Dynamic Load Split Control for Gas Turbine Fuel Efficiency
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Solution Overview
Problem
Combined cycle power plants face inefficiencies and component wear due to fluctuations in power generation demand, which conventional control methods fail to address effectively, leading to suboptimal fuel consumption and reduced lifespan of gas turbines.
Innovation Solution
A method is developed to model and control combined cycle power plants by generating a power plant model at ambient and load conditions, determining fuel consumption, creating a variant split ratio between gas turbines, and adjusting operations to meet a quality threshold for reduced fuel consumption, thereby optimizing power output and extending turbine lifespan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional control methods are used to manage power plant operations during demand fluctuations, then the power plant can respond to varying load conditions, but fuel consumption increases and component lifespan decreases
Solution Approach 1:
The patent implements dynamic load split ratio adjustment between multiple gas turbines based on real-time operating conditions. The control system continuously varies the load distribution to optimize fuel consumption while adapting to changing demand, rather than using fixed load allocations. This dynamic optimization resolves the contradiction by enabling adaptability to varying demands while minimizing energy loss through intelligent load management.
Solution Approach 2:
The patent changes the operational parameters of gas turbines by adjusting the load split ratio between multiple turbines. By varying the load distribution parameters dynamically based on ambient conditions and plant state, the system achieves both adaptability to demand fluctuations and reduced fuel consumption, resolving the technical contradiction through parameter optimization.
2Adaptability or versatility
If conventional control methods are used to manage power plant operations during demand fluctuations, then the power plant can respond to varying load conditions, but component lifespan is reduced
Solution Approach 1:
The dynamic load split ratio adjustment allows the system to adapt to varying demands while distributing wear and thermal stress more evenly across multiple gas turbines. By continuously optimizing load allocation based on turbine operating conditions, the system extends component lifespan while maintaining adaptability to demand changes.
Solution Approach 2:
The control system uses feedback from turbine operating parameters to adjust load split ratios in real-time. This feedback mechanism enables the system to adapt to varying demands while preventing excessive stress on individual turbines, thereby extending component lifespan through intelligent load management.
3Ease of operation
If the load split ratio between gas turbines is not optimized, then operations are simpler, but fuel consumption increases
Solution Approach 1:
The control system automatically optimizes the load split ratio between gas turbines based on real-time operating conditions without requiring manual intervention. This self-service approach maintains operational simplicity while achieving fuel consumption optimization through automated load distribution adjustments.
Solution Approach 2:
The system automatically changes operational parameters (load split ratios) to optimize fuel consumption while maintaining ease of operation. The automated parameter adjustment eliminates the need for complex manual optimization procedures, resolving the contradiction between operational simplicity and energy efficiency.
4Loss of energy
If the load split ratio is dynamically adjusted to optimize fuel consumption, then fuel efficiency improves, but control complexity increases
Solution Approach 1:
The control system integrates multiple functions into a unified load management platform that simultaneously optimizes fuel consumption, monitors turbine conditions, and adjusts load split ratios. This multi-functional approach reduces control complexity by consolidating optimization tasks rather than requiring separate systems for each function.
Solution Approach 2:
The system uses automated parameter optimization algorithms that dynamically adjust load split ratios based on operating conditions. This approach achieves fuel consumption optimization through systematic parameter changes while maintaining manageable control complexity through algorithmic automation rather than complex mechanical or manual control mechanisms.
Data Source
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AI summary
Embodiments of the disclosure provide a method for operating a combined cycle power plant (CCPP) (12). The method may include generating a power plant model (68) for operating the CCPP (12), determining whether at least two gas turbines (30A, 30B) in the power plant model (68) generate a power output, and modeling a fuel consumption of the CCPP (12) for a baseline split ratio (226) between the at least two gas turbines (30A, 30B). The method may also include determining whether the variant split ratio (226) meets a quality threshold for the CCPP (12), and adjusting the CCPP (12) to use the variant split ratio (226) in response to the variant split ratio (226) meeting the quality threshold.