PVPMC_Webinar_Martin_Herrerias

:: :: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: :: Towards a Generalized, Fully-anisotropic Transposition Model PVPMC Webinar on Solar Resource Assessment June 24, 2020 Martín Herrerías Azcué HLRS - NUM, Stuttgart herrerias@hlrs.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 (O&M) – Short-term forecasting – Site assessment – Bi-Facial modeling? – . Martín Herrerías Azcué (HLRS) 6/24/2020 2 Source: Capdevila, Herrerías & Marola (2014) :: :: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: :: Prior art • View factors – Evans & Coombe (1959) / Anderson (1964) • Continuous Distribution Models (CDM s) for Sky Luminance – Hooper & Brunger (1980) – Nakamura (1985) – Perez et al (1993) – Kittler & Darula (2002) • Masks on CDM s – [Forest Ecology / Building Simulation] – Bosch et al. (2010) – Ivanova (2013) • Discretized CDM s – Satel-Light Project (1996) – Goss et al. (2014) Martín Herrerías Azcué (HLRS) 6/24/2020 3 Source: Evans & Coombe (1959) :: :: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: :: Irradiance Transposition 6/24/2020Martín Herrerías Azcué (HLRS) 4 :: :: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: :: Transposition by View Factors 6/24/2020Martín Herrerías Azcué (HLRS) 5 :: :: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: :: View Factors by Custom Projection 6/24/2020Martín Herrerías Azcué (HLRS) 6 :: :: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: :: CDM s as Transposition Model *Martín Herrerías Azcué (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/m²sr], not luminance [cd/m²sr] • Discretizing the model seems like extra-steps, but can reduce computational effort and memory requirements: :: :: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: :: CDM s as Transposition Model 24.06.2020Martín Herrerías Azcué (HLRS) 8 • NREL s Data for Validating Models - Marion et al. (2014) – Eugene, Oregon (44°N, 123°W, 145 mASL, 44° tilt) :: :: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: :: CDM s as Transposition Model 24.06.2020Martín Herrerías Azcué (HLRS) 9 • NREL s Data for Validating Models - Marion et al. (2014) – Golden, Colorado (39.7°N, 105.2°W, 1798 mASL, 40° tilt) :: :: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: :: CDM s as Transposition Model 24.06.2020Martín Herrerías Azcué (HLRS) 10 • NREL s Data for Validating Models - Marion et al. (2014) – Cocoa, Florida (28.4°N, 80.5°W, 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 don t seem to reduce error, except in simplest cases *Martín Herrerías Azcué (HLRS) * :: :: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: :: Effect of discretization (cont.) 24.06.2020Martín Herrerías Azcué (HLRS) 12 :: :: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: :: Estimating Radiance using View Factors 6/24/2020Martín Herrerías Azcué (HLRS) 13 :: :: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: :: Least-Norm Solution 6/24/2020Martín Herrerías Azcué (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 6/24/2020Martín Herrerías Azcué (HLRS) 15 :: :: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: :: Least-Norm Solution: GHI + S 45° (Uni. Oldenburg) 6/24/2020Martín Herrerías Azcué (HLRS) 16 :: :: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: :: Least-Norm Solution: GHI + S 60° + SE 45° (Uni. Oldenburg) 6/24/2020Martín Herrerías Azcué (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 6/24/2020Martín Herrerías Azcué (HLRS) 18 :: :: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::::: ::24.06.2020Martín Herrerías Azcué (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., López, 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., Herrerías, 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 6/24/2020Martín Herrerías Azcué (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 distribution—Preliminary configuration and validation. Solar Energy 50, 235–245. https://doi.org/10.1016/0038-092X(93)90017-I 6/24/2020Martín Herrerías Azcué (HLRS) 21