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Review and Prospects of Artificial Intelligence Technology in Virtual Power Plants

Author

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  • Xinxing Liu

    (College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China)

  • Ciwei Gao

    (College of Electrical Engineering, Southeast University, Nanjing 210096, China)

Abstract

With the rapid development of global renewable energy, the virtual power plant (VPP), as an emerging power management model, has attracted increasing attention. Traditional manual management is difficult to effectively deal with because of the complexity and uncertainty of the VPP. The application of artificial intelligence (AI) technology provides new solutions for the VPP to cope with these problems. This review presents the research of AI technology in the VPP. Firstly, the basic concepts and theoretical framework of the VPP are presented. Then, the application of AI technology in VPP functional modules is discussed. Finally, the challenges of the VPP in coping with uncertainty, improving algorithmic interpretability and ensuring data security are pointed out, which provides theoretical support for subsequent research in the field of VPPs.

Suggested Citation

  • Xinxing Liu & Ciwei Gao, 2025. "Review and Prospects of Artificial Intelligence Technology in Virtual Power Plants," Energies, MDPI, vol. 18(13), pages 1-26, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3325-:d:1686791
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    References listed on IDEAS

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    1. Alabi, Tobi Michael & Lu, Lin & Yang, Zaiyue, 2022. "Data-driven optimal scheduling of multi-energy system virtual power plant (MEVPP) incorporating carbon capture system (CCS), electric vehicle flexibility, and clean energy marketer (CEM) strategy," Applied Energy, Elsevier, vol. 314(C).
    2. Li, Jiaxin & Xu, Zhanbo & Zhou, Yuzhou & Li, Yuting & Wu, Jiang & Guan, Xiaohong, 2024. "Optimal scheduling method and fast-solving algorithm for large-scale virtual power plants communication networks," Applied Energy, Elsevier, vol. 371(C).
    3. Wu, Haochi & Qiu, Dawei & Zhang, Liyu & Sun, Mingyang, 2024. "Adaptive multi-agent reinforcement learning for flexible resource management in a virtual power plant with dynamic participating multi-energy buildings," Applied Energy, Elsevier, vol. 374(C).
    4. Tianfeng Chu & Xingchen An & Wuyang Zhang & Yan Lu & Jiaqi Tian, 2023. "Multiple Virtual Power Plants Transaction Matching Strategy Based on Alliance Blockchain," Sustainability, MDPI, vol. 15(8), pages 1-16, April.
    5. Jiaqi Liu & Hongji Hu & Samson S. Yu & Hieu Trinh, 2023. "Virtual Power Plant with Renewable Energy Sources and Energy Storage Systems for Sustainable Power Grid-Formation, Control Techniques and Demand Response," Energies, MDPI, vol. 16(9), pages 1-28, April.
    6. Xu, Biao & Luan, Wenpeng & Yang, Jing & Zhao, Bochao & Long, Chao & Ai, Qian & Xiang, Jiani, 2024. "Integrated three-stage decentralized scheduling for virtual power plants: A model-assisted multi-agent reinforcement learning method," Applied Energy, Elsevier, vol. 376(PA).
    7. Li, Yiran & Chang, Weiguang & Yang, Qiang, 2025. "Deep reinforcement learning based hierarchical energy management for virtual power plant with aggregated multiple heterogeneous microgrids," Applied Energy, Elsevier, vol. 382(C).
    8. Gengsheng He & Yu Huang & Guori Huang & Xi Liu & Pei Li & Yan Zhang, 2024. "Assessment of Low-Carbon Flexibility in Self-Organized Virtual Power Plants Using Multi-Agent Reinforcement Learning," Energies, MDPI, vol. 17(15), pages 1-20, July.
    9. Wang, Siyi & Sheng, Wanxing & Shang, Yuwei & Liu, Keyan, 2024. "Distribution network voltage control considering virtual power plants cooperative optimization with transactive energy," Applied Energy, Elsevier, vol. 371(C).
    10. Zhichao Qiu & Ye Tian & Yanhong Luo & Taiyu Gu & Hengyu Liu, 2024. "Wind and Photovoltaic Power Generation Forecasting for Virtual Power Plants Based on the Fusion of Improved K-Means Cluster Analysis and Deep Learning," Sustainability, MDPI, vol. 16(23), pages 1-24, December.
    11. Huang, Qian & Li, Jinghua & Zhu, Mengshu, 2020. "An improved convolutional neural network with load range discretization for probabilistic load forecasting," Energy, Elsevier, vol. 203(C).
    12. Zhu Liu & Guowei Guo & Dehuang Gong & Lingfeng Xuan & Feiwu He & Xinglin Wan & Dongguo Zhou, 2025. "Bi-Level Game Strategy for Virtual Power Plants Based on an Improved Reinforcement Learning Algorithm," Energies, MDPI, vol. 18(2), pages 1-16, January.
    13. Nadimi, Reza & Goto, Mika, 2025. "A novel decision support system for enhancing long-term forecast accuracy in virtual power plants using bidirectional long short-term memory networks," Applied Energy, Elsevier, vol. 382(C).
    14. Alam, Khandoker Shahjahan & Kaif, A.M.A. Daiyan & Das, Sajal K., 2024. "A blockchain-based optimal peer-to-peer energy trading framework for decentralized energy management with in a virtual power plant: Lab scale studies and large scale proposal," Applied Energy, Elsevier, vol. 365(C).
    15. Lu, Xin & Qiu, Jing & Zhang, Cuo & Lei, Gang & Zhu, Jianguo, 2024. "Seizing unconventional arbitrage opportunities in virtual power plants: A profitable and flexible recruitment approach," Applied Energy, Elsevier, vol. 358(C).
    16. Li, Xiangyu & Luo, Fengji & Li, Chaojie, 2024. "Multi-agent deep reinforcement learning-based autonomous decision-making framework for community virtual power plants," Applied Energy, Elsevier, vol. 360(C).
    17. Weigang Jin & Peihua Wang & Jiaxin Yuan, 2024. "Key Role and Optimization Dispatch Research of Technical Virtual Power Plants in the New Energy Era," Energies, MDPI, vol. 17(22), pages 1-24, November.
    18. 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).
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