Load Embeddings for Scalable AC-OPF Learning
Load Embeddings for Scalable AC-OPF Learning
复制标题
用于可扩展 AC-OPF 学习的负载嵌入
DOI：
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发表时间：
2021
期刊：
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通讯作者：
Pascal VanHentenryck
中科院分区：
文献类型：
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作者：
Terrence W.K. Mak;Ferdinando Fioretto;Pascal VanHentenryck
—交流最佳潮流 (AC-OPF) 是电力系统优化的基本构建模块。这个问题通常会被反复解决，特别是在可再生能源发电普及率较高的地区，以避免违反运营限制。最近的工作表明，深度学习可以有效地提供高精度的 AC-OPF 近似值。然而，深度学习方法可能会遇到可扩展性问题，特别是当应用于大型现实网格时。本文解决了这些可扩展性限制，并提出了一种使用三步方法的负载嵌入方案。第一步将负载嵌入问题表述为可以使用惩罚方法求解的双层优化模型。第二步学习编码优化，以快速为新的 OPF 实例生成负载嵌入。第三步是深度学习模型，它使用负载嵌入来生成准确的 AC-OPF 近似值。该方法在 NESTA 库中的大规模测试用例上进行了实验评估。结果表明，所提出的方法在训练收敛性和预测准确性方面产生了一个数量级的改进。
—AC Optimal Power Flow (AC-OPF) is a fundamental building block in power system optimization. It is often solved repeatedly, especially in regions with large penetration of renewable generation, to avoid violating operational limits. Recent work has shown that deep learning can be effective in providing highly accurate approximations of AC-OPF. However, deep learning approaches may suffer from scalability issues, especially when applied to large realistic grids. This paper addresses these scalability limitations and proposes a load embedding scheme using a 3-step approach. The first step formulates the load embedding problem as a bilevel optimization model that can be solved using a penalty method. The second step learns the encoding optimization to quickly produce load embeddings for new OPF instances. The third step is a deep learning model that uses load embeddings to produce accurate AC-OPF approximations. The approach is evaluated experimentally on large-scale test cases from the NESTA library. The results demonstrate that the proposed approach produces an order of magnitude improvements in training convergence and prediction accuracy.