Research Article Electricity Generation and Energy Cost …downloads.hindawi.com/journals/jen/2014/613681.pdf · 2019-07-31 · Research Article Electricity Generation and Energy

Research ArticleElectricity Generation and Energy Cost Estimation ofLarge-Scale Wind Turbines in Jarandagh Iran

Kasra Mohammadi1 Ali Mostafaeipour2 Yagob Dinpashoh3 and Nima Pouya4

1 Faculty of Mechanical Engineering University of Kashan Kashan Iran2 Industrial Engineering Department University of Yazd Yazd Iran3Department of Water Engineering University of Tabriz Tabriz Iran4 Electrical Engineering Department Islamic Azad University South Tehran Branch Tehran Iran

Correspondence should be addressed to Ali Mostafaeipour mostafaeiyazdacir

Received 12 February 2014 Revised 30 September 2014 Accepted 8 October 2014 Published 30 October 2014

Academic Editor S Venkata Mohan

Copyright copy 2014 Kasra Mohammadi et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

Currently wind energy utilization is being continuously growing so that it is regarded as a large contender of conventional fossilfuels This study aimed at evaluating the feasibility of electricity generation using wind energy in Jarandagh situated in QazvinProvince in north-west part of Iran The potential of wind energy in Jarandagh was investigated by analyzing the measured windspeed data between 2008 and 2009 at 40m height The electricity production and economic evaluation of four large-scale windturbine models for operation at 70m height were examined The results showed that Jarandagh enjoys excellent potential for windenergy exploitation in 8 months of the yearThemonthly wind power at 70m height was in the range of 45028ndash166162Wm2 andalso the annual wind power was 75440Wm2 The highest capacity factor was obtained using Suzlon S66125MW turbine modelwhile in terms of electricity generation Repower MM82205MW model showed the best performance with total annual energyoutput of 5705MWh The energy cost estimation results convincingly demonstrated that investing on wind farm constructionusing all nominated turbines is economically feasible and among all turbines Suzlon S66125MW model with energy cost of00357 $kWh is a better option

1 Introduction

Depletion of fossil fuel negative effects of CO2emission and

high price of crude oil are major concerns which influencethe motivation and adoption of the renewable energies likewind solar geothermal and so on Among all renewableenergy sources wind energy as an abundant source of energyhas the highest annual growing rate of about 30 [1] Theaforementioned problems during the recent year have forcedmany scientists across the globe to pay more attention toclean energy sources like wind which is environmentallyfriendly and renewable [2] It is worthwhile to mention thatthe economic growth and environmental pressure shouldbe decoupled as evidenced by international agreements onenvironmental policy such as the Kyoto Protocol for reducinggreenhouse gas emissions [3]

The government plans to install more wind stations inIran and therefore further regions should be explored Based

on Iranian Renewable Energy Organization (SUNA) datasome recent studies indicated that Jarandagh is one of thepromising locations for wind power generation [4] Exploita-tion of the wind power potential is significant and profitablefor the regions particularly for reducing carbon emissionNevertheless reliable data is of great importance for thepurpose of wind resource assessment within large areas alsothey provide enough information to estimate the economicviability of wind farm projects [5] The global economicpotential and technical issues of onshore wind energy wereinvestigated by Hoogwijk et al [6] They illustrated cost-supply curves of wind electricity for economic potentialThey found that the regionally highest technical potentialof onshore wind energy belonged to USA However lowestpotentials were found for South East Asia Southern andWestern Africa and Japan

Many studies related to wind energy potential andassessment have been performed in various locations around

Hindawi Publishing CorporationJournal of EnergyVolume 2014 Article ID 613681 8 pageshttpdxdoiorg1011552014613681

2 Journal of Energy

the world Abbes and Belhadj [7] estimated the wind resour-ces and also wind park design in El-Kef region TunisiaThey investigated the characteristic of wind speed usingWeibull distribution function and estimated the capacityfactors for different wind turbine configurations They per-formed economic evaluation to examine the feasibility oftheir project Mohammadi and Mostafaeipour [8] utilizeddifferent methods for comprehensive study of wind turbineutilization in Zarrineh Iran They used hourly monthlyseasonal and yearly wind data analysis It was concludedthat the location was marginal for harnessing wind energyAlso the standard deviation and power density method wereperformed to determine best method for evaluation of windpower Akpinar [9] evaluated the wind energy potential forcoastal locations along the north eastern coasts of TurkeyThey illustrated that the monthly mean wind speed in theregion varied between 153ms and 406ms Also they foundthat the maximum annual mean wind power density andwind energy density were 5996Wm2 and 52525 kWhm2respectively Keyhani et al [10] studied assessment of windenergy potential as a power generation source in the capitalof Iran Tehran Long termmeasured wind speed data at 10mheight was used for this study They calculated the annualaverage wind power densities which were between 7400 and12248Wm2 They concluded that the wind energy poten-tial for Tehran was suitable only for battery charging andwater pumping Islam et al [11] investigated the assessmentof wind energy potential at Kudat and Labuan Malaysiausing Weibull distribution function They used 10m heightmeasured wind speed data and found that highest monthlymean wind speeds were 48ms and 43ms at Kudat andLabuan respectively Also they showed that the maximumwind power densities of Kudat and Labuan were 6740Wm2and 5081Wm2 respectively They concluded that the twolocations were suitable only for small-scale wind energyapplications

Mirhosseini et al [12] studied the potential of windpower generation for five cities in Semnan province in IranMostafaeipour andAbarghooei [13] performed analysis of thewind speed data for six stations in Manjil area in north ofIran Saeidi et al [14] analyzed the wind potential and windpower of four locations in two provinces of North and SouthKhorasan in Iran Mostafaeipour et al [15] studied the windenergy potential of Binalood located in north-east of IranMohammadi andMostafaeipour [16] appraised the economicviability of installing 6 different wind turbines models inAligoodarz situated in west part of Iran Mostafaeipour et al[17] examined the wind energy potential and economicevaluation of small wind turbines for city of Zahedan insouth-east of Iran

The aim of this study is to perform economic evaluationof installing wind turbines in Jarandagh located in Iran Thispaper illustrates research work involved in estimating theelectricity generation and energy cost of wind turbines Forperformance evaluation of the wind turbines and finding theamount of energy that could be harnessed from windturbines in Jarandagh area four large-scale wind turbines(Suzlon S66125MW HewindHW771500KW GamesaG802000KW and RepowerMM82205MW)with differentrated powers are nominated

Thenext section offers an overviewof geographic descrip-tion of the region In Section 3 themethodology is discussedThe review of wind data analysis is presented in Section 4Performance assessment of nominated wind turbines isbrought forward in Section 5 Energy cost estimation is donein Section 6 and finally concluding remarks are presented inSection 7

2 Geographic Description

Qazvin Province with an area of 15821 km2 is located inthe north-west part of Iran between 35∘371015840N and 36∘451015840Nand also between 48∘451015840E and 50∘501015840E This province withonly 1 of the total country area is involved in 5 percent ofIranrsquos economy and production Based on 2010 survey thetotal population of Qazvin Province was 572916 QazvinProvince consists of six counties namely Takestan AbyekBoin-Zahra Avaj andAlborz and city of Qazvin the center ofthe province Also the province has 1543 villages Jarandaghis a small village in suburb of Takestan located at 36∘111015840N and49∘481015840E with total population of 449 [18] Figure 1 shows thelocation of Qazvin Province as well as all of its six countiesincluding Jarandagh

The data used for this study includemeasured wind speedover period of two years from January 2008 to December2009 in the time interval of 10min at 40m height [4] Thedata was recorded at the meteorological mast installed byrenewable energies organization of Iran (SUNA) For thepurpose of wind speed data preparing the ten-minuterecorded data were averaged to drive hourly values Thenthe hourly values were used to obtain daily monthly andyearly values Finally the obtained data were averaged overthe period of two years and calculation procedure was donebased on these averaged values

3 Methodology

Knowledge of the wind speed frequency distribution playsa substantial role in order to estimate the potential of windin any location Various probability density functions existto fit and describe the wind speed frequency over a periodof time In this study the Weibull distribution function isused because of its simplicity and high accuracy for winddata analysis In fact Weibull distribution function is widelyemployed and adopted as an alternative method to evaluatewind energy potential and wind turbine energy output

31 Weibull Distribution Function The probability densityfunction of Weibull distribution can be estimated as [23 24]

119891119908(V) =

119896

119888(V119888)119896minus1

exp(minus(V119888)119896

) (1)

where 119891119908(V) is the wind speed probability for speed V 119896 is

shape parameter (dimensionless) and 119888 is scale parameter(ms) 119896 and 119888 are determined using standard deviationmethod as follows [23 24]

119896 = (120590

V)minus1086

1 le 119896 le 10

119888 =V

Γ (1 + 1119896)

(2)

Journal of Energy 3

Iran

Persian Gulf

Avaj

Jarandagh

Takestan

Boin-Zahra

Qazvin

Alborz Abyek

Figure 1 Location of Jarandagh and major counties of Qazvin Province on the Iranian map

where V and 120590 are mean wind speed and standard deviationAlso Γ(119909) is the gamma function

The best way to judge the wind potential of a location isevaluation of thewind power densityThewind power densityusing Weibull probability density function can be calculatedas [25]

119875

119860=1

2120588intinfin

0

V3119891119908(V) 119889V =

1

21205881198883Γ (1 +

3

119896) (3)

where 120588 is the air density

32 Extrapolation ofWind Data with Height Thewind speeddata measured at height of 40m are used in this studyGenerally wind blows slowly at lower heights Hence it isdesirable to estimate the wind data as well as the performanceof the turbines at higher hub heightsTheWeibull probabilitydensity function is used to obtain the extrapolated values ofwind speed at higher heights The shape parameter 119896

ℎand

scale parameter 119888ℎat desired height ℎ are related to the shape

parameter 119896119900and scale parameter 119888

119900at measurement height

ℎ119900as follows [26]

119896ℎ=119896119900[1 minus 0088 ln (ℎ

11990010)]

[1 minus 0088 ln (ℎ10)]

119888ℎ= 119888119900(ℎ

ℎ119900

)119899

(4)

where 119899 is the power law exponent (coefficient) and is definedby [26]

119899 =[037 minus 0088 ln (119888

119900)]

[1 minus 0088 ln (ℎ10)] (5)

33 Energy Generated by Wind Turbine and Capacity FactorOne of the key parameters that influence the performance ofa wind turbine is the power response to different wind speedsusually specified by the power curve of the turbine In facteach wind turbine has a particular power curve The typicalpower curve of a sample wind turbine is shown in Figure 2

According to Figure 2 two major performance regionsexist which can generate energy named performance region 1

Pow

er Performanceregion 1

Performanceregion 2

i r o

0 5 10 15 20 25 30

Wind speed (ms)

Figure 2 Typical power curve of a sample pitch controlled windturbine

and performance region 2 For these performance regionsthe power curve may be approximate with the followingequations [27]

119875 (V) = 119875119903(V119899 minus V119899

119894

V119899119903minus V119899119894

) V119894le V le V

119903 (6a)

119875 (V) = 119875119903 V119903le V le V

119900 (6b)

where V119894 V119903 V119900 and 119875

119903are cut-in speed rated speed cut-

out speed and rated power respectively 119899 is the power-speedproportionality Here the ideal power-speed proportionalityis assumed to be 3 The output generated energy by windturbines in the time period of 119879 using Weibull distributionfunction can be expressed as follows [27]

119864out = 119879intV119900

V119894

119875 (V) 119891119908(V) 119889V (7)

The total produced energy is the summation of producedenergy in region 1 and region 2 By substituting (6a) and (6b)in (7) after some mathematical manipulation the produced

4 Journal of Energy

14

12

10

8

6

4

2

0

40m70m

Mea

n w

ind

spee

d (m

s)

Jan

Feb

Mar

Apr

May Jun Jul

Aug

Sep

Oct

Nov Dec

Ann

ual

Figure 3 Monthly and annual mean wind speed (ms) at 40 and 70heights

energy in regions 1 and 2 can be estimated by the followingrelations

119864out1 =1198751199031198791198883

(V3119903minus V3119894)int119909119903

119909119894

1199093119896 119890minus119909119889119909

minus119875119903119879V3119894

(V3119903minus V3119894)(119890minus119909119894 minus 119890minus119909119903)

119864out2 = 119875119903119879 (119890minus119909119903 minus 119890minus119909119900)

(8)

where 119909119894 119909119903 and 119909

119900are given respectively by [27]

119909119894= (

V119894

119888)119896

119909119903= (

V119903

119888)119896

119909119900= (

V119900

119888)119896

(9)

The capacity factor 119862119865is a very important index of wind

turbine productivity and represents the fraction of the outputenergy by the wind turbine over period of time to theenergy which can be generated at the rated power 119862

119865can be

calculated by [27]

119862119865=119864out119864119903

=119864out119875119903119879 (10)

4 Wind Data Analysis

In this study the measured wind speed data at 40m for theperiod of January 2008 toDecember 2009 are analyzedUsing(4)-(5) the wind data at 40m height are extrapolated to the70m height Figure 3 shows monthly and annual mean windspeed at two heights of 40 and 70m Clearly wind speedat Jarandagh site follows constant pattern such that windspeed increases from January till July and August and thendecreases till December The maximum and minimum windspeed occur in January and December At 40m and 70mheights the wind speeds values are in the range of 587ndash1125ms and 669ndash1245ms respectively In addition thecalculation results show that annual wind speed at heights

1800

1600

1400

1200

1000

800

600

400

200

0

Mea

n w

ind

pow

er (W

m2)

40m70m

Jan

Feb

Mar

Apr

May Jun Jul

Aug

Sep

Oct

Nov Dec

Ann

ual

Figure 4 Monthly and annual mean wind power (Wm2) at 40 and70 heights

of 40m and 70m are 774ms and 873ms respectivelyThe monthly and annual mean wind power at 40m and70m heights are illustrated in Figure 4 It is noticed that thewind power does not follow similar pattern with respect towind speed throughout the year The reason is due to higherstandard deviation of wind speed in some months whichresults in higher values of wind power even with lower windspeed The maximum and minimum wind power happen inJuly and December respectively Due to high variation ofwind power in different months it can be concluded thatin case of wind turbines installation in Jarandagh region theenergy output from systems would be subjected to significantdifferences throughout the year However the results specifythat at 40m and 70m heights the wind power vary from32470 to 126706Wm2 and from 45028 to 166162Wm2respectively Also the annual wind power is 55743 and75440Wm2 respectively The Battelle-Pacific NorthwestLaboratory (PNL) proposed a wind power classification forthree heights of 10 30 and 50m to categorize the windresource into 7 classes [28] By interpolation of PNL windpower classification at 30m and 50m it is achieved that at40m height the wind power in 8 months from March toSeptember falls into classes 5 to 7 which shows excellentpotential of wind resource for wind farm construction [29]In the remaining months Jarandagh wind resource falls intoclasses 3 and 4 which means moderate and good potentialfor wind energy harnessing [29] However the better con-clusion can be obtained in terms of annual analysis whichdemonstrates that Jarandagh wind resource ranked in class6 and consequently the region enjoys excellent potential forutilizing wind turbines

The monthly and annual shape and scale parameters at40 and 70m heights are listed in Table 1 It is observed thatmaximum andminimumWeibull parameters are obtained inJuly and January respectively At 40m and 70m heights theannual shape parameters are 195 and 206 while the annualscale parameters are 883ms and 985ms respectively

Journal of Energy 5

Table 1 Monthly and annual shape and scale parameters at heightsof 40 and 70m

119896 (mdash) 40m 119888 (ms) 40m 119896 (mdash) 70m 119888 (ms) 70mJan 141 645 149 741Feb 164 682 173 782Mar 151 774 160 880Apr 177 810 188 918May 217 854 230 965Jun 178 954 189 1072Jul 283 1263 299 1394Aug 269 1244 285 1375Sep 226 1024 239 1145Oct 183 762 194 867Nov 177 709 188 810Dec 172 692 182 792Annual 195 873 206 985

DecNovOctSepAugJulJunMayAprMarFebJan

07

06

05

04

03

02

01

00

Months

Suzlon S66125MWHewind HW771500kW

Gamesa G802000 kWRepower MM82205MW

Mon

thly

capa

city

fact

or

Figure 5 Monthly capacity factor for selected wind turbines atJarandagh site

5 Performance Assessment ofNominated Wind Turbines

For the wind turbines performance evaluation and findingthe amount of energy that could be harnessed from windturbines in Jarandagh area four large-scale wind turbines(Suzlon S66125MW Hewind HW771500KW GamesaG802000KW and RepowerMM82205MW)with differentrated powers are nominatedThemain technical informationof nominated wind turbines are summarized in Table 2These turbines were chosen from an inventory of availablewind turbines in the market The selected wind turbines areconsidered for operation at one of their standard hub heightsequal to 70m

The monthly capacity factors calculated for four windturbines nominated in this study are presented in Figure 5Capacity factor is function of the characteristic speed ofthe wind turbine (ie cut-in speed rated speed and cut-out speed) as well as the wind regime characteristic of


900

800

700

600

500

400

300

200

100

0

Months

Mon

thly

ener

gy o

utpu

t (M

Wh)



Figure 6 Total monthly energy output from selected wind turbinesat Jarandagh site

the location Among characteristic speed of wind turbinesthe rated wind speed has a significant influence on theamount of capacity factor [27] According to Figure 5capacity factor values vary significantly from each month toanother also from each turbine to another For all selectedwind turbines the maximum and minimum values areachieved in June and January respectively It is noticedthat the Suzlon S66125MW turbine model has the highestcapacity factor whose values vary between 0257 and 0676while Gamesa G802000 kW model has the lowest capacityfactors in the range of 0176 and 0560 The main reasonfor difference between capacity factors from each turbineto another as mentioned before is due to the influenceof rated wind speed on the amount of capacity factorTotal amount of energy output from each wind turbinein different months is illustrated in Figure 6 Despite thesuperior performance of Suzlon S66125MW turbine modelin terms of capacity factor the Repower MM82205MWmodel because of higher rated power produces the highestamount of electricity in all months whereas the Suzlon S66125MW wind turbine model generates lowest amount ofelectricity The total monthly energy output from the SuzlonS66125MW and the Repower MM82205MW models isthe range of 23894ndash62889MWh and 29339ndash90122MWhrespectively

The annual capacity factor and total annual of energy out-put for nominated wind turbines are shown in Figures 7 and8 respectively The annual capacity factor of selected windturbines falls within the very satisfactory range of 029ndash040However similar to the monthly analysis the lowest andhighest capacity factors belong to the Gamesa G802000 kWand Suzlon S66125MW models respectively From Fig-ure 8 the lowest and highest total annual energy output areachieved using Suzlon S66125MW and Repower MM82205MWmodels equal to 4441MWhand 5705MWh respec-tively At present annual average of electricity consumptionfor each Iranian family is approximately 2500 kWh [30]Consequently it seems that each wind turbine can be used

6 Journal of Energy

Table 2 Technical data of nominated wind turbines [19ndash22]

Wind turbine model Rated power(KW)

Cut-in speed(ms)

Rated speed(ms)

Cut-outspeed (ms)

Hub height(m)

Rotordiameter (m)

Swept area(m2)

Suzlon S66125MW 1250 4 12 20 72 66 3421HewindHW771500KW 1500 3 13 25 614 70 80 77 4654

GamesaG802000KW 2000 4 15 25 60ndash100 80 5027

RepowerMM82205MW 2050 35 145 25 59ndash90 82 5281

04

03

02

01

00

032029

038040

Wind turbine model

Ann

ual c

apac

ity fa

ctor

MM82205MWG802MWHW7715MWS66125MW

Figure 7 Annual capacity factor for nominated wind turbines atJarandagh site

6000

5000

4000

3000

2000

1000

0

570551654987

4441

Wind turbine model

Ann

ual e

nerg

y ou

tput

(MW

h)


Figure 8 Total annual energy output from nominated windturbines at Jarandagh site

effectively to meet the electricity demand for several homesas well as other applications in the Jarandagh region andneighboring

6 Energy Cost Estimation

Economic feasibility of wind turbine projects is usuallyrelevant to the cost of energy generated by wind turbines In

this regard the project should be optimized for the lowestpossible cost per kWh energy generation The cost of energyproduced by wind turbines is function of many factors likewind speed tax installation operation and maintenanceWith exception of wind turbine cost others are locationdependent [31]The cost of wind turbinesmay vary accordingto the manufactures However the average specific cost ofwind turbines for rated power of more than 200 kW can betaken as 1150 $kW [31] In this study the estimation of energycost produced by wind turbines is conducted by calculatingthe energy cost per kilowatt hour (119862) which is the ratio ofthe accumulated present value of all costs (PVC) to the totalenergy generated by wind turbines during their lifetime (119899)[27]

The accumulated present value of all costs (PVC) includ-ing total initial investment cost of the wind turbine installa-tion project (119862

119868) is [27]

PVC = 1198621198681 + 119898[

(1 + 119868)119899 minus 1

119868 (1 + 119868)119899] (11)

where119898 is the annual operation and maintenance cost and 119868is the real discount rate

The output energy (119864out) produced by the wind turbine inone year according to (10) is [27]

119864out = 8760119875119903119862119865 (12)

where 119875119903and 119862

119865are rated power and capacity factor of the

turbineTherefore cost of electricity generated by wind turbine in

terms of moneykWh can be calculated by [27]

119862 =PWC119864out

=119862119868

8760 119899(

1

119875119903119862119865

)1 + 119898[(1 + 119868)119899 minus 1

119868 (1 + 119868)119899]

(13)

The following assumptions are considered in this study foreconomic evaluation [27]

(1) The other initial costs including installation trans-portation custom fee and grid integration areassumed 40 of the turbine cost Installation periodis neglected

(2) The real discount rate 119868 can approximately be takenas the difference between interest rate and inflationrate Interest rate and inflation rate are considered20 and 16 respectively So the real discount rateis equal to 4

Journal of Energy 7

005

004

003

002

001

000

Wind turbine model

0044800483

0037500357

Cos

t of e

nerg

y ($

kW

h)


Figure 9 Cost of energy (119862) produced by selected wind turbines interms of $kWh

(3) Annual operation and maintenance costs plus theland rent119898 are taken to be 4 of the turbine cost

(4) Expected useful life 119899 of the turbines is 20 years

Figure 9 shows the results of the energy cost per kWhfor selected wind turbines Currently the purchase tariff forelectricity produced by renewable energy sources adapted byIranian government is 013 $kWh [32] Noticeably the cost ofenergy produced by all nominatedwind turbines at Jarandaghsite is much lower than approved purchase tariff hence anyinvestment by national and international private markets forconstruction of wind farms in Jarandagh region seems veryprofitable According to Figure 9 the lowest energy cost isachieved using Suzlon S66125MW turbine model equal to00357 $kWh while the highest energy cost is obtained withGamesa G802000 kW model equal to 00483 $kWh Thusaccording to the energy cost estimation results the SuzlonS66125MW wind turbine model is suggested as the mosteconomical option for wind farms constructing in Jarandaghregion

7 Conclusion

In the current study the possibility of electricity productionusing wind energy in Jarandagh located in north-west partof Iran was investigated The wind energy potential was eval-uated by analyzing the measured wind speed data between2008 and 2009 at 40m height Besides the performance andeconomic assessment of four large-scale wind turbinemodelsfor operation at 70m height were studied The followingconclusions can be drawn from the results of this study

(1) The results at 70m height were achieved by extrapo-lating of wind data It was found that at the heightof 70m the mean wind speed values vary between669 and 1245ms in different months of the yearThe annual wind speed is 873msThemonthlymeanwind power ranges from 45028 to 166162Wm2respectively Also the annual mean wind power is75440Wm2 respectively

(2) The analysis results illustrated that in 8 months fromMarch to September Jarandagh enjoys excellent windenergy potential for wind farm construction whosewind power falls in classes 5 to 7 while in the remain-ing months Jarandagh wind resource falls into classes3 and 4 that means moderate and good potential forwind energy harnessing Besides in terms of annualanalysis it was observed that Jarandagh wind resourceranked in class 6 therefore the region enjoys excellentpotential for utilizing wind turbines

(3) The highest and lowest capacity factor were obtainedusing Suzlon S66125MW and Gamesa G802000 kW wind turbine models with annual valuesof 04 and 029 respectively In terms of electricitygeneration the maximum and minimum energyoutput were found for Repower MM82205MWand Suzlon S66125MWmodels which can generate4441MWh and 5705MWh electricity in the wholeyear respectively

(4) The obtained results for energy cost estimationshowed that the cost of energy produced by allnominated wind turbines at Jarandagh site is muchlower than current renewable energy purchase tariffin Iran (013 $kWh) hence any investment by privatemarkets for wind farms construction in Jarandaghregion seems very profitable Furthermore the leastenergy cost is achieved using Suzlon S66125MWturbine model equal to 00357 $kWh

The study result highly encourages the constructionof wind farms in Jarandagh for the purpose of electricitygeneration which provide a sustainable energy base for theregion In addition the S66125MW wind turbine model isrecommended as the most attractive option

Conflict of Interests

The authors declare that there is no conflict if interestsregarding the publication of this paper

References

[1] M J Shawon L El Chaar and L A Lamont ldquoOverview ofwind energy and its cost in theMiddle Eastrdquo Sustainable EnergyTechnologies and Assessments vol 2 no 1 pp 1ndash11 2013

[2] A Mostafaeipour A Sedaghat A A Dehghan-Niri and VKalantar ldquoWind energy feasibility study for city of Shahrbabakin Iranrdquo Renewable and Sustainable Energy Reviews vol 15 no6 pp 2545ndash2556 2011

[3] AMiketa andPMulder ldquoEnergy productivity across developedand developing countries in 10 manufacturing sectors patternsof growth and convergencerdquo Energy Economics vol 27 no 3 pp429ndash453 2005

[4] November 2013 httpwwwsunaorg[5] J F Manwell J G McGowan and A L Rogers Wind Energy

ExplainedmdashTheory Designand Application JohnWiley amp SonsNew York NY USA 2002

[6] M Hoogwijk B de Vries and W Turkenburg ldquoAssessment ofthe global and regional geographical technical and economic

8 Journal of Energy

potential of onshore wind energyrdquo Energy Economics vol 26no 5 pp 889ndash919 2004

[7] M Abbes and J Belhadj ldquoWind resource estimation and windpark design in El-Kef region Tunisiardquo Energy vol 40 no 1 pp348ndash357 2012

[8] K Mohammadi and A Mostafaeipour ldquoUsing different meth-ods for comprehensive study of wind turbine utilization inZarrineh IranrdquoEnergyConversion andManagement vol 65 pp463ndash470 2013

[9] A Akpinar ldquoEvaluation of wind energy potentiality at coastallocations along the north eastern coasts of Turkeyrdquo Energy vol50 no 1 pp 395ndash405 2013

[10] A Keyhani M Ghasemi-Varnamkhasti M Khanali and RAbbaszadeh ldquoAn assessment of wind energy potential as apower generation source in the capital of Iran Tehranrdquo Energyvol 35 no 1 pp 188ndash201 2010

[11] M R Islam R Saidur and N A Rahim ldquoAssessment ofwind energy potentiality at Kudat and Labuan Malaysia usingWeibull distribution functionrdquo Energy vol 36 no 2 pp 985ndash992 2011

[12] M Mirhosseini F Sharifi and A Sedaghat ldquoAssessing thewind energy potential locations in province of Semnan in IranrdquoRenewable and Sustainable Energy Reviews vol 15 no 1 pp449ndash459 2011

[13] AMostafaeipour andH Abarghooei ldquoHarnessingwind energyatManjil area located in north of IranrdquoRenewableamp SustainableEnergy Reviews vol 12 no 6 pp 1758ndash1766 2008

[14] D Saeidi M Mirhosseini A Sedaghat and A MostafaeipourldquoFeasibility study of wind energy potential in two provinces ofIran North and South Khorasanrdquo Renewable and SustainableEnergy Reviews vol 15 no 8 pp 3558ndash3569 2011

[15] A Mostafaeipour A Sedaghat M Ghalishooyan et al ldquoEval-uation of wind energy potential as a power generation sourcefor electricity production in Binalood Iranrdquo Renewable Energyvol 52 pp 222ndash229 2013

[16] K Mohammadi and A Mostafaeipour ldquoEconomic feasibility ofdeveloping wind turbines in Aligoodarz Iranrdquo Energy Conver-sion and Management vol 76 pp 645ndash653 2013

[17] A Mostafaeipour M Jadidi K Mohammadi and A SedaghatldquoAn analysis of wind energy potential and economic evaluationin Zahedan Iranrdquo Renewable and Sustainable Energy Reviewsvol 30 pp 641ndash650 2014

[18] httpwwwwikipediacom[19] June 2013 httpwwwsuzloncom[20] httpwwwhewindcom[21] httpwwwgamesacorpcom[22] June 2013 httpwwwrepowercom[23] C G Justus W R Hargraves A Mikhail and D Graber

ldquoMethods for estimating wind speed frequency distributionsrdquoJournal of Applied Meteorology vol 17 no 3 pp 350ndash353 1978

[24] A N Celik ldquoWeibull representative compressed wind speeddata for energy and performance calculations of wind energysystemsrdquo Energy Conversion and Management vol 44 no 19pp 3057ndash3072 2003

[25] E K Akpinar and S Akpinar ldquoAn assessment on seasonalanalysis ofwind energy characteristics andwind turbine charac-teristicsrdquo Energy Conversion andManagement vol 46 no 11-12pp 1848ndash1867 2005

[26] B Safari and J Gasore ldquoA statistical investigation of windcharacteristics and wind energy potential based on the Weibull

and Rayleighmodels in Rwandardquo Renewable Energy vol 35 no12 pp 2874ndash2880 2010

[27] S MathewWind Energy Fundamentals Resource Analysis andEconomics Springer Berlin Germany 2006

[28] D L Elliott and M N Schwartz ldquoWind energy potential in theUnited Statesrdquo NTIS no DE94001667 PNL-SA-23109 PacificNorthwest Laboratory Richland Wash USA 1993

[29] X Yu andH Qu ldquoWind power in China opportunity goes withchallengerdquo Renewable and Sustainable Energy Reviews vol 14no 8 pp 2232ndash2237 2010

[30] httpwwwirnairfaNews[31] M S Adaramola S S Paul and S O Oyedepo ldquoAssessment of

electricity generation and energy cost of wind energy conver-sion systems in North-central Nigeriardquo Energy Conversion andManagement vol 52 no 12 pp 3363ndash3368 2011

[32] httpwwwmojnewscomenMiscellaneousViewContentsaspxI

TribologyAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

FuelsJournal of


Journal ofPetroleum Engineering


Industrial EngineeringJournal of


Power ElectronicsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

CombustionJournal of


Journal of


Renewable Energy

Submit your manuscripts athttpwwwhindawicom


StructuresJournal of


RotatingMachinery


EnergyJournal of


Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy


Nuclear InstallationsScience and Technology of


Solar EnergyJournal of


Wind EnergyJournal of


Nuclear EnergyInternational Journal of


High Energy PhysicsAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

2 Journal of Energy

the world Abbes and Belhadj [7] estimated the wind resour-ces and also wind park design in El-Kef region TunisiaThey investigated the characteristic of wind speed usingWeibull distribution function and estimated the capacityfactors for different wind turbine configurations They per-formed economic evaluation to examine the feasibility oftheir project Mohammadi and Mostafaeipour [8] utilizeddifferent methods for comprehensive study of wind turbineutilization in Zarrineh Iran They used hourly monthlyseasonal and yearly wind data analysis It was concludedthat the location was marginal for harnessing wind energyAlso the standard deviation and power density method wereperformed to determine best method for evaluation of windpower Akpinar [9] evaluated the wind energy potential forcoastal locations along the north eastern coasts of TurkeyThey illustrated that the monthly mean wind speed in theregion varied between 153ms and 406ms Also they foundthat the maximum annual mean wind power density andwind energy density were 5996Wm2 and 52525 kWhm2respectively Keyhani et al [10] studied assessment of windenergy potential as a power generation source in the capitalof Iran Tehran Long termmeasured wind speed data at 10mheight was used for this study They calculated the annualaverage wind power densities which were between 7400 and12248Wm2 They concluded that the wind energy poten-tial for Tehran was suitable only for battery charging andwater pumping Islam et al [11] investigated the assessmentof wind energy potential at Kudat and Labuan Malaysiausing Weibull distribution function They used 10m heightmeasured wind speed data and found that highest monthlymean wind speeds were 48ms and 43ms at Kudat andLabuan respectively Also they showed that the maximumwind power densities of Kudat and Labuan were 6740Wm2and 5081Wm2 respectively They concluded that the twolocations were suitable only for small-scale wind energyapplications

Mirhosseini et al [12] studied the potential of windpower generation for five cities in Semnan province in IranMostafaeipour andAbarghooei [13] performed analysis of thewind speed data for six stations in Manjil area in north ofIran Saeidi et al [14] analyzed the wind potential and windpower of four locations in two provinces of North and SouthKhorasan in Iran Mostafaeipour et al [15] studied the windenergy potential of Binalood located in north-east of IranMohammadi andMostafaeipour [16] appraised the economicviability of installing 6 different wind turbines models inAligoodarz situated in west part of Iran Mostafaeipour et al[17] examined the wind energy potential and economicevaluation of small wind turbines for city of Zahedan insouth-east of Iran

The aim of this study is to perform economic evaluationof installing wind turbines in Jarandagh located in Iran Thispaper illustrates research work involved in estimating theelectricity generation and energy cost of wind turbines Forperformance evaluation of the wind turbines and finding theamount of energy that could be harnessed from windturbines in Jarandagh area four large-scale wind turbines(Suzlon S66125MW HewindHW771500KW GamesaG802000KW and RepowerMM82205MW)with differentrated powers are nominated

Thenext section offers an overviewof geographic descrip-tion of the region In Section 3 themethodology is discussedThe review of wind data analysis is presented in Section 4Performance assessment of nominated wind turbines isbrought forward in Section 5 Energy cost estimation is donein Section 6 and finally concluding remarks are presented inSection 7

2 Geographic Description

Qazvin Province with an area of 15821 km2 is located inthe north-west part of Iran between 35∘371015840N and 36∘451015840Nand also between 48∘451015840E and 50∘501015840E This province withonly 1 of the total country area is involved in 5 percent ofIranrsquos economy and production Based on 2010 survey thetotal population of Qazvin Province was 572916 QazvinProvince consists of six counties namely Takestan AbyekBoin-Zahra Avaj andAlborz and city of Qazvin the center ofthe province Also the province has 1543 villages Jarandaghis a small village in suburb of Takestan located at 36∘111015840N and49∘481015840E with total population of 449 [18] Figure 1 shows thelocation of Qazvin Province as well as all of its six countiesincluding Jarandagh

The data used for this study includemeasured wind speedover period of two years from January 2008 to December2009 in the time interval of 10min at 40m height [4] Thedata was recorded at the meteorological mast installed byrenewable energies organization of Iran (SUNA) For thepurpose of wind speed data preparing the ten-minuterecorded data were averaged to drive hourly values Thenthe hourly values were used to obtain daily monthly andyearly values Finally the obtained data were averaged overthe period of two years and calculation procedure was donebased on these averaged values

3 Methodology

Knowledge of the wind speed frequency distribution playsa substantial role in order to estimate the potential of windin any location Various probability density functions existto fit and describe the wind speed frequency over a periodof time In this study the Weibull distribution function isused because of its simplicity and high accuracy for winddata analysis In fact Weibull distribution function is widelyemployed and adopted as an alternative method to evaluatewind energy potential and wind turbine energy output

31 Weibull Distribution Function The probability densityfunction of Weibull distribution can be estimated as [23 24]

119891119908(V) =

119896

119888(V119888)119896minus1

exp(minus(V119888)119896

) (1)

where 119891119908(V) is the wind speed probability for speed V 119896 is

shape parameter (dimensionless) and 119888 is scale parameter(ms) 119896 and 119888 are determined using standard deviationmethod as follows [23 24]

119896 = (120590

V)minus1086

1 le 119896 le 10

119888 =V

Γ (1 + 1119896)

(2)

Journal of Energy 3

Iran

Persian Gulf

Avaj

Jarandagh

Takestan

Boin-Zahra

Qazvin

Alborz Abyek




119875

119860=1

2120588intinfin

0

V3119891119908(V) 119889V =

1

21205881198883Γ (1 +

3

119896) (3)



ℎand





119896ℎ=119896119900[1 minus 0088 ln (ℎ

11990010)]

[1 minus 0088 ln (ℎ10)]

119888ℎ= 119888119900(ℎ

ℎ119900

)119899

(4)


119899 =[037 minus 0088 ln (119888

119900)]

[1 minus 0088 ln (ℎ10)] (5)



Pow


Performanceregion 2

i r o

0 5 10 15 20 25 30

Wind speed (ms)



119875 (V) = 119875119903(V119899 minus V119899

119894

V119899119903minus V119899119894

) V119894le V le V

119903 (6a)

119875 (V) = 119875119903 V119903le V le V

119900 (6b)

where V119894 V119903 V119900 and 119875



119864out = 119879intV119900

V119894

119875 (V) 119891119908(V) 119889V (7)


4 Journal of Energy

14

12

10

8

6

4

2

0

40m70m

Mea

n w

ind

spee

d (m

s)

Jan

Feb

Mar

Apr

May Jun Jul

Aug

Sep

Oct

Nov Dec

Ann

ual



119864out1 =1198751199031198791198883

(V3119903minus V3119894)int119909119903

119909119894

1199093119896 119890minus119909119889119909

minus119875119903119879V3119894



(8)

where 119909119894 119909119903 and 119909


119909119894= (

V119894

119888)119896

119909119903= (

V119903

119888)119896

119909119900= (

V119900

119888)119896

(9)



119865can be

calculated by [27]

119862119865=119864out119864119903

=119864out119875119903119879 (10)



1800

1600

1400

1200

1000

800

600

400

200

0

Mea

n w

ind

pow

er (W

m2)

40m70m

Jan

Feb

Mar

Apr

May Jun Jul

Aug

Sep

Oct

Nov Dec

Ann

ual




Journal of Energy 5




07

06

05

04

03

02

01

00

Months



Mon

thly

capa

city

fact

or






900

800

700

600

500

400

300

200

100

0

Months

Mon

thly

ener

gy o

utpu

t (M

Wh)






6 Journal of Energy



Cut-in speed(ms)

Rated speed(ms)

Cut-outspeed (ms)

Hub height(m)

Rotordiameter (m)

Swept area(m2)


GamesaG802000KW 2000 4 15 25 60ndash100 80 5027


04

03

02

01

00

032029

038040

Wind turbine model

Ann

ual c

apac

ity fa

ctor



6000

5000

4000

3000

2000

1000

0

570551654987

4441

Wind turbine model

Ann

ual e

nerg

y ou

tput

(MW

h)








119868) is [27]

PVC = 1198621198681 + 119898[

(1 + 119868)119899 minus 1

119868 (1 + 119868)119899] (11)



119864out = 8760119875119903119862119865 (12)

where 119875119903and 119862




119862 =PWC119864out

=119862119868

8760 119899(

1

119875119903119862119865

)1 + 119898[(1 + 119868)119899 minus 1

119868 (1 + 119868)119899]

(13)




Journal of Energy 7

005

004

003

002

001

000

Wind turbine model

0044800483

0037500357

Cos

t of e

nerg

y ($

kW

h)






7 Conclusion









References







8 Journal of Energy





























FuelsJournal of







Advances in



Journal of


Renewable Energy





RotatingMachinery


EnergyJournal of

















Journal of Energy 3

Iran

Persian Gulf

Avaj

Jarandagh

Takestan

Boin-Zahra

Qazvin

Alborz Abyek




119875

119860=1

2120588intinfin

0

V3119891119908(V) 119889V =

1

21205881198883Γ (1 +

3

119896) (3)



ℎand





119896ℎ=119896119900[1 minus 0088 ln (ℎ

11990010)]

[1 minus 0088 ln (ℎ10)]

119888ℎ= 119888119900(ℎ

ℎ119900

)119899

(4)


119899 =[037 minus 0088 ln (119888

119900)]

[1 minus 0088 ln (ℎ10)] (5)



Pow


Performanceregion 2

i r o

0 5 10 15 20 25 30

Wind speed (ms)



119875 (V) = 119875119903(V119899 minus V119899

119894

V119899119903minus V119899119894

) V119894le V le V

119903 (6a)

119875 (V) = 119875119903 V119903le V le V

119900 (6b)

where V119894 V119903 V119900 and 119875



119864out = 119879intV119900

V119894

119875 (V) 119891119908(V) 119889V (7)


4 Journal of Energy

14

12

10

8

6

4

2

0

40m70m

Mea

n w

ind

spee

d (m

s)

Jan

Feb

Mar

Apr

May Jun Jul

Aug

Sep

Oct

Nov Dec

Ann

ual



119864out1 =1198751199031198791198883

(V3119903minus V3119894)int119909119903

119909119894

1199093119896 119890minus119909119889119909

minus119875119903119879V3119894



(8)

where 119909119894 119909119903 and 119909


119909119894= (

V119894

119888)119896

119909119903= (

V119903

119888)119896

119909119900= (

V119900

119888)119896

(9)



119865can be

calculated by [27]

119862119865=119864out119864119903

=119864out119875119903119879 (10)



1800

1600

1400

1200

1000

800

600

400

200

0

Mea

n w

ind

pow

er (W

m2)

40m70m

Jan

Feb

Mar

Apr

May Jun Jul

Aug

Sep

Oct

Nov Dec

Ann

ual




Journal of Energy 5




07

06

05

04

03

02

01

00

Months



Mon

thly

capa

city

fact

or






900

800

700

600

500

400

300

200

100

0

Months

Mon

thly

ener

gy o

utpu

t (M

Wh)






6 Journal of Energy



Cut-in speed(ms)

Rated speed(ms)

Cut-outspeed (ms)

Hub height(m)

Rotordiameter (m)

Swept area(m2)


GamesaG802000KW 2000 4 15 25 60ndash100 80 5027


04

03

02

01

00

032029

038040

Wind turbine model

Ann

ual c

apac

ity fa

ctor



6000

5000

4000

3000

2000

1000

0

570551654987

4441

Wind turbine model

Ann

ual e

nerg

y ou

tput

(MW

h)








119868) is [27]

PVC = 1198621198681 + 119898[

(1 + 119868)119899 minus 1

119868 (1 + 119868)119899] (11)



119864out = 8760119875119903119862119865 (12)

where 119875119903and 119862




119862 =PWC119864out

=119862119868

8760 119899(

1

119875119903119862119865

)1 + 119898[(1 + 119868)119899 minus 1

119868 (1 + 119868)119899]

(13)




Journal of Energy 7

005

004

003

002

001

000

Wind turbine model

0044800483

0037500357

Cos

t of e

nerg

y ($

kW

h)






7 Conclusion









References







8 Journal of Energy





























FuelsJournal of







Advances in



Journal of


Renewable Energy





RotatingMachinery


EnergyJournal of

















4 Journal of Energy

14

12

10

8

6

4

2

0

40m70m

Mea

n w

ind

spee

d (m

s)

Jan

Feb

Mar

Apr

May Jun Jul

Aug

Sep

Oct

Nov Dec

Ann

ual



119864out1 =1198751199031198791198883

(V3119903minus V3119894)int119909119903

119909119894

1199093119896 119890minus119909119889119909

minus119875119903119879V3119894



(8)

where 119909119894 119909119903 and 119909


119909119894= (

V119894

119888)119896

119909119903= (

V119903

119888)119896

119909119900= (

V119900

119888)119896

(9)



119865can be

calculated by [27]

119862119865=119864out119864119903

=119864out119875119903119879 (10)



1800

1600

1400

1200

1000

800

600

400

200

0

Mea

n w

ind

pow

er (W

m2)

40m70m

Jan

Feb

Mar

Apr

May Jun Jul

Aug

Sep

Oct

Nov Dec

Ann

ual




Journal of Energy 5




07

06

05

04

03

02

01

00

Months



Mon

thly

capa

city

fact

or






900

800

700

600

500

400

300

200

100

0

Months

Mon

thly

ener

gy o

utpu

t (M

Wh)






6 Journal of Energy



Cut-in speed(ms)

Rated speed(ms)

Cut-outspeed (ms)

Hub height(m)

Rotordiameter (m)

Swept area(m2)


GamesaG802000KW 2000 4 15 25 60ndash100 80 5027


04

03

02

01

00

032029

038040

Wind turbine model

Ann

ual c

apac

ity fa

ctor



6000

5000

4000

3000

2000

1000

0

570551654987

4441

Wind turbine model

Ann

ual e

nerg

y ou

tput

(MW

h)








119868) is [27]

PVC = 1198621198681 + 119898[

(1 + 119868)119899 minus 1

119868 (1 + 119868)119899] (11)



119864out = 8760119875119903119862119865 (12)

where 119875119903and 119862




119862 =PWC119864out

=119862119868

8760 119899(

1

119875119903119862119865

)1 + 119898[(1 + 119868)119899 minus 1

119868 (1 + 119868)119899]

(13)




Journal of Energy 7

005

004

003

002

001

000

Wind turbine model

0044800483

0037500357

Cos

t of e

nerg

y ($

kW

h)






7 Conclusion









References







8 Journal of Energy





























FuelsJournal of







Advances in



Journal of


Renewable Energy





RotatingMachinery


EnergyJournal of

















Journal of Energy 5




07

06

05

04

03

02

01

00

Months



Mon

thly

capa

city

fact

or






900

800

700

600

500

400

300

200

100

0

Months

Mon

thly

ener

gy o

utpu

t (M

Wh)






6 Journal of Energy



Cut-in speed(ms)

Rated speed(ms)

Cut-outspeed (ms)

Hub height(m)

Rotordiameter (m)

Swept area(m2)


GamesaG802000KW 2000 4 15 25 60ndash100 80 5027


04

03

02

01

00

032029

038040

Wind turbine model

Ann

ual c

apac

ity fa

ctor



6000

5000

4000

3000

2000

1000

0

570551654987

4441

Wind turbine model

Ann

ual e

nerg

y ou

tput

(MW

h)








119868) is [27]

PVC = 1198621198681 + 119898[

(1 + 119868)119899 minus 1

119868 (1 + 119868)119899] (11)



119864out = 8760119875119903119862119865 (12)

where 119875119903and 119862




119862 =PWC119864out

=119862119868

8760 119899(

1

119875119903119862119865

)1 + 119898[(1 + 119868)119899 minus 1

119868 (1 + 119868)119899]

(13)




Journal of Energy 7

005

004

003

002

001

000

Wind turbine model

0044800483

0037500357

Cos

t of e

nerg

y ($

kW

h)






7 Conclusion









References







8 Journal of Energy





























FuelsJournal of







Advances in



Journal of


Renewable Energy





RotatingMachinery


EnergyJournal of

















6 Journal of Energy



Cut-in speed(ms)

Rated speed(ms)

Cut-outspeed (ms)

Hub height(m)

Rotordiameter (m)

Swept area(m2)


GamesaG802000KW 2000 4 15 25 60ndash100 80 5027


04

03

02

01

00

032029

038040

Wind turbine model

Ann

ual c

apac

ity fa

ctor



6000

5000

4000

3000

2000

1000

0

570551654987

4441

Wind turbine model

Ann

ual e

nerg

y ou

tput

(MW

h)








119868) is [27]

PVC = 1198621198681 + 119898[

(1 + 119868)119899 minus 1

119868 (1 + 119868)119899] (11)



119864out = 8760119875119903119862119865 (12)

where 119875119903and 119862




119862 =PWC119864out

=119862119868

8760 119899(

1

119875119903119862119865

)1 + 119898[(1 + 119868)119899 minus 1

119868 (1 + 119868)119899]

(13)




Journal of Energy 7

005

004

003

002

001

000

Wind turbine model

0044800483

0037500357

Cos

t of e

nerg

y ($

kW

h)






7 Conclusion









References







8 Journal of Energy





























FuelsJournal of







Advances in



Journal of


Renewable Energy





RotatingMachinery


EnergyJournal of

















Journal of Energy 7

005

004

003

002

001

000

Wind turbine model

0044800483

0037500357

Cos

t of e

nerg

y ($

kW

h)






7 Conclusion









References







8 Journal of Energy





























FuelsJournal of







Advances in



Journal of


Renewable Energy





RotatingMachinery


EnergyJournal of

















8 Journal of Energy





























FuelsJournal of







Advances in



Journal of


Renewable Energy





RotatingMachinery


EnergyJournal of





















FuelsJournal of







Advances in



Journal of


Renewable Energy





RotatingMachinery


EnergyJournal of

















Documents

Research Article Electricity Generation and Energy Cost …downloads.hindawi.com/journals/jen/2014/613681.pdf · 2019-07-31 · Research Article Electricity Generation and Energy