Strategies for Improving the Resiliency of Distribution Networks in Electric Power Systems during Typhoon and Water-Logging Disasters

Nan Ma, Ziwen Xu, Yijun Wang, Guowei Liu, Lisheng Xin, Dafu Liu, Ziyu Liu, Jiaju Shi and Chen Chen ()
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Nan Ma: Shenzhen Power Supply Co., Ltd., Shenzhen 518020, China
Ziwen Xu: School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Yijun Wang: Shenzhen Power Supply Co., Ltd., Shenzhen 518020, China
Guowei Liu: Shenzhen Power Supply Co., Ltd., Shenzhen 518020, China
Lisheng Xin: Shenzhen Power Supply Co., Ltd., Shenzhen 518020, China
Dafu Liu: School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Ziyu Liu: School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Jiaju Shi: School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Chen Chen: School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Energies, 2024, vol. 17, issue 5, 1-16

Abstract: Coastal cities often face typhoons and urban water logs, which can cause power outages and significant economic losses. Therefore, it is necessary to study the impact of these disasters on urban distribution networks and improve their flexibility. This paper presents a method for predicting power-grid failure rates in typhoons and water logs and suggests a strategy for improving network elasticity after the disaster. It is crucial for the operation and maintenance of power distribution systems during typhoon and water-logging disasters. By mapping the wind speed and water depth at the corresponding positions in the evolution of wind and water logging disasters to the vulnerability curve, the failure probability of the corresponding nodes is obtained, the fault scenario is generated randomly, and the proposed dynamic reconstruction method, which can react in real-time to the damage the distribution system received, has been tested on a modified 33-node and a 118-node distribution network, with 3 and 11 distribution generators loaded, respectively. The results proved that this method can effectively improve the resiliency of the distribution network after a disaster compared with the traditional static reconstruction method, especially in the case of long-lasting wind and flood disasters that have complex and significant impacts on the distribution system, with about 26% load supply for the 33-node system and nearly 95% for the 118-node system.

Keywords: water-logging disaster; distribution network restoration; dynamic network reconstruction; elastic distribution network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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