A Survey on Applications of Machine Learning for Optimal Power Flow

Hasan, Fouad; Kargarian, Amin; Mohammadi, Ali

doi:10.1109/tpec48276.2020.9042547

Cited by 55 publications

(25 citation statements)

References 36 publications

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“…ML can help improve the existing (centralized) process of scheduling and dispatch by speeding up power system optimization problems and improving the quality of optimization solutions [333,608]. For instance, ML can be used to approximate or simplify existing optimization problems [75,242,311,871], find good starting points for optimization [52,196,382], identify redundant constraints [541], learn from the actions of power system control engineers [197], or do some combination of these [858].…”

Section: High Leveragementioning

confidence: 99%

Tackling Climate Change with Machine Learning

et al. 2022

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Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.

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Section: High Leveragementioning

confidence: 99%

Tackling Climate Change with Machine Learning

et al. 2022

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“…In particular, in the context of OPF, these approaches have involved generating a dataset by running multiple instances of OPF, training a supervised ML model on the corresponding input/output pairs, and then using this model to generate (approximate) OPF solutions. As described in a review by Hasan et al (62), early naïve approaches in this vein could not guarantee the feasibility or optimality of their solutions, limiting their viability in practice. As a result, recent approaches have attempted to incorporate pertinent structure from OPF into deep learning-based approximators, in order to increase their chances of success.…”

Section: Accelerating Centralized Power System Optimization Modelsmentioning

confidence: 99%

Machine Learning for Sustainable Energy Systems

Donti

Kolter

2021

Annu. Rev. Environ. Resour.

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In recent years, machine learning has proven to be a powerful tool for deriving insights from data. In this review, we describe ways in which machine learning has been leveraged to facilitate the development and operation of sustainable energy systems. We first provide a taxonomy of machine learning paradigms and techniques, along with a discussion of their strengths and limitations. We then provide an overview of existing research using machine learning for sustainable energy production, delivery, and storage. Finally, we identify gaps in this literature, propose future research directions, and discuss important considerations for deployment. Expected final online publication date for the Annual Review of Environment and Resources, Volume 46 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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“…Machine learning, including DNNs, has been applied to challenging constrained optimization for decades [16]- [18]. For brevity, we focus on applying learning-based methods to solve constrained optimization problems, divided into two two categories.…”

Section: Related Workmentioning

confidence: 99%

“…• Step 4. Check whether feasibility at the identified adversary sample θ t at Step 1 (with violation) is restored by the post-trained DNN (line [14][15][16][17][18]. If so, proceed to the next round t + 1 and go to Step 1.…”

Section: Adversary Sample-aware Algorithmmentioning

confidence: 99%

Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problems

Zhao¹,

Peng²,

Chen³

et al. 2021

Preprint

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Ensuring solution feasibility is a key challenge in developing Deep Neural Network (DNN) schemes for solving constrained optimization problems, due to inherent DNN prediction errors. In this paper, we propose a "preventive learning" framework to systematically guarantee DNN solution feasibility for problems with convex constraints and general objective functions. We first apply a predict-and-reconstruct design to not only guarantee equality constraints but also exploit them to reduce the number of variables to be predicted by DNN. Then, as a key methodological contribution, we systematically calibrate inequality constraints used in DNN training, thereby anticipating prediction errors and ensuring the resulting solutions remain feasible. We characterize the calibration magnitudes and the DNN size sufficient for ensuring universal feasibility. We propose a new Adversary-Sample Aware training algorithm to improve DNN's optimality performance without sacrificing feasibility guarantee. Overall, the framework provides two DNNs. The first one from characterizing the sufficient DNN size can guarantee universal feasibility while the other from the proposed training algorithm further improves optimality and maintains DNN's universal feasibility simultaneously. We apply the preventive learning framework to develop DeepOPF+ for solving the essential DC optimal power flow problem in grid operation. It improves over existing DNN-based schemes in ensuring feasibility and attaining consistent desirable speedup performance in both light-load and heavy-load regimes. Simulation results over IEEE Case-30/118/300 test cases show that DeepOPF+ generates 100% feasible solutions with <0.5% optimality loss and up to two orders of magnitude computational speedup, as compared to a state-of-the-art iterative solver. I. INTRODUCTIONRecently, there have been increasing interests in developing neural network schemes, including those based on deep neural networks (DNN), to solve constrained optimization problems in various problem domains, especially those needed to be solved repeatedly in real-time. The idea behind these machine learning approaches is to leverage the universal approximation capability of DNNs [1]- [3] to learn the mapping between the input parameters to the

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A Survey on Applications of Machine Learning for Optimal Power Flow

Cited by 55 publications

References 36 publications

Tackling Climate Change with Machine Learning

Tackling Climate Change with Machine Learning

Machine Learning for Sustainable Energy Systems

Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problems

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