Risk evaluation for low-voltage distribution network based on VMD-LSTM load forecasting model

Addressing the risks of voltage violations and transformer heavy-overloading in low-voltage distribution network caused by impact loads and distributed energy integration, this paper proposes a dynamic comprehensive risk evaluation framework. First, a composite risk index (CRI) is constructed, integrating the system voltage violation risk index (SVRI) and the system active power heavy-overload risk index (SAPHORI). A risk period composite index (RPCI) based on time-decay weighting is also designed. Second, a VMD-LSTM model is built for ultra-short-term load forecasting. Combined with Monte Carlo simulation for future system state sampling, this enables comprehensive future risk evaluation. Case studies using a modified IEEE 13-bus system and actual data from a distribution network in Southwest China demonstrate that the forecasting model achieves over 97 % accuracy. The evaluation results provide warnings several hours in advance regarding risk accumulation trends, offering crucial data support for power grid dispatching.

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Publication Details

Journal
Energy Reports
Published
2026-01-06
DOI
https://doi.org/10.1016/j.egyr.2025.108963
Primary Topic
Power System Reliability and Maintenance
Type
article
Field-Weighted Citation Impact
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Risk evaluation for low-voltage distribution network based on VMD-LSTM load forecasting model

Ye Lü, Yiqin Shen, Lei Sha, Zehui Shi et al.
Energy Reports
Power System Reliability and Maintenance
article

Risk evaluation for low-voltage distribution network based on VMD-LSTM load forecasting model

Ye Lü, Yiqin Shen, Lei Sha, Zehui Shi, Liang Liang, Chao Lu
article en

Abstract

Addressing the risks of voltage violations and transformer heavy-overloading in low-voltage distribution network caused by impact loads and distributed energy integration, this paper proposes a dynamic comprehensive risk evaluation framework. First, a composite risk index (CRI) is constructed, integrating the system voltage violation risk index (SVRI) and the system active power heavy-overload risk index (SAPHORI). A risk period composite index (RPCI) based on time-decay weighting is also designed. Second, a VMD-LSTM model is built for ultra-short-term load forecasting. Combined with Monte Carlo simulation for future system state sampling, this enables comprehensive future risk evaluation. Case studies using a modified IEEE 13-bus system and actual data from a distribution network in Southwest China demonstrate that the forecasting model achieves over 97 % accuracy. The evaluation results provide warnings several hours in advance regarding risk accumulation trends, offering crucial data support for power grid dispatching.

Energy ReportsVol. 15
Shenzhen Institute of Information Technology (CN), Harbin Institute of Technology (CN), Shenzhen Academy of Robotics (CN), Shanghai Electric (China) (CN)
Affordable and clean energy
Openalex Percentile: Top 97%
Power System Reliability and Maintenance
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