Risk-Aware Learning for Scalable Voltage Optimization in Distribution Grids
This addresses voltage management in power grids for grid operators, but appears incremental as it builds on existing neural network approaches with risk quantification.
The paper tackles voltage regulation in distribution grids by developing a risk-aware learning algorithm for decentralized control of distributed energy resources, demonstrating reduced voltage violations in tests on the IEEE 123-bus system.
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.