Optimizing solar energy collection potential in high-rise residential buildings in urban areas

As cities grow, the demand for energy-efficient solutions becomes paramount. Implementing solar systems on buildings becomes essential to transform them from energy consumers to active contributors, addressing urban energy challenges and meeting carbon emission reduction targets. Several factors contribute to solar energy gain by buildings, including location, building type, solar collection type and local conditions. Residential buildings present unique challenges due to architectural complexity like projections, recessions, and balconies, affecting solar collection potential. Additionally, varied energy demands and usage patterns require a distinct investigation of solar energy collection.

PV emerges as the most frequently studied system in recent studies (Bushra, 2022). Despite their benefits, in compact urban environments, building-integrated photovoltaic (BIPV) performance is considerably affected by shading from other structures. Consequently, maximizing sunlight utilization on limited roof and façade areas is crucial for optimal energy output. Moreover, previous research often simplifies residential buildings into uniform box-like models, neglecting the complexities of non-uniform façades (Bushra, 2022; Wu et al., 2022). This simplification ignores critical factors like balcony shading and window size impact on PV configurations and daylight penetration, affecting both PV electrical output and building thermal performance. Larger windows can increase solar gain and daylight, beneficial in winter, but potentially leading to overheating in summer. Therefore, optimizing window size and PV configuration is essential to balance energy gains and reliance on grid electricity during peak loads.

This research aims to optimize solar energy collection through PV systems and windows in urban residential buildings by considering overheating, building codes like maximum window-to-wall ratio, peak loads and detailed geometries. Optimization variables include PV and window sizes with objectives to minimize net energy consumption and carbon emissions. By using EnergyPlus and employing an evolutionary optimization algorithm, like Non-dominated Sorting Genetic Algorithms (NSGA-II), the study aims to find the best trade-offs between the objectives and present the non-dominated solutions in the Pareto frontier.

Expected findings suggest that a holistic approach to optimizing solar collection by considering the non-uniform design of facades and PV limitations, incorporating PV power generation and solar heat gain and daylight through windows can significantly demonstrate the real approach of solar energy collection potential in residential buildings. This integrated strategy promises more reliable energy solutions by balancing thermal and visual comfort with energy production, advancing buildings as active participants in urban energy systems, and bridging the gap between research findings and practical implementation of solar systems.