As electricity demand from individual power plants is expected to fluctuate increasingly due to the growing share of renewables, operators of large Combined Cycle Gas Turbine power plants will have to deal with increasing load variations and rapid load changes. To keep up reliability and availability of the plants, it is useful to accurately keep track of plant performance by comparing actual cycle data with a steady state base case model. This paper presents various aspects of the performance modeling of Alstom’s GT26 gas turbine as recently installed in the Netherlands. The modeling environment is GSP, a component based zero-dimensional software tool. Firstly, the modeling strategy is presented, taking into account the specific features of this sequential combustion gas turbine. Secondly, the method of processing field measurements to model inputs is shown and furthermore, the influence of measurement uncertainty on model parameter estimation is assessed. Procedures will be proposed to use this model in daily operation, to keep track of actual component loading. Later on, the recorded performance data can be used to evaluate maintenance as a function of actual operational history, as a basis for future strategies.

No abstract

A Holistic Approach to GTCC Operational Efficiency Improvement StudiesBecause o f the increasing share o f renewables in the energy market, part load operation o f gas turbine combined cycle (GTCC) power plants has become a major issue. In combi nation with the variable ambient conditions and fuel quality, load variations cause these plants to be operated across a wide range o f conditions and settings. However, efficiency improvement and optimization studies are often focused on single operating points. The current study assesses efficiency improvement possibilities fo r 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 fo r revealing correlations between the exergy losses in major system components, indicating component interac tions. This is achieved with a set o f numerical simulations, in which operational condi tions 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 fo r decision variables depend on the regime o f operation.

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.

No abstract