Intelligent State Space Pruning with local search for power system reliability evaluation

A methodology called Intelligent State Space Pruning (ISSP) has recently been developed and applied in order to reduce the computational resources necessary to achieve convergence when using non-sequential Monte Carlo Simulation (MCS). The main application of this algorithm has been the probabilistic evaluation of composite power system reliability. ISSP has been shown to perform differently when implemented using different population based metaheuristic algorithms, though computation resources are typically reduced by more than 50%. This reduction in computation resources is particularly important when considering the smart grid - a system whose complexity will be far beyond that of the present power grid. In order to further this line of research, this paper focuses on four contributions: 1) The presentation of a binary version of the deterministic Central Force Optimization (CFO) optimization algorithm, 2) The role of this new algorithm regarding ISSP, 3) The integration of a local search technique with three flavors of the ISSP algorithm in order to improve performance, and 4) A discussion of the role that ISSP may play in the reliability evaluation of the smart grid.

[1]  Fred W. Glover,et al.  Tabu Search - Part I , 1989, INFORMS J. Comput..

[2]  Chanan Singh,et al.  Incorporating the DC load flow model in the decomposition-simulation method of multi-area reliability evaluation , 1996 .

[3]  Robert C. Green,et al.  Power system reliability assessment using intelligent state space pruning techniques: A comparative study , 2010, 2010 International Conference on Power System Technology.

[4]  Richard A. Formato,et al.  CENTRAL FORCE OPTIMIZATION: A NEW META-HEURISTIC WITH APPLICATIONS IN APPLIED ELECTROMAGNETICS , 2007 .

[5]  Robert C. Green,et al.  State space pruning for reliability evaluation using binary particle swarm optimization , 2011, 2011 IEEE/PES Power Systems Conference and Exposition.

[6]  Chanan Singh,et al.  Composite system reliability evaluation using state space pruning , 1997 .

[7]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[8]  Robert C. Green,et al.  Training neural networks using Central Force Optimization and Particle Swarm Optimization: Insights and comparisons , 2012, Expert Syst. Appl..

[9]  M. Ben Ghalia,et al.  Particle swarm optimization with an improved exploration-exploitation balance , 2008 .

[10]  Probability Subcommittee,et al.  IEEE Reliability Test System , 1979, IEEE Transactions on Power Apparatus and Systems.

[11]  Lingfeng Wang,et al.  Population-Based Intelligent Search in Reliability Evaluation of Generation Systems With Wind Power Penetration , 2008, IEEE Transactions on Power Systems.

[12]  Richard A. Formato,et al.  Pseudorandomness in Central Force Optimization , 2010, ArXiv.

[13]  Chanan Singh,et al.  Reliability assurance of cyber-physical power systems , 2010, IEEE PES General Meeting.

[14]  S. Dreyfus,et al.  Thermodynamical Approach to the Traveling Salesman Problem : An Efficient Simulation Algorithm , 2004 .

[15]  Chanan Singh,et al.  Pruning and simulation for determination of frequency and duration indices of composite power systems , 1999 .

[16]  Fred Glover,et al.  Tabu Search - Part II , 1989, INFORMS J. Comput..

[17]  Joydeep Mitra,et al.  Composite system reliability analysis using particle swarm optimization , 2010, 2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems.

[18]  Mark Lauby,et al.  Reliability considerations from the integration of smart grid , 2012, 2012 IEEE PES Innovative Smart Grid Technologies (ISGT).

[19]  Khosrow Moslehi,et al.  A Reliability Perspective of the Smart Grid , 2010, IEEE Transactions on Smart Grid.