Applying GIS Technology for optimum selection of Photovoltaic Panels “Spatially at Defined Urban Area in Alexandria, Egypt”

Abstract

This paper introduces an improved method to specify the potential areas at buildings' top surface for installation of photovoltaic (PV) power units in a defined urban area (UA). Additionally, optimum selection between various (PV) modules is addressed. The proposed approach is based on spatial data analysis and implementation of probabilistic approach (PA) in order compute the power capacity factor (CF) of the PV modules. According this estimation the module with highest average capacity factor is selected for installation at the defined UA. A dedicated case study is proposed and implemented through three main stages. In the first stage, the spatial data of studied buildings are analyzed based on the digitized SIR-DS using Google Earth imagery and ArcGIS software as a Geo-Model. Thus, the planner defines the potential areas for installing PV modules which linked with the buildings' database. In the second stage, various PV modules which produced by different manufacturers, are compared together based on the concept of the highest average CF estimated. In proceedings, firstly, a mathematical modeling of solar irradiance data-set (SIR-DS) is presented using statistical probability distribution function (PDF). These data are approved by Egyptian Meteorological Authority, and collected over a long-term period (7 years). Then, the most fitted PDF in matching with the measured data is then utilized to determine the average output power of each PV module. After that, the CF is estimated for all modules analyzed, such that the module with the highest average CF over the year is identified. Finally, the last stage integrates the results obtained from the prior stages. Accordingly, this paper introduces effective solution for the optimum selection between different PV modules at a given UA, in addition to specifying the potential areas for PV system installation which subjected to the studied buildings' database. Keywords: Photovoltaic (PV), Capacity factor (CF), Google Earth imagery, Geographical information system (GIS), Solar radiation (SR)