Examinando por Autor "Mannino, Gaetano"
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Ítem Enhanced thermal models of photovoltaic modules by electrical operating conditions dependency(Elsevier, 2026-01-15) Tina , Giuseppe Marco; Osama, Amr; Gagliano, Antonio; Mannino, Gaetano; Muñoz-Rodríguez, Francisco José; Jiménez-Castillo, GabinoThe increasing penetration of photovoltaic (PV) systems poses challenges to the reliability and adequacy of power systems. To support grid stability, PV systems must evolve to be capable of providing frequency regulation and reserve services—including not only down frequency reserve but also up reserve. This latter service requires PV modules to operate away from their maximum power point (MPP), a condition that requires an enhancement in PV module thermal behavior assessment. Consequently, there is a growing need for advanced thermal models that account for electrical operating conditions to ensure accurate temperature prediction under all operating scenarios. While traditional thermal models primarily depend on meteorological inputs, they typically neglect the Electrical Operating Status (EOS). Overlooking this issue can lead to significant prediction errors—up to 5–7 ◦C—especially during operation away from MPP. The proposed investigation developed an enhanced thermal model incorporating EOS dependency by including the ratio of measured current to the calculated current at MPP as an additional input. Two cases of the Faiman and Sandia models were optimized using Genetic Algorithm, Particle Swarm Optimization, non-linear least squares, and polynomial regression. Optimization is performed using three identical PV systems operating under reference EOS conditions: open circuit, short circuit, and MPP. Results demonstrate that EOS-integrated models significantly improve temperature prediction accuracy. The EOS sensitive models achieved prediction errors as low as 0.1–1.13 % and R2 values above 0.91, outperforming traditional models that exhibited errors from 2 to 29 %. These findings support the need for EOSaware thermal modelling in modern PV system design and operationÍtem Nonlinear and multivariate regression models of current and voltage at maximum power point of bifacial photovoltaic strings(Elsevier, 2024-02) Mannino, Gaetano; Tina, Giuseppe Marco; Jiménez-Castillo, Gabino; Cacciato, Mario; Bizzarri, Fabrizio; Canino, AndreaThe bifacial photovoltaic (PV) modules are able to convert the irradiance that hits both the front and the back side of the modules into electrical energy, this allows to increase the output power compared to monofacial modules. However, the mathematical models used for traditional PV modules do not consider the contribution of rear irradiance, even recent works deal with the modeling of the rear irradiance influence on bPV power output. In the present work some empirical models capable of estimating the current and voltage at maximum power point conditions are unveiled; the models consider only the front irradiance, as monofacial PV modules, or the back irradiance through the concept of equivalent irradiance and module temperature. In addition, some modifications to the current and voltage models have been proposed. In all cases, the optimal parameters of the models are obtained starting from a dataset of experimental data acquired from a string of bifacial photovoltaic modules installed in Catania (Italy). The PV plant under study was monitored for an entire year, thus allowing the use of data acquired in different weather conditions. The method description includes the filtering of the input signals and the searching method of the empirical coefficients in order to estimate the current and voltage at the maximum power point (MPP) for bifacial photovoltaic modules.