Supervisory Power Plant Control Using Marginal Effect Maps
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Solution Overview
Problem
Current control schemes for power generating assets, such as wind turbines, are inflexible and do not effectively manage trade-offs between energy production, component damage, and risk of failure, leading to suboptimal economic operation.
Innovation Solution
A supervisory control system with independent applications and a central optimizer module that generates marginal effect maps to determine optimal operational setpoints for power generating assets, considering various factors like energy production, component health, and failure modes, to maximize economic value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional control schemes are used for power generating assets, then the control implementation is simple, but the economic value optimization is insufficient due to inflexible monolithic implementations that cannot manage trade-offs between energy production, component damage, and failure risk
Solution Approach 1:
The control system is divided into multiple independent applications, each responsible for specific control goals (energy production, component protection, failure risk management). This segmentation allows each application to operate independently with its own logic, providing adaptability without requiring a complex monolithic system. The modular structure enables flexible combination of control strategies while maintaining system manageability.
Solution Approach 2:
The control system is designed to perform multiple functions simultaneously through independent applications that can be activated based on operational conditions. The system can switch between different control modes (energy maximization, component protection, risk mitigation) as needed, providing universal adaptability across various operating scenarios without requiring separate specialized systems for each function.
2Ease of manufacture
If narrow constraint control approaches are used, then the control system is easier to implement, but the economic value is reduced due to inability to balance multiple competing objectives
Solution Approach 1:
By segmenting the control system into independent applications, each with a specific control goal, the implementation becomes simpler for each individual application while the overall system achieves comprehensive economic optimization. Each application can be developed, tested, and implemented separately, reducing implementation complexity while maintaining the ability to balance multiple objectives through coordinated operation of the segmented applications.
Data Source
AI summary
A method for operating a power generating plant having one or more power generating assets includes receiving, via a plurality of independent applications of a supervisory controller, a plurality of operational parameters relating to the one or more power generating assets in the power generating plant. The method also includes generating, via the plurality of independent applications of the supervisory controller, a plurality of marginal effect maps based on the plurality of operational parameters. The method further includes receiving, via a central optimizer module, the plurality of marginal effect maps from the plurality of independent applications and determining one or more operational setpoints for the power generating asset(s) based on the marginal effect maps to optimize an economic value of operating the one or more power generating assets. Moreover, the method includes communicating the operational setpoint(s) to the power generating asset(s).