Quantification of energy flexibility and uncertainty in airport terminals unlocked by occupancy-based multi-zone indoor temperature set-point optimization

Building energy flexibility is crucial for developing sustainable and resilient power grids. Airport terminals with high energy intensity and substantial carbon footprints are increasingly recognized as promising flexibility providers in electricity market. However, their dynamic occupancy patterns, omission of load aggregator, and stringent passenger service requirement pose significant challenges to their participation in demand response (DR) programs. To address these challenges, this work develops a multi-zone indoor temperature set-point optimization method based on spatiotemporal occupancy and passenger complaint constraint to unlock energy flexibility. First, a relation between allowable temperature offsets and spatiotemporal occupancy under the passenger complaint constraint was determined, and a DR-oriented PMV model to activate multi-zone flexibilities across different functional areas and seasons were established. Next, non-linear programming was used to generate optimal indoor temperature set-point profiles to balance economy-environment-passenger benefits. Last, a data-driven MPC for indoor temperature was conducted to unlock energy flexibility, and the Monte Carlo technique was employed to quantify its uncertainty associated with weather and holidays under multi-aggregation and multi-duration scales. The results showed that the proposed method enhanced the allowable temperature offset by 0.73 ℃, achieved a load-shifting capacity of 0.25 kWh/m², and reduced carbon emissions by 30%, saved electricity costs by 16% during the cooling season. The uncertainty of energy flexibility for cluster aggregating 63k m² consistently remained below 14% over periods ranging from one week to two months. This work provides a practical methodology and a reliable quantitative reference for airport terminals to participate in DR programs.