| This thesis investigates and examines the impact and effect of uncertainty within power system analysis in an effort to develop effective solution procedures for two different types of optimization problems: Probabilistic Optimal Power Flow (P-OPF) and Stochastic Optimal Power Flow (S-OPF). Solutions to both types of problems are developed based on a Cumulant Method framework and numerical results are included based on the IEEE test systems. Bus loading is considered to be random and both Gaussian and Gamma distributions are used to describe the behaviour of these variables.;The proposed methodologies are validated through the use of Monte Carlo Simulation (MCS) techniques; in all cases, the MCS results presented are comprised of 10,000 samples. The results are compared by evaluating the performance of the mean and variance computations directly as well as through the use of a normalized sum of squares error technique.;The numerical results presented demonstrate the effectiveness of the proposed techniques. |
