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Título : A New Methodology for Estimating the Potential for Photovoltaic Electricity Generation on Urban Building Rooftops for Self-Consumption Applications
Autor: Villa Avila, Edisson Andres
Arevalo Cordero, Wilian Paul
Ochoa Correa, Danny Vinicio
Villa Avila, Michael Francisco
Sempertegui Moscoso, Maria Emili
Correspondencia: Villa Avila, Edisson Andres, edisson.villa2809@ucuenca.ec
Palabras clave : Photovoltaic rooftop systems
Roof-Solar-Max methodology
Solar energy potential assessment
Urban solar energy planning
Área de conocimiento FRASCATI amplio: 2. Ingeniería y Tecnología
Área de conocimiento FRASCATI detallado: 2.2.1 Ingeniería Eléctrica y Electrónica
Área de conocimiento FRASCATI específico: 2.2 Ingenierias Eléctrica, Electrónica e Información
Área de conocimiento UNESCO amplio: 07 - Ingeniería, Industria y Construcción
ÁArea de conocimiento UNESCO detallado: 0713 - Electricidad y Energia
Área de conocimiento UNESCO específico: 071 - Ingeniería y Profesiones Afines
Fecha de publicación : 2024
Volumen: Volumen 7, número 6
Fuente: Smart Cities
metadata.dc.identifier.doi: 10.3390/smartcities7060146
Tipo: ARTÍCULO
Abstract: 
Urban PV solutions utilize city rooftops to address energy challenges. The Roof-Solar-Max method optimizes photovoltaic panel placement in urban areas. Significant energy potential aligns with substantial power needs in cities. Policy insights and grid surplus solutions provide valuable guidance for policymakers. The research promotes cleaner and more sustainable global energy solutions. What are the main findings? The Roof-Solar-Max method successfully optimizes the placement of photovoltaic (PV) panels on urban rooftops, significantly increasing energy generation potential. The methodology demonstrated that PV energy generation in the urban district studied can exceed the local electricity demand by more than six times, highlighting the feasibility of surplus energy contribution to the grid. What is the implication of the main finding? This approach offers a practical and scalable solution for urban planners to maximize the use of rooftop spaces, facilitating the widespread adoption of renewable energy in cities. By utilizing surplus energy through grid integration, the method can contribute to national energy systems, reduce reliance on non-renewable sources, and promote sustainability. Highlights: As the world increasingly embraces renewable energy as a sustainable power source, accurately assessing of solar energy potential becomes paramount. Photovoltaic (PV) systems, especially those integrated into urban rooftops, offer a promising solution to address the challenges posed by aging energy grids and rising fossil fuel prices. However, optimizing the placement of PV panels on rooftops remains a complex task due to factors like building shape, location, and the surrounding environment. This study introduces the Roof-Solar-Max methodology, which aims to maximize the placement of PV panels on urban rooftops while avoiding shading and panel overlap. Leveraging geographic information systems technology and 3D models, this methodology provides precise estimates of PV generation potential. Key contributions of this research include a roof categorization model, identification of PV-ready rooftops, optimal spatial distribution of PV panels, and innovative evaluation technology. Practical implementation in a real urban setting demonstrates the methodology’s utility for decision making in the planning and development of solar energy systems in urban areas. The main findings highlight substantial potential for PV energy generation in the studied urban area, with capacities reaching up to 444.44 kW. Furthermore, implementing PV systems on residential rooftops has proven to be an effective strategy for reducing CO2 emissions and addressing climate change, contributing to a cleaner and more sustainable energy mix in urban environments.
URI : https://www.scopus.com/record/display.uri?eid=2-s2.0-85213426466&doi=10.3390%2fsmartcities7060146&origin=inward&txGid=b1c1d00bb5de513d48558913d4bb9b0a
URI Fuente: https://www.mdpi.com/journal/smartcities
ISSN : 2624-6511
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