19
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Multi-objective optimal power flow with wind–solar–tidal systems including UPFC using Adaptive Improved Flower Pollination Algorithm(AIFPA)

, , , &
Received 27 Sep 2023, Accepted 12 May 2024, Published online: 04 Jun 2024

References

  • Panda A, Tripathy M. Security constrained optimal power flow solution of wind-thermal generation system using modified bacteria foraging algorithm. Energy. 2015;93:816–827. doi: 10.1016/j.energy.2015.09.083
  • Al-Bahran LT, Abdulrasool AQ. Multi objective functions of constraint optimal power flow based on modified ant colony system optimization technique. In: IOP Conference Series: Materials Science and Engineering; Iraq, Mustansiriyah University. Vol. 1105. IOP Publishing; 2021. p. 012015.
  • Beg MS, Waoo AA. Modified artificial bee colony (abc) algorithm using dynamic technique. Asian J Appl Sci. 2021;10(1):20–34. doi: 10.51983/ajes-2021.10.1.2863
  • Abido MA. Optimal power flow using tabu search algorithm. Electric Power Compon Sys. 2002;30(5):469–483. doi: 10.1080/15325000252888425
  • Sivasubramani S, Swarup KS. Multi-objective harmony search algorithm for optimal power flow problem. I Int J Electr Power Energy Syst. 2011;33(3):745–752. doi: 10.1016/j.ijepes.2010.12.031
  • Bouchekara HREH. Optimal power flow using black-hole-based optimization approach. Appl Soft Comput. 2014;24:879–888. doi: 10.1016/j.asoc.2014.08.056
  • Rakesh S, Mahesh S. A comprehensive overview on variants of cuckoo search algorithm and applications. In: 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT); Mysuru, India. IEEE; 2017. p. 1–5.
  • Sailaja Kumari M, Maheswarapu S. Enhanced genetic algorithm based computation technique for multi-objective optimal power flow solution. I Int J Electr Power Energy Syst. 2010;32(6):736–742. doi: 10.1016/j.ijepes.2010.01.010
  • Mandal B, Kumar Roy P. Multi-objective optimal power flow using quasi-oppositional teaching learning based optimization. Appl Soft Comput. 2014;21:590–606. doi: 10.1016/j.asoc.2014.04.010
  • Yang X-S. Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation; Tampa, FL, USA. Springer; 2012. p. 240–249.
  • Yang X-S, Karamanoglu M, He X. Multi-objective flower algorithm for optimization. Procedia Comput Sci. 2013;18:861–868. doi: 10.1016/j.procs.2013.05.251
  • Pandya KS, Dabhi DA, Joshi SK. Comparative study of bat & flower pollination optimization algorithms in highly stressed large power system. In: 2015 Clemson University Power Systems Conference (PSC); Clemson, SC, USA. IEEE; 2015. p. 1–5.
  • Mohan Dubey H, Pandit M, Panigrahi BK. Hybrid flower pollination algorithm with time-varying fuzzy selection mechanism for wind integrated multi-objective dynamic economic dispatch. Renewable Energy. 2015;83:188–202. doi: 10.1016/j.renene.2015.04.034
  • Sakti FP, Pramono Hadi S. Optimal power flow based on flower pollination algorithm. In: 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE); Bali, Indonesia. IEEE; 2018. p. 329–334.
  • Kahraman HT, Akbel M, Duman S, et al. Unified space approach-based dynamic switched crowding (DSC): a new method for designing Pareto-based multi/many-objective algorithms. Swarm Evol Comput. 2022;75:101196. doi: 10.1016/j.swevo.2022.101196
  • Bakir H, Guvenc U, Tolga Kahraman H. Optimal operation and planning of hybrid AC/DC power systems using multi-objective grasshopper optimization algorithm. Neural Comput Appl. 2022;34(24):22531–22563. doi: 10.1007/s00521-022-07670-y
  • Sonmez Y, Duman S, Kahraman HT, et al. Fitness-distance balance based artificial ecosystem optimisation to solve transient stability constrained optimal power flow problem. J Exp Theor Artif Intell. 2022;1–40. doi: 10.1080/0952813X.2022.2104388
  • Taranto GN, Pinto LMVG, Veiga Ferraz Pereira M. Representation of facts devices in power system economic dispatch. IEEE Trans Power Syst. 1992;7(2):572–576. doi: 10.1109/59.141761
  • Prasad Padhy N, Abdel Moamen MA. A generalized newton’s optimal power flow modelling with facts devices. Int J Model Simulat. 2008;28(3):229–238. doi: 10.1080/02286203.2008.11442473
  • Ambriz-Perez H, Acha E, Fuerte-Esquivel CR. Advanced svc models for newton-raphson load flow and newton optimal power flow studies. IEEE Trans Power Syst. 2000;15(1):129–136. doi: 10.1109/59.852111
  • Khunkitti S, Siritaratiwat A, Premrudeepreechacharn S, et al. A hybrid da-pso optimization algorithm for multiobjective optimal power flow problems. Energies. 2018;11(9):2270. doi: 10.3390/en11092270
  • Singh RP, Mukherjee V, Prasad D, et al. Solution of optimal power flow problem of system with facts devices using mde algorithm. In: 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS); Riyadh, Saudi Arabia. IEEE; 2020. p. 1–6.
  • Bakr H, Guvenc U, Duman S, et al. Optimal power flow for hybrid AC/DC electrical networks configured with VSC-MTDC transmission lines and renewable energy sources. IEEE Syst J. 2023;17(3):3938–3949. doi: 10.1109/JSYST.2023.3248658
  • Bakr H, Duman S, Guvenc U, et al. Improved adaptive gaining-sharing knowledge algorithm with FDB-based guiding mechanism for optimization of optimal reactive power flow problem. Electr Eng. 2023;105(5):3121–3160. doi: 10.1007/s00202-023-01803-9
  • Duman S, Kahraman HT, Kati M. Economical operation of modern power grids incorporating uncertainties of renewable energy sources and load demand using the adaptive fitness-distance balance-based stochastic fractal search algorithm. Eng Appl Artif Intell. 2023;117:105501. doi: 10.1016/j.engappai.2022.105501
  • Duman S, Akbel M, Tolga Kahraman H. Development of the multi-objective adaptive guided differential evolution and optimization of the mo-acopf for wind/pv/tidal energy sources. Appl Soft Comput. 2021;112:107814. doi: 10.1016/j.asoc.2021.107814
  • Immanuel A, Challa Babu PS, Rao Atyam N, et al. Adaptive fpa algorithm based opf with unified power flow controller. EAI Endorsed Trans Energy Web. 2022;9(40):e4. doi: 10.4108/ew.v9i40.150
  • Khan B, Singh P. Optimal power flow techniques under characterization of conventional and renewable energy sources: a comprehensive analysis. JF Eng. 2017;2017:1–16. doi: 10.1155/2017/9539506
  • Rodrigues D, de Rosa GH, Passos LA, et al. Adaptive improved flower pollination algorithm for global optimization. Nature-Inspired Computation In Data Mining And Machine Learning. 2020;855:1–21.
  • Gouda PK, Kumar Sahoo A, Hota PK. Optimal power flow including unified power flow controller in a deregulated environment. Int J Appl Eng Res. 2015;10(1):505–522.
  • Avvari RK, Vinod Kumar DM. A new hybrid evolutionary algorithm for multi-objective optimal power flow in an integrated we, pv, and pev power system. Electr Power Syst Res. 2023;214:108870. doi: 10.1016/j.epsr.2022.108870
  • Mohamed AA, Kamel S, Hassan MH, et al. Northern goshawk optimization algorithm for optimal power flow with facts devices in wind power integrated electrical networks. Electric Power Compon Sys. 2023;52(8):1293–1315. doi: 10.1080/15325008.2023.2239226
  • Maheshwari A, Raj Sood Y, Jaiswal S. Investigation of optimal power flow solution techniques considering stochastic renewable energy sources: review and analysis. Wind Eng. 2023;47(2):464–490. doi: 10.1177/0309524X221124000
  • Duman S, Li J, Wu L, et al. Symbiotic organisms search algorithm-based security-constrained ac–dc opf regarding uncertainty of wind, pv and pev systems. Soft Comput. 2021;25(14):9389–9426. doi: 10.1007/s00500-021-05764-8
  • Fernandes IG, Pereira FB, Gomes TL, et al. An optimal power flow approach including wind and tidal generation. In: 2019 IEEE PES Innovative Smart Grid Technologies Conference-Latin America (ISGT Latin America); Gramado, Brazil. IEEE; 2019. p. 1–6.
  • Maheshwari A, Raj Sood Y, Jaiswal S. Flow direction algorithm-based optimal power flow analysis in the presence of stochastic renewable energy sources. Electr Power Syst Res. 2023;216:109087. doi: 10.1016/j.epsr.2022.109087
  • Hashish MS, Hasanien HM, Ji H, et al. Monte carlo simulation and a clustering technique for solving the probabilistic optimal power flow problem for hybrid renewable energy systems. Sustainability. 2023;15(1):783. doi: 10.3390/su15010783
  • Kahraman HT, Duman S. Multi-objective adaptive guided differential evolution for multi-objective optimal power flow incorporating wind-solar-small hydro-tidal energy sources. Differential evolution: from theory to practice. Singapore: Springer; 2022. p. 341–365.
  • Ozkaya B, Kahraman HT, Duman S, et al. Fitness-Distance-Constraint (FDC) based guide selection method for constrained optimization problems. Appl Soft Comput. 2023:110479. doi: 10.1016/j.asoc.2023.110479
  • Kahraman HT, Aras S, Gedikli E. Fitness-distance balance (FDB): a new selection method for meta-heuristic search algorithms. Knowledge-Based Syst. 2020;190:105169. doi: 10.1016/j.knosys.2019.105169
  • Kahraman HT, Katı M, Aras S, et al. Development of the natural survivor method (NSM) for designing an updating mechanism in metaheuristic search algorithms. Eng Appl Artif Intell. 2023;122:106121. doi: 10.1016/j.engappai.2023.106121
  • Meng Ong K, Ong P, Kiong Sia C. A new flower pollination algorithm with improved convergence and its application to engineering optimization. Decis Anal J. 2022;5:100144. doi: 10.1016/j.dajour.2022.100144
  • Pavlyukevich I. Lévy flights, non-local search and simulated annealing. J Comput Phys. 2007;226(2):1830–1844. doi: 10.1016/j.jcp.2007.06.008
  • Atashpaz-Gargari E, Lucas C. IEEE congress on evolutionary computation. 2007;2007:4661–4667.
  • Chakraborty D, Saha S, Dutta O. De-fpa: a hybrid differential evolution-flower pollination algorithm for function minimization. In: 2014 international conference on high performance computing and applications (ICHPCA); Bhubaneswar, India. IEEE; 2014. p. 1–6.
  • Kessel P, Glavitsch H. Estimating the voltage stability of a power system. IEEE Trans Power Delivery. 1986;1(3):346–354. doi: 10.1109/TPWRD.1986.4308013
  • Abdel-Basset M, Shawky LA. Flower pollination algorithm: a comprehensive review. Artif Intell Rev. 2019;52:2533–2557. doi: 10.1007/s10462-018-9624-4
  • Boghdady TA, Mohamed YA. Reactive power compensation using statcom in a pv grid connected system with a modified mppt method. Ain Shams Eng J. 2023;14(8):102060. doi: 10.1016/j.asej.2022.102060
  • Guo W, Xu W. Research on optimization strategy of harmonic suppression and reactive power compensation of photovoltaic multifunctional grid connected inverter. I Int J Electr Power Energy Syst. 2023;145:108649. doi: 10.1016/j.ijepes.2022.108649
  • Huang Y, Liu G-P, Hu W. Priori-guided and data-driven hybrid model for wind power forecasting. ISA Trans. 2023;134:380–395. doi: 10.1016/j.isatra.2022.07.028
  • Zhang W, He Y, Yang S. A multi-step probability density prediction model based on gaussian approximation of quantiles for offshore wind power. Renewable Energy. 2023;202:992–1011. doi: 10.1016/j.renene.2022.11.111
  • Alghamdi AS. Optimal power flow of renewable-integrated power systems using a gaussian bare-bones levy-flight firefly algorithm. Front Energy Res. 2022;10:921936. doi: 10.3389/fenrg.2022.921936
  • Ali MA, Kamel S, Hassan MH, et al. Optimal power flow solution of power systems with renewable energy sources using white sharks algorithm. Sustainability. 2022;14(10):6049. doi: 10.3390/su14106049
  • Li N, Zhou G, Zhou Y, et al. Multi-objective pathfinder algorithm for multi-objective optimal power flow problem with random renewable energy sources: wind, photovoltaic and tidal. Sci Rep. 2023;13(1):10647. doi: 10.1038/s41598-023-37635-7
  • Yang C, Sun Y, Zou Y, et al. Optimal power flow in distribution network: a review on problem formulation and optimization methods. Energies. 2023;16(16):5974. doi: 10.3390/en16165974
  • Alghamdi AS. Optimal power flow of hybrid wind/solar/thermal energy integrated power systems considering costs and emissions via a novel and efficient search optimization algorithm. Appl Sci. 2023;13(8):4760. doi: 10.3390/app13084760
  • Bakr H, Duman S, Guvenc U, et al. A novel optimal power flow model for efficient operation of hybrid power networks. Comp Elec Eng. 2023;110:108885. doi: 10.1016/j.compeleceng.2023.108885
  • Pandya SB, Ravichandran S, Manoharan P, et al. Multi-objective optimization framework for optimal power flow problem of hybrid power systems considering security constraints. IEEE Access. 2022;10:103509–103528. doi: 10.1109/ACCESS.2022.3209996
  • Badoozadeh S, Nikdel N, Galvani S, et al. Probabilistic optimal power flow in wind energy integrated power system based on the k-medoids data clustering method considering correlated uncertain variables. IET Renewable Power Gener. 2023;17(13):3179–3194. doi: 10.1049/rpg2.12834
  • Kumar A, Deng Y, He X, et al. Impact of demand side management approaches for the enhancement of voltage stability loadability and customer satisfaction index. Appl Energy. 2023;339:120949. doi: 10.1016/j.apenergy.2023.120949
  • Wang T, Liu Y, Qiu G, et al. Deep learning-driven evolutionary algorithm for power system voltage stability control. Energy Rep. 2022;8:319–324. doi: 10.1016/j.egyr.2022.02.170

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.