Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6936355 | Transportation Research Part C: Emerging Technologies | 2016 | 13 Pages |
Abstract
Most existing studies on EV charging infrastructure planning take a central planner's perspective, by assuming that investment decision on charging facilities can be controlled by a single decision entity. In this paper, we establish modeling and computational methods to support business-driven EV charging infrastructure investment planning problem, where the infrastructure system is shaped by collective actions of multiple decision entities who do not necessarily coordinate with each other. A network-based multi-agent optimization modeling framework is developed to simultaneously capture the selfish behaviors of individual investors and travelers and their interactions over a network structure. To overcome computational difficulty imposed by non-convexity of the problem, we rely on recent theoretical development on variational convergence of bivariate functions to design a solution algorithm with analysis on its convergence properties. Numerical experiments are implemented to study the performance of proposed method and draw practical insights.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Zhaomiao Guo, Julio Deride, Yueyue Fan,