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

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  • 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
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    References listed on IDEAS

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    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. Manoharan, S. & Gnanambal, K., 2019. "Optimized FOPID controller for improving steady state and transient response of Microturbine Generation system," Energy, Elsevier, vol. 189(C).
    3. 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.
    4. 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).
    5. Fang, Fang & Yu, Songyuan & Liu, Mingxi, 2020. "An improved Shapley value-based profit allocation method for CHP-VPP," Energy, Elsevier, vol. 213(C).
    6. 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).
    7. 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.
    8. 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.
    9. 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.
    10. 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).
    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.
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    Cited by:

    1. 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).
    2. 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).
    3. 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).

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