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Adaptive bidding strategy for real-time energy management in multi-energy market enhanced by blockchain

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  • Wang, Longze
  • Liu, Jinxin
  • Yuan, Rongfang
  • Wu, Jing
  • Zhang, Delong
  • Zhang, Yan
  • Li, Meicheng

Abstract

Energy trading in the multi-energy market is affected by many uncertainties, especially the fluctuation of renewable energy sources and intermittent demand behavior of customers. The real-time energy management can effectively solve the impact of various uncertainties, ensure the instantaneous balance of energy and improve trading returns. A bidding strategy for multi-energy market is presented, in which reserve price adjustment and dynamic compensation mechanism is innovatively integrated into adaptive learning process. All energy trading participants conduct adaptive learning bidding adjustment based on real-time information in order to obtain higher transaction rate and transaction income. Meanwhile, adding dynamic compensation to the quoted price of fossil energy increases the consumption rate of renewable energy and reduces the emissions of pollutants. Furthermore, blockchain technology can ensure the seamless and effective performance of the presented bidding strategy. In the case study, the results show that our bidding strategy has an obvious advantage in social welfare and allocation efficiency than existing bidding strategies. Moreover, the problem of environmental pollution can be solved to a certain extent through flexible dynamic compensation. Finally, a decentralized application of blockchain is developed to demonstrate how the system could realize real-time energy management and dynamic trading in practice.

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  • Wang, Longze & Liu, Jinxin & Yuan, Rongfang & Wu, Jing & Zhang, Delong & Zhang, Yan & Li, Meicheng, 2020. "Adaptive bidding strategy for real-time energy management in multi-energy market enhanced by blockchain," Applied Energy, Elsevier, vol. 279(C).
    • Handle: RePEc:eee:appene:v:279:y:2020:i:c:s0306261920313398
    DOI: 10.1016/j.apenergy.2020.115866
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    6. Lu, Xiaohui & Yang, Yang & Wang, Peifang & Fan, Yiming & Yu, Fangzhong & Zafetti, Nicholas, 2021. "A new converged Emperor Penguin Optimizer for biding strategy in a day-ahead deregulated market clearing price: A case study in China," Energy, Elsevier, vol. 227(C).
    7. Elarbi Badidi, 2022. "Edge AI and Blockchain for Smart Sustainable Cities: Promise and Potential," Sustainability, MDPI, vol. 14(13), pages 1-30, June.
    8. Wang, Longze & Jiao, Shucen & Xie, Yu & Xia, Shiwei & Zhang, Delong & Zhang, Yan & Li, Meicheng, 2022. "Two-way dynamic pricing mechanism of hydrogen filling stations in electric-hydrogen coupling system enhanced by blockchain," Energy, Elsevier, vol. 239(PC).
    9. Yap, Kah Yung & Chin, Hon Huin & Klemeš, Jiří Jaromír, 2023. "Blockchain technology for distributed generation: A review of current development, challenges and future prospect," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
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