- Email: [email protected]
Simulation of electricity generation by marine current turbines at Istanbul Bosphorus Strait
Energy 95 (2016) 41e50
Contents lists available at ScienceDirect
Energy journal homepage: www.elsevier.com/locate/energy
Simulation of electricity generation by marine current turbines at Istanbul Bosphorus Strait Hasan Yazicioglu a, K.M. Murat Tunc b, Muammer Ozbek b, *, Tolga Kara b a b
Technical University of Denmark, Department of Wind Energy, Denmark Istanbul Bilgi University, Faculty of Engineering and Natural Sciences, Turkey
a r t i c l e i n f o
a b s t r a c t
Article history: Received 9 July 2015 Received in revised form 29 October 2015 Accepted 17 November 2015 Available online 24 December 2015
In this work, several simulations and analyses are carried out to investigate the feasibility of generating electricity from underwater sea currents at Istanbul Bosphorus Strait. Bosphorus is a natural canal which forms a border between Europe and Asia by connecting Black Sea and Marmara Sea. The differences in elevation and salinity ratios between these two seas cause strong marine currents. Depending on the morphology of the canal the speed of the flow varies and at some specific locations the energy intensity reaches to sufficient levels where electricity generation by marine current turbines becomes economically feasible. In this study, several simulations are performed for a 10 MW marine turbine farm/cluster whose location is selected by taking into account several factors such as the canal morphology, current speed and passage of vessels. 360 different simulations are performed for 15 different virtual sea states. Similarly, 8 different configurations are analyzed in order to find the optimum spacing between the turbines. Considering the spatial variations in the current speed within the selected region, the analyses are performed for three different flow speeds corresponding to ±10% change in the average value. For each simulation the annual energy yield and cluster efficiency are calculated. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Renewable energy Marine current turbine Energy yield simulations Cluster/farm optimization Offshore engineering Dynamic interactions
1. Introduction The growing world population and rapid industrialization seen in developing countries cause a continuous increase in the global energy demand. Today the major source of energy comes from fossil fuels such as oil, coal and natural gas. However, considering the rate of increase in the consumption, it can easily be realized that these limited sources cannot be a long term solution to satisfy the global energy demand and are definitely bound to run out. Besides, using fossil fuels as primary source of energy has irreversible negative impacts on the environment which force many countries to seek for alternative environmental friendly renewable energy sources. Turkey, as a rapidly growing economy with very limited national hydrocarbon resources, is also heavily dependent on fossil fuels (e.g. natural gas) imported for electricity production [1]. However, some recent political instabilities in the supplier countries, the heavy economic burden of importing these resources and the most
* Corresponding author. E-mail address: [email protected] (M. Ozbek). http://dx.doi.org/10.1016/j.energy.2015.11.038 0360-5442/© 2015 Elsevier Ltd. All rights reserved.
importantly, the increasing awareness of environmental issues have been encouraging policy makers to increase the use of renewable energy sources. Indeed, very detailed investigations and analyses were performed to determine the wind, solar and geothermal energy capacity of the country [1]. However, the potential of harnessing some other renewable sources, particularly sea current energy has not been fully realized yet. Compared to the other types of renewable energy such as wind and solar, current energy can still be considered in development phase and is not commercially available in large scales. Existing marine turbine systems are mostly in prototype testing stage. Although initial results are quite promising [2e9] some further verification for long term performance and durability under severe environmental conditions is still required. The average current speed needed for most commercial turbines is approximately 4e5 knots (2e2.5 m/s). Areas that typically experience high marine current flows are in narrow straits, between islands and around headlands. Entrances to lochs, bays and large harbors often also have high marine current flows. Generally the resource is largest where the water depth is relatively shallow and a good tidal range exists [10].
42
H. Yazicioglu et al. / Energy 95 (2016) 41e50
The flow in Bosphorus does not originate from tidal currents but the differences in elevation and salinity ratios between two seas and wind and pressure variations [11]. The unique characteristics of the strait enable very high energy intensities to be reached at some locations and sections. This paper aims at investigating the feasibility of generating electricity from the streams at Bosphorus by using marine current turbines. Extensive simulations and analyses are performed for a 10 MW marine turbine farm (10 e SeaGen 1 MW) where several important design parameters such as the size, orientation, depth and spacing of the turbines are optimized according to the specific morphology and flow patterns seen at Bosphorus. 2. Morphology of Bosphorus and flow characteristics Bosphorus is a natural canal which forms a border between Europe and Asia by connecting Black Sea and Marmara Sea. The strait is nearly 31 km long and runs in a north-south direction approximately. The narrowest section is between Anadolu and Rumeli Fortresses and 700 m wide. However, the width increases significantly at both northern (Black Sea) and southern (Marmara) ends and reach to values of 3400 m and 3200 m, respectively. The depth of the strait is 30e35 m on average and reaches to its maximum value of 110 m in Kandilli region. The orientation of the canal can be seen in Fig. 1. In the figure, the plot on the right shows the location and layout of the cluster. Due to the specific flow pattern, the cluster is designed as a single row to be able to benefit the high speed current as much as possible. The currents seen in the Bosphorus Strait is an example of a two layer exchange flow that is characterized by brackish waters originating in the Black Sea and moving southward and more saline, denser waters from the Marmara Sea and flowing northward [12]. Black Sea has a positive water balance due to the excess hydrological input from five major and countless minor rivers. The only exit point of this flow is the Bosphorus resulting in a net outflow of 300 km3 water through the strait in a year. The sea level difference between the two ends of the strait has an average value of 30 cm. The Elevation difference caused by river flows, leads surface currents with speeds reaching 7e8 knots at windy seasons.
It is very well known that the surface current has a very high potential for energy production [13,14]. However, Istanbul Strait is a major sea access route and has one of the heaviest maritime traffics in the world. According to the statistics [15] 38,000 vessels have passed through the strait in 2014. More than 15,000 of these vessels were large size crude oil tankers having lengths of 200 m or more. Considering the intensity of the traffic load it can be realized that electricity generation from surface currents is not feasible due to high risk of accident. Similarly, the difference in salinity ratios and density between two seas cause a deep layer current flowing in the opposite direction, namely from south to north. The salinity ratios of Black Sea and Marmara Sea are 1.8% and 2.5% respectively. Compared to the surface current, the speed of deep layer current is low (2e3 knots). However, depending on the morphology at certain locations the energy intensity of this flow can reach to sufficient levels making electricity production feasible. In this work, location of the farm/cluster together with other design parameters such as the size, orientation, depth and spacing of the turbines are determined according to the specific morphology and flow pattern seen at Bosphorus. 1 MW SeaGen marine current turbine [16] with a rotor diameter of 18 m is considered to be the most appropriate turbine for the project. In order not to obstruct the passage of vessels, the turbines are placed at an average depth of 42 m. This enables a clearance of 24 m to be left for the ships passing over the region. It should be noted that the power generated by the turbine is proportional to the square of the rotor diameter. Therefore, leaving a shorter clearance and using a larger turbine will definitely result in a higher production capacity. However, due to hydrodynamic load limitations, state of the art MW scale marine current turbines have rotor sizes changing between 15 and 20 m. In order to make a reasonable estimation by using commercially available and technically proven systems, in this study 1 MW SeaGen turbine is used in the analyses. It is believed that the rapid progress seen in composite materials technology will enable larger and higher capacity turbines to be designed in near future. The airfoil of the blade is NACA 631424 and remains same along the blade. The turbine selected for the project and the corresponding power curve can be seen in Figs. 2 and 3, respectively.
Fig. 1. The layout of the Istanbul Bosphorus Strait.
H. Yazicioglu et al. / Energy 95 (2016) 41e50
43
3. Modeling The similarities between horizontal axis wind turbines and current turbines suggest that the latter can benefit by transferring experience and modeling tools from the wind industry. Some recent works [18,19] show that wind turbine load and performance simulation code GH Bladed can easily be modified and used for marine turbine applications. Similarly, Yucheng LiuA methodology [20e22], which was developed by EPRI (Electric Power Research Institute) based on a modified Gamma spectrum, is used for analyzing the potential for WEC (wave energy conversion) in a desired geographical area. This methodology allows WEC developers to easily and efficiently predict the potential wave power available to their devices and to facilitate the prediction of power output performance in a given year, season, month, etc., at a specific location [20]. A recent approach proposed in Ref. [23] partitions the measured spectra into separate wave systems, each of which is represented by a modified JONSWAP (Joint North Sea Wave Project) spectrum. The methodology applied in this work can be summarized as follows; - The simulations are performed for 15 different virtual sea states (5 significant wave heights and 3 peak periods). For each state, the downstream velocity profile is interpolated and centerline velocities for the turbines are determined by using irregular wave theory. This technique can be considered as the expansion of linear wave theory by using the sea state frequency distribution acquired from JONSWAP spectrum. - The centerline velocity calculated for the hub (31 m below the sea surface) is then superimposed on the average current speed and the resultant flow speed is obtained. - Frandsen model is used to represent the dynamic interactions among the turbines and the resulting wake effects and to calculate the current speeds experienced by each turbine. Wake analyses are conducted for 8 different configurations in order to find the optimum spacing between the turbines. - Considering the spatial variations in the current speed within the selected region, these analyses are performed for three different flow speeds. The simulations are repeated for speeds corresponding to 10% increase and decrease in the average
Fig. 3. 1 MW tidal-current turbine power curve [17].
value. For each simulation the corresponding AEP (Annual Energy Production) is calculated and stored in a database.
3.1. Representation of irregular wave behavior This paper does not aim at focusing on the theoretical background of the utilized analysis methods but on the results obtained for different cases and scenarios. Interested reader is referred to related publications for further information. In linear wave theory, the velocity profile below the surface can be described as follows [24];
uðx; z; tÞ ¼
uH coshðkðz þ hÞÞ cosðut kxÞ 2 sinhðkhÞ
(1)
In Eq. (1) u, h and z are the wave velocity, total depth and the vertical distance from the surface, respectively. H represents the wavelength. Linear dispersion is shown by u. Similarly, k and x are used to describe the wave number and the horizontal position of the wave. In order to expand this expression and to take into account the wave irregularity some important parameters such as linear dispersion, wave number and the wave length should be taken as random variables. This also requires the surface elevation and downstream velocity profile to be updated. For a generated time interval and wave spectrum, the surface elevation hðx; tÞ and the
Fig. 2. SeaGen marine current turbine [16].
44
H. Yazicioglu et al. / Energy 95 (2016) 41e50
corresponding velocity profile uðx; z; tÞ are calculated as shown in Eqs. (2) and (3), respectively.
hðx; tÞ ¼ ap cosðun t kn x þ ∅n Þ uðx; z; tÞ ¼ ap un
coshðz þ hÞ cosðun t kn xÞ sinhðkn hÞ
(2) (3)
In Eq. (2) ∅n is phase angle and has a random value changing between 0 and 2p. As can be seen, Eq. (3) has the same structure as Eq. (1). The additional term ap represents the amplitude. In practice, instead of a finite number of waves kn , one assumes that the surface is made up of infinitely many waves, all having different amplitudes and wave numbers. Therefore, the calculation of ap depends on some other spectra such as JONSWAP spectrum. Estimation of the velocity profile at a certain depth through the use of surface wind/wave speeds is widely used in calculation of the loads acting on submerged structures such as pipelines and/or the foundations of offshore platforms and wind turbines. Several researchers reported that these estimations are in very good coherence with the real velocity profiles measured both at shallow and deep waters [25e28]. 3.2. JONSWAP spectrum JONSWAP [29] spectrum is a widely used spectral analysis method which establishes a relation between wind speed and wave spectrum. The technique is mainly based on some improvements made on PiersoneMoskowitz spectrum [30] by using the data collected during the North Sea Wave Observation Project called as JONSWAP. In this method it is assumed that the wave spectrum cannot be relied on fully developed sea state. Therefore, it requires the continuous non-linear waveewave interactions to be considered. Then, a small correction to PiersoneMoskowitz spectrum should be made by adding an artificial scaling factor in order to improve fit to the measurements. The difference between the two spectra is that waves continue to expand with distance from the shore and spectrum is clearer in the JONSWAP spectrum. In spectrum calculations clearance is a very important feature which allows representing non-linear interactions changing in time. In order to standardize the spectra the key variables should be related to main sea climate parameters rather than wind states. Hs and TP are two important parameters used to describe the spectrum and represent significant wave height and peak period, respectively. Hs , significant wave height can be defined as the average of the highest one-third (33%) of waves that occur in a given time interval (e.g. month or year). Similarly, the peak period, TP , is the wave period with the highest energy. The relation between Hs and TP can be approximated by using the equations shown below [31].
sffiffiffiffiffiffi sffiffiffiffiffiffi Hs Hs TP 14:4 11:1 g g
computed with the present model. It also includes the boundary layer conditions from the up and down streams. The model assumes that there is an initial wake expansion in front of the turbines. However, this expansion is neglected for the 1st turbine and the 1st velocity ratio C1 is considered to be equal to 1. The velocity ratios in the wake in front of the following turbines can be calculated by using the equation below.
Cnþ1 ¼ 1 ð
An 1 AR ð1 Cn Þ þ C Cn 2 Anþ1 T Anþ1
Where Cn and An are the velocity ratio and wake area before the nth turbine, respectively. AR is the swept area of the rotor and CT is the thrust coefficient of the turbine [32]. The main parameters of the Frandsen model can also be seen in Fig. 4. As can be seen in Eq. (5), the method requires the wake areas to be calculated. The first area is initial wake expansion which is neglected. This results that the area of the wake before the first turbine A1 is equal to the swept area AR. Furthermore, the wake expands immediately after the turbine, where the wake area expands with a factor b. The ratio b is computed as shown in Eq. (6) below.
pffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 1 þ 1 CT pffiffiffiffiffiffiffiffiffiffiffiffiffiffi b¼ 2 1 CT
(6)
This expression can be used to calculate wake area before the second turbine A2.
A2 ¼ b AR
(7)
In order to calculate the wake areas before other turbines, Frandsen model includes an expression (shown in Eq. (8)) that describes an expansion when the velocity ratio has reached an asymptotic value.
Anþ1 An ¼ AR a Sr
(8)
Frandsen also states that the decay factor a is to be determined experimentally. The value relies on the asymptotic value of velocity ratio from the N.O. Jensen Wake model [33,34]. The relation between decay factor and the velocity ratio at the infinity as follows.
(4)
In Eq. (4) g is the gravitational acceleration. In this work the effect of wave height is investigated for 5 different values between 1.5 and 2.5 m changing with an increment of 0.25 m. 3.3. Frandsen Wake Model and calculation of the spacing between the turbines The Frandsen Wake Model [32] is a more recent version than many other wake models and attempts to include more elements of reality. Flows from different directions through a large farm can be
(5)
Fig. 4. The parameters of the Frandsen Model [32].
H. Yazicioglu et al. / Energy 95 (2016) 41e50
a¼
1 CT Cw 2 Sr ð1 Cw Þ
(9)
Where, Cw is the velocity ratio at infinity taken from N.O. Jensen's model which is function of spacing x0 =D0 . In wake analysis, the spacing between turbines is described as the ratio of the absolute distance x0 between the turbines to the rotor diameter D0. In this work, in order to find the optimum layout and spacing between the turbines wake analyses are conducted for 8 different configurations, namely for distances corresponding to 5,6,7,8,9,10,12 and 15 times the rotor diameter D0 (18 m). In all configurations, the turbines are placed in tandem, i.e. all the turbines are placed one after another in downstream direction. Unless sufficient spacing is left between the turbines, such a strategy definitely increases the dynamic interactions between the turbines and the corresponding wake losses. Unfortunately, a configuration where all the turbines are placed side by side facing an inflow of the same and largest possible speed is not possible due to several reasons. The depth required to leave a safe clearance for the ships passing over the region can only be obtained in a narrow zone close to the centerline of the canal. Similarly, high speed steady flow is seen within the same narrow corridor. The stream close to shore has unsteady turbulent flow characteristics and is not suitable for energy production through the use of marine turbines. Therefore, due to the limitations caused by the specific morphology of the strait, a tandem configuration is considered as the most efficient layout for the cluster. However, in this configuration it becomes very crucial to analyze the dynamic interactions among the turbines and to determine the optimum spacing required for maximizing the energy output. Fig. 5 shows how the current speeds experienced by the 10 turbines in the cluster change depending on the spacing ratio x0 =D0 . In Fig. 5, the x axis shows the number of the turbine in the cluster. Similarly, the y axis represents the current speed which is normalized with respect to the current speed experienced by the 1st turbine in the row.
45
The centerline velocity time history (31 m deep from the sea surface) and the power generated by the first turbine can be seen in Fig. 6. In Fig. 6 the first plot shows the change in current velocity depending on the wind speed. The variation in current velocity is simulated for a period of one year (8760 h). Similarly, the second plot shows the power generated by the 1st turbine for the corresponding current speed profile. In these plots the x axis represents a time period of one year (8760) hours. In order to make it easier to visualize, in the third plot, the generated power is redrawn for a period of one week (168 h). As can be seen in Fig. 6 there may be some short duration fluctuations in the power due to the change in wind speed. These fluctuations are in the range of ±10%. Fig. 7 shows the Probability Density Function calculated for the current speed distribution used in the analysis. In Fig. 7, the x axis represents the current speed and measured in m/sec. The y axis shows the probability of having the current speeds shown in the x axis. In the figure, probability is described as percentage. However, the same graph can also be represented as a Dwelling Time vs. Current Speed graph simply by multiplying the y axis by 8760 h.
4. AEP (Annual energy production) calculation The power generated by each turbine in the cluster is calculated separately by using Eq. (10) shown below.
Pn ¼
1 Ar r u3n Cp 2
(10)
where; Ar , r , and Cp represent rotor area, density of water and power coefficient, respectively. Similarly un is the effective current speed experienced by the nth turbine in the row and is calculated by using Frandsen Wake Model. The resultant flow speed acting on the 1st turbine is calculated by superimposing the wind speed effect on the average current speed. The speeds experienced by different
Fig. 5. Change in current speed experienced by the turbines in the farm for different spacing ratios.
46
H. Yazicioglu et al. / Energy 95 (2016) 41e50
Centerline Velocity Time Series (for 1 year or 8760 hours) Wave Speed [m/s]
2,0
1,9
1,8
1,7 0
1000
2000
3000
4000
5000
6000
7000
8000
9000
7000
8000
9000
Time [Hour]
Generated Power kW]
Generated Power Time Series (for 1 year or 8760 hours) 1000
800
600
400 0
1000
2000
3000
4000
5000
6000
Time [Hour]
Generated Power [kW]
Generated Power Time Series (for 1 week or 168 hours) 1000
800
600
400 300
320
340
360
380
400
420
440
460
Time [Hour] Fig. 6. Centerline velocity and generated power graphs.
turbines are then scaled by using the normalized velocity ratios shown in Fig. 5. The centerline velocity at each turbine can be fitted to Weibull distribution in order to get distribution parameters. Due to statistical modeling, the Weibull coefficients are as follows: A ¼ 2.1,
q ¼ 8.97. The frequency of the AEP (Annual Energy Production) ðFAEP Þ is calculated by using power curve of the SeaGen generator (shown in Fig. 3). Annual energy production is then calculated by using equations below.
Fig. 7. Probability density graph calculated for the current speed distribution.
H. Yazicioglu et al. / Energy 95 (2016) 41e50
Fðui < u0 < uiþ1 ÞAEP
ðAEPÞn ¼
N1 X i¼1
u q u q exp iþ1 ¼ exp i A A
1 ðP þ Piþ1 Þð8760Þ Fðui < u0 < uiþ1 ÞAEP 2 i
(11)
(12)
In Eq. (11), q is the shape factor and A is the scale factor parameter of the Weibull distribution. Similarly P [watt] represents the power at ith point and taken from the power curve of the SeaGEn turbine, u represents the current speed. It should be noted that while calculating AEP it is assumed that there will be approximately 10% energy loss in the drive train (gearbox and generator). Fig. 8 shows how the cluster efficiency changes depending on the significant wave height (Hs) and peak period (TP) values used in the analyses. Efficiency can be defined as the ratio of the total power generated in the cluster in a real case scenario to the power corresponding to a hypothetical case where all the turbines experience maximum available current speed without obstructing each other. Such a hypothetical case can only be possible when the distance between the turbines is sufficiently long (approaching to ∞). However, in practice to due to space limitations and installation costs the turbines are not placed very far from each other resulting in avoidable wake effects and decrease in the flow speed (as shown in Fig. 5). Therefore, efficiency can be used to evaluate whether the distances between the turbines are sufficiently long or not. Fig. 8 presents the results of the simulations performed to determine the effect of significant wave height ðHs Þ and peak period ðTP Þ on the generated power or efficiency. The analyses are
47
performed for 5 different Hs values chancing between 1.5 and 2.5 m and 3 different TP values namely, minimum, maximum and mean values obtained from Eq. (4). In Fig. 8 the plots shown on the right are obtained by zooming in the red squares shown on the left plots. As can be seen, using minimum or maximum TP value recommended by Eq. (4) has a very little effect on the AEP or cluster efficiency. Similarly, increasing wave height Hs causes only a negligible increase in the generated power which is usually less than 1%. Although Fig. 6 clearly reveals that there may be some short duration fluctuations in the power due to the change in wind speed, these fluctuations are in the range of ±10% and are within the tolerable limits of the power regulating systems. The effect of instantaneous changes in sea state/wind becomes negligible when long term periods (e.g. one year) are considered. Fig. 9 shows the change of cluster efficiency depending turbine spacing ratio. In the figure x and y axes represent the turbine spacing ratio and the relative efficiency of the cluster, respectively. Unless a very detailed and high resolution 3D bathymetric map or model is utilized, there may be some unpredictable spatial variations in the current speed due to the complicated morphology of the strait. Considering the spatial variations within the cluster area, in this work, the analyses are performed for three different flow speeds. The simulations are repeated for speeds corresponding to 10% increase (2.0 m/s) and decrease (1.7 m/sec) in the average value of 1.85 m/s. For each simulation the annual energy yields and corresponding cluster efficiencies are calculated. The total energy produced by the cluster increases as the effective current speed increases. However, when the generated energy is normalized with the maximum theoretical energy corresponding to that speed, the effect of flow speed is eliminated and it becomes possible to investigate the effect of spacing ratio
Fig. 8. Change in efficiency for varying Hs and TP.
48
H. Yazicioglu et al. / Energy 95 (2016) 41e50
Fig. 9. Change of cluster efficiency depending on turbine spacing ratio.
specifically. Indeed, Fig. 9 shows that cluster efficiency is directly related to the distance between turbines. As the spacing ratio is increased the dynamic interactions between the turbines and the resulting wake loss decrease. The results presented in Fig. 9 are consistent with the velocity ratios shown in Fig. 5. As can be seen in Fig. 5, for a spacing ratio of 15, the average current speed for the turbines can be assumed as 0,9. Since the generated power is proportional to the 3rd power of the flow velocity, the normalized power is expected to be close to 0.70. Table 1 shows the annual energy production [GWh] results calculated for varying current speeds and turbine spacing. In the table, the 1st row represents spacing ratios changing between 5 and 15. Similarly the 1st column includes the average currents speeds. The generated power calculated for a specific value of current speed and spacing ratio can be obtained simply by intersecting the corresponding row and column. It should be noted that in this work the number of turbines is 10 and considered as a fixed parameter in the analyses. However, we also examined whether the acquired results can be used to roughly estimate the total power and relative efficiency of the clusters having variable number of turbines or not. This approach is discussed in more detail in the following sections. In practice the parameters which determine the size and capacity of the cluster is related to not only the efficiency but also the morphology, other physical limitations and installation costs. If the length of the available cluster area is considered as the limiting parameter, selecting a smaller spacing ratio makes it possible to install more turbines within the same region. Such an approach results in an increase in the total energy generated but it also decreases the cluster efficiency.
In this project, the length of the cluster area is 2000 m which is equal to approximately 110 times the rotor diameter (18 m). If this length is considered as the limiting parameter, the number of marine turbines corresponding to a specific spacing ratio can be calculated by dividing the total length ð110 DÞ by the ratio ðx=DÞ. Assuming that velocity ratios shown in Fig. 5 are constant after the 5th turbine in the row, we can estimate the velocity distribution for clusters having more than 10 turbines. The estimated current speeds are then used to calculate the annual energy production for the clusters having different number of turbines. Table 2 shows the results of AEP calculations performed for varying number of turbines. It should be noted that in these analyses the available cluster area (2000 m) is assumed to be constant. Fig. 10 shows the change in total power and cluster efficiency depending on the spacing ratio. Two different approaches namely, power maximizing and efficiency maximizing approaches can be followed in the design of the cluster. In power maximizing approach 22 (1 MW) marine turbines can be installed at the site with a spacing of 90 m (5 times the rotor diameter). In this case the average cluster efficiency is very low 41% but the generated total energy (AEP) is high 71.7 GWh. In efficiency maximizing approach 7 (1 MW) marine turbines can be installed at the site with a spacing of 270 m (15 times the rotor diameter). In this case the average cluster efficiency is the highest 73% but the generated total energy (AEP) is just 42.6 GWh. Decision of which approach will be applied is directly related to the design objectives. The 10 MW cluster analyzed in this work corresponds to a spacing ratio of 11 and can be considered as a combination of power and efficiency maximizing approaches. 5. Conclusion
Table 1 Annual energy production AEP matrix for 10 turbines. Spacing ratio x/D
5
Current speed [m/s]
Total energy generated [GWh]
6
1.70 1.85 2.00
20 26 32
22 28 36
7
24 31 39
8
26 34 42
9
28 36 45
10
12
15
29 38 47
32 41 52
35 45 57
This paper aims at investigating the feasibility of generating electricity from sea underwater currents at Istanbul Bosphorus Strait. Several simulations are performed for a 10 MW marine turbine farm/cluster where 360 different scenarios are analyzed for 15 different sea states (for 5 significant wave heights and 3 peak periods). Similarly, 8 different configurations are modeled in order to find the optimum spacing between the turbines. Dynamic
H. Yazicioglu et al. / Energy 95 (2016) 41e50
49
Table 2 Annual Energy Production AEP matrix for varying number of turbines. Spacing Ratio x/D Number of turbines Cluster Efficiency Normalized Equivalent Power
5 22 0.41 9.1
6 19 0.46 8.5
7 16 0.50 8.0
Fig. 10. Change in generated power and cluster efficiency depending on turbine spacing ratio.
interactions among the turbines and the corresponding wake effects are taken into account by using a wake model approach. Considering that the complicated morphology of the strait may cause some spatial variations in the current speed within the selected region, the analyses are performed for three different flow speeds. The simulations are conducted for speeds corresponding to 10% increase (2.0 m/s) and decrease (1.70 m/s) in the average value of 1.85 m/s. For each simulation the annual energy yields and cluster efficiencies are calculated. The analyses performed in this work are not based on a detailed 3D bathymetric map which would have made it possible to conduct a comprehensive CFD (computational fluid dynamics) analysis and to obtain a very accurate velocity profile. In this work, since the flow within the narrow zone close to the centerline of the canal is known to be quite steady, the average speed values are assumed to be valid for the whole cluster. Some spatial variations and their possible effects on the generated power are taken into account by allowing a ±10% change in the speed values. It is believed that the methodology proposed in this work can be used to obtain very realistic results if additional information for velocity distribution is provided through CFD simulations or field tests. The results of the simulations show that, using minimum or maximum value of peak period (TP) recommended by Eq. (4) has a very little effect on the AEP or cluster efficiency. Similarly, increasing wave height from 1.5 m to 2.5 m causes a negligible increase in the generated power which is usually less than 1%. The analyses also reveal that there may be some short duration fluctuations in the power due to the change in wind speed. However, these fluctuations are in the range of ±10% and within the tolerable limits of the power regulating systems. The effect of change in sea state/wind becomes negligible when long term periods (e.g. one year) are considered. The dynamic interactions and wake effects are modeled by using Frandsen Wake Model. A more detailed analysis can be conducted by using a high resolution 3D bathymetric map or model which enables spatial variations in the current speed (due to the complicated morphology of the strait) to be calculated more accurately.
8 14 0.54 7.5
9 12 0.57 7.1
10 11 0.61 6.7
12 9 0.66 6.1
15 7 0.73 5.4
In order to determine how the distance between the turbines affect the generated power, the calculations are repeated for 8 different spacing ratios changing between 5 and 15. Although the simulations are performed for a cluster of 10 (1 MW of each) marine turbines, the obtained results are also used to roughly estimate the total power and relative efficiencies of the clusters having more than 10 turbines. The results of the preliminary analyses show that two different approaches namely, power maximizing and efficiency maximizing approaches can be followed in designing the cluster. If the length of the available cluster area is considered as the limiting parameter, selecting a smaller spacing ratio makes it possible to install more turbines within the same region. Such an approach results in an increase in the total energy but it also decreases the cluster efficiency. Analytical assessment carried out in this work shows that depending on the morphology of the canal, at some specific locations the energy intensity reaches to sufficient levels where electricity generation by marine current turbines becomes economically feasible. However, there may still be some issues such as environmental effects which need further analyses and investigations. The results obtained in this work are expected to encourage the government and private enterprises to provide additional funding for future research projects and also for the design, development and installation of marine current turbine prototypes. References [1] Website 1: Republic of Turkey, Ministry of Energy and Natural Resources. http://www.enerji.gov.tr/en-US/Pages/Natural-Gas. [Accessed in July 2015]. [2] Liu P, Veitch B. Design and optimization for strength and integrity of tidal turbine rotor blades. Energy 2012;46(1):393e404. [3] Barbarelli S, Florio G, Amelio M, Scornaienchi NM, Cutrupi A, Lo Zupone G. Design procedure of an innovative turbine with rotors rotating in opposite directions for the exploitation of the tidal currents. Energy 2014;77:254e64. [4] Lewis M, Neill SP, Robins PE, Hashemi MR. Resource assessment for future generations of tidal-stream energy arrays. Energy 2015;83:403e15. [5] Sanchez M, Carballo R, Ramos V, Iglesias G. Floating vs. bottom-fixed turbines for tidal stream energy: a comparative impact assessment. Energy 2014;72: 691e701. nchez M, Iglesias G. A port towards energy [6] Ramos V, Carballo R, Alvarez M, Sa self-sufficiency using tidal stream power. Energy 2014;71:432e44. [7] Liu P, Bose N, Frost R, Macfarlane G, Lilienthal T, Penesis I, et al. Model testing of a series of bi-directional tidal turbine rotors. Energy 2014;67:397e410. [8] S anchez M, Carballo R, Ramos V, Iglesias G. Energy production from tidal currents in an estuary: a comparative study of floating and bottom-fixed turbines. Energy 2014;77:802e11. nchez M, Iglesias G. Assessment of the [9] Ramos V, Carballo R, Alvarez M, Sa impacts of tidal stream energy through high-resolution numerical modeling. Energy 2013;61:541e54. [10] Development of Marine Energy in New Zealand, Technical Report by EECA Energy Efficiency and Conservation Authority, 2009. € [11] Gregg MC, Ozsoy E, Latif MA. Quasi-steady exchange flow in the Bosphorus. Geophys Res Lett 1999;26(1):83e6. [12] Jarosz E, Teague WJ, Book JW, Bes¸iktepe S. On flow variability in the Bosphorus Strait. J Geophys Res Oceans 2011;116(8). [13] Oguz T, Ozsoy E, Latif MA, Sur HI, Unluata U. Modelling of hydraulically controlled exchange flow in the Bosphorus Strait. J Phys Oceanogr 1990;20(7): 945e65. [14] S¸en Z. Energy generation possibility from ocean currents: Bosphorus, Istanbul. Ocean Eng 2012;50:31e7. [15] Website 2: Republic of Turkey, Ministry of Transport, Maritime Affairs and Communications of Turkey. http://www.ubak.gov.tr/BLSM_WIYS/UBAK/en/ en_new_html/20091202_121818_204_2_64.php. [Accessed in July 2015]. [16] Website 3: Marine Current Turbines. http://www.marineturbines.com. [Accessed in July 2015].
50
H. Yazicioglu et al. / Energy 95 (2016) 41e50
[17] Boehme T, Taylor J, Wallace R, Bialek J. Matching renewable electricity generation with demand. Technical Report. Scottish Government Publications; 2006. ISBN 0 7559 502. [18] Mccann GN, Rawlinson-Smith R, Argyriadis K. Load Simulation for Tidal Turbines using Wind Turbine Experience. Technical Report Garrard&Hassan Ltd. [19] Bossanyi EA. GH bladed user manual. Garrard&Hassan Ltd; 2003. [20] Pastor J, Liu Y. Wave energy resource analysis for use in wave energy conversion. J Offshore Mech Arct Eng 2015;137(1). [21] Pastor J, Liu Y. Frequency and time domain modeling and power output for a heaving point absorber wave energy converter. Int J Energy Environ Eng 2014;5(2e3):1e13. [22] Pastor J, Liu Y. Power absorption modeling and optimization of a point absorbing wave energy converter using numerical method. J Energy Resour Technol Trans ASME 2014;136(2). [23] Kerbiriou MA, Prevosto M, Maisondieu C, Clement A, Babarit A. Influence of sea-states description on wave energy production assessment. In: The 7th European Wave and Tidal Energy Conference Proceedings. Porto, Portugal; 2007. [24] Krogstad HE, Arntsen ØA. Linear wave theory. Technical Report. Norwegian University of Science and Technology; 2003. [25] Gudmestad OT. Measured and predicted deep water wave kinematics in regular and irregular seas. Mar Struct 1993;6:1e73.
€ dinger method for [26] Trulsen K, Gudmestad OT, Velarde MG. The nonlinear Schro water wave kinematics on finite depth. Wave Motion 2001;33:379e95. [27] Madsen PA, Bingham HB, Liu H. A new Boussinesq method for fully nonlinear waves from shallow to deep water. J Fluid Mech 2002;462:1e30. [28] Madsen PA, Bingham HB, Sch€ affer HA. Boussinesq-type formulations for fully nonlinear and extremely dispersive water waves: derivation and analysis. Proc R Soc A Math Phys Eng Sci 2003;459(2033):1075e104. [29] Hasselmann K, Barnett TP, Bouws E, Carlson H, Cartwright DE, Enke K, et al. Measurements of wind-wave growth and swell decay during the Joint North Sea Wave Project (JONSWAP). Technical Report. Deutches Hydrographisches Institut; 1973. [30] Moskowitz L. Estimates of the power spectrums for fully developed seas for wind speeds of 20 to 40 knots. J Geophys Res 1964;69(24). [31] IEC 61400-3:2009 Design requirements for offshore wind turbines. International Electrotechnical Commission Standards 2009. [32] Frandsen S, Barthelmie R, Pryor S, Rathmann O, Larsen S, Højstrup J, et al. Analytical modelling of wind speed deficit in large offshore wind farms. Wind Energy 2006;9(1e2):39e53. [33] Jensen NO. A note on wind generator interaction. Technical Report. ISSN01062840. Risø National Laboratory; 1984, ISBN 87-550-0971-9. Risø-M-2411. [34] Katic I, Højstrup J, Jensen NO. A simple model for cluster efficiency. In: EWEC European wind energy Association Conference Proceedings. Rome, Italy; 1986.
Contents lists available at ScienceDirect
Energy journal homepage: www.elsevier.com/locate/energy
Simulation of electricity generation by marine current turbines at Istanbul Bosphorus Strait Hasan Yazicioglu a, K.M. Murat Tunc b, Muammer Ozbek b, *, Tolga Kara b a b
Technical University of Denmark, Department of Wind Energy, Denmark Istanbul Bilgi University, Faculty of Engineering and Natural Sciences, Turkey
a r t i c l e i n f o
a b s t r a c t
Article history: Received 9 July 2015 Received in revised form 29 October 2015 Accepted 17 November 2015 Available online 24 December 2015
In this work, several simulations and analyses are carried out to investigate the feasibility of generating electricity from underwater sea currents at Istanbul Bosphorus Strait. Bosphorus is a natural canal which forms a border between Europe and Asia by connecting Black Sea and Marmara Sea. The differences in elevation and salinity ratios between these two seas cause strong marine currents. Depending on the morphology of the canal the speed of the flow varies and at some specific locations the energy intensity reaches to sufficient levels where electricity generation by marine current turbines becomes economically feasible. In this study, several simulations are performed for a 10 MW marine turbine farm/cluster whose location is selected by taking into account several factors such as the canal morphology, current speed and passage of vessels. 360 different simulations are performed for 15 different virtual sea states. Similarly, 8 different configurations are analyzed in order to find the optimum spacing between the turbines. Considering the spatial variations in the current speed within the selected region, the analyses are performed for three different flow speeds corresponding to ±10% change in the average value. For each simulation the annual energy yield and cluster efficiency are calculated. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Renewable energy Marine current turbine Energy yield simulations Cluster/farm optimization Offshore engineering Dynamic interactions
1. Introduction The growing world population and rapid industrialization seen in developing countries cause a continuous increase in the global energy demand. Today the major source of energy comes from fossil fuels such as oil, coal and natural gas. However, considering the rate of increase in the consumption, it can easily be realized that these limited sources cannot be a long term solution to satisfy the global energy demand and are definitely bound to run out. Besides, using fossil fuels as primary source of energy has irreversible negative impacts on the environment which force many countries to seek for alternative environmental friendly renewable energy sources. Turkey, as a rapidly growing economy with very limited national hydrocarbon resources, is also heavily dependent on fossil fuels (e.g. natural gas) imported for electricity production [1]. However, some recent political instabilities in the supplier countries, the heavy economic burden of importing these resources and the most
* Corresponding author. E-mail address: [email protected] (M. Ozbek). http://dx.doi.org/10.1016/j.energy.2015.11.038 0360-5442/© 2015 Elsevier Ltd. All rights reserved.
importantly, the increasing awareness of environmental issues have been encouraging policy makers to increase the use of renewable energy sources. Indeed, very detailed investigations and analyses were performed to determine the wind, solar and geothermal energy capacity of the country [1]. However, the potential of harnessing some other renewable sources, particularly sea current energy has not been fully realized yet. Compared to the other types of renewable energy such as wind and solar, current energy can still be considered in development phase and is not commercially available in large scales. Existing marine turbine systems are mostly in prototype testing stage. Although initial results are quite promising [2e9] some further verification for long term performance and durability under severe environmental conditions is still required. The average current speed needed for most commercial turbines is approximately 4e5 knots (2e2.5 m/s). Areas that typically experience high marine current flows are in narrow straits, between islands and around headlands. Entrances to lochs, bays and large harbors often also have high marine current flows. Generally the resource is largest where the water depth is relatively shallow and a good tidal range exists [10].
42
H. Yazicioglu et al. / Energy 95 (2016) 41e50
The flow in Bosphorus does not originate from tidal currents but the differences in elevation and salinity ratios between two seas and wind and pressure variations [11]. The unique characteristics of the strait enable very high energy intensities to be reached at some locations and sections. This paper aims at investigating the feasibility of generating electricity from the streams at Bosphorus by using marine current turbines. Extensive simulations and analyses are performed for a 10 MW marine turbine farm (10 e SeaGen 1 MW) where several important design parameters such as the size, orientation, depth and spacing of the turbines are optimized according to the specific morphology and flow patterns seen at Bosphorus. 2. Morphology of Bosphorus and flow characteristics Bosphorus is a natural canal which forms a border between Europe and Asia by connecting Black Sea and Marmara Sea. The strait is nearly 31 km long and runs in a north-south direction approximately. The narrowest section is between Anadolu and Rumeli Fortresses and 700 m wide. However, the width increases significantly at both northern (Black Sea) and southern (Marmara) ends and reach to values of 3400 m and 3200 m, respectively. The depth of the strait is 30e35 m on average and reaches to its maximum value of 110 m in Kandilli region. The orientation of the canal can be seen in Fig. 1. In the figure, the plot on the right shows the location and layout of the cluster. Due to the specific flow pattern, the cluster is designed as a single row to be able to benefit the high speed current as much as possible. The currents seen in the Bosphorus Strait is an example of a two layer exchange flow that is characterized by brackish waters originating in the Black Sea and moving southward and more saline, denser waters from the Marmara Sea and flowing northward [12]. Black Sea has a positive water balance due to the excess hydrological input from five major and countless minor rivers. The only exit point of this flow is the Bosphorus resulting in a net outflow of 300 km3 water through the strait in a year. The sea level difference between the two ends of the strait has an average value of 30 cm. The Elevation difference caused by river flows, leads surface currents with speeds reaching 7e8 knots at windy seasons.
It is very well known that the surface current has a very high potential for energy production [13,14]. However, Istanbul Strait is a major sea access route and has one of the heaviest maritime traffics in the world. According to the statistics [15] 38,000 vessels have passed through the strait in 2014. More than 15,000 of these vessels were large size crude oil tankers having lengths of 200 m or more. Considering the intensity of the traffic load it can be realized that electricity generation from surface currents is not feasible due to high risk of accident. Similarly, the difference in salinity ratios and density between two seas cause a deep layer current flowing in the opposite direction, namely from south to north. The salinity ratios of Black Sea and Marmara Sea are 1.8% and 2.5% respectively. Compared to the surface current, the speed of deep layer current is low (2e3 knots). However, depending on the morphology at certain locations the energy intensity of this flow can reach to sufficient levels making electricity production feasible. In this work, location of the farm/cluster together with other design parameters such as the size, orientation, depth and spacing of the turbines are determined according to the specific morphology and flow pattern seen at Bosphorus. 1 MW SeaGen marine current turbine [16] with a rotor diameter of 18 m is considered to be the most appropriate turbine for the project. In order not to obstruct the passage of vessels, the turbines are placed at an average depth of 42 m. This enables a clearance of 24 m to be left for the ships passing over the region. It should be noted that the power generated by the turbine is proportional to the square of the rotor diameter. Therefore, leaving a shorter clearance and using a larger turbine will definitely result in a higher production capacity. However, due to hydrodynamic load limitations, state of the art MW scale marine current turbines have rotor sizes changing between 15 and 20 m. In order to make a reasonable estimation by using commercially available and technically proven systems, in this study 1 MW SeaGen turbine is used in the analyses. It is believed that the rapid progress seen in composite materials technology will enable larger and higher capacity turbines to be designed in near future. The airfoil of the blade is NACA 631424 and remains same along the blade. The turbine selected for the project and the corresponding power curve can be seen in Figs. 2 and 3, respectively.
Fig. 1. The layout of the Istanbul Bosphorus Strait.
H. Yazicioglu et al. / Energy 95 (2016) 41e50
43
3. Modeling The similarities between horizontal axis wind turbines and current turbines suggest that the latter can benefit by transferring experience and modeling tools from the wind industry. Some recent works [18,19] show that wind turbine load and performance simulation code GH Bladed can easily be modified and used for marine turbine applications. Similarly, Yucheng LiuA methodology [20e22], which was developed by EPRI (Electric Power Research Institute) based on a modified Gamma spectrum, is used for analyzing the potential for WEC (wave energy conversion) in a desired geographical area. This methodology allows WEC developers to easily and efficiently predict the potential wave power available to their devices and to facilitate the prediction of power output performance in a given year, season, month, etc., at a specific location [20]. A recent approach proposed in Ref. [23] partitions the measured spectra into separate wave systems, each of which is represented by a modified JONSWAP (Joint North Sea Wave Project) spectrum. The methodology applied in this work can be summarized as follows; - The simulations are performed for 15 different virtual sea states (5 significant wave heights and 3 peak periods). For each state, the downstream velocity profile is interpolated and centerline velocities for the turbines are determined by using irregular wave theory. This technique can be considered as the expansion of linear wave theory by using the sea state frequency distribution acquired from JONSWAP spectrum. - The centerline velocity calculated for the hub (31 m below the sea surface) is then superimposed on the average current speed and the resultant flow speed is obtained. - Frandsen model is used to represent the dynamic interactions among the turbines and the resulting wake effects and to calculate the current speeds experienced by each turbine. Wake analyses are conducted for 8 different configurations in order to find the optimum spacing between the turbines. - Considering the spatial variations in the current speed within the selected region, these analyses are performed for three different flow speeds. The simulations are repeated for speeds corresponding to 10% increase and decrease in the average
Fig. 3. 1 MW tidal-current turbine power curve [17].
value. For each simulation the corresponding AEP (Annual Energy Production) is calculated and stored in a database.
3.1. Representation of irregular wave behavior This paper does not aim at focusing on the theoretical background of the utilized analysis methods but on the results obtained for different cases and scenarios. Interested reader is referred to related publications for further information. In linear wave theory, the velocity profile below the surface can be described as follows [24];
uðx; z; tÞ ¼
uH coshðkðz þ hÞÞ cosðut kxÞ 2 sinhðkhÞ
(1)
In Eq. (1) u, h and z are the wave velocity, total depth and the vertical distance from the surface, respectively. H represents the wavelength. Linear dispersion is shown by u. Similarly, k and x are used to describe the wave number and the horizontal position of the wave. In order to expand this expression and to take into account the wave irregularity some important parameters such as linear dispersion, wave number and the wave length should be taken as random variables. This also requires the surface elevation and downstream velocity profile to be updated. For a generated time interval and wave spectrum, the surface elevation hðx; tÞ and the
Fig. 2. SeaGen marine current turbine [16].
44
H. Yazicioglu et al. / Energy 95 (2016) 41e50
corresponding velocity profile uðx; z; tÞ are calculated as shown in Eqs. (2) and (3), respectively.
hðx; tÞ ¼ ap cosðun t kn x þ ∅n Þ uðx; z; tÞ ¼ ap un
coshðz þ hÞ cosðun t kn xÞ sinhðkn hÞ
(2) (3)
In Eq. (2) ∅n is phase angle and has a random value changing between 0 and 2p. As can be seen, Eq. (3) has the same structure as Eq. (1). The additional term ap represents the amplitude. In practice, instead of a finite number of waves kn , one assumes that the surface is made up of infinitely many waves, all having different amplitudes and wave numbers. Therefore, the calculation of ap depends on some other spectra such as JONSWAP spectrum. Estimation of the velocity profile at a certain depth through the use of surface wind/wave speeds is widely used in calculation of the loads acting on submerged structures such as pipelines and/or the foundations of offshore platforms and wind turbines. Several researchers reported that these estimations are in very good coherence with the real velocity profiles measured both at shallow and deep waters [25e28]. 3.2. JONSWAP spectrum JONSWAP [29] spectrum is a widely used spectral analysis method which establishes a relation between wind speed and wave spectrum. The technique is mainly based on some improvements made on PiersoneMoskowitz spectrum [30] by using the data collected during the North Sea Wave Observation Project called as JONSWAP. In this method it is assumed that the wave spectrum cannot be relied on fully developed sea state. Therefore, it requires the continuous non-linear waveewave interactions to be considered. Then, a small correction to PiersoneMoskowitz spectrum should be made by adding an artificial scaling factor in order to improve fit to the measurements. The difference between the two spectra is that waves continue to expand with distance from the shore and spectrum is clearer in the JONSWAP spectrum. In spectrum calculations clearance is a very important feature which allows representing non-linear interactions changing in time. In order to standardize the spectra the key variables should be related to main sea climate parameters rather than wind states. Hs and TP are two important parameters used to describe the spectrum and represent significant wave height and peak period, respectively. Hs , significant wave height can be defined as the average of the highest one-third (33%) of waves that occur in a given time interval (e.g. month or year). Similarly, the peak period, TP , is the wave period with the highest energy. The relation between Hs and TP can be approximated by using the equations shown below [31].
sffiffiffiffiffiffi sffiffiffiffiffiffi Hs Hs TP 14:4 11:1 g g
computed with the present model. It also includes the boundary layer conditions from the up and down streams. The model assumes that there is an initial wake expansion in front of the turbines. However, this expansion is neglected for the 1st turbine and the 1st velocity ratio C1 is considered to be equal to 1. The velocity ratios in the wake in front of the following turbines can be calculated by using the equation below.
Cnþ1 ¼ 1 ð
An 1 AR ð1 Cn Þ þ C Cn 2 Anþ1 T Anþ1
Where Cn and An are the velocity ratio and wake area before the nth turbine, respectively. AR is the swept area of the rotor and CT is the thrust coefficient of the turbine [32]. The main parameters of the Frandsen model can also be seen in Fig. 4. As can be seen in Eq. (5), the method requires the wake areas to be calculated. The first area is initial wake expansion which is neglected. This results that the area of the wake before the first turbine A1 is equal to the swept area AR. Furthermore, the wake expands immediately after the turbine, where the wake area expands with a factor b. The ratio b is computed as shown in Eq. (6) below.
pffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 1 þ 1 CT pffiffiffiffiffiffiffiffiffiffiffiffiffiffi b¼ 2 1 CT
(6)
This expression can be used to calculate wake area before the second turbine A2.
A2 ¼ b AR
(7)
In order to calculate the wake areas before other turbines, Frandsen model includes an expression (shown in Eq. (8)) that describes an expansion when the velocity ratio has reached an asymptotic value.
Anþ1 An ¼ AR a Sr
(8)
Frandsen also states that the decay factor a is to be determined experimentally. The value relies on the asymptotic value of velocity ratio from the N.O. Jensen Wake model [33,34]. The relation between decay factor and the velocity ratio at the infinity as follows.
(4)
In Eq. (4) g is the gravitational acceleration. In this work the effect of wave height is investigated for 5 different values between 1.5 and 2.5 m changing with an increment of 0.25 m. 3.3. Frandsen Wake Model and calculation of the spacing between the turbines The Frandsen Wake Model [32] is a more recent version than many other wake models and attempts to include more elements of reality. Flows from different directions through a large farm can be
(5)
Fig. 4. The parameters of the Frandsen Model [32].
H. Yazicioglu et al. / Energy 95 (2016) 41e50
a¼
1 CT Cw 2 Sr ð1 Cw Þ
(9)
Where, Cw is the velocity ratio at infinity taken from N.O. Jensen's model which is function of spacing x0 =D0 . In wake analysis, the spacing between turbines is described as the ratio of the absolute distance x0 between the turbines to the rotor diameter D0. In this work, in order to find the optimum layout and spacing between the turbines wake analyses are conducted for 8 different configurations, namely for distances corresponding to 5,6,7,8,9,10,12 and 15 times the rotor diameter D0 (18 m). In all configurations, the turbines are placed in tandem, i.e. all the turbines are placed one after another in downstream direction. Unless sufficient spacing is left between the turbines, such a strategy definitely increases the dynamic interactions between the turbines and the corresponding wake losses. Unfortunately, a configuration where all the turbines are placed side by side facing an inflow of the same and largest possible speed is not possible due to several reasons. The depth required to leave a safe clearance for the ships passing over the region can only be obtained in a narrow zone close to the centerline of the canal. Similarly, high speed steady flow is seen within the same narrow corridor. The stream close to shore has unsteady turbulent flow characteristics and is not suitable for energy production through the use of marine turbines. Therefore, due to the limitations caused by the specific morphology of the strait, a tandem configuration is considered as the most efficient layout for the cluster. However, in this configuration it becomes very crucial to analyze the dynamic interactions among the turbines and to determine the optimum spacing required for maximizing the energy output. Fig. 5 shows how the current speeds experienced by the 10 turbines in the cluster change depending on the spacing ratio x0 =D0 . In Fig. 5, the x axis shows the number of the turbine in the cluster. Similarly, the y axis represents the current speed which is normalized with respect to the current speed experienced by the 1st turbine in the row.
45
The centerline velocity time history (31 m deep from the sea surface) and the power generated by the first turbine can be seen in Fig. 6. In Fig. 6 the first plot shows the change in current velocity depending on the wind speed. The variation in current velocity is simulated for a period of one year (8760 h). Similarly, the second plot shows the power generated by the 1st turbine for the corresponding current speed profile. In these plots the x axis represents a time period of one year (8760) hours. In order to make it easier to visualize, in the third plot, the generated power is redrawn for a period of one week (168 h). As can be seen in Fig. 6 there may be some short duration fluctuations in the power due to the change in wind speed. These fluctuations are in the range of ±10%. Fig. 7 shows the Probability Density Function calculated for the current speed distribution used in the analysis. In Fig. 7, the x axis represents the current speed and measured in m/sec. The y axis shows the probability of having the current speeds shown in the x axis. In the figure, probability is described as percentage. However, the same graph can also be represented as a Dwelling Time vs. Current Speed graph simply by multiplying the y axis by 8760 h.
4. AEP (Annual energy production) calculation The power generated by each turbine in the cluster is calculated separately by using Eq. (10) shown below.
Pn ¼
1 Ar r u3n Cp 2
(10)
where; Ar , r , and Cp represent rotor area, density of water and power coefficient, respectively. Similarly un is the effective current speed experienced by the nth turbine in the row and is calculated by using Frandsen Wake Model. The resultant flow speed acting on the 1st turbine is calculated by superimposing the wind speed effect on the average current speed. The speeds experienced by different
Fig. 5. Change in current speed experienced by the turbines in the farm for different spacing ratios.
46
H. Yazicioglu et al. / Energy 95 (2016) 41e50
Centerline Velocity Time Series (for 1 year or 8760 hours) Wave Speed [m/s]
2,0
1,9
1,8
1,7 0
1000
2000
3000
4000
5000
6000
7000
8000
9000
7000
8000
9000
Time [Hour]
Generated Power kW]
Generated Power Time Series (for 1 year or 8760 hours) 1000
800
600
400 0
1000
2000
3000
4000
5000
6000
Time [Hour]
Generated Power [kW]
Generated Power Time Series (for 1 week or 168 hours) 1000
800
600
400 300
320
340
360
380
400
420
440
460
Time [Hour] Fig. 6. Centerline velocity and generated power graphs.
turbines are then scaled by using the normalized velocity ratios shown in Fig. 5. The centerline velocity at each turbine can be fitted to Weibull distribution in order to get distribution parameters. Due to statistical modeling, the Weibull coefficients are as follows: A ¼ 2.1,
q ¼ 8.97. The frequency of the AEP (Annual Energy Production) ðFAEP Þ is calculated by using power curve of the SeaGen generator (shown in Fig. 3). Annual energy production is then calculated by using equations below.
Fig. 7. Probability density graph calculated for the current speed distribution.
H. Yazicioglu et al. / Energy 95 (2016) 41e50
Fðui < u0 < uiþ1 ÞAEP
ðAEPÞn ¼
N1 X i¼1
u q u q exp iþ1 ¼ exp i A A
1 ðP þ Piþ1 Þð8760Þ Fðui < u0 < uiþ1 ÞAEP 2 i
(11)
(12)
In Eq. (11), q is the shape factor and A is the scale factor parameter of the Weibull distribution. Similarly P [watt] represents the power at ith point and taken from the power curve of the SeaGEn turbine, u represents the current speed. It should be noted that while calculating AEP it is assumed that there will be approximately 10% energy loss in the drive train (gearbox and generator). Fig. 8 shows how the cluster efficiency changes depending on the significant wave height (Hs) and peak period (TP) values used in the analyses. Efficiency can be defined as the ratio of the total power generated in the cluster in a real case scenario to the power corresponding to a hypothetical case where all the turbines experience maximum available current speed without obstructing each other. Such a hypothetical case can only be possible when the distance between the turbines is sufficiently long (approaching to ∞). However, in practice to due to space limitations and installation costs the turbines are not placed very far from each other resulting in avoidable wake effects and decrease in the flow speed (as shown in Fig. 5). Therefore, efficiency can be used to evaluate whether the distances between the turbines are sufficiently long or not. Fig. 8 presents the results of the simulations performed to determine the effect of significant wave height ðHs Þ and peak period ðTP Þ on the generated power or efficiency. The analyses are
47
performed for 5 different Hs values chancing between 1.5 and 2.5 m and 3 different TP values namely, minimum, maximum and mean values obtained from Eq. (4). In Fig. 8 the plots shown on the right are obtained by zooming in the red squares shown on the left plots. As can be seen, using minimum or maximum TP value recommended by Eq. (4) has a very little effect on the AEP or cluster efficiency. Similarly, increasing wave height Hs causes only a negligible increase in the generated power which is usually less than 1%. Although Fig. 6 clearly reveals that there may be some short duration fluctuations in the power due to the change in wind speed, these fluctuations are in the range of ±10% and are within the tolerable limits of the power regulating systems. The effect of instantaneous changes in sea state/wind becomes negligible when long term periods (e.g. one year) are considered. Fig. 9 shows the change of cluster efficiency depending turbine spacing ratio. In the figure x and y axes represent the turbine spacing ratio and the relative efficiency of the cluster, respectively. Unless a very detailed and high resolution 3D bathymetric map or model is utilized, there may be some unpredictable spatial variations in the current speed due to the complicated morphology of the strait. Considering the spatial variations within the cluster area, in this work, the analyses are performed for three different flow speeds. The simulations are repeated for speeds corresponding to 10% increase (2.0 m/s) and decrease (1.7 m/sec) in the average value of 1.85 m/s. For each simulation the annual energy yields and corresponding cluster efficiencies are calculated. The total energy produced by the cluster increases as the effective current speed increases. However, when the generated energy is normalized with the maximum theoretical energy corresponding to that speed, the effect of flow speed is eliminated and it becomes possible to investigate the effect of spacing ratio
Fig. 8. Change in efficiency for varying Hs and TP.
48
H. Yazicioglu et al. / Energy 95 (2016) 41e50
Fig. 9. Change of cluster efficiency depending on turbine spacing ratio.
specifically. Indeed, Fig. 9 shows that cluster efficiency is directly related to the distance between turbines. As the spacing ratio is increased the dynamic interactions between the turbines and the resulting wake loss decrease. The results presented in Fig. 9 are consistent with the velocity ratios shown in Fig. 5. As can be seen in Fig. 5, for a spacing ratio of 15, the average current speed for the turbines can be assumed as 0,9. Since the generated power is proportional to the 3rd power of the flow velocity, the normalized power is expected to be close to 0.70. Table 1 shows the annual energy production [GWh] results calculated for varying current speeds and turbine spacing. In the table, the 1st row represents spacing ratios changing between 5 and 15. Similarly the 1st column includes the average currents speeds. The generated power calculated for a specific value of current speed and spacing ratio can be obtained simply by intersecting the corresponding row and column. It should be noted that in this work the number of turbines is 10 and considered as a fixed parameter in the analyses. However, we also examined whether the acquired results can be used to roughly estimate the total power and relative efficiency of the clusters having variable number of turbines or not. This approach is discussed in more detail in the following sections. In practice the parameters which determine the size and capacity of the cluster is related to not only the efficiency but also the morphology, other physical limitations and installation costs. If the length of the available cluster area is considered as the limiting parameter, selecting a smaller spacing ratio makes it possible to install more turbines within the same region. Such an approach results in an increase in the total energy generated but it also decreases the cluster efficiency.
In this project, the length of the cluster area is 2000 m which is equal to approximately 110 times the rotor diameter (18 m). If this length is considered as the limiting parameter, the number of marine turbines corresponding to a specific spacing ratio can be calculated by dividing the total length ð110 DÞ by the ratio ðx=DÞ. Assuming that velocity ratios shown in Fig. 5 are constant after the 5th turbine in the row, we can estimate the velocity distribution for clusters having more than 10 turbines. The estimated current speeds are then used to calculate the annual energy production for the clusters having different number of turbines. Table 2 shows the results of AEP calculations performed for varying number of turbines. It should be noted that in these analyses the available cluster area (2000 m) is assumed to be constant. Fig. 10 shows the change in total power and cluster efficiency depending on the spacing ratio. Two different approaches namely, power maximizing and efficiency maximizing approaches can be followed in the design of the cluster. In power maximizing approach 22 (1 MW) marine turbines can be installed at the site with a spacing of 90 m (5 times the rotor diameter). In this case the average cluster efficiency is very low 41% but the generated total energy (AEP) is high 71.7 GWh. In efficiency maximizing approach 7 (1 MW) marine turbines can be installed at the site with a spacing of 270 m (15 times the rotor diameter). In this case the average cluster efficiency is the highest 73% but the generated total energy (AEP) is just 42.6 GWh. Decision of which approach will be applied is directly related to the design objectives. The 10 MW cluster analyzed in this work corresponds to a spacing ratio of 11 and can be considered as a combination of power and efficiency maximizing approaches. 5. Conclusion
Table 1 Annual energy production AEP matrix for 10 turbines. Spacing ratio x/D
5
Current speed [m/s]
Total energy generated [GWh]
6
1.70 1.85 2.00
20 26 32
22 28 36
7
24 31 39
8
26 34 42
9
28 36 45
10
12
15
29 38 47
32 41 52
35 45 57
This paper aims at investigating the feasibility of generating electricity from sea underwater currents at Istanbul Bosphorus Strait. Several simulations are performed for a 10 MW marine turbine farm/cluster where 360 different scenarios are analyzed for 15 different sea states (for 5 significant wave heights and 3 peak periods). Similarly, 8 different configurations are modeled in order to find the optimum spacing between the turbines. Dynamic
H. Yazicioglu et al. / Energy 95 (2016) 41e50
49
Table 2 Annual Energy Production AEP matrix for varying number of turbines. Spacing Ratio x/D Number of turbines Cluster Efficiency Normalized Equivalent Power
5 22 0.41 9.1
6 19 0.46 8.5
7 16 0.50 8.0
Fig. 10. Change in generated power and cluster efficiency depending on turbine spacing ratio.
interactions among the turbines and the corresponding wake effects are taken into account by using a wake model approach. Considering that the complicated morphology of the strait may cause some spatial variations in the current speed within the selected region, the analyses are performed for three different flow speeds. The simulations are conducted for speeds corresponding to 10% increase (2.0 m/s) and decrease (1.70 m/s) in the average value of 1.85 m/s. For each simulation the annual energy yields and cluster efficiencies are calculated. The analyses performed in this work are not based on a detailed 3D bathymetric map which would have made it possible to conduct a comprehensive CFD (computational fluid dynamics) analysis and to obtain a very accurate velocity profile. In this work, since the flow within the narrow zone close to the centerline of the canal is known to be quite steady, the average speed values are assumed to be valid for the whole cluster. Some spatial variations and their possible effects on the generated power are taken into account by allowing a ±10% change in the speed values. It is believed that the methodology proposed in this work can be used to obtain very realistic results if additional information for velocity distribution is provided through CFD simulations or field tests. The results of the simulations show that, using minimum or maximum value of peak period (TP) recommended by Eq. (4) has a very little effect on the AEP or cluster efficiency. Similarly, increasing wave height from 1.5 m to 2.5 m causes a negligible increase in the generated power which is usually less than 1%. The analyses also reveal that there may be some short duration fluctuations in the power due to the change in wind speed. However, these fluctuations are in the range of ±10% and within the tolerable limits of the power regulating systems. The effect of change in sea state/wind becomes negligible when long term periods (e.g. one year) are considered. The dynamic interactions and wake effects are modeled by using Frandsen Wake Model. A more detailed analysis can be conducted by using a high resolution 3D bathymetric map or model which enables spatial variations in the current speed (due to the complicated morphology of the strait) to be calculated more accurately.
8 14 0.54 7.5
9 12 0.57 7.1
10 11 0.61 6.7
12 9 0.66 6.1
15 7 0.73 5.4
In order to determine how the distance between the turbines affect the generated power, the calculations are repeated for 8 different spacing ratios changing between 5 and 15. Although the simulations are performed for a cluster of 10 (1 MW of each) marine turbines, the obtained results are also used to roughly estimate the total power and relative efficiencies of the clusters having more than 10 turbines. The results of the preliminary analyses show that two different approaches namely, power maximizing and efficiency maximizing approaches can be followed in designing the cluster. If the length of the available cluster area is considered as the limiting parameter, selecting a smaller spacing ratio makes it possible to install more turbines within the same region. Such an approach results in an increase in the total energy but it also decreases the cluster efficiency. Analytical assessment carried out in this work shows that depending on the morphology of the canal, at some specific locations the energy intensity reaches to sufficient levels where electricity generation by marine current turbines becomes economically feasible. However, there may still be some issues such as environmental effects which need further analyses and investigations. The results obtained in this work are expected to encourage the government and private enterprises to provide additional funding for future research projects and also for the design, development and installation of marine current turbine prototypes. References [1] Website 1: Republic of Turkey, Ministry of Energy and Natural Resources. http://www.enerji.gov.tr/en-US/Pages/Natural-Gas. [Accessed in July 2015]. [2] Liu P, Veitch B. Design and optimization for strength and integrity of tidal turbine rotor blades. Energy 2012;46(1):393e404. [3] Barbarelli S, Florio G, Amelio M, Scornaienchi NM, Cutrupi A, Lo Zupone G. Design procedure of an innovative turbine with rotors rotating in opposite directions for the exploitation of the tidal currents. Energy 2014;77:254e64. [4] Lewis M, Neill SP, Robins PE, Hashemi MR. Resource assessment for future generations of tidal-stream energy arrays. Energy 2015;83:403e15. [5] Sanchez M, Carballo R, Ramos V, Iglesias G. Floating vs. bottom-fixed turbines for tidal stream energy: a comparative impact assessment. Energy 2014;72: 691e701. nchez M, Iglesias G. A port towards energy [6] Ramos V, Carballo R, Alvarez M, Sa self-sufficiency using tidal stream power. Energy 2014;71:432e44. [7] Liu P, Bose N, Frost R, Macfarlane G, Lilienthal T, Penesis I, et al. Model testing of a series of bi-directional tidal turbine rotors. Energy 2014;67:397e410. [8] S anchez M, Carballo R, Ramos V, Iglesias G. Energy production from tidal currents in an estuary: a comparative study of floating and bottom-fixed turbines. Energy 2014;77:802e11. nchez M, Iglesias G. Assessment of the [9] Ramos V, Carballo R, Alvarez M, Sa impacts of tidal stream energy through high-resolution numerical modeling. Energy 2013;61:541e54. [10] Development of Marine Energy in New Zealand, Technical Report by EECA Energy Efficiency and Conservation Authority, 2009. € [11] Gregg MC, Ozsoy E, Latif MA. Quasi-steady exchange flow in the Bosphorus. Geophys Res Lett 1999;26(1):83e6. [12] Jarosz E, Teague WJ, Book JW, Bes¸iktepe S. On flow variability in the Bosphorus Strait. J Geophys Res Oceans 2011;116(8). [13] Oguz T, Ozsoy E, Latif MA, Sur HI, Unluata U. Modelling of hydraulically controlled exchange flow in the Bosphorus Strait. J Phys Oceanogr 1990;20(7): 945e65. [14] S¸en Z. Energy generation possibility from ocean currents: Bosphorus, Istanbul. Ocean Eng 2012;50:31e7. [15] Website 2: Republic of Turkey, Ministry of Transport, Maritime Affairs and Communications of Turkey. http://www.ubak.gov.tr/BLSM_WIYS/UBAK/en/ en_new_html/20091202_121818_204_2_64.php. [Accessed in July 2015]. [16] Website 3: Marine Current Turbines. http://www.marineturbines.com. [Accessed in July 2015].
50
H. Yazicioglu et al. / Energy 95 (2016) 41e50
[17] Boehme T, Taylor J, Wallace R, Bialek J. Matching renewable electricity generation with demand. Technical Report. Scottish Government Publications; 2006. ISBN 0 7559 502. [18] Mccann GN, Rawlinson-Smith R, Argyriadis K. Load Simulation for Tidal Turbines using Wind Turbine Experience. Technical Report Garrard&Hassan Ltd. [19] Bossanyi EA. GH bladed user manual. Garrard&Hassan Ltd; 2003. [20] Pastor J, Liu Y. Wave energy resource analysis for use in wave energy conversion. J Offshore Mech Arct Eng 2015;137(1). [21] Pastor J, Liu Y. Frequency and time domain modeling and power output for a heaving point absorber wave energy converter. Int J Energy Environ Eng 2014;5(2e3):1e13. [22] Pastor J, Liu Y. Power absorption modeling and optimization of a point absorbing wave energy converter using numerical method. J Energy Resour Technol Trans ASME 2014;136(2). [23] Kerbiriou MA, Prevosto M, Maisondieu C, Clement A, Babarit A. Influence of sea-states description on wave energy production assessment. In: The 7th European Wave and Tidal Energy Conference Proceedings. Porto, Portugal; 2007. [24] Krogstad HE, Arntsen ØA. Linear wave theory. Technical Report. Norwegian University of Science and Technology; 2003. [25] Gudmestad OT. Measured and predicted deep water wave kinematics in regular and irregular seas. Mar Struct 1993;6:1e73.
€ dinger method for [26] Trulsen K, Gudmestad OT, Velarde MG. The nonlinear Schro water wave kinematics on finite depth. Wave Motion 2001;33:379e95. [27] Madsen PA, Bingham HB, Liu H. A new Boussinesq method for fully nonlinear waves from shallow to deep water. J Fluid Mech 2002;462:1e30. [28] Madsen PA, Bingham HB, Sch€ affer HA. Boussinesq-type formulations for fully nonlinear and extremely dispersive water waves: derivation and analysis. Proc R Soc A Math Phys Eng Sci 2003;459(2033):1075e104. [29] Hasselmann K, Barnett TP, Bouws E, Carlson H, Cartwright DE, Enke K, et al. Measurements of wind-wave growth and swell decay during the Joint North Sea Wave Project (JONSWAP). Technical Report. Deutches Hydrographisches Institut; 1973. [30] Moskowitz L. Estimates of the power spectrums for fully developed seas for wind speeds of 20 to 40 knots. J Geophys Res 1964;69(24). [31] IEC 61400-3:2009 Design requirements for offshore wind turbines. International Electrotechnical Commission Standards 2009. [32] Frandsen S, Barthelmie R, Pryor S, Rathmann O, Larsen S, Højstrup J, et al. Analytical modelling of wind speed deficit in large offshore wind farms. Wind Energy 2006;9(1e2):39e53. [33] Jensen NO. A note on wind generator interaction. Technical Report. ISSN01062840. Risø National Laboratory; 1984, ISBN 87-550-0971-9. Risø-M-2411. [34] Katic I, Højstrup J, Jensen NO. A simple model for cluster efficiency. In: EWEC European wind energy Association Conference Proceedings. Rome, Italy; 1986.