mlsanityData to 7 Oct 2026

Learning Optimal Power Flow Value Functions with Input-Convex Neural Networks

arXiv 2310.04605 ↗First posted 6 Oct 2023cs.LG math.OCcited by 7

Not on the map. Why?

Abstract (arXiv)

The Optimal Power Flow (OPF) problem is integral to the functioning of power systems, aiming to optimize generation dispatch while adhering to technical and operational constraints. These constraints are far from straightforward; they involve intricate, non-convex considerations related to Alternating Current (AC) power flow, which are essential for the safety and practicality of electrical grids. However, solving the OPF problem for varying conditions within stringent time frames poses practical challenges. To address this, operators resort to model simplifications of varying accuracy. Unfortunately, better approximations (tight convex relaxations) are often computationally intractable. This research explores machine learning (ML) to learn convex approximate solutions for faster analysis in the online setting while still allowing for coupling into other convex dependent decision problems. By trading off a small amount of accuracy for substantial gains in speed, they enable the efficient exploration of vast solution spaces in these complex problems.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 15 of its 39 references that are in the map. First posted on arXiv 6 Oct 2023.

Builds on

15 of 39 references.

References · 15

  1. 2016Input Convex Neural Networkscited by 407
  2. 2021End-to-End Constrained Optimization Learning: A Surveycited by 154
  3. 2019Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methodscited by 93
  4. 2019The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithmscited by 63
  5. 2018Fashionable Modelling with Fluxcited by 53
  6. 2018Averaging Stochastic Gradient Descent on Riemannian Manifoldscited by 39
  7. 2021Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatchcited by 29
  8. 2020Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networkscited by 28
  9. 2020A Convex Neural Network Solver for DCOPF with Generalization Guaranteescited by 17
  10. 2021Modeling the AC Power Flow Equations with Optimally Compact Neural Networks: Application to Unit Commitmentcited by 17
  11. 2021Assessing the Cost of Network Simplifications in Long-Term Hydrothermal Dispatch Planning Modelscited by 2
  12. 2022Benchmarking Large-Scale ACOPF Solutions and Optimality Boundscited by 2
3 more
  1. 2015Regularized Decomposition of High-Dimensional Multistage Stochastic Programs with Markov Uncertaintycited by 1
  2. 2022The Parametric Cost Function Approximation: A new approach for multistage stochastic programmingcited by 1
  3. 2022Enabling Fast Unit Commitment Constraint Screening via Learning Cost Modelcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 2.

7 papers.

  1. 2025ICNN-enhanced 2SP: Leveraging input convex neural networks for solving two-stage stochastic programmingcited by 3
  2. 2024Fast and Reliable N-k Contingency Screening with Input-Convex Neural Networkscited by 2
  3. 2025Tightening optimality gap with confidence through conformal predictioncited by 2
  4. 2025Sobolev Training of End-to-End Optimization Proxiescited by 2
  5. 2025Constraint-Informed Active Learning for End-to-End ACOPF Optimization Proxiescited by 1
  6. 2026SOC-ICNN: From Polyhedral to Conic Geometry for Learning Convex Surrogate Functionscited by 1
  7. 2026Input convex neural networks as surrogates in mathematical optimisationcited by 0