Photovoltaic potential in a Lisbon suburb using LiDAR data

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1 Available online at Solar Energy 86 (2012) Photovoltaic potential in a Lisbon suburb using LiDAR data M.C. Brito a,, N. Gomes b, T. Santos b, J.A. Tenedório b a IDL, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, Lisboa, Portugal b e-geo, Centro de Estudos de Geografia e Planeamento Regional, Faculdade de Ciências Sociais e Humanas, Universidade Nova de Lisboa, Avenida de Berna, 26 C, Lisboa, Portugal Received 5 December 2010; received in revised form 14 June 2011; accepted 28 September 2011 Available 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 is described. The total PV potential of the 538 identified buildings is around 11.5 GWh/year for an installed capacity of 7 MW, which corresponds to 48% of the local electricity demand. It is shown that for a low PV penetration (about 10% of total roof area) the PV potential can be well estimated by considering no shade and local optimum inclination and orientation. For high PV penetration (i.e. covering all roof 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 analysis of mutual shading on a Surface Digital Model. Ó 2011 Elsevier Ltd. All rights reserved. Keywords: Photovoltaic potential; LiDAR; Urban; Lisbon 1. Introduction It is widely accepted that photovoltaic (PV) solar energy is an abundant, clean and secure source of electricity. Its deployment in the urban environment and, in particular on roofs, has been strongly supported by governments throughout the world, acknowledging its many advantages including its modular and silent nature, the avoidance of transmission and distribution losses since electricity is generated at the point of use or its high correlation with peak loads in cities where demand is dominated by air conditioning (Ruther et al., 2008), allowing the combination of energy production with other functions of the buildings (e.g. cladding or heat insulation), lower costs since no high-value land and no separate support structure are required, or even the increase in environment awareness Corresponding author. address: mcbrito@fc.ul.pt (M.C. Brito). and energy savings behavior associated to residential PV systems (Hondo and Baba, 2010). The development of policies for the successful deployment of PV systems in urban environment, including financing schemes, utility planning or accommodating grid capacity, critically depends on the assessment of the local potential which is determined by local realities, meteorological data, reference technologies, and economic factors (Wittmann et al., 1997). At the city scale, the use of georeferenced urban fabric models associated to solar radiation tools to determine the incoming solar radiation (insolation) is particularly interesting since it allows the modeling of inclined surfaces, while taking into account shadows from surrounding buildings or other topographic features. The assessment of photovoltaic potential in urban areas has been addressed by many authors and different approaches with different levels of complexity. For example, Wiginton et al. (2010) determines the available roof area from extrapolation of roof area-population relation X/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi: /j.solener

2 284 M.C. Brito et al. / Solar Energy 86 (2012) ships and then applies a statistically based sequence of available roof area reduction factors in order to take into consideration shading, other uses and roof orientation. On the other hand, Izquierdo et al. (2008) use easily accessible data (such as land uses and population and building densities) and stratified statistical sampling based on the definition of representative building typologies. The use of Light Detection And Ranging (LiDAR) data to build a local Digital Surface Model was explored by Tereci et al. (2009). Using the GeoMedia Grid program they were able to classify the type of roofs in the area and then determine the annual PV potential of those roofs identified as suitable for solar modules (flat or inclined roofs, south facing and not to steep) by considering the average annual global insolation on the installation location. Hofierka and Kanuk (2009) have proposed a methodology for PV potential assessment in urban areas based on the r.sun radiation model developed by Suri et al. (2007) included in the open source GRASS GIS (Neteler and Mitasova, 2004), which was later developed into a web-based PV estimation utility, PVGIS (Suri et al., 2005). This method uses a Digital Surface Model (DSM) created from LiDAR data and takes into consideration shadowing, orientation and inclination of roof tops, and models for beam, diffuse and reflected irradiation. Carneiro et al. (2008) started by developing a 2.5-D urban surface model from LiDAR data, 2-D vectorial digital maps of buildings footprints and alphanumerical data containing altimetric information about buildings heights. The insolation at each data point is calculated using a Matlab routine using irradiance values (clear sky conditions only) obtained from statistical analysis of local historical data. The application of this method is not limited to roof area as it allows the analysis of the PV potential of building facades. The present work describes a case study for the assessment of the insolation and PV potential of Lisbon suburb, using LiDAR data and the ArcGIS extension for modeling solar radiation Solar Analyst (Fu and Rich, 1999). This extension enables the mapping and analysis of solar irradiation over a geographic area for specific time periods. It accounts for atmospheric effects, site latitude and elevation, inclination (steepness or slope) and orientation (compass direction or aspect), daily and seasonal shifts of the sun angle, and effects of shadows cast by surrounding buildings and topography. The analysis of this case study leads to the realization that for a low PV penetration, the PV potential is well estimated by considering no shade and the local optimum inclination and orientation. For unrealistic high PV penetration (i.e. covering close to all roof area) the PV potential is well estimated by considering a horizontal surface with the footprint area of the buildings. 2. Methodology The methodology for the assessment of the PV potential in urban areas consists of three main steps (Santos Silva et al., 2011a): 1. Building of a Digital Terrain Model (DTM) and a Digital Surface Model (DSM) from high resolution LiDAR data and aerial digital photography. 2. Estimation of population distribution. 3. Solar radiation modeling using the Solar Analyst extension for ArcGIS. The creation of the SDM included a sequence of steps for the subtraction of vegetation and identification of the built area, data smoothing (using the PAEK algorithm Polynomial Approximation with Exponential Kernel) and polygonal aggregation, as well as data interpolation using the inverse distance weighted algorithm followed by low frequency filtering with a 3 3 mesh. A semi-automatic procedure for the functional identification of the buildings was also developed. For further details on the DSM construction methodology see (Gomes, 2011). The population distribution was estimated from the National 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 beam and diffuse irradiation (the fraction of diffuse and direct on the global irradiation, respectively) for the area under study. In order to have a better description of the local insolation, one can determine the monthly averaged coefficients (e.g. from the PVGIS database) and separately run the model for each month of the year (Santos Silva et al., 2011b). This approach reduces the model monthly insolation variation with respect to the PVGIS database from about 20% to less than 1% (Gomes, 2011). One should notice that this method does not compute insolation on the building facades, and therefore we can only determine the PV potential of the roof area. Also, reflected radiation on neighboring buildings is not taken into account. The Solar Analyst tool determines the solar irradiation at the surface level. Then one may determine the solar irradiation on the roof-tops, by identifying roof-top pixels using the building s footprint. We have considered only roof data points with inclination below 45. This threshold was applied in order to avoid overestimation of the insolation on the edges of the roofs (since the DSM resolution is about 1 point/m 2, the interpolated edge of the roof is described 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 sample, 538 buildings were identified with a total footprint area of 85,000 m 2, thus with an average building footprint area of about 160 m 2. Fig. 1 shows the cumulative histogram of the footprint area of the buildings identified in the sample. It shows that half of the buildings have a footprint area above 130 m 2. The dash line shows the cumulative histogram of the estimated population as function of the

3 M.C. Brito et al. / Solar Energy 86 (2012) Fig. 1. Cumulative histograms of buildings (solid line) and estimated population (dashed line) as a function of footprint area. buildings footprint area, revealing that half of the population occupies buildings with a footprint area above 220 m 2. These typically correspond to large apartment blocks. Indeed, as shown in Fig. 2, most residents (93%) live in flats that only occupy 58% of the footprint area. The remaining 7% live in detached houses (that occupy 14% of the footprint area). Just over 28% of the footprint area is occupied by buildings for services, including schools. The roof orientation in the area considered in this study is represented in Fig. 3. It shows that there is a slight higher frequency of south and southwest facing roofs (15%). The plot also shows the fraction of available energy for each orientation (dotted line). As expected, more favorable orientations are exposed to more insolation. The global horizontal and latitude-tilted annual average irradiation for the site are 1.64 and 1.83 kwh/m 2 /year, respectively. The total solar energy received on the roofs is 96 GWh/year, which means that, on average, each building receives about 180 MWh/year. Fig. 4 shows the cumulative histogram of buildings and population according to the annual insolation. It shows that about half of the buildings receive more than 150 MWh/year and that half of the population resides in buildings exposed to over 220 MWh/ year of solar radiation. To determine the photovoltaic potential of the buildings in this sample one may convert insolation falling on a given area (kwh/m 2 /year) into the energy that a standard PV Fig. 3. Orientation distribution of roof area (solid line). Irradiation distribution as a function of roof orientation (dotted line). system would produce in those irradiation conditions, i.e. with units kwh/wp/year, by dividing the yearly irradiation by 1000 W/m 2 (the standard test illumination for PV modules) and multiplying by the system efficiency, typically 80% (due to temperature effects and cables and inverter losses). One may thus construct a map of the photovoltaic potential, as shown in Fig. 5. This approach is helpful for determining the most interesting spots for installing PV systems. It may be equally relevant to consider the PV potential in units of kwh/m 2 /year. In this case, one needs to multiply the annual insolation per unit area by the module and system efficiencies. The scale in Fig. 5 assumes a typical PV system total efficiency of 12% (which corresponds to module efficiency of 15% and, as above, system efficiency of 80%). The overall photovoltaic roof potential of this Oeiras quarter under analysis is therefore of the order of 11.5 GWh/year, for an installed capacity of about 7 MW. Considering the local residents and an average electricity consumption of 4.71 MWh/year/person (INE, 2007) we Fig. 2. Distribution of footprint area (dark boxes) and estimated population (white boxes) according to building typology. Fig. 4. Cumulative histogram of buildings (solid line) and population (dashed line) as a function of annual solar irradiation.

4 286 M.C. Brito et al. / Solar Energy 86 (2012) Fig. 5. Photovoltaic potential map: annual average solar electricity yield per nominal installed power and per unit area, assuming a typical system efficiency of 12%. conclude that covering all roofs with photovoltaic systems, with current PV technology, would satisfy 48% of the local electricity demand. 4. Discussion The total PV potential of a given urban region would only be realized if the cost of the PV systems was essentially negligible. Since it is clearly not the case, one ought to investigate what are the most favorable locations to install PV systems, e.g. roofs with optimum inclination, appropriate orientation and free from shade from surrounding buildings throughout the year. Fig. 6 shows the fraction of available energy as a function of occupied roof area. This plot was built by sorting all DSM data points (1 m 2 aggregates 1 ) according to decreasing annual irradiation. 1 The aggregation of data points into 1 m 2 pixels assumes that this is the minimum size for a PV module. Some authors define a minimum system size of 15 m 2 (Ludwig et al., 2009) or20m 2 (Carneiro et al., 2009) but we consider that the emergence of AC modules (low-power inverters attached at the back of each panel) (Rodriguez and Amaratunga, 2008) should soon overcome that limitation. Technically, the data point aggregation attributes the least favorable irradiation to the whole 1 m 2 area. This option is justified by the fact that in a string of solar cells in series, its energy output is determined by the cell that has the lowest current. One may observe that by carefully choosing the location for the PV systems, 10% of the available roof area will collect 13% of the available energy. Similarly, if the most favorable half of the roof area was covered with photovoltaic modules, one would receive 57% of the total available insolation. The dashed line describes the fraction of available energy as a function of the used roof area using the DTM. Since the terrain is roughly horizontal, in this case the used energy is almost linear with the used roof area. Fig. 7 shows the annual insolation per unit area as a function of roof area. Once again, the pixels were arranged by decreasing annual insolation (from the most favorable to the least favorable location). As an eye guide, the local yearly irradiation per unit area for a horizontal surface is shown (1.64 MWh/m 2 /year). The plot shows that almost a third of the roof area has better insolation than a horizontal flat surface. In order to compare the determination of the PV potential using the DSM and a straightforward 2D approach, one can compute the ratio between the annual insolation determined from the DSM (which takes into consideration roof orientation and inclination, as well as sky view, i.e. shade from other buildings) and the DTM (assuming horizontal modules covering the buildings footprint area). The result is shown in Fig. 8. As expected, the most favorable

5 M.C. Brito et al. / Solar Energy 86 (2012) Curiously, Fig. 8 suggests two simple rules of thumb for determination of PV potential from 2D mappings: Fig. 6. Fraction of available insolation as a function of occupied fraction of total available roof area, for the DSM (solid line) and DTM (dashed line). 1. for low PV penetration (around 10% of the total roof area) the PV potential is similar to optimum orientation and inclination with no shades from surrounding buildings; 2. for high (unrealistic) PV penetration (occupied roof area close to 100%) the shades from the surroundings and the non-ideal orientation and/or inclination of some roofs cancel out the increase in roof area with respect to the footprint area and lead to an overall PV potential very close (2% difference in this case under study) to what one would get if one considered that all PV systems were laid in the horizontal. These rules of thumb will naturally depend on the buildings typology and require further analysis if they are to be extended to other regions. In particular for sites at low latitude, where the horizontal irradiation levels are similar to the levels reaching the shallow tilts commonly found in more equatorial building roof covers, these rules might be extended even to higher penetration levels. 5. Conclusions Fig. 7. Annual insolation per unit area as a function of occupied fraction of total available roof area. Dotted line indicates annual irradiation per unit area on a horizontal surface for the same location. Fig. 8. Ratio of cumulative insolation for SDM and TDM as a function of occupied fraction of total available roof area (solid line). Dotted line indicates overall ratio (assuming 100% roof coverage) and dashed line indicates 100% for easier reading. roof spots for PV systems (i.e. appropriate inclination and orientation, no shade from other buildings) perform better than a horizontal surface. As one considers less favorable roof spots (i.e. not perfect inclination/orientation or under shade from other buildings) the overall performance decreases. We have described the development of a procedure for estimating the PV potential of an urban region using LiDAR data based on the Solar Analyst extension for Arc- GIS. Using this method, we have estimated that the total PV potential of the 538 identified buildings to be around 11.5 GWh/year for an installed capacity of 7 MW, which corresponds 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 by considering no shade and the local optimum inclination and orientation. For high PV penetration (i.e. covering close to all roof area) the PV potential is well estimated by considering a horizontal surface with the footprint area of the buildings. These results suggest a simple procedure for a first approximation estimate of the PV potential of urban areas from the 2-D footprint area of the buildings, without the need for a more complete analysis of the mutual shading in time and resource-consuming Digital Surface Model analysis. Acknowledgements The authors would like to thank Municípa, S.A., for the opportunity of using the LiDAR data set and the DMC image, and to the Câmara Municipal de Oeiras for the contour lines, that allowed the DTM production. References Carneiro, C., Morello, E., Ratti, C., Golay, F., Solar radiation over the urban texture: LiDAR data and image processing techniques for

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