Analysis of Wave energy potential in Caspian Sea

Analysis of Wave energy potential in Caspian Sea

Babak Mohammadi

¹

,Melika Jafari

²

1

- Master student Water resources engineering at Tabriz University.

2

- Lecturer In Qom- Payamenour University ,Master Degree of Industrial Engineering Babakmsh@yahoo.com

Abstract

In this paper, in determining the wave energies were considered to specify wave energy potentials in the southern Caspian Sea. For this purpose,

8

years of ECMWF data in

181

points were collected for the study area to analysis. a SWAN model for wave modeling was performed and then, the wave energies were calculated using conventional analysis. At next step, peak wave period, significant wave height in each peak wave period, and wave energy converter . Results showed that analysis results in almost

259

lower average wave power Possibility and

119

less exploitable energy than conventional analysis..

Distribution of wind power density in the study area illustrates that in contrast to the results of the wave power density, the wind power density is higher in western part of the study area than eastern parts.

Keywords: Wave energy,Caspian Sea ,Renewable energy

1

. INTRODUCTION

Wave energy is also another source of renewable energy, which is accessible for countries connected to seas. resource assessment, many studies has been carried out to investigate the wave energy potential in various regions [71,6,17,8]. Moreover, additional researches are conducted to investigate the wave energy converter technologies [7,12].

Developing industries and increasing demand for energy have made providing the energy resources a crisis for societies since last decades [71]. On the other hand, burning fossil fuels caused dramatic consequences such as global warming. Hence, increasing energy demand and limitations in fossil fuels as well as the prospective low carbon energy systems have leaded countries to invest on renewable energies [71]. Therefore, renewable energies play an important role in energy supply of current and future of the world.

Iran has surrounded by three huge seas in northern (Caspian Sea) and southern parts (Persian Gulf and Gulf of Oman see and these water bodies have not been studied deeply from offshore Wave energy viewpoint, since the available data are limited in these regions. Furthermore, there is no operating wave energy converter project so far; however, long coastlines in southern and northern parts of Iran make it feasible to harness the wave energy for different purposes [18]. Regarding the wave energy assessment of Iranian seas, many studies has been conducted [2,12] which most of them used numerical method to solve mathematical models in order to generate the wave characteristics.

The wave energy are dependent on several parameters with uncertainties based on the various situations.

These parameters may occur in air density, where it may change as a function of water vapor pressure and temperature on sea surface in different seasons. Also, sea surface roughness which is a function of wave height, has important effects on wind speed. Hence, addressing the uncertainties of these leading parameters helps to estimate a more accurate energy potential in the region. The first step to address the uncertainty is to know probability models for important parameters. For this purpose, analyzing the observed data during the past years and identifying the wind filed characteristics (as source of the wave) and probability models for multiple simulations can help. In this regard, observed parameters which are important for wind and wave

analysis of wind and wave filed as well as power generations [11, 13]. Implementing uncertainty analysis helps to obtain an accurate enough estimation and assessment of wave energy resources.

In this research, an 8 years (1224-7117) ECMWF (European Centre for Medium-Range Weather Forecasts) data over 181 points were used in the southern Caspian Sea. The ECMWF data in southern Caspian Sea was previously validated with local wind measurements [11]. The data were analyzed in each point, and probability models were established for uncertain parameters. At the first step, probability distribution of wind speed at 11 m height was extracted and used as SWAN (Simulating WAve Nearshore) model [4] input in order to simulate the peak wave period and significant wave height. The SWAN model was utilized completely based on in southern Caspian Sea. The results of wave modeling using SWAN were used to find the probability distribution of the peak wave period at the specific point. Then, the uncertainty of sea surface roughness-which directly affects the wind speed estimation was estimated using peak wave period and significant wave height. Until now, similar studies have concentrated on onshore sites, where the surface roughness depends only on the terrain. In the current research, sea surface roughness, which is dependent on wind speed, wave period and wave height was considered through a coupled procedure. In addition, air density during 71 years was observed in four different points in the region and probability model for air density was established. The wind and wave energy potential can be estimated by determining air density distribution, wind speed distribution at hub height and wave characteristics.

7. Materials and methods

This research focused on offshore wind and wave energy potential of the southern Caspian sea in north of Iran, as an offshore resource of energy to complete the previous assessments about Iran wind and wave energy resources and potential maps [1]. Wave energy potential in southern Caspian Sea has been studied before, Therefore, in this paper, a coupled method is provided for analysis of wave energy simultaneously.

For this purpose, the previous observations were used to establish the probability models for important parameters of wave. Then, these probability models were used for long term couples wave simulation to better estimate the wave energy potential.

7.1. Study area and data

Caspian Sea is located in the north part of Iran, between

42.14

E,

54.14

E,

36.54

N and

42.14

N and is surrounded by six countries . The huge water body and consequently, lack of enough and frequent weather stations, results in using satellite observations and mathematical models for wind and wave resource assessments in this region. ECMWF [

3

,

15

], QSCAT/NCEP [

14

], In this paper,

8

years of ECMWF data in

181

points were provided in southern part of the Caspian Sea. The data has a

1.74

spatial resolution, which means each point is representative of almost

71

kilometers.

SWAN model was implemented for hindcasting the wave characteristics such as peak wave period and significant wave height for

8

years in each observed data point. The SWAN model was carried out similar to previously conducted studies and parameters, details, and verification can be found in [

11

] and the results were used for conventional assessment of wave resources in the region. Also, the wave characteristics obtained from SWAN were later used for analysis of the wave energies.

7.7. Wind Speed

Wind speeds over

8

years were collected from

181

points on southern part of the Caspian Sea. For

predicting wind speed in a site, commonly used Rayleigh and Weibull distributions were utilized [

5

]. In this study, wind data in each point was analyzed and maximum likelihood method was used to check the goodness of fit on wind data. Table

1

represents the average log likelihood of goodness of fit for Weibull and Rayleigh distributions for all points.

Table

1

. Average logarithm likelihood distributions on wind speed data in the region

Distribution Weibull Rayleigh

Log Likelihood -62836 -21172

Weibull and Rayleigh distributions were well matched on the data. Since the Weibull distribution had slightly better goodness of fit and it deals with two parameters and is more flexible to change, the Weibull distribution was selected for probability density of wind speed at

11

m height.

The probability density function (PDF) of Weibull distribution is expressed in eq.

( ) ( )

^K 1

exp( ( ) )

^K

V

V V

P K C C







(

1

) Where 𝑉 is the wind speed in (m/s) at

11

m height, 𝑘 is the shape factor (𝑘 >

1

) and is the scale factor of the distribution .

7.3. Analysis of Wave Energy

SWAN model was performed in two-dimensional non-stationary mode with the computational time step of 11 min in the southern Caspian Sea domain (181 points) where the output grid resolution was 1.34 with a time step of 7 hrs. In the model, Komen's formulation was used for exponential growths of wind input [2, 16]. The bathymetry information was obtained from ETOPO7 dataset from NOAA’s national geophysical data center with a resolution of 1 min .This model was also previously used for wave energy assessment of southern Caspian Sea [2] for conventional wave energy assessment. More details about SWAN model in southern Caspian Sea can be found in [2].

The wave power can also be presented as eq. 7:

wave wave g

P  E C

(

7

⁾

7.4. Peak wave period and significant wave height

After performing SWAN model, peak wave period and significant wave heights were computed for each location. The SWAN results for peak wave period in the whole domain were analyzed and Normal, Rayleigh and Weibull distribution was fitted on the data at each point. In order to assess the goodness of fit for each distribution, Table 7 presents the average logarithm likelihood of each distribution, in which the Weibull distribution fits the best among the other distributions.

Table 7- Average logarithm likelihood distributions on peak wave period results

Distribution Weibull Normal Rayleigh

Log Likelihood -48262 -42652 -57311

7.5. Monte Carlo Simulation

After quantifying all the uncertainties and introducing appropriate models which are , Monte Carlo simulation can be used for long term simulation of the parameters to estimate the energy potential. In this study, the simulation was carried out for 1111 years to better understand the average wave potential and annual energy production in each point. The steps used for Monte Carlo simulation based on the introduced probability models are presented in Table 3.

Table 3. Parameters and adopted probability models

Parameter Probability Distribution Wind speed Weibull

Weibull shape parameter Normal Peak wave period Weibull Significant wave height in each

peak wave period Normal

3. CONCLUSIONS

After running the Monte Carlo simulation at each point for 1111 years, the conventional methods were also used to compute wave energy density. the wave power density results obtained from conventional and uncertainty analysis are presented where the uncertainty analysis shows almost 259 lower average wave power density. This difference is due to the coupled uncertainties in peak wave period and significant wave height distributions during long period of time.

Distribution of wind power density in the study area shows that in contrast to the results of the wave power density, the wind power density is higher in western part of the study area than eastern parts. The results implied almost 45% higher wind power density values in eastern parts rather than western parts. After comparing the wave and wind density results, it can be comprehended that however the wind potential in western parts is higher than the eastern parts, the deeper areas in western parts and probably the direction of the dominant wind cause fully wave developing, which results in higher waves there. These high waves increase the sea surface roughness which reduces the wind speed at turbine hub height. This process occurs alternatively and results in great wave power densities in eastern and western parts, respectively. Although, there is a transition area in the middle part, where the

wave and wind power densities are both high. These areas are located in deep water, where high winds can completely generate high waves.

In this study, 8 years of ECMWF wind data in 181 points were collected for the southern Caspian Sea in order to evaluate the wave energy potential taking into account their Parameters. Firstly, wave energy calculation were carried out using conventional analysis and then, the results were compared with the results obtained from Parameters analysis. For Parameters analysis, the uncertainties of wind speed, wind speed distribution parameters, peak wave period, significant wave height in each peak wave period and wave energy converter were considered.

Calculation of exploitable wave energy showed that the exploitable energy computed by uncertainty analysis is almost 119 less than the value obtained using conventional method. By locating a virtual wave energy converter in each point and considering its Parameters.

4. R

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