# On Solving Unit Commitment with Alternating Current Optimal Power Flow on GPU

arXiv: https://arxiv.org/abs/2310.13145 · HTML: https://mlsanity.com/paper/2310.13145/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2023.

## Built on by (most cited first, 2 of 2)

- [QCQP-Net: Reliably Learning Feasible Alternating Current Optimal Power Flow Solutions Under Constraints](https://mlsanity.com/paper/2401.06820/) (2024)
- [Towards Systematic Generalization for Power Grid Optimization Problems](https://mlsanity.com/paper/2605.02026/) (2026)

## Abstract (arXiv)

We consider the unit commitment (UC) problem that employs the alternating current optimal power flow (ACOPF) constraints, which is formulated as a mixed-integer nonlinear programming problem and thus challenging to solve in practice. We develop a new scalable algorithm based on alternating direction method of multiplier (ADMM), which enables quickly finding a good feasible solution of the UC-ACOPF problem. Our algorithm employs the component-based decomposition that solves a large number of independent small subproblems, each of which represents an individual grid component (e.g., generator, bus, and transmission line), as well as the UC subproblem. We implement the algorithm in Julia, as part of the existing package textttExaAdmm.jl, which can efficiently run on GPUs as well as CPUs. The numerical results are reported by using IEEE test instances.