mlsanityData to 7 Oct 2026

Risk-Aware Learning for Scalable Voltage Optimization in Distribution Grids

arXiv 2110.01490 ↗First posted 4 Oct 2021cs.LG cs.SY eess.SYcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 20 of its 42 references that are in the map. First posted on arXiv 4 Oct 2021.

Builds on

20 of 42 references.

References · 20

  1. 2015Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approachcited by 162
  2. 2013Mini-batch Stochastic Approximation Methods for Nonconvex Stochastic Composite Optimizationcited by 142
  3. 2014Optimizing the CVaR via Samplingcited by 98
  4. 2019Adaptive Sampling for Stochastic Risk-Averse Learningcited by 39
  5. 2020Learning Bounds for Risk-sensitive Learningcited by 33
  6. 2020Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networkscited by 28
  7. 2014Convex Relaxation of Optimal Power Flow, Part I: Formulations and Equivalencecited by 17
  8. 2019Two-Timescale Voltage Control in Distribution Grids Using Deep Reinforcement Learningcited by 15
  9. 2018Risk-Averse Stochastic Convex Banditcited by 13
  10. 2018Towards Distributed Energy Services: Decentralizing Optimal Power Flow with Machine Learningcited by 8
  11. 2019Designing reactive power control rules for smart inverters using support vector machinescited by 7
  12. 2020Noisy Linear Convergence of Stochastic Gradient Descent for CV@R Statistical Learning under Polyak-Lojasiewicz Conditionscited by 6
8 more
  1. 2015Fast Local Voltage Control under Limited Reactive Power: Optimality and Stability Analysiscited by 5
  2. 2017Load-Flow in Multiphase Distribution Networks: Existence, Uniqueness, Non-Singularity and Linear Modelscited by 5
  3. 2021Controlling Smart Inverters using Proxies: A Chance-Constrained DNN-based Approachcited by 5
  4. 2021Graph Neural Networks for Learning Real-Time Prices in Electricity Marketcited by 5
  5. 2018Matrix Completion for Low-Observability Voltage Estimationcited by 4
  6. 2019Data-driven Voltage Regulation in Radial Power Distribution Systemscited by 3
  7. 2019Dynamic Distribution State Estimation Using Synchrophasor Datacited by 2
  8. 2020On the Impact of Different Voltage Unbalance Metrics in Distribution System Optimizationcited by 1

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Cited by year

Citing papers per year: 2021: 1; 2022: 3; 2023: 0; 2024: 1.

5 papers.

  1. 2022Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutionscited by 28
  2. 2022Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systemscited by 9
  3. 2021Scalable Learning for Optimal Load Shedding Under Power Grid Emergency Operationscited by 3
  4. 2022Minimizing Worst-Case Violations of Neural Networkscited by 3
  5. 2024Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingencycited by 2