Probabilistic Load Flow in Correlated Uncertain Environment Using Unscented Transformation

As a matter of course, the unprecedented ascending penetration of distributed energy resources, mainly harvesting renewable energies, is a direct consequence of environmental concerns. This type of energy resource brings about more uncertainties in power system operation and planning; consequently, it necessitates probabilistic analyses of the system performance. This paper develops a new approach for probabilistic load flow (PLF) evaluation using the unscented transformation (UT) method. The UT method is recognized as a powerful approach in assessing stochastic problems with/without correlated uncertain variables. The capability of the UT method in modeling correlated uncertain variables is very appealing in the power system context, in which noticeable inherent correlation exists. The salient features of the UT method in probabilistic applications have been well proven in other engineering aspects. Following adaptation of the UT method for the PLF problem, three dimensionally different case studies are examined in order to inspect the performance of the proposed methodology. The obtained results are then compared with those of the Monte Carlo simulation as well as two-point estimation method with regards to both accuracy and execution time criteria.