Author(s): Hongrui Liu; Xin-An Yin; Zhihao Xu; Yanpeng Cai; Wei Yang
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Keywords: No Keywords
Abstract: The discrete number of storage states (DNSS) in dynamic programming models is a key factor affecting the reservoir’s operation policy. In most dynamic programming studies, DNSS has been determined based on computational efficiency and economic benefits. Little consideration has been given to the ecological disturbance caused by different DNSS-based operation policies. To analyze the impact of DNSS, we built a deterministic dynamic programming model to explore the relationship among DNSS, the flow regime alteration (ecological disturbance) and cumulative annual power generation (economic benefit) by setting a range of DNSS scenarios. We used three reservoirs with different storage coefficients (ratios of usable storage to annual average runoff) as examples and used the range of variability approach to assess the ecological disturbance under these scenarios. We compared the results with those of a stochastic dynamic programming (SDP) model and a Bayesian SDP (BSDP) model. We found that when DNSS is low, increasing DNSS improves economic performance but causes a more severe ecological disturbance; when DNSS is high, increasing DNSS improves the economic benefit only slightly, without exacerbating the ecological disturbance; for a given DNSS, the BSDP model provides a higher economic bene fit than the SDP model and a similar disturbance of the riverine ecosystem; and larger reservoirs more often cause more severe disturbance of riverine ecosystems because monthly mean flows and annual extreme flows change more drastically. Our results will help to improve economic benefits and protection of riverine ecosystems if reservoir operation managers consider DNSS using dynamic programming models.
Year: 2018