The thesis focuses on developing a data-driven algorithm, based on machine
learning, to solve the stochastic alternating current (AC) chance-constrained
(CC) Optimal Power Flow (OPF) problem. Although the AC CC-OPF problem has been
successful in academic circles, it is highly nonlinear