Data-driven aggregation of thermal dynamics within building virtual power plants

Virtual power plants (VPPs) possess the capability to aggregate flexible resources to provide grid services in the distributed network operation. The potential for flexibility utilization in building loads, particularly thermal-controlled ones, has drawn attention when aggregated into VPPs. This work proposes a data-driven approach to overcome the challenges in modeling and aggregation of thermal dynamics within clustered buildings. Specifically, we utilize piecewise linear equations to represent the impact of various input features on thermal dynamics (i.e., zone temperature variation), which is more precise in extracting the nonlinear relationship through adaptive model order adjustment and breakpoint determination. Additionally, we transform the identified thermal dynamic process into virtual storage models and aggregate them into the optimization-based system dispatch process. The proposed two-stage aggregation approach facilitates the determination of accurate dispatch decisions by iteratively updating the power bounds of flexibility regions. It ensures disaggregation feasibility simultaneously to generate individual power signals for each building. Case simulations verify the accuracy of the proposed method on modeling and aggregation of thermal dynamics within clustered buildings.