Power Plant Load Scheduling via Component Risk Indices
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Solution Overview
Problem
Current load scheduling in power plants lacks consideration for actual operating conditions and component states, leading to inefficient maintenance scheduling and increased operational costs due to unforeseen maintenance activities and downtime.
Innovation Solution
A method and system for optimizing load scheduling that analyzes the operating state of power plant components with associated risk indices, updates objective functions to reflect these conditions, and optimizes maintenance scheduling using a control system with a plant model and failure model to minimize costs and maximize efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If maintenance schedule is delayed to reduce unnecessary maintenance activities, then maintenance cost decreases, but unplanned maintenance increases and system reliability deteriorates
Solution Approach 1:
The system dynamically adjusts maintenance scheduling parameters based on real-time component state parameters (risk indices, operating conditions) rather than fixed time intervals. This allows optimization of maintenance timing to balance cost reduction with reliability maintenance by scheduling maintenance when component degradation reaches optimal thresholds.
Solution Approach 2:
The system enables components to 'self-report' their health status through continuous monitoring and risk index calculation, allowing the optimization system to automatically determine when maintenance is truly needed versus when it can be deferred, reducing unnecessary maintenance while preventing failures.
2Reliability
If maintenance schedule is advanced to prevent unplanned maintenance, then system reliability improves, but unnecessary maintenance activities increase and cost increases
Solution Approach 1:
The system uses dynamic parameter adjustment to schedule maintenance only when component risk indices indicate actual need, avoiding premature maintenance. The optimization algorithm continuously updates maintenance timing based on real-time operating conditions and component degradation rates.
Solution Approach 2:
The system implements continuous feedback loops where component monitoring data feeds into risk index calculations, which then inform maintenance scheduling decisions. This closed-loop approach ensures maintenance is scheduled based on actual component needs rather than predetermined schedules, eliminating unnecessary maintenance activities.
3Loss of substance
If load scheduling is optimized based only on cost considerations, then operational cost decreases, but actual operating conditions and component states are not considered leading to suboptimal decisions
Solution Approach 1:
The system merges cost optimization algorithms with component monitoring and risk assessment modules into a unified load scheduling system. This integration allows simultaneous consideration of operational costs, component states, and risk indices to generate comprehensive optimization decisions that balance economic and technical factors.
Solution Approach 2:
The system introduces risk indices as intermediary parameters that translate component state information into quantifiable metrics usable by the optimization algorithm. These risk indices serve as bridges between physical component conditions and economic optimization objectives, enabling informed decision-making.
Data Source
AI summary
A method and control system are disclosed for optimizing load scheduling for a power plant having one or more generation units. The method can include analyzing the operating state of one or more components of generation units in terms of one or more risk indices associated with one or more components of generation units; updating an objective function that reflects the state of one or more components of generation units; solving the objective function to optimize a schedule of the one or more generation units and operating state of one or more components of generation units; and operating the one or more generation units at optimized schedule and operating state.