Workshop “ENERGIA DAL MARE - Le Nuove Tecnologie Per i Mari Italiani”
ENEA - Roma, 1 Luglio 2014
Accordo di Programma MiSE-ENEA
RICERCA DI SISTEMA ELETTRICO
Gianmaria Sannino1, A. Carillo1, E. Caiaffa1, M. Pollino2, L. La Porta2, E. Lombardi1
ENEA, Unità Tecnica di Modellistica Energetica e Ambientale (UTMEA)
Laboratorio di Modellistica Climatica e Impatti
(2)Laboratorio di Analisi e Osservazioni sul Sistema Terra
(1)
utmea.enea.it
Background – Global ocean energy distribution
The RE resource in the ocean comes from six distinct sources, each with different origins and requiring
different technologies for conversion.
■  Waves: derived from the transfer of the kinetic energy of the wind to the upper
surface of the ocean.
■  Tidal Range (tidal rise and fall): derived from the gravitational forces of the
Earth-Moon-Sun system.
■  Tidal currents: water flow resulting from the filling and emptying of coastal
regions as a result of the tidal rise and fall.
■  Ocean Currents: derived from wind-driven and thermohaline ocean circulation.
■  Ocean Thermal Energy Conversion (OTEC):derived from temperature
differences between solar energy stored as heat in upper ocean layers and colder
seawater, generally below 1,000 m.
■  Salinity Gradients (osmotic power): derived from salinity differences between
fresh and ocean water at river mouths.
Workshop Energia dal Mare 2014 – Roma 1 July Background – Mediterranean energy
The RE resource in the ocean comes from six distinct sources, each with different origins and requiring
different technologies for conversion.
■  Waves: derived from the transfer of the kinetic energy of the wind to the upper
surface of the ocean.
■  Tidal Range (tidal rise and fall): derived from the gravitational forces of the
Earth-Moon-Sun system.
■  Tidal currents: water flow resulting from the filling and emptying of coastal
regions as a result of the tidal rise and fall.
■  Ocean Currents: derived from wind-driven and thermohaline ocean circulation.
■  Ocean Thermal Energy Conversion (OTEC):derived from temperature
differences between solar energy stored as heat in upper ocean layers and colder
seawater, generally below 1,000 m.
■  Salinity Gradients (osmotic power): derived from salinity differences between
fresh and ocean water at river mouths.
Workshop Energia dal Mare 2014 – Roma 1 July Background – Mediterranean energy
The RE resource in the ocean comes from six distinct sources, each with different origins and requiring
different technologies for conversion.
■  Waves: derived from the transfer of the kinetic energy of the wind to the upper
surface of the ocean.
■  Tidal Range (tidal rise and fall): derived from the gravitational forces of the
Earth-Moon-Sun system.
■  Tidal currents: water flow resulting from the filling and emptying of coastal
regions as a result of the tidal rise and fall.
■  Ocean Currents: derived from wind-driven and thermohaline ocean circulation.
■  Ocean Thermal Energy Conversion (OTEC):derived from temperature
differences between solar energy stored as heat in upper ocean layers and colder
seawater, generally below 1,000 m.
■  Salinity Gradients (osmotic power): derived from salinity differences between
fresh and ocean water at river mouths.
Workshop Energia dal Mare 2014 – Roma 1 July Tidal energy assessment for the Mediterranean
Mediterranean and strait models
■  Tidal currents: water flow resulting from the filling and emptying of coastal
regions as a result of the tidal rise and fall.
Workshop Energia dal Mare 2014 – Roma 1 July Tidal energy assessment for the Mediterranean
Mediterranean and strait models
■  Tidal currents: water flow resulting from the filling and emptying of coastal
regions as a result of the tidal rise and fall.
Strait of Messina topography Gibraltar topography Workshop Energia dal Mare 2014 – Roma 1 July Strait of Gibraltar Background
Tarifa Narrow!
Camarinal Sill!
Espartel Sill!
Chart of the Strait of Gibraltar, adapted from Armi & Farmer (1988), showing the principal geographic features referred to in the text. Areas deeper than 400 m are shaded Workshop Energia dal Mare 2014 – Roma 1 July 14
A. Sánchez-Román et al. / Journal of Marine Systems 98–99 (2012) 9–17
Strait
of Gibraltar Background:
Physics
semidiurnal constituents and particularly for M2. The opposite behavior
is found in the Atlantic layer, where amplitude increases monotonically
from east to west. Phases of semidiurnal constituents also show such
monotonic characteristic. This behavior was reported by GarcíaLafuente et al. (2000) between the eastern exit of the strait and the
main sill of Camarinal and the analysis here not only confirms this result
but also extends it to the western exit of the strait. Between GC and CS,
the amplitude of M2 transport in the Mediterranean layer is reduced by
more than 50% while it is nearly 6 times greater in the Atlantic layer at
CS (Table 2). A very clear transfer of the tidal signal from the Mediterranean to the Atlantic layer takes place between GC and CS during the
flood tide and vice-versa during the ebb tide. This fact led Bray et al.
(1990) to propose the conceptual model of a membrane-like interface
between CS and GC sections whose role would be the transfer of the
tidal signal from one layer to the other. Phase difference between
these locations in the Atlantic layer indicates that tidal transport propagates from CS to GC, a fact that is ascribed to the strong baroclinic nature
of the internal tide. An inspection of values in Table 2 indicates that the
same behavior applies between CS and ES sections.
The two-layer sketches presented in Fig. 4 illustrate key aspects of
the tidal dynamics in the strait and explain the aforementioned divergences. Fig. 4.a represents the hypothetical mean exchange and mean
interface position in the absence of tidal forcing. Tidal transports associated with M2 within each layer (small two-headed arrows with
size proportional to the transport, which is indicated by the numbers
beside) and the total barotropic transport (large arrows with numbers
inside) have been included for ES, CS and GC sections (see Fig. 1 for locations). Notice that M2 transport by itself (3.1 Sv) would reverse the
mean flow, which is around 0.8 to 1 Sv in each layer (Baschek et al.,
2001; Garcia-Lafuente et al., 2002a; Sánchez-Román et al., 2009), if it
were equally distributed among both layers. This is not the case at the
strait's boundaries because most of the tidal flow moves through the
passive, slow-flowing layer (Mediterranean layer in the east, Atlantic
Image of acousQc backscaRer during ebb Qde over Camarinal Sill in layer in the west), making them reverse periodically. At these boundthe Strait of Gibraltar (Wesson and Gregg, 1994) aries, however, the tidal transport in the active, fast-flowing layers is insufficient to reverse the total flow. In CS the amplitude of M in any
Strong mixing and entrainment mainly driven by the very intense Qdes. A. Sánchez-­‐Román et al, JGR 2012 OWEMES 2014 – Roma 30 June Sub-basin Model: Cadiz – Gibraltar - Alboran
Modified POM Minimal Hor. Resolu4on: < 500 m External Time-­‐Step: 0.1 sec O1 K1 diurnal 4dal component M2 S2 diurnal 4dal component • Sannino et al, JGR-Book, 2013
• Sannino et al, JGR, 2007
• Sannino et al, JPO, 2009
• Sannino et al , NC, 2005
• Sanchez et al, JGR, 2009
• Sannino et al, JGR, 2004
• Garrido et al, JGR, 2008
• Sannino et al, JGR, 2002
• Garcia-Lafuente et al, JGR, 2007
Workshop Energia dal Mare 2014 – Roma 1 July Sub-basin Model: Cadiz – Gibraltar - Alboran
salinity along-strait section
Workshop Energia dal Mare 2014 – Roma 1 July Sub-basin Model: Cadiz – Gibraltar - Alboran
Tidal Components comparison Surface elevaQon Max Differences:
Amp: 3.6 cm
Pha: 11°
Sannino et al.,JPO, 2009 Tidal Components comparison Along-­‐strait velocity Max Differences:
Amp: 10 cm s-1
Pha: 20°
Sánchez-­‐Román et al., JGR, 2009 Workshop Energia dal Mare 2014 – Roma 1 July POM model and Gibraltar tidal circulation
Are the results model depended? OWEMES 2014 – Roma 30 June MITgcm vs POM : model grids
POM grid Max resolu4on 300 m MITgcm grid Max resolu4on 25 m (only 25% of the actual grid is shown) OWEMES 2014 – Roma 30 June MITgcm vs POM : model bathymetry
Real bathymetry POM bathymetry 32 sigma-­‐levels MITgcm bathymetry 53 ver4cal zeta-­‐levels (par4al cell) OWEMES 2014 – Roma 30 June MITgcm model simulation
Sannino et AGU-­‐BOOK 2014 Lafuente et al. JMS 2013 Garrido et al. JGR 2011 Interface depth evolution OWEMES 2014 – Roma 30 June MITgcm vs POM – Internal bore evolution
POM MITgcm Garrido et al. jgr 2011 OWEMES 2014 – Roma 30 June Observed and models interface layer thickness
a)  LocaQons of historical conducQvity-­‐temperature-­‐
depth data (CTD, black dots) collected in the Strait. b)  Interface layer thickness computed from CTD data. POM MIT c)  BoRom topography along the central axis of the Strait. OWEMES 2014 – Roma 30 June MITgcm simulation: Gibraltar Energy assessment
P=
Layer1: 25m-­‐70m Calero Quesada et al. 2014 1 3
ρV
2
Layer2:70m-­‐115m Mean Energy Flux in W/m2 OWEMES 2014 – Roma 30 June Tidal energy assessment for the Mediterranean
Mediterranean and strait models
■  Tidal currents: water flow resulting from the filling and emptying of coastal
regions as a result of the tidal rise and fall.
Strait of Messina topography Gibraltar topography Workshop Energia dal Mare 2014 – Roma 1 July Tidal energy assessment for the Mediterranean
Mediterranean and strait models
■  Tidal currents: water flow resulting from the filling and emptying of coastal
regions as a result of the tidal rise and fall.
Gibraltar topography Workshop Energia dal Mare 2014 – Roma 1 July Tidal energy assessment for the Mediterranean
Mediterranean and strait models
■  Tidal currents: water flow resulting from the filling and emptying of coastal
regions as a result of the tidal rise and fall.
Gibraltar topography Workshop Energia dal Mare 2014 – Roma 1 July Tidal energy assessment for the Strait of Messina
Messina tidal model
!
Workshop Energia dal Mare 2014 – Roma 1 July Background – Mediterranean energy
The RE resource in the ocean comes from six distinct sources, each with different origins and requiring
different technologies for conversion.
■  Waves: derived from the transfer of the kinetic energy of the wind to the upper
surface of the ocean.
■  Tidal Range (tidal rise and fall): derived from the gravitational forces of the
Earth-Moon-Sun system.
■  Tidal currents: water flow resulting from the filling and emptying of coastal
regions as a result of the tidal rise and fall.
■  Ocean Currents: derived from wind-driven and thermohaline ocean circulation.
■  Ocean Thermal Energy Conversion (OTEC):derived from temperature
differences between solar energy stored as heat in upper ocean layers and colder
seawater, generally below 1,000 m.
■  Salinity Gradients (osmotic power): derived from salinity differences between
fresh and ocean water at river mouths.
Workshop Energia dal Mare 2014 – Roma 1 July Background – Mediterranean energy
The RE resource in the ocean comes from six distinct sources, each with different origins and requiring
different technologies for conversion.
■  Waves: derived from the transfer of the kinetic energy of the wind to the upper
surface of the ocean.
■  Tidal Range (tidal rise and fall): derived from the gravitational forces of the
Earth-Moon-Sun system.
■  Tidal currents: water flow resulting from the filling and emptying of coastal
regions as a result of the tidal rise and fall.
■  Ocean Currents: derived from wind-driven and thermohaline ocean circulation.
■  Ocean Thermal Energy Conversion (OTEC):derived from temperature
differences between solar energy stored as heat in upper ocean layers and colder
seawater, generally below 1,000 m.
■  Salinity Gradients (osmotic power): derived from salinity differences between
fresh and ocean water at river mouths.
Workshop Energia dal Mare 2014 – Roma 1 July Background – Mediterranean wave energy
Energy assessment at different time scales
Workshop Energia dal Mare 2014 – Roma 1 July Wave energy assessment along the Italian coasts
Traditional approach: using the Italian Wave measuring Network (Rete Ondametrica Nazionale, RON)
managed by Institute for Environmental Protection and Research (ISPRA)
2001
2002
2003
2004
2005
2006
2007
2008
100
Cetraro
100
Monopoli
100
Ponza
100
Ortona
100
La Spezia
100
Crotone
100
Catania
100
Ancona
100
Alghero
1 2 3 4 5 6 7 8 9 101112 1 2 3 4 5 6 7 8 9 101112 1 2 3 4 5 6 7 8 9 101112 1 2 3 4 5 6 7 8 9 101112 1 2 3 4 5 6 7 8 9 101112 1 2 3 4 5 6 7 8 9 101112 1 2 3 4 5 6 7 8 9 101112 1 2 3 4 5 6 7 8 9 101112
0
Mesi
Buoy
First Record
Last Record
Alghero
Catania
Crotone
La Spezia
Mazara del Vallo
Monopoli
Ortona
Ponza
Cetraro
Ancona
Capo Comino
Capo Gallo
Capo Linaro
Punta della Maestra
Cagliari
01/07/1989
01/07/1989
01/07/1989
01/07/1989
01/07/1989
01/07/1989
01/07/1989
01/07/1989
28/02/1999
10/03/1999
01/01/2004
01/01/2004
02/01/2004
02/01/2004
06/02/2007
05/04/2008
05/10/2006
15/07/2007
31/03/2007
04/04/2008
05/04/2008
24/03/2008
31/03/2008
05/04/2008
31/05/2006
12/09/2005
31/03/2008
12/09/2006
24/11/2004
02/03/2008
3hr Records
after 1/1/2001
15283
12549
14962
10952
15323
15641
12786
14479
16630
10212
3813
9001
5441
2616
1986
Expected
3hr records
21210
16827
19093
18240
21207
21209
21113
21169
21209
15812
5664
12408
7872
7872
3120
Efficiency
(%)
72.1
80.2
78.4
60.0
72.3
73.7
60.6
68.4
78.4
64.6
67.3
72.5
69.1
62.8
63.7
Workshop Energia dal Mare 2014 – Roma 1 July Efficienza
100
Mazara del Vallo
Numerical modeling approach
Numerical Wave model description
Model computaQonal domain and bathymetry Model implemented: WAM (Wave predic+on Model) Resolu4on 1/16° x 1/16° (about 7Km) ECMWF (European Centre for Medium range Weather Forecast) wind field data (analysis 2001-­‐2010) Workshop Energia dal Mare 2014 – Roma 1 July Wave energy assessment along the Italian coasts
Our approach: using a relatively high resolution wave model
www.cresco.enea.it Workshop Energia dal Mare 2014 – Roma 1 July Numerical wave model validation (Hs)
Numerical Wave model vs buoy: ALGHERO
8
500
6
250
50
4
Entries
(m)
Hs - Model
100
20
10
2
5
0
0
0
2
4
Hs - Buoy
6
8
(m)
CorrelaQon between buoy and model Hs at Alghero. Dashed line is the best fit line between model and buoy data points. Period considered 2001-­‐2010 Workshop Energia dal Mare 2014 – Roma 1 July Numerical wave model validation (Hs)
Numerical Wave model vs buoy: CROTONE
8
500
6
250
50
4
Entries
(m)
Hs - Model
100
20
10
2
5
0
0
0
2
4
Hs - Buoy
6
8
(m)
CorrelaQon between buoy and model Hs at Crotone. Dashed line is the best fit line between model and buoy data points. Period considered 2001-­‐2010 Workshop Energia dal Mare 2014 – Roma 1 July Numerical wave model validation (Hs)
Numerical Wave model vs buoy: LA SPEZIA
8
500
6
250
50
4
Entries
(m)
Hs - Model
100
20
10
2
5
0
0
0
2
4
Hs - Buoy
6
8
(m)
CorrelaQon between buoy and model Hs at La Spezia. Dashed line is the best fit line between model and buoy data points. Period considered 2001-­‐2010 Workshop Energia dal Mare 2014 – Roma 1 July Numerical wave model validation (Hs)
Numerical Wave model vs buoy: PONZA
8
500
6
250
50
4
Entries
(m)
Hs - Model
100
20
10
2
5
0
0
0
2
4
Hs - Buoy
6
8
(m)
CorrelaQon between buoy and model Hs at Ponza. Dashed line is the best fit line between model and buoy data points. Period considered 2001-­‐2010 Workshop Energia dal Mare 2014 – Roma 1 July Numerical wave model validation (Hs)
Numerical Wave model vs buoy
8
500
6
250
50
4
Entries
(m)
Hs - Model
100
20
10
2
5
0
0
0
2
4
Hs - Buoy
6
8
(m)
CorrelaQon between buoy and model Hs at Mazara del Vallo. Dashed line is the best fit line between model and buoy data points. Period considered 2001-­‐2010 Workshop Energia dal Mare 2014 – Roma 1 July Numerical wave model validation (Hs)
Numerical Wave model vs buoy: statistics
n
1X
bias =
(yi xi ) ,
n i=1
v
u
n
u 1 X
2
rmse = t
(yi xi ) ,
n 1 i=1
si =
1
n
rmse
Pn
i=1 yi
,
Pn
x i yi
.
slope = Pni=1
x
x
i
i
i=1
Buoy
(1)
Alghero
Ancona
Catania(2)
Crotone
(3)
La Spezia
Mazara del Vallo
Ortona(4)
Ponza
Monopoli
Cetraro
Capo Gallo
Bias
(m)
-0.005
-0.214
-0.178
0.004
-0.143
0.013
-0.150
-0.103
-0.124
-0.070
0.019
Rmse
(m)
0.311
0.361
0.308
0.276
0.283
0.257
0.284
0.273
0.307
0.241
0.255
Slope
Si
0.985
0.725
0.747
0.993
0.851
1.022
0.753
0.892
0.836
0.897
1.040
0.278
0.477
0.501
0.374
0.354
0.253
0.460
0.328
0.427
0.341
0.339
StaQsQcs of buoy and model significant wave height (Hs) comparison. Period considered 2001-­‐2010 Workshop Energia dal Mare 2014 – Roma 1 July Numerical wave model validation (Direction)
Numerical Wave model vs buoy: statistics
Buoy
1
C¯ =
n
i=1
n
X
sin (yi
xi ),
cos (yi
xi ),
i=1
1
2
¯ = C¯ 2 + S¯2 ,
R
¯ C¯ ,
bias = arctan S/
¯ .
var = 1 R
0.6
Frequency
1
S¯ =
n
n
X
Model
(1)
(2)
0.4
(3)
(4)
0.2
0.0
0
(5)
100
200
Average Wave Direction
300
(Deg.)
Frequency distribuQon of model and buoy average wave direcQon at Alghero. Only records with Hs > 1 m are considered. Incoming wave direcQon is indicated as degrees in clockwise direcQon from the north. Period considered 2001-­‐2010 Workshop Energia dal Mare 2014 – Roma 1 July Numerical wave model validation (Direction)
Numerical Wave model vs buoy: statistics
Buoy
1
C¯ =
n
i=1
n
X
sin (yi
xi ),
cos (yi
xi ),
i=1
1
2
¯ = C¯ 2 + S¯2 ,
R
¯ C¯ ,
bias = arctan S/
¯ .
var = 1 R
0.6
Frequency
1
S¯ =
n
n
X
Model
(1)
(2)
0.4
(3)
(4)
0.2
0.0
0
(5)
100
200
Average Wave Direction
300
(Deg.)
Frequency distribuQon of model and buoy average wave direcQon at Mazara del Vallo. Only records with Hs > 1 m are considered. Incoming wave direcQon is indicated as degrees in clockwise direcQon from the north. Period considered 2001-­‐2010 Workshop Energia dal Mare 2014 – Roma 1 July Numerical wave model validation (Direction)
Numerical Wave model vs buoy: statistics
Buoy
n
1X
sin (yi
S¯ =
n i=1
xi ),
n
1X
cos (yi
C¯ =
n i=1
xi ),
1
¯ = C¯ 2 + S¯2 2 ,
R
¯ C¯ ,
bias = arctan S/
¯ .
var = 1 R
(1)
Alghero
Ancona
Catania
(2)
Crotone
La Spezia
(3) del Vallo
Mazara
Ortona
(4)
Ponza
(5)
Monopoli
Cetraro
Capo Gallo
Bias
(Deg.)
4.51
9.41
14.81
8.09
2.94
11.00
13.48
8.76
5.00
6.39
-5.21
V ar
0.036
0.230
0.053
0.085
0.056
0.057
0.101
0.116
0.121
0.063
0.026
Circular staQsQcs of buoy and model wave average spectral direcQon comparison. Period considered 2001-­‐2010 Workshop Energia dal Mare 2014 – Roma 1 July Numerical wave model validation
Numerical Wave model vs satellite altimeter
5°W
0°
5°E
10°E
15°E
20°E
25°E
30°E
35°E
45°N
40°N
35°N
Satellite Tracks
Envisat, ERS-2
Topex-Poseidon
Jason-1, Jason-2
Ground tracks of satellites considered in model validaQon. Thick grey lines idenQfy Jason-­‐1 and Jason-­‐2 tracks. Black lines parQally overlying the grey ones symbolize Topex-­‐Poseidon tracks while thin dashed lines represent Envisat and ERS-­‐2 tracks Satellite
Topex-Poseidon
Jason-1
Jason-2
Envisat
ERS-2
Repeat Cycle
(days)
10
10
10
35
35
Used Period
Jan.
Jan.
Jun.
Oct.
Jan.
Jan.
2001 - Oct.
2002 - Dec.
2008 - Dec.
2002 - Oct.
2001 - Dec.
2008 - Dec.
2005
2010
2010
2010
2006
2010
Track Separation
at Equator (km)
315
315
315
80
80
CharacterisQcs of satellites used in this study. Workshop Energia dal Mare 2014 – Roma 1 July Numerical wave model validation
Numerical Wave model vs satellite altimeter
12
500
10
Hs - Model
(m)
100
6
50
4
Entries
250
8
10
5
2
0
0
ScaRer plot of model vs. Jason-­‐1 Hs for the enQre Mediterranean. Value pairs are grouped in 0.25 m wide bins, corresponding areas are painted according to the number of entries in each bin. Dashed line is the best fit line between model and satellite data points. 0
2
4
6
8
Hs - Satellite
(m)
Satellite
Samples
Topex-Poseidon
Jason-1
Jason-2
Envisat
ERS-2
457,000
910,133
242,766
695,768
363,336
Bias
(m)
-0.128
-0.028
0.024
-0.141
-0.011
10
Rmse
(m)
0.331
0.362
0.366
0.385
0.426
12
Slope
Si
0.912
0.979
1.018
0.921
0.962
0.279
0.304
0.303
0.310
0.400
StaQsQcs of satellite and model significant wave height Hs comparison for the enQre Mediterranean. Workshop Energia dal Mare 2014 – Roma 1 July Wave energy assessment for the Mediterranean
Yearly mean climatological energy flux
DistribuQon of average power per unit crest in the Mediterranean between 2001 and 2010. ⇢g 2
Te Hs2
J=
64⇡
(1)
Workshop Energia dal Mare 2014 – Roma 1 July Wave energy assessment for the Mediterranean
Seasonal average energy flux
Seasonal distribuQon of average power per unit crest in the Mediterranean. Averages are calculated for the enQre ten years simulaQon. Workshop Energia dal Mare 2014 – Roma 1 July Wave energy assessment along the Italian coasts
Yearly average variability
DistribuQon of the Coefficient of VariaQon (COV ) of the yearly average power fluxes for years 2001-­‐2010 around Italy. Standard deviaQon (yearly) µ
Averaged yearly value COV =
µ
Workshop Energia dal Mare 2014 – Roma 1 July Wave energy assessment along the Italian coasts
Distribution of average wave power flux along Sicily and west Sardinia
DistribuQon of average wave power flux per unit crest on western Sardinia and Sicilian coastline. Values are calculated on a line located 12 km off the coast. Workshop Energia dal Mare 2014 – Roma 1 July Wave energy assessment along the Italian coasts
Distribution of yearly average wave energy along west Sardinia
DistribuQon of wave energy as a funcQon of significant wave period and significant wave height at specific points. Lower lej panel shows the average yearly energy associated with sea states idenQfied by Te and Hs couples. DoRed lines mark reference power levels. Upper panel shows the energy distribuQon as a funcQon of Te only; right panel as a funcQon of Hs only. Red lines in the upper and right panels are the cumulaQve energy as a percentage of the total. Red dots on the cumulaQve lines mark each 10th percenQle. Rose plot in the upper right panel shows energy distribuQon over wave incoming direcQon. Each circle represents 20% fracQons of the total energy. Workshop Energia dal Mare 2014 – Roma 1 July Wave energy assessment along the Italian coasts
Distribution of yearly average wave energy along west Sardinia
DistribuQon of wave energy as a funcQon of significant wave period and significant wave height at specific points. Lower lej panel shows the average yearly energy associated with sea states idenQfied by Te and Hs couples. DoRed lines mark reference power levels. Upper panel shows the energy distribuQon as a funcQon of Te only; right panel as a funcQon of Hs only. Red lines in the upper and right panels are the cumulaQve energy as a percentage of the total. Red dots on the cumulaQve lines mark each 10th percenQle. Rose plot in the upper right panel shows energy distribuQon over wave incoming direcQon. Each circle represents 20% fracQons of the total energy. Workshop Energia dal Mare 2014 – Roma 1 July Wave energy assessment along the Italian coasts
Distribution of yearly average wave energy along west Sardinia
DistribuQon of wave energy as a funcQon of significant wave period and significant wave height at specific points. Lower lej panel shows the average yearly energy associated with sea states idenQfied by Te and Hs couples. DoRed lines mark reference power levels. Upper panel shows the energy distribuQon as a funcQon of Te only; right panel as a funcQon of Hs only. Red lines in the upper and right panels are the cumulaQve energy as a percentage of the total. Red dots on the cumulaQve lines mark each 10th percenQle. Rose plot in the upper right panel shows energy distribuQon over wave incoming direcQon. Each circle represents 20% fracQons of the total energy. Workshop Energia dal Mare 2014 – Roma 1 July Wave energy assessment along the Italian coasts
2500
2000
1500
1000
750
500
475
450
425
400
375
350
325
300
275
250
225
200
175
150
125
100
75
50
25
0
8
100 kW/m
50 kW/m
(m)
Hs
6
4
20 kW/m
10 kW/m
2
0
2
5 kW/m
2 kW/m
4
6
8
10
Te
120 5 10 15
Yearly Energy
(s)
(MWh/m)
(kWh/m)
N
15 Yr. Energy: 66.1 (MWh/yr)
10 Av. Power: 7.6 (kW/m)
5
0
10
200 kW/m
2.g
Yearly Energy
(MWh/m)
Yearly Energy
Distribution of yearly average wave energy near PANTELLERIA
DistribuQon of wave energy as a funcQon of significant wave period and significant wave height at specific points. Lower lej panel shows the average yearly energy associated with sea states idenQfied by Te and Hs couples. DoRed lines mark reference power levels. Upper panel shows the energy distribuQon as a funcQon of Te only; right panel as a funcQon of Hs only. Red lines in the upper and right panels are the cumulaQve energy as a percentage of the total. Red dots on the cumulaQve lines mark each 10th percenQle. Rose plot in the upper right panel shows energy distribuQon over wave incoming direcQon. Each circle represents 20% fracQons of the total energy. Workshop Energia dal Mare 2014 – Roma 1 July High resolution numerical modeling approach
Numerical Wave model description
WAM (1/16°x1/16°) WAM/SWAN (1/120°x/120°) Workshop Energia dal Mare 2014 – Roma 1 July High resolution numerical modeling approach
Numerical Wave model description for western Sardinia
8∞E
8∞E
8∞30'E
8∞30'E
Depth (m)
5000 -­‐ 2000
999 -­‐ 750
749 -­‐ 500
499 -­‐ 400
41∞N
41∞30'N
1999 -­‐ 1000
399 -­‐ 300
41∞N
299 -­‐ 200
49 -­‐ 40
39 -­‐ 30
29 -­‐ 20
19 -­‐ 10
39∞30'N
b
a
c
DistribuQon of average power per unit crest computed over years 2001-­‐2010 39∞N
39∞N
39∞30'N
40∞N
9 -­‐ 0
40∞N
Alghero
40∞30'N
149 -­‐ 100
99 -­‐ 50
40∞30'N
Alghero
199 -­‐ 150
Energy Flux (kW/m)
>15.0
14.0 -­‐ 15.0
13.0 -­‐ 14.0
12.0 -­‐ 13.0
11.0 -­‐ 12.0
10.0 -­‐ 11.0
9.00 -­‐ 10.0
8.00 -­‐ 9.00
7.00 -­‐ 8.00
6.00 -­‐ 7.00
5.00 -­‐ 6.00
4.00 -­‐ 5.00
3.00 -­‐ 4.00
2.50 -­‐ 3.00
2.00 -­‐ 2.50
1.50 -­‐ 2.00
1.00 -­‐ 1.50
0.50 -­‐ 1.00
0.00 -­‐ 0.50
Model domain and bathymetry Workshop Energia dal Mare 2014 – Roma 1 July High resolution numerical modeling approach
4
6
8
10
120 5 10 15
Yearly Energy
8
100 kW/m
50 kW/m
(m)
Hs
6
4
20 kW/m
10 kW/m
2
0
2
5 kW/m
2 kW/m
4
6
8
10
Te
120 5 10 15
Yearly Energy
(s)
(MWh/m)
2000
1500
1000
750.0
500.0
475.0
450.0
425.0
400.0
375.0
350.0
325.0
300.0
275.0
250.0
225.0
200.0
175.0
150.0
125.0
100.0
75.0
50.0
25.0
0
N
15 Yr. Energy: 19.3 (MWh/m)
10 Av. Power: 2.2 (kW/m)
5
0
10
200 kW/m
c 2500
2000
1500
1000
750.0
500.0
475.0
450.0
425.0
400.0
375.0
350.0
325.0
300.0
275.0
250.0
225.0
200.0
175.0
150.0
125.0
100.0
75.0
50.0
25.0
0
8
100 kW/m
39∞N
b Upper-right panel: percentage of yearly energy by incoming direction. Circle are 20 % spaced increments.
ENEA\WaveClimateChange\Analisi\AnalisiEnergia\PlotEnergyHsTmDist.pro
r 30 17:05:08 2013
39∞30'N
(s)
(MWh/m)
N
15 Yr. Energy: 61.0 (MWh/m)
10 Av. Power: 7.0 (kW/m)
5
Average yearly energy dependency on significant period Te, significant wave height Hs and average direction.
0
Region:
Point: (21,222) Lon: 8.10417, Lat:40.3542, Av. Power: 10.42 kW/m , Av. Yearly Energy : 91.21 MWh/m
10
2500
200
kW/m 29176 observations.
Model RON files suffix: g. 2001/1/1
- 2010/12/31,
(kWh/m)
(MWh/m)
Yearly Energy
Te
6
50 kW/m
4
20 kW/m
10 kW/m
2
0
2
5 kW/m
2 kW/m
4
6
Te
8
10
120 5 10 15
Yearly Energy
(s)
(MWh/m)
(kWh/m)
0
2
a
a,b,c: three points located at 19600 m, 9300 m and 830 m respecQvely from the coast. These points are distributed along a straight line poinQng offshore from Alghero, at decreasing depths ranging from 13m to 114 m Yearly Energy
2 kW/m
c
(MWh/m)
5 kW/m
2
b
Yearly Energy
10 kW/m
Alghero
Hs
20 kW/m
40∞N
4
Yearly Energy
Hs
(m)
50 kW/m
Energy Flux (kW/m)
>15.0
14.0 -­‐ 15.0
13.0 -­‐ 14.0
12.0 -­‐ 13.0
11.0 -­‐ 12.0
10.0 -­‐ 11.0
9.00 -­‐ 10.0
8.00 -­‐ 9.00
7.00 -­‐ 8.00
6.00 -­‐ 7.00
5.00 -­‐ 6.00
4.00 -­‐ 5.00
3.00 -­‐ 4.00
2.50 -­‐ 3.00
2.00 -­‐ 2.50
1.50 -­‐ 2.00
1.00 -­‐ 1.50
0.50 -­‐ 1.00
0.00 -­‐ 0.50
(m)
100 kW/m
41∞N
8
8∞30'E
(kWh/m)
2500
2000
1500
1000
750.0
500.0
475.0
450.0
425.0
400.0
375.0
350.0
325.0
300.0
275.0
250.0
225.0
200.0
175.0
150.0
125.0
100.0
75.0
50.0
25.0
0
40∞30'N
a 6
8∞E
N
15 Yr. Energy: 91.2 (MWh/m)
10 Av. Power: 10.4 (kW/m)
5
0
10
200 kW/m
Yearly Energy
(MWh/m)
Yearly Energy
Numerical Wave model description for western Sardinia
Workshop Energia dal Mare 2014 – Roma 1 July High resolution numerical modeling approach
Numerical Wave model description
!
MIKE21 SW WAM (1/16°x1/16°) Prof. Felice Arena WAM/SWAN (1/120°x/120°) Workshop Energia dal Mare 2014 – Roma 1 July Wave energy assessment for Pantelleria island
Distribution of yearly average (2001-2010) wave energy around PANTELLERIA
SWAN model laterally forced by the WAM simulaQon !
7 Km res. 800 m res. Workshop Energia dal Mare 2014 – Roma 1 July Wave energy assessment for Pantelleria island
Distribution of yearly average (2001-2010) wave energy around PANTELLERIA
ComputaQonal Mesh used for Pantelleria. !
!
!
10 km REWEC3 7 Km res. 800 m res. Workshop Energia dal Mare 2014 – Roma 1 July Wave energy ATLAS – data freely available from WEB
WEB-GIS
Wave ATLAS
utmea.enea.it/energiadalmare/
Wave Forecast
http://utmea.enea.it/waves/
Workshop Energia dal Mare 2014 – Roma 1 July Wave energy FORECAST – available from WEB
Workshop Energia dal Mare 2014 – Roma 1 July Wave energy ATLAS – available from WEB
utmea.enea.it/energiadalmare/
Workshop Energia dal Mare 2014 – Roma 1 July European initiative
The EERA Ocean Energy JP is based around six key research themes. These themes have been developed based on exisQng research roadmaps which idenQfy the criQcal areas of research required for the successful growth of the industry. The Research Themes are here: •  Resource •  Devices and Technology •  Deployment and OperaQons •  Environmental Impact •  Socio-­‐economic Impact •  Research Infrastructure, EducaQon and Training Within each Research Theme a number of sub-­‐topics have been idenQfied as key long term research objecQves. IniQal EERA Ocean Energy JP acQviQes are not able to cover all of the objecQves idenQfied but have been prioriQzed in the first year’s programme by need and the current availability of funding. The gap between what has been idenQfied as a key long term objecQve and what the EERA Ocean Energy JP is actually able to deliver in the first year will help idenQfy issues that need future funding and coordinated research efforts. Workshop Energia dal Mare 2014 – Roma 1 July First workshop on sea energy in Italy
Workshop Energia dal Mare 2014 – Roma 1 July