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A Holistic Approach to GTCC Operational Efficiency Improvement Studies

[+] Author Affiliations
Sowande Z. Boksteen

Rotterdam University, Rotterdam, The Netherlands

Jos P. van Buijtenen

Delft University of Technology, Delft, The Netherlands

Dick van der Vecht

GDF SUEZ Energie Europe, Zwolle, The Netherlands

Paper No. GT2014-25900, pp. V03AT21A009; 14 pages
doi:10.1115/GT2014-25900
From:
  • ASME Turbo Expo 2014: Turbine Technical Conference and Exposition
  • Volume 3A: Coal, Biomass and Alternative Fuels; Cycle Innovations; Electric Power; Industrial and Cogeneration
  • Düsseldorf, Germany, June 16–20, 2014
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-4565-3
  • Copyright © 2014 by ASME

abstract

Because of the increasing share of renewables in the energy market, part load operation of gas turbine combined cycle (GTCC) power plants has become a major issue. In combination with the variable ambient conditions and fuel quality, load variations cause these plants to be operated across a wide range of conditions and settings. However, efficiency improvement and optimization studies are often focused on single operating points. The current study assesses efficiency improvement possibilities for the KA26 GTCC plant, as recently built in Lelystad, The Netherlands, taking into account that the plant is operated under frequently varying conditions and load settings. In this context, free operational parameters play an important role: these are the process parameters, which can be adjusted by the operator without compromising safety and other operational objectives. The study applies a steady state thermodynamic model with second-law analysis for exploring the entire operational space. A method is presented for revealing correlations between the exergy losses in major system components, indicating component interactions. This is achieved with a set of numerical simulations, in which operational conditions and settings are randomly varied, recording plant efficiency and exergy losses in major components. The resulting data is used to identify distinct operational regimes for the GTCC. Finally, the free operational parameters are used as decision variables in a genetic algorithm, optimizing plant efficiency in the operational regimes identified earlier. The results show that the optimal settings for decision variables depend on the regime of operation.

Copyright © 2014 by ASME

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