  Renewable energy resources (RERs) have been increasingly integrated into
large-scale distributed power systems. Considering uncertainties and voltage
fluctuation issues introduced by RERs, in this paper, we propose a deep
reinforcement learning (DRL)-based strategy leveraging spatial-temporal (ST)
graphical information of power systems, to dynamically search for the optimal
operation, i.e., optimal power flow (OPF), of power systems with a high uptake
of RERs. Specifically, we formulate the OPF problem as a multi-objective
optimization problem considering generation cost, voltage fluctuation, and
transmission loss, and employ deep deterministic policy gradient (DDPG) to
learn an optimal allocation strategy for OPF. Moreover, given that the nodes in
power systems are self-correlated and interrelated in temporal and spatial
views, we develop a multi-grained attention-based spatial-temporal graph
convolution network (MG-ASTGCN) for extracting ST graphical correlations and
features, aiming to provide prior knowledge of power systems for its sequential
DDPG algorithm to more effectively solve OPF. We validate our algorithm on
modified IEEE 33, 69, and 118-bus radial distribution systems and demonstrate
that our algorithm outperforms other benchmark algorithms. Our experimental
results also reveal that our MG-ASTGCN can significantly accelerate DDPG's
training process and performance in solving OPF.
