@Article{ee.2026.087166,
AUTHOR = {Vivek Komarina, Suraj Wagle, Bhaskaran Gopalakrishnan, Hailin Li},
TITLE = {Simulation-Based Feasibility Assessment of Electric Vehicle Charging Stations in Public Facilities},
JOURNAL = {Energy Engineering},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/energy/online/detail/28083},
ISSN = {1546-0118},
ABSTRACT = {The expansion of public electric vehicle (EV) charging infrastructure requires facility-level planning methods that account for uncertain vehicle arrivals, charger availability, queueing behavior, electrical demand, and tariff-based operating cost. This study presents a simulation-based decision support framework for estimating the feasibility of installing Electric Vehicle Supply Equipment (EVSE) in public facilities. The method integrates a Monte Carlo arrival model with a discrete-event charging simulation and a demand-sensitive cost model. Daily EV arrivals are generated using weather severity and calendar-based demand factors, split by vehicle type, and converted into hourly annual arrival schedules. The discrete-event model routes vehicles to compatible chargers, represents charger occupancy, waiting, charging delay, release, vehicle departure without charging, and records service and utilization statistics. The resulting charging-event and hourly-utilization data are then used to estimate energy consumption, monthly peak demand, energy charges, demand charges, and total operating cost. A representative facility case is used to evaluate weather-severity sensitivity and charging-station configuration sensitivity. Results show that monthly demand peaks and operating costs are sensitive not only to the number and power rating of chargers but also to the coincidence between EVSE charging activity and existing facility load. The framework supports preliminary EVSE planning by allowing public facility managers to compare charger-count scenarios, estimate utility-bill impacts, and identify months with elevated peak-demand risk before investment decisions are finalized.},
DOI = {10.32604/ee.2026.087166}
}