A Microscopic Approach for Electric Vehicle Demand Estimation

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 project attempted 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 applied an agent-based modeling approach to produce high-resolution daily driving profiles within an urban-scale context using MATSim. Subsequently, the authors performed an EV assignment based on socioeconomic attributes to determine EV adopters. An energy consumption model and a public charging rule were specified for generating synthetic public charging demand, and such demand was validated against real-world public charging records to guarantee the robustness of simulation results. In the second step, the authors applied a location approach — the 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 provides practical support for future public EVSE installations.

• Record URL:
  • https://www.ugpti.org/resources/reports/details.php?id=1229
• Record URL:
  • https://rosap.ntl.bts.gov/view/dot/78994
• Summary URL:
  • https://rosap.ntl.bts.gov/view/dot/79675
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  University of Utah, Salt Lake City

  Department of Civil and Environmental Engineering
  Salt Lake City, UT  United States 

  Mountain-Plains Consortium

  North Dakota State University
  Fargo, ND  United States  58108

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • Liu, Xiaoyue Cathy
  • Yi, Zhiyan
• Publication Date: 2024-10

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Figures; References; Tables;
• Pagination: 43p

Subject/Index Terms

• TRT Terms: Demand; Electric vehicle charging; Electric vehicles; Energy consumption; Location; Optimization; Resource allocation; Simulation
• Identifier Terms: MATSim (Computer program)
• Subject Areas: Energy; Highways; Planning and Forecasting; Terminals and Facilities; Vehicles and Equipment;

Filing Info

• Accession Number: 01941293
• Record Type: Publication
• Report/Paper Numbers: MPC-697, MPC-24-566
• Files: UTC, NTL, TRIS, USDOT
• Created Date: Dec 30 2024 9:58AM