Joined Probabilistic Load Flow and Sensitivity Analysis of Distribution Networks Based on Polynomial Chaos Method
Name
Final_Version.pdf
Size
227.69 KB
Format
Adobe PDF
Checksum (MD5)
bf373586f064364f5620ab6a39c54c23
Author(s)
Gruosso, Giambattista
Netto, Roberto S.
Daniel, Luca
Maffezzoni, Paolo
Date Issued
January 2020
Journal
IEEE Transactions on Power Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Gruosso, Giambattista et al. "Joined Probabilistic Load Flow and Sensitivity Analysis of Distribution Networks Based on Polynomial Chaos Method." IEEE Transactions on Power Systems 35, 1 (January 2020): 618 - 627. © 2020 IEEE
Version
Final published version
Abstract
Due to the statistical uncertainty of loads and power sources found in smart grids, effective computational tools for probabilistic load flow analysis and planning are now becoming indispensable. In this paper, we describe a unified simulation framework that allows quantifying the probability distributions of a set of observation variables as well as evaluating their sensitivity to potential variations in the power demands. The proposed probabilistic technique relies on the generalized polynomial Chaos algorithm and on a regionwise aggregation/description of the time-varying load profiles. It is shown how detailed statistical distributions of some important figures of merit, which includes voltage unbalance factor in distribution networks, can be calculated with a two orders of magnitude acceleration compared to standard Monte Carlo analysis. In addition, it is highlighted how the associated sensitivity analysis is of guidance for the optimal allocation and planning of new loads.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/tpwrs.2019.2928674
