Techno-economic analysis and predictive model for heavy-duty gas turbine, in relation to cooling air system degradation.

Modern gas turbines are featured with cooling air systems that allow them to operate at temperatures much higher than the melting points of most of their hot gas path components. When these systems degrade, creep life consumption is accelerated. If the risk is not assessed and managed in time, the engine could operate with high lifting risk. In the world of power industry, the operators are in most cases presented with two options when a lifting risk in their engine is identified. They can undertake a forced opening of the engine, against their existing planning of maintenance events, aiming to re-instate cooling air system’s functionality. Alternatively, they can derate the engine, by decreasing firing temperature, to a level that will allow continuous operation until the next planned overhaul event. The model presented in this work allows the operator to follow a third path. The operator can still manage effectively the lifting risk but also, critically, to do it in a commercially optimised way by taking into consideration electricity market conditions and the incentives that the market may present. This is important as this model fills a gap in the relevant research where the majority of the works are focused in managing lifting risks through a binary time-interval-reduction or firing- temperature-decrease approach. The developed in this work tool uses a combination of performance modelling, analytical and computational methods, as well as Monte Carlo simulations. The cooling air system degradation is detected and assessed and the effect on cooling air temperature is determined with use of CFD. The impact on blades metal temperature is calculated with a combination of analytical model and Monte Carlo simulations. In a similar combination of analytical method and Monte Carlo, stress is calculated and together with OEM’s life expectancy, a creep life estimation model then is derived. The lifting risk is quantified and appropriate mitigation is proposed. The mitigation is in the form of a novel method, which uses market triggered factors to adjust dynamically engine’s firing temperature and define by this way engine’s performance. This hybrid approach results is an optimised operation in which the lifting risk is effectively managed, the original maintenance planning is respected and, critically, the commercial losses due to engine derate are significantly trimmed. The tool, with its risk-versus-value function, allows the operator to exercise a more or less conservative operation, following a case-by-case approach.