New evolutionary algorithm for optimizing hydropower generation considering multireservoir systems

• Main Authors: Ehteram, M., Koting, S.B., Afan, H.A., Mohd, N.S., Malek, M.A., Ahmed, A.N., El-shafie, A.H., Onn, C.C., Lai, S.H., El-Shafie, A.
• Format: Article
• Language: English
• Published: 2020
• Institution: Universiti Tenaga Nasional

In recent decades, solving complex real-life optimization problems has attracted the full attention of researchers. Dam and reservoir operation rules are considered one of the most complicated optimization engineering problems. In fact, the operation rules of dams and reservoirs are multisystematic and highly stochastic and have highly nonlinear system constraints due to the direct influence of environmental conditions: Therefore, these rules are considered highly complex optimization problems. Recently, metaheuristic methods inferred from nature have been broadly utilized to elucidate the way optimal solutions are provided for several complex optimization engineering applications, and these methods have achieved interesting results. The major advantage of these metaheuristic methods over conventional methods is the unnecessity to identify a particular initial condition, convexity, continuity, or differentiability. The present study investigated the potential of using a new metaheuristic method (i.e., the crow algorithm (CA)) to provide optimal operations for multireservoir systems, with the aim of optimally improving hydropower generation. A multireservoir system in China was considered to examine the performance of the proposed optimization algorithm for several operation scenarios. The results obtained the average hydropower generation by considering all examined operation scenarios based on the operation rule achieved using the CA, which outperformed the other metaheuristic methods. In addition, compared to other metaheuristic methods, the proposed CA lessened the time required to search for the optimal solution. In conclusion, the proposed CA has high potential for achieving optimal solutions to complex optimization problems associated with dam and reservoir operations. © 2019 by the authors.