TITLE:
Design and Development of Electric Vehicle Charging Station Infrastructure Integrated with a Solar PV-Based Microgrid for Traffic-Responsive Load Management
AUTHORS:
Mohammad Ali
KEYWORDS:
Electric Vehicle Charging Station, Solar Photovoltaic Microgrid, Battery Energy Storage System, Traffic-Responsive Load Management, Energy Management System, Peak Load Shaving, Smart Grid, Sustainable Transportation
JOURNAL NAME:
World Journal of Engineering and Technology,
Vol.14 No.4,
October
23,
2026
ABSTRACT: The rapid proliferation of electric vehicles (EVs) is placing an increasingly non-uniform and spatially concentrated demand on distribution networks, particularly at public electric vehicle charging stations (EVCS) situated along high-traffic urban corridors. Uncoordinated charging at such locations can produce sharp power demand peaks, voltage deviations, and elevated dependency on grid-supplied energy, while simultaneously under-utilizing the significant rooftop and canopy area available for distributed solar photovoltaic (PV) generation. This paper presents the design and development of an EVCS infrastructure integrated with a solar PV-based microgrid in which the charging power allocation and battery schedule are governed by road traffic conditions, referred to here as traffic-responsive load management (TRLM). The proposed architecture combines a rooftop/canopy-mounted PV array with maximum power point tracking (MPPT), a battery energy storage system (BESS) interfaced through a bidirectional DC-DC converter, a grid-tied bidirectional inverter, and multiple Level-2 and DC fast-charging points, all coordinated by a centralized Energy Management System (EMS). An upstream traffic sensing layer estimates a Traffic Density Index (TDI); its few-minute detection-to-arrival lead time is used for fast power allocation, while a TDI forecast built from the site’s historical profile and corrected in real time is used to plan a battery reserve and a grid peak-shaving threshold ahead of the commute peaks. Mathematical models are developed for the PV array, battery state-of-charge dynamics with separate charge and discharge efficiencies, the microgrid power balance, a calibrated Poisson EV arrival process with an explicit pass-by capture fraction and lead time, class-wise queue stability, and the traffic-anticipatory dispatch. A Monte Carlo time-series simulation (300 independent days, 1-min resolution, common random numbers) compares TRLM with a fully specified time-of-use/SOC rule-based baseline operating under identical physical constraints. TRLM increases the PV self-consumption ratio by 8.4 percentage points (95% CI 7.9 - 8.9), reduces the grid dependency ratio by 3.9 points and reduces the mean daily evening 15-min peak grid demand by 10.9 kW (12.1%), with the battery kept within its 20% - 90% SOC band; waiting time during high-traffic intervals is essentially unchanged (−0.16 min) and daily energy cost rises by 0.9%. The findings indicate that coupling distributed solar generation, battery storage and traffic forecasting at the EVCS level is a technically viable means of reducing coincident evening peak loading, with a benefit that depends on forecast quality and on the evening energy deficit relative to battery capacity.