assessments of wind-energy potential in selected sites from three geopolitical zones in nigeria:...

Research ArticleAssessments of Wind-Energy Potential in SelectedSites from Three Geopolitical Zones in Nigeria Implications forRenewableSustainable Rural Electrification

Joshua Olusegun Okeniyi1 Olayinka Soledayo Ohunakin1 and Elizabeth Toyin Okeniyi2

1Department of Mechanical Engineering Covenant University Ota 112001 Nigeria2Department of Petroleum Engineering Covenant University Ota 112001 Nigeria

Correspondence should be addressed to Joshua Olusegun Okeniyi joshuaokeniyicovenantuniversityedung

Received 13 June 2014 Accepted 25 November 2014

Academic Editor Hua Bai

Copyright copy 2015 Joshua Olusegun Okeniyi et al This is an open access article distributed under the Creative CommonsAttribution License which permits unrestricted use distribution and reproduction in any medium provided the original work isproperly cited

Electricity generation in rural communities is an acute problem militating against socioeconomic well-being of the populace inthese communities in developing countries including Nigeria In this paper assessments of wind-energy potential in selected sitesfrom three major geopolitical zones of Nigeria were investigated For this daily wind-speed data from Katsina in northern Warriin southwestern and Calabar in southeastern Nigeria were analysed using the Gumbel and theWeibull probability distributions forassessing wind-energy potential as a renewablesustainable solution for the countryrsquos rural-electrification problems Results showedthat the wind-speed models identified Katsina with higher wind-speed class than both Warri and Calabar that were otherwiseidentified as low wind-speed sites However econometrics of electricity power simulation at different hub heights of low wind-speed turbine systems showed that the cost of electric-power generation in the three study sites was converging to affordable costper kWhof electric energy from thewind resource at each siteThese power simulations identified costkWhof electricity generationat Kaduna as C00507 at Warri as C00774 and at Calabar as C00819 These bare positive implications on renewablesustainablerural electrification in the study sites even as requisite options for promoting utilization of this viable wind-resource energy in theremote communities in the environs of the study sites were suggested

1 Introduction

At the year 2010 according to reports in [1] about 50 of thetotal population globally 60 of the population in Africa55 of the population in West Africa and 79441 out of the158423 million (ie 5014) of the people in Nigeria dwell inthe rural areas In the country Nigeria electricity generationcapacity for the entire populace is most often lesser than4000MW in comparison to required energy demand thatcould be as high as 25000MW and due to this 65 to 72of the Nigerian rural populace lack access to electricity [2ndash4] This lack of basic infrastructural requirement on whichmajor productive activities for developments in modernsociety are dependent [5 6] leads to gross impoverishmentof the populace in rural communities in Nigeria and in otherdeveloping countries with similar energy situations [2 7 8]

These affect socioeconomic well-being of the rural dwellersin the country [7 9] a situation that is not much differentfrom what is obtained in developing countries globallyFor instance estimations by the World Health Organization(WHO) indicated that annual premature death of womenand young children attains 25 million while as high as 6of global populace are infected with acute respiratory illnessfrom hazardous gases and fumes emitted from traditionalbiomass stoves [9] By the neglects of rural populace inNigeria [5 7ndash9] only 25 are economically active in agri-culture which is supposed to be their major occupation58 have no access to improved drinking water sourcesand 72 have no access to improved sanitation facilities [1]These are caused by energy-related problems Many farmersare dissuaded from engaging beyond subsistence farming forlack of modern storage facilities which are energy dependent

Hindawi Publishing Corporatione Scientific World JournalVolume 2015 Article ID 581679 13 pageshttpdxdoiorg1011552015581679

2 The Scientific World Journal

(a) (b)

(c)

Figure 1 The study sites and environs in Nigeria (a) Katsina Katsina State (b) Warri Delta State (c) Calabar Cross River State

Lack of electricity leads to lack of water pumping facilitythat could both ensure good drinking water and make wateravailable for improved sanitation facility which is basicallya water-dependent hygienic issue

It had been identified in studies [2 4] that among thetwo options that could be employed for supplying electricityto the rural communities in developing countries includingNigeria the off-grid alternatives exhibited preference to theon-grid connection The off-grid method of rural electrifica-tion becomes imperative due to the insufficiency of electricitysupply available from the national grid and the remotenessof the rural communities from the grid which leads to pro-hibitive cost of electric-power installationsmaintenance andoperations [2 4 7] For the off-grid electrification option forthe rural areas in the country adoption of renewable energyportends potential of suitability for the remote communitiesthat could be sustainable with the additional advantage ofreducing unwholesome environmental effects from the use offossil fuel sources

A viable renewable energy option that could find appli-cability for most regions of remote rural communities inthe country includes the use of wind energy for generatingelectricity for the electrification of the rural areas Howeveruseful exploitation of energy from wind sources for a regionrequires assessment of the wind-energy potential in theenvirons of such region for requisite knowledge of energyavailability as well as viability of installing wind-energysystems in such locations for electric-energy generationWhile many studies [5 10ndash17] have deliberated on wind char-acteristics and energy potential from different parts of thecountry none have undertaken such study for establishinghow the wind energy could have been applicable as a solutionfor rural-electrification problems in the country Thereforethe objective of this study was to investigate wind-energy

potential of selected sites from three geopolitical zones inNigeria for the purpose of gaining insights into the usage ofwind-energy systems for sustainable rural electrification inthe selected parts of Nigeria For this selected sites employedinclude Katsina from Katsina State in the northern partWarri fromDelta State in the southwestern part and Calabarfrom Cross River State in the southeastern part of thecountry Aerial images showing topologies of these studysites and environs are shown in Figure 1 Motivation forthese study sites was from the consideration that environsof these locations are characteristically identified with ruralcommunities characterised with nonavailability of electricpower due to their nonconnection to the national grid ofelectricity supply in the country [3 7 18] Also Katsinarepresents a high altitude location compared to Warri whichis a low altitude site while the altitude of Calabar lies betweenthat of Katsina and Warri These make the study sites ofinterests for investigating how the different modes of wind-energy potential in the sites could be assessed for solvingrural-electrification problems in the environs of the studysites and in other similar regions of the world

2 Materials and Methods

21 Description of Selected Sites and Wind-Speed Data Dailywind-speed data that were measured from January 2006to December 2010 were obtained from Nigerian Meteoro-logical Agency (NIMET) Abuja Nigeria for the selectedsites of Katsina from northern Warri from southwesternand Calabar from the southeastern parts of Nigeria Inthese locations themeteorological stationswere respectivelylocated as given in Table 1 which also includes the air density120588 (kgm3) at each of the stations The wind-speed data

The Scientific World Journal 3

Table 1 Description of meteorological stations at the selected sites

SNo Site Latitude(∘N)

Longitude(∘E)

Altitude (above sea level)(m)

Air density 120588(kgm3)

1 Katsina 1301 0741 5176 116532 Warri 0531 0544 61 122433 Calabar 0458 0821 619 12179

were captured in each of the stations using cup-generatoranemometer at a height of 10m

22 Statistical Distribution Analyses ofWind-Speed Data Forstudying the statistics for describing wind-speed data themeasuredwind speedwas subjected to the extreme-value dis-tribution fittings of theGumbel and of theWeibull probabilitydensity functions (pdfs)This followed recommendations forsuitability study of probability density function for describingwind-speed data by [19] but in spite of this there is paucityof studies in which the Gumbel pdf has been employed fordetailing wind-speed frequency

The Gumbel distribution [21 22] employed for fittingvariable of wind-speed data 119909 in this study has its cumulativedistribution function given by the following expression

119865 (119909) = 1 minus expminus exp [minus(119909 minus 120582120573)] (1)

where 120582 equiv the location and 120573 equiv the scale parameters thatwere estimated from 119899 sample sized wind-speed data fromregression of the linearized cumulative distribution functionwhich assumes the following form

ln minus ln [1 minus 119865 (119909)] = minus119909120573+120582

120573 (2)

Estimated 120582 and 120573 values from this were employed forevaluating Gumbel mean 120583

119866 of the wind speed from the

following expression [21ndash23]

V119898119866equiv 120583119866= 120582 minus 120573Γ

1015840(1) (3)

where Γ1015840(1) = 120574 which is Eulerrsquos constant and the value of119889Γ(119899)119889119899 when this differential is evaluated at 119899 = 1

In similar manner Weibull distribution [5 6 21 24ndash26] utilized for fitting variable of wind-speed data 119909 in thisstudy has its cumulative distribution function given by thefollowing expression

119865 (119909) = 1 minus expminus(119909120573)

120578

(4)

where 120578 is the shape and 120573 is the scale parameters [21 23 27ndash29] that were evaluated from 119899 sample sizedwind-speed datafrom regression of the linearized cumulative distributionfunction in the following form

ln minus ln [1 minus 119865 (119909)] = 120578 ln119909 minus 120578 ln120573 (5)

Estimated values of 120578 and 120573 obtained from this were also usedfor evaluating the Weibull mean 120583

119882 of the wind speed from

the following expression [10 14 29ndash32]

V119898119882

equiv 120583119882= 120573Γ(1 +

1

120578) (6)

where Γ(sdot) is the gamma function of (sdot)

23 Fitting Performance of Probability Distribution ModelsCriteria employed for evaluating fitting performance ofthe probability distribution models include the correlationcoefficient 119877 and the Nash-Sutcliffe coefficient of efficiencyCoE [15 33 34] These are respectively estimated from theformula

119877 =sum119899

119894=1(119900119894minus 119900) (119901

119894minus 119901)

radicsum119899

119894=1(119900119894minus 119900)2times radicsum

119873

119894=1(119901119894minus 119901)2

CoE = 1 minussum119899

119894=1(119900119894minus 119901119894)2

radicsum119899

119894=1(119900119894minus 119900)2

(7)

where 119900 and 119901 are the observed and the predicted values ofthe wind-speed data

24 Wind-Power Modeling and Electric-Power SimulationThe mean wind-power density (Wm2) available at theanemometer height of 10m could be calculated for each pdfas [5 31]

119875119898=1

2120588V3119898pdf (8)

Also electric-power output 119875119890 simulation employs the rated

electric power 119875119890119877

for wind turbine model through thefollowing expression [5 10 13]

119875119890=

0 V lt V119862

119875119890119877(V120578 minus V120578

119862

V120578119877minus V120578119862

) V119862le V le V

119877

119875119890119877

V119877le V le V

119865

0 V gt V119865

(9)

where V119862 V119877 and V

119865are the cut-in the rated and the cut-off

speeds respectively of the wind turbine model Evaluationof the average power output 119875

119890ave of the wind turbine modelfor determining total energy production and the total income


from the wind-energy conversion system was obtained from[5]

119875119890ave = 119875119890119877 (

119890minus(V119862120573)

120578

minus 119890minus(V119877120573)

120578

(V119877120573)120578minus (V119862120573)120578minus 119890minus(V119865120573)

120578

) (10)

It is worth noting that the simulation of electric-power output119875119890in (9) and average power output 119875

119890ave in (10) was for windspeed measured at height ℎ

0= 10m For simulating these

quantities for different hub heights ℎ of wind turbine modelthe extrapolations of scale and shape parameters of the pdfs1205730and 120578

0at the measurement height ℎ

0= 10m to the hub

height ℎ are required This could be obtained from [5 35]

120573 (ℎ) = 1205730(ℎ

10)

120576

120578 (ℎ) =1205780

[1 minus 0088 ln (ℎ10)]

(11)

where the exponent 120576 was evaluated from

120576 =[037 minus 0088 ln120573

0]

[1 minus 0088 ln (ℎ10)] (12)

From the simulated wind-energy system the capacity factorevaluation employs the ratio of the averaged turbine power tothe turbine rated power [5 24 36]

119862119891=119875119890ave

119875119890119877

(13)

25 Analyses of Econometric Implications and Prospects ofWind-Energy System The analyses of the econometric impli-cation and prospect of wind-energy system development inthe study sites employ computation of the present value costPVC for the price 119910 of a given turbine over the lifetime 119905 ofthe turbine operation from the following equation [5 10 3537 38]

PVC = 119910 (1 + 119903119862) +119910

119905119903OMR [

1 + 119894

119903119868minus 119894] times [1 minus (

1 + 119894

1 + 119903119868

)

119905

]

minus 119903SC sdot 119910 (1 + 119903119862) times (1 + 119894

1 + 119903119868

)

119905

(14)

where 119903119862= rate of the price of turbine for civilstructural

works and other connections 119903OMR = annual rate of the priceof turbine for operation maintenance and repair 119894 = interestrate 119903

119868= inflation rate and 119903SC = rate of the price of turbine

and civil work for the scrap value of the turbine after theexpiration of the turbine lifetime By these the cost per kWhof electricity generation through the wind turbine systemwasobtained from [5]

Cost(per kWh) =

PVC119875119890ave times 119905

(15)

3 Results and Discussion

31 Estimated Parameters for the Study Sites Plots of theestimated parameters of the Gumbel and the Weibull distri-bution functions are presented in Figures 2 and 3 respec-tively for the monthly seasonal and all-year (JanuaryndashDecember) considerations all from 2006 to 2010 For thisit is worth noting that rainy season in Katsina northernNigeria spans through June to September four monthswhile the remaining eight months constitute dry season inthe prevalent Sahel (tropical dry) climate In contrast rainyseason spans through March to July and then Septemberto October while dry season spans through November toMarch and a short dry season in August termed ldquoAugustbreakrdquo in the tropical rain forest (or equatorial monsoon)to which the southern part of Nigeria thus Warri andCalabar belongs From Figure 2 it could be observed thatthe location parameters of the Gumbel pdf fittings of Katsinawind-speed data were of higher value than the locationparameters obtained from the fittings from Warri and fromCalabar in all the periods of the years studied Specificallythe Gumbel location parameter Figure 2(a) ranged from448ms in October to 831ms in March at Katsina but from220ms in January to 331ms in April at Warri or from297ms in August to 361ms in April at Calabar Also theGumbel scale parameter Figure 2(b) ranged from 281msin August to 510ms in January at Katsina while it rangedfrom 162ms in September to 242ms in June at Warri orfrom 141ms in February to 244ms in March at CalabarBy these results the Gumbel parameters bare suggestions ofsimilarities of wind-speed characteristics in the study sites ofWarri in the southwestern and of Calabar in the southeasternNigeria

Figure 3 also showed that the scale parameters of theWeibull pdf fittings of Katsina wind-speed data were ofhigher value than those from the wind-speed data fittingsfrom Warri and from Calabar The Weibull scale parameterFigure 3(a) ranged from 734ms in September to 1234msin January at Katsina while it ranged from 373ms inDecember to 532 in April at Warri or 466ms in Novemberto 560ms in June at Calabar However unlike the statisticalparameters considered thus far the Weibull shape parame-ters Figure 3(b) estimated from the wind-speed data fromCalabar overshot those estimated from the wind-speed datain Warri for all periods of the year and from Katsina for9 out of the 12 months the dry rainy and the all-year windspeedTheWeibull scale parameter ranged from211 inMarchto 305 in February at Calabar while it ranged from 172 inNovember to 263 inMarch at Katsina or from 166 in Januaryto 217 in September at Warri

These Weibull shape parameter values are of specificstatistical importance because they bare indications of theuniformity of the wind-speed data in the study sitesThus thehigher Weibull shape parameters at Calabar indicated higherslope of the Weibull fitting model for this study site in (5)and also implied that the modeled wind speed at Calabarexhibited more uniformity than the modeled wind speedfrom the other sites


000

100

200

300

400

500

600

700

800

900

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the yearKatsinaWarriCalabar

Gum

bel l

ocat

ion

para

met

er120582

(ms

)

(a)Ja

nFe

bM

ar

Apr

May

Ju

n Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year


000

100

200

300

400

500

600

Gum

bel s

cale

par

amet

er120573

(ms

)

(b)

Figure 2 Estimated parameters of the Gumbel distribution model for the study sites (a) Gumbel location parameter and (b) Gumbel scaleparameter

200

400

600

800

1000

1200

1400

Wei

bull

scal

e par

amet

er120573

(ms

)

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year


(a)

150

170

190

210

230

250

270

290

310

330

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year


Wei

bull

shap

e par

amet

er120578

(b)

Figure 3 Estimated parameters of the Weibull distribution model for the study sites (a) Weibull scale parameter and (b) Weibull shapeparameter


000

020

040

060

080

100

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year


Cor

relat

ion

coeffi

cien

t (R)

by G

umbe

l pdf

(a)

000

020

040

060

080

100

All-

year

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason


by G

umbe

l pdf

Coe

ffici

ent o

f effi

cien

cy (C

oE)

(b)

Figure 4 Performance modeling of the Gumbel distribution fitting of wind-speed data for the study sites (a) Correlation coefficient (119877) and(b) Nash-Sutcliffe coefficient of efficiency (CoE)

32 Performance Model of the Distribution Fittings of Wind-Speed Data Plots of the performance model of the fittingsof wind-speed data in the three study sites by the Gum-bel distribution are presented in Figure 4 using evaluatedvalues of correlation coefficient 119877 shown in Figure 4(a)and of Nash-Sutcliffe coefficient of efficiency CoE shown inFigure 4(b) This showed that by the monthly considerationcorrelation coefficient and coefficient of efficiency (119877 CoE)of the Gumbel pdf fitting of Katsina wind-speed data rangedfrom 7890 6225 in November to 9023 8142 inMarch These bare indications that the modeling efficiencyof the wind-speed data in Katsina by the Gumbel pdfmodel ranged from ldquogoodrdquo efficiency model in Novemberto ldquoexcellentrdquo efficiency model in March as per the modelefficiency classification from [39] By that classification in[39] the Gumbel fitting of the rainy season wind-speed datain Katsina at 119877 CoE of 9175 8419 also indicated ldquoverygoodrdquo efficiency model However the modeling efficienciesof the Gumbel pdf fittings of the dry season and the all-yearwind-speed data in Katsina indicated ldquoexcellentrdquo efficiencymodel at the 119877 CoE of 9574 9166 for the dry season and9657 9327 for the all-year period

The Gumbel fitting of wind-speed data in Warri rangedfrom 119877 CoE of 7195 5177 indicating ldquogoodrdquo modelefficiency in October to 9065 8218 indicating ldquoverygoodrdquo model efficiency in December Also the dry seasonrainy season and the all-year wind speed in Warri werefitted by the Gumbel pdf at the respective 119877 CoE of 92488553 9061 8211 and 9417 8867 all of which areclassified to the ldquovery goodrdquo model efficiency The Gumbelfitting of Calabar wind-speed data ranged from 119877 CoE of6381 4072 in March to 8889 7901 in August Bythese the fitting model of March wind speed in Calabar isclassified to ldquofairrdquo model efficiency while that of August isclassified to ldquovery goodrdquo model efficiency In similar mannerthe dry season rainy season and all-year wind speed were

fitted at ldquovery goodrdquo model efficiency by the Gumbel pdf 119877CoE of 9218 8497 9386 8809 and 9460 8950respectively

Performance models of the Weibull fitting of wind-speeddata for the three study sites are presented in Figure 5where estimations of correlation coefficient 119877 are plottedin Figure 5(a) and estimations of Nash-Sutcliffe coefficientof efficiency CoE are plotted in Figure 5(b) These showedthat performance model of Weibull fitting of monthly wind-speed data ranged from (8707 7581) ldquovery goodrdquo inOctober to (9762 9531) ldquoexcellentrdquo in March at Katsinabut from (7056 4978) ldquofairrdquo in January to (83677000) ldquogoodrdquo in July at Warri

TheWeibull fitting performancemodel of monthly wind-speed data ranged from (7498 5621) ldquogoodrdquo in March to(9227 8513) ldquovery goodrdquo in April at Calabar By similarconsiderations performance models of the Weibull fittingof seasonal and all-year wind-speed data are classified asldquoexcellentrdquo at Katsina ldquogoodrdquo at Warri and ldquovery goodrdquo atCalabar

33 Mean Wind-Speed and Wind-Power Density Models forthe Study Sites The mean wind-speed and mean-powerdensity models for Katsina are presented in Figure 6 for themonthly seasonal and all-year analyses of wind-speed databy the probability distribution models

From the figure it could be observed that Katsina innorthern Nigeria was windier in the months constituting itsdry seasons with January being the windiest month thanin the months of its rainy season where September was theleast windy month Thus monthly mean wind speed andmean power density in the form (raw data Gumbel modeland Weibull model) ranged from (644 646 and 650)msand (15560 15712 and 15980)Wm2 in September to (10651068 and 1094)ms and (70315 70993 and 76192)Wm2in January The mean wind speed and power density for


000

020

040

060

080

100by

Wei

bull

pdf

All-

year

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason


Cor

relat

ion

coeffi

cien

t (R)

(a)

000

020

040

060

080

100

by W

eibu

ll pd

f

All-

year

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason


Coe

ffici

ent o

f effi

cien

cy (C

oE)

(b)

Figure 5 Performance modeling of the Weibull distribution fitting of wind-speed data for the study sites (a) Correlation coefficient (119877) and(b) Nash-Sutcliffe coefficient of efficiency (CoE)

000200400600800

10001200

Win

d sp

eed

(ms

)

Katsina

Raw dataGumbel modelWeibull model

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

(a)

Katsina

000

20000

40000

60000

80000


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

Pow

er d

ensit

y (W

m2)

(b)

Figure 6 Wind-speed and power density models for Katsina (a) mean wind-speed plots and (b) mean power density plots

the windier dry season were (917 917 and 920)ms and(44912 44973 and 45410)Wm2 for the rainy season were(761 762 and 764)ms and (25711 25775 and 25955)Wm2and for the all-yearmodel were (866 866 and 866)ms and(37776 37812 and 37846)Wm2

The probability distribution models of the mean wind-speed and mean power-density models for Warri are pre-sented in Figure 7 for the monthly seasonal and all-yearanalyzed wind-speed data

From the figure it could be deduced that the wind-speed model in Warri in southwestern Nigeria patterneddifferently especially in the seasonal consideration fromwhat is obtained in the Katsina model Unlike the wind-speed model in Katsina the rainy season was windier thanthe dry season in Warri and the windiest month was April

a month in the rainy season at Warri while the least windymonth was December a month in the dry season at WarriAt Warri monthly mean wind speed and power density alsoin the form (raw data Gumbel model and Weibull model)ranged from (318 319 and 330)ms and (1968 1990 and2205)Wm2 in December to (454 456 and 471)ms and(5732 5788 and 6400)Wm2 in AprilThemeanwind speedand power density for the windier rainy season were (402403 and 412)ms and (3991 3998 and 4270)Wm2 for thedry season were (360 360 and 372)ms and (2849 2857and 3141)Wm2 and for the all-year season were (386 386and 395)ms and (3513 3518 and 3764)Wm2

Mean wind-speed and mean power-density models forCalabar are presented in Figure 8 also for the monthly


000

100

200

300

400

500W

ind

spee

d (m

s)

Warri


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

(a)

Warri

0001000200030004000500060007000

Pow

er d

ensit

y (W

m2)


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

(b)

Figure 7 Wind-speed and power density models for Warri (a) mean wind-speed plots and (b) mean power density plots

000

100

200

300

400

500

Win

d sp

eed

(ms

)


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

Calabar

(a)

000

2000

4000

6000

Pow

er d

ensit

y (W

m2)


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

Calabar8000

(b)

Figure 8 Wind-speed and power density models for Calabar (a) mean wind-speed plots and (b) mean power density plots

seasonal and all-yearmodeling ofwind-speed data by the sta-tistical distribution fitting functions The figure also showedthat the rainy season in Calabar in southeastern Nigeria waswindier than the dry season thus finding similarity withwhatwas obtained at Warri in southwestern Nigeria This baressupports for the prepotency of windiness of the rainy seasonover the dry season in the tropical rain forest (equatorialmonsoon) climate predominant both inWarri and inCalabarNigeria However while the probability distribution fittingshad exhibited agreements thus far in Katsina and in Warriat identifying the windiest month the windiest month bythe raw data and Gumbel pdf reckonings differs from whatis identified by the Weibull pdf While the raw data andthe Gumbel pdf models identified May with mean windspeed of 488ms (raw data) or 490ms (Gumbel) as thewindiest month the month of June was identified as thewindiest month by theWeibull pdf at its modeled mean wind

speed of 496ms (Weibull) These months of May and Junewhich both fall within the rainy season at Calabar have meanpower densities of 7080Wm2 (May by raw data model) or7344Wm2 (May by theGumbel pdfmodel) and 7428Wm2(June by the Weibull pdf model) In spite of these all thestatistical models identified August a dry season month inCalabar the ldquoAugust breakrdquo as the least windy month withwind speed and power density of 397ms and 3806Wm2

(raw data) or 399ms and 3855Wm2 (Gumbel) or 403msand 3987Wm2 (Weibull) The mean wind speed and powerdensity for the windier rainy season were (449 449 and451)ms and (5509 5521 and 5575)Wm2 for the dryseason were (432 433 and 434)ms and (4919 4929 and4982)Wm2 and for the all-year season were (441 441 and442)ms and (5221 5227 and 5272)Wm2 in the form (rawdata Gumbel model and Weibull model)


Table 2 Characteristics of the wind turbine model BTPS6500 system

Characteristics Hub height ℎ(m)

Blade diameter(m)

V119888

(ms)V119877

(ms)V119865

(ms)119875119890119877

(W)Value 10 17 02 139 170 1500

Table 3 Annual and all-year simulations of electric-power output at various turbine hub heights in the study sites

Location Period ℎ = 10m ℎ = 30m ℎ = 50m ℎ = 70m ℎ = 90m h = 110m119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W)

Katsina

2006 59415 59516 89236 66821 116014 67111 142630 65117 169898 62203 198201 589362007 77020 63102 113289 65248 145087 62956 176174 59639 207595 56097 239830 526112008 45136 54215 74006 68738 101779 72266 130777 71384 161706 68467 194945 646342009 43431 51050 66290 63988 87275 68404 108456 69152 130428 67983 153482 657702010 35894 42931 51511 55542 65410 61967 79108 65284 93040 66670 107409 66782

All-year 52229 55958 77856 65469 100866 67501 123729 66753 147143 64705 171438 62028

Warri

2006 7284 9882 11844 16435 16463 23060 21457 30090 26940 37427 32985 447982007 11493 15177 17657 23851 23608 31968 29819 39827 36441 47206 43556 538362008 11920 14909 16911 21726 21511 27874 26143 33798 30934 39484 35947 448292009 13764 17108 19347 24669 24441 31305 29532 37493 34767 43223 40216 484142010 12799 16131 18315 23680 23412 30436 28556 36842 33886 42854 39473 48354

All-year 10796 13916 15966 21108 20860 27797 25889 34380 31181 40778 36802 46816

Calabar

2006 6552 9015 11008 15479 15627 22178 20709 29433 26369 37135 32689 449822007 8159 11095 13315 18513 18531 25965 24166 33751 30350 41666 37165 493442008 6664 9181 11224 15800 15957 22665 21166 30088 26973 37942 33460 458982009 6429 8862 10847 15277 15441 21948 20506 29196 26159 36915 32480 448042010 5481 7619 9461 13432 13678 19604 18392 26454 23715 33943 29727 41830

All-year 6172 8533 10494 14821 15015 21404 20022 28601 25630 36319 31922 44265

4 Simulations and Econometric Implicationsof Electric-Power Generation

41 Wind Turbine System and Electric-Power Output Simu-lations The foregoing results of modeled wind speed andpower density identified Katsina with wind-speed class thatcould be favourable for electricity generation from windturbine system application while Warri and Calabar wereidentifiable as low wind-speed sites [5 40] However thisstudy is not deliberating on the comparison of the study sitesbut on investigating how the available wind in each of the sitescould find usefulness for electricity generation in the remoteareas By this it is worth noting that usage of conventionalwind turbine could be highly cost intensive for off-gridconnections desired for the remote rural communities in thestudy sites including Katsina in spite of the higher windmodeled for that northern geopolitical region Based onthese considerations a low-cost wind turbine system withthe added advantage of low cut-in wind speed such that itwould be also applicable to the low wind-speed sites wasidealized for electricity generation from the wind-power inthe three study sites The Honeywell BTPS6500 wind turbineby WindTronics [5] having the characteristics presented inTable 2 finds suitability for this criterion

In spite of the identification of this low cut-in windturbine system to the study sites it is worth noting fromTable 2 that even the mean wind speed of the higher-class

windmodeled for Katsina still falls short of the 139ms ratedwind speed of the idealized wind turbine system That thisrated wind speed would be required for the delivery of therated power of the applied wind turbine system necessitatesinvestigating further hub heights ℎ for improving the windspeeds at the study sites towards the turbine rated wind speedand for improving wind-power output This was done byrequisite applications of (9) to (12) and values in Table 2 tothe Weibull modeled results and by these applications thepower simulation fromheights ℎ = 10m to 110m is presentedin Table 3 It should be noted that for this important wind-power simulation the Gumbel model could not be usedbecause of its intrinsic lack of shape parameter 120578 which isa required quantity for electric-power simulation from windin (9) and (10)

The power output simulations from the wind turbinesystem in Table 3 showed that the electric power119875

119890that could

be generated increasedwith turbine hub heightsℎ for each ofthe periods studied and in each of the study sites At Katsinain northernNigeria the electric-power output even exhibitedpotency of surpassing the rated power of the turbine systemat the turbine hub height ℎ = 110m at which 119875

119890= 1714

W compared to the turbine 119875119890119877

= 1500 W In spite of thesethe average power output 119875

119890ave that kept increasing withincreasing hub heights at Warri and at Calabar in all theperiods studied peaked at Katsina when hub height ℎ = 50mand kept decreasing thereafter in the years 2006 to 2008 and


05

101520253035404550

0 10 20 30 40 50 60 70 80 90 100 110 120

KatsinaWarriCalabar

Turbine hub height h (m)

Capa

city

fact

orC

f(

)

Figure 9 Capacity factor against turbine hub height for the all-yearsimulation in the study sites

the all-yearmodel Averaged power output119875119890ave also peaked

at Katsina in the year 2009 when hub height ℎ = 70m anddecreased thereafter thus leaving only the year 2010 as theonly year in which the average power output kept increasingwith hub heights of the wind turbine system at KatsinaThesepatterns of simulated power output are further highlighted bythe plots of capacity factor 119862

119891 against turbine hub height ℎ

for the all-year model of each of the study sites presented inFigure 9 Apart from the power output patterns it could alsobe deduced from the figure that the capacity factors of thelow wind-speed sites Warri and Calabar tend towards thoseof the higher wind-speed class Katsina as the turbine hubheight increases

42 Econometric Implications of Electric-Power GenerationFor the econometric modeling applications to the study sitesusing (14) the price 119910 for the selected BTPS6500 windturbine and its smart box invertercontroller system was setat 119910 = C4 20002 equiv 1932 46967 and the turbine lifetime wasset at 119905 = 20 years [35] Other assumed parameters necessaryfor the requisite applications of (14) for econometric analyseswere presented in Table 4This includes the special indicationthat a progressive increase of 5 was utilized for 119903

119862 the

rate of turbine price for civilstructural works and otherconnections at each hub height increment for catering foradditional materials that may be required for increasing thewind turbine height ℎ

Econometrics implications of the modeled wind-energypotential obtained from substituting values from Tables 2and 4 into (10) and (14) with other modeled results asrequired are presented in Table 5 for turbine hub heightsranging from 10m to 110m at each of the study sitesThese tabulated econometric implications further support theresults from electric-power simulations from the modeledwind speed by the continuous increase of the present valuecost PVC for all the study sites with increasing hub heightof the wind turbine system For Warri and Calabar annual

Table 4 Parameters employed for econometric analyses

Parameter 119903lowast

119862119903OMR 119894

dagger119903dagger

119868119903SC

Value () 20 25 625 118 10lowastIncreased progressively by 5 at each hub height incrementdaggerSourced from [20]

11287 11125 11336

14760

12587 11718

17614

13330

11819

00010002000300040005000600070008000

000005010015020025030035040

0 10 20 30 40 50 60 70 80 90 100 110 120 130Turbine hub height h (m)

KatsinaWarriCalabar

Elec

tric

gen

erat

ion

cost

per k

Wh

(C)

Elec

tric

gen

erat

ion

cost

per k

Wh

(1)

Figure 10 Costs of electricity generation per kWh in each of thestudy sites

power output and the total power output through the turbineservice life 119905 = 20 years were also increasing with increasinghub heights of the wind turbine system In contrast to thesethe annual power output and the total power output increasedas the turbine height increased from 10m to 50m at whichthe output power simulations peaked and started to decreasethereafter as the turbine hub height further increases Thesebare implications of the 50m as the optimal hub height of thewind turbine model for optimum electric-power generationat Katsina

From the foregoing tabulated values of present valuecost and total power output the modeled cost of generating1 kWh of electrical power at the various turbine heights wasevaluated using (15) and the results from these evaluations arepresented in Figure 10

This figure showed that the cost of generating 1 kWh ofelectricity at Katsina decreased with increasing hub heightfrom C00580 equiv 11287 when ℎ = 10m until ℎ = 50mwhere it was C00507 equiv 11125 Further increase in turbinehub height from this 50m culminated in increased costkWhof electricity generation which attained C00602 equiv 11336as the hub height increased to ℎ = 110m This furthersupports the considerations from the electric-power outputsimulations that the turbine hub height ℎ = 50m was theoptimum hub height for favorable generation of electricitypower also potent with the least costkWh electricity fromthe wind resource at Katsina northern Nigeria

In furtherance of this Figure 10 also showed that thecostkWh of electric-power generation fromWarri and fromCalabar which were indicated by the modeled wind speedand mean power density as low wind-speed sites decreasedwith increasing hub height of the wind turbine system


Table 5 Econometrics implications of electric-power generation from modeled wind at study sites

Hub heightℎ (m)

Katsina Warri Calabar

PVC(C)

Annualpower output119875119890aveyr(kWh)

Total poweroutput119875119890ave times 119905

(kWh)

PVC(C)



(kWh)

PVC(C)



(kWh)

10 550002 474468 9489360 550002 128258 2565152 550002 80191 160382130 570244 561230 11224601 570244 193354 3867087 570244 137533 275066950 590486 582898 11657970 590486 253415 5068299 590486 196856 393712970 610727 579170 11583397 610727 311944 6238883 610727 260911 521822390 630969 563127 11262544 630969 368258 7365169 630969 328677 6573531110 651210 540899 10817977 651210 420884 8417673 651210 397456 7949115

At Warri costkWh model for electricity generation contin-uously decreased with increasing turbine hub heights fromC02144 equiv 14760 at ℎ = 10m to C00774 equiv 11718 atℎ = 110m At Calabar costkWh modeled for generatingelectric power also decreased continuously with increasingturbine heights from C03429 equiv 17614 at ℎ = 10m toC00819 equiv 11819 at ℎ = 110m From these it could benoted that large discrepancies in electric-power generationcosts ensuing from well-pronounced differences in wind-speed class models still culminated in converging costkWhmodel of electric-power generation with requisite increase inturbine hub heights

5 Implications of the Modeled Wind-Energy Potential for Renewable RuralElectrification

Themodeled costs of electric-power generation11125kWhat Katsina11718kWh at Warri and11819kWh at Calabarbare potency of cheapaffordable electricity generation fromwind that could be used as off-grid solution for the electrifica-tion of the rural communities at the study sites This form ofelectricity generation from renewable wind-resource energywill not only constitute a sustainable form of cleangreenelectric power for the remote rural areas predominant in theenvirons of the study sites but will also bare potencies ofadded advantages some of which include

(i) amelioration of energy poverty in the populace ofthe rural communities by the readily available andaffordable electric power which could be generated atthe affordable costkWh of electricity modeled for thestudy sites

(ii) improvement of the socioeconomic well-being ofthe populace where the electric generation could beutilized for improving access to potable water andaccess to water for sanitation purposes for suchneeded water resource could now be pumped fromboreholes

(iii) improved commercial activities in the rural commu-nities where the available electricity power could

be employed for agricultural produce storagepreser-vation through electric refrigeration techniques aswell as utilization of the electricity for other smallscale commercial activities prevalent in the ruralcommunities for example tailoringfashion designshairdressing and saloon and cateringrefreshments aswell as relaxation centers

(iv) improvements in the living standards conditions andcomforts of the dwellers in the rural communitiesthrough such access to usage of electric fans electricwashing machines electric lightings at nights andradio and television powered from clean renew-ableaffordable energy which could stem rural-urbanmigration and reduce pressures on facilities even inthe urban centers

(v) potency of enabling environments for attractingindustries that could in turn improvewealth andwell-being in the rural communities through for instancejob availability and other corporate social responsi-bilities that could attend locating such industries inthe community such as improved access roads andtransportation systems and establishment of schoolsand health centers for their members of staff thatwould eventually serve the general community

Although the modeled wind-resource energy from this studyconstitutes affordable clean and sustainable electric powerfrom renewable source the initial cost could still be costintensive for the current rural populace in the environs ofthe study sites However many options could be suggestedfor surmounting this initial cost and ensuring rural elec-trification using the wind-energy resource in these ruralcommunities Some of the options that could be suggestedinclude

(i) joint cooperative actions by the rural dwellers thatcould take the form of contributions or launching forattracting donations for the procurementinstallationof the wind turbine system for the clean and renew-able wind-resource energy being proposed in thisstudy


(ii) formation or initiation of public-private partnershipwith governments or the grassroots arm of govern-ment available in the environs of the rural commu-nities whereby both the community and the govern-ment jointly fund the procurementinstallation of theproposed wind-resource energy system

(iii) partnership with other corporate financing bod-iesinstitutions that could produce the initial fundingfor the procurementinstallation of the wind turbinesystem and with whom payback of the fund couldbe designed say with mild interest such paybackcould take the form for example of electric billspayment say at the double of the costkWh modeledfor the electricity generation from wind-energy forthis would also be affordable by the dwellers ofthe rural community to such bodiesinstitutions thispayback could be sustained until the pay-off of theinitial fund and the requisite mild interest after whichthe renewable energy generating system could totallybelong to the community

(iv) provision of land properties by the rural communitiesfor industries for example agroprocessing and otherallied industries in exchange for corporate socialresponsibilities by such industries that would includeprovision of the proposed wind-resource turbine sys-tem for renewable electric energy generation Theseindustries themselves could use such cleanaffordableelectric power for running their operations as wellas for the electrification of the rural communities inwhich the industries are sited

6 Conclusions

Potentials of wind-energy resources from three geopoliticalzones in Nigeria Katsina in Northern Nigeria Warri insouthwestern Nigeria and Calabar in southeastern Nigeriawere assessed in this paper for investigating how the windenergy could be used for solving rural-electrification prob-lem Results showed that the wind speed by raw data andby Gumbel and Weibull models respectively ranged from644 646 and 650ms to 1065 1068 and 1094ms atKatsina 318 319 and 330ms to 454 456 and 471msat Warri and 397 399 and 403ms to 488 490 and496ms at Calabar These wind speed models and estimatedpower densities from the models identified Katsina as a highwind-speed class whileWarri and Calabar were identified bythe models as low wind-speed sites However econometricanalyses using wind turbine system at various hub heightsshowed that the cost per kWh of electricity at the threestudy sites tends to be converging at increasing hub heightof the wind turbine system These eventually culminated incheapaffordable and sustainable models of electricity powergeneration from the clean and renewable wind resourcein the environs of the study sites by which costkWh ofelectricity generation at Kaduna = C00507 at Warri =C00774 and at Calabar = C00819 Advantages that couldaccrue from such renewable energy generation from windand the suggestions for surmounting the initial cost-intensive

funding for procurementinstallation of the wind turbinesystem were detailed in the study

Conflict of Interests

The authors declare that there is no conflict of interests in thepublication of this paper

References

[1] United Nations Department of Economic and Social Affairsand Population Division Rural Population Development andthe Environment United Nations 2011 httpwwwunorg

[2] I A Adejumobi O I Adebisi and S A Oyejide ldquoDevelopingsmall hydropower potentials for rural electrificationrdquo Interna-tional Journal of Research and Reviews in Applied Sciences vol17 no 1 pp 105ndash110 2013

[3] N Ayara U Essia and P Ubi ldquoOverview of electric powerdevelopment gaps in Cross River State Nigeriardquo InternationalJournal of Management and Business Studies vol 3 no 7 pp101ndash109 2013

[4] C S Kaunda C Z Kimambo and T K Nielsen ldquoPotentialof small-scale hydropower for electricity generation in Sub-Saharan Africardquo ISRN Renewable Energy vol 2012 Article ID132606 15 pages 2012

[5] J O Okeniyi I F Moses and E T Okeniyi ldquoWind character-istics and energy potential assessment in Akure South WestNigeria econometrics and policy implicationsrdquo InternationalJournal of Ambient Energy 2013

[6] M Gokcek A Bayulken and S Bekdemir ldquoInvestigation ofwind characteristics and wind energy potential in KirklareliTurkeyrdquo Renewable Energy vol 32 no 10 pp 1739ndash1752 2007

[7] J T Afa ldquoProblems of rural electrification in Bayelsa StaterdquoAmerican Journal of Scientific and Industrial Research vol 4 no2 pp 214ndash220 2013

[8] J O Okeniyi E U Anwan and E T Okeniyi ldquoWaste character-isation and recoverable energy potential using waste generatedin a model community in Nigeriardquo Journal of EnvironmentalScience and Technology vol 5 no 4 pp 232ndash240 2012

[9] M S Agba ldquoEnergy poverty and the leadership question inNigeria an overview and implication for the futurerdquo Journal ofPublic Administration and Policy Research vol 3 no 2 pp 48ndash51 2011

[10] O O Ajayi R O Fagbenle J Katende S A Aasa and J OOkeniyi ldquoWind profile characteristics and turbine performanceanalysis in Kano north-western Nigeriardquo International Journalof Energy and Environmental Engineering vol 4 no 27 pp 1ndash152013

[11] O O Ajayi R O Fagbenle J Katende and J O Okeniyi ldquoAvail-ability of wind energy resource potential for power generationat Jos Nigeriardquo Frontiers in Energy vol 5 no 4 pp 376ndash3852011

[12] O S Ohunakin ldquoWind characteristics and wind energy poten-tial assessment in Uyo Nigeriardquo Journal of Engineering andApplied Sciences vol 6 no 2 pp 141ndash146 2011

[13] R O Fagbenle J Katende O O Ajayi and J O OkeniyildquoAssessment of wind energy potential of two sites in NorthmdashEast Nigeriardquo Renewable Energy vol 36 no 4 pp 1277ndash12832011

[14] O O Ajayi R O Fagbenle J Katende J O Okeniyi and O AOmotosho ldquoWind energy potential for power generation of a


local site in Gusau Nigeriardquo International Journal of Energy fora Clean Environment vol 11 no 1ndash4 pp 99ndash116 2010

[15] D A Fadare ldquoStatistical analysis of wind energy potential inIbadan Nigeria based o n weibull distribution functionrdquo ThePacific Journal of Science and Technology vol 9 no 1 pp 110ndash119 2008

[16] A D Asiegbu and G S Iwuoha ldquoStudies of wind resourcesin umudike South East Nigeria-an assessment of economicviabilityrdquo Journal of Engineering and Applied Sciences vol 2 no10 pp 1539ndash1541 2007

[17] G M Ngala B Alkali and M A Aji ldquoViability of windenergy as a power generation source inMaiduguri Borno stateNigeriardquo Renewable Energy vol 32 no 13 pp 2242ndash2246 2007

[18] H M Aliero and S S Ibrahim ldquoDoes access to finance reducepoverty Evidence from Katsina Staterdquo Mediterranean Journalof Social Sciences vol 3 no 2 pp 575ndash581 2012

[19] A N Celik ldquoOn the distributional parameters used in assess-ment of the suitability of wind speed probability densityfunctionsrdquo Energy Conversion and Management vol 45 no 11-12 pp 1735ndash1747 2004

[20] Central Bank of Nigeria ldquoCentral Bank of Nigeria annualreportmdash2011rdquo Tech Rep Central Bank of Nigeria AbujaNigeria 2011

[21] J O Okeniyi I J Ambrose S O Okpala et al ldquoProbability den-sity fittings of corrosion test-data Implications on C

6H15NO3

effectiveness on concrete steel-rebar corrosionrdquo SadhanamdashAcademy Proceedings in Engineering Science vol 39 no 3 pp731ndash764 2014

[22] J O Okeniyi I O Oladele I J Ambrose et al ldquoAnalysisof inhibition of concrete steel-rebar corrosion by Na

2Cr2O7

concentrations implications for conflicting reports on inhibitoreffectivenessrdquo Journal of Central South University vol 20 no 12pp 3697ndash3714 2013

[23] S Kotz and S Nadarajah Extreme Value Distributions Theoryand Applications Imperial College Press London UK 2000

[24] Y Ditkovich and A Kuperman ldquoComparison of three methodsforwind turbine capacity factor estimationrdquoTheScientificWorldJournal vol 2014 Article ID 805238 7 pages 2014

[25] R Belu andD Koracin ldquoStatistical and spectral analysis of windcharacteristics relevant to wind energy assessment using towermeasurements in complex terrainrdquo Journal of Wind Energy vol2013 Article ID 739162 12 pages 2013

[26] A Ucar and F Balo ldquoEvaluation of wind energy potential andelectricity generation at six locations in TurkeyrdquoApplied Energyvol 86 no 10 pp 1864ndash1872 2009

[27] R-D Reiss and M Thomas Statistical Analysis of ExtremeValues Birkhauser Basel Switzerland 3rd edition 2007

[28] P Kvam and J-C Lu ldquoStatistical reliability with applicationsrdquo inSpringer Handbook of Engineering Statistics H Pham Ed pp49ndash61 Springer London UK 2006

[29] J O Okeniyi C A Loto and A P I Popoola ldquoElectrochemicalperformance of anthocleista djalonensis on steel-reinforcementcorrosion in concrete immersed in salinemarine simulating-environmentrdquo Transactions of the Indian Institute of Metals vol67 no 6 pp 959ndash969 2014

[30] J O Okeniyi O A Omotosho O O Ajayi and C A LotoldquoEffect of potassium-chromate and sodium-nitrite on concretesteel-rebar degradation in sulphate and salinemediardquoConstruc-tion and Building Materials vol 50 pp 448ndash456 2014

[31] A Keyhani M Ghasemi-Varnamkhasti M Khanali and RAbbaszadeh ldquoAn assessment of wind energy potential as a

power generation source in the capital of Iran Tehranrdquo Energyvol 35 no 1 pp 188ndash201 2010

[32] J O Okeniyi O M Omoniyi S O Okpala C A Lotoand A P I Popoola ldquoEffect of ethylenediaminetetraaceticdisodium dihydrate and sodium nitrite admixtures on steel-rebar corrosion in concreterdquo European Journal of Environmentaland Civil Engineering vol 17 no 5 pp 398ndash416 2013

[33] J O Okeniyi I J Ambrose I O Oladele C A Loto and P A IPopoola ldquoElectrochemical performance of sodium dichromatepartial replacement models by triethanolamine admixtures onsteel-rebar corrosion in concretesrdquo International Journal ofElectrochemical Science vol 8 no 8 pp 10758ndash10771 2013

[34] T Mandal and V Jothiprakash ldquoShort-term rainfall predictionusing ANN and MT techniquesrdquo ISH Journal of HydraulicEngineering vol 18 no 1 pp 20ndash26 2012

[35] A S A Shata and R Hanitsch ldquoApplications of electricitygeneration on the western coast of the Mediterranean Sea inEgyptrdquo International Journal of Ambient Energy vol 29 no 1pp 35ndash44 2008

[36] S Dagnall A Tipping and M Janes ldquoUK onshore windcapacity factors 1998ndash2005rdquo International Journal of AmbientEnergy vol 28 no 2 pp 83ndash88 2007

[37] A H Al-Badi ldquoWind power potential in Omanrdquo InternationalJournal of Sustainable Energy vol 30 no 2 pp 110ndash118 2011

[38] H S Bagiorgas M N Assimakopoulos DTheoharopoulos DMatthopoulos and G K Mihalakakou ldquoElectricity generationusing wind energy conversion systems in the area of WesternGreecerdquo Energy Conversion and Management vol 48 no 5 pp1640ndash1655 2007

[39] R Coffey S Dorai-Raj V OrsquoFlaherty M Cormican and ECummins ldquoModeling of pathogen indicator organisms in asmall-scale agricultural catchment using SWATrdquo Human andEcological Risk Assessment vol 19 no 1 pp 232ndash253 2013

[40] G P Kyle S J Smith M A Wise J P Lurz and D BarrieLong-TermModeling ofWind Energy in the United States PacificNorthwest National Laboratory 2007

TribologyAdvances in

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Power ElectronicsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

CombustionJournal of


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Renewable Energy

Submit your manuscripts athttpwwwhindawicom


StructuresJournal of


RotatingMachinery


EnergyJournal of


Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

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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


(a) (b)

(c)

Figure 1 The study sites and environs in Nigeria (a) Katsina Katsina State (b) Warri Delta State (c) Calabar Cross River State

Lack of electricity leads to lack of water pumping facilitythat could both ensure good drinking water and make wateravailable for improved sanitation facility which is basicallya water-dependent hygienic issue

It had been identified in studies [2 4] that among thetwo options that could be employed for supplying electricityto the rural communities in developing countries includingNigeria the off-grid alternatives exhibited preference to theon-grid connection The off-grid method of rural electrifica-tion becomes imperative due to the insufficiency of electricitysupply available from the national grid and the remotenessof the rural communities from the grid which leads to pro-hibitive cost of electric-power installationsmaintenance andoperations [2 4 7] For the off-grid electrification option forthe rural areas in the country adoption of renewable energyportends potential of suitability for the remote communitiesthat could be sustainable with the additional advantage ofreducing unwholesome environmental effects from the use offossil fuel sources

A viable renewable energy option that could find appli-cability for most regions of remote rural communities inthe country includes the use of wind energy for generatingelectricity for the electrification of the rural areas Howeveruseful exploitation of energy from wind sources for a regionrequires assessment of the wind-energy potential in theenvirons of such region for requisite knowledge of energyavailability as well as viability of installing wind-energysystems in such locations for electric-energy generationWhile many studies [5 10ndash17] have deliberated on wind char-acteristics and energy potential from different parts of thecountry none have undertaken such study for establishinghow the wind energy could have been applicable as a solutionfor rural-electrification problems in the country Thereforethe objective of this study was to investigate wind-energy

potential of selected sites from three geopolitical zones inNigeria for the purpose of gaining insights into the usage ofwind-energy systems for sustainable rural electrification inthe selected parts of Nigeria For this selected sites employedinclude Katsina from Katsina State in the northern partWarri fromDelta State in the southwestern part and Calabarfrom Cross River State in the southeastern part of thecountry Aerial images showing topologies of these studysites and environs are shown in Figure 1 Motivation forthese study sites was from the consideration that environsof these locations are characteristically identified with ruralcommunities characterised with nonavailability of electricpower due to their nonconnection to the national grid ofelectricity supply in the country [3 7 18] Also Katsinarepresents a high altitude location compared to Warri whichis a low altitude site while the altitude of Calabar lies betweenthat of Katsina and Warri These make the study sites ofinterests for investigating how the different modes of wind-energy potential in the sites could be assessed for solvingrural-electrification problems in the environs of the studysites and in other similar regions of the world

2 Materials and Methods

21 Description of Selected Sites and Wind-Speed Data Dailywind-speed data that were measured from January 2006to December 2010 were obtained from Nigerian Meteoro-logical Agency (NIMET) Abuja Nigeria for the selectedsites of Katsina from northern Warri from southwesternand Calabar from the southeastern parts of Nigeria Inthese locations themeteorological stationswere respectivelylocated as given in Table 1 which also includes the air density120588 (kgm3) at each of the stations The wind-speed data




Longitude(∘E)










120573 (2)




V119898119866equiv 120583119866= 120582 minus 120573Γ

1015840(1) (3)



119865 (119909) = 1 minus expminus(119909120573)

120578

(4)






V119898119882

equiv 120583119882= 120573Γ(1 +

1

120578) (6)



119877 =sum119899

119894=1(119900119894minus 119900) (119901

119894minus 119901)

radicsum119899


119873

119894=1(119901119894minus 119901)2


119894=1(119900119894minus 119901119894)2

radicsum119899

119894=1(119900119894minus 119900)2

(7)



119875119898=1

2120588V3119898pdf (8)


electric power 119875119890119877


119875119890=

0 V lt V119862

119875119890119877(V120578 minus V120578

119862

V120578119877minus V120578119862

) V119862le V le V

119877

119875119890119877

V119877le V le V

119865

0 V gt V119865

(9)







119875119890ave = 119875119890119877 (

119890minus(V119862120573)

120578

minus 119890minus(V119877120573)

120578


120578

) (10)






0= 10m to the hub


120573 (ℎ) = 1205730(ℎ

10)

120576

120578 (ℎ) =1205780

[1 minus 0088 ln (ℎ10)]

(11)


120576 =[037 minus 0088 ln120573

0]

[1 minus 0088 ln (ℎ10)] (12)


119862119891=119875119890ave

119875119890119877

(13)


PVC = 119910 (1 + 119903119862) +119910

119905119903OMR [

1 + 119894


1 + 119894

1 + 119903119868

)

119905

]


1 + 119903119868

)

119905

(14)





Cost(per kWh) =

PVC119875119890ave times 119905

(15)






000

100

200

300

400

500

600

700

800

900

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year


Gum

bel l

ocat

ion

para

met

er120582

(ms

)

(a)Ja

nFe

bM

ar

Apr

May

Ju

n Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year


000

100

200

300

400

500

600

Gum

bel s

cale

par

amet

er120573

(ms

)

(b)


200

400

600

800

1000

1200

1400

Wei

bull

scal

e par

amet

er120573

(ms

)

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year


(a)

150

170

190

210

230

250

270

290

310

330

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year


Wei

bull

shap

e par

amet

er120578

(b)



000

020

040

060

080

100

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year


Cor

relat

ion

coeffi

cien

t (R)

by G

umbe

l pdf

(a)

000

020

040

060

080

100

All-

year

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason


by G

umbe

l pdf

Coe

ffici

ent o

f effi

cien

cy (C

oE)

(b)










000

020

040

060

080

100by

Wei

bull

pdf

All-

year

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason


Cor

relat

ion

coeffi

cien

t (R)

(a)

000

020

040

060

080

100

by W

eibu

ll pd

f

All-

year

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason


Coe

ffici

ent o

f effi

cien

cy (C

oE)

(b)


000200400600800

10001200

Win

d sp

eed

(ms

)

Katsina


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

(a)

Katsina

000

20000

40000

60000

80000


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

Pow

er d

ensit

y (W

m2)

(b)








000

100

200

300

400

500W

ind

spee

d (m

s)

Warri


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

(a)

Warri

0001000200030004000500060007000

Pow

er d

ensit

y (W

m2)


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

(b)


000

100

200

300

400

500

Win

d sp

eed

(ms

)


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

Calabar

(a)

000

2000

4000

6000

Pow

er d

ensit

y (W

m2)


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

Calabar8000

(b)








Blade diameter(m)

V119888

(ms)V119877

(ms)V119865

(ms)119875119890119877

(W)Value 10 17 02 139 170 1500



119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W)

Katsina

2006 59415 59516 89236 66821 116014 67111 142630 65117 169898 62203 198201 589362007 77020 63102 113289 65248 145087 62956 176174 59639 207595 56097 239830 526112008 45136 54215 74006 68738 101779 72266 130777 71384 161706 68467 194945 646342009 43431 51050 66290 63988 87275 68404 108456 69152 130428 67983 153482 657702010 35894 42931 51511 55542 65410 61967 79108 65284 93040 66670 107409 66782

All-year 52229 55958 77856 65469 100866 67501 123729 66753 147143 64705 171438 62028

Warri

2006 7284 9882 11844 16435 16463 23060 21457 30090 26940 37427 32985 447982007 11493 15177 17657 23851 23608 31968 29819 39827 36441 47206 43556 538362008 11920 14909 16911 21726 21511 27874 26143 33798 30934 39484 35947 448292009 13764 17108 19347 24669 24441 31305 29532 37493 34767 43223 40216 484142010 12799 16131 18315 23680 23412 30436 28556 36842 33886 42854 39473 48354

All-year 10796 13916 15966 21108 20860 27797 25889 34380 31181 40778 36802 46816

Calabar

2006 6552 9015 11008 15479 15627 22178 20709 29433 26369 37135 32689 449822007 8159 11095 13315 18513 18531 25965 24166 33751 30350 41666 37165 493442008 6664 9181 11224 15800 15957 22665 21166 30088 26973 37942 33460 458982009 6429 8862 10847 15277 15441 21948 20506 29196 26159 36915 32480 448042010 5481 7619 9461 13432 13678 19604 18392 26454 23715 33943 29727 41830

All-year 6172 8533 10494 14821 15015 21404 20022 28601 25630 36319 31922 44265






119890that could


119890= 1714





05

101520253035404550

0 10 20 30 40 50 60 70 80 90 100 110 120

KatsinaWarriCalabar


Capa

city

fact

orC

f(

)







119862 the





119862119903OMR 119894

dagger119903dagger

119868119903SC


11287 11125 11336

14760

12587 11718

17614

13330

11819

00010002000300040005000600070008000

000005010015020025030035040


KatsinaWarriCalabar

Elec

tric

gen

erat

ion

cost

per k

Wh

(C)

Elec

tric

gen

erat

ion

cost

per k

Wh

(1)








Hub heightℎ (m)


PVC(C)



(kWh)

PVC(C)



(kWh)

PVC(C)



(kWh)

10 550002 474468 9489360 550002 128258 2565152 550002 80191 160382130 570244 561230 11224601 570244 193354 3867087 570244 137533 275066950 590486 582898 11657970 590486 253415 5068299 590486 196856 393712970 610727 579170 11583397 610727 311944 6238883 610727 260911 521822390 630969 563127 11262544 630969 368258 7365169 630969 328677 6573531110 651210 540899 10817977 651210 420884 8417673 651210 397456 7949115
















6 Conclusions





References
























6H15NO3



2Cr2O7

























FuelsJournal of







Advances in



Journal of


Renewable Energy





RotatingMachinery


EnergyJournal of




















Longitude(∘E)










120573 (2)




V119898119866equiv 120583119866= 120582 minus 120573Γ

1015840(1) (3)



119865 (119909) = 1 minus expminus(119909120573)

120578

(4)






V119898119882

equiv 120583119882= 120573Γ(1 +

1

120578) (6)



119877 =sum119899

119894=1(119900119894minus 119900) (119901

119894minus 119901)

radicsum119899


119873

119894=1(119901119894minus 119901)2


119894=1(119900119894minus 119901119894)2

radicsum119899

119894=1(119900119894minus 119900)2

(7)



119875119898=1

2120588V3119898pdf (8)


electric power 119875119890119877


119875119890=

0 V lt V119862

119875119890119877(V120578 minus V120578

119862

V120578119877minus V120578119862

) V119862le V le V

119877

119875119890119877

V119877le V le V

119865

0 V gt V119865

(9)







119875119890ave = 119875119890119877 (

119890minus(V119862120573)

120578

minus 119890minus(V119877120573)

120578


120578

) (10)






0= 10m to the hub


120573 (ℎ) = 1205730(ℎ

10)

120576

120578 (ℎ) =1205780

[1 minus 0088 ln (ℎ10)]

(11)


120576 =[037 minus 0088 ln120573

0]

[1 minus 0088 ln (ℎ10)] (12)


119862119891=119875119890ave

119875119890119877

(13)


PVC = 119910 (1 + 119903119862) +119910

119905119903OMR [

1 + 119894


1 + 119894

1 + 119903119868

)

119905

]


1 + 119903119868

)

119905

(14)





Cost(per kWh) =

PVC119875119890ave times 119905

(15)






000

100

200

300

400

500

600

700

800

900

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year


Gum

bel l

ocat

ion

para

met

er120582

(ms

)

(a)Ja

nFe

bM

ar

Apr

May

Ju

n Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year


000

100

200

300

400

500

600

Gum

bel s

cale

par

amet

er120573

(ms

)

(b)


200

400

600

800

1000

1200

1400

Wei

bull

scal

e par

amet

er120573

(ms

)

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year


(a)

150

170

190

210

230

250

270

290

310

330

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year


Wei

bull

shap

e par

amet

er120578

(b)



000

020

040

060

080

100

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year


Cor

relat

ion

coeffi

cien

t (R)

by G

umbe

l pdf

(a)

000

020

040

060

080

100

All-

year

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason


by G

umbe

l pdf

Coe

ffici

ent o

f effi

cien

cy (C

oE)

(b)










000

020

040

060

080

100by

Wei

bull

pdf

All-

year

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason


Cor

relat

ion

coeffi

cien

t (R)

(a)

000

020

040

060

080

100

by W

eibu

ll pd

f

All-

year

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason


Coe

ffici

ent o

f effi

cien

cy (C

oE)

(b)


000200400600800

10001200

Win

d sp

eed

(ms

)

Katsina


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

(a)

Katsina

000

20000

40000

60000

80000


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

Pow

er d

ensit

y (W

m2)

(b)








000

100

200

300

400

500W

ind

spee

d (m

s)

Warri


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

(a)

Warri

0001000200030004000500060007000

Pow

er d

ensit

y (W

m2)


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

(b)


000

100

200

300

400

500

Win

d sp

eed

(ms

)


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

Calabar

(a)

000

2000

4000

6000

Pow

er d

ensit

y (W

m2)


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

Calabar8000

(b)








Blade diameter(m)

V119888

(ms)V119877

(ms)V119865

(ms)119875119890119877

(W)Value 10 17 02 139 170 1500



119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W)

Katsina

2006 59415 59516 89236 66821 116014 67111 142630 65117 169898 62203 198201 589362007 77020 63102 113289 65248 145087 62956 176174 59639 207595 56097 239830 526112008 45136 54215 74006 68738 101779 72266 130777 71384 161706 68467 194945 646342009 43431 51050 66290 63988 87275 68404 108456 69152 130428 67983 153482 657702010 35894 42931 51511 55542 65410 61967 79108 65284 93040 66670 107409 66782

All-year 52229 55958 77856 65469 100866 67501 123729 66753 147143 64705 171438 62028

Warri

2006 7284 9882 11844 16435 16463 23060 21457 30090 26940 37427 32985 447982007 11493 15177 17657 23851 23608 31968 29819 39827 36441 47206 43556 538362008 11920 14909 16911 21726 21511 27874 26143 33798 30934 39484 35947 448292009 13764 17108 19347 24669 24441 31305 29532 37493 34767 43223 40216 484142010 12799 16131 18315 23680 23412 30436 28556 36842 33886 42854 39473 48354

All-year 10796 13916 15966 21108 20860 27797 25889 34380 31181 40778 36802 46816

Calabar

2006 6552 9015 11008 15479 15627 22178 20709 29433 26369 37135 32689 449822007 8159 11095 13315 18513 18531 25965 24166 33751 30350 41666 37165 493442008 6664 9181 11224 15800 15957 22665 21166 30088 26973 37942 33460 458982009 6429 8862 10847 15277 15441 21948 20506 29196 26159 36915 32480 448042010 5481 7619 9461 13432 13678 19604 18392 26454 23715 33943 29727 41830

All-year 6172 8533 10494 14821 15015 21404 20022 28601 25630 36319 31922 44265






119890that could


119890= 1714





05

101520253035404550

0 10 20 30 40 50 60 70 80 90 100 110 120

KatsinaWarriCalabar


Capa

city

fact

orC

f(

)







119862 the





119862119903OMR 119894

dagger119903dagger

119868119903SC


11287 11125 11336

14760

12587 11718

17614

13330

11819

00010002000300040005000600070008000

000005010015020025030035040


KatsinaWarriCalabar

Elec

tric

gen

erat

ion

cost

per k

Wh

(C)

Elec

tric

gen

erat

ion

cost

per k

Wh

(1)








Hub heightℎ (m)


PVC(C)



(kWh)

PVC(C)



(kWh)

PVC(C)



(kWh)

10 550002 474468 9489360 550002 128258 2565152 550002 80191 160382130 570244 561230 11224601 570244 193354 3867087 570244 137533 275066950 590486 582898 11657970 590486 253415 5068299 590486 196856 393712970 610727 579170 11583397 610727 311944 6238883 610727 260911 521822390 630969 563127 11262544 630969 368258 7365169 630969 328677 6573531110 651210 540899 10817977 651210 420884 8417673 651210 397456 7949115
















6 Conclusions





References
























6H15NO3



2Cr2O7

























FuelsJournal of







Advances in



Journal of


Renewable Energy





RotatingMachinery


EnergyJournal of



















119875119890ave = 119875119890119877 (

119890minus(V119862120573)

120578

minus 119890minus(V119877120573)

120578


120578

) (10)






0= 10m to the hub


120573 (ℎ) = 1205730(ℎ

10)

120576

120578 (ℎ) =1205780

[1 minus 0088 ln (ℎ10)]

(11)


120576 =[037 minus 0088 ln120573

0]

[1 minus 0088 ln (ℎ10)] (12)


119862119891=119875119890ave

119875119890119877

(13)


PVC = 119910 (1 + 119903119862) +119910

119905119903OMR [

1 + 119894


1 + 119894

1 + 119903119868

)

119905

]


1 + 119903119868

)

119905

(14)





Cost(per kWh) =

PVC119875119890ave times 119905

(15)






000

100

200

300

400

500

600

700

800

900

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year


Gum

bel l

ocat

ion

para

met

er120582

(ms

)

(a)Ja

nFe

bM

ar

Apr

May

Ju

n Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year


000

100

200

300

400

500

600

Gum

bel s

cale

par

amet

er120573

(ms

)

(b)


200

400

600

800

1000

1200

1400

Wei

bull

scal

e par

amet

er120573

(ms

)

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year


(a)

150

170

190

210

230

250

270

290

310

330

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year


Wei

bull

shap

e par

amet

er120578

(b)



000

020

040

060

080

100

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year


Cor

relat

ion

coeffi

cien

t (R)

by G

umbe

l pdf

(a)

000

020

040

060

080

100

All-

year

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason


by G

umbe

l pdf

Coe

ffici

ent o

f effi

cien

cy (C

oE)

(b)










000

020

040

060

080

100by

Wei

bull

pdf

All-

year

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason


Cor

relat

ion

coeffi

cien

t (R)

(a)

000

020

040

060

080

100

by W

eibu

ll pd

f

All-

year

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason


Coe

ffici

ent o

f effi

cien

cy (C

oE)

(b)


000200400600800

10001200

Win

d sp

eed

(ms

)

Katsina


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

(a)

Katsina

000

20000

40000

60000

80000


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

Pow

er d

ensit

y (W

m2)

(b)








000

100

200

300

400

500W

ind

spee

d (m

s)

Warri


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

(a)

Warri

0001000200030004000500060007000

Pow

er d

ensit

y (W

m2)


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

(b)


000

100

200

300

400

500

Win

d sp

eed

(ms

)


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

Calabar

(a)

000

2000

4000

6000

Pow

er d

ensit

y (W

m2)


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

Calabar8000

(b)








Blade diameter(m)

V119888

(ms)V119877

(ms)V119865

(ms)119875119890119877

(W)Value 10 17 02 139 170 1500



119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W)

Katsina

2006 59415 59516 89236 66821 116014 67111 142630 65117 169898 62203 198201 589362007 77020 63102 113289 65248 145087 62956 176174 59639 207595 56097 239830 526112008 45136 54215 74006 68738 101779 72266 130777 71384 161706 68467 194945 646342009 43431 51050 66290 63988 87275 68404 108456 69152 130428 67983 153482 657702010 35894 42931 51511 55542 65410 61967 79108 65284 93040 66670 107409 66782

All-year 52229 55958 77856 65469 100866 67501 123729 66753 147143 64705 171438 62028

Warri

2006 7284 9882 11844 16435 16463 23060 21457 30090 26940 37427 32985 447982007 11493 15177 17657 23851 23608 31968 29819 39827 36441 47206 43556 538362008 11920 14909 16911 21726 21511 27874 26143 33798 30934 39484 35947 448292009 13764 17108 19347 24669 24441 31305 29532 37493 34767 43223 40216 484142010 12799 16131 18315 23680 23412 30436 28556 36842 33886 42854 39473 48354

All-year 10796 13916 15966 21108 20860 27797 25889 34380 31181 40778 36802 46816

Calabar

2006 6552 9015 11008 15479 15627 22178 20709 29433 26369 37135 32689 449822007 8159 11095 13315 18513 18531 25965 24166 33751 30350 41666 37165 493442008 6664 9181 11224 15800 15957 22665 21166 30088 26973 37942 33460 458982009 6429 8862 10847 15277 15441 21948 20506 29196 26159 36915 32480 448042010 5481 7619 9461 13432 13678 19604 18392 26454 23715 33943 29727 41830

All-year 6172 8533 10494 14821 15015 21404 20022 28601 25630 36319 31922 44265






119890that could


119890= 1714





05

101520253035404550

0 10 20 30 40 50 60 70 80 90 100 110 120

KatsinaWarriCalabar


Capa

city

fact

orC

f(

)







119862 the





119862119903OMR 119894

dagger119903dagger

119868119903SC


11287 11125 11336

14760

12587 11718

17614

13330

11819

00010002000300040005000600070008000

000005010015020025030035040


KatsinaWarriCalabar

Elec

tric

gen

erat

ion

cost

per k

Wh

(C)

Elec

tric

gen

erat

ion

cost

per k

Wh

(1)








Hub heightℎ (m)


PVC(C)



(kWh)

PVC(C)



(kWh)

PVC(C)



(kWh)

10 550002 474468 9489360 550002 128258 2565152 550002 80191 160382130 570244 561230 11224601 570244 193354 3867087 570244 137533 275066950 590486 582898 11657970 590486 253415 5068299 590486 196856 393712970 610727 579170 11583397 610727 311944 6238883 610727 260911 521822390 630969 563127 11262544 630969 368258 7365169 630969 328677 6573531110 651210 540899 10817977 651210 420884 8417673 651210 397456 7949115
















6 Conclusions





References
























6H15NO3



2Cr2O7

























FuelsJournal of







Advances in



Journal of


Renewable Energy





RotatingMachinery


EnergyJournal of


















000

100

200

300

400

500

600

700

800

900

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year


Gum

bel l

ocat

ion

para

met

er120582

(ms

)

(a)Ja

nFe

bM

ar

Apr

May

Ju

n Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year


000

100

200

300

400

500

600

Gum

bel s

cale

par

amet

er120573

(ms

)

(b)


200

400

600

800

1000

1200

1400

Wei

bull

scal

e par

amet

er120573

(ms

)

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year


(a)

150

170

190

210

230

250

270

290

310

330

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year


Wei

bull

shap

e par

amet

er120578

(b)



000

020

040

060

080

100

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year


Cor

relat

ion

coeffi

cien

t (R)

by G

umbe

l pdf

(a)

000

020

040

060

080

100

All-

year

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason


by G

umbe

l pdf

Coe

ffici

ent o

f effi

cien

cy (C

oE)

(b)










000

020

040

060

080

100by

Wei

bull

pdf

All-

year

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason


Cor

relat

ion

coeffi

cien

t (R)

(a)

000

020

040

060

080

100

by W

eibu

ll pd

f

All-

year

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason


Coe

ffici

ent o

f effi

cien

cy (C

oE)

(b)


000200400600800

10001200

Win

d sp

eed

(ms

)

Katsina


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

(a)

Katsina

000

20000

40000

60000

80000


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

Pow

er d

ensit

y (W

m2)

(b)








000

100

200

300

400

500W

ind

spee

d (m

s)

Warri


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

(a)

Warri

0001000200030004000500060007000

Pow

er d

ensit

y (W

m2)


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

(b)


000

100

200

300

400

500

Win

d sp

eed

(ms

)


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

Calabar

(a)

000

2000

4000

6000

Pow

er d

ensit

y (W

m2)


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

Calabar8000

(b)








Blade diameter(m)

V119888

(ms)V119877

(ms)V119865

(ms)119875119890119877

(W)Value 10 17 02 139 170 1500



119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W)

Katsina

2006 59415 59516 89236 66821 116014 67111 142630 65117 169898 62203 198201 589362007 77020 63102 113289 65248 145087 62956 176174 59639 207595 56097 239830 526112008 45136 54215 74006 68738 101779 72266 130777 71384 161706 68467 194945 646342009 43431 51050 66290 63988 87275 68404 108456 69152 130428 67983 153482 657702010 35894 42931 51511 55542 65410 61967 79108 65284 93040 66670 107409 66782

All-year 52229 55958 77856 65469 100866 67501 123729 66753 147143 64705 171438 62028

Warri

2006 7284 9882 11844 16435 16463 23060 21457 30090 26940 37427 32985 447982007 11493 15177 17657 23851 23608 31968 29819 39827 36441 47206 43556 538362008 11920 14909 16911 21726 21511 27874 26143 33798 30934 39484 35947 448292009 13764 17108 19347 24669 24441 31305 29532 37493 34767 43223 40216 484142010 12799 16131 18315 23680 23412 30436 28556 36842 33886 42854 39473 48354

All-year 10796 13916 15966 21108 20860 27797 25889 34380 31181 40778 36802 46816

Calabar

2006 6552 9015 11008 15479 15627 22178 20709 29433 26369 37135 32689 449822007 8159 11095 13315 18513 18531 25965 24166 33751 30350 41666 37165 493442008 6664 9181 11224 15800 15957 22665 21166 30088 26973 37942 33460 458982009 6429 8862 10847 15277 15441 21948 20506 29196 26159 36915 32480 448042010 5481 7619 9461 13432 13678 19604 18392 26454 23715 33943 29727 41830

All-year 6172 8533 10494 14821 15015 21404 20022 28601 25630 36319 31922 44265






119890that could


119890= 1714





05

101520253035404550

0 10 20 30 40 50 60 70 80 90 100 110 120

KatsinaWarriCalabar


Capa

city

fact

orC

f(

)







119862 the





119862119903OMR 119894

dagger119903dagger

119868119903SC


11287 11125 11336

14760

12587 11718

17614

13330

11819

00010002000300040005000600070008000

000005010015020025030035040


KatsinaWarriCalabar

Elec

tric

gen

erat

ion

cost

per k

Wh

(C)

Elec

tric

gen

erat

ion

cost

per k

Wh

(1)








Hub heightℎ (m)


PVC(C)



(kWh)

PVC(C)



(kWh)

PVC(C)



(kWh)

10 550002 474468 9489360 550002 128258 2565152 550002 80191 160382130 570244 561230 11224601 570244 193354 3867087 570244 137533 275066950 590486 582898 11657970 590486 253415 5068299 590486 196856 393712970 610727 579170 11583397 610727 311944 6238883 610727 260911 521822390 630969 563127 11262544 630969 368258 7365169 630969 328677 6573531110 651210 540899 10817977 651210 420884 8417673 651210 397456 7949115
















6 Conclusions





References
























6H15NO3



2Cr2O7

























FuelsJournal of







Advances in



Journal of


Renewable Energy





RotatingMachinery


EnergyJournal of


















000

020

040

060

080

100

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year


Cor

relat

ion

coeffi

cien

t (R)

by G

umbe

l pdf

(a)

000

020

040

060

080

100

All-

year

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason


by G

umbe

l pdf

Coe

ffici

ent o

f effi

cien

cy (C

oE)

(b)










000

020

040

060

080

100by

Wei

bull

pdf

All-

year

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason


Cor

relat

ion

coeffi

cien

t (R)

(a)

000

020

040

060

080

100

by W

eibu

ll pd

f

All-

year

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason


Coe

ffici

ent o

f effi

cien

cy (C

oE)

(b)


000200400600800

10001200

Win

d sp

eed

(ms

)

Katsina


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

(a)

Katsina

000

20000

40000

60000

80000


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

Pow

er d

ensit

y (W

m2)

(b)








000

100

200

300

400

500W

ind

spee

d (m

s)

Warri


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

(a)

Warri

0001000200030004000500060007000

Pow

er d

ensit

y (W

m2)


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

(b)


000

100

200

300

400

500

Win

d sp

eed

(ms

)


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

Calabar

(a)

000

2000

4000

6000

Pow

er d

ensit

y (W

m2)


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

Calabar8000

(b)








Blade diameter(m)

V119888

(ms)V119877

(ms)V119865

(ms)119875119890119877

(W)Value 10 17 02 139 170 1500



119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W)

Katsina

2006 59415 59516 89236 66821 116014 67111 142630 65117 169898 62203 198201 589362007 77020 63102 113289 65248 145087 62956 176174 59639 207595 56097 239830 526112008 45136 54215 74006 68738 101779 72266 130777 71384 161706 68467 194945 646342009 43431 51050 66290 63988 87275 68404 108456 69152 130428 67983 153482 657702010 35894 42931 51511 55542 65410 61967 79108 65284 93040 66670 107409 66782

All-year 52229 55958 77856 65469 100866 67501 123729 66753 147143 64705 171438 62028

Warri

2006 7284 9882 11844 16435 16463 23060 21457 30090 26940 37427 32985 447982007 11493 15177 17657 23851 23608 31968 29819 39827 36441 47206 43556 538362008 11920 14909 16911 21726 21511 27874 26143 33798 30934 39484 35947 448292009 13764 17108 19347 24669 24441 31305 29532 37493 34767 43223 40216 484142010 12799 16131 18315 23680 23412 30436 28556 36842 33886 42854 39473 48354

All-year 10796 13916 15966 21108 20860 27797 25889 34380 31181 40778 36802 46816

Calabar

2006 6552 9015 11008 15479 15627 22178 20709 29433 26369 37135 32689 449822007 8159 11095 13315 18513 18531 25965 24166 33751 30350 41666 37165 493442008 6664 9181 11224 15800 15957 22665 21166 30088 26973 37942 33460 458982009 6429 8862 10847 15277 15441 21948 20506 29196 26159 36915 32480 448042010 5481 7619 9461 13432 13678 19604 18392 26454 23715 33943 29727 41830

All-year 6172 8533 10494 14821 15015 21404 20022 28601 25630 36319 31922 44265






119890that could


119890= 1714





05

101520253035404550

0 10 20 30 40 50 60 70 80 90 100 110 120

KatsinaWarriCalabar


Capa

city

fact

orC

f(

)







119862 the





119862119903OMR 119894

dagger119903dagger

119868119903SC


11287 11125 11336

14760

12587 11718

17614

13330

11819

00010002000300040005000600070008000

000005010015020025030035040


KatsinaWarriCalabar

Elec

tric

gen

erat

ion

cost

per k

Wh

(C)

Elec

tric

gen

erat

ion

cost

per k

Wh

(1)








Hub heightℎ (m)


PVC(C)



(kWh)

PVC(C)



(kWh)

PVC(C)



(kWh)

10 550002 474468 9489360 550002 128258 2565152 550002 80191 160382130 570244 561230 11224601 570244 193354 3867087 570244 137533 275066950 590486 582898 11657970 590486 253415 5068299 590486 196856 393712970 610727 579170 11583397 610727 311944 6238883 610727 260911 521822390 630969 563127 11262544 630969 368258 7365169 630969 328677 6573531110 651210 540899 10817977 651210 420884 8417673 651210 397456 7949115
















6 Conclusions





References
























6H15NO3



2Cr2O7

























FuelsJournal of







Advances in



Journal of


Renewable Energy





RotatingMachinery


EnergyJournal of


















000

020

040

060

080

100by

Wei

bull

pdf

All-

year

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason


Cor

relat

ion

coeffi

cien

t (R)

(a)

000

020

040

060

080

100

by W

eibu

ll pd

f

All-

year

Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason


Coe

ffici

ent o

f effi

cien

cy (C

oE)

(b)


000200400600800

10001200

Win

d sp

eed

(ms

)

Katsina


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

(a)

Katsina

000

20000

40000

60000

80000


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

Pow

er d

ensit

y (W

m2)

(b)








000

100

200

300

400

500W

ind

spee

d (m

s)

Warri


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

(a)

Warri

0001000200030004000500060007000

Pow

er d

ensit

y (W

m2)


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

(b)


000

100

200

300

400

500

Win

d sp

eed

(ms

)


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

Calabar

(a)

000

2000

4000

6000

Pow

er d

ensit

y (W

m2)


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

Calabar8000

(b)








Blade diameter(m)

V119888

(ms)V119877

(ms)V119865

(ms)119875119890119877

(W)Value 10 17 02 139 170 1500



119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W)

Katsina

2006 59415 59516 89236 66821 116014 67111 142630 65117 169898 62203 198201 589362007 77020 63102 113289 65248 145087 62956 176174 59639 207595 56097 239830 526112008 45136 54215 74006 68738 101779 72266 130777 71384 161706 68467 194945 646342009 43431 51050 66290 63988 87275 68404 108456 69152 130428 67983 153482 657702010 35894 42931 51511 55542 65410 61967 79108 65284 93040 66670 107409 66782

All-year 52229 55958 77856 65469 100866 67501 123729 66753 147143 64705 171438 62028

Warri

2006 7284 9882 11844 16435 16463 23060 21457 30090 26940 37427 32985 447982007 11493 15177 17657 23851 23608 31968 29819 39827 36441 47206 43556 538362008 11920 14909 16911 21726 21511 27874 26143 33798 30934 39484 35947 448292009 13764 17108 19347 24669 24441 31305 29532 37493 34767 43223 40216 484142010 12799 16131 18315 23680 23412 30436 28556 36842 33886 42854 39473 48354

All-year 10796 13916 15966 21108 20860 27797 25889 34380 31181 40778 36802 46816

Calabar

2006 6552 9015 11008 15479 15627 22178 20709 29433 26369 37135 32689 449822007 8159 11095 13315 18513 18531 25965 24166 33751 30350 41666 37165 493442008 6664 9181 11224 15800 15957 22665 21166 30088 26973 37942 33460 458982009 6429 8862 10847 15277 15441 21948 20506 29196 26159 36915 32480 448042010 5481 7619 9461 13432 13678 19604 18392 26454 23715 33943 29727 41830

All-year 6172 8533 10494 14821 15015 21404 20022 28601 25630 36319 31922 44265






119890that could


119890= 1714





05

101520253035404550

0 10 20 30 40 50 60 70 80 90 100 110 120

KatsinaWarriCalabar


Capa

city

fact

orC

f(

)







119862 the





119862119903OMR 119894

dagger119903dagger

119868119903SC


11287 11125 11336

14760

12587 11718

17614

13330

11819

00010002000300040005000600070008000

000005010015020025030035040


KatsinaWarriCalabar

Elec

tric

gen

erat

ion

cost

per k

Wh

(C)

Elec

tric

gen

erat

ion

cost

per k

Wh

(1)








Hub heightℎ (m)


PVC(C)



(kWh)

PVC(C)



(kWh)

PVC(C)



(kWh)

10 550002 474468 9489360 550002 128258 2565152 550002 80191 160382130 570244 561230 11224601 570244 193354 3867087 570244 137533 275066950 590486 582898 11657970 590486 253415 5068299 590486 196856 393712970 610727 579170 11583397 610727 311944 6238883 610727 260911 521822390 630969 563127 11262544 630969 368258 7365169 630969 328677 6573531110 651210 540899 10817977 651210 420884 8417673 651210 397456 7949115
















6 Conclusions





References
























6H15NO3



2Cr2O7

























FuelsJournal of







Advances in



Journal of


Renewable Energy





RotatingMachinery


EnergyJournal of


















000

100

200

300

400

500W

ind

spee

d (m

s)

Warri


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

(a)

Warri

0001000200030004000500060007000

Pow

er d

ensit

y (W

m2)


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

(b)


000

100

200

300

400

500

Win

d sp

eed

(ms

)


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

Calabar

(a)

000

2000

4000

6000

Pow

er d

ensit

y (W

m2)


Jan

Feb

Mar

Ap

rM

ay

Jun Jul

Aug

Sep

Oct

N

ov

Dec

D

ry se

ason

Rain

y se

ason

All-

year

Period of the year

Calabar8000

(b)








Blade diameter(m)

V119888

(ms)V119877

(ms)V119865

(ms)119875119890119877

(W)Value 10 17 02 139 170 1500



119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W)

Katsina

2006 59415 59516 89236 66821 116014 67111 142630 65117 169898 62203 198201 589362007 77020 63102 113289 65248 145087 62956 176174 59639 207595 56097 239830 526112008 45136 54215 74006 68738 101779 72266 130777 71384 161706 68467 194945 646342009 43431 51050 66290 63988 87275 68404 108456 69152 130428 67983 153482 657702010 35894 42931 51511 55542 65410 61967 79108 65284 93040 66670 107409 66782

All-year 52229 55958 77856 65469 100866 67501 123729 66753 147143 64705 171438 62028

Warri

2006 7284 9882 11844 16435 16463 23060 21457 30090 26940 37427 32985 447982007 11493 15177 17657 23851 23608 31968 29819 39827 36441 47206 43556 538362008 11920 14909 16911 21726 21511 27874 26143 33798 30934 39484 35947 448292009 13764 17108 19347 24669 24441 31305 29532 37493 34767 43223 40216 484142010 12799 16131 18315 23680 23412 30436 28556 36842 33886 42854 39473 48354

All-year 10796 13916 15966 21108 20860 27797 25889 34380 31181 40778 36802 46816

Calabar

2006 6552 9015 11008 15479 15627 22178 20709 29433 26369 37135 32689 449822007 8159 11095 13315 18513 18531 25965 24166 33751 30350 41666 37165 493442008 6664 9181 11224 15800 15957 22665 21166 30088 26973 37942 33460 458982009 6429 8862 10847 15277 15441 21948 20506 29196 26159 36915 32480 448042010 5481 7619 9461 13432 13678 19604 18392 26454 23715 33943 29727 41830

All-year 6172 8533 10494 14821 15015 21404 20022 28601 25630 36319 31922 44265






119890that could


119890= 1714





05

101520253035404550

0 10 20 30 40 50 60 70 80 90 100 110 120

KatsinaWarriCalabar


Capa

city

fact

orC

f(

)







119862 the





119862119903OMR 119894

dagger119903dagger

119868119903SC


11287 11125 11336

14760

12587 11718

17614

13330

11819

00010002000300040005000600070008000

000005010015020025030035040


KatsinaWarriCalabar

Elec

tric

gen

erat

ion

cost

per k

Wh

(C)

Elec

tric

gen

erat

ion

cost

per k

Wh

(1)








Hub heightℎ (m)


PVC(C)



(kWh)

PVC(C)



(kWh)

PVC(C)



(kWh)

10 550002 474468 9489360 550002 128258 2565152 550002 80191 160382130 570244 561230 11224601 570244 193354 3867087 570244 137533 275066950 590486 582898 11657970 590486 253415 5068299 590486 196856 393712970 610727 579170 11583397 610727 311944 6238883 610727 260911 521822390 630969 563127 11262544 630969 368258 7365169 630969 328677 6573531110 651210 540899 10817977 651210 420884 8417673 651210 397456 7949115
















6 Conclusions





References
























6H15NO3



2Cr2O7

























FuelsJournal of







Advances in



Journal of


Renewable Energy





RotatingMachinery


EnergyJournal of




















Blade diameter(m)

V119888

(ms)V119877

(ms)V119865

(ms)119875119890119877

(W)Value 10 17 02 139 170 1500



119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W) 119875119890(W) 119875

119890ave (W)

Katsina

2006 59415 59516 89236 66821 116014 67111 142630 65117 169898 62203 198201 589362007 77020 63102 113289 65248 145087 62956 176174 59639 207595 56097 239830 526112008 45136 54215 74006 68738 101779 72266 130777 71384 161706 68467 194945 646342009 43431 51050 66290 63988 87275 68404 108456 69152 130428 67983 153482 657702010 35894 42931 51511 55542 65410 61967 79108 65284 93040 66670 107409 66782

All-year 52229 55958 77856 65469 100866 67501 123729 66753 147143 64705 171438 62028

Warri

2006 7284 9882 11844 16435 16463 23060 21457 30090 26940 37427 32985 447982007 11493 15177 17657 23851 23608 31968 29819 39827 36441 47206 43556 538362008 11920 14909 16911 21726 21511 27874 26143 33798 30934 39484 35947 448292009 13764 17108 19347 24669 24441 31305 29532 37493 34767 43223 40216 484142010 12799 16131 18315 23680 23412 30436 28556 36842 33886 42854 39473 48354

All-year 10796 13916 15966 21108 20860 27797 25889 34380 31181 40778 36802 46816

Calabar

2006 6552 9015 11008 15479 15627 22178 20709 29433 26369 37135 32689 449822007 8159 11095 13315 18513 18531 25965 24166 33751 30350 41666 37165 493442008 6664 9181 11224 15800 15957 22665 21166 30088 26973 37942 33460 458982009 6429 8862 10847 15277 15441 21948 20506 29196 26159 36915 32480 448042010 5481 7619 9461 13432 13678 19604 18392 26454 23715 33943 29727 41830

All-year 6172 8533 10494 14821 15015 21404 20022 28601 25630 36319 31922 44265






119890that could


119890= 1714





05

101520253035404550

0 10 20 30 40 50 60 70 80 90 100 110 120

KatsinaWarriCalabar


Capa

city

fact

orC

f(

)







119862 the





119862119903OMR 119894

dagger119903dagger

119868119903SC


11287 11125 11336

14760

12587 11718

17614

13330

11819

00010002000300040005000600070008000

000005010015020025030035040


KatsinaWarriCalabar

Elec

tric

gen

erat

ion

cost

per k

Wh

(C)

Elec

tric

gen

erat

ion

cost

per k

Wh

(1)








Hub heightℎ (m)


PVC(C)



(kWh)

PVC(C)



(kWh)

PVC(C)



(kWh)

10 550002 474468 9489360 550002 128258 2565152 550002 80191 160382130 570244 561230 11224601 570244 193354 3867087 570244 137533 275066950 590486 582898 11657970 590486 253415 5068299 590486 196856 393712970 610727 579170 11583397 610727 311944 6238883 610727 260911 521822390 630969 563127 11262544 630969 368258 7365169 630969 328677 6573531110 651210 540899 10817977 651210 420884 8417673 651210 397456 7949115
















6 Conclusions





References
























6H15NO3



2Cr2O7

























FuelsJournal of







Advances in



Journal of


Renewable Energy





RotatingMachinery


EnergyJournal of


















05

101520253035404550

0 10 20 30 40 50 60 70 80 90 100 110 120

KatsinaWarriCalabar


Capa

city

fact

orC

f(

)







119862 the





119862119903OMR 119894

dagger119903dagger

119868119903SC


11287 11125 11336

14760

12587 11718

17614

13330

11819

00010002000300040005000600070008000

000005010015020025030035040


KatsinaWarriCalabar

Elec

tric

gen

erat

ion

cost

per k

Wh

(C)

Elec

tric

gen

erat

ion

cost

per k

Wh

(1)








Hub heightℎ (m)


PVC(C)



(kWh)

PVC(C)



(kWh)

PVC(C)



(kWh)

10 550002 474468 9489360 550002 128258 2565152 550002 80191 160382130 570244 561230 11224601 570244 193354 3867087 570244 137533 275066950 590486 582898 11657970 590486 253415 5068299 590486 196856 393712970 610727 579170 11583397 610727 311944 6238883 610727 260911 521822390 630969 563127 11262544 630969 368258 7365169 630969 328677 6573531110 651210 540899 10817977 651210 420884 8417673 651210 397456 7949115
















6 Conclusions





References
























6H15NO3



2Cr2O7

























FuelsJournal of







Advances in



Journal of


Renewable Energy





RotatingMachinery


EnergyJournal of



















Hub heightℎ (m)


PVC(C)



(kWh)

PVC(C)



(kWh)

PVC(C)



(kWh)

10 550002 474468 9489360 550002 128258 2565152 550002 80191 160382130 570244 561230 11224601 570244 193354 3867087 570244 137533 275066950 590486 582898 11657970 590486 253415 5068299 590486 196856 393712970 610727 579170 11583397 610727 311944 6238883 610727 260911 521822390 630969 563127 11262544 630969 368258 7365169 630969 328677 6573531110 651210 540899 10817977 651210 420884 8417673 651210 397456 7949115
















6 Conclusions





References
























6H15NO3



2Cr2O7

























FuelsJournal of







Advances in



Journal of


Renewable Energy





RotatingMachinery


EnergyJournal of





















6 Conclusions





References
























6H15NO3



2Cr2O7

























FuelsJournal of







Advances in



Journal of


Renewable Energy





RotatingMachinery


EnergyJournal of


























6H15NO3



2Cr2O7

























FuelsJournal of







Advances in



Journal of


Renewable Energy





RotatingMachinery


EnergyJournal of





















FuelsJournal of







Advances in



Journal of


Renewable Energy





RotatingMachinery


EnergyJournal of

















assessments of wind-energy potential in selected sites from three geopolitical zones in nigeria:...

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