Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis

Applied Energy xxx (2016) xxx–xxx

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

Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis Taehoon Hong a, Minhyun Lee a,⇑, Choongwan Koo a,b, Kwangbok Jeong a, Jimin Kim a a b

Department of Architectural Engineering, Yonsei University, Seoul 03722, Republic of Korea Division of Construction Engineering and Management, Purdue University, West Lafayette, IN 47906, United States

h i g h l i g h t s
This study developed a method for estimating the rooftop solar PV potential.
Available rooftop areas were calculated using Hillshade analysis on an hourly basis.
Physical, geographic, and technical potentials were estimated for 27,774 buildings.
The technical potential in the Gangnam district was determined to be 1,130,371 MW h.
The estimated rooftop solar PV potential can be used in establishing solar policies.

a r t i c l e

i n f o

Article history: Received 21 March 2016 Received in revised form 26 May 2016 Accepted 1 July 2016 Available online xxxx Keywords: Solar photovoltaic (PV) system Rooftop solar photovoltaic (PV) potential Available rooftop area Geographical information system (GIS) Hillshade analysis Building shadow

a b s t r a c t The solar photovoltaic (PV) system is known as one of the most outstanding new renewable energy systems for achieving the nearly zero energy building (nZEB). For the continuous deployment of the solar PV system in urban environments, it is crucial to estimate the rooftop solar PV potential. Urban areas, however, where high-rise buildings abound, are not always suitable for solar PV installation. Therefore, it is important to accurately estimate the available rooftop area considering the shadows from the surrounding buildings for reliable rooftop solar PV potential estimation. Therefore, this study proposed a method for estimating the rooftop solar PV potential by analyzing the available rooftop area through Hillshade analysis. Toward this end, the rooftop solar PV potential was estimated through the following hierarchical process: (i) calculation of the physical potential; (ii) calculation of the geographic potential; and (iii) calculation of the technical potential. For accurate estimation of the rooftop solar PV potential, the geographic potential (i.e., the available rooftop area) was explored in detail by analyzing the shadow based on the location of the sun through Hillshade analysis. By applying the proposed method to the Gangnam district in Seoul, South Korea, this study estimated the physical, geographic, and technical potentials on hourly, monthly, and annual bases. Overall, the physical, geographic, and technical potentials in the Gangnam district were found to be 9,287,982 MW h, 4,964,118 m2, and 1,130,371 MW h, respectively. These rooftop solar PV potential results can be used in establishing solar policies by analyzing the different levels of the rooftop solar PV potential on hourly, monthly, and annual bases. Ó 2016 Elsevier Ltd. All rights reserved.

1. Introduction The growing interest in global warming, environmental pollution, and resource depletion has led to various efforts to save energy and reduce greenhouse gas emissions worldwide [1–9]. As a part of such efforts, new renewable energy (NRE) is drawing ⇑ Corresponding author at: Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea. E-mail address: [email protected] (M. Lee).

much attention, which makes it possible to achieve and distribute the nearly zero energy building (nZEB), where buildings become nearly independent of the energy coming from the grid. As the building sector accounts for about 40% of the global energy consumption, the deployment of NRE in buildings and cities plays an important role in saving energy and achieving the nZEB [10,11]. One of the most outstanding and recognizable NRE systems for achieving the nZEB is the solar photovoltaic (PV) system, which is easy to install in and apply to a building and an urban environment with a clean and unlimited energy source [12–18].

http://dx.doi.org/10.1016/j.apenergy.2016.07.001 0306-2619/Ó 2016 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Hong T et al. Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.07.001

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T. Hong et al. / Applied Energy xxx (2016) xxx–xxx

To continue promoting the deployment of the solar PV system in urban environments and to successfully establish relevant policies and future directions, it is crucial to understand and accurately evaluate the rooftop solar PV potential [19–23]. Basically, the rooftop solar PV potential can be hierarchically categorized into three different levels: (i) the physical potential: the total amount of solar energy reaching the target surface, which can be referred to as the total solar radiation on the rooftop; (ii) the geographic potential: the spatial availability of the building rooftop, where solar energy can be obtained, which can be referred to as the available rooftop area for solar PV installation; and (iii) the technical potential: the total amount of electricity considering the technical characteristics of the solar PV system (i.e., module efficiency), which can be referred to as electricity generation [18–20]. Urban areas, however, where high-rise buildings abound, are not always suitable and applicable for installing the solar PV system. Unlike non-urban environments, where there is almost no surrounding obstacle, in urban environments, the geographic potential of the rooftop solar PV system (i.e., the available rooftop area for solar PV installation) largely depends on the shadows from the surrounding buildings. Therefore, for reliable rooftop solar PV potential estimation in urban areas, it is extremely important to accurately estimate the geographic potential of the rooftop solar PV system considering the shadows due to the surrounding buildings [18–20,24,25]. Accordingly, many previous studies analyzed and investigated the geographic potential of the rooftop solar PV system (i.e., the available rooftop area) to ultimately estimate the rooftop solar PV potential in different geographic environments using various methods [18–20,24–48]. First, various studies estimated the rooftop solar PV potential of a region by simply approximating the available rooftop area with a certain percentage [26–28]. IEA [26] approximated the available rooftop area using the utilization factor, which was expressed in the percentage of the ground floor area, to estimate the rooftop solar PV potential in Europe. Similarly, Peng and Lu [27] also approximated the available rooftop area using architectural and solar suitability factor, which was expressed in the percentage of the ground floor area, to estimate the rooftop solar PV potential in Hong Kong. These studies were able to analyze the rooftop solar PV potential in a simple and easy way, without much effort, but they were not able to validate the results. Second, some studies estimated the rooftop solar PV potential of a region by estimating the available rooftop area based on various social factors (i.e., population, land uses, and building footprint) [19,20,29–36]. Izquierdo et al. [19] estimated the available rooftop area by integrating information on population, number of buildings, and land uses with various coefficients (e.g., shadowing) to estimate the rooftop solar PV potential in Spain. Wiginton et al. [29] estimated the available rooftop area by extrapolating the calculated rooftop area of the sampled area to the entire region using the population data to estimate the rooftop solar PV potential in Canada. These methods may be useful if the building rooftop data are not available, but they still cannot guarantee accurate results and are somewhat time-consuming. Mainly, most of these methods do not fully consider the localized rooftop characteristics for the entire study area in a macro scale, and therefore, the results can only be an approximation. Third, other studies estimated the rooftop solar PV potential of a region by computing the available rooftop area with a geographic information system (GIS), based on the localized rooftop characteristics [37–45]. Ko et al. [37] computed the shaded areas on rooftops by conducting Hillshade analysis with the sampled building data, but approximated the available rooftop area for the rest of the region based on the sampled building data, to estimate the rooftop solar PV potential in Taiwan. Strzalka et al. [38] computed the

Fig. 1. Research framework.

shaded areas on rooftops using the CityGML model with all buildings in the study area, but the electricity generation of the rooftop solar PV system for the case study building was extrapolated to all suitable rooftop areas in the study area, to estimate the rooftop solar PV potential in Germany. These methods can consider the localized rooftop characteristics for more realistic results, but they still cannot overcome the following limitations: (i) only certain types of buildings or partial areas are taken into account for GIS simulation; (ii) the changing sun location and relevant local conditions are not considered on hourly, monthly, and annual bases; and (iii) the methods can be time-consuming and inefficient, requiring much data and efforts. Therefore, this study aimed to develop a method for estimating the rooftop solar PV potential, with following differences from the previous studies: (i) this study considers the localized rooftop characteristics such as the actual buildings’ elevation for the entire study area in a macro scale; (ii) this study considers the location of the sun, which changes throughout the year, for building shadow analysis; and (iii) this study estimates the rooftop solar PV potential on hourly, monthly, and annual bases. Toward the aforementioned end, this study was conducted in the following three steps: (i) step 1: calculation of the physical potential; (ii) step 2: calculation of the geographic potential; and (iii) step 3: calculation of the technical potential (refer to Fig. 1). Among these three steps, this study mainly focused on step 2 (i.e., calculation of the geographic potential) for accurate and reliable rooftop solar PV potential estimation. Thus, step 2 was conducted as follows: (i) step 2.1: data collection and conversion; (ii) step 2.2: building shadow analysis; and (iii) step 2.3: estimation of the available rooftop area. To analyze the building shadow on the rooftop and to calculate the available rooftop area based on the altitude and azimuth of the sun in step 2.2, this study used Hillshade analysis. Hillshade analysis was conducted every month, on the 15th, at hourly intervals, resulting in a total of 156 simulations per year. The proposed estimation method was applied to the Gangnam district in Seoul, South Korea, where there are many high-rise buildings, to show the step-by-step procedure.

2. Materials and method This study developed a method for estimating the rooftop solar PV potential in three steps: (i) step 1: calculation of the physical potential; (ii) step 2: calculation of the geographic potential; and (iii) step 3: calculation of the technical potential. The physical, geographic, and technical potentials were estimated for the entire Gangnam district in Seoul, South Korea. The Gangnam district,

Please cite this article in press as: Hong T et al. Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.07.001

T. Hong et al. / Applied Energy xxx (2016) xxx–xxx

one of the 25 local government districts of the city of Seoul, has a 39.5 km2 area with over 20,000 buildings. The said district is also well known as a popular metropolitan county where high-rise office buildings abound [49].

2.1. Step 1: Calculation of the physical potential In step 1, the solar radiation data in Seoul were collected to analyze the physical potential of the rooftop solar PV system in the Gangnam district. Solar radiation data are generally provided on a monthly basis by various organizations worldwide, including Korea Meteorological Administration, but this study needed hourly solar radiation data as it calculated the available rooftop area and ultimately estimated the rooftop solar PV potential on an hourly basis. Accordingly, in this study, the hourly solar radiation data in Seoul were collected from World Radiation Data Centre (WRDC).

3

WRDC, sponsored by the World Meteorological Organization, collects, archives, and offers solar radiation data throughout the world to ensure data accessibility for research purposes [50]. WRDC has provided hourly solar radiation data in Seoul since 1986, and in this study, the most recent five-year data (2010–2014) were used to calculate the rooftop solar PV potential in the Gangnam district.

2.2. Step 2: Calculation of the geographic potential In step 2, the available building rooftop area for solar PV installation was estimated to analyze the geographic potential of the rooftop solar PV system in the Gangnam district. The available rooftop area for PV installation was estimated in three steps: (i) step 2.1: data collection and conversion; (ii) step 2.2: building shadow analysis using Hillshade analysis; and (iii) step 2.3: estimation of the available rooftop area (refer to Fig. 2). To estimate the avail-

Fig. 2. Framework for estimating the geographic potential of the rooftop solar PV system.

Please cite this article in press as: Hong T et al. Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.07.001

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T. Hong et al. / Applied Energy xxx (2016) xxx–xxx

able rooftop area in these three steps, this study used ArcMap 10.1, a GIS software widely used for geospatial analysis [51]. 2.2.1. Step 2.1: Data collection and conversion In step 2.1, the building data in the Gangnam district were collected and then converted into a data form which allows Hillshade analysis. First, the building data in the Gangnam district were collected from Spatial Information Industry Promotion Institute (SPACEN) under the Ministry of Land, Infrastructure, and Transport of the Korean government. SPACEN is a foundation corporation which manages and operates the spatial information open platform called ‘‘Vworld” for promoting better use of geospatial information to meet social demand and to create new business opportunities [52]. As shown in Fig. 3, the building data in the Gangnam district were provided in shapefile format as polygon data, and various building information, such as the building elevation and area, were included as attributes. As a result, the building data for a total of 27,774 buildings were collected and used for estimating the geographic potential of the rooftop solar PV system. Second, the building elevation data from the building data in the Gangnam district were converted into a data form which allows Hillshade analysis. The following two steps should be conducted to convert building elevation data into a data form for Hillshade analysis: (i) the polygon data should be converted into raster data; and (ii) a 0 value should be assigned to the raster cells with no building elevation data. The existence of raster cells with no building elevation data indicates that there is no building in the relevant cells, and therefore, a value representing the ground level (i.e., 0) should be assigned to these raster cells. 2.2.2. Step 2.2: Building shadow analysis In step 2.2, the building shadow was analyzed and the shaded area was calculated based on the altitude and azimuth of the sun using the Hillshade algorithm. To analyze the building shadow and to calculate the shaded area using the Hillshade algorithm, three input data are required: (i) the raster data of the building elevation; (ii) the altitude of the sun above the horizon; and (iii) the azimuth of the sun. First, the raster data of the building elevation, which was processed in step 1, was used in this study. Second, the altitude and azimuth of the sun, which was calculated using the Sun Altitude and Azimuth Calculation tool from Korea Astronomy & Space Science Institute, was used [53]. As the altitude and

azimuth of the sun changes throughout the year and the day, in this study, the altitude and azimuth of the sun was calculated every hour from 6 a.m. to 7 p.m. (when the sun is over the horizon) on the 15th (when the sun is in the average position of each month) of each month from January to December in Seoul. Using these three input data, Hillshade analysis was conducted for 12 days (on the 15th of each month from January to December) at hourly intervals (from 6 a.m. to 7 p.m.), resulting in a total of 156 times. Each output raster is presented in gray scale, ranging from 0 to 255: (i) a gray scale value of 0 represents the shaded area (displayed in black); (ii) a low gray scale value (e.g., 1) represents the dark area (displayed in dark gray); and (iii) a high gray scale value (e.g., 255) represents the bright area (displayed in light gray or white).

2.2.3. Step 2.3: Estimation of the available rooftop area In step 2.3, the available rooftop area for solar PV installation was estimated excluding the shaded rooftop area and the unsuitable installation area. First, the shaded rooftop area was excluded from the total rooftop area based on the existence of building shadows. To exclude the shaded rooftop area from the total rooftop area, null values were assigned to the raster cells according to the following two conditions: (i) the area without a building: raster cells with no building elevation data in the original raster; and (ii) the shaded area: raster cells with a 0 gray scale value in the output raster of Hillshade analysis. As a result, the output raster of the unshaded rooftop area where the solar PV system can perform at the optimal level without any disturbance by the building shadow could be extracted from the output raster of Hillshade analysis. Second, in this study, the unsuitable installation area where the solar PV system cannot be installed architecturally was additionally excluded. To exclude the unsuitable installation area, the output raster of the unshaded rooftop area was converted to polygon data, and the polygons that fell below the minimum requirement for installing a solar PV system were removed. Korea New and Renewable Energy Center, a government agency for supporting and promoting NRE in South Korea, has suggested the following minimum installation areas per kW for a solar PV system: (i) fixed-tilt system: 16.5 m2; (ii) single-axis tracker system: 26.4 m2; (iii) dual-axis tracker system: 33 m2 [54]. Accordingly, in this study, a 33 m2 installation area was used as the minimum requirement for installing a solar PV system, and thus, polygons

Fig. 3. The building data in the Gangnam district.

Please cite this article in press as: Hong T et al. Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.07.001

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T. Hong et al. / Applied Energy xxx (2016) xxx–xxx

smaller than 33 m2 were removed from the available rooftop area. As a result, the output polygon of the available rooftop area where the solar PV system can be installed could be extracted by excluding the small and irregular rooftop area, where it is hard to install the solar PV system. 2.3. Step 3: Calculation of the technical potential In step 3, the solar PV module efficiency was defined to analyze the technical potential of the rooftop solar PV system in the Gangnam district. The solar PV module efficiency varies widely among the different types and manufacturers of solar panels [55,56]. The highest solar PV module efficiency that has been confirmed and reported so far under experimental conditions is 22.9%, but that of the commercial solar PV modules remains at 15–18% [57,58]. Therefore, in this study, 15% solar PV module efficiency was used for calculating the technical potential of the rooftop solar PV system in the Gangnam district to reflect the current technology level of the solar PV market and industry. Aside from the solar PV module efficiency, the tilt angle of the solar PV panel and the PV array spacing may also influence the technical potential of the rooftop solar PV system [15]. Since this study focused on calculating the unshaded rooftop area based on the actual location of the sun, the unshaded rooftop area where the solar PV system can perform at the optimal level without any disturbance by the building shadow widely varies by time. This fact makes it difficult to figure out the optimal placement of the solar PV system considering the tilt angle and the PV array spacing, since the solar PV system remains at the same location during its service life once they are installed. Therefore, in order to fully reflect the hourly geographical potential for estimating the technical potential of the rooftop solar PV system, this study assumed that the solar PV panels are installed horizontally with no tilt on the entire rooftops. In this way, it is possible to estimate a realistic and practical technical potential of the rooftop solar PV system by excluding the shaded rooftop area where it is impossible to generate electricity from the solar PV system, on an hourly basis.

Fig. 4. The Hillshade analysis concept.

considering the surface elevation and location of a hypothetical light source: in most cases, the sun (refer to Fig. 4) [59]. In this study, ArcMap 10.1 was used to compute the Hillshade value based on the building elevation. To compute the Hillshade value for each cell in the output raster, the altitude and azimuth of the light source (i.e., the sun) is required. The altitude and azimuth of the sun as well as the aspect and slope are used for determining the final Hillshade value for each cell in the output raster [60,61]. Each variable used in Eq. (2) is explained below in detail.

Hillshade ¼ 255  f½cosðZenithrad Þ  cosðSloperad Þ þ ½sinðZenithrad Þ  sinðSloperad Þ  cosðAzimuthrad Þ  cosðAspectrad Þg

3. Theory/calculation 3.1. Calculation of the physical potential The physical potential (i.e., the total solar radiation on the rooftop) of the rooftop solar PV system can be calculated using Eq. (1), by incorporating the total rooftop area and the solar radiation of the target region.

PhysicalT ¼ TRA 

12 18 n X X X SRijk i¼1

j¼6

!!

;

ð1Þ

k¼1

where PhysicalT stands for the total physical potential of the rooftop solar PV system for a year (MW h), TRA stands for the total rooftop area of the target region (total rooftop area of the Gangnam district: 7,517,837 m2), SRijk stands for the solar radiation on day k of month i at time j to j + 1 (MW h/m2), i stands for the month (i = 1, 2, 3, . . ., 12), j stands for the time in 24-h format (j = 6, 7, 8, . . ., 18), k stands for the day of a month (k = 1, 2, 3, . . ., 31), and n stands for the total number of days in month i. 3.2. Calculation of the geographic potential using Hillshade algorithm To analyze the building shadow and to calculate the shaded area based on the altitude and azimuth of the sun, this study used Hillshade analysis. Hillshade analysis computes the Hillshade value for each cell in the raster and creates a Hillshade raster by

ð2Þ

3.2.1. Calculation of the illumination angle The illumination angle, which is the altitude of the sun, is expressed in degrees above the horizon, but to calculate the Hillshade value using Eq. (2), the angle should satisfy the following conditions: (i) the angle should be in radians; and (ii) the angle should be the deflection from the vertical. Therefore, the altitude angle should be converted to the solar zenith angle, which is the angle between the zenith (i.e., an imaginary point directly above the surface) and the sun, expressed in radians. The altitude angle can be converted to the zenith angle using Eq. (3), and the zenith angle in degrees can be converted to radians using Eq. (4).

Zenithdeg ¼ 90  Altitude

ð3Þ

Zenithdeg  p 180

ð4Þ

Zenithrad ¼

3.2.2. Calculation of the illumination direction The illumination direction, which is the azimuth of the sun, is expressed in degrees, but to calculate the Hillshade value using Eq. (2), the angle should be in radians. Therefore, the azimuth angle in degrees should be converted to radians. The azimuth angle in its geographic unit (compass direction) can be converted to a mathematic unit (right angle) using Eq. (5), and the azimuth angle in a mathematic unit can be converted to radians using Eq. (6).

Please cite this article in press as: Hong T et al. Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.07.001

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T. Hong et al. / Applied Energy xxx (2016) xxx–xxx

 Azimuthmath ¼

360  Azimuthdeg þ 90

if Azimuthmath < 360

Azimuthmath  360

if Azimuthmath P 360

3.3. Calculation of the technical potential

ð5Þ Azimuthrad ¼

Azimuthmath  p 180

The technical potential of the rooftop solar PV system can be calculated using Eq. (13), by incorporating the solar radiation and available rooftop area.

ð6Þ TechnicalT ¼ ePV 

12 18 X X i¼1

3.2.3. Calculation of the slope and aspect To calculate the slope and aspect value, a 3  3 grid, which has a total of nine cells, moves to each cell in the input raster and calculates the slope and aspect for each cell in the center of the grid, according to the values of the eight neighboring cells. Each of the nine cells in a 3  3 grid has an assigned letter from a (upper-left corner) to i (lower-right corner), with e representing the cell in the center, where the slope and aspect will be calculated. To calculate the slope, which is the steepness of a surface, Eqs. (7)–(9) can be used as follows. First, the rate of change in the x and y directions for cell e can be calculated using Eqs. (7) and (8), respectively.

dz ðc þ 2f þ iÞ  ða þ 2d þ gÞ ¼ dx ð8  cellsizeÞ

ð7Þ

dz ðg þ 2h þ iÞ  ða þ 2b þ cÞ ¼ dy ð8  cellsizeÞ

ð8Þ

Second, by incorporating the values obtained using Eqs. (7) and (8), the slope in radians can eventually be calculated using Eq. (9).

Sloperad ¼ tan

1

0sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1  2  2 dz dz A @ þ dx dy

ð9Þ

To calculate the aspect, which is the direction of the slope, Eqs. (10) and (11) can be used as follows. dz –0, First, when dx

Aspect rad ¼

0 1 8 > dz > Þ < tan1 @ðdx A > > :

dz dy

2p þ Aspectrad

Second, when

Aspect rad ¼

8 > > <

dz dx

2

2p  p

2 > > : Aspect

rad

n X SRijk

!!

;

ð13Þ

k¼1

where TechnicalT stands for the total technical potential of the rooftop solar PV system for a year (MW h), ePV stands for the solar PV module efficiency (solar PV module efficiency used in this study: 15%), ARAij stands for the available rooftop area on the 15th of month i at time j to j + 1 (m2), SRijk stands for the solar radiation on day k of month i at time j to j + 1 (MW h/m2), i stands for the month (i = 1, 2, 3, . . ., 12), j stands for the time in 24-h format (j = 6, 7, 8, . . ., 18), k stands for the day of a month (k = 1, 2, 3, . . ., 31), and n stands for the total number of days in month i. By multiplying (i) the summation of the solar radiation at a certain time period (e.g., 12 a.m.–1 p.m.) for an entire month, (ii) the available rooftop area at a certain time period on the 15th of a month, and (iii) the solar PV module efficiency (i.e., 15%), it is possible to calculate the technical potential of a month at a certain time period (i.e., hourly technical potential). When this process is done for every time period from 6–7 a.m. to 6–7 p.m. for a month, it is possible to calculate the technical potential of a month (i.e., monthly technical potential). When this entire process is done from January to December, it is possible to calculate the technical potential of a year (i.e., annual technical potential). As a result, this study can calculate a realistic and practical rooftop solar PV potential considering the localized rooftop characteristics and the location of the sun, which changes throughout the year on hourly, monthly, and annual bases. 4. Results and discussion 4.1. The physical potential

if Aspect rad P 0

:

ð10Þ

if Aspect rad < 0

¼ 0,

p

j¼6

ARAij 

if

dz dy

>0

if

dz dy

<0

if

dz dy

¼0

ð11Þ

3.2.4. Calculation of the geographic potential The geographic potential of the rooftop solar PV system can be calculated using Eq. (12), by subtracting the shaded rooftop area and unsuitable installation area from the total rooftop area of the target region.

Geographicijk ¼ TRA  SRAijk  UIAijk ;

ð12Þ

where Geographicijk stands for the geographic potential of the rooftop solar PV system on day k of month i at time j to j + 1 (m2), TRA stands for the total rooftop area of the target region (total rooftop area of the Gangnam district: 7,517,837 m2), SRAijk stands for the shaded rooftop area on day k of month i at time j to j + 1 (m2), UIAijk stands for the unsuitable installation area on day k of month i at time j to j + 1 (m2), i stands for the month (i = 1, 2, 3, . . ., 12), j stands for the time in 24-h format (j = 6, 7, 8, . . ., 18), and k stands for the day of a month (k = 1, 2, 3, . . ., 31).

The physical potential of the rooftop solar PV system, which can be referred to as the total solar radiation on the rooftop, was analyzed for the Gangnam district in Seoul (refer to Table 1 and Fig. 5). Fig. 5 shows the hourly solar radiation on the rooftop for each month in the Gangnam district, based on the average value of the five-year data from 2010 to 2014. In Fig. 5, the hourly solar radiation on the rooftop is presented in colored lines by month, according to the seasonal characteristics, as follows: (i) spring (colored green1): March, April, and May; (ii) summer (colored red): June, July, and August; (iii) fall (colored orange): September, October, and November; and (iv) winter (colored blue): December, January, and February. First, the physical potential of the rooftop solar PV system (i.e., the total solar radiation on the rooftop) in the Gangnam district was the highest from 12 to 1 p.m., when the sun is at its highest point in the sky (i.e., noon), for all months, with its value ranging from 79,117 to 147,222 MW h. Meanwhile, the physical potential was the lowest from 6 to 7 a.m., when the sun is at its lowest point on the eastern horizon (i.e., sunrise), from January to August, whereas it was the lowest from 6 to 7 p.m., when the sun is at its lowest point on the western horizon (i.e., sunset), from September to December. Second, the physical potential of the rooftop solar PV system was the highest in May, with a value of 1,149,709 MW h, whereas 1 For interpretation of color in Figs. 5 and 9, the reader is referred to the web version of this article.

Please cite this article in press as: Hong T et al. Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.07.001

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T. Hong et al. / Applied Energy xxx (2016) xxx–xxx Table 1 Analysis results of the physical potential of the rooftop solar PV system (unit: MW h). Month

6–7 a.m. 7–8 a.m. 8–9 a.m. 9–10 a.m. 10–11 a.m. 11 a.m. – 12 p.m. 12–1 p.m. 1–2 p.m.

2–3 p.m.

3–4 p.m. 4–5 p.m. 5–6 p.m. 6–7 p.m. Total

January February March April May June July August September October November December

0 0 423 6182 16,093 17,210 9098 6752 3196 506 0 0

115 1995 14,385 27,189 42,196 40,133 23,600 26,275 22,385 15,477 4402 590

72,016 78,992 106,828 113,705 125,631 112,969 80,644 93,733 95,941 90,145 59,552 57,401

48,101 58,727 83,697 91,101 105,131 97,570 68,462 76,293 74,073 63,834 39,642 36,097

Total

59,460

218,741 496,029 808,877

13,007 19,607 43,245 54,905 72,611 66,591 42,483 48,952 49,501 46,054 24,338 14,735

39,480 44,869 75,415 85,404 101,398 92,600 61,355 69,473 76,110 75,635 47,866 39,272

65,708 71,154 103,894 109,737 125,558 115,042 77,868 83,462 96,949 100,155 67,280 63,400

83,624 90,730 125,574 126,070 139,850 128,289 87,935 95,445 109,512 116,180 80,198 78,084

92,521 98,766 128,613 129,114 147,222 130,049 94,577 101,048 114,102 119,501 83,530 79,117

87,937 93,289 122,415 125,981 141,176 125,067 92,356 95,518 110,421 110,217 76,005 72,402

21,910 32,530 53,385 62,873 74,308 71,635 53,560 53,239 48,707 33,394 14,714 12,657

2438 9305 23,612 33,429 42,138 45,020 34,868 30,520 21,743 6903 632 313

0 47 1702 9336 16,396 20,735 16,194 11,096 2590 16 0 0

526,858 600,012 883,189 975,026 1,149,709 1,062,910 742,998 791,806 825,229 778,017 498,160 454,069

1,080,208

1,261,491

1,318,160 1,252,784 1,087,558 842,728 532,913 250,921 78,111

9,287,982

Fig. 5. Analysis results of the physical potential of the rooftop solar PV system.

it was the lowest in December, with a value of 454,069 Wh/m2. Accordingly, the physical potential tends to be high in the spring months (i.e., March, April, and May) compared to the other seasons, with a value ranging from 883,189 to 1,149,709 MW h, whereas it tends to be low in the winter months (i.e., December, January, and February) compared to the other seasons, with a value ranging from 454,069 to 600,012 MW h. Meanwhile, the physical potentials in the summer and fall months had different tendencies. Among the summer months, June showed a high physical potential, the second highest (1,062,910 MW h) among all the months, whereas July and August showed relatively low physical potentials, the fifth (742,998 MW h) and seventh (791,806 MW h) lowest among all the months, respectively. Among the fall months, November showed a low physical potential, the second lowest (498,160 MW h) among all the months, whereas September and October showed relatively high physical potentials, the fifth (825,229 MW h) and seventh (778,017 MW h) highest among all the months, respectively. 4.2. The geographic potential The geographic potential of the rooftop solar PV system, which can be referred to as the available rooftop area, was analyzed for the Gangnam district in Seoul (refer to Table 2 and Figs. 6 and 7). Fig. 6 shows the Hillshade shade analysis results from 7 a.m. to

6 p.m. for the buildings in the Gangnam district. The areas in white, gray, and black in Fig. 6 represent the following: (i) white: the unshaded rooftop area where the solar PV system can perform at the optimal level; (ii) gray: the unshaded ground area where the solar PV system cannot be installed; and (iii) black: the total shaded area where the solar PV system cannot perform at the optimal level. As shown in Fig. 6, the unshaded rooftop area where the solar PV system can perform at the optimal level and the shaded area where the solar PV system cannot perform at the optimal level continuously change from 7 a.m. to 6 p.m. Fig. 7 shows the hourly available rooftop area for each month in the Gangnam district based on the Hillshade analysis results shown in Fig. 6. As with Fig. 5, in Fig. 7, the hourly available rooftop area is presented in colored lines by month, according to the seasonal characteristics. First, the geographic potential of the rooftop solar PV system (i.e., available rooftop area) in the Gangnam district was the highest from 12 to 2 p.m., when the sun is at its highest point in the sky (i.e., noon), for all months, with the available rate (i.e., the ratio of the available rooftop area to the total rooftop area) ranging from 77.18% to 92.74%. Meanwhile, the physical potential was the lowest from 6 to 7 a.m., when the sun is at its lowest point on the eastern horizon (i.e., sunrise), for all months, with the available rate ranging from 0% to 50.53%. Second, the geographic potential of the rooftop solar PV system was the highest in June, with the highest available rate (92.74%)

Please cite this article in press as: Hong T et al. Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.07.001

8

Month

6–7 a.m.

7–8 a.m.

8–9 a.m.

9–10 a.m.

10–11 a.m.

11 a.m. – 12 p.m.

12–1 p.m.

1–2 p.m.

2–3 p.m.

3–4 p.m.

4–5 p.m.

5–6 p.m.

6–7 p.m.

Average

January

Valuea Ratiob

0 0.00%

0 0.00%

1,525,608 20.29%

4,348,935 57.85%

5,323,155 70.81%

5,728,084 76.19%

5,865,232 78.02%

5,922,430 78.78%

5,775,211 76.82%

5,469,986 72.76%

4,937,476 65.68%

3,314,985 44.09%

0 0.00%

3,708,546 49.33%

February

Valuea Ratiob

0 0.00%

0 0.00%

3,230,056 42.97%

4,957,373 65.94%

5,767,064 76.71%

6,018,958 80.06%

6,159,338 81.93%

6,213,772 82.65%

6,107,971 81.25%

5,927,920 78.85%

5,479,815 72.89%

4,610,421 61.33%

1,589,526 21.14%

4,312,478 57.36%

March

Valuea Ratiob

0 0.00%

2,030,820 27.01%

4,682,638 62.29%

5,565,147 74.03%

6,081,201 80.89%

6,333,158 84.24%

6,426,871 85.49%

6,435,487 85.60%

6,381,268 84.88%

6,246,194 83.08%

5,811,764 77.31%

5,199,502 69.16%

3,639,307 48.41%

4,987,181 66.34%

April

Valuea Ratiob

919,165 12.23%

4,429,717 58.92%

5,484,715 72.96%

5,970,400 79.42%

6,346,271 84.42%

6,571,094 87.41%

6,687,916 88.96%

6,642,061 88.35%

6,589,876 87.66%

6,423,280 85.44%

6,044,093 80.40%

5,536,339 73.64%

4,526,783 60.21%

5,551,670 73.85%

May

Valuea Ratiob

3,341,725 44.45%

5,066,889 67.40%

5,821,575 77.44%

6,153,846 81.86%

6,460,545 85.94%

6,680,240 88.86%

6,878,901 91.50%

6,806,692 90.54%

6,740,674 89.66%

6,524,646 86.79%

6,157,023 81.90%

5,786,859 76.98%

4,949,360 65.83%

5,951,460 79.16%

June

Valuea Ratiob

3,798,482 50.53%

5,202,345 69.20%

5,811,305 77.30%

6,254,157 83.19%

6,484,823 86.26%

6,713,144 89.30%

6,972,247 92.74%

6,935,103 92.25%

6,845,684 91.06%

6,563,063 87.30%

6,282,426 83.57%

5,849,598 77.81%

5,198,288 69.15%

6,070,051 80.74%

July

Valuea Ratiob

3,322,844 44.20%

5,031,033 66.92%

5,740,864 76.36%

6,186,561 82.29%

6,439,923 85.66%

6,673,340 88.77%

6,917,633 92.02%

6,892,203 91.68%

6,811,782 90.61%

6,599,396 87.78%

6,268,497 83.38%

5,893,440 78.39%

5,214,097 69.36%

5,999,355 79.80%

August

Valuea Ratiob

1,604,237 21.34%

4,644,248 61.78%

5,656,059 75.24%

6,019,025 80.06%

6,376,419 84.82%

6,608,422 87.90%

6,775,996 90.13%

6,734,391 89.58%

6,667,830 88.69%

6,545,750 87.07%

6,128,215 81.52%

5,736,053 76.30%

4,879,895 64.91%

5,721,272 76.10%

September

Valuea Ratiob

0 0.00%

3,910,989 52.02%

5,265,099 70.03%

5,857,663 77.92%

6,267,257 83.37%

6,474,054 86.12%

6,543,630 87.04%

6,508,908 86.58%

6,463,120 85.97%

6,237,388 82.97%

5,848,871 77.80%

5,211,620 69.32%

3,752,207 49.91%

5,256,985 69.93%

October

Valuea Ratiob

0 0.00%

2,243,629 29.84%

4,688,640 62.37%

5,550,913 73.84%

6,082,941 80.91%

6,280,711 83.54%

6,302,613 83.84%

6,258,822 83.25%

6,169,615 82.07%

5,950,432 79.15%

5,323,333 70.81%

4,243,122 56.44%

0 0.00%

4,545,752 60.47%

November

Valuea Ratiob

0 0.00%

0 0.00%

3,607,714 47.99%

5,095,089 67.77%

5,640,053 75.02%

5,919,884 78.74%

6,030,872 80.22%

5,971,925 79.44%

5,797,029 77.11%

5,507,062 73.25%

4,675,887 62.20%

2,547,488 33.89%

0 0.00%

3,907,154 51.97%

December

Valuea Ratiob

0 0.00%

0 0.00%

2,049,762 27.27%

4,503,119 59.90%

5,277,607 70.20%

5,648,251 75.13%

5,813,536 77.33%

5,802,208 77.18%

5,607,833 74.59%

5,225,490 69.51%

4,445,248 59.13%

1,874,535 24.93%

0 0.00%

3,557,507 47.32%

1,082,204 14.40%

2,713,306 36.09%

4,463,670 59.37%

5,538,519 73.67%

6,045,605 80.42%

6,304,112 83.86%

6,447,899 85.77%

6,427,000 85.49%

6,329,825 84.20%

6,101,717 81.16%

5,616,887 74.71%

4,650,330 61.86%

2,812,455 37.41%

4,964,118 66.03%

Average a b

Refers to the geographic potential values in m2. Refers to the available rate (i.e., the ratio of the available rooftop area to the total rooftop area) in %.

T. Hong et al. / Applied Energy xxx (2016) xxx–xxx

Please cite this article in press as: Hong T et al. Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.07.001

Table 2 Analysis results of the geographic potential of the rooftop solar PV system.

T. Hong et al. / Applied Energy xxx (2016) xxx–xxx

9

Fig. 6. The Hillshade shade analysis results by hour in the Gangnam district.

Fig. 7. Analysis results of the geographic potential of the rooftop solar PV system.

Please cite this article in press as: Hong T et al. Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.07.001

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T. Hong et al. / Applied Energy xxx (2016) xxx–xxx

Table 3 Analysis results of the technical potential of the rooftop solar PV system (unit: MW h). Month

6–7 a.m. 7–8 a.m. 8–9 a.m. 9–10 a.m. 10–11 a.m. 11 a.m. – 12 p.m. 12–1 p.m. 1–2 p.m. 2–3 p.m. 3–4 p.m. 4–5 p.m. 5–6 p.m. 6–7 p.m. Total

January February March April May June July August September October November December

0 0 0 113 1073 1304 603 216 0 0 0 0

0 0 583 2403 4266 4166 2369 2435 1747 693 0 0

396 1264 4040 6008 8434 7721 4866 5524 5200 4308 1752 603

3426 4438 8374 10,174 12,450 11,555 7574 8343 8895 8377 4866 3528

6979 8188 12,606 13,895 16,185 14,885 10,005 10,618 12,123 12,156 7571 6676

9557 10,896 15,868 16,529 18,640 17,184 11,709 12,585 14,146 14,559 9473 8800

10,827 12,138 16,492 17,229 20,207 18,092 13,054 13,662 14,897 15,028 10,051 9177

10,391 11,566 15,719 16,696 19,173 17,306 12,701 12,835 14,340 13,764 9056 8382

Total

3310

18,661

50,118

92,001

131,888

159,946

170,854

161,928 139,015 104,661 62,531

8298 9627 13,602 14,951 16,897 15,430 10,960 12,470 12,372 11,097 6888 6423

5250 6946 10,431 11,676 13,686 12,777 9015 9964 9219 7579 4356 3764

2158 3557 6191 7582 9129 8979 6699 6510 5684 3547 1373 1123

161 856 2450 3693 4865 5255 4100 3493 2261 584 32 12

0 1 124 843 1619 2151 1685 1080 194 0 0 0

57,445 69,476 106,478 121,792 146,624 136,805 95,339 99,736 101,079 91,692 55,419 48,487

27,762

7697

1,130,371

Fig. 8. Analysis results of the technical potential of the rooftop solar PV system.

from 12 to 1 p.m., among all the months. It decreased with the passage of time from June, and it showed the lowest potential in December, with the highest available rate (77.33%) from 12 to 1 p.m., among all the months. Similar to the physical potential, the geographic potential tends to be low in the winter months (i.e., December, January, and February), with a 79.09% available rate from 12 to 1 p.m. on average. On the other hand, unlike the physical potential, the geographic potential tends to be high in the summer months (i.e., June, July, and August), with a 91.63% available rate from 12 to 1 p.m. on average. 4.3. The technical potential The technical potential of the rooftop solar PV system, which can be referred to as the electricity generation, was analyzed for the Gangnam district in Seoul (refer to Table 3 and Fig. 8). Fig. 8 shows the hourly technical potential for each month in the Gangnam district, assuming that the solar PV system was installed on the available rooftop area with 15% module efficiency. As with Figs. 5 and 7, in Fig. 8, the hourly technical potential is presented in colored lines by month, according to the seasonal characteristics. First, the technical potential of the rooftop solar PV system (i.e., electricity generation) in the Gangnam district was the highest from 12 to 1 p.m., when the sun is at its highest point in the sky

(i.e., noon), for all months, with a value ranging from 9177 to 20,207 kW h, similar to the physical potential. Meanwhile, the technical potential was the lowest from 6 to 7 a.m., when the sun is at its lowest point on the eastern horizon (i.e., sunrise), for all months, with a value ranging from 0 to 1304 kW h, similar to the geographic potential. Second, similar to the physical potential, the technical potential of the rooftop solar PV system was the highest in May among all the months, with a 146,624 kW h value, whereas it was the lowest in December among all the months, with a 48,487 kW h value. Accordingly, the technical potential tends to be high in the spring months (i.e., March, April, and May) compared to the other seasons, with a value ranging from 106,478 to 146,624 kW h, whereas it tends to be low in the winter months (i.e., December, January, and February) compared to the other seasons, with a value ranging from 48,487 to 69,476 kW h. Meanwhile, the technical potentials in the summer and fall months had different tendencies, as with the physical potential. Among the summer months, June showed a high technical potential, the second highest (136,805 kW h) among the summer months, whereas July and August showed relatively low technical potentials, the fifth (95,339 kW h) and seventh (99,736 kW h) lowest among the summer months, respectively. Among the fall months, November showed a low technical potential, the second lowest (55,419 kW h) among the fall months, whereas September and October showed relatively

Please cite this article in press as: Hong T et al. Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.07.001

T. Hong et al. / Applied Energy xxx (2016) xxx–xxx

high technical potentials, the fifth (101,079 kW h) and seventh (91,692 kW h) highest among the fall months, respectively. 4.4. Comparison of the physical, geographic, and technical potentials The physical, geographic, and technical potentials of the rooftop solar PV system were compared to each other for the Gangnam district in Seoul (refer to Fig. 9). Fig. 9 shows the monthly physical, geographic, and technical potentials of the rooftop solar PV system in the Gangnam district in two ways: (i) in its original values (refer to Fig. 9(a)); and (ii) in normalized values ranging from 0 to 1 (refer to Fig. 9(b)) for better comparison. In Fig. 9, the physical, geographic, and technical potentials are presented in blue, orange, and green lines, respectively. First, as shown in Fig. 9(a), the monthly physical and technical potentials of the rooftop solar PV system in the Gangnam district had similar characteristics, showing an M-shaped pattern throughout a year, indicating that the potentials increase as spring and fall near, and decrease as summer and winter near. Meanwhile, the

11

geographic potential is the highest in summer and continuously decreases with the passage of time after summer. Second, as shown in Fig. 9(b), the monthly physical and technical potentials of the rooftop solar PV system in the Gangnam district had similar patterns in summer, but the technical potential became slightly lower than the physical potential with the passage of time after summer. This is because the geographic potential is the highest in summer, which allows the technical potential to be as high as the physical potential in normalized values in summer. The geographic potential decreases, however, as winter nears, which makes the technical potential become lower than the physical potential in normalized values as winter nears. Overall, the annual physical potential of the rooftop solar PV system in the Gangnam district is 9,287,982 MW h whereas the annual technical potential is 1,130,371 MW h. This implies that only 12.17% of the physical potential can be generated as electricity with the current spatial availability and technology levels. But still, the annual technical potential of the rooftop solar PV system in the Gangnam district (1,130,371 MW h) can cover 303,496

Fig. 9. Comparison results of the rooftop solar PV potentials.

Please cite this article in press as: Hong T et al. Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.07.001

12

T. Hong et al. / Applied Energy xxx (2016) xxx–xxx

households based on the annual average electricity consumption per household in Seoul (i.e., 3724.5 kW h). That is, electricity generated from the rooftop solar PV system in the Gangnam district can be supplied to 303,496 households – almost 1.5 times more than the households in Gangnam district (i.e., 202,906) [62]. Meanwhile, the average geographic potential in the Gangnam district is 4,964,118 m2, which accounts for 66.03% of the total rooftop area and 12.57% of the total land area in the district. By using these rooftop solar PV potential results, it is possible to analyze the electricity supply and demand for the entire study area, Gangnam district in this case, and provide some solutions and insights for planning local energy policies and NRE strategies.

5. Conclusions This study developed a method for estimating the rooftop solar PV potential by analyzing the available rooftop area using Hillshade analysis. Toward this end, the rooftop solar PV potential was estimated in the following hierarchical procedure: (i) calculation of the physical potential; (ii) calculation of the geographic potential; and (iii) calculation of the technical potential. Among these three rooftop solar PV potentials, the geographic potential was mostly taken into account to acquire an accurate and reliable estimation result. Accordingly, in this study, the geographic potential of the solar PV system, which can be referred to as the available rooftop area, was calculated by analyzing the building shadow on the rooftop based on the altitude and azimuth of the sun through Hillshade analysis. Hillshade analysis was conducted for every month, on the 15th, at hourly intervals, resulting in a total of 156 simulations per year. The proposed estimation method was applied to the Gangnam district in Seoul, South Korea, where there are many high-rise buildings. As a result, this study estimated the physical, geographic, and technical potentials of the rooftop solar PV system on hourly, monthly, and annual bases. First, all the three levels of the rooftop solar PV potential (i.e., the physical, geographic, and technical potentials) in the Gangnam district tend to show the highest value from 12 to 2 p.m., when the sun is at its highest point in the sky (i.e., noon), regardless of the season. Meanwhile, all the three levels of the rooftop solar PV potential tend to show the lowest value from 6 to 7 a.m., when the sun is at its lowest point on the eastern horizon (i.e., sunrise), and from 6 to 7 p.m., when the sun is at its lowest point on the western horizon (i.e., sunset). Second, the physical and technical potentials of the rooftop solar PV system in the Gangnam district tend to be high in the spring months (i.e., March, April, and May) compared to the other seasons, with the highest values in May, whereas the geographic potential tends to be high in the summer months (i.e., June, July, and August), with the highest available rate (92.74%) in June, from 12 to 1 p.m. Meanwhile, all the three levels of the rooftop solar PV potential tend to be low in the winter months (i.e., December, January, and February) compared to the other seasons, with the lowest values in December. In summary, the total annual physical potential of the rooftop solar PV system in the Gangnam district was determined to be 9,287,982 MW h whereas the total annual technical potential was found to be 1,130,371 MW h, indicating that only 12.17% of the physical potential can be generated as electricity with the current spatial availability and technology levels. Meanwhile, the average geographic potential in the Gangnam district was found to be 4,964,118 m2, which accounts for 66.03% of the total rooftop area in the district. The proposed method is superior to the methods from the previous studies in terms of accuracy and reliability in that it (i) considers the actual buildings’ elevation in a macro scale; (ii) considers

the location of the sun, which changes throughout the year, for calculating the shaded rooftop area; and (iii) considers the hourly solar radiation and hourly rooftop conditions. The rooftop solar PV potential estimated using the proposed method can be used in several ways, as follows: (i) for analyzing and comparing the different levels of the rooftop solar PV potential; (ii) for analyzing and comparing the rooftop solar PV potential on hourly, monthly, and annual bases; (iii) for evaluating the buildings in the community or zone levels according to the estimated rooftop solar PV potential; and (iv) for establishing and improving the country’s longterm solar policies. The method proposed in this study can be further extended and applied to the future research for estimating the rooftop solar PV potential of the entire city or country. Acknowledgements This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korean government (MSIP; Ministry of Science, ICT & Future Planning) (No. NRF2015R1A2A1A05001657). The building data used in this work was supported by Spatial Information Industry Promotion Institute (SPACEN) under the Ministry of Land, Infrastructure and Transport of the Korean government. References [1] Koo C, Hong T, Lee M, Park H. Development of a new energy efficiency rating system for the existing residential buildings. Energy Policy 2014;68:218–31. [2] Hong T, Koo C, Kim H, Park H. Decision support model for establishing the optimal energy retrofit strategy for existing multi-family housing complexes. Energy Policy 2014;66:157–69. [3] Hong T, Koo C, Lee S. Benchmarks as a tool for free allocation through comparison with similar projects: focused on multi-family housing complex. Appl Energy 2014;114(2):663–75. [4] Park J, Hong T. Analysis of South Korea’s economic growth, carbon dioxide emission, and energy consumption using the Markov switching model. Renew Sustain Energy Rev 2013;18(1):543–51. [5] Hong T, Koo C, Kim H. A decision support model for improving a multi-family housing complex based on CO2 emission from electricity consumption. J Environ Manage 2012;112(15):67–78. [6] Park J, Hong T. Maintenance management process for reducing CO2 emission in the complex shopping mall. Energy Build 2011;43(4):894–904. [7] Koo C, Hong T, Hyun C, Park S, Seo J. A study on the development of a cost model based on the owner’s decision making at the early stages of a construction project. Int J Strategic Property Manage 2010;14(2):121–37. [8] Han S, Hong T, Lee S. Production prediction of conventional and global positioning system-based earthmoving systems using simulation and multiple regression analysis. Can J Civ Eng 2008;35(6):574–87. [9] Hong T, Hastak M. Simulation study on construction process of FRP bridge deck panels. J Autom Constr 2007;16(5):620–31. [10] Hong T, Koo C, Kwak T, Park H. An economic and environmental assessment for selecting the optimum new renewable energy system for educational facility. Renew Sustain Energy Rev 2014;29:286–300. [11] Hong T, Koo C, Kwak T. Framework for the implementation of a new renewable energy system in an educational facility. Appl Energy 2013;103:539–51. [12] Lee M, Hong T, Koo C, Kim C. A break-even analysis and impact analysis of residential solar photovoltaic systems considering state solar incentives. Scheduled for publication in technological and economic development of economy 2016. [13] Lee M, Hong T, Koo C. An economic impact analysis of state solar incentives for improving financial performance of residential solar photovoltaic systems in the United States. Renew Sustain Energy Rev 2016;58:590–607. [14] Lee M, Koo C, Hong T, Park H. Framework for the mapping of the monthly average daily solar radiation using an advanced case-based reasoning and a geostatistical technique. Environ Sci Technol 2014;28:4604–12. [15] Hong T, Koo C, Park J, Park H. A GIS (geographic information system)-based optimization model for estimating the electricity generation of the rooftop PV (photovoltaic) system. Energy 2014;65:190–9. [16] Koo C, Hong T, Park H, Yun G. Framework for the analysis of the potential of the rooftop photovoltaic system to achieve the net-zero energy solar buildings. Prog Photovoltaics Res Appl 2014;22:462–78. [17] Koo C, Hong T, Lee M, Park H. Estimation of the monthly average daily solar radiation using geographical information system and advanced case-based reasoning. Environ Sci Technol 2013;47:4829–39. [18] Freitas S, Catita C, Redweik P, Brito MC. Modelling solar potential in the urban environment: state-of-the-art review. Renew Sustain Energy Rev 2015;41:915–31.

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Please cite this article in press as: Hong T et al. Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis. Appl Energy (2016), http://dx.doi.org/10.1016/j.apenergy.2016.07.001