Available Capacity Analysis and Business Expansion Decision Method Based on Distribution Network Edge Agent

To fully leverage the advantages of distribution network edge computing and enhance the refined operation and management of distribution networks aimed at the problems of rough calculation of distribution network available capacity, relying on manual experience and not effectively using data enabling decision‐making, a distribution network available capacity calculation and optimization decision‐making method of distribution network expansion based on distribution network edge agent is proposed. First, considering the seasonal and daily load characteristics, the distribution network edge feeder partition and the public transformer available capacity are calculated based on the depth‐first traversal method, which includes seasonal and daily time series characteristics. Second, considering multiple objectives such as the matching of available capacity and load curve, load balancing, and three‐phase balance of station area, the optimal decision model of service expansion and installation is established, and the refined load optimal access scheme is obtained at two levels from the feeder area to the station area. Finally, an IEEE standard power distribution system and a real power distribution network are used to verify the efficiency of the proposed available capacity calculation method. The proposed service expansion decision method can effectively apply the available capacity analysis results to obtain a refined load access scheme that takes into account multiple objectives.

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Publication Details

Journal
Journal of Electrical and Computer Engineering
Published
2025-01-01
DOI
https://doi.org/10.1155/jece/8862201
Citations
1
Primary Topic
Power Systems and Technologies
Type
article
Field-Weighted Citation Impact
0.73

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Available Capacity Analysis and Business Expansion Decision Method Based on Distribution Network Edge Agent

Ning Guo, Tuo Ji, Xiaolong Xiao, Xinyi Zheng et al.
1 citations
Journal of Electrical and Computer Engineering
Power Systems and Technologies
0.73
article

Available Capacity Analysis and Business Expansion Decision Method Based on Distribution Network Edge Agent

Ning Guo, Tuo Ji, Xiaolong Xiao, Xinyi Zheng, Shufeng Dong, Zhitong Xue, Jinming Chen, Xiaoxing Lu
article en
1 citations

Abstract

To fully leverage the advantages of distribution network edge computing and enhance the refined operation and management of distribution networks aimed at the problems of rough calculation of distribution network available capacity, relying on manual experience and not effectively using data enabling decision‐making, a distribution network available capacity calculation and optimization decision‐making method of distribution network expansion based on distribution network edge agent is proposed. First, considering the seasonal and daily load characteristics, the distribution network edge feeder partition and the public transformer available capacity are calculated based on the depth‐first traversal method, which includes seasonal and daily time series characteristics. Second, considering multiple objectives such as the matching of available capacity and load curve, load balancing, and three‐phase balance of station area, the optimal decision model of service expansion and installation is established, and the refined load optimal access scheme is obtained at two levels from the feeder area to the station area. Finally, an IEEE standard power distribution system and a real power distribution network are used to verify the efficiency of the proposed available capacity calculation method. The proposed service expansion decision method can effectively apply the available capacity analysis results to obtain a refined load access scheme that takes into account multiple objectives.

Journal of Electrical and Computer EngineeringVol. 2025(1)
Electric Power Research Institute (US), Shanghai Electric (China) (CN), Zhejiang University of Technology (CN), Zhejiang University (CN)
State Grid Jiangsu Electric Power
Openalex Percentile: Top 25%
Power Systems and Technologies
0.73
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