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Solar Energy 86 (2012) 283–288

Photovoltaic potential in a Lisbon suburb using LiDAR data

M.C. Brito a,⇑, N. Gomes b, T. Santos b, J.A. Tenedorio b

a IDL, Faculdade de Ciencias da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugalb e-GEO, Centro de Estudos de Geografia e Planeamento Regional, Faculdade de Ciencias Sociais e Humanas, Universidade Nova de Lisboa,

Avenida de Berna, 26 C, 1069-061 Lisboa, Portugal

Received 5 December 2010; received in revised form 14 June 2011; accepted 28 September 2011Available online 5 November 2011

Communicated by: Associate Editor David Renne

Abstract

A procedure for estimating the photovoltaic (PV) potential of an urban region from LiDAR data using the Solar Analyst tool isdescribed. The total PV potential of the 538 identified buildings is around 11.5 GWh/year for an installed capacity of 7 MW, which cor-responds to 48% of the local electricity demand. It is shown that for a low PV penetration (about 10% of total roof area) the PV potentialcan be well estimated by considering no shade and local optimum inclination and orientation. For high PV penetration (i.e. covering allroof area available) the PV potential can be well estimated by considering a horizontal surface with the footprint area of the buildings.These results suggest a simple first-approximation estimation of the PV potential of an urban area without the need for a full 3D analysisof mutual shading on a Surface Digital Model.� 2011 Elsevier Ltd. All rights reserved.

Keywords: Photovoltaic potential; LiDAR; Urban; Lisbon

1. Introduction

It is widely accepted that photovoltaic (PV) solar energyis an abundant, clean and secure source of electricity. Itsdeployment in the urban environment and, in particularon roofs, has been strongly supported by governmentsthroughout the world, acknowledging its many advantagesincluding its modular and silent nature, the avoidance oftransmission and distribution losses since electricity is gen-erated at the point of use or its high correlation with peakloads in cities where demand is dominated by air condition-ing (Ruther et al., 2008), allowing the combination ofenergy production with other functions of the buildings(e.g. cladding or heat insulation), lower costs since nohigh-value land and no separate support structure arerequired, or even the increase in environment awareness

0038-092X/$ - see front matter � 2011 Elsevier Ltd. All rights reserved.

doi:10.1016/j.solener.2011.09.031

⇑ Corresponding author.E-mail address: mcbrito@fc.ul.pt (M.C. Brito).

and energy savings behavior associated to residential PVsystems (Hondo and Baba, 2010).

The development of policies for the successful deploy-ment of PV systems in urban environment, includingfinancing schemes, utility planning or accommodating gridcapacity, critically depends on the assessment of the localpotential which is determined by local realities, meteoro-logical data, reference technologies, and economic factors(Wittmann et al., 1997). At the city scale, the use of geo-referenced urban fabric models associated to solarradiation tools to determine the incoming solar radiation(insolation) is particularly interesting since it allows themodeling of inclined surfaces, while taking into accountshadows from surrounding buildings or other topographicfeatures.

The assessment of photovoltaic potential in urban areashas been addressed by many authors and differentapproaches with different levels of complexity. For exam-ple, Wiginton et al. (2010) determines the available roofarea from extrapolation of roof area-population relation-

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ships and then applies a statistically based sequence ofavailable roof area reduction factors in order to take intoconsideration shading, other uses and roof orientation.On the other hand, Izquierdo et al. (2008) use easily acces-sible data (such as land uses and population and buildingdensities) and stratified statistical sampling based on thedefinition of representative building typologies. The useof Light Detection And Ranging (LiDAR) data to builda local Digital Surface Model was explored by Tereciet al. (2009). Using the GeoMedia Grid program they wereable to classify the type of roofs in the area and then deter-mine the annual PV potential of those roofs identified assuitable for solar modules (flat or inclined roofs, south fac-ing and not to steep) by considering the average annualglobal insolation on the installation location. Hofierkaand Kanuk (2009) have proposed a methodology for PVpotential assessment in urban areas based on the r.sun radi-ation model developed by Suri et al. (2007) included in theopen source GRASS GIS (Neteler and Mitasova, 2004),which was later developed into a web-based PV estimationutility, PVGIS (Suri et al., 2005). This method uses a Dig-ital Surface Model (DSM) created from LiDAR data andtakes into consideration shadowing, orientation and incli-nation of roof tops, and models for beam, diffuse andreflected irradiation. Carneiro et al. (2008) started by devel-oping a 2.5-D urban surface model from LiDAR data, 2-Dvectorial digital maps of buildings footprints and alphanu-merical data containing altimetric information about build-ings heights. The insolation at each data point is calculatedusing a Matlab routine using irradiance values (clear skyconditions only) obtained from statistical analysis of localhistorical data. The application of this method is not lim-ited to roof area as it allows the analysis of the PV potentialof building facades.

The present work describes a case study for the assess-ment of the insolation and PV potential of Lisbon suburb,using LiDAR data and the ArcGIS extension for modelingsolar radiation Solar Analyst (Fu and Rich, 1999). Thisextension enables the mapping and analysis of solar irradi-ation over a geographic area for specific time periods. Itaccounts for atmospheric effects, site latitude and elevation,inclination (steepness or slope) and orientation (compassdirection or aspect), daily and seasonal shifts of the sunangle, and effects of shadows cast by surrounding buildingsand topography. The analysis of this case study leads to therealization that for a low PV penetration, the PV potentialis well estimated by considering no shade and the localoptimum inclination and orientation. For unrealistic highPV penetration (i.e. covering close to all roof area) thePV potential is well estimated by considering a horizontalsurface with the footprint area of the buildings.

2. Methodology

The methodology for the assessment of the PV potentialin urban areas consists of three main steps (Santos Silvaet al., 2011a):

1. Building of a Digital Terrain Model (DTM) and a Dig-ital Surface Model (DSM) from high resolution LiDARdata and aerial digital photography.

2. Estimation of population distribution.3. Solar radiation modeling using the Solar Analyst exten-

sion for ArcGIS.

The creation of the SDM included a sequence of steps forthe subtraction of vegetation and identification of thebuilt area, data smoothing (using the PAEK algorithm– Polynomial Approximation with Exponential Kernel)and polygonal aggregation, as well as data interpolationusing the inverse distance weighted algorithm followed bylow frequency filtering with a 3 � 3 mesh. A semi-automaticprocedure for the functional identification of the buildingswas also developed. For further details on the DSM con-struction methodology see (Gomes, 2011).

The population distribution was estimated from theNational Information Reference Geographical Database(INE, 2001) taking into consideration the average height,i.e. the number of floors, of each residential building.

The Solar Analyst tool for ArcGIS requires as input,among others, the annual average coefficients of beamand diffuse irradiation (the fraction of diffuse and directon the global irradiation, respectively) for the area understudy. In order to have a better description of the localinsolation, one can determine the monthly averaged coeffi-cients (e.g. from the PVGIS database) and separately runthe model for each month of the year (Santos Silva et al.,2011b). This approach reduces the model monthly insola-tion variation with respect to the PVGIS database fromabout 20% to less than 1% (Gomes, 2011). One shouldnotice that this method does not compute insolation onthe building facades, and therefore we can only determinethe PV potential of the roof area. Also, reflected radiationon neighboring buildings is not taken into account.

The Solar Analyst tool determines the solar irradiationat the surface level. Then one may determine the solar irra-diation on the roof-tops, by identifying roof-top pixelsusing the building’s footprint. We have considered onlyroof data points with inclination below 45�. This thresholdwas applied in order to avoid overestimation of the insola-tion on the edges of the roofs (since the DSM resolution isabout 1 point/m2, the interpolated edge of the roof isdescribed by a curve with varying slope).

3. Results

The area under study is located at Carnaxide, Oeiras,Portugal (38.43� N, 9.8 W), with 5036 residents. In the sam-ple, 538 buildings were identified with a total footprint areaof 85,000 m2, thus with an average building footprint areaof about 160 m2. Fig. 1 shows the cumulative histogramof the footprint area of the buildings identified in the sam-ple. It shows that half of the buildings have a footprint areaabove 130 m2. The dash line shows the cumulativehistogram of the estimated population as function of the

Fig. 1. Cumulative histograms of buildings (solid line) and estimatedpopulation (dashed line) as a function of footprint area.

Fig. 3. Orientation distribution of roof area (solid line). Irradiationdistribution as a function of roof orientation (dotted line).

M.C. Brito et al. / Solar Energy 86 (2012) 283–288 285

buildings footprint area, revealing that half of the popula-tion occupies buildings with a footprint area above220 m2. These typically correspond to large apartmentblocks. Indeed, as shown in Fig. 2, most residents (93%) livein flats that only occupy 58% of the footprint area. Theremaining 7% live in detached houses (that occupy 14% ofthe footprint area). Just over 28% of the footprint area isoccupied by buildings for services, including schools.

The roof orientation in the area considered in this studyis represented in Fig. 3. It shows that there is a slight higherfrequency of south and southwest facing roofs (15%). Theplot also shows the fraction of available energy for eachorientation (dotted line). As expected, more favorable ori-entations are exposed to more insolation.

The global horizontal and latitude-tilted annual averageirradiation for the site are 1.64 and 1.83 kWh/m2/year,respectively. The total solar energy received on the roofsis 96 GWh/year, which means that, on average, each build-ing receives about 180 MWh/year. Fig. 4 shows the cumu-lative histogram of buildings and population according tothe annual insolation. It shows that about half of the build-ings receive more than 150 MWh/year and that half of thepopulation resides in buildings exposed to over 220 MWh/year of solar radiation.

To determine the photovoltaic potential of the buildingsin this sample one may convert insolation falling on a givenarea (kWh/m2/year) into the energy that a standard PV

Fig. 2. Distribution of footprint area (dark boxes) and estimatedpopulation (white boxes) according to building typology.

system would produce in those irradiation conditions, i.e.with units kWh/Wp/year, by dividing the yearly irradiationby 1000 W/m2 (the standard test illumination for PV mod-ules) and multiplying by the system efficiency, typically80% (due to temperature effects and cables and inverterlosses). One may thus construct a map of the photovoltaicpotential, as shown in Fig. 5. This approach is helpful fordetermining the most interesting spots for installing PVsystems.

It may be equally relevant to consider the PV potentialin units of kWh/m2/year. In this case, one needs to multiplythe annual insolation per unit area by the module and sys-tem efficiencies. The scale in Fig. 5 assumes a typical PVsystem total efficiency of 12% (which corresponds to mod-ule efficiency of 15% and, as above, system efficiency of80%).

The overall photovoltaic roof potential of this Oeirasquarter under analysis is therefore of the order of11.5 GWh/year, for an installed capacity of about 7 MW.Considering the local residents and an average electricityconsumption of 4.71 MWh/year/person (INE, 2007) we

Fig. 4. Cumulative histogram of buildings (solid line) and population(dashed line) as a function of annual solar irradiation.

Fig. 5. Photovoltaic potential map: annual average solar electricity yield per nominal installed power and per unit area, assuming a typical systemefficiency of 12%.

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conclude that covering all roofs with photovoltaic systems,with current PV technology, would satisfy 48% of the localelectricity demand.

4. Discussion

The total PV potential of a given urban region wouldonly be realized if the cost of the PV systems was essentiallynegligible. Since it is clearly not the case, one ought toinvestigate what are the most favorable locations to installPV systems, e.g. roofs with optimum inclination, appropri-ate orientation and free from shade from surroundingbuildings throughout the year. Fig. 6 shows the fractionof available energy as a function of occupied roof area.This plot was built by sorting all DSM data points (1 m2

aggregates1) according to decreasing annual irradiation.

1 The aggregation of data points into 1 m2 pixels assumes that this is theminimum size for a PV module. Some authors define a minimum systemsize of 15 m2 (Ludwig et al., 2009) or 20 m2 (Carneiro et al., 2009) but weconsider that the emergence of AC modules (low-power inverters attachedat the back of each panel) (Rodriguez and Amaratunga, 2008) should soonovercome that limitation. Technically, the data point aggregationattributes the least favorable irradiation to the whole 1 m2 area. Thisoption is justified by the fact that in a string of solar cells in series, itsenergy output is determined by the cell that has the lowest current.

One may observe that by carefully choosing the locationfor the PV systems, 10% of the available roof area will col-lect 13% of the available energy. Similarly, if the mostfavorable half of the roof area was covered with photovol-taic modules, one would receive 57% of the total availableinsolation. The dashed line describes the fraction of avail-able energy as a function of the used roof area using theDTM. Since the terrain is roughly horizontal, in this casethe used energy is almost linear with the used roof area.

Fig. 7 shows the annual insolation per unit area as afunction of roof area. Once again, the pixels were arrangedby decreasing annual insolation (from the most favorableto the least favorable location). As an eye guide, the localyearly irradiation per unit area for a horizontal surface isshown (1.64 MWh/m2/year). The plot shows that almosta third of the roof area has better insolation than a hori-zontal flat surface.

In order to compare the determination of the PV poten-tial using the DSM and a straightforward 2D approach,one can compute the ratio between the annual insolationdetermined from the DSM (which takes into considerationroof orientation and inclination, as well as sky view, i.e.shade from other buildings) and the DTM (assuming hor-izontal modules covering the buildings footprint area). Theresult is shown in Fig. 8. As expected, the most favorable

Fig. 6. Fraction of available insolation as a function of occupied fractionof total available roof area, for the DSM (solid line) and DTM (dashedline).

Fig. 7. Annual insolation per unit area as a function of occupied fractionof total available roof area. Dotted line indicates annual irradiation perunit area on a horizontal surface for the same location.

Fig. 8. Ratio of cumulative insolation for SDM and TDM as a function ofoccupied fraction of total available roof area (solid line). Dotted lineindicates overall ratio (assuming 100% roof coverage) and dashed lineindicates 100% for easier reading.

M.C. Brito et al. / Solar Energy 86 (2012) 283–288 287

roof spots for PV systems (i.e. appropriate inclination andorientation, no shade from other buildings) perform betterthan a horizontal surface. As one considers less favorableroof spots (i.e. not perfect inclination/orientation or undershade from other buildings) the overall performancedecreases.

Curiously, Fig. 8 suggests two simple rules of thumb fordetermination of PV potential from 2D mappings:

1. for low PV penetration (around 10% of the total roofarea) the PV potential is similar to optimum orientationand inclination with no shades from surroundingbuildings;

2. for high (unrealistic) PV penetration (occupied roof areaclose to 100%) the shades from the surroundings and thenon-ideal orientation and/or inclination of some roofscancel out the increase in roof area with respect to thefootprint area and lead to an overall PV potential veryclose (2% difference in this case under study) to whatone would get if one considered that all PV systems werelaid in the horizontal.

These rules of thumb will naturally depend on the build-ings typology and require further analysis if they are to beextended to other regions. In particular for sites at low lat-itude, where the horizontal irradiation levels are similar tothe levels reaching the shallow tilts commonly found inmore equatorial building roof covers, these rules mightbe extended even to higher penetration levels.

5. Conclusions

We have described the development of a procedure forestimating the PV potential of an urban region usingLiDAR data based on the Solar Analyst extension for Arc-GIS. Using this method, we have estimated that the totalPV potential of the 538 identified buildings to be around11.5 GWh/year for an installed capacity of 7 MW, whichcorresponds to 48% of the local electricity demand.

It was noticed that for a low PV penetration (about 10%of total roof area) the PV potential is well estimated byconsidering no shade and the local optimum inclinationand orientation. For high PV penetration (i.e. coveringclose to all roof area) the PV potential is well estimatedby considering a horizontal surface with the footprint areaof the buildings. These results suggest a simple procedurefor a first approximation estimate of the PV potential ofurban areas from the 2-D footprint area of the buildings,without the need for a more complete analysis of themutual shading in time and resource-consuming DigitalSurface Model analysis.

Acknowledgements

The authors would like to thank Municıpa, S.A., for theopportunity of using the LiDAR data set and the DMCimage, and to the Camara Municipal de Oeiras for the con-tour lines, that allowed the DTM production.

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