Fleet Generating Asset Control Method for Power Plant Optimization
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Solution Overview
Problem
Power plant operators face challenges in maximizing economic return due to the complexity of modern power plants with multiple generating units, as conventional control systems struggle to account for variable ambient conditions and machine degradation, leading to inefficient operation and suboptimal utilization.
Innovation Solution
A control method that optimizes a fleet of generating assets by receiving real-time and historical operating parameter measurements, deriving relational expressions, selecting competing operating modes, calculating result sets, and evaluating them based on a cost function to designate optimized operating modes, which includes power sharing recommendations between power blocks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional control systems are used to manage power plant generating units, then the control system structure is simple, but the operation efficiency deteriorates due to inability to account for variable ambient conditions and machine degradation
Solution Approach 1:
The control system transitions from static control profiles to dynamic control that continuously adapts to changing ambient conditions and machine degradation states. The system uses real-time sensor data and machine learning models to adjust control parameters dynamically, enabling the power plant to optimize performance under varying operating conditions while accounting for equipment wear and environmental changes.
Solution Approach 2:
The system implements closed-loop feedback mechanisms where sensor measurements of ambient conditions, machine performance, and degradation indicators are continuously fed back to the control algorithm. This feedback enables the system to learn from actual operating data and adjust control strategies in real-time, improving operational efficiency while adapting to changing conditions and machine states.
2Measurement precision
If static control profiles are used for thermal generating units, then the control system is easy to operate, but the performance prediction accuracy deteriorates under varying ambient conditions
Solution Approach 1:
The control system employs self-learning capabilities through machine learning algorithms that automatically adapt to changing ambient conditions and machine degradation patterns. The system autonomously updates its performance models and control strategies based on accumulated operational data, eliminating the need for manual recalibration while maintaining high prediction accuracy across diverse operating scenarios.
Solution Approach 2:
The system dynamically adjusts control parameters based on ambient conditions and machine state by modifying key operational parameters such as fuel flow rates, turbine inlet temperatures, and valve positions. These parameter changes are optimized in real-time to maintain peak efficiency while adapting to environmental variations and equipment degradation, achieving high prediction accuracy without increasing operational complexity.
3Productivity
If conventional economic dispatch processes are used, then the dispatch process is simple, but the economic return deteriorates due to suboptimal utilization of generating units
Solution Approach 1:
The system performs preliminary optimization by pre-calculating optimal dispatch schedules and control strategies based on forecasted ambient conditions, load demands, and predicted machine degradation trajectories. This advance planning enables the power plant to proactively adjust its operating strategy to maximize economic returns while accounting for future equipment wear and environmental changes, rather than reacting to conditions as they occur.
Solution Approach 2:
The economic dispatch process incorporates continuous feedback from actual performance measurements and degradation monitoring to refine optimization algorithms and update dispatch decisions. This feedback-driven approach enables the system to learn from past operational outcomes and improve economic returns over time by adapting to actual machine behavior and market conditions, transforming the dispatch process from a static administrative procedure to a dynamic optimization system.
Data Source
AI summary
A method for optimizing a fleet of generating assets in which groupings include remotely located power blocks that operate so to collectively generate a fleet output level. The method may include: receiving real-time and historical measured values of operating parameters; for each of the generating assets, deriving a relational expression between the measured values of the process inputs and the measured values of the process outputs; defining a selected operating period; selecting competing operating modes for the fleet; based on the relational expressions and a generating configuration of the competing operating modes, calculating a result set for the operation of the fleet proposed during the selected operating period; defining a cost function; and evaluating each of the result sets pursuant to the cost function and, based thereupon, designating one of the competing operating modes as an optimized operating mode. The optimized operating mode may include a power sharing recommendation between the power blocks of the fleet.