This paper proposes an integrated bottom-up approach designed to assist those dealing with the strategic analysis of BIPV installations to estimate the power generation potential and energy demand of urban buildings on a regional scale. On the demand side, dynamic building simulations are used to generate hourly energy distributions, taking into account the actual urban morphology. On the supply side, projections of the system's power generation take into account the direct and indirect components of solar radiation, as well as local climate variables. The python-based algorithm editor uses Grasshopper to connect four types of modeling and simulation tools, such as 1) generation of 3D models, 2) solar radiation analysis, 3) formatting weather files (TMY datasets), and 4) dynamic energy demand. Compiled by Chen Jiaoyun
The methodology has been demonstrated in 20 buildings of Yasar University in Izmir (Turkey), and the study found that the BIPV system can reach a share of 23% of renewable energy per year, in line with the 20% target of the Renewable Energy Directive. A quantitative comparison of the supply and demand information at each time point shows that BIPV can only meet part of the energy demand, and appropriate matching strategies are needed to better utilize the renewable energy potential.
