LAUSR

This paper presents a scheduling framework based algorithm for reducing/shaving the peak loads in a team of cooperating microgrids (TCM) powered smart buildings taking advantages of vehicle-to-building (V2B) concept and operational flexibilities of electric vehicles (EVs). Each microgrid includes a roof-top solar PV, energy storage system, EVs, loads, and advanced metering and communication infrastructure. The main objective is to formulate a constrained optimization problem embedded in a model predictive control (MPC) scheme to optimally control the operation of each microgrid to reduce/shave the peak load in case of occurrence, optimizing the power flows exchanges and energy storages, while ensuring a high quality of service to the EVs owners in each microgrid. The developed predictive model is implemented as a smart energy management based high-level control of the TCM to reduce/shave the peak loads and satisfy the EVs power demands through a coordination of the power exchanges between the microgrids. The algorithm has been tested through a case study to demonstrate its performance and effectiveness.