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Investigation of cold storage performance to improve management of power generation in thermal power plants in Iran

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  • Beigi, Behnam Feizollah
  • Mehdipour, Ramin

Abstract

During the hot seasons, the balance between power generation and consumption is disrupted due to the higher demand for cooling load and the lower power plant efficiency occurred due to the enhanced temperature of the inlet air to the turbine. The costly cold storage method in consumption centers (residential or official) is usually suggested to overcome the power generation problem in hot seasons. In this paper, the application of the cold storage system in the inlet of the gas turbine is proposed and the performance of the power generation system with cold storage is evaluated. The improvement of power generation with cold storage system compared to other cooling methods such as fog and electrical chiller, is discussed to determine which cooling method works best in reducing the network peak problem. Different methods of cold storage (partial/full) are also compared. The results for the proposed cooling system with cold storage used in hot seasons showed that, the 16-h cold storage method with the air condenser performed better during peak time compared to the other cooling methods. In June, July and August, this method improved the net generated power by 11.07%, 19.06% and 18.40%, respectively, compared with the normal condition of power plant without any cooling solution. the results of comparing the different storage strategies showed that the net output power in the full storage strategy was 2.14% higher than the partial storage strategy. Using a water condenser instead of an air condenser results in a slight increase of 0.96% in the net power output of the turbine.

Suggested Citation

  • Beigi, Behnam Feizollah & Mehdipour, Ramin, 2020. "Investigation of cold storage performance to improve management of power generation in thermal power plants in Iran," Energy, Elsevier, vol. 213(C).
    • Handle: RePEc:eee:energy:v:213:y:2020:i:c:s0360544220319502
    DOI: 10.1016/j.energy.2020.118843
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    References listed on IDEAS

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    Cited by:

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    3. Monjazeb, Mohammad Reza & Amiri, Hossein & Movahedi, Akram, 2024. "Wholesale electricity price forecasting by Quantile Regression and Kalman Filter method," Energy, Elsevier, vol. 290(C).

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