Electrical Network Congestion Forecasting via Sensitive Bus Reduction
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Solution Overview
Problem
Current congestion management in electrical networks is reactive and inefficient, often relying on costly oversizing of conductors and lacking proactive forecasting due to computational complexity, inaccurate data models, and the integration of renewable energy sources, which exacerbates the problem.
Innovation Solution
A method and system for congestion forecasting in electrical networks that involves reducing network complexity by identifying a subset of highly sensitive buses, using a sensitivity matrix to predict power flow, and incorporating historical data and weather forecasts to generate proactive congestion reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AC power flow with Monte-Carlo simulation is used for congestion forecasting, then measurement precision is improved, but device complexity increases and productivity decreases due to computational inefficiency
Solution Approach 1:
The patent segments the electrical network into a region of interest and external equivalents, allowing focused analysis on critical areas. This segmentation enables the sensitivity matrix to be calculated only for relevant buses, reducing computational complexity while maintaining forecasting accuracy for congestion-prone regions.
Solution Approach 2:
The patent extracts and identifies a subset of highly sensitive buses from the complete network using the sensitivity matrix. By focusing computational resources on these extracted critical buses rather than analyzing the entire network, the system achieves real-time forecasting capability while maintaining measurement precision.
2Reliability
If comprehensive network modeling is performed for congestion forecasting, then reliability is improved, but device complexity increases due to high dimensional models
Solution Approach 1:
The patent divides the complex network model into manageable segments by identifying a region of interest and representing external areas as equivalents. This segmentation reduces model dimensionality while preserving the essential dynamics needed for reliable congestion forecasting in critical regions.
Solution Approach 2:
The patent extracts the critical subset of highly sensitive buses that most influence congestion conditions. By taking out and focusing on these key elements rather than modeling the entire high-dimensional network, the system achieves reliable forecasting with reduced computational complexity.
3Measurement precision
If detailed sensitivity analysis is performed on all buses, then measurement precision is improved, but loss of time increases due to computational expense
Solution Approach 1:
The patent extracts and identifies only the highly sensitive buses that have significant impact on congestion conditions. By performing detailed sensitivity analysis on this extracted subset rather than all buses in the network, the system achieves precise measurements while minimizing calculation time for real-time operation.
Data Source
AI summary
An example method comprises receiving an initial topology of an electrical, receiving a selection of a region of interest, determining one or more external equivalents of the electrical network that are external to the region of interest, determining one or more internal equivalents of the region of interest, calculating a sensitivity matrix based on electrical impedances of at least one of the one or more internal equivalents and based on an amount of power exchanged when in operation, determining a subset of the sensitivity matrix as indicating highly sensitive buses, receiving historical data regarding power flows, predicting power flow for each highly sensitive buses, comparing the predicted power flow to at least one predetermined threshold to determine possible network congestion, and generating a report regarding network congestion and locations of possible network congestion in the region of interest based on the comparison.