Development of a tool for the estimation of rooftop solar photovoltaic generation

Abstract

In this project, a tool has been created capable of estimating the solar photovoltaic energy generation potential of any building, taking into account its characteristics, the typical meteorological information of the chosen location, and the shadows cast on the roof. To carry it out, we have worked on the integration of two different parts using Python programming language. The first part consists of the validation of a first shadow module created by the CIMNE (Centre Internacional de Mètodes Numèrics a l'Enginyeria). This program studies the obstacles located around the interested roof and how they affect the solar radiation on the roof depending on the solar position. The validation of the shadow module has been carried out by comparing the results obtained with the obstacles adjacent to the building under study and taking into account the sun's path at different times of the day and times of the year. In the second part, a module has been developed and validated to estimate the hourly generation of a possible photovoltaic installation. This program has been created from the generation of a Python code from the SAM (System Advisor Model) software, taking into account the shading matrix obtained in the first module. To make these estimates, the characteristics of the installation to be simulated are defined (solar panels, inverters, installation losses, etc.). The validation of the power generation estimation module was carried out by comparing the results of the code created with the executions using the aforementioned SAM software. The validation of the entire program has been carried out by comparing the output of the radiation after shading on the roof of the complete execution and the same parameter estimated by PVGIS (a web page that provides information on solar radiation and the performance of photovoltaic systems). Both PVGIS and the program created that estimates the solar potential of roofs use the same meteorological data.