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Ashofteh, P. (2023). Application of the Slime Mould Optimization Algorithm (SMOA) in the Hydropower Three-Reservoir System Optimal Operation. , 13(4), 929-944. doi: 10.22059/jwim.2023.364129.1096
Parisa-Sadat Ashofteh. "Application of the Slime Mould Optimization Algorithm (SMOA) in the Hydropower Three-Reservoir System Optimal Operation". , 13, 4, 2023, 929-944. doi: 10.22059/jwim.2023.364129.1096
Ashofteh, P. (2023). 'Application of the Slime Mould Optimization Algorithm (SMOA) in the Hydropower Three-Reservoir System Optimal Operation', , 13(4), pp. 929-944. doi: 10.22059/jwim.2023.364129.1096
Ashofteh, P. Application of the Slime Mould Optimization Algorithm (SMOA) in the Hydropower Three-Reservoir System Optimal Operation. , 2023; 13(4): 929-944. doi: 10.22059/jwim.2023.364129.1096

Application of the Slime Mould Optimization Algorithm (SMOA) in the Hydropower Three-Reservoir System Optimal Operation

مدیریت آب و آبیاری
Volume 13, Issue 4, January 2024, Pages 929-944    PDF (856.67 K)
Document Type: Research Paper
DOI: 10.22059/jwim.2023.364129.1096
Author
Parisa-Sadat Ashofteh*
Department of Civil Engineering, University of Qom, Qom, Iran.
Abstract
Hydropower energy management is essential under climate change conditions. In this research, Slime Mould Optimization Algorithm (SMOA) is used to optimize the system of three hydropower reservoirs, and its results are compared with Genetic Algorithm (GA) to evaluate the performance of SMOA. First of all, SMOA performance is measured for Akley test function, which has been successfully performed. In the next step, the optimization is used on the three-reservoir hydropower system of Sazbon reservoir, Seymareh and Karkheh stream located in the Karkheh basin (Iran). The optimization of the hydropower problem is carried out for the baseline period of 1976-2005 and the future period of 2040-2069 under the RCP8.5 climate change scenario. The objective function is to minimize the lack of hydropower supply. For the optimization of the three-reservoir hydropower problem, the results show that the value of the objective function based on SMOA is close to the absolute optimal value, especially in the period of climate change. In general, the performance of SMOA to achieve the optimal value of the objective function in climate change periods is better than the baseline period and the solutions are more stable. In the comparison between SMOA and GA for the three-reservoir operation mode in the baseline and future period under the RCP8.5 scenario, the performance of SMOA in reaching the desired value of the objective function is much better, the speed of convergence is higher, the run-time is shorter, and the solutions of the objective function are more stable.
Keywords
Climate change; Genetic algorithm; Hydropower three-reservoir system; Slime mould optimization algorithm
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