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Improved Differential Evolution Algorithm to solve multi-objective of optimal power flow problem

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EN
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EN
This article presents a new efficient optimization technique namely the Multi- Objective Improved Differential Evolution Algorithm (MOIDEA) to solve the multiobjective optimal power flow problem in power systems. The main features of the Differential Evolution (DE) algorithm are simple, easy, and efficient, but sometimes, it is prone to stagnation in the local optima. This paper has proposed many improvements, in the exploration and exploitation processes, to enhance the performance of DE for solving optimal power flow (OPF) problems. The main contributions of the DE algorithm are i) the crossover rate will be changing randomly and continuously for each iteration, ii) all probabilities that have been ignored in the crossover process have been taken, and iii) in selection operation, the mathematical calculations of the mutation process have been taken. Four conflicting objective functions simultaneously have been applied to select the Pareto optimal front for the multi-objective OPF. Fuzzy set theory has been used to extract the best compromise solution. These objective functions that have been considered for setting control variables of the power system are total fuel cost (TFC), total emission (TE), real power losses (RPL), and voltage profile (VP) improvement. The IEEE 30-bus standard system has been used to validate the effectiveness and superiority of the approach proposed based on MATLAB software. Finally, to demonstrate the effectiveness and capability of the MOIDEA, the results obtained by this method will be compared with other recent methods.
Rocznik
Strony
641--657
Opis fizyczny
Bibliogr. 46 poz., rys., tab., wz.
Twórcy
Bibliografia
  • [1] Osman M., Abo-Sinna M., Mousa A., A solution to the optimal power flow using genetic algorithm, Applied Mathematics and Computation, vol. 155, no. 2, pp. 391–405 (2004), DOI: 10.1016/S0096-3003(03)00785-9.
  • [2] Abido M., Optimal power flow using particle swarm optimization, International Journal of Electrical Power and Energy Systems, vol. 24, no. 7, pp. 563–571 (2002), DOI: 10.1016/S0142-0615(01)00067-9.
  • [3] Al-kaabi M., Al-Bahrani L., Modified Artificial Bee Colony Optimization Technique with Different Objective Function of Constraints Optimal Power Flow, International Journal of Intelligent Engineering and Systems, vol. 13, no. 4, pp. 378–388 (2020), DOI: 10.22266/ijies2020.0831.33.
  • [4] Al-Bahrani L., Al-kaabi M., Al-saadi M., Dumbrava V., Optimal power flow based on differential evolution optimization technique, U.P.B. Sci. Bull., Series C, vol. 82, no. 1, pp. 378–388 (2020).
  • [5] Youssef H., Kamel S., Ebeed M., Optimal power flow considering loading margin stability using lightning attachment optimization technique, in 2018 Twentieth International Middle East Power Systems Conference (MEPCON), pp. 1053–1058 (2018), DOI: 10.1109/MEPCON.2018.8635110.
  • [6] Reddy S., Rathnam C., Optimal power flow using glowworm swarm optimization, Int. J. Electr. Power Energy Syst., vol. 80, pp. 128–139 (2016), DOI: 10.1016/j.ijepes.2016.01.036.
  • [7] Sayed G., Khoriba G., Haggag M., A novel chaotic salp swarm algorithm for global optimization and feature selection, Appl. Intell., vol. 48, no. 10, pp. 3462–3481 (2018), DOI: 10.1007/s10489-018-1158-6.
  • [8] Panda A., Tripathy M., Barisal A., Prakash T., A modified bacteria foraging based optimal power flow framework for Hydro-Thermal-Wind generation system in the presence of STATCOM, Energy, vol. 124, pp. 720–740 (2017), DOI: 10.1016/j.energy.2017.02.090.
  • [9] Elattar E., El-Sayed S., Modified JAYA algorithm for optimal power flow incorporating renewable energy sources considering the cost, emission, power loss and voltage profile improvement, Energy, vol. 178, pp. 598–609 (2019), DOI: 10.1016/j.energy.2019.04.159.
  • [10] Khan A., Hizam H., bin Abdul Wahab N., Lutfi Othman M., Optimal power flow using hybrid firefly and particle swarm optimization algorithm, PLoS One, vol. 15, no. 8, p. e0235668 (2020), DOI: 10.1371/journal.pone.0235668.
  • [11] Arsyad H., Suyuti A., Said S., Akil Y., Multi-objective dynamic economic dispatch using Fruit Fly Optimization method, Archives of Electrical Engineering, vol. 70, no. 2, pp. 351–366 (2021), DOI: 10.24425/aee.2021.136989.
  • [12] AlRashidi M., El-Hawary M., Applications of computational intelligence techniques for solving the revived optimal power flow problem, Electr. Power Syst. Res., vol. 79, no. 4, pp. 694–702 (2009), DOI: 10.1016/j.epsr.2008.10.004.
  • [13] Abou El Ela A., Abido M., Spea S., Optimal power flow using differential evolution algorithm, Electr. Power Syst. Res., vol. 80, no. 7, pp. 878–885 (2010), DOI: 10.1016/j.epsr.2009.12.018.
  • [14] Roy P., Paul C., Optimal power flow using krill herd algorithm, Int. Trans. Electr. Energy Syst., vol. 25, no. 8, pp. 1397–1419 (2015), DOI: 10.1002/etep.1888.
  • [15] Davoodi E., Babaei E., Mohammadi-ivatloo B., An efficient covexified SDP model for multiobjective optimal power flow, Int. J. Electr. Power Energy Syst., vol. 102, pp. 254–264 (2018), DOI: 10.1016/j.ijepes.2018.04.034.
  • [16] Datta R., Deb K., Segev A., A bi-objective hybrid constrained optimization (HyCon) method using a multi-objective and penalty function approach, in 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 317–324 (2017), DOI: 10.1109/CEC.2017.7969329.
  • [17] Deb K., Multi-objective ptimisation using evolutionary algorithms: an introduction, Multi-objective Evolutionary Optimisation for Product Design and Manufacturing, Springer, pp. 3–34 (2011).
  • [18] Mazza A., Chicco G., Russo A., Optimal multi-objective distribution system reconfiguration with multi criteria decision making-based solution ranking and enhanced genetic operators, Int. J. Electr. Power Energy Syst., vol. 54, pp. 255–267 (2014), DOI: 10.1016/j.ijepes.2013.07.006.
  • [19] Hazra J., Sinha A.K., A multi-objective optimal power flow using particle swarm optimization, Eur. Trans. Electr. power, vol. 21, no. 1, pp. 1028–1045 (2011), DOI: 10.1002/etep.494.
  • [20] Mirjalili S., Dragonfly algorithm: a new meta-heuristic optimization technique for solving single objective, discrete, and multi-objective problems, Neural Comput. Appl., vol. 27, no. 4, pp. 1053–1073 (2016), DOI: 10.1007/s00521-015-1920-1.
  • [21] Tan S., Lin S., Yang L., Zhang A., Shi W., Feng H., Multi-objective optimal power flow model for power system operation dispatching, in 2013 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), pp. 1–6 (2013), DOI: 10.1109/APPEEC.2013.6837208.
  • [22] Varadarajan M., Swarup K.S., Solving multi-objective optimal power flow using differential evolution, IET Gener. Transm. Distrib., vol. 2, no. 5, pp. 720–730 (2008), DOI: 10.1049/iet-gtd:20070457.
  • [23] Islam M. et al., A Harris Hawks Optimization Based Single-and Multi-Objective Optimal Power Flow Considering Environmental Emission, Sustainability, vol. 12, no. 13, p. 5248 (2020), DOI: 10.3390/su12135248.
  • [24] Abd El-Sattar S., Kamel S., El Sehiemy R., Jurado F., Yu J., Single-and multi-objective optimal power flow frameworks using Jaya optimization technique, Neural Comput. Appl., vol. 31, no. 12, pp. 8787–8806 (2019), DOI: 10.1007/s00521-019-04194-w.
  • [25] Abou El-Ela A., El-Sehiemy R., Mouwafi M., Salman D., Multiobjective fruit fly optimization algorithm for OPF solution in power system, 2018 Twentieth International Middle East Power Systems Conference (MEPCON), pp. 254–259 (2018), DOI: 10.1109/MEPCON.2018.8635232.
  • [26] Sivasubramani S., Swarup K., Multi-objective harmony search algorithm for optimal power flow problem, Int. J. Electr. Power Energy Syst., vol. 33, no. 3, pp. 745–752 (2011), DOI: 10.1016/j.ijepes. 2010.12.031.
  • [27] Niknam T., Rasoul Narimani M., Jabbari M., Malekpour A., A modified shuffle frog leaping algorithm for multi-objective optimal power flow, Energy, vol. 36, no. 11, pp. 6420–6432 (2011), DOI: 10.1016/j.energy.2011.09.027.
  • [28] Adaryani M., Karami A., Artificial bee colony algorithm for solving multi-objective optimal power flow problem, Int. J. Electr. Power Energy Syst., vol. 53, pp. 219–230 (2013), DOI: 10.1016/j.ijepes. 2013.04.021.
  • [29] Wahab M., Nefti-Meziani S., Atyabi A., A comprehensive review of swarm optimization algorithms, PLoS One, vol. 10, no. 5, p. e0122827 (2015), DOI: 10.1371/journal.pone.0122827.
  • [30] Storn R., Price K., Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces, J. Glob. Optim., vol. 11, no. 4, pp. 341–359 (1997), DOI: 10.1023/A:1008202821328.
  • [31] Barocio E., Regalado J., Cuevas E., Uribe F., Zúńiga P., Torres P., Modified bio-inspired optimisation algorithm with a centroid decision making approach for solving a multi-objective optimal power flow problem, IET Gener. Transm. Distrib., vol. 11, no. 4, pp. 1012–1022 (2017), DOI: 10.1049/ietgtd.2016.1135.
  • [32] Shaheen A., El-Sehiemy R., Farrag S., Solving multi-objective optimal power flow problem via forced initialised differential evolution algorithm, IET Gener. Transm. Distrib., vol. 10, no. 7, pp. 1634–1647 (2016), DOI: 10.1049/iet-gtd.2015.0892.
  • [33] Bahmani-Firouzi B., Farjah E., Azizipanah-Abarghooee R., An efficient scenario-based and fuzzy self-adaptive learning particle swarm optimization approach for dynamic economic emission dispatch considering load and wind power uncertainties, Energy, vol. 50, pp. 232–244 (2013), DOI: 10.1016/j.energy.2012.11.017.
  • [34] Al-Bahrani L., Al-Kaabi M., Al-Hasheme J., Solving Optimal Power Flow Problem Using Improved Differential Evolution Algorithm, International Journal of Electrical and Electronic Engineering and Telecommunications (IJEEET), vol. 11, no. 2, pp. 146–155 (2021), DOI: 10.18178/ijeetc.11.2.146-155.
  • [35] Al-Kaabi M., Al-Bahrani L., Dumbrava V., and Eremia M., Optimal Power Flow with Four Objective Functions using Improved Differential Evolution Algorithm: Case Study IEEE 57-bus power system, in 2021 10th International Conference on ENERGY and ENVIRONMENT (CIEM), Bucharest, Romania, pp. 1–5 (2021), DOI: 10.1109/CIEM52821.2021.9614925.
  • [36] Chen G., Qian J., Zhang Z., Li S., Application of modified pigeon-inspired optimization algorithm and constraint-objective sorting rule on multi-objective optimal power flow problem, Appl. Soft Comput., p. 106321 (2020), DOI: 10.1016/j.asoc.2020.106321.
  • [37] Pulluri H., Naresh R., Sharma V., An enhanced self-adaptive differential evolution based solution methodology for multiobjective optimal power flow, Appl. Soft Comput., vol. 54, pp. 229–245 (2017), DOI: 10.1016/j.asoc.2017.01.030.
  • [38] Chaib A., Bouchekara H., Mehasni R., Abido M., Optimal power flow with emission and non-smooth cost functions using backtracking search optimization algorithm, Int. J. Electr. Power Energy Syst., vol. 81, pp. 64–77 (2016), DOI: 10.1016/j.ijepes.2016.02.004.
  • [39] Chen G., Yi X., Zhang Z., Wang H., Applications of multi-objective dimension-based firefly algorithm to optimize the power losses, emission, and cost in power systems, Appl. Soft Comput., vol. 68, pp. 322–342 (2018), DOI: 10.1016/j.asoc.2018.04.006.
  • [40] Chen G., Qian J., Zhang Z., Sun Z., Applications of novel hybrid bat algorithm with constrained Pareto fuzzy dominant rule on multi-objective optimal power flow problems, IEEE Access, vol. 7, pp. 52060–52084 (2019), DOI: 10.1109/ACCESS.2019.2912643.
  • [41] Ouafa H., Linda S., Tarek B., Multi-objective optimal power flow considering the fuel cost, emission, voltage deviation and power losses using Multi-Objective Dragonfly algorithm, proceedings of the International Conference on Recent Advances in Electrical Systems, Tunisia (2017).
  • [42] Medina M., Das S., Coello C., Ramírez J., Decomposition-based modern metaheuristic algorithms for multi-objective optimal power flow–A comparative study, Eng. Appl. Artif. Intell., vol. 32, pp. 10–20 (2014), DOI: 10.1016/j.engappai.2014.01.016.
  • [43] Ghasemi M., Ghavidel S., Ghanbarian M., Gharibzadeh M., Vahed A., Multi-objective optimal power flow considering the cost, emission, voltage deviation and power losses using multi-objective modified imperialist competitive algorithm, Energy, vol. 78, pp. 276–289 (2014), DOI: 10.1016/j.energy. 2014.10.007.
  • [44] Abido M., Al-Ali N., Multi-objective optimal power flow using differential evolution, Arab. J. Sci. Eng., vol. 37, no. 4, pp. 991–1005 (2012), DOI: 10.1007/s13369-012-0224-3.
  • [45] Bouchekara H., Optimal power flow using black-hole-based optimization approach, Appl. Soft Comput., vol. 24, pp. 879–888 (2014), DOI: 10.1016/j.asoc.2014.08.056.
  • [46] Bhattacharya A., Chattopadhyay P., Application of biogeography-based optimisation to solve differentoptimal power flow problems, IET Gener. Transm. Distrib., vol. 5, no. 1, pp. 70–80 (2010), DOI: 10.1049/iet-gtd.2010.0237.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e753c0b6-87da-4343-8ab7-3b40cf2336e6
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