Capacity allocations of SOPs considering distribution network resilience through elastic models. (January 2022)

• Record Type: Journal Article
• Authors: Qin, Qing; Han, Bei; Li, Guojie; Wang, Keyou; Xu, Jin; Luo, Lingen
• Is Part Of: International journal of electrical power & energy systems. Volume 134(2022)
• Publication Date: 2022-01
• Subjects: Resilience -- Soft open points -- Elastic models -- Capacity allocations -- Optimizing method; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2021.107371
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: Mechanical mapping model is used to evaluate resilience enhancement in distribution networks with SOPs. Both normal operations and power restoration are considered to maximize resilience enhancement. A bi-level programming algorithm is proposed to determine the siting and sizing of SOPs to boost resilience. Abstract: Disturbances such as natural disasters or man-made attacks exert serious influences on power system, resulting in an increasing awareness of resilience enhancement strategies. As the integration of microgrids, distributed generators (DGs) and power electronic devices adds to the vulnerability of power distribution network to disruptions, it is highly required to enhance the resilience against failure. Soft open points (SOPs) are flexible power electronic devices, which can balance power distribution under normal operation, and supply restoration under abnormal conditions. Thus, the application of SOPs can boost resilience both in pre-failure prevention and post-failure recovery. In this paper, to maximize the effect of SOPs on the boost of resilience of distribution network, a mixed integer non-linear optimization problem is proposed to schedule the siting and sizing of SOPs based on multi-stage elastic mechanical model. A bi-level algorithm is used to tackle the SOP planning problem. The selection of location and capacity is solved by genetic algorithm, after which the control strategies of SOP are optimized by particle swarm algorithm with given planning scheme to obtain the maximum resilience. Finally, case studies on the IEEE 33-bus system and IEEE 123-node test feeder are used to verify the effectiveness and efficiency of the proposed method.