Renewable energy resources (RERs) have been increasingly integrated into
modern power systems, especially in large-scale distribution networks (DNs). In
this paper, we propose a deep reinforcement learning (DRL)-based approach to
dynamically search for the optimal operation point, i.e., optimal power flow
(OPF), in DNs with a high uptake of RERs. Considering uncertainties and voltage
fluctuation issues caused by RERs, we formulate OPF into a multi-objective
optimization (MOO) problem. To solve the MOO problem, we develop a novel DRL
algorithm leveraging the graphical information of the distribution network.
Specifically, we employ the state-of-the-art DRL algorithm, i.e., deep
deterministic policy gradient (DDPG), to learn an optimal strategy for OPF.
Since power flow reallocation in the DN is a consecutive process, where nodes
are self-correlated and interrelated in temporal and spatial views, to make
full use of DNs’ graphical information, we develop a multi-grained
attention-based spatial-temporal graph convolution network (MG-ASTGCN) for
spatial-temporal graph information extraction, preparing for its sequential
DDPG. We validate our proposed DRL-based approach in modified IEEE 33, 69, and
118-bus radial distribution systems (RDSs) and show that our DRL-based approach
outperforms other benchmark algorithms. Our experimental results also reveal
that MG-ASTGCN can significantly accelerate the DDPG training process and
improve DDPG’s capability in reallocating power flow for OPF. The proposed
DRL-based approach also promotes DNs’ stability in the presence of node faults,
especially for large-scale DNs.