Author:

Keywords:

Science & Technology, Technology, Construction & Building Technology, Energy & Fuels, Engineering, Civil, Engineering, Building Information Model (BIM), Distributed PV design, Radiation analysis, Cost-to-power ratio, Coordination transformation, OPTIMUM TILT ANGLE, DESIGN OPTIMIZATION, PERFORMANCE, SIMULATION, MODEL, ORIENTATION, BUILDINGS, FRAMEWORK, ARRAYS, 09 Engineering, 12 Built Environment and Design, Building & Construction, 33 Built environment and design, 40 Engineering

Abstract:

© 2017 Elsevier B.V. Distributed PV systems have been widely deployed on building rooftops or facade in more complex environments thanks to their low-carbon footprint and commercial value. As a fact, the shapes and locations of building exteriors as well as their surrounding obstacles highly impact the power output of distributed PV systems. Since they are complex and correlated in nature, these environmental factors have not been precisely dealt with so far in the current manual design practice. Actually, it is evident that if in the design stage these factors can be fully taken into account, the power output versus investment would be largely enhanced. To this end, this paper proposes an automatic distributed PV system design tool, which fully utilizes and integrates the existing information and modeling techniques, i.e. information from Building Information Model (BIM), as well as advanced optimization methods, to facilitate precise PV system simulation and optimization. The proposed tool firstly makes the detailed shade and radiation analysis on the basis of the existing BIM model, and, according to these results, executes an automatic PV design process oriented by the goal of the minimal cost-to-power ratio. Afterwards, a coordinate transform mechanism is designed to automatically transfer the calculated PV design into the BIM model, which facilitates the realization of automatic deployment. This tool has been applied to a roof PV project and verified via both numerical and empirical studies. Results show that compared to human-based design, this tool can improve up to 265% design efficiency, increase 36.1% power output and reduce around 4.5% capital investment per unit power output.