Logistics-aware supervisory temperature setpoint scheduling for industrial cold storage

Industrial cold-storage warehouses are energy-intensive assets whose refrigeration demand is affected by both thermal boundary conditions and daily logistics activity. In automated facilities, pallet inflow and outflow, door-related air exchange, and handling intensity can change the cooling load seen by the refrigerated volume over the day. This paper presents a logistics-aware supervisory scheduling strategy for an industrial cold-storage warehouse. A control-oriented dynamic model is used to describe the interaction among warehouse temperature, refrigeration effort, pallet-flow activity, door-related disturbance, and logistics-related electrical demand. The model is parameterized from technical and operational information associated with the SINERGIA industrial case study. The proposed supervisory layer updates the warehouse temperature setpoint and the admissible cooling ceiling on an hourly basis, while a local refrigeration controller remains responsible for thermal tracking and safety enforcement. The peak-aware cooling cap is formulated using the same part-load COP relation adopted in the refrigeration-power model, so that electrical peak constraints and cooling-ceiling decisions remain consistent. Comparative simulations were performed for moderate- and high-disturbance operating scenarios. In the nominal simulations, the proposed strategy reduced refrigeration energy by 5.78% and 6.23%, and peak total electrical power by 9.45% and 8.75%, respectively, with no temperature violations. A Monte Carlo uncertainty analysis with 300 randomized parameter sets per scenario confirmed positive energy and peak-demand reductions across the tested uncertainty range, again without any violation of the admissible storage-temperature limits. The results show that logistics information can be usefully incorporated at the supervisory level to improve cold-storage operation while preserving thermal safety.