Learning Solutions for Intertemporal Power Systems Optimization with Recurrent Neural Networks
Learning Solutions for Intertemporal Power Systems Optimization with Recurrent Neural Networks
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DOI：
10.1109/pmaps53380.2022.9810638
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
M. Mohammadian;K. Baker;M. H. Dinh;Ferdinando Fioretto
中科院分区：
文献类型：
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作者：
M. Mohammadian;K. Baker;M. H. Dinh;Ferdinando Fioretto
学习系统负荷与最优调度方案之间的映射关系是电力系统和机器学习领域近期感兴趣的一个课题。然而，以往的研究忽略了诸如发电机爬坡限制和其他跨时段要求等实际电力系统约束。此外，最优潮流计算并非独立于先前的时间步长进行——在大多数情况下，代表系统当前状态的最优潮流（OPF）解与先前时间步长的最优潮流解密切相关。在本文中，我们训练了一个递归神经网络，它嵌入了时间步长之间的自然关系，以预测具有跨时段约束的凸电力系统优化问题的最优解。与传统的预测方法相比，该技术的计算优势可使运营商快速模拟系统运行的预测以及相应的最优解，从而更全面地了解未来系统状态。
Learning mappings between system loading and optimal dispatch solutions has been a recent topic of interest in the power systems and machine learning communities. However, previous works have ignored practical power system constraints such as generator ramp limits and other intertemporal requirements. Additionally, optimal power flow runs are not performed independently of previous timesteps - in most cases, an OPF solution representing the current state of the system is heavily related to the OPF solution from previous timesteps. In this paper, we train a recurrent neural network, which embeds natural relationships between timesteps, to predict the optimal solution of convex power systems optimization problems with intertemporal constraints. In contrast to traditional forecasting methods, the computational benefits from this technique can allow operators to rapidly simulate forecasts of system operation and corresponding optimal solutions to provide a more comprehensive view of future system states.