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Quantifying rooftop solar photovoltaic potential for
regional renewable energy policy
L. K. Wiginton, H.T. Nguyen, J.M. Pearce
To cite this version:
L. K. Wiginton, H.T. Nguyen, J.M. Pearce. Quantifying rooftop solar photovoltaic potential for
regional renewable energy policy. Computers, Environment and Urban Systems, Elsevier, 2010, 34
(4), pp.345-357. �10.1016/j.compenvurbsys.2010.01.001�. �hal-02120495�
Electronic copy available at: http://ssrn.com/abstract=2006710
Published in: L.K. Wiginton, H. T. Nguyen, J.M. Pearce, “Quantifying Solar Photovoltaic Potential on a Large Scale
for Renewable Energy Regional Policy”, Computers, Environment and Urban Systems 34, (2010) pp. 345-357.
http://dx.doi.org/10.1016/j.compenvurbsys.2010.01.001
Quantifying Rooftop Solar Photovoltaic Potential for Regional Renewable Energy Policy
L.K. Wiginton, H. T. Nguyen, J.M. Pearce
Abstract
Solar photovoltaic (PV) technology has matured to become a technically viable large-
scale source of sustainable energy. Understanding the rooftop PV potential is critical for
utility planning, accommodating grid capacity, deploying financing schemes and
formulating future adaptive energy policies. This paper demonstrates techniques to
merge the capabilities of geographic information systems and object-specific image
recognition to determine the available rooftop area for PV deployment in an example
large-scale region in south eastern Ontario. A five-step procedure has been developed for
estimating total rooftop PV potential which involves geographical division of the region;
sampling using the Feature Analyst extraction software; extrapolation using roof area-
population relationships; reduction for shading, other uses and orientation; and
conversion to power and energy outputs. Limitations faced in terms of the capabilities of
the software and determining the appropriate fraction of roof area available are discussed.
Because this aspect of the analysis uses an integral approach, PV potential will not be
georeferenced, but rather presented as an agglomerate value for use in regional policy
making. A relationship across the region was found between total roof area and
population of 70.0 m
2
/capita ± 6.2%. With appropriate roof tops covered with
commercial solar cells, the potential PV peak power output from the region considered is
5.74 GW (157% of the region’s peak power demands) and the potential annual energy
production is 6909 GWh (5% of Ontario’s total annual demand). This suggests that 30%
of Ontario’s energy demand can be met with province-wide rooftop PV deployment. This
new understanding of roof area distribution and potential PV outputs will guide energy
policy formulation in Ontario and will inform future research in solar PV deployment and
its geographical potential.
Keywords: GIS; roof area; Feature Analyst; renewable energy; solar photovoltaic;
sustainable future
1. Introduction
Global climate destabilization as a result of the anthropogenic emission of green house
gases (GHGs) is one of today’s most urgent issues (IPCC, 2007; Jacobson, 2009; Sims, et
al., 2007; UN, 1992). Being that more than half of anthropogenic GHG emissions
comprise carbon dioxide from fossil fuel combustion (IPCC, 2007), the mitigation of
climate change predominantly concerns our energy use. Renewable energy technologies
are recognized as a vital part of energy use reform (Jacobson, 2009; Neuhoff, 2005;
Pearce, 2002; Sanden, 2004).
In particular, the direct conversion of sunlight into electricity by solar photovoltaic (PV)
technology possesses great untapped potential and represents a technically viable and
sustainable solution to energy demands (Neuhoff, 2005; Pearce, 2002). The use of PV
Electronic copy available at: http://ssrn.com/abstract=2006710
Published in: L.K. Wiginton, H. T. Nguyen, J.M. Pearce, “Quantifying Solar Photovoltaic Potential on a Large Scale
for Renewable Energy Regional Policy”, Computers, Environment and Urban Systems 34, (2010) pp. 345-357.
http://dx.doi.org/10.1016/j.compenvurbsys.2010.01.001
power is still dwarfed, however, by conventional (largely fossil fuel-based) energy
production methods. In fact, despite staggering growth rates of 110% in the last year
(Solarbuzz, 2009), PV accounts for less than 1% of the global energy supply (IEA,
2008a). Resources to deploy solar PV are not the limiting factor: PV remains an “infant
technology” primarily because of its prohibitively high levelized cost of electricity and
lack of market experience, resulting in a low rate of uptake in absolute terms (Neuhoff,
2005; Pearce, 2008; Sanden, 2004). To improve the rate of PV deployment, governments
throughout the world have introduced incentives such as Ontario’s pending feed in tariff
(FIT). The Ontario FIT is predicted to increase the uptake of PV across the province; in
particular, it will encourage rooftop PV deployment as a result of its sliding-scale pricing
structure (OPA, 2009). Yet, the maximum energy potential if PV is deployed on every
appropriate rooftop in the region remains unknown because data concerning roof area in
most regions simply does not exist. Understanding the rooftop PV potential is critical for
utility planning, accommodating grid capacity, deploying financing schemes and
formulating future adaptive policies.
In order to overcome these challenges, this paper will merge geographic sampling with
object-specific image recognition to determine the available rooftop area for PV
deployment in a large-scale region in south eastern Ontario, referred to as the
“Renewable Energy Region (RER)” (Mabee and Carpenter, 2009). It will apply the
Visual Learning Systems’ ArcGIS extension, Feature Analyst, to produce advanced
feature classification algorithms for extracting rooftop features from batches of high-
resolution digital orthophotos. Limitations of the product for this application will be
discussed. From this rooftop extraction on a representative geographical sample of the
region, the relationship between population and roof area will be explored and
extrapolated to the entire region. Relating roof area to population will be shown to be
highly important, not only for understanding the rooftop PV potential, but also with
regards to other applied urban sustainability initiatives such as solar thermal heating,
green roofs and stormwater runoff management.
From total roof area, an estimate of rooftop PV potential will be produced by considering
factors such as shading, other uses, and orientation of rooftops, PV panel efficiencies and
average solar insolation in the region. Because this aspect of the analysis uses an integral
approach, PV potential will not be georeferenced, but rather presented as an agglomerate
value for use in regional policy making. Power and energy outputs will be compared to
provincial and regional demands. These important and previously unknown figures can
be used to direct government, banking and utility-related policy in Ontario immediately
and in the future.
The focus of this paper is on the development of an approximation of total roof area. A
separate simulation for determining PV potential from total roof area was outside the
scope of this project as it is highly location dependent, thus, methods were taken from the
literature. Future research can expand upon these aspects of this paper’s results.
Published in: L.K. Wiginton, H. T. Nguyen, J.M. Pearce, “Quantifying Solar Photovoltaic Potential on a Large Scale
for Renewable Energy Regional Policy”, Computers, Environment and Urban Systems 34, (2010) pp. 345-357.
http://dx.doi.org/10.1016/j.compenvurbsys.2010.01.001
2. Background
2.1 Related Work
Several authors have applied GIS techniques to the topic of PV deployment and/or
impervious urban fabric (Gadsden et al., 2003; Ghosh & Vale, 2006; Izquierdo et al.,
2008; Kraines et al., 2003; Kraines et al., 2001; Rylatt et al., 2001). Image recognition,
both object-based and spectrally-based has been used as a means of studying urban fabric
and determining roof area (Akbari et al., 2003; Guindon et al., 2004; Ratti & Richens,
2009; Richens, 1997; Taubenbock et al., 2008).
Unfortunately, this past research is not directly applicable to determining the rooftop PV
potential in Ontario for one of the following reasons: (1) the technique was applied a
single building, neighbourhood or city, not a large-scale region (Gadsden et al, 2003;
Ghosh & Vale, 2006; Rylatt et al., 2001); (2) the goal is to classify land use designations
rather than extract roof area (Akbari et al., 2003; Guindon et al., 2004) or (3) the input
data is different from that which exists for Ontario (Aramaki et al., 2001; Grosso, 1998;
Izquierdo et al., 2008; Kraines et al., 2003; Kraines et al., 2001; Ratti & Richens, 1999;
Richens, 1997).
In particular, Feature Analyst (FA) has been used in the assessment of buildings and/or
land use. Psaltis and Ioannidis (2008) and Ioannidis et al. (2009) use FA in detecting
building change in Greece, while Yuan (2008) detects land-use/land-cover change.
Feature Analyst has also been used for quantifying impervious land cover for hydrology
studies (Kunapo, 2006), tsunami vulnerability assessments (Surmaryono et al., 2008) and
for studying trends in salamander populations (Miller, 2005). None of the work in FA to
date, however, has studied roof area quantification for PV deployment.
Further, several authors have explored the relationship between population and roof area
in Brazil (Ghisi, 2006), Germany/Western Europe (Lehmann & Peter, 2003), India
(Kumar, 2004; Pillai & Banerjee, 2007), Spain (Izquierdo et al., 2008) and the United
Kingdom (Pratt, 1999). Guindon et al. (2004) have studied the relationship between
building density and population density in Canada; however, they did not study roof area
in particular. In addition to the improvements in methodology, this paper will contribute
to this literature by exploring population-roof area relationships in the province of
Ontario, Canada and identifying similarities and differences to other regions.
2.2 Government Incentives for Renewable Energy
Governments have an important role to play in reducing GHG emission trends (IEA,
2008b). By using careful policy measures, governments have the means to increase the
Published in: L.K. Wiginton, H. T. Nguyen, J.M. Pearce, “Quantifying Solar Photovoltaic Potential on a Large Scale
for Renewable Energy Regional Policy”, Computers, Environment and Urban Systems 34, (2010) pp. 345-357.
http://dx.doi.org/10.1016/j.compenvurbsys.2010.01.001
uptake of PV, thereby spurring associated innovation and increasing economic
competitiveness through economies of scale (Sanden, 2004; Pearce, 2008). By increasing
reliance on distributed sources of renewable sources of energy, particularly roof-mounted
PV, governments of any size possess the power to reduce their regions’ environmental
impact through a reduction in GHG emissions from carbon use (Pearce, 2002; Caamaño-
Martín, 2008; Herig, 2003; IEA, 2008b). Further, renewable energy technologies address
regional and national security (IEA, 2008b) in that they decrease reliance on other
regions for energy sources, particularly fossil fuels (Pimentel et al., 1994; Caamaño-
Martín, 2008), and can also provide greater reliability during times of high demand and
pending blackouts (Caamaño-Martín, 2008; Herig, 2003; Perez & Collins, 2004).
Additionally, renewable energy technologies facilitate the establishment of distributed
generation which reduces transmission and distribution costs as well as system losses
(Caamaño-Martín et al., 2008; Pearce and Harris, 2007; Shalaby, 2008). Finally,
renewable energy technologies eliminate the need for the construction of new large-scale
fossil fuel power plants and the associated economic risks that accompany these projects
(Caamaño-Martín, 2008; Pearce and Harris, 2007; Shalaby, 2008). Overall it can be seen
that there are many reasons for which governments have an interest in the expansion of
distributed generation of renewable energy such as roof-mounted PV in their regions.
2.2.1 Feed-in Tariffs
Feed-in tariffs (FITs) have proven to be the most effective government incentive program
for renewable technologies: countries who have adopted FITs have been shown to have
the largest growth rates in renewable energy technology deployment (Pietruszko, 2006;
REN21, 2009; EPIA, 2008). In fact, half of the world’s PV installations are due to FITs
(Peters & Weis, 2008). FITs for PV are being utilized around the globe: in early 2009, 45
countries and 18 states/provinces/territories had FITs (REN21, 2009). In Germany, a FIT
program has been offered to PV operators for nearly two decades. The tariff is altered
throughout the years to spur innovation and effectively stimulate the market (EEG, 2007).
The success of the FIT enabled Germany to reach its goal of having a 12.5% renewable
energy supply three years early, in 2007 (EEG, 2007; Peters & Weis, 2008) and
encouraged 18 other EU countries to adopt similar programs (EEG, 2007). Another
country to successfully pursue FITs was Spain: in 2008, Spain saw a five-fold increase in
PV capacity from the previous year. Germany and Spain possessed 5.4 and 3.3 GW of
PV power capacity in 2008, representing the majority of the world’s 13 GW total
(REN21, 2009). Other countries/regions with FIT programs in include California,
Ireland, Portugal, the Slovak Republic, Switzerland, Turkey, Bulgaria, Greece, France,
Kenya, the Philippines, Poland and South Africa (REN21, 2009).
2.2.2 The Ontario FIT
