Article Sidebar

Download
PDF
Statistic
Read Counter : 19 Download : 2

Main Article Content

Abstract

The integration of high-penetration renewable energy sources (RES) into global power systems necessitates advanced scheduling strategies to ensure supply-demand balance. Virtual Power Plants (VPPs) serve as critical aggregators for distributed resources; however, coordinating VPPs across multiple regions is hindered by the curse of dimensionality, partial observability, and stochastic volatility. Conventional centralized optimization lacks scalability for real-time applications, while single-agent approaches fail to effectively address complex collaborative dynamics. To overcome these limitations, this paper proposes a collaborative scheduling framework based on Multi-Agent Reinforcement Learning (MARL). We model the global system as a multi-regional environment where heterogeneous agents operate under a Centralized Training with Decentralized Execution (CTDE) architecture. A composite reward function is designed to balance economic efficiency with RES absorption, utilizing an attention-based mechanism to exploit time-zone complementarity. Simulation results demonstrate that the proposed method significantly outperforms baseline strategies. Specifically, it achieves a global RES accommodation rate of 94.2% and maintains a minimal tie-line violation rate of 0.8%, compared to only 76.5% accommodation with rule-based heuristics. Furthermore, the approach exhibits superior robustness in extreme-volatility scenarios where standard methods degrade. This study validates the efficacy of distributed intelligence in solving large-scale energy dispatch problems, offering a scalable and privacy-preserving pathway for managing the Global Energy Interconnection.

Keywords

Multi-agent reinforcement learning Virtual power plant Collaborative scheduling Renewable energy consumption Global energy system

Article Details

How to Cite
Zhang, M. (2026). Multi-agent reinforcement learning for global virtual power plant collaborative scheduling: a new approach to optimizing renewable energy consumption. Future Technology, 5(2), 326–335. Retrieved from https://fupubco.com/futech/article/view/858
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
Bookmark and Share

References

  1. Jayanetti, A., Halgamuge, S., & Buyya, R. (2024). Multi-agent deep reinforcement learning framework for renewable energy-aware workflow scheduling on distributed cloud data centers. IEEE Transactions on Parallel and Distributed Systems, 35(4), 604-615. https://doi.org/10.1109/tpds.2024.3360448
  2. Tang, X., & Wang, J. (2025). Deep reinforcement learning-based multi-objective optimization for virtual power plants and smart grids: maximizing renewable energy integration and grid efficiency. Processes, 13(6), 1809. https://doi.org/10.3390/pr13061809
  3. He, G., Huang, Y., Huang, G., Liu, X., Li, P., & Zhang, Y. (2024). Assessment of low-carbon flexibility in self-organized virtual power plants using multi-agent reinforcement learning. Energies, 17(15), 3688. https://doi.org/10.3390/en17153688
  4. Zhang, X., Wang, Q., Yu, J., Sun, Q., Hu, H., & Liu, X. (2023). A multi-agent deep-reinforcement-learning-based strategy for safe distributed energy resource scheduling in energy hubs. Electronics, 12(23), 4763. https://doi.org/10.3390/electronics12234763
  5. Vetter, V., Wohlgenannt, P., Kepplinger, P., & Eder, E. (2025). Deep reinforcement learning approaches the MILP optimum of a multi-energy optimization in energy communities. Energies, 18(17), 4489. https://doi.org/10.20944/preprints202508.0033.v1
  6. Sun, Z., & Lu, T. (2024). Collaborative operation optimization of distribution system and virtual power plants using multi‐agent deep reinforcement learning with parameter‐sharing mechanism. IET Generation, Transmission & Distribution, 18(1), 39-49. https://doi.org/10.1049/gtd2.13037
  7. Li, Y., Chang, W., & Yang, Q. (2025). Deep reinforcement learning based hierarchical energy management for virtual power plant with aggregated multiple heterogeneous microgrids. Applied Energy, 382, 125333. https://doi.org/10.1016/j.apenergy.2025.125333
  8. Aoun, A., Adda, M., Ilinca, A., Ghandour, M., & Ibrahim, H. (2024). Optimizing virtual power plant management: A novel MILP algorithm to minimize levelized cost of energy, technical losses, and greenhouse gas emissions. Energies, 17(16), 4075. https://doi.org/10.3390/en17164075
  9. Guo, B., Li, F., Yang, J., Yang, W., & Sun, B. (2024). The application effect of the optimized scheduling model of virtual power plant participation in the new electric power system. Heliyon, 10(11). https://doi.org/10.1016/j.heliyon.2024.e31748
  10. Yoon, S. J., Ryu, K. S., Kim, C., Nam, Y. H., Kim, D. J., & Kim, B. (2024). Optimal Bidding Scheduling of Virtual Power Plants Using a Dual-MILP (Mixed-Integer Linear Programming) Approach under a Real-Time Energy Market. Energies, 17(15), 3773. https://doi.org/10.3390/en17153773
  11. Arévalo, P., Ochoa-Correa, D., Villa-Ávila, E., Iñiguez-Morán, V., & Astudillo-Salinas, P. (2025). Systematic Review of Hierarchical and Multi-Agent Optimization Strategies for P2P Energy Management and Electric Machines in Microgrids. Applied Sciences, 15(9), 4817. https://doi.org/10.3390/app15094817
  12. Guerra, P., Gil, E., & Hinojosa, V. H. (2024). Improving the Computational Efficiency of the Unit Commitment Problem in Hydrothermal Systems by Using Multi-Agent Deep Reinforcement Learning. IEEE Access, 12, 53266-53276. https://doi.org/10.1109/access.2024.3383442
  13. Michailidis, P., Michailidis, I., & Kosmatopoulos, E. (2025). Reinforcement learning for optimizing renewable energy utilization in buildings: A review on applications and innovations. Energies, 18(7), 1724. https://doi.org/10.3390/en18071724
  14. Liu, Z. (2015). Global energy interconnection. Academic Press. DOI: 10.1016/C2015-0-01255-2
  15. Chatzivasileiadis, S., Ernst, D., & Andersson, G. (2013). The global grid. Renewable Energy, 57, 372-383. https://doi.org/10.1016/j.renene.2013.01.032
  16. Darvishi, M., Tahmasebi, M., Shokouhmand, E., Pasupuleti, J., Bokoro, P., & Raafat, J. S. (2023). Optimal operation of sustainable virtual power plant considering the amount of emission in the presence of renewable energy sources and demand response. Sustainability, 15(14), 11012. https://doi.org/10.3390/su151411012
  17. Jendoubi, I., & Bouffard, F. (2023). Multi-agent hierarchical reinforcement learning for energy management. Applied Energy, 332, 120500. https://doi.org/10.1016/j.apenergy.2022.120500