Electrical Network Congestion Forecasting Using Sensitive Bus Equivalents
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Solution Overview
Problem
Current electrical network congestion management is reactive and inefficient, lacking proactive solutions due to high computational costs of simulating complex network models, inefficiencies in AC power flow simulations, the impact of renewable energy, and inaccuracies in data-only approaches that fail to account for network topology.
Innovation Solution
A method that simplifies the electrical network topology by identifying highly sensitive buses, calculating a sensitivity matrix based on electrical impedances, and using historical data to predict power flows, comparing them to thresholds to forecast potential congestion, and generating reports on congestion locations, while also incorporating weather predictions and external power injections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AC power flow simulations with Monte-Carlo simulation are used for congestion forecasting, then measurement precision is improved, but computational efficiency deteriorates due to high dimensional models
Solution Approach 1:
The patent segments the electrical network into a region of interest and external equivalents, focusing computational resources only on the critical subset of buses and lines that contribute most to congestion. This segmentation reduces the dimensionality of the simulation model while maintaining forecasting accuracy for the most problematic areas.
Solution Approach 2:
The patent extracts and removes less critical components from the simulation model by identifying and eliminating buses and lines with low sensitivity to congestion. This extraction process reduces computational complexity by eliminating unnecessary calculations while preserving the accuracy of congestion predictions for critical network elements.
2Reliability
If detailed network models with wide-range scenarios are created for simulations, then reliability is improved, but device complexity increases and computational cost rises
Solution Approach 1:
The patent applies local quality by creating detailed models only for the region of interest where congestion is most likely to occur, while using simplified equivalent models for external network areas. This approach maintains high reliability for forecasting local congestion without requiring complex detailed models of the entire network.
Solution Approach 2:
The patent performs preliminary action by pre-identifying the region of interest and critical buses before running simulations. This preliminary identification allows the system to focus computational resources on the most relevant network portions, reducing overall model complexity while maintaining forecasting reliability.
3Ease of operation
If data-only approaches are used for line-flow forecasts, then ease of operation is improved, but measurement precision deteriorates due to overfitting and inability to process network topology
Solution Approach 1:
The patent introduces sensitivity analysis as an intermediary between raw data and congestion forecasts. This intermediary layer processes network topology information and combines it with historical data, allowing the system to maintain simplicity while improving accuracy by incorporating structural network knowledge without requiring complex detailed models.
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.