I have spent quite a bit of time pursuing an area of research concerned with State Space Pruning for Monte Carlo  Simulation (MCS) when calculating Reliability Indices for Power Systems. This is a necessity due to the curse of dimensionality. Simply stated, in a power system with 32 generators one finds themselves with a need to examine states. Expand this to a larger system and suddenly the state space size explodes. In this work we’ve used Genetic Algorithms (GA), Repulsive Binary Particle Swarm Optimization (RBPSO), and Ant Colony Optimization (ACO) in order to reduce MCS time and iterations before convergence. In all cases we have used a binary representation (each bit represents a generator’s on/off state) with a DC Optimal Power Flow (DCOPF) that has been tailored to minimize load curtailment instead of cost. The algorithms are customized in order to server our particular needs (specifically generating states in which there is no load curtailment quickly). We have had a rather good success rate over two different test systems: IEEE-RTS79 and IEEE-RTS96. For those not familiar with them, these two test systems are designed specifically for reliability testing (RTS = Reliability Testing System). RTS-79 came first and RTS-96 (for the most part) is simply 3 RTS-79 that are connected to each other with minor changes. The table below shows a brief comparison between the two systems.

1. R. Green, L. Wang, Z. Wang, M. Alam, and C. Singh, “Power System Reliability Assessment Using Intelligent State Space Pruning Techniques: A Comparative Study” Submitted to 2010 Conference on Power System Technology, Hangzou China, October 2010.
2. R. Green, L. Wang, M. Alam, and C. Singh, “State space pruning for Reliability Evaluation using Binary Particle Swarm Optimization,” Submitted to Hawaii International Conference on System Sciences,University of Hawaii at Manoa, January 2011.
3. R. Green, L. Wang, and C. Singh, “State space pruning for power system reliability evaluation using genetic algorithms,” IEEE Power & Energy Society General Meeting 2010, Minneapolis, MN, July 2010.
4. R. Green, Z. Wang, L. Wang, M. Alam, and C. Singh, “Evaluation of loss of load probability for power systems using intelligent search based state space pruning,” The 11th International Conference on Probabilistic Methods Applied to Power Systems, Singapore, June 2010

Some further resources for this work that may be helpful to others include:

1. Matpower Formulation of IEEE-RTS79 [IEEE-RTS79 MatPower]
2. Matpower Formulation of IEEE-RTS96 [IEEE-RTS96 MatPower]
3. DCOPF for RTS79 in LP_Solve format [RTS-79 DC Optimal Power Flow]
4. DCOPF for RTS96 in LP_Solve format [RTS-96 DC Optimal Power Flow]

As well as some references:

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Particle Swarm Optimization Code

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