A Sensitivity Matrix-Based Temperature-Augmented Probabilistic Load Flow Study

This paper proposes a hybrid method for probabilistic load flow (PLF) study to analyze the influence of uncertain photovoltaic generations and load demands on transmission system performance. Besides, the paper focuses on accurate approximation of multimodal distributions of result variables in a temperature-augmented PLF model without using any series expansion methods. The effect of uncertain ambient temperature on result variables is discussed. Multiple correlation cases between the input bus powers are considered. The performance of the proposed method is investigated on modified New England 39-bus power system. The results are compared with four well-established analytical methods and Monte Carlo simulation. The effect of multiple input correlations on probability distributions of result variables is analyzed.