A Bi-Level Advanced Control Framework for Large-Scale Control of Buildings with System-Level Impact

Increased electricity consumption combined with new forms of generation is testing the reliability of our grid infrastructure. This work describes a method to improve the reliability of the grid through large-scale advanced building control. This paper develops a bi-level distributed control framework to shift the load of 153 buildings to achieve a system-level objective of tracking a power reference signal. This bi-level control is based on the previously-developed ANPV-MPC, a predictive controller that uses a Bayesian neural network to generate an accurate control model and adapt to changing conditions over time. By shifting the building electricity demand to better match the available power, the grid system supplying the buildings is more reliable as evidenced by the analysis of node voltages across an IEEE 13-bus distribution system. The proposed bi-level control framework tracks the system-level power reference with enough accuracy to regulate node voltages across the IEEE 13-bus distribution system within ANSI limits of +-5%. Additionally, the adaptive nature of ANPV-MPC allows each building across the system to adapt to changing conditions, further amplifying the system-level reliability.. Authors: Wald, Dylan [National Laboratory of the Rockies, Golden, CO (United States)] (ORCID:0000000297425288); Doronina, Olga [National Laboratory of the Rockies, Golden, CO (United States)]; Johnson, Kathryn [National Laboratory of the Rockies, Golden, CO (United States)] (ORCID:0000000197717718); King, Ryan [National Laboratory of the Rockies, Golden, CO (United States)]; Sinner, Michael [National Laboratory of the Rockies, Golden, CO (United States)] (ORCID:0000000187660711). DOE Contract: AC36-08GO28308. Subjects: 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; 97 MATHEMATICS AND COMPUTING; building modeling; Model predictive control