Paper Info
Title
Preallocation of Electric Buses for Resilient Restoration of Distribution Network: A Data-Driven Robust Stochastic Optimization Method
Abstract
In recent years, severe hurricanes have occurred frequently, posing a huge challenge to the distribution network (DN) operation. Electric buses (EBs) possess large-capacity batteries and are widely used in public transit under normal circumstances. Before a hurricane occurs, some idle EBs can be preallocated to different charging stations equipped with vehicle-to-grid and served as sources for emergency power supply. This article proposes a prehurricane EB preallocation method to assist the resilience enhancement of a fragile DN. A two-stage data-driven robust stochastic programming technique is applied to build this model. Different scenarios are generated, and the corresponding posthurricane restoration processes are considered in the determination of the preallocation strategy. Also, the uncertainties of hurricane-induced physical damages are considered in modeling, which is described as a strengthened confidence set. The established model aims at exploring the optimal preallocation strategy of EBs with minimum load losses under the worst-case distribution. A column-and-constraint generation algorithm is then used to solve this proposed model. The proposed method is tested on a modified IEEE 33-bus system with 50 EBs. The results indicate that preallocating EBs to charging stations can improve the resilience of the posthurricane DN effectively.
Year
DOI
Venue
2022
10.1109/JSYST.2021.3123623
IEEE Systems Journal
Keywords
DocType
Volume
Data-driven stochastic program,distribution network (DN),electric bus (EB),load restoration,preallocation,resilience
Journal
16
Issue
ISSN
Citations 
2
1932-8184
1
PageRank 
References 
Authors
0.36
17
6
Name
Order
Citations
PageRank
Boda Li110.36
Ying Chen2216.97
Wei Wei35614.87
Shengwei Mei419634.27
Yunhe Hou511422.07
Shanshan Shi610.36