Abstract
Operating rules have been used widely in the reservoir long-term operation duo to its characteristics of coping with inflow uncertainty and easy implementation. And implicit stochastic optimization (ISO) has been widely applied to derive reservoir operation rules, based on linear regression or nonlinear fitting method. However, the maximum goodness-of-fit criterion of fitting method may be unreliable to determine the effective rules. Therefore, this paper develops a self-optimization system dynamics (SD) simulation of reservoir operation for optimizing the operating rules, by taking advantages of feedback loops in SD simulation. A deterministic optimization operation model is firstly established, and then resolved using dynamic programming (DP). Simultaneously, the initial operating rules (IOR) are derived using the linear fitting method. Finally, the refined optimal operating rules (OOR) are obtained by improving the IOR based on the self-optimization SD simulation. China’s Three Gorges Reservoir is used as a case study. The results show that the SD simulation is competent in simulating a complicated hydropower system with feedback and causal loops. Moreover, it makes a contribution to improve the IOR derived by fitting method within an ISO frame. And the OOR improve effectively the guarantee rate of power generation on the premise of ensuring power generation.
Highlights
Reservoir operation is an extremely complicated partly due to the uncertainty of reservoir inflow and the stochastic fluctuation of load demand [1]
Within an implicit stochastic optimization (ISO) framework, the operating rules derived by fitting method with the maximum goodness-of-fit criterion may be unreliable to guide reservoir operation because of the existence of outliers partly
This study focuses on realizing the self-optimization system dynamics (SD) simulation of operating rules so as to improve initial operating rules derive by fitting method
Summary
Reservoir operation is an extremely complicated partly due to the uncertainty of reservoir inflow and the stochastic fluctuation of load demand [1]. Operating rules have been used widely in the reservoir optimization operation. Taking random factors into account, especially stochastic nature of inflow, the explicit stochastic optimization (ESO) has been proposed, and often used in studies to derive the operating rules [5]. The implicit stochastic optimization (ISO) is an efficient alternative to ESO. ISO is a deterministic approach, but based on the long representative hydrologiclal data, the past records or the synthetic streaflow, most stochastic aspects of reservoir operation problem are implicitly considered [2, 5]. As a simple and efficient method, ISO has been widely used to deduce opermal reservoir operating rules
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