Journal
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
Volume 68, Issue -, Pages 215-227Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trc.2016.04.010
Keywords
EV charging infrastructure; Multi-agent optimization; Nash equilibrium; Lopsided convergence
Categories
Funding
- Sustainable Transportation Energy Pathways (STEPS) program
- National Center on Sustainable Transportation (NCST) at UC Davis
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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. (C) 2016 Elsevier Ltd. All rights reserved.
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