Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

Small-signal Stability Constrained Optimal Power Flow Considering Eigenvalue Distribution
Author:
Affiliation:

1.School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China;2.Cardiff University, Cardiff CF24 3AA, U.K

Fund Project:

This work was supported by the National Natural Science Foundation of China (No. 62203395), the Postdoctoral Research Project of Henan Province (No. 202101011), and the Key R&D and Promotion Project of Henan Province (No. 222102220041).

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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.

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History
  • Received:March 03,2023
  • Revised:June 30,2023
  • Adopted:
  • Online: July 30,2024
  • Published: