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Research Papers

Multivariable Closed Control Loop Methodology for Heliostat Aiming Manipulation in Solar Central Receiver Systems

[+] Author and Article Information
Jesús García

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

Yen Chean Soo Too

CSIRO Energy Centre,
Mayfield West 2304, NSW, Australia
e-mail: yenchean@csiro.au

Ricardo Vasquez Padilla

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

Andrew Beath

CSIRO Energy Centre,
Mayfield West 2304, NSW, Australia
e-mail: andrew.beath@csiro.au

Jin-Soo Kim

CSIRO Energy Centre,
Mayfield West 2304, NSW, Australia
e-mail: jin-soo.kim@csiro.au

Marco E. Sanjuan

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

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 July 24, 2017; final manuscript received December 19, 2017; published online March 13, 2018. Assoc. Editor: Marc Röger.

J. Sol. Energy Eng 140(3), 031010 (Mar 13, 2018) (17 pages) Paper No: SOL-17-1309; doi: 10.1115/1.4039255 History: Received July 24, 2017; Revised December 19, 2017

Maintaining receiver’s thermal stresses and corrosion below the material limits are issues that need careful attention in solar thermal towers. Both depend on heliostats’ aiming points over the central receiver and available direct solar radiation at any instant. Since this technology relies on an unavoidable time-changing resource, aiming points need to be properly manipulated to avoid excessive hot spots. This paper proposes a new aiming point strategy based on a multivariable model predictive control (MPC) approach. It shows an alternative approach by introducing an agent-based group behavior over heliostats’ subsets, which makes possible either concentrating or dispersing solar radiation as required by the MPC algorithm. Simulated results indicate that it is feasible to develop a closed-loop control procedure that distributes solar irradiance over the central receiver according to the predefined heat flux limits. The performance of the proposed approach is also compared with the results found in the available literature that uses a different methodology.

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References

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Figures

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Fig. 1

Main procedures considered in the proposed closed loop aiming strategy

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Fig. 2

(a) A schematic diagram of a typical solar central receiver system and (b) main parameters accounted in the developed model

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Fig. 3

A schematic diagram of the solar central receiver used in Gemasolar (a) lateral and (b) top view [6]

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Fig. 4

Gemasolar heliostat distribution. Data taken from Ref.[6]

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Fig. 5

Percent deviation in cosine efficiency between the developed model and Solar Pilot at different solar times

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Fig. 6

Percent deviation in blocking and shading between the developed model and Solar Pilot at various solar times

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Fig. 7

Flux density profiles obtained from various models throughout the western receiver panels with equatorial aiming at summer solstice noon. Reference model [6].

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Fig. 8

Maximum AFD and flux distribution on the western receiver panels with equatorial aiming at solstice summer noon. Data taken from Ref. [6].

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Fig. 9

A basic operational scheme of the implemented group behavior for different pairs of manipulated variables κ and yCent

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Fig. 10

Distribution of r value for two different values of κ parameter for an interval of [−10,+10]

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Fig. 11

Distances for calculating the movement of aiming points within the five-heliostat group

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Fig. 12

Continuous iteration procedure to determine aiming points locations at each time-step

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Fig. 13

Aiming points’ movement for a κ parameter from a low value to a high value at four different times

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Fig. 14

Maximum speed in elevation and azimuth obtained using k1 and k2 equal to 0.35 in a process identification step test for κ values from one to 20 and back to one

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Fig. 15

Gemasolar heliostat field layout divided into 54 groups using the proposed aiming strategy

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Fig. 16

An example of responses obtained from step tests in κ-value of group 1 of section E1 and its neighbor panels (E2 andW1)

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Fig. 17

An example of responses obtained from step tests in the centroid location of group 1 of section E1 and its neighbor panels (E2, E3, W1, and W2)

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Fig. 18

A schematic diagram of a feedback control loop using the DMC control law

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Fig. 19

Performance comparison between different two distinctive cases from the 32 runs of the fractional factorial design: (a) flux density profiles (case 18), (b) flux density profiles (case 23), (c) aiming points distribution (case 18), (d) aiming points distribution (case 23), (e) controlled variables (case 18), and (f) controlled variables (case 23)

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Fig. 20

Comparison of incident flux distribution of the receiver panels between proposed and Ref. [6] aiming strategies at different solar hours: (a) 12 pm, (b) 9 am, and (c) 7 am

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Fig. 21

Aiming points distribution of the receiver panels using the proposed aiming strategy at different solar hours: (a) 12 pm, (b) 9 am, and (c) 7 am

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Fig. 22

Incident flux distribution of the receiver panels using the proposed aiming strategy at different solar hours: (a) 12 pm, (b) 9 am, and (c) 7 am

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Fig. 23

Amount of aiming points shared by the corresponding primary and neighbor panels at various solar hours

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Fig. 24

Total incident energy on the receiver panels between proposed and Ref. [6] aiming strategies at different solar hours: (a) west side and (b) east side

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