A Mixed-Integer Programming Framework for Economic and Environmental EV Fleet Charging

Widespread fleet electrification is concentrating electricity demand at commercial depots that face volatile prices, tight feeder limits and scarce chargers. This paper proposes a forecast‑aware mixed‑integer linear program (MILP) that co‑optimises vehicle charging, battery‑energy‑storage dispatch and photovoltaic self‑consumption. The model minimises energy cost plus state‑of‑charge (SOC) penalties, while enforcing charger exclusivity, battery‑health bounds and continuous priority weights. It is evaluated on a 48‑interval weekday data set comprising 20 electric vehicles, two 11 kW chargers, half‑hourly solar forecasts, factory‑load predictions and Iberian day‑ahead prices. Relative to an uncontrolled first‑come/first‑served baseline, the optimiser cuts total charging expenditure by 49 %, inceases SOC compliance from 35 % to 65 %, increases PV self‑consumption from 33.4 % to 35.5 % and lowers grid‑attributed CO₂ emissions by 66 %. A modest rise in instantaneous demand is held within transformer limits through strategic battery discharge. These results confirm that predictive scheduling transforms depot charging from a passive load into a cost‑optimal, carbon‑aware asset and motivate future extensions that embed stochastic forecasts, vehicle‑to‑grid services. route‑energy coupling and Keywords. EV fleet charging; mixed‑integer linear programming; battery energy self‑consumption; predictive scheduling