Development of a rooftop solar photovoltaic rating system considering the technical and economic suitability criteria at the building level

Energy 160 (2018) 213e224

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Development of a rooftop solar photovoltaic rating system considering the technical and economic suitability criteria at the building level Minhyun Lee a, Taehoon Hong a, *, Jaewook Jeong b, Kwangbok Jeong a a b

Department of Architecture and Architectural Engineering, Yonsei University, Seoul, 03722, Republic of Korea CBM Singapore, Sen Coretech Inc., Seoul, 07230, Republic of Korea

a r t i c l e i n f o

a b s t r a c t

Article history: Received 15 March 2018 Received in revised form 27 June 2018 Accepted 6 July 2018 Available online 7 July 2018

The successful deployment of distributed solar generation in urban areas involves the evaluation of how suitable a building is for installing the rooftop solar photovoltaic (PV) system within a region. When evaluating the rooftop solar PV suitability of a building, it is crucial to consider not only its technical but also its economic performance as the difference in technical performance can lead to different financial returns for rooftop solar PV adopters. Towards this end, this study aimed to propose a method for developing a rooftop solar PV rating system using cluster analysis based on the technical and economic suitability criteria. As a result, a rooftop solar PV rating system was developed for 21,681 buildings in the Gangnam district in Seoul, South Korea by dividing them into four grades according to their rooftop solar PV potential, return on investment, and payback period. The effectiveness of the proposed rating system was validated by comparing it with the rating systems developed using the classification method and suitability criteria from the previous studies. This study has significant contributions in that it can provide the rooftop solar PV suitability information on a grade scale for intuitive decision-making based on scientific evidence and reasonable criteria. © 2018 Elsevier Ltd. All rights reserved.

Keywords: Olar photovoltaic rating system Rooftop solar photovoltaic suitability Hillshade analysis Life cycle cost Cluster analysis K-means clustering

1. Introduction One of the most powerful solutions to climate change and global warming, distributed generation (DG) with renewable energy is gaining much attention as it can supply electricity directly to the nearby end users with clean energy sources [1e5]. Particularly, distributed solar generation (DSG) with the rooftop solar photovoltaic (PV) system is considered a powerful solution that can be applied in the urban environment, which has plenty of building rooftops [6e10]. As not all the buildings in urban areas are suitable for rooftop solar PV system installation due to the shadows on the rooftop from the surrounding buildings, it is crucial to evaluate and determine which building is suitable for rooftop solar PV system installation (i.e., rooftop solar PV suitability) within certain urban boundaries [11e13]. The first step in evaluating the rooftop solar PV suitability of a building is to investigate its potential for generating electricity from the rooftop solar PV system (i.e., rooftop solar PV potential).

* Corresponding author. Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea. E-mail address: [email protected] (T. Hong). https://doi.org/10.1016/j.energy.2018.07.020 0360-5442/© 2018 Elsevier Ltd. All rights reserved.

Accordingly, many previous studies estimated the rooftop solar PV potential in a region using various methods, mostly geographic information system (GIS) and Light Detection And Ranging (LiDAR) data [12,14e18]. Most of the previous studies, however, simply estimated the rooftop solar PV potential in a region, without evaluating the rooftop solar PV suitability according to certain criteria. Accordingly, these previous studies could not identify which building is suitable for installing the rooftop solar PV system. Meanwhile, few attempts have been made to evaluate the rooftop solar PV suitability based on the technical performance of the rooftop solar PV system (i.e., the rooftop solar PV potential) in other previous studies [19e22]. Lukac et al. [19] rated the suitability of the rooftop segment in Slovenia by classifying them into five categories (i.e., very high suitability to unsuitable) according to its -vis the maximum rooftop solar PV potential (i.e., 0.75, ratio vis-a 0.5, 0.25, and 0). Santos et al. [20] rated the rooftop solar PV suitability of the buildings in Portugal by classifying them into quartiles (i.e., four categories) according to the rooftop solar PV potential. These previous studies, however, evaluated the rooftop solar PV suitability of rooftop segments or buildings according to only their technical aspects, without considering their economic aspects. Accordingly, they could not determine which building is suitable

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for installing the rooftop solar PV system in terms of economic benefits. The difference in the technical performance of the rooftop solar PV system, however, can lead to different financial returns for the rooftop solar PV adopters, which actually affects their decisionmaking [23,24]. Therefore, it is crucial to identify and evaluate how suitable a building is for installing the rooftop solar PV system considering not only its technical but also its economic performance. There was a limited study, however, that actually considered the economic performance of the rooftop solar PV system as one of the criteria for evaluating the rooftop solar PV suitability [25e27]. Jakubiec and Reinhart [25] estimated the rooftop solar PV potential using Daysim, an advanced daylight simulation software, based on LiDAR and GIS data, and divided the estimated results into four bins (i.e., excellent, good, poor, and n/a). The minimum threshold (i.e., n/ a) for the evaluation was determined as 121 kWh/m2, the energy yield required for a 10-year payback period (PP) in the U.S. The thresholds for the other bins, however, were simply based on the ratio of the total rooftop area. In summary, the previous studies had limitations in evaluating the rooftop solar PV suitability in a reasonable and acceptable manner, as shown below.
Most of the previous studies failed to evaluate the rooftop solar PV suitability at the building level within certain regional boundaries, as they rated the rooftop solar PV suitability either for the entire region, from a macroscopic view, or for a single building, from a microscopic view.
Most of the previous studies simply rated the rooftop solar PV suitability by equally classifying the number of buildings or estimated rooftop solar PV potential, without dividing it based on reasonable and acceptable criteria.
Most of the previous studies rated the rooftop solar PV suitability without considering the economic performance of the rooftop solar PV system, which could highly affect the decisionmaking on the rooftop solar PV adoption. To overcome these limitations, this study aimed to propose a method for evaluating the rooftop solar PV suitability of a building, and to ultimately develop a rooftop solar PV rating system. Namely, this study not only suggest an approach to evaluate the rooftop solar PV suitability of a building by calculating suitability scores, but also provide a rating system with grades (e.g., grades A, B, C, etc.) which can be further applied as a policy measure for public use. Towards this end, this study first calculated the technical and economic performance of the rooftop solar PV system (i.e., rooftop solar PV potential and profitability, respectively) using Hillshade analysis and life cycle cost (LCC) analysis, to consider them as the technical and economic suitability criteria, respectively. To evaluate the rooftop solar PV suitability according to the technical and economic suitability criteria, the rooftop solar PV suitability scores were calculated based on the rooftop solar PV potential and profitability. Finally, this study suggested a method for developing a rooftop solar PV rating system for buildings using two cluster analysis, hierarchical cluster analysis (HCA) and k-means clustering, validated statistical approaches (refer to Fig. 1). Through this process, this study can be differentiated from the previous studies in the following ways: (i) this study computed the rooftop solar PV potential and profitability for each building in the study area considering the actual building characteristics based on the bottom-up approach (ii) this study used scientific measures (i.e., HCA and k-means clustering); for evaluating the rooftop solar PV suitability of a building; and (iii) this study considered both the technical and economic suitability criteria for evaluating the rooftop solar PV suitability of a building.

Fig. 1. Research framework.

2. Materials and methods 2.1. Step 1: calculation of the technical performance of the rooftop solar PV system This study calculated the rooftop solar PV potential of each building in the study area to consider the expected technical performance of the rooftop solar PV system as one of the suitability criteria (i.e., technical suitability criteria). First, this study calculated the available rooftop area for solar PV installation by removing the shaded rooftop area, where the solar PV system cannot perform at its optimal level, from the total rooftop area for each building in the Gangnam district. The shaded rooftop area was calculated using Hillshade analysis based on the following input data: (i) the raster data of the building elevation from Spatial Information Industry Promotion Institute under the Ministry of Land, Infrastructure, and Transport of the South Korean government [28]; and (ii) the location data (i.e., altitude and azimuth) of the sun from Korea Astronomy & Space Science Institute [29]. Based on these input data, Hillshade analysis was conducted for 12 days (from January to December, on the 15th of each month) at hourly intervals (from 6 a.m. to 7 p.m.) using ArcMap 10.1, a widely used GIS software developed by Environmental Systems Research Institute [30]. Second, this study calculated the rooftop solar PV potential of each building in the Gangnam district by normalizing the total solar radiation on the available rooftop area of a building with the total rooftop area, and by considering the solar PV module efficiency. Accordingly, the rooftop solar PV potential of a building can be calculated using Eq. (1), considering various key factors, including the available rooftop area, hourly solar radiation, and solar PV module efficiency. Among these key factors, the data on the hourly solar radiation in Seoul in 2016 were collected from World Radiation Data Center, sponsored by World Meteorological Organization, to calculate the rooftop solar PV potential on an hourly basis [31]. The

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solar PV module efficiency was defined to reflect the current technology level of the solar PV industry. Despite the difference in module efficiency by type and manufacturer, the efficiency of the commercial solar PV modules mostly stayed between 15 and 18% [32,33]. Particularly, most of the previous studies and simulators that aimed to evaluate the technical and economic performance of the solar PV system adopted 15e16% solar PV module efficiency for their energy analysis [11,34e37]. Therefore, the solar PV module efficiency was assumed to be 16% for calculating the rooftop solar PV potential to represent the typical module efficiency of the current solar PV technology and to ensure consistency with the other study results. Apart from these key factors, the rooftop solar PV system was assumed to be installed horizontally on rooftops to fully consider the hourly calculated results of the available rooftop area. For a detailed description of the calculation of the rooftop solar PV potential, please refer to Hong et al. [11].

RSPPGB ¼

12 P

18 P

i¼1

j¼6

ARAGBij 

n P k¼1

TRAGB

!! SRijk

 ePV (1)

where RSPPGB stands for the rooftop solar PV potential of a given building for a year per unit area (kWh/m2), ARAGBij stands for the available rooftop area of a given building on the 15th of month i at time j to jþ1 (m2), SRijk stands for the solar radiation on day k of month i at time j to jþ1 (kWh/m2), ePV stands for the solar PV module efficiency (16.2%), TRAGB stands for the total rooftop area of a given building (m2), i stands for the month (i ¼ 1, 2, 3, …, 12), j stands for the time in 24-h format (j ¼ 6, 7, 8, …, 18), k stands for the day of a month (k ¼ 1, 2, 3, …, 31), and n stands for the total number of days in month i. As shown in Eq. (1), the rooftop solar PV potential of a given building in month i at a certain time frame (e.g., 12 to 1 p.m.) can be calculated by multiplying (i) the total solar radiation at a certain time frame for an entire month; (ii) the available rooftop area of a given building at a certain time frame in month i; and (iii) the solar PV module efficiency. This process was repeated and summed up for every time frame and every month to calculate the total rooftop solar PV potential of a given building for an entire year. Finally, the total annual rooftop solar PV potential of a given building was normalized with its total rooftop area to calculate the rooftop solar PV potential per unit area (i.e., rooftop solar PV potential) of a given building. The calculated rooftop solar PV potential of each given building was used as the technical suitability criterion to represent the expected technical performance of the solar PV system installed on the rooftop.

2.2. Step 2: calculation of the economic performance of the rooftop solar PV system This study calculated the rooftop solar PV profitability of each building in the study area to consider the expected economic performance of the rooftop solar PV system as one of the suitability criteria (i.e., economic suitability criteria). First, the following assumptions were defined for the LCC analysis of the rooftop solar PV system (refer to Table 1): (i) analysis approach; (ii) analysis period; (iii) analysis point; (iv) real discount rate; (v) inflation rate; (vi) significant cost of ownership; and (vii) system specifications [7].
Analysis approach, period, and point: The present-worth method based on discounted cash flow analysis was used as an analysis approach. The analysis period was assumed to be 25 years based on the useful life and warranty period of the solar PV panel [7,8,38]. The analysis point was set to 2016 to reflect the current

215

state of the industry, using the most recent available data on the rooftop solar PV system.
Real discount rate: The real discount rate was calculated using Eq. (2), based on the following data: (i) nominal interest rate: base rate (2007e2016) from the Economic Statistics System (ECOS) of Bank of Korea [39]; (ii) inflation rate: inflation rate (2007e2016) from ECOS of Bank of Korea [39]; (iii) electricity price growth rate: retail price of electricity and system marginal price (SMP) (2007e2016) from Power Big Data Center and the Electric Power Statistics Information System (EPSIS), respectively [40,41]; and (iv) renewable energy certificate (REC) price growth rate: REC price (2012e2016) from the REC trading system [42].

i¼

ð1 þ in Þ ­1 ð1 þ f Þ

(2)

where i stands for the real discount rate; f stands for one of the followings: the inflation rate, electricity price growth rate, or REC price growth rate; and in stands for nominal interest rate.
Significant cost of ownership: The significant cost of ownership can be classified into two types of items: (i) cost items; and (ii) benefit items. The cost items include the installation cost, operation and maintenance (O&M) cost, and rooftop rental cost. The installation cost was assumed to be US$1404/kW (equivalent to KRW1,500,000 based on the KRW1,068.50/USD exchange rate as of January 20, 2018) for residential buildings and US$1872/kW (equivalent to KRW2,000,000 based on the KRW1,068.50/USD exchange rate as of January 20, 2018) for commercial buildings in 2016, based on the industry average provided by HAEZOOM, the most popular solar PV consulting company in South Korea [43]. The annual O&M cost was assumed to be 1% of the installation cost of the rooftop solar PV system based on the previous studies [7,8,38]. Meanwhile, the benefit items depend on the two different installation purposes of the rooftop solar PV system: (i) selfconsumption; and (ii) electricity business. The benefit items of rooftop solar PV installation for self-consumption puposes include the benefits from receiving government subsidies and saving the electricity bill. The electricity generated from the rooftop solar PV system for self-consumption purposes can be directly supplied to and used in the installed building, and can save the electricity bill. The amount of electricity bill savings was determined based on the monthly average retail price of electricity in 2016 in the Gangnam district from Power Big Data Center according to the building type (i.e., residential, commercial, or educational) [40]. At the same time, the rooftop solar PV system installed for self-consumption purposes can receive subsidies and grants from the federal or city government. The amount of subsidies and grants from the government was determined based on the data from Korea New & Renewable Energy Center (KNREC) according to the building type (i.e., residential, non-residential, or public) and system size [44]. The benefit items of rooftop solar PV installation for electricity business puposes include the benefits from selling electricity and REC and receiving the Seoul feed-in tariff (FIT). The electricity generated from the rooftop solar PV system can be sold to the grid at SMP. The SMP was determined based on the monthly weighted average price in 2016 in the Gangnam district from EPSIS [41]. At the same time, the REC can also be sold on the REC spot market at the REC spot price. The REC price was determined based on the monthly average REC spot price from the REC Trading System [42]. In addition to the REC profits, the Seoul FIT can be received from the Seoul metropolitan government. The Seoul FIT was determined based on the data from the Energy White Paper of the Seoul metropolitan government [45].

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Table 1 Assumptions for LCC analysis. Classification

Description

Reference

Note

Analysis approach Analysis period Analysis point Real discount rate

Present worth method 25 years 2016 0.95% 1.35% 4.44% 1404 1872 1% of IC 0.14 0.12 0.11 627

e [7,8,38] e ECOS [39] EPSIS [41] REC Trading System [42] HAEZOOM [43]

e e e e e e Industry average

[7,8,38] KEPCO [47]

Anually Monthly average

KNREC [44]

up to 3 kW per household for single family house up to 30 kW per building for multi family house up to 50 kW e Monthly weighted average Monthly average e Anually for 20 years

Significant cost of ownership

Inflation Rate Electricity price REC price Cost

Benefit

SCc

ICa (US$/kW) O&M costb Retail price of electricity (US$/kWh) Subsidies and grants (US$/kW)

Residential Commercial Residential Commercial Educational Home subsidy

749 Building subsidy Local grants EB

d

SMP (US$/kWh) REC price (US$/MWh) Seoul FIT (US$/kWh)

System degradation rate a b c d

1020 50% of IC 0.07 127.46 0.09 0.80%

EPSIS [41] REC Trading System [42] Seoul [45] [10,38,46]

IC refers to the installation cost. O&M cost refers to the operation & maintenance cost. SC refers to the self-consumption. and EB refers to the electricity business.


System degradation rate: The technical performance of the rooftop solar PV system was assumed to be degraded by 20% during its useful life (i.e., 25 years), based on the previous studies and the actual PV panel data [10,38,46]. Second, the rooftop solar PV profitability of each building in the study area was calculated using LCC analysis in terms of two relative financial indices: (i) return on investment (ROI) and (ii) PP, two of the most commonly used financial indices for making decisions on solar PV adoption [48,49]. ROI refers to the ratio of the discounted cash inflows and outflows, and the rooftop solar PV system is considered economically viable when it exceeds “1” [7,8]. It can be said that the higher the ROI becomes beyond “1,” the better the economic performance of the rooftop solar PV system. Meanwhile, PP refers to the length of time, usually in years, required for the investment cost to be repaid by the income, and to reach the breakeven point. PP is simple and easy to apply for understanding the economic performance of the rooftop solar PV system, as it can intuitively answer how long it will take the rooftop solar PV system to pay for itself [8,50,51]. The rooftop solar PV system with a shorter PP is typically considered better and desirable for the potential adoptors. Among various financial indices, PP was selected as an indicator for the rooftop solar PV suitability as almost all the previous studies regarding this issue used PP as a critical economic criterion [23,24,52]. In addition to PP, ROI was selected as an indicator of rooftop solar PV suitability as it can directly explain the level of profitability. Using Eqs. (3)e(7), the expected ROI and PP of the rooftop solar PV system per kW for each building in the study area was calculated based on the discounted cash inflows (i.e., benefit) and outflows (i.e., cost). As the rooftop solar PV system can be installed for two purposes, (i) self-consumption, and (ii) electricity business, its benefit was calculated differently using Eq. (5). For selfconsumption purposes, the benefit from the electricity generation of the rooftop solar PV system was calculated based on the amount of subsidy and the monthly average retail price of electricity by building type. For electricity business purposes, the benefit from the electricity generation was calculated based on the monthly

weighted average SMP, REC price, and Seoul FIT. The cost of the rooftop solar PV system for both self-consumption and electricity business purposes was calculated in the same way using Eq. (6). n P

ROIGB ¼

t¼1 n P t¼1

Bt ð1þrÞt

PPGB ¼ T;

Bt ¼ St þ

T P

when

12 X

12 X i¼1

Bt

t¼1 ð1

t

þ rÞ



!

EGGBi t

i¼1

þ

(3)

Ct ð1þrÞt

ð1 þ dÞ ! EGGBi ð1 þ dÞt

 EPi

þ

T X

12 X

EGGBi ¼

j¼6

¼0

(4)

ðNRECi  RECi  wREC Þ

i¼1

 SeoulFIT

Ct ¼ ICt þ OMC 18 P

Ct

t t¼1 ð1 þ rÞ

(5)

(6)

ARAGBij 

n P k¼1

TRAGB

! SRijk

 PR (7)

where ROIGB stands for the expected ROI of the rooftop solar PV system per kW for a given building, PPGB stands for the expected PP of the rooftop solar PV system per kW for a given building (years), Bt stands for the benefit from the electricity generation per kW in year t (US$), Ct stands for the cost from the rooftop solar PV system per kW in year t (US$), r stands for the real discount rate, n stands for the analysis period, St stands for the amount of subsidy per kW in year t (US$), EGGBi stands for the expected electricity generation of the rooftop solar PV system per kW for a given building in month i (kWh), d stands for the annual degradation rate of the rooftop solar PV system, EPt stands for the electricity price per kWh in year t

M. Lee et al. / Energy 160 (2018) 213e224

(US$/kWh; average retail price of electricity for self-consumption and SMP for electricity business), NRECi stands for the number of RECs issued in month i, RECi stands for the REC price per kWh in year t (US$), wREC stands for the REC weight for installing the solar PV system on buildings (1.5), SeoulFITt stands for the Seoul FIT in year t (US$/kWh), ICt stands for the installation cost of the rooftop solar PV system in year t (US$), OMCt stands for the O&M cost in year t (US$), ARAGBij stands for the available rooftop area of a given building on the 15th of month i at time j to jþ1 (m2), SRijk stands for the solar radiation on day k of month i at time j to jþ1 (kWh/m2), PR stands for the performance ratio (89% [34,53]), TRAGB stands for the total rooftop area of a given building (m2), i stands for the month (i ¼ 1, 2, 3, …, 12), j stands for the time in 24-h format (j ¼ 6, 7, 8, …, 18), k stands for the day of a month (k ¼ 1, 2, 3, …, 31), and n stands for the total number of days in month i.

2.3. Step 3: evaluation of the rooftop solar PV suitability of a building This study evaluated the rooftop solar PV suitability of each building in the study area based on the expected technical (i.e., rooftop solar PV potential) and economic performance (i.e., expected ROI and PP) of the rooftop solar PV system. First, the technical rooftop solar PV suitability score (i.e., technical RSPS score) was calculated to evaluate the technical rooftop solar PV suitability of a building based on its rooftop solar PV potential, using Eq. (8). The technical RSPS score was presented as a relative score ranging from 0 to 100 indicating how “good” the technical peroformance of the rooftop solar PV system for a certain building is compared to other buildings. For example, if the solar PV system of a given building can perform at its full scale without any disturbance by the building shadow, the technical RSPS score will be calculated as 100. If the solar PV system of a given building is expected to produce less electricity than the maximum level obtainable in that region, the technical RSPS score will be calculated as below 100 and will approach 0 as the rooftop solar PV potential decreases.

Technical RSPS scoreGB ¼

RSPPGB  RSPPmin  100 RSPPmax  RSPPmin

(8)

where Technical RSPS scoreGB stands for the technical RSPS score of a given building (0e100), RSPPGB stands for the rooftop solar PV potential of a given building for a year per unit area (kWh/m2), RSPPmin stands for the minimum rooftop solar PV potential for a year per unit area obtainable in a region (1.48 kWh/m2), and RSPPmax stands for the maximum rooftop solar PV potential for a year per unit area obtainable in a region (195.42 kWh/m2). Second, the economic rooftop solar PV suitability score (i.e., economic RSPS score) was calculated to evaluate the rooftop solar PV suitability of a building based on its expected ROI and PP, using Eqs. (9) and (10), respectively. The economic RSPS score was also presented as a relative score ranging from 0 to 100 indicating how “good” the economic performance of the rooftop solar PV system for a certain building is. For example, if the solar PV system of a given building can reach its expected ROI and PP at the optimal level obtainable in that region, the economic RSPS score for ROI and PP will be calculated as 100, respectively. As the expected ROI decreases and the PP increases due to the low benefit from the electricity generation, the economic RSPS score will also decrease and will approach 0.

Economic RSPS scoreGB for ROI ¼

ROIGB  ROImin  100 ROImax  ROImin

(9)

217

Economic RSPS scoreGB for PP ¼

PPmax  PPGB  100 PPmax  PPmin

(10)

where Economic RSPS scoreGB stands for the economic RSPS score of a given building (0e100), ROIGB stands for the expected ROI of the rooftop solar PV system for a given building, ROImax stands for the maximum ROI of the rooftop solar PV system for a building obtainable in a region (3.01 for self-consumption and 2.09 for electricity business), and ROImin stands for the minimum ROI of the economically viable rooftop solar PV system in a region (1.00), PPGB stands for the expected PP of the rooftop solar PV system for a given building (years), PPmax stands for the maximum PP of the economically viable rooftop solar PV system in a region (analysis period: 25 years), and PPmin stands for the minimum PP of the rooftop solar PV system for a building obtainable in a region (5.30 years for self-consumption and 5.36 years for electricity business). 2.4. Step 4: development of the rooftop solar PV rating system for buildings This study developed the rooftop solar PV rating system in the study area considering the technical and economic suitability criteria (i.e., technical and economic RSPS scores). To develop the rooftop solar PV rating system in a reasonable and scientific manner, two types of cluster analysis were used for classifying the buildings into different grades, as follows: (i) HCA; and (ii) k-means clustering. The technical and economic RSPS scores were used as variables for conducting HCA and k-means clustering. This way, it can be expected that the appropriate number of grades and their intervals can be determined for developing the rooftop solar PV rating system according to the technical and economic suitability criteria. First, the appropriate number of grades was determined using HCA. HCA is a method of establishing a hierarchy of clusters by repetitively merging the two closest clusters until they become a single cluster [54]. Accordingly, HCA can be used for cases where the number of clusters is unknown, unlike other types of cluster analysis [55,56]. To conduct HCA, this study used the Euclidean distance, the most common method of measuring the distance between two observations, as the similarity metric (refer to Eq. (11)), and Ward’s method, a complex method for merging and linking clusters based on the minimum increase in the sum of squared error (SSE), as the linkage criteria (refer to Eq. (12)) [54]. Using the Elbow method, a representative and intuitive method for finding the appropriate number of clusters, the optimal number of clusters (i.e., grade) can be determined at k-value using Eq. (12), where the SSE tends to decrease substantially as the k-value increases [55,57].

Edx;y

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u n uX ¼t ðxi  yi Þ2

(11)

i¼1

SSE ¼

k X X

EdX;c2i

(12)

i¼1 X2ci

where Edx,y stands for the Euclidean distance between observation x and y, SSE is the sum of squared error, k is the number of clusters (i.e., grade), X is the set of observations, and ci is the centroid of each cluster. Second, the appropriate intervals for each grade were determined using k-means clustering, based on the number of grades established by HCA. k-means clustering is a non-hierarchical

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cluster method of classifying all observations into the userspecified number of clusters (i.e., k), by assigning each observation to the cluster with the closest centroid (i.e., usually the mean of the set of observations) [54,58]. Eventually, each building can be rated as a certain grade based on the distribution of the rooftop solar PV suitability in the region. The within-cluster sum of squares (WCSS) used in k-means clustering can be calculated using Eq. (13) [55,59].

WCSS ¼ args min

k X X i¼1 X2si

Ed2X;mi

(13)

where WCSS is the within-cluster sum of squares, k is the number of clusters (i.e., grade), X is the set of observations, si is the cluster i, EdX,mi stands for the Euclidean distance between X and mi, and mi is the centroid of each cluster. When determining the appropriate intervals for each grade using k-means clustering, buildings that are hard to expect the economic feasibility from installing the solar PV system were first filtered out to regard them as unsuitable. To sort buildings out with unsuitable rooftop where the solar PV system cannot guarantee its financial returns, the buildings that could not reach break even point (i.e., buildings with the expected ROI below 1 and expected PP above 25 years) by rooftop solar PV adoption were removed from the database for k-means clustering and were included to the rooftop solar PV rating system as the lowest grade (i.e., unsuitable). 3. Results and discussion 3.1. Development of the rooftop solar PV rating system A rooftop solar PV rating system was developed based on the technical and economic suitability criteria (i.e., technical and economic RSPS scores) for the total of 21,681 buildings in the Gangnam district. As the economic performance of the rooftop solar PV system changes according to its installation purpose (i.e., selfconsumption or electricity business), the rooftop solar PV rating systems were developed separately for both of these two different purposes. First, the appropriate number of grades for the rooftop solar PV rating system was determined using HCA and the Elbow method. HCA was conducted with the 21,681 building data of the Gangnam district, based on their technical and economic RSPS scores for both the self-consumption and electricity business purposes. Figs. 2 and 3 are the scree plots showing the results of HCA and Elbow for finding the optimal number of grades. As shown in Figs. 2 and 3, the SSE decreased drastically as the number of grades became larger

Fig. 2. Scree plot of the HCA result for self-consumption purposes.

Fig. 3. Scree plot of the HCA results for electricity business purposes.

than four for both the self-consumption and electricity business purposes. Thus, it was determined that four would be the optimal number of grades for the rooftop solar PV rating system. Second, the appropriate intervals for these four grades of the rooftop solar PV rating system were determined using k-means clustering. Same as HCA, k-means clustering was also conducted with the 21,681 building data of the Gangnam district, based on their technical and economic RSPS scores for both the selfconsumption and electricity business purposes. Figs. 4 and 5 show the scatter plots of the k-means clustering results according to the four grades, from grade A (very suitable) to grade D (unsuitable), and their centroids. Based on these results of k-means clustering, the decision criteria for the four grades of the rooftop solar PV rating systems for the self-consumption and electricity business purposes are shown in Table 2. The developed rooftop solar PV rating system for selfconsumption purposes using two cluster analysis, HCA and kmeans clustering, can be interpreted as follows. First, 7740 buildings (i.e., 35.70% of all the buildings in the Gangnam district) were determined to be grade A, with centroids of 82.84, 71.79, and 91.48 for the technical RSPS score, economic RSPS scoreROI, and economic RSPS scorePP, respectively. These values for the centroid of grade A correspond to a rooftop solar PV potential of 162.15 kWh/m2, an expected ROI of 2.44, and an expected PP of 6.97 years, respectively, indicating that the grade A buildings can expect relatively high technical and economic performances of the rooftop solar PV system. Second, 7249 buildings (i.e., 33.43% of all the buildings in the Gangnam district) were determined to be grade B, with centroids of 82.09, 40.30, and 75.78 for the technical RSPS score, economic RSPS scoreROI, and economic RSPS scorePP, respectively. These values for the centroid of grade B correspond to a rooftop solar PV potential of 160.68 kWh/m2, an expected ROI of 1.81, and an expected PP of 10.07 years, respectively, indicating that a similar technical performance but a lower economic performance of the rooftop solar PV system can be expected for the grade B buildings compared to the grade A buildings. Still, however, the grade B buildings can expect an overall high economic performance from installing the rooftop solar PV system, showing an ROI of over 1.5 and a PP of within 13 years. Meanwhile, 4404 buildings (i.e., 20.31% of all the buildings in the Gangnam district) were determined to be grade C, with centroids of 68.30, 31.58, and 67.10 for the technical RSPS score, economic RSPS scoreROI, and economic RSPS scorePP, respectively. These values for the centroid of grade C correspond to a rooftop solar PV potential of 133.95 kWh/m2, an expected ROI of 1.63, and an expected PP of 11.78 years, respectively, indicating that lower technical and economic performances of the rooftop solar PV

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Fig. 4. Scatter plot of the k-means clustering result for self-consumption purposes.

Fig. 5. Scatter plot of the k-means clustering result for electricity business purposes.

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Table 2 Decision criteria for the four grades of the rooftop solar PV rating system. Installation purpose Grade No. of buildings % of buildings Technical suitability criteria

Self-consumption

Electricity business

a b

A B C D A B C D

7740 7249 4404 2288 8396 6914 3742 2629

35.70% 33.43% 20.31% 10.55% 38.73% 31.89% 17.26% 12.13%

Economic suitability criteria

RSPS Score Rooftop solar PV potential (kWh/m2)

RSPS ScoreROI ROI

RSPS ScorePP PP (years)

Ctda

Avgb

Range

Ctda

Avgb Range

Ctda

Avgb

Range

82.84 82.09 68.30 50.27 82.55 81.30 69.23 52.43

162.15 160.68 133.95 98.98 161.57 159.16 135.75 103.16

over 134.50 136.56e195.42 98.06e153.10 under 131.98 over 143.92 130.47e195.42 102.98e180.82 under 173.72

71.79 40.30 31.58 8.54 69.92 34.68 18.79 0.94

2.44 1.81 1.63 1.11 1.76 1.38 1.20 0.86

91.48 75.78 67.10 36.69 89.91 69.34 52.24 23.27

6.97 10.07 11.78 17.77 7.28 11.34 14.71 20.43

under 8.26 7.99e13.61 7.96e15.87 over 11.77 under 9.17 8.78e18.48 10.49e19.55 over 16.75

over 2.14 1.53e2.14 1.32e2.15 under 1.60 over 1.55 1.05e1.58 1.02e1.43 under 1.12

Ctd refers to the centroid RSPS score for each grade. and Avg refers to the average rooftop solar PV potential, ROI, or PP for each grade.

system can be expected for the grade C buildings compared to the grade B buildings. Still, however, the installation of the rooftop solar PV system will be economically viable for the grade C buildings, showing an ROI of over 1.3 and a PP of within 15 years. Third, 2288 buildings (i.e., 10.55% of all the buildings in the Gangnam district) were determined to be grade D, with centroids of 50.27, 8.54, and 36.69 for the technical RSPS score, economic RSPS scoreROI, and economic RSPS scorePP, respectively. These values for the centroid of grade D correspond to a rooftop solar PV potential of 98.98 kWh/m2, an expected ROI of 1.11, and an expected PP of 17.77 years, respectively, indicating that the grade D buildings can expect relatively low technical and economic performances of the rooftop solar PV system. Of the 2288 grade D buildings, 26.57% (i.e., 608 buildings) will not be able to reach the break-even point within 25 years of their useful life when they install the rooftop solar PV system. Accordingly, the grade D buildings can be regarded as unsuitable for rooftop solar PV system installation. Similarly, the developed rooftop solar PV rating system for electricity business purposes using two cluster analysis, HCA and kmeans clustering, can be interpreted as follows. First, 8396 buildings (i.e., 38.73% of all the buildings in the Gangnam district) were determined to be grade A, with centroids of 82.55, 69.92, and 89.91 for the technical RSPS score, economic RSPS scoreROI, and economic RSPS scorePP, respectively. These values for the centroid of grade A correspond to a rooftop solar PV potential of 161.57 kWh/m2, an expected ROI of 1.76, and an expected PP of 7.28 years, respectively, indicating that the grade A buildings can expect relatively high technical and economic performances of the rooftop solar PV system. Second, 6914 buildings (i.e., 31.89% of all the buildings in the Gangnam district) were determined to be grade B, with centroids of 81.30, 34.68, and 69.34 for the technical RSPS score, economic RSPS scoreROI, and economic RSPS scorePP, respectively. These values for the centroid of grade B correspond to a rooftop solar PV potential of 159.16 kWh/m2, an expected ROI of 1.38, and an expected PP of 11.34 years, respectively, indicating that a similar technical performance but a lower economic performance of the rooftop solar PV system can be expected for the grade B buildings compared to the grade A buildings. Meanwhile, 3742 buildings (i.e., 17.26% of the total buildings in the Gangnam district) were determined as grade C, with a centroid of 69.23, 18.79, and 52.24 for the technical RSPS score, economic RSPS scoreROI and economic RSPS scorePP, respectively. These values for the centroid of grade C correspond to a rooftop solar PV potential of 135.75 kWh/m2, an expected ROI of 1.20, and an expected PP of 14.71 years, respectively, indicating that a lower technical and economic performance of the rooftop solar PV system could be expected for the buildings in grade C, compared

to those for buildings in grade B. Overall, the installation of the rooftop solar PV system will be economically viable for the grades B and C buildings, showing an ROI of over 1 and a PP of within 20 years. Third, 2629 buildings (i.e., 12.13% of all the buildings in the Gangnam district) were determined to be grade D, with centroids of 52.43, 0.94, and 23.27 for the technical RSPS score, economic RSPS scoreROI, and economic RSPS scorePP, respectively. These values for the centroid of grade D correspond to a rooftop solar PV potential of 103.16 kWh/m2, an expected ROI of 0.86, and an expected PP of 20.43 years, respectively, indicating that the grade D buildings can expect overall low technical and economic performances of the rooftop solar PV system. Of the 2250 grade D buildings, 74.63% (i.e., 1962 buildings) will not be able to reach the break-even point within 25 years of their useful life when they install the rooftop solar PV system. Accordingly, the grade D buildings can be regarded as unsuitable for rooftop solar PV system installation. 3.2. Comparative analysis of the rooftop solar PV rating system This study developed and proposed the rooftop solar PV rating system in the study area using cluster analysis (i.e., HCA and kmeans clustering) and considering both the technical and economic suitability criteria. To ensure and validate the effectiveness of the proposed rooftop solar PV rating system, this study conducted a comparative analysis of the rooftop solar PV rating system by (i) used classification method, and (ii) considered suitability criteria. Towards this end, the rooftop solar PV rating system developed using the classification method (i.e., k-means clustering) and suitability criteria (i.e., technical and economic suitability criteria) proposed in this study was compared with the rating systems developed using the classification method and suitability criteria proposed in the previous studies. 3.2.1. Comparative analysis of the rooftop solar PV rating system by classification method The rooftop solar PV rating systems developed using the following three different classification methods were compared to demonstrate the superiority of the proposed method: (i) quantile; (ii) equal interval; and (iii) k-means clustering (i.e., the proposed classification method). The two most frequently used classification methods for rating the rooftop solar PV suitability in the previous studies, quantile and equal interval, determine the intervals of each grade by assigning an equal number of buildings to each grade and by dividing the range of the technical RSPS scores into equal-sized subranges, respectively. As this comparative analysis was primarily intended to verify the superiority of the proposed classification

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and B), the rooftop solar PV rating system developed using the proposed classification method reduced the discrepancy in the quantity of buildings in each grade compared to that developed using equal interval breaks. This indicates that the rooftop solar PV rating system developed using the proposed method, k-means clustering, has been improved by determining the appropriate intervals for each grade according to the similarity of the technical RSPS scores for the buildings within a grade. 3.2.2. Comparative analysis of the rooftop solar PV rating system by considered suitability criteria From the aforementioned comparative analysis, it has been confirmed that the rooftop solar PV rating system can be improved by applying the k-means algorithm. In this comparative analysis, three different rooftop solar PV rating systems by considered suitability criteria were compared to demonstrate the superiority of the proposed approach, as follows: (i) considering only the technical suitability criteria; (ii) considering only the economic suitability criteria; and (iii) considering both the technical and economic suitability criteria (i.e., the proposed approach). As the superiority of the proposed classification method (i.e., k-means clustering) was already verified in the previous section, all three rooftop solar PV rating systems by considered suitability criteria were developed using k-means clustering. First, when the rooftop solar PV rating system was developed considering only the technical suitability criteria, the technical performance of the rooftop solar PV system was reasonably divided into each grade, but the buildings with widely different economic performances (i.e., expected ROI and PP) were put in the same grade. This difference in economic performance within a grade tend to be more remarkable for a higher grade, except for grade D (i.e., most remarkable for grade A) in terms of ROI, whereas for a lower grade (i.e., most remarkable for grade D) in terms of PP. As shown in Fig. 7 and Table 4, grades A and B contained buildings with broad and similar ROI ranges (i.e., 1.53e3.01 and 1.31e2.44, respectively) and PP ranges (i.e., 5.30e13.61 and 6.81e15.88, respectively). Meanwhile, grades C and D contained buildings both with and without rooftop solar PV system economic viability. This indicates that there was no clear difference in the expected ROI and PP among the different grades of the rooftop solar PV rating system developed considering only the technical suitability criteria, and therefore, this rating system cannot classify buildings well according to their economic suitability. Second, when the rooftop solar PV rating system was developed considering only the economic suitability criteria, the economic performance of the rooftop solar PV system was reasonably divided into each grade, but the buildings with widely different technical performances (i.e., rooftop solar PV potential) were put in the same grade. As shown in Fig. 7 and Table 4, the rooftop solar PV potential of the grade A-C buildings rarely showed superiority to one another, ranging from 150.19 to 191.18, from 118.29 to 195.42, and from 86.81 to 189.39, respectively. This indicates that the grades of the rooftop solar PV rating system developed considering only the

Fig. 6. Rooftop solar PV rating system by classification method in terms of the number of buildings and the range of technical RSPS scores.

method (i.e., k-means clustering), only the technical suitability criteria were taken into account for developing the three different rooftop solar PV rating systems. First, when the rooftop solar PV rating system was developed using quantile breaks, the number of buildings was evenly distributed to each grade, but the buildings with high technical RSPS scores tended to have low rooftop solar PV rating grades, showing a high disparity in the range of technical RSPS scores. As shown in Fig. 6 and Table 3, the buildings with similarly high technical RSPS scores (i.e., 71.36e100) were placed in different grades (i.e., grades A-C), and the buildings with widely different scores (i.e., 0e71.36) were put in the same grade (i.e., grade D). This indicates that the rooftop solar PV rating system developed using quantile breaks can be biased by downgrading buildings with high technical RSPS scores. Second, when the rooftop solar PV rating system was developed using equal interval breaks, the range of technical RSPS scores was evenly divided into each grade, but majority of the buildings tended to have high rooftop solar PV rating grades, showing a high disparity in the quantity of buildings in each grade. As shown in Fig. 6 and Table 3 and 95.72% of all the buildings were placed in the upper grades (i.e., grades A and B) while only 4.28% of all the buildings were placed in the lower grades (i.e., grades C and D). This indicates that the rooftop solar PV rating system developed using equal interval breaks can be biased by upgrading the overall rooftop solar PV suitability of all the buildings. Third, when the rooftop solar PV rating system was developed using k-means clustering, the range of technical RSPS scores for each grade was adjusted to make it more consistent with the similarity of the score values for the buildings within a grade. As shown in Fig. 6 and Table 3, the discrepancy in the range of technical RSPS score values was improved as the range for grades A-C was adjusted to be widened from 71.36 to 100 to 47.50e100 compared to that using quantile breaks. Meanwhile, as the majority of the buildings were still placed in the upper grades (i.e., grades A

Table 3 Comparative analysis results of the rooftop solar PV rating system by classification method. Grade

Quantile a

A B C D a b c

Equal interval b

N

%

5421 5420 5420 5420

25.00% 25.00% 25.00% 25.00%

c

a

Range

N

over 84.13 79.58e84.13 71.36e79.58 under 71.36

14,323 6431 840 87

N refers to the number of buildings in each grade. % refers to the percentage of buildings in each grade. and Range refers to the range of the RSPS score for each grade.

k-means clustering %

b

66.06% 29.66% 3.87% 0.40%

Range

c

over 75.00 50.00e75.00 25.00e50.00 under 25.00

Na

%b

Rangec

11,780 6472 2675 754

54.33% 29.85% 12.34% 3.48%

over 78.50 65.50e78.50 47.50e65.50 under 47.50

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Fig. 8. Snapshot of the rooftop solar PV rating system as an application example.

Fig. 7. Rooftop solar PV rating system by considered suitability criteria according to the technical and economic performances of the rooftop solar PV system.

economic suitability criteria cannot classify buildings well according to their technical suitability, and cannot explain why certain buildings are technically suitable for rooftop solar PV system installation.

Third, when the rooftop solar PV rating system was developed considering both the technical and economic suitability criteria, the centroids of both the technical and economic RSPS scores were adjusted to balance the technical and economic performances of the rooftop solar PV system (i.e., rooftop solar PV potential, expected ROI, and PP). In terms of the rooftop solar PV potential, the grade A and B buildings still did not show superiority to one another by considering both the technical and economic suitability criteria, ranging from 134.50 to 191.18 and from 136.56 to 195.42, respectively, similar to the rating system considering only the economic suitability criteria. The rooftop solar PV potential of the grade C and D buildings, however, was adjusted downward compared to the rating system considering only the economic suitability criteria, showing a clear difference in rooftop solar PV potential among grades B-D. In terms of the expected ROI and PP, unlike the rating system considering only the technical suitability criteria, the rating system considering both the technical and economic suitability criteria showed a clear difference in the expected ROI and PP among grades A-D. The differences in the expected ROI and PP between grades B and C, however, were not as clear as those among the other grades due to the inversely proportional relationship between the expected ROI and PP. Overall, it can be said that the rooftop solar PV rating system developed using the proposed approach, which considers both the technical and economic suitability criteria, has been improved by determining the appropriate intervals for each grade according to both the technical and economic performances of the rooftop solar PV system.

Table 4 Comparative analysis results of the rooftop solar PV rating system by considered suitability criteria. Classification

Decision criteria

Technical and economic performance

a b c d e f

Technical suitability criteria

Technical & economic suitability criteria

Economic suitability criteria

Grade

ScoreTa

ScoreROIb

ScorePPc

ScoreTa

ScoreROIb

ScorePPc

ScoreTa

ScoreROIb

ScorePPc

A B C D

84.34 73.25 58.55 37.01

57.88 40.94 21.66 2.29

84.47 73.82 51.05 35.93

85.06 78.91 77.40 53.82

80.54 58.31 34.23 10.22

95.07 85.69 70.99 40.24

82.84 82.09 68.30 50.27

71.79 40.30 31.58 8.54

91.48 75.78 67.10 36.69

Grade

RangeTd

RangeROIe

RangePPf

RangeTd

RangeROIe

RangePPf

RangeTd

RangeROIe

RangePPf

A B C D

over 153.73 128.53e153.72 93.67e128.51 under 93.58

over 1.53 1.31e2.44 1.00e2.05 under 1.52

under 13.61 6.81e15.88 8.44e24.98 over 12.60

over 150.19 118.29e195.42 86.81e189.39 under 152.35

over 2.39 1.90e2.39 1.43e1.93 under 1.48

under 7.25 6.98e9.43 9.23e13.77 over 13.77

over 134.50 136.56e195.42 98.06e153.10 under 131.98

over 2.14 1.53e2.14 1.32e2.15 under 1.60

under 8.26 7.99e13.61 7.96e15.87 over 11.77

ScoreT refers to the centroid technical RSPP score for each grade. ScoreROI refers to the centroid economic RSPP scoreROI for each grade. ScorePP refers to the centroid economic RSPP scorePP for each grade. RangeT refers to the range of the rooftop solar PV potential for each grade. RangeROI refers to the range of ROI for each grade. and RangePP refers to the range of PP for each grade.

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4. Conclusion This study proposed a method of evaluating the rooftop solar PV suitability at the building level within the urban boundaries by developing a rooftop solar PV rating system based on both the technical and economic suitability criteria. Towards this end, this study calculated a realistic rooftop solar PV potential and profitability (i.e., expected ROI and PP) for each building in the Gangnam district, to be used as the technical and economic suitability criteria, respectively, using Hillshade analysis and LCC analysis. Then the rooftop solar PV suitability was evalutated by calculating the technical and economic RSPS scores for each building in the Gangnam district based on its rooftop solar PV potential and profitability, respectively. Finally, the rooftop solar PV rating system was developed using two validated types of cluster analysis, HCA and k-means clustering, based on the technical and economic suitability criteria. As a result, the total of 21,681 buildings in the Gangnam district were classified into four grades (i.e., grades A-D) according to the technical RSPS score, economic RSPS scoreROI, and economic RSPS scorePP for self-consumption purposes. It was shown that 37.69% of all the buildings in the Gangnam district were rated as grade A and can thus expect relatively high technical and economic performances of the rooftop solar PV system; 37.08% of the buildings in the Gangnam district were rated as grade B and can thus expect a similar technical performance but a lower economic performance of the rooftop solar PV system compared to the grade A buildings; and 19.39% of the buildings in the Gangnam district were rated as grade C with the installation of the rooftop solar PV system being economically viable for most of such buildings, showing an ROI of over 1 and a PP of within 25 years. Only 5.83% of the buildings in the Gangnam district were rated as grade D and many of such buildings will not be able to reach the break-even point within 25 years when they install the rooftop solar PV system, showing low rooftop solar PV suiability. To validate the effectiveness and superiority of the proposed rooftop solar PV rating system, it was compared to rating systems using different approaches from the previous studies in terms of two aspects: (i) classification method; and (ii) suitability criteria.
Classification method: The rooftop solar PV rating system developed using the classification method proposed in this study (i.e., k-means clustering) was compared to the rating systems developed using the classification method proposed in the previous studies (i.e., quantile and equal intervals). It was shown that the high disparity in the range of technical RSPS scores for each grade (i.e., problems using quantile breaks) and in the quantity of buildings in each grade (i.e., problems using equal interval breaks) had been reduced by using k-means clustering for developing the rooftop solar PV rating system.
Suitability criteria: The rooftop solar PV rating system developed considering the suitability criteria proposed in this study (i.e., both the technical and economic suitability criteria) was compared to the rating systems developed considering the suitability criteria proposed in the previous studies (i.e., only the technical or economic suitability criteria). It was shown that the rooftop solar PV rating system developed considering only one of the two suitability criteria could not classify buildings well according to the other suitability criteria. Meanwhile, the rooftop solar PV rating system developed considering both the technical and economic suitability criteria had been improved, showing relatively clear differences in the values of the rooftop solar PV potential, expected ROI, and PP among the grades. The proposed method for developing the rooftop solar PV

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system is superior to the methods from the previous studies in terms of applicability and usefulness in that it (i) considers the actual building characteristics for calculating the technical and economic performances of the rooftop solar PV system for each building in the study area, using Hillshade analysis and LCC analysis; (ii) considers scientific evidence and minimizes the randomness of evaluating the rooftop solar PV suitability of a building by using cluster analysis; and (iii) considers both the technical and economic suitability criteria for evaluating the rooftop solar PV suitability of a building. One of the main advantages of this study is that it made it possible to evaluate the rooftop solar PV suitability of a building based on a grade scale from A (very suitable) to D (unsuitable) for intuitive decision-making based on reasonable and acceptable criteria (refer to Fig. 8). This approach will also support more individual- and community-oriented decision-making for deploying DSG by providing the rooftop solar PV suitability information at the building level based on the bottom-up approach. Knowing which building is suitable and how suitable it is for rooftop solar PV system installation can accelerate the effective market penetration of DSG in urban areas. 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.NRF2018R1A2A1A19020868). References [1] Lee M, Koo C, Hong T, Park HS. 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;48:4604e12. https:// doi.org/10.1021/es405293u. [2] Koo C, Hong T, Lee M, Park HS. Estimation of the monthly average daily solar radiation using geographic information system and advanced case-based reasoning. Environ Sci Technol 2013;47:4829e39. https://doi.org/10.1021/ es303774a. [3] Hong T, Koo C, Kwak T, Park HS. An economic and environmental assessment for selecting the optimum new renewable energy system for educational facility. Renew Sustain Energy Rev 2014;29:286e300. https://doi.org/10.1016/ j.rser.2013.08.061. [4] Hong T, Koo C, Park J, Park HS. A GIS (geographic information system)-based optimization model for estimating the electricity generation of the rooftop PV (photovoltaic) system. Energy 2014;65:190e9. https://doi.org/10.1016/ j.energy.2013.11.082. [5] Koo C, Hong T, Park HS, 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;22:462e78. https://doi.org/10.1002/ pip.2448. [6] Koo C, Hong T, Lee M, Kim J. An integrated multi-objective optimization model for determining the optimal solution in implementing the rooftop photovoltaic system. Renew Sustain Energy Rev 2016;57:822e37. https://doi.org/ 10.1016/j.rser.2015.12.205. [7] Lee M, Hong T, Koo C, Kim C-J. A break-even analysis and impact analysis of residential solar photovoltaic systems considering state solar incentives. Technol Econ Dev Econ 2017;4913:1e25. https://doi.org/10.3846/ 20294913.2016.1212745. [8] 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;58:590e607. https:// doi.org/10.1016/j.rser.2015.12.297. [9] Lee M, Hong T, Yoo H, Koo C, Kim J, Jeong K, et al. Establishment of a base price for the Solar Renewable Energy Credit (SREC) from the perspective of residents and state governments in the United States. Renew Sustain Energy Rev 2017;75:1066e80. https://doi.org/10.1016/j.rser.2016.11.086. [10] Lee M, Hong T, Kang H, Koo C. Development of an integrated multi-objective optimization model for determining the optimal solar incentive design. Int J Energy Res 2017;41:1749e66. https://doi.org/10.1002/er.3744. [11] Hong T, Lee M, Koo C, Jeong K, Kim J. Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis. Appl Energy 2017;194:320e32. https:// doi.org/10.1016/j.apenergy.2016.07.001. [12] 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:

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