Solar Radiation in Africa

8/12/2019 Solar Radiation in Africa

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Solar Energy Vol. 70, No. 1, pp. 112, 20012001 Elsevier Science Ltd

Pergamon P I I : S 0 0 3 8 0 9 2 X ( 0 0 )0 0 1 2 6 2 All rights reserved. Printed in Great Britain0038-092X/01 /$ - see front matter

www.elsevier.com/locate/solener

ASSESSMENT OF SOLAR ELECTRICITY POTENTIALS IN NORTH AFRICABASED ON SATELLITE DATA AND A GEOGRAPHIC INFORMATION

SYSTEM

, ,1 ,H. BROESAMLE*, H. MANNSTEIN**, C. SCHILLINGS* *** and F. TRIEB****NEVAG/Enersys GmbH, Rheingaustr. 184, D-65203 Wiesbaden, Germany

**Institute of Atmospheric Physics, DLR Oberpfaffenhofen, D-82234 Weling, Germany

***Institute of Technical Thermodynamics, DLR Stuttgart, Pfaffenwaldring 38-40, D-70569 Stuttgart,

Germany

Received 10 February 2000; revised version accepted 31 July 2000

Communicated by RICHARD PEREZ

AbstractSolar thermal power plants will provide a major share of the renewable energy sources needed inthe future. STEPS, an evaluation system for solar thermal power stations, was designed to calculate theperformance of such power stations as a function of direct solar radiation, geographical conditions (land slope,land cover, distance from cooling water resources, etc.), infrastructure (pipelines, electricity grids, streets etc.)and the configuration and performance of a selected solar thermal power plant concept. A cloud index derivedfrom METEOSAT satellite images is used to calculate the direct solar radiation resource. A geographicinformation system (GIS) is used to process all the parameters for site assessment. In order to demonstrate theconcept, an analysis of Northern Africa was performed with STEPS providing a ranking of sites with respect tothe potential and cost of solar thermal electricity for a particular power plant configuration. Results wereobtained with high spatial and temporal resolution. 2001 Elsevier Science Ltd. All rights reserved.

1. INTRODUCTION

Providing the basis for solar power build outscenarios.Solar thermal power plants use concentrated solar

Sensitivity analysis of the performance of solarradiation in order to generate high pressure steam

thermal power stations regarding site condi-for electricity generation in conventional steam

tions.turbines. Because fuel is substituted by solar

STEPS has a clear modular structure (Fig. 1). Thecollectors, an additional investment and additional

main module defines the interaction of all otherspace at the plant site is required. The evaluation

modules. Temporal and spatial resolution can betool STEPS allows the selection and ranking of

varied according to specific needs. Results aresites for solar thermal power plant construction,

typically shown as maps using a geographicanalysing a large region, country or even a

information system (GIS). The first application ofcontinent (Broesamle, 1999). Among others, the

STEPS was the estimation of the potential and

following services can be provided by STEPS. present cost of solar thermal power plants in Maps of the direct normal irradiation (DNI)

North Africa. The following topics were treatedresource in high spatial and temporal resolu-

within the study.tion (best resolution: 2.5 km 3 2.5 km, hourly

Determination of the geographical andmean values).

meteorological frame conditions. Assessment of the technical and economic

Determination of all suitable sites for thepotential of solar power generation in a defined

construction of solar thermal power plants.region.

Development of a map of the direct normal Ranking and selection of sites for the construc-

irradiation (DNI) for North Africa.tion of solar thermal power stations.

Calculation of the solar electricity yield per2

km of land.Author to whom correspondence should be addressed. Tel.:

Calculation of power generation costs per149-711-686-2423; fax: 149-711-686-2783; e-mail:kWh.

[email protected] The procedure and the results of this analysis areFormer affiliation: Geographical Institutes of the University

of Bonn, D-53115 Bonn, Germany. described in the following.

1


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2 H. Broesamle et al.

Fig. 1. The modular structure of STEPS.

2. ASSESSMENT OF THE GEOGRAPHIC cultivated land are considered unsuitable for theFRAME CONDITIONS construction of such plants. Sand deserts are not

considered to be a criteria for exclusion, but may

All available data sets (satellite data, digital elevate the cost.

maps etc.) are processed in a geographic infor- Land cover and land use data sets are createdmation system (GIS). The usually heterogeneous by the US Geological Survey (USGS), the Uni-

data sets have to be transferred into a uniform versity of NebraskaLincoln (UNL) and the

geographic projection and data format. Important European Commission for Research Co-operation.

data for the ranking and evaluation of potential These data can be obtained partially from the

sites of solar thermal power plants are e.g. land Earth Resources Observation System (EROS)

use, land cover, slope and water surfaces. Data Centre via the internet. For the determination

Solar thermal power stations have a relatively of the land cover and land use, the global

big area demand in comparison to conventional ecosystems classification by Olson (1994) is used.

power stations. The specific area demand for a A reduction to 10 classes of land cover has been2

parabolic trough power station is | 1 km per applied.

50 MW of installed electric capacity. Typical sites The land slope should be less than 5% forare hot, dry regions like deserts or semi-deserts. parabolic trough plants. We used the digital

Surface water, forests, settlements, arable and elevation model (DEM) called GTOPO30 of the


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Assessment of solar electricity potentials in North Africa based on satellite data and a geographic information system 3

EROS Data Centre with a spatial resolution of with x5 t/ 24 3 2p and t5decimal hours of the

nearly 1 km 3 1 km in order to calculate the satellite (UTC).a gives the daily mean tempera-0slope. The data were considered to be acceptable ture, a the temperature amplitude, a influences1 2for the first application of STEPS, but a land slope the width and steepness of the daily temperature

of maximum 2% instead of 5% was selected as wave and a gives the phase shift, which is3threshold in order to compensate for the limited dominated by the local solar time. The coeffi-

resolution. For more detailed project studies, more cients a are fitted for every daily period andn

accurate DEM may be used, as e.g. the Shuttle every pixel using the fit from the former time

Radar Topography Mission (SRTM) by NASA interval and the corresponding new cloud free

with a 30 m spatial resolution. The shading of temperature.

mountains, especially in the morning and after- We use the following differences of properties

noon, can also be calculated with DEM data, but of clouds versus surface for a first cloud detection.

this feature has been neglected in the first applica- Clouds are cold. The weight of every pixel

tion of STEPS. with a temperature colder than estimated is

Fig. 2 shows some of the results of this reduced proportional to the difference.2

analysis. In North Africa, 12.6 Mio km fulfil the Clouds move. We compare the data to the

criteria of suitability for the construction of solar previous image and the image of the day

thermal power plants with respect to land slope before. Clouds are colder and show up as localand land cover. differences.

Surface temperature has a regular daily vari-

ation and depends on the landscape. We com-3. SOLAR RADIATION RESOURCE

pare the data to the predicted reference tem-ASSESSMENT

perature image. Clouds again show up as local

differences.The most important parameter for the site Weather patterns have a larger scale than pixelselection of solar thermal power plants is the

size. We allow for deviations from the pre-direct normal irradiation (DNI). The direct normaldicted temperatures if they are common withinsolar irradiation on the ground is described by:regions of pixels with similar surface prop-

DNI 5E ? t ? t ? t ? t ? t ? t (1)s d0 R Ozon Gas WV Ae Clerties.For corrections of the visible channel ofwhere E is the extraterrestrial irradiation. t ,0 R

METEOSAT, we analysed 1 year of VIS data tot , t , t , t , t are the transmittanceOzon Gas WV Ae Clextract the distribution of counts with respect tofunctions for Rayleigh scattering, ozone absorp-the solar zenith angle and the angular distancetion, absorption by uniformly mixed gas (oxygenbetween sun and satellite seen from the surface. Aand carbon dioxide), water vapour absorption,further correction is made to account for atmos-aerosol extinction and cloud extinction, respec-pheric influences like forward and backwardtively. The formula is based on the clear skyscattering within the atmosphere by defining andmodel (Bird, 1984; Iqbal, 1983). For calculatingsubtracting a minimum count from the satellitethe DNI in STEPS, we have modified that model,values (Mannstein et al., 1999). VIS data isadding a coefficient of transmission (t ) thatClincluded into the decision process at locations,takes into account the attenuation of irradiation bywhere the cosine of the solar zenith angle isclouds. We derive a cloud index from the visiblegreater than 0.1 (the sun is more than 5.78 over(VIS) and infrared (IR) image channel of thethe horizon). Similar to the IR analysis, we deriveMETEOSAT weather satellite based on self ad-a reference image, which is in this case notjusting, local thresholds which represent the spa-variable throughout the day. The VIS images aretial and temporal variation of the surface prop-compared against the predicted image and theerties (Mannstein et al., 1999). We look at longprevious image. The corrected count has to betime series of METEOSAT IR-data to achieve ahigher then a threshold derived from the predictedlocal temperature threshold which is close to thecloud free scene.temperature of the cloud-free surface. The refer-

Both IR and VIS information are combined toence temperature of land surface is described as athe final result by linear interpolation between thefunction of time for every pixel:

expected cloud free value and a threshold for aT5 a 1 a cos x2 a 1 sin a 3 sin x2 as s s d s dd0 1 3 2 3 fully cloudy pixel (2408C in the IR and acorrected count of 150 in the VIS channel). The1 0.1 3 sin x2 a (2)s dd3


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Fig. 2. North Africa determination of geographic frame conditions and non-suitable ( black) are


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higher of both values is given as the result. The from 15% to 215% with respect to the annual

interpolation scheme and the values of these sums of DNI (Broesamle, 1999). It must be

thresholds might be changed, if better validation mentioned that those data sets were obtained from

data are available. In a first rough assumption, the rough estimates and did cover different time

cloud-index is converted by a linear function into intervals, so the comparison does not necessarily

a coefficient of cloud transmittance. show the quality of the satellite-derived data. All

Not only clouds, but also aerosols have an in all, the satellite data was more complete andimportant influence on solar radiation. We use the had a much better coverage and resolution than

Global Aerosol Data Set by Kopke et al. (1997) the available ground measurements.

to calculate the aerosol transmittance. This data For the analysis of North Africa, METEOSAT

set has a spatial resolution of 583 58 and a images from 1998 were used for the calculation of

temporal resolution of two values per year (sum- the direct normal irradiation. STEPS calculates

mer and winter). We took the aerosol optical the DNI for every hour and every location (8760

thickness (AOT) for the wavelengths 0.5 mm and values for each location) with a spatial resolution

0.35 mm and for a relative humidity of 50%. of |535 km. These values are used by the power

These wavelengths are required by the Bird plant simulation module to determine the per-

aerosol transmittance function. We extended the formance and energy yield. The results were also

summer values from June until November, the used to create a map of the yearly sums of solarwinter values from December until May (Hess, direct normal irradiation for North Africa for

1998). 1998 (Fig. 4). A long term climatology of solar

The low resolution of the available aerosol data radiation was not used within this study.

has been considered critical for our application.

Therefore, aerosol transmittances were selected4. SOLAR POWER PLANT PERFORMANCE

considering only those values relevant for the

main areas of interest (deserts and semi-deserts). In the first prototype of STEPS, a solar power

Furthermore, aerosol transmittance values were plant simulation model was integrated that repre-

reduced by 20% for every 1000 m of altitude, sents a 200 MW parabolic trough solar electricity

taking into consideration the reduced atmospheric generating system (SEGS) in solar only operation

turbidity at elevated sites. Values for water vapour mode and without thermal energy storage (Fig. 5).and ozone are taken from the NASA Water Vapour The model is made up of two parts, one that

Project (NVAP) and the NASA Total Ozone simulates the energy balance of the solar field,

Mapping Spectrometer project (TOMS), respec- and a second that represents the conversion

tively. efficiency of the Rankine steam cycle as a func-

The direct solar irradiation obtained by this tion of time. The module calculates the hourly

method shows good agreement (65% with re- thermal power output of the solar field and the

spect to the annual mean) with data from selected electricity yield of the SEGS from the solar direct

sites derived from WMO-WRDC data, where DNI normal radiation generated in the meteorology

had to be calculated from global horizontal values module for each point of the map. For the

using empirical conversion models (Mannstein et simulation of the collector field energy output, a

al., 1999). Measured direct radiation data is very simplified stationary model of the physical prop-

poor in the regions in question, so we had to erties and behaviour of the collector is applied.compare our results to hourly time series from The physical parameters represent the LS-3

1998 measured in Almeria, Southern Spain. With parabolic trough collectors installed in some of

respect to this high quality data set, we observed the plants in California (Table 1). A detailed

errors of less than 65% for monthly sums of DNI description of the performance model and the

(Broesamle, 1999; Mannstein et al., 1999; Schil- related set of parameters can be found in

lings, 1999). On an hourly basis, the coincidence Broesamle (1999).

is very good for clear days, but unsatisfactory for A one-axis tracked parabolic trough collector

cloudy days, although the general daily pattern shows certain losses that depend only on its

and the daily sum of DNI is again represented geometrical structure and on the angle of inci-

quite well (Fig. 3). Other data sets displaying one dence. The following geometric losses are consid-

typical day per month in hourly resolution for ered in the model.Taroudant and Ouarzazate (Morocco), Ouwairah Cosine losses represented by j consider theCO S(Jordan), Tahrir (Egypt) and Tenerife (Spain) smaller active area of projection of the collec-

were compared to our results showing differences tor due to non-perpendicular irradiation.


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Fig. 3. Measured and calculated direct normal irradiation on clear and cloudy days in Almeria, Spain (Mannstein et al., 1999).

The ground measured data was kindly provided by Schlaich, Bergermann & Partner, Stuttgart.

The incident angle modifier represented by the absorber tubes at the end of each collector

j considers the distortion of the reflected row. End losses are described by the interceptIA Mimage of the sun at non-perpendicular incident factor j .Eangles. Shading losses within the solar field are de-

Collector end-losses are the portion of the scribed by the shading factor j .Ssunlight that is reflected outside of the range of The second important group of loss mechanisms

Fig. 4. North Africa annual sum of direct normal irradiation (1998).


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Fig. 5. Basic solar thermal power plant configuration simulated by STEPS.

are the optical losses that occur by non ideal thermal losses of the hot collector elements duringreflection and absorption of solar radiation. The operation. Losses caused by thermal convection

optical efficiency is described by the: are in a first approximation proportional to the

reflectivity of the mirrors r, difference of the mean surface temperature of the

transmission factor of the mirror glass cover absorber tube T (653 K) and the ambient tem-At , perature perceived by the absorber tube exposed1

optical precision of the mirror surface (quality to the sunlight T (330 K), that both areambfactor) g, assumed to be constant during operation. Convec-

transmission factor of the glass tube that tion losses are quantified by the convection loss2

surrounds the absorber tube t , factor U (W/m K). Thermal radiation losses are2 coefficient of absorption of the absorber tube described by a term that is proportional to the

a. difference of the same temperatures but to theThe third group of loss mechanisms considers the power of four. The intensity of thermal radiative

Table 1. Selected properties of the LS-2 and LS-3 parabolic trough collectors

LS-2 LS-3

Aperture 5.00 m 5.76 mLength SCA (solar collector assembly) 48 m 99 mDistance between rows 1215 m 1617 m

2 2Reflecting surface per SCA 235 m 545 m

2 2Convection loss factor 2 W/ m K 2 W/ m KDiameter of the absorber tube 0.07 m 0.07 mConcentration ratio 72 82Reflectivity of mirror 0.93 0.93Coefficient of absorption of absorber tube 0.94 0.96Coefficient of emission of absorber tube 0.24 0.17Coefficient of transmission of mirror 0.98 0.98Coefficient of transmission of glass tube 0.95 0.96Collector peak efficiency 66% 68%


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emission losses is described by the coefficient of of cooling systems, that is dry cooling system,

emission of the absorber tube surface and the evaporation cooling tower and once-through cool-2 4

Boltzmann constant s (W/m K ). The simula- ing. The resulting net power is integrated to the

tion model yields the thermal power output of the net annual solar electricity yield E for eachyearsolar field according to the formula: point of the map (Fig. 6).

The simulation showed an acceptable accuracy,

~Q 5A ? DNISF SF representing well the geometrical effects of the

angle of incidence varying with time and place forp? U]]]F? j h 2 ? T 2 Ts dgeo ? opt A amb all latitudes between 0 and 408 North and South.C? DNI

A comparison to measurements at the originalp? ? s 4 4]]] G2 ? T 2 T (3)s d SEGS in California (Cohen et al., 1999; DudleyA ambC? DNI

et al., 1994) showed a very good qualitative andwith the collector area of the solar field A , the quantitative agreement with the actual physicalSFfactor of concentration of the parabolic trough C, behaviour of those plants (Fig. 7). The achievedthe direct normal irradiation DNI, the geometric accuracy of the average daily energy yield ofefficiency j 5j j j j and the optical better than 65% was considered to be sufficientgeo IAM S E COSefficiencyh 5 a r g t t (Table 1). for the purpose of the first prototype of STEPS.opt 1 2

The power block model is a very simple Most of the parameters of the simulation modelformula that considers the nominal conversion have been considered as constants. Further im-efficiency of the steam cycle, its part load be- provements of the performance model will behaviour and the parasitic losses of the power achieved in the future by considering the in-plant. The net electric power output of the plant is fluence of varying ambient and operation tem-

peratures. For example, ambient temperatures alsok

~QSF can be derived from satellite data (Broesamle,~ ]]P 5 Q ?h ? 2 P 2 Pnet S F nom Par,SF Par,PBF G~Q 1999) and processed by the GIS. Dynamic start-SF,nomup behaviour, thermal inertia of the solar field,(4)wind effects, thermal energy storage and hybrid

fuel-solar operation modes are other topics forbeing h the nominal efficiency of the powerno m~

future enhancement of the simulation model.cycle and Q the rated nominal thermalSF,nompower output of the solar field. The exponent k

describes the partial load behaviour of the power5. COST ESTIMATE

cycle efficiency. P and P represent thePar,SF Par,PBThe electricity costC of a solar thermal powerparasitic electricity consumption of the solar field el

station operating in solar-only mode dependsand of the power block, respectively. Themainly on its investment cost I , the infra-parasitic losses are also a function of load. The plantstructure cost for connecting the plant to roadsmodel considers a minimum irradiation intensity

2and the public grid I , the annual runningof 200 W/ m for power block start-up. Efficiency in fexpenses of operation and maintenance C , theand parasitic losses are calculated for three types O& M

2Fig. 6. North Africa annual solar electricity yield per km of land area.


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Fig. 7. Measured and calculated overall net solar-to-electricity efficiency including parasitic losses of SEGS VI on July 1, 1997

(Cohen et al., 1999). The original measured radiation data was used as input to the model. A considerable difference between the

measured and the predicted efficiency occurs during start-up, as the model neglects the thermal inertia of the solar field and the

power block (lower graph). In the real plant, part of the energy used for heating up in the morning is recovered during the day

and in the evening. In this period, the model underestimates plant efficiency. Therefore, this source of error compensates itself to

a large extent in the course of a day. As a consequence, the measured and calculated daily electricity yield differs by only 2%.

economic lifetime n, the mean capital interest rate distinction has been made between the different

i, and the net annual solar electricity yield E at economic environments of the countries of theyearthe respective site Maghreb. Especially the cost of infrastructure and

personnel will vary strongly from country ton

i ? (1 1 i ) country. Country-specific parameters (country]]]] ? (I 1I ) 1 C

n plant inf O&M(1 1 i) 2 1]]]]]]]]]]C 5 (5)el Eyear

Table 2. Sensitivity of the electricity cost of a 200 MW SEGSain solar only mode (Broesamle, 1999)The sensitivity of the electricity cost to the

Parameter varied by e.g. 1100% RelativeC -elvariation of selected input parameters is given in variationTable 2. Table 3 shows the most important Annual direct normal irradiation 21.40

Annual cloud index 21.40economic parameters used as reference for theGeometrical and optical efficiency 21.35study on Northern Africa. Due to the influence onOverall investment 10.95

the power plant efficiency, the different types of Power block investment 10.94Average interest rate 10.53cooling systems require different sizes of the solarAnnual aerosol optical thickness 10.41collector and the corresponding investment inThermal losses 10.31

order to yield the same rated power of 200 MW. Insurance cost 10.08Cost of operation and maintenance 10.08Fig. 8 shows the procedure of calculating theMean salaries 10.05

infrastructure cost from the distances to the Atmospheric water vapour 10.04nearest road, grid and cooling water source de- Atmospheric ozone 10.02

arived from the well known Digital Chart of the Values indicate relative sensitivities. An absolute 100%World (DCW). In this first version of STEPS, no variation may not be realistic for some parameters.


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Table 3. Selected parameters of the economic model for Northern Africa

Type of power plant Parabolic trough, LS-3

Capacity 200 MW

Solar collector aperture area2

using a dry cooling system 1.228 km2

using an evaporation cooling tower 1.124 km2using a once-through cooling system 1.075 km

Required land area (cost free) |3 times solar collector area

Plant investment (I )plant

using a dry cooling system 460 Mio USDusing an evaporation cooling tower 420 Mio USDusing a once-through cooling system 405 Mio USD

Infrastructure costs (I )in f

per km road 185,000 USDper km high tension grid 125,000 USDper km pipeline (once-through) 2 Mio USDper km pipeline (evaporation) 305,000 USD

Operating costs (C )O&MPersonnel 2.7 Mio USD per yearOperation and maintenance 1% of investment per yearInsurance 1% of investment per year

Economic lifetime (n) 25 yearsAverage interest rate (i ) 8%

data base) and a more detailed calculation of the tool can be used for the assessment, evaluation

economic performance will be integrated in a and ranking of sites of solar thermal power plant

future version of STEPS. projects, giving project developers, governments,

The resulting solar electricity cost is displayed intergovernmental institutions and other decisionin Fig. 9. Figs. 7 and 9 show the large technical makers a well founded basis for planning and

and economic potential of solar power generation designing the build out of solar power capacity

in North Africa. Theoretically, on less than 1% of world wide. The tool has been applied successful-

the suitable area in North Africa, the total 1997 ly on North Africa, showing the large technical

world electricity demand of 12,000 TWh / year and economical potential of solar thermal power

could be generated at a cost of less than 12 in this region.

cents / kWh (price level 1998, solar only opera- It must be pointed out that the results of the

tion, see also Table 4). According to the remain- first prototype of STEPS shown here should not

ing potential of cost reduction of solar thermal be used directly for investment decisions, as they

power technology, this cost will come down only represent one possible power plant configu-

within a decade to less than 6 cents / kWh (Ener- ration and are based on low resolution and

modal Engineering Ltd, 1999; Trieb, 1999). With partially insufficient input data (e.g. no radiationavailable support from the World Bank and in climatology was used). They are primarily meant

hybrid operation mode, a competitive electricity for demonstrating the concept.

cost can already be achieved today. For in-depth analysis of a countrys solar

thermal power potential and for detailed site

ranking on a feasibility study level, STEPS is

presently enhanced by an extensive country data6. CONCLUSIONS

base, further GIS data on natural and politicalSTEPS enables the computer-based assessment risks, soils, hydrology and natural reserve areas, a

of solar irradiation, geographic frame conditions, more sophisticated and variable performancerequirements of infrastructure and the expected model, a more accurate method of cloud and

electricity potential and cost of solar thermal aerosol assessment, a long term radiationpower stations for large regions, providing results climatology and other issues. With the recentwith high spatial and temporal resolution. The Space Shuttle Radar Topography Mission, higher


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Fig. 8. Calculation of infrastructure costs from the distances to roads, grids and cooling water.


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Fig. 9. North Africa solar electricity cost per kWh (200 MW SEGS, solar only, price level 1998).

Table 4. Ranking of areas by the solar thermal power gene- Cohen G. E., Kearney D. W. and Price H. W. (1999) Per-a ,b

ration cost per kWh in North Africa formance history and future costs of parabolic trough solarelectricity systems. In Proceedings of the 9th International

Power Available PotentialSymposium on Solar Thermal Concentrating Technologies,

generation area in of power2 Odeillo, France, 1998, J. Phys. IV, EDP Sciences.

costs in 1000 km generationDudley V. E., Kolb G. J., Mahoney A. R., Mancini T. R.,cents / kWh in TWh

Matthews C. W., Sloan M. and Kearney D. (1994). Test#12 297.1 37,994 Results of SEGS LS-2 Solar Collector, Sandia, Albuquer-

1213 1107.9 138,047 que, Sandia report SAND94-1884.1314 2999.4 339,939 Enermodal Engineering Ltd (1999). Cost Reduction Study for1415 3896.9 412,603 Solar Thermal Power Plants, World Bank/ GEF, Washington1516 1986.3 203,575 DC.1617 1340.2 135,973 Hess M. (1998). Personal correspondence.$17 980.6 93,425 Iqbal M. (1983). An Introduction to Solar Radiation, Academ-

Total 12,608.4 1,361,556 ic Press, Toronto.a Kopke P., Hess M., Schult I. and Shettle E. P. (1997). GlobalTotal size of the analysed area in North Africa was 14.3

2 Aerosol Data Set, Max Planck Institute for Meteorology,Mio km .b Hamburg, Report no. 243.Suitable area with respect to land cover and land slope was

212.6 Mio km . Mannstein H., Broesamle H., Schillings C. and Trieb F. (1999)

Using a METEOSAT cloud index to model the performanceof solar thermal power stations. In Proceedings of the

EUMETSAT Meteorological Satellite Data Users Confer-resolution topographic data is now available forence, Copenhagen.

more detailed siting purposes. Olson J. S. (1994) Global ecosystem framework-definitions. InUSGS EROS Data Centre Internal Report, USGS EROSData Centre, Sioux Falls, SD, 37 S.

Schillings C. (1999). Calculation of the Direct-normal Ir-REFERENCES radiation for the using in Solar Thermal Power Stations.

Application of a Physical Clear-sky Model using Satellite

Bird R. E. (1984) A simple solar spectral model for direct- Data, University of Bonn, Masters thesis.normal and diffuse horizontal irradiation. Solar EnergyTrieb F. (1999) Competitive solar thermal power stations until

32(4), 461.2010 the challenge of market introduction. In 9th

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Solar Radiation in Africa