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Solar Power Plant Performance

  • Jesús PoloEmail author
Chapter
Part of the Green Energy and Technology book series (GREEN)

Abstract

A key part in the development of any project for deployment a solar power plant is the analysis of the expected energy yield production. The system energy production depends on the plant design, the technology used for power conversion, the solar resource, and the characteristics of the site. Due to the intrinsic variability of the solar resource, the prediction of long-term electricity production is also crucial for the financial evaluation of solar power plants. The energy yield performance is thus the process of predicting the annual average energy output for the lifetime of the solar power plant. For that purpose, a number of system performance models and tools have been developed; many of them are updated regularly. In addition, several international programs deliver recommendations and guidelines for yield performance analysis. Thus, in the case of photovoltaic (PV) plants the PVPS program from the International Energy Agency (IEA) publishes regularly updated reports on many aspects of PV generation (http://www.iea-pvps.org/). In addition, the Sandia National Laboratories is facilitating a collaborative group called PV Performance Modeling Collaborative (PVPMC) with regular activities focused on improving the accuracy of PV performance analysis (https://pvpmc.sandia.gov/). On the other hand, in the case of Concentrated Solar Power (CSP), the SolarPACES program of the IEA (http://www.solarpaces.org/) is developing guidelines for solar thermal energy (STE) yield assessment. This chapter summarizes the main aspects included in the tools and software for estimating yield performance of PV and CSP power plants and the long-term characterization of yield energy, risk analysis, and uncertainty quantification.

Notes

Acknowledgements

The author/editor, Jesús Polo, wishes to acknowledge the PVCastSOIL Proyect (ENE2017-83790-C3-1, 2 and 3), which is funded by the Spanish Ministerio de Economía y Competitividad and co-financed by the European Regional Development Fund.

References

  1. Ayodele TR, Ogunjuyigbe ASO, Ekoh EE (2016) Evaluation of numerical algorithms used in extracting the parameters of a single-diode photovoltaic model. Sustain Energy Technol Assessments 13:51–59.  https://doi.org/10.1016/j.seta.2015.11.003CrossRefGoogle Scholar
  2. Blair N, Dobos AP, Freeman J et al (2014) System advisor model, sam, 2014.1.14: General description. Tech Report, NREL/TP-6A20-61019, Golden, USAGoogle Scholar
  3. Cameron CP, Stein JS, Tasca CA (2011) PV performance modeling workshop summary report. Rep SAND2011-3419, Alburquerque, USA 1–92Google Scholar
  4. Cebecauer T, Suri M (2015) Typical meteorological year data: solarGIS approach. Energy Procedia 69:1958–1969.  https://doi.org/10.1016/j.egypro.2015.03.195CrossRefGoogle Scholar
  5. Cucumo M, De Rosa A, Ferraro V et al (2007) Experimental testing of models for the estimation of hourly solar radiation on vertical surfaces at Arcavacata di Rende. Sol Energy 81:692–695.  https://doi.org/10.1016/j.solener.2006.09.002CrossRefGoogle Scholar
  6. De Soto W, Klein SA, Beckman WA (2006) Improvement and validation of a model for photovoltaic array performance. Sol Energy 80:78–88.  https://doi.org/10.1016/j.solener.2005.06.010CrossRefGoogle Scholar
  7. Demain C, Journée M, Bertrand C (2013) Evaluation of different models to estimate the global solar radiation on inclined surfaces. Renew Energy 50:710–721.  https://doi.org/10.1016/j.renene.2012.07.031CrossRefGoogle Scholar
  8. Dobos A, Neises T, Wagner M (2014) Advances in CSP simulation technology in the system advisor model. Energy Procedia 49:2482–2489.  https://doi.org/10.1016/j.egypro.2014.03.263CrossRefGoogle Scholar
  9. Dobos AP, Gilman P, Kasberg M (2012) P50/ P90 analysis for solar energy systems using the system advisor model. In: 2012 World renewable energy forumGoogle Scholar
  10. Elbaset AA, Ali H, Abd-El Sattar M (2014) Novel seven-parameter model for photovoltaic modules. Sol Energy Mater Sol Cells 130:442–455.  https://doi.org/10.1016/j.solmat.2014.07.016CrossRefGoogle Scholar
  11. Espinar B, Blanc P, Wald L (2012) Report on the production S4 “TMY FOR PRODUCTION.” Proj ENDORSE 12Google Scholar
  12. Et-Torabi K, Nassar-Eddine I, Obbadi A et al (2017) Parameters estimation of the single and double diode photovoltaic models using a gaussian seidel algorithm and analytical method: a comparative study.  https://doi.org/10.1016/j.enconman.2017.06.064CrossRefGoogle Scholar
  13. Fanego VL, Rubio JP, Peruchena CMF, et al (2017) A novel procedure for generating solar irradiance TSYs. In: AIP conference proceedings 1850.  https://doi.org/10.1063/1.4984523
  14. Fernández-Peruchena CM, Gastón M, Sánchez M et al (2015) MUS: a multiscale stochastic model for generating plausible meteorological years designed for multiyear solar energy yield simulations. Sol Energy 120:244–256.  https://doi.org/10.1016/j.solener.2015.07.037CrossRefGoogle Scholar
  15. Fernández Peruchena CM, Ramírez L, Silva-Pérez MA et al (2016a) A statistical characterization of the long-term solar resource: towards risk assessment for solar power projects. Sol Energy 123:29–39.  https://doi.org/10.1016/j.solener.2015.10.051CrossRefGoogle Scholar
  16. Fernández Peruchena CM, Ramírez L, Silva M et al (2016b) A methodology for calculating percentile values of annual direct normal solar irradiation series. 101:120010–150005.  https://doi.org/10.1063/1.4949234CrossRefGoogle Scholar
  17. Festa R, Ratto CF (1993) Proposal of a numerical procedure to select reference years. Sol Energy 50:9–17.  https://doi.org/10.1016/0038-092X(93)90003-7CrossRefGoogle Scholar
  18. Finkelstein JM, Schafer RE (1971) Improved goodness-of-fit tests. Biometrika 58:641–645.  https://doi.org/10.1093/biomet/58.3.641CrossRefzbMATHGoogle Scholar
  19. Ghani F, Rosengarten G, Duke M, Carson JK (2014) The numerical calculation of single-diode solar-cell modelling parameters. Renew Energy 72:105–112.  https://doi.org/10.1016/j.renene.2014.06.035CrossRefGoogle Scholar
  20. Gilman P (2015) SAM photovoltaic model technical reference. Technical Report NREL/TP-6A20-64102, Golden CO, USAGoogle Scholar
  21. Habte A, Lopez A, Sengupta M, Wilcox S (2014) Temporal and spatial comparison of gridded TMY, TDY, and TGY data sets. NREL/TP-5D00-60886, National Renewable Energy lab Report, Golden CoGoogle Scholar
  22. Hall IJ, Prairie RR, Anderson HE, Boes EC (1978) Generation of typical meteorological years for 26 SOLMET stations. Albuquerque (USA)Google Scholar
  23. Hirsch T, Dernsch J, Fluri T et al (2017) SolarPACES guideline for bankable STE yield assessment. IEA technology collaboration programme solarPACESGoogle Scholar
  24. Jain A (2004) Exact analytical solutions of the parameters of real solar cells using Lambert W-function. Sol Energy Mater Sol Cells 81:269–277.  https://doi.org/10.1016/j.solmat.2003.11.018CrossRefGoogle Scholar
  25. Janjai S, Deeyai P (2009) Comparison of methods for generating typical meteorological year using meteorological data from a tropical environment. Appl Energy 86:528–537.  https://doi.org/10.1016/j.apenergy.2008.08.008CrossRefGoogle Scholar
  26. JCGM (2008) Evaluation of measurement data—guide to the expression of uncertainty in measurement. Int Organ Stand Geneva ISBN 50:134.  https://doi.org/10.1373/clinchem.2003.030528CrossRefGoogle Scholar
  27. Jiang Y (2010) Generation of typical meteorological year for different climates of China. Energy 35:1946–1953.  https://doi.org/10.1016/j.energy.2010.01.009CrossRefGoogle Scholar
  28. Kambezidis HD, Psiloglou BE, Gueymard C (1994) Measurements and models for total solar irradiance on inclined surface in Athens, Greece. Sol Energy 53:177–185.  https://doi.org/10.1016/0038-092X(94)90479-0CrossRefGoogle Scholar
  29. Khalil SA, Shaffie AM (2013) A comparative study of total, direct and diffuse solar irradiance by using different models on horizontal and inclined surfaces for Cairo Egypt. Renew Sustain Energy Rev 27:853–863.  https://doi.org/10.1016/j.rser.2013.06.038CrossRefGoogle Scholar
  30. Khorasanizadeh H, Mohammadi K, Mostafaeipour A (2014) Establishing a diffuse solar radiation model for determining the optimum tilt angle of solar surfaces in Tabass. Iran. Energy Convers Manag 78:805–814.  https://doi.org/10.1016/j.enconman.2013.11.048CrossRefGoogle Scholar
  31. King BH, Hansen CW, Riley D et al (2016) Procedure to determine coefficients for the Sandia Array Performance Model (SAPM). Sandia ReportGoogle Scholar
  32. King DL, Boyson WE, Kratochvill JA (2004) Photovoltaic array performance model. Sandia Rep.  https://doi.org/10.2172/919131
  33. King DL, Gonzalez S, Galbraith GM, Boyson WE (2007) Performance model for grid-connected photovoltaic inverters, SAND2007-5036. Contract 38:655–660Google Scholar
  34. Lee K, Yoo H, Levermore GJ (2013) Quality control and estimation hourly solar irradiation on inclined surfaces in South Korea. Renew Energy 57:190–199.  https://doi.org/10.1016/j.renene.2013.01.028CrossRefGoogle Scholar
  35. Lohmann S, Riihimaki L, Vignola F, Meyer R (2007) Trends in direct normal irradiance in Oregon: comparison of surface measurements and ISCCP derived irradiance. Geophys Res Lett 34:1–4CrossRefGoogle Scholar
  36. Lohmann S, Schillings C, Mayer B, Meyer R (2006) Long-term variability of solar direct and global radiation derived from ISCCP data and comparison with reanalysis data. Sol Energy 80:1390–1401.  https://doi.org/10.1016/j.solener.2006.03.004CrossRefGoogle Scholar
  37. Loutzenhiser PG, Manz H, Felsmann C et al (2007) Empirical validation of models to compute solar irradiance on inclined surfaces for building energy simulation. Sol Energy 81:254–267.  https://doi.org/10.1016/j.solener.2006.03.009CrossRefGoogle Scholar
  38. Lund H (1995) The design reference year. Users manual. IEA, Solar heating and Cooling Task 9 ReportGoogle Scholar
  39. Mares O, Paulescu M, Badescu V (2015) A simple but accurate procedure for solving the five-parameter model. Energy Convers Manag 105:139–148.  https://doi.org/10.1016/j.enconman.2015.07.046CrossRefGoogle Scholar
  40. Marion W, Urban K (1985) User’s Manual for TMY2 s. Typical Meteorological Years. NREL/TP-463-7668, National Renewable Enery Laboratory, Golden COGoogle Scholar
  41. Mefti A, Bouroubi MY, Adane A (2003) Generation of hourly solar radiation for inclined surfaces using monthly mean sunshine duration in Algeria. Energy Convers Manag 44:3125–3141.  https://doi.org/10.1016/S0196-8904(03)00070-0CrossRefGoogle Scholar
  42. Mermoud AA, Wittmer B (2014) Pvsyst user’s manual. PVSYST SAGoogle Scholar
  43. Mohammadi K, Khorasanizadeh H (2015) A review of solar radiation on vertically mounted solar surfaces and proper azimuth angles in six Iranian major cities. Renew Sustain Energy Rev 47:504–518.  https://doi.org/10.1016/j.rser.2015.03.037CrossRefGoogle Scholar
  44. Muneer T, Saluja GS (1985) A brief review of models for computing solar radiation on inclined surfaces. Energy Convers Manag 25:443–458.  https://doi.org/10.1016/0196-8904(85)90009-3CrossRefGoogle Scholar
  45. Padovan A, Del Col D (2010) Measurement and modeling of solar irradiance components on horizontal and tilted planes. Sol Energy 84:2068–2084.  https://doi.org/10.1016/j.solener.2010.09.009CrossRefGoogle Scholar
  46. Pagh Nielsen K, Blanc P, Vignola F et al (2017) Discussion of current used practices for: creation of meteorological data sets for CSP/STE performance simulations. Technical report SolarPACES Task VGoogle Scholar
  47. Pandey CK, Katiyar AK (2011) A comparative study of solar irradiation models on various inclined surfaces for India. Appl Energy 88:1455–1459.  https://doi.org/10.1016/j.apenergy.2010.10.028CrossRefGoogle Scholar
  48. Pelland S, Maalouf C, Kenny R et al (2016) Solar Energy Assessments: When Is a Typical Meteorological Year Good Enough? In: Proceedings of the American solar energy society national 2016 conference 1–7.  https://doi.org/10.18086/solar.2016.01.17
  49. Peng J, Lu L, Yang H, Ma T (2015) Validation of the Sandia model with indoor and outdoor measurements for semi-transparent amorphous silicon PV modules. Renew Energy 80:316–323.  https://doi.org/10.1016/j.renene.2015.02.017CrossRefGoogle Scholar
  50. Peruchena CMF, García-Barberena J, Guisado MV, Gastón M (2016) A clustering approach for the analysis of solar energy yields: a case study for concentrating solar thermal power plants. p 070008Google Scholar
  51. Petrucelli JD, Nandram B, Chen M (1999) Applied statistics for scientists and engineers, Prentice-Hall IncGoogle Scholar
  52. Polo J, Fernández-Peruchena C, Gastón M (2017) Analysis on the long-term relationship between DNI and CSP yield production for different technologies. Sol Energy 115:1121–1129.  https://doi.org/10.1016/j.solener.2017.07.059CrossRefGoogle Scholar
  53. Polo J, Téllez FM, Tapia C (2016) Comparative analysis of long-term solar resource and CSP production for bankability. Renew Energy 90:38–45.  https://doi.org/10.1016/j.renene.2015.12.057CrossRefGoogle Scholar
  54. Pusat S, Ekmekçi İ, Akkoyunlu MT (2015) Generation of typical meteorological year for different climates of Turkey. Renew Energy 75:144–151.  https://doi.org/10.1016/j.renene.2014.09.039CrossRefGoogle Scholar
  55. Ramírez L, Barnechea B, Bernardos A et al (2012) Towards the standardization of procedures for solar radiation data series generation. In: Proceedings of the solarPACES conference. p 5Google Scholar
  56. Ramírez L, Pagh Nielsen K, Vignola F et al (2017) Road map for creation of advanced meteorological data sets for CSP performance simulations. Technical report SolarPACES Task VGoogle Scholar
  57. Richter M, Kalisch J, Schmidt T et al (2015) Best Practice Guide On Uncertainty in PV ModellingGoogle Scholar
  58. Riihimaki L, Vignola F (2005) Trends in direct normal solar irradiance in Oregon from 1979–2003. In: ISES Solar world congress 2005Google Scholar
  59. Riihimaki L, Vignola F, Lohmann S, Meyer R (2005) Observing changes of surface solar irradiance in Oregon: a comparison of satellite and ground-based time-series. In: AGU fall meeting, 2005-12-05–2005-12-09, San Francisco, CO (USA). pGoogle Scholar
  60. Röttinger N, Remann F, Meyer R, Telsnig T (2015) Calculation of CSP Yields with probabilistic meteorological data sets: a case Study in Brazil. Energy Procedia 69:2009–2018.  https://doi.org/10.1016/j.egypro.2015.03.210CrossRefGoogle Scholar
  61. Sengupta M, Habte A, Gueymard C, Wilbert S, Renné D (2017) Best practices handbook for the collection and use of solar resource data for solar energy applications, second edn.  https://doi.org/10.18777/ieashc-task46-2015-0001
  62. Skeiker K (2004) Generation of a typical meteorological year for Damascus zone using the Filkenstein-Schafer statistical method. Energy Convers Manegement 45:99–112.  https://doi.org/10.1016/S0196-8904(03)00106-7CrossRefGoogle Scholar
  63. Stein JS, Farnung B (2017) PV performance modeling methods and practices results from the 4th PV performance modeling collaborative workshop. IEA PVPS Task 13Google Scholar
  64. Sudhakar Babu T, Prasanth Ram J, Sangeetha K et al (2016) Parameter extraction of two diode solar PV model using Fireworks algorithm. Sol Energy 140:265–276.  https://doi.org/10.1016/j.solener.2016.10.044CrossRefGoogle Scholar
  65. Thevenard D, Driesse A, Turcotte D et al (2010) Uncertainty in long-term photovoltaic yield predictionsGoogle Scholar
  66. Thevenard D, Pelland S (2013) Estimating the uncertainty in long-term photovoltaic yield predictions. Sol Energy 91:432–445.  https://doi.org/10.1016/j.solener.2011.05.006CrossRefGoogle Scholar
  67. Tjengdrawira C, Moser D, Jahn U et al (2017) PV investment technical risk management: best practice guidelines for risk identification, assessment and mitigationGoogle Scholar
  68. Vignola F, Grover C, Lemon N, McMahan A (2012) Building a bankable solar radiation dataset. Sol Energy 86:2218–2229CrossRefGoogle Scholar
  69. Wagner MJ (2008) Simulation and predictive performance modeling of utility-scale central receiver system power plants. Thesis at the University of Wisconsin-MadisonGoogle Scholar
  70. Wagner MJ, Gilman P (2011) Technical manual for the SAM physical trough model. NREL Report No. NREL/TP-5500-51825Google Scholar
  71. Wagner MJ, Zhu G (2012) A direct-steam linear fresnel performance model for NREL’s system advisor model. In: Proceedings of the ASME 2012 6th international conference on energy sustainability and 10th fuel cell science, engineering and technology conference, San Diego, CA, USA, 23–26 July 2012Google Scholar
  72. Wattan R, Janjai S (2016) An investigation of the performance of 14 models for estimating hourly diffuse irradiation on inclined surfaces at tropical sites. Renew Energy 93:667–674.  https://doi.org/10.1016/j.renene.2016.02.076CrossRefGoogle Scholar
  73. Wilcox S, Marion W (2008) Users manual for TMY3 data sets. NREL/TP-581-43156, National Renewable Energy Laboratory, Golden COGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Photovoltaic Solar Energy Unit, Renewable Energy Division (Energy Department)CIEMATMadridSpain

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