Occupancy Driven Control and Optimization of Energy Cost for Smart Buildings and Communities

This dissertation proposes and develops an occupancy-based control and optimization framework for buildings-to-grid integration that reduces energy consumption and cost for smart buildings and communities. A novel occupancy model is used for building control. Various models of buildings systems are designed based on ideal conditioning, small or medium building conditioning, and large building conditioning. Distributed energy resources, such as battery storage and Photovoltaic generation, are also modeled and controlled. A hierarchical building Model Predictive Control is designed for large scale simulation. A high-level grid distribution network is further integrated via the decentralized control strategy to solve the optimal power flow problem and provide frequency regulation in grid operation. The final decentralized utility-scale BtG integrations with building occupancy, building air conditioning systems, battery energy storages, and photovoltaics generations, are simulated on different IEEE grid configurations. The final results show that integrated buildings-to-grid control can achieve up to 26.74% total operation cost saving.