Integrating Knowledge-Based and Data-Driven Approaches for TTC Assessment in Power Systems With High Renewable Penetration

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.