Electric vehicle charging stations: Model, algorithm, simulation, location, and capacity planning.

Area of Science:

• Sustainable Transportation
• Environmental Science
• Urban Planning

Background:

• Electric vehicles (EVs) are crucial for sustainable transportation but face adoption barriers.
• Insufficient charging infrastructure hinders widespread EV adoption globally.
• Optimizing electric vehicle charging station (EVCS) locations is vital for EV uptake.

Purpose of the Study:

• To develop a comprehensive approach for determining optimal EVCS locations and capacities.
• To integrate location modeling with demand forecasts and market penetration for EVCS planning.
• To address gaps in existing literature regarding demand and market penetration in EVCS optimization.

Main Methods:

• Literature review on facility location models for EV charging infrastructure.
• Utilized a genetic algorithm to solve the p-median problem for EVCS location selection.
• Employed Arena 14 simulation software for traffic modeling and optimizing charging unit configurations.

Main Results:

• The proposed model identifies optimal public areas for EVCS, considering demand and accessibility.
• Results demonstrate minimized travel distances and reduced waiting times for EV users.
• The model offers a scalable solution adaptable to future growth in the EV market.

Conclusions:

• A robust and strategically placed EVCS network is essential for enhancing EV feasibility and attractiveness.
• Proactive infrastructure development, informed by accurate demand predictions, supports the transition to sustainable transportation.
• This study provides a sustainable and economical model for EVCS placement and capacity planning.