Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

An Adaptive Many-objective Robust Optimization Model of Dynamic Reactive Power Sources for Voltage Stability Enhancement
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State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044, China

Fund Project:

This work was supported by the International Postdoctoral Exchange Fellowship Program (Talent-Introduction Program) (No. YJ20210337) and the Fundamental Research Funds for the Central Universities (No. 2022CDJXY-007).

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    Abstract:

    The deployment of dynamic reactive power source can effectively improve the voltage performance after a disturbance for a power system with increasing wind power penetration level and ubiquitous induction loads. To improve the voltage stability of the power system, this paper proposes an adaptive many-objective robust optimization model to deal with the deployment issue of dynamic reactive power sources. Firstly, two metrics are adopted to assess the voltage stability of the system at two different stages, and one metric is proposed to assess the tie-line reactive power flow. Then, a robustness index is developed to assess the sensitivity of a solution when subjected to operational uncertainties, using the estimation of acceptable sensitivity region (ASR) and D-vine Copula. Five objectives are optimized simultaneously total equipment investment; adaptive short-term voltage stability evaluation; tie-line power flow evaluation; prioritized steady-state voltage stability evaluation; and robustness evaluation. Finally, an angle-based adaptive many-objective evolutionary algorithm (MaOEA) is developed with two improvements designed for the application in a practical engineering problem: adaptive mutation rate; and elimination procedure without a requirement for a threshold value. The proposed model is verified on a modified Nordic 74-bus system and a real-world power system. Numerical results demonstrate the effectiveness and efficiency of the proposed model.

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History
  • Received:July 20,2022
  • Revised:September 18,2022
  • Adopted:
  • Online: November 16,2023
  • Published: