mlsanityData to 14 Oct 2026

Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitments

arXiv 2211.15755 ↗First posted 28 Nov 2022cs.LG math.OCcited by 12

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Abstract (arXiv)

Reliability Assessment Commitment (RAC) Optimization is increasingly important in grid operations due to larger shares of renewable generations in the generation mix and increased prediction errors. Independent System Operators (ISOs) also aim at using finer time granularities, longer time horizons, and possibly stochastic formulations for additional economic and reliability benefits. The goal of this paper is to address the computational challenges arising in extending the scope of RAC formulations. It presents RACLearn that (1) uses a Graph Neural Network (GNN) based architecture to predict generator commitments and active line constraints, (2) associates a confidence value to each commitment prediction, (3) selects a subset of the high-confidence predictions, which are (4) repaired for feasibility, and (5) seeds a state-of-the-art optimization algorithm with feasible predictions and active constraints. Experimental results on exact RAC formulations used by the Midcontinent Independent System Operator (MISO) and an actual transmission network (8965 transmission lines, 6708 buses, 1890 generators, and 6262 load units) show that the RACLearn framework can speed up RAC optimization by factors ranging from 2 to 4 with negligible loss in solution quality.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 43 of its 82 references that are in the map. First posted on arXiv 28 Nov 2022.

Builds on

43 of 82 references.

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31 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 5; 2024: 2; 2025: 3; 2026: 2.

12 papers.

  1. 2023Compact Optimization Learning for AC Optimal Power Flowcited by 11
  2. 2023Spatio-Temporal Deep Learning-Assisted Reduced Security-Constrained Unit Commitmentcited by 6
  3. 2023Two-Stage Learning For the Flexible Job Shop Scheduling Problemcited by 4
  4. 2025PROPEL: Supervised and Reinforcement Learning for Large-Scale Supply Chain Planningcited by 4
  5. 2023Optimization-based Learning for Dynamic Load Planning in Trucking Service Networkscited by 3
  6. 2023Self-Supervised Learning for Large-Scale Preventive Security Constrained DC Optimal Power Flowcited by 3
  7. 2025Flow-Aware GNN for Transmission Network Reconfiguration via Substation Breaker Optimizationcited by 3
  8. 2025Constraint Matters: Multi-Modal Representation for Reducing Mixed-Integer Linear programmingcited by 2
  9. 2024Graph neural networks for power grid operational risk assessment under evolving grid topologycited by 1
  10. 2024Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placementcited by 1
  11. 2026Fusing Backdoors, Machine Learning, and Optimization for Large-Scale Parametric Mixed-Integer Programscited by 0
  12. 2026Graph Machine Learning: An Opportunity for Power Systemscited by 0