A convex-optimization method to propagate uncertainty in power flow

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper presents a convex-optimization-based method to estimate maximum and minimum bounds on state variables in the power flow problem while acknowledging worst-case parametric and input uncertainties in the model. The approach leverages a second-order Taylor-series expansion of the states around a nominal (known) power-flow solution. Maximum and minimum bounds are then estimated from semidefinite relaxations of quadratically constrained quadratic programs. The objective of these problems is to maximize / minimize the quadratic approximation of the states recovered from the Taylor series expansion over the convex set in which the uncertainties lie. Numerical case studies validate the approach for the IEEE 118-bus system.

Original languageEnglish (US)
Title of host publication2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages846-850
Number of pages5
ISBN (Electronic)9781509045457
DOIs
StatePublished - Apr 19 2017
Event2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Washington, United States
Duration: Dec 7 2016Dec 9 2016

Publication series

Name2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings

Other

Other2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
CountryUnited States
CityWashington
Period12/7/1612/9/16

Keywords

  • Power flow
  • QCQP
  • SDP
  • Sensitivity
  • Uncertainty propagation

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