• Energy Research

Availability of effective estimation of the power profiles of photovoltaic systems is essential for studying how to increase the share of intermittent renewable sources in the electricity mix of many countries. For this purpose, weather forecasts, together with historical data of the meteorological quantities, provide fundamental information. The weak point of the forecasts depends on variable sky conditions, when the clouds successively cover and uncover the solar disc. This causes remarkable positive and negative variations in the irradiance pattern measured at the photovoltaic (PV) site location. This paper starts from 1 to 3 days-ahead solar irradiance forecasts available during one year, with a few points for each day. These forecasts are interpolated to obtain more irradiance estimations per day. The estimated irradiance data are used to classify the sky conditions into clear, variable or cloudy. The results are compared with the outcomes of the same classification carried out with the irradiance measured in meteorological stations at two real PV sites. The occurrence of irradiance spikes in “broken cloud” conditions is identified and discussed. From the measured irradiance, the Alternating Current (AC) power injected into the grid at two PV sites is estimated by using a PV energy conversion model. The AC power errors resulting from the PV model with respect to on-site AC power measurements are shown and discussed.

The validation of a new explicit empirical model for general photovoltaic devices, providing current and voltage at Maximum Power Point (MPP) and current-voltage/power-voltage characteristics under arbitrary conditions of temperature and irradiance, is presented. One of the main advantages of this model is the fact that the equivalent circuit parameters - such as series and shunt resistance, dark-saturation currents, etc. - are not needed, as the sole model input data are the device parameters commonly reported in the datasheets. Moreover, the model is explicit so that its application is very affordable from the computational standpoint. The model is applied to three different types of photovoltaic modules representing some of the most widely diffused technologies in the current market: multi-crystalline silicon, CdTe and CIGS. The calculated voltages, currents and powers at maximum power point are compared with the ones measured for three modules working at the photovoltaic test facility of the University of Trieste. A statistical analysis is presented in order to prove the effectiveness and reliability of the model at maximum power point. Finally, the results of the new explicit model are compared with those obtained by a polynomial regression, Artificial Neural Network (ANN), the well-known single-diode model and an additional, different explicit model. This work shows that the electric performance of a photovoltaic module can be predicted with a high degree of accuracy on the sole basis of parameters that are always found in the photovoltaic device's datasheet

In this article, a novel embedded low-cost system for real-time fault diagnosis of photovoltaic (PV) modules is proposed. The idea aims to develop an embedded application to classify certain defects that can frequently occur on PV modules based on infrared (IR) images in different regions (desert and Mediterranean climates). The investigated faults are sand accumulation, dirt on PV modules, degradation, and junction box overheating. After several inspections, these are the most commonly observed defects on PV modules in both regions (south and north of Algeria). A tiny convolutional neural network (TinyCNN) was developed, optimized, and integrated into a low-cost and low-power microcontroller (Arduino Nano 33 BLE sense). In this regard, a database of IR thermography images was built and used. The developed TinyCNN-based model could be run locally, without the need to send the data to the cloud for analysis and processing. Another microcontroller [Arduino Nano 33 Internet of Things (IoT)] was used to remotely monitor the state of the PV modules. Thanks to IoT technology, the results have been visualized and posted online on a dedicated monitoring webpage. The proposed embedded solution could be integrated into an unmanned aerial vehicle for real-time applications. Furthermore, it assists operators in diagnosing their PV modules and making a maintenance schedule. The proposed technique outperforms the existing solutions in terms of cost, consuming power, simplicity, and execution time. Simulation and experimental results clearly report the feasibility of the proposed embedded system, which has an average cost of around 120 US dollars.

As well known, potential induced degradation (PID) strongly decreases the performance of photovoltaic (PV) strings made of several crystalline silicon modules in hot and wet climates. In this paper, PID tests have been performed on commercial copper indium gallium selenide (CIGS) modules to investigate if this degradation may be remarkable also for CIGS technology. The tests have been conducted inside an environmental chamber where the temperature has been set to 85 °C and the relative humidity to 85%. A negative potential of 1000 V has been applied to the PV modules in different configurations. The results demonstrate that there is a degradation affecting the maximum power point and the fill factor of the current-voltage (I-V) curves. In fact, the measurement of the I-V curves at standard test condition show that all the parameters of the PV modules are influenced. This reveals that CIGS modules suffer PID under high negative voltage: this degradation occurs by different mechanisms, such as shunting, observed only in electroluminescence images of modules tested with negative bias. After the stress test, PID is partially recovered by applying a positive voltage of 1000 V and measuring the performance recovery of the degraded modules. The leakage currents flowing during the PID test in the chamber are measured with both positive and negative voltages; this analysis indicates a correlation between leakage current and power losses in case of negative potential.

The integration of photovoltaic power plants in the distribution or transmission level is already a reality. Usually, the location chosen for these power plants consider high solar irradiance, but the temperature could be a drawback. Thus, the aim of this paper is to show the effect of solar irradiance and ambient temperature on the power generation of a photovoltaic power plant. For this, a real photovoltaic power plant is chosen in the Ecuadorian line in South America. The results show that the active power is reduced around 0.1 to 0.3 p.u when the ambient temperature is higher than 25 Celsius degrees although the solar irradiance is high.

The Italian photovoltaic market is since 2011 the world's largest and represents a success story having attained grid parity for the commercial and industrial (C&I) market of electricity. In this paper, the Levelized Cost Of Energy (LCOE) is calculated for three representative locations in Northern, Central, and Southern Italy, and compared with the C&I end-user electricity price. The grid parity is shown under certain conditions showing that the photovoltaic market is already ready to survive without the feed-in tariff mechanism.

Abstract Forecasting of solar irradiance is in general significant for planning the operations of power plants which convert renewable energies into electricity. In particular, the possibility to predict the solar irradiance (up to 24 h or even more) can became – with reference to the Grid Connected Photovoltaic Plants (GCPV) – fundamental in making power dispatching plans and – with reference to stand alone and hybrid systems – also a useful reference for improving the control algorithms of charge controllers. In this paper, a practical method for solar irradiance forecast using artificial neural network (ANN) is presented. The proposed Multilayer Perceptron MLP-model makes it possible to forecast the solar irradiance on a base of 24 h using the present values of the mean daily solar irradiance and air temperature. An experimental database of solar irradiance and air temperature data (from July 1st 2008 to May 23rd 2009 and from November 23rd 2009 to January 24th 2010) has been used. The database has been collected in Trieste (latitude 45°40′N, longitude 13°46′E), Italy. In order to check the generalization capability of the MLP-forecaster, a K -fold cross-validation was carried out. The results indicate that the proposed model performs well, while the correlation coefficient is in the range 98–99% for sunny days and 94–96% for cloudy days. As an application, the comparison between the forecasted one and the energy produced by the GCPV plant installed on the rooftop of the municipality of Trieste shows the goodness of the proposed model.