Transactional Node Segmentation for Fast Power Grid Feasibility Testing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional technologies for Simultaneous Feasibility Test (SFT) in electric power grids face computational challenges due to the large scale of modern power grids, leading to inefficiencies in computation time and memory usage, which hinders the ability to provide practical solutions within time constraints.
Innovation Solution
The implementation of innovative computational methods, including precomputing matrices, using sparse matrices, reducing matrix rank, and organizing operations to minimize multiplication, allows for efficient SFT evaluation by focusing on transactional nodes rather than all nodes, enabling faster and more memory-efficient computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional SFT methods are used on large-scale power grids, then comprehensive feasibility testing can be performed, but computation time and memory usage become excessively high
Solution Approach 1:
The patent segments the power grid into transactional nodes and infrastructure nodes, focusing computation only on transactional nodes where power injections occur. This segmentation reduces the problem size from O(J) where J is total nodes to O(N) where N is transactional nodes, dramatically reducing computation time while maintaining feasibility testing accuracy for security constraints.
Solution Approach 2:
The patent precomputes sensitivity matrices and contingency matrices before performing SFT evaluations. These precomputed matrices capture the relationship between power injections and branch flows, allowing rapid evaluation of multiple candidate solutions without repeating full power flow calculations, thus reducing real-time computation time.
2Reliability
If conventional SFT methods are used on large-scale power grids, then comprehensive feasibility testing can be performed, but memory storage requirements become excessively high
Solution Approach 1:
By segmenting the computation to focus only on transactional nodes rather than all grid nodes, the patent reduces memory storage requirements from storing full J×J or J×I matrices to storing only N×I sensitivity matrices and contingency matrices, where N < J. This segmentation dramatically reduces memory footprint while maintaining the ability to test security constraints.
Solution Approach 2:
The patent computes only the necessary portions of sensitivity and contingency matrices related to transactional nodes and monitored branches, rather than computing complete system-wide matrices. This partial computation approach reduces memory storage to only the essential data needed for feasibility testing.
3Manufacturing precision
If SFT is called frequently from SCUC solvers, then solution quality improves, but computational burden increases
Solution Approach 1:
The patent precomputes sensitivity matrices and contingency matrices that capture the essential system characteristics. These precomputed structures enable rapid SFT evaluations that can be called frequently from SCUC solvers without imposing excessive computational burden, as each evaluation reuses the precomputed matrices rather than performing full power flow calculations.
Solution Approach 2:
The patent changes the computational parameters by working with reduced-order matrices based on transactional nodes rather than full system matrices. This parameter change enables faster matrix operations and reduces the computational complexity of each SFT evaluation, allowing frequent calls from SCUC solvers to improve solution quality without overwhelming computational resources.
Data Source
AI summary
Fast simultaneous feasibility testing (SFT) for management of an electrical power grid is achieved through various innovations. The computation problem relates to evaluation of candidate solutions for external power flows into a power grid, with respect to predetermined constraints and contingencies. Storage and computations are reduced by formulating the problem in terms of transactional nodes (e.g. third party connections for generators and loads) instead of the larger number of bus nodes. Further advantages are achieved by precomputing matrices that can be reused across multiple SFT invocations, organizing matrices and operations to reduce storage and computation, and eliminating branches that have no contingency violations. The fast SFT further enables new applications for integrating SFT with a Security-Constrained Unit Commitment (SCUC) optimizer for efficient single pass solutions; operating SFT as a service to multiple SCUC solvers; or integration with Security-Constrained Economic Dispatch (SCED) optimization.