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 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.
This project integrates physical power grids with distributed cyberinfrastructure by developing distributed estimation and control algorithms for monitoring and controlling large power systems. The researchers also conduct experiments using advanced networking to connect smart grid data from thousands of sensors—called phasor measurement units, or PMUs—to cloud computing resources for analysis and visualization. The PMUs are distributed across the transmission grid, and connect a wide range of energy generating plants, including wind turbines and solar panels. Sensors collect as many as 120 data points per second; high-speed networks with guaranteed bandwidth teransport the data to computing resources at many sites; each site provisions a slice of virtual machines (VMs); and the VMs run algorithms to analyze and visualize the data in real time.
RENCI’s networking research group uses the ExoGENI test bed to link real-time sensor data to on-demand virtual computing resources at ExoGENI nodes across the U.S. ExoGENI is part of the National Science Foundation’s Global Environment of Network Innovations (GENI) project and connects scientific resources at GENI sites nationwide. The researchers first used the ExoGENI test bed to show how sensor data can be quickly transported for analysis. In 2015, they began using more complex algorithms to demonstrate that sensor data can be used to monitor grid instabilities.
- Aranya Chakrabortty, North Carolina State University (Principal Investigator)
- Ilya Baldin
- Yufeng Xin
National Science Foundation Cyber-Physical Systems program