论文标题
基于学习的AC-OPF求解器现实网络和现实负载
Learning-based AC-OPF Solvers on Realistic Network and Realistic Loads
论文作者
论文摘要
近年来，正在积极研究中，针对流动 - 最佳功率流（AC-OPF）问题的深度学习方法。在这一研究领域的一个常见缺点是缺乏包括现实的电力网络拓扑结构和相应现实负载的数据集。为了解决此问题，我们构建了一个称为TAS-97的AC-OPF公式的数据集，该数据集包含塔斯马尼亚州电力网络的现实网络信息和现实的总线负载。我们发现，塔斯马尼亚州的现实载荷在总线之间相关，它们显示出潜在的多元正态分布的迹象。在构造的数据集上对可行性优化的端到端深度神经网络模型进行了训练和测试。接受了由拟合的多元正态分布产生的公共汽车负载的样品培训，我们基于学习的AC-OPF求解器达到了0.13％的成本最佳差距，99.73％的可行性率和38.62倍的现实测试样品的加速速度38.62倍。
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.
