Identification of Building-Surrounded Obstacle Parameter Using Automated Simulation to Support Building Integrated Photovoltaic ( BIPV ) Layout Planning in Thailand

The overall aim of this study was to explore the comparative effects between obstacle’s distance and obstacle’s orientation parameters that cause partially shading effects and influence the potential solar power generation of a photovoltaic (PV) system. An automatic collaboration of a BIM authoring software and a cloud-based building performance analysis tool were used to simulate the annual cumulative insolation obtained from rooftop PV surfaces of eight different orientations and forty-three different distances between the BIPV and building obstacle. Two public healthcare buildings, an OPD and a ward building that widely established throughout Thailand were our case study. This study also explores that orientation and distance of a surrounding obstacle are both important parameters that influencing the annual cumulative insolation of PV surfaces but in the different contexts. The findings of this study also support decision making for BIPV designers and planners to acknowledge which the BIPV and the obstacle placement is highly effective, and which one is encountering a problem and its solution.


Introduction 1.1 Introduction of the Problem: Building Integrated Photovoltaic (BIPV)
The sun is the widest and huge amount of expansive energy source.The potential for solar energy to make a significant contribution to global electricity demand has been widely recognized and solar photovoltaic (PV) is considered as a major contributor to solar energy supply (Ekici, 2014;Yang, 2015).Photovoltaic systems are one of the most beneficial plants in this clean electricity production which is easy to install in and apply to a building and an urban environment.Building integrated photovoltaics (BIPVs) are solar PV materials that replace conventional building materials in parts of the building envelopes, such as the rooftop or walls, that serve as building envelope material and power generator simultaneously.Additionally, the BIPV technology also reduces the total building cost and mounting cost as BIPV panels serve as building components (Shukla, et al., 2016;Hong, Lee, Koo, Jeong, & Kim, 2017;Tripathy, Yadav, Sadhu, & Panda, 2017).Apart from attaining optimum technique and aesthetic solutions.Other key factors to achieve wide-scale implementation of BIPV involve minimizing the production costs, reducing the environmental impact and especially increasing the final efficiency of the system (Mulcué-Nieto & Mora-López, 2015).The efficiency of any BIPV systems can be estimated by solar insolation, i.e., a measure of solar radiation energy received on a specific PV surface area at a given time.Solar insolation is affected by factors such as atmosphere, angle of the sun and distance.The thinner the atmosphere in which the sun is passing through, the higher degree the insolation.The insolation of an area determines how much energy a square meter of solar panel can provide on any given day.When the insolation rate of an area is low, more area of panel is required to maximize energy output (Sinovoltaics, 2014).A BIPV system directly converts sunlight into electricity so it is sensitively affected with the change in the intensity of solar radiation.These fluctuations cause troubles between demand and supply and reduce the power quality (Ekici, 2014).Main challenging issues about BIPV applications including partial shading, incorrect specifications of the BIPV systems, non-optimal tilt and azimuthal deviations are commonly encountered: therefore, the importance of these technical performance issues is indisputable, particularly in designing processes (Lam, Close, & E.W.C., 2006;Celik, Karatepe, Silvestre, Gokman, & Chouder, 2015;Yang, 2015;Zomer & Rüther, 2017).

An Introduction to the Partial Shading Effects
PV system performance is significantly affected by the environmental and surrounding factors which involve; surrounding-reflected radiation and shading effects of the environmental obstacles (Yoo, 2011;Celik, Karatepe, Silvestre, Gokman, & Chouder, 2015).The available total solar irradiance on PV modules is composed of three components: beam (direct), diffuse from sky, and surrounding-reflected components.Beam radiation is the component directly comes from the sun without being scattered through the atmosphere but diffuse radiation is highly scattered by different types of particles of clouds, dust or haze in the atmosphere.Beam radiation calculation is purely geometric and directly dependent upon the solar geometry-azimuth and altitude, straightforwardly.On the other hand, surrounding-reflected radiation is a complicated factor which is structurally formulated by both beam and diffuse radiation reflected from the surrounding such as nearby buildings and ground, and reach the PV module again.The surrounding-reflected radiation also depends on many factors such as surrounding reflectance, absorptance, emittance, and transmittance which influences the incidence solar irradiation on the PV modules (Yoo, 2011;Gökmen, 2016).Practically, one of the most significant and complicated effect in dealing with BIPV performance estimation is the partial shading effect on PV modules due to the surrounding obstacles, it plays important role in the efficiency of PV systems by their convoluted, non-uniform and dynamic conditions, especially when the PV system locates in a dense urban environment.Partially shaded PV modules receive less solar radiation than the unshaded PV modules and partial shading effects may cause irreversible damage to the module due to the hot spot effect.The surrounding obstacles including trees, utility poles, surrounding buildings and so on, furthermore, only the building itself on which the system is integrated is responsible for approximately 5-10 % decrease of the overall BIPV performance (Celik, Karatepe, Gokman, & Silvestre, 2013;Masa-bote & Caamaño-Martín, 2014;Frontini, Bouziri, Corbellini, & Medici, 2016;Zomer & Rüther, 2017).To acknowledge the impacts of the shadow that project on the surfaces of a PV system, examination of three main conceptual parameters are required including (A) solar properties, (B) surroundings, and (C) related-BIPV which are described as follow (Yoo, 2011;Celik, Karatepe, Gokman, & Silvestre, 2013;Masa-bote & Caamaño-Martín, 2014): (A) The parameter of solar property includes (A-1) the sun altitude, (A-2) azimuth angle, and (A-3) solar irradiation.The radiant energy from the sun is measured and reported as the solar irradiance, it is a crucial parameter using for calculating the solar insolation of PV modules (Zeil, 2017).(B) The parameter of surroundings consists of two subcategories; (B-1) the parameter of surrounding-reflected radiation and (B-2) surrounding obstacle.The parameter of surrounding-reflected radiation includes (B-1-1) reflectance, (B-1-2) absorptance, (B-1-3) emittance and (B-1-4) transmittance as previously described, while (B-2) the parameter of a surrounding obstacle comprises of (B-2-1) obstacle's location, (B-2-2) obstacle's shape, and (B-2-3) obstacle's orientation, (Figure 1).The surrounding obstacles block and eliminate the beam element of the solar radiation from fully hitting on a PV surface.The projected shadow from the three-dimensional coordinates of the obstacle on PV surfaces determined by the solar azimuth and solar altitude angles that dynamically change all the time during day.The dynamical variation makes the parameters of a surrounding obstacle one of the subtlest factors; however, clarification of such parameter assists in a more accurate estimation of partial shading effects.(C) The parameter of related-BIPV consists of two subcategories: (C-1) the parameter of a PV surface geometry includes (C-1-1) PV surface orientation, (C-1-2) PV surface tilt angle, (C-1-3) PV surface shape, and (C-1-4) PV surface location, these four parameters are illustrated, in relation with the parameter of a surrounding obstacle, in Figure 1.(C-2) the parameter of a PV module properties comprises of (C-2-1) PV materials, (C-2-2) BIPV product type, and (C-2-3) BIPV system type.PV materials which are semiconductors create voltage and current from movement of electron between anode and cathode poles to generate electricity.There are two broad categories of PV cells technologies-Crystalline Silicon and Thin Film.Crystalline Silicon cells gain the majority of market share at almost 90 percent of the world's PV materials and they provide efficiency of 12-16% (Chaianong & Pharino, 2015;Shukla, Sudhakar, & Baredar, 2016).BIPV products are classified into five main categories including: (1) BIPV's foil products, (2) BIPV's tile products, (3) BIPV's module products, (4) BIPV's solar cell glazing products and (5) building attached photovoltaic (BAPV) products.In the current BIPV market application, about 80% of BIPV installations are rooftop mounted, while the remaining 20% are façade mounted.Rooftop solar PV systems generally and practically meet requirements of most cases where ground space is limited and unused large roof space is available.Similarly, in Thailand, many BIPV end-users have gained greater interest in the solar rooftop technology especially rooftop BIPV's module products due to their high efficiency performance, competitive pricing among suppliers, easily applicable and suitable for pitched roofs.The rooftop BIPV solar PV module products may be somewhat similar to conventional solar PV modules.The difference, however, is that the BIPV solar modules are made with weather skin solutions (Jelle, Breivik, & Røkenes, 2012;Chaianong & Pharino, 2015;Shukla, Sudhakar, & Baredar, 2016).A BIPV system-(C-2-3) is considered as building integrated energy storage system which is comprised of a charge controller, a power storage system, power conversion equipment including an inverter, and it may include backup power suppliers such as diesel generators (Strong, 2011;Biyik, et al., 2017).As previously mentioned, the projection of shadow on the PV modules has been determined by using the three-dimensional coordinates that derived from the parameters of the surrounding obstacle which are determined by solar azimuth and solar altitude angle that constantly change through time.The projection of shadow on PV modules directly determines the shaded PV surfaces, shown in Figure 2, that have been dynamically and continuously changed by the movement of the sun. Figure 3 illustrates an example of dynamic change of shadow at 2.30 p.m. and 3.30 p.m. on winter solstice for buildings located in Bangkok, Thailand (13N 10030'E), east facing (AZ = 90).Figure 3   BIPV and the orientation between building obstacle and BIPV affect the potential to generate power of a BIPV.This study furtherly hypothesized that is orientation has more impact on the annual cumulative insolation on PV surfaces than distance.Thus, the conceptual framework of the study can be established and illustrated in Figure 5.

Methodology
2.1 Related Techniques in building performance simulation of BIPV 2.1.1BIPV and Building Performance Simulation (BPS) Though BIPV technology has great potential for reducing carbon emissions from building energy consumption.However, there are currently some obstacles in the general adoption of this technology.One of the obstacles is evidence-based design that needed on the effectiveness of the maximum-efficient design of BIPV buildings as well as the benefits of BIPV to convince owners to opt for BIPV buildings (Kuo, Hsieh, Guo, & Chan, 2016).In order to quantify the benefit of BIPV design, it is required to estimate the potentials of the BIPV electricity production first by quantitative assessment of irradiance on the required surfaces to install PV modules by using Building Performance Simulation (BPS) tools.The main purpose of Building Performance Simulation (BPS) is to quantitatively justify design decisions as a result of predicting real physical conditions in a building by using a computational model, and to support building design processes by providing a high integrity representation of the dynamic, connected and non-linear physical processes that govern the disparate performance aspects that dictate overall acceptability of building and their related energy supply systems, particularly, the BPS involves a scientific basis in its simulation algorithms and the level of building information detail required as input data (Bazjanac, et al., 2011;Hitchcock & Wong, 2011;Clarke & Hensen, 2015;Shen & Lu, 2016).PV simulation tools are useful to perform detailed analysis of system performance and assess the viability of a PV system in terms of energy production (Eltawil & Zhao, 2010).For the purpose of optimal PV system design, many models and studies have been proposed in literature (Ning, et al., 2017) ranging from the solar radiation model (Liu & Jordan, 1960;Goswami, Kreith, & Kreider, 2000), PV panel conversion model for unshaded PV (Clark, Klein, & Beckman, 1984;Goss, Cole, Betts, & Gottschalg, 2014;Ramli, Twaha, Ishaque, & Al-Turki, 2017), the power mismatch models for the partially shaded PV systems (Alonso-Garcia, Ruiz, & Hermann, 2006; Karatepe, Boztepe, & Çolak, 2007;Dolara, Lazaroiu, Leva, & Manzolini, 2013;Bai, et al., 2015) and to evaluating and optimizing the efficiency of PV systems working in partial shading conditions (Woyte, Nijs, & Belmans, 2003;Celik, Karatepe, Gokman, & Silvestre, 2013;Celik, Karatepe, Silvestre, Gokman, & Chouder, 2015;Ning, et al., 2017).However, these research results have not been commonly used in practical design practices yet, and have mainly been limited in a few academic research studies.One of the major barriers, it is argued, lies in the complexities in accessing or reconstructing a large number of related information, especially building's information as it varies in locations, shapes and obstacles (Asl, Zarrinmehr, Bergin, & Yan, 2015;Ning, et al., 2017).The current practice involves collecting the mentioned information from a variety of sources and manually transforming this information into the specific input required by performance simulation (Gupta, Cemesova, Hopfe, Rezgui, & Sweet, 2014).While based on professional expertise, this manual process tends to be uniquely performed by each practitioner according to methods, arbitrary judgements, rules-of-thumb developed over time by that individual.The results is a non-standardized process that produces energy models that can widely vary from one modeler to the next, even given the same initial building design information and these BPS models have been achieved with much duplication of efforts, time-consuming, and significant deficiencies remain.This is applicable not only to various BPS tools but also to various renewable energy simulation tools including solar PV simulation models as well (Bazjanac, et al., 2011;Hitchcock & Wong, 2011;Clarke & Hensen, 2015;Ning, et al., 2017).2.1.2Automated BPS and Building Information Modeling (BIM) An intelligent approach to better deal with these deficiencies in BPS, from the time-consuming, cumbersome and error-prone of manual data generation and use of improvised defined data that may invalidate the results, is the automation of BPS input data acquisition and transformation, it has been a goal of the buildings industry for decades (Bazjanac, et al., 2011;Hitchcock & Wong, 2011;Clarke & Hensen, 2015) Reusing of existing data by interoperable processes would significantly reduce the time and overhead associated with the creation of simulation models (Hand, Crawley, Donn, & Lawrie, 2005;Bazjanac, et al., 2011;O'Donnell, et al., 2011).An interoperable, intelligent and object-oriented simulation model would enable bi-directional data exchange with a Building Information Modelling (BIM) authoring applications, reusing of geometric and other data from different models significantly reduces the overhead associated with the definition of input data and has the potential to eliminate error-prone manual processes (O 'Donnell, et al., 2011;Ning, et al., 2017).Nowadays, it is generally accepted by the Architecture, Engineering, Construction, and Operations (AECO) industries that Building Information Modeling (BIM) is the most promising technology for enhancing the performance and quality of construction (Kuo, Hsieh, Guo, & Chan, 2016;Somboonwit, Boontore, & Rugwongwan, 2017).A BIM is a tool / methodology / paradigm / process of virtual design and construction involving the generation and management of digital representations of physical and functional characteristics of a facility which creates and uses the coordinated, consistent, computable information of the 3D models of the project components interconnect with the holistic information that conceived as a source of shared knowledge to support decision-making, through the life cycle of the building.When completed, these computer-generated-semantic-3D models contain precise geometry and data needed to support the construction, fabrication, and procurement activities through which the building is realized (Krygiel & Nies, 2008;Kymmell, 2008;Eastman, Teicholz, Sacks, & Liston, 2011;Matthew, Jason, Melissa, Seokho, & Fiona, 2013;Ladenhauf, et al., 2015;Agugiaro, 2016;Kuo, Hsieh, Guo, & Chan, 2016;Somboonwit, Boontore, & Rugwongwan, 2017).There is a very useful feature of BIM is that building geometry can be extracted from a BIM model to support the assessment of alternative sustainable design principles.BIPV design processes involve elements of expertise deriving from multiple disciplines such as architects, civil, mechanical and electrical engineers (Negendahl, 2015).With numerous unified tools that act both as a design tool and BPS tool exist, building designers still seem to prefer to crate and explore design options in dedicated design tool such as AutoCAD, ArchiCAD, Revit, SketchUp, etc., as they support the concept of a sketch and the freedoms associated with design tools.The integration of a design tool and a BPS tools is fundamentally changing building design into a faster, performance-aware and more flexible process, which eases the production of multiple design alternatives that provide model foundations for BIPV design optimization (Negendahl, 2015;Ning, et al., 2017).Furthermore, many buildings have already been modelled with BIM authoring tools, in which the features of most building components, e.g.shape, size, materials, locations as well as building's environment, has been accurately described (Ning, et al., 2017).

Simulation of Partial Shading Effects
It is important to include an accurate methodology for evaluating the fluctuation of potential PV power generation caused by partial shadow effects.Therefore the quantitative assessment of the incident irradiance on their surface is the most important issue, which affects the performance of the PV systems directly (Yoon, Song, & Lee, 2011;Celik, Karatepe, Gokman, & Silvestre, 2013;Yang, 2015).There are several studies that provide theoretical models to simulate the behavior of PV modules and generators in conditions of non-uniform radiation and also studies that provide models to estimate the effective irradiation, thus, after incorporating shading effects and these models are able to be used to estimate the electricity losses and mismatch losses in which the PV system incurs due to the effect of inhomogeneous irradiation (Norton, et al., 2011 Kim, 2017;Zomer & Rüther, 2017).However, there are a very few studies of the optimization of facility planning and buildings placement that strengthen solar energy utilization of a building in an interconnected composition with surrounding buildings to reduce the impacts of shading conditions due to dynamic changing of solar geometry.Kanters and Wall (2014) explored the effect of four factors on the solar potential of building blocks typically used in urban planning, i.e. form, density, orientation and roof type.The results showed that density (the closer the distance between buildings is, the higher the urban density becomes) was the most influential factor, while the effect of orientation was not that clear (Kanters & Wall, 2014).Bhattacharjee, Noble, Kensek and Schiler (2016) studied solar envelope for sites with existing buildings using a computational design tool for generating solar envelopes that allowed additional volume to be added to existing building geometry without further casting shadows on neighboring sites.While the usable floor area has been increased but the shape of the buildings has been transformed into something that irregular and eccentric, furthermore, if the physical boundaries of the site and the surroundings the determine the solar envelope of the buildings have been changed, it possibly that the added volume might not be positively contented complying with the change of solar envelope.Thus, it possibly implies that focusing on the shape of surrounding obstacles to enhance solar energy utilization might be an endless task (Bhattacharjee, Noble, Kensek, & Schiler, 2016).In high-density urban areas or campuses, the solar access of a building has been affected directly, especially the clustered facility developments that put pressures on land usage and create large buildings occupying maximum plot ratio that create solar obstructions on rooftop PV systems that installed on lower-rise buildings, as shown in Figure 6.Without proper siting and buildings placement, a structure cannot be designed for maximum power generation from a solar PV system.Referring to a previously mentioned argument, there are just a few studies of the investigation of the parameter of surrounding obstacles but there is no previous study that explores the most critical parameters of surrounding obstacle determining the projection of shadow on PV surfaces that influence over the potential to power generation of a PV system through automated processes of BPS and BIM.
Figure 1.A

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