Células solares fotovoltaicas : desarrollo de sistemas de monitorización y seguimiento de sistemas fotovoltaicos

En la UMA se investiga tanto el control y análisis de este tipo de dispositivos, como en las características eléctricas de módulos de distintas tecnologías que ayudan, entre otras cosas, a conocer enn directo indicadores como la producción real de energía de estas plataformas

Models for the Optimization and Evaluation of Photovoltaic Self-Consumption Facilities

The results obtained for the modeling and optimization of photovoltaic self-consumption facilities are presented. The study has been carried out for three Spanish cities with different climatic conditions. The self-consumption and self-sufficiency curves for different hourly consumption profiles have been obtained based on the installed peak power and the size of the battery. Different models of machine learning are proposed to predict these parameters. The input variables of these models are related to the configuration of the installation, its location and the type of consumption profile. The model with best predictions of self-sufficiency is Random Forest, which in cross-validation has a relative error of 5%. For the prediction of self-consumption, the model that performs best is the multilayer perceptron, with an average absolute error of 0.55 and an absolute relative error of 3%

Characterisation of hourly temperature of a thin-film module from weather conditions by artificial intelligence techniques

The aim of this paper is the use and validation of artificial intelligence techniques to predict the temperature of a thin-film module based on tandem CdS/CdTe technology. The cell temperature of a module is usually tens of degrees above the air temperature, so that the greater the intensity of the received radiation, the greater the difference between these two temperature values. In practice, directly measuring the cell temperature is very complicated, since cells are encapsulated between insulation materials that do not allow direct access. In the literature there are several equations to obtain the cell temperature from the external conditions. However, these models use some coefficients which do not appear in the specification sheets and must be estimated experimentally. In this work, a support vector machine and a multilayer perceptron are proposed as alternative models to predict the cell temperature of a module. These methods allow us to achieve an automatic way to learn only from the underlying information extracted from the measured data, without proposing any previous equation. These proposed methods were validated through an experimental campaign of measurements. From the obtained results, it can be concluded that the proposed models can predict the cell temperature of a module with an error less than 1.5 °C.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

New software tool to characterize photovoltaic modules from commercial equipment

A software platform has been developed in order to unify the different measurements obtained from different manufacturers in the photovoltaic system laboratory of the University of Malaga, Spain. These measurements include the current-voltage curve of PV modules and several meteorological parameters such as global and direct irradiance, temperature and spectral distribution of solar irradiance. The measurements are performed in an automated way by a stand-alone application that is able to communicate with a pair of multimeters and a bipolar power supply that are controlled in order to obtain the current–voltage pairs. In addition, several magnitudes, that can be configured by the user, such as irradiance, module temperature or wind speed, are incorporated to register the conditions of each measurement. Moreover, it is possible to attach to each curve the spectral distribution of the solar radiation at each moment. Independently of the source of the information, all these measurements are stored in a uniform relational database. These data can be accessed through a public web site that can generate several graphics from the data.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Junta de Andalucía. Proyecto de Excelencia P11-RNM-711

Modeling the solar spectrum including clearness index and average photon index to calculate a-si thin film modules performance.

Accurate prediction of the energy produced by photovoltaic modules is a key issue to make a proper integration into the energy grid. Even though crystalline silicon based modules have a bigger presence on photovoltaic installations, thin film technologies are getting more market share. The effect of varying spectrum is not always taken into account but several studies indicates that spectrum effect should not be obviated in thin film technologies, especially those with narrower spectral response, as happens for amorphous silicon technologies. A new method to show the performance ratio of thin film modules having into account the spectrum is developed in this work by clustering all the spectra into a few groups. For this characterization, both, statistical and data mining techniques have been used to cluster all spectra in groups by means of its APE value. Afterwards a depiction using contour graphs of modules PR indexed by clearness index and module temperature has been carried out in every one of the APE clusters. The data used have been registered in the Photovoltaic Laboratory of the University of Malaga for more than one year in order to take into account seasonal effects that could occur

Analysis and characterization of photovoltaic modules of three different thin-film technologies in outdoor conditions

The instantaneous performance ratio of thin-film photovoltaic modules of three different technologies is analyzed and characterized using contour graphs for different outdoor conditions. This parameter changes when modules are working in outdoor conditions depending on several variables. The most explanatory parameters we have found are the module temperature, the atmospheric clearness index and the solar spectral irradiance distribution; this latter has been included in the characterization using the average photon energy index as it has been identified as a good indicator of the solar spectral irradiance distribution. Moreover, the variance of instantaneous performance ratio for each studied technology has been analyzed. We can conclude that the joint use of all these parameters in contour graphs allows us to better characterize the performance of modules and to reduce the uncertainty observed in previous proposals that only use two of these parameters

Optimization of Energy Distribution in Solar Panel Array Configurations by Graph Theory and Minkowski’s Paths

Nowadays, the development of the photovoltaic (PV) technology is consolidated as a source of renewable energy. The research in the topic of maximum improvement on the energy efficiency of the PV plants is today a major challenge. The main requirement for this purpose is to know the performance of each of the PV modules that integrate the PV field in real time. In this respect, a PLC communications based Smart Monitoring and Communications Module, which is able to monitor at PV level their operating parameters, has been developed at the University of Malaga. With this device you can check if any of the panels is suffering any type of overriding performance, due to a malfunction or partial shadowing of its surface. Since these fluctuations in electricity production from a single panel affect the overall sum of all panels that conform a string, it is necessary to isolate the problem and modify the routes of energy through alternative paths in case of PV panels array configuration.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Analysis of CO2 reduction with micro CHP facility: Renewable energies and Stirling engine.

The Cogeneration laboratory is a research facility in the University of Málaga (UMA) that allows for the behavioural study of a renewable energy installation combining solar resources and micro-CHP. Energy generation in the system is provided by a 3 kWp photovoltaic array, two solar thermal connectors and a Whispergen EU1 Stirling micro-CHP unit. Energy storage in the facility is provided by water tank and lithium-ion battery. This laboratory is managed through a programmable Mitsubishi PLC that permits the simulation of different thermal and electrical load profiles, as well as the mode of operation. The electrical energy management is controlled by the solar inverter. Environmental data, are measured using a top of the line weather station.The system’s real time status is logged through the programmable PLC. All this data is transferred and analysed in a purpose-built MATLAB-based software, where power and energy balances are conducted, efficiencies are calculated, and a CO2 emissions evaluation is studied.The CO2 emissions analysis is carried to evaluate the carbon dioxide emissions generated by the facility when the electrical and thermal demand are provided by the joint solar and micro-CHP system. These emissions come from the burning of natural gas in the micro-CHP Stirling engine, and the usage of electricity from the grid. With the current mode of operation, a reduction of up to 70% in CO2 emissions has been achieved, with an energy generation that exceeds the demandUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech