  Probabilistic optimal power flow (POPF) is an important analytical tool to
ensure the secure and economic operation of power systems. POPF needs to solve
enormous nonlinear and nonconvex optimization problems. The huge computational
burden has become the major bottleneck for the practical application. This
paper presents a deep learning approach to solve the POPF problem efficiently
and accurately. Taking advantage of the deep structure and reconstructive
strategy of stacked denoising auto encoders (SDAE), a SDAE-based optimal power
flow (OPF) is developed to extract the high-level nonlinear correlations
between the system operating condition and the OPF solution. A training process
is designed to learn the feature of POPF. The trained SDAE network can be
utilized to conveniently calculate the OPF solution of random samples generated
by Monte-Carlo simulation (MCS) without the need of optimization. A modified
IEEE 118-bus power system is simulated to demonstrate the effectiveness of the
proposed method.
