Abstract
In this paper, independent optimization operation strategy and global optimization operation strategy for cascaded hydropower plants are studied in aim to maximize total power energy of all the cascaded hydropower plants. In the first strategy, upstream hydropower plants are optimally operated first and then obtained results are used to operate downstream hydropower plants. On the contrary, all hydropower plants are operated simultaneously in the second strategy. The two strategies are accomplished by using an improved cuckoo search algorithm (ICSA) together with particle swarm optimization (PSO), cuckoo search algorithm (CSA), Salp Swarm Algorithm (SSA), sunflower optimization algorithm (SFO), equilibrium optimizer (EO) and marine predator algorithm (MPA). Result comparisons can lead to the evaluation that the first strategy can bring more benefits for upstream plants whereas the second strategy is more suitable for downstream hydroelectric plants. For the purpose of maximizing total energy of all plants, the second strategy is more effective than the first strategy. Compared to PSO, CSA, SSA, SFO, EO and MPA, ICSA method finds higher energy with a highly faster speed. Thus, the paper suggests the second strategy should be executed for hydroelectric plants in cascaded reservoir systems and ICSA can be a favorable method for implementing the recommended optimization operation strategy.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Abbreviations
- α1,n, α2,n, α3n, α4,n, α5,n :
-
Water discharge function coefficients of the nth hydroelectric plant
- α :
-
Scaling factor selected between 0 and 1
- C rp1 , C rp2 , C rp3 , C rp4 , C rp5 , C rp6 :
-
Randomly picked solutions from the set solution of Ck
- \({H}_{n}^{min},{H}_{n}^{max},{H}_{n,i}\) :
-
The lowest, highest and output of power of the nth hydroelectric plant
- I, N :
-
Interval and hydropower plant number
- If n,i :
-
Inflow into the reservoir n at the period i
- λ1, λ2, λ3 :
-
Randomly generated numbers within 0 and 1
- N sol :
-
Population size
- T m,n :
-
Time of flowing water from the reservoir m to the reservoir n
- \({Vl}_{n}^{min}, {Vl}_{n}^{max}\) :
-
The minimum and maximum volume of the nth reservoir
- Vl n,i –1, Vl n,i :
-
The nth reservoir volume at the end of the (i–1)th and ith intervals
- Wd n,i :
-
Water discharge through turbines of the nth reservoir at the ith interval
- \({Wd}_{n}^{min},{Wd}_{n}^{max}\) :
-
The minimum and maximum discharges through turbines of the nth reservoir
- \({Wd}_{m,i-{T}_{m,n}},\mathrm{ }{Ws}_{m,i-{T}_{m,n}}\) :
-
Discharge and spillage from the upstream reservoir m of the nth reservoir
- Ws n,i :
-
Spillage from the reservoir n at the interval i
- V ctv, V dpv :
-
Control and dependent variable number
- F(C k), F(A k):
-
Fitness function of the solutions Ck and Ak
- C Best :
-
The best solution among the set solution of Ck
- F(C Best):
-
Fitness function of CBest
- Wd kn,I :
-
Discharge of the nth hydroelectric plant at the ith interval in the kth solution
References
Afzalan E, Joorabian M (2015) An improved cuckoo search algorithm for power economic load dispatch. Int Trans Elect Energy Syst 25(6):958–975. https://doi.org/10.1002/etep.1878
Ahmed J, Salam Z (2014) A maximum power point tracking (MPPT) for PV system using Cuckoo Search with partial shading capability. Appl Energy 119:118–130. https://doi.org/10.1016/j.apenergy.2013.12.062
Alizadeh Bidgoli M, Payravi AR, Ahmadian A, Yang W (2021) Optimal day-ahead scheduling of autonomous operation for the hybrid micro-grid including PV, WT, diesel generator, and pump as turbine system. J Ambient Intell Humaniz Comput 12:961–977. https://doi.org/10.1007/s12652-020-02114-8
Basu M, Chowdhury A (2013) Cuckoo search algorithm for economic dispatch. Energy 60:99–108. https://doi.org/10.1016/j.energy.2013.07.011
Chi R, Su Y, Qu Z, Chi X (2019) A hybridization of cuckoo search and differential evolution for the logistics distribution center location problem. Math Probl Eng. https://doi.org/10.1155/2019/7051248
Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut Comput 1(1):3–18. https://doi.org/10.1016/j.swevo.2011.02.002
Ding J, Wang Q, Zhang Q, Ye Q, Ma Y (2019) A hybrid particle swarm optimization-cuckoo search algorithm and its engineering applications. Math Probl Eng. https://doi.org/10.1155/2019/5213759
Faramarzi A, Heidarinejad M, Mirjalili S, Gandomi AH (2020) Marine predators algorithm: a nature-inspired Metaheuristic. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2020.113377
Gheisarnejad M (2018) An effective hybrid harmony search and cuckoo optimization algorithm based fuzzy PID controller for load frequency control. Appl Soft Compu 65:121–138. https://doi.org/10.1016/j.asoc.2018.01.007
Gomes GF, da Cunha SS, Ancelotti AC (2019) A sunflower optimization (SFO) algorithm applied to damage identification on laminated composite plates. Eng Comput 35(2):619–626. https://doi.org/10.1007/s00366-018-0620-8
Kang T, Yao J, Jin M, Yang S, Duong T (2018) A novel improved cuckoo search algorithm for parameter estimation of photovoltaic (PV) models. Energies 11(5):1060. https://doi.org/10.3390/en11051060
Li J, Li YX, Tian SS, Xia JL (2019) An improved cuckoo search algorithm with self-adaptive knowledge learning. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04178-w
Ma HS, Li SX, Li SF, Lv ZN, Wang JS (2019) An improved dynamic self-adaption cuckoo search algorithm based on collaboration between subpopulations. Neural Comput Appl 31(5):1375–1389. https://doi.org/10.1007/s00521-018-3512-3
Mehrabi P, Honarbari S, Rafiei S, Jahandari S, Bidgoli MA (2021) Seismic response prediction of FRC rectangular columns using intelligent fuzzy-based hybrid metaheuristic techniques. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02776-4
Meng X, Chang J, Wang X, Wang Y (2019) Multi-objective hydropower station operation using an improved cuckoo search algorithm. Energy 168:425–439. https://doi.org/10.1016/j.energy.2018.11.096
Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp Swarm Algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002
Mo L, Lu P, Wang C, Zhou J (2013) Short-term hydro generation scheduling of Three Gorges-Gezhouba cascaded hydropower plants using hybrid MACS-ADE approach. Energy Convers Manage 76:260–273. https://doi.org/10.1016/j.enconman.2013.07.047
Naresh R, Sharma J (1999) Two-phase neural network based solution technique for short term hydrothermal scheduling. IEEE Proc-Gener Transm Distrib 146(6):657–663. https://doi.org/10.1049/ip-gtd:19990855
Nguyen KP, Fujita G (2018) Self-Learning Cuckoo search algorithm for optimal power flow considering tie-line constraints in large-scale systems. GMSARN Int J 12(2):118–126
Nguyen TT, Truong AV (2015) Distribution network reconfiguration for power loss minimization and voltage profile improvement using cuckoo search algorithm. Int J Elect Power Energy Syst 68:233–242. https://doi.org/10.1016/j.ijepes.2014.12.075
Nguyen TT, Truong AV, Phung TA (2016) A novel method based on adaptive cuckoo search for optimal network reconfiguration and distributed generation allocation in distribution network. Int J Elect Power Energy Syst 78:801–815. https://doi.org/10.1016/j.ijepes.2015.12.030
Nguyen TT, Nguyen TT, Vo DN (2018a) An effective cuckoo search algorithm for large-scale combined heat and power economic dispatch problem. Neural Comput Appl 30(11):3545–3564. https://doi.org/10.1007/s00521-017-2941-8
Nguyen TT, Vo DN, Dinh BH (2018b) An effectively adaptive selective cuckoo search algorithm for solving three complicated short-term hydrothermal scheduling problems. Energy 155:930–956. https://doi.org/10.1016/j.energy.2018.05.037
Nguyen TT, Nguyen TT, Pham TD (2020) Applications of metaheuristic algorithms for optimal operation of cascaded hydropower plants. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05418-0
Niu WJ, Feng ZK, Cheng CT, Wu XY (2018) A parallel multi-objective particle swarm optimization for cascade hydropower reservoir operation in southwest China. Appl Soft Comput 70:562–575. https://doi.org/10.1016/j.asoc.2018.06.011
Ong P, Zainuddin Z (2019) Optimizing wavelet neural networks using modified cuckoo search for multi-step ahead chaotic time series prediction. Appl Soft Comput 80:374–386. https://doi.org/10.1016/j.asoc.2019.04.016
Pham LH, Dinh BH, Nguyen TT, Phan VD (2021) Optimal operation of wind-hydrothermal systems considering certainty and uncertainty of wind. Alexandria Eng J 60(6):5431–5461. https://doi.org/10.1016/j.aej.2021.04.025
Rakhshani H, Rahati A (2017) Snap-drift cuckoo search: A novel cuckoo search optimization algorithm. Appl Soft Comput 52:771–794. https://doi.org/10.1016/j.asoc.2016.048
Shen J, Cheng C, Zhang X, Zhou B (2018) Coordinated operations of multiple-reservoir cascaded hydropower plants with cooperation benefit allocation. Energy 153:509–518. https://doi.org/10.1016/j.energy.2018.04.056
Soares S, Lyra C, Tavares H (1980) Optimal generation scheduling of hydrothermal power systems. IEEE Trans Power Apparat Syst 3:1107–1118. https://doi.org/10.1109/TPAS.1980.319741
Tang X, Zhou J (2012) A future role for cascade hydropower in the electricity system of China. Energy Policy 51:358–363. https://doi.org/10.1016/j.enpol.2012.08.025
Vo DN, Schegner P, Ongsakul W (2013) Cuckoo search algorithm for non-convex economic dispatch. IET Gener Transm Distrib 7(6):645–654. https://doi.org/10.1049/iet-gtd.2012.0142
Wang C, Shahidehpour SM (1993) Power generation scheduling for multi-area hydro-thermal systems with tie line constraints, cascaded reservoirs and uncertain data. IEEE Trans Power Syst 8(3):1333–1340. https://doi.org/10.1109/59.260860
Wardlaw R, Sharif M (1999) Evaluation of genetic algorithms for optimal reservoir system operation. J Water Resour Plan Manage. https://doi.org/10.1061/(ASCE)0733-9496(1999)125:1(25)
Xiao L, Dridi M, Hajjam El Hassani A, Fei H, Lin W (2018) An improved cuckoo search for a patient transportation problem with consideration of reducing transport emissions. Sustainability 10(3):793. https://doi.org/10.3390/su10030793
Yang XS (2014) Nature-inspired optimization algorithms. Elsevier
Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC). pp 210–214. IEEE. https://doi.org/10.1109/NABIC.2009.5393690
Yang XS, Deb S (2010) Engineering optimisation by cuckoo search. Int J Math Model Numer Optimis 1(4):330–343. https://doi.org/10.1504/IJMMNO.2010.03543
Yang B, Miao J, Fan Z, Long J, Liu X (2018) Modified cuckoo search algorithm for the optimal placement of actuators problem. Appl Soft Comput 67:48–60. https://doi.org/10.1016/j.asoc.2018.03.004
Zhang Y, Zhao H, Cao Y, Liu Q, Shen Z, Wang J, Hu M (2018) A hybrid ant colony and cuckoo search algorithm for route optimization of heating engineering. Energies 11(10):2675. https://doi.org/10.3390/en11102675
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations
Appendix
Appendix
See Table 8.
Rights and permissions
About this article
Cite this article
Nguyen, T.T., Nguyen, T.T. & Pham, T.D. Finding optimal solutions for reaching maximum power energy of hydroelectric plants in cascaded systems. J Ambient Intell Human Comput 13, 4369–4384 (2022). https://doi.org/10.1007/s12652-021-03361-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-021-03361-z