  Real-time coordination of distributed energy resources (DERs) is crucial for
regulating the voltage profile in distribution grids. By capitalizing on a
scalable neural network (NN) architecture, one can attain decentralized DER
decisions to address the lack of real-time communications. This paper develops
an advanced learning-enabled DER coordination scheme by accounting for the
potential risks associated with reactive power prediction and voltage
deviation. Such risks are quantified by the conditional value-at-risk (CVaR)
using the worst-case samples only, and we propose a mini-batch selection
algorithm to address the training speed issue in minimizing the
CVaR-regularized loss. Numerical tests using real-world data on the IEEE
123-bus test case have demonstrated the computation and safety improvements of
the proposed risk-aware learning algorithm for decentralized DER decision
making, especially in terms of reducing feeder voltage violations.
