Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow
Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow
复制标题
结合深度学习和优化以实现安全受限的最佳潮流
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
P. V. Hentenryck
中科院分区：
文献类型：
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作者：
Alexandre Velloso;P. V. Hentenryck
安全约束最优潮流（SCOPF）是电力系统中的基本问题，它将同步发电机的自动一次响应（APR）与短期计划联系起来。每天，SCOPF问题都要针对各种输入进行反复求解，以确定给定一组突发事件的鲁棒调度。不幸的是，在SCOPF问题中的APR建模导致复杂的大规模混合整数规划，这是很难解决的。为了应对这一挑战，利用丰富的可用历史数据，本文提出了一种结合深度学习和鲁棒优化技术的新方法。与最近的机器学习应用程序的目的是减轻精确求解器的计算负担不同，所提出的方法直接预测SCOPF可实现的解决方案。可行性分两步实施。首先，在训练过程中，拉格朗日对偶方法惩罚违反物理和操作约束的行为，这些约束根据需要通过列和约束生成算法（CCGA）迭代地添加到机器学习模型中。其次，另一种不同的CCGA通过找到最接近预测的可行解来恢复可行性。大型测试用例的实验表明，该方法的结果在显着的时间减少获得可行的解决方案的最优性差距小于0.1%。
The security-constrained optimal power flow (SCOPF) is fundamental in power systems and connects the automatic primary response (APR) of synchronized generators with the short-term schedule. Every day, the SCOPF problem is repeatedly solved for various inputs to determine robust schedules given a set of contingencies. Unfortunately, the modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs, which are hard to solve. To address this challenge, leveraging the wealth of available historical data, this paper proposes a novel approach that combines deep learning and robust optimization techniques. Unlike recent machine-learning applications where the aim is to mitigate the computational burden of exact solvers, the proposed method predicts directly the SCOPF implementable solution. Feasibility is enforced in two steps. First, during training, a Lagrangian dual method penalizes violations of physical and operations constraints, which are iteratively added as necessary to the machine-learning model by a Column-and-Constraint-Generation Algorithm (CCGA). Second, another different CCGA restores feasibility by finding the closest feasible solution to the prediction. Experiments on large test cases show that the method results in significant time reduction for obtaining feasible solutions with an optimality gap below 0.1%.
安全约束最优潮流的精确且可扩展的问题分解
影响因子：
3.9
作者：
Velloso, Alexandre;Van Hentenryck, Pascal;Johnson, Emma S.
通讯作者：
Johnson, Emma S.