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

Document Type : Research Paper

Author

Department of Civil Engineering, University of Qom, Qom, Iran.

10.22059/jwim.2023.364129.1096

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.

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  1. real-time operation rules: a new genetic programming approach. Proceedings of the Institution of Civil Engineers, Water Management, 167 (10), 561-576.
  2. Akbari-Alashti, H., Soncini, A., Dinpashoh, Y., Fakheri-Fard, A., Talatahari, S., & Bocchiola, D. (2018). Operation of two major reservoirs of Iran under IPCC scenarios during the XXI century. Hydrological Processes, 32 (21), 3254-3271.
  3. (2009). Meteorological and Climate Studies Report. Iran Water and Power Resources Development Company, Karkheh Watershed System Studies, 1, 1-319. (In Persian).
  4. (2010). Water Resources Planning Studies. Iran Water and Power Resources Development Company, Karkheh Watershed System Studies, Vol. 5, 1-361. (In Persian).
  5. (2014). Report on Seymareh Project. Mahab Ghodss Consulting Company, Vol. 1, 1-100. (In Persian).
  6. Arunkumar, R., & Jothiprakash, V. (2012). Optimal reservoir operation for hydropower generation using non-linear programming model. Journal of The Institution of Engineers (India): Series A, 93 (2), 111-120.
  7. Bolouri-Yazdeli, Y., Bozorg-Haddad, O., Fallah-Mehdipour, E., & Marino, M. A. (2014). Evaluation of real-time operation rules in reservoir systems operation. Water Resources Management, 28, 715-729.
  8. Chang, J., Wang, X., Li, Y., Wang, Y., & Zhang, H. (2018). Hydropower plant operation rules optimization response to climate change. Energy, 160, 886-897.
  9. Deb, K. (2012). Optimization for engineering design: Algorithms and examples. Second Edition, PHI Learning Private Limited.
  10. Garousi-Nejad, I., Bozorg-Haddad, O., Loáiciga, H. A., & Mariño, M. A. (2016). “Application of the firefly algorithm to optimal operation of reservoirs with the purpose of irrigation supply and hydropower production”, Journal of Irrigation and Drainage Engineering, 142 (10), 04016041.
  11. Holland, J. H. (1992). Genetic algorithms. Scientific American, 267 (1), 66-72.
  12. Hosseini-Moghari, S. M., Morovati, R., Moghadas, M., & Araghinejad, S. (2015). Optimum operation of reservoir using two evolutionary algorithms: imperialist competitive algorithm (ICA) and cuckoo optimization algorithm (COA). Water Resources Management, 29 (10), 3749-3769.
  13. IPCC (Intergovernmental Panel on Climate Change). (2013). Climate change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Rep. of the Intergovernmental Panel on Climate Change, T. F. Stocker, et al., eds., Cambridge University Press, Cambridge, U.K.
  14. Jahandideh-Tehrani, M., Bozorg-Haddad, O., & Loáiciga, H. A. (2015). Hydropower reservoir management under climate change: The Karoon reservoir system. Water Resources Management, 29 (3), 749-770.
  15. Liu, S., Xie, Y., Fang, H., Huang, Q., Huang, Sh., Wang, J., & Li, Zh. (2020). Impacts of inflow variations on the long term operation of a multi-hydropower-reservoir system and a strategy for determining the adaptable operation rule. Water Resources Management, 34, 1649-1671, DOI: 1007/s11269-020-02515-6.
  16. Liu, X., Lu, J,. Zou, Ch., Deng, B., Liu, L., & Yan, Sh. (2023). Research on sustainable scheduling of cascade reservoirs based on improved crow search algorithm. Water, 15 (3), 578, DOI: 10.3390/w15030578.
  17. Li, Sh., Chen, H., Wang, M., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems, 111, 300-323.
  18. Qiu, H., Hu, T., Zhang, S., & Xiao, Y. (2023). “Deriving operating rules of hydropower reservoirs using multi-strategy ensemble Henry Gas solubility optimization driven support vector machine. Water, 15 (3), 437, DOI: 10.3390/w15030437.
  19. Rahmati, K., Ashofteh, P.-S., Afzali, R., & Loáiciga, H. A. (2022). System-dynamics approach to multireservoir energy generation under climate change. Journal of Hydrologic Engineering, 27 (9), DOI: 10.1061/%28ASCE%29HE.1943-5584.0002197.
  20. Rahmati, K., Ashofteh, P.-S., & Loáiciga, H. A. (2021). Application of the grasshopper optimization algorithm (GOA) to the optimal operation of hydropower reservoir systems under climate change. Water Resources Management, 35, 4325-4348, Doi: 10.1007/s11269-021-02950-z.
  21. Ren, X., Zhao, Y., Hao, D., Sun, Y., Chen, Sh., & Gholinia, F. (2021). Predicting optimal hydropower generation with help optimal management of water resources by Developed Wildebeest Herd Optimization (DWHO). Energy Reports, 7, 968-980.
  22. Sharifi, M. R., Akbarifard, S., Madadi, M. R., Qaderi, K., & Akbarifard, H. (2022). Optimization of hydropower energy generation by 14 robust evolutionary algorithms. Scientific Reports, 12, DOI: 1038/s41598-022-11915-0.
  23. Zaman, M., Fang, G., Huang, X., Shuo, X., & Xin, W. (2014). Optimization of the Xin'anjiang hydropower station using particle swarm optimization and genetic algorithm. In 2014 10th International Conference on Natural Computation (ICNC) (pp. 1-6). IEEE.
  24. Zhang, X., Liu, P., Xu, Ch.-Y., Guo, Sh., Gong, Y., & Li, H. (2019). Derivation of hydropower rules for multireservoir systems and its application for optimal reservoir storage allocation. Journal of Water Resources Planning and Management, 145 (5), 04019010.
  25. Zolghadr-Asli, B. (2017). Investigation of hydropower energy generation’s uncertainties under climate change conditions. M.Sc. Thesis, College of Agriculture and Natural Resources, Faculty of Agriculture Engineering and Technology, Department of Irrigation and Reclamation Engineering, University of Tehran.