Probabilistic optimal power flow using unscented transformation

Renewable energy-based generation causes uncertainties in power system operation and planning due to its stochastic nature. Probabilistic optimal power flow (POPF) is a tool to solve for the random variables of this nonlinear power flow problem. In this paper, the unscented transformation (UT) is utilized to solve POPF with correlated random variables taken into account. The UT is utilized to calculate the statistical characteristics (i.e. mean and variance) of OPF results. The method is tested on the IEEE 30-bus system and the results are compared with the Monte Carlo Simulation (MCS) to demonstrate the effectiveness of the UT.

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University of Nevada Reno
United States

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