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A hierarchical model in short-term hydro scheduling with unit commitment and head-dependency

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  • Cheng, Xianliang
  • Feng, Suzhen
  • Zheng, Hao
  • Wang, Jinwen
  • Liu, Shuangquan

Abstract

The short-term hydro scheduling (STHS) problem has long been studied for decades, with great efforts having been made on improving their solution methods or algorithms. This work instead aims to solve a real-world problem with a hierarchical modeling strategy presented to decouple the STHS problem into two sub-problems: a load distribution (LD) and a unit commitment (UC), which are both solved with the Mixed Integer Linear Programming (MILP) and then coordinated through their coupling variables. The linearity errors in the model can be eliminated with an efficient successive water head updating procedure that adjusts the searching range based on the improvement of the objective. Taking into account the hydraulic connection, unit operating zones, head-dependency, generation and water level targets, the optimal quarter-hourly schedule for Lancang Cascade that consists of 11 hydropower reservoirs and 56 units can be obtained in 1 min for not only all the hydro-plants in the cascade but also every unit within hydro plant. The application results demonstrate that the convergence of the water head updating procedure is intermittent yet guaranteed after about 20 iterations.

Suggested Citation

  • Cheng, Xianliang & Feng, Suzhen & Zheng, Hao & Wang, Jinwen & Liu, Shuangquan, 2022. "A hierarchical model in short-term hydro scheduling with unit commitment and head-dependency," Energy, Elsevier, vol. 251(C).
    • Handle: RePEc:eee:energy:v:251:y:2022:i:c:s0360544222008118
    DOI: 10.1016/j.energy.2022.123908
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    2. Han, Shuo & Yuan, Yifan & He, Mengjiao & Zhao, Ziwen & Xu, Beibei & Chen, Diyi & Jurasz, Jakub, 2024. "A novel day-ahead scheduling model to unlock hydropower flexibility limited by vibration zones in hydropower-variable renewable energy hybrid system," Applied Energy, Elsevier, vol. 356(C).
    3. Favaro, Pietro & Dolányi, Mihály & Vallée, François & Toubeau, Jean-François, 2024. "Neural network informed day-ahead scheduling of pumped hydro energy storage," Energy, Elsevier, vol. 289(C).

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