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

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  • Çetin, Gürcan
  • Keçebaş, Ali

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.

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  • Çetin, Gürcan & Keçebaş, Ali, 2021. "Optimization of thermodynamic performance with simulated annealing algorithm: A geothermal power plant," Renewable Energy, Elsevier, vol. 172(C), pages 968-982.
    • Handle: RePEc:eee:renene:v:172:y:2021:i:c:p:968-982
    DOI: 10.1016/j.renene.2021.03.101
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    2. Yang, Bo & Wu, Shaocong & Li, Qiang & Yan, Yingjie & Li, Danyang & Luo, Enbo & Zeng, Chunyuan & Chen, Yijun & Guo, Zhengxun & Shu, Hongchun & Li, Zilin & Wang, Jingbo, 2023. "Jellyfish search algorithm based optimal thermoelectric generation array reconfiguration under non-uniform temperature distribution condition," Renewable Energy, Elsevier, vol. 204(C), pages 197-217.
    3. Wang, Qinggang & Ming, Tingzhen & Chen, Qiong & Wu, Yongjia, 2024. "A novel TLFD-GA algorithm for multi-objectives optimization of ORC power plants with the effect of pressure drop considered," Energy, Elsevier, vol. 293(C).
    4. Vaccari, Marco & Pannocchia, Gabriele & Tognotti, Leonardo & Paci, Marco, 2023. "Rigorous simulation of geothermal power plants to evaluate environmental performance of alternative configurations," Renewable Energy, Elsevier, vol. 207(C), pages 471-483.

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