IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v277y2023ics0360544223010794.html
   My bibliography  Save this article

Solution framework for short-term cascade hydropower system optimization operations based on the load decomposition strategy

Author

Listed:
  • Liao, Shengli
  • Liu, Huan
  • Liu, Benxi
  • Liu, Tian
  • Li, Chonghao
  • Su, Huaying

Abstract

The optimal operation of short-term cascade hydropower systems, with dozens of hydropower stations, multistage calculation periods and complex hydraulic constraints, faces a serious “curse of dimensionality” problem, and it is difficult to solve this problem directly or obtain a precise optimal hydropower dispatching plan in an acceptable time. This study presents an efficient solution framework based on the load decomposition strategy to alleviate the dimensionality problem. First, the load decomposition strategy is exploited by reducing the number of optimization stages to reduce the dimensionality of the original complicated optimization problem. By continuously adjusting the number of time periods and load of each stage, the load decomposition strategy can divide the original optimization problem into two subproblems with different optimization periods, namely, the segmented load process and burr load process. Second, a hydropower station classification method is proposed, in which all stations are divided into balanced power stations for the burr load and main dispatching stations for the base load, significantly reducing the participation of most hydropower stations in the frequent dispatch process. Finally, according to the transformation of the water balance relationship, a mathematical expression that accurately describes the complex hydraulic connection problem between the two subproblems is constructed to obtain more refined solution results. Practical project cases involving a large-scale hydropower system with 7 stations on the Wu River of China are used to test the sensitivity and efficiency of the proposed method. The sensitivity analysis indicates that the different optimization stages in this method can quickly obtain a globally optimal result and improved computational efficiency. Moreover, compared with the 24-point and 96-point traditional optimization scheduling methods, the water discharge of the presented method is reduced by 6.6% and 7.8% in the dry season and 4.0% and 4.9% in the flood season in a limited time, respectively, which suggests that the solving difficulties caused by the “curse of dimensionality” can be effectively alleviated and the solution efficiency can be greatly improved. The simulation calculations for different seasons and scales also indicate that the proposed method is an effective tool for the optimal operation of large-scale hydropower systems.

Suggested Citation

  • Liao, Shengli & Liu, Huan & Liu, Benxi & Liu, Tian & Li, Chonghao & Su, Huaying, 2023. "Solution framework for short-term cascade hydropower system optimization operations based on the load decomposition strategy," Energy, Elsevier, vol. 277(C).
  • Handle: RePEc:eee:energy:v:277:y:2023:i:c:s0360544223010794
    DOI: 10.1016/j.energy.2023.127685
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223010794
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2023.127685?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Liao, Shengli & Liu, Zhanwei & Liu, Benxi & Cheng, Chuntian & Wu, Xinyu & Zhao, Zhipeng, 2021. "Daily peak shaving operation of cascade hydropower stations with sensitive hydraulic connections considering water delay time," Renewable Energy, Elsevier, vol. 169(C), pages 970-981.
    2. Shengli Liao & Jie Liu & Benxi Liu & Chuntian Cheng & Lingan Zhou & Huijun Wu, 2020. "Multicore Parallel Dynamic Programming Algorithm for Short-Term Hydro-Unit Load Dispatching of Huge Hydropower Stations Serving Multiple Power Grids," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(1), pages 359-376, January.
    3. Cheng, Chun-Tian & Shen, Jian-Jian & Wu, Xin-Yu & Chau, Kwok-wing, 2012. "Operation challenges for fast-growing China's hydropower systems and respondence to energy saving and emission reduction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 2386-2393.
    4. Bo Ming & Pan Liu & Tao Bai & Rouxin Tang & Maoyuan Feng, 2017. "Improving Optimization Efficiency for Reservoir Operation Using a Search Space Reduction Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(4), pages 1173-1190, March.
    5. He, Zhongzheng & Wang, Chao & Wang, Yongqiang & Wei, Bowen & Zhou, Jianzhong & Zhang, Hairong & Qin, Hui, 2021. "Dynamic programming with successive approximation and relaxation strategy for long-term joint power generation scheduling of large-scale hydropower station group," Energy, Elsevier, vol. 222(C).
    6. Wang, Peilin & Yuan, Wenlin & Su, Chengguo & Wu, Yang & Lu, Lu & Yan, Denghua & Wu, Zening, 2022. "Short-term optimal scheduling of cascade hydropower plants shaving peak load for multiple power grids," Renewable Energy, Elsevier, vol. 184(C), pages 68-79.
    7. Liu, Benxi & Cheng, Chuntian & Wang, Sen & Liao, Shengli & Chau, Kwok-Wing & Wu, Xinyu & Li, Weidong, 2018. "Parallel chance-constrained dynamic programming for cascade hydropower system operation," Energy, Elsevier, vol. 165(PA), pages 752-767.
    8. Masood, Zahid & Khan, Shahroz & Qian, Li, 2021. "Machine learning-based surrogate model for accelerating simulation-driven optimisation of hydropower Kaplan turbine," Renewable Energy, Elsevier, vol. 173(C), pages 827-848.
    9. Yong Peng & Anbang Peng & Xiaoli Zhang & Huicheng Zhou & Lin Zhang & Wenzhong Wang & Zixin Zhang, 2017. "Multi-Core Parallel Particle Swarm Optimization for the Operation of Inter-Basin Water Transfer-Supply Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 27-41, January.
    10. Yufei Ma & Ping-an Zhong & Bin Xu & Feilin Zhu & Jieyu Li & Han Wang & Qingwen Lu, 2021. "Cloud-Based Multidimensional Parallel Dynamic Programming Algorithm for a Cascade Hydropower System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2705-2721, July.
    11. Zhong-kai Feng & Wen-jing Niu & Zhi-qiang Jiang & Hui Qin & Zhen-guo Song, 2020. "Monthly Operation Optimization of Cascade Hydropower Reservoirs with Dynamic Programming and Latin Hypercube Sampling for Dimensionality Reduction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(6), pages 2029-2041, April.
    12. Gianluca Nastasi & Valentina Colla & Silvia Cateni & Simone Campigli, 2018. "Implementation and comparison of algorithms for multi-objective optimization based on genetic algorithms applied to the management of an automated warehouse," Journal of Intelligent Manufacturing, Springer, vol. 29(7), pages 1545-1557, October.
    13. Jianjian Shen & Chuntian Cheng & Jun Zhang & Jianyu Lu, 2015. "Peak Operation of Cascaded Hydropower Plants Serving Multiple Provinces," Energies, MDPI, vol. 8(10), pages 1-20, October.
    14. Iram Parvez & Jianjian Shen & Ishitaq Hassan & Nannan Zhang, 2021. "Generation of Hydro Energy by Using Data Mining Algorithm for Cascaded Hydropower Plant," Energies, MDPI, vol. 14(2), pages 1-28, January.
    Full references (including those not matched with items on IDEAS)

    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. Fang, Zhou & Liao, Shengli & Cheng, Chuntian & Zhao, Hongye & Liu, Benxi & Su, Huaying, 2023. "Parallel improved DPSA algorithm for medium-term optimal scheduling of large-scale cascade hydropower plants," Renewable Energy, Elsevier, vol. 210(C), pages 134-147.
    2. Liu, Benxi & Liu, Tengyuan & Liao, Shengli & Wang, Haidong & Jin, Xiaoyu, 2023. "Short-term operation of cascade hydropower system sharing flexibility via high voltage direct current lines for multiple grids peak shaving," Renewable Energy, Elsevier, vol. 213(C), pages 11-29.
    3. Liao, Shengli & Yang, Hualong & Liu, Benxi & Zhao, Hongye & Liu, Huan & Ma, Xiangyu & Wu, Huijun, 2022. "Daily peak-shaving model of cascade hydropower serving multi-grids considering an HVDC channel shared constraint," Renewable Energy, Elsevier, vol. 199(C), pages 112-122.
    4. Wang, Peilin & Yuan, Wenlin & Su, Chengguo & Wu, Yang & Lu, Lu & Yan, Denghua & Wu, Zening, 2022. "Short-term optimal scheduling of cascade hydropower plants shaving peak load for multiple power grids," Renewable Energy, Elsevier, vol. 184(C), pages 68-79.
    5. Zhong-kai Feng & Wen-jing Niu & Zhi-qiang Jiang & Hui Qin & Zhen-guo Song, 2020. "Monthly Operation Optimization of Cascade Hydropower Reservoirs with Dynamic Programming and Latin Hypercube Sampling for Dimensionality Reduction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(6), pages 2029-2041, April.
    6. Jin, Xiaoyu & Liu, Benxi & Liao, Shengli & Cheng, Chuntian & Yan, Zhiyu, 2022. "A Wasserstein metric-based distributionally robust optimization approach for reliable-economic equilibrium operation of hydro-wind-solar energy systems," Renewable Energy, Elsevier, vol. 196(C), pages 204-219.
    7. Liao, Shengli & Liu, Huan & Liu, Zhanwei & Liu, Benxi & Li, Gang & Li, Shushan, 2021. "Medium-term peak shaving operation of cascade hydropower plants considering water delay time," Renewable Energy, Elsevier, vol. 179(C), pages 406-417.
    8. Su, Chengguo & Wang, Peilin & Yuan, Wenlin & Wu, Yang & Jiang, Feng & Wu, Zening & Yan, Denghua, 2022. "Short-term optimal scheduling of cascade hydropower plants with reverse-regulating effects," Renewable Energy, Elsevier, vol. 199(C), pages 395-406.
    9. Yuan, Wenlin & Zhang, Shijie & Su, Chengguo & Wu, Yang & Yan, Denghua & Wu, Zening, 2022. "Optimal scheduling of cascade hydropower plants in a portfolio electricity market considering the dynamic water delay," Energy, Elsevier, vol. 252(C).
    10. Li, He & Liu, Pan & Guo, Shenglian & Ming, Bo & Cheng, Lei & Yang, Zhikai, 2019. "Long-term complementary operation of a large-scale hydro-photovoltaic hybrid power plant using explicit stochastic optimization," Applied Energy, Elsevier, vol. 238(C), pages 863-875.
    11. Feng, Zhong-kai & Niu, Wen-jing & Cheng, Chun-tian, 2019. "China’s large-scale hydropower system: operation characteristics, modeling challenge and dimensionality reduction possibilities," Renewable Energy, Elsevier, vol. 136(C), pages 805-818.
    12. Feng, Zhong-kai & Niu, Wen-jing & Cheng, Chun-tian, 2018. "Optimization of hydropower reservoirs operation balancing generation benefit and ecological requirement with parallel multi-objective genetic algorithm," Energy, Elsevier, vol. 153(C), pages 706-718.
    13. Xinyu Wu & Ruixiang Cheng & Chuntian Cheng, 2022. "A Simplified Solution Method for End-of-Term Storage Energy Maximization Model of Cascaded Reservoirs," Energies, MDPI, vol. 15(12), pages 1-18, June.
    14. Thibaut Cuvelier & Pierre Archambeau & Benjamin Dewals & Quentin Louveaux, 2018. "Comparison Between Robust and Stochastic Optimisation for Long-term Reservoir Management Under Uncertainty," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1599-1614, March.
    15. Mohammad Ehteram & Hojat Karami & Saeed Farzin, 2018. "Reducing Irrigation Deficiencies Based Optimizing Model for Multi-Reservoir Systems Utilizing Spider Monkey Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(7), pages 2315-2334, May.
    16. Hegazy Rezk & A. G. Olabi & Mohammad Ali Abdelkareem & Abdul Hai Alami & Enas Taha Sayed, 2023. "Optimal Parameter Determination of Membrane Bioreactor to Boost Biohydrogen Production-Based Integration of ANFIS Modeling and Honey Badger Algorithm," Sustainability, MDPI, vol. 15(2), pages 1-13, January.
    17. Xie, Xiaomin & Jiang, Xiaoyun & Zhang, Tingting & Huang, Zhen, 2019. "Regional water footprints assessment for hydroelectricity generation in China," Renewable Energy, Elsevier, vol. 138(C), pages 316-325.
    18. Mohammad Ehteram & Hojat Karami & Sayed Farhad Mousavi & Saaed Farzin & Alcigeimes B. Celeste & Ahmad-El Shafie, 2018. "Reservoir Operation by a New Evolutionary Algorithm: Kidney Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(14), pages 4681-4706, November.
    19. Jin, Xiaoyu & Liu, Benxi & Liao, Shengli & Cheng, Chuntian & Zhang, Yi & Zhao, Zhipeng & Lu, Jia, 2022. "Wasserstein metric-based two-stage distributionally robust optimization model for optimal daily peak shaving dispatch of cascade hydroplants under renewable energy uncertainties," Energy, Elsevier, vol. 260(C).
    20. Xu, Xiao & Hu, Weihao & Du, Yuefang & Liu, Wen & Liu, Zhou & Huang, Qi & Chen, Zhe, 2020. "Robust chance-constrained gas management for a standalone gas supply system based on wind energy," Energy, Elsevier, vol. 212(C).

    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:eee:energy:v:277:y:2023:i:c:s0360544223010794. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.