Machine Learning Assisted Stochastic Unit Commitment: A Feasibility Study
Machine Learning Assisted Stochastic Unit Commitment: A Feasibility Study
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DOI：
10.1109/naps50074.2021.9449789
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
F. Mohammadi;M. Sahraei-Ardakani;D. Trakas;N. Hatziargyriou
中科院分区：
文献类型：
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作者：
F. Mohammadi;M. Sahraei-Ardakani;D. Trakas;N. Hatziargyriou
随机机组组合是一种有效的发电调度模型，在存在大量的不确定性。然而，有效性伴随着大量的计算成本。一般来说，随机机组组合比确定性机组组合等标准方法需要更多的时间来求解机组组合问题。在本文中，初步可行性研究的结果，旨在找出使用基于机器学习的模型，可以方便地解决随机机组组合问题。一个真实世界的大规模测试案例被用来展示机器学习算法在减少计算时间而不牺牲准确性方面的能力和缺点。我们的可行性研究表明，虽然不太可能训练机器学习模型作为独立平台来解决问题，但可以使用经过训练的机器学习模型来帮助加速随机模型的解决方案。
Stochastic unit commitment is an effective model for generation scheduling, in the presence of substantial uncertainty. However, effectiveness comes with substantial computational cost. Generally, stochastic unit commitment needs more time than other standard methods such as deterministic, to solve the unit commitment problem. In this paper, the results of initial feasibility studies are presented aiming to find out if using machine learning-based models can facilitate solving stochastic unit commitment problems. A real-world, large-scale test case is used to demonstrate the capabilities and shortcomings of machine learning algorithms in reducing the calculation time without sacrificing accuracy. Our feasibility study reveals that while it is unlikely to train a machine learning model to solve the problem as a standalone platform, it is possible to use a trained machine learning model to assist in accelerating the solution of the stochastic model.