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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. A team led by researchers at North Carolina State University, with partners from the Renaissance Computing Institute (RENCI) at UNC Chapel Hill and the University of Illinois at Urbana-Champaign, are using NSF-funded 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. This process — which is currently only available using the GENI infrastructure — could someday evolve into the standard method for monitoring and troubleshooting smart grids.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.

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