论文标题
Smart-PGSIM：使用神经网络加速AC-OPF电网模拟
Smart-PGSim: Using Neural Network to Accelerate AC-OPF Power Grid Simulation
论文作者
论文摘要
最佳功率流（OPF）问题是电网操作中最重要的优化问题之一。它计算了承诺生成单元的最佳计划。在本文中，我们开发了一种神经网络方法来通过生成智能初始解决方案来加速当前最佳功率流（AC-OPF）的问题。最初解决方案的高质量和由神经网络产生的其他输出的指导能够更快地融合到解决方案，而不会失去传统方法计算的最终解决方案的最佳性。 Smart-PGSIM生成了一种新型的多任务学习神经网络模型，以加速AC-OPF模拟。 Smart-PGSIM还将模拟的物理约束自动施加了仿真的物理约束。对于原始的AC-OPF实施，Smart-PGSIM平均带来了49.2％的性能提高（高达91％），计算出10,000多个问题模拟，而不会失去最终解决方案的最佳性。
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.
