An integrated multi-objective optimization model for determining the optimal solution in the solar thermal energy system

Jimin Kim, Taehoon Hong, Jaemin Jeong, Myeonghwi Lee, Choongwan Koo, Minhyun Lee, Changyoon Ji and Jaewook Jeong

Energy, 2016, vol. 102, issue C, 416-426

Abstract: The STE (solar thermal energy) system is considered an important new renewable energy resource. While various simulations are used as decision-making tools in implementing the STE system, it has a limitation in considering both diverse impact factors and target variables. Therefore, this study aimed to develop an integrated multi-objective optimization model for determining the optimal solution in the STE system. As the optimization algorithm, this study utilizes GA (genetic algorithm) to select optimal STE system solution. Using crossover and mutation, GA investigates optimal STE system solution. The proposed model used GA based on the software program Evolver 5.5. The proposed model presents high available and efficient results as decision-making tools. First, to determine the optimal solution, a total of 30,407,832 possible scenarios were generated by considering various factors in terms of their high availability. Second, in terms of efficiency, an average of 131 s were used to determine the optimal solution out of the previously proposed various scenarios. The proposed model can become a tool for consumers to decide on the optimal solution for the design of the STE system.

Keywords: Solar thermal energy system; Multi-objective optimization; Generic algorithm; Economic and environmental assessment (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544216301669
Full text for ScienceDirect subscribers only

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:eee:energy:v:102:y:2016:i:c:p:416-426

DOI: 10.1016/j.energy.2016.02.104

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().