Applied Energy xxx (2012) xxx–xxx

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Applied Energy

journal homepage: www.elsevier .com/locate /apenergy

Large scale integration of photovoltaics in cities

Aneta Strzalka ⇑, Nazmul Alam, Eric Duminil, Volker Coors, Ursula EickerUniversity of Applied Sciences Stuttgart, Schellingstr. 24, 70174 Stuttgart, Germany

a r t i c l e i n f o a b s t r a c t

Article history:Received 27 July 2011Received in revised form 6 December 2011Accepted 9 December 2011Available online xxxx

Keywords:Urban photovoltaic potentialCityGML modelShadow effect

0306-2619/$ - see front matter � 2011 Elsevier Ltd. Adoi:10.1016/j.apenergy.2011.12.033

⇑ Corresponding author.E-mail address: aneta.strzalka@hft-stuttgart.de (A.

Please cite this article in press as: Strzalkj.apenergy.2011.12.033

For a large scale implementation of photovoltaics (PV) in the urban environment, building integration is amajor issue. This includes installations on roof or facade surfaces with orientations that are not ideal formaximum energy production. To evaluate the performance of PV systems in urban settings and compareit with the building user’s electricity consumption, three-dimensional geometry modelling was combinedwith photovoltaic system simulations. As an example, the modern residential district of ScharnhauserPark (SHP) near Stuttgart/Germany was used to calculate the potential of photovoltaic energy and to eval-uate the local own consumption of the energy produced.

For most buildings of the district only annual electrical consumption data was available and onlyselected buildings have electronic metering equipment. The available roof area for one of these multi-family case study buildings was used for a detailed hourly simulation of the PV power production, whichwas then compared to the hourly measured electricity consumption. The results were extrapolated to allbuildings of the analyzed area by normalizing them to the annual consumption data. The PV systems canproduce 35% of the quarter’s total electricity consumption and half of this generated electricity is directlyused within the buildings.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Photovoltaic systems are ideally suited for decentral electricitysupply in urban structures with a large electric energy demand.Germany is currently a key player in PV installations worldwideand has 17 GW installed capacity. However only a very small frac-tion of the installed capacity is building integrated. Especiallybuilding roofs represent large resources of areas for the conversionof solar energy [1].

The motivation to install photovoltaic systems with crystallineor thin film technologies can be economic (feed-in tariff and ownconsumption), based on the desire for backup power or uninter-rupted power supply solutions, visual properties or environmentalbenefits [2]. However, electricity produced by photovoltaic sys-tems is still more expensive than other renewable energy re-sources. Therefore, for PV-systems it is of special importance tolower energy losses and to achieve a high reliability and very longlifetime [3]. Especially in case of the implementation of PV-systemsat city scale, the economical aspect should be considered.

Feed-in-tariffs (FITs) have proven to be the most effectiveincentive program for renewable technologies; countries, whichhave adopted FITs, have the largest growth rates in renewable en-ergy technology deployment [4]. Half of the world’s PV installa-tions are supported by FITs [5]. The success of FITs enabled e.g.

ll rights reserved.

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a A et al. Large scale inte

Germany to reach its goal of having 12.5% renewable energy supplyin 2007 and 20% today [6]. Building integration is supported inmany countries by increased feed-in tariffs.

The contribution of PV power to cover decentral energy con-sumption depends not only on building size but also on differentconsumer types. Some countries already provide higher FITs, ifthe PV electricity is directly used by the consumer. The estimationof solar potential and the evaluation of energy demand for specificend-use activities are the basic steps to determine the best policiesfor large-scale deployment of different solar energy applications[7]. There is still a lack of studies regarding the impact of differentuser behaviors. Several studies have shown that occupant behaviorplays a prominent role in the variation in energy consumption indifferent households [8].

1.1. Background and motivation

Today, different methodologies for the estimation of PV potentialat an urban scale are proposed. Nowak et al. [9] uses statistical infor-mation to estimate the available building stock combined withassumptions to correct the surfaces obtained for architectural suit-ability for solar utilization. Defaix [10] developed a refined methodto estimate the surfaces and potentials for all 27 EU member states.In his work, the technical potential for Building Integrated Photovol-taics (BIPVs) starts with floor areas and population data availablefrom public databases. The floor area together with the number offloors is used to calculate the ground floor area. The technical

gration of photovoltaics in cities. Appl Energy (2012), doi:10.1016/

Nomenclature

Qel electricity consumption (kW h)PVprod PV power production (kW h)PVfeed-in PV power feed-in (kW h)

AbbreviationsBIPVs Building Integrated PhotovoltaicsCityGML City Geography Markup Language

EnBW Energy Baden-WürttembergFA Feature AnalystGIS Geo-information SystemLOD Level of Detail

1 GTA Geoinformatik Gmbh. Solar Potential Analysis. Berlin: ISPRS; 2010.2 CPA Systems Gmbh. Support GIS-Solar. Berlin: ISPRS; 2010.

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potential of BIPV is then calculated using an irradiance database andthe technical parameters of PV-systems.

In further work [11], a methodology for estimating the rooftopsolar feasibility on an urban scale for public buildings in the city ofPhoenix was developed. This methodology, which uses AutoCADand Sketch-up to prepare the aerial images from Google Earth,can be then applied to the whole city. Also the work of Taguchiand Kurokawa [12] uses aerial images to determine suitable roofareas at urban scale. A more advanced methodology was developedby Wiginton et al. [13], who created a five-step procedure for esti-mating the total roof PV potential. This procedure involves: (1)geographical division of the analyzed region, (2) sampling usingthe Feature Analyst (FA) extraction software, (3) extrapolationusing roof-area-population relationships, (4) reduction for shadingand other uses and orientation and (5) conversion to power andenergy outputs. Feature analyst is an advanced feature extractionprogram, which exists as an extension to ArcGIS. This extensionuses Orthophotos or aerial images as an input. Regarding the FA,none of the previous works has studied roof area quantificationfor PV deployment.

In Germany, a method using geo-information systems [14] wasdeveloped, where the suitable roof areas are obtained from laserscanner and plan view data. Special algorithms are able to identifythe necessary data, like outer form, inclination, orientation of eachroof. Several authors, like Gadsden et al. [15], Izquierdo et al. [16]or Kraines et al. [17], have also applied GIS techniques to estimatethe PV potential of building roofs in urban areas. These techniqueswere applied to a single building, group of buildings (e.g. 396dwellings by Gadsden et al. [15]), but not to the large-scale region.Thus, there is a lack of research works, which consider the urbanscale in the PV potential analysis; most of them rely on singlebuilding or small scale areas. Furthermore, all above mentionedmethodologies are not based on 3D city models.

A recent development in the estimation of PV potential in urbanareas is the use of 3D city models. For the purposes of urban scalesimulation, it is important to achieve a good compromise betweenmodeling accuracy, computational overheads and data availability[18]. Especially in case of 3D city models the maximum level of de-tail is mainly restricted to the detailed roof structure, which is avery important for the estimation of PV potential of building roofs[19]. Joachem et al. [20] uses full 3D information for both featureextraction and solar potential analysis using LIDAR point clouds.A very good data basis for the automatic detection of best fittingroof surfaces for photovoltaics in terms of energy performanceand integration possibilities is CityGML [21,22]. CityGML enablesto describe 3D city and landscape models including geometry,semantic, topology and appearance. It is a multifunctional model,which can be used for geospatial transactions, data storage, data-base modelling and provides a basis for 3D geospatial visualization,analyzing, simulation and exploration tools. Calculation of roofsarea and tilt as well as shadow effects based on roofs structuresand neighborhoods and also the attribute extraction such as geo-graphical position and orientation of building surfaces are possiblebased on CityGML models. Carrión et al. [23] proposed a method

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for the estimation of the energetic rehabilitation state of buildingsusing CityGML. It is possible to calculate geometric characteristicsby using a library. After the calculation, results for every buildingare written to new generic attributes within the CityGML file.The approach of using geometric properties, calculating geometriccharacteristics and writing generic attributes is also suitable forthe PV analysis. Wen et al. [24] have pointed out solar collectorsas one of the dynamic features in 3D city models as an energy sys-tem. GTA Geoinformatik Gmbh1 has produced a solar map for show-ing the potential roofs for solar energy also using CityGML models.CPA Systems Gmbh2 has a Java based software component calledSupportGIS/Java3D, which is based on a 3D-information systemusing CityGML. It has the ability of continuously updating informa-tion of the official geobasis data. At the University of Applied Sci-ences Stuttgart (HFT), a 3D management framework based onCityGML model has been developed. This framework can be usedin different scenarios, e.g. urban planning [25], but also to providedata to the SolarCity3D engine in the photovoltaic potential analysisscenario [26]. Also Baumanns and Löwner [27] presents a decisiontree for a refined solar energy plant potential estimation on roofareas using CityGML. This approach is seen as a contribution fordecision makers and private households to estimate the return on in-vest of solar energy plants.

While accurate 3D modelling is thus gaining importance, theconnection to accurate photovoltaic system simulation includingmutual shading of buildings in urban settings is still quite weak[28]. However, shading is one of the major loss mechanisms inphotovoltaic energy production, which was shown as early as dur-ing the German ‘‘1000-Roofs-Program’’ of the 1990ies [29]. PV-sys-tems integrated into the built environment are frequently subjectto partial shading resulting from the roof-landscape itself, otherbuildings located in the proximity of the array, minor obstaclessuch as antennas, lighting protection masts and electric poles.Shading of a single cell within a PV-module leads to a reverse biasoperation of the cell, which may results in hot-spots and potentialbreakdown of the shaded cell. Castro [30] programmed a computertool for simulating the electrical behavior of shaded PV-modulesand determining the performance loss. Here, shading of the beamradiation on to a surface is calculated by ray tracing techniques.Diffuse irradiance reduction by buildings has been analyzed byQuaschning [29], using surface polygons for all surrounding build-ings. These methods are not yet applied to an urban scale and it isnot possible to solve it with a 2D approach. The methodology forshadow effect calculation proposed in this paper is advanced asit is based on 3D-model in CityGML format.

Also the weak point of the methodologies developed until now isthe lack of validation process at urban scale. According to Mathews[31], the success of the model development process depends on itsvalidation, but this is very often neglected due to the difficulty inobtaining good data sets. In the work of Jin and Otanicar [32], avalidation process for 932 government and commercial buildings

gration of photovoltaics in cities. Appl Energy (2012), doi:10.1016/

A. Strzalka et al. / Applied Energy xxx (2012) xxx–xxx 3

(of 2800 buildings in total) of the analyzed area is available. In thiswork, PV energy potential and measured user influenced electricityconsumption are compared, based on average annual electricityconsumption data, measured from 2006 to 2008. The result showedthat the renewable energy generation source could cover 10% ofthe total electricity consumption of all the buildings within theanalyzed area (including also the residential buildings). In thiscomparison, the own consumption ratio is not considered; thereforeour work is of benefit in this issue due to the availability of hourlymeasured electricity consumption data, which can be compared tothe hourly produced PV electricity generation.

In summary, the work presented in this paper uses innovative3D geoinformation technology including shading algorithms tosimulate the photovoltaic energy potential and compare it to mea-sured consumption values to obtain the possible own consumptionratios in urban areas.

1.2. Case study area

The case study area of Scharnhauser Park (SHP) is an urban con-version and development area of 150 hectares in the community ofOstfildern on the southern border of Stuttgart. This area is a formermilitary area converted to a mixed residential and commercialtown quarter with 7000 inhabitants. About 80% of the heating en-ergy and 35% of the electricity demand of the whole area is sup-plied by renewable energies. The main portion of the heating andelectrical energy is delivered to the buildings by a wood-fired co-generation plant. A small portion of electricity is delivered by exist-ing roof top PV-systems. For all buildings in SHP, annual electricityconsumption data are available and additionally hourly electricityconsumption data for one case study building were monitored overone year. This building, which is a multi-family house with 12apartments and a gross heated area of 1688 m2, serves as a casestudy building to validate the developed methodology.

2. Methodology

The first step of the developed methodology was to organize amap in DXF-format and laser scanner data for the buildings ofthe analyzed residential district. The DXF-map of the analyzed areaoriginates from the city of Ostfildern and includes the buildingcontours. For the case study building, detailed architectural planswere available from the company Siedlungswerk Stuttgart.

The laser scanner data for the area of Scharnhauser Park are ob-tained from the Land Survey Office Baden-Württemberg. The pointdensity is of 4 points per m2 with a vertical resolution of 0.2 m [33].Airbone LIDAR (Light Detection and Ranging) data establish thedigital surface models and the filtering approaches enable toseparate the bare ground from natural objects, which cover thetopographic surface. Large objects like buildings are a specialchallenge for filtering approaches [34].

Global horizontal irradiance and outside temperature have beenmeasured by a local weather station. This data has been used as in-put for the hourly PV power simulations.

In order to calculate the hourly own consumption ratio for thecase study building, measured hourly electricity consumption datawere used. These data have been monitored in the time period be-tween October 2009 and September 2010 using smart meters ofthe company Energy Baden-Württemberg (EnBW). These smartmetering systems have been installed in almost all flats of the casestudy building.

Furthermore, measured annual electricity consumption data forall buildings for the year 2005 were used, which have also beenobtained from EnBW. Additionally, the information about thenumber of floors for each building, which originate from the cityof Ostfildern was used to estimate the own consumption ratio.

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In order to determine the PV potential of the building roofs on acity scale, a flow chart was developed, which includes the wholeprocess from the data acquisition and their preparation, throughthe PV and shadow calculation until the validation of the results,as shown in Fig. 1.

The flow chart shown in Fig. 1 presents a methodology for atwo-tier process of the PV potential analysis. On the left side of thisflow chart, only one case study building is analyzed in details. Onthe right side, all buildings in SHP are considered.

2.1. Estimation of PV-suitable roof areas

For the case study building, the PV-suitable roof area was man-ually extracted from detailed architectural plans. For all otherbuildings of SHP, laser scanner data and the DXF-map have beenemployed. On the basis of these materials, by using a GeographicInformation System (GIS) the detection of the roofs with optimumconditions for producing solar power was possible. The intersec-tion of the laser scanner data and the building contours [35] en-abled to estimate the roof type, its inclination, orientation andsize (see workflow in Fig. 2).

The height of each point was calculated by the subtraction of la-ser scanner data of first pulse (building + vegetation points) andlast pulse (ground points) measurement. Then, the relative averageheight of all points within each of the building contours was calcu-lated, which is seen in Fig. 3. These values are then used as a basisfor the development of a 3D city model of SHP.

An important step was the classification of the building roofs ofSHP regarding their type, by considering the distribution of thehigh points in order to divide them into flat and inclined roofs.The classification showed that 80% of the roofs are of flat type.After the automatic calculation of the areas of these roofs usingthe standard functions of the GIS system used, these areas wereclassified as PV-suitable.

The analysis of the inclined roofs happened to be more compli-cated. Not only the size, but also the orientation and the inclinationof the roofs had to be calculated using additional GIS-applications.A special raster calculator enabled to filter only the roof areas,which have an orientation of 145–215� (south is 180�) and theroofs with optimal inclination (between 15� and 45�) in order tominimize the reduction of the solar irradiance. Lower (flat roof)or higher inclinations (>70�) reduce the solar gain by about 20%.The results of the calculation of the orientation for some buildingroofs are shown in Fig. 4.

In SHP about 20% of the roofs are inclined and could be poten-tially suitable for PV-installation. Nevertheless, these roofs were allclassified as not suitable, because they include windows, dormerwindows and chimneys, which was analyzed manually by viewingthe aerial photographs of these roofs.

2.2. 3D city model

The next step of the proposed methodology is the generation ofa topologically consistent 3D city model on the basis of the givenbuilding footprints and the measured building height [36]. Hereby,the average building height, estimated from laser scanner data, asdescribed in Section 2.1, was used. This model, as shown in Fig. 5, isa simple block model, with the Level of Detail 1 (LOD1), which isused for the shadowing simulation.

3. Photovoltaic yield simulations

The next step of the process is to use the extracted surface areasfor the calculation of the PV power production. The photovoltaicsystem simulation for the case study building was carried out

gration of photovoltaics in cities. Appl Energy (2012), doi:10.1016/

Fig. 2. Work flow of the analysis.

Fig. 1. Process of estimating the PV power potential at urban scale.

4 A. Strzalka et al. / Applied Energy xxx (2012) xxx–xxx

using the commercially available software environment INSEL83,to which some of the authors still contribute in development andwhere all the source code is available to the authors. Thephotovoltaic modules are simulated using the one-diode currentvoltage equation with 6 free parameters, which have been derivedfrom the manufacturer’s information. For each time step, the maxi-mum power point is iteratively determined for the given irradiance.Module temperatures are simulated in each time step to account fortemperature related voltage drops. The resulting DC power is thenfed into an inverter model with 3 free parameters to calculate partialload performance. Details of the photovoltaic simulation algorithmscan be found in the block module handbook of the INSEL8 software.

Using this simulation tool, the calculation of the hourly PVpower production for one case study building was performed.The suitable roof area of the case study multi-family house of theresidential area SHP is of 230 m2 (flat roof). The PV-simulationwas done for a roof generator with 15 kW peak power and a tiltangle of 25� facing south, using as input hourly irradiance and

3 INSEL 8 is developed by doppelintegral GmbH, http://www.insel.eu.

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temperature, which was measured on the weather station placedin SHP. There are 74 Suntechnics 210F mono-crystalline siliconmodules. The modules are connected to one SMA SMC 10 000and one SMA Sunnyboy 5000 inverter, with respectively 5 stringswith 10 modules and 4 strings with 6 modules. The annual PV

ig. 3. Classification of the buildings by average building height in [m] (cut-outom GIS).

Ffr

gration of photovoltaics in cities. Appl Energy (2012), doi:10.1016/

0

20

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1 2 3 4 5 6 7 8

Ow

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nsum

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ergy

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io in

%

Number of floors

10 kWh/m2a 15 kWh/m2a 20 kWh/m2a

25 kWh/m2a 30 kWh/m2a 35 kWh/m2a

Case study building

Fig. 7. Correlation for the case study building divided into different values ofelectricity consumption.

0

5

10

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30

35

40

Ele

ctri

city

con

sum

ptio

n in

kW

h/m

2 a

Flat

Annual value Average_value

Fig. 8. Annual electricity consumption values for separate flats of the case studybuilding (Oct. 2009-Sept. 2010).

Fig. 5. Topologically consistent 3D block model of SHP.

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ergy

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io in

%

Number of floors

Own consumption ratio PV-fraction of electr. consumption

PV

-coverage ratio in %

Case study building

Fig. 6. Correlation for the case study building.

Fig. 4. Result of the calculation of the roof orientation.

A. Strzalka et al. / Applied Energy xxx (2012) xxx–xxx 5

energy produced is 15.27 MW h for this particular installation,which amounts to 980 kW h/kWp and a performance ratio of 85%.

In order to calculate the own consumption ratio on an hourlybasis, the measured electricity consumption was subtracted fromthe simulated PV power production at each time step to obtainthe remaining feed-in PV power. The minimum feed-in value iszero.

PVfeed�in;i ¼ PVprod;i � Q el;i ð1Þ

The summation of the hourly data resulted in the annual own con-sumption ratio.

Q own ¼P8760

i¼1 ðPVprod;i � PVfeed�in;iÞP8760i¼1 PVprod;i

ð2Þ

In the next step, a correlation between the own consumptionratio and the number of floors has been calculated on the basisof the available data for the case study building. Here, the mea-sured specific electricity consumption (for four floors of this build-ing) was extrapolated to different numbers of floors (from 1 to 8).The own consumption ratio was calculated for different numbersof the floors with a fixed given PV-roof installation, as seen inFig. 64.

This correlation is used as an input for the calculation of theown consumption ratio of each building of the analyzed areaSHP. The red line represents the PV-coverage ratio of the annualmeasured electricity consumption.

In addition to the number of floors, different levels of electricityconsumption have to be taken into account (see Fig. 7). If the elec-tricity consumption increases from 10 to 35 kW h/m2a, the ownconsumption increases from 5% to 30% for a single storey building,

4 For interpretation of color in Figs. 3–7, 9, 10 and 13, the reader is referred to theweb version of this article.

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from 35% to 75% or a 4 storey building, and from 55% to 97% for aneight floor building.

The own consumption ratio depends strongly on the consump-tion level, i.e. the user. This has been analyzed for individual flats inthe case study building, where the electricity consumption variesbetween 15 and 35 kW h/m2a, as seen in Fig. 8.

4. Shadow effect

In order to consider shadow for direct radiation a model hasbeen developed, which determines the exact shadow projectedonto each of the roof surfaces of the analyzed area. The method

gration of photovoltaics in cities. Appl Energy (2012), doi:10.1016/

6 A. Strzalka et al. / Applied Energy xxx (2012) xxx–xxx

includes several steps like sun angles detection, potential surfacefiltering, surface subdivision, sun’s ray calculation, potential sha-dow caster filtering, shadow calculation and surface regeneration.The CityGML model, where each of the surfaces consists of a setof polygons, was taken as the basis for the estimation of the sha-dow effect. Only the roof surfaces and façades were taken into ac-count. The shadowing calculation for the area of SHP in hourlyinterval was done for December 21, which is the worst day ofthe year concerning shading with the lowest sun height angle.

For detecting shadows a surface subdivision approach has beendeveloped, where a single shadow can be detected. According toFig. 9, the first step is to read these sets of polygons. The secondstep is triangulation of each polygon. Then, to achieve a fine reso-lution each triangle is further triangulated. The middle point ofeach side is connected and thus the triangle is divided into foursmaller triangles, the process is repeated until the length of thesmallest side is larger than the desired resolution. Then, thecentroid of the triangle is measured and a line towards the sun’sdirection is calculated representing the sun’s ray. The next step isto look if the sun’s ray intersects any of the surfaces. For thispurpose it is checked if the line intersects with any of the trianglesfound in the second step. If any intersection point is found then thetriangle can be declared as a shaded triangle and joining theshaded triangles together results in a shadow polygon on anyfaceset. The process may face problem with thin triangles. So, fortriangles with very narrow angles triangulation can be done bydividing the triangles according to the longest side. Thus the prob-lem with thin triangles can be avoided and a high quality result canbe obtained. Centroids of final subdivided triangles are assumed asthe target points for which shadow will be calculated. Whether thetriangle will be in shadow or not will be determined by this point.For each target point, a distant point is measured in sun’s directionat a minimum distance and above the top most point in the citymodel.

For each point, the whole city model is divided into four quad-rants, divided by the north-south and east west axis. The quadrant,which contains the sun, is marked as active quadrant and surfaces,which have at least one vertex in this area, are selected as potentialshadow caster surface. Surfaces, which are below the target point,are further filtered from the selection by comparing the elevationor height of each vertex of the surface with the target point. Theprocedure has to be applied to every time step of the simulationdue to the changing solar position. Diffuse radiation reductionthrough shading is not yet considered.

Fig. 9. Workflow of the estim

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4.1. Shadow calculation

This is the main step, where shadow is calculated. A line-planeintersection check is performed here. A line can be expressed as:

la þ ðlb � laÞt; t 2 R ð3Þ

where target point la = (xa,ya,za) and distant point lb = (xb,yb,zb)A surface can be expressed as:

P0 þ ðP � 1� P0Þuþ ðP2 � P0Þv ; u;v 2 R ð4Þ

where, subdivided triangle of potential shadow caster surfacePk = (xk,yk,zk); k = 0,1,2, which represents three points of thetriangle.

At intersection point the point on line will be equal to the pointon surface so by solving the equation in matrix form:

xa � x0

ya � y0

za � z0

264

375 ¼

xa � xb x1 � x0 x2 � x0

ya � yb y1 � y0 y2 � y0

za � zb z1 � z0 z2 � z0

264

375

t

u

v

264

375 ð5Þ

If t, u, v, e [0,1] and u + v 6 1, then the line and the triangle inter-sects and the point is marked as shadowed.

If the line lies upon the surface or parallel then lb � la, P1 � P0,P2 � P0 will be linearly independent. In this case, if line lies uponthe surface then the point must be also marked as shadowed. Todetermine if the line lays upon the surface a further line–lineintersection check is performed for each side of the triangle withthe sun’s ray by solving equation of two lines. If two lines areMa + (Mb �Ma)t1 and Na + (Nb � Na)t2 then t1 and t2 can be obtainedby solving the equations for x and y. If the value for t1 and t2 alsosatisfies the equations for z then there is an intersection. Then itis checked if the intersection point lies within the lines. If allrequirements are fulfilled for intersection then the point is markedas shadowed point.

4.2. Surface generation

Only the surfaces where at least one subdivided triangle isshadowed are considered for surface regeneration. This step is onlynecessary when a visual output for real time shadow is required foran instance of time. If the calculation is carried out for any longertime period like and hourly or minutely shadow calculation thenthis step might be excluded. Neighboring subdivided triangles withsame shadow status are joined together to form shadow and

ation of the shadow [28].

gration of photovoltaics in cities. Appl Energy (2012), doi:10.1016/

Fig. 10. Comparison of electricity consumption, solar power production and own consumption (summer week).

54%43%

3%

suitable roof areas

Fig. 11. Classification of the roofs in the urban area Scharnhauser Park.

A. Strzalka et al. / Applied Energy xxx (2012) xxx–xxx 7

non-shadow region. These regions are further merged with otherneighboring region with similar shadow status.

The whole process produces result for an instance of time. Toget hourly or minutely shadow calculation this has been repeat-edly applied and the result has been presented in a tabular form.

5. Results

5.1. Case study building

Fig. 10 shows the comparison between the hourly measuredelectricity consumption, hourly simulated PV-power productionand hourly calculated own consumption for one summer week.The total own consumption ratio for this week was 48%; in com-parison the own consumption ratios for spring and winter weekshave values of 60% and 66%.

On the basis of hourly data of simulated PV-power productionand measured electricity consumption for the time period betweenOctober 2009 and September 2010 an annual own consumptionenergy ratio of 56% has been calculated, i.e. 44% of the produced

Table 1PV-coverage ratio depending on the assumptions made.

Buildings considered

Nr Calculation method of the PV production

1 PV power production (total)2 PV power production (own consumption)

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PV energy is exported to the grid. The solar fraction with directlyused electricity corresponds to 25% of the total building electricityconsumption. The solar fraction based on the annual ratio of gen-erated PV energy to electricity consumption is higher and amountsto 44%. The simulation of the shadow effect showed that no shad-owing occurs on the case study building roof during the shortestday of the year.

5.2. Whole district

The classification of the roofs, which was done within the GIS-system using laser scanner data allowed to divide all roofs of SHPinto suitable and non-suitable for PV-installation as well as for theroofs, which have already installed PV-systems, as seen in Fig. 11.

As seen in Fig. 11, 54% of all roofs in SHP can be used for theinstallation of PV-systems. The calculation of the PV-power pro-duction for the case study building was extrapolated to all suitableroofs of SHP. The annual energy balance simulation showed thatabout 35% of the total measured electricity consumption of thewhole district SHP, which was 10,700 MW h (year 2005), couldbe covered by the electrical energy produced from PV-modules.Taking into consideration the measured electricity consumptionof only the buildings with suitable roof areas, the PV-coverage isof 54% (see Table 1).

In case, when the own power consumption ratio (from Table 1)was considered, only 17% of the total electricity consumption ofthe district SHP could be directly covered by the PV power, and26% when taking into consideration only the electricity consump-tion of the buildings with suitable roof areas (Nr 2 in Table 1). Theremaining 18% (total district) or 28% (buildings with suitable roofareas) are fed into the electrical grid.

On an individual building level, the percentage of the PV-cover-age of the electricity consumption strongly depends on thebuilding size and the user behavior and varies from 5% to 100%.

Whole district Buildings with suitable roof areas

PV coverage ratio [%] PV coverage ratio [%]

35 5417 26

gration of photovoltaics in cities. Appl Energy (2012), doi:10.1016/

Fig. 12. Distribution of the PV-fraction of the measured annual electricityconsumption when only annual energy balances are considered (total) and whenhourly production and consumption are balanced (own consumption).

8 A. Strzalka et al. / Applied Energy xxx (2012) xxx–xxx

There are also many building, where the solar power produced is2–10 times higher than the annual electricity consumption. Thedistribution of the PV coverage ratio of the annual electricity con-sumption for all residential buildings with PV-suitable roof areas isshown in Fig. 12. The two distributions show the ratio of annual

Fig. 13. Average measured electricity consu

Please cite this article in press as: Strzalka A et al. Large scale intej.apenergy.2011.12.033

energy production to consumption and the hourly power balances.For similar building types, the ratio of PV production to own con-sumption can vary by a factor 2.

Considering only the buildings with PV-suitable roof areas, theshadowing simulation showed that 97% of the total roof area ofthese buildings is completely unshaded over the whole year.

5.3. Visualization

Finally, the GIS-system was employed to visualize and publishthe results of this analysis. For data protection issues, only averagevalues of the electricity consumption for similar building types areshown in Fig. 13.

5.4. Error analysis

Elberink and Vosselman [37] discuss an approach of targetbased graph matching with both complete and incomplete laserdata for 3D building reconstruction. Major problems are missingdata features, deflected or absorbed laser pulses, missing lasersegments, intersection lines, trees or cars, and occlusions, etc.Uncertainties in our analysis are due to the lack of laser scannerdata in case of buildings built after the year of 2002 (the heightvalue was assumed to 7.5 m). This could lead to underestimate

mption per building group [kW h/m2a].

gration of photovoltaics in cities. Appl Energy (2012), doi:10.1016/

A. Strzalka et al. / Applied Energy xxx (2012) xxx–xxx 9

the building’s height and therefore influence the shadowing effectto the neighbor buildings.

Some more errors can occur due to missing data of the numberof floors for several new buildings, which have been built after theyear of 2006 (the data about the number of floors have been re-corded in 2006). The lack of this information can influence the cal-culated own consumption ratio.

6. Conclusions

The paper uses geo-information systems, 3D city model and ad-vanced extraction algorithms combined with PV system simulationsto quantify potential rooftop solar photovoltaic deployment at an ur-ban level. The developed methodology is a few steps semi-auto-mated process, which consists mainly of the extraction of roofsurfaces and orientations based on airborne laser scanner data andof the simulation of the solar power production using the modularsimulation environment INSEL. Additionally, a general concept hasbeen introduced for using 3D city models to calculate shadow effectsin a solar potential analysis. Here, a special tool was programmed todetermine the shaded parts of each suitable roof for PV-installation.

The analysis of the PV own consumption ratio was based on acase study building, for which detailed measured hourly electricityconsumption values were available. On the basis of hourly data ofsimulated PV-power production and measured electricity con-sumption an own consumption energy ratio of 56% has been calcu-lated. The annual own consumption represents about 25% of theannual measured electricity consumption for the case study build-ing. A correlation between the own consumption ratio and thenumber of floors was extracted from the case study building andfinally extrapolated to all buildings of the urban district with PV-suitable roof areas.

The ratio of PV own consumption and the total measured electric-ity consumption of 10,700 MW h of the whole district was 17%. Thetotal PV energy production (feed-in and own consumption) is 35% ofthe total electricity consumption of the district, i.e. about half of thelocally produced PV energy is exported to the grid. The shadowingsimulation showed that only 3% of the total roof area of the buildingswith PV-suitable roof areas is partially shaded.

Acknowledgments

We would like to thank the project RegioEnergie and the EU-Project POLYCITY (REF EC: TREN/05FP6EN/S07.43964/513481/),for funding part of this research.

The authors would like to thank Hugo Ledoux and Martijn Maij-ers from TU Delft for their help generating the topologically correct3D city model.

Special thanks also to Prof. R. Kettemann, Prof. D. Schröder, andMr. Arefi for the support by preparing the laser scanner data for ourPV-analysis.

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