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.

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Fig. 4

Graphical LSD results among the studied groups

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Fig. 3

Features integrated into the implemented model [12]

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Fig. 2

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

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Fig. 1

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

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Fig. 12

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

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Fig. 11

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

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Fig. 5

Variation of both components of the objective function

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Fig. 6

Optimization procedure flowchart

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Fig. 7

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

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Fig. 8

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

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Fig. 9

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

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Fig. 10

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




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