Control Issues in Solar Systems

  • Eduardo F. CamachoEmail author
  • Manuel Berenguel
  • Francisco R. Rubio
  • Diego Martínez
Part of the Advances in Industrial Control book series (AIC)


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.


Solar Irradiance Solar Collector Exponentially Weighted Move Average Global Irradiance Solar Plant 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 6.
    Almorox, J., Hontoria, C.: Global solar radiation estimation using sunshine duration in Spain. Energy Convers. Manag. 45, 1529–1535 (2004) CrossRefGoogle Scholar
  2. 14.
    Ångström, A.: Solar and terrestrial radiation. Q. J. R. Meteorol. Soc. 50, 121–126 (1924) CrossRefGoogle Scholar
  3. 20.
    ASHRAE: ASHRAE Handbook: HVAC Applications. ASHRAE, Atlanta (1999) Google Scholar
  4. 38.
    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. (1994)
  5. 42.
    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) CrossRefGoogle Scholar
  6. 56.
    Blanco-Muriel, M., Alarcón-Padilla, D.C., López-Moratalla, T., Lara-Coira, M.: Computing the solar vector. Sol. Energy 70(5), 431–441 (2001) CrossRefGoogle Scholar
  7. 62.
    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) CrossRefGoogle Scholar
  8. 63.
    Box, G.E.P., Luceno, A., Paniagua, M.C.: Statistical Control by Monitoring and Adjustment. Wiley, New York (2009) CrossRefzbMATHGoogle Scholar
  9. 68.
    Brockwell, P.J., Davis, R.A.: Introduction to Time Series and Forecasting. Springer, Berlin (2002) CrossRefzbMATHGoogle Scholar
  10. 85.
    Camacho, E.F., Berenguel, M., Rubio, F.R.: Advanced Control of Solar Plants. Springer, Berlin (1997) CrossRefGoogle Scholar
  11. 91.
    Cao, J., Cao, S.: Forecast of solar irradiance using recurrent neural networks combined with wavelet analysis. Appl. Therm. Eng. 25, 161–172 (2005) CrossRefGoogle Scholar
  12. 92.
    Cao, J., Cao, S.: Study of forecasting solar irradiance using neural networks with preprocessing sample data by wavelet analysis. Energy 31, 3435–3445 (2006) CrossRefGoogle Scholar
  13. 101.
    Castro, M.A.: Simulation of solar energy plants. Application to energy management. PhD Thesis, ESII Madrid, Spain (1988) (in Spanish) Google Scholar
  14. 102.
    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) CrossRefGoogle Scholar
  15. 123.
    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. 132.
    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) CrossRefGoogle Scholar
  17. 161.
    Goswami, Y., Kreith, F., Kreider, J.F.: Principles of Solar Engineering. Taylor and Francis, London (2000) Google Scholar
  18. 163.
    Grena, R.: An algorithm for the computation of the solar position. Sol. Energy 82, 462–470 (2008) CrossRefGoogle Scholar
  19. 165.
    Gueymard, C.A.: Direct solar transmittance and irradiance predictions with broadband models. Part I: Detailed theoretical performance assessment. Sol. Energy 74, 355–379 (2003) CrossRefGoogle Scholar
  20. 166.
    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) CrossRefGoogle Scholar
  21. 167.
    Gueymard, C.A.: Importance of atmospheric turbidity and associated uncertainties in solar radiation and luminous efficacy modeling. Energy 30, 1603–1621 (2005) CrossRefGoogle Scholar
  22. 168.
    Gueymard, C.A.: Prediction and validation of cloudless shortwave solar spectra incident on horizontal, tilted, or tracking surfaces. Sol. Energy 82, 260–271 (2008) CrossRefGoogle Scholar
  23. 175.
    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. 180.
    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. 184.
    Ibáñez, M., Rosell, J., Rosell Urrutia, J.: Tecnología Solar. Editorial MP, Madrid (2005) Google Scholar
  26. 188.
    Iqbal, M.: An Introduction to Solar Radiation. Academic Press, Toronto (1983) Google Scholar
  27. 193.
    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) CrossRefGoogle Scholar
  28. 214.
    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. 248.
    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) CrossRefGoogle Scholar
  30. 249.
    Mellit, A., Kalogirou, S.A.: Artificial intelligence techniques for photovoltaic applications: a review. Prog. Energy Combust. Sci. 34, 547–632 (2008) CrossRefGoogle Scholar
  31. 250.
    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) CrossRefGoogle Scholar
  32. 261.
    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. 263.
    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) CrossRefGoogle Scholar
  34. 268.
    NIST: Engineering statistics handbook. Technical Report. (2006)
  35. 279.
    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. 283.
    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) CrossRefGoogle Scholar
  37. 288.
    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. 291.
    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) CrossRefGoogle Scholar
  39. 305.
    Rabl, A.: Active Solar Collectors and Their Applications. Oxford University Press, New York (1985) Google Scholar
  40. 311.
    Reikard, G.: Predicting solar radiation at high resolutions: a comparison of time series forecasts. Sol. Energy 83(3), 342–349 (2009) CrossRefGoogle Scholar
  41. 312.
    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. 337.
    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. 359.
    Stine, W.B., Geyer, M.: Power from the Sun. (2001)
  44. 374.
    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) CrossRefGoogle Scholar
  45. 378.
    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) CrossRefGoogle Scholar
  46. 381.
    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. 396.
    Welch, G., Bishop, G.: An introduction to the Kalman filter. Technical Report, Chapel Hill, NC, USA (2006) Google Scholar
  48. 399.
    Wong, L.T., Chow, W.K.: Solar radiation model. Appl. Energy 69, 191–224 (2001) CrossRefGoogle Scholar
  49. 409.
    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. 410.
    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. 420.
    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) CrossRefGoogle Scholar
  52. 421.
    Zhang, G.P., Qi, M.: Neural network forecasting for seasonal and trend time series. Eur. J. Oper. Res. 160, 501–514 (2005) CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  • Eduardo F. Camacho
    • 1
    Email author
  • Manuel Berenguel
    • 2
  • Francisco R. Rubio
    • 1
  • Diego Martínez
    • 3
  1. 1.Departamento de Ingeniería de Sistemas y Automática, Escuela Superior de IngenierosUniversidad de SevillaSevilleSpain
  2. 2.Departamento de Lenguajes y Computación, Escuela Superior de IngenieríaUniversidad de AlmeríaAlmeríaSpain
  3. 3.Plataforma Solar de Almería, Centro Europeo de Ensayos de Energía SolarCIEMATTabernasSpain

Personalised recommendations