Solar energy scenarios in Brazil, Part one: Resource ... - LEPTEN

ARTICLE IN PRESS2846F.R. Martins et al. / Energy Policy 36 (2008) 2843–2854conditions.CCI ¼ ½L L clrŠ(1)½L cld L clr ŠThe L clr and L cld values for each image pixel are obtained bystatistical analysis of satellite images (Martins et al., 2007). Thekey point in this methodology is its independence from thecalibration factors and electronic degradation of satellite sensors.It was assumed that CCI equal to 1.0 denotes overcast sky withaltostratus (As) clouds. Previous studies showed that As clouds arethe most common cloud type in continental areas in the latituderange where Brazil is located (Warren et al., 1986).It was assumed that the entire cloud coverage is formed by asingle type of cloud with its base at 500 mb and uniformly distributedin two atmospheric layers in order to solve the solar radiative transferequation for the overcast sky condition. The Stephens parameterizationwas assumed to determine the total extinction coefficient and thetotal liquid water content of the cloud (Stephens, 1978).The diffuse solar radiation is determined by subtracting thedirect component from the global solar radiation at the horizontalsurface. The normal direct irradiation (DNI) is estimated assumingthat the absorption by clouds is not significant and that thecontribution of scattering of the solar radiation by clouds may beadded to the atmospheric transmittance in clear sky conditions.The solar irradiation on a tilted plane was obtained by using thePerez et al. (1987) methodology.The reliability of the solar irradiation estimates provided bymodel BRASIL-SR was evaluated in two tasks: (i) throughcomparison with estimates provided by the core radiation transfermodels adopted by the SWERA to map the solar energy in othercountries taking part in the project and (ii) through thecomparison among the estimates with solar radiation fluxmeasured at the surface (ground truth). The results obtained inthe first task demonstrated that BRASIL-SR presents a similarperformance as any other radiative transfer model adopted inSWERA (Beyer et al., 2004; Martins, 2003).In the second task, the solar flux estimates provided by theBRASIL-SR were compared with measured values acquired atseveral ground sites spread along the Brazilian territory—ProjectSONDA (Martins et al., 2005) and AWS (Automated WeatherStations) operated by CPTEC/INPE (www.cptec.inpe.br/aws). Themodel BRASIL-SR achieved a similar performance in all geographicregions of the country with a slight overestimation of the globalsolar flux—mean bias error (MBE) roughly 6%. The root meansquare error (RMSE) was about 13% throughout the Brazilianterritory. Table 1 presents a summary for mean deviationobserved in solar radiation estimates provided by BRASIL-SR.The larger deviations were observed in the Amazon region, whichpresents larger precipitation all through the year and the groundstations maintenance was more complex reducing the confidenceof ground data (Pereira et al., 2006).The direct beam solar irradiation (DNI) values provided byBRASIL-SR overestimated the ground data and presented largerTable 1Summary of the deviations presented by global solar estimates provided byBRASIL-SR in each Brazilian geographic regionGeographical region MBE (Wh/m 2 ) MBE (%) RMSE (%) Correlation factorNorthern 353.48 7 15 0.85Northeastern 306.75 6 13 0.97Midwestern 272.11 5 13 0.89Southeastern 249.10 5 14 0.93Southern 259.49 5 12 0.97The last column presents the correlation factor among ground data and modelestimates.Table 2Deviations presented by DNI estimates provided by BRASIL-SR when comparedwith the ground data acquired in SONDA networkGround site MBE (Wh/m 2 ) MBE (%) RMSE (%) Correlation factorSão Martinho da Serra 872.8 13.0 19.7 0.96Florianópolis 1147.5 22.7 29.2 0.95Petrolina 718.4 12.7 18.3 0.96All sites 903.2 15.1 21.6 0.95The last column presents the correlation factor among ground data and modelestimates.deviations (MBE E15% and RMSE E22%). The simple parameterizationused to model the cloud transmittance of the direct beamis responsible for the overestimation of direct beam irradiation,mainly in the overcast days (Pereira et al., 2006). Unfortunately,only few ground sites collect direct solar beam radiation in Brazilwith the high quality required to be used in model validation.Table 2 presents the estimate deviations for all the ground sitesavailable for DNI validation.3.2. Global solar irradiationFig. 3 presents annual and seasonal averages of daily globalsolar irradiation in Brazil. In spite of the different climate andenvironmental characteristics of the Brazilian territory, one canobserve that the global irradiation is fairly uniform. The maximumvalue—more than 6.5 kWh/m 2 day—occurs in the semi-arid areaof the Brazilian Northeastern region (around 101S/431W). Thisarea presents a semi-arid climate with low annual precipitation(around 300 mm) and the lowest mean cloud cover of Brazil(INMET, 2007) due to the influence of the tropical high pressureassociated with the South Atlantic tropical anticyclone.The minimum value, around 4.25 kWh/m 2 day, was obtainedfor the coastal area of the Southern region of Brazil, which ischaracterized by a relatively large precipitation. The temperateclimate and the influence of the cold systems associated with theAntarctic Polar anticyclone contribute to enhance the nebulosityin the region, mainly in winter months. Fig. 4 shows theclimatology of annual precipitation and cloud cover for theBrazilian territory taking into consideration ground data collectedfrom 1931 till 1990.The annual sum of daily horizontal global solar irradiation inany Brazilian region (1500–2500 kWh/m 2 ) is greater than thosefor the majority of the European countries such as Germany(900–1250 kWh/m 2 ), France (900–1650 kWh/m 2 ) and Spain(1200–1850 kWh/m 2 ) where projects to harness solar resourcesare greatly disseminated, some with huge government incentives(European Database for Daylight and Solar Radiation, 2007).It is worthy of note that the Southern region is subjected tolarger mean irradiances than the Northern region during thesummer season notwithstanding the latter being much closer tothe equator. This is explained by climate characteristics of Amazonregion with large precipitation and persistent cloud cover duringthe summer months owing to the strong influence of theIntertropical Convergence Zone (ITCZ). The North and Centralregions of Brazil receive the largest solar irradiances particularlyduring the dry season from July till September, when precipitationis low, and clear sky days predominate.Fig. 5 shows the annual and seasonal variability of daily sumsof global solar irradiation. The annual variability, V annual , wasdefined here as the average of the intra-annual variabilityobserved for each day of the year. The intra-annual variability,V d,y , for the day d of the year y is defined as presented in Eq. (2).SD d stands for the mean quadratic deviation of global solar