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CHARGE-MAP: An integrated framework to study the multicriteria EV charging infrastructure expansion problem.
Kazi Ashik Islam1, Aparna Kishore1, Rounak Meyur2
1Department of Computer Science, Biocomplexity Institute, University of Virginia, Charlottesville, VA 22903.
Summary
Charge-map, a new framework, optimizes electric vehicle (EV) charging infrastructure by simulating demand and planning station placement. This data-driven approach significantly reduces charging detours and wait times, supporting widespread EV adoption.
Area of Science:
- Urban Planning and Transportation Science
- Electrical Engineering and Power Systems
- Computational Science and Simulation
Background:
- The rapid growth of electric vehicles (EVs) necessitates robust charging infrastructure.
- Expanding EV charging infrastructure is complex, involving demand, grid capacity, and cost factors.
Purpose of the Study:
- To present charge-map, a data-driven simulation-optimization framework for effective EV charging infrastructure planning.
- To ensure a positive charging experience for individual EV owners.
Main Methods:
- Developed charge-map, integrating agent-based simulation for demand prediction and optimization for station placement.
- Modeled EV adopter mobility and charging behavior to estimate spatiotemporal demand.
- Optimized new station/charger locations and capacities to minimize detour distances and wait times within budget constraints.
- Assessed power grid impact and transformer capacity requirements.
Main Results:
- charge-map can support ~198,600 EVs with 1,305 new stations and 2,164 chargers in Virginia.
- Reduced average detour distances by 66% and wait times by 72%.
- Identified minimal transformer upgrades needed (1.8% residential, >80% commercial supportable with 25-50 kVA).
Conclusions:
- charge-map offers data-driven insights for cost-effective EV charging infrastructure expansion.
- The framework facilitates targeted investments for sustainable EV integration.
- Provides crucial data for policymakers and urban planners in developing EV infrastructure.
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