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 language||English (US)|
|Title of host publication||2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - Apr 19 2017|
|Event||2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Washington, United States|
Duration: Dec 7 2016 → Dec 9 2016
|Name||2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings|
|Other||2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016|
|Period||12/7/16 → 12/9/16|
Bibliographical noteFunding Information:
This work was supported in part by the National Science Foundation under the CAREER award 1453921 and CyberSEES grant 1442686.
© 2016 IEEE.
Copyright 2017 Elsevier B.V., All rights reserved.
- Power flow
- Uncertainty propagation