论文标题
将深度学习和优化结合到安全受限的最佳功率流
Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow
论文作者
论文摘要
安全受限的最佳功率流（SCOPF）是电力系统的基础，并将同步发电机的自动主要响应（APR）与短期时间表联系起来。每天，对于各种输入，都会反复解决SCOPF问题，以确定一组意外情况。不幸的是，SCOPF问题中APR的建模导致复杂的大规模混合构成程序，这很难解决。为了应对这一挑战，利用可用的历史数据的财富，本文提出了一种新颖的方法，将深度学习和强大的优化技术结合在一起。与最近的机器学习应用程序不同，其目的是减轻确切求解器的计算负担，而拟议的方法直接预测了SCOPF可实施的解决方案。可行性分为两个步骤。首先，在训练期间，拉格朗日双重方法惩罚了违反物理和操作约束的行为，这些方法是通过列和构造生成算法（CCGA）迭代添加到机器学习模型中的。其次，另一个不同的CCGA通过找到最接近预测的可行解决方案来恢复可行性。大型测试案例的实验表明，该方法可显着减少以低于0.1％的最佳差距获得可行解决方案。
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%.
