A Resilience Enhancement Model for Complex Distribution Network Coupling with Human Resources and Traffic Network. (7th June 2021)

• Record Type: Journal Article
• Authors: Chen, Biyun; Shi, Yumo; Chen, Yanni
• Other Names: Wei Chun Academic Editor.
• Is Part Of: Complexity. Volume 2021(2021)
• Publication Date: 2021-06-07
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2021/2051719
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : Resilience is the ability of a system to withstand and recover from deliberate accidents; as to distribution systems, it means the ability to withstand and recover from natural disasters or other serious accidents and ensure electricity supply. Generally, promotion strategies of distribution network resilience mostly focus on electrical topology planning and reinforcement. The operation strategies in emergency repair stage are frequently ignored, especially the complex coupling relationship between distribution network, traffic network, and maintenance teams. A model of resilience improvement for a complicated distribution-traffic-human coupling system under hurricane disasters considering is presented. Firstly, based on the influence spreading mechanism of a hurricane acting on the distribution and traffic part of the system, a fault analysis model of rush repair is constructed. Secondly, according to the function of human resources in shortening the repair time and improving system resilience, an optimization model of emergency repair strategy is proposed. Taking into account the repair demand, traffic cost, and personnel operating and executing ability, the optimized strategy can minimize the social loss in the whole repair and recovery process after hurricane disaster. Furthermore, three indices, including system adaptability, repair rate, and economic loss rate, are proposed to quantify the resilience of distribution network. Finally, case studies on the IEEE33 bus system are implemented to verify the effectiveness of the proposed model.