Abstract
In the long-term operation of hydropower reservoirs, operating rules have been used widely to decide reservoir operation because they can help operators make an approximately optimal decision with limited runoff information. However, the problems faced by reservoir managers is how to make and select an efficient operating rule properly. This study focuses on identifying efficient and reliable operating rules for the long-term operation of hydropower reservoirs using system dynamics (SD) approach. A stochastic hydrological model of reservoir inflow time series was established and used to generate a large number of inflow scenarios. A deterministic optimization operation model of hydropower reservoirs was constructed and then resolved using dynamic programming (DP) algorithm. Simultaneously, within implicit stochastic optimization (ISO) framework, different operating rules were derived using linear fitting methods. Finally, the most efficient one of the existing operating rules was identified based on SD simulation coupled with the operating rules. The Three Gorges Reservoir (TGR) in central China was used as a case study. The results show that the SD simulation is an efficient way to simulate a complicated reservoir system using feedback and causal loops. Moreover, it can directly and efficiently guide reservoir managers to make and identify efficient operating rules for the long-term operation of hydropower reservoirs.
Highlights
Reservoir operation is an extremely complicated and comprehensive problem partly due to the uncertainty of reservoir inflow and the stochastic fluctuation of water use demand [1,2,3,4]
While the purpose of this study is to identifying the efficient operating rules for long-term operation of hydropower reservoirs by system dynamics (SD) simulation coupled with operating rules
This study focuses on identifying an efficient operating rule for long-term operation of hydropower reservoirs by SD simulation coupled with operating rules
Summary
Reservoir operation is an extremely complicated and comprehensive problem partly due to the uncertainty of reservoir inflow and the stochastic fluctuation of water use demand [1,2,3,4]. There are two ways to generate operating rules considering the uncertainty of decision input information—explicit stochastic optimization (ESO) [24] and implicit stochastic optimization (ISO) [17]. These two frameworks deal explicitly and implicitly with the stochasticity of inflow respectively [2,7] and are shown in Figures 2 and 5 in the literature [7]. According to the literatures [23,27,28,29], the ISO method is an efficient alternative to ESO for considering uncertainty of reservoir inflow.
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