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Hybrid swarm evolutionary programming for optimal distributed generation in distribution system | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 12، Special Issue، اسفند 2021، صفحه 1075-1090 اصل مقاله (507.33 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.5570 | ||
| نویسندگان | ||
| Siti Rafidah Abdul Rahim* 1؛ Yeap Chuan Liang1؛ Muhamad Hatta Hussain1؛ Ismail Musirin2؛ Syahrul Ashikin Azmi1؛ Azralmukmin Azmi1 | ||
| 1Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis, Perlis, Malaysia | ||
| 2School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia | ||
| تاریخ دریافت: 27 خرداد 1400، تاریخ پذیرش: 21 شهریور 1400 | ||
| چکیده | ||
| This paper discusses the incorporation of the Hybrid Swarm Evolutionary Programming (SEP) for optimal distributed generation (DG) in the distribution system. High load demand will result in unstable control power distribution due to power transmission loss. The compensation process can be implemented by installing a compensation device to prevent this from happening. It is required the optimal sizing and location for the devices to achieve the objective and this can be done by using an optimization technique. Thus, this project aims to develop a hybrid computational intelligence technique is called hybrid SEP for loss minimization. The proposed method embedded the element of Particle Swarm Optimization (PSO) into traditional Evolutionary Programming (EP) to improve precision of traditional EP algorithm. The purpose of this study is to investigate the maximum benefits of DG integration to be gained. The proposed techniques are validated on IEEE-69 bus radial system with multiple units of DG. The results showed that the most effective type of DG inject to IEEE-69 bus radial system is with DG Type III, with 95% of active power loss reduction and the best on voltage profile improvement. Hybrid SEP shows superior to EP in term of loss minimization. | ||
| کلیدواژه‌ها | ||
| Distributed generation؛ Evolutionary programming؛ Hybrid Swarm evolutionary programming | ||
| مراجع | ||
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[1] S. Alam, G. Dobbie, Y. S. Koh, P. Riddle, and S. Ur Rehman, Research on particle swarm optimization based clustering: A systematic review of literature and techniques, Swarm and Evolutionary Computation, 17 (2014) 1–13, doi: 10.1016/j.swevo.2014.02.001. [2] H. Algorithm, Optimal Integration of Multi Distributed Generation, (2018), doi: 10.3390/en11030628. [3] A. Eid, Allocation of distributed generations in radial distribution systems using adaptive PSO and modified GSA multi-objective optimizations, Alexandria Engineering Journal, 59 (6) (2020) 4771–4786, doi: 10.1016/j.aej.2020.08.042. [4] A. Embedded and C. Evolutionary, Keywords: Artificial intelligence; Adaptive Embedded Clonal Evolutionary Programming; loss minimization., 1 (2016) 187–196. [5] M. Gomes Pereira de Lacerda, L. F. de Araujo Pessoa, F. Buarque de Lima Neto, T. B. Ludermir, and H. Kuchen, A systematic literature review on general parameter control for evolutionary and swarm-based algorithms, Swarm and Evolutionary Computation, 60 (January 2020) (2021) 100777, doi: 10.1016/j.swevo.2020.100777. [6] L. F. Grisales-Nore˜na, O. D. Montoya, C. A. Ramos-Paja, Q. Hernandez-Escobedo, and A. J. Perea-Moreno, Optimal location and sizing of distributed generators in dc networks using a hybrid method based on parallel pbil and pso, Electronics (Switzerland), 9 (11) (2020) 1–27, doi: 10.3390/electronics9111808. [7] W. Haider, S. J. Ul Hassan, A. Mehdi, A. Hussain, G. O. M. Adjayeng, and C. H. Kim, Voltage profile enhancement and loss minimization using optimal placement and sizing of distributed generation in reconfigured network, Machines, 9 (1) (2021) 1–16, doi: 10.3390/machines9010020. [8] A. S. Hassan, Y. Sun, and Z. Wang, Multi-objective for optimal placement and sizing DG units in reducing loss of power and enhancing voltage profile using BPSO-SLFA, Energy Reports, 6 (2020) 1581–1589, doi: 10.1016/j.egyr.2020.06.013. [9] E. H. Houssein, A. G. Gad, K. Hussain, and P. N. Suganthan, Major Advances in Particle Swarm Optimization: Theory, Analysis, and Application, Swarm and Evolutionary Computation, 63 (March)(2021) 100868, doi: 10.1016/j.swevo.2021.100868. [10] E. Karatepe, F. Ugranli, and T. Hiyama, Comparison of single- and multiple-distributed generation concepts in terms of power loss, voltage profile, and line flows under uncertain scenarios, Renewable and Sustainable Energy Reviews, 48 (2015), doi: 10.1016/j.rser.2015.04.027. [11] S. Kayalvizhi and V. K. Vinod, Optimal planning of active distribution networks with hybrid distributed energy resources using grid-based multi-objective harmony search algorithm, Applied Soft Computing Journal, 67(2018) 387–398, doi: 10.1016/j.asoc.2018.03.009. [12] D. S. Kourkoumpas, G. Benekos, N. Nikolopoulos, S. Karellas, P. Grammelis, and E. Kakaras, A review of key environmental and energy performance indicators for the case of renewable energy systems when integrated with storage solutions, Applied Energy, 231 (March) (2018)380–398, doi: 10.1016/j.apenergy.2018.09.043. [13] Y. v. Kovalenko, On Hybrid Evolutionary Algorithms for Scheduling Problem with Tardiness Criterion, Journal of Physics: Conference Series, 1791 (1) (2021), doi: 10.1088/1742-6596/1791/1/012076. [14] J. Y. Lee, R. Verayiah, K. H. Ong, A. K. Ramasamy, and M. B. Marsadek, Distributed generation: A review on current energy status, grid-interconnected pq issues, and implementation constraints of dg in malaysia, Energies, 13 (24) (2020), doi: 10.3390/en13246479. [15] W. Liu, H. Xu, S. Niu, and J. Xie, Optimal distributed generator allocation method considering voltage control cost, Sustainability (Switzerland), 8 (2) (2016), doi: 10.3390/su8020193. [16] S. V. Oprea, A. Bˆara, A. I. Ut¸˘a, A. Pˆırjan, and G. C˘arut¸a¸su, Analyses of distributed generation and storage effect on the electricity consumption curve in the smart grid context, Sustainability (Switzerland), 10 (7) (2018), doi: 10.3390/su10072264. [17] S. Pirouzi et al., Hybrid planning of distributed generation and distribution automation to improve reliability and operation indices, International Journal of Electrical Power & Energy Systems, 135 (May 2021) (2022) 107540, doi: 10.1016/j.ijepes.2021.107540.[18] E. Rokrok, M. Shafie-khah, and J. P. S. Catal˜ao, Review of primary voltage and frequency control methods for inverter-based islanded microgrids with distributed generation, Renewable and Sustainable Energy Reviews, 82 (October 2017)(2018) 3225–3235, doi: 10.1016/j.rser.2017.10.022. [19] A. Rahiminejad, B. Vahidi, M. A. Hejazi, and S. Shahrooyan, Optimal scheduling of dispatchable distributed generation in smart environment with the aim of energy loss minimization, Energy, 116 (2016) 190–201, doi: 10.1016/j.energy.2016.09.111. [20] A. Slowik and H. Kwasnicka, Evolutionary algorithms and their applications to engineering problems, Neural Computing and Applications, 32 (16) (2020) 12363–12379, doi: 10.1007/s00521-020-04832-8. [21] Z. Z. Wang and A. Sobey, A comparative review between Genetic Algorithm use in composite optimisation and the state-of-the-art in evolutionary computation, Composite Structures, 233 (October 2019) (2020) 111739, doi: 10.1016/j.compstruct.2019.111739. | ||
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