Incorporating Reliability into the Optimal Design of Multi-hydropower Systems: A Cellular Automata-based Approach
Document Type
Article
Abstract
In recent years, Cellular Automata (CA) has emerged as a powerful tool for solving optimization problems in water resources engineering and, in particular, reservoir operation problems. This study utilizes the capabilities of the CA-based method to maximize the firm energy yield of multi-reservoir hydropower systems. Installed capacities (ICs) are selected as decision variables in the design phase and be determined in an iterative procedure. The reservoir storages at the beginning and the end of the periods are used as the decision variables in the operation phase and be calculated by the CA. The process starts with specifying an arbitrary initial installed IC for each reservoir determined based on long-term average annual inflow. Then, the system is optimally operated to maximize the energy yield under user-specified reliability of the system using a hybrid approach, Cellular Automata-Simulating Annealing (CA-SA). The system’s ICs are then increased/decreased depending on whether the system’s energy yield reliability is greater/less than the target reliability. This iterative process lasts till the system’s energy yield reliability is equal to the target reliability. The proposed method is used to optimally design the three-reservoir Khersan hydropower system and also the largest hydropower reservoir system in Iran composed of 16 reservoirs. The results are presented and compared with those of the existing methods in the literature. The results show that the proposed method can be efficiently and effectively used for improving the firm energy yield of real-world multi-reservoir hydropower systems.
Digital Object Identifier (DOI)
Publication Info
Published in Volume 604, 2022.
Rights
© 2021 Elsevier B.V.
APA Citation
Azizipour, M., Sattari, A., Afshar, M. H., & Goharian, E. (2022). Incorporating reliability into the optimal design of multi-hydropower systems: A cellular automata-based approach. Journal of Hydrology, 604, 127227. https://doi.org/10.1016/j.jhydrol.2021.127227