Skip to main content
SpringerLink
Log in

Control Issues in Solar Systems

  • Chapter
Control of Solar Energy Systems

Part of the book series: Advances in Industrial Control ((AIC))

Abstract

This chapter is devoted to introduce the main issues involved in the control of solar energy systems. Four different levels can be distinguished: (i) the control of the solar collector units, (ii) solar radiation estimation and forecast, (iii) the control of the energy conversion systems and (iv) the overall control of the complete process.

The control of the solar collecting systems consists of controlling the solar collectors’ movements in such a way that the maximum solar energy is collected at any time. The controller has to compute the Sun vector, which depends of the geographical position of the collector and the date and time of day. Fine tracking is obtained in some cases by using signals which depend on the angle formed by the collector surface normal and the solar vector.

In order to control solar energy system, it is very important both to know the actual values of solar irradiance and even to be able to forecast this variable within different time windows to be used for control and operation planning purposes. Thus, adequate sensors to obtain values for solar irradiance are used in these kinds of plant (mainly pyranometers and pyrheliometers) and different algorithms to provide estimations of future values of solar irradiance where the solar plant is located.

The control of the variables associated to the solar conversion units depends very much on the type of system. In the case of photovoltaic (PV) systems, this involves the control of the voltage and intensity produced by the solar cells in order to operate at the maximum efficiency point and the controls of the associated DC/AC conversion power electronics. In the case of thermal solar plants, the solar energy heats up a fluid, which is then used to produce steam necessary to drive the turbines. The variables to be controlled at this level are the temperatures and flows of the heat collecting fluid.

The upper control level takes care of the operation of the complete solar system. The control decides what amount of energy is produced and delivered and what amount is stored, and which are the set points of the main process variables at any given moment. Since the solar radiation varies along the day, the plant is rarely in a steady-state condition and the determination of the optimal operating points should be done dynamically.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    This is the amount of total energy that contains the extraterrestrial solar irradiance, integrated in all the spectrum of wave lengths. The value used is E c =1367 W/m2.

References

  1. Almorox, J., Hontoria, C.: Global solar radiation estimation using sunshine duration in Spain. Energy Convers. Manag. 45, 1529–1535 (2004)

    Article  Google Scholar 

  2. Ångström, A.: Solar and terrestrial radiation. Q. J. R. Meteorol. Soc. 50, 121–126 (1924)

    Article  Google Scholar 

  3. ASHRAE: ASHRAE Handbook: HVAC Applications. ASHRAE, Atlanta (1999)

    Google Scholar 

  4. Berenguel, M., Camacho, E.F., Rubio, F.R.: Simulation software package for the Acurex field. Internal Report, Dpto. de Ingeniería de Sistemas y Automática, ESI Sevilla, Spain. www.esi2.us.es/~rubio/libro2.html (1994)

  5. Berenguel, M., Arahal, M.R., Camacho, E.F.: Modeling free response of a solar plant for predictive control. Control Eng. Pract. 6, 1257–1266 (1998)

    Article  Google Scholar 

  6. Blanco-Muriel, M., Alarcón-Padilla, D.C., López-Moratalla, T., Lara-Coira, M.: Computing the solar vector. Sol. Energy 70(5), 431–441 (2001)

    Article  Google Scholar 

  7. Bosch, J.L., López, G., Batlles, F.J.: Daily solar irradiation estimation over a mountainous area using artificial neural networks. Renew. Energy 33, 1622–1628 (2008)

    Article  Google Scholar 

  8. Box, G.E.P., Luceno, A., Paniagua, M.C.: Statistical Control by Monitoring and Adjustment. Wiley, New York (2009)

    Book  MATH  Google Scholar 

  9. Brockwell, P.J., Davis, R.A.: Introduction to Time Series and Forecasting. Springer, Berlin (2002)

    Book  MATH  Google Scholar 

  10. Camacho, E.F., Berenguel, M., Rubio, F.R.: Advanced Control of Solar Plants. Springer, Berlin (1997)

    Book  Google Scholar 

  11. Cao, J., Cao, S.: Forecast of solar irradiance using recurrent neural networks combined with wavelet analysis. Appl. Therm. Eng. 25, 161–172 (2005)

    Article  Google Scholar 

  12. Cao, J., Cao, S.: Study of forecasting solar irradiance using neural networks with preprocessing sample data by wavelet analysis. Energy 31, 3435–3445 (2006)

    Article  Google Scholar 

  13. Castro, M.A.: Simulation of solar energy plants. Application to energy management. PhD Thesis, ESII Madrid, Spain (1988) (in Spanish)

    Google Scholar 

  14. Chen, Y.T., Lim, B.H., Lim, C.S.: Sun tracking formula for heliostats with arbitrarily oriented axes. J. Sol. Energy Eng. 128, 245–251 (2006)

    Article  Google Scholar 

  15. Crispim, E.M., Ferreira, P.M., Ruano, A.E.: Solar radiation prediction using RBF neural networks and cloudiness indices. In: 2006 Int. Joint Conf. on Neural Networks, Vancouver, BC, Canada, 2006

    Google Scholar 

  16. Ertekin, C., Evrendilek, F.: Spatio-temporal modeling of global solar radiation dynamics as a function of sunshine duration for Turkey. Agric. For. Meteorol. 145, 36–47 (2007)

    Article  Google Scholar 

  17. Goswami, Y., Kreith, F., Kreider, J.F.: Principles of Solar Engineering. Taylor and Francis, London (2000)

    Google Scholar 

  18. Grena, R.: An algorithm for the computation of the solar position. Sol. Energy 82, 462–470 (2008)

    Article  Google Scholar 

  19. Gueymard, C.A.: Direct solar transmittance and irradiance predictions with broadband models. Part I: Detailed theoretical performance assessment. Sol. Energy 74, 355–379 (2003)

    Article  Google Scholar 

  20. Gueymard, C.A.: Direct solar transmittance and irradiance predictions with broadband models. Part II: Validation with high-quality measurements. Sol. Energy 74, 381–395 (2003)

    Article  Google Scholar 

  21. Gueymard, C.A.: Importance of atmospheric turbidity and associated uncertainties in solar radiation and luminous efficacy modeling. Energy 30, 1603–1621 (2005)

    Article  Google Scholar 

  22. Gueymard, C.A.: Prediction and validation of cloudless shortwave solar spectra incident on horizontal, tilted, or tracking surfaces. Sol. Energy 82, 260–271 (2008)

    Article  Google Scholar 

  23. Heinemann, D., Lorenz, E., Girodo, M.: Forecasting of solar radiation. In: Dunlop, E.D., Wald, L., Suri, M. (eds.) Solar Energy Resource Management for Electricity Generation from Local Level to Global Scale. Nova Publishers, New York (2006) (Chap. 7)

    Google Scholar 

  24. Hibbert, H., Pedreira, C., Souza, R.: Combining neural networks and ARIMA models for hourly temperature forecast. In: Proc. of the Int. Conf. on Neural Networks, IJCNN 2000, Como, Italy, pp. 414–419 (2000)

    Google Scholar 

  25. Ibáñez, M., Rosell, J., Rosell Urrutia, J.: Tecnología Solar. Editorial MP, Madrid (2005)

    Google Scholar 

  26. Iqbal, M.: An Introduction to Solar Radiation. Academic Press, Toronto (1983)

    Google Scholar 

  27. Janjai, S., Pankaew, P., Laksanaboonsong, J.: A model for calculating hourly global solar radiation from satellite data in the tropics. Appl. Energy 86, 1450–1457 (2009)

    Article  Google Scholar 

  28. Kleissl, J., Harper, J., Dominguez, A.: A solar resource measurement network for solar intermittency at high spatio-temporal resolution. In: Proc. of the SOLAR 2010 Conf., Phoenix, Arizona, USA, 2010

    Google Scholar 

  29. Mechlouch, R.F., Brahim, A.B.: A global solar radiation model for the design of solar energy systems. Asian J. Sci. Res. 1(3), 231–238 (2008)

    Article  Google Scholar 

  30. Mellit, A., Kalogirou, S.A.: Artificial intelligence techniques for photovoltaic applications: a review. Prog. Energy Combust. Sci. 34, 547–632 (2008)

    Article  Google Scholar 

  31. Mellit, A., Massi-Pavan, A.: A 24-h forecast of solar irradiance using artificial neural network: application for performance prediction of a grid-connected PV plant at Trieste, Italy. Sol. Energy 84, 807–821 (2010)

    Article  Google Scholar 

  32. Moreno-Muñoz, A., de la Rosa, J.J.G., Posadillo, R., Bellido, F.: Very short term forecasting of solar radiation. In: Photovoltaic Specialists Conf., San Diego, CA, USA, 2008

    Google Scholar 

  33. Mousazadeh, H., Keyhani, A., Javadi, A., Mobli, H., Abrinia, K., Sharifi, A.: A review of principle and sun-tracking methods for maximizing solar systems output. Renew. Sustain. Energy Rev. 13, 1800–1818 (2009)

    Article  Google Scholar 

  34. NIST: Engineering statistics handbook. Technical Report. http://www.itl.nist.gov/div898/handbook/ (2006)

  35. Olfati-Saber, R.: Distributed Kalman filter with embedded consensus filters. in: 44th IEEE Conf. on Decision and Control and European Control Conf., CDC–ECC’05, Seville, Spain, pp. 8179–8184 (2005)

    Google Scholar 

  36. Paoli, C., Voyant, C., Muselli, M., Nivet, M.L.: Solar radiation forecasting using ad-hoc time series preprocessing and neural-network. Emerg. Intell. Comput. Technol. Appl. SpringerLink 5754, 898–907 (2009)

    Article  Google Scholar 

  37. Pawlowski, A., Guzmán, J.L., Rodríguez, F., Berenguel, M., Sánchez, J.: Application of time-series methods to disturbance estimation in predictive control problems. In: Proc. of the 2010 IEEE Symp. on Industrial Electronics, Bari, Italy, 2010

    Google Scholar 

  38. Perez, R., Moore, K., Wilcox, S., Renne, D., Zelenka, A.: Forecasting solar radiation: preliminary evaluation of an approach based upon the national forecast database. Sol. Energy 81, 809–812 (2007)

    Article  Google Scholar 

  39. Rabl, A.: Active Solar Collectors and Their Applications. Oxford University Press, New York (1985)

    Google Scholar 

  40. Reikard, G.: Predicting solar radiation at high resolutions: a comparison of time series forecasts. Sol. Energy 83(3), 342–349 (2009)

    Article  Google Scholar 

  41. Remund, J., Perez, R., Lorenz, E.: Comparison of solar radiation forecasts for the USA. In: 2008 European PV Conf., Valencia, Spain, 2008

    Google Scholar 

  42. Sarkka, S., Vehtari, A., Lampinen, J.: Time series prediction by Kalman smoother with cross-validated noise density. In: Proc. of the IEEE Int. Joint Conf. on Neural Networks, Budapest, Hungary, pp. 1615–1619 (2004)

    Google Scholar 

  43. Stine, W.B., Geyer, M.: Power from the Sun. http://www.powerfromthesun.net/book.html (2001)

  44. Tovar-Pescador, J., Pozo-Vázquez, D., Ruiz-Arias, J.A., Batlles, J., López, G., Bosch, J.L.: On the use of the digital elevation model to estimate the solar radiation in areas of complex topography. Meteorol. Appl. 13(3), 279–287 (2006)

    Article  Google Scholar 

  45. Tymvios, F.S., Jacovides, C.P., Michaelides, S.C., Scouteli, C.: Comparative study of Angström’s and artificial neural networks’ methodologies in estimating global solar radiation. Sol. Energy 78, 752–762 (2005)

    Article  Google Scholar 

  46. Vadakkoot, R., Shah, M.D., Shrivastava, S.: Enhanced moving average computation. In: World Congress on Computer Science and Information Engineering. Los Angeles, USA, 2009

    Google Scholar 

  47. Welch, G., Bishop, G.: An introduction to the Kalman filter. Technical Report, Chapel Hill, NC, USA (2006)

    Google Scholar 

  48. Wong, L.T., Chow, W.K.: Solar radiation model. Appl. Energy 69, 191–224 (2001)

    Article  Google Scholar 

  49. Yona, A., Senjyu, T.: One-day-ahead 24-hours thermal energy collection forecasting based on time series analysis technique for solar heat energy utilization system. In: Proc. of IEEE T&D Asia 2009, Seoul, Korea, 2009

    Google Scholar 

  50. Zaharim, A., Razali, A.M., Gim, T.P., Sopian, K.: Time series analysis of solar radiation data in the tropics. Eur. J. Sci. Res. 25(4), 672–678 (2009)

    Google Scholar 

  51. Zavala, V.M., Constantinescu, E.M., Krause, T., Anitescu, M.: On-line economic optimization of energy systems using weather forecast information. J. Process Control 19, 1725–1736 (2009)

    Article  Google Scholar 

  52. Zhang, G.P., Qi, M.: Neural network forecasting for seasonal and trend time series. Eur. J. Oper. Res. 160, 501–514 (2005)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Departamento de Ingeniería de Sistemas y Automática, Escuela Superior de Ingenieros, Universidad de Sevilla, Seville, Spain

    Prof. Eduardo F. Camacho & Prof. Francisco R. Rubio

  2. Departamento de Lenguajes y Computación, Escuela Superior de Ingeniería, Universidad de Almería, Almería, Spain

    Manuel Berenguel

  3. Plataforma Solar de Almería, Centro Europeo de Ensayos de Energía Solar, CIEMAT, Tabernas, Spain

    Diego Martínez

Authors
  1. Prof. Eduardo F. Camacho
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manuel Berenguel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Prof. Francisco R. Rubio
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Diego Martínez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eduardo F. Camacho .

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag London Limited

About this chapter

Cite this chapter

Camacho, E.F., Berenguel, M., Rubio, F.R., Martínez, D. (2012). Control Issues in Solar Systems. In: Control of Solar Energy Systems. Advances in Industrial Control. Springer, London. https://doi.org/10.1007/978-0-85729-916-1_2

Download citation

  • DOI: https://doi.org/10.1007/978-0-85729-916-1_2

  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-915-4

  • Online ISBN: 978-0-85729-916-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation