Power Grid Feasibility Testing with State-Contingency Perturbation
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Solution Overview
Problem
Conventional methods for Simultaneous Feasibility Test (SFT) in electrical power grids face computational challenges due to the scale of modern power grids, requiring excessive computation time and resources, especially when evaluating multiple time periods and contingencies, which strain available computing resources.
Innovation Solution
The method involves innovative formulations and organizations of computations, including decoupling state and contingency perturbations, using sparse matrices, precomputing matrices, and organizing operations to reduce the number of matrix factorizations, inversions, and storage requirements, allowing for faster SFT evaluations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional SFT methods are used to evaluate candidate solutions for modern power grids, then security constraints can be verified, but computation time and resources become excessive
Solution Approach 1:
The patent segments the SFT computation by decoupling state perturbations from contingency perturbations. Instead of evaluating all contingencies for all states simultaneously, the method separates the computation into independent state-level perturbations and contingency-level perturbations, allowing parallel processing and reducing overall computation time while maintaining verification reliability
Solution Approach 2:
The patent applies preliminary action by precomputing the state perturbation factors and organizing the computation structure before evaluating contingencies. The state perturbation matrices are factorized and stored in advance, so that when contingencies need to be evaluated across multiple states, the computationally intensive matrix operations have already been performed, significantly reducing the time required for actual feasibility testing
2Adaptability or versatility
If conventional SFT methods evaluate multiple time periods in one planning cycle, then comprehensive grid management is achieved, but available computing resources are strained
Solution Approach 1:
The patent creates a universal computation framework that handles multiple time periods through a single perturbation analysis structure. The state perturbation factors computed for one state can be reused across multiple contingencies and time periods, making the computational engine multi-functional and adaptable to comprehensive grid management requirements without proportionally increasing computational complexity
Solution Approach 2:
The patent discards redundant computations by identifying and eliminating repeated matrix factorizations across multiple time periods. The state perturbation matrices are computed once and recovered/reused for evaluating multiple contingencies and time periods, avoiding redundant calculations while maintaining the ability to evaluate comprehensive multi-time period scenarios
3Productivity
If the number of matrix factorizations and inversions is reduced through perturbation decoupling, then computation time decreases, but the formulation complexity increases
Solution Approach 1:
The patent introduces intermediary perturbation factor matrices that mediate between the original power flow equations and the final feasibility evaluation. These intermediate state perturbation factors (dP/dq matrices) serve as a bridge, allowing the decoupling of state and contingency analyses while maintaining mathematical correctness. The intermediaries organize the complex formulation into manageable, reusable components that improve computation speed despite the increased formulation structure
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. A perturbation approach with precomputation is extended to encompass grid states (e.g. time periods) in addition to contingencies. Advantages derive from: fewer factorizations or inversions of large matrices; decoupling of state-dependent and contingency-dependent perturbations, leaving relatively few perturbations jointly dependent on both state and contingency; or making approximations by discarding small jointly dependent terms. Significant computation reductions allow a single workstation to perform SFT for 36 hours of a day-ahead cycle.