Applied Energy 86 (2009) 388–396

Contents lists available at ScienceDirect

Applied Energy

journal homepage: www.elsevier .com/ locate/apenergy

Review

Wind energy potential assessment at four typical locations in Ethiopia

Getachew Bekele *, Björn Palm 1

Department of Energy Technology, KTH, 10044 Stockholm, Sweden

a r t i c l e i n f o a b s t r a c t

Article history:Received 17 January 2008Received in revised form 19 May 2008Accepted 19 May 2008Available online 1 July 2008

Keywords:Wind energyEthiopiaDiurnal patternProbability density function (PDF)Cumulative density function (CDF)Duration curve (DC)

0306-2619/$ - see front matter � 2008 Elsevier Ltd. Adoi:10.1016/j.apenergy.2008.05.012

* Corresponding author. Tel.: +46 (0) 87907435; faE-mail addresses: getachew@energy.kth.se (G. Bek

Palm).1 Tel.: +46 (0) 87907453; fax: +46 (0) 8204161.

The wind energy potential at four different sites in Ethiopia – Addis Ababa (09:02N, 38:42E), Mekele(13:33N, 39:30E), Nazret (08:32N, 39:22E), and Debrezeit (8:44N, 39:02E) – has been investigated bycompiling data from different sources and analyzing it using a software tool. The results relating to windenergy potential are given in terms of the monthly average wind speed, wind speed probability densityfunction (PDF), wind speed cumulative density function (CDF), and wind speed duration curve (DC) for allfour selected sites. In brief, for measurements taken at a height of 10 m, the results show that for three ofthe four locations the wind energy potential is reasonable, with average wind speeds of approximately4 m/s. For the fourth site, the mean wind speed is less than 3 m/s. This study is the first stage in a longerproject and will be followed by an analysis of solar energy potential and finally the design of a hybridstandalone electric energy supply system that includes a wind turbine, PV, diesel generator and battery.

� 2008 Elsevier Ltd. All rights reserved.

Contents

1. Background. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3882. Previous studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3893. Data sources and data treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3894. Software input data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3945. Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3946. Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396

1. Background

It is well known that Ethiopia has been suffering from cyclicaldroughts, which hamper the sustainability of the agro-ecologicalenvironment. The land has been degraded over time, soil has beeneroded and vegetation cover has been removed. As in most devel-oping countries, Ethiopia has relied heavily on biomass to meet itsurban and rural energy needs. The conventional electricity supplyproduced by the centralized energy production authority, whichincludes hydroelectric power plants and engine-driven generators,is not only inequitably distributed but also insufficient to meet theeconomic needs of the rural and urban population. A lack of water,

ll rights reserved.

x: +46 (0) 8204161.ele), bpalm@energy.kth.se (B.

electricity, and communication facilities in rural areas has forcedpeople to migrate to urban areas, resulting in an irregular popula-tion distribution. The dependence on wood and agricultural wastesfor fuel has inevitably led to deforestation and desertification dueto the lack of re-plantation and soil rehabilitation schemes.

As part of a solution to these problems, the government decidedto resettle about two million people to fertile or unused land acrossthe country where they will be able to farm and, it is assumed, leada better life. This resettlement program has been partially imple-mented and over a million people have already been moved totheir new land. These resettled people need energy for their vari-ous daily needs. The current study is imperative with or withoutthe implementation of the resettlement programs, as the majorityof the population still reside in the countryside and are not con-nected to the grid supply system.

The situation clearly highlights the need to exploit all the coun-try’s potential energy resources immediately and by any means

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eed

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Fig. 1. Monthly average wind speed: Meteonorm [6].

G. Bekele, B. Palm / Applied Energy 86 (2009) 388–396 389

possible in order to alleviate the problems and provide energy ser-vices for the people in need. At the same time, the development ofthe energy sector would help in the appraisal and developmentof the industrial economy which is, in its own way, the future goalof the country.

When looking into the sources of energy available to the coun-try, hydropower occupies the major share. Unfortunately it is notaccessible to most rural areas in the foreseeable future due to lim-its in both production facilities and the distribution system (powerlines). In many areas, solar and wind energy are the main locallyavailable energy resources. Due to its geographical location thecountry enjoys a considerable amount of sunshine for mostmonths of the year. Furthermore, due to the fact that the summermonsoon, tropical easterlies, and local convergence over the RedSea control the wind regimes, there is significant wind energy, witha varying annual mean speed which decreases from east to west[1]. Altitude and temperature variations across the country alsocontribute to the amount of wind. Human needs, poor energy dis-tribution systems, and the availability of solar and wind energymake it interesting to investigate these resources more in detail.

This article is just the first part of an ongoing research project,which is a collaboration between Addis Ababa University and theRoyal Institute of Technology in Stockholm. The overall objectiveof the project is to propose a feasible standalone power supply sys-tem consisting of wind turbines, photovoltaic cells, engine-drivengenerators and batteries to be used by remote communities notreached by the national grid. The scope of this article is limitedto finding out the wind energy potential at the selected sites. Astudy into solar energy potential will be carried out subsequently,and following this, the feasibility of a standalone solar/wind hybridelectric power supply system for a 200 family community will beinvestigated. This number is selected as being typical of suburbanvillages all over the country, including communities of the resettle-ment program.

2. Previous studies

There are a couple of previous studies which provide substantialresults regarding the wind energy potential of the country [1,2].Both studies are based on data from the National MeteorologicalServices Agency (NMSA), and one of them claims to include addi-tional information from neighboring countries [1]. The studiesidentified the wind regimes in most areas of the country and alsoprovided estimates of wind energy potential. However, the dataused in the studies is relatively old and was probably meant to pro-vide a general understanding of the country’s weather conditions,for aviation purposes, or possibly for agricultural weather needs.The most recent data used in the second study [2] was collectedduring the period 1968–1973 and was recorded only three timesa day, at 6:00, 12:00, and 18:00 for 20 locations across the country.The rest of the data used was recorded during the period 1937–1940 and was likewise recorded three times a day at 8:00, 14:00and 19:00. The referenced source for the latter is entitled ‘‘CON-TRIBUTO ALLA CLIMATOLOGIA DEL RISULTATI E TABLEU METYE-ROROGICHE E PLUVIOMETRICHE”, by the author Amilcare Fantoli,published by the Ministero Degli Affari Esteri, Cooperazione Scien-tifica E Technica, 1965 [3].

Data used in the first study [1] was collected over a period of 5–10 years, 1979–1990 and the source is again the NMSA which wasthen called the Ethiopian Meteorological Service Agency (EMSA).The data was collected at 60 different locations across the countryand the recording was carried out, according to the author, 4–7times per day and at a height of 2 m but subsequently recalculatedto a height of 10 m. The author also stated that he included datasurveys from neighboring countries to compensate for the incom-plete data from within Ethiopia. The authors of the present work,

however, are not able to find any of this data in the archives ofNMSA.

3. Data sources and data treatment

Within this piece of work, wind speed data specific to four loca-tions, assumed to be fairly representative of many of the areas ofthe country, have been considered. The four locations investigatedare Addis Ababa, 0900 020N, 3800 420E, 2408 m (AMSL); Mekele, 1300

330N, 3900 300E, 2130 m; Nazret 08:32N, 39:22E, 1690 m; andDebrezeit, 0800440N, 3900 020E 1850 m. The data is obtained fromthe same source, the NMSA. This data has never previously beenpublished and is also relatively recent, from the years 2000–2003, taken at a height of 10 m and recorded five times daily, at6:00, 9:00, 12:00, 15:00, and 18:00 for three consecutive years.The data can be claimed to be fairly complete for the given periodwith only a few recordings missing here and there. The missingdata has been replaced by the averages of the preceding and fol-lowing readings. This procedure was necessary in order to calcu-late average monthly values. For verification purposes, data fromother sources has also been investigated at those sites for whichdata is available. One of the sources is a website known as Weath-erbaseSM [4], which according to the source holds monthly weatherrecords and averages for more than 16,439 cities worldwide. Theother is a report on a 6 month study of wind potential in the north-ern part of the country close to Mekele (one of the sites selected forthis study) by Deutsche Gesellschaft für Technische Zusammenar-beit (GTZ) [5]. Additionally, software and satellite derived data ob-tained from the Meteonorm [6] and NASA [7] websites have alsobeen checked. Figs. 1–4 show the monthly average wind speeds gi-ven by these sources.

In comparison to the data obtained from the above sources, Fig. 5shows the wind speed results obtained within this study. Theseresults are obtained by running the available raw data throughthe software, as will be explained later. As can be seen in the figuresthe results reported by the other sources show differences in bothmagnitude and, to some extent, shape, which shows the importanceof the current investigation.

It is to be noted that the data used in the previous studies, andalso as a basis for this one, is recorded only between 6:00 and18:00 and that there is no recorded data for the period between18:00 in the evening and 6:00 in the morning. However, in thisstudy further investigations have been carried out to compensatefor the lack of nighttime data. As a stage in this process a number

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J F M A M J J A S O N DMonths

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Fig. 2. Monthly average wind speeds: NASA [7].

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Fig. 3. Monthly average wind speeds: Wolde-Ghiorgis W. [2].

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Addis Ababa

Fig. 4. Monthly average wind speed: Weatherbase [4].

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F A J A O D

Fig. 5. Software generated monthly average wind speeds derived from themeasured data.

390 G. Bekele, B. Palm / Applied Energy 86 (2009) 388–396

of assumptions have been made for the nighttime wind speeds atone of the sites, Addis Ababa, and the resulting monthly energyhistograms have been plotted.

� The first assumption is to use only data from the daytime,recorded between 6:00 and 18:00 at intervals of 3 h. This meansneglecting possible energy capture during the night. Out of apossible 24 h the five interval measurements thus represent atotal of 15 h. From an energy point of view this is equivalentto assuming that there is no wind from 19:30 to 4:30.

� The second assumption is to replace the missing nighttime datawith the minimum wind speed recorded during the day.

� The third is to consider the average daily wind speed to be thenighttime wind speed from 19.30 to 4.30.

� The fourth assumption is to use the averages of the morning,6:00, and the evening, 18:00, readings for the missing nighttimedata.

� The final assumption is to distribute the daytime data over a24 h period, stretching the time interval from 3 h to approxi-mately 5 h. This is equivalent to assuming that the average dailywind speed is the same as the average for 24 h. The total energyof the wind (per square meter) is calculated as the sum of theaverages of each 5 h period of the month.

Fig. 6 shows the average energy in Addis Ababa in January forthe years 2001–2003 given the different assumptions.

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60

Assum 1: Assum 2: Assum 3: Assum 4: Assum 5:

Ave

rage

ene

rgy

(kW

h/m

2 )

Fig. 6. Addis Ababa energy histogram for January 2001–2003 under differentassumptions.

G. Bekele, B. Palm / Applied Energy 86 (2009) 388–396 391

As can be observed in the histogram plot the values for assump-tions 1 and 2 are quite close to each other, 34.09 and 35.06 kWh/m2, respectively. If we look at assumption 3, where the averageof the day’s recordings is used in place of the missing data, the en-ergy is boosted up to 50.3 kWh/m2, which is more than 40% higherthan the values for assumptions 1 and 2.

Considering assumptions 4 and 5, the total energy increases to51.08 and 54.55 kWh/m2, respectively. The conclusion of this isthat assumptions concerning nighttime wind are very important.

Except for the unpublished short-timescale data recently col-lected by GTZ [5], there is no data recorded continuously over along period to help understand the behavior of nighttime windspeeds in relation to those recorded during the daytime. However,from practical observations at those locations, nighttime windspeed is often lower than that recorded during the daytime. Loca-tions close to strong orographic influences usually show suchbehavior [8] and it is known that most regions of the country aremountainous. Fig. 7 [5] shows data taken at a location close toMekele, one of the sites under investigation in this study; it canbe used as an example to confirm this hypothesis for one of the se-lected sites. While assumptions 1 and 2 are obviously too low, it isprobable that assumptions 3, 4 and 5 give too high a value for theenergy of the wind.

Whilst assumption 1 gives the lowest energy level, it is possiblethat the actual energy is somewhere between the results ofassumption 1 and assumption 4 (average of morning and evening).Given the available data it is impossible to determine the availablewind energy more precisely than by using this method.

Another method used here to fill in the gaps of the missingnighttime data is to use the available measured data and synthe-size new wind data with the help of a simulation tool. Hybrid opti-mization model for electric renewables (HOMER) computersoftware [9] has been used for this purpose. This piece of softwareis a micropower optimization model for both off-grid and grid-con-nected power systems in a variety of applications. Here it is used toevaluate the wind energy potential of the selected sites. Parame-ters required by the software, such as Weibull K, Autocorrelationfactor, and diurnal pattern, have been determined by calculationand/or by trial and error. This has been achieved by repeatedly run-

Fig. 7. Wind speed recorded by GTZ on the

ning the software using different values for the parameters, withinthe typical range of values that each parameter could have, andthen comparing the results against the measured data. Hence,the value selected for Weibull K is 2, for autocorrelation factor itis 0.85, for diurnal pattern 0.25 and for peak hour 15. The typicalranges for these parameters are 1.5–2.5 for Weibull K, 0.80–0.95for autocorrelation factor, 0.0–0.4 for diurnal pattern, and 15–18for the hour of peak wind speed [9]. The procedure followed willbe explained in more detail in a later paragraph.

The principal parameter required by the program to synthesizethe hourly data is the average monthly wind speed. In an effort tofind a model which can generate the measured daytime data, andthereby at the same time generate the most probable nighttimedata, two different approaches are taken:

In the first approach, a scaling factor is determined. The mea-sured monthly average for the daytime is fed into the software(assumption 1 above). The software interprets this input as amonthly average. Fig. 8 shows the monthly average of the mea-sured daytime data for Addis Ababa (curve A) together with themonthly average of the hourly data (24 h) synthesized by the soft-ware (curve B). As can be seen, the agreement is very good. Fromthe synthesized data, the daytime data corresponding to the timesof the actual measurements is extracted. The monthly average ofthis daytime data is plotted as curve C in Fig. 8. As expected, thiscurve is above the 24 h average data, as we anticipate the windspeed to be higher during the daytime.

Finally, the extracted daytime data (curve C) is scaled down byapplying a scaling factor, which was chosen in order to produce themeasured average. The best agreement is found when the scalingfactor is set to 92%. Fig. 9 shows the fit between the monthly aver-ages of the measured daytime data (curve A) and those of the syn-thesized daytime data after the scaling factor is applied (curve C).The monthly averages of the simultaneously generated hourly(24 h) data, which is believed to be the actual wind potential ofthe site, is shown by curve B in the same figure.

In the second approach, no scaling factor is used. Instead, twodifferent ways of approximating the nighttime data are tested, cor-responding to two of the assumptions discussed above: Assump-tion 2 (wind speed throughout the night is equal to the lowest

16th of January 2005 for Mekele [5].

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A - Monthly average of the measured wind data

B - Monthly average wind speed simultaneously generated with the daytime monthlyaverage; input is the average of the measured dataC - Daytime monthly average filtered out from generated hourly data; input is theaverage of the measured dataD - Daytime monthly average filtered out from generated hourly data; input is based onassumption 2.E - Daytime monthly average filtered out from generated hourly data; input is based onassumption 4.

Fig. 8. Comparison of the measured averages to the assumption based software generated daytime average wind speeds.

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A - Monthly average of the measured wind data

B - Monthly average wind speed simultaneously generated with the scaleddown daytime monthly average; input is the average of the measured dataC - Daytime monthly average filtered out from generated hourly data andscaled down by 8%; input is the average of the measured data.D - Daytime monthly average filtered out from generated hourly data; input isbased on assumption 2.E - Daytime monthly average filtered out from generated hourly data; input isbased on assumption 4.

F A J A O D

Fig. 9. Comparison of the scaled down daytime output to the measured average and to the assumptions based daytime outputs.

392 G. Bekele, B. Palm / Applied Energy 86 (2009) 388–396

wind speed measured during the day) and Assumption 4 (windspeed throughout the night is equal to the average of the windspeeds measured in the morning, 6:00, and in the evening,18.00). In this way, two complete sets of 24 h data are produced,which are then used to calculate two different sets of average

monthly wind speeds. Subsequently, these are used to synthesizetwo sets of hourly data for each month of the year, correspondingto each of the two assumptions. From each of these sets of data, thedaytime data was filtered out and the average monthly daytimedata calculated. In Fig. 9 curve D shows the averages based on

G. Bekele, B. Palm / Applied Energy 86 (2009) 388–396 393

Assumption 2 and curve E shows the averages based on Assump-tion 4. The Figure shows that Assumption 2 gives daytime averageswhich are much too low, while Assumption 4 gives average valuesquite close to those averages based on the measured data. How-ever, none of these simple assumptions give an agreement withthe average of the measured data quite as strong as that given byadapting a scaling factor to minimize the difference, as suggestedin the first approach.

In order to evaluate the discrepancies between the results theMean Square Error method given in Eq. (1) has been used.

Mean Square Error; MSEð%Þ ¼ 1001

Um

� � X E2i

M

!1=2

ð1Þ

where, M, is the total number of observation points and Um, thearithmetic mean value of the measured data; Ei ¼ Uassumed�Umeasured i ¼ 1; 2; :::; M.

Table 1 shows the mean square error between the averages ofthe measured daytime data and the averages of the three sets ofsynthesized daytime data. The second column reveals thatassumption 2 (curve D) gives values for the average daytime datawhich are about 17% too low, while assumption 4 (curve E) givesvalues which are about 4% higher than the measured values. The

Table 1Percentage mean square errors of curves B, C, D, and E against the average of themeasured values (curve A)

Compared items Mean square error in %(unity scaling factor)

Mean square error in %(92% scaling factor)

B 0.05 7.89C 8.81 1.69D 16.91E 3.95

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A - Monthly average of the measur

B - Monthly average wind speed sidown daytime monthly average; in

F - Generated monthly average ou

G - Generated monthly average ou

Fig. 10. Measured and the assumptions based generate

right hand column shows that by using the adapted scaling factor,the difference between the measured data (curve A) and the scaleddown data (curve C) is only 1.69%, which shows that the measureddaytime data is generated effectively. Curve B, which is generatedby the software, is about 8% lower than curve A in relation to thedaytime values. It is to be noted here that all curves, with theexception of curve B in Fig. 9, are daytime (6:00, 9:00, 12:00,15:00, and 18:00 hours) averages.

The unfiltered average monthly data for all three assumptions isgiven in Fig. 10. As can be seen in the figure, the monthly average ofthe measured data is the highest of all and it is expected to be so, asit represents only the daytime (between 6:00 and 18:00). The othercurves are all monthly averages of the software generated data.

Concluding this investigation, whilst it is not possible to tell theactual wind speed precisely, the most probable wind speed at eachlocation is believed to be that given by curve B, the monthly aver-age of the hourly data generated by the software, based on themeasured average of the daytime data, scaled down by approxi-mately 8%.

To evaluate the potentials found in this study with respect tothe wind power classification of the US Department of Energy(DOE), wind speed at a height of 50 m is calculated using Eq. (2).Table 2 shows the results of the calculations and the correspondingclassifications.

J A S O N Dnths

ed wind data

multaneously generated with the scaledput is the average of the measured data

tput; input is basedon assumption 2.

tput; input is based on assumption 4.

d monthly average wind speeds for Addis Ababa.

Table 2Wind power classes

Elevation v (z) (10 m) V (z) (50 m) Wind power class

Addis Ababa 2408 4.21 5.90 Class 2Mekele 2130 3.76 5.27 Class 1Nazret 1690 4 5.60 Class 2Debrezeit 1850 2.51 3.52 Class 1

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Weatherbase MeteonormNASA Wolde-Ghiorgis W.Present work

Fig. 12. Average wind speeds from different sources.

394 G. Bekele, B. Palm / Applied Energy 86 (2009) 388–396

vðzÞ � ln zr

z0

� �¼ vðzrÞ � ln

zz0

� �ð2Þ

where, zr is the reference height (10 m); z0 is the roughness length,which is 0.1–0.25 m for cropland and an average value of 0.18 hasbeen used in the calculation.

Accordingly, Addis Ababa and Nazret are class 2 types whileMekele and Debrezeit are in class 1. Whereas class 2 potential isconsidered marginally good for wind energy development, class1 potential is in general considered to be unsuitable [10]. However,average annual wind speeds of 3–4 m/s may be adequate for non-grid-connected electrical and mechanical applications such as bat-tery charging and water pumping.

To see the variation in wind energy over the course of a day thecorresponding diurnal pattern for Addis Ababa has been calculatedfrom the hourly data generated by the software for the years 2001–2003 and is shown in Fig. 11.

4. Software input data

The method described above was used to determine the windpotential for all four locations in which the wind energy potentialcan be evaluated: Addis Ababa, Mekele, Debrezeit and Nazret. Theresults produced by the software are the monthly average windspeed, the wind speed probability density function (PDF), the windspeed cumulative density function (CDF) and the wind speed dura-tion curve (DC). The potential revealed in this study will be usedlater on to design a model of a standalone hybrid power supplysystem for a small model community.

The input parameters required for the software have been cal-culated and/or estimated as follows: The shape parameter K, whichis an indication of the breadth of the distribution of wind speeds, iscalculated by applying Eq. (3) [10] and also by repeatedly runningthe program, by way of trial and error, checking the results againstthe measured data. The value that fits best for K is found to be 2.

K ¼ rU

U

� ��1:086

ð3Þ

where, U is the mean wind speed and rU is the standard deviation.The anemometer height is 10 m according to the data source,

NMSA. Typical values for diurnal pattern strength range from 0 to0.4 [9]; by varying the values within the range, repeatedly runningthe software and checking the results against the measured data, avalue of 0.25 has been selected. The autocorrelation function is ameasure of the tendency of what a wind speed is likely to be, givenwhat it was earlier [10]. For complex topography the autocorrela-tion factor is (0.70–0.80) while for a uniform topography the range

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Fig. 11. Diurnal pattern calculated from hourly data (2001–2003) generated by thesoftware for Addis Ababa.

is higher (0.90–0.97). A typical range for the autocorrelation factoris 0.8–0.95 [9]. An average value of 0.85 is used here because the se-lected areas are of averagely uniform topography. The typical rangefor the time of peak wind speed, which is the time of day that tends,on average, to be windiest throughout the year, is 14:00–16:00 [5].This has also been observed in the available raw data for some of themonths. In addition to this, the software has been run for differenttimes between 14:00 and 18:00, the results have been checkedagainst the measured data and the time of 15:00 has been chosenfor the calculations.

5. Results and discussion

It is believed that this study provides a deeper understandingof the wind energy situation for the majority, if not for thewhole, of the country. Fig. 12 shows what has been obtainedfrom different sources and includes the results from this study.From the figure it can be observed that there are differences ofabout 1 m/s or more in the magnitudes of the wind speeds be-tween the sources. This difference is substantial considering thelevel of the wind.

As is well known the wind speed probability density function(PDF) describes the likelihood that wind speed has a particular va-lue for a certain candidate site and may also be used as the basis for

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0 5 10 15Value (m/s)

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uenc

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Addis AbabaMekeleNazretDebrezeit

Fig. 13. Best-fit curves for wind speed probability density functions (PDFs).

G. Bekele, B. Palm / Applied Energy 86 (2009) 388–396 395

design decisions. For a shape parameter, K, equal to 2, the PDF iscommonly known as the Rayleigh density function [10]. Fig. 13shows the best fit curves of the PDF obtained from HOMER for allthe selected sites.

Weibull K and scale factor C, corresponding to each of the bestfit curves, as generated by HOMER, is given in Table 3.

Table 3HOMER generated Weibull K and scale factor C for the locations

Place Addis Ababa Mekele Nazret Debrezeit

Weibull K 2.01 2.01 1.97 2.01Scale parameter 4.24 4.24 4.50 2.83

Fig. 14. Wind speed cumulative density functions (CDFs).

Fig. 15. Wind speed duration curves (DCs).

Fig. 16. Wind speeds profil

Figs. 14 and 15, respectively show the wind speed cumulativedensity function, CDF, and the wind speed duration curve, DC. Thewind speed profiles for all locations have also been given in Fig. 16.

It is to be noted that the results shown are for wind speeds mea-sured at an anemometer height of 10 m. Wind speed at a higher le-vel is also higher and this has to be taken into consideration whenplanning for standalone power supply systems at the small com-munity level.

6. Conclusions

In general, based on the data available at the time, previousstudies have given valuable information about the wind energy po-tential across Ethiopia. The data used in the studies is relatively

es of the selected sites.

396 G. Bekele, B. Palm / Applied Energy 86 (2009) 388–396

old, from the late 60s or early 70s or even earlier [2] and the datawas recorded only 3 times a day, at 6:00, 12:00, and 18:00.

On the other hand, in addition to being specific to just four cho-sen locations, this study is also based on recently recorded data(2000–2003) and the readings have been taken five times a day.Furthermore, unlike in the previous studies, the data as it is, col-lected from dawn to dusk, is not claimed to be the wind energy po-tential of the sites but is instead used as an input into a piece ofsoftware (HOMER) and it is the resulting 24 h output of the soft-ware that is claimed to be the most probable wind energy potentialof the locations.

The results obtained in this study for the selected sites showthat the wind energy potential of one of the sites, Debrezeit, is sub-stantially lower than the others. In general, although the potentialmay not be sufficient for independent wind energy conversion sys-tems, it is believed that wind energy is feasible if integrated intoother energy conversion systems such as PV, diesel generator andbattery. The results of this study can be considered applicable tomost of the country’s regions which have similar climaticconditions.

The methods followed in this study to determine wind potentialfrom incomplete data are also of significance to similar researchprojects in other locations.

This study is only the first phase of a project aiming to establisha standalone solar-wind hybrid electric power supply system for amodel community in a remote area detached from the main gridsystem.

Acknowledgements

The authors would like to thank the National Renewable EnergyLaboratories (NREL) for providing the Hybrid Optimization Modelfor Electric Renewables (HOMER) software for free. The authorsare also grateful to Dr. Tom Lambert for his advice on the use ofthe software.

References

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