DYNAMIC ENERGY DISTRIBUTION IN SMART GRIDS ENABLED BY INTERNET OF THINGS SENSORS AND HYBRID TELECOM NETWORKS

1 online resource (177 pages) : PDF

2019

University of North Carolina at Charlotte

ABSTRACTRAVIL BIKMETOV. Dynamic Energy Distribution in Smart Grids Enabled by Internet of Things Sensors and Hybrid Telecom Networks. (Under the direction of DR. YASIN RAJA)Ability to manage energy consumption and generation is a major feature of the developed smart grid (SG) paradigm. Implementation of machine-to-machine (M2M) communications supported by hybrid telecom networks and Internet of Things (IoT) sensors plays an important role in dynamic energy management in SGs. As an innovative application of demand energy management, a resilient and secure layered architecture of automated charging station for unsupervised electric vehicles have been proposed. To demonstrate the feasibility of the architecture, an analytical framework has been developed using a bottom-up approach. The main goal of charging station’s operation is to optimize scheduling of electric vehicles for their charging service considering an efficient energy distribution. A divide-and-conquer strategy is employed for such scheduling optimization at the operational level real-time decision-making. A mixed-integer linear programming model is considered to solve this online optimal scheduling procedure. A mathematical model and corresponding simulation platform have been developed to perform a further analysis of charging station’s operation at various levels of decision-making hierarchy. An illustrative example of the scheduling solution and the developed simulation have been obtained by a Matlab code combined with the Gurobi optimization solver. Operation of the proposed autonomous charging station has been demonstrated based on different decisions on the number of sharable pumps in its tiers. In the demonstrated example the optimization of charging station’s operation can be performed by development of rules for dynamic pump sharing and profit-pricing models. As a part of SG, the proposed architecture of charging station relies on available dynamic load scheduling techniques and utilizes M2M communications supported by existing hybrid telecom network infrastructure.

doctoral dissertations

Computer science
Power resources
Operations research

Ph.D.

Autonomous Charging
Dynamic Load Scheduling
Electric Vehicles
Energy Distribution Optimization
Smart Grids

Optical Science & Engineering

Raja, Yasin
Kazi, Khurram

Lim, Churlzu
Fiddy, Michael
Chowdhury, Badrul
Hadzikadic, Mirsad

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2019.

This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). For additional information, see http://rightsstatements.org/page/InC/1.0/.

Copyright is held by the author unless otherwise indicated.

Bikmetov_uncc_0694D_12040

http://hdl.handle.net/20.500.13093/etd:1991