Modeling the potential for PV installation in residential buildings in dense urban areas

Vulkan, Adi; Kloog, Itai; Dorman, Michael; Erell, Evyatar

doi:10.1016/j.enbuild.2018.03.052

Cited by 108 publications

(42 citation statements)

References 20 publications

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“…To summarize, the shadow package can be used to calculate shadow, SVF and solar radiation in an urban environment using widely available polygonal building data, inside the R environment (e.g. Vulkan et al, 2018). Potential use cases include urban environment applications such as evaluation of micro-climatic influence for urban planning, studying urban well-being (e.g.…”

Section: Discussionmentioning

confidence: 99%

shadow: R Package for Geometric Shadow Calculations in an Urban Environment

Dorman¹,

Erell²,

Vulkan³

et al. 2019

The R Journal

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This paper introduces the shadow package for R. The package provides functions for shadowrelated calculations in the urban environment, namely shadow height, shadow footprint and Sky View Factor (SVF) calculations, as well as a wrapper function to estimate solar radiation while taking shadow effects into account. All functions operate on a layer of polygons with a height attribute, also known as "extruded polygons" or 2.5D vector data. Such data are associated with accuracy limitations in representing urban environments. However, unlike 3D models, polygonal layers of building outlines along with their height are abundantly available and their processing does not require specialized closed-source 3D software. The present package thus brings spatio-temporal shadow, SVF and solar radiation calculation capabilities to the open-source spatial analysis workflow in R. Package functionality is demonstrated using small reproducible examples for each function. Wider potential use cases include urban environment applications such as evaluation of micro-climatic influence for urban planning, studying urban climatic comfort and estimating photovoltaic energy production potential.

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Section: Discussionmentioning

confidence: 99%

shadow: R Package for Geometric Shadow Calculations in an Urban Environment

Dorman¹,

Erell²,

Vulkan³

et al. 2019

The R Journal

Self Cite

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“…and 15 180 T   (7)  is the sun elevation angle,  is the sun azimuth angle with respect to south,  is the solar…”

Section: Shadow On a South Facing Walls Cast By A Horizontal Polementioning

confidence: 99%

“…The right edge "c" casts a shadow on the wall by the solar rays at point "a". (2) By substituting Equation (2) into Equation (1), we get the coordinates of the shadow point "a", i.e., the shadow variation on the wall during the day maybe calculated: 15 180 T   (7)  is the sun elevation angle,  is the sun azimuth angle with respect to south,  is the solar From Figure 2 it follows:…”

Section: Shadow On a South Facing Walls Cast By A Horizontal Polementioning

confidence: 99%

“…and ω = 15T − 180 (7) α is the sun elevation angle, γ is the sun azimuth angle with respect to south, ω is the solar angle (ω = 0 noon, ω < 0 morning and ω > 0 afternoon) and T is the solar time. The solar inclination angle δ is given by: δ = 23.45 sin (284 + n) 365 (8) Figure 3 shows the shadow component,F x and F y , of a pole attached prependicular to a wall at the origin 0,0,0 calculated according Equations (3)-(8) for Tel Aviv (latitude φ = 32 • N) for the months December to April on 21st day.…”

Section: Shadow On a South Facing Walls Cast By A Horizontal Polementioning

confidence: 99%

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Shading by Overhang PV Collectors

2019

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Photovoltaic modules integrated into buildings may provide shading to windows, doors and walls to protect against sun rays and at the same time generate ancillary electrical energy. The study develops the methodology for calculating the shadow variation cast by overhangs on doors, windows, carports, and calculates the annual incident energy (beam, diffuse and global energy) on overhangs made up of conventional and bifacial PV modules. The methodology of the present study is different from published articles including software programs. The study starts with shadows on walls cast by a horizontal pole and follows by shadows on walls cast by horizontal plates, inclined pole, inclined plate, and shaded area. The study deals also with overhangs placed one above the other. The calculation of the diffuse radiation involves the calculation of view factors to sky, to ground and between overhangs. In addition, the present study suggests using bifacial PV modules for overhangs and calculates the contribution of the reflective energy (5% and more) from walls and ground to the rear side of the bifacial PV module.

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“…Another important step for reducing emissions is to use the solar irradiance to produce electricity with photovoltaic (PV) systems. There are several studies that show methods on how to analyze the PV potential in urban areas [21,22]. Most of the studies investigating the PV potential are based on the 3D geometry of the buildings and their roofs [23][24][25][26] since knowing the exact roof shape and orientation helps to calculate more accurate values for the PV potential energy yield.…”

Section: Introductionmentioning

confidence: 99%

Renewable Energy Generation Scenarios Using 3D Urban Modeling Tools—Methodology for Heat Pump and Co-Generation Systems with Case Study Application †

2019

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In the paper, a method was developed to automatically dimensionalize and calculate central energy generation and supply scenarios with a district heating system for cities based on 3D building models in the CityGML format and their simulated heat demand. In addition, the roof geometry of every individual building is used to model photovoltaic energy generation potential. Two types of supply systems, namely a central heat pump (HP) system and a large co-generation (combined heat and power-CHP) system (both with a central storage and district distribution system), are modeled to supply the heat demand of the area under investigation. Both energy generation models are applied to a case study town of 1610 buildings. For the HP scenario, it can be shown that the case study town's heat demand can be covered by a monovalent, low-temperature system with storage, but that the PV only contributes 15% to the HP electricity requirement. For the CHP scenario, only 61% of the heat demand can be covered by the CHP, as it was designed for a minimum of 4000 operating hours. Both the PV and the CHP excess electricity are fully injected into the grid. As a result, the primary energy comparison of both systems strongly depends on the chosen primary energy factors (PEF): with given German regulations the CHP system performs better than the HP system, as the grid-injected electricity has a PEF of 2.8. In the future, with increasingly lower PEFs for electricity, the situation reverses, and HPs perform better, especially if the CHP continues to use natural gas. Even when renewable gas from a power to gas (P2G) process is used for the CHP, the primary energy balance of the HP system is better, because of high conversion losses in the P2G process.

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Modeling the potential for PV installation in residential buildings in dense urban areas

Cited by 108 publications

References 20 publications

shadow: R Package for Geometric Shadow Calculations in an Urban Environment

shadow: R Package for Geometric Shadow Calculations in an Urban Environment

Shading by Overhang PV Collectors

Renewable Energy Generation Scenarios Using 3D Urban Modeling Tools—Methodology for Heat Pump and Co-Generation Systems with Case Study Application †

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