Risk Assessment of Distribution Network Operation Based on Generalized Load

With the widespread use of distributed generation and electric vehicles, the uncertainty of distribution network operation is increased, challenging risk assessment. This paper proposes a generalized load modeling and risk assessment method based on GNG–Informer–WOA. GNG adaptively clusters load curves to identify typical patterns and noise; WOA optimizes Informer’s hyperparameters for high-precision prediction. An index system covering voltage out-of-limit, regulation capacity, and new energy consumption risks is established, with weights determined by fusing AHP and PCA via game theory. Case studies on the improved IEEE 33-bus system show the method effectively characterizes generalized load characteristics and accurately evaluates risks under different scenarios, supporting safe operation.

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Publication Details

Journal
Energies
Published
2026-03-07
DOI
https://doi.org/10.3390/en19051369
Primary Topic
Power System Reliability and Maintenance
Type
article
Field-Weighted Citation Impact
0.00

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Risk Assessment of Distribution Network Operation Based on Generalized Load

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Risk Assessment of Distribution Network Operation Based on Generalized Load

Mingshen Wang, Yi Ru, Qikai Zhao, Manqian Yu, Ying Wang, Jiamin Lv
article en

Abstract

With the widespread use of distributed generation and electric vehicles, the uncertainty of distribution network operation is increased, challenging risk assessment. This paper proposes a generalized load modeling and risk assessment method based on GNG–Informer–WOA. GNG adaptively clusters load curves to identify typical patterns and noise; WOA optimizes Informer’s hyperparameters for high-precision prediction. An index system covering voltage out-of-limit, regulation capacity, and new energy consumption risks is established, with weights determined by fusing AHP and PCA via game theory. Case studies on the improved IEEE 33-bus system show the method effectively characterizes generalized load characteristics and accurately evaluates risks under different scenarios, supporting safe operation.

EnergiesVol. 19(5)
China Jiliang University (CN)
State Grid Corporation of China, Science and Technology Project of State Grid
Affordable and clean energy
Openalex Percentile: Top 44%
Power System Reliability and Maintenance
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