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The paper proposes a novel End-to-End Learning and Repair (E2ELR) architecture for training optimization proxies for economic dispatch problems.
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W. Huang and M. Chen, “DeepOPF-NGT: Fast No Ground Truth Deep Learning-Based Approach for AC-OPF Problems,” in ICML 2021 Workshop Tackling Climate Change with Machine Learning , 2021. [Online]. Available: https://www.climatechange.ai/papers/icml2021/18
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O. Stover, P. Karve, S. Mahadevan, W. Chen, H. Zhao, M. Tanneau, and P. V. Hentenryck, “Just-In-Time Learning for Operational Risk Assessment in Power Grids,” 2022
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R. Nellikkath and S. Chatzivasileiadis, “Physics-informed neural networks for ac optimal power flow,” Electric Power Systems Research , vol. 212, p. 108412, 2022
2022
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M. Zhou, M. Chen, and S. H. Low, “DeepOPF-FT: One Deep Neural Network for Multiple AC-OPF Problems With Flexible Topology,” IEEE Transactions on Power Systems , vol. 38, no. 1, pp. 964–967, 2022
2022
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2023
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2023
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R. Ferrando, L. Pagnier, R. Mieth, Z. Liang, Y. Dvorkin, D. Bienstock, and M. Chertkov, “A Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study,” 2023. [Online]. Available: https://doi.org/10.48550/arXiv.2304.00062
2023
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X. Pan, M. Chen, T. Zhao, and S. H. Low, “DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems,” IEEE Systems Journal , vol. 17, no. 1, pp. 673–683, 2023
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X. Pan, W. Huang, M. Chen, and S. H. Low, “DeepOPF-AL: Augmented Learning for Solving AC-OPF Problems with a Multi-Valued Load-Solution Mapping,” in Proceedings of the 14th ACM International Conference on Future Energy Systems , ser. e-Energy ’23. New York, NY, USA: Association for Computing Machinery, 2023, p. 42–47
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M. Gao, J. Yu, Z. Yang, and J. Zhao, “A Physics-Guided Graph Convolution Neural Network for Optimal Power Flow,” IEEE Transactions on Power Systems , 2023
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M. Mitrovic, A. Lukashevich, P. Vorobev, V. Terzija, S. Budennyy, Y. Maximov, and D. Deka, “Data-driven stochastic AC-OPF using Gaussian process regression,” International Journal of Electrical Power & Energy Systems , vol. 152, p. 109249, 2023
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Z. Hu and H. Zhang, “Optimal power flow based on physical-model-integrated neural network with worth-learning data generation,” 2023
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S. Park and P. Van Hentenryck, “Self-supervised primal-dual learning for constrained optimization,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 4, 2023, pp. 4052–4060
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M. Li, S. Kolouri, and J. Mohammadi, “Learning to Solve Optimization Problems With Hard Linear Constraints,” IEEE Access , vol. 11, pp. 59 995–60 004, 2023
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W. Chen, M. Tanneau, and P. Van Hentenryck, “End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatch,” arXiv (preprint) , 2023
2023
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MISO, “Schedule 28 – Demand Curves for TOperating Reserves,” 2023. [Online]. Available: https://www.misoenergy.org/legal/tariff/
2023
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