Effects of design parameters on rooftop photovoltaic economics in the urban environment: A case study in Melbourne, Australia

Abstract

Many researchers found high potential of adopting building photovoltaic (PV) systems in urban areas, especially on building rooftop, to improve the sustainability of urban environment. However, the optimal energy output performance and economic benefit of the PV system are affected by the usable roof area, PV array layout, and shading effect considering high city density. This study aims to understand the effects of these design parameters in the urban environment of rooftop PV’s economic performance. This study carries out a case study in the urban area of Melbourne with 90 PV designs under three shading conditions to generate 270 scenarios. Through a lifecycle cost-benefit analysis, including net present value (NPV), NPV per kW, internal return rate (IRR), and payback year, the results can help in developing a comprehensive understanding of the economic performance of rooftop PV designs that cover most of the urban areas of Melbourne. The optimal PV design scenarios for the urban environment are identified, thereby providing investors and industry professionals with useful information on value-for-money PV design. Meanwhile, the maximum shading loss that makes the PV systems financially unfeasible is investigated, and design scenarios with greatest ability to sustain the shading effect are identified. This research can also support the policy makers’ decision on the development and deployment of the roof PV systems in urban planning.

Keywords

building photovoltaics / tilt and azimuth / orientation / shading analysis / economic analysis

Introduction

Increasing renewable energy sources (RESs) in the urban context is important to improve the sustainability of a city. Among all the available RESs, solar photovoltaic (PV) energy is one of the most promising candidates due to its continuous cost reduction and technological improvement (^{Freitas et al., 2018}). Rooftop PV systems are extremely common in urban areas due to easy installation. A well-designed rooftop PV system cannot only exhibit considerable environmental benefit but also become an investment choice in adding value to building owners. However, many PV design parameters are constrained by the urban environment, which may cause considerable influences on the optimal energy output performance and economic benefit of rooftop PV systems. This study focuses on the tilt and azimuth angles of the PV system, usable roof area, and shading effect by the surrounding environment.

The tilt and azimuth angles of the PV arrays are the most important design parameters that have direct influences on obtaining solar irradiation and energy outputs (^{Jantsch et al., 1991}; ^{Bhattacharya et al., 2014}; ^{Singh and Banerjee, 2016}). The common practice to simplify the PV array design is making the tilt angle equal to the geographic latitude and the azimuth angle equal to the due south in the northern hemisphere for a fixed PV array (^{Rowlands et al., 2011}). Given that the solar condition differs based on the geographic location, a large amount of research has investigated the optimal tilt angle and azimuth angle for a certain location around the globe. For example, ^{Khahro et al. (2015)} identified the annual optimum tilt angle in the southern region of Sindh, Pakistan. The optimum tilt angle is 23°, which is slightly smaller than the local latitude (25°). Similar research can also be conducted in Saudi Arabia (^{Kaddoura et al., 2016}), Turkey (^{Bakirci, 2012}), and China’s Taiwan (^{Chang, 2010}). In addition to discovering the optimal tilt angle alone, many studies regarding the optimal setting of tilt and azimuth angles have been conducted to maximize the monthly or yearly energy yield, such as in Canada (^{Rowlands et al., 2011}), Iran (^{Talebizadeh et al., 2011}), and Abu Dhabi (^{Jafarkazemi and Saadabadi, 2013}). Various models are established for the optimization of the PV system layout.

However, in terms of the rooftop PV design in urban areas, the possible effect of building orientation on the usable roof area for PV system is often neglected. Conflict may arise between the building orientation and the optimal PV array layout. In many cities, general building orientations are evident in urban blocks, which have been determined for decade by the metropolitan planning scheme. For example, in Melbourne, “Hoddle Grid” is the layout of streets in its central business district (CBD), which was established in 1837 and became the first formal town plan in the city (^{University of Melbourne, 2008}; ^{City of Melbourne, 2012}). Two main building orientations are apparent in Melbourne. In this densely built district of Melbourne, most buildings are aligned with the layout of the streets, which is 20° counter-clockwise from true north. Most buildings outside the CBD are oriented at approximately 10° clockwise from true north (^{National Centers for Environmental Information, 2018}). The capacity of a PV system is mainly affected by the number of PV panels placed on the available roof area. To achieve the maximum number of PV panels, researchers supposed that the orientation of the PV array, namely, the azimuth angle, is equal to the building orientation. However, the amount of annual solar irradiation varies depending on the azimuth angle. The general rule indicates that the surface facing due south achieves the most in the northern hemisphere and vice versa in the southern hemisphere (^{Mondol et al., 2007}). Research on the economic assessment of the optimal design for rooftop PV in urban areas by considering both the main building orientations of the city and PV panel azimuth is limited.

Meanwhile, shading in the urban environment can have a larger influence on the PV system performance than any other design parameter (^{Horn, 2011}). Shading is a major challenge imposed by surrounding obstruction, such as tall neighboring buildings or trees. In particular, in the urban environment, shading is becoming a major risk. As the cities increase in size, and buildings become tall, the likelihood of overshadowing buildings is high, which will result in considerable energy losses. Shading challenges can be the difference between a viable and a nonviable PV project. To guide the application of PV systems, many researchers investigated the solar potential of cities worldwide (^{Compagnon, 2004}; ^{Redweik et al., 2013}; ^{Sarralde et al., 2015}; ^{Mohajeri et al., 2016}; ^{Wong et al., 2016}). Most studies evaluated the availability of solar irradiation under the effect of shading by the surrounding buildings through geographic information system-based methods (^{Melius et al., 2013}). Some researchers analyzed the shading effects on the system yield and performance ratio reduction through experiment or by using a software (^{Zomer et al., 2014}; ^{Frontini et al., 2016}; ^{Zomer et al., 2016}; ^{Bana and Saini, 2017}). However, research associating the shading effect to the economic performance of rooftop PV systems is lacking, especially in increasingly dense urban environment.

Considering the research gaps in the area, the main focus of this study is to investigate the effects of design parameters including the usable roof area, PV array layout, and shading ratio in the urban environment on rooftop PV’s economic performance. This work carries out a case study in Melbourne, Australia with 90 PV designs under 3 proposed shading conditions to generate 270 scenarios. A life cycle cost-benefit analysis, including net present value (NPV), NPV per kW, and payback year (PB), is carried out to study the effects of shading, building orientation, and azimuth angle of a PV system. Meanwhile, a maximum shading loss that each PV system can withstand to remain financially feasible is identified through the economic analysis. The outcomes of the study can benefit both the investors and urban planners regarding the development and deployment of rooftop building PV in urban environment. This paper is organized as follows: Section 2 describes the research method, with detailed explanation on each investigation step, including design scenario development (Section 2.1), shading condition and maximum shading loss determination (Section 2.2), and cost-benefit analysis (Section 2.3); Section 3 presents the results and discussion; and Section 4 provides the conclusions and implications.

Method

The case building of this investigation belonged to an educational institute in the urban area of Melbourne, Australia. The total built-up area of this building was approximately 1131 m². Figure 1 illustrates the case building (in blue) in the selected urban block. As shown in Fig. 1, the surrounding environment of the case building can be considered as a typical example of modern urban environment, with high and low buildings around the case building. The height of the case building was approximately 6.9 m, while those of surrounding buildings varied from 3.1 to 29 m. The case building was also selected because it had a regular rectangle shape, which aligned with the prevailing building designs. The shading condition of the selected areas was also analyzed and discussed in Section 2.2. The proposed PV systems were located on the roof of the case building. The design results are summarized in Appendix. Figure 2 provides an illustration of the PV design scenarios when the PV tilt angle was 38°.

Multiple data sources and tools were utilized in the study to carry out the economic analysis, as follows. (1) Building drawings and information were collected from the educational institute to create a SketchUp model of the case building and its surrounding environment. Google Earth was also used to assist the modeling process. (2) The detailed information of the selected PV module was obtained from a third-party PV design firm in Melbourne, who can provide the professional industry knowledge to the designs. (3) A total of 90 scenarios of PV layouts were proposed through the Skelion, which is a SketchUp plugin software with expertise in PV system design in 3D modeling. The explanation of the design process was provided in Section 2.1. Meanwhile, three different shading conditions were developed for the study (Section 2.2). (4) After the PV designs were developed, PV suppliers and installers were approached to obtain the capital cost information. A local utility provider advised the electricity price, and the feed-in-tariff (FIT) was obtained from the government website. (5) The hourly building consumption data were provided by the building owner. (6) Solar irradiation data were obtained from the NREL’s PVWatts Calculator (^{NREL, 2018}), which was used for energy output calculation. (7) Energy outputs were calculated based on the solar irradiation, system efficiency, and loss. Energy consumption and output data were compared to identify the possible energy export to the public grid. (8) The NPV, NPV per kW, and PB year were calculated to show the economic performance of all the designs. The design scenario with the best or worst economic performance was identified. (9) The maximum shading loss was calculated for each design scenario when the NPV became negative.

Developing design scenarios

When establishing the rooftop PV design, the present study considered the building orientation and the tilt angle and azimuth angle of the PV system. On the one hand, this study attempted to investigate the relationship between the building orientation and the PV panel orientation (azimuth) to maximize usable roof area. On the other hand, this study aimed to discover the optimal setting of PV tilt and azimuth angles in Melbourne. In this study, an orientation/azimuth angle of 0° means north facing, and an orientation/azimuth angle of 90° is for true east.

The building orientation was classified as true north (0), 10° clockwise from true north (10), and 20° counter-clockwise from true north (340). The two classifications, namely, 10° clockwise from true north (10) and 20° counter-clockwise from true north (340), were included to align with the typical building orientations in Melbourne. In the SketchUp model, the orientation of the case building was changed, while the surrounding environment remained constant.

Regarding the azimuth angle of the PV design, we chose five sets of azimuth angle (i.e., from 20° counter-clockwise from true north (340) to 20° clockwise from true north (20)). The maximum PV usage area can be achieved when the building orientation was equal to the azimuth angle of the proposed PV system.

Six tilt angles were proposed (i.e., from 10° to 60°). For each building orientation, 30 alternative design scenarios were developed to determine the effects of tilt and azimuth angles on the economic performance of rooftop PV systems, as shown in Table 1. These combinations of tilt and azimuth angles were selected based on the study of the Australian Clean Energy Council (^{Clean Energy Council, 2009}), as shown in Table 2. Instead of using a tilt angle of 40°, 38° was applied in the study, which was close to 40° and equal to the geographic latitude of Melbourne. Appendix provides a summary of all the design results in the study, where B represents building orientation, and A and T stand for the PV azimuth and tilt angles, respectively. For example, B0A0T10 indicates the combination of the building facing the true north, the PV azimuth angle of 0°, and the tilt angle of 10°. This setting remained the same throughout this study. These 90 design scenarios can help in developing a relatively comprehensive understanding of the rooftop PV design that covers the most urban areas of Melbourne.

The PV system design of each scenario was made with the principle to install as many PV panels as possible. The 90 scenarios of PV layouts were proposed through the Skelion. A popular polycrystalline silicon PV product was selected for the application, and the specification of the PV module input in the software is presented in Table 3. The distance for all the scenarios was set to guarantee that all PV panels can achieve at least 6 h of sunlight during winter solstice. According to PV selection and distance setting, the design results generated by the software are summarized in the Appendix to investigate the relationship between the building orientation and PV panel orientation (azimuth). The design results are discussed in Section 3.

Shading condition and maximum shading loss determination

The shading effect on the PV system in many studies refers to the solar irradiance or generation loss due to shading effect, which is generally in the form of percentage (^{Woyte et al., 2003}; ^{Loulas et al., 2012}; ^{Nguyen and Pearce, 2012}). ^{Nguyen and Pearce (2012)} investigated the shading effect at the municipal scale in downtown Kingston, Ontario and showed that shading leads to a 25% generation loss in the average for >12 months. ^{Loulas et al. (2012)} studied the shading loss of rooftop PV system on a building block in Greece. According to the study of ^{Loulas et al. (2012)}, the annual performance loss due to shading varies from approximately 8% to 23%. A similar investigation on the shading loss was carried out for the block where the case building is located, as shown in Fig. 3 (^{Yang and Carre, 2018}). The result of this study showed that the average annual performance loss of rooftop PV system in this building block is 16%. ^{Deline et al. (2012)} also indicated that the annual shading losses in a typical urban environment are 7%, 19%, and 25% under light, moderate, and heavy shading scenarios, respectively. Therefore, the present study adopts the three shading effect patterns not only to reflect on the current urban environment in Melbourne but also provide indications to urban developmental changes and other cities in Australia.

This study investigated a maximum shading loss that makes each design scenario financially unattractive (i.e., the NPV becomes negative) through the cost-benefit analysis. The results of maximum shading loss indicate the ability of each PV design scenario to sustain the shading loss in the urban environment.

Cost-benefit analysis

A 25-year cost-benefit analysis was carried out to study the actual value of all 90 PV designs under three shading conditions. The cost consists of the initial investment cost of each system and maintenance cost for 25 years. The cost information regarding the PV system was obtained from local PV suppliers and installers.

For the calculation of the benefit of the PV system, the first step was to calculate the energy output of the PV system by using Eq. (1), as follows:

(1)E=A\cdot r\cdot H\cdot PR,

where E is the energy output in kW∙h, A is the total solar cell area in m², which is equal to the number of PV panel in each system timing the area of PV cell on one PV panel, r is the PV product efficiency in percentage, H is the hourly solar radiation in kW∙h/m², and PR is the system performance ratio.

The area of solar cell on the PV panel and the r of the PV panel used in the study are provided in Table 3. The corresponding H statistics that were used in each scenario were collected from a popular solar modelling tool named PVWatts (^{NREL, 2018}). H represents the actual solar irradiation without additional effects on building shading. The shading loss in the study was applied on the PR, which was 90%×(1-Shading loss). The percentage in the equation, that is, 90%, is the system performance ratio provided by the PV supplier. Three shading losses, namely, 7%, 19%, and 25% for light, moderate, and heavy shading conditions, were examined, respectively. The attenuation rate of the PV system was assumed to be 0.7% per year for all the cases.

The second step was to compare the energy output with the building energy consumption. The building energy consumption of every 15 min interval was measured for 1 year. The hourly energy consumption data were generated from the original data and compared with the hourly energy output. If the energy output exceeds the building consumption, then the surplus energy will be sold to the public grid at the price of FIT set by the government.

The final equation of the benefit generated by the PV system is shown in Eq. (2). The detailed information used in the study is shown in Table 4.

(2)B(n)={\displaystyle {\sum }_{\mathrm{25}}e{p}_n{(\mathrm{1}+\Delta ep)}^n{E}_{\mathrm{1}}(h)+\mathrm{F}\mathrm{I}\mathrm{T}\cdot E_{\mathrm{2}}(h)},

where n is the number of the year, ep_h is the electricity price of that h, ∆ep is the compounded growth rate of the electricity price of Melbourne calculated from the government report (^{Australian Energy Regulator, 2017}), E₁(h) is the hourly energy generation using Eq. (1) and consumed by the building, FIT is the FIT set by the government (^{State Government of Victoria, 2018}), and E₂(h) is the surplus energy sold to the public grid. In this study, we assumed that the energy consumption of the case building remained the same for 25 years. However, with the increase in energy consumption in the future, the demand for additional solar energy for self-consumption will increase, thereby leading to a highly feasible solution to use PV. We presented a strict scenario here.

The NPV, NPV per kW, PB, and internal return rate (IRR) were selected for the cost-benefit analysis in the study. Equations (3) and (4) were used to calculate NPV and IRR in the study. The NPV per kW was applied in the following sections to compare each scenario with different PV capacities for the standardization of the comparison instead of NPV. PB is the number of years when the NPV becomes positive.

(3)\mathrm{N}\mathrm{P}\mathrm{V}\mathrm{=-}{C}_{\mathrm{0}}+{\displaystyle {\sum }_{n=\mathrm{1}}^N\frac{B_n}{{(\mathrm{1}+r)}^n}}-{\displaystyle {\sum }_{n=\mathrm{1}}^N\frac{M_n}{{(\mathrm{1}+r)}^n}},

(4)0\mathrm{=-}{C}_{\mathrm{0}}+{\displaystyle {\sum }_{n=\mathrm{1}}^N\frac{B_n}{{(\mathrm{1}+\mathrm{I}\mathrm{R}\mathrm{R})}^n}}-{\displaystyle {\sum }_{n=\mathrm{1}}^N\frac{M_n}{{(\mathrm{1}+\mathrm{I}\mathrm{R}\mathrm{R})}^n}},

where C₀ is the initial investment cost, B(n) is the benefit generated by the PV system using Eq. (2), M_n is the maintenance cost, which is assumed to be the cost of changing the invertors every 10 years, and r is the discount rate, which is assumed to be 7% in this study (^{Office of Best Practice Regulation, 2016}).

Results and discussion

The results of the study are presented and discussed in this section. The three subsections are as follows: (1) the analysis of the design results to investigate the effects of building orientation and PV tilt and azimuth angles on the usable roof area, (2) economic performance of 90 different design scenarios under three shading conditions, and (3) maximum shading loss that makes the system financially unviable.

Analysis of 90 PV design results

From the geometrical perspective, the number of PV panels will reach its maximum when all panels are parallel to the building’s orientation. However, as shown in Appendix, the design results indicated that the number of PV panels reached the maximum when the PV azimuth angle was 0° (i.e., facing north) regardless of the building orientation. When the PV arrays were parallel to the building orientation, scenario B10A10 had the second most PV panels among all five B10 scenarios, while scenario B340A340 possessed the second least panels among all five B340 scenarios. The finding was due to the distance between PV arrays. A suitable space should be provided between arrays to avoid shading effects among the arrays and guarantee that all PV panels can achieve at least 6 h of exposure to sunlight during winter solstice.

The results also showed that the number of PV panel decreased when the PV arrays had a large tilt angle setting. The largest number of PV panels for each set of building orientation and azimuth angle can be reached when the PV tilt angle was the smallest (10°) because the large tilt angle can increase the shading effects among the arrays, which required a large distance between the two adjacent PV panels, thereby reducing the number of PV panels.

Performance of proposed PV system designs under three shading conditions

The NPV, NPV per kW, and PB results of all the scenarios under three shading conditions are illustrated in Figs. 4–7. In general, the results of the economic indicators showed a consistent patent under three shading conditions. As shown in Fig. 4, among all building orientations, the T10 groups with the highest NPV and NPV decreased when the tilt angle increased. According to Figs. 5 and 7, NPV per kW and IRR reach their peaks when the tilt angle ranged from 30° to 38°. Meanwhile, the PV system demonstrated the shortest PB when the tilt angle was set from 20° to 50° under three shading conditions.

A summary table (Table 5) was generated to improve the comparison in terms of the performance of all 90 design scenarios. The table lists the best and worst design scenarios of the three shading conditions. The percentage difference between the two scenarios was also provided. In addition to all the economic indicators (i.e., NPV, NPV per kW, PB, and IRR), the total generation of the PV system and self-consumption ratio was included in the table to understand the economic results.

Regardless of the building orientation, A0T10 exhibited the best NPV, while A340T60 had the worst NPV under all three shading conditions. As shown in Appendix, the A0T10 of all three building orientations had the largest system capacity, thereby indicating that it possessed the largest area of the solar cells and highest initial investment cost. By contrast, the A340T60 of all three building orientations had the smallest or second smallest system capacity. As a result, B0A0T10, B10A0T10, and B340A0T10 presented the highest 25-year energy output under three shading conditions, while B0A340T60 and B10A340T60 showed the least total energy generation during the entire lifespan. Although A0T10 had the highest energy generation, its self-consumption ratio was the lowest, thereby indicating that more surplus energy was sold to the public grid than the other design scenarios. A340T60 showed the opposite result with the least energy generation and almost 100% self-consumption ratio under all three shading conditions.

Given that the 90 scenarios had a variety of system capacities on the basis of the available roof areas, the NPV per kW can provide an improved understanding of the system benefit per unit. With regard to NPV per kW, the best scenarios for the three building orientations occurred when the PV azimuth angle was either 10° or 20°, and the title angle was equal to 20° or 38°. On the contrary, the combination of A0T10, A340T60, and A350T10 should be avoided for all three building orientations under different shading conditions due to the lowest NPV per kW. Meanwhile, the IRR performance was similar to that of NPV per kW.

In terms of PB, the difference among the scenarios under three shading conditions was insignificant. All scenarios can be breakeven in the 13th and 15th year under light and heavy shading conditions, respectively. The difference in PB between the best and worst scenarios was either 1 or 2 years.

Maximum shading loss

Considering the ongoing growth of urban environment, the maximum annual shading loss that resulted in the NPV of 0 was identified, as shown in Fig. 8. The maximum shading loss varied from 50% to 56%. As shown in Fig. 8, for the three building orientations, A0T30, A10T30, A20T30, and A10T38 were the best designs to sustain the shading loss, while A340T60 was the worst. Although the PV design scenario reached the largest panel area and highest system capacity when the azimuth angle was 0°, the performance of these A0 scenarios was not the optimum when coping with shading loss. High solar irradiation instead of the large panel area made the system sustain substantial shading. As shown in Table 6, the average solar irradiation of the four best scenarios was approximately 4.83 kW∙h∙m⁻²∙d⁻¹, which was the highest among all other angle combinations in this study (^{NREL, 2018}). The result indicated that the combination of tilt and azimuth angles to obtain solar irradiation was the critical factor to deal with shading loss instead of building orientation.

Conclusions and implications

The solar PV system is one of the most promising RESs that can improve the sustainability of the urban environment. However, many design parameters are constrained by the urban context that can affect the PV system performance. This study investigates the effects of tilt and azimuth angles of the PV system, building orientation, and shading effect by the surrounding environment on the rooftop PV design for urban areas from the economic perspective. This work carries out a case study on the urban area of Melbourne. The 90 design scenarios are generated by considering both the building orientation and PV panel layout. The design results show that the combination of A0T10 for buildings of all three orientations in Melbourne can provide that largest panel area and system capacity.

A lifecycle cost-benefit analysis is conducted to investigate the economic performance of the 90 PV designs under three shading conditions, which generates 270 scenarios. NPV, NPV per kW, PB, and IRR are used to evaluate the economic performance of all the scenarios. The results of the economic analysis provide a comprehensive understanding of the economic performance of rooftop PV system design in the urban areas of Melbourne under different shading prospects. The best PV design scenarios for the urban environment are identified, thereby providing investors and industry professionals with useful information on the value-for-money PV design.

This study also identifies the value of maximum shading loss that makes the proposed PV systems financially unfeasible. The results show that the maximum shading loss varies from 50% to 56%. Regardless of the building orientation, the PV design combination of A0T30, A10T30, A20T30, and A10T38 has the optimal capability of sustaining overshadowing.

This research can also support policy makers’ decision on the deployment of the roof PV systems in the urban areas. For the existing roof PV systems and envisioned PV projects, future urban development plans should consider the shading effects of buildings in the increasing high-density urban environment.

For building PV design, a large number of design parameters can affect the system performance and economic benefit (^{Wijeratne et al., 2018}). This work has several limitations as a result of the case study. First, we only study the PV layout on the case building roof with a regular rectangular shape. The PV layout can be easily arranged when the building roof and PV module are rectangular in shape. However, the rectangular roof is the most popular roof shape in the case city. Hence, the results can be easily generalized. Second, the case building is an institutional building. Thus, the load profile and energy consumption data used in the study can only represent this kind of building. Third, we have not considered the potential change of energy consumption in the case building. However, if the building energy consumption increases, then the self-consumption ratio will increase, and the economic performance of the PV system will also improve. Finally, the shading effect is quantified by percentage values. Future research will explore the method to visualize shading loss, which can further facilitate the uptake and diffusion of rooftop PV system in the urban environment.

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