Date of Award:

2016

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Electrical and Computer Engineering

Advisor/Chair:

Jacob H. Gunther

Abstract

Advanced Metering Infrastructure (AMI) is one of the most important components of smart grid (SG) which aggregates data from smart meters (SMs) and sends the collected data to the utility center (UC) to be analyzed and stored. In traditional centralized AMI architecture, there is one meter data management system to process all gathered information in the UC, therefore, by increasing the number of SMs and their data rates, this architecture is not scalable and able to satisfy SG requirements, e.g., delay and reliability. Since scalability is one of most important characteristics of AMI architecture in SG, we have investigated the scalability of different AMI architectures and proposed a scalable hybrid AMI architecture. We have introduced three performance metrics. Based on these metrics, we formulated each AMI architecture and used a genetic-based algorithm to minimize these metrics for the proposed architecture. We simulated different AMI architectures for five demographic regions and the results proved that our proposed AMI hybrid architecture has a better performance compared with centralized and decentralized AMI architectures and it has a good load and geographic scalability.

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