Optimal Planning for Public Charging Infrastructure by Behaviour‐Based Electric Vehicle Charging Demand Simulations and Geographic Information System

ABSTRACT The rapid expansion of electric vehicles (EVs) in Thailand necessitates a strategic approach to developing an efficient EV charging station (EVCS) infrastructure. This research study introduces dual‐strategy algorithms for EVCS deployment, aligning with the national 30@30 policy target to promote sustainable transportation. Monte Carlo simulations are employed to estimate EV charging demand, accounting for user behaviour, public charging needs, EV fleet data, and various charging scenarios. Simulated demand profiles and utilisation factors are used to determine the required number of chargers for different EV types. Charging station locations along nationwide highways and within provinces are optimised using the maximum coverage and Dijkstra's shortest path algorithms. These approaches leverage geographic information system (GIS) data from OpenStreetMap, which allows realistic representations of road networks, travel distances, and route complexities. The strategy prioritises traffic density and incorporates constraints such as demand, coverage radius, existing infrastructure and network traffic patterns. Charger distribution for highways and urban areas is optimised using the knapsack problem framework to minimise waiting times and improve sharing efficiency. This methodology can be updated annually using new EV growth projections and infrastructure needs. Case studies include long‐term planning for EVCS deployment on inter‐provincial highways and in metropolitan areas, demonstrating its effectiveness in achieving the country's ambitious sustainable transportation targets.