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A fully decentralized dual consensus method for carbon trading power dispatch with wind power

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  • Qian, Tong
  • Tang, Wenhu
  • Wu, Qinghua

Abstract

The global-based and partition-based dynamic power dispatch problems with wind power integrated into the carbon emission trading system are established and investigated. To meet this challenge, a distributed dual consensus algorithm based the alternating direction method of multipliers is implemented by sharing Lagrangian multipliers associated with coupling constraints between partitioned subproblems rather than phase angles on adjacent buses that are usually shared, thus protecting the key private information of each subsystem. Furthermore, a fully decentralized algorithm is proposed by adopting the finite-time average consensus algorithm, which enables each partition to iteratively approach a consensus of its shared information in a finite number of steps. For comparison purposes, a global-based centralized optimization is implemented at first, adopting the effect of carbon price on the operation of a modified IEEE-30 bus system, followed by tests of the proposed algorithms with three different partitioning methods of power systems. Results illustrate that a higher carbon price can be regarded as an incentive to decrease the wind curtailment rates and spur the increased use of clean fuel. Compared with the results of the centralized optimization, both the algorithms can achieve satisfactory convergence accuracies, although the fully decentralized algorithm requires slightly longer time for computation.

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  • Qian, Tong & Tang, Wenhu & Wu, Qinghua, 2020. "A fully decentralized dual consensus method for carbon trading power dispatch with wind power," Energy, Elsevier, vol. 203(C).
    • Handle: RePEc:eee:energy:v:203:y:2020:i:c:s0360544220307416
    DOI: 10.1016/j.energy.2020.117634
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    4. Javadi, Mohammad Sadegh & Esmaeel Nezhad, Ali & Jordehi, Ahmad Rezaee & Gough, Matthew & Santos, Sérgio F. & Catalão, João P.S., 2022. "Transactive energy framework in multi-carrier energy hubs: A fully decentralized model," Energy, Elsevier, vol. 238(PB).
    5. Tiwari, Shubham & Singh, Jai Govind & Garg, Ankit, 2024. "A static robust energy management approach for modelling low emission multi-vectored energy hub including emission markets and power-to-gas units," Energy, Elsevier, vol. 294(C).
    6. Wu, Qunli & Li, Chunxiang, 2023. "Modeling and operation optimization of hydrogen-based integrated energy system with refined power-to-gas and carbon-capture-storage technologies under carbon trading," Energy, Elsevier, vol. 270(C).
    7. Du, Dajun & Zhu, Minggao & Wu, Dakui & Li, Xue & Fei, Minrui & Hu, Yukun & Li, Kang, 2024. "Distributed security state estimation-based carbon emissions and economic cost analysis for cyber–physical power systems under hybrid attacks," Applied Energy, Elsevier, vol. 353(PA).
    8. Yang, Meng & Liu, Yisheng, 2023. "Research on multi-energy collaborative operation optimization of integrated energy system considering carbon trading and demand response," Energy, Elsevier, vol. 283(C).
    9. Jin, Jingliang & Wen, Qinglan & Cheng, Siqi & Qiu, Yaru & Zhang, Xianyue & Guo, Xiaojun, 2022. "Optimization of carbon emission reduction paths in the low-carbon power dispatching process," Renewable Energy, Elsevier, vol. 188(C), pages 425-436.
    10. Qian, Tong & Chen, Xingyu & Xin, Yanli & Tang, Wenhu & Wang, Lixiao, 2022. "Resilient decentralized optimization of chance constrained electricity-gas systems over lossy communication networks," Energy, Elsevier, vol. 239(PB).
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