IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i14p6236-d1439846.html
   My bibliography  Save this article

P2P Optimization Operation Strategy for Photovoltaic Virtual Power Plant Based on Asymmetric Nash Negotiation

Author

Listed:
  • Xiyao Gong

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
    Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China)

  • Wentao Huang

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
    Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China)

  • Jiaxuan Li

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
    Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China)

  • Jun He

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
    Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China)

  • Bohan Zhang

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
    Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China)

Abstract

Under the guidance of the “dual-carbon” target, the utilization of and demand for renewable energy have been growing rapidly. In order to achieve the complementary advantages of renewable energy in virtual power plants with different load characteristics and improve the rate of consumption, an interactive operation strategy for virtual power plants based on asymmetric Nash negotiation is proposed. Firstly, the photovoltaic virtual power plant is proposed to establish the optimal scheduling model for the operation of the virtual power plant, and then the asymmetric Nash negotiation method is adopted to achieve the fair distribution of benefits. Finally, the ADMM distribution is used to solve the proposed model in the solution algorithm. The simulation results show that the revenue enhancement rates are 28.27%, 1.09%, and 12.37%, respectively. The participating subjects’ revenues are effectively enhanced through P2P power sharing. Each subject can obtain a fair distribution of benefits according to the size of its power contribution, which effectively improves the enthusiasm of the PV virtual power plant to participate in P2P interactions and thus promotes the development and consumption of renewable energy.

Suggested Citation

  • Xiyao Gong & Wentao Huang & Jiaxuan Li & Jun He & Bohan Zhang, 2024. "P2P Optimization Operation Strategy for Photovoltaic Virtual Power Plant Based on Asymmetric Nash Negotiation," Sustainability, MDPI, vol. 16(14), pages 1-18, July.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:14:p:6236-:d:1439846
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/14/6236/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/14/6236/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dong, Haiyan & Fu, Yanbo & Jia, Qingquan & Wen, Xiangyun, 2022. "Optimal dispatch of integrated energy microgrid considering hybrid structured electric-thermal energy storage," Renewable Energy, Elsevier, vol. 199(C), pages 628-639.
    2. Zeng, Yu & Wei, Xuan & Yao, Yuan & Xu, Yinliang & Sun, Hongbin & Kin Victor Chan, Wai & Feng, Wei, 2023. "Determining the pricing and deployment strategy for virtual power plants of peer-to-peer prosumers: A game-theoretic approach," Applied Energy, Elsevier, vol. 345(C).
    3. Wang, Xuejie & zhao, Huiru & Lu, Hao & Zhang, Yuanyuan & Wang, Yuwei & Wang, Jingbo, 2022. "Decentralized coordinated operation model of VPP and P2H systems based on stochastic-bargaining game considering multiple uncertainties and carbon cost," Applied Energy, Elsevier, vol. 312(C).
    4. Cui, Xueyuan & Liu, Shu & Ruan, Guangchun & Wang, Yi, 2024. "Data-driven aggregation of thermal dynamics within building virtual power plants," Applied Energy, Elsevier, vol. 353(PB).
    5. Zhao, Kaifang & Qiu, Kai & Yan, Jian & Shaker, Mir Pasha, 2023. "Technical and economic operation of VPPs based on competitive bi–level negotiations," Energy, Elsevier, vol. 282(C).
    6. Gao, Hongchao & Jin, Tai & Feng, Cheng & Li, Chuyi & Chen, Qixin & Kang, Chongqing, 2024. "Review of virtual power plant operations: Resource coordination and multidimensional interaction," Applied Energy, Elsevier, vol. 357(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Han, Fengwu & Zeng, Jianfeng & Lin, Junjie & Zhao, Yunlong & Gao, Chong, 2023. "A stochastic hierarchical optimization and revenue allocation approach for multi-regional integrated energy systems based on cooperative games," Applied Energy, Elsevier, vol. 350(C).
    2. Fatemeh Marzbani & Akmal Abdelfatah, 2024. "Economic Dispatch Optimization Strategies and Problem Formulation: A Comprehensive Review," Energies, MDPI, vol. 17(3), pages 1-31, January.
    3. Xu, Jiazhu & Yi, Yuqin, 2023. "Multi-microgrid low-carbon economy operation strategy considering both source and load uncertainty: A Nash bargaining approach," Energy, Elsevier, vol. 263(PB).
    4. Zheng Shi & Lu Yan & Yingying Hu & Yao Wang & Wenping Qin & Yan Liang & Haibo Zhao & Yongming Jing & Jiaojiao Deng & Zhi Zhang, 2024. "Optimization of Operation Strategy of Multi-Islanding Microgrid Based on Double-Layer Objective," Energies, MDPI, vol. 17(18), pages 1-20, September.
    5. Yiyun Liu & Jun Wu & Jianjun Li & Jingjing Huang, 2023. "The Diffusion Rule of Demand-Oriented Biogas Supply in Distributed Renewable Energy System: An Evolutionary Game-Based Approach," Sustainability, MDPI, vol. 15(19), pages 1-16, September.
    6. 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).
    7. Wei Li & Ruixin Jin & Xiaoyong Ma & Guozun Zhang, 2023. "Capacity Optimal Allocation Method and Frequency Division Energy Management for Hybrid Energy Storage System Considering Grid-Connected Requirements in Photovoltaic System," Energies, MDPI, vol. 16(10), pages 1-16, May.
    8. Qin, Peijia & Tan, Xianlin & Huang, Youbin & Pan, Mingming & Ouyang, Tiancheng, 2023. "Two-stage robust optimal scheduling framework applied for microgrids: Combined energy recovery and forecast," Renewable Energy, Elsevier, vol. 214(C), pages 290-306.
    9. Wang, Beibei & Xu, Lun & Wang, Jialei, 2023. "A privacy-preserving trading strategy for blockchain-based P2P electricity transactions," Applied Energy, Elsevier, vol. 335(C).
    10. Uda Bala & Wei Li & Wenguo Wang & Yuying Gong & Yaheng Su & Yingshu Liu & Yi Zhang & Wei Wang, 2024. "The Sharing Energy Storage Mechanism for Demand Side Energy Communities," Energies, MDPI, vol. 17(21), pages 1-19, October.
    11. Zheng, Weiye & Lu, Hao & Zhu, Jizhong, 2023. "Incentivizing cooperative electricity-heat operation: A distributed asymmetric Nash bargaining mechanism," Energy, Elsevier, vol. 280(C).
    12. Bao, Peng & Xu, Qingshan & Yang, Yongbiao & Nan, Yu & Wang, Yucui, 2024. "Efficient virtual power plant management strategy and Leontief-game pricing mechanism towards real-time economic dispatch support: A case study of large-scale 5G base stations," Applied Energy, Elsevier, vol. 358(C).
    13. Hou, Hui & Ge, Xiangdi & Yan, Yulin & Lu, Yanchao & Zhang, Ji & Dong, Zhao Yang, 2024. "An integrated energy system “green-carbon” offset mechanism and optimization method with Stackelberg game," Energy, Elsevier, vol. 294(C).
    14. Duan, Jiandong & Liu, Fan & Yang, Yao, 2022. "Optimal operation for integrated electricity and natural gas systems considering demand response uncertainties," Applied Energy, Elsevier, vol. 323(C).
    15. Guixing Yang & Haoran Liu & Weiqing Wang & Junru Chen & Shunbo Lei, 2023. "Distributed Optimal Coordination of a Virtual Power Plant with Residential Regenerative Electric Heating Systems," Energies, MDPI, vol. 16(11), pages 1-15, May.
    16. Huiru Zhao & Chao Zhang & Yihang Zhao & Xuejie Wang, 2022. "Low-Carbon Economic Dispatching of Multi-Energy Virtual Power Plant with Carbon Capture Unit Considering Uncertainty and Carbon Market," Energies, MDPI, vol. 15(19), pages 1-25, October.
    17. Yuchen Liu & Zhenhai Dou & Zheng Wang & Jiaming Guo & Jingwei Zhao & Wenliang Yin, 2024. "Optimal Configuration of Electricity-Heat Integrated Energy Storage Supplier and Multi-Microgrid System Scheduling Strategy Considering Demand Response," Energies, MDPI, vol. 17(21), pages 1-23, October.
    18. Nihuan Liao & Zhihong Hu & Vedran Mrzljak & Saber Arabi Nowdeh, 2024. "Stochastic Techno-Economic Optimization of Hybrid Energy System with Photovoltaic, Wind, and Hydrokinetic Resources Integrated with Electric and Thermal Storage Using Improved Fire Hawk Optimization," Sustainability, MDPI, vol. 16(16), pages 1-30, August.
    19. Liu, Fang & Mo, Qiu & Zhao, Xudong, 2023. "Two-level optimal scheduling method for a renewable microgrid considering charging performances of heat pump with thermal storages," Renewable Energy, Elsevier, vol. 203(C), pages 102-112.
    20. Ding, Zhetong & Li, Yaping & Zhang, Kaifeng & Peng, Jimmy Chih-Hsien, 2024. "Two-stage dynamic aggregation involving flexible resource composition and coordination based on submodular optimization," Applied Energy, Elsevier, vol. 360(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:14:p:6236-:d:1439846. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.