Emended Snake Optimizer to Solve Multiobjective Hybrid Energy Generation Scheduling
DOI:
https://doi.org/10.2298/YJOR240315018KKeywords:
Coordinated generation scheduling, renewable energy, Snake optimization algorithm, simplex search method, opposition-based learning, metaheuristics optimization, Optimization problemAbstract
This paper proposes an emended snake optimizer (ESO) for solving hydrothermal, pumped hydro, and solar generators’ non-convex, highly constrained, and non-linear power generation scheduling problem. The generation scheduling problem aims to reduce thermal generator operating costs and pollutants by maximizing hydro volume and utilizing solar power generation. The minimization of operating costs and pollutants is subjected to various constraints, like meeting load demand, active power generation violations, water volume utilization, etc. The conflicting objectives of the multiobjective generation scheduling are handled using the non-interactive approach exploiting the price-penalty method. The direct heuristics search is utilized to satisfy the load demand and water volume constraints. The snake optimization algorithm (SOA) often gets stuck in the local minima while solving complex engineering optimization problems, resulting in sluggish convergence behavior. The basic SOA is emended through simple search and opposition-based learning, enhancing exploitation, convergence behavior, and procuring near to global solutions. The simulation studies involve solving unconstrained standard benchmark problems and electric power system problems. The proposed emended snake optimizer offers significant cost savings for electric power systems ranging from 10-15%. Statistical analysis using Wilcoxon signed-rank test and Friedman’s test justifies the amendment. The rapid convergence behavior and Whisker box plots justify the proposed ESO’s robustness.References
A. J. Wood, B. F. Wollenberg, and G. B. Sheblé, “ Power generation, operation, and control,” John Wiley & Sons; 2013.
C.A. Coello, G. T. Pulido, and M. S. Lechuga, “Handling multiple objectives with particle swarm optimization,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 256-79, 2004, https://doi.org/10.1109/TEVC.2004.826067.
T. T. Anilkumar, S. P. Simon, and N. P. Padhy, “Residential electricity cost minimization model through open well-pico turbine pumped storage system,” Applied Energy, vol. 194, pp. 23-35, 2017, https://doi.org/10.1016/j.apenergy.2017.03.020.
M. Basu, “Fuel constrained short-term hydrothermal generation scheduling,” Energy, vol. 239, pp. 122352, 2022, https://doi.org/10.1016/j.energy.2021.122352.
J. I. Pérez-Díaz, and J. Jiménez, “ Contribution of a pumped-storage hydropower plant to reduce the scheduling costs of an isolated power system with high wind power penetration,” Energy, vol. 109, pp. 92-104, 2016, https://doi.org/10.1016/j.energy.2016.04.014.
P. T. Ha, D.T.Tran, and T.T. Nguyen, “Electricity generation cost reduction for hydrothermal systems with the presence of pumped storage hydroelectric plants,” Neural Computing and Applications, vol. 34, no. 12, pp. 9931-53, 2022, https://doi.org/10.1007/s00521-022-06977-0.
K. Deb, “Optimization for engineering design: Algorithms and examples,” PHI Learning Pvt. Ltd; 2012 Nov 18.
F. Kholardi, M. Assili, M. A. Lasemi, and A. Hajizadeh, “Optimal management of energy hub with considering hydrogen network," In 2018 International Conference on Smart Energy Systems and Technologies (SEST), 10-12 Sep 2018, pp. 1-6, IEEE. https://doi.org/10.1109/SEST.2018.8495664.
J. Xu, F. Wang, C. Lv, Q. Huang, and H. Xie, “Economic-environmental equilibrium based optimal scheduling strategy towards wind-solar-thermal power generation system under limited resources,” Applied Energy, vol. 231, pp. 355-71, 2018, https://doi.org/10.1016/j.apenergy.2018.09.113.
A.E. Nezhad, M.S. Javadi, and E. Rahimi, “Applying augmented ɛ-constraint approach and lexicographic optimization to solve multi-objective hydrothermal generation scheduling considering the impacts of pumped-storage units,” International Journal of Electrical Power and Energy Systems, vol. 55, pp. 195-204, 2014, https://doi.org/10.1016/j.ijepes.2013.09.006.
S. A. Bukhari, W. Cao, X. Chen, F. Jandan, and D. Goswami, “Hybrid electric energy storage and its dynamic performance,” Cases on Green Energy and Sustainable Development 2020 (pp. 406-437). IGI Global.
N. A. Khan, A. B. Awan, A. Mahmood, I. E. Member, S. Razzaq, A. Zafar, and G. A. Sidhu, “ Combined emission economic dispatch of power system including solar photo voltaic generation,” Energy Conversion and management; vol. 92, pp. 82-91, 2015, https://doi.org/10.1016/j.enconman.2014.12.029.
Y. Zhu, J. Wang, and B. Qu, “ Multi-objective economic emission dispatch considering wind power using evolutionary algorithm based on decomposition,” International Journal of Electrical Power & Energy Systems; vol. 63, pp. 434-45, 2014, https://doi.org/10.1016/j.ijepes.2014.06.027.
M. S. Fakhar, S. Liaquat, S. A. Kashif, A .Rasool, M. Khizer, M. A. Iqbal, M. A. Baig, and S. Padmanaban, “ Conventional and metaheuristic optimization algorithms for solving short term hydrothermal scheduling problem: A review,” IEEE Access; vol. 9, pp. 25993-6025, 2021, https://doi.org/10.1109/ACCESS.2021.3055292
P. P. Biswas, P. N. Suganthan, and G. A. Amaratunga, “Optimal power flow solutions incorporating stochastic wind and solar power,” Energy Conversion and Management, vol. 148, pp. 1194-207, 2017, https://doi.org/10.1016/j.enconman.2017.06.071.
M. Dahmani, M. Fellah, N. Hezil, M.C. Benoudia, M. Abdul Samad, A. Alburaikan, H. Abd El-Wahed khalifa, and A. Obrosov, “Structural and mechanical evaluation of a new Ti-Nb-Mo alloy produced by high-energy ball milling with variable milling time for biomedical applications,” The International Journal of Advanced Manufacturing Technology, vol. 129, no. 11, pp. 4971-91, 2023, https://doi.org/10.1007/s00170-023-12650-0.
R. M. Zulqarnain, W. X. Ma, I. Siddique, A. Alburaikan, H. Abd El-Wahed Khalifa, and A. M. Alanzi, “ Prioritization of thermal energy storage techniques using TO SIS method based on correlation coefficient for interval-valued intuitionistic fuzzy hypersoft set,” Symmetry, vol. 15, no. 3, pp. 615, 2023, https://doi.org/10.3390/sym15030615.
A. Ullah, R. A. ZeinEldin, and H. A. Khalifa, “Investigation of the three-dimensional hybrid casson nanofluid flow: a cattaneo-christov theory,” ACS omega, vol. 8, no. 12, pp. 10991-1002, 2023, https://doi.org/10.1021/acsomega.2c07750.
M. Basu, “ Multiobjective generation scheduling of fixed head hydrothermal power systems through an interactive fuzzy satisfying method and evolutionary programming technique,” Electric Power Components and Systems, vol. 32, no. 12, pp. 1287-99, 2004, https://doi.org/10.1080/15325000490446764.
M. Clerc, and J. Kennedy, “The particle swarm-explosion, stability, and convergence in a multidimensional complex space,” IEEE transactions on Evolutionary Computation, vol. 6, no. 1, pp. 58-73, 2002, https://doi.org/10.1109/4235.985692.
N. Yang, Z. Dong, L. Wu, L. Zhang, X. Shen, D. Chen, B. Zhu, and Y. Liu, “A comprehensive review of security-constrained unit commitment,” Journal of Modern Power Systems and Clean Energy, vol. 10, no. 3, pp. 562-76, 2021, https://doi.org/10.35833/MPCE.2021.000255.
I. Abdou, and M. Tkiouat, “Unit ommitment roblem in Electrical Power System: A Literature Review,” International Journal of Electrical & Computer Engineering, vol. 8, no. 3, pp. 2088-8708, 2018, https://doi.org/10.11591/ijece.v8i3.pp1357-1372.
W. Van Ackooij, I. Danti Lopez, A. Frangioni, F. Lacalandra, and M. Tahanan, “Large-scale unit commitment under uncertainty: an updated literature survey,” Annals of Operations Research, vol. 271, no. 1, pp. 11-85, 2018, https://doi.org/10.1007/s10479-018-3003-z.
R. Swain, and U. C. Mishra, “To the hief Editor, Electric Power System Researchshort-Term Hydrothermal Scheduling Using Grey Wolf Optimization Algorithm,” Electric Power System Researchshort-Term Hydrothermal Scheduling Using Grey Wolf Optimization Algorithm, https://dx.doi.org/10.2139/ssrn.4468244.
V. K. Kamboj, A. Nandi, A. Bhadoria, and S. Sehgal, “An intensify Harris Hawks optimizer for numerical and engineering optimization problems,” Applied Soft Computing, vol. 89, pp. 106018, 2020, https://doi.org/10.1016/j.asoc.2019.106018.
I. G. Damousis, A. G. Bakirtzis, and P. S. Dokopoulos, “A solution to the unit-commitment problem using integer-coded genetic algorithm,” IEEE Transactions on Power Systems, vol. 19, no. 2, pp. 1165-72, 2004, https://doi.org/10.1109/TPWRS.2003.821625.
B. K. Das, R. Hassan, M. S. Tushar, F. Zaman, M. Hasan, and P. Das, “Techno-economic and environmental assessment of a hybrid renewable energy system using multi-objective genetic algorithm: A case study for remote Island in Bangladesh,” Energy Conversion and Management, vol. 230, pp. 113823, 2021, https://doi.org/10.1016/j.enconman.2020.113823.
L. Xie, P. M. Carvalho, L. A. Ferreira, J. Liu, B. H. Krogh, N. Popli, and M. D. Ilić, “Wind integration in power systems: Operational challenges and possible solutions,” Proceedings of the IEEE, vol. 99, no. 1, pp. 214-32, 2010, https://doi.org/10.1109/JPROC.2010.2070051.
N. Muralikrishnan, L. Jebaraj, and C. C. Rajan, “A comprehensive review on evolutionary optimization techniques applied for unit commitment problem,” IEEE Access, vol. 8, pp. 132980-3014, 2020, https://doi.org/10.1109/ACCESS.2020.3010275.
C. P. Cheng, C. W. Liu, and C.C. Liu, “Unit commitment by Lagrangian relaxation and genetic algorithms,” IEEE transactions on power systems, vol. 15, no. 2, pp. 707-14, 2000, https://doi.org/10.1109/59.867163.
L. Richter, . Lehna, S. archand, . Scholz, A. Dreher, S. Klaiber, and S. Lenk, “Artificial intelligence for electricity supply chain automation,” Renewable and Sustainable Energy Reviews, vol. 163, pp. 112459, 2022, https://doi.org/10.1016/j.rser.2022.112459.
H. Mori, and O. Matsuzaki, “Application of priority-list-embedded tabu search to unit commitment in power systems,” IEEE Transactions on Power and Energy; vol. 121, no. 4, pp. 535-41, 2001.
T. O. Ting, M. V. Rao, and C. K. Loo, “A novel approach for unit commitment problem via an effective hybrid particle swarm optimization,” IEEE Transactions on Power Systems, vol. 21, no. 1, pp. 411-8, 2006, https://doi.org/10.1109/TPWRS.2005.860907.
A. Y. Saber, T. Senjyu, N. Urasaki, and T. Funabashi, “Unit commitment computation-A novel fuzzy adaptive particle swarm optimization approach,” IEEE Power Systems Conference and Exposition, pp. 1820-1828, 2006, https://doi.org/10.1109/PSCE.2006.296189.
P. Sriyanyong, and Y. H. Song, “Unit commitment using particle swarm optimization combined with Lagrange relaxation,” IEEE Power Engineering Society General Meeting, pp. 2752-2759, 2005, https://doi.org/10.1109/PES.2005.1489390.
Z.L. Gaing, “Discrete particle swarm optimization algorithm for unit commitment,” IEEE Power Engineering Society General Meeting, vol. 1, pp. 418-424, 2003, https://doi.org/10.1109/PES.2003.1267212.
A. H. antawy, “A genetic algorithm solution to a new fuzzy unit commitment model,” Electric power systems research, vol. 72, no. 2, pp. 171-8, 2004, https://doi.org/10.1016/j.epsr.2004.04.003.
M. M. El-Saadawi, M. A. Tantawi, and E. Tawfik, “A fuzzy optimization-based approach to large scale thermal unit commitment,” Electric Power Systems Research, vol. 72, no. 3, pp. 245-52, 2004, https://doi.org/10.1016/j.epsr.2004.04.009.
P. Arévalo, M. Tostado-Véliz, and F. Jurado, “A novel methodology for comprehensive planning of battery storage systems,” Journal of Energy Storage, vol. 37, pp. 102456, 2021, https://doi.org/10.1016/j.est.2021.102456.
N. P. Padhy, “Unit commitment using hybrid models: a comparative study for dynamic programming, expert system, fuzzy system and genetic algorithms,” International Journal of Electrical Power & Energy Systems, vol. 23, no. 8, pp. 827-36, 2001, https://doi.org/10.1016/S0142-0615(00)00090-9.
T. T. Nguyen, D. N. Vo, B. H. Dinh, “An effectively adaptive selective cuckoo search algorithm for solving three complicated short-term hydrothermal scheduling problems,” Energy, vol. 155, pp. 930-56, 2018, https://doi.org/10.1016/j.energy.2018.05.037.
J. Jian, S. Pan, and L. Yang, “Solution for short-term hydrothermal scheduling with a logarithmic size mixed-integer linear programming formulation,” Energy, vol. 171, pp. 770-84, 2019, https://doi.org/10.1016/j.energy.2019.01.038.
H. Yin, F. Wu, X. Meng, Y. Lin, J. Fan, and A. Meng, “ Crisscross optimization based short-term hydrothermal generation scheduling with cascaded reservoirs,” Energy, vol. 203, pp. 117822, 2020, https://doi.org/10.1016/j.energy.2020.117822.
R. S. Patwal, N. Narang, and H. Garg, “A novel TVA -PSO based mutation strategies algorithm for generation scheduling of pumped storage hydrothermal system incorporating solar units,” Energy, vol. 142, pp. 822-37, 2018, https://doi.org/10.1016/j.energy.2017.10.052.
M. Kaur, J. S. Dhillon, and D. P. Kothar, “ Crisscross differential evolution algorithm for constrained hydrothermal scheduling,” Applied Soft Computing, vol. 93, pp. 106393, 2020, https://doi.org/10.1016/j.asoc.2020.106393.
N. Narang, “Short-term hydrothermal generation scheduling using improved predator influenced civilized swarm optimization technique,” Applied Soft Computing, vol. 58, pp. 207-24, 2017, https://doi.org/10.1016/j.asoc.2017.04.065.
S. Özyön, “Optimal short-term operation of pumped-storage power plants with differential evolution algorithm,” Energy, vol. 194, pp. 116866, 2020, https://doi.org/10.1016/j.energy.2019.116866.
P. H. Chen, “ Pumped-storage scheduling using evolutionary particle swarm optimization,” IEEE Transactions on Energy Conversion, vol. 23, no. 1, pp. 294-301, 2008, https://doi.org/10.1109/TEC.2007.914312.
F. A. Hashim, and A. G. Hussien, “Snake Optimizer: A novel meta-heuristic optimization algorithm,” Knowledge-Based Systems, vol. 242, pp. 108320, 2022, https://doi.org/10.1016/j.knosys.2022.108320.
P. V. Klimov, J. Kelly, J. M. Martinis, and H. Neven, “The snake optimizer for learning quantum processor control parameters,” arXiv preprint arXiv:2006.04594, 2020, https://doi.org/10.48550/arXiv.2006.04594
R. A. Khurma, D. Albashish, M. Braik, A. Alzaqebah, A. Qasem, and O. Adwan, “An augmented Snake Optimizer for diseases and COVID-19 diagnosis,” Biomedical Signal Processing and Control, vol. 84, pp. 104718, 2023, https://doi.org/10.1016/j.bspc.2023.104718.
I. Al-Shourbaji, P. H. Kachare, S. Alshathri, S. Duraibi, B. Elnaim, and M. Abd Elaziz, “An efficient parallel reptile search algorithm and snake optimizer approach for feature selection,” Mathematics, vol. 10, no. 13, pp. 2351, 2022, https://doi.org/10.3390/math10132351.
M. Kumar, and J. S. Dhillon, “Hybrid artificial algae algorithm for economic load dispatch,” Applied Soft Computing, vol. 71, pp. 89-109, 2018, https://doi.org/10.1016/j.asoc.2018.06.035.
J. S. Dhaliwal, and J. S. Dhillon, “A synergy of binary differential evolution and binary local search optimizer to solve multi-objective profit-based unit commitment problem,” Applied Soft Computing, vol. 107, pp. 107387, 2021,
N. Narang, J. S. Dhillon, and D. P. Kothari, “ Multi-objective fixed head hydrothermal scheduling using integrated predator-prey optimization and owell search method,” Energy, vol. 47, no. 1, pp. 237-52, 2012, https://doi.org/10.1016/j.energy.2012.09.004.
M. Nemati, M. Braun, and S. Tenbohlen, “Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming,” Applied Energy, vol. 210, pp. 944-63, 2018, https://doi.org/10.1016/j.apenergy.2017.07.007.
A. Kumar, and J. S. Dhillon, “Emended Harris Hawk Optimizer for Mixed Energy Generation Scheduling roblem,” Electric Power Components and Systems, vol. 52, no. 8, pp. 1235-1268, 2023, https://doi.org/10.1080/15325008.2023.2239224.
J. A. Duffie, and W. A Beckma, “Solar engineering of thermal processes,” John Willey & Sons, New York, 1980.
V. B. Vommi, and R. Vemula, “A very optimistic method of minimization (VO I) for unconstrained problems,” Information Sciences, vol. 454, pp. 255-74, 2018, https://doi.org/10.1016/j.ins.2018.04.046.
W. G. Spendley, G. R. Hext, and F. R. Himsworth, “Sequential application of simplex designs in optimisation and evolutionary operation. Technometrics,” vol. 4, no. 4, pp. 441-61, 1962, https://doi.org/10.1080/00401706.1962.10490033.
S. Rahnamayan, H. R. Tizhoosh, and M. M. Salama, “Opposition-based differential evolution,” IEEE Transactions on Evolutionary computation, vol. 12, no. 1, pp. 64-79, 2008, https://doi.org/10.1109/TEVC.2007.894200.
Z. Seif, and M. B. Ahmadi, “An opposition-based algorithm for function optimization,” Engineering Applications of Artificial Intelligence, vol. 37, pp. 293-306, 2015, https://doi.org/10.1016/j.engappai.2014.09.009.
A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H.Chen, “Harris hawks optimization: Algorithm and applications,” Future Generation Computer Systems, vol. 97, pp. 849-72, 2019, https://doi.org/10.1016/j.future.2019.02.028.
N. Narang, J. S. Dhillon, and D. P. Kothari, “Scheduling short-term hydrothermal generation using predator-prey optimization technique,” Applied Soft Computing, vol. 21, pp. 298-308, 2014, https://doi.org/10.1016/j.asoc.2014.03.029.
J. S. Dhillon, J. S. Dhillon, and D. P. Kothari, “Real coded genetic algorithm for stochastic hydrothermal generation scheduling,” Journal of Systems Science and Systems Engineering, vol. 20, pp. 87-109, 2011, https://doi.org/10.1007/s11518-011-5158-x.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) YUJOR
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
