Towards improved cover glasses for photovoltaic devices

For the solar energy industry to increase its competitiveness there is a global drive to lower the cost of solar generated electricity. Photovoltaic (PV) module assembly is material-demanding and the cover glass constitutes a significant proportion of the cost. Currently, 3 mm thick glass is the predominant cover material for PV modules, accounting for 10-25% of the total cost. Here we review the state-of-the-art of cover glasses for PV modules and present our recent results for improvement of the glass. These improvements were demonstrated in terms of mechanical, chemical and optical properties by optimizing the glass composition, including addition of novel dopants, to produce cover glasses that can provide: (i) enhanced UV protection of polymeric PV module components, potentially increasing module service lifetimes; (ii) re-emission of a proportion of the absorbed UV photon energy as visible photons capable of being absorbed by the solar cells, thereby increasing PV module efficiencies; (iii) Successful laboratory-scale demonstration of proof-of-concept, with increases of 1-6% in Isc and 1-8% Ipm. Improvements in both chemical and crack resistance of the cover glass were also achieved through modest chemical reformulation, highlighting what may be achievable within existing manufacturing technology constraints

Real-time procedure to detect losses in photovoltaic generators using the instantaneous and the translated Performance Ratio

In the past the diagnosis of PV panels was mainly devoted to evaluate the performance degradation due to the manufacturing process and to guarantee the 25 years lifetime; these analysis was essentially based on off-line costly laboratory measurement. Recent studies shown that the degradation of photovoltaic (PV) panels is accelerated by various unpredictable and unavoidable phenomena, e.g, the extreme environmental and operating conditions, or by the type of electrical connections thus, in order to prevent the stop of the PV plants, the development of on-line diagnostic techniques is assuming great of interest for the customers and manufacturers of PV systems. The real-time monitoring of the electrical and environmental operating conditions, devoted to the energy productivity analysis, is a feature already offered to the PV customers. However these analyses are mainly used to quantify the return of the investment of the PV plants. The estimation of the global energy productivity is a too coarse information for understanding the problems affecting the modules and for performing the right repairing. Thus, new and even more advanced on-line diagnostic algorithms must be developed in order to monitor the status of the cells and to predict failures before they lead to a significant reduction on the energy produced by the module. Such failures might be not only due to cells malfunctioning, but also to faults of the bypass diode the module is equipped with. This might happen due to repeated shadowing or to lightning. The aim of this lecture is to give an overview of the PV diagnostic methodologies proposed in literature. The main features of some PV commercial products are also shown. Finally a new approach for the on-line diagnostic will be discussed.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Econometric Forecasting

Several principles are useful for econometric forecasters: keep the model simple, use all the data you can get, and use theory (not the data) as a guide to selecting causal variables. But theory gives little guidance on dynamics, that is, on which lagged values of the selected variables to use. Early econometric models failed in comparison with extrapolative methods because they paid too little attention to dynamic structure. In a fairly simple way, the vector autoregression (VAR) approach that first appeared in the 1980s resolved the problem by shifting emphasis towards dynamics and away from collecting many causal variables. The VAR approach also resolves the question of how to make long-term forecasts where the causal variables themselves must be forecast. When the analyst does not need to forecast causal variables or can use other sources, he or she can use a single equation with the same dynamic structure. Ordinary least squares is a perfectly adequate estimation method. Evidence supports estimating the initial equation in levels, whether the variables are stationary or not. We recommend a general-to-specific model-building strategy: start with a large number of lags in the initial estimation, although simplifying by reducing the number of lags pays off. Evidence on the value of further simplification is mixed. If cointegration among variables, then error-correction models (ECMs) will do worse than equations in levels. But ECMs are only sometimes an improvement eve