  Renewable energy resources (RERs) have been increasingly integrated into
distribution networks (DNs) for decarbonization. However, the variable nature
of RERs introduces uncertainties to DNs, frequently resulting in voltage
fluctuations that threaten system security and hamper the further adoption of
RERs. To incentivize more RER penetration, we propose a deep reinforcement
learning (DRL)-based strategy to dynamically balance the trade-off between
voltage fluctuation control and renewable accommodation. To further extract
multi-time-scale spatial-temporal (ST) graphical information of a DN, our
strategy draws on a multi-grained attention-based spatial-temporal graph
convolution network (MG-ASTGCN), consisting of ST attention mechanism and ST
convolution to explore the node correlations in the spatial and temporal views.
The continuous decision-making process of balancing such a trade-off can be
modeled as a Markov decision process optimized by the deep deterministic policy
gradient (DDPG) algorithm with the help of the derived ST information. We
validate our strategy on the modified IEEE 33, 69, and 118-bus radial
distribution systems, with outcomes significantly outperforming the
optimization-based benchmarks. Simulations also reveal that our developed
MG-ASTGCN can to a great extent accelerate the convergence speed of DDPG and
improve its performance in stabilizing node voltage in an RER-rich DN.
Moreover, our method improves the DN's robustness in the presence of generator
failures.
