Novel Computational Methods for the Reliability Evaluation of Composite Power Systems using Computational Intelligence and High Performance Computing Techniques

The probabilistic reliability evaluation of power systems is a complex and highly dimensional problem that often requires a large amount of computational resources, particularly processing power and time. The complexity of this problem is only increasing with the advent of the smart grid and its accompanying technologies, such as plug-in hybrid electric vehicles (PHEVs). Such technologies, while they add convenience, intelligence, and reduce environmental impacts, also add dynamic and stochastic loads that challenge the current reliability and security of the power grid.;One method that is often used to evaluate the reliability of power systems is Monte Carlo simulation (MCS). As the complexity and dimensionality of a power system grows, MCS requires more and more resources leading to longer computational times. Multiple methods have previously been developed that aid in reducing the computational resources necessary for MCS in order to achieve a more efficient and timely convergence while continuing to accurately assess the reliability of a given system. Examples include analytical state space decomposition, population based metaheuristic algorithms (PBMs), and the use of high performance computing (HPC).;In order to address these issues, this dissertation is focused on improving the performance of algorithms used to examine the level of reliability in composite power systems through the use of computational intelligence (CI) and HPC, while also investigating the impact of PHEVs on the power grid at the composite and distribution levels. Contributions include the development and exploration of 3 variations of a new, hybrid algorithm called intelligent state space pruning (ISSP) that combines PBMs with non-sequential MCS in order to intelligently decompose, or prune, a given state space and improve computational efficiency, an evaluation of the use of latin hypercube sampling and low discrepancy sequences in place of MCS, the use of serial and parallel support vector machines for state classification when evaluating power system reliability using MCS, an investigation of the impact that PHEVs will have when integrated into the power grid at the distribution level, and the development of a new model for the probabilistic evaluation of composite system reliability that includes one of the key technologies in the smart grid, PHEVs.