Model predictive control energy dispatch to optimize renewable penetration for a microgrid with battery and thermal storage

Abstract

As intermittent renewable energy becomes a larger fraction of the overall energy mix in the US, algorithms that efficiently utilize this energy are necessary. In this work, a model predictive control (MPC) method is developed to perform real-time optimization to maximize the power delivery from a renewable supply to a building. An isolated microgrid scenario is considered, consisting of a mixed-use residential and commercial building, renewable power supply, battery storage, hot water tank thermal storage, and a backup supply. The MPC strategy utilizes predictions of the building's electrical and hot water loads, on an hourly basis, along with predictions of the output from the renewable supply. At each time step, these predictions are used to create an optimized power dispatching strategy between the microgrid elements, to maximize renewable energy use. For a fixed size microgrid, the performance of this MPC approach is compared to the performance of a simple non-predictive dispatching strategy.