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

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.

Inspec keywords: distribution networks; wind power; distributed power generation; fuzzy set theory; computational complexity; transforms; power system interconnection; load flow; radial basis function networks; power engineering computing; Monte Carlo methods; hybrid electric vehicles

Other keywords: plug-in hybrid electric vehicles; PV microgrids; solar distributed energy resources; PPF algorithm; nonlinear mapping; active distribution networks; conventional power flow algorithms; uncertain loads; probabilistic power flow; uncertainty quantification; computation time; fuzzy unscented transform; possibilistic power flow; Monte Carlo simulation; correlated wind microgrids; test power systems; radial basis function neural networks; large interconnected power systems; RBFNN; historical data

Subjects: Distribution networks; Monte Carlo methods; Integral transforms; Distributed power generation; Transportation; Energy resources; Integral transforms; Monte Carlo methods; Computational complexity; Neural computing techniques; Combinatorial mathematics; Combinatorial mathematics; Power engineering computing

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