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Market Applications and Uncertainty Handling for Virtual Power Plants

Author

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  • Yujie Jin

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

  • Ciwei Gao

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

Abstract

Virtual power plants achieve the flexible scheduling and management of power systems by integrating distributed energy resources such as renewable energy sources, energy storage systems, and controllable loads. However, due to the instability of renewable energy generation, load demand fluctuations, and market price uncertainty, virtual power plants face a gigantic challenge operating and participating in electricity markets. First, this paper outlines the functions and uncertainties of virtual power plants; then, it describes the uncertainties of virtual power plants in terms of aggregation, participation in market bidding, and optimal dispatch; finally, it summarizes the review.

Suggested Citation

  • Yujie Jin & Ciwei Gao, 2025. "Market Applications and Uncertainty Handling for Virtual Power Plants," Energies, MDPI, vol. 18(14), pages 1-27, July.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:14:p:3743-:d:1701944
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    References listed on IDEAS

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    1. Zamani, Ali Ghahgharaee & Zakariazadeh, Alireza & Jadid, Shahram, 2016. "Day-ahead resource scheduling of a renewable energy based virtual power plant," Applied Energy, Elsevier, vol. 169(C), pages 324-340.
    2. Wang, Yuqing & Fu, Wenjie & Wang, Junlong & Zhen, Zhao & Wang, Fei, 2024. "Ultra-short-term distributed PV power forecasting for virtual power plant considering data-scarce scenarios," Applied Energy, Elsevier, vol. 373(C).
    3. Yetuo Tan & Yongming Zhi & Zhengbin Luo & Honggang Fan & Jun Wan & Tao Zhang, 2023. "Optimal Scheduling of Virtual Power Plant with Flexibility Margin Considering Demand Response and Uncertainties," Energies, MDPI, vol. 16(15), pages 1-14, August.
    4. Yan, Laiqing & Zhang, Xiaoyu & Ullah, Zia & Qazi, Hasan Saeed & Hasanien, Hany M., 2025. "A novel solution strategy for scheduling optimization of virtual power plant considering multiple participants and Peak Energy Market," Renewable Energy, Elsevier, vol. 250(C).
    5. Feng, Jie & Ran, Lun & Wang, Zhiyuan & Zhang, Mengling, 2024. "Optimal energy scheduling of virtual power plant integrating electric vehicles and energy storage systems under uncertainty," Energy, Elsevier, vol. 309(C).
    6. Mei, Shufan & Tan, Qinliang & Trivedi, Anupam & Srinivasan, Dipti, 2024. "A two-step optimization model for virtual power plant participating in spot market based on energy storage power distribution considering comprehensive forecasting error of renewable energy output," Applied Energy, Elsevier, vol. 376(PB).
    7. 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).
    8. Zhang, Jinliang & Liu, Ziyi & Liu, Yishuo, 2025. "A scheduling optimization model for a gas-electricity interconnected virtual power plant considering green certificates-carbon joint trading and source-load uncertainties," Energy, Elsevier, vol. 315(C).
    9. Kong, Xiangyu & Xiao, Jie & Liu, Dehong & Wu, Jianzhong & Wang, Chengshan & Shen, Yu, 2020. "Robust stochastic optimal dispatching method of multi-energy virtual power plant considering multiple uncertainties," Applied Energy, Elsevier, vol. 279(C).
    10. Loßner, Martin & Böttger, Diana & Bruckner, Thomas, 2017. "Economic assessment of virtual power plants in the German energy market — A scenario-based and model-supported analysis," Energy Economics, Elsevier, vol. 62(C), pages 125-138.
    11. Zhai, Xiangyu & Li, Zening & Li, Zhengmao & Xue, Yixun & Chang, Xinyue & Su, Jia & Jin, Xiaolong & Wang, Peng & Sun, Hongbin, 2025. "Risk-averse energy management for integrated electricity and heat systems considering building heating vertical imbalance: An asynchronous decentralized approach," Applied Energy, Elsevier, vol. 383(C).
    12. Cao, Jinye & Xu, Chunlei & Siqin, Zhuoya & Yu, Miao & Diao, Ruisheng, 2025. "Scenario-driven distributionally robust optimization model for a rural virtual power plant considering flexible energy-carbon-green certificate trading," Applied Energy, Elsevier, vol. 379(C).
    13. Nadimi, Reza & Goto, Mika, 2025. "Uncertainty reduction in power forecasting of virtual power plant: From day-ahead to balancing markets," Renewable Energy, Elsevier, vol. 238(C).
    14. Meng, He & Jia, Hongjie & Xu, Tao & Hatziargyriou, Nikos & Wei, Wei & Wang, Rujing, 2025. "Internal pricing driven dynamic aggregation of virtual power plant with energy storage systems," Energy, Elsevier, vol. 321(C).
    15. Liu, Xin & Li, Yang & Wang, Li & Tang, Junbo & Qiu, Haifeng & Berizzi, Alberto & Valentin, Ilea & Gao, Ciwei, 2024. "Dynamic aggregation strategy for a virtual power plant to improve flexible regulation ability," Energy, Elsevier, vol. 297(C).
    16. Guoqiang Sun & Weihang Qian & Wenjin Huang & Zheng Xu & Zhongxing Fu & Zhinong Wei & Sheng Chen, 2019. "Stochastic Adaptive Robust Dispatch for Virtual Power Plants Using the Binding Scenario Identification Approach," Energies, MDPI, vol. 12(10), pages 1-23, May.
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