A flexible black‐start network partitioning strategy considering subsystem recovery time balance

Summary When black out occurs, the whole power grid is divided into several subsystems to increase the efficiency and shorten the duration of power system restoration by using parallel restoration strategy. Therefore, reasonable network partitioning in black start phase after large-scale power failures is important in both theory and practice. In this paper, a flexible black start network partitioning strategy considering the subsystem recovery time balance is proposed. Based on the rules of zone division and analyzing initial black start partitioning model of subsystem shortest recovery path, subsystem recovery time balance index is defined to represent disparity of every subsystem's restoration process. Meanwhile, a flexible factor of total recovery time is introduced as part of objective function with the time balance index, aiming at reducing waiting time of the synchronization operation and strengthens efficiency of black start at the cost of the least total time increment. The classic Dijkstra algorithm and improved genetic algorithm are employed to obtain the optimal restoration plan in the paper. Case study on IEEE 118-bus test system shows the validity of the methodology proposed. Copyright © 2014 John Wiley & Sons, Ltd.

[1]  Abbas Ketabi,et al.  An approach for optimal units start-up during bulk power system restoration , 2001, LESCOPE 01. 2001 Large Engineering Systems Conference on Power Engineering. Conference Proceedings. Theme: Powering Beyond 2001 (Cat. No.01ex490).

[2]  M. M. Adibi,et al.  Power system restoration planning , 1994 .

[3]  T Bi,et al.  Efficient multiway graph partitioning method for fault section estimation in large-scale power networks , 2002 .

[4]  M. M. Adibi,et al.  Power System Restoration - The Second Task Force Report , 1987, IEEE Transactions on Power Systems.

[5]  R. R. Lindstrom Simulation and field tests of the black start of a large coal-fired generating station utilizing small remote hydro generation , 1990 .

[6]  S. Nourizadeh,et al.  A power system build‐up restoration method based on wide area measurement systems , 2011 .

[7]  Steven M. Halladay,et al.  The application of network science principles to knowledge simulation , 2004, 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the.

[8]  Sang-Seung Lee,et al.  Power system restoration plan using the characteristics of scale‐free networks , 2008 .

[9]  R. Kafka,et al.  Power System Restoration - A Task Force Report , 1987, IEEE Transactions on Power Systems.

[10]  Chong Wang,et al.  OBDD-Based Sectionalizing Strategies for Parallel Power System Restoration , 2011, IEEE Transactions on Power Systems.

[11]  Dana S. Richards,et al.  Distributed genetic algorithms for the floorplan design problem , 1991, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[12]  Hao Zhou,et al.  Division algorithm and interconnection strategy of restoration subsystems based on complex network theory , 2011 .

[13]  Yi-Ting Chou,et al.  Development of a Black Start Decision Supporting System for Isolated Power Systems , 2013, IEEE Transactions on Power Systems.

[14]  T. Nagata,et al.  An autonomous agent for power system restoration , 2004, IEEE Power Engineering Society General Meeting, 2004..

[15]  Malcolm Irving,et al.  A genetic algorithm for network partitioning in power system state estimation , 1996 .