www.elsevier.com/locate/solener

Solar Energy 81 (2007) 1295–1305

Potential of solar electricity generation in the European Unionmember states and candidate countries

Marcel Suri *, Thomas A. Huld, Ewan D. Dunlop, Heinz A. Ossenbrink

European Commission, DG Joint Research Centre, Institute for Environment and Sustainability, Renewable Energies Unit, TP 450,

via E. Fermi 1, I-21020 Ispra (VA), Italy

Received 22 June 2006; received in revised form 20 November 2006; accepted 26 December 2006Available online 14 February 2007

Communicated by: Associate Editor Hansjorg Gabler

Abstract

During the years 2001–2005, a European solar radiation database was developed using a solar radiation model and climatic data inte-grated within the Photovoltaic Geographic Information System (PVGIS). The database, with a resolution of 1 km · 1 km, consists ofmonthly and yearly averages of global irradiation and related climatic parameters, representing the period 1981–1990. The databasehas been used to analyse regional and national differences of solar energy resource and to assess the photovoltaic (PV) potential inthe 25 European Union member states and 5 candidate countries. The calculation of electricity generation potential by contemporaryPV technology is a basic step in analysing scenarios for the future energy supply and for a rational implementation of legal and financialframeworks to support the developing industrial production of PV. Three aspects are explored within this paper: (1) the expected averageannual electricity generation of a ‘standard’ 1 kWp grid-connected PV system; (2) the theoretical potential of PV electricity generation;(3) determination of required installed capacity for each country to supply 1% of the national electricity consumption from PV. The anal-ysis shows that PV can already provide a significant contribution to a mixed renewable energy portfolio in the present and future Euro-pean Union.� 2007 Elsevier Ltd. All rights reserved.

Keywords: Solar radiation; Photovoltaic electricity generation; Geographical information system

1. Introduction

The generation of solar electricity from photovoltaics(PV) is beginning to penetrate the energy market in thosecountries, where clear and stable policy commitments havebeen made. In Europe, the example of Germany demon-strates how a policy has stimulated PV growth even inregions with moderate solar energy resource. Although inrecent years other European countries have adopted simi-lar policies (e.g. Spain, Italy, Greece, and the Czech Repub-lic), PV technology is still not fully appreciated in many

0038-092X/$ - see front matter � 2007 Elsevier Ltd. All rights reserved.

doi:10.1016/j.solener.2006.12.007

* Corresponding author. Tel.: +39 0332 786661; fax: +39 0332 789992.E-mail addresses: marcel.suri@jrc.it (M. Suri), thomas.huld@jrc.it

(T.A. Huld), ewan.dunlop@ec.europa.eu (E.D. Dunlop), heinz.ossenbrink@ec.europa.eu (H.A. Ossenbrink).

regions, one of the main reasons being a lack of clearunderstanding of its potential.

One of the four factors1 determining the economic per-formance of the PV system is the solar energy arriving atthe surface of the Earth. Although the total amount of thisenergy resource far exceeds human needs, its exploitation isdetermined by the knowledge of geographical variabilityand time dynamics. The geographical analysis of the avail-ability of the primary solar energy resource can improveour understanding of the potential PV contribution tothe future energy and economic structures and thus con-tribute to setting up effective policies.

1 The other three factors being the cost per unit or installed peak power(€/kWp), the lifetime, and the operational cost including capital cost.

1296 M. Suri et al. / Solar Energy 81 (2007) 1295–1305

The geographical dependency and distributed nature ofsolar electricity generation impose questions that requirespecific location-dependent answers. Although various dat-abases and estimation tools are available worldwide (Euro-pean Solar Radiation Atlas, Meteonorm, NASA SSE,SODA, Satel-Light, etc.; see Wald, 2006), none of themfully matched our needs:

• open data and software architecture;• climatic and geographic data at higher spatial resolu-

tion, integrated into a GIS system;• map-based interface providing easy-understandable

information also for non-professionals.

This has led to the development of the PhotovoltaicGeographic Information System (PVGIS) at the JointResearch Centre of the European Commission since theyear 2001. PVGIS combines the long-term expertise fromlaboratory research, monitoring and testing with geograph-ical knowledge. It is used as a research tool for the perfor-mance assessment of PV technology in geographicalregions, and as a support system for policy-making in theEuropean Union. The web interface was developed to pro-vide interactive access to the data, maps and tools to otherresearch and education institutes, decision-makers, PV pro-fessionals and system owners as well as to the generalpublic.

The aim of this paper is to provide an analysis ofnational and regional differences of solar electricity gener-ation from photovoltaic systems in the 25 member states,and 5 candidate countries (Bulgaria, Croatia, the formerYugoslav Republic of Macedonia, Romania, and Turkey)of the European Union (abbreviated as EU25+5). We takeinto account PV systems with flat modules mounted in hor-izontal, vertical and optimally tilted position. The theoret-ical potential is compared to what can be achieved in theshort-term, assuming the current PV growth. Rather thanfocusing on primary solar radiation, we have looked atthe generated kilowatt-hours from each kilowatt-peak(kWp) of a typical PV system, as this information can bedirectly used in economic and environmental assessments.Although this analysis focuses on the European Unioncountries, the data and maps cover the whole Europeansubcontinent and the neighbouring regions.

2. Data and methodology

2.1. European solar radiation database in PVGIS

The solar radiation database for the European subcon-tinent was developed using the solar radiation modelr.sun (Suri and Hofierka, 2004) and dedicated programsintegrated into the GIS software GRASS (Neteler andMitasova, 2002; GRASS, 2006). The r.sun algorithms arebased on equations published in the European Solar Radi-ation Atlas (ESRA, 2000). The model estimates beam, dif-fuse and reflected components of the clear-sky and real-sky

global irradiance/irradiation for horizontal or inclined sur-faces. The main input parameters to the model were solarradiation from 566 ground meteorological stationstogether with the ratio of diffuse to global radiation fromthe same set of stations (source: ESRA, 2000), the Linkeatmospheric turbidity (Remund et al., 2003) and a digitalelevation model (DEM) derived from SRTM-30 data(SRTM, 2006). The models account for sky obstruction(shadowing) by local terrain features, calculated from theDEM.

The spatial resolution of the resulting grid data layers is1 km · 1 km. The primary database represents the period1981–1990 and it contains 12 monthly averages and theyearly average of the following climatic parameters:

• daily global irradiation on a horizontal surface;• ratio of diffuse to global horizontal irradiation;• clear-sky index (characterizes cloudiness of the sky).

The 1-km grid resolution is determined by incorporationof the DEM data, and therefore the detailed structure ofthe terrain features (elevation and shadowing) is well repre-sented in the solar radiation data. On the other hand, thelimited number of available ground measurements andthe information content and accuracy of the Linke turbid-ity factor do not represent the atmospheric conditions inthe same level of spatial detail.

The accuracy of the modelled values in the database wasevaluated against the input meteorological data used in thecomputation. Comparing the yearly averages of the dailyglobal horizontal irradiation, the mean bias error (MBE)is 8.9 Wh/m2 (0.3%) and the root mean square error(RMSE) is 118 Wh/m2 (3.7%) for the whole dataset. Thisanalysis provides information about the errors only in loca-tions for which the measurements are known. Therefore across-validation was applied (using the same input meteo-rological data) to estimate the predictive accuracy of themodel that better describes the distribution of errors fur-ther from the locations with known measurements. Theaverage yearly MBE from cross-validation is smaller:1.1 Wh/m2 (0.03%), but the range of monthly averages ofMBE is higher – from �2.5 Wh/m2 in January to4.4 Wh/m2 in August. The cross-validation RMSE ishigher, and the yearly average is 146 Wh/m2 (4.5%). ThePVGIS method simulating irradiation for inclined planeshas been compared with measurements at the Ispra meteo-rological station by Kenny et al. (2006). This resulted in anannual overestimation by PVGIS by 3.2%, one of the rea-sons being partial shadowing of measured values by nearbybuildings and trees.

The details of the solar radiation model and computa-tional approach can be consulted in our previous works(Suri and Hofierka, 2004; Suri et al., 2005).

The primary data (representing average values of theperiod 1981–1990) are used in combination with developedtools for calculation of various products that are related tosolar electricity generation, such as:

M. Suri et al. / Solar Energy 81 (2007) 1295–1305 1297

• global irradiation for horizontal and inclined surfaces;• clear-sky and average real-sky daily profile of irradi-

ances (considering also terrain shadowing);• average beam, diffuse and reflected components of the

global radiation;• average electricity generation from fixed and tracking

PV systems;• optimum inclination and orientation of fixed PV mod-

ules to maximize energy yields;• electricity output of PV systems considering also ambi-

ent temperature.

2.2. Estimation of solar electricity potential

The PVGIS solar radiation database was used for anassessment of the potential solar electricity generation byPV modules mounted at horizontal, vertical and optimalinclination. Horizontal mounting is not often used exceptwhen building integration considerations demand it. How-ever, it is useful as a baseline estimate, also because manysources of radiation data only provide the irradiation ona horizontal plane. Comparing the results from horizontalmounting with those of optimal and vertical mounting willaid in using the present results to make estimates forinclined mountings when using different data sets givingonly horizontal irradiation. We have considered the mostwidespread grid-connected PV technology, installed withinthe existing building infrastructure in residential areas. Theannual total of electricity generated from a PV system, E

(kWh), was calculated using the following equation:

E ¼ P kPRG; ð1Þ

where Pk is the unit peak power (assumed to be 1 kWp inour calculation), PR is the system performance ratio, andG is the yearly sum of global irradiation on a horizontal,vertical or inclined plane of the PV module (kWh/m2).

The size of PV systems (installed peak power, Pk) is typ-ically measured in watt-peak (Wp) and it characterizes thenominal power output of the PV modules at Standard TestConditions (STC; see IEC/TS 61836, 1997), i.e. when theirradiance in the plane of the PV modules is 1000 W/m2

and the temperature of the modules is 25�C. The advantageof using this measure is that it does not require knowledgeof the PV conversion efficiency or the module area. In the-ory the PR in Eq. (1) would equal 1 for a system operatingconstantly with the STC efficiency. In practice, the outputof a PV system is lower than the peak power, even at anirradiance of 1000 W/m2. One reason is the operating tem-perature that is typically higher than 25 �C and which tendsto lower the PV efficiency. The other factors are losses dueto angular and spectral variation, and system losses ininverters and cables. The ratio between the actual outputand the nominal output is therefore expressed by a grossmeasure, the performance ratio PR (see IEC 61724,1998). A typical value for a roof-mounted system withmodules from mono- or polycrystalline silicon is around

0.75 and this value is assumed in our furtherconsiderations.

A part of the analysis was focused on urban residentialareas where most people live. To extract data for residentialareas, the estimated solar electricity potential was overlaidwith the CORINE Land Cover (CLC90) database (Hey-mann et al., 1994), namely with class 11 (urban fabric, i.e.we did not consider other urban land such as industrialand commercial sites, transport infrastructure, city parks,etc.). The CLC90 database is available at 100-metres gridresolution for most of the EU25+5 countries online(EEA, 2006). However, in Sweden, Malta, Cyprus, Croatiaand Turkey the CLC90 data are not available and we had touse the less accurate urban boundaries excerpted from mapsand from the Global Land Cover 2000 database (seeGLC2000, 2006). These data do not distinguish betweenresidential and non-residential zones in settlements.

In regional planning and decision making, the informa-tion is analyzed at the level of administrative boundaries.Administrative boundaries (according to Eurostat NUTS,level 3) were therefore used to synthesize PV estimationsand to calculate statistics (average, minimum, maximum,probability distribution) at the level of individual countriesand their regions.

3. Results

The results reveal significant national and regional dif-ferences within the 25 EU member states, and 5 candidatecountries, determined by latitude, continentality, terrainand local climatic variations.

To outline geographic regions of solar electricity pro-duction, we first assume horizontally-mounted PV mod-ules. Inclining the PV modules southwards to anoptimum angle maximises yearly energy yields and this isthe most typical way how PV modules are installed. Onthe other hand, PV is also used as a building integratedmaterial (cladding) on facades of buildings. Therefore wehave compared the energy gains and losses for PV modulesinclined at the optimum angle and vertically.

The two maps in Fig. 1 present the potential energy pro-duction for each installed kWp of a PV system withmodules mounted horizontally, and at optimum angle (cal-culated from Eq. (1)). The regional data assuming all threetypes of mounting are further summarized to compare thepotential between the EU25+5 countries as well as betweenregions within each country (Fig. 2). The countries aresorted in descending order according to the national aver-ages. The extremes of the dash line show the minimum andmaximum values within each country. Our aim was tofocus on areas where people live and where PV is mostlyinstalled. Therefore the upper and lower edges of the boxesdelineate 5% minimum and 95% maximum occurrenceprobability of power production in urban residential areas.These limits for populated areas are taken out to eliminatethe most extreme values from the analysis (mainly in highmountains and deep valleys).

Fig. 1. Yearly sum of electricity generation from a 1 kWp PV configuration with modules: (a) at horizontal position; (b) optimally inclined, to maximiseyearly energy yield (kWh/kWp).

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3.1. PV modules mounted horizontally

The yearly sum of the electricity generated for each kWp

of PV with horizontal modules in EU25+5 countriesranges from about 470 up to 1390 kWh (Fig. 2a). Thelower limit is strongly determined by the shadowing effectof terrain in mountains – for unshadowed locations theyearly sums do not go below 530 kWh/kWp in Northern

Scandinavia. Taking into account only populated areas,the range of the solar electricity potential is much narrower– 630 kWh/kWp in the Northern Finland up to 1330 kWh/kWp in Malta. In other words – comparing only areaswhere people live – the same PV system with horizontalmodules will produce about 2.1-times more electricalenergy in Malta than in the extreme North of Fin-land. However, there are large geographical differences

Fig. 2. Yearly sum of the electricity generated by a typical 1 kWp PV system in the EU 25 Member States and 5 Candidate Countries (kWh/kWp) withmodules mounted: (a) horizontally; (b) at the optimum angle; and (c) vertically. The solid line represents the country’s average value. The extremes of thedash lines show the minimum and maximum values in each country. The box plot depicts the 90% of occurrence of values in urban residential areas.

M. Suri et al. / Solar Energy 81 (2007) 1295–1305 1299

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and – when focusing to the urban residential areas in theEU25+5 countries – five climatic regions can be identified.

1. It is obvious that the highest potential for solar electric-ity generation is in Portugal, and in the Mediterraneanregion with strong peaks in cloudless summer (Malta,Cyprus, most parts of Spain, Italy and Croatia, South-ern France and Corsica, Greece and Southern Turkey).In this region in the urban residential areas, a typicalcrystalline silicon PV system generates annual electricitybetween 1100 and 1330 kWh per installed kWp.

2. Favourable climatic conditions are also found in theNorthern parts of Spain, Italy, Croatia, in FYR of Mac-edonia, and around the Black Sea (Romania, Bulgariaand Turkey) with abundance of solar resource and PVpotential in the range of 1000–1100 kWh/kWp per year.

3. Good conditions are found in France (except in theNorth) and also in most regions of Central Europe (Hun-gary, Slovenia, Austria, Slovakia and Southern Germany)with more continental summers, where yearly generationusually falls into the range of 800–1000 kWh/kWp.

4. Northwest Europe (Southern Ireland, England andWales, North France and Germany, Benelux, and Den-mark), Northern part of Central Europe (Poland, andmost parts of the Czech Republic) and the Baltic states(Estonia, Latvia and Lithuania) including South Swedenand Finland, have less favourable conditions. The dif-fuse radiation has a higher share and yearly generationis here expected to be mainly within the interval from700 to 800 kWh/kWp. Due to long daylight in summer,the yearly sums of solar electricity generation in the Bal-tic region are almost the same as in the lower latitudes ofWestern Europe, where a more humid climate isstrongly influenced by the Atlantic Ocean.

Fig. 3. Optimum inclination angle for a South-facing PV module, i.e. the angirradiation (degrees). The solid line represents the country’s average value. Th

5. From the point of view of solar electricity generation,the poorest regions in the European Union are in Scot-land and the North Sweden and Finland, where yearlygeneration falls below 700 kWh per installed kWp.

3.2. PV modules mounted at optimum angle

The main factors determining optimum inclinationangle of the PV modules (Fig. 3) are the geographical lati-tude, share of diffuse to global radiation, and – in moun-tainous areas – shadowing by local terrain features. Ingeneral, the optimum orientation of PV modules is dueSouth in the Northern Hemisphere. However, in someareas the optimum orientation might be slightly offsettowards East or West due to shadowing by localmountains.

If considering populated areas only, the optimummounting angle of the PV modules within Europe rangesfrom 28� in Western Peloponnesos (with a high concentra-tion of aerosols in the atmosphere) to 47� in NorthernScandinavia. In large parts of Europe (mainly between lat-itudes from 45–55�), the latitudinal gradient is weak, thediffuse component relatively high, and the optimum anglestays in the range of 33–36�, with some fluctuation depend-ing on regional climate.

The optimum inclination of PV in mountains is morevariable than in lowlands, as the energy yield stronglydepends on local dynamics of cloudiness and terrain shad-owing. This effect is noticeable for regions with high moun-tains, such as the Pyrenees, the Alps, Carpathians, and inScandinavia. The extreme case of Sweden shows that inlocations with strong terrain shadowing the module incli-nation close to horizontal provides best yields, due to thefact that very little direct sunlight reaches the modules,

le at which the module receives the largest amount of total yearly globale meaning of the lines and boxes in the plot is the same as in Fig. 2.

M. Suri et al. / Solar Energy 81 (2007) 1295–1305 1301

and hence the optimum angle is very flat or horizontal tocapture as much diffuse sunlight as possible.

Inclining the PV modules southwards from horizontal tooptimum angle increases the yearly electricity productionin urban areas by 9–26%, i.e. to levels of 760 (in Scotlandand Northern Scandinavia) up to 1510 kWh/kWp (inMalta and Portugal). The lowest relative contribution fromthe optimum inclination can be expected in SouthernGreece (9–10%). In Cyprus, and most regions of Greece,Turkey, FYR of Macedonia, and Bulgaria this contribu-tion does not exceed 12%. Optimum angle mounting inmost of the EU25+5 states increases electricity productionin the range of 12–16%. The highest benefit (above 16%)can obviously be reached in Scandinavia and Balticcountries.

In absolute numbers (Fig. 2b), the most electricity canbe generated in the Mediterranean islands, Portugal, inlarge parts of Spain, Southern France, and in the centraland Southern regions of Italy, Greece, Croatia, FYR ofMacedonia, and Turkey (above 1200 kWh/kWp per year).On the opposite site is the Baltic region, Scandinavia,British Isles, but also parts of Central Europe, NorthernFrance, and Benelux countries where the yearly yield goesbelow 900 kWh/kWp. In the rest of the EU the yearly yieldsare in the range of 900–1200 kWh.

3.3. PV modules mounted on facades

Compared to the optimum angle, PV modules mountedvertically have yearly yields from about 42–33% less in Por-tugal, and in the Mediterranean and Black Sea zone(Fig. 2c). From these regions, in direction to Central andNorthern Europe the difference diminishes to about 28%.Lower differences compared to the optimally inclinedmodules can be expected also in the Alps, Pyrenees,Carpathians and Scandinavian mountains. In SouthernScandinavia, Eastern regions of the UK and Baltic statesthe yearly loss from vertical mounting of PV modules issmaller than 28%. In the Northern Sweden and Finlandthis difference goes below 20%.

Fig. 4. Seasonal variation expressed by relative deviation of monthly averageBratislava (SK) and Stockholm (SE) for PV modules mounted: (a) horizontallrelative standard deviation (%).

Due to the abundance of sunlight, the highest yieldsfrom vertically mounted PV modules are still found inMalta, Sicily, Southern regions of Spain, France, Turkey,and Portugal (above 900 kWh/kWp per year). In the restof the Mediterranean region and in the Black Sea theyearly yields from 1 kWp system are in the range of 650–900 kWh with overlaps in France, FYR of Macedonia,Bulgaria and Romania, and countries of Central Europe.The electricity yields reduce to 650 kWh/kWp in the CzechRepublic, Poland Germany, Benelux, British Isles, Balticstates and Scandinavia.

Although the yields for vertical PV installation are smal-ler, one advantage is a better balanced seasonal profile(compare with Fig. 4). The vertically mounted PV modules,when mounted on buildings, can also contribute to savingson the conventional cladding material.

3.4. Seasonal variability

The seasonal distribution of the solar resource in Eur-ope is uneven and has to be considered for planning anoff-grid system or in electricity grid management by theutilities once PV starts to become a significant part of theelectricity production. The typical PV system with modulesmounted at the optimum angle produces 40% (in Spain) to60% (in Finland) of the yearly electricity yield in just foursummer months (May, June, July and August, i.e. onethird of the year). The seasonal variability increases fromSouth to North. While monthly averages of PV outputmay decrease from the yearly average in a range from�30% (Southeastern Spain) up to �100% (North fromthe Polar Circle) in winter, they increase in summer in arange from +20% (Southeastern Spain) up to +85% (Cen-tral Sweden). The seasonal variability is lower for verticallymounted modules, as can be seen in Fig. 4, providing anexample of three cities – Alicante (ES), Bratislava (SK)and Stockholm (SE). Fig. 5 aggregates the seasonal varia-tion of monthly averages of PV electricity generation (mod-ules mounted at the optimum angle) from the yearlyaverage expressed by the relative standard deviation (%).

s of PV electricity generation from the yearly average for Alicante (ES),y; (b) at optimum angle, and (c) vertically. Values in the brackets express

Fig. 5. Seasonal variation of monthly averages of PV electricity generation (modules mounted at the optimum angle) from the yearly average, expressed asrelative standard deviation (%).

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A similar variation can be observed within a day. Tak-ing into consideration that daily electricity consumptionpeaks around noon, solar electricity can provide a signifi-cant contribution to satisfying peak load demand and peakpower shaping, such as those originating from the increas-ing demand of air-conditioning systems.

3.5. Regional differences within countries

Large geographical differences can be observed not onlyat the continental level but also within countries. Fig. 6indicates regional disparities in some countries that mightinfluence the national strategies for implementation ofphotovoltaic solar electricity. To make a comparison atthe level of administrative territorial units, the regionalaverages of solar electricity yields for the urban residentialareas were calculated. The calculation for optimally-inclined PV modules is assumed, as this is the most typicalway how PV is mounted.

The results demonstrate that the largest variability ofPV electricity generation at the national level can be seenin France, Spain, and Italy. This is due to the geographicalextent of the countries as well as transitions in their cli-mates from the Atlantic (in the case of Spain and France)and the Alpine (in case of Italy) to the Mediterranean.

The differences between regions in solar electricity gen-eration in France reach as much as 500 kWh/kWp (whichis about 47% of the country’s average value), and in Italy470 kWh/kWp (38% of the average value). ConsiderableNorth–South differences in PV output, within quite short

geographical distances, can be seen in Croatia and Turkey,by about 380 and 370 kWh, respectively (34% and 28%,respectively) per year from each installed kWp, and some-what less in Greece 310 kWh and Portugal 220 kWh(25% and 16%, respectively).

In Spain, the difference in yearly solar electricity gener-ation between the provinces Huelva and Asturias for a1 kWp system with optimally-inclined modules can exceed450 kWh from each installed kWp, and this representsalmost 33% of the country’s average. As kWh are equiva-lent to Euro, for the end user this can create a considerableimpact on investment decisions.

The diversity of the regional solar electricity potential incountries such as FYR of Macedonia, Romania, Germany,and UK is smaller though not insignificant (the range ishigher than 150 kWh/kWp).

3.6. Theoretical PV potential

Today, PV electricity tends to come from a large num-ber of small power generators that are distributed domi-nantly in the residential areas. In order to lay out thescope and extent of realistic generation potential in a givencountry one of the first questions to address is, ‘how mucharea has to be covered by PV modules to meet the fullnational electricity consumption?’ Using the baseline of1 kWp system; this consists of PV modules with a total areaof �9.5 m2; then dividing the total national consumption(IEA, 2004) by the yearly electricity yield of a 1 kWp sys-tem mounted at the optimum angle will give outline estima-

Fig. 6. Regional differences of solar electricity generation from 1 kWp system (modules mounted at the optimum angle) compared to the country’s average(kWh/kWp).

M. Suri et al. / Solar Energy 81 (2007) 1295–1305 1303

tions. The theoretical surface of the PV modules in eachcountry depends on the electricity consumption and solarresource available and therefore it varies between 0.1%(Baltic states, Romania and Turkey) and 3.6% (Beneluxstates and Malta) of the country’s surface area (Fig. 7).On average, covering �0.6% of the EU25+5 territory byPV modules would theoretically satisfy its electricity con-sumption. This estimate is somewhat conservative; the lat-est PV module technologies have higher efficiency, so thearea covered per kWp is likely to decrease in the future.Furthermore, this calculation ignores cross-border electric-

Fig. 7. Theoretical PV potential: surface of PV modules mounted at the optimconsumption (expressed as % of the country’s area). The dashed line represen

ity trade (so for instance no allowance is made for Dutch orGerman electricity consumption being covered by PV inSpain). On the other hand, of course, the PV area doesnot translate directly into land area covered unless thePV modules are placed horizontally. From the geometricalpoint of view, the area of land needed for inclined modulesshould be less, but in practice this depends on the type ofinstallation and the need to avoid modules shadowing eachother.

For comparison, using the CORINE Land Cover data,we have identified that in countries such as Estonia and

um angle that would be needed to completely satisfy country’s electricityts the EU25+5 average 0.6%.

1304 M. Suri et al. / Solar Energy 81 (2007) 1295–1305

Bulgaria this theoretical surface of PV modules corre-sponds to the current extent of land fill and mineral extrac-tion sites. In countries such as Czech Republic, Lithuania,Latvia and Romania the theoretical PV surface is abouttwice that of existing land fill and mineral extraction sites.

3.7. Photovoltaic capacity needed to cover 1% of

electricity consumption

The average electricity generation of a typical 1 kWp PVconfiguration at the optimum angle was used to estimatethe installed PV capacity that would be needed in eachcountry of EU25+5 to provide 1% of the national electric-ity consumption (IEA, 2004).

Fig. 8. Surface of the modules (m2) per capita needed to satisfy 1% of the natisurface of a TV satellite dish with diameter 0.85 m.

Fig. 9. PV capacity needed to satisfy 1% of country’s electricity consumptionyearly world production of PV cells (MWp) in period 2000–2005 (Source: Eur

The supply of 1% of national electricity consumption bysolar electricity would require an installation of 0.1 m2–0.9 m2 of photovoltaics per capita (Fig. 8). Three countriesfall outside this range due to exceptionally high consump-tion per capita. This dimension of less than 1 m2 corre-sponds to what we regularly see installed on roofs,facades and balconies in the form of TV satellite dishes.Interestingly, in many countries the installation of theseTV reception dishes is directly subsidised by nationalgovernments.

The indicated installed capacity for 1% of share by PV isshown in Fig. 9. The closest to the PV share of 1% in Eur-ope is Germany, where at the end of year 2005, theinstalled PV capacity reached 1537 MWp (Eurobserver,

onal electricity consumption. For comparison, the dashed line represents a

(MWp). For comparison, the small bars in the upper right represent theobserver, 2006).

M. Suri et al. / Solar Energy 81 (2007) 1295–1305 1305

2006), which covers �0.25% of the electricity consumptionin Germany.

4. Discussion and conclusions

Grid-connected PV in Europe is still dependent onmarket support programmes. The success of individualnational initiatives demonstrates how tailored programscan drive the long-term growth of solar electricity. How-ever, many EU countries still do not consider photovoltaicsolar electricity as a key future technology to be addressedby policies. This may be due to the lack of knowledge ofthe solar electricity generation potential. This is despitethe fact that in many regions of Europe the solar energyresource is more generous than in Germany; a countrywhich, thanks in part to the contribution from its Renew-able Energy Act, has become a leading world player in arapidly expanding market.

Within our previous activities we have developed a map-based system to provide overall information in order toclearly and unambiguously present the European-wide sit-uation for solar electricity generation and to provide anobjective analysis of what current PV technology offers toEurope. In this paper we have focused on the EU memberstates and candidate countries, as development of renew-able-energies policies is high on the political agenda there.Our results contribute to understanding the spatial andtemporal complexity the solar electricity generation on acontinental scale where policy-making needs to take intoaccount geographical variability.

Further development of the PVGIS system is under wayalong two parallel lines:

• Implementing a new solar resource database that isderived from a 20-years series of Meteosat satelliteimages. This will provide higher regional accuracy andbetter statistics;

• Incorporation of technological and socio-economicparameters to the database that will enable analyses ofeconomic, technological and environmental aspects,such as cost of PV energy generation, energy paybacktime, and avoidance of CO2 emissions.

PVGIS is primarily meant to be an in-house decisionsupport system. However, to provide an access to the data-base and estimations to professionals and the general pub-lic, we have developed web-based interactive applications.Any location in Europe can be chosen by browsing andclicking on a map, choosing a country and city from a list,or by directly setting latitude/longitude values. The

monthly and yearly values are displayed in a separatewindow. For a selected module inclination and orientationthe user can get also an average daily profile of clear-skyand real-sky irradiances for a chosen month. The webapplications and supporting documentation can beaccessed at http://re.jrc.ec.europa/pvgis/.

References

EEA (European Environmental Agency), 2006. Corine land cover(CLC1990) 250 m – version 12/2000. <http://dataservice.eea.eu.int/dataservice/metadetails.asp?id=571>.

ESRA (European Solar Radiation Atlas), 2000. Fourth edition, includingCD-ROM. Editors: Greif, J., Scharmer, K.; Scientific advisors:Dogniaux, R., Page, J.K.; Authors: Wald, L., Albuisson, M., Czeplak,G., Bourges, B., Aguiar, R., Lund, H., Joukoff, A., Terzenbach, U.,Beyer, H.G., Borisenko, E.P.; published for the Commission of theEuropean Communities by Presses de l’Ecole des Mines de Paris, Paris,France.

Eurobserver, 2006. Barometre du solaire photovoltaıque. Systemessolaires No. 172. <http://www.observ-er.org/>.

GLC2000 (Global Land Cover database 2000), 2006. <http://www-gvm.jrc.it/glc2000/>.

GRASS (Geographic Resources Analysis Support System) GIS, 2006.<http://grass.itc.it/>.

Heymann, Y., Steenmans, C., Croisille, G., Bossard, S., 1994. CORINELand Cover Technical Guide. Office for Official Publications of theEuropean Communities, Luxembourg, EUR 12585 EN.

IEA Key World Energy Statistics, Edition 2004. International EnergyAgency. <http://www.iea.org/bookshop/add.aspx?id=144>.

IEC 61724, 1998. Photovoltaic System Performance Monitoring –Guidelines for Measurement, Data Exchange and Analysis. Interna-tional Electrotechnical Commission, TC 82 – Solar photovoltaicenergy systems.

IEC/TS 61836, 1997. Solar Photovoltaic Energy Systems – Terms andSymbols. Technical Report – Type 2. International ElectrotechnicalCommission, TC 82 – Solar photovoltaic energy systems.

Kenny, R.P., Huld, T.A., Iglesias, S., 2006. Energy rating of PV modulesbased on PVGIS irradiance and temperature database. In: Proceedingsfrom 21st European Photovoltaic Solar Energy Conference andExhibition, 4–8 October 2006, Dresden, Germany.

Neteler, M., Mitasova, H., 2002. Open Source GIS: A GRASS GISApproach. Kluwer Academic Publishers, Boston.

Remund, J., Wald, L., Lefevre, M., Ranchin, T., Page, J., 2003.Worldwide Linke turbidity information. In: Proceedings of ISESSolar World Congress. Solar Energy for a Sustainable Future, 14–19June 2003, Goteborg, Sweden.

SRTM (Shuttle Radar Topography Mission), 2006. <http://www2.jpl.na-sa.gov/srtm/>.

Suri, M., Hofierka, J., 2004. A new GIS-based solar radiation model andits application to photovoltaic assessments. Transactions in GIS 8,175–190.

Suri, M., Dunlop, E.D., Huld, T.A., 2005. PV-GIS: A web based solarradiation database for the calculation of PV potential in Europe.International Journal of Sustainable Energy 24 (2), 55–67.

Wald, L., 2006. Available Databases, Products and Services. In: Dunlop,E., Wald, L., Suri, M. (Eds.), Solar Energy Resource Management forElectricity Generation from Local to Global Scale. Nova SciencePublishers., Hauppauge.