Sizing methodology for photovoltaic systems considering coupling of solar energy potential and the electric load: dynamic simulation and financial assessment

• Autores: Milón Guzmán, Juan José, Zúñiga Torres, Juan Carlos, Del Carpio Beltrán, Herbert Jesús
• Formato: artículo
• Fecha de Publicación: 2020
• Institución: Universidad Tecnológica del Perú
• Repositorio: UTP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.utp.edu.pe:20.500.12867/3226
• Enlace del recurso: https://hdl.handle.net/20.500.12867/3226; https://doi.org/10.1051/e3sconf/202018102003
• Nivel de acceso: acceso abierto
• Materia: Photovoltaic system; https://purl.org/pe-repo/ocde/ford#2.11.04

A reliable methodology for the dimensioning of photovoltaic systems is presented in this paper. This method generates technical-financial variables that aid in the choice of the most adequate photovoltaic power system for each project. The techniques that are usually used determine the size of PV power plants considering the monthly average of the solar energy potential of the month with the lowest solar radiation and the electricity to be supplied to satisfy the demand. These conventional techniques generate an uncertainty of at least 40 %, mainly due to the daily dispersion of the solar energy availability and of the electric load. The proposed methodology takes into account a region’s own photovoltaic energy potential and the detailed characteristics of the electric load, matching both with different PV power plants sizes, and analyzing the whole during a time period that guarantees the reliability of the results. The energy coupling is performed integrating the energy parameters (solar energy and electric load) in short time intervals (30 minutes maximum) to determine the supplied energy, the unsupplied energy demand and the unused solar energy. The daily integration of the three factors, using a dynamic simulation and performing a financial evaluation, allows for the identification of the most appropriate PV power plant size for every project. The results indicate that this methodology reduces the uncertainty of the solar power-electric load coupling from 40 % to 2.2 %, which allows a better definition of the financial variables that determine the most appropriate installed solar power for a photovoltaic project.