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

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Bikmetov, R. (2019). DYNAMIC ENERGY DISTRIBUTION IN SMART GRIDS ENABLED BY INTERNET OF THINGS SENSORS AND HYBRID TELECOM NETWORKS. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

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.

Details
Author

Bikmetov, Ravil

Title

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

Physical Description

1 online resource (177 pages) : PDF

Date

2019

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

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.

Genre

doctoral dissertations

Subjects--Topics

Computer science
Power resources
Operations research

Degree

Ph.D.

Keywords

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

Subject Area

Optical Science & Engineering

Advisor(s)

Raja, Yasin
Kazi, Khurram

Committee Members

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

Degree Note

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

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Rights Holder Information

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Identifier

Bikmetov_uncc_0694D_12040

Permalink

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