Power systems have faced significant changes in recent years due to the integration of renewable energies in the power grid. Thanks to its multiple advantages, the so-called clean energies play an important role in the development of the electrical system, allowing the active participation of end users in energy markets. However, the intermittent nature of these sources has delayed their full integration into power systems; unless operated in conjunction with energy storage systems (e.g. batteries, ultracapacitors, etc.) to smooth out the generation and match the demand. This article presents a power sharing methodology for the exchange of energy between batteries and ultracapacitors in a photovoltaic installation. The case study comprises a hybrid energy storage system, on-site generation and a residential user, where both the load and generation profiles are analyzed using wavelet transform. The high- and low-frequency components of both profiles are used to calculate the energy that is injected into the energy storage system. Results show that by separating the components of the signal in high and low frequencies it is possible to take advantage of the characteristics of each storage technology, extending its life cycle.
La Comisión de Energía y Gas (CREG) estableció a finales del 2016 una metodología para el cálculo del Cargo por Confiabilidad de plantas solares fotovoltaicas. En esta se establece que se requieren series históricas de más de diez años de irradiación solar y temperatura ambiente en el sitio de instalación de la planta para el cálculo de dicho cargo, sin embargo, en Colombia no se cuenta con datos históricos con dicha información, por lo que se requiere de estrategias que permitan la estimación de dichas series a partir de otras variables de entrada. El presente artículo emplea Redes Neuronales Artificiales para estimar datos de radicación solar requeridos utilizando como datos de entrada la humedad relativa, temperatura ambiente y mes del año.
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