IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v228y2024ics0960148124007353.html
   My bibliography  Save this article

Optimization method of dynamic reconfiguration in virtual power plants

Author

Listed:
  • Sun, Lingling
  • Li, Haibin
  • Jia, Qingquan
  • Zhang, Gong

Abstract

With the continuous expansion of distributed energy resources (DERs), virtual power plants (VPPs) have emerged as an efficient solution for their aggregation and management. This study proposes a dynamic reconfiguration method for VPPs, addressing the problem of optimally aggregating DERs within a VPP. To enhance DER participation, an innovative membership rewards mechanism is introduced. Furthermore, we develop a method for DER feature extraction and classification to deeply understand their performance. We also assess DERs' willingness to participate, which is the precondition for effective aggregation. The core of our method is an optimized strategy for multi-stage dynamic planning of VPPs, designed to accommodate the dynamic evolution of DERs and electricity markets conditions, and formulated as a two-layer model to offer optimal solutions for DERs aggregation. Numerical simulations validate the superiority of our method in economic benefits and operational efficiency of VPP, highlighting our contributions to enhance the feasibility and efficiency of VPP aggregation through multi-stage planning, dynamic adaptability, and strategic incentives.

Suggested Citation

  • Sun, Lingling & Li, Haibin & Jia, Qingquan & Zhang, Gong, 2024. "Optimization method of dynamic reconfiguration in virtual power plants," Renewable Energy, Elsevier, vol. 228(C).
    • Handle: RePEc:eee:renene:v:228:y:2024:i:c:s0960148124007353
    DOI: 10.1016/j.renene.2024.120667
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148124007353
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2024.120667?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yan, Xingyu & Gao, Ciwei & Meng, Jing & Abbes, Dhaker, 2024. "An analytical target cascading method-based two-step distributed optimization strategy for energy sharing in a virtual power plant," Renewable Energy, Elsevier, vol. 222(C).
    2. Li, Qiang & Wei, Fanchao & Zhou, Yongcheng & Li, Jiajia & Zhou, Guowen & Wang, Zhonghao & Liu, Jinfu & Yan, Peigang & Yu, Daren, 2023. "A scheduling framework for VPP considering multiple uncertainties and flexible resources," Energy, Elsevier, vol. 282(C).
    3. Luo, Zhe & Hong, SeungHo & Ding, YueMin, 2019. "A data mining-driven incentive-based demand response scheme for a virtual power plant," Applied Energy, Elsevier, vol. 239(C), pages 549-559.
    4. Li, Jinghua & Lu, Bo & Wang, Zhibang & Zhu, Mengshu, 2021. "Bi-level optimal planning model for energy storage systems in a virtual power plant," Renewable Energy, Elsevier, vol. 165(P2), pages 77-95.
    5. Bhuiyan, Erphan A. & Hossain, Md. Zahid & Muyeen, S.M. & Fahim, Shahriar Rahman & Sarker, Subrata K. & Das, Sajal K., 2021. "Towards next generation virtual power plant: Technology review and frameworks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    6. Shang, Yuping & Sang, Shenghu & Tiwari, Aviral Kumar & Khan, Salahuddin & Zhao, Xin, 2024. "Impacts of renewable energy on climate risk: A global perspective for energy transition in a climate adaptation framework," Applied Energy, Elsevier, vol. 362(C).
    7. Bates, John J., 2024. "Pivoting from a known base when predicting choices using logit models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    8. Petrucci, Andrea & Ayevide, Follivi Kloutse & Buonomano, Annamaria & Athienitis, Andreas, 2023. "Development of energy aggregators for virtual communities: The energy efficiency-flexibility nexus for demand response," Renewable Energy, Elsevier, vol. 215(C).
    9. Chang, Weiguang & Dong, Wei & Wang, Yubin & Yang, Qiang, 2022. "Two-stage coordinated operation framework for virtual power plant with aggregated multi-stakeholder microgrids in a deregulated electricity market," Renewable Energy, Elsevier, vol. 199(C), pages 943-956.
    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. Mei, Shufan & Tan, Qinliang & Liu, Yuan & Trivedi, Anupam & Srinivasan, Dipti, 2023. "Optimal bidding strategy for virtual power plant participating in combined electricity and ancillary services market considering dynamic demand response price and integrated consumption satisfaction," Energy, Elsevier, vol. 284(C).
    2. Ebrahimi, Mahoor & Ebrahimi, Mahan & Shafie-khah, Miadreza & Laaksonen, Hannu, 2024. "EV-observing distribution system management considering strategic VPPs and active & reactive power markets," Applied Energy, Elsevier, vol. 364(C).
    3. Kong, Xiangyu & Wang, Zhengtao & Liu, Chao & Zhang, Delong & Gao, Hongchao, 2023. "Refined peak shaving potential assessment and differentiated decision-making method for user load in virtual power plants," Applied Energy, Elsevier, vol. 334(C).
    4. 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).
    5. Li, Qiang & Zhou, Yongcheng & Wei, Fanchao & Li, Shuangxiu & Wang, Zhonghao & Li, Jiajia & Zhou, Guowen & Liu, Jinfu & Yan, Peigang & Yu, Daren, 2024. "Multi-time scale scheduling for virtual power plants: Integrating the flexibility of power generation and multi-user loads while considering the capacity degradation of energy storage systems," Applied Energy, Elsevier, vol. 362(C).
    6. Jia, Kunqi & Guo, Ge & Xiao, Jucheng & Zhou, Huan & Wang, Zhihua & He, Guangyu, 2019. "Data compression approach for the home energy management system," Applied Energy, Elsevier, vol. 247(C), pages 643-656.
    7. Ewelina Kochanek, 2021. "Evaluation of Energy Transition Scenarios in Poland," Energies, MDPI, vol. 14(19), pages 1-13, September.
    8. Liu, Bo & Hou, Yufan & Luan, Wenpeng & Liu, Zishuai & Chen, Sheng & Yu, Yixin, 2023. "A divide-and-conquer method for compression and reconstruction of smart meter data," Applied Energy, Elsevier, vol. 336(C).
    9. Lau, Jat-Syu & Jiang, Yihuo & Li, Ziyuan & Qian, Qian, 2023. "Stochastic trading of storage systems in short term electricity markets considering intraday demand response market," Energy, Elsevier, vol. 280(C).
    10. Jani, Ali & Jadid, Shahram, 2023. "Two-stage energy scheduling framework for multi-microgrid system in market environment," Applied Energy, Elsevier, vol. 336(C).
    11. Xu, Fangyuan & Zhu, Weidong & Wang, Yi Fei & Lai, Chun Sing & Yuan, Haoliang & Zhao, Yujia & Guo, Siming & Fu, Zhengxin, 2022. "A new deregulated demand response scheme for load over-shifting city in regulated power market," Applied Energy, Elsevier, vol. 311(C).
    12. Michał Jasiński & Tomasz Sikorski & Dominika Kaczorowska & Jacek Rezmer & Vishnu Suresh & Zbigniew Leonowicz & Paweł Kostyła & Jarosław Szymańda & Przemysław Janik & Jacek Bieńkowski & Przemysław Prus, 2021. "A Case Study on Data Mining Application in a Virtual Power Plant: Cluster Analysis of Power Quality Measurements," Energies, MDPI, vol. 14(4), pages 1-14, February.
    13. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    14. Mariusz Niekurzak, 2021. "The Potential of Using Renewable Energy Sources in Poland Taking into Account the Economic and Ecological Conditions," Energies, MDPI, vol. 14(22), pages 1-17, November.
    15. Das, Laya & Garg, Dinesh & Srinivasan, Babji, 2020. "NeuralCompression: A machine learning approach to compress high frequency measurements in smart grid," Applied Energy, Elsevier, vol. 257(C).
    16. Dominika Kaczorowska & Jacek Rezmer & Michal Jasinski & Tomasz Sikorski & Vishnu Suresh & Zbigniew Leonowicz & Pawel Kostyla & Jaroslaw Szymanda & Przemyslaw Janik, 2020. "A Case Study on Battery Energy Storage System in a Virtual Power Plant: Defining Charging and Discharging Characteristics," Energies, MDPI, vol. 13(24), pages 1-22, December.
    17. Chang, Weiguang & Yang, Qiang, 2023. "Low carbon oriented collaborative energy management framework for multi-microgrid aggregated virtual power plant considering electricity trading," Applied Energy, Elsevier, vol. 351(C).
    18. Rong Xia & Jun Dai & Xiangjie Cheng & Jiaqing Fan & Jing Ye & Qiangang Jia & Sijie Chen & Qiang Zhang, 2024. "Demand Response of Integrated Zero-Carbon Power Plant: Model and Method," Energies, MDPI, vol. 17(14), pages 1-15, July.
    19. Hou, Guolian & Huang, Ting & Zheng, Fumeng & Huang, Congzhi, 2024. "A hierarchical reinforcement learning GPC for flexible operation of ultra-supercritical unit considering economy," Energy, Elsevier, vol. 289(C).
    20. Mota, Bruno & Faria, Pedro & Vale, Zita, 2024. "Energy cost optimization through load shifting in a photovoltaic energy-sharing household community," Renewable Energy, Elsevier, vol. 221(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:eee:renene:v:228:y:2024:i:c:s0960148124007353. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.