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Resilience Planning of Power Distribution System Emergency Resources Considering Multiple Uncertainties Under Heavy Rain Disasters
Received date: 2023-11-21
Revised date: 2023-11-28
Accepted date: 2023-12-11
Online published: 2024-01-15
In recent years, the impact of heavy rain disasters on the reliable power supply of distribution networks has become increasingly prominent, and it is extremely challenging to ensure the supply of important loads in the case of large-scale and multi-point serious faults. Rational deployment of emergency resources can help reduce the loss of important loads in the system, thereby improving the resilience of the distribution system. Therefore, a resilience planning method for emergency resources in the distribution system considering multiple uncertainties under heavy rain disasters is proposed in this paper, and a two-tier planning model for the resilience improvement of the distribution system is established considering the emergency power supply and emergency energy storage, with the upper model aiming to minimize the net cost of investment cost and load loss, while the lower model aiming to minimize the comprehensive load loss in multiple island scenarios. Additionally, a stochastic simulation generation method for multi-island scenarios in distribution network under heavy rain disasters is proposed, and a scene reduction method based on clustering algorithm is proposed based on the probability and severity of each scenario to serve the scene screening of the lower-level optimization model. The analysis of the actual 62-node system of distribution network shows that the proposed method can effectively reduce the load loss of the system in disasters while considering investment economy, and improve the power supply capacity of the distribution network for important loads.
SHEN Zeyuan , ZHAO Haibo , WANG Chao , LI Jia , WANG Yao , LI Qi . Resilience Planning of Power Distribution System Emergency Resources Considering Multiple Uncertainties Under Heavy Rain Disasters[J]. Journal of Shanghai Jiaotong University, 2025 , 59(11) : 1742 -1753 . DOI: 10.16183/j.cnki.jsjtu.2023.594
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