An agent-based modeling approach for public charging demand estimation and charging station location optimization at urban scale

As the market penetration of electric vehicles (EVs) increases, the surge of charging demand could potentially overload the power grid and disrupt infrastructure planning. Hence, an efficient deployment strategy of electrical vehicle supply equipment (EVSE) is much needed. This study attempts to address the EVSE problem from a microscopic perspective by formulating the problem in two steps: public charging demand simulation and charging station location optimization. Specifically, the authors apply agent-based modeling approach to produce high-resolution daily driving profiles within an urban-scale context using MATSim. Subsequently, the authors perform EV assignment based on socioeconomic attributes to determine EV adopters. Energy consumption model and public charging rule are specified for generating synthetic public charging demand and such demand is validated against real-world public charging records to guarantee the robustness of simulation results. In the second step, the authors apply a location approach – capacitated maximal coverage location problem (CMCLP) model – to reallocate existing charging stations with the objective of maximizing the coverage of total charging demands generated from the previous step under the budget and load capacity constraints. The entire framework is capable of modeling the spatiotemporal distribution of public charging demand in a bottom-up fashion, and provide practical support for future public EVSE installation.

• Record URL:
  https://doi.org/10.1016/j.compenvurbsys.2023.101949
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0198971523000121
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01989715
• Supplemental Notes:
  • © 2023 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Yi, Zhiyan
  • Chen, Bingkun
  • Liu, Xiaoyue Cathy
  • 0000-0002-5162-891X
  • Wei, Ran
  • Chen, Jianli
  • Chen, Zhuo
• Publication Date: 2023-4

Language

• English

Media Info

• Media Type: Web
• Features: Figures; Maps; References; Tables;
• Pagination: 101949
• Serial:
  • Computers, Environment and Urban Systems
  • Volume: 101
  • Issue Number: 0
  • Publisher: Elsevier Science
  • ISSN: 0198-9715
  • EISSN: 1873-7587
  • Serial URL: https://www.sciencedirect.com/journal/computers-environment-and-urban-systems

Subject/Index Terms

• TRT Terms: Demand; Electric vehicle charging; Location; Optimization; Service stations; Simulation
• Identifier Terms: MATSim (Computer program)
• Subject Areas: Data and Information Technology; Energy; Highways; Terminals and Facilities;

Filing Info

• Accession Number: 01877636
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 28 2023 9:56AM