Framework for Optimization and Control in Buildings-to-Grid Integration

Electricity consumption projection rate suggests that the world's energy consumption will grow tremendously in future. Building loads contribute to a major share in the modern day energy demand. Due to this reason, it is very important that these building loads actively participate in grid operations to help improve the frequency profile and to generate power efficiently. To serve this purpose, this thesis proposes a mathematical framework for Buildings-to-Grid (BtG) integration in smart cities. The framework explicitly couples power grid and building's control actions and operational decisions and can be utilized by buildings and power grids operators to simultaneously optimize their performance. High-level dynamics of building clusters and building-integrated power networks with algebraic equations are presented both operating at different time-scales. A model predictive control (MPC)-based algorithm that formulates the BtG integration and accounts for the time-scale discrepancy is developed. The formulation captures dynamic and algebraic power flow constraints of power networks and is shown to be numerically advantageous, as a high-fidelity discretization is used. Case studies demonstrate building energy savings and significant frequency regulation, while these findings carry over in network simulations with nonlinear power flows and mismatch in load predictions, weather forecasts, and building model parameters.