  There is an emerging need for efficient solutions to stochastic AC Optimal
Power Flow ({AC-}OPF) to ensure optimal and reliable grid operations in the
presence of increasing demand and generation uncertainty. This paper presents a
highly scalable data-driven algorithm for stochastic AC-OPF that has extremely
low sample requirement. The novelty behind the algorithm's performance involves
an iterative scenario design approach that merges information regarding
constraint violations in the system with data-driven sparse regression.
Compared to conventional methods with random scenario sampling, our approach is
able to provide feasible operating points for realistic systems with much lower
sample requirements. Furthermore, multiple sub-tasks in our approach can be
easily paralleled and based on historical data to enhance its performance and
application. We demonstrate the computational improvements of our approach
through simulations on different test cases in the IEEE PES PGLib-OPF benchmark
library.
