Fuzzy unscented transform for uncertainty quantification of correlated wind/PV microgrids: possibilistic–probabilistic power flow based on RBFNNs

Abstract

The probabilistic power flow (PPF) of active distribution networks and microgrids based on the conventional power flow algorithms is almost impossible or at least cumbersome. Always, Mont Carlo simulation is a reliable solution. However, its computation time is relatively high that makes it unattractive to be a reliable solution for large interconnected power systems. This study presents a new method based on fuzzy unscented transform and radial basis function neural networks (RBFNN) for possibilistic-PPF in the microgrids including uncertain loads, correlated wind and solar distributed energy resources and plug-in hybrid electric vehicles. When sufficient historical data of the system variables is not available, a probability density function might not be defined, while they must be represented in another way namely possibilistically. When some of system uncertain variables are probabilistic and some are possibilistic, neither the conventional pure probabilistic nor pure possibilistic methods can be implemented. Hence, a combined solution methodology is needed. The proposed method exploits the ability of RBFNN and unscented transform in non-linear mapping with an acceptable level of accuracy, robustness and reliability. Simulation results for the proposed PPF algorithm and its comparison with the reported methods for different test power systems reveals its efficiency, accuracy, robustness and authenticity.