Risk Assessment in Probabilistic Load Flow via Monte Carlo Simulation and Cross-Entropy Method

Abstract

In planning and operation of electric power systems, it is essential to perform several evaluations using the power flow algorithm to estimate and manage the operating point of the network being studied. The probabilistic load flow (PLF) algorithm aims to overcome the limitations imposed by the use of the conventional deterministic tool, allowing the consideration of input uncertainties. This paper proposes a new method for calculating risk indices (e.g., probability of overloads in transmission equipment), based on Monte Carlo simulation (MCS) using importance sampling techniques via the cross-entropy method. In fact, the probability associated with any target region of the output PLF variables can be precisely evaluated. The flexibility and accuracy of the conventional MCS are maintained, while the computational time required is greatly reduced. The proposed method is applied to different IEEE test systems and the obtained results are widely discussed.