Setting intelligent city tiling strategies for urban shading simulations

Assessing accurately the solar potential of all building surfaces in cities, including shading and multiple reflections between buildings, is essential for urban energy modelling. However, since the number of surface interactions and radiation exchanges increase exponentially with the scale of the district, innovative computational strategies are needed, some of which will be introduced in the present work. They should hold the best compromise between result accuracy and computational efficiency, i.e. computational time and memory requirements. In this study, different approaches that may be used for the computation of urban solar irradiance in large areas are presented. Two concrete urban case studies of different densities have been used to compare and evaluate three different methods: the Perez Sky model, the Simplified Radiosity Algorithm and a new scene tiling method implemented in our urban simulation platform SimStadt, used for feasible estimations on a large scale. To quantify the influence of shading, the new concept of Urban Shading Ratio has been introduced and used for this evaluation process. In high density urban areas, this index may reach 60% for facades and 25% for roofs. Tiles of 500 m width and 200 m overlap are a minimum requirement in this case to compute solar irradiance with an acceptable accuracy. In medium density areas, tiles of 300 m width and 100 m overlap meet perfectly the accuracy requirements. In addition, the solar potential for various solar energy thresholds as well as the monthly variation of the Urban Shading Ratio have been quantified for both case studies, distinguishing between roofs and facades of different orientations

Assessment of the photovoltaic potential at urban level based on 3D city models: A case study and new methodological approach

The use of 3D city models combined with simulation functionalities allows to quantify energy demand and renewable generation for a very large set of buildings. The scope of this paper is to determine the solar photovoltaic potential at an urban and regional scale using CityGML geometry descriptions of every building. An innovative urban simulation platform is used to calculate the PV potential of the Ludwigsburg County in south-west Germany, in which every building was simulated by using 3D city models. Both technical and economic potential (considering roof area and insolation thresholds) are investigated, as well as two different PV efficiency scenarios. In this way, it was possible to determine the fraction of the electricity demand that can be covered in each municipality and the whole region, deciding the best strategy, the profitability of the investments and determining optimal locations. Additionally, another important contribution is a literature review regarding the different methods of PV potential estimation and the available roof area reduction coefficients. An economic analysis and emission assessment has also been developed. The results of the study show that it is possible to achieve high annual rates of covered electricity demand in several municipalities for some of the considered scenarios, reaching even more than 100% in some cases. The use of all available roof space (technical potential) could cover 77% of the region’s electricity consumption and 56% as an economic potential with only high irradiance roofs considered. The proposed methodological approach should contribute valuably in helping policy-making processes and communicating the advantages of distributed generation and PV systems in buildings to regulators, researchers and the general public

From roofs to renewables : Deep learning and geographic information systems insights into a comprehensive urban solar photovoltaic assessment for Stonehaven

Peer reviewe

A new simplified vector-based model to support solar energy planning at urban scale

Evaluation of solar potential is a necessary step for integrating solar technologies in buildings in order to properly assess the benefits of harvesting solar energy and draw well-informed decisions in various design phases. Solar energy planning at urban scale requires large-scale solar analysis to support various decision-making contexts, such as making urban solar targets, prioritizing urban zones or buildings for solar integration, and optimizing solar technologies tailored for targeting buildings. Existing tools have the following major limitations to support such decision-making situations. (1) Current advanced simulation models based on ray trace and ray interception techniques are not effectively scalable to evaluate solar potential at urban scale due to the expensive modelling process and computational cost. (2) Simple and statistical models developed for large-scale analysis are not suitable to accurately predict solar irradiance on individual surfaces with proper consideration of urban shading and reflection. This dissertation addresses the need for developing scalable, efficient analysis methods to support the solar energy planning process. This dissertation has developed a simplified vector-based model that effectively predicts the solar potential of urban areas on the basis of consideration of the urban context. The proposed model is based on vector-based methods without the use of ray trace and ray interception techniques, and consists of new methods that suitably account for the non-uniform solar radiation of the sky, obstruction by urban surfaces, and reflection by urban surfaces in urban areas. The proposed model establishes three new methods to simplify the calculation in the context of urban applications: (1) a two-segment discretisation model, (2) an edge-angle detection obstruction model, and (3) a unified view-angle-based reflection model. This dissertation demonstrates the usability of the new model in supporting decision-making in the solar energy planning process. It addresses the following two hypotheses to examine the usability of the new model: (1) Simplified, vector-based model, tailored to urban applications, predict accurate solar radiation on urban surfaces to effectively support urban-scale analysis and (2) solar analysis with full representation of urban surroundings is necessary in the calculation of urban shading and solar reflection to correctly support distributed PV planning. For the first hypothesis, the performance of the method is compared against the advanced daylight simulation program RADIANCE and measurements obtained from controlled experiments. The first comparison demonstrates the new method provides flexible setting options for different resolution and prediction accuracy requirements and generates reasonably accurate predictions. The second comparison further confirms the prediction accuracy against the measurements for the horizontal and vertical surfaces under different shading and reflection conditions. The comparison with the ray interception approach demonstrates the computational efficiency of the proposed obstruction model for solar analysis that substantially reduces calculation iterations for detecting sky and building obstructions. For the second hypothesis, predictions and decisions derived by the developed method are compared against those by a lower fidelity models to investigate the importance of modelling urban shading and reflection with full representation of urban surroundings in three decision making contexts of urban-scale distributed PV planning process. Additionally, the second hypothesis is furthered examined and highlighted by investigating the effect of an additional dynamic PV model on decision-makings in comparison with the effect of the proposed high-fidelity solar radiation model for urban shading and solar reflection. The new model is demonstrated to enable cost-efficient solar potential analysis based on urban contexts for supporting solar energy planning at urban scale.CSC Cambridge Scholarshi

Data driven tools to assess the location of photovoltaic facilities in urban areas

Urban sustainability is a significant factor in combating climate change. Replacing polluting by renewable energies is fundamental to reduce the emission of greenhouse gases. Photovoltaic (PV) facilities harnessing solar energy, and particularly self-consumption PV facilities, can be widely used in cities throughout most countries. Therefore, locating spaces where photovoltaic installations can be integrated into urban areas is essential to reduce climate change and improve urban sustainability. An open-source software (URSUS-PV) to aid decision-making regarding possible optimal locations for photovoltaic panel installations in cities is presented in this paper. URSUS-PV is the result of a data mining process, and it can extract the characteristics of the roofs (orientation, inclination, latitude, longitude, area) in the urban areas of interest. By combining this information with meteorological data and characteristics of the photovoltaic systems, the system can predict both the next-day hourly photovoltaic energy production and the long-term photovoltaic daily average energy production.This work has been supported by the project RTI2018-095097-B-I00 at the 2018 call for I+D+i Project of the Ministerio de Ciencia, Innovación y Universidades, Spain. Funding for open access charge: Universidad de Málaga/CBUA, Spain

Method for estimating solar energy potential based on photogrammetry from unmanned aerial vehicles

This study presents a method to estimate the solar energy potential based on 3D data taken from unmanned aerial devices. The solar energy potential on the roof of a building was estimated before the placement of solar panels using photogrammetric data analyzed in a geographic information system, and the predictions were compared with the data recorded after installation. The areas of the roofs were chosen using digital surface models and the hemispherical viewshed algorithm, considering how the solar radiation on the roof surface would be affected by the orientation of the surface with respect to the sun, the shade of trees, surrounding objects, topography, and the atmospheric conditions. The results show that the efficiency percentages of the panels and the data modeled by the proposed method from surface models are very similar to the theoretical efficiency of the panels. Radiation potential can be estimated from photogrammetric data and a 3D model in great detail and at low cost. This method allows the estimation of solar potential as well as the optimization of the location and orientation of solar panels

Estimating Solar Energy Production in Urban Areas for Electric Vehicles

Cities have a high potential for solar energy from PVs installed on buildings\u27 rooftops. There is an increased demand for solar energy in cities to reduce the negative effect of climate change. The thesis investigates solar energy potential in urban areas. It tries to determine how to detect and identify available rooftop areas, how to calculate suitable ones after excluding the effects of the shade, and the estimated energy generated from PVs. Geographic Information Sciences (GIS) and Remote Sensing (RS) are used in solar city planning. The goal of this research is to assess available and suitable rooftops areas using different GIS and RS techniques for installing PVs and estimating solar energy production for a sample of six compounds in New Cairo, and explore how to map urban areas on the city scale. In this research, the study area is the new Cairo city which has a high potential for harvesting solar energy, buildings in each compound have the same height, which does not cast shade on other buildings affecting PV efficiency. When applying GIS and RS techniques in New Cairo city, it is found that environmental factors - such as bare soil - affect the accuracy of the result, which reached 67% on the city scale. Researching more minor scales, such as compounds, required Very High Resolution (VHR) satellite images with a spatial resolution of up to 0.5 meter. The RS techniques applied in this research included supervised classification, and feature extraction, on Pleiades-1b VHR. On the compound scale, the accuracy assessment for the samples ranged between 74.6% and 96.875%. Estimating the PV energy production requires solar data; which was collected using a weather station and a pyrometer at the American University in Cairo, which is typical of the neighboring compounds in the new Cairo region. It took three years to collect the solar incidence data. The Hay- Devis, Klucher, and Reindl (HDKR) model is then employed to extrapolate the solar radiation measured on horizontal surfaces β =0°, to that on tilted surfaces with inclination angles β =10°, 20°, 30° and 45°. The calculated (with help of GIS and Solar radiation models) net rooftop area available for capturing solar radiation was determined for sample New Cairo compounds . The available rooftop areas were subject to the restriction that all the PVs would be coplanar, none of the PVs would protrude outside the rooftop boundaries, and no shading of PVs would occur at any time of the year; moreover typical other rooftop occupied areas, and actual dimensions of typical roof top PVs were taken into consideration. From those calculations, both the realistic total annual Electrical energy produced by the PVs and their daily monthly energy produced are deduced. The former is relevant if the PVs are tied to a grid, whereas the other is more relevant if it is not; optimization is different for both. Results were extended to estimate the total number of cars that may be driven off PV converted solar radiation per home, for different scenarios

Assessment of potential rooftop solar PV electricity at a suburban scale, and a comparative analysis based on topographical obstruction and seasonality

Long-term climate change mitigation calls for a switch from the current global non-renewable energy system to low greenhouse gas (GHG) emission energy solutions. Many nations have started adopting energy-efficient technology as part of their climate change programs and the built environment has been identified as a key lever for reducing emissions linked to energy efficiency. Building rooftop photovoltaic (PV) system is an effective technology to reduce emissions through the use of solar energy. In recent years, rooftop PV systems have become the main source of solar-generated energy, and forecasting their output is critical when assessing a site\u27s PV energy potential. However, integrating topographical features with seasonal considerations to estimate solar PV energy is challenging. There are some studies available that estimate solar PV energy on rooftops using geospatial tool modeling, but these have limitations in functionality, accuracy, and calculation speed. This study uses a geospatial tool to assess the solar PV potential of suitable rooftops in the suburbs of Wollongong, Australia, namely, Wombarra and Cringila. The model used in this study compares the energy potential of these two suburbs based on the topographical feature (escarpment), seasonality, rooftop slope, and aspect. The digital surface model (DSM) is created using LiDAR data, and then the DSM, building footprints, and suburb boundaries data are used to calculate the solar PV energy potential. A total of 1594 buildings from two suburbs were considered. Subsequently, solar radiation modeling for four common seasons in a year and a comparison of solar radiation output, suitable rooftop area, and electricity output are being done for both suburbs. Wombarra\u27s building rooftops are shadowed by the escarpment, whereas Cringila\u27s aren\u27t. Even though the weather in both suburbs is similar, the escarpment\u27s shadow affects solar PV energy output. Wombarra has 178 kWh/m2/building lesser yearly solar radiation than Cringila. Hence, Cringila offers more solar rooftop installation potential per building. The average annual potential electricity generation per dwelling in Wombarra is 20.6 kWh/m2/day, and the same for Cringila is 27.6 kWh/m2/day. The outcome reveals that 1352 building rooftops, with a usable area of 75481 m2, are the best locations for installing solar panels. According to the Australian Government\u27s Energy Made Easy statistics, the annual electricity consumption per household in Wollongong is 5707.6 kWh (Australian Energy Regulator 2022). The estimated yearly electricity production is 12705 Mwh (Wombarra: 2778.3 Mwh, Cringila: 9926.7 Mwh), which would be sufficient to meet local electricity consumption. An excess of 17% from Wombarra and 48% from Cringila can be exported back to the grid, which can be used by 3 neighbouring areas. Tiseo (2021) reported that Australia\u27s power sector released 656.4 grams/kWh of CO2 in 2020. Therefore, solar PV panels on all suitable rooftops of both suburbs could prevent 8339.5 tonnes of CO2 emissions. To achieve the goal of clean energy, future development can use the study\u27s findings as a guide. The proposed approach can assist in influencing policies and subsidies to boost deployment. This research can be made more in-depth by taking into account social and economic factors like consumer choices and return on investment, and physically inspecting specific building rooftop impediments

Ener3DMap-SolarWeb roofs: A geospatial web-based platform to compute photovoltaic potential

[EN] The effective exploitation and management of renewable energies requires knowledge not only of the energy intensity at the exploitation site but also of the influence of the geometry of the site and its surroundings. For this reason, the efficient processing and interpretation of combined geospatial and energy data is a key issue. This paper presents the development of a web-based tool for the automatic computation of photovoltaic potential on rooftops and on parcels without buildings. The tool called Ener3DMap-SolarWeb Roofs is based on Leaflet and supports WMS, GeoJSON, GeoCSV and KML formats, among others. With these data formats, base maps, geometric data from the rooftops automatically computed from LiDAR and imagery data with self-developed processing algorithms, cadastral data and a solar radiation model are integrated in the tool. These different types of data, the high level of automation and the different scales for which energy data is calculated (hourly, monthly and annually) are the main contributions of the presented tool compared to other existing solutions. The capacities of the tool are tested through its application to analyze the solar potential of rooftops with different shapes and for different solar panel configurations. The accuracy of the results is ensured through the integration of a validated methodology for the computation of geometry and a validated solar radiation model, PVGIS