A Model Predictive Control Approach for Heliostat Field Power Regulatory Aiming Strategy under Varying Cloud Shadowing Conditions

Ruidi Zhu and Dong Ni ()
Additional contact information
Ruidi Zhu: Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province, Zhejiang University, 38 Zheda Rd., Xihu District, Hangzhou 310027, China
Dong Ni: Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province, Zhejiang University, 38 Zheda Rd., Xihu District, Hangzhou 310027, China

Energies, 2023, vol. 16, issue 7, 1-19

Abstract: Weather conditions have significant impacts on the solar concentration processes of the heliostat fields in solar tower power plants. The cloud shadow movements may cause varying solar irradiance levels received by each heliostat. Hence, fixed aiming strategies may not be able to guarantee the solar concentrating performance. Dynamic aiming strategies are able to optimize the aiming strategy based on real-time shadowing conditions and short-term forecast, and, therefore, provide much more robust solar concentration performance compared to fixed strategies. In this work, a model predictive control approach for s heliostat field power regulatory aiming strategy was proposed to regulate the total concentrated solar flux on the central receiver. The model predictive control method obtains the aiming strategy, leveraging real-time and forecast shadowing conditions based on the solar concentration model of the heliostat field. The allowable flux density of the receiver and the aiming angle adjustment limits are also considered as soft and hard constraints in the aiming strategy optimization. A Noor III-like heliostat field sector was studied with a range of shadow-passing scenarios, and the results demonstrated the effectiveness of the proposed method.

Keywords: dynamic aiming strategy; model predictive control; cloud shadowing; optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/7/2997/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/7/2997/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:7:p:2997-:d:1107006

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().