A stochastic dynamic programming model for hydropower scheduling with state-dependent maximum discharge constraints
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DOI: 10.1016/j.renene.2022.05.106
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Cited by:
- 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).
- 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.
- 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.
- Zhao, Lulin & Yin, Linfei, 2024. "Knowledge-shareable adaptive deep dynamic programming for hierarchical generation control of distributed high-percentage renewable energy systems," Renewable Energy, Elsevier, vol. 228(C).
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Keywords
Environmental factors; Hydroelectric power generation; Optimisation methods; Power generation scheduling; Stochastic processes;All these keywords.
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