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In this paper, a model predictive control (MPC) based scheduling method for a building microgrid was proposed. Firstly, a dynamic model to simulate heating/cooling energy consumption for a building was proposed. The model consists of several transient energy balance equations for external walls and internal air, in which the convective heat transfer, conductive heat transfer and heat storage in the heat transfer process are considered. The proposed model has been implemented utilizing the Simulink/MATLAB platform. Then, a MPC based scheduling method for the building microgrid considering the dynamic thermal characteristics of the building was proposed. The MPC method aims to minimize the total energy consumption of the building microgrid by combining the model predictive and the short-term control, and guarantees the customer temperature comfort level at the same time. Two comparative cases are presented to verify the effectiveness of the proposed MPC method. Numerical studies demonstrate that the proposed MPC based scheduling method can not only contribute to cost reduction, but also contribute to better renewables hosting capability for the building microgrid.

This paper proposes a simplified microturbine (MT) model which allows for dynamic heat and power output prediction. Considering the time-scale difference of various dynamic processes occuring within MTs, the electromechanical subsystem is treated as a fast quasi-linear system while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A subspace model identification method is utilized to capture dominant dynamics and predict outputs of the electromechanical subsystem. For the thermo-mechanical process, a fast recursive algorithm assisted radial basis function model is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 MT show that the proposed modeling method can well capture the system dynamics and produce a good model to predict the interrelated heat and electricity outputs.

In this paper, a building based virtual storage system (VSS) model is developed by utilizing the heat storage characteristics of the building. Then, the VSS is integrated to the optimal scheduling model of the building microgrid (BM) for operation cost reduction. The indoor temperature of the building is adjusted within the customer temperature comfort level range to manage the charging/discharging power of the VSS. Finally, two different types of BM cases under the summer refrigeration scenario are carried out to demonstrate the effectiveness of the proposed optimal scheduling approach. Numerical studies demonstrate that the proposed optimal scheduling approach can make full use of the potential of the VSS and furthermore contribute to the operation cost reduction of the BM, while guarantee the customer temperature comfort level at the same time.