Infrastructure planning for fast charging stations in a competitive market |
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| Institution: | 1. Department of Civil and Environmental Engineering, University of California, Davis, CA 95616, United States;2. Department of Mathematics, University of California, Davis, CA 95616, United States;1. Department of Civil Engineering, University of Coimbra, Rua Luis dos Reis Santos, room SA3.4, 3030-788 Coimbra, Portugal;2. Department of Transport and Planning, Delft University of Technology, Stevinweg 1, 2628 CN Delft, Netherlands;3. Department of Mathematics, University of Coimbra, 3001-454 Coimbra, Portugal;1. School of Management, Huazhong University of Science and Technology, Wuhan 430074, China;2. School of Information Management, Wuhan University, Wuhan 430072, China;1. Department of Civil Engineering, Tsinghua University, Beijing 100084, PR China;2. Department of Industrial Engineering, Tsinghua University, Beijing 100084, PR China;3. Department of Civil and Environmental Engineering, University of California Berkeley, Berkeley, CA 94720, United States;4. Department of Industrial Engineering and Operations Research, University of California Berkeley, Berkeley, CA 94720, United States;1. Glenn Department of Civil Engineering, Clemson University, 109 Lowry Hall, Clemson, SC 29634, United States;2. Department of Industrial Engineering, Clemson University, 273 Freeman Hall, Clemson, SC 29634, United States;1. Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong;2. Stanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong |
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| 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. |
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| Keywords: | EV charging infrastructure Multi-agent optimization Nash equilibrium Lopsided convergence |
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