Author ORCID Identifier

https://orcid.org/0000-0002-5515-2234

Semester

Summer

Date of Graduation

2023

Document Type

Dissertation

Degree Type

PhD

College

Statler College of Engineering and Mineral Resources

Department

Lane Department of Computer Science and Electrical Engineering

Committee Chair

Anurag Srivastava

Committee Member

Muhammad Choudhry

Committee Member

Katerina Goseva-Popstojanova

Committee Member

Sarika Khushalani-Solanki

Committee Member

Laurentiu Dan Marinovici

Abstract

The smart distribution grid achieves enhanced efficiency and flexibility through the utilization of advanced communication infrastructure, digital devices, and robust computation and control capabilities. Grid-edge devices including distributed energy resources (DERs) like solar photovoltaics (PV), battery storage systems, and intelligent electric loads like electric vehicles and smart appliances, are contributing to the growth of the smart distribution grid. This evolution is transforming the grid into a multifaceted network of interconnected devices and systems, enabling bidirectional data communication and power flows. However, these new layers of data integration and control in the smart grid introduce vulnerabilities to cyberattacks and accidental failures, posing significant threats to the critical infrastructure of the distribution grid.

To ensure the cybersecure and resilient operation of the smart distribution grid, it is imperative to comprehend the interdependencies between these devices and the grid itself. Additionally, monitoring and controlling are critical for system operation with edge devices. This research focuses on conducting cyber-power co-simulation studies to gain insights into the interplay between the cyber and physical layers of a smart distribution grid. A test-bed for cyber-power co-simulation has been developed, employing OpenDSS as the power network simulator and Mininet as the cyber network emulator. This test-bed integrates distributed coordination, cyber-attack modeling, and anomaly detection and mitigation techniques to evaluate the performance of various distributed control and optimization applications against cyberattacks.

Furthermore, this research has devised resiliency metrics to enable the monitoring of smart distribution grids equipped with edge devices and facilitate informed decision-making. These metrics include the IoT Trustability Score (ITS), which leverages a neural network with federated learning to assess the impact of IoT devices. Additionally, Primary level Node Resiliency (PNR) and Distribution System Resiliency (DSR) metrics have been developed, utilizing the cyber-physical attributes of a smart distribution grid. Finally, a resiliency-driven reconfiguration algorithm has been developed, incorporating demand response strategies to maximize the supply to critical loads. Multiple case studies have been conducted to validate these methodologies on different distribution test systems, yielding satisfactory results.

Embargo Reason

Publication Pending

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