÞ 16
Cg ðTe ; dÞ ¼
rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 2kd gL 1þ tanhðkdÞ 2 sinh 2 kd 2p
(3)
(4)
where, Cg ðTe ; dÞ is the group velocity corresponding to the energy period (Te), r is the sea water density taken as 1025 kg/m3, g is the gravitational constant, k and L are the wave number and wave length respectively, computed with ‘Te’ for the depth ‘d’ using the linear wave ‘dispersion relationship’. Now the energy conservation equation (1) may be rewritten as [see ref. 24, page 170],
CR2 þ CT2 þ ðPCRÞ2 ¼ 1 Fig. 8. Validation of WAMIT results for Pelamis wave energy converter with experimental results from (McCormick, 2010). Three hinges with different PTO damping (hinge 1 ¼ 1.9 106 Nms, hinge 2 ¼ 1.2 106 Nms and hinge 3 ¼ 1.9 106 Nms) have been used.
suitable tool to study wave forecasting or hindcasting. On the other hand the wave structure interaction tool WAMIT, as demonstrated through various studies is powerful and accurate in modelling any type of fixed and floating offshore structures, but its downside is that it cannot be applied to varying bathymetry or wave forecasting or hindcasting and therefore its use in modelling realistic oceanic scale array to assess environmental impact is less proved. Hence the method to use WAMIT to determine the energy absorption, reflection and transmission characteristics of WECs and then transfer these into MIKE 21 for large scale array modelling and at the same time for studying WECs interaction with marine environment on a wider scale is chosen. The facility available with MIKE 21 allows one to model the WEC as a line or point structure, and the former was chosen for this study to model the WECs. MIKE 21 also allows the structure to be modelled as a submerged, emergent, or sub-aerial structure. The following methodology was adopted in modelling the devices. The energy transmission through any WEC structure can be represented by the energy balance equation,
CR2 þ CT2 þ CL2 ¼ 1
(1)
where, CT is the transmission coefficient, CR is the reflection coefficient and CL is the energy loss coefficient. The reflection and transmission coefficients are calculated in normalised form as ratios of reflected wave height to incident wave height, and, transmitted wave height to incident wave height respectively. The information on energy loss or energy absorbed by a device could be obtained from power matrix produced by developers. The power matrices for Pelamis and Oyster devices are extracted using the data from (Babarit et al., 2012; Diaconu and Rusu, 2013) and a metric known as Power Capture Ratio (PCR) is produced. The PCR which is a measure of wave power absorbed by a device can be calculated for different range of significant wave heights (Hm0) and energy periods (Te) as,
PCR ¼
Pm P
(2)
where, Pm is the power produced by a device based on its power matrix for a chosen pair of wave height and energy period and P is theoretical energy flux calculated for the same pair of wave
(5)
while CT, CR and CL (or PCR) are wave period dependent which means they change with sea states, determining the energy coefficients for every wave frequency, while it is possible to do, is laborious, and considering the time and resources available, the wave periods that correspond to the maximum power output from both the attenuator (Pelamis) and terminator (Oyster type device) have been selected and corresponding energy coefficients have been chosen to model the WECs in MIKE 21 SW model. This approach may be justified as the main interest is to evaluate the impacts on the environment when the devices operate at its best. However, before using these coefficients in MIKE 21, the WAMIT results are to be validated which was done by comparing the results produced from WAMIT with published literature. Two different WAMIT models have been constructed, one representing the Pelamis device (of length 120 m and diameter 3.5 m) and another for Oyster type devices which are hereafter denoted as OWSC (Oscillating Wave Surge Converter). Two types of OWSCs were tested; OWSC 1 has a width of 18 m, immersion depth of 9.4 m and thickness of 4 m and operating in a constant water depth of 10.9 m, and OWSC 2 has a width of 26 m, immersion depth of 9 m and thickness of 4 m and is operating at a water depth of 12.5 m. The results are shown in Figs. 8 and 9 for Pelamis and OWSCs respectively. For Pelamis type device, the PTO was modelled by three hinges but with different PTO (Power Take-Off) damping (hinge 1 ¼ 1.9 106 Nms, hinge 2 ¼ 1.2 106 Nms and hinge 3 ¼ 1.9 106 Nms) as was done in experiments by Retzler (2006), and a good agreement between WAMIT and experiments for power capture width can be seen in Fig. 8. Similarly for Oscillating Wave Surge Converters, The values of wave excitation force, added inertia, radiation damping and q-factor, all presented against the wave periods (see Fig. 9) for OWSC 2 showed an excellent match with Renzi et al. (2014), though not graphically reproduced here. Thus the WAMIT model has been verified, further simulations were carried out for the wave conditions corresponding to the maximum power production for each device and the values of transmission and reflection coefficients have been obtained. The values of the wave transmission/reflection coefficients for Pelamis type devices selected were CT ¼ 0.99 and CR ¼ 0.01, and for OWSC, CT ¼ 0.75 and CR ¼ 0.25. To verify the validity of the above coefficients and method, wave climate modification for an array of devices from both WAMIT and MIKE 21 have been compared in Fig. 10, for significant wave height Hm0 ¼ 3 m and peak wave period Tp ¼ 7.5s. In total, three Pelamis type devices (Fig. 10, subplots at the top) and five OWSCs (Fig. 10, bottom subplots) were simulated using JOWNSWAP wave spectrum with a peak enhancement factor of 3.3 and a directional standard deviation ¼ 5 . Despite some differences in wave height
Please cite this article in press as: Venugopal, V., et al., Numerical modelling of wave energy resources and assessment of wave energy extraction by large scale wave farms, Ocean & Coastal Management (2017), http://dx.doi.org/10.1016/j.ocecoaman.2017.03.012
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V. Venugopal et al. / Ocean & Coastal Management xxx (2017) 1e12
5
2.0
4
1.5
3
1.0
2
0.5
0.0
OWSC 1 n=0 4
6
1
OWSC2 n=1 n=2 8
10
12
14
12.5
(b)
10.0
Ia (x107 kg.m2)
(a)
Fext for OWSC 1 (x106N.m)
Fext for OWSC 2 (x107 N.m)
2.5
7.5
5.0 OWSC 1 n=0
2.5
0.0
0
4
6
8
(c)
OWSC 1 n=0
1.2
OWSC 2 n=1 n=2
14
(d)
1.0 0.8
7.5
OWSC 2 n=1 n=2
0.6
q
Ba (x107 kg.m2/s)
10.0
12
T (s)
T (s) 12.5
10
OWSC 2 n=1 n=2
0.4
5.0
0.2 2.5
0.0
0.0 4
6
8
10
12
14
-0.2
4
6
8
T (s)
10
12
14
T (s)
Fig. 9. Validation of hydrodynamic coefficients for OWSC 1 (width ¼ 18 m, immersion depth ¼ 9.4 m, thickness ¼ 4 m, operating water depth ¼ 10.9 m) and OWSC 2 (width ¼ 26 m, immersion depth ¼ 9 m, thickness ¼ 4 m, operating water depth ¼ 12.5 m). (a) Wave excitation forces, (b) Added inertia, (c) radiation damping and (c) q-factor.
distribution, the general wave propagation pattern in MIKE 21 agreed well with WAMIT. It is also clear that MIKE 21 shows a large reduction in wave height immediately downstream of the device and no alteration to the upstream wave field; this is expected as the wave diffraction which was included in WAMIT was not considered in MIKE 21. Nevertheless, as the far field wave conditions being less affected in MIKE 21, and no other single software tool that can model both WEC and wave environments, it was decided to accept
this solutions for further modelling. A maximum difference of up to 30% in wave heights between WAMIT and MIKE 21 results are seen, particularly very close to downstream of the WECs, however, this difference is found to be minimum further downstream. 6. Results and discussions The WEC array models were simulated for the same time period
Fig. 10. Comparison of WEC performance between WAMIT and MIKE21 SW models for (a) surface attenuator and (b) OWSC. Wave conditions: Hm0 ¼ 3 m, Tp ¼ 7.5s and q ¼ 0deg.
Please cite this article in press as: Venugopal, V., et al., Numerical modelling of wave energy resources and assessment of wave energy extraction by large scale wave farms, Ocean & Coastal Management (2017), http://dx.doi.org/10.1016/j.ocecoaman.2017.03.012
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Fig. 11. Mean significant wave height for the period January to December 2010 with energy extraction.
Fig. 12. Absolute difference of mean significant wave height with and without energy extraction for the period January to December 2010.
as the baseline model (as in Fig. 6), to be able to evaluate the wave farm impact by comparing model outputs with and without WECs. Following the implementation of the WEC arrays (both attenuator and terminators) in the model for 2010 it is observed that the mean significant wave height is decreased downwave of the arrays and
9
this is visible in Fig. 11. Further, the absolute mean difference in the significant wave height as a result of inclusion of the WEC arrays is shown in Fig. 12. A clear reduction of wave height is observed downwave following the inclusion of WEC arrays with the largest differences being visible in the region immediately behind the wave array. At the point of maximum impact, i.e., downstream of array close to coast, a large decrease in wave height based on the annual mean wave conditions is noticed and this constitutes a reduction of maximum of up to 1 m or just above (i.e difference in mean wave height computed with WEC minus no WEC) of the incident wave height. The wave height is decreased because of the energy extraction by the WEC array, which is indicated by a negative value in the downstream of the arrays (Fig. 12). Furthermore, the results show that as the downstream distance increases the individual effects of each device reduces and an evenly distributed reduction in wave height is observed. The impact of the wave farms decreases with increasing distance, and this is due to restoring processes e.g. diffracted wave energy penetrating into the lee of the wave farm from both sides. A comparison of the baseline data time series against the WEC array scenario is shown in Fig. 13 for a location indicated by the red coloured point to the west of the Bay of Skaill in a water depth of 8 m. Significant wave height, peak wave period and mean wave direction of the both cases are presented for the year 2010, and, it is clear that a significant reduction in wave height is obvious. The fact that, the wave heights are reduced and the wave directions and wave periods are less affected, which demonstrates that changes to sediments and beach erosion at this site might be less significant, however this needs further research. In order to provide a quantitative comparison between the baseline scenario and the model outputs with WEC arrays included, scatter plots for the hindcast significant wave height, peak wave period and mean wave direction are shown in Fig. 14, for the same location in the Bay of Skaill. It is evident from the scatter plot that the significant wave height predicted with included WEC arrays is smaller than for the baseline scenario without WECs. The correlation between baseline and with WECs case is highest for wave period and lowest for wave height indicating that the impact of WEC deployments on wave period is less than on wave height. To provide a detailed analysis of energy extraction, the percentage change in significant wave height for each node is calculated and shown in Fig. 15. The location of each device is indicated by a large reduction in wave height leeward of the device as indicated in the theoretical test in Fig. 10. The maximum percentage height difference results from a situation where a very small original wave height undergoes a substantial change due to wave farm even though the absolute difference is minimal. To better visualise the impact, the variation in the significant wave height along a line transect (B1eB2) through the mid-section of an array and extending up to the 8 m depth contour line was computed from mean conditions and is shown in Fig. 16. This plot also has two other transects (A1-A2 and C1eC2). The model results indicate a reduction of significant wave height across the arrays for all locations. The transect line B1eB2 ending at a point in Bay of Skaill shows a wave height reduction of up to 60% from offshore to nearshore due to the cumulative effects of all wave farms. Large reductions have also been noticed for transects A1-A2 and C1eC2, and the maximum impact is observed for transect A1-A2 directly downwave of the WEC array. These changes of significant wave height represent the net change of wave conditions due to energy absorption by WECs, interaction between devices within an array and between adjacent arrays, scattering by the array and local bathymetry effect. The magnitude of reduction clearly varies with both the number of devices installed in each row and their alignment. It is to be kept in mind that the results presented above are
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Fig. 13. Comparison of time series of wave height, wave period and wave direction at Bay of Skaill between Baseline and WEC scenarios.
Fig. 14. Scatter plot of significant wave height, peak period and mean wave direction at Bay of Skaill, comparing model predictions with and without WECs.
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purely numerical simulation based and no validation with real scale deployments has been done, hence usage of this data to be treated with caution.
7. Conclusions
Fig. 15. Percentage change in significant wave height.
Arrays of two different wave energy converters (WECs) proposed by the device developers in the Orkney Islands have been modelled using the industry standard software tool MIKE 21 spectral wave model, to assess the consequences of energy extraction by large scale wave farms on the regional sea state distribution around the farms. The proportions of wave energy absorption, reflection and transmission by the WECs within the MIKE tool have been modelled by their respective energy coefficients obtained from the state of art wave-structure interaction software WAMIT after a validation exercise. This study demonstrated that the WEC arrays altered the wave climate within an array and also the neighbouring array. Cumulative wave height reduction downstream of the WEC array and farther down the coastline appears to be significant, and, its magnitude depends on the array layout and number of co-located arrays. The average yearly reduction in wave height immediately to the lee side of the array was observed to be very high, and its magnitude varies from arrays to arrays. With increasing distance from the arrays towards the shoreline, a recovery in wave heights or energy restore is evident, and energy progresses into the downwave shadow region of the arrays from the sides. In this study the effect of devices is modelled using frequency independent transmission coefficients, rather than using a more detailed approach, due to unavailability of precise device specific data. Further work is required in investigating frequency dependent behaviour and dynamic response characteristics of coefficients for absorption, transmission and reflection.
Fig. 16. Wave height reduction across three transect lines post array deployments.
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Acknowledgements Work presented in this paper is part of the TeraWatt project and the authors wish to acknowledge support from the UK Engineering and Physical Sciences Research Council (EPSRC) under grant reference number EP/J010170/1. The authors are also grateful to Cefas (UK) for wave buoys data, European Centre for Medium-Range Weather Forecasts (ECMWF) for providing wind data, European Marine Energy Centre (EMEC) for providing wave buoy data for Orkney, The Crown Estate, UKHO and Marine Scotland Sciences for providing bathymetry data. References Babarit, A., et al., 2012. Numerical benchmarking study of a selection of marine energy converters. Renew. Energy 41, 44e63. Beels, C., Troch, P., De Backer, G., Vantorre, M., De Rouck, J., 2010. Numerical implementation and sensitivity analysis of a wave energy converter in a timedependent mild-slope equation model. Coast. Eng. 57 (no. 5), 471e492. Diaconu, S., Rusu, E., 2013. Evaluation of various MEC devices in the Romanian near shore. In: WSEAS International Conference on Energy and Environment Technologies and Equipment (EEETE'13). Brasov, Romania. ECMWF. http://www.ecmwf.int/; (Accessed 28 May 2014). GEBCO. http://www.gebco.net/; (Accessed 28 May 2014). Hasselmann, K., 1974. On the spectral dissipation of ocean waves due to whitecapping. Bound. Layer. Meteor 6, 107e127. Janssen, P.A.E.M., 1989. Wave induced stress and the drag of airflow over sea waves. J. Phys. Oceanogr. 19, 745e754. Janssen, P.A.E.M., 1991. Quasi-linear theory of wind wave generation applied to wave forecasting. J. Phys. Oceanogr. 21, 1631e1642. Komen, G.J., Cavaleri, L., Donelan, M., Hasselmann, K., Hasselmann, S., Janssen, P.A.E.M., 1994. Dynamics and Modelling of Ocean Waves. Cambridge Univ. Press, New York, p. 532. Marine Scotland. http://www.scotland.gov.uk/Topics/marine/science/
MSInteractive/datatype/Bathymetry/data. (Accessed 28 May 2014). McCormick, M.E., 2010. Ocean Engineering Mechanics: with Applications. Cambridge University Press, Cambridge; New York. MIKE 21, 2012. Wave Modelling User Guide, Scientific Document. Danish Hydraulic Institute, Denmark. Millar, D.L., Smith, H.C.M., Reeve, D.E., 2007. Modelling analysis of the sensitivity of shoreline change to a wave farm. Ocean. Eng. 34 (no. 5e6), 884e901. Monk, K., Zou, Q., Conley, D., 2013. An approximate solution for the wave energy shadow in the lee of an array of overtopping type wave energy converters. Coast. Eng. 73, 115e132. Nelson, R.C., 1987. Design wave heights on very mild slopes: an experimental study. Civ. Eng. Trans. Inst. Eng. 29, 157e161. Nelson, R.C., 1994. Depth limited wave heights in very flat regions. Coast. Eng. 23, 43e59. Nørgaard, J.H., Andersen, T.L., 2012. Investigation of wave transmission from a floating wave dragon wave energy converter. Isope 4, 509e516. Palha, A., Mendes, L., Fortes, C.J., Brito-Melo, A., Sarmento, A., 2010. The impact of wave energy farms in the shoreline wave climate: Portuguese pilot zone case study using Pelamis energy wave devices. Renew. Energy 35 (no. 1), 62e77. Renzi, E., Abdolali, A., Bellotti, G., Dias, F., 2014. Wave-power absorption from a finite array of oscillating wave surge converters. Renew. Energy 63, 55e68. Retzler, C., 2006. Measurements of the slow drift dynamics of a model Pelamis wave energy converter. Renew. Energy 31 (2), 257e269. Ruessink, B.G., Walstra, D.J.R., Southgate, H.N., 2003. Calibration and verification of a parametric wave model on barred beaches. Coast. Eng. 48, 139e149. Venugopal, V., Nemalidinne, R., 2015. Wave resource assessment for Scottish waters using a large scale North Atlantic Model. Renew. Energy 76, 503e525. Venugopal, V., Smith, G.H., 2007. Wave climate investigation for an array of wave power devices. In: 7th Eur. Wave Tidal Energy Conf., pp. 1e10. € gler A. Impact of temporal variation of wind Venugopal V, Nemalidinne R and Vo input on wave parameters prediction using numerical wave model. In: Proceedings of EWTEC 12th European Wave and Tidal Energy Conference. Nantes, France, 6e11, September 2015. Weber, S.L., 1991. Bottom friction for wind sea and swell in extreme depth-limited situations. J. Phys. Oceanogr. 21, 149e172. Young, I.R., 1999. Wind generated ocean waves. In: Bhattacharyya, R., McCormick, M.E. (Eds.), Elsevier Ocean Engineering Book Series, vol. 2. ISBN: 978-0-08-043317-2.
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Contents lists available at ScienceDirect
Ocean & Coastal Management journal homepage: www.elsevier.com/locate/ocecoaman
Numerical modelling of wave energy resources and assessment of wave energy extraction by large scale wave farms € gler b Vengatesan Venugopal a, *, Reddy Nemalidinne a, Arne Vo a
Institute for Energy Systems, School of Engineering, The University of Edinburgh, Faraday Building, King's Buildings, Colin Maclaurin Road, Edinburgh, EH9 3DW, UK b Marine Energy Research Group, Lews Castle College, University of the Highlands and Islands, Stornoway, Isle of Lewis, Scotland, GB-HS2 0XR, UK
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 June 2016 Received in revised form 2 January 2017 Accepted 9 March 2017 Available online xxx
This study reports the practicalities of applying a numerical model to assess the wave energy extraction at proposed development sites in Orkney Waters, Scotland. A state-of-the-art phase averaged spectral wave model, MIKE 21 Spectral Wave, in association with the wave-structure interaction software tool WAMIT, has been employed to study the impact of energy extraction by large arrays of Wave Energy Converters (WECs) on the wave height alteration in the neighbourhood of WEC arrays. Two generic types of WEC, one representing surface attenuators (deployed in deep water) and other representing Oscillating Wave Surge Converters (deployed in shallow waters) are used for numerical modelling. The power extraction performance of the WECs are initially modelled using WAMIT and validated with data from literature. As MIKE 21 SW has limited ability in modelling complete dynamics of a moving structure, each WEC has been modelled as a generic structure but with appropriate reflection, transmission and energy absorption properties derived from WAMIT, and this methodology is found to have worked well. In total there are 198 attenuators and 120 Oscillating Wave Surge Converters are constructed within the numerical model at potential energy locations in Orkney Waters, and the wave-WECs array interactions for the year 2010 have been simulated. The results suggest that the wave farm will have an impact on the wave climate immediately in the lee of the array, although the magnitude of these effects decreases monotonically with distance from the farm. © 2017 Elsevier Ltd. All rights reserved.
Keywords: Wave energy converter Wave energy extraction Wave farms Spectral wave modelling
1. Introduction and overview Scotland is geographically well placed on the globe where large energetic waves from the North Atlantic Ocean provide a high level of sustainable wave power resources. The Scottish Government has committed to the development of a successful marine renewable energy industry in Scotland as the country has an estimated 25% of Europe's tidal potential and 10% of its wave potential (http://www. gov.scot/Topics/Business-Industry/Energy/Facts). However, harvesting this energetic resource increases the number of challenges associated with it. One of such challenges is to understand how nearshore coastal processes may be modified by energy extraction, as the installed capacity of wave farms increases to hundreds of MW in capacity. The extraction of wave energy from a wave farm
* Corresponding author. E-mail addresses: [email protected] (V. Venugopal), reddy.nemalidinne@ € gler). gmail.com (R. Nemalidinne), [email protected] (A. Vo
produces a wave energy deficit or shadow down region which in turn may affect the downstream sediment transport and may thus result in beach erosion/deposition. The quantification of the wave energy reduction and identification of induced wave height gradients is desired for an accurate assessment of the environmental impact of a Wave Energy Converter (WEC) array on the nearby marine environment. The ability to predict the wave shadow is of topical interest due to significant stakeholder concerns about potential impacts from wave shadowing arising from wave energy device installations. The primary aim of this work is to assess the consequences wave energy extraction by large scale wave arrays in the areas of the Crown Estate Round 1 lease sites in the Pentland Firth and Orkney Waters (PFOW). Based on guidance from commercial stakeholders two state-of-the-art modelling suites were initially selected for the TeraWatt project, namely MIKE by Danish Hydraulic Institute and Delft3D by Deltares to predict the physical effects of wave energy extraction and compare the advantages and disadvantages of each
http://dx.doi.org/10.1016/j.ocecoaman.2017.03.012 0964-5691/© 2017 Elsevier Ltd. All rights reserved.
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modelling suite. Both models have produced good results of comparative quality. The results included in the present paper are based on MIKE suite only as due to unforeseen circumstances data from Delft3D model is not available for demonstration. Although much of the existing literature on wave energy is related to device development, a small number of studies have been carried out to investigate the wave height reduction in the lee of WEC arrays. Millar et al. (2007) used the third generation phase averaged spectral wave model, SWAN, to investigate the effects of the scale of energy extraction and incident wave parameters on the far field wave energy deficit. Venugopal and Smith (2007) used the MIKE 21 Boussinesq wave (BW), which is a phase resolving model, to assess the resulting wave field around a single row of overtopping type WECs which partially reflect energy to achieve a wave energy deficit in the down wave region rather than by specifically extracting it. Palha et al. (2010) used the parabolic mild slope wave model REF/DIF to assess the wave shadow in the lee of a series of large energy sinks that represent clusters of devices. Beels et al. (2010) used the time dependent mild slope wave model MILDWave to assess the wave shadow in the lee of a 2D array of Wave dragon overtopping type WEC devices. Nørgaard and Andersen (2012) investigated potential benefits to shore protection by simulating an array of Wave Dragon overtopping type WECs in the relatively near-shore region using the MIKE 21 Boussinesq wave model. Monk et al. (2013) developed an approximate analytical solution to assess the distributed wave field about a single row array of overtopping WEC devices, represented as a series of partially absorbing and transmitting breakwater segments. In the named studies WECs were implemented through different means, e.g. as small islands or obstructions with defined coefficients for transmission and absorption, and where possible reflection. A common problem to all previous, and also to the model discussed in this paper, is the lack of validation data from field deployments. Another challenge is related to the performance parameters of different WEC systems, as these are often not available to the scientific community for reasons of confidentiality. In this paper the MIKE 21 Spectral Wave model (SW) (MIKE 21, 2012) is used to simulate large scale WEC array based on the planned developments in Orkney waters. Energy extraction is represented through arrays of surface attenuators in deep or intermediate water depth, together with oscillating wave surge energy converters in the shallow waters and results are compared against a baseline scenario without WEC deployments. Modelling a realistic wave energy device with energy extraction in MIKE 21 SW module is not a direct process as this tool does not have the ability to model moving bodies dynamics, however, the hydrodynamic coefficients describing energy loss, wave scattering, wave diffraction and wave transmission can be implemented if these coefficients are known for a particular energy device. In order to generate the hydrodynamic coefficients the state of art wavestructure interaction software tool WAMIT (http://www.wamit. com/), which is capable of analysing wave interactions with fixed and floating offshore structures has been used, and the wave absorption, reflection and transmission characteristics of the devices are obtained. These hydrodynamic parameters were then input to MIKE 21 SW to model the wave device. The initial part of this paper provides a detailed description of the model and this is followed by an overview of results obtained for the year 2010 without the implementation of energy extraction (baseline). Following on from that baseline scenario details are given on the approach used in this paper for the representation of WECs, and the simulated consequences of the large scale energy extraction on the surrounding wave field are presented and compared against the baseline.
2. Study area Exposed to energetic seas from the Atlantic and strong tidal currents driven by a semidiurnal ebb/flood cycle that combines the Atlantic with the North Sea, the waters surrounding the Orkney Islands, together with the Pentland Firth at the north of Scotland are a core area for wave and tidal energy extraction targeted by government and licensing authorities. In 2010 lease agreements for site developments were in place between project developers and The Crown Estate for a total rated capacity of 1,600 MW shared between six wave and five tidal energy extraction projects (see Fig. 1). A number of prototype device deployments have been completed at the European Marine Energy Centre (EMEC), and as part of previous and ongoing research and site development projects considerable baseline data has been gathered in terms of bathymetry and wave observations in the area. The combination of real sites with planned developments and access to relevant datasets required for setting up and running resource models made the area around Orkney an obvious candidate for use as model domain in this project. 3. Model overview MIKE 21 SW is a third generation spectral wind-wave model utilising unstructured meshes (MIKE 21, 2012) to simulate the growth, decay and transformation of wind-generated sea and ocean swells in offshore and coastal areas. The wind waves are expressed by the wave action density spectrum. The MIKE 21 spectral wave model includes two methods of wave simulation, namely, (i) the directional decoupled parametric formulation and (ii) the fully spectral formulation, both based on the wave action conservation equations in either Cartesian (for small scale applications) or spherical (for large scale applications) co-ordinate systems (Komen et al., 1994; Young et al., 1999). The model accounts for the physical phenomenon of wave growth from wind, energy transfer due to non-linear quadruplet or triad waveewave interaction, and includes energy dissipation terms for white-capping, bottom friction and depth-induced breaking. The wind input is based on Janssen's (1989, 1991) quasi-linear theory. A cell-centered finite volume method is applied in the discretization of the governing equations in geographical and spectral space and a multisequence explicit method is applied for the wave propagation with the time integration carried out using a fractional step approach. The model generates phase averaged wave parameters as output for either the total computational domain, or selected parts thereof. Further details can be found in the MIKE 21 wave modelling user guide (MIKE 21, 2012). 3.1. Model set-up An unstructured computational mesh (see Fig. 2) was constructed for the North Atlantic region (10oE e 75oW and 10oN70oN) using bathymetry data compiled from General Bathymetric Chart for Oceans (GEBCO) and Marine Scotland Science (Marine Scotland). GEBCO bathymetry data with a spatial resolution of 30 arc seconds was used for most parts of the model domain and only the area around Orkney, Pentland Firth, Shetland and northwest of the Isle of Lewis was covered by bathymetry datasets obtained from Marine Scotland Science. The unstructured mesh of the computational domain visible in Fig. 1 was triangulated using the natural neighbour interpolation method (MIKE 21, 2012). Finer mesh resolutions were produced for Pentland Firth and Orkney Waters with a mesh area of 0.0005 square degrees (approx. 1700 m2), for the Hebrides and northwest Scotland of 0.001 square degrees and 0.75 square degrees (approx. 2.5 km2)
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Fig. 1. Location of Pentland Firth showing wave and tidal energy leasing sites (http://www.thecrownestate.co.uk/energy-and-infrastructure/wave-and-tidal/the-resources-andtechnologies/3).
Fig. 2. Computational domain for North Atlantic wave model.
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Fig. 3. Comparison of significant wave height, between measurements and MIKE21 model: (a) Blackstone - top, (b) Orkney-E middle (c) Firth of Forth e bottom.
Table 1 Performance parameters for Significant wave height, Hm0, for the year 2010. Site
Mean
Bias
RMSE
SI
R
Blackstone Orkney-E Firth of Forth
2.3 m 1.67 m 1.15 m
1.23 m 1.3 m 0.10 m
1.45 m 1.31 m 1.25 m
0.22 0.19 0.22
0.94 0.95 0.96
for the North Atlantic Ocean. Further details on the model domain and setup are available in Venugopal and Nemalidinne (2015). 3.2. Model forcing and physical processes The model was forced with wind data obtained from the operational model of the European Centre for Medium-Range Weather Forecasts (ECMWF) at 6 hourly intervals with a spatial resolution of 0.125 x 0.125 . To ensure fetch unlimited wave growth, decay and
transformation of wind sea and swells, the model was run in ‘fully spectral’ mode with ‘Instationary formulation’. The number of frequencies used for the model was 25 with fmin ¼ 0.04 Hz and a logarithmic frequency distribution with a frequency factor of 1.1. The directional discretization had 24 directional bins, each with 15 resolution. A low order fast algorithm has been chosen as the solution technique with the ‘maximum number of levels in the transport calculation’ set as 32. A quadruplet-wave interaction has been applied. No current, ice coverage and diffraction were included into the model. Dissipation due to whitecapping based on the theory of Hasselmann (1974) and Komen et al. (1994), bottom friction according to Nikuradse roughness (Weber, 1991) and depth-induced wave breaking based on the specified gamma model (Nelson, 1987; Nelson, 1994; Ruessink et al., 2003), were considered in the simulations and the energy transfer was activated. For a detailed description of the above source terms and basis for selection of the appropriate values the reader is referred to the MIKE 21 SW user guide (MIKE 21, 2012) and (Venugopal and Nemalidinne, 2015).
Fig. 4. Wave rose diagram of the MIKE21 model data at Orkney location: The left hand plot is for Hm0 (m) and the right hand plot is for Tp(s).
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Fig. 5. Comparison between measurements and MIKE21 model at Orkney location: Shown is Hm0 (m) on the right, and Tp (s) to the left.
3.3. Validation of the wave model Measured wave data from scientific buoys deployed around Scotland was used to calibrate and validate the model. The successful calibration of the model is described in detail in (Venugopal and Nemalidinne, 2015) and for this paper a further validation was carried out for the year 2010 for Scottish waters, based on available measured wave data for Blackstone (56.062 N 007.056 W, depth ¼ 97 m), Orkney-E (58.970 N 003.390 W, depth ¼ 53 m), and Firth of Forth (56.188 N 002.503 W, depth ¼ 65 m) and the results are presented in Fig. 3. The results have shown good comparison with measurements for most of the time period for all three sites and this was particularly true for the significant wave height. As the hindcasting data covers a full calendar year including both summer and winter months, it is evident that the model is able to resolve wave conditions for different sea states throughout all seasons. Although the wind input used here has a temporal resolution of 6 h, the impact of varying the resolution to 1 h and 3 h has also been examined in another publication by the authors (see Venugopal V and Nemalidinne, 2015), however, it was noticed that the temporal variation of wind speed has less impact on the resulting wave parameters when large scale modelling is considered. The performance indices (or quality parameters) for the significant wave height for the model period of 2010 were calculated for the three buoy locations Blackstone, Orkney-E, and Firth of Forth and are shown in Table 1. A good agreement between model and buoy data is observed for all three locations and this is particularly remarkable for the Firth of Forth location, where the significant wave height for the majority of the time period in the year 2010 is less than 2 m and yet the model was still able to predict this accurately. Alternative ways to inspect model results or to compare against measured data are to represent the data as wave rose and scatter plots as shown in Figs. 4 and 5. For the Orkney-E site, both the data for significant wave height and peak wave period from the model and buoy measurements are found to be in close proximity to or on the equality line illustrating that the significant wave height was highly accurately resolved, and this is also indicated by low values of Bias, RMSE, Scatter Index (SI) and high Correlation Coefficient (R) values as shown in Table 1.
Matrices are a standard way of expressing WEC performance against particular sea states and the selection of the most appropriate site and technology for wave farm operation in a specific region should be based on a thorough analysis of the energy production of different combinations of different types of WECs at a range of sites. To be able to undertake such an assessment an accurate and detailed resource characterisation at each location of interest has to be conducted. The approach taken here is to run the MIKE 21 Spectral Wave model for the year 2010, with and without WECs, and to subtract the results from each other to produce maps of the differences in wave parameters following the inclusion of WECs. The evolution of mean significant wave height (Hm0) for the pre-device model, for the study area around Orkney is shown in Fig. 6 for the period January to December 2010, which is extracted from the validated model described in section (3) above. Note that
4. Predictions without energy extraction The efficiency of Wave Energy Converters (WEC) depends on the characteristics of the site specific resource, and key parameters are the wave height and period of the different sea states. Power
Fig. 6. Mean significant wave height for the period January to December 2010 without energy extraction.
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relative to one another as waves pass along the length of the machine and the wave induced motion causes hydraulic cylinders to pump high pressure oil through electric generators. Based on the power matrix of an early prototype the Pelamis machine attains its maximum power of 750 kW for a range of values of the wave height and period, generally with Hm0 >5 m and periods around 6e12 s. The second device, an OWSC, used in this study is based on the Oyster 800 by Aquamarine Power, an Oscillating Wave Surge Converter that is fixed to the seabed with a hinged flap protruding the surface, where it is pushed backwards and forwards in an oscillating motion through the wave action. For the first generation Oyster prototypes power is produced onshore by pumping water through a closed loop towards a Pelton wheel. The maximum power of this device is rated as 800 kW. The WEC arrays proposed by the developers are: i) Marwick Head: An array of 66 Pelamis type devices with a 350 400 m (cross stream x down stream) staggered spacing across 4 rows was considered. ii) West Orkney: Arrays of 132 Pelamis devices with a 400 400 m distance between the WECs in two staggered rows, and 10 times the device length between the arrays (1800 m) were considered. iii) Brough Head: Single row of 120 Oyster devices (26 m wide and 45 m spacing) was distributed as 4 arrays along the 12.5 m depth contour. For the detailed methodology used for the array layouts see O'Hara Murray et al., (this issue). 5.2. Implementation of energy removal in MIKE 21 spectral wave
Fig. 7. Map of the Orkney showing the layout of 300 WEC devices.
this is one of the strategic deployment zone identified by the Crown Estate in Fig. 1. As expected the wave height decreases as the wave progresses from a westerly direction across the domain into reduced water depths. The annual average wave height varies from about 1.6 m to about 2.5 m at the sites where nearshore arrays are proposed (refer to Fig. 1).
5. Implementation of energy extraction 5.1. Wave energy converters and its array layouts Marine Scotland Science developed possible array layout scenarios from Environmental Statements submitted by developers. WECs were considered as arrays at the following three sites identified within the TeraWatt project to enhance the understanding of the impact of removing wave energy on physical processes within the region (see Fig. 7). Two generic types of WEC are used, a surface based line attenuator and an Oscillating Wave Surge Converter (OWSC). The first device is based on the Pelamis Wave Energy Converter, which has been in operation for over a decade with considerable sea trial experience, and is an attenuator designed to operate in deep water. This converter type consists of semi-submerged cylindrical sections, moored perpendicular to the wave front. The segments move
This study explores a new method of removing energy from the model domain as the MIKE 21 SW model has no built-in algorithm for simulating WECs. By including individual WECs or WEC arrays as additional source terms representing the energy extracted and redistributed by the WECs in spectral wave models as used in this study, it is possible to assess the hydrodynamic behaviour and power performance of WEC array. Such an approach in the representation of WECs in the most commonly used coastal modelling packages has significant practical implications on the development of software tools for the planning of wave farms. Since the horizontal dimensions of WECs are usually smaller than the mesh resolution used in the computational grid, the generic wave energy device in the MIKE 21 SW are modelled using a sub-grid scaling technique. The location of a WEC is given by a number of geo-referenced points which together make up a polyline. The location and geometry of these polyline structures are included when the mesh is created. The values of the wave energy transmission factors are still not completely understood (due to a lack of installed systems for validation) or openly disclosed by the WEC developers. Moreover, these values depend not only on the farm geometry, but on a wider range of factors, and they often appear to have a dynamic behaviour relationship with the impacting wave conditions. Given that this work is concerned with surface attenuator and Oscillating Wave Surge Converter type technology as represented by Pelamis and Oyster devices respectively, the values of the reflection, transmission and energy loss coefficients were obtained from a proper wave-structure interaction prediction tool, WAMIT, to represent various scenarios. Although the MIKE21 SW model does not have the ability to model the entire dynamics of a floating wave energy device, it can model wave propagation accurately over varying complex bathymetry in coastal to ocean scale regions, thus it makes a highly
Please cite this article in press as: Venugopal, V., et al., Numerical modelling of wave energy resources and assessment of wave energy extraction by large scale wave farms, Ocean & Coastal Management (2017), http://dx.doi.org/10.1016/j.ocecoaman.2017.03.012
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parameters, using Eqns (3) and (4),
Pzrg
Hm02 Cg ðTe ; dÞ 16
Cg ðTe ; dÞ ¼
rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 2kd gL 1þ tanhðkdÞ 2 sinh 2 kd 2p
(3)
(4)
where, Cg ðTe ; dÞ is the group velocity corresponding to the energy period (Te), r is the sea water density taken as 1025 kg/m3, g is the gravitational constant, k and L are the wave number and wave length respectively, computed with ‘Te’ for the depth ‘d’ using the linear wave ‘dispersion relationship’. Now the energy conservation equation (1) may be rewritten as [see ref. 24, page 170],
CR2 þ CT2 þ ðPCRÞ2 ¼ 1 Fig. 8. Validation of WAMIT results for Pelamis wave energy converter with experimental results from (McCormick, 2010). Three hinges with different PTO damping (hinge 1 ¼ 1.9 106 Nms, hinge 2 ¼ 1.2 106 Nms and hinge 3 ¼ 1.9 106 Nms) have been used.
suitable tool to study wave forecasting or hindcasting. On the other hand the wave structure interaction tool WAMIT, as demonstrated through various studies is powerful and accurate in modelling any type of fixed and floating offshore structures, but its downside is that it cannot be applied to varying bathymetry or wave forecasting or hindcasting and therefore its use in modelling realistic oceanic scale array to assess environmental impact is less proved. Hence the method to use WAMIT to determine the energy absorption, reflection and transmission characteristics of WECs and then transfer these into MIKE 21 for large scale array modelling and at the same time for studying WECs interaction with marine environment on a wider scale is chosen. The facility available with MIKE 21 allows one to model the WEC as a line or point structure, and the former was chosen for this study to model the WECs. MIKE 21 also allows the structure to be modelled as a submerged, emergent, or sub-aerial structure. The following methodology was adopted in modelling the devices. The energy transmission through any WEC structure can be represented by the energy balance equation,
CR2 þ CT2 þ CL2 ¼ 1
(1)
where, CT is the transmission coefficient, CR is the reflection coefficient and CL is the energy loss coefficient. The reflection and transmission coefficients are calculated in normalised form as ratios of reflected wave height to incident wave height, and, transmitted wave height to incident wave height respectively. The information on energy loss or energy absorbed by a device could be obtained from power matrix produced by developers. The power matrices for Pelamis and Oyster devices are extracted using the data from (Babarit et al., 2012; Diaconu and Rusu, 2013) and a metric known as Power Capture Ratio (PCR) is produced. The PCR which is a measure of wave power absorbed by a device can be calculated for different range of significant wave heights (Hm0) and energy periods (Te) as,
PCR ¼
Pm P
(2)
where, Pm is the power produced by a device based on its power matrix for a chosen pair of wave height and energy period and P is theoretical energy flux calculated for the same pair of wave
(5)
while CT, CR and CL (or PCR) are wave period dependent which means they change with sea states, determining the energy coefficients for every wave frequency, while it is possible to do, is laborious, and considering the time and resources available, the wave periods that correspond to the maximum power output from both the attenuator (Pelamis) and terminator (Oyster type device) have been selected and corresponding energy coefficients have been chosen to model the WECs in MIKE 21 SW model. This approach may be justified as the main interest is to evaluate the impacts on the environment when the devices operate at its best. However, before using these coefficients in MIKE 21, the WAMIT results are to be validated which was done by comparing the results produced from WAMIT with published literature. Two different WAMIT models have been constructed, one representing the Pelamis device (of length 120 m and diameter 3.5 m) and another for Oyster type devices which are hereafter denoted as OWSC (Oscillating Wave Surge Converter). Two types of OWSCs were tested; OWSC 1 has a width of 18 m, immersion depth of 9.4 m and thickness of 4 m and operating in a constant water depth of 10.9 m, and OWSC 2 has a width of 26 m, immersion depth of 9 m and thickness of 4 m and is operating at a water depth of 12.5 m. The results are shown in Figs. 8 and 9 for Pelamis and OWSCs respectively. For Pelamis type device, the PTO was modelled by three hinges but with different PTO (Power Take-Off) damping (hinge 1 ¼ 1.9 106 Nms, hinge 2 ¼ 1.2 106 Nms and hinge 3 ¼ 1.9 106 Nms) as was done in experiments by Retzler (2006), and a good agreement between WAMIT and experiments for power capture width can be seen in Fig. 8. Similarly for Oscillating Wave Surge Converters, The values of wave excitation force, added inertia, radiation damping and q-factor, all presented against the wave periods (see Fig. 9) for OWSC 2 showed an excellent match with Renzi et al. (2014), though not graphically reproduced here. Thus the WAMIT model has been verified, further simulations were carried out for the wave conditions corresponding to the maximum power production for each device and the values of transmission and reflection coefficients have been obtained. The values of the wave transmission/reflection coefficients for Pelamis type devices selected were CT ¼ 0.99 and CR ¼ 0.01, and for OWSC, CT ¼ 0.75 and CR ¼ 0.25. To verify the validity of the above coefficients and method, wave climate modification for an array of devices from both WAMIT and MIKE 21 have been compared in Fig. 10, for significant wave height Hm0 ¼ 3 m and peak wave period Tp ¼ 7.5s. In total, three Pelamis type devices (Fig. 10, subplots at the top) and five OWSCs (Fig. 10, bottom subplots) were simulated using JOWNSWAP wave spectrum with a peak enhancement factor of 3.3 and a directional standard deviation ¼ 5 . Despite some differences in wave height
Please cite this article in press as: Venugopal, V., et al., Numerical modelling of wave energy resources and assessment of wave energy extraction by large scale wave farms, Ocean & Coastal Management (2017), http://dx.doi.org/10.1016/j.ocecoaman.2017.03.012
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5
2.0
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OWSC 1 n=0 4
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OWSC2 n=1 n=2 8
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(b)
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Ia (x107 kg.m2)
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Fext for OWSC 1 (x106N.m)
Fext for OWSC 2 (x107 N.m)
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OWSC 1 n=0
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OWSC 2 n=1 n=2
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q
Ba (x107 kg.m2/s)
10.0
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T (s)
Fig. 9. Validation of hydrodynamic coefficients for OWSC 1 (width ¼ 18 m, immersion depth ¼ 9.4 m, thickness ¼ 4 m, operating water depth ¼ 10.9 m) and OWSC 2 (width ¼ 26 m, immersion depth ¼ 9 m, thickness ¼ 4 m, operating water depth ¼ 12.5 m). (a) Wave excitation forces, (b) Added inertia, (c) radiation damping and (c) q-factor.
distribution, the general wave propagation pattern in MIKE 21 agreed well with WAMIT. It is also clear that MIKE 21 shows a large reduction in wave height immediately downstream of the device and no alteration to the upstream wave field; this is expected as the wave diffraction which was included in WAMIT was not considered in MIKE 21. Nevertheless, as the far field wave conditions being less affected in MIKE 21, and no other single software tool that can model both WEC and wave environments, it was decided to accept
this solutions for further modelling. A maximum difference of up to 30% in wave heights between WAMIT and MIKE 21 results are seen, particularly very close to downstream of the WECs, however, this difference is found to be minimum further downstream. 6. Results and discussions The WEC array models were simulated for the same time period
Fig. 10. Comparison of WEC performance between WAMIT and MIKE21 SW models for (a) surface attenuator and (b) OWSC. Wave conditions: Hm0 ¼ 3 m, Tp ¼ 7.5s and q ¼ 0deg.
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Fig. 11. Mean significant wave height for the period January to December 2010 with energy extraction.
Fig. 12. Absolute difference of mean significant wave height with and without energy extraction for the period January to December 2010.
as the baseline model (as in Fig. 6), to be able to evaluate the wave farm impact by comparing model outputs with and without WECs. Following the implementation of the WEC arrays (both attenuator and terminators) in the model for 2010 it is observed that the mean significant wave height is decreased downwave of the arrays and
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this is visible in Fig. 11. Further, the absolute mean difference in the significant wave height as a result of inclusion of the WEC arrays is shown in Fig. 12. A clear reduction of wave height is observed downwave following the inclusion of WEC arrays with the largest differences being visible in the region immediately behind the wave array. At the point of maximum impact, i.e., downstream of array close to coast, a large decrease in wave height based on the annual mean wave conditions is noticed and this constitutes a reduction of maximum of up to 1 m or just above (i.e difference in mean wave height computed with WEC minus no WEC) of the incident wave height. The wave height is decreased because of the energy extraction by the WEC array, which is indicated by a negative value in the downstream of the arrays (Fig. 12). Furthermore, the results show that as the downstream distance increases the individual effects of each device reduces and an evenly distributed reduction in wave height is observed. The impact of the wave farms decreases with increasing distance, and this is due to restoring processes e.g. diffracted wave energy penetrating into the lee of the wave farm from both sides. A comparison of the baseline data time series against the WEC array scenario is shown in Fig. 13 for a location indicated by the red coloured point to the west of the Bay of Skaill in a water depth of 8 m. Significant wave height, peak wave period and mean wave direction of the both cases are presented for the year 2010, and, it is clear that a significant reduction in wave height is obvious. The fact that, the wave heights are reduced and the wave directions and wave periods are less affected, which demonstrates that changes to sediments and beach erosion at this site might be less significant, however this needs further research. In order to provide a quantitative comparison between the baseline scenario and the model outputs with WEC arrays included, scatter plots for the hindcast significant wave height, peak wave period and mean wave direction are shown in Fig. 14, for the same location in the Bay of Skaill. It is evident from the scatter plot that the significant wave height predicted with included WEC arrays is smaller than for the baseline scenario without WECs. The correlation between baseline and with WECs case is highest for wave period and lowest for wave height indicating that the impact of WEC deployments on wave period is less than on wave height. To provide a detailed analysis of energy extraction, the percentage change in significant wave height for each node is calculated and shown in Fig. 15. The location of each device is indicated by a large reduction in wave height leeward of the device as indicated in the theoretical test in Fig. 10. The maximum percentage height difference results from a situation where a very small original wave height undergoes a substantial change due to wave farm even though the absolute difference is minimal. To better visualise the impact, the variation in the significant wave height along a line transect (B1eB2) through the mid-section of an array and extending up to the 8 m depth contour line was computed from mean conditions and is shown in Fig. 16. This plot also has two other transects (A1-A2 and C1eC2). The model results indicate a reduction of significant wave height across the arrays for all locations. The transect line B1eB2 ending at a point in Bay of Skaill shows a wave height reduction of up to 60% from offshore to nearshore due to the cumulative effects of all wave farms. Large reductions have also been noticed for transects A1-A2 and C1eC2, and the maximum impact is observed for transect A1-A2 directly downwave of the WEC array. These changes of significant wave height represent the net change of wave conditions due to energy absorption by WECs, interaction between devices within an array and between adjacent arrays, scattering by the array and local bathymetry effect. The magnitude of reduction clearly varies with both the number of devices installed in each row and their alignment. It is to be kept in mind that the results presented above are
Please cite this article in press as: Venugopal, V., et al., Numerical modelling of wave energy resources and assessment of wave energy extraction by large scale wave farms, Ocean & Coastal Management (2017), http://dx.doi.org/10.1016/j.ocecoaman.2017.03.012
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Fig. 13. Comparison of time series of wave height, wave period and wave direction at Bay of Skaill between Baseline and WEC scenarios.
Fig. 14. Scatter plot of significant wave height, peak period and mean wave direction at Bay of Skaill, comparing model predictions with and without WECs.
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purely numerical simulation based and no validation with real scale deployments has been done, hence usage of this data to be treated with caution.
7. Conclusions
Fig. 15. Percentage change in significant wave height.
Arrays of two different wave energy converters (WECs) proposed by the device developers in the Orkney Islands have been modelled using the industry standard software tool MIKE 21 spectral wave model, to assess the consequences of energy extraction by large scale wave farms on the regional sea state distribution around the farms. The proportions of wave energy absorption, reflection and transmission by the WECs within the MIKE tool have been modelled by their respective energy coefficients obtained from the state of art wave-structure interaction software WAMIT after a validation exercise. This study demonstrated that the WEC arrays altered the wave climate within an array and also the neighbouring array. Cumulative wave height reduction downstream of the WEC array and farther down the coastline appears to be significant, and, its magnitude depends on the array layout and number of co-located arrays. The average yearly reduction in wave height immediately to the lee side of the array was observed to be very high, and its magnitude varies from arrays to arrays. With increasing distance from the arrays towards the shoreline, a recovery in wave heights or energy restore is evident, and energy progresses into the downwave shadow region of the arrays from the sides. In this study the effect of devices is modelled using frequency independent transmission coefficients, rather than using a more detailed approach, due to unavailability of precise device specific data. Further work is required in investigating frequency dependent behaviour and dynamic response characteristics of coefficients for absorption, transmission and reflection.
Fig. 16. Wave height reduction across three transect lines post array deployments.
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