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Simplified method for shading-loss analysis in BIPV systems – part 1: Theoretical study
Accepted Manuscript Title: Simplified method for shading-loss analysis in BIPV systems – Part 1: Theoretical study ¨ Authors: Clarissa ZOMER, Ricardo RUTHER PII: DOI: Reference:
S0378-7788(16)31521-3 http://dx.doi.org/doi:10.1016/j.enbuild.2017.02.042 ENB 7403
To appear in:
ENB
Received date: Revised date: Accepted date:
11-11-2016 18-1-2017 15-2-2017
¨ Please cite this article as: Clarissa ZOMER, Ricardo RUTHER, Simplified method for shading-loss analysis in BIPV systems – Part 1: Theoretical study, Energy and Buildings http://dx.doi.org/10.1016/j.enbuild.2017.02.042 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Energy and Buildings Revised manuscript submitted for publication by C.D. Zomer and R. Rüther in January 2017
Simplified method for shading-loss analysis in BIPV systems – Part 1: Theoretical study Clarissa ZOMER and Ricardo RÜTHER Universidade Federal de Santa Catarina (UFSC), Caixa Postal 476, 88040-900, Florianópolis – SC, Brazil * Email: [email protected], Tel: +55 (48) 3721 4598
Highlights
Demonstration of how shadings on BIPV systems can be translated into energy losses. Comparison of shading percentages and incident irradiation reductions. Validation through a physical experiment and three computer software packages. Annual shading percentage can be used in energy estimations of BIPV systems.
Abstract This paper proposes a simplified method to determine an index to quantify the influence of partial shadings on the performance of BIPV systems based on the relation between the shading percentages and the reduction of the incident irradiation on a given surface. The research is divided in two papers: Part 1: Theoretical study and Part 2: Application in case studies. Part 1 consists in identifying and quantifying the shading on a surface, and relates the fraction of shaded area with the percentage of incident irradiation reduction during the same period, in order to propose a shading index (SI) that represents the energy losses due to shadings on PV systems. The method was developed for a theoretical shaded case study simulated in two cities located at low latitude, tropical regions: Singapore (1.35°N) and Florianópolis-Brazil (27.48°S). Results showed that the shading percentage on the analysed surface on an annual basis is closer to the percentage of incident irradiation reduction at same period than when these values are compared on other time bases, as hourly, daily or monthly. Therefore, in this case, the annual percentage of shading can be adopted as the SI. SI was validated using different computer software packages and it was proved to be a convenient way of estimation the PV generation of similar cases of partially shaded PV systems, that could be used even before the PV electrical desing has been done. Keywords: Photovoltaic generation; building-integrated photovoltaic systems (BIPV); shading analysis for PV systems.
1. INTRODUCTION In 2015, some 60 GWp of new photovoltaic (PV) systems were installed globally [1] 1
Energy and Buildings Revised manuscript submitted for publication by C.D. Zomer and R. Rüther in January 2017
bringing the total worldwide installed capacity to nearly 250 GWp, with Asia leading the wave of new installations [2]. Information Handling Services Inc. (IHS) has raised its global solar PV forecasts for 2016 to 65 GWp, and over 70 GWp is expected to be installed in 2019 [3]. By 2020, the cumulative global market for solar PV is expected to triple to around 700 GWp [4]. In addition to being a renewable and pollution-free energy generation technology with no moving parts, PV modules can also be integrated into buildings as BIPV systems, adding aesthetic value [5]. When installed in an optimised way, BIPV systems can reduce heat transferred through the envelope and reduce cooling load components decreasing the CO2 emissions [6]. Apart from some façade installations, the rooftop segment represented more than 23 GWp of total installations in 2015, with projections of more than 35 GWp to be installed by 2018 [2]. Under all scenarios, PV will continue to increase its share of the energy mix around the world [2], and, in this context, a great number of new BIPV systems will be installed in urban environments in the coming years. Nowadays, it is already possible to see very advanced BIPV systems installed with high architectural quality, but they are still not large in numbers [7]. Architects and urban planners are becoming aware of the importance of high-quality BIPV systems, therefore, with the view to regulate the adoption of the PV modules in buildings and to define a suitable design approach when using the solar technology in a sensitive urban environment (where architectural, historical or cultural value is considerable), a multidisciplinary project was launched in Canton Ticino, Switzerland in order to define a set of criteria and recommendations for such sensible areas and guarantee a proper balance, or compromise, between technical and aesthetic requirements [8]. The International Energy Agency (IEA) encourages and facilitates the adoption of PV modules by architects and engineers as a design element already at an initial design stage, by presenting guidelines and inspiring them by giving good examples of high quality architectural integration [9, 10]. Usually, BIPV system design follows classical PV system design practices, with tilt angles matching a location’s latitude and orientation towards the opposite hemisphere in which the site is located in order to maximise energy generation [11-15]. While there exists clearly a technical possibility for the integration of PV and solar thermal on a building façade, most of the potential is intrinsically related to intervention in the roof, especially in sensitive urban environment [8]. Although maximisation of yield and performance ratio (PR) represent a better return of investment, the differences between an optimal PV system and a non-optimal PV system can be a couple of percentage points in some cases [16]. Challenges such as partial shading and non-optimal tilt and azimuthal deviations are common in BIPV systems; therefore it is important to be aware of the consequences on their performance. We have previously shown [17] that even with a non-optimal combination of azimuthal deviations and tilt angles, some PV systems can perform better than PV systems installed in more ideal conditions (according to the common practices), as long as the PV systems have been well designed, with a suitable PV string electrical configuration [17, 18]. Designers and building owners should be aware of the consequences in terms of energy generation of each architectural choice. Knowledge on the concurrent, and sometimes conflicting, consequences between the way in which modules are installed and the associated energy generation then 2
Energy and Buildings Revised manuscript submitted for publication by C.D. Zomer and R. Rüther in January 2017
becomes of technical, scientific, as well as of economic interest [18]. Estimating the energy provided by PV systems is important in order to analyse their economic viability and supervise their operation. However, this is not trivial; there are a lot of methods for PV energy calculation [19] and a lot of scientific studies showing different methods [19-22]. Sometimes they require some inputs difficult to obtain [23-25] and the method can not be easily adopted. One of the most difficult variables to consider in estimations is the energy losses caused by partial shadings and BIPV systems usually are strongly affected by complex and dynamic shadings, especially when located in dense urban environments [26, 27]. A detailed shading analysis can assist on the design phase of a PV system; can contribute to its optimisation, by putting shaded modules together in the same string; and also allow to a more precise estimation of PV generation, avoiding oversizing and undersizing of a PV system [28]. In addition, it doesn’t only enhance the yearly output of the system but also allows a longer lifetime for the system since it avoids mismatch losses by gathering modules within a defined range of irradiation under the same MPPT [26]. A shading mask can immediately show for how long and for which extent a surface is shaded [29]; therefore it can be used to help in a number of design decisions. The shadings on PV systems and their impacts on energy generation has been extensely studied as the partial shading was identified as the main cause of yield and performance ratio (PR) reductions in grid connected PV systems [30-38]. Different models can be found in the literature and the most of them are based on numerical algoritms or based on very detailed electrical configurations [26, 39-47]. In order to visualize and/or to quantify the shadings, it is possible to analyse three-dimensional models using softwares like Ecotect [48], PVSyst [49] and Sketch Up [50]. A plug in for SketchUp (Solar 3DBR) was developed by [51] that also estimates the shadings from a 3D model. Some studies based on these softwares can be found at [26, 44, 52-55]. Considering the state of the art in this field, the main focus of this paper is the evaluation and estimation of the energy generation of partially shaded BIPV systems in a most simple way, that could be used even before the PV electrical desing has been done. The paper proposes a simplified method based on the relation between the shading percentage and the reduction of the incident irradiation on a given surface to determine an index that quantifies the partial shading influence on PV performance in BIPV systems.
2. METHOD The proposed method is divided in the following phases:
Phase 1: Computer simulation for 3D model inserted in two locations: Singapore, 1.35ºN; 103.98ºE and Florianópolis – Brazil, 27.48ºS; 48.5ºW. The first phase is divided in: a) shading identification; b) shading quantification (shading percentage (%) – SP); c) reduction of incident irradiation (RII); d) relation between SP and RII (%). Phase 2: Validation of of shading percentage and reduction of incident irradiation using a physical model and different computer software. 3
Energy and Buildings Revised manuscript submitted for publication by C.D. Zomer and R. Rüther in January 2017
Phase 3: Shading Index proposal.
2.1. Phase 1: Computer simulation 2.1.1. Shading identification After the visual identification and the neighbouring data collecting, both the PV system and the environment were modelled in 3D using the SketchUp [50] software. All existing elements around the PV system were measured (in terms of height and distance) and geographically located in relation to the object of study. Through the 3D model and the shadow tool of SketchUp, the shadings over the surfaces could be visualized.
2.1.2. Shading quantification: Shading percentage (SP) The 3D model developed in SketchUp in the previous phase was then imported to the Ecotect software [48] to perform the shading quantification. Quantification of shading percentage in each surface was based on two different tools from the Ecotect software: shading masks and sun exposure calculations; and was calculated for different time bases: hourly, daily, monthly, and annual. The shading percentage represents the fraction of shadowed area in a given time. Therefore, the hourly shading percentage represents the percentage of shadowed area in that moment (i.e. at 8am or at 2 pm, for instance). The daily shading percentage represents the average of every hourly shading percentages, from the sunrises until the sunset of each day. That is why in summer days the average will takes into account more hours than in winter days. The monthly shading percentage is the average of every daily shading percentages inside that month. Finally, the annual shading percentage is the average of every monthly shading percentages. The 3D model with its environment was then inserted in the correct geographical orientation and with a weather data from U.S. Department of Energy [56].
2.1.3. Reduction of incident irradiation (RII) In order to compare how a partial shading can influence the irradiation over a selected surface, a simulation using the software Ecotect was carried out. Two identical horizontal surfaces, at the same location and same weather data were compared, one partially shaded and the other with no shadings. This simulation was performed for two cartographic positions, one in an equatorial region: Singapore, and the other in a subtropical region: Florianópolis – Brazil. The reduction of incident irradiation (RII) was calculated following the equation (1): RII = 1-(IIwS/IInS) x 100 (1) Where RII is the reduction of incident irradiation (%), IIwS is the incident irradiation with shadings (W/m²); and IInS is the incident irradiation with no shadings (W/m²).
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2.1.4. Shading percentage versus reduction of incident irradiation The following step was used to calculate the relation between the shading percentage and the reduction of incident irradiation percentage in different time basis (hourly, daily, monthly and annually), for the two selected cities: Singapore and Florianópolis – Brazil. With this study, it was possible to compare the seasonality of irradiation reduction and shading profile for each city.
2.2. Phase 2: Validation of shading percentage and reduction of incident irradiation In order to validate the previous results from Ecotect in relation to shading percentage and reduction of incident irradiation over a surface, three studies were carried out: one with a physical model and two computer simulations using two different software packages: PVSyst and a plug-in for SketchUp, the Solar3DBR, developed at Universidade de São Paulo (USP) and presented in a M.Sc. thesis [51]. The experiment was performed using a physical Styrofoam model representing the surface analysed in computer simulations and its immediate surroundings in order to collect irradiance data using a silicon global irradiance sensor (Mini-KLA [57]) on the surface and compare the projection of shadows on the spot between simulation and reality. This experiment was conducted only in Florianopolis - Brazil. Besides the physical experiment, two different software were used to calculate the shading percentage and yield of PV systems under study and results were compared to the results previously presented. The yield of a PV system is the net energy output divided by the nominal power of the installed PV array and represents the number of hours that the PV array would need to operate at its rated power to provide the same energy. The units are hours or kWh/kW, with the latter preferred by the authors because it describes the quantities used to derive the parameter. The yield normalizes the energy produced with respect to the system size; consequently, it is a convenient way to compare the energy produced by PV systems of differing size [58].
2.3. Phase 3: Shading index proposal Based on relations between shading percentage and reduction of incident irradiation in different time basis, a shading index was proposed in order to be used in energetic generation estimations for partially shaded PV systems.
3.
RESULTS
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Energy and Buildings Revised manuscript submitted for publication by C.D. Zomer and R. Rüther in January 2017
3.1. Phase 1: Computer simulation 3.1.1. Shading identification The surface – a flat roof – and its surroundings, modelled using SketchUp and analysed using Ecotect, can be seen in Figure 1. The higher buildings around it can cause shadows in some months per year. Weather data used for simulations are from the U.S. Department of Energy [56] website, in the .epw extension. The source of weather data from Singapore is the International Weather for Energy Calculations (IWEC) of ASHRAE, based on ten years of data between 1983 and 1999, and the source of weather data for Florianópolis is the Solar and Wind Energy Research Assessment project (SWERA), based on ten years of data between 1973 and 2000, which has the support of the National Research Institute Space [24] in providing meteorological data in Brazil and validation of satellite data by ground stations carried out by UFSC. Figure 2 presents the values of horizontal global and diffuse solar irradiation in daily averages for each month for Florianópolis (red circles) and Singapore (blue triangles).
According to IWEC data, the typical horizontal global irradiation is 1,596 kWh/m².year in Singapore, with 68% of diffuse irradiation. According to SWERA data, the same values for Florianópolis are 1,649 kWh/m².year and 44%, respectively. Even showing almost the same annually average of solar irradiation, the cities present different profiles throughout the year, and a distinctively different diffuse fraction, which will have a direct effect on shading. Florianópolis has four well-defined seasons while Singapore is hot and humid during the whole year. Singapore skies are overcast most of the days, and Florianópolis has mostly clear skies, especially during the winter. The shading identification analysed using the “shadow range” tool at Ecotect software, demonstrates the roof fraction shaded in each half-hour, for a specific day of the year. The darkest greys represent the most shaded parts of the surface. Figure 3 presents the shadow range from 8 am to 5 pm at 30-minute intervals for Singapore and Florianópolis. From these analyses, it can be seen that in the higher latitude (Florianópolis) the surface is more shaded than the same surface in the lower latitude (Singapore).
3.1.2. Shading quantification: Shading Percentage (SP) The shading quantification was carried out using shading masks for both cities. Figure 4 shows the shading mask for Singapore (left) and Florianópolis (right). The lighter shades represent a smaller percentage of shaded area and the darkest points, a higher percentage of shaded area. The shading percentage corresponds to an instantaneous measure, directly related to the geometry of the analysed surface, its environment and the sun’s trajectory during the year. In Singapore, there is partial shading on the surface before 9 am in all months of the year and 6
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in the months of May, June, July and August continued to occur up to 2 pm. In Florianopolis, there is partial shading in the early morning hours in every month of the year. Shading is still occurring in the hours during the months of autumn and winter. In the late afternoom the surface is not shaded by its surroundings. The shading masks show the instantaneous shading percentages for each city. The averages of those values from sunrise until the sunset were computed by the Ecotect software and the monthly and annual shading percentages can be seen in Table 1.
The next step was performing calculations using the solar exposure tool. The advantage of this tool is that the shading percentages can be read in a higher resolution: daily and hourly, for an entire year, but with the disadvantage of taking longer time. Figure 5 shows graphs where the x-axis corresponds to the months of the year and the y-axis corresponds to the hour of day, for Singapore and Florianópolis, respectively. The yellow colour represents a lower percentage of shading, transitioning to the blue colour, which represents that the surface is completely shaded or it is night time.
In Florianópolis, the shaded area percentage is always higher in winter months (June, July for South Hemisphere). In Singapore, although there are no well-defined seasons, the shadow behaviour also changed seasonally, with the largest percentage of shading values appearing during summer months (June, July for North Hemisphere). Seeking to compare the results provided by the shading mask and the analysis of sun exposure, the monthly shading percentages of each analysis were compared and the difference in percentage points is presented in Figure 6.
Results from Figure 7 showed that the variation was from 0 up to 4% in Singapore and from 0 up to 8% in Florianopolis on the monthly basis, when both ways to get the shading percentages available on the Ecotect software were compared. On the yearly basis, the difference was smaller: 0.03% for Singapore and 1.62% for Florianopolis. This analysis showed that the annual shading percentage quickly calculated by the shading mask method shows results very close to the results of more detailed analyses (sun exposure with hourly results), unlike what happens with the monthly figures and, therefore, the annual shading percentage can be a most convenient and reliable factor in estimating losses of annual energy generation by partially shaded solar PV systems.
3.1.3. Shading percentage versus Incident irradiation reduction The shading percentage was compared to the incident irradiation reduction in four time bases: hourly, daily, monthly and annually.
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3.1.3.1. Hourly basis In order to compare the shading percentage (shaded area) at a certain time with the percentage of the incident irradiation reduction at that moment in a given area, a day of each month was selected for both cities. For each day, the incident irradiation was calculated on the surface considering the obstacles causing shadows and removing them. Figure 7shows the direct (green line) and the diffuse irradiance (yellow line) available to a horizontal plane, as well the incident solar irradiance in the analysed surface with shadows (red line) and without shadows (blue line), for 13 April in both analysed cities. Also, it shows the shaded area percentage in each hour of the day (grey hatch).
Figure 7 presents a shading percentage of 20% at 12 pm in Singapore and a shading percentage of 40% in Florianopolis at the same time. The cities analysed have opposite characteristics in the ratio between global irradiation and diffuse irradiation; while Singapore showed 71% of diffuse irradiation in its overall composition, Florianopolis showed 31% for the day analysed. Through Figure 8, it is possible to see how shading occurs over the surface for both cities on this specific day, at 12 pm. The hourly percentages of incident irradiation reduction were then calculated and compared with the hourly shaded area percentages for the same day. The results can be seen in Figure 9 for Singapore and Florianopolis. According to Figure 9, although the shading percentage varies from 0 to 26% during the day in Singapore, the losses of irradiance were generally constant (~12%). In Florianópolis, the shading percentage was always higher than the percentage of irradiation reduction, getting closer only in the end of the day. In order to evaluate how this relation occurs during the year, but still on an hourly basis, the same comparison was carried out for each location, but now considering the average of 365 days, which results in typical-day graphs (Figure 10).
In Figure 10, it can be seen that in Singapore the fraction of irradiation reduction is constant over the hours, always around 10%, rising slightly as the shading fraction exceeds 10%. The annual average of shading fraction, in turn, shows a peak of 29% at 8 am and gets closer to zero over time. In Florianópolis, the fraction of irradiation reduction varied from 9% to 24%, and the shading fraction varied from 0% to 39%. On the annual average, even when there is no direct shading on the selected plane, like for example at 5 pm and 6 pm, the simple fact that there are buildings in the surroundings, resulted in reductions of 8% and 9%, that is, the surrounding buildings contributed in reducing the incidence diffuse irradiance. The mismatch between the shading fraction and the fraction of incident irradiation reduction is directly related to changes in solar irradiance throughout the day, as during the early hours 8
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in the morning and in the late afternoon, the incident irradiation is lower than in the middle of the day. In addition, the diffuse fraction of the incident irradiance is larger before 9 am and after 4 pm than in the rest of the day for the two cities analysed. Due to the position of the sun in the sky, and the surrounding buildings, in the early morning hours shading fractions tend to be higher than in the rest of the day. What happens, therefore, is that an area 50% shaded in the early morning will not result in a 50% reduction in incident radiation, because the shading significantly reduces the fraction of direct irradiation, but has much less impact on the fraction related to the diffuse radiation. Note, then, that the hourly shading percentage does not necessarily reflect the percentage of incident irradiation losses on a surface, since the shading fraction is related to a shaded area at a given time and the reduction of incident irradiation is related to the energy lost due to shade. The energy issue related to shading (reduction of incident irradiation - kWh) is therefore more complex than the issue of shading percentage (reduction of irradiation time – hours), because the solar intensity can vary over the hours of the day so much because of both weather conditions (cloud cover distribution), and the position of the sun in the sky (air mass). 3.1.3.2.
Daily basis
The next step was the analysis of the same data considering the average of shading fraction (average of shading fraction of each hour in a day) in each day, and the percentage of incident irradiation reduction in the same day. Figure 11 presents the daily percentage of incident irradiation reduction and the daily shading percentage for all days of the year for Singapore and Florianopolis. When both daily fraction were compared for all days of the year it was possible to conclude: In Singapore: The shading fraction was lower than the fraction of incident irradiation reduction during autumn and winter days for the North Hemisphere, low-latitude site. In these days, the reduction of incident irradiation was on average 10%. During spring and summer days in the North Hemisphere, low-latitude site, the shading percentage exceeded the percentage of incident irradiation reduction. On these days, the reduction was on average 14%. In Florianopolis: In the South Hemisphere, medium-latitude site, the spring and summer days presented similar fractions between shading and incident irradiation reduction, with averages of 11% e 12%, respectively. Autumn and winter days presented higher differences between shading fraction and incident irradiation reduction, and the shading fraction was always higher than the incident irradiation reduction 36% and 22%, respectively). When both cities are compared, one can see that the shading fraction is relatively uniform, following a continuous curve throughout the year, which is due to the fact that shading is an effect related purely to solar geometry. The reduction of the incident irradiation proved to be quite variable over the days, with greater intensity in Florianopolis. This larger variation in 9
Energy and Buildings Revised manuscript submitted for publication by C.D. Zomer and R. Rüther in January 2017
Florianópolis is related to the overall irradiation characteristics (composition between direct and diffuse irradiation), as previous shown in Figure 2.
3.1.3.3.
Monthly basis
From the daily values, monthly total values corresponding to the average monthly shaded area fraction, and the incident irradiation reduction fraction, were also compared and the result is shown in Figure 12 for Singapore and Florianopolis.
The same conclusions already arrived at through the analyses on a daily basis have been extended in the analysis on a monthly basis. Furthermore, it can be concluded that in both locations, the minimum monthly incident irradiation reduction was higher than the lowest monthly shading fraction. In Singapore, there are months with shading fractions lower than 5%, with an incident irradiation reduction of about 10%. Also, there are months with shading fractions larger than 22%, with reductions in incident radiation of only 14%. In Florianópolis, December and January were the months with the lowest percentage of shading (7% and 8%, respectively). During these months the reduction in incident irradiation was 9% for both months. During the winter months, the shading fraction was above 40%, and the reduction percentage of the incident radiation was approximately 25%. These simulations showed that, for the simple fact that there are buildings in the surroundings, even if they are not shadowing the analysed surface causing loss of direct sunlight, there will always be a loss of incident irradiation, i.e., there will be a part of diffuse irradiation blocked. As Singapore has a higher fraction of diffuse irradiation than Florianópolis, the presence of surroundings impacted more the reduction of incident radiation than in Florianópolis. Moreover, the difference in latitude between the two cities is another factor that has influenced these results. Although both cities are located within the range of low latitudes, the sun's relative position in the sky is different for each of them. In Singapore, the solar position remains higher than in Florianópolis throughout the year; so for this location, the shading fraction caused by the surroundings on the surface is lower in all months of the year.
3.1.3.4.
Annual basis
Finally, the shading fraction and the reduction of incident radiation calculated on the annual basis can be seen in Figure 13. Previous comparisons were based on the results of solar exposure tool only. On the annual basis, the shading percentage from shading mask was also included in the analysis. When the annual values were compared, it was found that for Singapore, a simple shading simulation would be sufficient to provide the incident radiation reduction with an error margin of only two points considering the solar exposure tool and only one point using the shading mask tool. In Florianópolis, the margin of error would be slightely higher, with a difference of seven or six percentage points, respectively. 10
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The simulations for these two cities showed that Singapore, closer to the Equator and with a larger fraction of diffuse radiation, showed greater coincidence between the fraction of shading and the reduction on incident irradiation than Florianópolis, which is 27° South of the Equator. Also, it was found that averaging the hourly shading percentage values for a typical day in each analysed city (Figure 10) would also lead to the same values presented in Figure 13, what reinforces the convinience of adopting only the annual shading percentage from shading masks.
3.2. Phase 2: Validation of shading percentage and reduction of incident irradiation In order to validate the results of the shading fraction (shadowed area) and incident irradiation reduction, three experiments were carried out: one physical model in Florianopolis and two computer simulations using two different software packages, PVsyst and the SketchUp plug-in SOLAR3DBR, developed and presented in the M.Sc. thesis of Emerson Melo [51] . 3.2.1. Physical model The styrofoam physical model and the irradiation sensor (Mini KLA) can be seen in Figure 14, and some comparisons between the real model situation and the Ecotect simulation can be seen in Figure 15.
Based on the images presented, it is possible to see a perfect relation between the Ecotect estimatives of shadow and the real situation. From the irradiation sensor data, a graph was generated showing the irradiance with and without the surroundings, i.e., with and without shadows (Figure 16). This graph also shows the shading percentage calculated with Ecotect, as well as the fraction of incident irradiation reduction at each time of the day.
The daily irradiation measured by the irradiation sensor was 3.78 kWh/day without surroundings and 2.82 kWh/day with surroundings. During the day analysed, the incident irradiation reduction was 25% and the shading percentage was 37%. This study showed that, as in the computer simulations, the shading fraction does not directly correlate to the incident irradiation reduction. While the shading fraction is related to the solar geometry and position of the analysed surface with their surroundings and latitude, the incident irradiation reduction depends on climatic conditions, as characteristic of direct and diffuse irradiation and clouds in the instant measured. In order to minimize the influence of clouds, this study was carried out on a clear day.
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3.2.2. PVSYST and Solar3DBR In order to validate the incident irradiation reduction results obtained from Ecotect, the same analyses were carried out with both PVSYST and the Solar3DBR SketchUp plug-in. For PVSYST, a 3D model was simulated, identical to the 3D model analysed with the Ecotect software. The simulations were carried out for both cities, Singapore and Florianópolis. On the SketchUp software, the Solar3DBR plug-in was installed and the same 3D model was used on the simulations. Both software were fed with the same irradiation data which was used on the Ecotect simulations, i.e, from IWEC for Singapore and from SWERA for Florianopolis. Both files are based on ten years of data. The comparison between the incident irradiation losses due shadings from the surroundings from the three computer software packages were then compared, and are shown in Figure 17 for Singapore and Florianópolis.
The comparison between monthly and annual results from the three software tools presented similar curves for both cities, with highest differences during winter months in Florianópolis (9 percentage points). On the annual basis, the three software tools presented very similar values (10, 11 and 12 %) for Singapore, and three identical values for Florianopolis (17%). From this analysis, the Ecotect study was considered validated, and it was confirmed that the annual value tends to be the most reliable when compared to other time bases.
3.3. Phase 3: Shading index proposal From studies with computer simulations for the two cities, it can be concluded that the percentage of shaded area for a given time is responsible for the reduction in incident irradiation; however, this effect does not occur at the same rate (for example, due to the contribution of scattered radiation, an area 50% shaded at a given time does not represent a 50% reduction of incident irradiation at that point on the surface), and does not occur at all times of the day with the same intensity. When the relationship between the percentages of daily shading with the percentage of incident irradiation reduction was analysed, a few days showed greater coincidence than others. This fact invalidates adopting the shading fraction of a specific day in the estimation of PV generation. When the monthly basis was compared, one can also say that in most months the shading fraction was different from the incident irradiation reduction fraction: at times it was superior, and at other times it was inferior. Therefore, energy generation forecasts for BIPV based on calculations from shaded area fractions on a monthly basis will involve large errors. Finally, values were compared on an annual basis. On the annual basis, the percentage point differences were very small for Singapore (2%) and slightly larger for Florianópolis (7%); but in any case much lower than the differences found in the winter months (+ 20%) for this location, for example. Added to this, the fact that when comparing percentage reduction of incident irradiation from different computational tools, the annual percentage was identical or very close for both of the cities analysed. 12
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From the results presented in this work, it can be said that estimating the energy generation of a PV system based on average shaded area in the course of a year is the option that will results in the smallest error. Moreover, as PV generation forecasting studies are often estimated on an annual basis, using the annual shading fraction is the most simple, fast and convenient way to obtain energy generation values. Therefore, it is suggested that the annual shading fraction be considered in the power generation prediction calculations as the shading ratio, as can be seen in equation 2. SPANNUAL ~ RII ANNUAL = SI (2) Where: SPANNUAL = Annual shading percentage (%) RIIANNUAL = Annual reduction of incident irradiation (%) SI = Shading Index (%) The simplified flowchart presented in Figure 18 ilustrates the methodology used in this study.
4. APPLICATION OF SHADING INDEX ON COMPUTER SIMULATIONS FOR ENERGY ESTIMATIVE In order to validate the shading index estimated by computer simulations, the theoretical case studies for Singapore and Florianópolis presented in Phase 1 were tested. Computer simulations with and without environment, with the same electric design were carried. Theoretical studies were simulated for flat surfaces, i.e. extracting the environment, and the own edification shading fraction was set to zero for both cities. In this case, the adopted shading index coincided with the annual shading percentage of each surface. The simulation was performed using the software PVsyst. Table 2 shows the previously calculated values of incident radiation reduction percentage, the shading rate and the energy yield estimates for the PV generator with and without surroundings. Through the cases analysed, the percentage reduction in estimated yield due to the existence of partial shading showed an absolute deviation of -1% for Singapore and -6% for Florianópolis. Therefore, it is concluded that it is reasonable to perform a shading study using the software tools presented here, disregarding the shading on hourly basis and multiplying the resulting annual shading index by the software calculated yield as a way to estimate the energy generation in partially shaded PV systems. For both locations, the results are likely to be slightly conservative.
5. CONCLUSIONS Making use of established software tools typically used by architects and designers, such as three-dimensional models and shading masks, this paper aimed to demonstrate how the partial shading on BIPV systems can be translated into energy losses in a simple and direct way. The main objective was to propose a method to quantify the influence of partial shading on the operational performance of solar generators integrated onto buildings in low-latitude, tropical regions. Although this study has focused on the effect of shading on the performance 13
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of solar PV generators integrated on buildings (BIPV), the method developed here can also be applied to any type of PV solar generator. The method presented consists in simulating and analysing the influence of shading on a theoretical horizontal surface partially shaded by the surroundings, which was carried out for two sunbelt cities, Singapore (latitude 1,35ºN) and Florianópolis (27.48 ° S). The computer simulation was performed for different time bases, to compare how the shading percentage of the surface area relates to the percentage energy loss during the same period. Validation was carried out through an experiment with a real physical model and through comparisons of results from other computational software packages: PVSYST and Solar3DBR. For both cities, it was observed that in smaller time bases, such as hours, days and months, the percentage of shaded area and the percentage of energy losses are not much related. However, by expanding the time basis for a year, the percentage became very similar, reaching almost a perfect match for the simulation carried out for Singapore. Therefore, as a result of this research, it can be proposed that estimating the energy generation of a PV system based on shaded average area in the course of a year is the option that will bring the lowest percentage error, compared to the shaded area on a smaller time basis. The great advantage of this finding is that the achievement of the annual shading percentage resulting from a quick and easy procedure using the Ecotect software, may be calculated either through a shading mask or through sun exposure analysis, once the building and its surroundings are modelled, and the site climate file is loaded. A further advantage is that because PV generation forecasting studies are often estimated on an annual basis, adopting the annual shading percentage is the simplest, fast and more convenient way of obtaining energy generation estimatives. So, it is suggested that the annual shading percentage be considered in the calculation of prediction of energy generation as the shading index. The shading index was validated in computer simulations that overlook the shading in the calculation process. The theoretical case studies for Singapore and Florianópolis were used as an object of study, and the PVSYST software was used to perform energy simulations with and without surroundings. The results showed that the percentage of reduction of the estimated energy yield was very similar to the shading index. Therefore, it is concluded that it is reasonable to perform a shading study using the software tools presented here, disregarding the shading on hourly basis and multiplying the resulting annual shading index by the software calculated yield as a way to estimate the energy generation in partially shaded PV systems. For both locations, the results are likely to be slightly conservative. ACKNOWLEDGEMENTS The authors acknowledge the financial support of the Brazilian Research Council / CNPq and the ANEEL sponsored R&D Project with Tractebel Energia (now Engie) under call 013/2011. REFERENCES [1] M. Osborne, IHS remains cautions on PV market demand growth, in: London, 2015.
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[2] EPIA, Global Market Outlook for Photovoltaics 2014-2018, in: European Photovoltaic Industry Association, Brussels, Belgium,, 2014. [3] B. Beetz, IHS increases 2015 PV forecast to 59 GW, 2016 to 65 GW, in: PV Magazine Photovoltaic Markets & Technology, 2015. [4] GTM, Global PV Demand Outlook 2015-2020: Exploring Risk in Downstream Solar Markets, in: GTM Research, 2015. [5] D. Prasad, M. Snow, Designing with solar power - A source book for building integration photovoltaics (BiPV), Images Publishing, Australia, 2002. [6] M.S. ElSayed, Optimizing thermal performance of building-integrated photovoltaics for upgrading informal urbanization, Energy and Buildings, 116 (2016) 232-248. [7] IEA, Solar Energy and Architecture, IEA SHC Task 41 Solar Energy and Architecture, Annex Plan (2008) 16. [8] F. Frontini, M. Manfren, L.C. Tagliabue, A Case Study of Solar Technologies Adoption: Criteria for BIPV Integration in Sensitive Built Environment, Energy Procedia, 30 (2012) 1006-1015. [9] IEA, Solar Energy Systems in Architecture: Integration Criteria and Guidelines, IEA SHC Task 41 Solar energy and Architecture, T.41.A.2 (2012) 228. [10] IEA, Solar Design of Buildings for Architects: Review of Solar Design Tools, IEA SHC Task 41: Solar Energy and Architecture, T.41.B.3 (2012) 115. [11] C.J. Willmott, On the climatic optimization of the tilt and azimuth of flat-plate solar collectors, Solar Energy, 28 (3) (1982) 205-216. [12] H.M.S. Hussein, G.E. Ahmad, M.A. Mohamad, Optimization of operational and design parameters of plane reflector-tilted flat plate solar collector systems, Energy, 25 (6) (2000) 529-542. [13] V. Badescu, Optimum size and structure for solar energy collection systems, Energy, 31 (12) (2006) 1819-1835. [14] H. Yang, L. Lu, The Optimum Tilt Angles and Orientations of PV Claddings for Building-Integrated Photovoltaic (BIPV) Applications, Journal of Solar Energy Engineering, 129 (2) (2007) 253-255. [15] M. Hummon, P. Denholm, R. Margolis, Impact of photovoltaic orientation on its relative economic value in wholesale energy markets, Progress in Photovoltaics: Research and Applications, 2198 (2012). [16] C.D. Zomer, M.R. Costa, A. Nobre, R. Rüther, Performance compromises of building-integrated and building-applied photovoltaics (BIPV and BAPV) in Brazilian airports, Energy and Buildings, 66 (0) (2013) 607-615. [17] C. Zomer, A. Nobre, P. Cassatella, T. Reindl, R. Rüther, The balance between aesthetics and performance in building-integrated photovoltaics in the tropics, Progress in Photovoltaics: Research and Applications, 22 (7) (2014) 744-756. [18] J. Urbanetz, C.D. Zomer, R. Rüther, Compromises between form and function in grid-connected, building-integrated photovoltaics (BIPV) at low-latitude sites, Building and Environment, 46 (10) (2011) 2107-2113. [19] C. Rus-Casas, J.D. Aguilar, P. Rodrigo, F. Almonacid, P.J. Pérez-Higueras, Classification of methods for annual energy harvesting calculations of photovoltaic generators, Energy 15
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[35] R. Levinson, H. Akbari, M. Pomerantz, S. Gupta, Solar access of residential rooftops in four California cities, Solar Energy, 83 (12) (2009) 2120-2135. [36] D. Roche, H. Outhred, R.J. Kaye, Analysis and control of mismatch power loss in photovoltaic arrays, Progress in Photovoltaics: Research and Applications, 3 (2) (1995) 115-127. [37] M. Radike, J. Summhammer, Electrical and Shading Power Losses of Decorative PV Front Contact Patterns, Progress in Photovoltaics: Research and Applications, 7 (1999) 399-407. [38] R. Giral, C.A. Ramos-Paja, D. Gonzalez, J. Calvente, A. Cid-Pastor, L. Martinez-Salamero, Minimizing the effects of shadowing in a PV module by means of active voltage sharing, in: IEEE International Conference on Industrial Technology Viña del Mar, Valparaiso, Chile, 2010, pp. 943-948. [39] V. Quaschning, R. Hanitsch, Numeral simulation of photovoltaic generators with shaded cells, in: 30th Universities Power Engineering Conference, Greenwich, London, 1995, pp. 583-586. [40] H. Kawamura, K. Naka, N. Yonekura, S. Yamanaka, H. Kawamura, H. Ohno, K. Naito, Simulation of I-V characteristics of a PV module with shaded PV cells, Solar Energy Materials & Solar Cells, 75 (2003) 613-621. [41] W. Hermann, W. Wiesner, Modelling of PV modules - the effects of noum-uniform irradiance on performance measurements with solar simulators, in: 16th European Photovoltaic Solar Energy Conference and Exhibition, Glasgow, 2000, pp. 2338-2341. [42] M.C. Alonso-García, J.M. Ruíz, Analysis and modelling the reverse characteristic of photovoltaic cells, Solar Energy Materials and Solar Cells, 90 (7–8) (2006) 1105-1120. [43] C. Gonzalez, Photovoltaic array loss mechanisms, Solar Cells, 18 (1986) 373-382. [44] C. Zomer, A. Nobre, P. Cassatella, T. Reindl, R. Rüther, The balance between aesthetics and performance in building-integrated photovoltaics in the tropics, Progress in Photovoltaics: Research and Applications, 22 (7) (2013) 744-756. [45] A. Iliceto, R. Vigotti, The largest PV installation in Europe: Perspectives of multimegawatt PV, Renewable Energy, 15 (1–4) (1998) 48-53. [46] S.A. Omer, R. Wilson, S.B. Riffat, Monitoring results of two examples of building integrated PV (BIPV) systems in the UK, Renewable Energy, 28 (9) (2003) 1387-1399. [47] M. Seyedmahmoudian, B. Horan, T.K. Soon, R. Rahmani, A.M. Than Oo, S. Mekhilef, A. Stojcevski, State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems – A review, Renewable and Sustainable Energy Reviews, 64 (2016) 435-455. [48] Autodesk, Ecotect Analysis, in, Autodesk, 2011. [49] PVsyst, PVsyst Photovoltaic Software, in, 2013. [50] Trimble, SketchUp Pro, in, 2012. [51] E.G.d. Melo, Geração Solar Fotovoltaica: estimativa do fator de sombreamento e irradiação em modelos tridimensionais de edificações, M. Sc. Thesis, Escola Politécnica, Universidade de São Paulo, 2012. [52] G.M.d. Silva, A.S.A.C. Diniz, Análise da contribuição da micro-geração distribuída com energia solar fotovoltaica para a criação de um campus universitário verde na PUCMinas, in: 17
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VI Congresso Nacional de Engenharia Mecânica (CONEM), ABCM, Campina Grande/Paraíba - Brazil, 2010. [53] R. Herrero, E. Melo, S. Shimura, C. Biasi, T. Costa, R. Simplicio, J. Grimonni, M. Zuffo, Comparing energy yield simulation in grid-connected 450 kWp parking-integrated photovoltaics - Case study: Villa Lobos Project in São Paulo, Brazil, in: 29th European Photovoltaic Solar Energy Conference and Exhibition, Amsterdam, the Netherlands, 2014, pp. 2612-2619. [54] C. Zomer, A. Nobre, T. Reindl, R. Rüther, Shading analysis for rooftop BIPV embedded in a high-density environment: A case study in Singapore, Energy and Buildings, 121 (2016) 159-164. [55] C. Zomer, A. Nobre, D. Yang, T. Reindl, R. Rüther, Performance analysis for BIPV in high-rise, high-density cities: a case study in Singapore, in: World Conference on Photovoltaic Energy Conversion, WCPEC-6, Kyoto, Japan, 2014. [56] U.S.Department_of_Energy, EnergyPlus Energy Simulation Software: Weather Data, in, Washington DC, 2014. [57] I.M.T. GmbH, Mini-KLA - PV I-V Curve Analyser, in, 2014. [58] B. Marion, J. Adelstein, K. Boyle, H. Hayden, B. Hammond, T. Fletcher, B. Canada, D. Narang, D. shugar, H. Wenger, A. Kimber, L. Mitchell, G. Rich, T. Townsend, Performance Parameters for Grid-Connected PV Systems, in: 31st IEEE Photovoltaics Specialists Conference and Exhibition, NREL/CP-520-37358, Lake Buena Vista, Florida, 2005, pp. 1601-1606.
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Figure 1. 3D model for computer simulation, highlighting the surface analysed in this work.
Figure 2. Typical horizontal global and diffuse irradiation in daily averages for each month for Florianópolis and Singapore based in ten years of data .
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Figure 3. Shadow range analysis for Singapore (above) and Florianópolis (below) from 8 am to 5 pm at 30-minute intervals obtained using Ecotect software.
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Figure 4. Shading mask for Singapore (left) and Florianópolis (right) obtained using Ecotect software.
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Figure 5. Average of hourly shading percentage for each month in Singapore (above) and Florianópolis (below).
Figure 6. Difference between the shaded area percentage values obtained from the shading mask and from sun exposure analysis for Singapore (above) and Florianopolis (below), both from Ecotect software.
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Figure 7. Shading analysis for a specific day (April 13) in Singapore (left) and Florianopolis (right) in a partially shaded surface.
Figure 8. Shading analysis for Singapore (left) and Florianópolis (right) on April 13, at 12 pm.
Figure 9. Incident irradiance with and without shadings on April 13 in Singapore (left) and Florianópolis (right) and hourly percentages of incident irradiation reduction and shaded area.
Figure 10. Incident irradiance with and without shadings on a typical day (hourly average during one year) in 23
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Singapore (left) and Florianópolis (right), and hourly percentages of incident irradiation reduction and shaded area.
Figure 11. Percentage of incident irradiation reduction and shading fraction on a daily basis in Singapore (left) and Florianopolis (right).
Figure 12. Shading fraction and fraction of incident irradiation reduction on a monthly basis in Singapore (left) and Florianopolis (right).
Figure 13. Percentage of incident irradiation reduction and shading percentage from solar exposure and shading mask tools on an annual basis in Singapore and Florianópolis.
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Figure 14. Styrofoam physical model and irradiation sensor used to validate the computer simulations.
Figure 15. Comparison between Ecotect simulation and real situation for shadows projected on the styrofoam physical model.
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Figure 16. Incident irradiance with and without surroundings, shading percentage from Ecotect and percentage of incident irradiation reduction for Florianopolis, on June 2nd, 2014.
Figure 17. Simulations of incident irradiation reduction in Singapore (left) and Florianopolis (right) using Ecotect, PVSYST and the Solar3DBR SketchUp plug-in.
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Figure 18. Simplified flowchart to obtain the shading index.
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Table 1. Monthly and annual shading percentage over the selected surface for Singapore and Florianópolis during day hours using the shading mask tool from Ecotect.
Shading Percentage (SP) Month Singapore January 5% February 2% March 3% April 15% May 24% June 25% July 22% August 14% September 2% October 3% November 6% December 5% Summer 4% Winter 24% Annual 10%
Florianópolis 7% 13% 23% 34% 40% 41% 38% 32% 20% 11% 7% 7% 9% 40% 23%
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Table 2. Yield estimation calculated with the PVsyst software.
Incident irradiation reduction Singapore 11% Florianópolis 17%
Yield with no Shading surroundings Index (kWh/kWp.year) 9% 1,233 24% 1,252
Yield with surroundings (kWh/kWp.year) 1,133 1,021
Yield reduction 8% 18%
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S0378-7788(16)31521-3 http://dx.doi.org/doi:10.1016/j.enbuild.2017.02.042 ENB 7403
To appear in:
ENB
Received date: Revised date: Accepted date:
11-11-2016 18-1-2017 15-2-2017
¨ Please cite this article as: Clarissa ZOMER, Ricardo RUTHER, Simplified method for shading-loss analysis in BIPV systems – Part 1: Theoretical study, Energy and Buildings http://dx.doi.org/10.1016/j.enbuild.2017.02.042 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Energy and Buildings Revised manuscript submitted for publication by C.D. Zomer and R. Rüther in January 2017
Simplified method for shading-loss analysis in BIPV systems – Part 1: Theoretical study Clarissa ZOMER and Ricardo RÜTHER Universidade Federal de Santa Catarina (UFSC), Caixa Postal 476, 88040-900, Florianópolis – SC, Brazil * Email: [email protected], Tel: +55 (48) 3721 4598
Highlights
Demonstration of how shadings on BIPV systems can be translated into energy losses. Comparison of shading percentages and incident irradiation reductions. Validation through a physical experiment and three computer software packages. Annual shading percentage can be used in energy estimations of BIPV systems.
Abstract This paper proposes a simplified method to determine an index to quantify the influence of partial shadings on the performance of BIPV systems based on the relation between the shading percentages and the reduction of the incident irradiation on a given surface. The research is divided in two papers: Part 1: Theoretical study and Part 2: Application in case studies. Part 1 consists in identifying and quantifying the shading on a surface, and relates the fraction of shaded area with the percentage of incident irradiation reduction during the same period, in order to propose a shading index (SI) that represents the energy losses due to shadings on PV systems. The method was developed for a theoretical shaded case study simulated in two cities located at low latitude, tropical regions: Singapore (1.35°N) and Florianópolis-Brazil (27.48°S). Results showed that the shading percentage on the analysed surface on an annual basis is closer to the percentage of incident irradiation reduction at same period than when these values are compared on other time bases, as hourly, daily or monthly. Therefore, in this case, the annual percentage of shading can be adopted as the SI. SI was validated using different computer software packages and it was proved to be a convenient way of estimation the PV generation of similar cases of partially shaded PV systems, that could be used even before the PV electrical desing has been done. Keywords: Photovoltaic generation; building-integrated photovoltaic systems (BIPV); shading analysis for PV systems.
1. INTRODUCTION In 2015, some 60 GWp of new photovoltaic (PV) systems were installed globally [1] 1
Energy and Buildings Revised manuscript submitted for publication by C.D. Zomer and R. Rüther in January 2017
bringing the total worldwide installed capacity to nearly 250 GWp, with Asia leading the wave of new installations [2]. Information Handling Services Inc. (IHS) has raised its global solar PV forecasts for 2016 to 65 GWp, and over 70 GWp is expected to be installed in 2019 [3]. By 2020, the cumulative global market for solar PV is expected to triple to around 700 GWp [4]. In addition to being a renewable and pollution-free energy generation technology with no moving parts, PV modules can also be integrated into buildings as BIPV systems, adding aesthetic value [5]. When installed in an optimised way, BIPV systems can reduce heat transferred through the envelope and reduce cooling load components decreasing the CO2 emissions [6]. Apart from some façade installations, the rooftop segment represented more than 23 GWp of total installations in 2015, with projections of more than 35 GWp to be installed by 2018 [2]. Under all scenarios, PV will continue to increase its share of the energy mix around the world [2], and, in this context, a great number of new BIPV systems will be installed in urban environments in the coming years. Nowadays, it is already possible to see very advanced BIPV systems installed with high architectural quality, but they are still not large in numbers [7]. Architects and urban planners are becoming aware of the importance of high-quality BIPV systems, therefore, with the view to regulate the adoption of the PV modules in buildings and to define a suitable design approach when using the solar technology in a sensitive urban environment (where architectural, historical or cultural value is considerable), a multidisciplinary project was launched in Canton Ticino, Switzerland in order to define a set of criteria and recommendations for such sensible areas and guarantee a proper balance, or compromise, between technical and aesthetic requirements [8]. The International Energy Agency (IEA) encourages and facilitates the adoption of PV modules by architects and engineers as a design element already at an initial design stage, by presenting guidelines and inspiring them by giving good examples of high quality architectural integration [9, 10]. Usually, BIPV system design follows classical PV system design practices, with tilt angles matching a location’s latitude and orientation towards the opposite hemisphere in which the site is located in order to maximise energy generation [11-15]. While there exists clearly a technical possibility for the integration of PV and solar thermal on a building façade, most of the potential is intrinsically related to intervention in the roof, especially in sensitive urban environment [8]. Although maximisation of yield and performance ratio (PR) represent a better return of investment, the differences between an optimal PV system and a non-optimal PV system can be a couple of percentage points in some cases [16]. Challenges such as partial shading and non-optimal tilt and azimuthal deviations are common in BIPV systems; therefore it is important to be aware of the consequences on their performance. We have previously shown [17] that even with a non-optimal combination of azimuthal deviations and tilt angles, some PV systems can perform better than PV systems installed in more ideal conditions (according to the common practices), as long as the PV systems have been well designed, with a suitable PV string electrical configuration [17, 18]. Designers and building owners should be aware of the consequences in terms of energy generation of each architectural choice. Knowledge on the concurrent, and sometimes conflicting, consequences between the way in which modules are installed and the associated energy generation then 2
Energy and Buildings Revised manuscript submitted for publication by C.D. Zomer and R. Rüther in January 2017
becomes of technical, scientific, as well as of economic interest [18]. Estimating the energy provided by PV systems is important in order to analyse their economic viability and supervise their operation. However, this is not trivial; there are a lot of methods for PV energy calculation [19] and a lot of scientific studies showing different methods [19-22]. Sometimes they require some inputs difficult to obtain [23-25] and the method can not be easily adopted. One of the most difficult variables to consider in estimations is the energy losses caused by partial shadings and BIPV systems usually are strongly affected by complex and dynamic shadings, especially when located in dense urban environments [26, 27]. A detailed shading analysis can assist on the design phase of a PV system; can contribute to its optimisation, by putting shaded modules together in the same string; and also allow to a more precise estimation of PV generation, avoiding oversizing and undersizing of a PV system [28]. In addition, it doesn’t only enhance the yearly output of the system but also allows a longer lifetime for the system since it avoids mismatch losses by gathering modules within a defined range of irradiation under the same MPPT [26]. A shading mask can immediately show for how long and for which extent a surface is shaded [29]; therefore it can be used to help in a number of design decisions. The shadings on PV systems and their impacts on energy generation has been extensely studied as the partial shading was identified as the main cause of yield and performance ratio (PR) reductions in grid connected PV systems [30-38]. Different models can be found in the literature and the most of them are based on numerical algoritms or based on very detailed electrical configurations [26, 39-47]. In order to visualize and/or to quantify the shadings, it is possible to analyse three-dimensional models using softwares like Ecotect [48], PVSyst [49] and Sketch Up [50]. A plug in for SketchUp (Solar 3DBR) was developed by [51] that also estimates the shadings from a 3D model. Some studies based on these softwares can be found at [26, 44, 52-55]. Considering the state of the art in this field, the main focus of this paper is the evaluation and estimation of the energy generation of partially shaded BIPV systems in a most simple way, that could be used even before the PV electrical desing has been done. The paper proposes a simplified method based on the relation between the shading percentage and the reduction of the incident irradiation on a given surface to determine an index that quantifies the partial shading influence on PV performance in BIPV systems.
2. METHOD The proposed method is divided in the following phases:
Phase 1: Computer simulation for 3D model inserted in two locations: Singapore, 1.35ºN; 103.98ºE and Florianópolis – Brazil, 27.48ºS; 48.5ºW. The first phase is divided in: a) shading identification; b) shading quantification (shading percentage (%) – SP); c) reduction of incident irradiation (RII); d) relation between SP and RII (%). Phase 2: Validation of of shading percentage and reduction of incident irradiation using a physical model and different computer software. 3
Energy and Buildings Revised manuscript submitted for publication by C.D. Zomer and R. Rüther in January 2017
Phase 3: Shading Index proposal.
2.1. Phase 1: Computer simulation 2.1.1. Shading identification After the visual identification and the neighbouring data collecting, both the PV system and the environment were modelled in 3D using the SketchUp [50] software. All existing elements around the PV system were measured (in terms of height and distance) and geographically located in relation to the object of study. Through the 3D model and the shadow tool of SketchUp, the shadings over the surfaces could be visualized.
2.1.2. Shading quantification: Shading percentage (SP) The 3D model developed in SketchUp in the previous phase was then imported to the Ecotect software [48] to perform the shading quantification. Quantification of shading percentage in each surface was based on two different tools from the Ecotect software: shading masks and sun exposure calculations; and was calculated for different time bases: hourly, daily, monthly, and annual. The shading percentage represents the fraction of shadowed area in a given time. Therefore, the hourly shading percentage represents the percentage of shadowed area in that moment (i.e. at 8am or at 2 pm, for instance). The daily shading percentage represents the average of every hourly shading percentages, from the sunrises until the sunset of each day. That is why in summer days the average will takes into account more hours than in winter days. The monthly shading percentage is the average of every daily shading percentages inside that month. Finally, the annual shading percentage is the average of every monthly shading percentages. The 3D model with its environment was then inserted in the correct geographical orientation and with a weather data from U.S. Department of Energy [56].
2.1.3. Reduction of incident irradiation (RII) In order to compare how a partial shading can influence the irradiation over a selected surface, a simulation using the software Ecotect was carried out. Two identical horizontal surfaces, at the same location and same weather data were compared, one partially shaded and the other with no shadings. This simulation was performed for two cartographic positions, one in an equatorial region: Singapore, and the other in a subtropical region: Florianópolis – Brazil. The reduction of incident irradiation (RII) was calculated following the equation (1): RII = 1-(IIwS/IInS) x 100 (1) Where RII is the reduction of incident irradiation (%), IIwS is the incident irradiation with shadings (W/m²); and IInS is the incident irradiation with no shadings (W/m²).
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2.1.4. Shading percentage versus reduction of incident irradiation The following step was used to calculate the relation between the shading percentage and the reduction of incident irradiation percentage in different time basis (hourly, daily, monthly and annually), for the two selected cities: Singapore and Florianópolis – Brazil. With this study, it was possible to compare the seasonality of irradiation reduction and shading profile for each city.
2.2. Phase 2: Validation of shading percentage and reduction of incident irradiation In order to validate the previous results from Ecotect in relation to shading percentage and reduction of incident irradiation over a surface, three studies were carried out: one with a physical model and two computer simulations using two different software packages: PVSyst and a plug-in for SketchUp, the Solar3DBR, developed at Universidade de São Paulo (USP) and presented in a M.Sc. thesis [51]. The experiment was performed using a physical Styrofoam model representing the surface analysed in computer simulations and its immediate surroundings in order to collect irradiance data using a silicon global irradiance sensor (Mini-KLA [57]) on the surface and compare the projection of shadows on the spot between simulation and reality. This experiment was conducted only in Florianopolis - Brazil. Besides the physical experiment, two different software were used to calculate the shading percentage and yield of PV systems under study and results were compared to the results previously presented. The yield of a PV system is the net energy output divided by the nominal power of the installed PV array and represents the number of hours that the PV array would need to operate at its rated power to provide the same energy. The units are hours or kWh/kW, with the latter preferred by the authors because it describes the quantities used to derive the parameter. The yield normalizes the energy produced with respect to the system size; consequently, it is a convenient way to compare the energy produced by PV systems of differing size [58].
2.3. Phase 3: Shading index proposal Based on relations between shading percentage and reduction of incident irradiation in different time basis, a shading index was proposed in order to be used in energetic generation estimations for partially shaded PV systems.
3.
RESULTS
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3.1. Phase 1: Computer simulation 3.1.1. Shading identification The surface – a flat roof – and its surroundings, modelled using SketchUp and analysed using Ecotect, can be seen in Figure 1. The higher buildings around it can cause shadows in some months per year. Weather data used for simulations are from the U.S. Department of Energy [56] website, in the .epw extension. The source of weather data from Singapore is the International Weather for Energy Calculations (IWEC) of ASHRAE, based on ten years of data between 1983 and 1999, and the source of weather data for Florianópolis is the Solar and Wind Energy Research Assessment project (SWERA), based on ten years of data between 1973 and 2000, which has the support of the National Research Institute Space [24] in providing meteorological data in Brazil and validation of satellite data by ground stations carried out by UFSC. Figure 2 presents the values of horizontal global and diffuse solar irradiation in daily averages for each month for Florianópolis (red circles) and Singapore (blue triangles).
According to IWEC data, the typical horizontal global irradiation is 1,596 kWh/m².year in Singapore, with 68% of diffuse irradiation. According to SWERA data, the same values for Florianópolis are 1,649 kWh/m².year and 44%, respectively. Even showing almost the same annually average of solar irradiation, the cities present different profiles throughout the year, and a distinctively different diffuse fraction, which will have a direct effect on shading. Florianópolis has four well-defined seasons while Singapore is hot and humid during the whole year. Singapore skies are overcast most of the days, and Florianópolis has mostly clear skies, especially during the winter. The shading identification analysed using the “shadow range” tool at Ecotect software, demonstrates the roof fraction shaded in each half-hour, for a specific day of the year. The darkest greys represent the most shaded parts of the surface. Figure 3 presents the shadow range from 8 am to 5 pm at 30-minute intervals for Singapore and Florianópolis. From these analyses, it can be seen that in the higher latitude (Florianópolis) the surface is more shaded than the same surface in the lower latitude (Singapore).
3.1.2. Shading quantification: Shading Percentage (SP) The shading quantification was carried out using shading masks for both cities. Figure 4 shows the shading mask for Singapore (left) and Florianópolis (right). The lighter shades represent a smaller percentage of shaded area and the darkest points, a higher percentage of shaded area. The shading percentage corresponds to an instantaneous measure, directly related to the geometry of the analysed surface, its environment and the sun’s trajectory during the year. In Singapore, there is partial shading on the surface before 9 am in all months of the year and 6
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in the months of May, June, July and August continued to occur up to 2 pm. In Florianopolis, there is partial shading in the early morning hours in every month of the year. Shading is still occurring in the hours during the months of autumn and winter. In the late afternoom the surface is not shaded by its surroundings. The shading masks show the instantaneous shading percentages for each city. The averages of those values from sunrise until the sunset were computed by the Ecotect software and the monthly and annual shading percentages can be seen in Table 1.
The next step was performing calculations using the solar exposure tool. The advantage of this tool is that the shading percentages can be read in a higher resolution: daily and hourly, for an entire year, but with the disadvantage of taking longer time. Figure 5 shows graphs where the x-axis corresponds to the months of the year and the y-axis corresponds to the hour of day, for Singapore and Florianópolis, respectively. The yellow colour represents a lower percentage of shading, transitioning to the blue colour, which represents that the surface is completely shaded or it is night time.
In Florianópolis, the shaded area percentage is always higher in winter months (June, July for South Hemisphere). In Singapore, although there are no well-defined seasons, the shadow behaviour also changed seasonally, with the largest percentage of shading values appearing during summer months (June, July for North Hemisphere). Seeking to compare the results provided by the shading mask and the analysis of sun exposure, the monthly shading percentages of each analysis were compared and the difference in percentage points is presented in Figure 6.
Results from Figure 7 showed that the variation was from 0 up to 4% in Singapore and from 0 up to 8% in Florianopolis on the monthly basis, when both ways to get the shading percentages available on the Ecotect software were compared. On the yearly basis, the difference was smaller: 0.03% for Singapore and 1.62% for Florianopolis. This analysis showed that the annual shading percentage quickly calculated by the shading mask method shows results very close to the results of more detailed analyses (sun exposure with hourly results), unlike what happens with the monthly figures and, therefore, the annual shading percentage can be a most convenient and reliable factor in estimating losses of annual energy generation by partially shaded solar PV systems.
3.1.3. Shading percentage versus Incident irradiation reduction The shading percentage was compared to the incident irradiation reduction in four time bases: hourly, daily, monthly and annually.
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3.1.3.1. Hourly basis In order to compare the shading percentage (shaded area) at a certain time with the percentage of the incident irradiation reduction at that moment in a given area, a day of each month was selected for both cities. For each day, the incident irradiation was calculated on the surface considering the obstacles causing shadows and removing them. Figure 7shows the direct (green line) and the diffuse irradiance (yellow line) available to a horizontal plane, as well the incident solar irradiance in the analysed surface with shadows (red line) and without shadows (blue line), for 13 April in both analysed cities. Also, it shows the shaded area percentage in each hour of the day (grey hatch).
Figure 7 presents a shading percentage of 20% at 12 pm in Singapore and a shading percentage of 40% in Florianopolis at the same time. The cities analysed have opposite characteristics in the ratio between global irradiation and diffuse irradiation; while Singapore showed 71% of diffuse irradiation in its overall composition, Florianopolis showed 31% for the day analysed. Through Figure 8, it is possible to see how shading occurs over the surface for both cities on this specific day, at 12 pm. The hourly percentages of incident irradiation reduction were then calculated and compared with the hourly shaded area percentages for the same day. The results can be seen in Figure 9 for Singapore and Florianopolis. According to Figure 9, although the shading percentage varies from 0 to 26% during the day in Singapore, the losses of irradiance were generally constant (~12%). In Florianópolis, the shading percentage was always higher than the percentage of irradiation reduction, getting closer only in the end of the day. In order to evaluate how this relation occurs during the year, but still on an hourly basis, the same comparison was carried out for each location, but now considering the average of 365 days, which results in typical-day graphs (Figure 10).
In Figure 10, it can be seen that in Singapore the fraction of irradiation reduction is constant over the hours, always around 10%, rising slightly as the shading fraction exceeds 10%. The annual average of shading fraction, in turn, shows a peak of 29% at 8 am and gets closer to zero over time. In Florianópolis, the fraction of irradiation reduction varied from 9% to 24%, and the shading fraction varied from 0% to 39%. On the annual average, even when there is no direct shading on the selected plane, like for example at 5 pm and 6 pm, the simple fact that there are buildings in the surroundings, resulted in reductions of 8% and 9%, that is, the surrounding buildings contributed in reducing the incidence diffuse irradiance. The mismatch between the shading fraction and the fraction of incident irradiation reduction is directly related to changes in solar irradiance throughout the day, as during the early hours 8
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in the morning and in the late afternoon, the incident irradiation is lower than in the middle of the day. In addition, the diffuse fraction of the incident irradiance is larger before 9 am and after 4 pm than in the rest of the day for the two cities analysed. Due to the position of the sun in the sky, and the surrounding buildings, in the early morning hours shading fractions tend to be higher than in the rest of the day. What happens, therefore, is that an area 50% shaded in the early morning will not result in a 50% reduction in incident radiation, because the shading significantly reduces the fraction of direct irradiation, but has much less impact on the fraction related to the diffuse radiation. Note, then, that the hourly shading percentage does not necessarily reflect the percentage of incident irradiation losses on a surface, since the shading fraction is related to a shaded area at a given time and the reduction of incident irradiation is related to the energy lost due to shade. The energy issue related to shading (reduction of incident irradiation - kWh) is therefore more complex than the issue of shading percentage (reduction of irradiation time – hours), because the solar intensity can vary over the hours of the day so much because of both weather conditions (cloud cover distribution), and the position of the sun in the sky (air mass). 3.1.3.2.
Daily basis
The next step was the analysis of the same data considering the average of shading fraction (average of shading fraction of each hour in a day) in each day, and the percentage of incident irradiation reduction in the same day. Figure 11 presents the daily percentage of incident irradiation reduction and the daily shading percentage for all days of the year for Singapore and Florianopolis. When both daily fraction were compared for all days of the year it was possible to conclude: In Singapore: The shading fraction was lower than the fraction of incident irradiation reduction during autumn and winter days for the North Hemisphere, low-latitude site. In these days, the reduction of incident irradiation was on average 10%. During spring and summer days in the North Hemisphere, low-latitude site, the shading percentage exceeded the percentage of incident irradiation reduction. On these days, the reduction was on average 14%. In Florianopolis: In the South Hemisphere, medium-latitude site, the spring and summer days presented similar fractions between shading and incident irradiation reduction, with averages of 11% e 12%, respectively. Autumn and winter days presented higher differences between shading fraction and incident irradiation reduction, and the shading fraction was always higher than the incident irradiation reduction 36% and 22%, respectively). When both cities are compared, one can see that the shading fraction is relatively uniform, following a continuous curve throughout the year, which is due to the fact that shading is an effect related purely to solar geometry. The reduction of the incident irradiation proved to be quite variable over the days, with greater intensity in Florianopolis. This larger variation in 9
Energy and Buildings Revised manuscript submitted for publication by C.D. Zomer and R. Rüther in January 2017
Florianópolis is related to the overall irradiation characteristics (composition between direct and diffuse irradiation), as previous shown in Figure 2.
3.1.3.3.
Monthly basis
From the daily values, monthly total values corresponding to the average monthly shaded area fraction, and the incident irradiation reduction fraction, were also compared and the result is shown in Figure 12 for Singapore and Florianopolis.
The same conclusions already arrived at through the analyses on a daily basis have been extended in the analysis on a monthly basis. Furthermore, it can be concluded that in both locations, the minimum monthly incident irradiation reduction was higher than the lowest monthly shading fraction. In Singapore, there are months with shading fractions lower than 5%, with an incident irradiation reduction of about 10%. Also, there are months with shading fractions larger than 22%, with reductions in incident radiation of only 14%. In Florianópolis, December and January were the months with the lowest percentage of shading (7% and 8%, respectively). During these months the reduction in incident irradiation was 9% for both months. During the winter months, the shading fraction was above 40%, and the reduction percentage of the incident radiation was approximately 25%. These simulations showed that, for the simple fact that there are buildings in the surroundings, even if they are not shadowing the analysed surface causing loss of direct sunlight, there will always be a loss of incident irradiation, i.e., there will be a part of diffuse irradiation blocked. As Singapore has a higher fraction of diffuse irradiation than Florianópolis, the presence of surroundings impacted more the reduction of incident radiation than in Florianópolis. Moreover, the difference in latitude between the two cities is another factor that has influenced these results. Although both cities are located within the range of low latitudes, the sun's relative position in the sky is different for each of them. In Singapore, the solar position remains higher than in Florianópolis throughout the year; so for this location, the shading fraction caused by the surroundings on the surface is lower in all months of the year.
3.1.3.4.
Annual basis
Finally, the shading fraction and the reduction of incident radiation calculated on the annual basis can be seen in Figure 13. Previous comparisons were based on the results of solar exposure tool only. On the annual basis, the shading percentage from shading mask was also included in the analysis. When the annual values were compared, it was found that for Singapore, a simple shading simulation would be sufficient to provide the incident radiation reduction with an error margin of only two points considering the solar exposure tool and only one point using the shading mask tool. In Florianópolis, the margin of error would be slightely higher, with a difference of seven or six percentage points, respectively. 10
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The simulations for these two cities showed that Singapore, closer to the Equator and with a larger fraction of diffuse radiation, showed greater coincidence between the fraction of shading and the reduction on incident irradiation than Florianópolis, which is 27° South of the Equator. Also, it was found that averaging the hourly shading percentage values for a typical day in each analysed city (Figure 10) would also lead to the same values presented in Figure 13, what reinforces the convinience of adopting only the annual shading percentage from shading masks.
3.2. Phase 2: Validation of shading percentage and reduction of incident irradiation In order to validate the results of the shading fraction (shadowed area) and incident irradiation reduction, three experiments were carried out: one physical model in Florianopolis and two computer simulations using two different software packages, PVsyst and the SketchUp plug-in SOLAR3DBR, developed and presented in the M.Sc. thesis of Emerson Melo [51] . 3.2.1. Physical model The styrofoam physical model and the irradiation sensor (Mini KLA) can be seen in Figure 14, and some comparisons between the real model situation and the Ecotect simulation can be seen in Figure 15.
Based on the images presented, it is possible to see a perfect relation between the Ecotect estimatives of shadow and the real situation. From the irradiation sensor data, a graph was generated showing the irradiance with and without the surroundings, i.e., with and without shadows (Figure 16). This graph also shows the shading percentage calculated with Ecotect, as well as the fraction of incident irradiation reduction at each time of the day.
The daily irradiation measured by the irradiation sensor was 3.78 kWh/day without surroundings and 2.82 kWh/day with surroundings. During the day analysed, the incident irradiation reduction was 25% and the shading percentage was 37%. This study showed that, as in the computer simulations, the shading fraction does not directly correlate to the incident irradiation reduction. While the shading fraction is related to the solar geometry and position of the analysed surface with their surroundings and latitude, the incident irradiation reduction depends on climatic conditions, as characteristic of direct and diffuse irradiation and clouds in the instant measured. In order to minimize the influence of clouds, this study was carried out on a clear day.
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Energy and Buildings Revised manuscript submitted for publication by C.D. Zomer and R. Rüther in January 2017
3.2.2. PVSYST and Solar3DBR In order to validate the incident irradiation reduction results obtained from Ecotect, the same analyses were carried out with both PVSYST and the Solar3DBR SketchUp plug-in. For PVSYST, a 3D model was simulated, identical to the 3D model analysed with the Ecotect software. The simulations were carried out for both cities, Singapore and Florianópolis. On the SketchUp software, the Solar3DBR plug-in was installed and the same 3D model was used on the simulations. Both software were fed with the same irradiation data which was used on the Ecotect simulations, i.e, from IWEC for Singapore and from SWERA for Florianopolis. Both files are based on ten years of data. The comparison between the incident irradiation losses due shadings from the surroundings from the three computer software packages were then compared, and are shown in Figure 17 for Singapore and Florianópolis.
The comparison between monthly and annual results from the three software tools presented similar curves for both cities, with highest differences during winter months in Florianópolis (9 percentage points). On the annual basis, the three software tools presented very similar values (10, 11 and 12 %) for Singapore, and three identical values for Florianopolis (17%). From this analysis, the Ecotect study was considered validated, and it was confirmed that the annual value tends to be the most reliable when compared to other time bases.
3.3. Phase 3: Shading index proposal From studies with computer simulations for the two cities, it can be concluded that the percentage of shaded area for a given time is responsible for the reduction in incident irradiation; however, this effect does not occur at the same rate (for example, due to the contribution of scattered radiation, an area 50% shaded at a given time does not represent a 50% reduction of incident irradiation at that point on the surface), and does not occur at all times of the day with the same intensity. When the relationship between the percentages of daily shading with the percentage of incident irradiation reduction was analysed, a few days showed greater coincidence than others. This fact invalidates adopting the shading fraction of a specific day in the estimation of PV generation. When the monthly basis was compared, one can also say that in most months the shading fraction was different from the incident irradiation reduction fraction: at times it was superior, and at other times it was inferior. Therefore, energy generation forecasts for BIPV based on calculations from shaded area fractions on a monthly basis will involve large errors. Finally, values were compared on an annual basis. On the annual basis, the percentage point differences were very small for Singapore (2%) and slightly larger for Florianópolis (7%); but in any case much lower than the differences found in the winter months (+ 20%) for this location, for example. Added to this, the fact that when comparing percentage reduction of incident irradiation from different computational tools, the annual percentage was identical or very close for both of the cities analysed. 12
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From the results presented in this work, it can be said that estimating the energy generation of a PV system based on average shaded area in the course of a year is the option that will results in the smallest error. Moreover, as PV generation forecasting studies are often estimated on an annual basis, using the annual shading fraction is the most simple, fast and convenient way to obtain energy generation values. Therefore, it is suggested that the annual shading fraction be considered in the power generation prediction calculations as the shading ratio, as can be seen in equation 2. SPANNUAL ~ RII ANNUAL = SI (2) Where: SPANNUAL = Annual shading percentage (%) RIIANNUAL = Annual reduction of incident irradiation (%) SI = Shading Index (%) The simplified flowchart presented in Figure 18 ilustrates the methodology used in this study.
4. APPLICATION OF SHADING INDEX ON COMPUTER SIMULATIONS FOR ENERGY ESTIMATIVE In order to validate the shading index estimated by computer simulations, the theoretical case studies for Singapore and Florianópolis presented in Phase 1 were tested. Computer simulations with and without environment, with the same electric design were carried. Theoretical studies were simulated for flat surfaces, i.e. extracting the environment, and the own edification shading fraction was set to zero for both cities. In this case, the adopted shading index coincided with the annual shading percentage of each surface. The simulation was performed using the software PVsyst. Table 2 shows the previously calculated values of incident radiation reduction percentage, the shading rate and the energy yield estimates for the PV generator with and without surroundings. Through the cases analysed, the percentage reduction in estimated yield due to the existence of partial shading showed an absolute deviation of -1% for Singapore and -6% for Florianópolis. Therefore, it is concluded that it is reasonable to perform a shading study using the software tools presented here, disregarding the shading on hourly basis and multiplying the resulting annual shading index by the software calculated yield as a way to estimate the energy generation in partially shaded PV systems. For both locations, the results are likely to be slightly conservative.
5. CONCLUSIONS Making use of established software tools typically used by architects and designers, such as three-dimensional models and shading masks, this paper aimed to demonstrate how the partial shading on BIPV systems can be translated into energy losses in a simple and direct way. The main objective was to propose a method to quantify the influence of partial shading on the operational performance of solar generators integrated onto buildings in low-latitude, tropical regions. Although this study has focused on the effect of shading on the performance 13
Energy and Buildings Revised manuscript submitted for publication by C.D. Zomer and R. Rüther in January 2017
of solar PV generators integrated on buildings (BIPV), the method developed here can also be applied to any type of PV solar generator. The method presented consists in simulating and analysing the influence of shading on a theoretical horizontal surface partially shaded by the surroundings, which was carried out for two sunbelt cities, Singapore (latitude 1,35ºN) and Florianópolis (27.48 ° S). The computer simulation was performed for different time bases, to compare how the shading percentage of the surface area relates to the percentage energy loss during the same period. Validation was carried out through an experiment with a real physical model and through comparisons of results from other computational software packages: PVSYST and Solar3DBR. For both cities, it was observed that in smaller time bases, such as hours, days and months, the percentage of shaded area and the percentage of energy losses are not much related. However, by expanding the time basis for a year, the percentage became very similar, reaching almost a perfect match for the simulation carried out for Singapore. Therefore, as a result of this research, it can be proposed that estimating the energy generation of a PV system based on shaded average area in the course of a year is the option that will bring the lowest percentage error, compared to the shaded area on a smaller time basis. The great advantage of this finding is that the achievement of the annual shading percentage resulting from a quick and easy procedure using the Ecotect software, may be calculated either through a shading mask or through sun exposure analysis, once the building and its surroundings are modelled, and the site climate file is loaded. A further advantage is that because PV generation forecasting studies are often estimated on an annual basis, adopting the annual shading percentage is the simplest, fast and more convenient way of obtaining energy generation estimatives. So, it is suggested that the annual shading percentage be considered in the calculation of prediction of energy generation as the shading index. The shading index was validated in computer simulations that overlook the shading in the calculation process. The theoretical case studies for Singapore and Florianópolis were used as an object of study, and the PVSYST software was used to perform energy simulations with and without surroundings. The results showed that the percentage of reduction of the estimated energy yield was very similar to the shading index. Therefore, it is concluded that it is reasonable to perform a shading study using the software tools presented here, disregarding the shading on hourly basis and multiplying the resulting annual shading index by the software calculated yield as a way to estimate the energy generation in partially shaded PV systems. For both locations, the results are likely to be slightly conservative. ACKNOWLEDGEMENTS The authors acknowledge the financial support of the Brazilian Research Council / CNPq and the ANEEL sponsored R&D Project with Tractebel Energia (now Engie) under call 013/2011. REFERENCES [1] M. Osborne, IHS remains cautions on PV market demand growth, in: London, 2015.
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Figure 1. 3D model for computer simulation, highlighting the surface analysed in this work.
Figure 2. Typical horizontal global and diffuse irradiation in daily averages for each month for Florianópolis and Singapore based in ten years of data .
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Energy and Buildings Revised manuscript submitted for publication by C.D. Zomer and R. Rüther in January 2017
Figure 3. Shadow range analysis for Singapore (above) and Florianópolis (below) from 8 am to 5 pm at 30-minute intervals obtained using Ecotect software.
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Energy and Buildings Revised manuscript submitted for publication by C.D. Zomer and R. Rüther in January 2017
Figure 4. Shading mask for Singapore (left) and Florianópolis (right) obtained using Ecotect software.
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Energy and Buildings Revised manuscript submitted for publication by C.D. Zomer and R. Rüther in January 2017
Figure 5. Average of hourly shading percentage for each month in Singapore (above) and Florianópolis (below).
Figure 6. Difference between the shaded area percentage values obtained from the shading mask and from sun exposure analysis for Singapore (above) and Florianopolis (below), both from Ecotect software.
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Energy and Buildings Revised manuscript submitted for publication by C.D. Zomer and R. Rüther in January 2017
Figure 7. Shading analysis for a specific day (April 13) in Singapore (left) and Florianopolis (right) in a partially shaded surface.
Figure 8. Shading analysis for Singapore (left) and Florianópolis (right) on April 13, at 12 pm.
Figure 9. Incident irradiance with and without shadings on April 13 in Singapore (left) and Florianópolis (right) and hourly percentages of incident irradiation reduction and shaded area.
Figure 10. Incident irradiance with and without shadings on a typical day (hourly average during one year) in 23
Energy and Buildings Revised manuscript submitted for publication by C.D. Zomer and R. Rüther in January 2017
Singapore (left) and Florianópolis (right), and hourly percentages of incident irradiation reduction and shaded area.
Figure 11. Percentage of incident irradiation reduction and shading fraction on a daily basis in Singapore (left) and Florianopolis (right).
Figure 12. Shading fraction and fraction of incident irradiation reduction on a monthly basis in Singapore (left) and Florianopolis (right).
Figure 13. Percentage of incident irradiation reduction and shading percentage from solar exposure and shading mask tools on an annual basis in Singapore and Florianópolis.
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Energy and Buildings Revised manuscript submitted for publication by C.D. Zomer and R. Rüther in January 2017
Figure 14. Styrofoam physical model and irradiation sensor used to validate the computer simulations.
Figure 15. Comparison between Ecotect simulation and real situation for shadows projected on the styrofoam physical model.
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Energy and Buildings Revised manuscript submitted for publication by C.D. Zomer and R. Rüther in January 2017
Figure 16. Incident irradiance with and without surroundings, shading percentage from Ecotect and percentage of incident irradiation reduction for Florianopolis, on June 2nd, 2014.
Figure 17. Simulations of incident irradiation reduction in Singapore (left) and Florianopolis (right) using Ecotect, PVSYST and the Solar3DBR SketchUp plug-in.
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Energy and Buildings Revised manuscript submitted for publication by C.D. Zomer and R. Rüther in January 2017
Figure 18. Simplified flowchart to obtain the shading index.
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Energy and Buildings Revised manuscript submitted for publication by C.D. Zomer and R. Rüther in January 2017
Table 1. Monthly and annual shading percentage over the selected surface for Singapore and Florianópolis during day hours using the shading mask tool from Ecotect.
Shading Percentage (SP) Month Singapore January 5% February 2% March 3% April 15% May 24% June 25% July 22% August 14% September 2% October 3% November 6% December 5% Summer 4% Winter 24% Annual 10%
Florianópolis 7% 13% 23% 34% 40% 41% 38% 32% 20% 11% 7% 7% 9% 40% 23%
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Energy and Buildings Revised manuscript submitted for publication by C.D. Zomer and R. Rüther in January 2017
Table 2. Yield estimation calculated with the PVsyst software.
Incident irradiation reduction Singapore 11% Florianópolis 17%
Yield with no Shading surroundings Index (kWh/kWp.year) 9% 1,233 24% 1,252
Yield with surroundings (kWh/kWp.year) 1,133 1,021
Yield reduction 8% 18%
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