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The unprecedented increasing penetration of distributed energy resources, mainly harvesting renewable energies, is a direct result of environmental concerns. These types of energy resources bring about more uncertainties in the power system and, consequently, necessitate probabilistic analyses of the system performance. This paper develops a new approach for probabilistic optimal power flow (P-OPF) by adapting the unscented transformation (UT) method. The heart of the proposed method lies in how to produce the sampling points. Appropriate sample points are chosen to perform the P-OPF with a high degree of accuracy and less computational burden in comparison with the features of other existing methods. Another salient feature of the UT technique is its capability in modeling the correlated uncertain input variables. This attribute is very desirable in the accommodation of wind generations, which likely have a correlation regarding the geographical proximity. In order to examine the performance of the proposed method, 2 case studies are conducted and the results are compared with those of other existing methods, such as Monte Carlo simulation and 2-point estimation methods. The case studies are the Wood and Wollenberg 6-bus and the IEEE 118-bus test systems. A comparison of the results justifies the effectiveness of the proposed method with regards to the accuracy and execution time.

Unscented transformation-based probabilistic optimal power flow for modeling the effect of wind power generation