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

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.

Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

“…• Our approach is amenable to a bias-free variant of the rectification procedure proposed in [33], as discussed in Section 3.5.…”

“…Stochastic Interpolants, Rectified Flows, and Flow matching. Variants of the stochastic interpolant method presented in [1] were also presented in [33,31]. In [33], a linear interpolant was proposed with a focus on straight paths.…”

“…Variants of the stochastic interpolant method presented in [1] were also presented in [33,31]. In [33], a linear interpolant was proposed with a focus on straight paths. This was employed as a step toward rectifying the transport paths [32] through a procedure that improves sampling efficiency but introduces a bias.…”

“…A variety of recent works have formulated the Schrödinger problem in the context of a learnable diffusion [6,48,11]. In the interpolant framework, [1,33,31] all propose optimal transport extensions to the learning procedure. The method proposed in [33,32] allows one to sequentially lower the transport cost through rectification, at the cost of introducing a bias unless the velocity field is perfectly learned.…”

“…Dynamical approaches for deterministic and stochastic transport have become a central theme in contemporary generative modeling research. At the heart of progress is the idea to use ordinary or stochastic differential equations (ODEs/SDEs) to continuously transform samples from a base probability density function (PDF) ρ 0 into samples from a target density ρ 1 (or vice-versa), and the realization that inference over the velocity field in these equations can be formulated as an empirical risk minimization problem over a parametric class of functions [19,46,20,47,3,1,33,31].…”