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Feasibility study of Combined Cycle Gas Turbine (CCGT) power plant integration with Adiabatic Compressed Air Energy Storage (ACAES)

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  • Wojcik, Jacek D.
  • Wang, Jihong

Abstract

The paper presents the research outcome on integration of an Adiabatic Compressed Air Energy Storage system with a Combined Cycle Gas Turbine power plant to increase its operation flexibility. The study demonstrates the novel hybrid CCGT-ACAES plant including an extended operational load level range and increased operation flexibility which supports the power grid to allow more power generation from connected intermittent renewable energy sources. It is also shown that this new hybrid power plant will avoid the Combined Cycle plant gas turbine operating under the low load level. Lowering the minimum hybrid plant operational load level during air and Thermal Energy Storage charging process to the required minimum Heat Recovery Steam Generator load level eliminates the need for Combined Cycle Gas Turbine plant cycling operation increasing the lifetime of the plant components. Adiabatic Compressed Air Energy Storage plant concept is based on proved and well established direct two-tank Thermal Energy Storage technology used in Concentrated Solar Power plants. Improved hybrid plant flexibility is occupied by slight decrease (2%) in the plant efficiency. Further investigation into alternative advanced Thermal Energy Storage systems based on latent heat and chemical reaction heat would offer better hybrid plant round-trip efficiency across operational plant load level range.

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  • Wojcik, Jacek D. & Wang, Jihong, 2018. "Feasibility study of Combined Cycle Gas Turbine (CCGT) power plant integration with Adiabatic Compressed Air Energy Storage (ACAES)," Applied Energy, Elsevier, vol. 221(C), pages 477-489.
    • Handle: RePEc:eee:appene:v:221:y:2018:i:c:p:477-489
    DOI: 10.1016/j.apenergy.2018.03.089
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    18. Pang, Simian & Zheng, Zixuan & Xiao, Xianyong & Huang, Chunjun & Zhang, Shu & Li, Jie & Zong, Yi & You, Shi, 2022. "Collaborative power tracking method of diversified thermal loads for optimal demand response: A MILP-Based decomposition algorithm," Applied Energy, Elsevier, vol. 327(C).
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