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Energy network dispatch optimization under emergency of local energy shortage with web tool for automatic large group decision-making

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  • Xiang, Liu
Abstract
By large group decision-making, obtaining large group response plans within a limited amount of time plays a crucial role under emergency of local energy shortage. An energy network dispatch optimization under emergency of local energy shortage has been found to possess broad applications in energy system; however, the existing mechanism did not consider and failed to provide an approach to deal with the large group decision makers taking part in the energy network dispatch optimization. In this paper, the main contribution is that an energy network dispatch optimization under emergency of local energy shortage with web tool for automatic large group decision-making is proposed, in which anytime and anywhere, each expert can login in the system and provide his/her personal preference to the emergency of local energy shortage alternatives, and then the large group response plans to emergency of local energy shortage will be obtained within a limited amount of time. A case study and simulation results are presented that prove that the proposed model is feasible, and achieves better performance of energy network dispatch optimization under emergency of local energy shortage than the method proposed in the literature.

Suggested Citation

  • Xiang, Liu, 2017. "Energy network dispatch optimization under emergency of local energy shortage with web tool for automatic large group decision-making," Energy, Elsevier, vol. 120(C), pages 740-750.
    • Handle: RePEc:eee:energy:v:120:y:2017:i:c:p:740-750
    DOI: 10.1016/j.energy.2016.11.125
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    Cited by:

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    2. Feifan Chen & Haifeng Liang & Yajing Gao & Yongchun Yang & Yuxuan Chen, 2019. "Research on Double-Layer Optimal Scheduling Model of Integrated Energy Park Based on Non-Cooperative Game," Energies, MDPI, vol. 12(16), pages 1-16, August.
    3. Lan, Puzhe & Han, Dong & Xu, Xiaoyuan & Yan, Zheng & Ren, Xijun & Xia, Shiwei, 2022. "Data-driven state estimation of integrated electric-gas energy system," Energy, Elsevier, vol. 252(C).
    4. Xiang, Liu, 2022. "A large-scale equilibrium model of energy emergency production: Embedding social choice rules into Nash Q-learning automatically achieving consensus of urgent recovery behaviors," Energy, Elsevier, vol. 259(C).
    5. Heidary-Dahooie, Jalil & Rafiee, Mostafa & Mohammadi, Mehdi & Meidute-Kavaliauskienė, Ieva, 2022. "Proposing a new LSGDM framework based on BWM with hesitant fuzzy information for prioritizing blockchain adoption barriers in supply chain," Technology in Society, Elsevier, vol. 71(C).
    6. Xiang, Liu, 2020. "Energy emergency supply chain collaboration optimization with group consensus through reinforcement learning considering non-cooperative behaviours," Energy, Elsevier, vol. 210(C).

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