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Application of the Grasshopper Optimization Algorithm (GOA) to the Optimal Operation of Hydropower Reservoir Systems Under Climate Change

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Abstract

Hydropower is a low-carbon energy source, which may be adversely impacted by climate change. This work applies the Grasshopper Optimization Algorithm (GOA) to optimize hydropower multi-reservoir systems. Performance of GOA is compared with that of particle swarm optimization (PSO). GOA is applied to hydropower, three-reservoir system (Seymareh, Sazbon, and Karkheh), located in the Karkheh basin (Iran) for baseline period 1976–2005 and two future periods (2040–2069) and (2070–2099) under greenhouse gases pathway scenarios RCP2.6, RCP4.5, and RCP8.5. GOA minimizes the shortage of hydropower energy generation. Results from GOA optimization of Seymareh reservoir show that average objective function in baseline is 85 and minimum value of average objective function in 2040–2069 would be under RCP2.6 (equal to 0.278). Optimization of Seymareh-reservoir based on PSO shows that average value of objective function in baseline is less (that is, better) than value obtained with GOA (10.953). Optimization results for two-reservoir system (Sazbon and Karkheh) based on GOA optimization show that objective function in baseline is 5.44 times corresponding value obtained with PSO, standard deviation is 2.3 times that calculated with PSO, and run-time is 1.5 times PSO’s. Concerning three-reservoir systems it was determined that objective function based on PSO had the best value (the lowest energy deficit), especially in future. GOA converges close to the best objective function, especially in future-periods optimization, and convergence to solutions is more stable than PSO’s. A comparison of performance of GOA and PSO indicates PSO converges faster to optimal solution, and produces better objective function than GOA.

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References

  • Ahmadianfar I, Samadi-Koucheksaraee A, Bozorg-Haddad O (2017) Extracting optimal policies of hydropower multi-reservoir systems utilizing enhanced differential evolution algorithm. Water Resour Manag 31(14):4375–4397

    Article  Google Scholar 

  • Ahmadianfar I, Bozorg-Haddad O, Chu X (2019) Optimizing multiple linear rules for multi-reservoir hydropower systems using an optimization method with an adaptation strategy. Water Resour Manag 33:4265–4286

    Article  Google Scholar 

  • Ashofteh P-S, Bozorg-Haddad O, Loáiciga HA (2021) Application of bi-objective genetic programming (BO-GP) for optimizing irrigation rules using two reservoir performance criteria. Int J River Basin Manag. https://doi.org/10.1080/15715124.2019.1613415

    Article  Google Scholar 

  • Azadi F, Ashofteh P-S, Shokri A, Loáiciga HA (2021) Simulation-optimization of reservoir water quality under climate change. J Water Resour Plan Manag 147(9):04021054

    Article  Google Scholar 

  • Bozorg-Haddad O, Hosseini-Moghari SM, Loáiciga HA (2016) Biogeography-based optimization algorithm for optimal operation of reservoir systems. J Water Resour Plan Manag 142(1):04015034

    Article  Google Scholar 

  • Bozorg-Haddad O, Garousi-Nejad I, Loaiciga HA (2017) Extended multi-objective firefly algorithm for hydropower energy generation. J Hydroinform 19(5):734–751

    Article  Google Scholar 

  • Chang J, Wang X, Li Y, Wang Y, Zhang H (2018) Hydropower plant operation rules optimization response to climate change. Energy 160:886–897

    Article  Google Scholar 

  • Fallah-Mehdipour E, Bozorg-Haddad O, Loaiciga HA (2018) Calculation of multi-objective optimal tradeoffs between environmental flows and hydropower generation. Environ Earth Sci 77:453. https://doi.org/10.1007/s12665-018-7645-6

    Article  Google Scholar 

  • Fang R, Popole Z (2020) Multi-objective optimized scheduling model for hydropower reservoir based on improved particle swarm optimization algorithm. Environ Sci Pollut Res 27(12):12842–12850

    Article  Google Scholar 

  • Feng ZK, Liu S, Niu WJ, Li BJ, Wang WC, Luo B, Miao SM (2020) A modified sine cosine algorithm for accurate global optimization of numerical functions and multiple hydropower reservoirs operation. Knowl-Based Syst 208:106461

    Article  Google Scholar 

  • Garousi-Nejad I, Bozorg-Haddad O, Loáiciga HA (2016a) Modified firefly algorithm for solving multireservoir operation in continuous and discrete domains. J Water Resour Plan Manag 142(9):04016029

    Article  Google Scholar 

  • Garousi-Nejad I, Bozorg-Haddad O, Loáiciga HA, Mariño MA (2016b) Application of the firefly algorithm to optimal operation of reservoirs with the purpose of irrigation supply and hydropower production. J Irrig Drain Eng 142(10):04016041

    Article  Google Scholar 

  • Golfam P, Ashofteh P-S, Loáiciga HA (2021) Modeling adaptation policies to increase the synergies of water-climate-agriculture nexus under climate change. Environ Dev 37:100612. https://doi.org/10.1016/j.envdev.2021.100612

    Article  Google Scholar 

  • Hosseini-Moghari SM, 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 Resour Manag 29(10):3749–3769

    Article  Google Scholar 

  • Jahandideh-Tehrani M, Bozorg-Haddad O, Loáiciga HA (2020) Application of particle swarm optimization to water management: an introduction and overview. Environ Monit Assess 192(5):1–18

    Article  Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN'95-international conference on neural networks. IEEE 4:1942–1948

  • Khalifeh S, Esmaili K, Khodashenas S, Akbarifard S (2020) Data on optimization of the non-linear Muskingum flood routing in Kardeh River using Goa algorithm. Data Brief 30:105398

    Article  Google Scholar 

  • Saremi Sh, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47

    Article  Google Scholar 

  • Sarzaeim P, Bozorg-Haddad O, Zolghadr-Asli B, Fallah-Mehdipour E, Loáiciga HA (2018) Optimization of run-of-river hydropower plant design under climate change conditions. Water Resour Manag 32(12):3919–3934

    Article  Google Scholar 

  • Xu Y, Mei Y (2018) A modified water cycle algorithm for long-term multi-reservoir optimization. Appl Soft Comput 71:317–332

    Article  Google Scholar 

  • Zeynali MJ, Shahidi A (2018) Performance assessment of grasshopper optimization algorithm for optimizing coefficients of sediment rating curve. AUT J Civ Eng 2(1):39–48

    Google Scholar 

  • Zhang X, Liu P, Xu C-Y, Guo Sh, Gong Y, Li H (2019) Derivation of hydropower rules for multireservoir systems and its application for optimal reservoir storage allocation. J Water Resour Plan Manag 145(5):04019010

    Article  Google Scholar 

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KR developed theory and performed computations. P-SA verified analytical methods and encouraged KR to investigate specific aspects. P-SA supervised findings of this work, and HL helped supervise project. All authors discussed results and contributed to final manuscript. KR wrote manuscript with support from P-SA, and especially HL. P-SA conceived original idea.

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Correspondence to Parisa-Sadat Ashofteh.

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Rahmati, K., Ashofteh, PS. & Loáiciga, H.A. Application of the Grasshopper Optimization Algorithm (GOA) to the Optimal Operation of Hydropower Reservoir Systems Under Climate Change. Water Resour Manage 35, 4325–4348 (2021). https://doi.org/10.1007/s11269-021-02950-z

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