# Buildings-to-Grid Integration Framework

## TL;DR

This paper introduces a mathematical Buildings-to-Grid integration framework that optimizes combined building and power grid operations, improving efficiency, reducing costs, and providing effective frequency regulation in smart city networks.

## Contribution

It develops a novel MPC-based framework that explicitly couples building and grid control actions, accounting for different time-scales and dynamic constraints, which was not previously addressed.

## Key findings

- Reduces total system costs compared to decoupled control strategies.
- Achieves building energy savings and frequency regulation in simulations.
- Maintains performance under weather and load forecast uncertainties.

## Abstract

This paper puts forth 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. Simplified 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. The paper analytically establishes that the BtG integration yields a reduced total system cost in comparison with decoupled designs where grid and building operators determine their controls separately. The developed framework is tested on standard power networks that include thousands of buildings modeled using industrial data. 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 building model parameters. Finally, simulations indicate that the performance does not significantly worsen when there is uncertainty in the forecasted weather and base load conditions.