The Application of LiDAR to Assessment of Rooftop Solar Photovoltaic Deployment Potential in a Municipal District Unit

A methodology is provided for the application of Light Detection and Ranging (LiDAR) to automated solar photovoltaic (PV) deployment analysis on the regional scale. Challenges in urban information extraction and management for solar PV deployment assessment are determined and quantitative solutions are offered. This paper provides the following contributions: (i) a methodology that is consistent with recommendations from existing literature advocating the integration of cross-disciplinary competences in remote sensing (RS), GIS, computer vision and urban environmental studies; (ii) a robust methodology that can work with low-resolution, incomprehensive data and reconstruct vegetation and building separately, but concurrently; (iii) recommendations for future generation of software. A case study is presented as an example of the methodology. Experience from the case study such as the trade-off between time consumption and data quality are discussed to highlight a need for connectivity between demographic information, electrical engineering schemes and GIS and a typical factor of solar useful roofs extracted per method. Finally, conclusions are developed to provide a final methodology to extract the most useful information from the lowest resolution and least comprehensive data to provide solar electric assessments over large areas, which can be adapted anywhere in the world

A GIS Open-Data Co-Simulation Platform for Photovoltaic Integration in Residential Urban Areas

The rising awareness of environmental issues and the increase of renewable energy sources (RES) has led to a shift in energy production toward RES, such as photovoltaic (PV) systems, and toward a distributed generation (DG) model of energy production that requires systems in which energy is generated, stored, and consumed locally. In this work, we present a methodology that integrates geographic information system (GIS)-based PV potential assessment procedures with models for the estimation of both energy generation and consumption profiles. In particular, we have created an innovative infrastructure that co-simulates PV integration on building rooftops together with an analysis of households’ electricity demand. Our model relies on high spatiotemporal resolution and considers both shadowing effects and real-sky conditions for solar radiation estimation. It integrates methodologies to estimate energy demand with a high temporal resolution, accounting for realistic populations with realistic consumption profiles. Such a solution enables concrete recommendations to be drawn in order to promote an understanding of urban energy systems and the integration of RES in the context of future smart cities. The proposed methodology is tested and validated within the municipality of Turin, Italy. For the whole municipality, we estimate both the electricity absorbed from the residential sector (simulating a realistic population) and the electrical energy that could be produced by installing PV systems on buildings’ rooftops (considering two different scenarios, with the former using only the rooftops of residential buildings and the latter using all available rooftops). The capabilities of the platform are explored through an in-depth analysis of the obtained results. Generated power and energy profiles are presented, emphasizing the flexibility of the resolution of the spatial and temporal results. Additional energy indicators are presented for the self-consumption of produced energy and the avoidance of CO2 emission

A Decision Support System for Photovoltaic Potential Estimation

With knowledge on the photovoltaic potential of individual residential buildings, solar companies, energy service providers and electric utilities can identify suitable customers for new PV installations and directly address them in renewable energy rollout and maintenance campaigns. However, many currently used solutions for the simulation of energy generation require detailed information about houses (roof tilt, shading, etc.) that is usually not available at scale. On the other hand, the methodologies enabling extraction of such details require costly remote-sensing data from three-dimensional (3D) laser scanners or aerial images. To bridge this gap, we present a decision support system (DSS) that estimates the potential amount of electric energy that could be generated at a given location if a photovoltaic system would be installed. The DSS automatically generates insights about photovoltaic yields of individual roofs by analyzing freely available data sources, including the crowdsourced volunteered geospatial information systems OpenStreetMap and climate databases. The resulting estimates pose a valuable foundation for selecting the most prospective households (e.g., for personal visit and screening by an expert) and targeted solar panel kit offerings, ultimately leading to significant reduction of manual human efforts, and to cost-effective personalized renewables adoption

Solar simulators: Application to developing cities

Developing cities seeking to assess their policy and financing options for rooftop photovoltaic (PV) power generation can benefit from the increasing sophistication of rooftop PV simulators. This report from the International Renewable Energy Agency (IRENA) examines the evolution of solar PV simulators, from single-rooftop assessments at the household level to large-scale, aggregate-level analyses undertaken by municipal authorities and other large entities. Larger-scale simulator applications, typically preceding the establishment PV incentive programmes, help to determine the optimal power mix and ensure the long-term viability. The report also highlights the growing range of accessible, free online solar simulators. In the past, the significant costs and need for expertise to develop such simulators limited their use to advanced economies with well-established electricity markets and a strong research culture. However, the technology landscape has evolved, and solar simulators can now be deployed to maximum benefit anywhere in the world at an affordable cost. IRENA’s work on solar simulators aims to encourage further dialogue on energy planning in the urban context. This study also reinforces the case for greater use of proven data-driven techniques to create actionable, pragmatic policy and economic solutions. Solar simulators offer a clear example of how such techniques can enhance the energy sustainability of cities in developing countries

Assessment of potential for photovoltaic roof installations by extraction of roof slope from lidar data and aggregation to census geography

Leading topics in PV research include field performance and grid impact. A national understanding of roof features (slope, orientation, area) is essential for modelling the timing of PV installation scenarios with their associated irradiance data. However, such information is not currently available. This paper demonstrates the extraction of building characteristics from LIDAR (Light Detection and Ranging) data. These characteristics are then aggregated and scaled-up to produce a UK-wide map of PV potential, based on suitable roof tilts and azimuths

Energy Self-Sufficiency Urban Module (ESSUM): GIS-LCA-based multi-criteria methodology to analyze the urban potential of solar energy generation and its environmental implications

The concentration of the population in cities has turned them into sources of environmental pollution, however, cities have a great potential for generating clean energy through renewable sources such as a responsible use of solar energy that reaches its rooftops. This work proposes a methodology to estimate the level of energy self-sufficiency in urban areas, particularly in a district of the city of Zaragoza (Spain). First, the Energy Self-Sufficiency Urban Module concept (ESSUM) is defined, then the self-sufficiency capacity of the city or district is determined using Geographical Information Systems (GIS), Light Detection and Ranging (LiDAR) point clouds and cadastral data. Secondly, the environmental implications of the implementation of these modules in the rooftops of the city using the LCA methodology are calculated. The results obtained show that total self-sufficiency of Domestic Hot Water (DHW) can be achieved using 21 % of available rooftop area, meanwhile the rest of rooftop area, dedicated to photovoltaic (PV), can reach 20 % of electricity self-sufficiency, supposing a final balance of a reduction in CO2 emissions of 12,695.4 t CO2eq/y and energy savings of 372,468.5 GJ/y. This corresponds to a scenario where full self-sufficiency of DHW was prioritized, with the remaining roof area dedicated to PV installation. In addition, other scenarios have been analyzed, such as the implementation of the energy systems separately

Laboratory-based spectral data acquisition of roof materials

Roof characteristics such as material type and their properties information are essential to integrating urban agriculture (UA), rainwater harvesting systems (RWHS), and energy systems on roofs. Roof materials can be identified from their spectral signatures. However, this identification requires a priori knowledge of the materials’ spectral characteristics. The main perspective of this work is the future use of spectral data for roof classification. A common practice in mapping materials is the use of spectral libraries. In this regard, this work describes a novel framework for laboratory-based spectral data acquisition. The reflectance data of common, recently introduced (plastics and metals), and representative roof materials from the Mediterranean region were obtained. Data acquisition was conducted in a laboratory under controlled conditions using a high-spatial-resolution (HSR) sensor, which is usually used for airborne surveys. Large variations in the spectral reflectance data were observed due to the composition of the roof material. Flat spectral signatures were found for fibre cement, concrete, gravels and some metals, especially from the near-infrared (NIR) spectral region. Colour and surface finish greatly influence the visible (VIS) spectral range. It was confirmed that the view angle did not modify the spectral shapes. A collection of 39 spectral data of roof materials (ceramics, concrete, fibre cement, metals, plastics, paints, stone, and wood) were compiled into a spectral library that is available online.This work is part of the Fertilecity II project supported by the Spanish Ministry of Economy and Competitiveness (CTM2016-75772-C3-3-R and CTM2016-75772-C3-1-R, AEI/FEDER, UE); from the Spanish Ministry of Science, Innovation and Universities; through the María de Maeztu program for Units of Excellence (MDM-2015-0552). Authors want to thank to the University of Guadalajara (Mexico) for awarding a research scholarship to Perla Zambrano-Prado, and to the reviewers for their valuable comments.Peer ReviewedPostprint (author's final draft

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

BIM-based surface-specific solar simulation of buildings

Photovoltaic (PV) solar energy is rapidly growing as an attractive alternative to fossil fuels. PV panels can harvest the solar power and turn it into a clean source of electricity. Traditionally, PV panels are only used on the rooftops of buildings. However, with the emergence of building-integrated solar panels in recent years, other surfaces on the building façade can be considered for the installation of PV panels. Given that different panels have different cost and performance profiles, it is of a cardinal importance to properly design the PV panels on the building facades to ensure a maximum benefit-cost ratio. Existing simulation and optimization methods do not discriminate between different types of surfaces of the building and treat the building envelope as a set of polygons. This can result in under- or over- design since there is a strong relationship between the type of the surfaces and the type of PV panels that can be attached to them or integrated with them. The advent of Building Information Modeling (BIM) in recent years has provided a rich platform for object-based evaluation and analysis of buildings. Nonetheless, currently, BIM is not used for a detailed and surface-specific simulation of building surfaces. In this research, a BIM-based method is developed for a detailed simulation of a building envelope using its surface properties. A prototype is developed using Dynamo visual programming platform to demonstrate the feasibility of the proposed method, and a case study is presented for a building in Montreal, Canada. In the light of the result of the case study, it can be concluded that the proposed method is promising in terms of providing the input for a comprehensive planning of the solar panel layout