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.

References

1.
Berenguel
,
M.
, and
Rubio
,
F.
,
2012
,
Advanced Control of Solar Plants, Advances in Industrial Control
,
Springer
,
London
.
2.
Camacho
,
E.
,
Soria
,
M.
,
Rubio
,
F.
, and
Martínez
,
D.
,
2012
,
Control of Solar Energy Systems, Advances in Industrial Control
,
Springer
,
London
.
3.
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
.
4.
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
.
5.
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
.
6.
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
.
7.
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
.
8.
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
.
9.
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
.
10.
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
.
11.
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
.
12.
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
.
13.
Tomson
,
T.
,
2013
, “
Transient Processes of Solar Radiation
,”
Theor. Appl. Climatol.
,
112
(
3–4
), pp.
403
408
.
14.
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
.
15.
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
.
16.
Jackson
,
S.
,
2009
,
Statistics Plain and Simple
,
Cengage Learning
, Belmont, CA.
17.
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
.
18.
Montgomery
,
D. C.
,
1997
,
Design and Analysis of Experiments and Educational Version of Design Expert
, Wiley, New York.
19.
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
.
20.
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
.
21.
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
.
22.
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
.
23.
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
24.
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
.
25.
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
.
26.
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
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