Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods
Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods
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DOI：
10.1609/aaai.v34i01.5403
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发表时间：
2019-09
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影响因子：
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通讯作者：
Ferdinando Fioretto;Terrence W.K. Mak;Pascal Van Hentenryck
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文献类型：
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作者：
Ferdinando Fioretto;Terrence W.K. Mak;Pascal Van Hentenryck
最优潮流(OPF)问题是电力系统优化的基本组成部分。它是非线性和非凸性的，在给定一组负荷要求的情况下，计算发电机的功率和电压设定值。它经常在不同的条件下反复求解，无论是实时的还是大规模的研究。由于仪表前面和后面的可再生能源，电力系统的随机性越来越强，这一需求进一步加剧。为了应对这些挑战，本文提出了一种深度学习最优潮流的方法。该学习模型利用了系统相似状态下的信息(这在实际应用中通常是可用的)，以及一种双重拉格朗日方法来满足最优潮流中存在的物理和工程约束。在大量实际中型电力系统上对所提出的模型进行了评估。实验结果表明，其预测精度较高，平均误差为0.2%。此外，所提出的方法将被广泛采用的线性直流近似的精度提高了至少两个数量级。
The Optimal Power Flow (OPF) problem is a fundamental building block for the optimization of electrical power systems. It is nonlinear and nonconvex and computes the generator setpoints for power and voltage, given a set of load demands. It is often solved repeatedly under various conditions, either in real-time or in large-scale studies. This need is further exacerbated by the increasing stochasticity of power systems due to renewable energy sources in front and behind the meter. To address these challenges, this paper presents a deep learning approach to the OPF. The learning model exploits the information available in the similar states of the system (which is commonly available in practical applications), as well as a dual Lagrangian method to satisfy the physical and engineering constraints present in the OPF. The proposed model is evaluated on a large collection of realistic medium-sized power systems. The experimental results show that its predictions are highly accurate with average errors as low as 0.2%. Additionally, the proposed approach is shown to improve the accuracy of the widely adopted linear DC approximation by at least two orders of magnitude.