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Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning

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  1. Paudel, Diwas & Das, Tapas K., 2023. "A deep reinforcement learning approach for power management of battery-assisted fast-charging EV hubs participating in day-ahead and real-time electricity markets," Energy, Elsevier, vol. 283(C).
  2. Shi, Linjun & Lao, Wenjie & Wu, Feng & Lee, Kwang Y. & Li, Yang & Lin, Keman, 2023. "DDPG-based load frequency control for power systems with renewable energy by DFIM pumped storage hydro unit," Renewable Energy, Elsevier, vol. 218(C).
  3. Ren, Kezheng & Liu, Jun & Liu, Xinglei & Nie, Yongxin, 2023. "Reinforcement Learning-Based Bi-Level strategic bidding model of Gas-fired unit in integrated electricity and natural gas markets preventing market manipulation," Applied Energy, Elsevier, vol. 336(C).
  4. Elinor Ginzburg-Ganz & Itay Segev & Alexander Balabanov & Elior Segev & Sivan Kaully Naveh & Ram Machlev & Juri Belikov & Liran Katzir & Sarah Keren & Yoash Levron, 2024. "Reinforcement Learning Model-Based and Model-Free Paradigms for Optimal Control Problems in Power Systems: Comprehensive Review and Future Directions," Energies, MDPI, vol. 17(21), pages 1-54, October.
  5. Toni Alex Reis Borges & Filipe Cardoso Brito & Rafael Guimarães Oliveira dos Santos & Paulo de Tarso Nascimento & Celso Barreto da Silva & Roberta Mota Panizio & Hugo Saba & Aloísio Santos Nascimento , 2025. "Smart Technologies Applied in Microgrids of Renewable Energy Sources: A Systematic Review," Energies, MDPI, vol. 18(11), pages 1-20, May.
  6. Silvia Trimarchi & Fabio Casamatta & Laura Gamba & Francesco Grimaccia & Marco Lorenzo & Alessandro Niccolai, 2025. "A Review of Agent-Based Models for Energy Commodity Markets and Their Natural Integration with RL Models," Energies, MDPI, vol. 18(12), pages 1-23, June.
  7. Sifat, Md. Mhamud Hussen & Choudhury, Safwat Mukarrama & Das, Sajal K. & Pota, Hemanshu & Yang, Fuwen, 2025. "Novel abstractions and experimental validation for digital twin microgrid design: Lab scale studies and large scale proposals," Applied Energy, Elsevier, vol. 377(PC).
  8. Natalia Aizenberg & Evgeny Barakhtenko & Gleb Mayorov, 2024. "Cooperative Behavior of Prosumers in Integrated Energy Systems," Mathematics, MDPI, vol. 12(24), pages 1-29, December.
  9. Zhao, Yunlong & Han, Fengwu & Zeng, Jianfeng & Zhang, Shengnan & Wu, Tianyu & Zhou, Luming & Gao, Jianwei, 2024. "Coordinated optimization of integrated rural multiple regional energy systems considering electricity to ammonia and improved Shapley value revenue allocation," Energy, Elsevier, vol. 313(C).
  10. Savino, Sabrina & Minella, Tommaso & Nagy, Zoltán & Capozzoli, Alfonso, 2025. "A scalable demand-side energy management control strategy for large residential districts based on an attention-driven multi-agent DRL approach," Applied Energy, Elsevier, vol. 393(C).
  11. Lan, Penghang & Chen, She & Li, Qihang & Li, Kelin & Wang, Feng & Zhao, Yaoxun, 2024. "Intelligent hydrogen-ammonia combined energy storage system with deep reinforcement learning," Renewable Energy, Elsevier, vol. 237(PB).
  12. Anis ur Rehman & Muhammad Ali & Sheeraz Iqbal & Aqib Shafiq & Nasim Ullah & Sattam Al Otaibi, 2022. "Artificial Intelligence-Based Control and Coordination of Multiple PV Inverters for Reactive Power/Voltage Control of Power Distribution Networks," Energies, MDPI, vol. 15(17), pages 1-13, August.
  13. Tariq, Abdul Haseeb & Amin, Uzma, 2025. "Peer-to-peer multi-energy trading in a decentralized network: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 208(C).
  14. Seongwoo Lee & Joonho Seon & Byungsun Hwang & Soohyun Kim & Youngghyu Sun & Jinyoung Kim, 2024. "Recent Trends and Issues of Energy Management Systems Using Machine Learning," Energies, MDPI, vol. 17(3), pages 1-24, January.
  15. Li, Sichen & Hu, Weihao & Cao, Di & Chen, Zhe & Huang, Qi & Blaabjerg, Frede & Liao, Kaiji, 2023. "Physics-model-free heat-electricity energy management of multiple microgrids based on surrogate model-enabled multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 346(C).
  16. Wang, Mei & Liu, Songyuan & Liu, Jiageng & Li, Zhengjun, 2025. "Agent-based modeling of firm's heterogeneous preferences: Implications for trading and technology adoption in electricity-carbon markets," Energy Economics, Elsevier, vol. 148(C).
  17. Liu, Jiejie & Ma, Yanan & Chen, Ying & Zhao, Chunlu & Meng, Xianyang & Wu, Jiangtao, 2025. "Multi-agent deep reinforcement learning-based cooperative energy management for regional integrated energy system incorporating active demand-side management," Energy, Elsevier, vol. 319(C).
  18. Nasir, M. & Bansal, R.C. & Saloumi, M., 2025. "Reinforcement learning algorithms in AC, DC, and hybrid microgrids applications: A comprehensive review," Applied Energy, Elsevier, vol. 401(PC).
  19. Romain Mannini & Julien Eynard & Stéphane Grieu, 2022. "A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids," Energies, MDPI, vol. 15(19), pages 1-37, September.
  20. Ahsan, Syed Muhammad & Gholizadeh, Nastaran & Musilek, Petr, 2025. "Multi-agent systems in networked microgrids: Reinforcement learning and strategic pricing mechanisms," Renewable Energy, Elsevier, vol. 254(C).
  21. Cephas Samende & Zhong Fan & Jun Cao & Renzo Fabián & Gregory N. Baltas & Pedro Rodriguez, 2023. "Battery and Hydrogen Energy Storage Control in a Smart Energy Network with Flexible Energy Demand Using Deep Reinforcement Learning," Energies, MDPI, vol. 16(19), pages 1-20, September.
  22. Li, Yonggang & Su, Yaotong & Zhang, Yuanjin & Wu, Weinong & Xia, Lei, 2024. "Two-layered optimal scheduling under a semi-model architecture of hydro-wind-solar multi-energy systems with hydrogen storage," Energy, Elsevier, vol. 313(C).
  23. Mahmud, Sakib & Sayed, Aya Nabil & Himeur, Yassine & Nhlabatsi, Armstrong & Bensaali, Faycal, 2026. "A comprehensive review of deep reinforcement learning applications from centralized power generation to modern energy internet frameworks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PE).
  24. Liu, Songyuan & Zhou, Peng & Wang, Mei & Xu, Aobo, 2025. "An agent-based approach to modeling power firms' emission reduction strategies and market dynamics," Applied Energy, Elsevier, vol. 400(C).
  25. Cheng, Hangyu & Chen, Jiahui & Jung, Seunghun & Kim, Young-Bae, 2025. "Hierarchical rolling optimization strategy for hybrid electric-hydrogen system based on deep reinforcement learning," Energy, Elsevier, vol. 338(C).
  26. Hu, Ze & Zhu, Ziqing & Wei, Xiang & Chan, Ka Wing & Bu, Siqi, 2025. "Mixed strategy Nash equilibrium analysis in real-time pricing and demand response for future smart retail market," Applied Energy, Elsevier, vol. 391(C).
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