This study presents an offshore wind power density atlas of the Black Sea Region, including the Black Sea, the Sea of Azov, and the Sea of Marmara, by taking variable air density into account as a function of temperature, pressure, and water content. ECMWF ERA-Interim Reanalysis data spanned over 37 years have been analyzed and monthly average wind speeds, wind speed Weibull scale, and shape parameters, air density, wind power, and operational percentages were presented in the form of maps for 50 m of altitude. Cumulative wind power density frequency maps were generated for 6 percentiles. It is found out that the monthly mean air density has an annual variation of about 9%, which directly affects the available wind power. Results show that the wind power density in the region increases in the West and the North directions, having the maximum potential at the North-West part of the Black Sea and the Sea of Azov. The power density at these locations can be classified as class 4 wind power according to the classification defined by the U.S. Department of Energy.

1.
European Union
, https://ec.europa.eu/energy/node/163 for European Commission Renewable Energy Targets by 2030; accessed 3 December
2015
.
2.
S.
Rodrigues
,
C.
Restrepo
,
E.
Kontos
,
R.
Teixeira Pinto
, and
P.
Bauer
, “
Trends of offshore wind projects
,”
Renewable Sustainable Energy Rev.
49
,
1114
1135
(
2015
).
3.
Global Wind Energy Council (GWEC)
, http://www.gwec.net/ for Global Wind Statistics 2014,
2015
.
4.
Global Wind Energy Council (GWEC)
, http://www.gwec.net/ for Global Wind Energy Outlook 2014,
2015
.
5.
X.
Zhao
and
L.
Ren
, “
Focus on the development of offshore wind power in China: Has the golden period come?
,”
Renewable Energy
81
,
644
657
(
2015
).
6.
C.
Makridis
, “
Offshore wind power resource availability and prospects: A global approach
,”
Environ. Sci. Policy
33
,
28
40
(
2013
).
7.
M. D.
Esteban
,
J. J.
Diez
,
J. S.
López
, and
V.
Negro
, “
Why offshore wind energy?
,”
Renewable Energy
36
,
444
450
(
2011
).
8.
W.
Musial
and
S.
Butterfield
, “
Future of wind energy in the United States
,” in
Proceedings of the Energy Ocean 2004 Conference
(
2004
), Paper No. NREL/CP-500-36313.
9.
M.
Bilgili
,
A.
Yasar
, and
E.
Simsek
, “
Offshore wind power development in Europe and its comparison with onshore counterpart
,”
Renewable Sustainable Energy Rev.
15
,
905
915
(
2011
).
10.
Offshore Wind Energy (OWE)
, (2009), http://www.offshorewindenergy.org/ for Technology of OWE; accessed 3 December
2015
.
11.
M.
Brower
,
D. W.
Bernadett
, and
K. V.
Elsholz
,
Wind Resource Assessment: A Practical Guide to Developing a Wind Project
(
John Wiley & Sons
,
Somerset, NJ, USA
,
2012
).
12.
C. R.
Jones
and
J. R.
Eiser
, “
Understanding ‘local’ opposition to wind development in the UK: How big is a backyard?
,”
Energy Policy
38
,
3106
3117
(
2010
).
13.
European Environment Agency
, “
Europe's onshore and offshore wind energy potential: An assessment of environmental and economic constraints
,”
EEA Technical Report No. 6
, European Environment Agency, Copenhagen,
2009
.
14.
J.
Ladenburg
, “
Visual impact assessment of offshore wind farms and prior experience
,”
Appl. Energy
86
(
3
),
380
387
(
2009
).
15.
International Renewable Energy Agency (IRENA)
, https://www.irena.org/ for Renewable Energy Technologies: Cost Analysis Series-Wind Power,
2012
.
16.
J.
Appiott
,
A.
Dhanju
, and
B.
Cicin-Sain
, “
Encouraging renewable energy in the offshore environment
,”
Ocean Coastal Manage.
90
,
58
64
(
2014
).
17.
J. H.
Koh
and
E. Y. K.
Ng
, “
Downwind offshore wind turbines: Opportunities, trends and technical challenges
,”
Renewable Sustainable Energy Rev.
54
,
797
808
(
2016
).
18.
See http://www.ncep.noaa.gov/ for NOAA/National Weather Service.
19.
See http://www.ecmwf.int/ for European Centre for Medium-Range Weather Forecasts (ECMWF).
20.
A. M.
Sempreviva
,
J.
Barthelmie
, and
C.
Pryor
, “
Review of methodologies for offshore wind resource assessment in European seas
,”
Surv. Geophys.
29
,
471
497
(
2008
).
21.
M.
Morrissey
,
W.
Cook
, and
J.
Greene
, “
An improved method for estimating the wind power density distribution function
,”
J. Atmos. Oceanic Technol.
27
(
7
),
1153
1164
(
2010
).
22.
J.
Wang
,
S.
Qin
,
S.
Jin
, and
J.
Wu
, “
Estimation methods review and analysis of offshore extreme wind speeds and wind energy resources
,”
Renewable Sustainable Energy Rev.
42
,
26
42
(
2015
).
23.
A.
Ucar
and
F.
Balo
, “
Investigation of wind characteristics and assessment of wind generation potentiality in Uludag-Bursa, Turkey
,”
Appl. Energy
86
(
3
),
333
339
(
2009
).
24.
A. M.
Foley
,
P. G.
Leahy
,
A.
Marvuglia
, and
E. J.
McKeogh
, “
Current methods and advances in forecasting of wind power generation
,”
Renewable Energy
37
,
1
8
(
2012
).
25.
L.
Hong
and
B.
Möller
, “
Offshore wind energy potential in China: Under technical, spatial and economic constraints
,”
Energy
36
(
7
),
4482
4491
(
2011
).
26.
H. F.
Fang
, “
Wind energy potential assessment for the offshore areas of Taiwan west coast and Penghu Archipelago
,”
Renewable Energy
67
,
237
241
(
2014
).
27.
D. K. S.
Lima
,
R. P. S.
Leao
,
A. C. S.
dos Santos
,
F. D. C.
de Melo
,
V. M.
Couto
,
A. W. T.
de Noronha
, and
D. S.
Oliveira
, Jr.
, “
Estimating the offshore wind resources of the State of Ceara in Brazil
,”
Renewable Energy
83
,
203
221
(
2015
).
28.
Z. R.
Shu
,
Q. S.
Li
, and
P. W.
Chan
, “
Investigation of offshore wind energy potential in Hong Kong based on Weibull distribution function
,”
Appl. Energy
156
,
362
373
(
2015
).
29.
E.
Rusu
and
F.
Onea
, “
Evaluation of the wind and wave energy along the Caspian Sea
,”
Energy
50
,
1
14
(
2013
).
30.
I.
Balog
,
P. M.
Ruti
,
I.
Tobin
,
V.
Armenio
, and
R.
Vautard
, “
A numerical approach for planning offshore wind farms from regional to local scales over the Mediterranean
,”
Renewable Energy
85
,
395
405
(
2016
).
31.
J.
Waewsak
,
M.
Landry
, and
Y.
Gagnon
, “
Offshore wind power potential of the Gulf of Thailand
,”
Renewable Energy
81
,
609
626
(
2015
).
32.
J. W.
Hurrell
, “
Decadal Trends in the North Atlantic Oscillation: Regional Temperatures and Precipitation
,”
Science
269
,
676
679
(
1995
).
33.
N.
Valchev
,
I.
Davidan
,
Z.
Belberov
,
A.
Palazov
, and
N.
Valcheva
, “
Hindcasting and assessment of the Western Black Sea wind and wave climate
,”
J. Environ. Prot. Ecol
11
(3),
1001
1012
(
2010
).
34.
F.
Onea
and
E.
Rusu
, “
An evaluation of the wind energy in the North-West of the Black Sea
,”
Int. J. Green Energy
11
(
5
),
465
487
(
2014
).
35.
F.
Onea
and
E.
Rusu
, “
Wind energy assessments along the Black Sea Basin
,”
Meteorol. Appl.
21
,
316
329
(
2014
).
36.
F.
Onea
and
E.
Rusu
, “
Evaluation of the wind energy resources in the Black Sea Area
,” in
8th WSEAS International Conference on Energy, Environment, Ecosystems and Sustainable Development, Faro Portugal (EEESD'12)
(
2012
), Vol.
12
, pp.
26
32
.
37.
G. M.
Masters
,
Renewable and Efficient Electric Power Systems
(
John Wiley and Sons
,
Hoboken, NJ
,
2004
).
38.
M.
Elamouria
and
F.
Ben Amara
, “
Wind energy potential in Tunisia
,”
Renewable Energy
33
,
758
768
(
2008
).
39.
M.
Türk
and
S.
Emeis
, “
The dependence of offshore turbulence intensity on wind speed
,”
J. Wind Eng. Ind. Aerodyn.
98
,
466
471
(
2010
).
40.
S. E.
Gryning
,
R.
Floors
,
A.
Pena
,
E.
Batchvarova
, and
B.
Brümmer
, “
Weibull wind-speed distribution parameters derived from combination of wind—lidar and tall-mast measurements over land, coastal and marine sites
,”
Boundary-Layer Meteorol.
159
,
329
348
(
2016
).
41.
D.
Carvalho
,
A.
Rocha
,
M.
Gómez-Gesteira
, and
C.
Silva Santos
, “
WRF wind simulation and wind energy production estimates forced by different reanalyses: Comparison with observed data for Portugal
,”
Appl. Energy
117
,
116
126
(
2014
).
42.
J. E.
Stopa
and
K. F.
Cheung
, “
Intercomparison of wind and wave data from the ECMWF Reanalysis Interim and the NCEP Climate Forecast System Reanalysis
,”
Ocean Modell.
75
,
65
83
(
2014
).
43.
B.
Aydoğan
,
B.
Ayat
, and
Y.
Yüksel
, “
Black Sea wave energy atlas from 13 years hindcasted wave data
,”
Renewable Energy
57
,
436
447
(
2013
).
44.
B.
Ayat
, “
Wave power atlas of Eastern Mediterranean and Aegean seas
,”
Energy
54
,
251
262
(
2013
).
45.
D. P.
Dee
,
S. M.
Uppala
,
A. J.
Simmons
,
P.
Berrisford
,
P.
Poli
,
S.
Kobayashi
,
U.
Andrae
,
M. A.
Balmaseda
,
G.
Balsamo
,
P.
Bauer
,
P.
Bechtold
,
A. C. M.
Beljaars
,
L.
van de Berg
,
J.
Bidlot
,
N.
Bormann
,
C.
Delsol
,
R.
Dragani
,
M.
Fuentes
,
A. J.
Geer
,
L.
Haimberger
,
S. B.
Healy
,
H.
Hersbach
,
E. V.
Hólm
,
L.
Isaksen
,
P.
Kållberg
,
M.
Köhler
,
M.
Matricardi
,
A. P.
McNally
,
B. M.
Monge-Sanz
,
J. J.
Morcrette
,
B. K.
Park
,
C.
Peubey
,
P.
de Rosnay
,
C.
Tavolato
,
J. N.
Thépaut
, and
F.
Vitart
, “
The ERA-interim reanalysis: Configuration and performance of the data assimilation system
,”
Q. J. R. Meteorol. Soc.
137
,
553
597
(
2011
).
46.
See National Renewable Energy Laboratory, US Department of Energy
, http://rredc.nrel.gov/wind/pubs/atlas/ for “Wind Energy Resource Atlas of the United States,” 1986.
You do not currently have access to this content.