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...

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Bibliographic Details
Published in:2017 North American Power Symposium (NAPS) pp. 1 - 6
Main Authors: Sanchez, Edward, Nazari, Masoud H.
Format: Conference Proceeding
Language:English
Published: IEEE 01.09.2017
Subjects:
Buildings
energy management
Energy storage
Microgrids
mixed integer linear programming (MILP)
model predictive control (MPC)
Optimal scheduling
power management
Predictive models
Uncertainty
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Summary: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.
DOI:10.1109/NAPS.2017.8107299