Learning-Aided Optimal Power Flow Based Fast Total Transfer Capability Calculation

Ji’ang Liu, Youbo Liu, Gao Qiu and Xiao Shao
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Ji’ang Liu: College of Electrical Engineering Technology, Sichuan University, Chengdu 610065, China
Youbo Liu: College of Electrical Engineering Technology, Sichuan University, Chengdu 610065, China
Gao Qiu: College of Electrical Engineering Technology, Sichuan University, Chengdu 610065, China
Xiao Shao: State Grid Tianfu New Area Electric Power Supply Company, Chengdu 610041, China

Energies, 2022, vol. 15, issue 4, 1-14

Abstract: Total transfer capability (TTC) is a vital security indicator for power exchange among areas. It characterizes time-variants and transient stability dynamics, and thus is challenging to evaluate efficiently, which can jeopardize operational safety. A leaning-aided optimal power flow method is proposed to handle the above challenges. At the outset, deep learning (DL) is utilized to globally establish real-time transient stability estimators in parametric space, such that the dimensionality of dynamic simulators can be reduced. The computationally intensive transient stability constraints in TTC calculation and their sensitivities are therewith converted into fast forward and backward processes. The DL-aided constrained model is finally solved by nonlinear programming. The numerical results on the modified IEEE 39-bus system demonstrate that the proposed method outperforms several model-based methods in accuracy and efficiency.

Keywords: total transfer capability; surrogate assisted method; transient stability; deep learning; interior point method (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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