Resilent Networks Minitrack

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This minitrack focuses on enhancing the reliability of future electric power infrastructure. Advanced technologies will require sophisticated methods for understanding how they can be incorporated into increasingly complex and dynamic infrastructure. We invite papers that examine issues of resiliency and secure interoperability of future grid systems, testbeds that will demonstrate the robustness of advanced technologies, and the associated computational and communication challenges associated with operating the power system.

Minitrack Chair:

Jeffery E. Dagle
Pacific Northwest National Laboratory
Email: jeff.dagle@pnnl.gov


Session 1: Interdependencies and Testbeds
Session Organizer and Chair: Mladen Kezunovic, kezunov@ece.tamu.edu and Ian Dobson, dobson@iastate.edu

Blackouts of the electric grid not only deprive our society of electricity, but also can impair other essential networked infrastructures such as gas, communications, and water. Moreover, failures in other infrastructures can, in turn, propagate into the electrical grid. Further adverse infrastructure interactions can emerge as the infrastructures degrade. The combined cascading failure of electricity and other infrastructures greatly increases the discomfort, danger, and economic loss to society. These complex interactions are known anecdotally and by some simulations, but there are considerable challenges in modeling and coordinating the important interactions (possibly including human, market, or economic factors) and quantifying the adverse interactions so that their risk can be estimated, mitigated and controlled. It is also important to verify and quantify these interactions in large-scale testbeds.

The objective of this session is to describe new methods to analyze and quantify electric, gas, communications or water network outages and their interactions with each other so that they can be better mitigated. Novel test approaches that are enabling physical and virtual testing of the interactions are needed. Papers describing new approaches to modeling and testing complex infrastructure failures in the context of complex systems, complex networks, and probabilistic analyses of cascades among interacting networks are encouraged. Papers using testbeds should state the hypotheses being tested and discuss the conclusions about the hypotheses. Joint papers from multiple organizations that have federated testbed facilities are welcome.


Session 2: Data Analytics and Decision Support
Session Organizer and Chair: Le Xie, le.xie@tamu.edu

Power system operators now have an unprecedented wealth of data, coming from a variety of sources such as demand response participants, synchrophasors, and enhanced SCADA systems, which if managed properly can provide opportunities to increase the efficiency, reliability and system performance of the power system. With the increased adoption of grid modernization, demand response programs, and distributed generation that is often renewable and intermittent system operators need to manage vast amounts of data, making big data analytics a requirement for future electrical energy systems.

This session invites technical papers presenting new approaches, methods, and applications related to big data analytics in planning, designing and operating electric energy systems. This session will address some of the challenges and opportunities associated with big data in electrical energy systems, coming from a variety of sources such as behavior data in demand response, PMUs, weather, and enhanced SCADA systems.

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Recent Submissions

Now showing 1 - 5 of 7
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    When are Decentralized Infrastructure Networks Preferable to Centralized Ones?
    ( 2017-01-04) Hines, Paul ; Blumsack, Seth ; Schlaepfer, Markus
    Many infrastructure networks, such as power, water, and natural gas systems, have similar properties governing flows. However, these systems have distinctly different sizes and topological structures. This paper seeks to understand how these different features can emerge from relatively simple design principles. Specifically, we work to understand the conditions under which it is optimal to build small decentralized network infrastructures, such as a microgrid, rather than centralized ones, such as a large high-voltage power system. While our method is simple it is useful in explaining why sometimes, but not always, it is economical to build large, interconnected networks and in other cases it is preferable to use smaller, distributed systems. The results indicate that there is not a single set of infrastructure cost conditions that cause a transition from centralized networks being optimal, to decentralized architectures. Instead, as capital costs increase network sizes decrease gradually, according to a power-law. And, as the value of reliability increases, network sizes increase abruptly---there is a threshold at which large, highly interconnected networks are preferable to decentralized ones.
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    Robust Reconfiguration of A Distribution System
    ( 2017-01-04) Moradzadeh, Benyamin ; Liu, Guodong ; Tomsovic, Kevin
    In this paper, a robust reconfiguration approach based on Mixed Integer Programming (MIP) is proposed to minimize loss in distribution systems. A Depth-First Search (DFS) algorithm to enumerate possible loops provides radiality constraint. This provides a general solution to the radiality constraint for distribution system reconfiguration/expansion problems. Still, imprecision and ambiguity in net loads, i.e., load minus renewable generation, due to lack of sufficient measurements and high utilization of demand response programs and renewable resources, creates challenges for effective reconfiguration. Deterministic optimization of reconfiguration may no lead to optimal/feasible results. Two methods to address these uncertainties are introduced in this paper: one, based on a stochastic MIP (SMIP) formulation and two, based on a fuzzy MIP (FMIP) formulation. Case studies demonstrate the robustness and efficiency of the proposed reconfiguration methods.
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    Reflections on Twenty Years of Electric Power Research at HICSS
    ( 2017-01-04) Thomas, Robert
    The Electric Power Track activities at HICSS began twenty years ago. This is an account of its history, its focus, and its impact over those years.
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    Online Detection of False Data Injection Attacks to Synchrophasor Measurements: A Data-Driven Approach
    ( 2017-01-04) Wu, Meng ; Xie, Le
    This paper presents an online data-driven algorithm to detect false data injection attacks towards synchronphasor measurements. The proposed algorithm applies density-based local outlier factor (LOF) analysis to detect the anomalies among the data, which can be described as spatio-temporal outliers among all the synchrophasor measurements from the grid. By leveraging the spatio-temporal correlations among multiple time instants of synchrophasor measurements, this approach could detect false data injection attacks which are otherwise not detectable using measurements obtained from single snapshot. This algorithm requires no prior knowledge on system parameters or topology. The computational speed shows satisfactory potential for online monitoring applications. Case studies on both synthetic and real-world synchrophasor data verify the effectiveness of the proposed algorithm.
  • Item
    Automated Anomaly Detection in Distribution Grids Using uPMU Measurements
    ( 2017-01-04) Jamei, Mahdi ; Scaglione, Anna ; Roberts, Ciaran ; Stewart, Emma ; Peisert, Sean ; McParland, Chuck ; McEachern, Alex
    The impact of Phasor Measurement Units (PMUs) for providing situational awareness to transmission system operators \ has been widely documented. Micro-PMUs (uPMUs) \ are an emerging sensing technology that can provide similar \ benefits to Distribution System Operators (DSOs), enabling a \ level of visibility into the distribution grid that was previously \ unattainable. In order to support the deployment of these \ high resolution sensors, the automation of data analysis and \ prioritizing communication to the DSO becomes crucial. In this \ paper, we explore the use of uPMUs to detect anomalies on \ the distribution grid. Our methodology is motivated by growing \ concern about failures and attacks to distribution automation \ equipment. The effectiveness of our approach is demonstrated \ through both real and simulated data.