The optimal power flow (OPF) problem is one of the most important optimization problems for the operation of the power grid. It calculates the optimum scheduling of the committed generation units. In this paper, we develop a neural network approach to the problem of accelerating the current optimal power flow (AC-OPF) by generating an intelligent initial solution. The high quality of the initial solution and guidance of other outputs generated by the neural network enables faster convergence to the solution without losing optimality of final solution as computed by traditional methods. Smart-PGSim generates a novel multitask-learning neural network model to accelerate the AC-OPF simulation. Smart-PGSim also imposes the physical constraints of the simulation on the neural network automatically. Smart-PGSim brings an average of 49.2% performance improvement (up to 91%), computed over 10,000 problem simulations, with respect to the original AC-OPF implementation, without losing the optimality of the final solution.

中文翻译：

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Smart-PGSim：使用神经网络加速 AC-OPF 电网仿真

最优潮流（OPF）问题是电网运行最重要的优化问题之一。它计算承诺的发电机组的最佳调度。在本文中，我们开发了一种神经网络方法，通过生成智能初始解决方案来解决加速当前最优潮流 (AC-OPF) 的问题。高质量的初始解和神经网络生成的其他输出的指导能够更快地收敛到解，而不会失去通过传统方法计算的最终解的最优性。Smart-PGSim 生成一种新颖的多任务学习神经网络模型来加速 AC-OPF 仿真。Smart-PGSim 还自动将模拟的物理约束强加于神经网络。Smart-PGSim 带来平均 49 个。