QuantumFerryV2B: Weather-Robust Quantum-AI Control for Ferry-Port Vehicle-to-Building Energy Systems

Abstract

Battery-electric ferry ports are emerging as demanding nodes in maritime electrification: one shore transformer must deliver megawatt-scale charging in minute-long berth windows while serving terminal buildings, refrigerated cargo, electrified equipment, and cold-weather battery limits. We introduce QuantumFerryV2B, a real-data-grounded Quantum-AI framework that controls this coupled system as one weather-robust, thermally aware Vehicle-to-Building (V2B) problem rather than isolated charging, building, or battery subproblems. The framework combines a three-region benchmark, a physics-based energy and battery-thermal evaluator, a chance-constrained P90 weather reserve learned by a SciML surrogate, a QPU-ready QUBO day-ahead scheduler with deterministic feasibility repair, and a physics-shielded variational quantum circuit (VQC) controller. The benchmark uses Entur ferry timetables, Danish AIS traces, Washington State Ferries GTFS schedules, Open-Meteo weather and marine fields, NREL/OEDI port-load profiles, CALCE low-temperature lithium-ion data, EnergyPlus terminal simulation, and OpenModelica battery-thermal validation. Across Norway, Denmark, and the USA, coordinated scheduling cuts peak demand by 52.5--56.1\% and operating cost by 33.0--38.5\% relative to uncontrolled opportunity charging. Thermal preconditioning is necessary in cold-wave cases; the SciML P90 reserve converts brittle point-optimal schedules from 45--70\% held-out feasibility to 98--100\% robust feasibility at below 2.5\% cost premium. The QUBO scheduler matches the exact optimizer on demand peak, while the physics-shielded VQC sustains zero missed departures under IBM Heron-r2-class hardware noise with 16x slower parameter growth than a matched neural network. QuantumFerryV2B provides a reproducible benchmark and deployable Quantum-AI control architecture for robust ferry-port electrification.

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