Optimization of thermodynamic performance with simulated annealing algorithm: A geothermal power plant

Gürcan Çetin and Ali Keçebaş

Renewable Energy, 2021, vol. 172, issue C, 968-982

Abstract: Binary geothermal power plants (GPP) always attract researchers’ attention as they are renewable-energy operated, low-temperature, high-performance, environmentally-friendly, and baseload power plants. In addition, they need to be monitored, controlled and optimized due to their complex structure and functioning. This article presents the application of the Simulated Annealing (SA) algorithm for the thermodynamic performance optimization on the verified thermodynamic model of the SINEM GPP operating in Aydın, Turkey. This algorithm is also compared to the Gravitational Search Algorithm (GSA). By using these methods, 17 optimization parameters in the plant model are simultaneously optimized for maximum exergy efficiency. Study results show that the exergy analysis, gravitational search algorithm and simulated annealing algorithm respectively determined the exergy efficiency of the plant as 14.48%, 30.62%, and 38.49%. The SA algorithm has a better performance compared to the other two methods. System components such as condensers, vaporizers, and pumps are made more efficient using the SA algorithm. In addition, the most effective parameters of the plant are evaporator pressure differences and the mass flow of ORC’s working fluid. By using GSA and SA algorithm, the gross electricity generation in the power plant can be increased by 2.11 MW and 3.15 MW, respectively. While GSA uses the procedure of reducing the amount of component exergy destruction, the SA algorithm uses the procedure of reducing the amount of electricity spent in the operation of the plant equipment. The rate of non-condensing gas (NCG) outlet, which is harmful to the environment, can be reduced by using SA algorithm. In this way, a power plant can be operated more economically and in a more environmentally friendly manner.

Keywords: Geothermal power plant; Optimization; Exergy efficiency; Simulated annealing algorithm (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:172:y:2021:i:c:p:968-982

DOI: 10.1016/j.renene.2021.03.101

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