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Development of PID based control strategy in maximum exergy efficiency of a geothermal power plant

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  • Çetin, Gürcan
  • Özkaraca, Osman
  • Keçebaş, Ali

Abstract

The control strategy insures the efficiency, increased production and safety in new and complicated energy conversion systems. To this end, a thermodynamic model that simulates a real geothermal power plant is developed, and a new and optimal control strategy to increase exergy efficiency is presented on this model. In this study, real average daily data obtained during 2016 are used to verify the thermodynamic model of Sinem geothermal power plant in Turkey (S-GPP) and its control strategy. Thus, exergy efficiency of the system is maximized even under different and changed conditions by controlling flow rate, pressure and NCG percent content parameters in certain locations of S-GPP. Consequently, a new control strategy based on PID is developed. At the same time, different working conditions are provided by assigning disruptive values to these parameters. The thermodynamic model of S-GPP is used as a test system to demonstrate the efficiency of the proposed control strategy under various operating conditions. The results of the study prove that the model has a good statistical performance in terms of maximum exergy efficiency values. In addition, the proposed PID controller has a better performance compared to manual control, even in the presence of S-GPP. The effectiveness of the proposed control strategy is demonstrated by this case study, in which exergy efficiency of the system increased by 25% and power generation by 23%. Therefore, the proposed control strategy has the potential to create more reliable and controlled systems for geothermal and other power plants.

Suggested Citation

  • Çetin, Gürcan & Özkaraca, Osman & Keçebaş, Ali, 2021. "Development of PID based control strategy in maximum exergy efficiency of a geothermal power plant," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    • Handle: RePEc:eee:rensus:v:137:y:2021:i:c:s1364032120309072
    DOI: 10.1016/j.rser.2020.110623
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    References listed on IDEAS

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    Cited by:

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    2. Han, Kunlun & Yang, Kai & Yin, Linfei, 2022. "Lightweight actor-critic generative adversarial networks for real-time smart generation control of microgrids," Applied Energy, Elsevier, vol. 317(C).
    3. Jing Ma & Dan Liu & Zhengwen Wang, 2023. "Sponge City Construction and Urban Economic Sustainable Development: An Ecological Philosophical Perspective," IJERPH, MDPI, vol. 20(3), pages 1-17, January.
    4. Shi, Yao & Zhang, Zhiming & Chen, Xiaoqiang & Xie, Lei & Liu, Xueqin & Su, Hongye, 2023. "Data-Driven model identification and efficient MPC via quasi-linear parameter varying representation for ORC waste heat recovery system," Energy, Elsevier, vol. 271(C).
    5. Taler, Dawid & Sobota, Tomasz & Jaremkiewicz, Magdalena & Taler, Jan, 2022. "Control of the temperature in the hot liquid tank by using a digital PID controller considering the random errors of the thermometer indications," Energy, Elsevier, vol. 239(PE).

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