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This paper investigates the application of model predictive control (MPC) method in a MicroGrid with distributed energy resources (DERs), including distributed generators, energy storage and demand response to achieve higher penetration of renewable energy. A MicroGrid is an aggregation of network that is connected to a centralized grid and can be operated autogenously. MPC utilizes a simulation model to make decisions on the amount of power that the MicroGrid should draw from the connected main grid and each DER respectively, in a way that the economical cost is minimized and all operational constraints are satisfied. The potential of MPC is shown by simulations on an IEEE test feeder modeled on the OpenDSS simulator. [ABSTRACT FROM PUBLISHER]

This paper presents a model reduction method based on balanced realization for thermal and power models of buildings. System identification is firstly performed to obtain high-order state-space models. The purpose of model reduction is to simplify the model structure while preserving the major input-output relations, so as to lower the computational cost in the subsequent model predictive control (MPC) scheme. An economic objective function is designed to minimize the energy and demand charges of building energy systems. The effectiveness of the presented method is shown by simulation, and it is shown that the control performance is not significantly affected by using reduced models. [ABSTRACT FROM PUBLISHER]