Brian Goss

Large scale PV system monitoring - modules technology intercomparison

This paper presents an initial analysis of a large scale PV system monitoring campaign. Ongoing project aims to be a detailed inter-comparison of different modules technologies installed in a different types of climates, identifying optimal configurations in different climatic zones. Detailed description of created plants and developed monitoring facilities was shown. Appropriate performance indicators are discussed and applied to the measurements of two sites. The resulting performance analysis of different modules technologies, is presented and discussed. Differences between crystalline and thin film technologies were marked, with thin film technologies not performing as well as expected. This is most likely due to installation issues and will be rectified in the near future‥ Further investigations will be undertaken and reported in close future.

Detection of roof shading for PV based on LiDAR data using a multi-modal approach

There is a current drive to increase rooftop deployment of PV. Suitable roofs need to be located, especially as regards shading. A shadow cast on one small section of a solar panel can disproportionately undermine output of the entire system. Nevertheless, few shading figures are available to researchers and developers. This paper reviews and categorizes a number of methods of determining shade losses on photovoltaic systems. Two existing methods are tested on case study areas: shadow simulation from buildings and ambient occlusion. The first is conceptually simple and was found to be useful where data is limited. The second is slightly more demanding in terms of data input and mathematical models. It produces attractive shadow maps but is intended for speed and represents an approximation to ray-tracing. Accordingly, a new model was developed which is fast, flexible and accurately models solar radiation.

Large scale evaluation of photovoltaic technologies in different climates

Photovoltaic systems are typically optimised for performance or cost. In order to evaluate the wider parameter space and extensive measurement campaign has been designed that will provide guidance on future system designs. Four near-identical, grid-connected 200kW PV systems are being installed onto IKEA home furnishings stores in four countries with different climatic classification. The systems are integrated with comprehensive weather and power monitoring systems. This paper reports on the design, installation and scientific objectives of the project.

Modelling and prediction of PV module energy yield

Abstract At the heart of a photovoltaic (PV) system model is the modelling of the actual PV module, which is a group of PV cells in a weatherproof laminate. This chapter describes the physical and empirical approaches which are commonly used and why different applications favour certain models. The main input parameters for these models are described with a brief discussion of the commonly used datasets. The operating environment for PV is discussed alongside analysis of the primary variables and physical factors affecting net yield and generation time, with an overview of modelling techniques for these effects. An overview is given of advanced considerations such as mismatch and shading. Shading models of varying complexity are discussed, noting the assumptions and simplifications used in many commercial software packages in order to reduce computational time. Finally, a discussion of the modelling uncertainties finds that the greatest source of uncertainty lies with the accuracy of input data, such as the reference environmental conditions and predicted degradation rate. The chapter concludes that, for the most part, it is not the choice of model that makes the greatest contribution to modelling uncertainty but the input data. Therefore input data quality should be the focus for further reductions in modelling uncertainty and the associated project financial risks.