IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v280y2023ics0360544223015244.html
   My bibliography  Save this article

Predictive operation optimization of multi-energy virtual power plant considering behavior uncertainty of diverse stakeholders

Author

Listed:
  • Lin, Xiaojie
  • Lin, Xueru
  • Zhong, Wei
  • Zhou, Yi

Abstract

To address the inflexibility of traditional single-energy virtual power plants (VPPs) in accommodating high proportions of renewable energy, this study proposes a predictive optimization method for multi-energy VPPs. The method supports multi-energy complementary cooperative dispatch and participation in multiple markets, and is applicable to future multi-energy VPPs that integrate carbon capture technology, power-to-gas, and energy storage. The method takes into account the uncertain parameters of stakeholders' behavior and introduces sliding time windows to improve production stability and the feasibility of VPP dispatch schemes. The optimization scheme is obtained based on the scenario method when the risk is maximum. The case results show that the proposed method can increase the profit by 8.31% compared with that without considering the time window. After stabilization, the ramping power changes by 17.39%. It is also found that accounting for the uncertainty of stakeholders increased the maximum profit drop from 47.70% to 60.45%. The introduction of power-to-gas and carbon capture technology has effectively improved the overall economy of the VPP and reduced carbon emissions. The proposed VPP predictive optimization method and uncertainty analysis of stakeholders' behavior provide basis for operation control of multi-energy VPPs.

Suggested Citation

  • Lin, Xiaojie & Lin, Xueru & Zhong, Wei & Zhou, Yi, 2023. "Predictive operation optimization of multi-energy virtual power plant considering behavior uncertainty of diverse stakeholders," Energy, Elsevier, vol. 280(C).
    • Handle: RePEc:eee:energy:v:280:y:2023:i:c:s0360544223015244
    DOI: 10.1016/j.energy.2023.128130
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223015244
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.128130?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. 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. Fang, Fang & Yu, Songyuan & Liu, Mingxi, 2020. "An improved Shapley value-based profit allocation method for CHP-VPP," Energy, Elsevier, vol. 213(C).
    3. Shafiekhani, Morteza & Ahmadi, Abdollah & Homaee, Omid & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Optimal bidding strategy of a renewable-based virtual power plant including wind and solar units and dispatchable loads," Energy, Elsevier, vol. 239(PD).
    4. Thema, M. & Bauer, F. & Sterner, M., 2019. "Power-to-Gas: Electrolysis and methanation status review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 775-787.
    5. Frauke Oest & Malin Radtke & Marita Blank-Babazadeh & Stefanie Holly & Sebastian Lehnhoff, 2021. "Evaluation of Communication Infrastructures for Distributed Optimization of Virtual Power Plant Schedules," Energies, MDPI, vol. 14(5), pages 1-20, February.
    6. Hadayeghparast, Shahrzad & SoltaniNejad Farsangi, Alireza & Shayanfar, Heidarali, 2019. "Day-ahead stochastic multi-objective economic/emission operational scheduling of a large scale virtual power plant," Energy, Elsevier, vol. 172(C), pages 630-646.
    7. Manoharan, S. & Gnanambal, K., 2019. "Optimized FOPID controller for improving steady state and transient response of Microturbine Generation system," Energy, Elsevier, vol. 189(C).
    8. Levieux, Luis Ignacio & Ocampo-Martinez, Carlos & Inthamoussou, Fernando A. & De Battista, Hernán, 2021. "Predictive management approach for the coordination of wind and water-based power supplies," Energy, Elsevier, vol. 219(C).
    9. Nunes, Juliana Barbosa & Mahmoudi, Nadali & Saha, Tapan Kumar & Chattopadhyay, Debabrata, 2018. "A stochastic integrated planning of electricity and natural gas networks for Queensland, Australia considering high renewable penetration," Energy, Elsevier, vol. 153(C), pages 539-553.
    10. Kia, M. & Shafiekhani, M. & Arasteh, H. & Hashemi, S.M. & Shafie-khah, M. & Catalão, J.P.S., 2020. "Short-term operation of microgrids with thermal and electrical loads under different uncertainties using information gap decision theory," Energy, Elsevier, vol. 208(C).
    11. Naval, Natalia & Sánchez, Raul & Yusta, Jose M., 2020. "A virtual power plant optimal dispatch model with large and small-scale distributed renewable generation," Renewable Energy, Elsevier, vol. 151(C), pages 57-69.
    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. Lin, Xiaojie & Lin, Xueru & Zhong, Wei & Zhou, Yi, 2024. "Multi-time scale dynamic operation optimization method for industrial park electricity-heat-gas integrated energy system considering demand elasticity," Energy, Elsevier, vol. 293(C).
    2. 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).
    3. 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. Masoud Khatibi & Abbas Rabiee & Amir Bagheri, 2023. "Integrated Electricity and Gas Systems Planning: New Opportunities, and a Detailed Assessment of Relevant Issues," Sustainability, MDPI, vol. 15(8), pages 1-32, April.
    2. Mostafa Darvishi & Mehrdad Tahmasebi & Ehsan Shokouhmand & Jagadeesh Pasupuleti & Pitshou Bokoro & Jwan Satei Raafat, 2023. "Optimal Operation of Sustainable Virtual Power Plant Considering the Amount of Emission in the Presence of Renewable Energy Sources and Demand Response," Sustainability, MDPI, vol. 15(14), pages 1-25, July.
    3. 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).
    4. 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).
    5. Natalia Naval & Jose M. Yusta, 2020. "Water-Energy Management for Demand Charges and Energy Cost Optimization of a Pumping Stations System under a Renewable Virtual Power Plant Model," Energies, MDPI, vol. 13(11), pages 1-21, June.
    6. Guo, Hongye & Chen, Qixin & Shahidehpour, Mohammad & Xia, Qing & Kang, Chongqing, 2022. "Bidding behaviors of GENCOs under bounded rationality with renewable energy," Energy, Elsevier, vol. 250(C).
    7. Kanjanapon Borisoot & Rittichai Liemthong & Chitchai Srithapon & Rongrit Chatthaworn, 2023. "Optimal Energy Management for Virtual Power Plant Considering Operation and Degradation Costs of Energy Storage System and Generators," Energies, MDPI, vol. 16(6), pages 1-19, March.
    8. Andrea Barbaresi & Mirko Morini & Agostino Gambarotta, 2022. "Review on the Status of the Research on Power-to-Gas Experimental Activities," Energies, MDPI, vol. 15(16), pages 1-32, August.
    9. Zhihan Shi & Weisong Han & Guangming Zhang & Zhiqing Bai & Mingxiang Zhu & Xiaodong Lv, 2022. "Research on Low-Carbon Energy Sharing through the Alliance of Integrated Energy Systems with Multiple Uncertainties," Energies, MDPI, vol. 15(24), pages 1-20, December.
    10. Nyong-Bassey, Bassey Etim & Giaouris, Damian & Patsios, Charalampos & Papadopoulou, Simira & Papadopoulos, Athanasios I. & Walker, Sara & Voutetakis, Spyros & Seferlis, Panos & Gadoue, Shady, 2020. "Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty," Energy, Elsevier, vol. 193(C).
    11. Mansour-Saatloo, Amin & Pezhmani, Yasin & Mirzaei, Mohammad Amin & Mohammadi-Ivatloo, Behnam & Zare, Kazem & Marzband, Mousa & Anvari-Moghaddam, Amjad, 2021. "Robust decentralized optimization of Multi-Microgrids integrated with Power-to-X technologies," Applied Energy, Elsevier, vol. 304(C).
    12. Farrokhifar, Meisam & Nie, Yinghui & Pozo, David, 2020. "Energy systems planning: A survey on models for integrated power and natural gas networks coordination," Applied Energy, Elsevier, vol. 262(C).
    13. Yin, Linfei & He, Xiaoyu, 2023. "Artificial emotional deep Q learning for real-time smart voltage control of cyber-physical social power systems," Energy, Elsevier, vol. 273(C).
    14. Li, Yang & Wang, Bin & Yang, Zhen & Li, Jiazheng & Chen, Chen, 2022. "Hierarchical stochastic scheduling of multi-community integrated energy systems in uncertain environments via Stackelberg game," Applied Energy, Elsevier, vol. 308(C).
    15. 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).
    16. Georgios Varvoutis & Athanasios Lampropoulos & Evridiki Mandela & Michalis Konsolakis & George E. Marnellos, 2022. "Recent Advances on CO 2 Mitigation Technologies: On the Role of Hydrogenation Route via Green H 2," Energies, MDPI, vol. 15(13), pages 1-38, June.
    17. Navarro, J.C. & Baena-Moreno, F.M. & Centeno, M.A. & Laguna, O.H. & Almagro, J.F. & Odriozola, J.A., 2023. "Process design and utilisation strategy for CO2 capture in flue gases. Technical assessment and preliminary economic approach for steel mills," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    18. Rezaei, Navid & Pezhmani, Yasin & Khazali, Amirhossein, 2022. "Economic-environmental risk-averse optimal heat and power energy management of a grid-connected multi microgrid system considering demand response and bidding strategy," Energy, Elsevier, vol. 240(C).
    19. Song, Yuguang & Xia, Mingchao & Yang, Liu & Chen, Qifang & Su, Su, 2023. "Multi-granularity source-load-storage cooperative dispatch based on combined robust optimization and stochastic optimization for a highway service area micro-energy grid," Renewable Energy, Elsevier, vol. 205(C), pages 747-762.
    20. Tsao, Yu-Chung & Thanh, Vo-Van & Lu, Jye-Chyi, 2019. "Multiobjective robust fuzzy stochastic approach for sustainable smart grid design," Energy, Elsevier, vol. 176(C), pages 929-939.

    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:energy:v:280:y:2023:i:c:s0360544223015244. 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/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.