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Title: A Framework for Robust Long-Term Voltage Stability of Distribution Systems

Abstract

Power injection uncertainties in distribution power grids, which are mostly induced by aggressive introduction of intermittent renewable sources, may drive the system away from normal operating regimes and potentially lead to the loss of long-term voltage stability (LTVS). Naturally, there is an ever increasing need for a tool for assessing the LTVS of a distribution system. This paper presents a fast and reliable tool for constructing inner approximations of LTVS regions in multidimensional injection space such that every point in our constructed region is guaranteed to be solvable. Numerical simulations demonstrate that our approach outperforms all existing inner approximation methods in most cases. Furthermore, the constructed regions are shown to cover substantial fractions of the true voltage stability region. In conclusion, the paper will later discuss a number of important applications of the proposed technique, including fast screening for viable injection changes, constructing an effective solvability index and rigorously certified loadability limits.

Authors:
 [1];  [2];  [1];  [1]
  1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
  2. Washington State Univ., Pullman, WA (United States); Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1471070
Grant/Contract Number:  
AC05-76RL01830
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Smart Grid
Additional Journal Information:
Journal Name: IEEE Transactions on Smart Grid; Journal ID: ISSN 1949-3053
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; inner approximation; power flow; voltage stability

Citation Formats

Nguyen, Hung D., Dvijotham, Krishnamurthy, Yu, Suhyoun, and Turitsyn, Konstantin. A Framework for Robust Long-Term Voltage Stability of Distribution Systems. United States: N. p., 2018. Web. doi:10.1109/TSG.2018.2869032.
Nguyen, Hung D., Dvijotham, Krishnamurthy, Yu, Suhyoun, & Turitsyn, Konstantin. A Framework for Robust Long-Term Voltage Stability of Distribution Systems. United States. doi:10.1109/TSG.2018.2869032.
Nguyen, Hung D., Dvijotham, Krishnamurthy, Yu, Suhyoun, and Turitsyn, Konstantin. Thu . "A Framework for Robust Long-Term Voltage Stability of Distribution Systems". United States. doi:10.1109/TSG.2018.2869032. https://www.osti.gov/servlets/purl/1471070.
@article{osti_1471070,
title = {A Framework for Robust Long-Term Voltage Stability of Distribution Systems},
author = {Nguyen, Hung D. and Dvijotham, Krishnamurthy and Yu, Suhyoun and Turitsyn, Konstantin},
abstractNote = {Power injection uncertainties in distribution power grids, which are mostly induced by aggressive introduction of intermittent renewable sources, may drive the system away from normal operating regimes and potentially lead to the loss of long-term voltage stability (LTVS). Naturally, there is an ever increasing need for a tool for assessing the LTVS of a distribution system. This paper presents a fast and reliable tool for constructing inner approximations of LTVS regions in multidimensional injection space such that every point in our constructed region is guaranteed to be solvable. Numerical simulations demonstrate that our approach outperforms all existing inner approximation methods in most cases. Furthermore, the constructed regions are shown to cover substantial fractions of the true voltage stability region. In conclusion, the paper will later discuss a number of important applications of the proposed technique, including fast screening for viable injection changes, constructing an effective solvability index and rigorously certified loadability limits.},
doi = {10.1109/TSG.2018.2869032},
journal = {IEEE Transactions on Smart Grid},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {9}
}

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Figures / Tables:

Fig. 1 Fig. 1: The performance of the fast screening algorithm against a CPF-based approach

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