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Analysis of Rooftop Solar Potential on Australian Residential Buildings

Conference Paper

Analysis of Rooftop Solar Potential on Australian Residential Buildings

Abstract

Deployment of rooftop photovoltaics (PV) is technically constrained by the availability of suitable roof space as well as by the ability of the distribution network to absorb exported generation. Although Australian rooftop PV installations are at record levels, deployment is uneven across different building types, with commercial, industrial and multi-occupancy residential buildings lagging behind the worldleading penetration on detached residential buildings. An understanding of the amount and distribution of usable rooftop space on different building classifications is therefore useful in guiding appropriate policy incentives to increase deployment, as well as in network planning. The APVI Solar Potential Tool (SunSPoT) contributes to this understanding by using 3D building models or LiDAR building elevation data, vegetation layers and weather data to calculate the rooftop solar potential of specific buildings. This method has been extended in a number of APVI reports to calculate the rooftop solar potential in some of Australia’s major urban centres using both 3D building models and low-resolution LiDAR data. In this study, we combine these methods with residential building classification data to determine utilisation factors (the proportion of a building’s roof area that is usable for PV deployment) for different types of residential building. The potential PV capacity per dwelling and an estimate for the potential capacity per unit of floor area is also calculated for different classes of residential building. These results are combined with Australian Bureau of Statistics (ABS) census data to estimate the total residential potential for different dwelling types in each state or territory. National residential solar potential is estimated to be between 43GWp and 61GWp, of which 6.5% is on multi-occupancy buildings. As well as the slope and orientation of the roof planes and the degree of shading from neighbouring buildings and trees, utilisation factors are also affected by the presence of rooftop obstructions (such as air-conditioning units, skylights, perimeter walls, access equipment) which are not always captured by 3D models or low-resolution LiDAR data. Using high-resolution aerial imagery, we visually assess the roofs of case study buildings to better understand the effect of these factors.
Asia Pacific Solar Research Conference, Sydney, December 4-6 2018
Mike B Roberts
Analysis of Rooftop Solar Potential on Australian Residential Buildings
Mike B Roberts1,2, Jessie Copper1, Anna Bruce1,2
1School of Photovoltaic and Renewable Energy Engineering
2Centre for Energy & Environmental Markets
University of New South Wales, Sydney 2052, Australia
E-mail: m.roberts@unsw.edu.au
Abstract
Deployment of rooftop photovoltaics (PV) is technically constrained by the availability of suitable roof
space as well as by the ability of the distribution network to absorb exported generation. Although
Australian rooftop PV installations are at record levels, deployment is uneven across different building
types, with commercial, industrial and multi-occupancy residential buildings lagging behind the world-
leading penetration on detached residential buildings. An understanding of the amount and distribution
of usable rooftop space on different building classifications is therefore useful in guiding appropriate
policy incentives to increase deployment, as well as in network planning. The APVI Solar Potential
Tool (SunSPoT) contributes to this understanding by using 3D building models or LiDAR building
elevation data, vegetation layers and weather data to calculate the rooftop solar potential of specific
buildings. This method has been extended in a number of APVI reports to calculate the rooftop solar
potential in some of Australia’s major urban centres using both 3D building models and low-resolution
LiDAR data.
In this study, we combine these methods with residential building classification data to determine
utilisation factors (the proportion of a building’s roof area that is usable for PV deployment) for different
types of residential building. The potential PV capacity per dwelling and an estimate for the potential
capacity per unit of floor area is also calculated for different classes of residential building. These
results are combined with Australian Bureau of Statistics (ABS) census data to estimate the total
residential potential for different dwelling types in each state or territory. National residential solar
potential is estimated to be between 43GWp and 61GWp, of which 6.5% is on multi-occupancy
buildings.
As well as the slope and orientation of the roof planes and the degree of shading from neighbouring
buildings and trees, utilisation factors are also affected by the presence of rooftop obstructions (such
as air-conditioning units, skylights, perimeter walls, access equipment) which are not always captured
by 3D models or low-resolution LiDAR data. Using high-resolution aerial imagery, we visually assess
the roofs of case study buildings to better understand the effect of these factors.
1. Introduction
Australia leads the world in deployment of distributed residential PV, with close to two million solar
households and penetration levels reaching 40% of stand-alone houses in some areas (APVI 2018).
Although 2018 has seen an increase in commercial scale PV deployment in Australia, the majority of
installations are still on residential buildings, with new installations averaging 6kWp in size. Driven by a
combination of increasing electricity prices and decreasing PV costs, the continuing increase in
distributed generation has implications for the electricity system, particularly the management of the
distribution network. Multi-occupancy buildings, often located in proximity to daytime-peaking
commercial loads, may present opportunities for significant network benefits from PV deployment, as
Asia Pacific Solar Research Conference, Sydney, December 4-6 2018
well as potential to address equity issues, such as exclusion of groups of consumers, including
apartment dwellers and renters, from the benefits of distributed energy in the energy transition. An
understanding of the scale and distribution of this residential rooftop potential can therefore inform
network planning as well as facilitating targeted policies for PV deployment.
Assessment of residential solar potential is hampered by a lack of data about the country’s residential
building stock. The Australian Bureau of Statistics (ABS) five-yearly Census of Population and
Housing (ABS 2016) includes information on the number of dwellings in each state, local government
area (LGA) and post office area (POA), broken down by dwelling structure. For stand-alone houses,
dwelling numbers are equal to building numbers, which, despite the diversity of house sizes, can be
used, with an understanding of the housing stock, to estimate roof area and therefore solar potential.
However, there are multiple attached dwellings in other types of residential buildings, such as multi-
storey apartment buildings, and the number and geographical distribution of buildings is therefore
difficult to extract from the census dwellings data. In recent years, ABS has also published statistics
regarding numbers and categories of building development approvals (ABS 2018) and financial value
of building completions which can help to reveal trends in multi-dwelling housing, but throws little light
on the existing building stock. However, at a city level, some local councils collect more detailed
building data which can be used to explore the relationship between solar potential and building
characteristics.
The assessment of rooftop solar potential in Australia, as internationally, has been the subject of
research for at least 20 years (Watt, Kaye et al. 1997). Space in this paper does not allow for a
comprehensive review of the many methodologies applied to the problem. However, researchers from
NREL have carried out such a review and used it as a basis for developing their own methodology
(Lopez, Roberts et al. 2012, Melius, Margolis et al. 2013, Gagnon, Margolis et al. 2016). This method
forms the basis for a number of APVI reports assessing the solar potential of Australian cities (e.g.
(Copper, Roberts et al. 2017)) which are based on the dataset used for the APVI’s Solar Potential Tool
(SunSPoT)(APVI 2018).
In this paper, an analysis of the rooftop solar potential of buildings in the City of Melbourne has been
conducted, and building census data provided by the City of Melbourne used to assess the usable roof
area and potential PV capacity of different types of residential buildings in the LGA. Using ABS census
data, these results have been extrapolated across the country. Assessment of visual imagery for a
small number of case study buildings has been carried out in order to facilitate discussion of the
accuracy of the methodology.
2. Method
2.1. Calculation of usable area and solar potential
Figure 1 shows the major steps in the process. Steps 1 to 7 share the data and methodology behind
the APVI’s SunSPoT tool, as detailed in (Copper and Bruce 2014,2) and (Copper and Bruce 2014,1),
which has also been used to assess the solar potential of major Australian cities as detailed in the
relevant reports (Copper, Roberts et al. 2017). A brief description follows.
Asia Pacific Solar Research Conference, Sydney, December 4-6 2018
Figure 1. Major process steps for the calculation of rooftop PV potential
(adapted from Copper, Roberts et al. 2017)
A 3D building model (built from photogrammetry and LiDAR data) and XYZ vegetation dot point, both
supplied by geospatial company AAM, were combined to create a 1m2 gridded raster-based digital
surface model of the City of Melbourne. ESRI’s ArcGIS tool was used to calculate tilt and orientation of
the roof surfaces in the model, and ArcGIS’s Area Solar Radiation tool was used to calculate monthly
and annual values of solar radiation, considering shading from surrounding buildings and vegetation
as well as from the building itself. These were then adjusted by a set of calibration factors which were
determined via a validation analysis (Copper and Bruce 2014,1) against hourly modelling undertaken
in NREL’s System Advisor Model (SAM) (NREL 2010) using a Typical Meteorological Year (TMY)
weather file. Two processes were then used to identify suitable planes for PV deployment: the first
based on NRELs hillshade and surface orientation method (Melius, Margolis et al. 2013) and the
second selecting areas exposed to 80% of the insolation incident on an unshaded horizontal surface.
The NREL method used the ArcGIS hillshade tool to calculate shading on the roof planes for each
hour on four days of the year (the equinoxes and solstices) and then find a metric for average sunlight
availability. Roof planes were selected if they were exposed for sufficient hours to produce 80%
generation on those four days, while excluding planes orientated between south-east and south-west
(in the southern hemisphere) or between north-east and north-west in the northern hemisphere. The
second method used annual daily average insolation (rather than the four days used in NRELs
method) and allowed all orientations of roof surface if they were exposed to sufficient insolation.
However, planes of under 10m2 contiguous area were discarded for both methods. For each usable
plane, potential PV system size (DC capacity) was calculated as per APVIs SPT methodology
(Copper and Bruce 2014,1) using DC size factor and array spacing methodologies (Copper, Sproul et
al. 2016) based on a generic 250W module with dimensions of 1m x 1.6m.
2.2. Building classification
City of Melbourne building footprints from 2015 (City of Melbourne 2015) were used to divide the roof
planes and allocate them to building identifiers, while discarding planes of less than 10m2 area per
building. Two datasets from the City of Melbourne 2017 Census of Land use and Employment (CLUE)
1"
Input"Data"Source:""
AAM"3D"Model"
2"
Calculation"of"roof"surface"
Tilt"and"Aspect"
3"
Calculation"of"Hillshades"
4"
Calculation"of"surface"
Insolation"
5"
Identi?ication"of"Unique"roof"
surfaces"
6"
Assessment"of"rooftop"
suitability:"
a)"Insolation""
b)"NREL"Hillshade"&"aspect"
7"
Calculation"of"PV"Capacity"
and"Yield"per"suitable"roof"
plane"
8"
Aggregation"of"usable"roof"
planes"for"each"building"
9"
Minimum"criteria"of"10m2"of"
contigous"area"
10"
Categorization"of"buildings"by"
use,"height"and"number"of"
dwellings"
11"
Aggregation"of"solar"potential"
for"each"dwel ling"type"in"City"
of"Melbourne"
12"
Extrapolation"nationally"
using"per-dwelling"potential"
and"ABS"census"data"
Asia Pacific Solar Research Conference, Sydney, December 4-6 2018
(City of Melbourne 2018) (Building Information and Residential Dwellings’) were used to identify
residential buildings according to predominant space use and categorise them by ABS dwelling types1.
Aggregate values for gross floor area (GFA) (approximated by the product of total footprint area and
number of floors above ground), usable area, insolation, PV potential and annual energy generation
were calculated for each building. 154 outliers (1.6% of the dataset) with PV capacities of 20 kW per
dwelling or above were removed from the dataset predominantly either dwellings attached to non-
residential buildings or new developments with incorrect dwelling numbers in the database. The
remaining data were used to generate averages for utilisation factor (the % of total roof area that is
usable for PV) and for PV potential per dwelling and per square meter of GFA.
2.3. Comparison with aerial imagery for case study buildings
The roofs of some case study buildings within the City of Melbourne were analysed using high
resolution aerial imagery from nearmap.com (Nearmap Ltd. 2015). This visual analysis allowed the
exclusion of roof surfaces with localised, building-specific obstructions or sources of shading below the
resolution of the 3D model (including air vents, HVAC installations, etc.) or otherwise unsuitable for PV
deployment. Details of the method can be found in (Copper, Roberts et al. 2017). Nearmap’s Solar
tool was used to design an array, by laying out 1.6m x 1.0m modules on the roof.
2.4. Application to Australia’s residential housing stock
The average figures for PV system size (in kWp/dwelling), combined with data from the ABS censuses
(ABS 2016) enumerating types of dwelling by State or Territory, were used to estimate the potential
residential PV capacity for each type of dwelling in 2011 and 2016. The rate of increase of each
dwelling type between the two census dates was projected forward to estimate dwelling numbers for
2018 and current solar potential for each state. PV potential was compared with the current installed
capacity of PV system less than 10kW (as PV systems <10kW are generally assumed by the
Australian PV industry to be residential systems). The data on PV systems was obtained from the
Clean Energy Regulator’s Small Scale renewable Energy Scheme (Clean Energy Regulator 2018)
database, accessed through the APVI PV Postcode Tool.
3. Results
3.1. Residential solar potential of City of Melbourne
Table 1 shows the total usable area, potential PV capacity and annual generation aggregated across
all the residential building roofs in the City of Melbourne, calculated using the two methods outlined in
Section 2.1. This represents approximately 24% of the total rooftop PV potential in the LGA (Copper,
Roberts et al. 2017) and nearly 50 times the approximately 2.1MW of small (under 10kWp) systems
currently installed in the LGA (Clean Energy Regulator 2018).
1 The full list of ABS Dwelling Structures (‘STRD’) is: Separate House; Semi-detached, row or terrace house, townhouse etc.
(with one storey / with two or more storeys); Flat or apartment (in a one or two storey block / in a three storey block / in a four
or more storey block / attached to a house); Other dwelling (caravan / cabin or houseboat / improvised home, tent, sleepers
out / house or flat attached to a shop, office, etc.).(ABS 2016)
Asia Pacific Solar Research Conference, Sydney, December 4-6 2018
Table 1. Total usable area, PV system size and energy generation for each residential building
type in City of Melbourne
80% Insolation method
NREL Method
Dwelling Type
#
Buildings
%
with
Flat
Roof
Potential
Capacity
(MWp)
Annual
Energy
(GWh)
Usable
Area
(Ha)
Potential
Capacity
(MWp)
Annual
Energy
(GWh)
House
7815
28%
32.6
48.8
56.7
30.8
46.2
53.8
Townhouse*
334
43%
5.3
8.0
9.5
4.9
7.5
8.8
1 or 2 storey apartment
339
32%
5.1
7.8
9.1
4.7
7.2
8.4
3 storey apartment
385
61%
8.4
13.0
15.5
8.2
12.6
15.0
4 or more storey apartment
553
86%
20.3
31.4
37.4
22.5
34.7
40.3
Total
9426
71.7
109.1
128.2
71.1
108.2
126.3
*The dwelling type ‘Townhouse’ includes terraced and semi-detached houses
Figure 2 shows the usable area normalized (left) by total roof area and (right) by number of dwellings.
Note that the 80% insolation method gives slightly higher values, on average, likely because of the
inclusion of low tilt, south-facing roofs that are excluded in the NREL method, whereas PV systems
are commonly installed on near-flat roofs in Australia. The increase of usable roof area between
houses, townhouses and low-rise apartments may be due to increasing proportion of flat roofs, or
decrease in highly tilted roofs (and therefore south facing roof areas), while the slight decrease for
high rise apartments is likely due to lift housings and other rooftop obstructions, but it is important to
note the wide distribution of values for all dwelling types. It is unsurprising that the per-dwelling usable
area is greater for houses than for apartments and lowest for high-rise apartments, although the
variability is large and the sample sizes for apartment buildings relatively small (Table 1).
Figure 2. Percentage usable area (left) and usable area per dwelling (right) for residential
dwellings in City of Melbourne
Table 2 shows the mean (and standard deviation) of usable area and potential PV capacity for each
dwelling type in the City of Melbourne. On average, low-rise apartment buildings have a greater
proportion of roof area available for PV deployment, perhaps because of a greater incidence of flat
roofs (Table 1) and therefore less South-facing planes. The results show that, on average, high-rise
Asia Pacific Solar Research Conference, Sydney, December 4-6 2018
apartments have approximately a quarter of the potential per-dwelling PV capacity of stand-alone
houses (but note the large standard deviation (SD) compared to the mean, due to the variety of
building heights, so that a usable area of 1.1m2/dwelling is one SD below the mean). On average,
three-storey apartment buildings have twice that, and one- or two-storey apartment buildings have
similar potential per-dwelling capacity to townhouses, semi-detached and terraced houses.
Residential electricity demand increases with dwelling occupancy and with space heating and cooling
loads, both of which are related to gross floor area (GFA). Table 2 gives approximate average GFA for
dwellings (based on the product of building footprint and number of floors) for each dwelling type (but
note that these include internal walls and common areas) and PV potential per square meter of GFA.
Table 2. Mean (standard deviation) usable area and PV potential per dwelling in City of
Melbourne by dwelling type
Dwelling Type
% Usable Area
Usable Area
(m2) per
dwelling
PV (kWp)
per dwelling
GFA (m2)
per dwelling
PV per GFA
(Wp/m2)
House
34.5% (15.0%)
40.3 (25.0)
6.0 (3.8)
171 (102)
39.2 (22.7)
Townhouse
39.6% (13.5%)
35.5 (21.4)
5.4 (3.3)
177 (112)
34.1 (18.1)
1 or 2 storey apartment
42.1% (15.1%)
34.9 (24.5)
5.3 (3.7)
153 (102)
36.1 (16.0)
3 storey apartment
45.3% (13.1%)
20.8 (13.2)
3.2 (2.0)
138 (107)
24.0 (7.0)
4 or more storey apartment
37.2% (15.4%)
10.3 (9.2)
1.6 (1.4)
178 (138)
10.1 (7.3)
The variability of percentage usable area and potential PV capacity per dwelling for each dwelling type
are shown in Figure 3. For all dwelling types, median percentage usable area is higher for flat roofs
than pitched roofs, while three storey apartments with flat roofs have a narrower distribution than all
other categories.
Asia Pacific Solar Research Conference, Sydney, December 4-6 2018
Figure 3. Percentage usable area by dwelling type and roof form (left), and
mean PV potential (kWp/dwelling) by dwelling type (right)
3.2. Observations from analysis of case studies
Figure 4 shows images of the usable planes (calculated using the 80% insolation method) for four
case study buildings (selected to demonstrate specific constraints), along with images showing PV
arrays arranged on the roof using Nearmaps. For all these buildings, there are constraints revealed by
the visual imagery that are not accounted for in the GIS analysis, and which reduce the potential PV
capacity of the roofs, as shown in Table 3.
Table 3. PV potential of case studies using 80% insolation method and visual analysis
Dwelling / Roof Type
PV capacity
kWp (80%
insolation)
PV capacity
kWp (Visual
analysis)
Nearmap array
as % of
theoretical
potential
Notes
(a)
Townhouse
Slope
11.0
10.3
93%
Some loss of potential on SW facing roof from
roof vents
(b)
Apt - 3F
Slope
76.5
55.8
73%
Loss of potential from air vents and tree
shading
(c)
Apt - 3F
slope
74.4
74.5
100%
Loss of potential from skylights, shading but
may have additional potential compared to
usable planes
(b)
Apt - 4+F
Flat
225.8
15.0
7%
Most potential lost through large HVAC
obstruction and roof garden.
For sites (a), (b) and (c), usable area is reduced by air vents and skylights on the roof space. Note that
the 3D model was not built for the purpose of analysing rooftop solar, so does not capture these small
roof features and is therefore likely to result in an overestimate of the solar potential. For the APVI
report on Melbourne’s solar potential (Copper, Roberts et al. 2017), repeating the analysis using
LiDAR data gave an average value for usable area of 31.3%, compared to 44.3% using the 3D model,
although that method may underestimate the total potential (and note that a smaller difference was
observed for other Australian cities). The visual assessments for case studies (a), (b) and (c) are
consistent with that range.
For (b) and (c), there is also additional shading from proximate trees, but the discrepancy between the
two results may be due to the time difference the vegetation data used to create the raster layer and
the visual imagery2. However, for site (c), the loss of usable area is compensated by additional roof
areas available for PV deployment that are excluded by the resolution of the polygons defining the
usable planes3, so the two methods produce similar results.
Site (d), however, was chosen to demonstrate a less common issue where the roof is largely occupied
by public areas (roof garden, pool, terraces) and HVAC equipment, with the results that very little of
the theoretical usable area can be used for PV deployment, unless additional structures such as
shade structures were erected.
Additionally, although the insolation method discards roof planes of less than 10 m2, constrains system
size to integer quantities of (250Wp, 1.6m x 1.0m) modules, and includes a PV occupancy factor of
2 The AAM vegetation points dataset was collected in 2010 while the data for the 3D model was from 2012 and the
Nearmaps visual imagery is from 2018.
3 The jagged edges of the usable plane polygons is an artefact of using a 1m2 gridded raster. A finer resolution of the raster
can be created using the 3D building model inputs but processing is highly resource-intensive.
Asia Pacific Solar Research Conference, Sydney, December 4-6 2018
98% for flush mounted panels, it does not account for fitting rectangular panels onto irregularly shaped
roof planes, which results in additional loss of usable area, particularly near roof edges, making the
effect likely more significant for smaller roof planes.
(a) (house)
(b) (3 storey apt)
(c) (3 storey apt)
(d) (13 storey apt)
Figure 4. Case study buildings showing (top) arrays designed using aerial imagery
and (bottom) usable roof planes by 80% insolation method (shown green)
3.3. Residential solar potential by state
Based on the results presented in Table 3, the estimated total (including existing) potential residential
PV capacity for each dwelling type in 2011 and 2016 is calculated by state in Table 4 and, along with
estimated 2018 potential and installed capacity, in Figure 5, though, as noted above, the 3D model
analysis may overestimate usable roof area. Dwelling numbers, and therefore potential capacity, are
increasing for all dwelling types in all states except for ‘1 and 2 storey apartments’ but the very large
increases in Semi-detached, row or terrace house, townhouse etc.may include dwellings previously
classified as apartments. Amongst apartments, the biggest increases are in buildings of four storeys
and above, with the lowest potential per-dwelling capacity, but the potential capacity on three-storey
apartment buildings also increased significantly, particularly in VIC and QLD.
Asia Pacific Solar Research Conference, Sydney, December 4-6 2018
Table 4. Estimated total potential PV capacity (MWp) by dwelling type and state
2016 (2011), % change
NSW
VIC
QLD
WA
SA
TAS
ACT
NT
House
12019 (11770)
2%
10992 (10449)
5%
8855 (8365)
6%
4953 (4539)
9%
3546 (3473)
2%
1266 (1203)
5%
640 (621)
3%
328 (305)
8%
Townhouse
2009 (1652)
22%
1930 (1183)
63%
1151 (831)
39%
839 (561)
50%
619 (425)
46%
77 (68)
14%
159 (116)
37%
57 (47)
22%
1 or 2 storey apartment
914 (987)
-7%
689 (1008)
-32%
518 (643)
-20%
155 (257)
-39%
215 (295)
-27%
62 (82)
-24%
31 (35)
-12%
37 (43)
-14%
3 storey apartment
626 (561)
2%
204 (165)
24%
216 (176)
23%
54 (52)
4%
18 (15)
14%
5 (4)
11%
29 (23)
25%
9 (8)
14%
4 or more storey apartment
429 (326)
32%
195 (109)
79%
139 (102)
37%
41 (30)
36%
13 (11)
17%
2 (1)
28%
19 (10)
96%
10 (5)
84%
Using the 3D model analysis, the total residential potential PV capacity in Australia in 2018 is
estimated to be 61GWp (ten times the capacity of existing sub-10kW installations), of which 4.0GWp
of potential is on apartment buildings. It would be instructive to repeat the analysis using LiDAR data
which might be expected to exclude more of the small roof obstructions. Applying the relationship
between the results from the two datasets averaged across all buildings in the LGA (Copper, Roberts
et al. 2017) (as discussed in Section 3.2) would suggest the total potential residential PV capacity to
be in the range 43GWp - 61GWp.
Figure 5. Estimated residential solar potential by state:
(l) by dwelling type for 2011 and 2016
(r) projected for 2018 showing existing capacity
The potential capacity on apartment buildings in NSW is more than twice that in QLD and three times
VIC and exceeds the total existing residential capacity in the state.
4. Discussion and Conclusion
This study has used a novel approach to estimate Australia’s residential solar potential for all dwelling
types by calculating per-dwelling potential for the building stock in City of Melbourne LGA and
extrapolating nationally using ABS census data.
The small number of case studies analysed visually suggest the method may overestimate potential
by excluding the constraints due to small rooftop obstructions and the shape of roof planes. Visual
analysis of a large number of buildings for each dwelling type would be useful to determine average
adjustment factors to account for these effects. The quantity of small obstructions is building-specific
but likely to be affected by the age of buildings (included in the CLUE dataset for 38% of buildings) as
well as by dwelling type and roof form, while potential losses due to fitting modules to irregularly
shaped roof planes is likely to be a factor of the size of the planes. These factors could be used to
categorise buildings and calculate average adjustment factors. Conversely, the roofs of houses in City
Asia Pacific Solar Research Conference, Sydney, December 4-6 2018
of Melbourne are likely to be smaller than the national average, which could result in an overestimate
of the per-dwelling potential capacity. It would therefore be useful to repeat the analysis using 3D
models and building data from suburban or rural areas.
Planned future work includes applying the analysis to high resolution LiDAR data and (as in the APVI
solar potential reports) comparing the results with the 3D model data to estimate upper and lower
bounds for the potential, as well as assessing further case-studies to determine how completely the
LiDAR analysis captures small localised obstructions. Applying the average results from a previous
analysis (Copper, Roberts et al. 2017) suggests the national residential potential to be in the range
43GWp - 61GWp, of which 6.5% (2.9GWp - 4.0GWp) is on multi-occupancy buildings.
Moreover, for some flat-roofed buildings, the use of roof space for other purposes, including roof
gardens and terraces, reduces PV potential and is hard to detect through analysis of 3D building
models. There may be potential to automate analysis of aerial imagery to detect some of these
features. Collection of data regarding rooftop facilities would also be a useful addition to any future
building census. Additionally, it should be noted that least-cost PV installations do not always allow for
efficient use of the whole roof space, and that economically optimal sizing of systems often utilises
only a proportion of the usable area but may exclude other areas from future installation.
Paradoxically, as the penetration of rooftop PV increases, the total potential PV capacity may
therefore be decreased, although the effect is likely to be counteracted by the ongoing growth of the
building stock.
A detailed understanding of rooftop PV potential and its distribution geographically and by building
type has application for federal, state and local governments to design incentives for PV deployment,
for not-for-profit PV advocates and for network planners. In particular, where potential PV capacity is
spatially aligned with distribution network locations having daytime capacity constrictions, there may
be opportunities to utilise targeted PV incentives to defer the cost of network upgrades. Additionally,
the scale of the potential opportunity on multi-occupancy buildings suggests value in exploring the
barriers to apartment PV deployment and in incentivising developers and strata bodies to install PV on
apartment buildings, and this is the subject of ongoing study by the authors.
Acknowledgements
The authors would like to thank Rebecca Hu for her assistance in preparation of this paper and
gratefully acknowledge support provided for the research by a grant from Energy Consumers
Australia, a studentship from the CRC for Low Carbon Living and an Australian Government Research
Training Program scholarship.
References
ABS (2016). Census of Population and Housing, Australian Bureau of Statistics.
ABS. (2016). "Census of Population and Housing: Census Dictionary." Retrieved 24/10/2018, from
http://www.abs.gov.au/ausstats/abs@.nsf/Lookup/2901.0Chapter9502016.
ABS (2018). Building Approvals March 2018. A. B. o. Statistics.
APVI. (2018). "Mapping Australian Photovoltaic Installations." Retrieved 22/10/18, 2018, from
http://pv-map.apvi.org.au/historical#4/-26.67/134.12.
APVI. (2018). "Solar Potential Tool (SunSPoT)." from http://pv-map.apvi.org.au/sunspot.
City of Melbourne. (2015, 17/6/2017). "Building Outlines." 2018, from
https://data.melbourne.vic.gov.au/Property-Planning/Building-outlines-2015/pv8y-ihee.
City of Melbourne. (2018). "Census of Land Use and Employment (CLUE)." Retrieved 26/10/2018,
from https://www.melbourne.vic.gov.au/about-melbourne/research-and-statistics/city-
economy/census-land-use-employment/Pages/clue.aspx.
Asia Pacific Solar Research Conference, Sydney, December 4-6 2018
Clean Energy Regulator. (2018, 11/10/2018). "Postcode data for small-scale installations." Retrieved
26/10/18, from http://www.cleanenergyregulator.gov.au/RET/Forms-and-resources/Postcode-data-for-
small-scale-installations.
Copper, J. and A. Bruce (2014). Validation of Methods Used in the APVI Solar Potential Tool. Asia
Pacific Solar Research Project.
Copper, J., M. Roberts and A. Bruce (2017). Spatial Analysis of Solar Potential in Melbourne, APVI.
Copper, J. K. and A. G. Bruce. (2014). "SunSPoT Data and Calculations." from
http://d37ms1bqmca9qk.cloudfront.net/assets/SunSPOT_data_and_calculations-
eb0145ede270216c5f4b38683fcaf7d3.pdf.
Copper, J. K., A. B. Sproul and A. G. Bruce (2016). "A method to calculate array spacing and potential
system size of photovoltaic arrays in the urban environment using vector analysis." Applied Energy
161.
Gagnon, P., R. Margolis, J. Melius, C. Phillips and R. Elmore (2016). Rooftop Solar Photovoltaic
Technical Potential in the United States: A Detailed Assessment, NREL.
Lopez, A., B. Roberts, D. Heimiller, N. Blair and G. Porro (2012). U.S. Renewable Energy Technical
Potentials: A GIS-Based Analysis, NREL.
Melius, J., R. Margolis and S. Ong (2013). Estimating Rooftop Suitability for PV: A Review of Methods,
Patents, and Validation Techniques, NREL.
Nearmap Ltd. (2015). "Nearmap." Retrieved 6/11/2017, from http://au.nearmap.com.
NREL. (2010). "System Advisor Model (SAM)." Retrieved 18/10/2017, from https://sam.nrel.gov/.
Watt, M., R. J. Kaye, D. L. Travers and I. F. MacGill (1997). Assessing the Potential of PV in Buildings.
14th European PV Solar Energy Conf. EUPSEC’97. Barcelona, Spain.
... The solar models based on GIS can be applied on different scales, such as in various countries, like Australia (ROBERTS et al. 2018), Sweden (LINGFORS et al., 2017), and Saudi Arabia (ASIF, 2016, in the province of Salta, in Argentina (Sarmiento et al., 2019), in the city of Berlin (KRÜGER and KOLBE, 2012), and in villages (MAVROMATIDIS et al., 2015). More recently, as a result of advances in the sphere of machine learning and processing of satellite images, it is worth mentioning solar platforms that have great potential as they are online, free, and international: Google Sunroo3(recently used by MARTÍN-JIMENEZ et al, 2020) and Mapdwell4 (MAPDWELL, 2018, FOX-PENNER, 2020, HEINRICH et al 2020, and LIAO, 2019. ...
... Regarding model calibration, Szewczyk (2018) presents calibration of cloud presence in the GRASS r.sun model and warns that in relation to the solar register, the two biggest difficulties are obtaining threedimensional data of the urban area and the insertion of information in respect to nebulosity over the year. This was the case of Roberts et al. (2018), who used ARCGIS Solar Analyst and data from NREL for calibration, while Szewczyk (2018) adjusted the GRASS r.sun model using data from the SoDa project and pointed out that the algorithm is precise for days with clear sky, even without adjustment. Adjusting the GIS models using ground data or data from satellites with thermal sensors is extremely relevant for energy, financial, environmental, and policy planning to be developed based on real values, avoiding excesses or losses in private projects or larger scale projects. ...
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A transição sustentável para matrizes energéticas mais sustentáveis é uma demanda mundial e atual. Nesse sentido, a modelagem da radiação solar em alta resolução espacial é utilizada para avaliar o potencial de geração fotovoltaica em qualquer tipo de superfície e fornecer informações para planejamento e dimensionamento de sistemas fotovoltaicos. A partir do potencial técnico de geração, pode-se estimar o tempo de retorno do investimento do sistema fotovoltaico e a quantidade de gás carbono que deixou de ser emitido ao adotar a energia fotovoltaica. No contexto quantitativo, o objetivo deste artigo foi abordar brevemente a metodologia técnica e construir um modelo de radiação solar incidente em prédios da EE-IGC-UFMG. No contexto da discussão das aplicações sustentáveis da ferramenta, o objetivo foi tratar de temas relevantes, tais como a construção de modelos de radiação e os potenciais associados, as escalas de aplicação e dificuldades e limitações da modelagem.
... For each building, we have calculated the total energy consumption per year presented in Table 1. The potential PV system size is generated from Nearmap images, as described by Roberts, M.B. et al., (2018b). A wide range of retail tariffs was studied, as described in the next sub-section. ...
... In the data files, we have 30-minute load data for individual units and CP throughout the year. The data file also includes the estimated PV generation at 30-minute intervals throughout the year, calculated using NREL's System Advisory Model (SAM) (Roberts, M.B., et al., 2018b). The reference files include a tariff lookup table that itemizes the structure and rates for all the tariffs used in the modelling. ...
Conference Paper
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In the context of increasing retail electricity prices, rooftop PV is a potential low-cost source of electricity for residential customers. Hence, over two million Australian homes have solar PV on their rooftops. However, the penetration of rooftop PV on apartment buildings is shallow in Australia compared to single-unit households, due to regulatory and strata issues. This paper builds on previous research comparing the financial outcomes of different technical arrangements, business models, and battery management strategies for the deployment of PV on virtual apartment buildings. Here, we applied the same techno-economic model for 12 months of household-level interval load data for four real Sydney apartment buildings and modeled PV generation data based on the usable area and insolation on the building roof. We compare the costs and benefits of different PV systems and deployment arrangements under a range of tariff settings, and thereby identify the optimal arrangement for each building, in terms of lower individual household bills and increasing the profits for stakeholders.
... The study combines methodologies from NREL [69] and others [70] to exclude small roof planes and areas with low insolation, and correlates the potential PV arrays with the number of dwellings in the building. Initial results for the City of Melbourne LGA suggest that, on average, 48% of roof space is usable for PV installation on low-rise (one to three storey) apartment buildings and 38% on, medium-and high-rise, compared to 35% and 40% on detached and attached houses respectively [71], although further work is required to account for obstructions and shading sources below the resolution of the 3D map used for the analysis, which will likely reduce these figures. For Melbourne, this gives an estimate of mean potential PV system size of 2.3-5.3 kWp/unit for low-rise and 1.1-1.6 kWp/unit for high-rise buildings. ...
... The total potential on City of Melbourne apartment buildings is estimated at 38-53 MWp, ten times the estimated installed capacity on all commercial, industrial and residential rooftops in the LGA [72]. Scaled by the number of dwellings in appropriate categories across Australia [18], this suggests between 2.9GWp and 4.0GWp of potential capacity on apartment buildings nationally [71]. ...
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This paper reviews opportunities for, and barriers to, increasing photovoltaic (PV) deployment on apartment buildings, with a particular focus on the Australian experience. As rapid urbanisation drives increasing housing density, PV penetration in multi-occupancy housing has been limited by comparison with stand-alone housing in many jurisdictions, including in Australia despite its worldleading residential PV penetration. Given the growing commercial attractiveness of residential PV, this also raises equity concerns for apartment households. PV can potentially be installed to supply electricity to common property, to serve individual apartments, or as a resource shared between multiple apartments through embedded networks, local energy trading or ‘behind the meter’ deployment models. Our study undertook a review of the academic literature in this space and of specific Australian regulatory arrangements, as well as conducting a series of semi-structured interviews with a range of relevant stakeholders. Barriers identified include the huge variety amongst existing apartment building stock, demographic factors and knowledge issues. However, the Australian regulatory context - including governance of apartment buildings, regulation of the energy market, and electricity tariff policies - also impacts on the options available to apartment residents. New business models for deploying PV on apartments are emerging, including initiatives from retailers, developers and community energy organisations. While some issues are specific to the Australian context, or to buildings governed under strata-type arrangements, broader lessons can be taken from the Australian experience, including to inform the design of the regulatory framework required to facilitate widespread PV deployment across all residential housing types.
... The work presented in the literature noted above does not provide an online tool that is available to researchers to investigate the potential of rooftop PV electricity in residential districts. This research gap was recently addressed by Roberts M. et al. [12,13], who developed an online tool calledSunSPoT (Australian PV Institute Solar Map, funded by the Australian Renewable Energy Agency), which can be used to estimate the total potential of PV rooftop systems via a roof space mapping tool that evaluates available roof spaces, and estimates shading and roof orientation. This work was conducted for different residential and commercial zones in states and local government areas in Australia. ...
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The installation of rooftop PV systems in residential buildings and dwellings has increased rapidly in the past decade, and these systems have become a major source of renewable energy in many countries. This paper presents a new method of estimating the potential of rooftop PV systems to meet energy demands in residential districts by introducing a roof suitability factor. The method of assessment is based on an online tool called SunSPot, which uses a solar radiation heat map layer of building roofs and the PVSYST solar performance software. A sample of 400 houses from four suburbs considered in the Sydney City Council 2030 sustainability plan was selected to conduct the performance analysis of rooftop PV systems and develop a formula that can estimate the suburban annual energy production. The results show that if the dwelling roofs in residential suburbs could be covered by PV arrays it would produce enough electricity to exceed the local electricity demand and, in some suburbs, a surplus of more than 87%.
... In Austria, 71 % of apartment buildings contain ten or less apartments, while in Australia 60 % of apartments were in buildings of less than four storeys in 2016 (although higher-rise buildings are more common in recent construction). This is significant for PV deployment, as low-rise apartment buildings have greater per-dwelling solar rooftop potential [60]. ...
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Full-text available
Although the amount of solar photovoltaic systems installed in residential buildings is increasing globally, it is largely limited to single-occupancy dwellings and is extremely uneven across jurisdictions. Deployment on apartment buildings remains low, even in Australia with its world-leading residential photovoltaic penetration, or in countries subject to specific enabling legislation, such as Austria. We present a comparative study of photovoltaic system deployment on multi-occupancy residential buildings in these two countries, examining the impact of their distinct climates, financial settings, heating and cooling technologies and regulatory environments. A mixed-integer linear optimisation model is used to compare cost-optimal photovoltaic system size and achievable cost savings for a nine-apartment building. We find that Australia's higher insolation and lower investment costs drive higher optimal system size and bill savings, but lower electricity tariffs and regulatory barriers constrain deployment. By contrast, European enabling legislation has not yet achieved success in overcoming Austria's higher investment costs and lower solar exposure, partly due to significant administrative hurdles. Our findings point to possible country-specific policy approaches to increase deployment in this important sector.
... However, 61% of apartments are in buildings of three storeys or less, with potential PV capacity to make a significant contribution to the building load. Our analysis of the solar potential of apartment building roofs in the City of Melbourne [10] found that, on average, apartment buildings have a greater proportion of total roof area suitable for PV installation than houses but, more importantly, that the average potential PV capacity on three-storey apartment buildings is 3.2kW per dwelling, slightly more than half the average potential on stand-alone houses in the LGA, and that buildings with four or more storeys have an average potential PV capacity of 1.6kW per apartment (see Table 2). Note that these are average figures and that, as with houses, solar potential is highly variable (as shown by the high standard deviation in Table 2) and dependent on specific building characteristics as well as shading. ...
Conference Paper
Full-text available
Over the last two decades, grid-connecte d solar photovoltaic systems have increased from a niche market to one of the leading power generation capacity additions annually. In 2019 the total worldwide installed photovoltaic electricity generation capacity exceeded 630 GW. It is forecasted that 1 TW will be reached by 2022. This further development is coupled with the question at what prices solar photovoltaic electricity can be provided and delivered to the customers. The installation of PV systems for self-consumption is already now an interesting option for many people but in general limited to those who have access to a rooftop they own or can use. Enabling residents of multi apartment buildings to commonly use electricity generated by a PV system (collective self-consumption) is a relatively new development and is still facing a lot of administrative and regulatory challenges. This paper provides an overview of existing regulatory schemes in IEA PVPS countries and presents and analysis of two self-consumption case studies .
Conference Paper
Full-text available
Over the last two decades, grid-connected solar photovoltaic systems have increased from a niche market to one of the leading power generation capacity additions annually. In 2018 the total installed photovoltaic electricity generation capacity exceeded 500 GW. Another doubling is forecasted until the early 2020s. Therefore, the further development is coupled with the question at what prices solar photovoltaic electricity can be provided and delivered to the customers. The installation of PV systems for self-consumption is already now an interesting option for many people but in general limited to those who have access to a rooftop they own or can use. A new envelopment to enable residents of multi apartment buildings to commonly use electricity generated by a PV system (collective self-consumption) is a relatively new development and is still facing a lot of administrative and regulatory challenges.
Technical Report
Full-text available
There is significant potential for rooftop solar PV in Australia. Rooftop solar PV is a key energy technology because it is leading the transition to consumer uptake of low-¬‐carbon demand-¬‐side energy technologies, which are providing new opportunities for consumer engagement and new clean energy business models to emerge. However, there is a lack of good information in the public domain about the potential for rooftop solar to contribute to low-¬‐carbon electricity generation in Australia's cities. This type of information is important for policymakers and planners, and to encourage public support for rooftop solar. This research uses the data and methodologies behind the APVI Solar Potential Tool http://pv-¬‐ map.apvi.org.au/potential, developed by researchers at UNSW, to estimate the Solar Potential in the Brisbane CBD. The report includes: 1. An assessment of PV Potential in Sydney CBD (bounded by the City of Sydney LGA) 2. An estimate of the potential impact of rooftop PV on local electricity consumption and emissions 3. Identification of rooftops with the largest PV potential (area available) in the CBD 4. Three case studies of PV Potential on landmark buildings in Sydney. The useable area suitable for PV deployment across Sydney's CBD was calculated using two different methods. The most conservative estimate of the two suggests the useable area suitable for rooftop PV deployment (the ratio between the area of PV panels that could be accommodated and the total roof area) is 25% corresponding to 393 MW of PV potential with an expected annual yield of 507 GWh. The equivalent CO 2 emission savings are 403 kt per year.
Article
Full-text available
This report presents the state-level results of a spatial analysis effort calculating energy technical potential, reported in square kilometers of available land, megawatts of capacity, and gigawatt-hours of generation, for six different renewable technologies. For this analysis, the system specific power density (or equivalent), efficiency (capacity factor), and land-use constraints were identified for each technology using independent research, published research, and professional contacts. This report also presents technical potential findings from previous reports.
Article
The standard mathematical approach used to calculate photovoltaic (PV) array spacing contains a number of assumptions that limits its use to PV arrays installed on horizontal surfaces. This paper utilises vector analysis to develop a new method to calculate array spacing and potential system size for any combination of PV array and surface tilt and orientation. This approach is validated by comparing the vector results with ray-tracing shadow visualisations utilising the Ecotect software package. The vector method is presented as an approach compatible with online solar/PV mapping tools after a review of the existing online tools indicated that rack mounted array functionalities were rarely included. The methodology is further demonstrated via results from the Australian PV Institute's (APVI's) Solar Potential Tool which utilises the array spacing method presented. This paper also applies the methodology to a general analysis of array spacing and power density (installed capacity/unit area) for an optimally tilted equator facing array on roof surfaces of a variety of tilts and orientations.
Mapping Australian Photovoltaic Installations
ABS (2018). Building Approvals March 2018. A. B. o. Statistics. APVI. (2018). "Mapping Australian Photovoltaic Installations." Retrieved 22/10/18, 2018, from http://pv-map.apvi.org.au/historical#4/-26.67/134.12.
Census of Land Use and Employment (CLUE)
APVI. (2018). "Solar Potential Tool (SunSPoT)." from http://pv-map.apvi.org.au/sunspot. City of Melbourne. (2015, 17/6/2017). "Building Outlines." 2018, from https://data.melbourne.vic.gov.au/Property-Planning/Building-outlines-2015/pv8y-ihee. City of Melbourne. (2018). "Census of Land Use and Employment (CLUE)." Retrieved 26/10/2018, from https://www.melbourne.vic.gov.au/about-melbourne/research-and-statistics/cityeconomy/census-land-use-employment/Pages/clue.aspx.
Postcode data for small-scale installations
Clean Energy Regulator. (2018, 11/10/2018). "Postcode data for small-scale installations." Retrieved 26/10/18, from http://www.cleanenergyregulator.gov.au/RET/Forms-and-resources/Postcode-data-forsmall-scale-installations.
Validation of Methods Used in the APVI Solar Potential Tool
  • J Copper
  • A Bruce
Copper, J. and A. Bruce (2014). Validation of Methods Used in the APVI Solar Potential Tool. Asia Pacific Solar Research Project.
Rooftop Solar Photovoltaic Technical Potential in the United States: A Detailed Assessment
  • P Gagnon
  • R Margolis
  • J Melius
  • C Phillips
  • R Elmore
Gagnon, P., R. Margolis, J. Melius, C. Phillips and R. Elmore (2016). Rooftop Solar Photovoltaic Technical Potential in the United States: A Detailed Assessment, NREL.
Estimating Rooftop Suitability for PV: A Review of Methods, Patents, and Validation Techniques
  • J Melius
  • R Margolis
  • S Ong
Melius, J., R. Margolis and S. Ong (2013). Estimating Rooftop Suitability for PV: A Review of Methods, Patents, and Validation Techniques, NREL. Nearmap Ltd. (2015). "Nearmap." Retrieved 6/11/2017, from http://au.nearmap.com. NREL. (2010). "System Advisor Model (SAM)." Retrieved 18/10/2017, from https://sam.nrel.gov/.
Assessing the Potential of PV in Buildings
  • M Watt
  • R J Kaye
  • D L Travers
  • I F Macgill
Watt, M., R. J. Kaye, D. L. Travers and I. F. MacGill (1997). Assessing the Potential of PV in Buildings.