Power Plant Operating Mode Optimization for Variable Conditions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Power plant operators face challenges in maximizing economic return due to the complexity of modern power plants and the need to account for variable ambient conditions and machine degradation, leading to inefficient operation and underutilization of generating units.
Innovation Solution
The development of a system and method for optimizing power plant performance by combining a power plant model that predicts performance under varying conditions with an economic model that includes economic constraints and objectives, allowing for real-time optimization of operating setpoints to maximize profitability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional control systems are used to govern power plant operation, then basic operational control is maintained, but the plant operates inefficiently due to inability to account for variable ambient conditions and machine degradation
Solution Approach 1:
The control system dynamically adjusts operating setpoints based on real-time ambient conditions and machine degradation state. The system transitions from static conventional control to dynamic adaptive control, continuously optimizing power plant efficiency by responding to changing conditions rather than following fixed operational parameters.
Solution Approach 2:
The system implements feedback mechanisms by monitoring ambient conditions, machine degradation, and operational performance, then using this information to adjust control setpoints. This closed-loop approach enables the control system to learn from and adapt to changing conditions, improving efficiency without requiring complete system redesign.
2Productivity
If operating setpoints are adjusted frequently to account for changing conditions, then operational efficiency improves, but control and measurement complexity increases
Solution Approach 1:
The system performs preliminary assessments of ambient conditions and machine degradation state to predict optimal operating setpoints before actual operational changes are needed. By anticipating required adjustments and preparing control parameters in advance, the system reduces the complexity of real-time decision-making while maintaining high operational efficiency.
3Productivity
If advanced optimization systems are implemented to maximize profitability, then economic return increases, but system complexity and implementation cost increase
Solution Approach 1:
The optimization system serves itself by using its own operational data and performance metrics to continuously improve its control algorithms. The system automatically identifies opportunities for profitability enhancement and implements adjustments without requiring external intervention or complex manual analysis, reducing implementation burden while maximizing economic return.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A system (10) including a power plant (12,302,501) having thermal generating units that operate according to multiple possible operating modes, which are differentiated by a unique operational or maintenance schedule. The system (10) further includes a hardware processor (82) and machine readable storage medium on which is stored instructions that cause the hardware processor (82) to execute a process related to optimizing the operational or maintenance schedule during a selected operating period. The process may include: receiving the selected operating period; selecting competing operating modes for the power plant (12,302,501) during the selected operating period according to a selection criteria; simulating the operation of the power plant (12,302,501) during the selected operating period for each of the competing operating modes and deriving simulation results therefrom; evaluating each of the simulation results pursuant to a cost function and, based thereupon, designating at least one of the competing operating modes as an optimized operating mode.