Learning-based AC-OPF Solvers on Realistic Network and Realistic Loads
This addresses a data gap for power grid optimization researchers, but it is incremental as it applies existing deep learning methods to a new realistic dataset.
The authors tackled the lack of realistic datasets for AC-OPF by constructing TAS-97, which includes realistic network topology and loads from Tasmania, and trained a deep learning solver that achieved a 0.13% cost optimality gap, 99.73% feasibility rate, and 38.62x speedup compared to PYPOWER.
Deep learning approaches for the Alternating Current-Optimal Power Flow (AC-OPF) problem are under active research in recent years. A common shortcoming in this area of research is the lack of a dataset that includes both a realistic power network topology and the corresponding realistic loads. To address this issue, we construct an AC-OPF formulation-ready dataset called TAS-97 that contains realistic network information and realistic bus loads from Tasmania's electricity network. We found that the realistic loads in Tasmania are correlated between buses and they show signs of an underlying multivariate normal distribution. Feasibility-optimized end-to-end deep neural network models are trained and tested on the constructed dataset. Trained on samples with bus loads generated from a fitted multivariate normal distribution, our learning-based AC-OPF solver achieves 0.13% cost optimality gap, 99.73% feasibility rate, and 38.62 times of speedup on realistic testing samples when compared to PYPOWER.