The Role of Simulators for Smart Grid Development

The implementation of highly realistic real-time, massive, online, multi-time frame simulations is proposed as a means for building a common vision of smart grid functions among politicians, regulators, managers, operators, engineers, and technicians. These massive simulations will include hundreds of participants that play roles of reliability coordinators, transmission operators, distribution operators, power plant operators, and substation operators. These highly visible drills can demonstrate how the new smart grid systems, people, and processes can all work together economically and reliably. The industry, especially smart grid system designers, can get feedback from low cost, safe, and easily configurable simulations instead of waiting for expensive and hardwired deployments. Direct load control of millions of customer appliances is identified as a silver bullet to build self-healing and maximal flow smart grids that can accommodate large penetrations of intermittent wind and solar generation and rapid load growth due to plug-in electric vehicles. The paper recommends that up to 50% of load be controlled with minimal inconvenience to customers to potentially enhance angle, voltage, frequency, and thermal stability. An expert operator decision model is described with a view to helping system developers build operator-centered and friendly smart grid control systems.

[1]  Ra Gawler,et al.  Load Modelling for Electrical Power System Studies , 1979 .

[2]  R. Podmore,et al.  An Advanced Dispatcher Training Simulator , 1982, IEEE Transactions on Power Apparatus and Systems.

[3]  Mark Waldron,et al.  Synthesis of Dynamic Load Models for Stability Studies , 1982, IEEE Transactions on Power Apparatus and Systems.

[4]  M. Prais,et al.  Operator Training Simulator: Algorithms and Test Results , 1989, IEEE Power Engineering Review.

[5]  G. Klein,et al.  A recognition-primed decision (RPD) model of rapid decision making. , 1993 .

[6]  M. Endsley The role of situation awareness in naturalistic decision making , 1997 .

[7]  H. Falk,et al.  Standards-based approach integrates utility applications , 2000 .

[8]  Anjan Bose,et al.  Simulation environment for development and testing of plug compatible power system applications , 2001, Proceedings of the 34th Annual Hawaii International Conference on System Sciences.

[9]  D. Kosterev,et al.  Load Modeling in WECC , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[10]  M. Sedighizadeh,et al.  Load Modeling for Power Flow and Transient Stability Computer Studies at BAKHTAR Network , 2007 .

[11]  J.P. Britton Achieving Uniformly Accurate and Up-to-date Models in Large Interconnected Power Systems , 2007, 2007 IEEE Power Engineering Society General Meeting.

[12]  Frank L. Greitzer,et al.  Human Factors Evaluation of Advanced Electric Power Grid Visualization Tools , 2009 .

[13]  Frank L. Greitzer,et al.  Naturalistic Decision Making for Power System Operators , 2010, Int. J. Hum. Comput. Interact..