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A model predictive control approach to economic scheduling for a building microgrid

Author: Edward Sanchez
Publisher: [Long Beach, California] : California State University, Long Beach, 2018.
Dissertation: M.S. California State University, Long Beach 2018
Series: California State University, Long Beach.; Master's thesis collection, Department of Electrical Engineering.
Edition/Format:   Thesis/dissertation : Thesis/dissertation : eBook   Computer File : English
Summary:
Abstract: This thesis presents a model predictive control (MPC) approach to economic scheduling for a building microgrid at California State University, Long Beach. First, components of the microgrid relevant to operational costs are modeled. Next, a peak demand cost model to extend MPC-based microgrid energy scheduling is proposed. The corresponding objective function is then formulated as a mixed-integer linear  Read more...
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Details

Genre/Form: Academic theses
Material Type: Thesis/dissertation, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Edward Sanchez
ISBN: 9780438208995 0438208994
OCLC Number: 1057365696
Description: 1 online resource (xii, 93 pages) : illustrations (some color)
Series Title: California State University, Long Beach.; Master's thesis collection, Department of Electrical Engineering.
Responsibility: by Edward Sanchez.

Abstract:

Abstract: This thesis presents a model predictive control (MPC) approach to economic scheduling for a building microgrid at California State University, Long Beach. First, components of the microgrid relevant to operational costs are modeled. Next, a peak demand cost model to extend MPC-based microgrid energy scheduling is proposed. The corresponding objective function is then formulated as a mixed-integer linear programming (MILP) problem. The MPC framework is implemented onto MILP optimization to construct MPC-MILP, which is formulated to compensate for uncertainties in day-ahead demand, photovoltaic (PV) power forecasts and system modeling. Next, the forecast modeling for demand and PV power to improve the accuracy of MPC-MILP is provided. The simulation results show that the MPC-MILP optimization approach provides superior cost minimization over other strategies such as MILP, which controls the microgrid subject to only one calculation using day-ahead forecasts.

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