On solving probabilistic load flow for radial grids using polynomial chaos

Publikationstyp
Conference Paper
Date Issued
2017
Sprache
English
Author(s)
Appino, Riccardo R.
Mühlpfordt, Tillmann
Faulwasser, Timm  
Hagenmeyer, Veit  
TORE-URI
https://hdl.handle.net/11420/46252
Article Number
7981264
Citation
2017 IEEE Manchester PowerTech : 18-22 June 2017. - Piscataway, NJ, 2017. - Art. no. 7981264
Contribution to Conference
IEEE Manchester PowerTech 2017  
Publisher DOI
10.1109/PTC.2017.7981264
Scopus ID
2-s2.0-85034732392
Publisher
IEEE
ISBN
978-1-5090-4237-1
978-1-5090-4238-8
The uncertain nature of electric energy production from distributed generation based on renewable resources has to be considered when managing and operating distribution grids. In several cases, this uncertainty can be described using non-Gaussian random variables, requiring appropriate probabilistic load flow techniques. The present paper proposes a method that, exploiting Polynomial Chaos Expansion and Galerkin projection, allows a reformulation of the probabilistic load flow for radial grids as an enlarged deterministic problem. For radial grids, the well known Backward-Forward-Sweep method is applicable. This method does not require any model simplification or assumptions on the probability density function of the input random variables, i.e. it is applicable to non-Gaussian uncertainties. We draw upon a real 84-node grid and compare results against those obtained from Monte Carlo simulation.
Subjects
Backward-Forward-Sweep method
non-Gaussian uncertainty
polynomial chaos expansion
probabilistic load flow
radial distribution grid
uncertain distributed generation
DDC Class
510: Mathematics