Fully Distributed Reinforcement Learning for Efficient Networked Microgrids Resource Management: Source-Load-Storage Coordination

Xiaowen Wang; Xinquan Shao; Shuai Liu

doi:10.1109/JAS.2025.125636

Volume 13 Issue 7

Jul. 2026

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 18.3, Top 1 (SCI Q1)

CiteScore: 28.2, Top 1% (Q1)
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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2026 > 13(7): 1755-1757

X. Wang, X. Shao, and S. Liu, “Fully distributed reinforcement learning for efficient networked microgrids resource management: Source-load-storage coordination,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 7, pp. 1755–1757, Jul. 2026. doi: 10.1109/JAS.2025.125636

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X. Wang, X. Shao, and S. Liu, “Fully distributed reinforcement learning for efficient networked microgrids resource management: Source-load-storage coordination,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 7, pp. 1755–1757, Jul. 2026. doi: 10.1109/JAS.2025.125636

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Fully Distributed Reinforcement Learning for Efficient Networked Microgrids Resource Management: Source-Load-Storage Coordination

doi: 10.1109/JAS.2025.125636

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References

[1]	L. Xu, J. M. Guerrero, A. Lashab, et al., “A review of DC shipboard microgrids—part I: Power architectures, energy storage, and power converters,” IEEE Trans. Power Electron., vol. 37, no. 5, pp. 5155–5172, 2022. doi: 10.1109/TPEL.2021.3128417
[2]	J. De La Cruz, Y. Wu, J. E. Candelo Becerra, and J. M. Guerrero, “Review of networked microgrid protection: Architectures, challenges, solutions, and future trends,” CSEE J. Power Energy Syst., vol. 10, no. 2, pp. 448–467, 2024.
[3]	A. Hussain, V. H. Bui, and H. M. Kim, “A resilient and privacy-preserving energy management strategy for networked microgrids,” IEEE Trans. Smart Grid, vol. 9, no. 3, pp. 2127–2139, 2018. doi: 10.1109/TSG.2016.2607422
[4]	W. J. Ma, J. Wang, V. Gupta, and C. Chen, “Distributed energy management for networked microgrids using online ADMM with regret,” IEEE Trans. Smart Grid, vol. 9, no. 2, pp. 847–856, 2018. doi: 10.1109/TSG.2016.2569604
[5]	F. Yang, J. Liu, and X. Guan, “Distributed fixed-time optimal energy management for microgrids based on a dynamic event-triggered mechanism,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 12, pp. 2396–2407, 2024. doi: 10.1109/JAS.2024.124686
[6]	J. Li, J. Lu, Y. Liu, and J. Cao, “Optimal energy management for networked microgrids via distributed neurodynamic,” IEEE Trans. Circuits Syst. I-Regul. Pap., vol. 71, no. 5, pp. 2182–2192, 2024. doi: 10.1109/TCSI.2024.3351942
[7]	X. Wang, S. Liu, Q. Xu, and X. Shao, “Distributed multi-agent reinforcement learning for multi-objective optimal dispatch of microgrids,” ISA Trans., vol. 158, pp. 130–140, 2025. doi: 10.1016/j.isatra.2025.01.009
[8]	B. Zhang, C. Dou, D. Yue, et al., “Event-triggered hierarchical multi-mode management strategy for source-load-storage in microgrids,” IEEE Trans. Circuits Syst. I-Regul. Pap., vol. 70, no. 5, pp. 2201–2214, 2023. doi: 10.1109/TCSI.2023.3248584
[9]	X. Xu, Z. Qiu, T. Zhang, and H. Gao, “Distributed source-load-storage cooperative low-carbon scheduling strategy considering vehicle-to-grid aggregators,” J. Mod. Power Syst. Clean Energy, vol. 12, no. 2, pp. 440–453, 2024. doi: 10.35833/MPCE.2023.000742
[10]	R. S. Sutton and A. G. Barto, “Reinforcement learning: An introduction,” Cambridge, USA: MIT Press, 1998.
[11]	B. Lai, W. Chiu, and Y. Tsai, “Multiagent reinforcement learning for community energy management to mitigate peak rebounds under renewable energy uncertainty,” IEEE Trans. Emerg. Top. Comput. Intell., vol. 6, no. 3, pp. 568–579, 2022. doi: 10.1109/TETCI.2022.3157026
[12]	S. Lee, J. Seon, Y. G. Sun, et al., “Novel architecture of energy management systems based on deep reinforcement learning in microgrid,” IEEE Trans. Smart Grid, vol. 15, no. 2, pp. 1646–1658, 2024. doi: 10.1109/TSG.2023.3317096
[13]	S. Li, D. Cao, W. Hu, et al., “Multi-energy management of interconnected multi-microgrid system using multi-agent deep reinforcement learning,” J. Mod. Power Syst. Clean Energy, vol. 11, no. 5, pp. 1606–1617, 2023.
[14]	R. Lu, Z. Jiang, H. Wu, et al., “Reward shaping-based actor-critic deep reinforcement learning for residential energy management,” IEEE Trans. Ind. Inform., vol. 19, no. 3, pp. 2662–2673, 2023. doi: 10.1109/TII.2022.3183802