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Integrating Knowledge-based and Data-driven Approaches for TTC Assessment in Power Systems with High Renewable Penetration
  • Yuhong Zhu ,
  • Yongzhi Zhou ,
  • Wei Wei
Yuhong Zhu
Zhejiang University

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Yongzhi Zhou
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Assessment of total transfer capability (TTC) is vital for determining the permissible power transfer between two areas of an interconnected power system. In the context of heightened volatility and time-variability in power system operating states after integrating high proportions of renewable energy, data-driven inferential assessment methods emerge as promising alternatives, offering faster assessment capabilities compared to knowledge-based iterative methods. However, data-driven methods typically struggle to establish reliable connections between assessment outcomes and security standards, hindering the guarantee of conservatism. A hybrid algorithm, combining knowledge-based and data-driven techniques, is proposed to accurately and efficiently assess TTC while strictly complying with pre-established security and stability constraints. Data-driven inference accelerates knowledge-based iterative processes by rapidly identifying reasonable initial values and providing adaptive step sizes, while knowledge-based analysis guides data-driven methods through offering stability margin information. This mechanism leverages the speed of data-driven methods while maintaining conservatism through knowledge-based approaches. The effectiveness of the proposed method is verified on benchmarks, including the IEEE 30-bus system and a real-world power system, which also exhibits conservatism and robustness in the face of increasing renewable energy penetration.