Augmented optimal control for buildings under high penetration of solar photovoltaic generation

This paper investigates the use of a collection of dispatchable heating, ventilation and air conditioning (HVAC) loads to absorb the slow (low-frequency) fluctuations in solar photovoltaic (PV) generation. We first formulate the building's thermal dynamics and the associated optimization problem. An optimization formulation is then designed based on traditional model predictive control algorithm, which provides the baseline performance. We then develop an augmented optimal control strategy to improve the solar tracking performance. To guarantee quality of service, in a fleet of residential/commercial buildings, a quadratic optimization problem is formulated to compute the optimal schedule for a given set of HVAC loads, while maintaining occupants comfort and PV generation constraints. Finally, we demonstrate the performance of the present algorithms through simulation, which validates that the proposed mechanism is able to achieve good PV tracking performance as well as obtain a minimal capacity of the required energy storage devices.