Abstract
Operation rule plays an important role in the scientific management of hydropower reservoirs, because a scientifically sound operating rule can help operators make an approximately optimal decision with limited runoff prediction information. In past decades, various effective methods have been developed by researchers all the over world, but there are few publications evaluating the performances of different methods in deriving the hydropower reservoir operation rule. To achieve satisfying scheduling process triggered by limited streamflow data, four methods are used to derive the operation rule of hydropower reservoirs, including multiple linear regression (MLR), artificial neural network (ANN), extreme learning machine (ELM), and support vector machine (SVM). Then, the data from 1952 to 2015 in Hongjiadu reservoir of China are chosen as the survey case, and several quantitative statistical indexes are adopted to evaluate the performances of different models. The radial basis function is chosen as the kernel function of SVM, while the sigmoid function is used in the hidden layer of ELM and ANN. The simulations show that three artificial intelligence algorithms (ANN, SVM, and ELM) are able to provide better performances than the conventional MLR and scheduling graph method. Hence, for scholars in the hydropower operation field, the applications of artificial intelligence algorithms in deriving the operation rule of hydropower reservoir might be a challenge, but represents valuable research work for the future.
Highlights
As a classical tool for adjusting natural runoff, reservoirs play an increasingly important role in the human society [1]
The active-storage volume of the Hongjiadu reservoir is rather large in comparison with its annual inflow volume, meaning it plays a large role in determining the efficiencies to be achieved by any operation rules
As compared with scheduling graph method (SGM), multiple linear regression (MLR), artificial neural network (ANN), and support vector machine (SVM), the extreme learning machine (ELM) method can generate the best solution with approximately 9.00%, 7.57%, 3.03%, and 1.73% improvements in average power generation (APG), respectively, while the generation assurance rate is improved by about 8.01%, 4.80%, 1.87%, and 0.27%, respectively
Summary
As a classical tool for adjusting natural runoff, reservoirs play an increasingly important role in the human society [1]. The reservoir operation optimization has become one of the most significant tasks in water resources and power. When the inflow per scheduling period is known, the global optimal solution for the reservoir operation problem can be obtained using the dynamic programming or other optimization methods [6]. This dispatching pattern is identified as the deterministic optimization and the corresponding scheduling result denotes the best solution found in this scenario [7]. A natural idea for handling the above issue is to examine the reservoir operation rule with actual data and planning data [8]
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