Demand Side Management (DSM) provides ancillary service to the electric grid by modifying customers electricity demand. Demand Response (DR) and Demand Flexibility (DF) programs from buildings are well-adopted ancillary services to reduce the peak demand in grids by altering the power consumption strategy. Heating, Ventilation and Air-Conditioning (HVAC) systems are one of the largest energy demands in commercial buildings. In addition, HVAC systems are flexible to provide DR service to the grid. In this study, two common configuration topologies of building integration with Energy Storage Systems (ESS) and renewables are considered. A real-time optimization framework based on Model Predictive Control (MPC) is designed to control the power flow from the grid, solar Photovoltaic (PV) panels, and ESS to a commercial building with HVAC systems. The MPC framework uses the inherent thermal mass storage of the building and the ESS as a means to provide DR. Deterministic and probabilistic analysis are studied to investigate the effectiveness of the proposed framework on Building-to-Grid (B2G) systems. Our deterministic results show that the proposed optimization and control framework for B2G systems can significantly reduce the maximum load ramp-rate of the electric grid to prevent duck-curve problems associated with increase in solar PV penetration into the grid. Based on probabilistic results, even under prediction uncertainties, electricity cost saving and ramp-rate reduction is achievable. The results show that this DR service does not affect the building indoor climate in a way noticeable to humans and its effect on the operational building costs is reduced. The B2G simulation testbed in this paper is based on the experimental data obtained from an office building, PV panels, and battery packs integrated with a three-phase unbalanced distribution test feeder. A Monte-Carlo simulation is carried out to account for uncertainties of the proposed method. Both deterministic and stochastic analyses show the effectiveness of the proposed predictive power flow control to decrease the building operation electricity costs and load ramp-rates. (C) 2017 Elsevier Ltd. All rights reserved.
