Uncertainty in power systems analysis
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AbstractThis 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.
Bibliography: p. 117-130