0
Research Papers

Dynamic Modeling of Solar Radiation Disturbances Based on a Biomimetic Cloud Shading Model

[+] Author and Article Information
Jesús García

Department of Mechanical Engineering,
Universidad del Norte,
Barranquilla 080001, Colombia
e-mail: jesusmg@uninorte.edu.co

Iván Portnoy

Department of Mechanical Engineering,
Universidad del Norte,
Barranquilla 080001, Colombia
e-mail: iportnoy@uninorte.edu.co

Ricardo Vasquez Padilla

School of Environment,
Science and Engineering,
Southern Cross University,
Lismore 2480, NSW, Australia
e-mail: ricardo.vasquez.padilla@scu.edu.au

Marco E. Sanjuan

Department of Mechanical Engineering,
Universidad del Norte,
Barranquilla 080001, Colombia
e-mail: msanjuan@uninorte.edu.co

1Correspondance author.

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING: INCLUDING WIND ENERGY AND BUILDING ENERGY CONSERVATION. Manuscript received March 14, 2017; final manuscript received December 12, 2017; published online January 31, 2018. Assoc. Editor: Jorge Gonzalez.

J. Sol. Energy Eng 140(2), 021008 (Jan 31, 2018) (9 pages) Paper No: SOL-17-1083; doi: 10.1115/1.4038961 History: Received March 14, 2017; Revised December 12, 2017

Variation in direct solar radiation is one of the main disturbances that any solar system must handle to maintain efficiency at acceptable levels. As known, solar radiation profiles change due to earth's movements. Even though this change is not manipulable, its behavior is predictable. However, at ground level, direct solar radiation mainly varies due to the effect of clouds, which is a complex phenomenon not easily predictable. In this paper, dynamic solar radiation time series in a two-dimensional (2D) spatial domain are obtained using a biomimetic cloud-shading model. The model is tuned and compared against available measurement time series. The procedure uses an objective function based on statistical indexes that allow extracting the most important characteristics of an actual set of curves. Then, a multi-objective optimization algorithm finds the tuning parameters of the model that better fit data. The results showed that it is possible to obtain responses similar to real direct solar radiation transients using the biomimetic model, which is useful for other studies such as testing control strategies in solar thermal plants.

FIGURES IN THIS ARTICLE
<>
Copyright © 2018 by ASME
Your Session has timed out. Please sign back in to continue.

References

Berenguel, M. , and Rubio, F. , 2012, Advanced Control of Solar Plants, Advances in Industrial Control, Springer, London.
Camacho, E. , Soria, M. , Rubio, F. , and Martínez, D. , 2012, Control of Solar Energy Systems, Advances in Industrial Control, Springer, London. [CrossRef]
Gagné, A. , Turcotte, D. , Goswamy, N. , and Poissant, Y. , 2016, “High Resolution Characterisation of Solar Variability for Two Sites in Eastern Canada,” Sol. Energy, 137, pp. 46–54. [CrossRef]
Bright, J. , Smith, C. , Taylor, P. , and Crook, R. , 2015, “Stochastic Generation of Synthetic Minutely Irradiance Time Series Derived From Mean Hourly Weather Observation Data,” Sol. Energy, 115, pp. 229–242. [CrossRef]
Nguyen, D. D. , and Lehman, B. , 2006, “Modeling and Simulation of Solar PV Arrays Under Changing Illumination Conditions,” IEEE Workshops on Computers in Power Electronics (COMPEL'06), Troy, NY, July 16–19, pp. 295–299.
Vijayalekshmy, S. , Bindu, G. , and Iyer, S. R. , 2014, “Estimation of Power Losses in Photovoltaic Array Configurations Under Moving Cloud Conditions,” Fourth International Conference on Advances in Computing and Communications (ICACC), Cochin, India, Aug. 27–29, pp. 366–369.
Cai, C. , and Aliprantis, D. C. , 2013, “Cumulus Cloud Shadow Model for Analysis of Power Systems With Photovoltaics,” IEEE Trans., 28(4), pp. 4496–4506.
Augsburger, G. , and Favrat, D. , 2013, “Modelling of the Receiver Transient Flux Distribution Due to Cloud Passages on a Solar Tower Thermal Power Plant,” Sol. Energy, 87, pp. 42–52. [CrossRef]
Haupt, S. E. , Jiménez, P. A. , Lee, J. A. , and Kosović, B. , 2017, “1—Principles of Meteorology and Numerical Weather Prediction,” Renewable Energy Forecasting, Woodhead Publishing Series in Energy, G. Kariniotakis , ed., Woodhead Publishing, Cambridge, UK, pp. 3–28. [CrossRef]
Jiménez, P. A. , Hacker, J. P. , Dudhia, J. , Haupt, S. E. , Ruiz-Arias, J. A. , Gueymard, C. A. , Thompson, G. , Eidhammer, T. , and Deng, A. , 2016, “WRF-Solar: Description and Clear-Sky Assessment of an Augmented NWP Model for Solar Power Prediction,” Bull. Am. Meteorol. Soc., 97(7), pp. 1249–1264. [CrossRef]
Jiménez, P. A. , Alessandrini, S. , Haupt, S. E. , Deng, A. , Kosović, B. , Lee, J. A. , and Delle Monache, L. , 2016, “The Role of Unresolved Clouds on Short-Range Global Horizontal Irradiance Predictability,” Mon. Weather Rev., 144(9), pp. 3099–3107. [CrossRef]
García, J. M. , Padilla, R. V. , and Sanjuan, M. E. , 2016, “A Biomimetic Approach for Modeling Cloud Shading With Dynamic Behavior,” Renewable Energy, 96(Pt. A), pp. 157–166. [CrossRef]
Tomson, T. , 2013, “Transient Processes of Solar Radiation,” Theor. Appl. Climatol., 112(3–4), pp. 403–408. [CrossRef]
Sengupta, M. , and Andreas, A. , 2010, “Oahu Solar Measurement Grid (1-Year Archive): 1-Second Solar Irradiance, Oahu, HI (Data),” National Renewable Energy Laboratory, Golden, CO, Report No. DA-5500-56506.
Kreft, J. U. , Booth, G. , and Wimpenny, J. W. , 1998, “BacSim, a Simulator for Individual-Based Modelling of Bacterial Colony Growth,” Microbiol. (Reading, England), 144(Pt. 1), pp. 3275–3287. [CrossRef]
Jackson, S. , 2009, Statistics Plain and Simple, Cengage Learning, Belmont, CA. [PubMed] [PubMed]
Ku, W. , Storer, R. H. , and Georgakis, C. , 1995, “Disturbance Detection and Isolation by Dynamic Principal Component Analysis,” Chemom. Intell. Lab. Syst., 30(1), pp. 179–196. [CrossRef]
Montgomery, D. C. , 1997, Design and Analysis of Experiments and Educational Version of Design Expert, Wiley, New York.
Venkatasubramanian, V. , Rengaswamy, R. , Yin, K. , and Kavuri, S. N. , 2003, “A Review of Process Fault Detection and Diagnosis—Part I: Quantitative Model-Based Methods,” Comput. Chem. Eng., 27(3), pp. 293–311. [CrossRef]
Venkatasubramanian, V. , Rengaswamy, R. , and Kavuri, S. N. , 2003, “A Review of Process Fault Detection and Diagnosis—Part II: Qualitative Models and Search Strategies,” Comput. Chem. Eng., 27(3), pp. 313–326. [CrossRef]
Venkatasubramanian, V. , Rengaswamy, R. , Kavuri, S. N. , and Yin, K. , 2003, “A Review of Process Fault Detection and Diagnosis—Part III: Process History Based Methods,” Comput. Chem. Eng., 27(3), pp. 327–346. [CrossRef]
Russell, E. L. , Chiang, L. H. , and Braatz, R. D. , 2000, Data-Driven Methods for Fault Detection and Diagnosis in Chemical Processes, Advances in Industrial Control, Springer, London. [CrossRef]
Ledesma, R. D. , and Valero-Mora, P. , 2007, “Determining the Number of Factors to Retain in EFA: An Easy-to-Use Computer Program for Carrying Out Parallel Analysis,” Pract. Assess., Res. Eval., 12(2), pp. 1–11. http://audibmw.info/pdf/retain/4.pdf
Franklin, S. B. , Gibson, D. J. , Robertson, P. A. , Pohlmann, J. T. , and Fralish, J. S. , 1995, “Parallel Analysis: A Method for Determining Significant Principal Components,” J. Veg. Sci., 6(1), pp. 99–106. [CrossRef]
Zwick, W. R. , and Velicer, W. F. , 1986, “Comparison of Five Rules for Determining the Number of Components to Retain,” Psychol. Bull., 99(3), p. 432. [CrossRef]
Stein, J. S. , Hansen, C. W. , and Reno, M. J. , 2012, “The Variability Index: A New and Novel Metric for Quantifying Irradiance and PV Output Variability,” Sandia National Laboratories, Albuquerque, NM, Report No. SAND2012-2088C. https://www.osti.gov/scitech/biblio/1068417

Figures

Grahic Jump Location
Fig. 1

Sample curve of direct solar radiation for Apr. 1, 2010. Oahu Solar Measurement Grid (NREL), UTC-10 [14].

Grahic Jump Location
Fig. 2

Reduced curve of direct solar radiation for Apr. 1, 2010 from 12:00 to 12:10. Data taken from Ref. [14].

Grahic Jump Location
Fig. 3

Features integrated into the implemented model [12]

Grahic Jump Location
Fig. 4

Graphical LSD results among the studied groups

Grahic Jump Location
Fig. 5

Variation of both components of the objective function

Grahic Jump Location
Fig. 6

Optimization procedure flowchart

Grahic Jump Location
Fig. 7

Environmental resource distribution used for levels I and II in the factorial design

Grahic Jump Location
Fig. 8

Wind vector field used for levels I and II in the factorial design

Grahic Jump Location
Fig. 9

Comparison between real measurements and model results for each one of the eight experimental conditions

Grahic Jump Location
Fig. 10

Comparison between real measurements and model results for each one of the eight experimental conditions

Grahic Jump Location
Fig. 11

Variation of direct solar radiation (W/m2) for several snapshots over time in a 2D domain

Grahic Jump Location
Fig. 12

Arrow head plot for each time series created using the cloud shading model

Tables

Errata

Discussions

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