Analysis of spatial fixed PV arrays configurations to maximize energy harvesting in BIPV applications

This paper presents a new approach for efficient utilization of building integrated photovoltaic (BIPV) systems under partial shading conditions in urban areas. The aim of this study is to find out the best electrical configuration by analyzing annual energy generation of the same BIPV system, in terms of nominal power, without changing physical locations of the PV modules in the PV arrays. For this purpose, the spatial structure of the PV system including the PV modules and the surrounding obstacles is taken into account on the basis of virtual reality environment. In this study, chimneys which are located on the residential roof-top area are considered to create the effect of shading over the PV array. The locations of PV modules are kept stationary, which is the main point of this paper, while comparing the performances of the configurations with the same surrounding obstacles that causes partial shading conditions. The same spatial structure with twelve distinct PV array configurations is considered. The same settling conditions on the roof-top area allow fair comparisons between PV array configurations. The payback time analysis is also performed with considering local and global maximum power points (MPPs) of PV arrays by comparing the annual energy yield of the different configurationsPeer ReviewedPostprint (author’s final draft

Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models

In the field of renewable energy, reliability analysis techniques combining the operating time of the system with the observation of operational and environmental conditions, are gaining importance over time. In this paper, reliability models are adapted to incorporate monitoring data on operating assets, as well as information on their environmental conditions, in their calculations. To that end, a logical decision tool based on two artificial neural networks models is presented. This tool allows updating assets reliability analysis according to changes in operational and/or environmental conditions. The proposed tool could easily be automated within a supervisory control and data acquisition system, where reference values and corresponding warnings and alarms could be now dynamically generated using the tool. Thanks to this capability, on-line diagnosis and/or potential asset degradation prediction can be certainly improved. Reliability models in the tool presented are developed according to the available amount of failure data and are used for early detection of degradation in energy production due to power inverter and solar trackers functional failures. Another capability of the tool presented in the paper is to assess the economic risk associated with the system under existing conditions and for a certain period of time. This information can then also be used to trigger preventive maintenance activities

Silver as a constraint for a large-scale development of solar photovoltaics? Scenario-making to the year 2050 supported by expert engagement and global sensitivity analysis

In this study we assess whether availability of silver could constrain a large-scale deployment of solar photovoltaics (PV). While silver-paste use in photovoltaics cell metallization is becoming more efficient, solar photovoltaics power capacity installation is growing at an exponential pace. Along photovoltaics, silver is also employed in an array of industrial and non-industrial applications. The trends of these uses are examined up to the year 2050. The technical coefficients for the expansion in photovoltaics power capacity and contraction in silver paste use have been assessed through an expert-consultation process. The trend of use in the non-PV sectors has been estimated through an ARIMA (auto-regressive integrated moving average) model. The yearly and cumulative silver demand are evaluated against the technological potential for increasing silver mining and the estimates of its global natural availability, respectively. The model implemented is tested with a quasi-random Monte Carlo variance-based global sensitivity analysis. The result of our inquiry is that silver may not represent a constraint for a very-large-scale deployment of photovoltaics (up to tens TW in installed power capacity) provided the present decreasing trend in the use of silver paste in the photovoltaics sector continues at an adequate pace. Silver use in non-photovoltaic sectors plays also a tangible influence on potential constraints. In terms of natural constraints, most of the uncertainty is dependent on the actual estimates of silver natural budget

Statistical modeling, parameter estimation and measurement planning for PV degradation

Photovoltaics (PV) degradation is a key consideration during PV performance evaluation. Accurately predicting power delivery over the course of lifetime of PV is vital to manufacturers and system owners. With many systems exceeding 20 years of operation worldwide, degradation rates have been reported abundantly in the recent years. PV degradation is a complex function of a variety of factors, including but not limited to climate, manufacturer, technology and installation skill. As a result, it is difficult to determine degradation rate by analytical modeling; it has to be measured. As one set of degradation measurements based on a single sample cannot represent the population nor be used to estimate the true degradation of a particular PV technology, repeated measures through multiple samples are essential. In this chapter, linear mixed effects model (LMM) is introduced to analyze longitudinal degradation data. The framework herein introduced aims to address three issues: 1) how to model the difference in degradation observed in PV modules/systems of a same technology that are installed at a shared location; 2) how to estimate the degradation rate and quantiles based on the data; and 3) how to effectively and efficiently plan degradation measurements