Environmental flow assessment of Kayan River: managing sustainability indicator of hydropower project

Nowadays, constructing a new hydropower plant is one of the most attractive solutions to overcome energy requirements. The Kayan Hydroelectric, built in the Kayan River, is projected to generate electricity of nine hundred megawatts. However, the dams have to be managed appropriately since alteration of river discharge will have a significant impact on the environment. This paper proposes an environmental flow assessment as an appropriate indicator to manage sustainability. Three environmental flow assessment methods were used: Flow Duration Curve Analysis (FDCA), Tennant method, and Building Block method. The environmental flow pattern was used as a benchmark to evaluate whether the operation rule of the dams fulfilled the sustainable requirement, particularly on the hydrological pattern of the river. Regarding the Tennant and FDCA method, the minimum discharge that has to be maintained for the minimum environmental flow of the river is about twenty-five cms (corresponds to ten percent of AFF) and thirty-five cms, respectively. Meanwhile, the Building block method informs a range of discharge from a hundred cms to twenty thousand cms during the flood. The environmental flow should be managed to guarantee that the river’s ecosystem and carrying capacity can be preserved.

Operation Rule Derivation of Hydropower Reservoirs by Support Vector Machine Based on Grey Relational Analysis

In practical applications, the rational operation rule derivation can lead to significant improvements in the middle and long-term joint operation of cascade hydropower stations. The key issue of actual optimal operation is to select effective attributions from the deterministic optimal operation results, however, there is still no general and mature method to solve this problem. Firstly, the joint optimal operation model of hydropower reservoirs considering backwater effects are established. Then, the dynamic programming and progressive optimality algorithm are applied to solve the joint optimal operation model and the deterministic optimization results are obtained. Finally, the grey relational analysis method is applied to select more effective factors from the obtained results as the input of a support vector machine for further operation rule derivation. The Xi Luo-du and Xiang Jia-ba cascade reservoirs in the upper Yangtze river of China are selected as a case study. The results show that the proposed method can obtain better input factors to improve the performance of SVM, and smallest value of root mean square error by the proposed method of Xi Luo-du and Xiang Jia-ba are 94.33 and 21.32, respectively. The absolute error of hydropower generation for Xi Luo-du and Xiang Jia-ba are 2.57 and 0.42, respectively. Generally, this study provides a well and promising alternative tool to guide the joint operation of hydropower reservoir systems.

Hybridizing ANN-NSGA-II Model with Genetic Programming Method for Reservoir Operation Rule Curve Determination (Case Study Zayandehroud Dam Reservoir)

One of the most important and effective works of water resource planning and management is determining the specific, applicable, regulated operating policies of the Zayandehroud dam reservoir, as a case study, in which it should be user-friendly and straightforward for the operator. For this purpose, different methods have been proposed in which each of them has its limitations. Due to the unique capabilities of the genetic programming (GP) model, here, this method is used to determine the operating rule curves and policies of the dam reservoir. For this purpose, here, two cases are proposed in which, in the first case, each month is individually simulated and modeled. However, in the second case, all months are simulated simultaneously. A second case is proposed here to determine simple and more applicable operation rule curves. In addition, two approaches are suggested for each case in which in the first approach, the influential input variables are selected by presenting the hybrid method. In the proposed hybrid method, the artificial neural network (ANN) model is equipped with non dominated sorting genetic (NSGA-II) algorithm leading to a hybrid method named the ANN-NSGA-II method. However, in the second approach, the influential input variables are selected automatically using the GP method. Here, the hybrid method is proposed and used to overcome the limitations of existing usual method. In other words, it is proposed to reduce the number of influential input variables of data-driven methods and select the effective ones. The obtained results of all proposed cases and approaches are presented and compared with the standard operation policy (SOP) method, stochastic dynamic programming (SDP), ANN model and, NLP method. Comparison of the results shows the acceptable performance of the proposed cases and approaches. In other words, the best- obtained values of (stability index) SI index and water deficit (objective function value) are 49.3% and 32, respectively.

Optimal water allocation method based on the genetic algorithm for a system of a reservoir and two pumping stations

The subtropical monsoon climate zone is featured with the abundant water resources but uneven temporal and spatial distribution, so seasonal water shortages are frequent. In order to reduce the water shortage and water spill in this region, a nonlinear optimization model for the joint operation of a system of a reservoir and two pumping stations is developed in this paper. In this model, the water supply of reservoir and pumping volume of pumping stations in each period are two types of decision variables, which are subjected to the annual available water in the reservoir, water rights of the two pumping stations and the operation rule of reservoir. However, the modern intelligent algorithms may fail in dealing with constraints of if statements like the operation rule of reservoir in this model. In light of the shortcoming of the classical genetic algorithm, a modified genetic algorithm is proposed by comparing the different methods for dealing with constraints. The modified algorithm showed a better adaptability to the operation rule. The modified genetic algorithm may provide a reference for similar modern intelligent algorithms to solve optimal water resources allocation for multiple reservoirs and multiple pumping stations system.

Operation Rule of Steam Ejector and Influence Analysis of Ejection Position

In order to analyze the operation rule of steam ejector used in sewage treatment field, this paper established the mathematical model of steam ejector, simulated the internal flow state, analyzed the influence rule of external parameters on the performance of steam ejector, and analyzed the influence rule of ejector position on the water production ratio of steam ejector multi-effect evaporation desalination system. The research results show that under the same steam ejector structure, as the pressure of the working steam increases, the mixing and disturbance of the working steam and the ejected steam become more intense, and the diamond shock wave structure increases. As the pressure of the ejected steam increases, the momentum exchange effect of the shear blending of working steam and ejected steam is accelerated, and the speed increase rate becomes larger, and the diamond shock wave structure gradually gathers toward the nozzle. As the pressure rise of the ejected steam inlet and outlet increases, the backflow phenomenon near the wall in the mixing chamber becomes more serious, and the diamond shock wave structure is reduced. With the forward movement of the ejector position of the thermal steam compressor, the pressure of the ejected steam increases, and the water production ratio of the steam ejector multi-effect evaporation seawater desalination system shows a downward trend. Therefore, choosing appropriate working condition parameters can effectively improve the working performance of steam ejector and improve its operating efficiency in the field of sewage treatment.