Abstract

Despite tremendous efforts on increasing power system reliability, outages and blackouts are inevitable. These losses can be greatly reduced by fast and reliable restoration mechanisms. Largescale distribution networks and increasing penetration of distributed energy resources (DERs) make restoration one of the most challenging problems in power systems. The prevailing centralized restoration scheme is costly, limited by information privacy of entities, unreliable due to dependence on central units, and might suffer from single-point failures. In this dissertation, a fully distributed restoration framework based on the alternating direction method of multipliers (ADMM) is proposed to address these challenges. Accordingly, the restoration problem is decomposed into small subproblems for multiple agents in the network, and solved by each of them through exchanging limited information within neighboring agents in an iterative procedure. First, a distributed load restoration based on ADMM in unbalanced active distribution networks is developed. During blackout events, this method provides step-by-step restorative actions for various components in the networks within a distributed framework. Next, this distributed framework is further extended to address the remaining challenge of binary decision variables for network reconfiguration or load pickup, which can disrupt the convergence of many distributed optimization methods due to the non-convexity nature. The proposed method can achieve a high-quality solution for the problem and show very promising results in implementations on large-scale networks with various operational constraints and scenarios. Finally, the distributed ADMM-based restoration framework is applied to consider the mutual impacts between transmission and distribution (T&D) networks as independent agents. It decomposes the problem into subproblems for each T&D operator within an iterative solution procedure while exchanging boundary bus information for optimal restoration procedures. The distributed restoration framework demonstrates effective performance on several IEEE test networks.

Notes

If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu

Graduation Date

2021

Semester

Spring

Advisor

Sun, Wei

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Electrical and Computer Engineering

Degree Program

Electrical Engineering

Format

application/pdf

Identifier

CFE0008935; DP0026214

URL

https://purls.library.ucf.edu/go/DP0026214

Language

English

Release Date

11-15-2022

Length of Campus-only Access

1 year

Access Status

Doctoral Dissertation (Open Access)

Download

Share

COinS