Monitoring nationwide ensembles of PV generators: Limitations and uncertainties. The case of the UK

Abstract Sources of error in the performance of large ensembles of spatially distributed photovoltaic generators are investigated and reported. Errors are propagated to estimate uncertainty in modeled global tilted radiation and performance ratio ( PR ) for the typical UK generator. Uncertainties in generators’ azimuth and elevation lead to typical monthly errors of 4% and 1% on global tilted radiation and PR . Interpolation of global horizontal irradiance is affected by an average 5% monthly error and the conversion to the inclined plane leads to an estimated error from 7% to 8% on tilted radiation and PR . This prediction has been verified against a set of twenty pyranometers on the plane of the array deployed across the UK, which gauge a 6% monthly error. Mutual cancellations lower this value to 4% for annual periodicity. The estimated monthly error on interpolated global horizontal irradiance is half of the 10% error affecting widely-used Photovoltaic Geographical Information System (PVGIS), which experiences larger errors also on the inclined plane. The assessed uncertainties impact the net present value of the investment required for deploying a PV generator; such an impact has been quantified. The yearly PR for the typical UK microgenerator is 84%, a value 8% (6%) higher than recent studies in France (Belgium). In winter, the typical UK performance ratio drops to 75%, because of an increase in shading. Summer performance ratio remains greater than the yearly mean, possibly reflecting the relatively short intervals during which direct sunlight heats the PV modules and the windy conditions over the British Isles. The monthly/annual error affecting the typical individual generator virtually cancel out for the whole national ensemble.

[1]  Mike Shelton,et al.  Energy Saving Trust , 2013 .

[2]  Takashi Oozeki,et al.  Performance trends in grid-connected photovoltaic systems for public and industrial use in Japan , 2010 .

[3]  C.C.Y. Ma,et al.  Statistical comparison of solar radiation correlations Monthly average global and diffuse radiation on horizontal surfaces , 1983 .

[4]  Jonathan Leloux,et al.  Review of the Performance of Residential PV systems in France , 2012 .

[5]  L. Wald,et al.  Using reduced data sets ISCCP-B2 from the Meteosat satellites to assess surface solar irradiance , 2007 .

[6]  Erik Lundtang Petersen,et al.  The European Wind Atlas , 1985 .

[7]  E. Riley,et al.  Mismatch Loss Reduction in Photovoltaic Arrays as a Result of Sorting Photovoltaic Modules by Max-Power Parameters , 2013 .

[8]  Kamaruzzaman Sopian,et al.  A review of solar energy modeling techniques , 2012 .

[9]  S. Klein Calculation of monthly average insolation on tilted surfaces , 1976 .

[10]  Peter Lehman,et al.  Effects of mismatch losses in photovoltaic arrays , 1995 .

[11]  R. Müller,et al.  A new solar radiation database for estimating PV performance in Europe and Africa , 2012 .

[12]  J. A. del Cueto Comparison of energy production and performance from flat-plate photovoltaic module technologies deployed at fixed tilt , 2002, PVSC 2002.

[13]  K. K. Gopinathan,et al.  Diffuse radiation models and monthly-average, daily, diffuse data for a wide latitude range , 1995 .

[14]  Ulrike Jahn,et al.  Operational performance of grid‐connected PV systems on buildings in Germany , 2004 .

[15]  F. Iannone,et al.  Monte Carlo techniques to analyse the electrical mismatch losses in large-scale photovoltaic generators , 1998 .

[16]  Jonathan Leloux,et al.  Review of the performance of residential PV systems in Belgium , 2012 .