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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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    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).

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