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Weekly hydropower scheduling of cascaded reservoirs with hourly power and capacity balances

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  • Feng, Suzhen
  • Zheng, Hao
  • Qiao, Yifan
  • Yang, Zetai
  • Wang, Jinwen
  • Liu, Shuangquan

Abstract

A medium/long-term hydropower scheduling routinely assumes the energy produced in each week will be fully utilized in peak-shaving the power demands during every day in that week, ignoring the fluctuation of the hourly power loads. This work improves the traditional weekly hydropower scheduling by integrating the hourly power and capacity balances (HPCB), which has rarely been investigated, mainly due to great challenges imposed in solving the model. The HPCB are formulated into mixed integer linear constraints, involving the spare, maintenance, disabled, reserve and working capacities, as well as the order and levels of hydroplants in peak-shaving the hourly power load curve, all to be optimized. The formulation is then improved with successive strategies in updating water heads and peak-shaving hours, aiming to boost the solution efficiency by excluding more binary variables. The case studies involving 6 cascaded hydropower reservoirs in Lancang River reveal that the traditional weekly hydropower scheduling overestimates the benefits significantly, strongly suggesting the necessity to include the HPBC into a long/mid-term hydropower scheduling. The experiments also recommend an improved model formulation that achieves very consistent results on the top three prioritized objectives, while taking less than 0.1 s to solve the problem involving all six cascaded reservoirs, demonstrating a great prospect in solving problems in large scale.

Suggested Citation

  • Feng, Suzhen & Zheng, Hao & Qiao, Yifan & Yang, Zetai & Wang, Jinwen & Liu, Shuangquan, 2022. "Weekly hydropower scheduling of cascaded reservoirs with hourly power and capacity balances," Applied Energy, Elsevier, vol. 311(C).
    • Handle: RePEc:eee:appene:v:311:y:2022:i:c:s0306261922000940
    DOI: 10.1016/j.apenergy.2022.118620
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    References listed on IDEAS

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    1. R. Arunkumar & V. Jothiprakash, 2013. "Chaotic Evolutionary Algorithms for Multi-Reservoir Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(15), pages 5207-5222, December.
    2. Sheng-li Liao & Ben-xi Liu & Chun-tian Cheng & Zhi-fu Li & Xin-yu Wu, 2017. "Long-Term Generation Scheduling of Hydropower System Using Multi-Core Parallelization of Particle Swarm Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(9), pages 2791-2807, July.
    3. 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).
    4. Xianliang Cheng & Suzhen Feng & Yanxuan Huang & Jinwen Wang, 2021. "A New Peak-Shaving Model Based on Mixed Integer Linear Programming with Variable Peak-Shaving Order," Energies, MDPI, vol. 14(4), pages 1-15, February.
    5. Yves Mbeutcha & Michel Gendreau & Gregory Emiel, 2021. "A hybrid dynamic programming - Tabu Search approach for the long-term hydropower scheduling problem," Computational Management Science, Springer, vol. 18(3), pages 385-410, July.
    6. Chuanxiong Kang & Cheng Chen & Jinwen Wang, 2018. "An Efficient Linearization Method for Long-Term Operation of Cascaded Hydropower Reservoirs," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3391-3404, August.
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    Cited by:

    1. Xiong, Xin & Hu, Xi & Tian, Tian & Guo, Huan & Liao, Han, 2022. "A novel Optimized initial condition and Seasonal division based Grey Seasonal Variation Index model for hydropower generation," Applied Energy, Elsevier, vol. 328(C).
    2. Chao Wang & Zhiqiang Jiang & Pengfei Wang & Yichao Xu, 2024. "A Fast Local Search Strategy Based on the Principle of Optimality for the Long-Term Scheduling of Large Cascade Hydropower Stations," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 137-152, January.
    3. Shuo Huang & Xinyu Wu & Yiyang Wu & Zheng Zhang, 2023. "Mid-Term Optimal Scheduling of Low-Head Cascaded Hydropower Stations Considering Inflow Unevenness," Energies, MDPI, vol. 16(17), pages 1-13, September.
    4. Rongqi Zhang & Shanghong Zhang & Xiaoxiong Wen & Zhu Jing, 2023. "Refined Scheduling Based on Dynamic Capacity Model for Short-term Hydropower Generation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 21-35, January.
    5. Li, Zekai & Hu, Xi & Guo, Huan & Xiong, Xin, 2023. "A novel Weighted Average Weakening Buffer Operator based Fractional order accumulation Seasonal Grouping Grey Model for predicting the hydropower generation," Energy, Elsevier, vol. 277(C).

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