CrossMark

Available online at www.sciencedirect.com

ScienceDirect

Energy Procedia 93 (2016) 141 - 145

Africa-EU Renewable Energy Research and Innovation Symposium, RERIS 2016, 8-10 March

2016, Tlemcen, Algeria

On the use of wind energy at Tlemcen, North-western region of

Algeria

Sidi Mohammed Boudiaa*, Sidahmed Berrachedb, Sihem Bourib

aCentre de Développement des Energies Renouvelables, CDER, 6340, Algiers, Algeria bDépartement de génie électrique et électronique, University of Tlemcen B. P. 230 Tlemcen, Algeria

Abstract

In this work, ten years of wind data from Tlemcen meteorological station have been used to evaluate the potential of wind power on the North occidental region of Algeria. The WAsP program was used to analyse the wind Atlas of the region to find the windiest areas. The study proposes to assess the wind energy produced and the cost per kWh of electricity produced by a wind farm of 18.4 MW rated capacity installed at the southern part of Tlemcen. Despite a relatively low potential assessed at the meteorological station, it was possible to delineate a favourable zone for the use of wind energy. Thus, the investigation at 10 m above ground at the location of the airport shows a low wind potential, with an annual mean wind speed equal to 2.42 m/s and an annual mean power density of 49W/m2 while, the temporal study gives the hottest months as the windiest. However, the windiest area received 23 wind turbines of 800 kW rated capacity and gives an annual energy production equal to 25.3 GWh. Concerning the economic analysis, it gives 0.0587 $ per kWh produced for a wind farm expected to cost about 30 million $ (US Dollar) for 20 years.

© 2016 The Authors. Published by ElsevierLtd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.Org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of the organizing committee of RERIS 2016 Keywords: Wind resources; Weibull parameters; economic study; Tlemcen; Algeria.

* Corresponding author. Tel.: +213-23-189-051; fax: +213-23-189-056.

E-mail address: m.boudia@cder.dz

1876-6102 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.Org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of the organizing committee of RERIS 2016 doi:10.1016/j.egypro.2016.07.162

1. Introduction

The geographical location and climate conditions of Algeria procure a great renewable energy potential and theoretically, some of the electrical energy needs can be produced by renewable energy [1-2]. The new Algerian governmental program consists to install 22.000 MW from renewable sources by 2030, which represent 40 % of total energy consumption [2].

Despite its relatively low potential, wind energy is not excluded from the new program as it constitutes the second axis of development with a share in electricity production expected to reach about 3 % in 2030 [3].

Wind resource studies can be assessed at the measurement points, but works must be thorough because the weather stations' locations are not well adequate to characterize the real wind potential. In order to make a reliable estimate of the wind resource assessment, it is required to know the vertical and horizontal profiles of the wind speed on the studied site. For this, we could use different software such as the Wind Atlas Analysis and Application Program (WAsP) [4] of the European Wind Atlas [5], which is one of the most preferred packet programs by commercial firms in wind potential analysis.

In this study, wind energy potential has been investigated at Tlemcen, on the northwestern region of Algeria, by using meteorological data provided from the measurement station within Zenata Airport. Before the assessment of cost and economical aspects, several fundamental properties in the region, such as land topography, rougher surfaces, wind speed probability distribution, wind direction frequency distribution, Weibull parameters, mean wind speed, power density variations were determined. The wind characteristics were studied according to the twelve months, the hours of the day and the whole period. According to the simulations, the wind speed distribution map of the region is obtained and the most suitable site for wind farms is determined, where the performance of a wind farm with a rated capacity of 18.4 MW designed for electricity generation is examined and economic evaluation of the wind energy is performed.

2. Methods

The wind data and the mathematical models used in the study as well as the cost analysis model are presented in this section. The three-hour data of the wind speed and direction collected at 10 m above the ground at the meteorological station of Zenata at Tlemcen, were used in this work. The geographical coordinates of the meteorological station are (-1.4591° for longitude and 35.0097° for latitude) while the measurements were done during the decade between 1981 and 1990.

The digital terrain map based on a resolution of 90 m derived from the free NASA SRTM (Shuttle Radar Terrain Mission) downloaded from the Jet Propulsion Laboratory [6] was used to prepare the necessary digitized topographical, sheltering obstacle data and roughness.

The Weibull function is used on WAsP to characterize the frequency distribution of wind speeds over time. It is defined by the following equation [7]:

Where f (v) ,is the probability of observing wind speed v, k is the dimensionless Weibull shape parameter, and A is the Weibull scale parameter.

The average wind speed Vm can be calculated on the basis of the Weibull parameters, as given below [8]:

Vm = A.r|l + k

The wind power density of a site based on a Weibull probability density function can be expressed as follows:

p=ispA3r(1+S

Where Sw is the blade sweep area (m2) and p the air density (1.225kg/m3).

Once the wind power density of a site is given, the wind energy density for a desired duration T , can be calculated as [9]:

In this study, the estimation of the specific cost per kilowatt hour of energy produced by a wind turbine is expressed as the present value of costs ( PVC ) of the investment divided by the energy output during the lifetime of a wind turbine [10].

Where I is the investment cost, COMR is the operation, maintenance and repair cost, i is the inflation rate (9 %), r is the interest rate (8 %), n is the lifetime of the machine (20 years) and S is the salvage value. The calculation of the cost has been done under the assumptions given in [11]. For electricity generation and cost analysis, the Nordex N50 wind turbine is chosen in this study, with 800 kW rated capacity.

3. Results and discussion

The wind rose and the frequency histogram of wind speeds were evaluated at the meteorological station, versus whole year measurement and the twelve months, in order to see whether the wind blows from a single direction throughout the year with the respective intensity. The monthly variation of the wind speed can help in forecasting the future trend of wind projects. Due to the random behaviour of the wind speed and its variation over time, it was represented using Weibull probability function. The annual results are shown in Fig. 1.

The monthly results of the two Weibull parameters, mean wind speed and mean wind power density at a height of 10 m a.g.l. are given in Table 1. The minimum value of mean wind speed is given in October with 2.13 m/s while the maximum value is assessed in April with 3.01 m/s. On the other hand, the investigation of monthly variation of shape factor lead to conclude that wind speed is more uniform in Spring period than in Winter period.

100,0-

Sector: All

U: 2,42 m/s P: 49 W/mJ - Emergent

u [m/s]

Fig. 1. Annual resource analysis at a height of 10m above ground level (a.g.l.) (a) Wind rose (b)Wind speed frequency with fitted Weibull distribution.

Table 1: Monthly resource analysis at a height of 10 m a.g.l.

Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Vm (m/s) 2.38 2.76 2.58 3.01 2.84 2.86 2.89 2.71 2.36 2.13 2.43 2.54

P (W/m2) 59 67 50 56 49 52 54 50 40 35 46 60

A (m/s) 2.3 2.8 2.6 3.3 3.1 3.1 3 2.8 2.4 2.1 2.5 2.6

k (-) 0.94 1.07 1.09 1.36 1.3 1.29 1.22 1.17 1.09 1.01 1.05 1.02

The wind resource assessment requires not only the annual and monthly probability distribution of wind velocities but also needs to assess the diurnal probabilities of wind speeds. The diurnal wind speed variations were determined. It is found that during the daytime, from 12 a.m. to 6 p.m., the studied region is windy for the whole studied periods, while the night time is relatively calm.

The Wind Atlas consisting of data obtained by the WAsP includes wind distributions for different roughness values, from 0.1 to 0.5 m. Roughness was digitalized starting from the digital terrain map topographic derived from the free NASA SRTM and georeferenced under WAsP, which helped us to evaluate the distributions of mean wind speed around the region. Fig.2 gives the illustration of the south windy area, where the feasibility studies of the turbines to be installed have been carried out, with the layout of 23 wind turbines of 800 kW rated capacity.

At the selected site, the WAsP calculations of the annual gross and net energy production (AEP) with the wake loss ratio for the wind park were assessed and given on Table 2. On the other hand, the cost of electricity per kWh was calculated using the present value cost method. The results give an annual energy production net equal to 25.3 GWh while the cost analysis gives 0.0587 $ per kWh produced for a wind farm expected to cost about 30 million $ for 20 years.

JS52000- ---

656000 65BD00 £№000 661000 6E4000

Fig. 2. Wind speed map at the chosen area at a height of 10 m a.g.l. with the layout of the wind turbines.

Table 2: Annual analysis of the energy production given by the wind farm.

AEP Gross (GWh) AEP Net (GWh) Wake loss (%)

Wind park of 18.4 MW 25.759 25.295 1.8

4. Conclusions

In this study, the wind energy potential and economic analysis for the region of Tlemcen, in the north occidental part of Algeria were made. The following conclusions can be given from the obtained results:

• The statistical analysis at 10 m above ground at the meteorological station, based on Weibull function, shows that Tlemcen has a low wind potential. The annual mean wind speed is equal to 2.42 m/s and the annual mean power density is equal to 49 W/m2. While the monthly investigation gives April as the windiest.

• The diurnal wind speed analysis gives a good potential in the daytime.

• The south region with a good potential have been chosen for the feasibility studies using wind turbine of 800 kW rated capacity for electricity generation.

• The net annual energy production of a wind farm containing 23 wind turbines reached 25.3 GWh.

• Concerning the economic analysis, it gives 0.0587 $ per kWh produced.

To conclude, despite a relatively low potential assessed at the meteorological station, Tlemcen highlands situated in the south can be suitable for wind generation.

References

[1] Boudghene Stambouli A. Algerian renewable energy assessment: The challenge of sustainability. Energy Policy 2011; 39 :4507-19.

[2] Boudghene Stambouli A, Khiat Z, Flazi S. Kitamura Y. A review on the renewable energy development in Algeria: Current perspective.

energy scenario and sustainability issues. Renew Sustain Energy Rev 2012; 16: 4445-60.

[3] Renewable Energy and Energy Efficiency Program. MEM. < http:// www.mem-algeria.org/ francais/uploads/enr/Programme_

ENR_et_efficacite_energetique_en.pdf > March 2011.

[4] Mortensen NG, Landberg L, Troen I, Petersen EL. Wind Atlas Analysis and Application Program (WAsP). Ris0 National Laboratory.

Roskilde. Denmark. 1993.

[5] Troen I, Petersen EL. European Wind Atlas. Ris0 National Laboratory. Roskilde. Denmark. 1989.

[6] Jet Propulsion Laboratory, California Institute of Technology. Shuttle Radar Topography Mission (SRTM). NASA. 1987.

<http://www2.jpl.nasa.gov/srtm/> June 2013

[7] Khahro SF, Tabbassum K, Soomro AM, Dong L, Liao X. Evaluation of wind power production prospective and Weibull parameter estimation

methods for Babaurband, Sindh Pakistan. Energy Convers Manage 2014;78:956-67.

[8] Akpinar EK, Akpinar S. An assessment on seasonal analysis of wind energy characteristics and wind turbine characteristics. Energy Convers

Manage 2005; 46:1848-67.

[9] Keyhani A. Ghasemi-Varnamkhasti M. Khanali M. Abbaszadeh R. An assessment of wind energy potential as a power generation source in

the capital of Iran. Tehran. Energy 2010;35:188-201.

[10] Bataineh KM, Dalalah D. Assessment of wind energy potential for selected areas in Jordan. Renew Energy 2013 ;59:75-81.

[11] Boudia SM, Guerri O. Investigation of wind power potential at Oran, northwest of Algeria. Energy Convers Manage 2015; 105:81-92.