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The Effects of Increasing Ambient Temperature and Sea Surface Temperature Due to Global Warming on Combined Cycle Power Plant

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  • Asiye Aslan

    (Electricity and Energy Department, Gönen Vocational School, Bandırma Onyedi Eylül University, Balıkesir 10200, Turkey)

  • Ali Osman Büyükköse

    (Enerjisa Enerji Uretim Inc., Istanbul 34746, Turkey)

Abstract

The critical consequence of climate change resulting from global warming is the increase in temperature. In combined cycle power plants (CCPPs), the Electric Power Output (PE) is affected by changes in both Ambient Temperature (AT) and Sea Surface Temperature (SST), particularly in plants utilizing seawater cooling systems. As AT increases, air density decreases, leading to a reduction in the mass of air absorbed by the gas turbine. This change alters the fuel–air mixture in the combustion chamber, resulting in decreased turbine power. Similarly, as SST increases, cooling efficiency declines, causing a loss of vacuum in the condenser. A lower vacuum reduces the steam expansion ratio, thereby decreasing the Steam Turbine Power Output. In this study, the effects of increases in these two parameters (AT and SST) due to global warming on the PE of CCPPs are investigated using various regression analysis techniques, Artificial Neural Networks (ANNs) and a hybrid model. The target variables are condenser vacuum (V), Steam Turbine Power Output (ST Power Output), and PE. The relationship of V with three input variables—SST, AT, and ST Power Output—was examined. ST Power Output was analyzed with four input variables: V, SST, AT, and relative humidity (RH). PE was analyzed with five input variables: V, SST, AT, RH, and atmospheric pressure (AP) using regression methods on an hourly basis. These models were compared based on the Coefficient of Determination (R 2 ), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The best results for V, ST Power Output, and PE were obtained using the hybrid (LightGBM + DNN) model, with MAE values of 0.00051, 1.0490, and 2.1942, respectively. As a result, a 1 °C increase in AT leads to a decrease of 4.04681 MWh in the total electricity production of the plant. Furthermore, it was determined that a 1 °C increase in SST leads to a vacuum loss of up to 0.001836 bar a . Due to this vacuum loss, the steam turbine experiences a power loss of 0.6426 MWh. Considering other associated losses (such as generator efficiency loss due to cooling), the decreases in ST Power Output and PE are calculated as 0.7269 MWh and 0.7642 MWh, respectively. Consequently, the combined effect of a 1 °C increase in both AT and SST results in a 4.8110 MWh production loss in the CCPP. As a result of a 1 °C increase in both AT and SST due to global warming, if the lost energy is to be compensated by an average-efficiency natural gas power plant, an imported coal power plant, or a lignite power plant, then an additional 610 tCO 2 e, 11,184 tCO 2 e, and 19,913 tCO 2 e of greenhouse gases, respectively, would be released into the atmosphere.

Suggested Citation

  • Asiye Aslan & Ali Osman Büyükköse, 2025. "The Effects of Increasing Ambient Temperature and Sea Surface Temperature Due to Global Warming on Combined Cycle Power Plant," Sustainability, MDPI, vol. 17(10), pages 1-27, May.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:10:p:4605-:d:1658201
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    References listed on IDEAS

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    1. Chen, Youliang & Huang, Xiaoguang & Li, Wei & Fan, Rong & Zi, Pingyang & Wang, Xin, 2023. "Application of deep learning modelling of the optimal operation conditions of auxiliary equipment of combined cycle gas turbine power station," Energy, Elsevier, vol. 285(C).
    2. Kaytez, Fazil, 2020. "A hybrid approach based on autoregressive integrated moving average and least-square support vector machine for long-term forecasting of net electricity consumption," Energy, Elsevier, vol. 197(C).
    3. Longfei Tian & Zhen Zhang & Zhiru He & Chen Yuan & Yinghui Xie & Kun Zhang & Ran Jing, 2025. "Predicting Energy-Based CO 2 Emissions in the United States Using Machine Learning: A Path Toward Mitigating Climate Change," Sustainability, MDPI, vol. 17(7), pages 1-18, March.
    4. Hundi, Prabhas & Shahsavari, Rouzbeh, 2020. "Comparative studies among machine learning models for performance estimation and health monitoring of thermal power plants," Applied Energy, Elsevier, vol. 265(C).
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