An Intelligent Energy Management System for Charging of Plug-in Hybrid Electric Vehicles at a Municipal Parking Deck

energy management system;optimization;phev;smart grid;control

Kulshrestha, Preetika ; Dr. Mo-Yuen Chow, Committee Chair,Dr. James J. Brickley, Committee Member,Dr. Hamid Krim, Committee Member,Kulshrestha, Preetika ; Dr. Mo-Yuen Chow ; Committee Chair ; Dr. James J. Brickley ; Committee Member ; Dr. Hamid Krim ; Committee Member

University:North Carolina State University

关键词: energy management system;    optimization;    phev;    smart grid;    control;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/474/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

There is a need to address potential problems due to the emergence of technologies that will affect the utility industry in a time horizon of less than 20 years. One such technology is the plug-in hybrid electric vehicle (PHEV); the emergence of these vehicles in the marketplace poses a potential threat to the existing power grid. With a large number of these vehicles ‘plugged-in’ for charging, in the absence ofcontrol over the power drawn, the additional load can result in grid instabilities and disruptions. As a solution to alleviate such a situation and to allow for smooth integration of PHEVs into the grid, an “intelligent energy management systemâ€Â(iEMS) is proposed in this thesis. The iEMS intelligently allocates power to the vehicle battery chargers through real time monitoring and control, to ensure optimal usage of available power, charging time and grid stability. The research presented here provides the conceptualization of the system architecture and the definition of its components, their attributes and interactions. A Simulink based simulator incorporating the dynamics of the real world scenario at a municipal parking deck with random plug-in/out times and varying initial states of charge is presented. A mathematical framework is provided for developing the iEMS algorithm for the optimal power allocation strategy under utility power constraints; taking into consideration the vehicle battery parameters and user preferences. The formulation and solution of the optimization is also proposed for a chosen objective function followed by the presentation of simulation results. The thesis concludes with the description of an experimental setup consisting of a Labview based GUI along with ZigBee communication nodes which is a first step towards validating the system performance in a real-world deployment.