Adaptable scheduling of smart building communities with thermal mapping and demand flexibility

This paper proposes a novel hierarchical optimization framework that couples the balancing problem of a community network with building energy management (BEM) to form an integrated model capable of capturing potential joint-flexibilities of connected buildings while co-optimizing the operation schedules of community-operated assets. The proposed framework comprises a community-level power flow-based model to solve the energy procurement problem of the community and a building-level physics-aware simulation model to estimate a set of day-ahead aggregate load scenarios with potential joint-flexibilities. At the building-level, a least-cost multi-objective optimization provides a sequence of optimal temperature setpoints for all thermal zones that is fed into an accurate Gradient Boosting Machine (GBM) to estimate heating, ventilation, and air conditioning (HVAC) load trajectories while considering human comfort, occupancy patterns, building thermal response, and intraday electricity prices. The community-level model co-optimizes day-ahead schedules of shared distributed energy resources (DERs) and electric vehicle (EV) chargings while directing the building cluster to adapt the joint-flexibilities to balance the community network under different operation conditions. Finally, a test building cluster located in Qatar University is investigated through several case studies. The simulation results reveal the model’s practicality in fully capturing the joint-flexibilities of the building cluster for an adaptable community operation which cuts the operation cost by 21.04%.