Abstract

An overview of photovoltaics, from cells to modules to systems, is presented. Various solar cell structures that have been commercially pursued over the years, as well as emerging solar cell technologies, are described. Approaches to cell-to-module manufacturing are discussed. The influence of temperature on cell/module performance and methods to mitigate it are briefly addressed. This is followed by a discussion of the variety of models and software tools that are being practiced in the industry today for simulation of solar photovoltaic systems. The capabilities, assumptions, limitations, costs, and licensing approaches of selected software tools are compared. An exploration of the use of Machine Learning (ML) techniques to perform: (a). detection of defects at the cell & panel level, (b). estimation of faults and voltage regulation at the PV system level, and (c). performance enhancement and forecasting at the cell and panel level, is presented based on existing studies. The increasing role of PV systems in distributed energy applications including microgrids and growing electricity demand from Data Centers, Electric Vehicle Charging Stations, and other energy-intensive applications, is examined. Opportunities and limitations associated with the development and increased utilization of PV technologies and software tools are presented.

Keywords

solar photovoltaics, solar cells, solar modules, influence of temperature, machine learning techniques, thermal management, microgrids