Optimal Planning of Electric Vehicle Fast-Charging Stations Considering Uncertain Charging Demands via Dantzig–Wolfe Decomposition

Luyun Wang and Bo Zhou ()
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Luyun Wang: College of Economics and Management, Southwest University, Chongqing 400715, China
Bo Zhou: College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 400074, China

Sustainability, 2023, vol. 15, issue 8, 1-23

Abstract: This study investigates the planning problem of fast-charging stations for electric vehicles with the consideration of uncertain charging demands. This research aims to determine where to build fast-charging stations and how many charging piles to be installed in each fast-charging station. Based on the multicommodity flow model, a chance-constrained programming model is established to address this planning problem. A scenario-based approach as well as a big-M coefficients generation algorithm are applied to reformulate the programming model into tractable one, then the Dantzig–Wolfe decomposition method is leveraged to find its optimal solution. Finally, a numerical experiment is conducted in a 25-node network to assess the efficiency of the proposed model and solution approach.

Keywords: planning of fast-charging stations; electric vehicle; uncertain charging demand; chance-constrained programming; Dantzig–Wolfe decomposition (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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