Ian R. Cole

Solar profiles and spectral modelling for CPV simulations

A computer model for the simulation of solar flux distribution in the direct and circumsolar regions of the beam irradiation is described. The model incorporates previous research into circumsolar ratios (CSRs) and spectral transmittance. It is used to demonstrate the importance of realistic solar flux distributions as source inputs in Concentrator Photovoltaic (CPV) simulations. It is shown that flux distribution for different circumsolar ratios varies significantly. Such variation will have a considerable effect on the optical image formed at the receiver of a solar concentration system and thus is a necessary consideration in CPV modelling. Flux distributions incident on lenses of various entry apertures are generated and used to investigate incident flux losses resulting from tracking errors and CSR variation. It is found that, for a concentrating system with an entry aperture of 0.5°, a 5% loss in incident flux occurs with a uni-axial tracking error of ∼0.28° for a CSR of 0.2 and with a uni-axial tracking error of ∼0.21° for a CSR of 0.8. This analysis is at the primary concentrator stage and hence only considers the flux incident on the surface of the initial concentrating lens. The summation of flux at this stage does not account for further losses at the receiver due to optical misalignment. Such losses will be amplified when extending the analysis from the surface of the primary lens to the receiver surface. In the full paper: ray tracing methods will be employed to further investigate and quantify the significance of performance degradation due to optical misalignment; changes in spectral constitution are considered; and a multijunction cell model is used to generate system energy predictions integrating and accounting for the phenomena described herein.

Improved Model for Circumsolar Irradiance Calculation as an Extended Light Source and Spectral Implications for High-Concentration Photovoltaic Devices

With concentrator photovoltaics (CPV) technology soon to enter a phase of further development in the USA Sunbelt region, it is imperative to update the predictive tools associated with the technology. This involves modeling the Sun as an extended light source with particular attention to circumsolar irradiance. An improvement to the standard extended light source solar profile model is presented based on an improved parameterization of the circumsolar irradiance. A case study for a solar profile pertaining to a circumsolar ratio (CSR) of 0.3 shows incident direct normal irradiance collection overestimations in the standard model of 0.5%-1.5%. The model presented here mitigates these errors and corrects for a misrepresentation of CSR in the standard model. For an input CSR resolution of 0.01, the mean and standard deviation of normalized output CSR are improved from 0.937 and 0.107 to 1.000 and 0.005. Furthermore, a model extension is presented, incorporating the spectral distribution differences in the central solar and circumsolar regions. A banded spectral analysis of CSR and air mass variation effects are presented, corresponding to the subcells of the multijunction cell architecture. Significant differences are found in the trends of each subcell regarding the relationship between these variables and usable input irradiance.

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.

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.

A fast and effective approach to modelling solar energy potential in complex shading environments

A fast and effective model for the computation of solar energy potential in complex shading environments is presented. Accurate calculation and identification of solar energy potential profiles is demonstrated over large areas. Calculation time is exceptionally fast, even on an average specification PC (typically under 1 min per 1 km2). Problems with commonly used low-resolution sky domes that can lead to irradiance calculation errors of ~5% are identified. Ideal placements are easily visually identified from resultant irradiance/irradiation profile images. Image processing techniques for spatially distributed optimization problems are described and an example of energy value optimization is presented by means of individual dwelling demand separation & comparison.