Robust Optimal Demand Response of Energy-efficient Commercial Buildings

Commercial buildings show a great potential for participating in demand response (DR) programs due to their extensive use of energy-intensive flexible loads such as heating, ventilation, and air conditioning (HVAC) systems. The capability of HVAC systems for responding to automated control and intelligent energy scheduling strategies makes them essential flexibility sources in commercial DR. This capability, in combination with the use of local energy storage systems (ESSs), can substantially enhance the energy management performance. This paper proposes a novel robust model predictive control (MPC) approach for online energy scheduling of multiple commercial buildings comprising individual HVAC systems, ESSs, and non-controllable loads. The proposed approach aims at minimizing the total expected energy costs while ensuring the occupants' thermal comfort under the presence of uncertainties in electricity market pricing. Moreover, operational constraints of the power grid and buildings' components are considered. To this aim, we firstly formulate the energy scheduling problem as a min-max robust optimization problem which is transformed into a mixed-integer linear programming problem using duality. Next, we apply MPC to solve the robust optimization problem iteratively based on the receding horizon concept. Finally, we assess the performance of the proposed approach on a simulated realistic case study.