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Smarter smart grids

ExoGENI testbed helps researchers develop methods for monitoring, controlling and troubleshooting power grids

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Researchers are developing tools for monitoring and responding to problems in smart grids.
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March 18, 2015

Smart grids--power grids that adapt to changes in demand and reconfigure as needed to avoid overloads and other problems--can reduce energy costs, help avoid blackouts and deter cyber attacks.

They also pose new challenges. As power generation--and the communication and information processing associated with it--shifts from centralized power stations to distributed, heterogeneous systems, massive amounts of sensor data from stations must be transmitted efficiently and effectively analyzed in real time.

A team led by researchers at North Carolina (NC) State University, with partners from the Renaissance Computing Institute (RENCI) at University of North Carolina at Chapel Hill and the University of Illinois at Urbana-Champaign, are using cloud computing resources to analyze smart grid data from thousands of sensors, called phasor measurement units, or PMUs.

The PMUs are distributed across the transmission grid, and connect a wide range of energy generating plants, including wind turbines and solar panels.

The research is funded by the National Science Foundation's (NSF) Cyber-Physical Systems program, and leverages resources developed through another NSF project called ExoGENI, part of the Global Environment for Network Innovations, or GENI.

Led by RENCI, the ExoGENI testbed combines computation, storage, and network capabilities with open cloud computing and dynamic circuit fabrics to address complex scientific and network engineering problems.

Through ExoGENI, the researchers linked real-time sensor data to on-demand virtual computing resources at ExoGENI nodes across the U.S. Sensors collected as many as 120 data points per second; high-speed networks with guaranteed bandwidth connected the data to computing resources at many sites; each site provisioned a slice of virtual machines, or VMs; and the VMs ran algorithms to analyze and visualize the data in real time.

This process--which is currently only available using the GENI infrastructure--could someday evolve into the standard method for monitoring and troubleshooting smart grids.

"We want to show how processing, analyzing, and monitoring power system data can be done using a distributed architecture instead of traditional centralized methods," said Aranya Chakrabortty, an assistant professor in the NC State department of electrical and computer engineering and principal investigator on the smart grid project.

The project launched in 2013 as an experimental system for monitoring and analyzing the status of power grids in real time. At the 2013 US Ignite Application Summit, the researchers demonstrated a proof-of-concept experiment showing how GENI can be used to transmit sensor data. At the Smart Future 2015 Summit, they will implement much more complex algorithms that allow sensor data to be used to monitor grid instabilities.

The work was recognized at the 2013 and 2014 US Ignite Application Summits for best application in the energy and sustainability sector.

"The advancements in the science of distributed sensing, communications, and cloud computing architecture, demonstrated by ExoGENI, will also play a critical role in building smarter transportation infrastructures and efficient manufacturing systems," said NSF Program Director, Kishan Baheti.

The team is currently in the process of extending the testbed to a completely closed-loop sensing and control system for wide-area control of power grids. In collaboration with the University of Southern California's Information Sciences Institute, the team is launching a project to detect and initiate action in cases of cyber attacks on the grid.

"As the number of phasor measurement units in the North American grid grows exponentially over the next five years, such a distributed data processing architecture will become imperative for monitoring and control, and eventually for initiating actions to solve problems," Chakrabortty said.

--  Karen Green, Renaissance Computing Institute 919-445-9648 kgreen@renci.org
--  Aaron Dubrow, NSF (703) 292-4489 adubrow@nsf.gov

Investigators
Aranya Chakrabortty
Yufeng Xin

Related Institutions/Organizations
North Carolina State University
University of Illinois Urbana-Champaign
Renaissance Computing Institute at UNC-Chapel Hill

Locations
Raleigh , North Carolina
Champaign , Illinois
Chapel Hill , North Carolina

Related Programs
Cyber-Physical Systems
Energy, Power, Control and Networks

Related Awards
#1001845 A Measurement based Framework for Dynamic Equivalencing of Large-Scale Power Systems using Synchrophasors
#1062811 A Measurement based Framework for Dynamic Equivalencing of Large-Scale Power Systems using Synchrophasors
#1230848 SEP Collaborative: Integrating Heterogeneous Energy Resources for Sustainable Power Networks - A Systems Approach
#1040161 MRI - Development of Real Time Simulator for Smart Grid Systems Integrated with Distributed Renewable Energy Sources
#1054394 CAREER: Wide-Area Control of Large Power Systems Using Distributed Synchrophasors: Where Network Theory Meets Power System Dynamics
#1329780 CPS: Synergy: Collaborative Research: Distributed Asynchronous Algorithms and Software Systems for Wide-Area Monitoring of Power Systems

Years Research Conducted
2010 - 2017

Total Grants
$3,402,598

Related Websites
US Ignite: https://us-ignite.org/
Renaissance Computing Institute: http://renci.org/
GENI (Global Environment for Network Innovations): http://www.geni.net/

graphic illustration showing a buildings and lights
Researchers linked real-time sensor data to on-demand virtual computing resources across the nation.
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Data flow diagram of smart grid monitoring system using ExoGENI
Data flow diagram of smart grid monitoring system using ExoGENI.
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