Power System Aggregation Device for Stability Analysis
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Solution Overview
Problem
The increasing complexity of power systems due to renewable energy integration leads to higher calculation loads in stability analysis, causing delays and accuracy issues in monitoring and stabilization systems, and existing aggregation models either create excessive aggregation groups or fail to sufficiently reduce calculation amounts.
Innovation Solution
A power system aggregation device that calculates aggregation fitness using coherency and electrical distance functions to create optimized aggregation groups, reducing calculation loads while maintaining analysis accuracy by aggregating generators and buses based on similarity and electrical connectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If the power system model is simplified to reduce calculation amount, then calculation time is reduced, but analysis accuracy deteriorates
Solution Approach 1:
The power system is segmented into multiple aggregation areas, where each area is aggregated independently into an equivalent model. This segmentation allows the system to maintain detailed representations in critical areas while simplifying less critical areas, thus balancing calculation speed and accuracy.
Solution Approach 2:
Different levels of aggregation are applied to different areas of the power system based on their importance and characteristics. Critical areas maintain higher detail while less critical areas are more heavily aggregated, creating a non-uniform model that optimizes both accuracy and computational efficiency.
2Productivity
If more generators and buses are aggregated into fewer groups, then calculation amount is reduced, but the number of aggregation groups increases excessively
Solution Approach 1:
The aggregation model dynamically adjusts the number and composition of aggregation groups based on system operating conditions. The system can reconfigure aggregation groups in response to changing load patterns, generation availability, and system topology, optimizing the balance between model detail and computational efficiency for each scenario.
3Measurement precision
If manual trial and error is used to create aggregation groups, then analysis accuracy can be improved, but work time increases significantly
Solution Approach 1:
The system automatically performs aggregation group creation and parameter optimization without requiring manual intervention. The automated algorithm evaluates multiple aggregation configurations and selects the optimal model based on predefined accuracy criteria and computational efficiency metrics, eliminating time-consuming manual trial and error while maintaining high accuracy.
Data Source
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AI summary
In a method for increasing the efficiency of an aggregation group which is a part of a power system aggregation process, the aggregation group is not optimum and a created aggregation model does not satisfy both low volume of calculations and high analysis accuracy. In order to solve this problem, a power system aggregation device of the present invention includes: a fitness evaluation function library in which two or more aggregation fitness evaluation functions are stored; and a processing unit which obtains an aggregation fitness from a detailed system model using the aggregation fitness evaluation functions, creates an aggregation group from the aggregation fitness, and creates an aggregation system model from the aggregation group. The power system aggregation device is characterized in that the processing unit calculates the aggregation fitness of two or more power system model constituent elements using two or more fitness evaluation functions, and creates the aggregation group on the basis of the aggregation fitness of the power system model constituent elements.