Application of Satellite-Based Spectrally-Resolved Solar Radiation Data to PV Performance Studies

Abstract

In recent years, satellite-based solar radiation data resolved in spectral bands have become available. This has for the first time made it possible to produce maps of the geographical variation in the solar spectrum. It also makes it possible to estimate the influence of these variations on the performance of photovoltaic (PV) modules. Here, we present a study showing the magnitude of the spectral influence on PV performance over Europe and Africa. The method has been validated using measurements of a CdTe module in Ispra, Italy, showing that the method predicts the spectral influence to within ±2% on a monthly basis and 0.1% over a 19-month period. Application of the method to measured spectral responses of crystalline silicon, CdTe and single-junction amorphous silicon (a-Si) modules shows that the spectral effect is smallest over desert areas for all module types, higher in temperate Europe and highest in tropical Africa, where CdTe modules would be expected to yield +6% and single-junction a-Si modules up to +10% more energy due to spectral effects. In contrast, the effect for crystalline silicon modules is less than ±1% in nearly all of Africa and Southern Europe, rising to +1% or +2% in Northern Europe.

Solar cell efficiency divergence due to operating spectrum variation

“…An impediment to defining additional "operating spectra," consistent with what is now required for temperature and irradiance, has been the uncertainty around what those operating spectra should be. Results presented here and in the literature (Alonso-Abella et al, 2014;Amillo et al, 2015;Cornaro and Andreotti, 2013;Dirnberger et al, 2015;Faine et al, 1991;Fernandez et al, 2015;Gottschalg et al, 2004;Huld and Gracia Amillo, 2015;Ishii et al, 2013;Kinsey et al, 2013;Kinsey, 2015;Lee et al, 2015;Marzo et al, 2017;Minemoto et al, 2007;Monokroussos et al, 2011;Nann and Emery, 1992;Peters and Buonassisi, 2018;Philipps et al, 2010;Schweiger, 2015;Simon and Meyer, 2011;Ye et al, 2014) indicate that this concern is subordinate to the uncertainty arising from reliance on testing under a single-value standard spectrum. Any additional standard spectrum will convert the current single-value specification into a performance range that, however limited, will enable interpolation and/or extrapolation to conditions of interest to the "customer".…”

Solar cell efficiency divergence due to operating spectrum variation

“…Single-value testing cannot, of course, provide insight into the impact of spectrum variation on solar operations worldwide. When comparing devices with different spectral responses for their usefulness in outdoor energy generation, the results of single-spectrum testing, if not representative of operating spectra, are potentially misleading (Amillo et al, 2015;Faine et al, 1991;Lee et al, 2015;Nann and Emery, 1992;Schweiger, 2015). This work maps the impact of operating spectrum variation over a range of latitudes and longitudes and offers a template for determining energy generation for various solar cell types.…”

A Characterization of Metrics for Comparing Satellite-Based and Ground-Measured Global Horizontal Irradiance Data: A Principal Component Analysis Application

“…Mean Square Error [3,11] Root Mean Square Error RMSE RMSE = √ MSE [3,[9][10][11]18,20,25,26,[28][29][30]34] Normalized RMSE nRMSE nRMSE = RMSE GH I 0 [7][8][9][10][11]18,19,22,23,25,28,30,34] Mean Bias Error [3,[9][10][11]18,20,25,[28][29][30]34] Normalized MBE nMBE nMBE = MBE GH I 0 [7,8,10,11,18,21,23,25,30,34] Mean Absolute Error [3,11,21,…”