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This paper develops an efficient multi-agent deep reinforcement learning algorithm for cooperative controls in powergrids.
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M. Savaghebi, A. Jalilian, J. C. Vasquez, and J. M. Guerrero, “Secondary control scheme for voltage unbalance compensation in an islanded droop-controlled microgrid,” IEEE Transactions on Smart Grid , vol. 3, no. 2, pp. 797–807, 2012
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Q. Shafiee, J. M. Guerrero, and J. C. Vasquez, “Distributed secondary control for islanded microgrids—a novel approach,” IEEE Transactions on power electronics , vol. 29, no. 2, pp. 1018–1031, 2013
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A. Bidram, A. Davoudi, F. L. Lewis, and J. M. Guerrero, “Distributed cooperative secondary control of microgrids using feedback linearization,” IEEE Transactions on Power Systems , vol. 28, no. 3, pp. 3462–3470, 2013
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F. Guo, C. Wen, J. Mao, and Y.-D. Song, “Distributed secondary voltage and frequency restoration control of droop-controlled inverter-based microgrids,” IEEE Transactions on industrial Electronics , vol. 62, no. 7, pp. 4355–4364, 2014
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J. W. Simpson-Porco, Q. Shafiee, F. Dörfler, J. C. Vasquez, J. M. Guerrero, and F. Bullo, “Secondary frequency and voltage control of islanded microgrids via distributed averaging,” IEEE Transactions on Industrial Electronics , vol. 62, no. 11, pp. 7025–7038, 2015
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J. Lai, H. Zhou, X. Lu, X. Yu, and W. Hu, “Droop-based distributed cooperative control for microgrids with time-varying delays,” IEEE Transactions on Smart Grid , vol. 7, no. 4, pp. 1775–1789, 2016
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X. Lu, X. Yu, J. Lai, J. M. Guerrero, and H. Zhou, “Distributed secondary voltage and frequency control for islanded microgrids with uncertain communication links,” IEEE Transactions on Industrial Informatics , vol. 13, no. 2, pp. 448–460, 2016
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V. K. Sood and H. Abdelgawad, “Chapter 1 - microgrids architectures,” in Distributed Energy Resources in Microgrids , R. K. Chauhan and K. Chauhan, Eds. Academic Press, 2019, pp. 1 – 31. [Online]. Available: http://www.sciencedirect.com/science/article/pii/B9780128177747000016
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M. Glavic, “(deep) reinforcement learning for electric power system control and related problems: A short review and perspectives,” Annual Reviews in Control , vol. 48, pp. 22–35, 2019
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R. Diao, Z. Wang, D. Shi, Q. Chang, J. Duan, and X. Zhang, “Autonomous voltage control for grid operation using deep reinforcement learning,” in 2019 IEEE Power & Energy Society General Meeting (PESGM) . IEEE, 2019, pp. 1–5
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J. Duan, D. Shi, R. Diao, H. Li, Z. Wang, B. Zhang, D. Bian, and Z. Yi, “Deep-reinforcement-learning-based autonomous voltage control for power grid operations,” IEEE Transactions on Power Systems , vol. 35, no. 1, pp. 814–817, 2019
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D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot et al. , “Mastering the game of go with deep neural networks and tree search,” nature , vol. 529, no. 7587, pp. 484–489, 2016
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J. Foerster, I. A. Assael, N. De Freitas, and S. Whiteson, “Learning to communicate with deep multi-agent reinforcement learning,” in Advances in neural information processing systems , 2016, pp. 2137–2145
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J. Lai, X. Lu, X. Yu, W. Yao, J. Wen, and S. Cheng, “Distributed voltage control for dc mircogrids with coupling delays & noisy disturbances,” in IECON 2017-43rd Annual Conference of the IEEE Industrial Electronics Society . IEEE, 2017, pp. 2461–2466
2017
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2017
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2017
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2019
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Q. Yang, G. Wang, A. Sadeghi, G. B. Giannakis, and J. Sun, “Two-timescale voltage control in distribution grids using deep reinforcement learning,” IEEE Transactions on Smart Grid , vol. 11, no. 3, pp. 2313–2323, 2019
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O. Vinyals, I. Babuschkin, W. M. Czarnecki, M. Mathieu, A. Dudzik, J. Chung, D. H. Choi, R. Powell, T. Ewalds, P. Georgiev et al. , “Grandmaster level in starcraft ii using multi-agent reinforcement learning,” Nature , vol. 575, no. 7782, pp. 350–354, 2019
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2019
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T. Chu, J. Wang, L. Codecà, and Z. Li, “Multi-agent deep reinforcement learning for large-scale traffic signal control,” IEEE Transactions on Intelligent Transportation Systems , vol. 21, no. 3, pp. 1086–1095, 2019
2019
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A. Mustafa, B. Poudel, A. Bidram, and H. Modares, “Detection and mitigation of data manipulation attacks in ac microgrids,” IEEE Transactions on Smart Grid , vol. 11, no. 3, pp. 2588–2603, 2019
2019
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S. Wang, J. Duan, D. Shi, C. Xu, H. Li, R. Diao, and Z. Wang, “A data-driven multi-agent autonomous voltage control framework using deep reinforcement learning,” IEEE Transactions on Power Systems , 2020
2020
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D. Chen, L. Jiang, Y. Wang, and Z. Li, “Autonomous driving using safe reinforcement learning by incorporating a regret-based human lane-changing decision model,” in 2020 American Control Conference (ACC) . IEEE, 2020, pp. 4355–4361
2020
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D. Cao, W. Hu, J. Zhao, Q. Huang, Z. Chen, and F. Blaabjerg, “A multi-agent deep reinforcement learning based voltage regulation using coordinated pv inverters,” IEEE Transactions on Power Systems , vol. 35, no. 5, pp. 4120–4123, 2020
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Y. Zhang, X. Wang, J. Wang, and Y. Zhang, “Deep reinforcement learning based volt-var optimization in smart distribution systems,” IEEE Transactions on Smart Grid , vol. 12, no. 1, pp. 361–371, 2020
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B. Ning, Q.-L. Han, and L. Ding, “Distributed secondary control of ac microgrids with external disturbances and directed communication topologies: A full-order sliding-mode approach,” IEEE/CAA Journal of Automatica Sinica , 2020
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Y. Gao, W. Wang, and N. Yu, “Consensus multi-agent reinforcement learning for volt-var control in power distribution networks,” IEEE Transactions on Smart Grid , 2021
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