IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v360y2024ics0306261924002125.html
   My bibliography  Save this article

Two-stage dynamic aggregation involving flexible resource composition and coordination based on submodular optimization

Author

Listed:
  • Ding, Zhetong
  • Li, Yaping
  • Zhang, Kaifeng
  • Peng, Jimmy Chih-Hsien

Abstract

Traditional virtual power plants (VPPs) with fixed resource composition and coordination strategies struggle to cost-effectively exploit the flexibility of large-scale resources for adapting variable regulation requirements and resources characteristics. To this end, this paper proposes a dynamic aggregation mechanism to flexibly select and coordinate individual resources for forming aggregators according to grids regulation requirements and resource characteristics. The proposed mechanism is operated through a two-stage dynamic aggregation model comprising resource selection and coordination. Considering the two-stage dynamic aggregation model is a combinational optimization problem with high computational complexity, the submodular optimization method is utilized to swiftly address this problem. First, the complementarity and submodularity of the dynamic aggregation process are formulated to elaborate how the aggregation regulation characteristics (ARCs) evolve with flexible resource composition and coordination. Next, a submodularity-based algorithm is developed to promptly solve dynamic aggregation model under three scenarios, where aggregation operators focus on the resources quantity, quality, and cost-effectiveness, respectively. The polynomial computational complexity of the proposed algorithm has also been evaluated. Simulations using the IEEE 39-bus (New England) system consists of 10,000 flexible resources were executed to assess the submodularity approach. The proposed algorithm demonstrates superior computing speed and better performance guaranteed results (90%, 97%, 90% in three scenarios) compared to other methods—making it more suitable for implementation in practice.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:360:y:2024:i:c:s0306261924002125
    DOI: 10.1016/j.apenergy.2024.122829
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924002125
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.122829?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. Yang, Qing & Wang, Hao & Wang, Taotao & Zhang, Shengli & Wu, Xiaoxiao & Wang, Hui, 2021. "Blockchain-based decentralized energy management platform for residential distributed energy resources in a virtual power plant," Applied Energy, Elsevier, vol. 294(C).
    2. 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).
    3. Teichgraeber, Holger & Lindenmeyer, Constantin P. & Baumgärtner, Nils & Kotzur, Leander & Stolten, Detlef & Robinius, Martin & Bardow, André & Brandt, Adam R., 2020. "Extreme events in time series aggregation: A case study for optimal residential energy supply systems," Applied Energy, Elsevier, vol. 275(C).
    4. Oshnoei, Arman & Kheradmandi, Morteza & Blaabjerg, Frede & Hatziargyriou, Nikos D. & Muyeen, S.M. & Anvari-Moghaddam, Amjad, 2022. "Coordinated control scheme for provision of frequency regulation service by virtual power plants," Applied Energy, Elsevier, vol. 325(C).
    5. Castagneto Gissey, Giorgio & Subkhankulova, Dina & Dodds, Paul E. & Barrett, Mark, 2019. "Value of energy storage aggregation to the electricity system," Energy Policy, Elsevier, vol. 128(C), pages 685-696.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).

    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. Meng, Yuan & Qiu, Jing & Zhang, Cuo & Lei, Gang & Zhu, Jianguo, 2024. "A Holistic P2P market for active and reactive energy trading in VPPs considering both financial benefits and network constraints," Applied Energy, Elsevier, vol. 356(C).
    2. Esfahani, Moein & Alizadeh, Ali & Amjady, Nima & Kamwa, Innocent, 2024. "A distributed VPP-integrated co-optimization framework for energy scheduling, frequency regulation, and voltage support using data-driven distributionally robust optimization with Wasserstein metric," Applied Energy, Elsevier, vol. 361(C).
    3. Seong-Hyeon Cha & Sun-Hyeok Kwak & Woong Ko, 2023. "A Robust Optimization Model of Aggregated Resources Considering Serving Ratio for Providing Reserve Power in the Joint Electricity Market," Energies, MDPI, vol. 16(20), pages 1-27, October.
    4. Sergio Cantillo-Luna & Ricardo Moreno-Chuquen & David Celeita & George J. Anders, 2024. "A Stochastic Decision-Making Tool Suite for Distributed Energy Resources Integration in Energy Markets," Energies, MDPI, vol. 17(10), pages 1-28, May.
    5. Zeyi Wang & Yao Wang & Li Xie & Dan Pang & Hao Shi & Hua Zheng, 2024. "Load Frequency Control of Multiarea Power Systems with Virtual Power Plants," Energies, MDPI, vol. 17(15), pages 1-10, July.
    6. Ju, Liwei & Yin, Zhe & Lu, Xiaolong & Yang, Shenbo & Li, Peng & Rao, Rao & Tan, Zhongfu, 2022. "A Tri-dimensional Equilibrium-based stochastic optimal dispatching model for a novel virtual power plant incorporating carbon Capture, Power-to-Gas and electric vehicle aggregator," Applied Energy, Elsevier, vol. 324(C).
    7. Andrea Calvagna & Giovanni Marotta & Giuseppe Pappalardo & Emiliano Tramontana, 2024. "A Blockchain-Based Real-Time Power Balancing Service for Trustless Renewable Energy Grids," Future Internet, MDPI, vol. 16(5), pages 1-22, April.
    8. Yap, Kah Yung & Chin, Hon Huin & Klemeš, Jiří Jaromír, 2023. "Blockchain technology for distributed generation: A review of current development, challenges and future prospect," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
    9. Giarola, Sara & Molar-Cruz, Anahi & Vaillancourt, Kathleen & Bahn, Olivier & Sarmiento, Luis & Hawkes, Adam & Brown, Maxwell, 2021. "The role of energy storage in the uptake of renewable energy: A model comparison approach," Energy Policy, Elsevier, vol. 151(C).
    10. Mokhtar Aly & Emad A. Mohamed & Abdullah M. Noman & Emad M. Ahmed & Fayez F. M. El-Sousy & Masayuki Watanabe, 2023. "Optimized Non-Integer Load Frequency Control Scheme for Interconnected Microgrids in Remote Areas with High Renewable Energy and Electric Vehicle Penetrations," Mathematics, MDPI, vol. 11(9), pages 1-31, April.
    11. Keck, Felix & Jütte, Silke & Lenzen, Manfred & Li, Mengyu, 2022. "Assessment of two optimisation methods for renewable energy capacity expansion planning," Applied Energy, Elsevier, vol. 306(PA).
    12. Huang, Z.F. & Soh, K.Y. & Islam, M.R. & Chua, K.J., 2023. "Development of a novel grid-free district cooling system considering blockchain-based demand response management," Applied Energy, Elsevier, vol. 342(C).
    13. Ioanna Andreoulaki & Aikaterini Papapostolou & Vangelis Marinakis, 2024. "Evaluating the Barriers to Blockchain Adoption in the Energy Sector: A Multicriteria Approach Using the Analytical Hierarchy Process for Group Decision Making," Energies, MDPI, vol. 17(6), pages 1-27, March.
    14. Tómasson, Egill & Hesamzadeh, Mohammad Reza & Wolak, Frank A., 2020. "Optimal offer-bid strategy of an energy storage portfolio: A linear quasi-relaxation approach," Applied Energy, Elsevier, vol. 260(C).
    15. Gourisetti, Sri Nikhil Gupta & Sebastian-Cardenas, D. Jonathan & Bhattarai, Bishnu & Wang, Peng & Widergren, Steve & Borkum, Mark & Randall, Alysha, 2021. "Blockchain smart contract reference framework and program logic architecture for transactive energy systems," Applied Energy, Elsevier, vol. 304(C).
    16. 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).
    17. Brandt, Adam R. & Teichgraeber, Holger & Kang, Charles A. & Barnhart, Charles J. & Carbajales-Dale, Michael A. & Sgouridis, Sgouris, 2021. "Blow wind blow: Capital deployment in variable energy systems," Energy, Elsevier, vol. 224(C).
    18. Ju, Liwei & Yin, Zhe & Zhou, Qingqing & Li, Qiaochu & Wang, Peng & Tian, Wenxu & Li, Peng & Tan, Zhongfu, 2022. "Nearly-zero carbon optimal operation model and benefit allocation strategy for a novel virtual power plant using carbon capture, power-to-gas, and waste incineration power in rural areas," Applied Energy, Elsevier, vol. 310(C).
    19. Castagneto Gissey, Giorgio & Zakeri, Behnam & Dodds, Paul E. & Subkhankulova, Dina, 2021. "Evaluating consumer investments in distributed energy technologies," Energy Policy, Elsevier, vol. 149(C).
    20. Hilbers, Adriaan P. & Brayshaw, David J. & Gandy, Axel, 2023. "Reducing climate risk in energy system planning: A posteriori time series aggregation for models with storage," Applied Energy, Elsevier, vol. 334(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:appene:v:360:y:2024:i:c:s0306261924002125. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.