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The Scheduling Research of a Wind-Solar-Hydro Hybrid System Based on a Sand-Table Deduction Model at Ultra-Short-Term Scales

Author

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  • Tianyao Zhang

    (State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China)

  • Weibin Huang

    (State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China)

  • Shijun Chen

    (State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China)

  • Yanmei Zhu

    (State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China)

  • Fuxing Kang

    (China Hua’neng Dingbian Renewable Energy Power Generation Company, Yulin 718600, China)

  • Yerong Zhou

    (State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China
    China Energy Investment Jinsha River Upstream Hydropower Development Company, Chengdu 610065, China)

  • Guangwen Ma

    (State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China)

Abstract

Establishing a wind-solar-hydro hybrid generation system is an effective way of ensuring the smooth passage of clean energy into the grid, and its related scheduling research is a complex and real-time optimization problem. Compared with the traditional scheduling method, this research investigates and improves the accuracy of the scheduling model and the flexibility of the scheduling strategy. The paper innovatively introduces a sand-table deduction model and designs a real-time adaptive scheduling algorithm to evaluate the source-load matching capability of the hybrid wind-solar-hydro system at ultra-short-term scales, and verifies it through arithmetic examples. The results show that the proposed adaptive sand-table scheduling model can reflect the actual output characteristics of the hybrid wind-solar-hydro system, track the load curve, and suppress the fluctuation of wind and solar energy, with good source-load matching capability.

Suggested Citation

  • Tianyao Zhang & Weibin Huang & Shijun Chen & Yanmei Zhu & Fuxing Kang & Yerong Zhou & Guangwen Ma, 2023. "The Scheduling Research of a Wind-Solar-Hydro Hybrid System Based on a Sand-Table Deduction Model at Ultra-Short-Term Scales," Energies, MDPI, vol. 16(7), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:7:p:3280-:d:1117253
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    References listed on IDEAS

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    1. Wang, Feifei & Chen, Diyi & Xu, Beibei & Zhang, Hao, 2016. "Nonlinear dynamics of a novel fractional-order Francis hydro-turbine governing system with time delay," Chaos, Solitons & Fractals, Elsevier, vol. 91(C), pages 329-338.
    2. Neves, Diana & Brito, Miguel C. & Silva, Carlos A., 2016. "Impact of solar and wind forecast uncertainties on demand response of isolated microgrids," Renewable Energy, Elsevier, vol. 87(P2), pages 1003-1015.
    3. Madhusudhan Pandey & Dietmar Winkler & Roshan Sharma & Bernt Lie, 2021. "Using MPC to Balance Intermittent Wind and Solar Power with Hydro Power in Microgrids," Energies, MDPI, vol. 14(4), pages 1-28, February.
    4. Chen, J.J. & Zhao, Y.L. & Peng, K. & Wu, P.Z., 2017. "Optimal trade-off planning for wind-solar power day-ahead scheduling under uncertainties," Energy, Elsevier, vol. 141(C), pages 1969-1981.
    5. Tianyao Zhang & Diyi Chen & Jing Liu & Beibei Xu & Venkateshkumar M, 2020. "A Feasibility Analysis of Controlling a Hybrid Power System over Short Time Intervals," Energies, MDPI, vol. 13(21), pages 1-21, October.
    6. Sun, Kaiqi & Li, Ke-Jun & Pan, Jiuping & Liu, Yong & Liu, Yilu, 2019. "An optimal combined operation scheme for pumped storage and hybrid wind-photovoltaic complementary power generation system," Applied Energy, Elsevier, vol. 242(C), pages 1155-1163.
    7. Li, Yan & Ming, Bo & Huang, Qiang & Wang, Yimin & Liu, Pan & Guo, Pengcheng, 2022. "Identifying effective operating rules for large hydro–solar–wind hybrid systems based on an implicit stochastic optimization framework," Energy, Elsevier, vol. 245(C).
    8. Lorestani, A. & Ardehali, M.M., 2018. "Optimization of autonomous combined heat and power system including PVT, WT, storages, and electric heat utilizing novel evolutionary particle swarm optimization algorithm," Renewable Energy, Elsevier, vol. 119(C), pages 490-503.
    9. Liu, Zifa & Zhang, Zhe & Zhuo, Ranqun & Wang, Xuyang, 2019. "Optimal operation of independent regional power grid with multiple wind-solar-hydro-battery power," Applied Energy, Elsevier, vol. 235(C), pages 1541-1550.
    10. Noah Serem & Lawrence K. Letting & Josiah Munda, 2021. "Voltage Profile and Sensitivity Analysis for a Grid Connected Solar, Wind and Small Hydro Hybrid System," Energies, MDPI, vol. 14(12), pages 1-26, June.
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