This paper presents an integrated bottom-up approach aimed at helping those dealing with strategical analysis of installation of Building Integrated Photo Voltaic (BIPV) to estimate the electricity production potential along with the energy needs of urban buildings at the district scale. On the demand side, hourly energy profiles is generated using dynamic building simulation taking into account actual urban morphologies. On the supply side, electricity generated from the system is predicted considering both the direct and indirect components of solar radiation as well as local climate variables. Python-based Algorithm editor Grasshopper is used to interlink four types of modelling and simulation tools as 1) generation of 3-D model, 2) solar radiation analysis, 3) formatting weather files (TMY data set) and 4) dynamic energy demand. The method has been demonstrated for a cluster of 20 buildings located in the Yasar University in Izmir (Turkey), for which it is found the BIPV system could achieve an annual renewable share of 23%, in line with the Renewable Energy Directive target of 20%. Quantitatively-compared demand and supply information at hourly time step shows that only some energy needs can be met by BIPV, so there is a need for an appropriate matching strategy to better exploit the renewable energy potential.

本文提出了一种自下而上的集成方法，旨在帮助那些对建筑集成光伏（BIPV）的安装进行战略分析的人员来估算区域规模内的电力生产潜力以及城市建筑的能源需求。在需求方面，考虑到实际的城市形态，使用动态建筑模拟生成每小时的能源分布。在供应方面，可以从系统产生的电能进行预测，同时考虑到太阳辐射的直接和间接成分以及当地的气候变量。基于Python的算法编辑器Grasshopper用于互连四种类型的建模和仿真工具，例如1）3-D模型的生成，2）太阳辐射分析，3）格式化天气文件（TMY数据集）和4）动态能量需求。该方法已在位于伊兹密尔Yasar大学（土耳其）的20栋建筑物的集群中得到了证明，发现BIPV系统可实现23％的年可再生能源份额，与可再生能源指令的20％目标相符％。在每小时时间步长上进行定量比较的需求和供应信息表明，BIPV只能满足部分能源需求，因此需要一种适当的匹配策略来更好地利用可再生能源的潜力。