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Real-time coordination of distributed energy resources (DERs) is crucial forregulating the voltage profile in distribution grids. By capitalizing on ascalable neural network (NN) architecture, one can attain decentralized DERdecisions to address the lack of real-time communications. This paper developsan advanced learning-enabled DER coordination scheme by accounting for thepotential risks associated with reactive power prediction and voltagedeviation. Such risks are quantified by the conditional value-at-risk (CVaR)using the worst-case samples only, and we propose a mini-batch selectionalgorithm to address the training speed issue in minimizing theCVaR-regularized loss. Numerical tests using real-world data on the IEEE123-bus test case have demonstrated the computation and safety improvements ofthe proposed risk-aware learning algorithm for decentralized DER decisionmaking, especially in terms of reducing feeder voltage violations.

Risk-Aware Learning for Scalable Voltage Optimization in Distribution Grids