This thesis focuses on uncertainty analysis in two areas in power system operations, which may encounter uncertainty in the model. First, the Probabilistic Power Flow (PPF) prob­lem is introduced where uncertainty is involved in demand or available generation. By employing Latin Supercube Sampling (LSS) the distribution of output variables in the PPF is estimated. A bin-by-bin histogram comparison is used to efficiently compare the perfor­mance of the LSS with other techniques.
Second, a Chance Constrained Optimization (CCO) is presented to handle uncertainty in control of transmission voltages. A control scheme is proposed using steady-state system model to achieve the goal of on-line voltage control and prevent long-term voltage instabil­ity. In order to model steady-state system response, the long-term model of governors and automatic voltage regulators are employed in the control scheme.