Small-signal stability constrained optimal power flow considering eigenvalue distribution

Huang, Zheng, Wang, Kewen, Wang, Yi, Han, Jing and Liang, Jun ORCID: https://orcid.org/0000-0001-7511-449X 2024. Small-signal stability constrained optimal power flow considering eigenvalue distribution. Journal of Modern Power Systems and Clean Energy 12 (4) , pp. 1052-1062. 10.35833/MPCE.2023.000135

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Abstract

In the existing small-signal stability constrained optimal power flow (SSSC-OPF) algorithms, only the rightmost eigenvalue or eigenvalues that do not satisfy a given threshold, e.g ., damping ratio threshold and real-part threshold of eigenvalue, are considered in the small-signal stability constraints. The effect of steady-state, i.e., operating point, changes on eigenvalues is not fully taken into account. In this paper, the small-signal stability constraint that can fully reflect the eigenvalue change and system dynamic performance requirement is formed by analyzing the eigenvalue distribution on the complex plane. The small-signal stability constraint is embedded into the standard optimal power flow model for generation rescheduling. The simultaneous solution formula of the SSSC-OPF is established and solved by the quasi-Newton approach, while penalty factors corresponding to the eigenvalue constraints are determined by the stabilization degree of constrained eigenvalues. To improve the computation speed, a hybrid algorithm for eigenvalue computation in the optimization process is proposed, which includes variable selection for eigenvalue estimation and strategy selection for eigenvalue computation. The effectiveness of the proposed algorithm is tested and validated on the New England 10-machine 39-bus system and a modified practical 68-machine 2395-bus system.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	2196-5625
Date of First Compliant Deposit:	9 September 2024
Date of Acceptance:	30 December 2023
Last Modified:	16 Sep 2024 13:22
URI:	https://orca.cardiff.ac.uk/id/eprint/171893

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