Power Grid State Estimation Using Subsection Consensus
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Solution Overview
Problem
State estimation in power distribution grids is challenging due to the large scale, dynamic behavior, and limited and less accurate measurements, making real-time estimation difficult and computationally intensive, with traditional methods taking hours to produce results.
Innovation Solution
The power grid is divided into smaller sub-sections, where an unscented Kalman Filter model is used for local state estimation in each sub-section, and the individual estimates are combined through a consensus process to achieve a near real-time aggregate state estimate, utilizing parallel processing on a multi-core processor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional state estimation methods are used on large power distribution grids, then comprehensive state coverage is achieved, but computation time increases to hours and numerical ill-conditioning occurs
Solution Approach 1:
The large power distribution grid is divided into multiple smaller sub-sections or clusters. Each sub-section is processed independently through local state estimation, and results are aggregated to form the overall grid state estimate. This segmentation reduces the computational burden from hours to near real-time (30-60 seconds) while maintaining estimation accuracy through coordinated boundary conditions between sub-sections.
2Productivity
If the grid is divided into smaller sub-sections for parallel processing, then computation time is reduced to near real-time, but measurement redundancy and uniform distribution become challenging
Solution Approach 1:
Local state estimation results from multiple sub-sections are merged through a consensus process that aggregates estimates and resolves boundary conditions. This merging ensures that measurement redundancy is maintained across the entire grid by combining information from all sub-sections, achieving both fast computation and reliable estimation through coordinated integration of local results.
Data Source
AI summary
Provided is a system and method for determining the state estimate of a power grid by dividing the power grid into smaller sub-sections, generating state estimates for each sub-section, and then generating a consensus among the sub-sections. In one example, the method may include partitioning a section of the power distribution grid into a plurality of sub-sections based on loads distributed within the section of the power distribution grid, generating a plurality of state estimates for the plurality of sub-sections based on load distribution within the plurality of sub-sections and a Kalman Filter model, generating an aggregate state estimate for the section of the power distribution grid based on an aggregate of the plurality of state estimates and a boundary consensus between the plurality of sub-sections from a previous state estimation, and displaying data about the aggregate state estimate via a user interface.