Y10. Distributed Grid Intelligence

Overview

If the SST is the heart of FREEDM, then DGI is the brain. DGI utilizes an energy management system based on fog computing — an architecture that distributes resources and services between the cloud and internet-enabled devices — to manage the cooperation of power electronics on the grid through localized intelligence. Consensus-based algorithms handle energy management, provide Volt/VAR support and fault detection, and manage the configuration when new sources join the system. Bringing computation and communication closer to the distribution system resolves issues with latency, network bandwidth and geographic focus.

Method

The FREEDM concept of cyber-physical security is an inherent part of DGI’s algorithms. As a fog, DGI supports multiple interacting security domains secured by common data and physical information flows. Static and dynamic invariants are evaluated among components to create this trusted system.

DGI’s common code has been installed on the Green Energy Hub testbed and Hardware-in-the-Loop testbeds at most FREEDM partner universities. DGI integrates with Open DSS, RTDS and PSCAD.

Results

In year 10, researchers migrated Volt/VAR control algorithms to the Green Energy Hub testbed. The scheme was extended to include traditional devices such as voltage regulators and new devices like the SST. Decomposing the problem into a slow voltage-control loop and a fast reactive power-control loop increased computational efficiency. Fundamental work on system stability using Lyapunov level sets is ongoing. DGI’s broader impacts have expanded significantly into securing other electric power and municipal water infrastructures.

References

  1. McMillin and T. Zhang, “Fog Computing for Smart Living,” in Computer, vol. 50, no. 2, pp. 5-5, Feb. 2017.
  2. Garcia-Molina, “Elections in a Distributed Computing System,” IEEE Transactions on Computers, Vol. 31, No. 1, January 1982, pp. 48 – 59.
  3. M. Chandy and L. Lamport, “Distributed snapshots: determining global states of distributed systems,” ACM Transactions on Computer Systems, Vol. 3, No. 1, February 1985, pp. 63 – 75.
  4. Akella, R., Meng, F., Ditch, D., McMillin, B., and Crow, M., “Distributed Power Balancing for the FREEDM System,” Proceedings of the 1st annual IEEE Smart Grid Communications Conference, Gaithersburg, MD, Oct 4-6, 2010.
  5. Peréz and G. Heydt, “Distribution system restoration via subgradient based lagrangian relaxation,” IEEE Transactions on Power Systems, Vol. 23, No. 3, August 2008, pp. 1162 – 1169.
  6. Raúl E. Pérez-Guerrero, G. T. Heydt, N. Jack, B. Keel and A. Castelhano, “Optimal restoration of distribution systems using dynamic programming,” IEEE Transactions on Power Delivery, Vol. 23, No. 3, July 2008, pp. 1589 – 1596.
  7. Olfati-Saber and R. M. Murray, “Consensus Problems in Networks of Agents With Switching Topology and Time-Delays,” IEEE Transactions on Automatic Control, Vol. 49, No. 9, September 2004, pp. 215 –233.
  8. Ott, “Experience with PJM market operation, system design, and implementation,” IEEE Transaction on Power System, vol. 18, no. 2. pp. 528–534, 2003.
  9. Howser and B. McMillin, “Using Information-Flow Methods to Analyze the Security of Cyber-Physical Systems,” in Computer, vol. 50, no. 4, pp. 17-26, April 2017. doi: 10.1109/MC.2017.112
  10. Roth and B. McMillin, “Physical Attestation in the Smart Grid for Distributed State Verification,” in IEEE Transactions on Dependable and Secure Computing, vol. 15, no. 2, pp. 275-288, March-April 1 2018. doi: 10.1109/TDSC.2016.2577021

Posters

Papers