analysis carried out would be that although the energy potential of the Black Sea is in general lower than in ... Significant advances in wave energy converters.
EVALUATION OF THE WAVE ENERGY POTENTIAL IN THE BLACK SEA USING REMOTELY SENSED DATA Florin Onea, Eugen Rusu and Ioan Strat University “Low Danube”, Faculty of Mechanical Engineering, 111 Domneasca St., 800201 Galati, Romania
Abstract In recent years, satellite data became available on various internet sites. The objective of the work proposed herewith is to evaluate the energetic potential in the Black Sea basin using such remotely sensed data. The period analyzed is the time interval 2005-2009, when relevant data are available for the target area. The average wind conditions were also analyzed since strong winds are characteristic to the Black Sea region. A general conclusion of the analysis carried out would be that although the energy potential of the Black Sea is in general lower than in the oceanic areas it may be however taken into considerations. This is especially related with the implementation of the mixed energetic farms wind-waves that have the advantage that can use the same infrastructure. The work is still ongoing and some relevant areas from energetic point of view were already identified. Keywords: environmental science, wind waves, wave climate, remote sensing data, Black Sea oceanography.
1. Introduction
The objective of this work is to identify, the most important wind and wave resources in the Black Sea as a first step to chose a suitable location for developing wind-wave mixed farms. Created by wind blowing over large sea surface areas (tens or hundreds of kilometres) for long periods of time (days), ocean waves are complex random phenomena that require the use of statistical methods for their characterization. For simplifications a sea state can be defined as stationary wave conditions on a scale of tens of kilometres and few hours. Sea wave measurements from satellites combined with global wave and atmospheric numerical models change the way of obtaining wind and wave information both for climatologically and operational purposes. Wave measurements have been developed during the last 30 years, creating in this way new possibilities for the monitoring and analysis of the wave climate. The systematic and long-term study the wave climate variability is a valuable tool for marine engineering and operational oceanographic applications as well as for scientific research. Currently, the knowledge of the wave climate characteristics in a specific area is required for designing of marine structures and offshore installations as well for the assessment of the local wave potential, and the efficient coastal zone management and protection. Moreover, real-time
wave data and operational wave forecasts play a vital role in safety of life and search-and-rescue missions at sea, optimal ship routing, extensive fishing activities and other operations taking place in the marine environment. Wave measurements are necessary for modelling and forecasting of weather, ocean circulation and wave-energy balance, but they are also important for the understanding and explanation of the physical processes at the ocean–atmosphere interface. Several parameters to describe and quantify the temporal variation of wave and wind resources are presented and discussed such as the significant wave height (Hs) and wind speed (Ws). The global warming, the excessive use of conventional energy reserves and the rising cost of electricity generation have resulted in a race for alternative energy resources and a significant part of this area is reserved for renewable marine energy. Significant advances in wave energy converters have been made in recent years, and it is expected, particularly in Europe, that this technologies will be ready for large scale deployments within the next five to ten years. Despite these exciting developments, the potential of the wave energy resources in many parts of the world remains poorly defined. In this category enters the Black Sea area where there have been found energetic features quite significant that are not neglected for this type of sea (enclosed sea). This study presents a clear, simple and accurate way for wave climate description suitable
for energetic farm site selections using statistical analysis, as shown in this case study that focuses on the near shore Romanian Black Sea area. The methodology that was followed to obtain these results is described in detail in the next chapters.
northeast winds appear and they determine predominance of relatively cold and dry spell weather. Development of cyclonic activity over the sea leads to strengthening of south winds, rainfalls and increase of the air temperature. 3. Observation sites and data
2. Main features of the Black Sea climate The Black Sea is located in the Northern hemisphere to relatively low latitudes between 46°30″ and 41°00″. This region is defined by over 1200 km from east to west and about 600 km in the north–south direction. The Black Sea can be characterized as a semi-enclosed basin. It has connection with the larger Mediterranean Sea by the strait of Bosphorus in southwest and by the Crimean strait to the small and shallow Azov Sea in the north. The Crimean peninsula formally divides the northern part of the sea into two relatively detached basins. Most of the basin is deep as the bottom rises only near the coasts. The extensive shallow water regions in the north and northwest side are determined by the flow of large European rivers (especially Danube). Besides the features concerning the geographic location, bathymetry and the complexity of the shores, the climate over the Black Sea and close land regions is affected by the atmospheric circulation conditions over the basin. The synoptic processes over the Black Sea are classified into nine types [1]. The classification is carried out by grouping the synoptic situations with respect to the main wind direction over the sea and by wind velocity levels. Seven synoptic types correspond to the main directions of the wind over the Black sea (north– east, east, south–east, south–west along with southwest, north–west and north), the eighth – the cyclonic type – cause predominance of east wind in the north regions and gradual transition to west wind in the south. The atmosphere circulation conditions above the Black Sea and the relevant weather conditions possess well-distinguished seasonal differences. During wintertime the Black Sea periodically is under the influence whether of the Siberian anticyclone spur, which spreads over the Eastern Europe or of cyclones that originate in the Mediterranean branch of the polar front and moves eastward. Above the whole sea under the anticyclone circulation strong and constant east and
The combination of several satellites enables high-precision altimetry and reduce to silence the compromise: spatial resolution versus temporal resolution, Topex/Poseidon-ERS and JasonEnvisat are good examples of how altimetry satellites can operate together. Even if the satellite measurements are quite intermittent compared to 3 hour in-situ measurements, it should be noted that to determine the long term distribution of significant wave height, Hs, three hours is unnecessarily dense (this sampling is primarily driven by operational rather than climatologically needs). In fact, it has been demonstrated that for the Norwegian Sea, the information content in a data set of significant wave height is equivalent to a subset of independent measurements taken every 40 to 50 hours (Krogstad et al). Based on satellite multi-mission measurements provided mostly by site www.aviso.oceanobs.com, wind and wave data, covering almost five years starting from December 2005 until May 2010, were analyzed. Several locations have been identified from data analysis concerning Hs. All these locations are situated in the western part of the Black Sea which confirms the analysis made in SWAN program [2], indicating that the west area has a higher energy potential much significant than any other part on the Black Sea.
Fig. 1. Location of the satellite node used as reference for analysis (B1, B2, B3 and B4).
Because of the interested to evaluate the energy features on the Romanian Black Sea area were selected the points that are closer to this region, in this case points B1, B2, B3 and B4 (fig.1) and estimate wave and wind characteristic by using satellite data and statistic instrument to give an answer about a suitable place to develop a mix wind-wave farm.
standard is a normal distribution, which has a kurtosis of 3. All data from December 2005 to May 2010 were considered to evaluate the significant wave height distribution. Table. 1. Overall measurements statistics for significant wave height for points B1, B2, B3 and B4 during December 2005 to May 2010.
4. Statistical analysis of satellite data 4.1. General In this section we will examine the basic statistics of the wave data set, for point B1, B2, B3 and B4. The main statistical parameters of the significant wave height data are: mean value: 1n (1) x Hi n i1 and Hi, i=1, 2, . . . ,n is the sample values of significant wave height, standard deviation 2
H
1n (Hi x) n i1
(2)
skewness S
(x x)
3
n 3
(3)
2
σ=
n
(4)
and N is the number of data points. The skewness for a normal distribution is zero, and any symmetric data should have skewness near zero. kurtosis (x x) K n 4
B1 0.20 3.90 0.98 0.80 0.70 0.57 1.71 3.70 25% 35% 40%
B2 0.30 4.30 0.98 0.80 0.60 0.56 1.77 4.33 25% 35% 40%
B3 0.20 3.50 0.91 0.80 0.50 0.51 1.67 3.68 27% 28% 44%
B4 0.10 3.60 0.86 0.70 0.50 0.49 1.69 4.03 30% 24% 46%
Table. 2. Winter season measurements statistics for significant wave height for point B1, B2, B3 and B4 during December 2005 to May 2010. Minimum Maximum Mean Median Mode Std dev Skewness Kurtosis 0…0.6m 0.6...1.2m >1.2m
B1 0.20 3.90 1.18 1.00 0.70 0.61 1.32 1.71 17% 49% 34%
B2 0.30 4.30 1.18 1.00 0.90 0.61 1.44 2.49 17% 49% 34%
B3 0.20 3.50 1.11 1.00 0.80 0.56 1.30 2.04 18% 44% 38%
B4 0.10 3.60 1.05 0.90 0.70 0.54 1.33 2.34 21% 39% 41%
4.2. Seasonal distribution and spatial variability of wave data.
σ is the standard deviation,
x x
Minimum Maximum Mean Median Mode Std dev Skewness Kurtosis 0…0.6m 0.6...1.2m >1.2m
4
(5)
Kurtosis is the height and sharpness of the peak relative to the rest of the data. Higher values indicate a higher, sharper peak; lower values indicate a lower, less distinct peak. The reference
Based on data from table 1, the four points located on the Romanian near shore can be grouped into two distinct areas: area 1 (B1, B2) area 2 (B3, B4). In area 1 waves occur between 0.2-3.9 m and most common waves are those 0.7m and 0.6 m, regarding area 2 wave are between 0.1-3.6 m and 0.5 m is the most common wave value. Normal distribution is calculated using mean and standard deviation, and indicate that there is a cumulative probability of 40% that wave to be over 1.2 m for area 1, this probability increases at location 2 where we have almost 44-46%. In figure 2, a histogram is presented for summer season and where the waves between 0.61.2 m occur more often.
Fig. 2. Classes of Hs for summer season during December 2005 to May 2010 . Figure Fig. 4 Typical annual distribution of Hs (m) during Table 1 and 2 indicate that wave fields in the December 2005 to May 2010. study area are characterized by significant changes during the year, the annual evolution is defined by 4.3 Wind statistics winter and summer seasons. Winter season is more In addition, statistical parameters of wind speed significant mainly due the strong wind blowing in data for the same location are produced and the winter and storms that are more common analyzed. Analysis of wind conditions are events for this time of the year. important both due to the influence they play in In winter season differences between the 2 production and development of sea waves, and in areas are almost the same as in global analysis, an the same time, to evaluate the potential of wind important difference is that wave occur more often speed in nearshore, necessary to develop a wind in class 0.6-1.2 this change is mainly due to a farm project. larger number of waves that occur in the interval The spatial distribution of wind speeds in the 0.7-0.9 m and so we have 49% for area 1 and target area is relatively homogenous ranging from almost 44% for the other area. 0.3 m/s to 16.1 m/s as shown in Table. 3. This significant wave distribution between 0.6 Average wind speed is 4.8 m/s 2.5 m/s. and 1.2 note that mean and median increase with Nearly 47% wind speed is between 3-8 m/s. almost 0.2 m in the winter time . Table. 3. Overall measurements statistics for wind Skeweness indicate a strong positive data speed[m/s] for point B1, B2, B3 and B4. distortion with an annual value exceeding 1.6 and B1 B2 B3 B4 for the winter season over 1.3. Minimum 0.3 0.5 0.5 0.3 Maximum 14.8 16.1 15.4 15.5 For kurtosis, data are distributed near to a Mean 4.8 4.8 4.9 4.8 normal distribution, with value under 2.5 for Median 4.4 4.4 4.5 4.4 winter season and over 3.7 for annual data. Mode 3.5 4.7 4.5 3.6 Figure 3 and 4 show annual distribution of Hs Std dev 2.5 2.4 2.4 2.5 Skewness 1.0 1.0 0.9 0.9 and the two main seasons, highest monthly average Kurtosis 0.9 1.12 0.99 0.84 are in January with almost 1.4 m and minimum 0…3m/s 24% 23% 21% 24% values in May and June with 0.6 m. Wave value 3...8 m/s 66% 68% 69% 67% for locations B1 and B2 is a little higher than B3 >8 m/s 10% 9% 10% 10% and B4, this fact is reflected in the Hs monthly distribution.
Fig. 3. Monthly mean Hs distribution ) during December 2005 to May 2010.
Fig. 5. Typical Black Sea wind climate [m/s] during December 2005 to May 2010
Wind map provided by site Windfinder.com confirm this results (fig.5) an indicate a moderate climate of wind conditions for the Romanian Black Sea area with wind speed between 3 and 9 m/s. Table. 4. Statistic wind speed [m/s] for winter season for point B1, B2, B3 and B4.
Minimum Maximum Mean Median Mode Std dev Skewness Kurtosis 0…3 m/s 3...8 m/s >8 m/s
B1 0.60 14.80 5.70 5.10 4.70 2.66 0.79 0.26 15% 65%
B2 0.90 16.10 5.72 5.30 5.30 2.51 0.79 0.62 14% 68%
B3 0.60 15.40 5.76 5.30 5.70 2.57 0.78 0.56 14% 67%
B4 0.30 15.50 5.73 5.30 3.40 2.66 0.75 0.44 15% 65%
19%
18%
19%
20%
Winter season, is characterized by the fact that minimum value are much higher than annual value with almost 0.6≈0.9 m/s excepting B4 that has the same values for the whole year (0.3m/s). Mean is around 5.7 m/s and median is close to 5.3 m/s. Chances are over 65% to find wind speed between 3 and 8 m/s . Skewnees indicate that there is a positive data distortion almost the same for the four locations: per year this is around 0.9≈1 and for the winter season there is a small tendency for data to be closer to mean value (0.75≈0.79). Regarding kurtosis this is very low, both for the for the winter and for entire years (between 0.75-1.12) which indicate that each class contains a similar proportion in all values.
Fig. 6. Monthly mean wind speed distribution during December 2005 to May 2010.
The monthly distribution of wind speeds is ranging from 3 m/s to 6 m/s and in Fig. 6. summer and winter seasons are clearly defined the average difference between this two seasons
is almost 3 m/s with high possibility during the winter period to have the larger mean wind speed around 6.4 m/s in January and the lowest one in May with 3.2 m/s. In summer wind is over 3 m/s while in the winter is over 13 m/s exception being the months September, October and December. Maximum wind speed distribution (fig. 7) shows that wind in over 8 m/s for summer and over 13 m/s in winter time (except-September, October and December ) Wind speed is above 5 m/s for nearly 50% of summer and 60% of winter time, which fits the requirements of wind generators because this turbine are productive if wind speed is 4.5 m/s or greater, this is good enough for a wind-wave mixed farm because this coupled wave energy device reduce intermittent wind/wave resources.
Fig. 7. Monthly maximum wind speed distribution during December 2005 to May 2010.
Figure 8 show the influence of wind on wave climate and monthly variation of maximum values of their characteristics for areas analyzed.
Fig. 8. Monthly evolution of wind and wave characteristics during December 2005 to May 2010.
Offshore wind farms present benefits that include higher wind resource (larger wind
velocities) with lower turbulence levels than adjacent land sites. The joint exploitation of offshore wind and wave energy resources can have a number of advantages that include: - higher availability of produced power when swells continue after the wind has declined; - higher quality of power delivered to the grid when mixing the power from wind and wave energy; - lower structural and erection costs per MW if the two converters share the same structure; - lower electric cable cost per MW by sharing the same transmission cable; - lower operation and maintenance costs and less area and environmental impact for combined farms. Moreover, the costs of offshore wind exploitation by itself are higher than the onshore ones. On the other hand, more convenient locations for sitting offshore wind farms are those that are not exposed to rough seas, which are the most appropriate for wave energy utilization [3, 4]. 5. Conclusions and future directions This study describes the statistical properties and the spatial variability of waves and wind near the Romania nearshore of the Black Sea using satellite data. Because Black Sea is an enclosed sea, comparing the results from the target area with more energetic areas, like for the Portuguese continental nearshore, the average value of the significant wave height registered by Sines buoy in the period 1994-2003 is about 1.7 meters, almost double than the average annual (0.98 meters) and with 0.5 meters more higher than the winter season. Analyzing the four selected locations, results that significant wave height shows slightly higher values for locations B1 and B2 compared with B3 and B4, however for this four points we have a moderate wave climate and an annual evolution of wave climate dominated by the summer and the winter seasons. Sites with a moderate and steady wave energy flux may well prove to be more attractive than sites where the resource is more energetic, but also unsteady and thus less reliable. Some wave energy converters can be
tuned for maximum efficiency in waves with a particular range of periods and heights. Moderate climate of the wave, strong winds which shows the same features as in nearshore oceanic areas sets the stage for implementation of mixed wind and wave farms. Future analysis will be to identify wind turbines and wave devices that are more suitable for the west part of the Black Sea climate, to produce forecasts that cover the life period for this devices and to analyse the feasibility to implement a system of combined wind and wave device considering seasonal environmental conditions. Acknowledgments "The work of Florin Onea was supported by Project SOP HRD - EFICIENT 61445"
References [1] Sorkina, A. I., 1964: Reference book on the Black Sea Climate, Gidrometeoizdat, pp 406-412. [2] Rusu, E, 2009: Wave energy assessments in the Black Sea, Journal of Marine Science and Technology, Springer, Volume 14, issue 3 pp. 359372. [3] Pontes, M.T., 1998: Assessing the European Wave Energy Resource. Journal of Offshore Mechanics and Arctic Engineering, vol. 120, p.226-231,. [4] Mollison, D. and Pontes, M.T., 1992: Assessing the Portuguese wave power resource, Energy-Pergamon, vol. 17, Nº3, 55-268,.
