Grid Reconstruction Manager Optimizes Load Step Sequences
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for power plant grid reconstruction after a blackout are limited by the operational capabilities of individual power generation units, which are constrained by factors like fuel supply, ambient conditions, and current operating points, leading to potential under-speed events and transient overloads, and lack an effective mechanism for determining optimal load step sizes.
Innovation Solution
A plant-based grid reconstruction manager uses high-fidelity modeling and optimization techniques, including mixed integer optimization, to determine enhanced load step sequences that consider current and future capabilities of power generation units, ambient conditions, and operational limitations, providing both automatic and advisory control mechanisms to optimize load step execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If load step commands are issued to power generation units during grid reconstruction, then grid power reestablishment is accelerated, but unit operability and controllability constraints may be violated causing under-speed events or transient overloads
Solution Approach 1:
The system performs preliminary positioning of power generation units to optimal operating points before grid reconstruction begins. This preliminary action ensures units are positioned to maximize their load step capabilities while maintaining operability constraints, enabling faster and more reliable grid reconstruction without causing under-speed events or transient overloads.
Solution Approach 2:
The system dynamically adjusts load step commands based on real-time unit responses and changing grid conditions. By continuously monitoring unit operability and controllability factors, the control system adapts load step sizing to prevent violations of operational constraints while maintaining accelerated grid reconstruction pace.
2Reliability
If ambient conditions and current operating points are considered in load step planning, then unit safety is improved, but computational complexity and data processing requirements increase
Solution Approach 1:
The system transforms complex operational constraints into simplified parameter-based decision rules. By converting ambient conditions and operating point considerations into standardized parameters that directly influence load step sizing, the system maintains high unit safety while reducing computational complexity and enabling practical implementation.
3Power
If load step capabilities are maximized for current step, then immediate power delivery is improved, but future load step capabilities may be compromised due to unit positioning
Solution Approach 1:
The system performs preliminary positioning of units to optimal operating points before grid reconstruction begins. This preliminary action ensures units are positioned to maximize their load step capabilities while maintaining operability constraints, enabling faster and more reliable grid reconstruction without causing under-speed events or transient overloads.
Solution Approach 2:
The system uses feedback from unit responses to each load step to adjust subsequent load step commands. By monitoring how units respond to load changes and their resulting operating points, the control system optimizes future load step sizing to maintain both current power delivery and future capability, preventing premature exhaustion of unit operational margins.
Data Source
Figure 1~3
Figure 2
AI summary
Methodology is provided for enhancing plant 200 level support for grid reconstruction following a blackout. A plant 200 based grid reconstruction manager develops solutions for reconstruction sequence steps to be carried out automatically or to be communicated to site personnel for manual implementation. The sequence steps are based on high-fidelity modeling of the capabilities all of the power production units present in a power plant 200 and take into consideration grid specified load expectations, ambient conditions including ambient temperature and gas turbine operating levels. The methodology also provides for consideration of possible subsequent steps in the sequence to maximize the ability to pick-up additional load in such subsequent steps.