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.

Fast Calculation of Probabilistic Power Flow: A Model-Based Deep Learning Approach

“…The results in this paper follows the direction of using artificial neural networks to approximate and speed up the power flow calculation process [18], [19]. The PPF processes numerous samples with a similar computational task (power flow calculation), which makes it a natural target application of neural networks.…”

“…To the best of our knowledge, [18] is the first paper to utilize this idea and develop a control scheme via a radial basis function (RBF) neural network. Followed by [19], RBF-based power flow is applied to the probabilistic PPF. However, the RBF networks are shallow models, which have difficulty extracting complex and abstract features.…”

Sizing and Allocation of Distributed Energy Resources for Loss Reduction using Heuristic Algorithms

“…When wind and solar power generation units are added to the DNs, and the statistical pattern of load variation is also considered (such as the mode of plugged-in hybrid electric vehicle (PHEVs)), statistical approaches are used [37]- [39]. In these kinds of approaches, the extension of the power-flow method of [35], for possibilistic/probabilistic power flow is usually useful [40].…”

Optimisation of the Structure of a Wind Farm—Kinetic Energy Storage for Improving the Reliability of Electricity Supplies

“…This is why scientific centres worldwide carry out research on ESS that can function in conjunction with RES-E, especially photovoltaic cells and wind turbines. References [9][10][11][12][13][14][15] focused on the issue of predicting the generating capacity of RES-E to adapt in advance with the power generated by other systems. There are the examples of papers [16][17][18][19][20][21][22][23][24] concern with the selection of the structure of energy sources and storages according to location needs and conditions.…”