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Development of an Adaptive Restoration Tool For a Self-Healing Smart Grid

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Date Issued:
2017
Abstract/Description:
Large power outages become more commonplace due to the increase in both frequency and strength of natural disasters and cyber-attacks. The outages and blackouts cost American industries and business billions of dollars and jeopardize the lives of hospital patients. The losses can be greatlyreduced with a fast, reliable and flexible restoration tool. Fast recovery and successfully adapting to extreme events are critical to build a resilient, and ultimately self-healing power grid. This dissertation is aimed to tackle the challenging task of developing an adaptive restoration decisionsupport system (RDSS). The RDSS determines restoration actions both in planning and real-time phases and adapts to constantly changing system conditions. First, an efficient network partitioning approach is developed to provide initial conditions for RDSS by dividing large outage network into smaller islands. Then, the comprehensive formulation of RDSS integrates different recovery phases into one optimization problem, and encompasses practical constraints including AC powerflow, dynamic reserve, and dynamic behaviors of generators and load. Also, a frequency constrained load recovery module is proposed and integrated into the RDSS to determine the optimal location and amount of load pickup. Next, the proposed RDSS is applied to harness renewable energy sources and pumped-storage hydro (PSH) units by addressing the inherent variabilities and uncertainties of renewable and coordinating wind and PSH generators. A two-stage stochastic and robust optimization problem is formulated, and solved by the integer L-shaped and column-and-constraintsgeneration decomposition algorithms. The developed RDSS tool has been tested onthe modified IEEE 39-bus and IEEE 57-bus systems under different scenarios. Numerical results demonstrate the effectiveness and efficiency of the proposed RDSS. In case of contingencies or unexpected outages during the restoration process, RDSS can quickly update the restoration plan and adapt to changing system conditions. RDSS is an important step toward a self-healing power grid and its implementation will reduce the recovery time while maintaining system security.
Title: Development of an Adaptive Restoration Tool For a Self-Healing Smart Grid.
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Name(s): Golshani, Amir, Author
Sun, Wei, Committee Chair
Qu, Zhihua, Committee CoChair
Vosoughi, Azadeh, Committee Member
Zhou, Qun, Committee Member
Zheng, Qipeng, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2017
Publisher: University of Central Florida
Language(s): English
Abstract/Description: Large power outages become more commonplace due to the increase in both frequency and strength of natural disasters and cyber-attacks. The outages and blackouts cost American industries and business billions of dollars and jeopardize the lives of hospital patients. The losses can be greatlyreduced with a fast, reliable and flexible restoration tool. Fast recovery and successfully adapting to extreme events are critical to build a resilient, and ultimately self-healing power grid. This dissertation is aimed to tackle the challenging task of developing an adaptive restoration decisionsupport system (RDSS). The RDSS determines restoration actions both in planning and real-time phases and adapts to constantly changing system conditions. First, an efficient network partitioning approach is developed to provide initial conditions for RDSS by dividing large outage network into smaller islands. Then, the comprehensive formulation of RDSS integrates different recovery phases into one optimization problem, and encompasses practical constraints including AC powerflow, dynamic reserve, and dynamic behaviors of generators and load. Also, a frequency constrained load recovery module is proposed and integrated into the RDSS to determine the optimal location and amount of load pickup. Next, the proposed RDSS is applied to harness renewable energy sources and pumped-storage hydro (PSH) units by addressing the inherent variabilities and uncertainties of renewable and coordinating wind and PSH generators. A two-stage stochastic and robust optimization problem is formulated, and solved by the integer L-shaped and column-and-constraintsgeneration decomposition algorithms. The developed RDSS tool has been tested onthe modified IEEE 39-bus and IEEE 57-bus systems under different scenarios. Numerical results demonstrate the effectiveness and efficiency of the proposed RDSS. In case of contingencies or unexpected outages during the restoration process, RDSS can quickly update the restoration plan and adapt to changing system conditions. RDSS is an important step toward a self-healing power grid and its implementation will reduce the recovery time while maintaining system security.
Identifier: CFE0007284 (IID), ucf:52169 (fedora)
Note(s): 2017-12-01
Ph.D.
Engineering and Computer Science, Electrical Engineering and Computer Engineering
Doctoral
This record was generated from author submitted information.
Subject(s): Power system restoration -- MILP optimization -- robust optimization -- stochastic optimization -- wind uncertainty -- dynamic reserve -- load recovery.
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0007284
Restrictions on Access: campus 2021-06-15
Host Institution: UCF

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