Date of Award

1-1-2016

Document Type

Masters Thesis

Degree Name

M.S.

Organizational Unit

Daniel Felix Ritchie School of Engineering and Computer Science

First Advisor

Amin Khodaei, Ph.D.

Second Advisor

Kimon Valavanis

Third Advisor

Wenzhong Gao

Fourth Advisor

Ryan Elmore

Keywords

Extreme event, Machine learning, Microgrids, Power system resilience

Abstract

Power system management in response to extreme events is one the most important operational aspects of power systems. In this thesis, a novel Event-driven Security Constrained Unit Commitment (E-SCUC) model and a statistical method, based on regression and data mining to estimate the system components outages, are proposed. The proposed models help consider the simultaneous outage of several system components represented by an N-1-m reliability criterion and accordingly determine the proper system response. In addition, an optimal microgrid placement model with the objective of minimizing the cost of unserved energy to enhance power system resilience is proposed.

The numerical simulations on the standard IEEE 30-bus and IEEE 118-bus test systems exhibit the merits and applicability of the proposed E-SCUC model, as well as the advantages of the data mining approach in estimating component outage, and the effectiveness of the optimal microgrid placement in ensuring an economic operation under normal conditions and a resilient operation under contingency cases.

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Rights Holder

Rozhin Eskandarpour

Provenance

Received from ProQuest

File Format

application/pdf

Language

en

File Size

60 p.

Discipline

Electrical Engineering

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