A Digital Twin Framework for Decision-Support and Optimization of EV Charging Infrastructure in Localized Urban Systems

TL;DR

The paper tackles the challenge of planning EV charging infrastructure in localized urban systems under energy variability and policy influences. It presents a digital twin that fuses agent-based modeling with embedded metaheuristic optimization and an interactive dashboard, demonstrated on a university campus in Hanoi. Key findings show seasonal solar fluctuations reducing energy self-sufficiency and profitability, policy interventions like real-time slot notifications boosting user satisfaction, and a balanced mix of fast (30 kW) and slow (11 kW) chargers coupled with solar energy yielding near-optimal configurations with significant computational savings. The framework offers a transferable, scalable tool for data-driven EV infrastructure planning and adaptive energy management from campus-scale to city-scale applications.

Abstract

As Electric Vehicle (EV) adoption accelerates in urban environments, optimizing charging infrastructure is vital for balancing user satisfaction, energy efficiency, and financial viability. This study advances beyond static models by proposing a digital twin framework that integrates agent-based decision support with embedded optimization to dynamically simulate EV charging behaviors, infrastructure layouts, and policy responses across scenarios. Applied to a localized urban site (a university campus) in Hanoi, Vietnam, the model evaluates operational policies, EV station configurations, and renewable energy sources. The interactive dashboard enables seasonal analysis, revealing a 20% drop in solar efficiency from October to March, with wind power contributing under 5% of demand, highlighting the need for adaptive energy management. Simulations show that real-time notifications of newly available charging slots improve user satisfaction, while gasoline bans and idle fees enhance slot turnover with minimal added complexity. Embedded metaheuristic optimization identifies near-optimal mixes of fast (30kW) and standard (11kW) solar-powered chargers, balancing energy performance, profitability, and demand with high computational efficiency. This digital twin provides a flexible, computation-driven platform for EV infrastructure planning, with a transferable, modular design that enables seamless scaling from localized to city-wide urban contexts.

Figures (11)

• Figure 1: The model environment replicates the campus, manages vehicle flow, parking, and charging, and distinguishes vehicle agents: gasoline (blue circle) & EVs (yellow circles).
• Figure 2: Decision-making process of vehicle agents, expanded from vanderKam2019lee-2020. Each timestep follows the same loop for gasoline and EVs, except in "Do Assign Slot," where EV parking depends on SoC and station preferences.
• Figure 3: Digital Twin dashboard for real-time EV charging visualization, integrating (1) Interactive parameters control panel, (2) GIS-based interactive multi-agent simulation, and (3) Performance charts tracking user satisfaction, energy efficiency, and financial viability.
• Figure 4: Average one-month simulation results for Quarter 1 & Quarter 3, showing total energy consumption (kWh), renewable energy usage (kWh), and self-sufficiency (%) without wind energy integration.
• Figure 5: Average one-month simulation results for Q1, comparing self-sufficiency (%) with (orange) and without (blue) wind energy integration using Wilcoxon Test.