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Safe deep reinforcement learning-based constrained optimal control scheme for active distribution networks

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  1. Prabawa, Panggah & Choi, Dae-Hyun, 2024. "Safe deep reinforcement learning-assisted two-stage energy management for active power distribution networks with hydrogen fueling stations," Applied Energy, Elsevier, vol. 375(C).
  2. Shen, Rendong & Zhong, Shengyuan & Wen, Xin & An, Qingsong & Zheng, Ruifan & Li, Yang & Zhao, Jun, 2022. "Multi-agent deep reinforcement learning optimization framework for building energy system with renewable energy," Applied Energy, Elsevier, vol. 312(C).
  3. Qingyan Li & Tao Lin & Qianyi Yu & Hui Du & Jun Li & Xiyue Fu, 2023. "Review of Deep Reinforcement Learning and Its Application in Modern Renewable Power System Control," Energies, MDPI, vol. 16(10), pages 1-23, May.
  4. Yu, Peipei & Zhang, Hongcai & Hu, Zechun & Song, Yonghua, 2025. "Voltage control of distribution grid with district cooling systems based on scenario-classified reinforcement learning," Applied Energy, Elsevier, vol. 377(PB).
  5. Jude Suchithra & Duane Robinson & Amin Rajabi, 2023. "Hosting Capacity Assessment Strategies and Reinforcement Learning Methods for Coordinated Voltage Control in Electricity Distribution Networks: A Review," Energies, MDPI, vol. 16(5), pages 1-28, March.
  6. Zhang, Zhengfa & da Silva, Filipe Faria & Guo, Yifei & Bak, Claus Leth & Chen, Zhe, 2021. "Double-layer stochastic model predictive voltage control in active distribution networks with high penetration of renewables," Applied Energy, Elsevier, vol. 302(C).
  7. Chen, Yongdong & Liu, Youbo & Zhao, Junbo & Qiu, Gao & Yin, Hang & Li, Zhengbo, 2023. "Physical-assisted multi-agent graph reinforcement learning enabled fast voltage regulation for PV-rich active distribution network," Applied Energy, Elsevier, vol. 351(C).
  8. Mudhafar Al-Saadi & Maher Al-Greer & Michael Short, 2023. "Reinforcement Learning-Based Intelligent Control Strategies for Optimal Power Management in Advanced Power Distribution Systems: A Survey," Energies, MDPI, vol. 16(4), pages 1-38, February.
  9. Xue, Lin & Zhang, Yao & Wang, Jianxue & Li, Haotian & Li, Fangshi, 2024. "Privacy-preserving multi-level co-regulation of VPPs via hierarchical safe deep reinforcement learning," Applied Energy, Elsevier, vol. 371(C).
  10. Zhang, Xiao & Wu, Zhi & Sun, Qirun & Gu, Wei & Zheng, Shu & Zhao, Jingtao, 2024. "Application and progress of artificial intelligence technology in the field of distribution network voltage Control：A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
  11. Kewei Wang & Yonghong Huang & Junjun Xu & Yanbo Liu, 2024. "A Flexible Envelope Method for the Operation Domain of Distribution Networks Based on “Degree of Squareness” Adjustable Superellipsoid," Energies, MDPI, vol. 17(16), pages 1-19, August.
  12. Jude Suchithra & Amin Rajabi & Duane A. Robinson, 2024. "Enhancing PV Hosting Capacity of Electricity Distribution Networks Using Deep Reinforcement Learning-Based Coordinated Voltage Control," Energies, MDPI, vol. 17(20), pages 1-27, October.
  13. 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).
  14. Jude Suchithra & Duane A. Robinson & Amin Rajabi, 2024. "A Model-Free Deep Reinforcement Learning-Based Approach for Assessment of Real-Time PV Hosting Capacity," Energies, MDPI, vol. 17(9), pages 1-12, April.
  15. Zhang, Yiwen & Lin, Rui & Mei, Zhen & Lyu, Minghao & Jiang, Huaiguang & Xue, Ying & Zhang, Jun & Gao, David Wenzhong, 2024. "Interior-point policy optimization based multi-agent deep reinforcement learning method for secure home energy management under various uncertainties," Applied Energy, Elsevier, vol. 376(PA).
  16. Yin, Linfei & Lu, Yuejiang, 2021. "Expandable deep width learning for voltage control of three-state energy model based smart grids containing flexible energy sources," Energy, Elsevier, vol. 226(C).
  17. Se-Heon Lim & Sung-Guk Yoon, 2022. "Dynamic DNR and Solar PV Smart Inverter Control Scheme Using Heterogeneous Multi-Agent Deep Reinforcement Learning," Energies, MDPI, vol. 15(23), pages 1-18, December.
  18. Gong, Xun & Wang, Xiaozhe & Cao, Bo, 2023. "On data-driven modeling and control in modern power grids stability: Survey and perspective," Applied Energy, Elsevier, vol. 350(C).
  19. Zhu, Dafeng & Yang, Bo & Liu, Yuxiang & Wang, Zhaojian & Ma, Kai & Guan, Xinping, 2022. "Energy management based on multi-agent deep reinforcement learning for a multi-energy industrial park," Applied Energy, Elsevier, vol. 311(C).
  20. Homod, Raad Z. & Togun, Hussein & Kadhim Hussein, Ahmed & Noraldeen Al-Mousawi, Fadhel & Yaseen, Zaher Mundher & Al-Kouz, Wael & Abd, Haider J. & Alawi, Omer A. & Goodarzi, Marjan & Hussein, Omar A., 2022. "Dynamics analysis of a novel hybrid deep clustering for unsupervised learning by reinforcement of multi-agent to energy saving in intelligent buildings," Applied Energy, Elsevier, vol. 313(C).
  21. He, Wangli & Li, Chengyuan & Cai, Chenhao & Qing, Xiangyun & Du, Wenli, 2024. "Suppressing active power fluctuations at PCC in grid-connection microgrids via multiple BESSs: A collaborative multi-agent reinforcement learning approach," Applied Energy, Elsevier, vol. 373(C).
  22. Lu, Renzhi & Li, Yi-Chang & Li, Yuting & Jiang, Junhui & Ding, Yuemin, 2020. "Multi-agent deep reinforcement learning based demand response for discrete manufacturing systems energy management," Applied Energy, Elsevier, vol. 276(C).
  23. Oh, Seok Hwa & Yoon, Yong Tae & Kim, Seung Wan, 2020. "Online reconfiguration scheme of self-sufficient distribution network based on a reinforcement learning approach," Applied Energy, Elsevier, vol. 280(C).
  24. Aras Ghafoor & Jamal Aldahmashi & Judith Apsley & Siniša Djurović & Xiandong Ma & Mohamed Benbouzid, 2024. "Intelligent Integration of Renewable Energy Resources Review: Generation and Grid Level Opportunities and Challenges," Energies, MDPI, vol. 17(17), pages 1-29, September.
  25. Du, Yan & Zandi, Helia & Kotevska, Olivera & Kurte, Kuldeep & Munk, Jeffery & Amasyali, Kadir & Mckee, Evan & Li, Fangxing, 2021. "Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning," Applied Energy, Elsevier, vol. 281(C).
  26. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
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