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3Rs for Power And Demand

Dynamic monitoring and decision systems maximize energy resources.

Fortnightly Magazine - December 2009

is key to the clean and cost-effective use of wind and responsive demand. The implementation of self-commitment would require a transformation of today’s SCADA systems into multi-directional, multi-layered interactive dynamic monitoring and decision systems (DYMONDS). However, if this is done systematically, at least the first generation DYMONDS would be a natural outgrowth of today’s SCADA, and wouldn’t require a major re-design. Today’s SCADA would have to be enhanced by interactive multi-directional information exchange between system operators, aggregators of variable resources (such as wind, solar and demand) and the resources themselves. The NIST standards and protocols under design must enable minimal information exchange from the system operators to the aggregators and resources, in both directions and multi-laterally. An IEEE test system has shown that this system is capable of integrating greater than 50 percent wind capacity with less than 3 percent demand elasticity during most hours, while observing the same transmission limits. Symmetric distributed risk management is beneficial for all industry participants as their value is aligned with what they are compensated for.



1) America’s Energy Future: Technology and Transformation , National Research Council Study Report, 2007.

2) Marija Ilic, Le Xie, and Jhi-Young Joo, “Dynamic Monitoring and Decision Systems (DYMONDS) for Efficient Coordination of Wind Power and Price-Responsive Demand: Proof-of-Concept on the IEEE RTS Test System, Electric Energy Systems Group Working Paper R-WP18,” Carnegie Mellon University, 2009.

3) Reliability Test System Task Force of the Application of Probability Methods Subcommittee, “The IEEE Reliability Test System-1996,”  IEEE Transactions on Power Systems , Vol. 14, Issue 3, pp. 1010-1020, August 1999.

4) GridWeek Meeting, Washington D.C., Sept. 21-24, 2009 .

5) Ilic, Marija, “Driving Efficiency and Optimization: Maximizing the Operational Value of Smart Grid,”  GridWeek.

6) Ilic, M., E. Allen, J. Chapman, C. King, J. Lang, and E. Litvinov, “Preventing Future Blackouts by Means of Enhanced Electric Power Systems Control: From Complexity to Order.”  IEEE Proceedings , November 2005 (Section VII).

7) Varaiya, Pravin, “Risk-Limiting Dispatch of the Smart Grid: A Research Agenda,” Keynote Talk, CPS Workshop, Baltimore, MD.

8) Ilic, M., Skantze, P., Yu, C-N., Fink, L.H., Cardell, J., “Power Exchange for Frequency Control (PXFC),”  Proceedings of the International Symposium on Bulk Power Systems Dynamics and Control-IV: Restructuring , Santorini, Greece, Aug. 23-28, 1998.

9) Audun Botterud, Tarjei Kristiansen, and Marija Ilic, “The Relationship Between Spot and Futures Prices in the Nord Pool Electricity Market,”  Energy Economics Journal , (under review).

10) Wu, Zhiyong and Marija Ilic, “Generation Investment under Stratum Energy Market Structure.” 2008 IEEE Power Engineering Society General Meeting, July 20-24, 2008. Pittsburgh, PA.

11) Wolak, F.A, “An Empirical Analysis of the Impact of Hedge Contracts on Bidding Behavior in Competitive Electricity Markets,”  International Economic Journal , vol. 14, no. 2, Summer 2000.

12) Ilic, Marija, “Dynamic Monitoring and Decision Systems (DYMONDS) and Smart Grids; One and the Same,” EESG WP, 2009.



1. Systems with large storage wouldn’t be as dependent on near-real time knowledge. Without storage, the large stand-by reserve is used. The first solution is still in the embryonic stages, and the second solution leads