Research Papers

Use of Image-Based Direct Normal Irradiance Forecasts in the Model Predictive Control of a Solar-Thermal Reactor

[+] Author and Article Information
Elizabeth Saade

e-mail: maria.saadesaade@colorado.edu

David E. Clough

e-mail: david.clough@colorado.edu

Alan W. Weimer

e-mail: alan.weimer@colorado.edu
Department of Chemical
and Biological Engineering,
University of Colorado,
Jennie Smoly Caruthers Biotechnology Building,
596 UCB,
Boulder, CO 80309-0596

1Corresponding author.

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING. Manuscript received August 2, 2013; final manuscript received October 20, 2013; published online November 19, 2013. Assoc. Editor: Aldo Steinfeld.

J. Sol. Energy Eng 136(1), 010905 (Nov 19, 2013) (9 pages) Paper No: SOL-13-1217; doi: 10.1115/1.4025825 History: Received August 02, 2013; Revised October 20, 2013

A model predictive control (MPC) system for a solar-thermal reactor was developed and applied to the solar-thermal steam-gasification of carbon. The controller aims at rejecting the disturbances in solar irradiance, caused by the presence of clouds. Changes in solar irradiance are anticipated using direct normal irradiance (DNI) forecasts generated using images acquired through a Total Sky Imager (TSI). The DNI predictor provides an estimation of the disturbances for the control algorithm, for a time horizon of 1 min. The proposed predictor utilizes information obtained through the analysis of sky images, in combination with current atmospheric measurements, to produce the DNI forecast. The predictions of the disturbances are used, in combination with a dynamic model of the process, to determine the required control moves at every time step. The performance of the proposed DNI predictor-controller scheme was compared to the performance of an equivalent MPC that does not use DNI forecasts in the calculation of the control signals. In addition, the performance of a controller fed with perfect DNI predictions was also evaluated.

Copyright © 2014 by ASME
Your Session has timed out. Please sign back in to continue.


Steinfeld, A., and Palumbo, R., 2001, “Solar Thermochemical Process Technology,” Encycl. Phys. Sci. Technol., 15, pp. 237–256. [CrossRef]
Petrasch, J., Osch, P., and Steinfeld, A., 2009, “Dynamics and Control of Solar Thermochemical Reactors,” Chem. Eng. J., 145(3), pp. 362–370. [CrossRef]
Skogestad, S., and Postlethwaite, I., 1996, Multivariable Feedback Control, John Wiley and Sons, Inc., New York.
Saade, E., Clough, D. E., and Weimer, A. W., “Model Predictive Control of a Solar-Thermal Reactor,” Solar Energy (submitted).
Camacho, E. F., and Bordons, C., 2004, Model Predictive Control, Springer-Verlag, London.
Camacho, E. F., Berenguel, M., Rubio, F. R., and Martinez, D., 2012, Control of Solar Energy Systems, Springer-Verlag, London.
Marquez, R., and Coimbra, C. F. M., 2011, “Forecasting of Global and Direct Solar Irradiance Using Stochastic Learning Methods, Ground Experiments and the NWS Database,” Solar Energy, 85(5), pp. 746–756. [CrossRef]
Mellit, A., and Pavan, A. M., 2010, “A 24-h Forecast of Solar Irradiance Using Artificial Neural Network: Application for Performance Prediction of a Grid-Connected PV Plant at Trieste, Italy,” Solar Energy, 84(5), pp. 807–821. [CrossRef]
Sfetsos, A., and Coonick, A. H., 2000, “Univariate and Multivariate Forecasting of Hourly Solar Radiation With Artificial Intelligence Techniques,” Solar Energy, 68(2), pp. 169–178. [CrossRef]
Martín, L., Zarzalejo, L. F., Polo, J., Navarro, A., Marchante, R., and Cony, M., 2010, “Prediction of Global Solar Irradiance Based on Time Series Analysis: Application to Solar Thermal Power Plants Energy Production Planning,” Solar Energy, 84(10), pp. 1772–1781. [CrossRef]
Perez, R., Ineichen, P., Moore, K., Kmiecik, M., Chain, C., George, R., and Vignola, F., 2002, “A New Operational Model for Satellite-Derived Irradiances: Description and Validation,” Solar Energy, 73(5), pp. 307–317. [CrossRef]
Hammer, A., Heinemann, D., Lorenz, E., and Lückehe, B., 1999, “Short-Term Forecasting of Solar Radiation: A Statistical Approach Using Satellite Data,” Solar Energy, 67(1), pp. 139–150. [CrossRef]
Chow, C. W., Urquhart, B., Lave, M., Dominguez, A., Kleissl, J., Shields, J., and Washom, B., 2011, “Intra-Hour Forecasting With a Total Sky Imager at the UC San Diego Solar Energy Testbed,” Solar Energy, 85(11), pp. 2881–2893. [CrossRef]
López-Martínez, M., Vargas, M., and Rubio, F., 2002, “Vision-Based System for the Safe Operation of a Solar Power Tower Plan,” Advances in Artificial Intelligence—IBERAMIA 2002 (Lecture Notes in Computer Science, Vol. 2527), Springer, Berlin, pp. 943–952. [CrossRef]
Marquez, R., Gueorguiev, V. G., and Coimbra, C. F. M., 2011, “Forecasting of Global Horizontal Irradiance Using Sky Cover Indices,” ASME Paper No. ES2011-54551 [CrossRef].
Marquez, R., and Coimbra, C. F. M., 2013, “Intra-Hour DNI Forecasting Based on Cloud Tracking Image Analysis,” Solar Energy, 91, pp. 327–336. [CrossRef]
Saade, E., Bingham, C., Clough, D. E., and Weimer, A. W., 2012, “Dynamics of a Solar-Thermal Transport-Tube Reactor,” Chem. Eng. J., 213, pp. 272–285. [CrossRef]
National Renewable Energy Laboratory, 1981, “Solar Radiation Research Laboratory,” http://www.nrel.gov/midc/srrl_bms/
Bemporad, A., Morari, M., and Ricker, N. L., 2004, “Model Predictive Control Toolbox,” Users Guide Version 2, MathWorks Inc., Natick, MA.
Yankee Environmental Systems, Inc., Turners Falls, MA., 2003, “TSI-880 Autmatic Total Sky Imager,”
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H., 2009, “The WEKA Data Mining Software: An Update,” Acm Sigkdd Explor. Newsl., 11(1), pp. 10–18. [CrossRef]
The Mathworks, Inc, Natick, MA, 2012, “Image Processing Toolbox for MATLAB.”
Eppley Lab, Newport, RI, 2013, “Normal Incidence Pyrheliometer,” http://www.eppleylab.com/PrdNormIncPyrhelmtr.htm
Campbell Scientific, Inc, Logan, UT, 2012, “Campbell Scientific,” http://www.campbellsci.com/hmp45c-l
NRG Systems, Inc, Hinesburg, VT, 2008, “NRG Systems, NRG #40C” http://www.nrgsystems.com/sitecore/content/Products/1900.aspx
NRG Systems, Inc, Hinesburg, VT, 2008, “NRG Systems, NRG #200P” http://www.nrgsystems.com/sitecore/content/Products/1904.aspx
Campbell Scientific, Inc, Logan, UT, 2007, “CS105/CS105MD Barometric Pressure Sensor, Instruction Manual.”
Reda, I., and Andreas, A., 2004, “Solar Position Algorithm for Solar Radiation Applications,” Solar Energy, 76(5), pp. 577–589. [CrossRef]
NOAA Earth System Research Laboratory, “NOAA Solar Calculator,” NOAA/ESRL, Boulder, CO, http://www.esrl.noaa.gov/gmd/grad/solcalc/
Jean, M., 1991, Astronomical Algorithms, Willmann-Bell, Richmond, VA.


Grahic Jump Location
Fig. 2

Diagram of proposed control system

Grahic Jump Location
Fig. 1

Schematic of solar-thermal reactor for the steam-gasification of carbon

Grahic Jump Location
Fig. 3

Strategy for the image-based DNI forecasts

Grahic Jump Location
Fig. 4

Output images after applying masks. (a) Mask 1, (b) Mask 2, (c) Mask 3.

Grahic Jump Location
Fig. 5

Four sky scenarios and main characteristics. (a) Sunny scenario. (b) Cloudy scenario. (c) Transition scenario, covered case. (d) Transition scenario, uncovered case.

Grahic Jump Location
Fig. 6

Example of characteristics obtained from images. (a) Uncovered case. (b) Covered case.

Grahic Jump Location
Fig. 8

Integration of image-based DNI predictor with MPC algorithm

Grahic Jump Location
Fig. 7

Comparison between the predicted one-minute-ahead DNI and the measured DNI, for two different dates. (a) February 27th, 2012. (b) November 14th, 2012.

Grahic Jump Location
Fig. 9

Comparison of MPC strategies. (a) Synthesis gas fraction. (b) CO:CO2 ratio.

Grahic Jump Location
Fig. 10

Manipulated variables using different MPC strategies. (a) Gas flow rate. (b) Steam flow.



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In