“20th PSCC 2018 papers submission and review platform

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Probabilistic Load Flow Method for Correlated Multimodally Distributed Input Variables

Due to growing uncertainties in the electricity grid, probabilistic load flow methods have achieved more attention. The Point Estimate Method (PEM) was introduced in 1975 and was enhanced since then. Within this method, statistical moments of the output variables are calculated. Afterwards, the probability density functions (PDF) and cumulative density functions (CDF) are determined using expansion methods. Nowadays, Cornish-Fisher-, Edgeworth- and Gram-Charlier-Expansion- (GCE) Methods are mainly used. Due to the combination of different renewable energy sources (RES) at the same grid node, correlated multimodally distributed input variables may result. The existing approaches of PEM and their expansion methods can only deal with normally or close to normally distributed input variables. This paper shows an enhancement to the two-PEM (2m-PEM) and GCE method in order to consider correlated multimodally distributed input variables. The proposed algorithm is demonstrated in a test grid and verified through the comparison of the results using Monte Carlo Simulation (MCS) as reference method.

Author(s):

Marie-Louise Kloubert    
TU Dortmund University
Germany

Christian Rehtanz    
TU Dortmund University
Germany

 

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