Multiple Strategies Based Salp Swarm Algorithm for Optimal Operation of Multiple Hydropower Reservoirs

Reasonable optimal operation policy for complex multiple reservoir systems is very important for the safe and efficient utilization of water resources. The operation policy of multiple hydropower reservoirs should be optimized to maximize total hydropower generation, while ensuring flood control safety by effective and efficient storage and release policy of multiple reservoirs. To achieve this goal, a new meta-heuristic algorithm, salp swarm algorithm (SSA), is used to optimize the joint operation of multiple hydropower reservoirs for the first time. SSA is a competitive bio-inspired optimizer, which has received substantial attention from researchers in a wide variety of applications in finance, engineering, and science because of its little controlling parameters and adaptive exploratory behavior. However, it still faces few drawbacks such as lack of exploitation and local optima stagnation, leading to a slow convergence rate. In order to tackle these problems, multiple strategies combining sine cosine operator, opposition-based learning mechanism, and elitism strategy are applied to the original SSA. The sine cosine operator is applied to balance the exploration and exploitation over the course of iteration; the opposition-based learning mechanism is used to enhance the diversity of the swarm; and the elitism strategy is adopted to find global optima. Then, the improved SSA (ISSA) is compared with six well-known meta-heuristic algorithms on 23 classical benchmark functions. The results obtained demonstrate that ISSA outperforms most of the well-known algorithms. Then, ISSA is applied to optimal operation of multiple hydropower reservoirs in the real world. A multiple reservoir system, namely Xiluodu Reservoir and Xiangjiaba Rservoir, in the upper Yangtze River of China are selected as a case study. The results obtained show that the ISSA is able to solve a real-world optimization problem with complex constraints. In addition, for the typical flood with a 100 return period in 1954, the maximum hydropower generation of multiple hydropower reservoirs is about 6671 GWh in the case of completing the flood control task, increasing by 1.18% and 1.77% than SSA and Particle Swarm Optimization (PSO), respectively. Thus, ISSA can be used as an alternative effective and efficient tool for the complex optimization of multiple hydropower reservoirs. The water resources in the river basin can be further utilized by the proposed method to cope with the increasingly serious climate change.

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.

Reconstructing the Changes in Sedimentation and Source Provenance in an East African Hydropower Reservoirs: A Case Study of Nyumba ya Mungu in Tanzania

This study aimed to reconstruct the sedimentation rates over time and identify the changing sources of sediment in a major hydropower reservoir in Tanzania, the Nyumba ya Mungu (NYM). Fallout 210Pb measurements were used to estimate age of sediment deposits and broad changes in sedimentation rates were reconstructed. Sedimentation peaks were cross referenced to geochemical profiles of allogenic and autogenic elemental constituents of the sediment column to confirm a causal link. Finally, geochemical fingerprinting of the sediment cores and potential sources were compared using a Bayesian mixing model (MixSIAR) to attribute the dominant riverine and land use sources to the reservoir together with changes through recent decades. Reservoir sedimentation generally increased from 0.1 g cm−2 yr−1 in the lower sediment column to 1.7 g cm−2 yr−1 in the most recent deposits. These results correlated to changes in allogenic and autogenic tracers. The model output pointed to one of two major tributaries, the Kikuletwa River with 60.3%, as the dominant source of sediment to the entire reservoir, while the other tributary, Ruvu River, contributed approximately 39.7%. However, downcore unmixing results indicated that the latest increases in sedimentation seem to be mainly driven by an increased contribution from the Ruvu River. Cultivated land (CU) was shown to be the main land use source of riverine sediment, accounting for 38.4% and 44.6% in Kikuletwa and Ruvu rivers respectively. This study explicitly demonstrated that the integration of sediment tracing and dating tools can be used for quantifying the dominant source of sediment infilling in East African hydropower reservoirs. The results underscore the necessity for catchment-wide management plans that target the reduction of both hillslope erosion reduction and the sediment connectivity from hillslope source areas to rivers and reservoirs, which will help to maintain and enhance food, water and energy security in Eastern Africa.