ExaAdmm.jl implements the two-level alternating direction method of multipliers for solving the component-based decomposition of alternating current optimal power flow problems on GPUs.
The package can be installed in the Julia REPL with the command below:
] ExaAdmmRunning the algorithms on GPU requires Nvidia GPUs with CUDA.jl.
Currently, ExaAdmm.jl supports electrical grid files in the MATLAB format. You can download them from here.
Below shows an example of solving case1354pegase.m using ExaAdmm.jl on GPUs.
env, mod = ExaAdmm.solve_acopf(
"case1354pegase.m";
rho_pq=1e1,
rho_va=1e3,
outer_iterlim=20,
inner_iterlim=20,
scale=1e-4,
tight_factor=0.99,
use_gpu=true
);The following table shows parameter values we used for solving pegase and ACTIVSg data.
| Data | rho_pq | rho_va | scale | obj_scale |
|---|---|---|---|---|
| 1354pegase | 1e1 | 1e3 | 1e-4 | 1.0 |
| 2869pegase | 1e1 | 1e3 | 1e-4 | 1.0 |
| 9241pegase | 5e1 | 5e3 | 1e-4 | 1.0 |
| 13659pegase | 5e1 | 5e3 | 1e-4 | 1.0 |
| ACTIVSg25k | 3e3 | 3e4 | 1e-5 | 1.0 |
| ACTIVSg70k | 3e4 | 3e5 | 1e-5 | 2.0 |
We have used the same tight_factor=0.99, outer_iterlim=20, and inner_iterlim=1000 for all of the above data.
- Youngdae Kim and Kibaek Kim. "Accelerated Computation and Tracking of AC Optimal Power Flow Solutions using GPUs" arXiv preprint arXiv:2110.06879, 2021
- Youngdae Kim, François Pacaud, Kibaek Kim, and Mihai Anitescu. "Leveraging GPU batching for scalable nonlinear programming through massive lagrangian decomposition" arXiv preprint arXiv:2106.14995, 2021
This research was supported by the Exascale ComputingProject (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. This material is based upon work supported by the U.S. Department of Energy, Office of Science, under contract number DE-AC02-06CH11357.