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Power Scheduling Optimization Method of Wind-Hydrogen Integrated Energy System Based on the Improved AUKF Algorithm

Author

Listed:
  • Yong Wang

    (Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education (Northeast Electric Power University), Jilin 132000, China)

  • Xuan Wen

    (School of Information Engineering, Nanchang University, Nanchang 330027, China)

  • Bing Gu

    (Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education (Northeast Electric Power University), Jilin 132000, China)

  • Fengkai Gao

    (Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education (Northeast Electric Power University), Jilin 132000, China)

Abstract

With the proposal of China’s green energy strategy, the research and development technologies of green energy such as wind energy and hydrogen energy are becoming more and more mature. However, the phenomenon of wind abandonment and anti-peak shaving characteristics of wind turbines have a great impact on the utilization of wind energy. Therefore, this study firstly builds a distributed wind-hydrogen hybrid energy system model, then proposes the power dispatching optimization technology of a wind-hydrogen integrated energy system. On this basis, a power allocation method based on the AUKF (adaptive unscented Kalman filter) algorithm is proposed. The experiment shows that the power allocation strategy based on the AUKF algorithm can effectively reduce the incidence of battery overcharge and overdischarge. Moreover, it can effectively deal with rapid changes in wind speed. The wind hydrogen integrated energy system proposed in this study is one of the important topics of renewable clean energy technology innovation. Its grid-connected power is stable, with good controllability, and the DC bus is more secure and stable. Compared with previous studies, the system developed in this study has effectively reduced the ratio of abandoned air and its performance is significantly better than the system with separate grid connected fans and single hydrogen energy storage. It is hoped that this research can provide some solutions for the research work on power dispatching optimization of energy systems.

Suggested Citation

  • Yong Wang & Xuan Wen & Bing Gu & Fengkai Gao, 2022. "Power Scheduling Optimization Method of Wind-Hydrogen Integrated Energy System Based on the Improved AUKF Algorithm," Mathematics, MDPI, vol. 10(22), pages 1-16, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4207-:d:968991
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    References listed on IDEAS

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    1. Yuan, Hao & Dai, Haifeng & Wei, Xuezhe & Ming, Pingwen, 2020. "A novel model-based internal state observer of a fuel cell system for electric vehicles using improved Kalman filter approach," Applied Energy, Elsevier, vol. 268(C).
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    2. Weng, Xuemeng & Xuan, Ping & Heidari, Ali Asghar & Cai, Zhennao & Chen, Huiling & Mansour, Romany F. & Ragab, Mahmoud, 2023. "A vertical and horizontal crossover sine cosine algorithm with pattern search for optimal power flow in power systems," Energy, Elsevier, vol. 271(C).
    3. Rasheed Abdulkader & Hayder M. A. Ghanimi & Pankaj Dadheech & Meshal Alharbi & Walid El-Shafai & Mostafa M. Fouda & Moustafa H. Aly & Dhivya Swaminathan & Sudhakar Sengan, 2023. "Soft Computing in Smart Grid with Decentralized Generation and Renewable Energy Storage System Planning," Energies, MDPI, vol. 16(6), pages 1-24, March.
    4. Monirul Islam Miskat & Protap Sarker & Hemal Chowdhury & Tamal Chowdhury & Md Salman Rahman & Nazia Hossain & Piyal Chowdhury & Sadiq M. Sait, 2023. "Current Scenario of Solar Energy Applications in Bangladesh: Techno-Economic Perspective, Policy Implementation, and Possibility of the Integration of Artificial Intelligence," Energies, MDPI, vol. 16(3), pages 1-27, February.
    5. Kheir Abadi, Majid & Davoodi, Vajihe & Deymi-Dashtebayaz, Mahdi & Ebrahimi-Moghadam, Amir, 2023. "Determining the best scenario for providing electrical, cooling, and hot water consuming of a building with utilizing a novel wind/solar-based hybrid system," Energy, Elsevier, vol. 273(C).
    6. Wu, Fan & Wang, Xingguo & Liu, Tao, 2023. "Sustainable development goals, natural resources and economic growth: Evidence from China," Resources Policy, Elsevier, vol. 83(C).

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