  The effective management of stochastic characteristics of renewable power
generations is vital for ensuring the stable and secure operation of power
systems. This paper addresses the task of optimizing the chance-constrained
voltage-stability-constrained optimal power flow (CC-VSC-OPF) problem, which is
hindered by the implicit voltage stability index and intractable chance
constraints Leveraging a neural network (NN)-based surrogate model, the
stability constraint is explicitly formulated and directly integrated into the
model. To perform uncertainty propagation without relying on presumptions or
complicated transformations, an advanced data-driven method known as adaptive
polynomial chaos expansion (APCE) is developed. To extend the scalability of
the proposed algorithm, a partial least squares (PLS)-NN framework is designed,
which enables the establishment of a parsimonious surrogate model and efficient
computation of large-scale Hessian matrices. In addition, a dimensionally
decomposed APCE (DD-APCE) is proposed to alleviate the "curse of
dimensionality" by restricting the interaction order among random variables.
Finally, the above techniques are merged into an iterative scheme to update the
operation point. Simulation results reveal the cost-effective performances of
the proposed method in several test systems.
