Abstract
This paper presents a practical and efficient scheduling optimization framework for reducing/shaving the peak load in an institutional building integrated microgrid. The proposed microgrid is designed to be equipped with a roof-top solar PV, battery energy storage system, loads, and advanced metering and communication infrastructure. The microgrid is designed to support the institutional building to reduce/shave the peak load in case of occurrence; otherwise, the microgrid will serve to charge both energy storage system and the electric vehicles (EVs). The main objective is to develop an optimization framework embedded in a model predictive control (MPC) scheme to control the operation of the microgrid and manage the power flows exchanges ensuring a high quality of service to the EVs owners. In this paper, we take advantages of vehicle-to-building (V2B) concept to support the microgrid, which enables the EVs to have a bidirectional power flows in the microgrid once they are connected and enhancing the efficiency and performance of the microgrid. The developed predictive model is implemented as a smart energy management based algorithm to reduce/shave the peak load and satisfy the EVs power demands.
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