  The alternating current (AC) chance-constrained optimal power flow (CC-OPF)
problem addresses the economic efficiency of electricity generation and
delivery under generation uncertainty. The latter is intrinsic to modern power
grids because of the high amount of renewables. Despite its academic success,
the AC CC-OPF problem is highly nonlinear and computationally demanding, which
limits its practical impact. For improving the AC-OPF problem
complexity/accuracy trade-off, the paper proposes a fast data-driven setup that
uses the sparse and hybrid Gaussian processes (GP) framework to model the power
flow equations with input uncertainty. We advocate the efficiency of the
proposed approach by a numerical study over multiple IEEE test cases showing up
to two times faster and more accurate solutions compared to the
state-of-the-art methods.
