4. iii
List of Figures
Figure 2-1; Details of the 12 hindcast outputlocations around the Isles of Scilly............................... 5
Figure 2-2; Maps the location of the 12 hindcast locations.............................................................. 6
Figure 2-3; Details the 7 buoys used for validation. Source; (van Nieuwkoopet al., 2013) ................. 6
Figure 2-4; Validation results from the original hindcast study. Source; (van Nieuwkoopet al., 2013) 7
Figure 2-5; Bias betweenmodelledvaluesandobservedvaluesfromPRIMaREwave buoyD.(van
Nieuwkoop et al., 2013) ................................................................................................................ 7
Figure 2-6; Results from regression analysis previously conducted, (van Nieuwkoop et al., 2013) ......8
Figure 3-1; Shows location of WaveNet buoy and local bathymetry;................................................ 9
Figure 3-2; Compares uncorrected annual means over hindcast period with spectral data for 2015 13
Figure 3-3; Compares corrected annual means over hindcast period with spectral data for 2015 .... 13
Figure 3-4; Showsuncorrectedandcorrectedminimum, maximumandaverage meansforeach
month with spectral data plotted for comparison......................................................................... 14
Figure 3-5; Showsuncorrectedandcorrectedmonthlysignificantheightswithspectral dataplotted
for comparison........................................................................................................................... 14
Figure 3-6; Showsuncorrectedandcorrectedmonthlyenergyperiodswithspectral dataplottedfor
comparison................................................................................................................................ 15
Figure 4-1; Power per meter of wave for each sea state................................................................ 17
Figure 4-2; Condensed table showing hours occurrence each year of each sea state at location 4 ... 19
Figure 4-3; Hours occurrence each year of each sea state at location 1.......................................... 21
Figure 4-4; Annual energy generated per meter of wave for each sea state at location 1 ................ 21
Figure 4-5; Hours occurrence each year of each sea state at location 4.......................................... 22
Figure 4-6; Annual energy generated per meter of wave for each sea state at location 4 ................ 22
Figure 4-7; Time series of power at location 1 .............................................................................. 24
Figure 4-8; Time series of power at location 4 .............................................................................. 24
Figure 4-9; Monthly averages over hindcast period at location 1................................................... 25
Figure 4-10; Monthly averages over hindcast period at location 4 ................................................. 25
Figure 4-11; Minimum, maximum and mean monthly average power per meter of wave ............... 26
Figure 4-12; Minimum, maximum and mean monthly average significant height............................ 26
Figure 4-13; Minimum, maximum and mean monthly average energy periods............................... 27
Figure 4-14; Annual mean power over hindcast period ................................................................. 27
Figure 4-15; Seasonal variation at location 1 ................................................................................ 28
Figure 4-16; Seasonal variation at location 14............................................................................... 28
Figure 4-17; Hourly occurrence of sea states from 0-180 degrees.................................................. 29
Figure 4-18; Hour occurrence table of sea states between 180-360 degrees .................................. 30
Figure 4-20; Wave rose for locations 1-4...................................................................................... 32
Figure 4-21; Map with wave roses superimposed ......................................................................... 33
Figure 4-22; Spatial variation of mean power............................................................................... 34
Figure 4-23; Spectral data plotted for each Hm0 and Tm-10 pair................................................... 35
Figure 4-24; Spectral data plotted by direction............................................................................. 36
Figure 5-1; Frequency distribution of significant heights at location 4 ................................ 38
Figure 5-2; Extreme significant heights with 95% confidence bounds at location 1 ......................... 41
Figure 5-3; Extreme significant heights with 95% confidence bounds at location 4 ......................... 41
Figure 5-4; Most energetic sea states recorded each year throughout the hindcast period ............. 42
Figure 5-5; Largest significant heights recordedeach year throughout the hindcast period............. 43
Figure 5-6; Spatial variation during extreme conditions................................................................. 44
Figure 6-1; Available substation capacity on Bryher and St Martins................................................ 47
5. iv
Figure 6-2; Available substation capacity at St Mary's ................................................................... 47
Figure 6-3; Geological survey....................................................................................................... 48
Figure 6-4; Marine designation and marine species and habitat concentrations ............................. 49
Figure 6-5; Mean annual power and marine designations ............................................................. 50
List of Tables
Table 2-1; Table of proposed correction factors.............................................................................. 8
Table 3-1; Spectral data for a single sea state............................................................................... 10
Table 4-1; Summary statistics for all location................................................................................ 23
Table 4-2; Summary table of power by direction........................................................................... 31
Table 5-1; Summary table of extreme conditions experienced at all locations ................................ 43
Table 6-1; Local constraint data sources....................................................................................... 46
6. v
Acknowledgements
Firstly, I would like to thank my supervisor Dr. Helen Smith for her continued support and
advice throughout the duration of this dissertation. Her help and advice has contributed to
the success of this report.
I would also like to thank Johanna van Nieuwkoop-McCall, Prof. George Smith and Lars
Johanning who, along with Dr. Helen Smith, produced the hindcast dataset analysed in this
report and whose research and work within the marine renewables industry continues to
lead the way and gain international recognition.
Special thanks should also be given to Julian Pearce, Senior Officer of Physical Assets and
Natural Resources within the Isles of Scilly council, for his continued patience and readiness
to share information and resources.
7. vi
Abstract
Due to their exposed location in the Atlantic Ocean, the Isles of Scilly experience some of
the UK’s largest waves. This report studies the variation in wave power at 12 locations
around the Isles by analysing a 23-year hindcast dataset. The dataset was produced using
the ERA-Interim global reanalysis dataset provided by the European Centre for Medium-
Range Weather Forecasts, (ECMWF). Knowledge of temporal and spatial variation, extreme
wave conditions and local constraints is essential for locating wave energy converters.
There is a large temporal and spatial variation around the Isles of Scilly. Locations to the
south-west of Isles experience a mean annual power of 37.5kW/m whilst on the sheltered
eastern side the mean annual power is as little as 6.7kW/m. At the most energetic sites
monthly mean power can vary from 3-7kW/m during summer months to over 100kW/m in
winter months. Extreme wave analysis shows there is potential for a 1 in 100 year wave to
have a significant height of almost 20m.
However, although there is an abundant wave resource the biologically diverse marine
environment, exposed location and unique setting of the Isles of Scilly can produce different
problems.
9. 1
1 Introduction
1.1 The Isles of Scilly
The Isles of Scilly are a small archipelago located 28 miles off the south-west tip of Cornwall.
The Isles consist of over 190 islets composed of granite rock dating back over 300 million
years. There are five inhabited islands and a permanent population of 2,203 residents at the
2011 census, (ONS, 2011).
The exposed location within the Atlantic Ocean has created a complex ecosystemof
significant cultural and environmental importance. The Isles and surrounding area are a
designated Marine Special Area of Conservation, (SAC), and there are 11 Marine
Conservation Zones around the Isles. The Islands themselves are a designated Area of
Outstanding Natural Beauty, Heritage Coast and 26 Sites of Specific Scientific Interest cover
34% of the Islands, (Natural England, 2013). Thus, highlighting the sensitive and complex
environment.
1.2 CurrentInfrastructure
In order to meet electricity requirements, the Isles rely on a single 33kV electricity
transmission line connected to the mainland. The 55km cable was installed by South
Western Electricity Board in 1988 and is currently owned and maintained by Western Power
Distribution, the current Distribution Network Operator for the South-West. The cable has a
capacity of 7.5MW, and can be used to back-feed 4MW to the mainland if required. This
rarely occurs and would primarily be from the 5.7MW backup power station on St Mary’s,
during times of blackout. The cable was exposed by the 2013/14 winter storms and is due
for replacement, at an estimated replacement cost of £25million, (Isles of Scilly Council,
2014).
St Mary’s, Tresco and St Martins are on a shared distribution network, and are effectively a
self-contained micro-grid. Bryher and St Agnes are on spurs from this loop and the lack of
opportunity to back feed has required two local back up power stations on the Islands. (Isles
of Scilly Council, 2007).
10. 2
Energy supply to the Isles of Scilly is restricted by the Islands’ peripheral location preventing
the local population access to certain energy sources. The 2011 census showed 40% of local
residents do not have central heating systems and electricity is currently used to meet a
large proportion of demand, including heating and cooking. Since the installation of the
cable, locals can receive the Economy 7 tariff which is widely used due to the use of electric
storage heaters. There is an early evening peak load of 4.5MW and a night time Economy 7
peak load of 4.5MW.
1.3 Wave Resource
Due to their exposed location in the Atlantic, the Isles experience some of the UK’s largest
waves. Earlier this year, (February 2016), the Isles were hit by storm Imogen and waves with
a significant height of over 13.5m were recorded, (Met Office, 2016). In an assessment of
the wave and tidal resource by the South West of England Regional Development Agency,
results found that throughout the South West region; “in water depths of around 50 m, the
all-year average wave power varies between 38 kW/m at exposed locations near the Isles of
Scilly and 19 kW/m in more sheltered sites near Lundy Island,” (SWRDA, 2004).
Utilising the abundant wave energy resource could help reduce dependence on electricity
supply from the mainland, help to cut carbon emissions and help the Isles meet their long
term goal of self-sustainability. As well as the positive environmental impacts the
development of wave energy projects could help to diversify the local economy. At the
moment At least 80% of the Islands economic income stems directly from tourism.
Currently there is only one wave energy project in planning around the Isles. 40 South
Energy have proposed a small project near St Mary’s airport with the installation of 3,
200kW devices. However, this project was first suggested in 2013 and is still awaiting
consent. Two of three devices have already been sold to an investment company and 40
South Energy have stated that they hope at least part of the third device can be owned by
the local community, (40 South Energy, 2016).
Wave power provides an opportunity for the Isles of Scilly to implement sustainable
technologies without having a significant visual impact on the surrounding landscape.
11. 3
1.4 ProjectAims
There has been little in depth research into the wave resource around the Isles of Scilly with
no academic journals existing on the topic. This report aims to study a 23-year hindcast
dataset produced by the University of Exeter in order to;
Quantify the available power around the Isles.
Analyse the temporal and spatial variation around the Isles.
Analyse extreme wave conditions.
Identify suitable locations by studying the wave climate and local constraints.
Assess the feasibility of using wave power to meet all the Isles electricity demand.
12. 4
2 Dataset
The University has produced a 23-year hindcast dataset using the spectral wave model
SWAN, (Simulating WAves Nearshore). SWAN is a third-generation wave model for
obtaining accurate estimates of fundamental wave parameters in large bodies of water
including; coastal areas, lakes, and estuaries. The model accounts for all processes that
generate, dissipate or redistribute wave energy. These include deep water processes of
wind input, whitecapping dissipation, and quadruplet nonlinear interaction. As well as
shallow water processes including, bottom friction dissipation, depth induced breaking and
triad wave-wave interactions, (Ris, Holthuijsen and Booij, 1994).
This section briefly summarises the main methodology of the original hindcast study, for full
details on the model set-up, sensitivity study and data validation please refer to the
following paper; “Wave resource assessment along the Cornish coast (UK) from a 23-year
hindcast dataset,” (van Nieuwkoop et al., 2013).
The model was set-up to cover the area of 4 to 7 degrees west and 49 to 51 degrees north,
encompassing the whole Cornwall coast and the Isles of Scilly. The model ran in non-
stationary mode and the wind and wave inputs were provided by the ECMWF, (European
Centre for Medium-Range Weather Forecasting), dataset. ECMWF runs the ERA-Interim, a
global atmospheric reanalysis utilising the wave model WAM, (Hasselmann et al. 1988).
Before the study was conducted a sensitivity study was done to determine the optimal
model settings. Results were compared to a reference simulation using default SWAN
settings and to recorded buoy data for two hindcast periods. The most significant change
occurred when the default whitecapping settings were changed. Settings were changed to
reduce dissipation at lower frequencies and increase dissipation at higher frequencies.
Other changes to default settings were found to have a negligible effect. SWAN models used
in Sections 3.2 and 4.2.4 of this report have used the similar settings to the original study.
In addition to locations around Cornwall, analysed in the original study, hourly readings
were produced at 12 locations around the Isles of Scilly over the 23-year hindcast period
from 1st Jan 1989 to 31st Dec 2011. Output parameters included; Hm0, Tm-10, Tm01, the mean
direction, time and date for each sea state. Details and positions of the output locations are
13. 5
shown Table 2.1 and Figure 2.2. Output locations are referred to throughout the report as
‘Hindcast Locations 1-12’, or simply Locations 1-12 were acceptable.
Figure 2-1; Details of the 12 hindcast output locations around the Isles of Scilly
Location Latitude Longitude Depth
1 -6.2490 49.9714 53.5
2 -6.3460 49.8743 60.1
3 -6.4100 49.8563 62.7
4 -6.4640 49.8833 54.6
5 -6.4280 49.9255 56.2
6 -6.4010 49.9507 67.7
7 -6.3730 49.9624 53.7
8 -6.3490 49.9759 50.8
9 -6.4530 49.9561 84.9
10 -6.4170 49.9741 77.7
11 -6.3880 49.9975 78.0
12 -6.3650 50.0110 80
14. 6
Figure 2-2; Maps the location of the 12 hindcast locations
Data outputs were validated against buoy measurements at 7 locations over available time
periods.
Figure 2-3; Details the 7 buoys used for validation. Source; (van Nieuwkoop et al., 2013)
In each comparison three statistical analysis techniques were applied and studied; the
relative bias, the root mean squared error, (RMSE), and the scatter index, (defined as the
15. 7
standard deviation of the difference between modelled and observed data, normalised by
the mean of the observations). Results are shown in Figures 2.4 and 2.5.
Figure 2-4; Validation results from the original hindcast study. Source; (van Nieuwkoop et al., 2013)
Computed values of Hm0 overall were underestimated by a few centimetres. Figure.2.5
shows relatively large negative bias for very steep or long waves and a large positive bias for
small waves less than 1m.
All bias for the calculation of Tm-10 is negative. The smallest bias is found on steep waves and
the largest bias is found for long small waves. The model performs best for medium height
waves between 0.5 and 3m and wave periods between 4 and 10 seconds but tends to
significantly underestimate larger waves.
The relationship between modelled data and observed data from the PRIMaRE wave buoy D
was studied and correction factors for both Hm0 and Tm-10 were calculated using regression
techniques. Figure 2.6 shows the error between modelled and computed data. Hm0 bias is
modelled as a quadratic in order to increase Hm0 for larger waves and reduce Hm0 for smaller
Figure 2-5; Bias between modelled values and observed values from PRIMaRE wave buoy D. (van Nieuwkoop et al., 2013)
16. 8
waves. There is a linear relationship between Tm-10 bias and is therefore modelled as a
constant.
Figure 2-6; Results from regression analysis previously conducted, (van Nieuwkoop et al., 2013)
Table 2-1; Table of proposed correction factors.
Hm0 Correction Factor y = -0.01x2 -0.08x +0.11
Tm-10 Correction Factor y = -1.2
For more information, please refer Appendix A of the original study, (van Nieuwkoop et al.,
2013).
17. 9
3 Data Validation
3.1 Introduction
This section aims to test the validity of the data for use at the 12 hindcast locations around
the Isles and assess the difference between uncorrected and corrected datasets.
3.2 Method
In order to assess the validity, spectral data from a nearby wave buoy has been analysed.
Data is from the 'SW Isles of Scilly WaveNet Site' and has been provided by Cefas, (Cefas,
2016). Data collection commenced on 11th October 2014. The buoy is still operational and
data has been downloaded and analysed until 1st March 2016. The buoy is situated at a
water depth of 90m and is located to the South of the Isles of Scilly at a 49°51'.01N,
6°32'.61W, as shown in Fig.3.1.
Figure 3-1; Shows location of WaveNet buoy and local bathymetry;
18. 10
Spectral data represents the sea state at a given moment of time, i.e. assumes that the sea
state is stationary. The WaveNet spectral data is recorded with a time step of 30 minutes
and shows the period, spectral density, wave direction and wave spread for 13 frequencies
at each time step. Data for a single time step is shown in Table 3.1.
Table 3-1; Spectral data for a single sea state
The power per meter of wave, (W/m), can be calculated using the omni-directional wave
power formulae, (EMEC, 2009):
Where the group velocity can be calculated as;
19. 11
Where k = 2π/ λ and has been calculated using an iterative Matlab script based on the
dispersion relationship;
The significant height and energy period have also been calculated from the moments of the
spectrum, where;
For example, m1 and m-1 can be calculated as;
The significant height and energy period are calculated as;
The power, significant height and energy period were calculated for each sea state within
the available period and analysed for comparison.
In order to compare the power output at the WaveNet site to the corrected and
uncorrected hindcast datasets a SWAN model was set up in order to determine the spatial
relationship between Hindcast Location 4 and the WaveNet site. Location 4 has been chosen
for comparison as its exposed location and geographical proximity to the WaveNet site
suggest conditions should be most similar to the WaveNet site.
The model was run in stationary mode using settings similar to the original hindcast study.
44 different sea states were analysed to approximate the spatial correlation between the
two sites. The sea states modelled are shown in section 4.2.4 Figure 4.2. The hourly
20. 12
occurrence of each sea state is known, (see section 4.2), this was used to calculate the
spatial correlation between Location 4 and the WaveNet buoy site.
Results from the SWAN model show the power per meter of wave is on average 12 percent
greater at the WaveNet site than Hindcast Location 4. The significant height is on average 8
percent greater at the WaveNet site and the energy period is on average 3 percent smaller
as the WaveNet site.
These results were used to adjust the spectral data to match conditions at Location 4. The
mean power per meter of wave for 2015 at the WaveNet site, is 40.67 kW/m. When
adjusted to match conditions at Location 4 the mean power is 36.50 kW/m.
The modified spectral data was then compared to both the corrected and uncorrected
hindcast datasets. Annual and monthly averages of power, significant height and energy
periods have been compared to the spectral data for both the corrected and uncorrected
datasets. Figures 3.3 and 3.4 show the annual means over the hindcast period compared to
the 2015 annual mean calculated from the spectral data. Figures 3.4 to 3.6 show the
minimum, maximum and mean monthly averages of; power output, significant height and
energy period. Monthly means of the spectral data have been plotted for comparison.
21. 13
3.3 Results
3.3.1 Comparison of Spectral Data
The overall annual mean for the uncorrected data is 29 kW/m whilst for the corrected
hindcast data the annual mean is 37.63. Results show that an annual mean power of 36.5
kW/m, calculated from the spectrum, is right at the top of the range for uncorrected data,
however it is close to the mean for the corrected dataset.
Figure 3-3; Compares corrected annual means over hindcast period with spectral data for 2015
Figure 3-2; Compares uncorrected annual means over hindcast period with spectral data for 2015
22. 14
When compared to the uncorrected hindcast dataset, the average power from the spectral
data throughout December is greater than any December over the entire 23-year hindcast
period. When compared to the corrected data, all monthly averages fit within the expected
range.
Figure 3-4; Shows uncorrected and corrected minimum, maximum and average means for each month with spectral data
plotted for comparison
There is little difference between corrected and uncorrected significant heights and the
spectral data fits within the range of both datasets.
Figure 3-5; Shows uncorrected and corrected monthly significant heights with spectral data plotted for comparison
23. 15
When plotted against the energy period for the uncorrected dataset, the spectral data falls
outside the expected range almost every month. Although this is possible, it is unlikely as in
other regions of the UK, 2015 was a normal year (Met Office, 2016b#). The original
validation study showed that the hindcast data consistently underestimates the energy
period, this can also be seen here. However, when hindcast data has been corrected there is
a very strong correlation between the energy periods calculated from the spectrum and the
hindcast energy periods.
Figure 3-6; Shows uncorrected and corrected monthly energy periods with spectral data plotted for comparison
The results from SWRDA, 2004, determined that the average power for exposed locations
around the Isles of Scilly is approximately 38kW/m. This fits extremely well with the spectral
data and the corrected hindcast dataset. However, the uncorrected dataset provides an
average wave power of only 29kW/m, which is well below what is expected.
3.4 Conclusion
Unfortunately, as there is no freely available buoy data in the region that covers the
hindcast period, it is difficult to correlate the dataset. However, based on the spectral data,
validation from the original study and results from the SWRDA assessment, it is clear that
the uncorrected datasets consistently underestimate Tm-10 and underestimate Hm0 for
larger waves. Therefore, the resource assessment will be carried out using only the
corrected hindcast dataset. It is worth noting here that corrected data may still contain
errors and should be used as a guide only and not the final assessment for any WEC
developments.
24. 16
4 Wave Resource
4.1 Intro
This section aims to quantify and analyse the available wave resource and study the
temporal, directional and spatial variation around the Isles.
4.2 Method
Characterization of the wave energy resource is achieved by analysing the corrected 23-year
hindcast dataset. Matlab has been used for all data analysis as it is a powerful tool for
grouping and presenting data in a number of ways. Due to the geographical proximity of a
number of the hindcast locations, full results will not be presented for all locations in the
main body of text, summary tables with all locations included will be shown where
necessary.
4.2.1 Joint OccurrenceTables, Energy Yield and Power Calculations
First data is binned into pairs of Hm0 and Tm-10 representing different sea states. This allows
for all 201,600 hourly data points at each location to be categorized within a defined
number of Hm0 and Tm-10 pairs. Hm0 and Tm-10 were originally binned in 0.5m and 0.5s
intervals respectively. However, the tables created were too large for display and the
intervals have been doubled to 1m and 1s. Joint occurrence tables were then created
showing the number of data points that fall within each bin.
From this it is possible to work out the number of hours each year that each sea state is
likely to occur by calculating the probability of each bin occurring and multiplying 8,760. By
multiplying the hours of occurrence each year by the power available per meter of wave for
each sea state, it is then possible to calculate the average annual energy generated per
meter of wave over a one-year period at each location. Both the hours of occurrence each
year and the energy generated for each sea state are shown in Figures 4.3 to 4.6. Only
locations 1 and 4 will be shown in full in the main body of text as they represent the
minimum and maximum power resource around the Isles, tables for all other locations have
been included in the appendix.
This allows WEC developers to establish whether the wave resource suits their needs and if
their device will be best placed to utilise the available energy yields at a given location.
25. 17
As spectral data is not available at the hindcast locations, the omni-directional power can be
calculated using Hm0 and Tm-10 with the approximation equation 4.1. Cg is calculated as a
function of the Tm-10 and water depth. There can be a slight error with the approximation
equation which can typically underestimate the power resource by 1 -3%, (Robertson et al.,
2016);
(4.1)
The available power per meter of wave for each sea state has been calculated using
equation 4.1, and results are shown in Figure.4.1.
The mean power per meter of wave at each hindcast location was found by calculating the
power for each hourly time step at each location, using equation 4.1, and taking the mean.
Figure 4-1; Power per meter of wave for each sea state
4.2.2 Temporal Variation
In order to assess the feasibility of using wave power to meet the Isles energy demands, it is
important to understand the temporal variations in the available resource. Data has been
analysed on monthly, seasonal and annual time periods. Again, only locations 1 and 4 have
been shown as they represent the minimum and maximum power resource around the
Isles.
2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 15.5
0.5 0.3 0.4 0.6 0.7 0.8 0.9 1.0 1.2 1.3 1.4 1.5 1.7 1.8 1.9
1.5 2.8 3.9 5.0 6.1 7.2 8.3 9.4 10.5 11.6 12.7 13.9 15.0 16.1 17.2
2.5 7.7 10.8 13.9 16.9 20.0 23.1 26.2 29.2 32.3 35.4 38.5 41.6 44.6 47.7
3.5 15.1 21.1 27.2 33.2 39.2 45.3 51.3 57.3 63.4 69.4 75.4 81.5 87.5 93.5
4.5 24.9 34.9 44.9 54.9 64.8 74.8 84.8 94.7 105 115 125 135 145 155
5.5 37.2 52.1 67.0 81.9 96.8 112 127 142 156 171 186 201 216 231
6.5 52.0 72.8 93.6 114 135 156 177 198 218 239 260 281 302 323
7.5 69.3 97.0 125 152 180 208 235 263 291 319 346 374 402 429
8.5 89.0 125 160 196 231 267 302 338 374 409 445 480 516 552
9.5 111 156 200 244 289 333 378 422 467 511 556 600 645 689
10.5 136 190 244 299 353 407 462 516 570 624 679 733 787 842
11.5 163 228 293 358 423 489 554 619 684 749 814 879 944 1010
12.5 192 269 346 423 500 577 654 731 808 885 962 1039 1116 1193
Te (s)Location 1
Mid Bin
Hmo (m)
26. 18
This has been done by studying the following;
Time series of wave power over the 23-year period.
Monthly averages over the 23 year hindcast period.
Minimum, maximum and mean monthly averages throughout the year.
Variations in the mean annual power.
Seasonal variations in wave power.
4.2.3 Directional Analysis
The prevailing wave direction is represented using wave roses to show the frequency of
occurrence of waves from all directions. A wave rose for locations 1, 2, 3 and 4 have been
presented in Figure.4.20. Wave roses for all other locations are attached in the Appendix
but have not been shown in the main body of text as they are very similar to location 4.
Wave roses taken from locations around the Isles have been superimposed on a map to
show the directional variation as waves move around the Isles.
Hindcast data has been binned into directional groups of 45 degree intervals to analyse the
variation in wave power from each direction. Joint occurrence tables showing the number of
hours each sea state occurs have been produced for all directional bins at location 4.
Location 4 has been chosen as it the most exposed site and represents waves that have not
been affected by any obstructions.
4.2.4 Spatial Variation
SWAN software has been used to analyse the spatial variation around the Isles. The model
covers the area from 6.5 to 6.1 degrees west and 49.7 to 50.1 degrees north, shown in
Fig.4.22, and uses the bathymetry data shown in Fig.3.1, (Digimaps, 2005). The model
results have been output every 0.005 degrees, approximately every 550 meters, creating an
81 x 81 grid. Results were also recorded at buoy locations for reference. Default settings
have been used except for the adjustments to whitecapping formulae as used in the original
hindcast study.
27. 19
A smaller joint occurrence table was created for Hindcast Location 4, with Tm-10 binned in
two second intervals. The model was run for all 44 sea states that occur at Hindcast Location
4, shown in Fig.4.2. The modelled boundary conditions for each sea state were set so the
significant height and energy period at Hindcast Location 4 were as close to the mid bin
values, shown in Fig.4.2, as possible. This was done through an iterative process making
small changes to input boundary conditions. Wave direction and wave spread input
conditions were found by taking the mean values for all data points within bin ranges.
Figure 4-2; Condensed table showing hours occurrence each year of each sea state at location 4
The significant height and energy period were recorded at each output location. This was
then used to calculate the power at each location for each sea state. As the results were
also recorded at buoy locations the number of hours each sea state occurred is known. I.e. if
the results at location 4 showed a significant height of 3.5m and an energy period of 10
seconds, by referencing the joint occurrence table it is shown how many hours each year
that sea state occurs.
This method serves as an approximation only as positive and negative bias within each bin is
unaccounted for.
The mean power at each grid point was then plotted to create a thematic map showing the
spatial variation across the Isles.
Mid Bin 2 4 6 8 10 12 14 16
0.5 1 269 415 106 1 0 0 0
1.5 0 81 1821 1280 227 7 0 0
2.5 0 0 302 1308 527 76 1 0
3.5 0 0 0 559 478 103 4 0
4.5 0 0 0 113 418 97 8 0
5.5 0 0 0 1 224 79 8 0
6.5 0 0 0 0 71 71 5 0
7.5 0 0 0 0 6 51 5 0
8.5 0 0 0 0 0 22 2 0
9.5 0 0 0 0 0 5 2 0
10.5 0 0 0 0 0 1 1 0
11.5 0 0 0 0 0 0 1 0
12.5 0 0 0 0 0 0 0 0
Te (s)
Hmo (m)
28. 20
4.2.5 Spectral Analysis
The spectral data from the WaveNet buoy has been used to analyse the sea states around
the Isles. All sea states have been binned into Hm0 and Tm-10 pairs and the mean spectrum for
each sea state has been plotted with frequency on the x-axis and energy density on the y-
axis. This shows the occurrence of swell and wind waves. As there is only a limited amount
of spectral data, not all sea states have been plotted. Only sea states which occur over 100
times have been plotted to ensure the mean is representative. As only the mean values
have been plotted the full variety of sea states is not shown.
The spectral data has also been binned by direction to analyse the variety the waves that
approach from various directions. As individual sea states are made up of waves from a
variety of directions, data has been plotted as a scatter diagram of frequency against
spectral density.
29. 21
4.3 Results
4.3.1 Joint OccurrenceTables and AnnualEnergy Yield
Location 1
Location 1 is the most sheltered of the 12 hindcast locations, with significant heights only
exceeding 2.5m for 2% of the year, and never exceeding 6m over the 23 year hindcast
period. The most commonly occurring sea states are between 0-1m and 4-6s. However, the
majority energy available throughout the year occurs from sea states between 1-3meters
and 5-10seconds.
The total energy available per meter of wave each year is on average 60.5MWh
Figure 4-3; Hours occurrence each year of each sea state at location 1
Figure 4-4; Annual energy generated per meter of wave for each sea state at location 1
2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 15.5
0.5 7 252 1,213 1,412 838 292 80 24 8 2 0 0 0 0
1.5 0 0 118 988 1,141 859 399 137 33 10 1 0 0 0
2.5 0 0 0 4 199 213 188 114 37 6 1 0 0 0
3.5 0 0 0 0 0 46 40 42 19 7 1 0 0 0
4.5 0 0 0 0 0 0 15 8 5 1 0 0 0 0
5.5 0 0 0 0 0 0 0 1 0 1 0 0 0 0
6.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
7.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
8.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
9.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
10.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
11.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
12.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Location 1
Mid Bin
Hmo (m)
Te (s)
2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 15.5
0.5 2 109 672 956 671 269 84 27 11 2 0 0 0 0
1.5 0 1 587 6,024 8,215 7,141 3,759 1,437 382 132 13 0 0 0
2.5 0 0 0 71 3,975 4,918 4,909 3,324 1,198 212 32 0 0 0
3.5 0 0 0 0 15 2,068 2,028 2,396 1,211 458 43 0 0 0
4.5 0 0 0 0 0 20 1,234 790 519 159 0 0 0 0
5.5 0 0 0 0 0 0 0 178 41 171 32 0 0 0
6.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
7.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
8.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
9.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
10.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
11.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
12.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Hmo (m)
Te (s)Location 1
Mid Bin
31. 23
Summary Information
The wave resource around the Isles varies considerably. The west of the Isles is exposed to
many more extreme conditions, whilst Location 1 on the east rarely sees severe conditions.
Although only locations 1 and 4 have been shown in full, the other locations experience
conditions somewhere in between.
Table 4-1; Summary statistics for all location
Location Mean Power
(kW/m)
Mean Hm0
(m)
Mean Tm-10
(s)
Annual Energy
Yield
(MWh/m)
1 6.7 1.18 6.33 60.5
2 10.7 1.32 6.69 96.1
3 30.9 2.26 7.90 273.9
4 37.5 2.44 7.96 331.8
5 31.5 2.26 7.84 279.4
6 31.7 2.24 7.94 280.7
7 21.1 1.80 7.90 188.1
8 23.6 1.94 7.85 209.7
9 36.6 2.41 7.91 324.2
10 34.7 2.35 7.89 308.1
11 34.0 2.35 7.84 301.6
12 33.4 2.35 7.78 295.6
4.3.2 Temporal Variation
Time Series of WavePower
The time series at both locations highlights the variability in the wave resource. Fluctuations
can vary from 0 to over 30 times the mean power. Location 4 regularly experiences peaks of
over 500 kW/m and on one occasion the power per meter of wave reached over 1000kW.
Whilst location 1 does not experience the same extreme conditions fluctuations are as large
proportionally.
32. 24
Figure 4-7; Time series of power at location 1
Figure 4-8; Time series of power at location 4
33. 25
Monthly AveragesthroughouttheHindcastPeriod
Monthly averages have been plotted in order to smooth the extreme fluctuations presented
in the time series. However, monthly power averages still fluctuate from 2kW/m to 23kW/m
at location 1 and 4kW/m to almost 200kW/m at location 4.
Figure 4-9; Monthly averages over hindcast period at location 1
Figure 4-10; Monthly averages over hindcast period at location 4
34. 26
Variation of Monthly Averages
Powerper Meter of Wave
Fig.4.11 highlights the monthly variation of the wave resource. At location 4 the monthly
mean in July is on average 10.7kW/m, almost 8 times lower than the 82.7kW/m mean in
January. Some years there is a factor of 10 difference between summer months and winter
months.
Figure 4-11; Minimum, maximum and mean monthly average power per meter of wave
SignificantHeight
The monthly variation in significant height follows a similar pattern to the monthly variation
in power. However, as power is proportional to Hm0
2, the fluctuations are less extreme. The
mean significant height in July is half the size of January.
Figure 4-12; Minimum, maximum and mean monthly average significant height
35. 27
Energy Period
The energy period varies less drastically than the power per meter of wave and significant
height. However, there is a clear reduction of approximately 20% throughout the year.
Figure 4-13; Minimum, maximum and mean monthly average energy periods
AnnualVariation
As well as monthly variations, there is also a large annual variation in the wave resource.
Mean power can vary significantly from one year to the next. At location 4 the annual mean
power varies from 23kW/m to 48kW/m.
Figure 4-14; Annual mean power over hindcast period
36. 28
4.3.2.1 Seasonal Variation
Comparison of winter months, (October to March), and summer months, (April to
September), further shows temporal variation.
Figure 4-15; Seasonal variation at location 1
Figure 4-16; Seasonal variation at location 14
37. 29
4.3.3 Directional Analysis
The wave roses presented in Fig.4.20 show that the prevailing wave direction is due west. At
location 4, 79% of waves approach the Isles from in between 225 to 315 degrees. The Isles
act as a barrier causing diffraction to occur as the waves wrap around the Isles, as shown by
Fig.4.21. The joint occurrence tables show that no large waves propagate from between 0-
180 degrees.
Figure 4-17; Hourly occurrence of sea states from 0-180 degrees
39. 31
Table 4. Shows that the most powerful waves come from between 225 to 250 degrees,
these waves have travelled directly across the Atlantic and can bring powerful storms and
large swell waves.
Table 4-2; Summary table of power by direction
40. 32
WaveRose
Wave roses for all locations except locations 1 and 2 show that the prevailing wave direction
is due west.
Figure 4-19; Wave rose for locations 1-4
42. 34
4.3.4 Spatial Variation
The results from the SWAN model have been plotted in Fig.4.22. The mean power increases
to the west of the Isles as the water depth increases. There is little variation between
locations 4 to 12, except locations 7 and 8 which are closer to land.
Figure 4-21; Spatial variation of mean power
43. 35
4.3.5 Spectral Analysis
The peak frequency for the smallest waves is over 0.2 Hz, showing that these smaller waves
are wind driven waves. Whilst the larger, more powerful waves with a frequency of 0.1Hz
and below are swell driven waves.
Figure 4-22; Spectral data plotted for each Hm0 and Tm-10 pair
44. 36
The most energetic frequencies for waves coming from between 0-180 degrees is close to
0.2Hz. For waves approaching from 180-360 degrees the most energetic frequencies are
closer to 0.1Hz and for waves between 225-315degrees the most energetic frequencies are
below 0.1Hz.
Figure 4-23; Spectral data plotted by direction
45. 37
4.4 Conclusion
There is a large temporal variation of the available wave power resource around the Isles.
There can be a factor of 10 difference in the average power per month within the same
year.
Locations 1 and 4 represent the maximum and minimum wave resources with all other
locations experiencing conditions in between. The majority of waves approach the Isles
from the west, with the largest waves approaching from between 225-270 degrees.
Therefore, the south-west of the Isles has the largest wave power resource and experiences
the most extreme wave conditions. Waves with a significant of up to 20m may occur within
a 100-year period. To the east of the Isles remains sheltered and has a relatively small wave
power resource.
Spectral analysis confirms that any waves approaching from between 0-180 degrees are
small wind waves. Whilst waves approaching from 180-360 are predominantly swell waves
with the most energetic waves approaching from 225-315 degrees. This supports the
hindcast data.
46. 38
5 Extreme Wave Analysis
5.1 Intro
Modelling extreme wave conditions is vital for the design and operation of WEC’s. Any
devices and moorings should be designed to withstand the most severe conditions. This
section aims to quantify the most extreme waves that may occur in each location within a
100-year period and analyse the frequency and severity of storm conditions.
5.2 Method
5.2.1 Extreme Value Analysis and the
100-Year Wave
A number of methods and techniques exist for
the statistical analysis of extreme values within
a dataset. Extreme value distributions can be
used to analyse the tail of a parent distribution.
In this instance the parent distribution is the
significant heights at hindcast locations. The
distribution of significant heights at location 4,
with a mean of 2.44 and standard deviation of 1.42, has been plotted.
There are two main methods for characterising and extracting extreme values. The first is
the block maxima approach. This approach takes the length of study, and divides into
equally spaced blocks and extracts the maximum values from each block. The block maxima
are then fitted to their own distribution. The second approach, which tends to be the more
popular approach, is the Peaks over Threshold, (POT), method. A threshold is set, and all
data points above the threshold are extracted and can also be characterised by their own
distribution.
Each approach has a specific distribution that can be used to characterise how the data
would converge. The block maxima technique fits the generalised extreme value, (GEV),
distribution. Whilst the POT technique should be used with the generalised Pareto
distribution, (GPD), (Rootzen and Tajvidi, 2006). Due to the large temporal variation and
Figure 5-1; Frequency distribution of significant heights at location
4
47. 39
limited data collected by the block maxima approach, the POT method fitted with the GPD
has been used.
There are a number of methods for estimating the unknown parameters of extreme value
distributions. The most commonly used are: probability weighted moments, (e.g. Hosking et
al. 1985), maximum likelihood, (e.g. Coles, 2001), and Bayesian methods, (Coles, 2001 and
Cooley et al. 2007). Due to the software used for analysis the probability weighted moments
methods has been used.
For more detail into the general methodology of statistical extreme value analysis the
following texts are of use; Coles, 2001, Beirlant et al. 2004, Hann and Ferreira, 2006.
Extreme wave analysis was conducted for all locations. However, only locations 1 and 4 are
shown in the main text, as they represent the most sheltered and exposed sites around the
Isles. All significant heights above a threshold were extracted from the hindcast data set and
modelled as an independent distribution. Data points were extracted so that two values
were never taken within the same 48-hour period, this allowed for each data point to
represent independent storms and stops particularly violent storms from dominating
results. The threshold hold was set using the WAFO toolbox, based on the dispersion index,
(variance to mean ratio), and mean residual life, (mean exceedance over threshold). WAFO
ensures that there is enough data for the extreme values to be fitted to the GPD, but not
too many that the tail of the new distribution again creates uncertainty. The thresholds for
Hindcast Locations 1 and 4 were 4.05m and 7.2m respectively.
The software was used to calculate the extreme significant heights that may occur at each
location within a 100-year period, with a 95% confidence bound, known as the 100-year
wave.
5.2.2 StormAnalysis
As well as using the WAFO toolbox, extreme conditions that occur over the 23-year hindcast
period have analysed. The number of 48 hour periods where wave conditions exceeded 100
kW/m and Hm0 exceeded 5m at locations 1 and 4 have been calculated and the most
extreme sea states that occur each year at locations 1 and 4 have been plotted. A summary
48. 40
table of most powerful waves experienced at each location over the 23 year hindcast period
has also been produced.
The spatial variation around the Isles during storm conditions has also been plotted using
SWAN software. All sea states at Hindcast Location 4 with a power per meter of wave over
300 kW have been grouped together and studied to find the mean direction, energy period
and significant height. Hindcast Location 4 has again been used as its exposed location
means that it is subject to the largest storms and greatest significant heights. This data has
been used as the input for a SWAN model. The model covers, and is set up as in section 4.24
with different input parameters. Model results have then been plotted to highlight areas
that are significantly influenced by extreme wave conditions.
5.3 Results
5.3.1 100-Year Wave
Figures 5.2 and 5.3 show the results from the WAFO analysis. Data is summarised in
Table.5.1. As a general rule, the maximum height of a wave is approximately twice as high as
Hm0, (NOAA, 2016). Therefore, at Hindcast Location 4, observed heights of 30-40m may
occur within a 100-year time frame. This should be considered for in the design of any WEC
and mooring systems. However, the 95% confidence bounds show the increasing
uncertainty of predicting of predicting wave heights for larger return periods.
On the other hand, Hindcast Location 1 is subject to far less extreme conditions. Over a 100-
year period significant heights of 8m may occur. This equates to a Hmax of approximately
16m, less than halve the 100-year Hmax at Hindcast Location 4. This shows the variability
around the Isles and indicates the sheltered environment to the east of the Isles.
49. 41
Figure 5-2; Extreme significant heights with 95% confidence bounds at location 1
Figure 5-3; Extreme significant heights with 95% confidence bounds at location 4
50. 42
5.3.2 StormAnalysis
Over the 23-year hindcast period, the power per meter wave at Location 4 exceeds 100kW
for 760 48-hour periods, whilst for 565 of those Hm0 is greater than 5m. This suggests that
on average there are nearly 26 isolated occasions each year of violent conditions with Hmax
exceeding 10m. At location 1 there is only 21 occasions over the hindcast period where
conditions exceeded 100kW/m. Of those only 7 had a significant height of over 5m.
Fig.5.4 shows the most powerful sea states that occur each year over the hindcast period at
Locations 1 and 4. The most powerful sea state experienced at Location 1 was 197 kW/m, at
Location 4 a sea state of 1040 kW/m was recorded in December 1989.
Figure 5-4; Most energetic sea states recorded each year throughout the hindcast period
Fig.5.5 shows the largest value of Hm0 recorded each year. Most years at Location 4 Hm0
reaches over 8m, this is similar to the worst case predicted 100-year wave at Location 1.
51. 43
Figure 5-5; Largest significant heights recorded each year throughout the hindcast period
Table 5-1; Summary table of extreme conditions experienced at all locations
Hindcast Location Maximum Power
Recorded Over
Hindcast Period
(kW/m)
Maximum Significant
Height Recorded
Over Hindcast Period
(m)
Significant Height
of 1 in 100 Year
Wave (m)
1 196 5.91 8.0
2 640 9.90 17.1
3 840 11.4 18.4
4 1040 12.5 19.8
5 795 11.0 18.5
6 805 11.0 18.7
7 636 9.80 14.9
8 653 10.0 16.3
9 901 11.7 17.2
10 870 11.5 18.1
11 855 11.4 19.5
12 840 11.3 18.6
52. 44
5.3.3 Spatial Variation during Storm Conditions
There are 1792 hourly readings with a power density of over 300kW/m. The mean direction
of these powerful waves is 253 degrees with an average Hm0 of 7.13m and an average Tm-10
of 11.84s. Boundary conditions for the SWAN model have been found through an iterative
process and set so these parameters are present at Location 4. Fig.5.6 shows the spatial
variation in power during storm conditions around the Isles.
Figure 5-6; Spatial variation during extreme conditions
The most powerful waves come from between 2225-270 degrees, as shown in Table 4.2,
therefore it is generally the South-West of the Isles that is most heavily effected from severe
conditions.
53. 45
5.4 Conclusion
The North and North-East of Isles are slightly sheltered during severe conditions. To the East
of the Isles remains protected throughout intense storms.
The 100-year wave at Location 1 is smaller than waves that regularly occur at Location 4,
again showing the variability around the Isles. Waves with in an observed height, (Hmax), may
reach 30-40m at exposed locations.
Any wave energy devices installed in the more energetic areas will have to withstand huge
forces and waves over 1MW/m.
54. 46
6 Local Constraints
6.1 Introduction
As well as simply considering the wave climate around the Isles, it is necessary to study
possible local constraints and influences that may affect the installation of WEC’s. This
section aims to look at local grid and infrastructure constraints, as well as briefly exploring
the environmental constraints presented from such a biologically diverse area. Data has
been collected from the following sources;
Table 6-1; Local constraint data sources
Data Type Source
1:50,000 and 1:250,000 Rasta OS Maps DigiMaps, (DigiMaps, 2005)
Bathymetry Data DigiMaps, (DigiMaps, 2005)
Geological Survey DigiMaps, (DigiMaps, 2005)
GIS Cultural/Historic Designations Historic England
GIS Environmental Constraints and
Designation Boundaries
Natural England
Available Substation Capacity WPD, Generation Capacity Map, (WPD,
2016)
6.2 Constraints
6.2.1 Infrastructureand Grid constraints
Large projects may be influenced by the ability to secure a grid connection. St Mary’s,
Tresco and St Martins are on a shared distribution network loop, allowing supply to be back-
fed if there is an issue with the supply cables. Bryher and St Agnes are on spurs from this
loop, and the lack of opportunity to back feed has required the two local back up power
stations. (Isles of Scilly Council, 2014).
Substations on the Islands of St Martins, and Tresco, have limited available capacity of
between 300-450kW, as shown by the Generation Capacity Map provided by WPD.
Substations on the Island of St Agnus have an available capacity of up to 1MW, however, as
St Agnus and Bryher are on spurs from the main distribution network it is likely that any
developments connected on these Islands will require infrastructure upgrades. Substations
on the larger island of St Mary’s have an available capacity of up to 5MW. This may impact
55. 47
on the location of future WEC’, as upgrading infrastructure or installing long subsea cables
can add significant costs to projects.
Figure 6-1; Available substation capacity on Bryher and St Martins
As aforementioned, the subsea cable can be
used to back-feed 4MW to the mainland if
required. This rarely occurs and at the
moment this would primarily be from the
5.7MW power station on St Mary’s, during
times of blackout. However, this does
represent an opportunity for any renewable
energy developments. The cable is due to
be replaced and it is unknown the size and
capacity of any future installation. However,
the Isles may consider increasing the
capacity to back-feed as this will limit the capacity of developments for the duration of the
25-50year lifetime of the new cable, estimated replacement costs of £25million mean there
is little opportunity to upgrade at a later date.
Figure 6-2; Available substation capacity at St Mary's
56. 48
6.3 Environmental Impact
Wave energy conversion projects may conflict with existing ocean uses or strategies for
protecting marine species and habitats’, (Kim et al. 2012). Before any projects can
commence it is first essential to assess any impacts on the natural environment and
surrounding areas. These impacts will be considered in three categories; physical, biological
and social impacts.
6.3.1 Physical
This section refers to any changes that may occur to the physical landscape such as
sedimentary movement. The Isles and surrounding offshore area are composed of granite
rock that dates back 300 million years. The granite bedrock has created a unique
environment and a number of ‘Rocky Reefs’.
Figure 6-3; Geological survey
6.3.2 Biological
The waters surrounding the Isles are a biologically diverse ecosystem of European and
international importance. The whole of the Isles have been a designated Special Area of
Conservation since 2000, in compliance with the EC Habitats Directive. The main reason for
the designation is the presence of Annex 1 habitats, including a number of ‘Rocky Reefs’,
(Natural England, 2013). As well as the entire region being an SAC, there are a number of
individual Marine Conservations Zones, (MCZ’s). The maps below highlight the marine
57. 49
designations and areas with a high concentration of marine species and habitats. A thematic
map showing the mean annual power has been superimposed to show the location of
marine designations in relation to the available power resource. Unfortunately as the
projection systems used differ between the OS map, (National Grid coordinate system), and
the thematic map, (degrees latitude and longitude), the combined image has been slightly
squashed.
Figure 6-4; Marine designation and marine species and habitat concentrations
58. 50
Figure 6-5; Mean annual power and marine designations
6.3.3 Social
In addition to the diverse marine habitats and species the Isles are a designated Area of
Natural Beauty, Conservation Area, and the entire coastline is designated Heritage Coast.
The Isles of Scilly have the highest density of scheduled monuments, (238 monuments with
over 900 archaeological sites), in the UK and numerous protected ship wreck sites.
The Isles rely heavily on tourism throughout the year, accounting for 83% of the economy
with over 100,000 visitors per annum. It is therefore essential that any developments do not
impinge on tourist hotspots or take away from the natural beauty that tourists expect.
59. 51
7 Conclusion
The Isles of Scilly experience the most energetic sea-states in the south-west. The annual
average power reaches nearly 38kW/m to the South West of the Isles. However, there is a
large temporal and spatial variation around the Isles. Some years the mean power during
summer months can be a factor of 10 less than during winter months and to the east of Isles
annual average power can be as low as 3-5kW/m.
Although there is an abundant wave power resource around the Isles of Scilly, the unique
character, isolation and biodiversity of the area creates some problems for the installation
of WEC’s. Locations 5 to 12 are situated in less biologically diverse waters outside of any
conservation zones. However, improvements are required to the existing infrastructure with
limited available capacity on the smaller Islands. The granite bedrock means unique mooring
solutions will be required, consisting of rock bolts or piled foundations. The nature of the
Isles means that any developments will be heavily scrutinised and the environmental impact
will rigorously assessed. This has been proven by the slow consent process for the wave
energy project proposed by 40 South Energy in 2013.
The temporal variation suggests that wave power alone will not be sufficient to meet all of
the Isles electricity demand. To cover peak demand of 4.5MW in the summer months would
require huge developments that would produce 45-50MW during the winter months, unless
large energy storage techniques were applied. Currently the subsea cable can back-feed
4MW to the mainland, this limits generation capacity as surplus generation will be unable to
be exported during times of low demand on the Isles.
Many locations will require the upgrade of insisting infrastructure. Small developments
could be spread around the Isles in order to connect to multiple substations. However, as
Bryher and St Agnes are on spurs from the main distribution network, upgrades to the
network would be necessary in order to back-feed electricity to the other Islands and to the
mainland.
The most energetic sea states approach the Isles from between 225-270 degrees. To the
north and north-by-north-east of the Isles remain slightly sheltered during extreme
conditions. However, mean power is not significantly reduced.
60. 52
It is difficult to define ideal locations for WEC’s as there is a wide variety of different wave
energy devices, with the industry yet to consolidate on a single design. Different designs and
operating principles generally perform better in different conditions. The wide variety of
conditions around the Isles will be able to provide a suitable environment for all types.
Although less power is available near location 1, the sheltered area may be attractive to
some developers.
61. 53
8 Discussion and Limitations
Unfortunately limited buoy data around the region creates uncertainty during validation.
The corrected hindcast dataset was used as analysis showed results were representative of
conditions typically experienced throughout the region. However, as no data within the
region is freely available that covers the hindcast period, it is difficult to ascertain complete
confidence.
Corrected data still contains error. As figure 2.6 shows, the correction factor is based on the
best fit between modelled and observed data. Therefore, individual corrected data values
still have bias but it is assumed that the positive and negative bias cancel over the total
dataset. The errors obtained from the hindcast study are likely due to the coarse resolution
of the ECMWF wave boundary and wind input. Hindcast datasets should not be used as a
final resource assessment but as an overview of the variation of conditions throughout the
modelled region.
The correction factors were calculated using simple regression techniques, and more
sophisticated techniques could be applied in the future. However, it was deemed
appropriate to only display results based on the corrected dataset. Is was considered to
display both corrected and uncorrected results, however, this created confusion and the
report lost clarity.
When modelling extreme wave conditions, removing bias from the dataset does not
necessarily remove bias from extreme wave conditions.
The quality of the thematic maps showing the spatial variation around the Isles are of a poor
resolution and quality. This is largely due to the processing power of the computer used and
the inability to deal with high resolution models and outputs.
Although there are certain limitations the report does provide a detailed study into the
wave resource around the Isles of Scilly. No project previously has assessed the resource in
this depth.
62. 54
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