Operational-Response-Aware Two-Stage Stochastic Scheduling of Renewable Port Microgrids under Arrival-Driven Congestion and Demand Amplification

Electrified container ports are coupled logistics–energy systems in which uncertain vessel arrivals determine berth occupancy, shore-power activation, cargo-handling intensity, and the residual demand balanced by renewable generation, battery storage, and the utility grid. This study develops a scenario-based stochastic framework linking a non-homogeneous vessel-arrival process to queue-consistent berth occupancy, congestion-responsive demand, photovoltaic and wind uncertainty, battery dispatch, and grid exchange. Physical arrival variability is separated from one-sided delayed Automatic Identification System (AIS) information, while a Gaussian-copula construction preserves adverse traffic–demand–renewable dependence. Five thousand stratified scenarios are reduced to 60 weighted representatives; the conditional extreme-congestion benchmark uses the nine retained Scenario-IV representatives. Across 30 independent paired out-of-sample replications, the proposed schedule reduces operating cost by 13.9% (95% Student-t CI: 13.60–14.20%), grid-import-based operational CO2 emissions by 17.1% (16.76–17.44%), probability-weighted scenario peak grid import by 18.4% (18.03–18.77%), and cumulative operational response by 34.1% (33.50–34.70%) relative to the deterministic reference. Battery throughput is 29.4% lower than under cost-only stochastic dispatch (28.73–30.07%). In the representative seed-2026 benchmark, the corresponding in-sample reductions are 14.8%, 18.2%, 19.9%, and 36.4% relative to B0, while battery throughput is 31.6% lower than under B3. The formulation is an optimistic two-stage perfect-continuous-recourse benchmark rather than a deployable rolling-horizon controller. Within the representative synthetic system, vessel-arrival uncertainty is therefore an operational energy disturbance rather than a purely logistical input.