Date of Award

1-1-2016

Document Type

Thesis

Degree Name

M.S.

Department

Electrical Engineering

First Advisor

Amin Khodaei

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.

Provenance

Recieved from ProQuest

Rights holder

Rozhin Eskandarpour

File size

60 p.

File format

application/pdf

Language

en

Discipline

Electrical engineering

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