PVPMC_Webinar_Martin_Herrerias

Towards a Generalized, Fully-anisotropic Transposition Model PVPMC Webinar on Solar Resource Assessment June 24, 2020 Martn Herreras Azcu HLRS - NUM, Stuttgart herreriashlrs.de * * Motivation Empirical transposition models are limited Fitted on shade-free conditions No room for arbitrary distributions clouds Diffuse shading is difficult to localize Rigid in their inputs – GTI sensors – Shaded sensors Applications – Nowcasting OM – Short-term forecasting – Site assessment – Bi-Facial modeling – . Martn Herreras Azcu HLRS 6/24/2020 2 Source Capdevila, Herreras Marola 2014 Prior art View factors – Evans Coombe 1959 / Anderson 1964 Continuous Distribution Models CDMs for Sky Luminance – Hooper Brunger 1980 – Nakamura 1985 – Perez et al 1993 – Kittler Darula 2002 Masks on CDMs – [Forest Ecology / Building Simulation] – Bosch et al. 2010 – Ivanova 2013 Discretized CDMs – Satel-Light Project 1996 – Goss et al. 2014 Martn Herreras Azcu HLRS 6/24/2020 3 Source Evans Coombe 1959 Irradiance Transposition 6/24/2020Martn Herreras Azcu HLRS 4 Transposition by View Factors 6/24/2020Martn Herreras Azcu HLRS 5 View Factors by Custom Projection 6/24/2020Martn Herreras Azcu HLRS 6 CDMs as Transposition Model *Martn Herreras Azcu HLRS * Empirical Continuous Radiance Distribution Models can be used as a drop- in replacement for transposition models Igawa, Koga, Matsuza Nakamura 2004 – Parametrization of std. gradation x scattering function in terms of a single „Sky-index“ – Fitted to radiance [W/msr], not luminance [cd/msr] Discretizing the model seems like extra-steps, but can reduce computational effort and memory requirements CDMs as Transposition Model 24.06.2020Martn Herreras Azcu HLRS 8 NRELs Data for Validating Models - Marion et al. 2014 – Eugene, Oregon 44N, 123W, 145 mASL, 44 tilt CDMs as Transposition Model 24.06.2020Martn Herreras Azcu HLRS 9 NRELs Data for Validating Models - Marion et al. 2014 – Golden, Colorado 39.7N, 105.2W, 1798 mASL, 40 tilt CDMs as Transposition Model 24.06.2020Martn Herreras Azcu HLRS 10 NRELs Data for Validating Models - Marion et al. 2014 – Cocoa, Florida 28.4N, 80.5W, 12 mASL, 28.5 tilt Effect of discretization Integration errors increase with patch size and steeper gradients Sign of bias gradation Final transposition error no shading rather insensitive to anything above 10 sky regions. Moving circumsolar regions dont seem to reduce error, except in simplest cases *Martn Herreras Azcu HLRS * Effect of discretization cont. 24.06.2020Martn Herreras Azcu HLRS 12 Estimating Radiance using View Factors 6/24/2020Martn Herreras Azcu HLRS 13 Least-Norm Solution 6/24/2020Martn Herreras Azcu HLRS 14 Test case Least L2 norm Ridge Regression Data from the Karl von Ossietzky University of Oldenburg Secondary Standard Pyranometers for – GHI, DHI – South 45 – South 60 – South-East 45 – South-West 45 10000 points at random, from 1 year of 1-minute data 6/24/2020Martn Herreras Azcu HLRS 15 Least-Norm Solution GHI S 45 Uni. Oldenburg 6/24/2020Martn Herreras Azcu HLRS 16 Least-Norm Solution GHI S 60 SE 45 Uni. Oldenburg 6/24/2020Martn Herreras Azcu HLRS 17 Future Work Decomposition Problem hard soft constraints, uncertainty and smoothness priors to reduce overfitting Estimating diffuse fraction Testing Validation, new data sets and sensor configurations Spectral content correction for individual components Obstacle terrain self shadows, non-Lambertian albedo Performance Optimization 6/24/2020Martn Herreras Azcu HLRS 18 24.06.2020Martn Herreras Azcu HLRS 19 Thanks Annete Hammer, Jorge Lezaca, Hugo Capdevila,. University of Oldenburg, NREL, GroundWork and PVPMC Everyone, for your attention References Anderson, M.C., 1964. Studies of the Woodland Light Climate I. The Photographic Computation of Light Conditions. Journal of Ecology 52, 27–41. https//doi.org/10.2307/2257780 Bosch, J.L., Batlles, F.J., Zarzalejo, L.F., Lpez, G., 2010. Solar resources estimation combining digital terrain models and satellite images techniques. Renewable Energy 35, 2853–2861. https//doi.org/10.1016/j.renene.2010.05.011 Capdevila, H., Herreras, M., Marola, A., 2014. Anisotropic Diffuse Shading Model for Sun-tracking Photovoltaic Systems. Energy Procedia, 2013 ISES Solar World Congress 57, 144–151. https//doi.org/10.1016/j.egypro.2014.10.018 Darula, S., Kittler, R., 2002. CIE general sky standard defining luminance distributions, in Proc. Conf. ESim 2002. Presented at the Canadian conference on building energy simulation, Montreal, Canada, p. 9. Evans, G.C., Coombe, D.E., 1959. Hemisperical and Woodland Canopy Photography and the Light Climate. Journal of Ecology 47, 103–113. https//doi.org/10.2307/2257250 Goss, B., Cole, I., Betts, T., Gottschalg, R., 2014. Irradiance modelling for individual cells of shaded solar photovoltaic arrays. Solar Energy 110, 410–419. https//doi.org/10.1016/j.solener.2014.09.037 Hooper, F.C., Brunger, A.P., 1980. A Model for the Angular Distribution of Sky Radiance. Journal of Solar Energy Engineering 102, 196. https//doi.org/10.1115/1.3266154 6/24/2020Martn Herreras Azcu HLRS 20 References cont. Igawa, N., Koga, Y., Matsuzawa, T., Nakamura, H., 2004. Models of sky radiance distribution and sky luminance distribution. Solar Energy 77, 137–157. https//doi.org/10.1016/j.solener.2004.04.016 Ineichen, P., 1996. Use of Meteosat data to produce sky luminance maps. Satellight, Commision of the European Communities, Bergen. Ivanova, S.M., 2013. Estimation of background diffuse irradiance on orthogonal surfaces under partially obstructed anisotropic sky. Part I – Vertical surfaces. Solar Energy 95, 376–391. https//doi.org/10.1016/j.solener.2013.01.021 Marion, B., Anderberg, A., Deline, C., Cueto, J. del, Muller, M., Perrin, G., Rodriguez, J., Rummel, S., Silverman, T.J., Vignola, F., Kessler, R., Peterson, J., Barkaszi, S., Jacobs, M., Riedel, N., Pratt, L., King, B., 2014. New data set for validating PV module performance models, in 2014 IEEE 40th Photovoltaic Specialist Conference PVSC. Presented at the 2014 IEEE 40th Photovoltaic Specialist Conference PVSC, pp. 1362–1366. https//doi.org/10.1109/PVSC.2014.6925171 Nakamura, H., Oki, M., Hayashi, Y., 1985. Luminance distribution of Intermediate Sky. Journal of Light Visual Environment 9, 1_6-1_13. https//doi.org/10.2150/jlve.9.1_6 Perez, R., Seals, R., Michalsky, J., 1993. All-weather model for sky luminance distributionPreliminary configuration and validation. Solar Energy 50, 235–245. https//doi.org/10.1016/0038-092X9390017-I 6/24/2020Martn Herreras Azcu HLRS 21