EconPapers    
Economics at your fingertips  
 

An Improved Bare Bone Multi-Objective Particle Swarm Optimization Algorithm for Solar Thermal Power Plants

Qun Niu, Han Wang, Ziyuan Sun and Zhile Yang
Additional contact information
Qun Niu: School of Mechanical Engineering and Automation, Shanghai Key Laboratory of Power Station Automation Technology, Shanghai University, Shanghai 200444, China
Han Wang: School of Mechanical Engineering and Automation, Shanghai Key Laboratory of Power Station Automation Technology, Shanghai University, Shanghai 200444, China
Ziyuan Sun: School of Mechanical Engineering and Automation, Shanghai Key Laboratory of Power Station Automation Technology, Shanghai University, Shanghai 200444, China
Zhile Yang: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

Energies, 2019, vol. 12, issue 23, 1-22

Abstract: Solar energy has many advantages, such as being abundant, clean and environmentally friendly. Solar power generation has been widely deployed worldwide as an important form of renewable energy. The solar thermal power generation is one of a few popular forms to utilize solar energy, yet its modelling is a complicated problem. In this paper, an improved bare bone multi-objective particle swarm optimization algorithm (IBBMOPSO) is proposed based on the bare bone multi-objective particle swarm optimization algorithm (BBMOPSO). The algorithm is first tested on a set of benchmark problems, confirming its efficacy and the convergency speed. Then, it is applied to optimize two typical solar power generation systems including the solar Stirling power generation and the solar Brayton power generation; the results show that the proposed algorithm outperforms other algorithms for multi-objective optimization problems.

Keywords: solar Stirling power system; solar Brayton power system; particle swarm optimization (PSO); multi-objective optimization problems (MOP) (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/23/4480/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/23/4480/ (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:12:y:2019:i:23:p:4480-:d:290577

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 ().

 
Page updated 2025-03-19
Handle: RePEc:gam:jeners:v:12:y:2019:i:23:p:4480-:d:290577