Open Access

ARTICLE

Knowledge-Based Efficient N-1 Analysis Calculation Method for Urban Distribution Networks with CIM File Data

Lingyu Liang¹, Xiangyu Zhao^1,*, Wenqi Huang¹, Liming Sun^2,3, Ziyao Wang³, Yaosen Zhan²

1 Digital Grid Research Institute of China Southern Power Grid, China Southern Power Grid Co., Ltd., Guangzhou, 510640, China
2 Software Development Department, Guangzhou Shuimu Qinghua Technology Co., Ltd., Guangzhou, 510640, China
3 College of Electric Power, South China University of Technology, Guangzhou, 510640, China

* Corresponding Author: Xiangyu Zhao. Email:

Energy Engineering 2023, 120(12), 2839-2856. https://doi.org/10.32604/ee.2023.042042

Received 16 May 2023; Accepted 28 July 2023; Issue published 29 November 2023

Abstract

The N-1 criterion is a critical factor for ensuring the reliable and resilient operation of electric power distribution networks. However, the increasing complexity of distribution networks and the associated growth in data size have created a significant challenge for distribution network planners. To address this issue, we propose a fast N-1 verification procedure for urban distribution networks that combines CIM file data analysis with MILP-based mathematical modeling. Our proposed method leverages the principles of CIM file analysis for distribution network N-1 analysis. We develop a mathematical model of distribution networks based on CIM data and transfer it into MILP. We also take into account the characteristics of medium voltage distribution networks after a line failure and select the feeder section at the exit of each substation with a high load rate to improve the efficiency of N-1 analysis. We validate our approach through a series of case studies and demonstrate its scalability and superiority over traditional N-1 analysis and heuristic optimization algorithms. By enabling online N-1 analysis, our approach significantly improves the work efficiency of distribution network planners. In summary, our proposed method provides a valuable tool for distribution network planners to enhance the accuracy and efficiency of their N-1 analyses. By leveraging the advantages of CIM file data analysis and MILP-based mathematical modeling, our approach contributes to the development of more resilient and reliable electric power distribution networks.

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Keywords

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