Learning for DC-OPF: Classifying active sets using neural nets
Learning for DC-OPF: Classifying active sets using neural nets
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DOI：
10.1109/ptc.2019.8810819
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发表时间：
2019-02
期刊：
影响因子：
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通讯作者：
Deepjyoti Deka;Sidhant Misra
中科院分区：
文献类型：
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作者：
Deepjyoti Deka;Sidhant Misra
最优潮流是电力系统运行规划中使用的一个优化问题，旨在满足需求和保持安全裕度的同时最大化经济效率。由于可再生能源发电和需求的不确定性和多变性，最优解需要根据观测到的不确定性实现情况或近实时的预测更新进行调整。为了解决对最优解进行如此频繁的实时更新计算的挑战，近期文献提出利用机器学习来学习不确定性实现情况与最优解之间的映射关系。此外，与直接学习最优解不同，学习最优情况下的有效约束集已被证明可显著简化机器学习任务，并且所学习的模型可用于实时预测最优解。在本文中，我们提出使用分类算法来学习不确定性实现情况与最优情况下的有效约束集之间的映射关系，从而进一步提高实时预测的计算效率。我们为此任务采用神经网络分类器，并在IEEE PES PGLib - OPF基准库中的多个系统上展示了这种方法的优异性能。
The optimal power flow is an optimization problem used in power systems operational planning to maximize economic efficiency while satisfying demand and maintaining safety margins. Due to uncertainty and variability in renewable energy generation and demand, the optimal solution needs to be updated in response to observed uncertainty realizations or near real-time forecast updates. To address the challenge of computing such frequent real-time updates to the optimal solution, recent literature has proposed the use of machine learning to learn the mapping between the uncertainty realization and the optimal solution. Further, learning the active set of constraints at optimality, as opposed to directly learning the optimal solution, has been shown to significantly simplify the machine learning task, and the learnt model can be used to predict optimal solutions in real-time. In this paper, we propose the use of classification algorithms to learn the mapping between the uncertainty realization and the active set of constraints at optimality, thus further enhancing the computational efficiency of the real-time prediction. We employ neural net classifiers for this task and demonstrate the excellent performance of this approach on a number of systems in the IEEE PES PGLib-OPF benchmark library.