期刊
IEEE ACCESS
卷 9, 期 -, 页码 24629-24636出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3057458
关键词
Microgrids; Buildings; Smart buildings; Energy storage; Electric vehicles; Production; Predictive control; Cooperative smart buildings; peak loads shaving; team decision problem; networked microgrids; model predictive control; solar PV
资金
- Qatar National Library (QNL)
The paper presents a scheduling framework based algorithm for reducing peak loads in a team of cooperating microgrids powered by smart buildings. The algorithm optimally controls the operation of each microgrid to reduce peak loads and maintain a high quality of service for EVs owners. The developed predictive model is implemented as a smart energy management based high-level control of the TCM.
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.
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