nach oben

Erschienen in:

2018 | OriginalPaper | Buchkapitel

Solar Resource Variability

verfasst von : Richard Perez, Philippe Lauret, Marc Perez, Mathieu David, Thomas E. Hoff, Sergey Kivalov

Erschienen in: Wind Field and Solar Radiation Characterization and Forecasting

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

This chapter aims at characterizing and modeling solar resource variability. It is shown that understanding solar energy variability requires a definition of the temporal and spatial context for which variability is assessed. This research describes a predictable, quantifiable variability-smoothing space–time continuum from a single point to thousands of kilometers and from seconds to days. Implications for solar penetration on the power grid and variability mitigation strategies are also discussed. Models for predicting intra-day or intra-hourly variability as a function of insolation conditions are also depicted.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Wind Field Probabilistic Forecasting

Nächstes Kapitel Solar Radiation Forecasting with Statistical Models

This velocity is a priori defined as the vector in the direction of the two considered locations. However, as will be discussed below, empirical evidence shows that a mean, local—directionless—velocity, can be an adequate input for assessing regional station pair correlations.

Aguiar R, Collares-Pereira M, Conde J (1988) Simple procedure for generating sequences of daily radiation values using a library of Markov transition matrices. Sol Energy 40(3):269–279. https://doi.org/10.1016/0038-092X(88)90049-7CrossRef

Arias-Castro E, Kleissl J, Lave, M (2014) A Poisson model for anisotropic solar ramp rate correlations. Sol Energy 101:192–202. https://doi.org/10.1016/j.solener.2013.12.028. http://linkinghub.elsevier.com/retrieve/pii/S0038092X13005549CrossRef

Badosa J, Haeffelin M, Chepfer H (2013) Scales of spatial and temporal variation of solar irradiance on Reunion tropical island. Sol Energy 88:42–56. https://doi.org/10.1016/j.solener.2012.11.007. http://linkinghub.elsevier.com/retrieve/pii/S0038092X12003982CrossRef

Becker S, Frew BA, Andresen GB, Zeyer T, Schramm S, Greiner M, Jacobson MZ (2014) Features of a fully renewable US electricity system: optimized mixes of wind and solar PV and transmission grid extensions. Energy 72:443–458. https://doi.org/10.1016/j.energy.2014.05.067. http://linkinghub.elsevier.com/retrieve/pii/S0360544214006343CrossRef

Bing J, Krishnani P, Bartholomy O, Hoff T, Perez R (2012) Solar monitoring, forecasting, and variability assessment at SMUD, Denver, CO

Bosch J, Kleissl J (2013) Cloud motion vectors from a network of ground sensors in a solar power plant. Sol Energy 95:13–20. https://doi.org/10.1016/j.solener.2013.05.027. http://linkinghub.elsevier.com/retrieve/pii/S0038092X13002193CrossRef

Bosch J, Zheng Y, Kleissl J (2013) Deriving cloud velocity from an array of solar radiation measurements. Sol Energy 87:196–203. https://doi.org/10.1016/j.solener.2012.10.020. http://linkinghub.elsevier.com/retrieve/pii/S0038092X12003854CrossRef

David M, Andriamasomanana FHR, Liandrat O (2014) Spatial and temporal variability of PV output in an insular grid: case of Reunion Island. Energy Procedia 57:1275–1282. https://doi.org/10.1016/j.egypro.2014.10.117. http://linkinghub.elsevier.com/retrieve/pii/S1876610214014842CrossRef

ESRA (1999) ESRA, European solar radiation atlas. https://hal.archives-ouvertes.fr/hal-00363667/document

10.

Frank J, Freedman J, Brower M, Schnitzer M (2011) Development of high frequency solar data, Raleigh, NC

11.

Fung V, Bosch JL, Roberts SW, Kleissl J (2014) Cloud shadow speed sensor. Atmos Meas Tech 7(6):1693–1700. https://doi.org/10.5194/amt-7-1693-2014. http://www.atmos-meas-tech.net/7/1693/2014/CrossRef

12.

Gueymard CA, Wilcox SM (2011) Assessment of spatial and temporal variability in the US solar resource from radiometric measurements and predictions from models using ground-based or satellite data. Sol Energy 85(5):1068–1084. https://doi.org/10.1016/j.solener.2011.02.030. http://linkinghub.elsevier.com/retrieve/pii/S0038092X11000855CrossRef

13.

Halsz G, Malachi Y (2014) Solar energy from Negev desert, Israel: assessment of power fluctuations for future PV fleet. Energy Sustain Dev 21:20–29. https://doi.org/10.1016/j.esd.2014.04.005. http://linkinghub.elsevier.com/retrieve/pii/S0973082614000404

14.

Hinkelmann L (2013) Differences between along-wind and cross-wind solar variability. Proc Sol Energy:192–203

15.

Hoff T, Norris B (2010) Mobile high-density: irradiance sensor network: cordelia junction results

16.

Hoff T, Perez R (2012) Predicting short-term variability of high-penetration PV, Denver, CO

17.

Hoff TE, Perez R (2010) Quantifying PV power output variability. Sol Energy 84(10):1782–1793. https://doi.org/10.1016/j.solener.2010.07.003. http://linkinghub.elsevier.com/retrieve/pii/S0038092X10002380CrossRef

18.

Hoff TE, Perez R (2012) Modeling PV fleet output variability. Sol Energy 86(8):2177–2189. https://doi.org/10.1016/j.solener.2011.11.005. http://linkinghub.elsevier.com/retrieve/pii/S0038092X11004154CrossRef

19.

Holger R, Schroedter-Homscheidt M, Beyer H, Meier F, Heilscher G (2014) Wolkenindikatoren fr die Sammelschienenspannung von Niederspannungsverteilnetztransformatoren, 29. In: Symposium Photovoltaische Solarenergie, Kloster Banz, Bad Staffelstein

20.

Holger R, Schroedter-Homscheidt M, Meier F, Heilscher G (2014) Application of meteorological data for state estimation of an electrical low voltage grid with a high amount of photovoltaic systems. In: 14th EMS annual meeting and 10th European conference on applied climatology, Prague

21.

Huang J, Troccoli A, Coppin P (2014) An analytical comparison of four approaches to modelling the daily variability of solar irradiance using meteorological records. Renew Energy 72:195–202. https://doi.org/10.1016/j.renene.2014.07.015. http://linkinghub.elsevier.com/retrieve/pii/S0960148114004017CrossRef

22.

Jamaly M, Bosch JL, Kleissl J (2013) Aggregate ramp rates of distributed photovoltaic systems in San Diego County. IEEE Trans Sustain Energy 4(2):519–526. https://doi.org/10.1109/TSTE.2012.2201966. http://ieeexplore.ieee.org/document/6232480/CrossRef

23.

Kankiewicz A, Sengupta M, Li J (2011) Cloud meteorology and utility scale variability, Raleigh, NC

24.

Kato T, Inoue T, Suzuki Y (2011) Estimation of total power output fluctuation of high penetration photovoltaic power generation system, Detroit, Michigan, USA

25.

Krige D (1951) A statistical approach to some basic mine valuation problems on the Witwatersrand. J Chem Metall Min Soc South Africa, 119–139

26.

Kuszamaul S, Ellis A, Stein J, Johnson L (2010) Lanai high-density irradiance sensor network for characterizing solar resource variability of MW-scale PV system, Honolulu, HI

27.

Lauret P, Perez R, Mazorra Aguiar L, Tapachs E, Diagne HM, David M (2016) Characterization of the intraday variability regime of solar irradiation of climatically distinct locations. Sol Energy 125:99–110. https://doi.org/10.1016/j.solener.2015.11.032. http://linkinghub.elsevier.com/retrieve/pii/S0038092X15006490CrossRef

28.

Lave M, Kleissl J (2010) Solar variability of four sites across the state of Colorado. Renew Energy 35(12):2867–2873. https://doi.org/10.1016/j.renene.2010.05.013. http://linkinghub.elsevier.com/retrieve/pii/S0960148110002314CrossRef

29.

Lave M, Kleissl J (2013) Cloud speed impact on solar variability scaling application to the wavelet variability model. Sol Energy 91:11–21. https://doi.org/10.1016/j.solener.2013.01.023. http://linkinghub.elsevier.com/retrieve/pii/S0038092X13000406CrossRef

30.

Lave M, Kleissl J, Arias-Castro E (2012) High-frequency irradiance fluctuations and geographic smoothing. Sol Energy 86(8):2190–2199. https://doi.org/10.1016/j.solener.2011.06.031. http://linkinghub.elsevier.com/retrieve/pii/S0038092X11002611CrossRef

31.

Lave M, Kleissl J, Stein JS (WVM) A wavelet-based variability modelfor solar PV power plants. IEEE Trans Sustain Energy 4(2):501–509 (2013). https://doi.org/10.1109/TSTE.2012.2205716. http://ieeexplore.ieee.org/document/6269913/

32.

Lonij VP, Brooks AE, Cronin AD, Leuthold M, Koch K (2013) Intra-hour forecasts of solar power production using measurements from a network of irradiance sensors. Sol Energy 97:58–66. https://doi.org/10.1016/j.solener.2013.08.002. http://linkinghub.elsevier.com/retrieve/pii/S0038092X13003125CrossRef

33.

Lorenz E, Scheidsteger T, Hurka J, Heinemann D, Kurz C (2011) Regional PV power prediction for improved grid integration. Prog Photovolt Res Appl 19(7):757–771. https://doi.org/10.1002/pip.1033. http://doi.wiley.com/10.1002/pip.1033CrossRef

34.

Lovejoy S (1982) Area-perimeter relation for rain and cloud areas. Science, 185–187. New SeriesCrossRef

35.

Marcos J, Marroyo L, Lorenzo E, Garca M (2012) Smoothing of PV power fluctuations by geographical dispersion: smoothing of PV power fluctuations. Prog Photovolt: Res Appl 20(2):226–237. https://doi.org/10.1002/pip.1127. http://doi.wiley.com/10.1002/pip.1127CrossRef

36.

Mazumdar BM, Saquib M, Das, AK (2014) An empirical model for ramp analysis of utility-scale solar PV power. Sol Energy 107:44–49. https://doi.org/10.1016/j.solener.2014.05.027. http://linkinghub.elsevier.com/retrieve/pii/S0038092X14002564CrossRef

37.

Mills A, Wiser R (2010) Implications of wide-area geographic diversity for short-term variability of solar power. Technical report, Lawrence Berkeley National Laboratory Technical Report LBNL-3884E

38.

Murata A, Yamaguchi H, Otani K (2009) A method of estimating the output fluctuation of many photovoltaic power generation systems dispersed in a wide area. Electr Eng Japan 166(4):9–19CrossRef

39.

Nguyen A, Velay M, Schoene J, Zheglov V, Kurtz B, Murray K, Torre B, Kleissl J (2016) High PV penetration impacts on five local distribution networks using high resolution solar resource assessment with sky imager and quasi-steady state distribution system simulations. Sol Energy 132:221–235. https://doi.org/10.1016/j.solener.2016.03.019. http://linkinghub.elsevier.com/retrieve/pii/S0038092X16002061CrossRef

40.

Norris B, Hoff T (2011) Determining storage reserves for regulating solar variability

41.

Ole-Morten M, Yordanov G, Ranaweera I (2014) Short-term intermittency of solar irradiance in Southern Norway, pp 2635–2638

42.

PEA: PREPA puerto rico electric power authority (2013). www.prepa.com

43.

Perez M (2015) Geographic dispersion and curtailment of VLS-PV electricity. Chapter 4 future technical options for the entire energy system, 2015. Technical report

44.

Perez M, Fthenakis V (2012) Quantifying long time scale solar resource variability, Denver, CO

45.

Perez MJ, Fthenakis VM (2015) On the spatial decorrelation of stochastic solar resource variability at long timescales. Sol Energy 117:46–58. https://doi.org/10.1016/j.solener.2015.04.020. http://linkinghub.elsevier.com/retrieve/pii/S0038092X1500208XCrossRef

46.

Perez R, Aguiar R, Collares-Pereira M, Dumortier D, Estrada-Cajigal V, Gueymard C, Ineichen P, Littlefair P, Lund H, Michalsky J, Olseth J, Renn D, Rymes M, Skartveit A, Vignola F, Zelenka A (2001) Solar resource assessment a review. Solar energy-the state of the art. James & James, London, pp 497–575

47.

Perez R, David M, Hoff TE, Jamaly M, Kivalov S, Kleissl J, Lauret P, Perez M (2016) Spatial and temporal variability of solar energy. Found Trends Renew Energy 1(1):1–44. https://doi.org/10.1561/2700000006. http://www.nowpublishers.com/article/Details/REN-006CrossRef

48.

Perez R, Hoff T (2013) Solar resource variability. In: Kleissl J (ed) Solar resource assessment and forecasting. ElsevierCrossRef

49.

Perez R, Hoff T, Dise J, Chalmers D, Kivalov S (2013) Mitigating short-term PV output variability, Paris, France

50.

Perez R, Hoff T, Kivalov S (2011) Spatial & temporal characteristics of solar radiation variability, Kassel, Germany

51.

Perez R, Ineichen P, Seals R, Zelenka A (1990) Making full use of the clearness index for parameterizing hourly insolation conditions. Sol Energy 45(2):111–114. https://doi.org/10.1016/0038-092X(90)90036-C. http://linkinghub.elsevier.com/retrieve/pii/0038092X9090036CCrossRef

52.

Perez R, Kankiewicz A, Schlemmer J, Hemker K, Kivalov S (2014) A new operational solar resource forecast service for PV fleet simulation

53.

Perez R, Kivalov S, Schlemmer J, Hemker K, Hoff T (2011) Parameterization of site-specific short-term irradiance variability. Sol Energy 85(7):1343–1353. https://doi.org/10.1016/j.solener.2011.03.016. http://linkinghub.elsevier.com/retrieve/pii/S0038092X11000995CrossRef

54.

Perez R, Kivalov S, Schlemmer J, Hemker K, Hoff TE (2012) Short-term irradiance variability: preliminary estimation of station pair correlation as a function of distance. Sol Energy 86(8):2170–2176. https://doi.org/10.1016/j.solener.2012.02.027. http://linkinghub.elsevier.com/retrieve/pii/S0038092X12000928CrossRef

55.

Perez R, Schlemmer J, Hemker K, Kivalov S, Kankiewicz A, Gueymard C (2015) Satellite-to-irradiance modeling a new version of the SUNY model

56.

Remund J, Calhau C, Marcel D, Perret L (2015) Spatio-temporal variability of PV production. In: Spatio-temporal variability of PV production. Proceedings of the European photovoltaic solar energy conference and exhibition (EUPVSEC), Hamburg, Germany

57.

Research CP, Behind-the-meter intelligence for distributed PV grid integration (2012). http://www.cleanpower.com/wp-content/uploads/SA-FleetView-Whitepaper-v060812.pdf

58.

Rowlands IH, Kemery BP, Beausoleil-Morrison I (2014) Managing solar-PV variability with geographical dispersion: An Ontarioa (Canada) case-study. Renew Energy 68:171–180. https://doi.org/10.1016/j.renene.2014.01.034. http://linkinghub.elsevier.com/retrieve/pii/S0960148114000639CrossRef

59.

Sengupta M (2011) Measurement and modeling of solar and PV output variability, Raleigh, NC

60.

Skartveit A, Olseth J (1992) The probability density and autocorrelation of short-term global and beam irradiance. Sol Energy 49(6):477–487. https://doi.org/10.1016/0038-092X(92)90155-4. http://linkinghub.elsevier.com/retrieve/pii/0038092X92901554CrossRef

61.

SSE, N: Surface meteorology and solar energy (2012). http://eosweb.larc.nasa.gov/sse/

62.

Stein J, Ellis A, Hansen C, Chadliev V (2011) Simulation of 1-minute power output from utility-scale photovoltaic generation systems, Raleigh, NC

63.

Stokes G, Schwartz S (1994) The atmospheric radiation measurement (ARM) program: programmatic background and design of the cloud and radiation test bed. Bull Am Meteorol Soc 1201–1221CrossRef

64.

Tomson T (2010) Fast dynamic processes of solar radiation. Sol Energy 84(2):318–323. https://doi.org/10.1016/j.solener.2009.11.013. http://linkinghub.elsevier.com/retrieve/pii/S0038092X09002795CrossRef

65.

Urquhart B, Kurtz B, Dahlin E, Ghonima M, Shields JE, Kleissl J (2014) Development of a sky imaging system for short-term solar power forecasting. Atmos Meas Tech Discuss 7(5):4859–4907. https://doi.org/10.5194/amtd-7-4859-2014. http://www.atmos-meas-tech-discuss.net/7/4859/2014/CrossRef

66.

Vignola F (2001) Variability of solar radiation over short time intervals, Washington, D.C

67.

Vindel J, Polo J (2014) Intermittency and variability of daily solar irradiation. Atmos Res 143:313–327CrossRef

68.

Wiemken E, Beyer H, Heydenreich W, Kiefer K (2001) Power characteristics of PV ensembles: experiences from the combined power production of 100 grid connected PV systems distributed over the area of Germany. Sol Energy 70(6):513–518. https://doi.org/10.1016/S0038-092X(00)00146-8. http://linkinghub.elsevier.com/retrieve/pii/S0038092X00001468CrossRef

69.

Woyte A, Belmans R, Nijs J (2007) Fluctuations in instantaneous clearness index: analysis and statistics. Sol Energy 81(2):195–206. https://doi.org/10.1016/j.solener.2006.03.001. http://linkinghub.elsevier.com/retrieve/pii/S0038092X0600079XCrossRef

70.

Yang D, Dong Z, Reindl T, Jirutitijaroen P, Walsh WM (2014) Solar irradiance forecasting using spatio-temporal empirical kriging and vector autoregressive models with parameter shrinkage. Sol Energy 103:550–562. https://doi.org/10.1016/j.solener.2014.01.024. http://linkinghub.elsevier.com/retrieve/pii/S0038092X14000425CrossRef

71.

Yordanov G, Mitdgard O, Norum L (2013) Overirradiance (cloud enhancement) events at high latitudes. IEEE J Photovolt 78:203–218

72.

Zagouras A, Pedro HT, Coimbra CF (2015) On the role of lagged exogenous variables and spatiotemporal correlations in improving the accuracy of solar forecasting methods. Renew Energy 78, 203–218. https://doi.org/10.1016/j.renene.2014.12.071. http://linkinghub.elsevier.com/retrieve/pii/S0960148115000051CrossRef

Titel: Solar Resource Variability
verfasst von: Richard Perez
Philippe Lauret
Marc Perez
Mathieu David
Thomas E. Hoff
Sergey Kivalov
Verlag: Springer International Publishing
Buch: Wind Field and Solar Radiation Characterization and Forecasting
Print ISBN: 978-3-319-76875-5

Electronic ISBN: 978-3-319-76876-2

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-76876-2_7

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"