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Artificial emotional deep Q learning for real-time smart voltage control of cyber-physical social power systems

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  • Yin, Linfei
  • He, Xiaoyu

Abstract

The volatility of renewable energy leads to numerous voltage changes in a short period, thus affecting the quality of the power supply. A real-time smart voltage control framework of cyber-physical social power systems is proposed to replace the traditional multi-timescale “reactive power optimization and voltage regulation” framework. This work combines artificial emotion, deep learning, and Q learning as an artificial emotional deep Q-learning algorithm. The proposed framework and algorithm are simulated in the developed parallel smart voltage control platform. The proposed algorithm has a strong-robust capability of real-time online updating, learning, and decision-making. Furthermore, four virtual systems simulating the actual smart voltage control systems are built. Compared to the conventional proportional-integral-derivative, the voltage deviation of the proposed algorithm is reduced by 68.95%. Among six uniform time-scale algorithms, the proposed algorithm has the highest control performance and the lowest control error. Besides, the parameters of the proposed algorithm are continuously optimized to enhance the control performance through the continuous interaction of the parallel systems. The numerical results verify the effectiveness and feasibility of the real-time smart voltage control framework of cyber-physical social power systems.

Suggested Citation

  • Yin, Linfei & He, Xiaoyu, 2023. "Artificial emotional deep Q learning for real-time smart voltage control of cyber-physical social power systems," Energy, Elsevier, vol. 273(C).
    • Handle: RePEc:eee:energy:v:273:y:2023:i:c:s0360544223006266
    DOI: 10.1016/j.energy.2023.127232
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    as
    1. Sun, Xu & Liu, Yanli & Deng, Liangchen, 2020. "Reliability assessment of cyber-physical distribution network based on the fault tree," Renewable Energy, Elsevier, vol. 155(C), pages 1411-1424.
    2. Zhang, Dongdong & Li, Chunjiao & Goh, Hui Hwang & Ahmad, Tanveer & Zhu, Hongyu & Liu, Hui & Wu, Thomas, 2022. "A comprehensive overview of modeling approaches and optimal control strategies for cyber-physical resilience in power systems," Renewable Energy, Elsevier, vol. 189(C), pages 1383-1406.
    3. Murray, William & Adonis, Marco & Raji, Atanda, 2021. "Voltage control in future electrical distribution networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    4. Utama, Christian & Meske, Christian & Schneider, Johannes & Ulbrich, Carolin, 2022. "Reactive power control in photovoltaic systems through (explainable) artificial intelligence," Applied Energy, Elsevier, vol. 328(C).
    5. Panda, Mitali & Nayak, Yogesh Kumar, 2022. "Impact analysis of renewable energy Distributed Generation in deregulated electricity markets: A Context of Transmission Congestion Problem," Energy, Elsevier, vol. 254(PC).
    6. Ranjbar, Hossein & Kazemi, Mostafa & Amjady, Nima & Zareipour, Hamidreza & Hosseini, Seyed Hamid, 2022. "Maximizing the utilization of existing grids for renewable energy integration," Renewable Energy, Elsevier, vol. 189(C), pages 618-629.
    7. Han, Kunlun & Yang, Kai & Yin, Linfei, 2022. "Lightweight actor-critic generative adversarial networks for real-time smart generation control of microgrids," Applied Energy, Elsevier, vol. 317(C).
    8. Dehghani, Nariman L. & Jeddi, Ashkan B. & Shafieezadeh, Abdollah, 2021. "Intelligent hurricane resilience enhancement of power distribution systems via deep reinforcement learning," Applied Energy, Elsevier, vol. 285(C).
    9. Yin, Linfei & Gao, Qi & Zhao, Lulin & Wang, Tao, 2020. "Expandable deep learning for real-time economic generation dispatch and control of three-state energies based future smart grids," Energy, Elsevier, vol. 191(C).
    10. Perez-Molina, M.J. & Larruskain, D.M. & Eguia Lopez, P. & Buigues, G. & Valverde, V., 2021. "Review of protection systems for multi-terminal high voltage direct current grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    11. Cao, Di & Zhao, Junbo & Hu, Weihao & Ding, Fei & Yu, Nanpeng & Huang, Qi & Chen, Zhe, 2022. "Model-free voltage control of active distribution system with PVs using surrogate model-based deep reinforcement learning," Applied Energy, Elsevier, vol. 306(PA).
    12. Zhang, Zhengfa & da Silva, Filipe Faria & Guo, Yifei & Bak, Claus Leth & Chen, Zhe, 2022. "Coordinated voltage control in unbalanced distribution networks with two-stage distributionally robust chance-constrained receding horizon control," Renewable Energy, Elsevier, vol. 198(C), pages 907-915.
    13. Chaoyang Zhang & Zhengxu Wang & Kai Ding & Felix T.S. Chan & Weixi Ji, 2020. "An energy-aware cyber physical system for energy Big data analysis and recessive production anomalies detection in discrete manufacturing workshops," International Journal of Production Research, Taylor & Francis Journals, vol. 58(23), pages 7059-7077, December.
    14. Yin, Linfei & Zhang, Bin, 2021. "Time series generative adversarial network controller for long-term smart generation control of microgrids," Applied Energy, Elsevier, vol. 281(C).
    15. Momchil S. Tomov & Eric Schulz & Samuel J. Gershman, 2021. "Multi-task reinforcement learning in humans," Nature Human Behaviour, Nature, vol. 5(6), pages 764-773, June.
    16. Xu, Luo & Guo, Qinglai & Sheng, Yujie & Muyeen, S.M. & Sun, Hongbin, 2021. "On the resilience of modern power systems: A comprehensive review from the cyber-physical perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    17. Hassani, Hossein & Razavi-Far, Roozbeh & Saif, Mehrdad, 2022. "Real-time out-of-step prediction control to prevent emerging blackouts in power systems: A reinforcement learning approach," Applied Energy, Elsevier, vol. 314(C).
    18. Huang, Manyun & Wei, Zhinong & Lin, Yuzhang, 2022. "Forecasting-aided state estimation based on deep learning for hybrid AC/DC distribution systems," Applied Energy, Elsevier, vol. 306(PB).
    19. Qiao, Dalei & Wu, Shun & Li, Ge & You, Jiaxing & Zhang, Juan & Shen, Bilong, 2022. "Wind speed forecasting using multi-site collaborative deep learning for complex terrain application in valleys," Renewable Energy, Elsevier, vol. 189(C), pages 231-244.
    20. Eros D. Escobar & Tatiana Manrique & Idi A. Isaac, 2022. "Campus Microgrid Data-Driven Model Identification and Secondary Voltage Control," Energies, MDPI, vol. 15(21), pages 1-19, October.
    21. Chen, Xiaoyang & Du, Yang & Lim, Enggee & Wen, Huiqing & Yan, Ke & Kirtley, James, 2020. "Power ramp-rates of utility-scale PV systems under passing clouds: Module-level emulation with cloud shadow modeling," Applied Energy, Elsevier, vol. 268(C).
    22. Shair, Jan & Li, Haozhi & Hu, Jiabing & Xie, Xiaorong, 2021. "Power system stability issues, classifications and research prospects in the context of high-penetration of renewables and power electronics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    23. Lai, Qiupin & Liu, Chengxi & Sun, Kai, 2021. "Vulnerability assessment for voltage stability based on solvability regions of decoupled power flow equations," Applied Energy, Elsevier, vol. 304(C).
    24. Naval, Natalia & Sánchez, Raul & Yusta, Jose M., 2020. "A virtual power plant optimal dispatch model with large and small-scale distributed renewable generation," Renewable Energy, Elsevier, vol. 151(C), pages 57-69.
    25. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    26. Alghamdi, Baheej & Cañizares, Claudio, 2022. "Frequency and voltage coordinated control of a grid of AC/DC microgrids," Applied Energy, Elsevier, vol. 310(C).
    27. Aragón, Gustavo & Pandian, Vinoth & Krauß, Veronika & Werner-Kytölä, Otilia & Thybo, Gitte & Pautasso, Elisa, 2022. "Feasibility and economical analysis of energy storage systems as enabler of higher renewable energy sources penetration in an existing grid," Energy, Elsevier, vol. 251(C).
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