Optimization of short-term hydropower scheduling with dynamic reservoir capacity based on improved genetic algorithm and parallel computing

• Published in: Journal of hydrology (Amsterdam) 2024-06, Vol.636, p.131238, Article 131238
• Main Authors: Zhang, Rongqi, Zhang, Shanghong, Wen, Xiaoxiong, Yue, Ziqi, Zhou, Yang
• Format: Article
• Language: English
• Subjects: Genetic Algorithm; OpenMP; Parallel Computing; Reservoir Optimization Scheduling; Three Gorges Reservoir
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2024.131238

•Modified dynamic reservoir capacity model used for short-term optimal scheduling.•Combination of genetic algorithm and parallel computing improves calculation speed.•Improved genetic algorithm effectively enhanced model convergence.•The model with parallel computing achieves 246 × speedup over the enumeration method. The optimal scheduling of hydropower plants is important for the efficient operation and management of hydropower energy systems. In the short-term optimization scheduling of river-type reservoirs, the dynamic reservoir capacity is a key factor. Currently, the short-term power generation scheduling model considering dynamic reservoir capacity was solved by the enumeration method, providing a more accurate simulation of the water flow process compared to the model considering static reservoir capacity. However, the solving process had the disadvantage of a large computational burden and excessive time consumption, which restricted application in the actual scheduling of reservoirs. Therefore, to solve the time-consuming problem of repeated calculation of hydrodynamics model in optimization scheduling, an improved genetic algorithm including adaptive and elitist-selection strategies combined with the parallel computing technique was proposed. The optimal scheduling plan generated by this model were compared with the optimization results of the enumeration method and the genetic algorithm scheduling model. The results showed that the optimal power generation selected by the three models were extremely similar and the optimization model could find the optimal value for the scheduling period. In terms of computing speed, the improved genetic algorithm parallel technique took approximately 0.8 h, around 246 times faster than the enumeration method and 20 times faster than the model without using parallel computing. It demonstrates that the present algorithm is practically usable, significantly enhancing the optimization efficiency of scheduling solutions. Furthermore, it satisfies the engineering application efficiency requirements and can provide technical support for the subsequent power generation scheduling of the Three Gorges Reservoir.