Robust chance-constrained programming approach for the planning of fast-charging stations in electrified transportation networks. (15th March 2020)

• Record Type: Journal Article
• Authors: Zhou, Bo; Chen, Guo; Song, Qiankun; Dong, Zhao Yang
• Is Part Of: Applied energy. Volume 262(2020)
• Publication Date: 2020-03-15
• Subjects: Plug-in electric vehicle -- Fast-charging station -- Transportation network -- Distribution network -- Robust chance constraint -- Mixed integer second order cone programming; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.114480
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: The planning model reflects the interactions among multiple factors in reality. Robust chance constraints are formulated to address the uncertain charging demands. Moment-based information is utilized to construct ambiguity set for uncertainties. The chance constraints are reformulated into mixed integer linear constraints. The modeling results are validated by experiments through real world applications. Abstract: In this paper, a bi-level programming model is established to address the planning issues of fast-charging stations in electrified transportation networks with the consideration of uncertain charging demands. The capacitated flow refueling location model is considered in the upper level to minimize the planning cost of fast-charging stations while the traffic assignment model is utilized in the lower level to determine the spatial and temporal distribution of plug-in electric vehicle flows over entire transportation networks. Such bi-level model unveils the inherent relationship among charging demands, electrical demands and the spatial and temporal distribution of plug-in electric vehicle flows. Robust chance constraints are formulated to characterize the service abilities of fast-charging stations under distribution-free uncertain charging demands, where the ambiguity set is constructed to estimate the potential values of the uncertainties based on their moment-based information, such that the robust chance constraints can exactly be reduced to mixed integer linear constraints. By introducing new variables, the bi-level model is then reformulated into a single-level mixed integer second-order cone programming model so as to be solved via off-the-shelf solvers, which guarantee the optimality of the solution. A case study is conducted to illustrate the effectiveness of the proposed planning model, which reveals three critical factors that significantly impact the planning outcomes.