Impacts of Electric Vehicle Charging Regimes and Infrastructure Deployments on System Performance: An Agent-Based Study

TL;DR

This study uses an agent-based model to analyze how different EV charging regimes and infrastructure layouts affect system costs and user behavior in Melbourne, highlighting the importance of tailored deployment strategies.

Contribution

It introduces a modeling framework that captures user behavior across multiple charging regimes and evaluates deployment strategies considering behavioral responses.

Findings

Utilization-refined deployments lower total system costs.

Effective AC slow charger allocation shifts charging behavior.

Accounting for user response improves infrastructure planning outcomes.

Abstract

The rapid growth of electric vehicles (EVs) requires more effective charging infrastructure planning. Infrastructure layout not only determines deployment cost, but also reshapes charging behavior and influences overall system performance. In addition, destination charging and en-route charging represent distinct charging regimes associated with different power requirements, which may lead to substantially different infrastructure deployment outcomes. This study applies an agent-based modeling framework to generate trajectory-level latent public charging demand under three charging regimes based on a synthetic representation of the Melbourne (Australia) metropolitan area. Two deployment strategies, an optimization-based approach and a utilization-refined approach, are evaluated across different infrastructure layouts. Results show that utilization-refined deployments reduce total system cost, accounting for both infrastructure deployment cost and user generalized charging cost, with the most significant improvement observed under the combined charging regime. In particular, a more effective allocation of AC slow chargers reshapes destination charging behavior, which in turn reduces unnecessary reliance on en-route charging and lowers detour costs associated with en-route charging. This interaction highlights the behavioral linkage between destination and en-route charging regimes and demonstrates the importance of accounting for user response and multiple charging regimes in charging infrastructure planning.