Model predictive energy scheduling for building microgrid

This paper presents a model predictive control (MPC) approach to economic scheduling for a building microgrid at California State University, Long Beach. We first propose a peak demand cost model to extend MPC-based microgrid energy scheduling. The corresponding objective function is then formulated as a mixed-integer linear programming (MILP) problem. The MPC framework is implemented into MILP optimization to construct MPC-MILP, which is formulated to compensate for uncertainties in day-ahead demand and photovoltaic (PV) power forecasts. Next, we provide the forecast modeling for demand and PV power to improve the accuracy of MPC-MILP. The simulation results show that the MPC-MILP optimization approach provides superior cost minimization over strategies such as MILP, which controls the microgrid subject to one calculation using day-ahead forecasts.