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Research Papers

Robust Topology Design of Complex Infrastructure Systems

[+] Author and Article Information
Joseph R. Piacenza

Mechanical Engineering,
California State University Fullerton,
E412, 800 N. State College Boulevard,
Fullerton, CA 92831
e-mail: jpiacenza@fullerton.edu

Scott Proper

Mechanical Engineering,
Oregon State University,
Dearborn Hall 102,
Corvallis, OR 97331
e-mail: scottproper99@gmail.com

Mir Abbas Bozorgirad

Optym,
7600 NW 5 Place,
Gainesville, FL 32607
e-mail: abbas.bozorgirad@gmail.com

Christopher Hoyle

Mechanical Engineering,
Oregon State University,
Rogers Hall 418,
Corvallis, OR 97331
e-mail: chris.hoyle@oregonstate.edu

Irem Y. Tumer

Mechanical Engineering,
Oregon State University,
Covell Hall 116,
Corvallis, OR 97331
e-mail: irem.tumer@oregonstate.edu

Manuscript received December 2, 2016; final manuscript received March 1, 2017; published online March 24, 2017. Assoc. Editor: Konstantin Zuev.

ASME J. Risk Uncertainty Part B 3(2), 021006 (Mar 24, 2017) (10 pages) Paper No: RISK-16-1143; doi: 10.1115/1.4036152 History: Received December 02, 2016; Revised March 01, 2017

Optimizing the topology of complex infrastructure systems can minimize the impact of cascading failures due to an initiating failure event. This paper presents a model-based design approach for the concept-stage robust design of complex infrastructure systems, as an alternative to modern network analysis methods. This approach focuses on system performance after cascading has occurred and examines design tradeoffs of the resultant (or degraded) system state. In this research, robustness is classically defined as the invariability of system performance due to uncertain failure events, implying that a robust network has the ability to meet minimum performance requirements despite the impact of cascading failures. This research is motivated by catastrophic complex infrastructure system failures such as the August 13th Blackout of 2003, highlighting the vulnerability of systems such as the North American power grid (NAPG). A mathematical model was developed using an adjacency matrix, where removing network connections simulates uncertain failure events. Performance degradation is iteratively calculated as failures cascade throughout the system, and robustness is measured by the lack of performance variability over multiple cascading failure scenarios. Two case studies are provided: an extrapolated IEEE 14 test bus and the Oregon State University (OSU) campus power network. The overarching goal of this research is to understand key system design tradeoffs between robustness, performance objectives, and cost, and explore the benefits of optimizing network topologies during the concept-stage design of these systems (e.g., microgrids).

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Figures

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Fig. 1

Parameter diagram for the NAPG

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Fig. 2

Visual interpretation between the performance-based and robust solution

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Fig. 3

IEEE 14 test case [38]

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Fig. 4

Visual representation of the 3-IEEE 14 subnetworks (corresponding to Fig. 5)

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Fig. 5

Adjacency matrix visualization representing subnetworks and interconnections (corresponding to Fig. 4)

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Fig. 6

Outline of topology design and fault implementation method

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Fig. 7

Pareto solutions for the 3-IEEE 14 network using the exhaustive search approach

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Fig. 8

Pareto solutions for the OSU campus microgrid using the ten-line removal method

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Fig. 9

This solution (using the ten-line removal method) is both lower-cost and more robust than the current OSU campus network. Blue, purple, and red lines respectively indicate low-, medium-, and high-voltage lines. Line width is proportional to power flow or load at the nodes. Green nodes indicate active power generators, and yellow nodes indicate reactive power generators.

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