IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i7p6208-d1115791.html

Research on Power System Day-Ahead Generation Scheduling Method Considering Combined Operation of Wind Power and Pumped Storage Power Station

Author

Listed:
  • Zhi Zhang

    (Department of Electrical Engineering, Tsinghua University, Beijing 100084, China)

  • Dan Xu

    (China Electric Power Research Institute, Beijing 100192, China)

  • Xuezhen Chan

    (Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, China Three Gorges University, Yichang 443002, China)

  • Guobin Xu

    (Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, China Three Gorges University, Yichang 443002, China)

Abstract

In the proposed wind-storage combined operation technology, the storage side is foreseen to play a significant role in power system day-ahead generation scheduling. Based on the operational characteristics of pumped storage power stations, the day-ahead dispatching method of a power system with wind farms and pumped storage power stations is studied. The dispatching mode that aims at the lowest operating cost is proposed, taking into consideration the coordination relationship between the scheduling benefit of pumped storage power stations and the total peak-shaving economy of the system and the fluctuation of new energy output. First, taking the constraint of reservoir capacity, the output power, and the daily pumping power of the pumped storage power station into account, a day-ahead generation scheduling model is constructed, with the objective of minimizing costs. Then, the imperial competition algorithm is applied to the proposed model. Finally, the algorithm is compared with the standard particle swarm optimization algorithm. The simulation results based on standard 4-unit and 10-unit systems indicate that the proposed method is effective and robust for a power system with wind power and pumped storage power stations.

Suggested Citation

  • Zhi Zhang & Dan Xu & Xuezhen Chan & Guobin Xu, 2023. "Research on Power System Day-Ahead Generation Scheduling Method Considering Combined Operation of Wind Power and Pumped Storage Power Station," Sustainability, MDPI, vol. 15(7), pages 1-14, April.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:6208-:d:1115791
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/7/6208/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/7/6208/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chen, J.J. & Qi, B.X. & Rong, Z.K. & Peng, K. & Zhao, Y.L. & Zhang, X.H., 2021. "Multi-energy coordinated microgrid scheduling with integrated demand response for flexibility improvement," Energy, Elsevier, vol. 217(C).
    2. Fang, Ping & Fu, Wenlong & Wang, Kai & Xiong, Dongzhen & Zhang, Kai, 2022. "A compositive architecture coupling outlier correction, EWT, nonlinear Volterra multi-model fusion with multi-objective optimization for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 307(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ren, Yan & Sun, Ketao & Zhang, Kai & Han, Yuping & Zhang, Haonan & Wang, Meijing & Jing, Xiang & Mo, Juhua & Zou, Wenhang & Xing, Xinyang, 2024. "Optimization of the capacity configuration of an abandoned mine pumped storage/wind/photovoltaic integrated system," Applied Energy, Elsevier, vol. 374(C).
    2. Baoyu Wei & Lu Gao & Hongbao Zhao, 2025. "Study on the Seismic Stability of Urban Sewage Treatment and Underground Reservoir of an Abandoned Mine Pumped Storage Power Station," Sustainability, MDPI, vol. 17(12), pages 1-25, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guanglei Huang & Tian Mao & Bin Zhang & Renli Cheng & Mingyu Ou, 2023. "An Intelligent Algorithm for Solving Unit Commitments Based on Deep Reinforcement Learning," Sustainability, MDPI, vol. 15(14), pages 1-19, July.
    2. Mansour-Saatloo, Amin & Pezhmani, Yasin & Mirzaei, Mohammad Amin & Mohammadi-Ivatloo, Behnam & Zare, Kazem & Marzband, Mousa & Anvari-Moghaddam, Amjad, 2021. "Robust decentralized optimization of Multi-Microgrids integrated with Power-to-X technologies," Applied Energy, Elsevier, vol. 304(C).
    3. Gao, Yang & Ai, Qian & He, Xing & Fan, Songli, 2023. "Coordination for regional integrated energy system through target cascade optimization," Energy, Elsevier, vol. 276(C).
    4. Ge, Haotian & Zhu, Yu & Zhong, Jiuming & Wu, Liang, 2024. "Day-ahead optimization for smart energy management of multi-microgrid using a stochastic-robust model," Energy, Elsevier, vol. 313(C).
    5. Wang, Liying & Lin, Jialin & Dong, Houqi & Wang, Yuqing & Zeng, Ming, 2023. "Demand response comprehensive incentive mechanism-based multi-time scale optimization scheduling for park integrated energy system," Energy, Elsevier, vol. 270(C).
    6. Haibing Wang & Chengmin Wang & Weiqing Sun & Muhammad Qasim Khan, 2022. "Energy Pricing and Management for the Integrated Energy Service Provider: A Stochastic Stackelberg Game Approach," Energies, MDPI, vol. 15(19), pages 1-15, October.
    7. Liu, Shuhan & Sun, Wenqiang, 2025. "Knowledge- and data-driven prediction of blast furnace gas generation and consumption in iron and steel sites," Applied Energy, Elsevier, vol. 390(C).
    8. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    9. Ren, Lina & Zhang, Kunpeng & Mehran, Kamyar & Ma, Kai, 2025. "Low-carbon economic dispatch of a hydrogen-based integrated energy system considering the coordinated operation of CHP-ORC-CSP and P2G-CCS," Energy, Elsevier, vol. 340(C).
    10. Li, Ling-Ling & Miao, Yan & Lim, Ming K. & Sethanan, Kanchana & Tseng, Ming-Lang, 2024. "Integrated energy system for low-carbon economic operation optimization: Pareto compromise programming and master-slave game," Renewable Energy, Elsevier, vol. 222(C).
    11. Liang, Ziwen & Mu, Longhua, 2024. "Multi-agent low-carbon optimal dispatch of regional integrated energy system based on mixed game theory," Energy, Elsevier, vol. 295(C).
    12. Mei, Peng & Karimi, Hamid Reza & Xie, Jiale & Chen, Fei & Ou, Lei & Yang, Shichun & Huang, Cong, 2024. "Battery state estimation methods and management system under vehicle–cloud collaboration: A Survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 206(C).
    13. Norouzi, Mohammadali & Aghaei, Jamshid & Pirouzi, Sasan & Niknam, Taher & Fotuhi-Firuzabad, Mahmud, 2022. "Flexibility pricing of integrated unit of electric spring and EVs parking in microgrids," Energy, Elsevier, vol. 239(PB).
    14. Chao Tan & Wenrui Tan & Yanjun Shen & Long Yang, 2023. "Multistep Wind Power Prediction Using Time-Varying Filtered Empirical Modal Decomposition and Improved Adaptive Sparrow Search Algorithm-Optimized Phase Space Reconstruction–Echo State Network," Sustainability, MDPI, vol. 15(11), pages 1-17, June.
    15. Li, Xinyu & Guan, Chaoran & Chai, Xiang & Liu, Xiaojing, 2025. "Study on the load-following ability of HeXe cooled SMR with close Brayton cycle for renewable energy integration," Energy, Elsevier, vol. 318(C).
    16. Saeian, Hosein & Niknam, Taher & Zare, Mohsen & Aghaei, Jamshid, 2022. "Coordinated optimal bidding strategies methods of aggregated microgrids: A game theory-based demand side management under an electricity market environment," Energy, Elsevier, vol. 245(C).
    17. Liu, Jiarui & Fu, Yuchen, 2023. "Decomposition spectral graph convolutional network based on multi-channel adaptive adjacency matrix for renewable energy prediction," Energy, Elsevier, vol. 284(C).
    18. Navid Rezaei & Abdollah Ahmadi & Mohammadhossein Deihimi, 2022. "A Comprehensive Review of Demand-Side Management Based on Analysis of Productivity: Techniques and Applications," Energies, MDPI, vol. 15(20), pages 1-28, October.
    19. Boyu Zhu & Dazhi Wang, 2024. "Master–Slave Game Optimal Scheduling for Multi-Agent Integrated Energy System Based on Uncertainty and Demand Response," Sustainability, MDPI, vol. 16(8), pages 1-27, April.
    20. Akulker, Handan & Aydin, Erdal, 2023. "Optimal design and operation of a multi-energy microgrid using mixed-integer nonlinear programming: Impact of carbon cap and trade system and taxing on equipment selections," Applied Energy, Elsevier, vol. 330(PA).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:6208-:d:1115791. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.