Abstract
Optimal placement of Distributed Generation (DG) is a crucial challenge for Distribution Companies (DISCO’s) to run the distribution network in good operating conditions. Optimal positioning of DG units is an optimization issue where maximization of DISCO’s additional benefit due to the installation of DG units in the network is considered to be an objective function. In this article, the self adaptive levy flight based black widow optimization algorithm is used as an optimization strategy to find the optimum position and size of the DG units. The proposed algorithm is implemented in the IEEE 15 and PG & E 69 bus management systems in the MATLAB environment. Based on the simulation performance, it has been found that with the correct location and size of the DG modules, the distribution network can be run with maximum DISCO’s additional benefit.
Acknowledgment
We thank S R Engineering College Warangal, Osmania University and JNTU Hyderabad for supporting us during this work.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
1. Veeramsetty, V, Chintham, V, Kumar, DMV. Probabilistic locational marginal price computation in radial distribution system based on active power loss reduction. In: IET generation, transmission & distribution. UK: IET; 2020, 14:2292–302 pp.10.1049/iet-gtd.2019.0952Search in Google Scholar
2. Rakesh, R, VenkataPapana, P, Keerthi, S. A hybrid algorithm for optimal allocation of dg in radial distribution system. In: 2017 IEEE region 10 symposium (TENSYMP). Cochin, India: IEEE; 2017:1–5 pp.10.1109/TENCONSpring.2017.8070009Search in Google Scholar
3. Nagaballi, S, Kale, VS. Pareto optimality and game theory approach for optimal deployment of dg in radial distribution system to improve techno-economic benefits. Appl Soft Comput 2020;92:106234. https://doi.org/10.1016/j.asoc.2020.106234.Search in Google Scholar
4. Almabsout, EA, El-Sehiemy, RA, An, ONU, Bayat, O. A hybrid local search-genetic algorithm for simultaneous placement of dg units and shunt capacitors in radial distribution systems. IEEE Access 2020;8:54465–81. https://doi.org/10.1109/access.2020.2981406.Search in Google Scholar
5. Lakshmi, GN, Jayalaxmi, A, Veeramsetty, V. Optimal placement of distributed generation using firefly algorithm. In: IOP conference series: materials science and engineering. Warangal, India: IOP Publishing; 2020, 981:042060 p.10.1088/1757-899X/981/4/042060Search in Google Scholar
6. Samala, RK, Kotapuri, MR. Optimal allocation of distributed generations using hybrid technique with fuzzy logic controller radial distribution system. SN Appl Sci 2020;2:1–14. https://doi.org/10.1007/s42452-020-1957-3.Search in Google Scholar
7. Truong, KH, Nallagownden, P, Elamvazuthi, I, Vo, DN. An improved meta-heuristic method to maximize the penetration of distributed generation in radial distribution networks. Neural Comput Appl 2020;32:10159–81. https://doi.org/10.1007/s00521-019-04548-4.Search in Google Scholar
8. Selim, A, Kamel, S, Nasrat, LS, Jurado, F. Voltage stability assessment of radial distribution systems including optimal allocation of distributed generators. Int J Interact Multimed Artif Intell 2020;6:32–40. https://doi.org/10.9781/ijimai.2020.02.004.Search in Google Scholar
9. Jayasree, M, Sreejaya, P, Bindu, G. Multi-objective metaheuristic algorithm for optimal distributed generator placement and profit analysis. Technol Econ Smart Grid Sustain Energy 2019;4:11. https://doi.org/10.1007/s40866-019-0067-z.Search in Google Scholar
10. Jalili, A, Taheri, B. Optimal sizing and sitting of distributed generations in power distribution networks using firefly algorithm. Technol Econ Smart Grid Sustain Energy 2020;5:1–14. https://doi.org/10.1007/s40866-020-00081-9.Search in Google Scholar
11. Aravinth, A, Vatul, VA, Narayanan, K, Muthukumar, K, Senjyu, T. A multi-objective framework to improve voltage stability in a distribution network. Int J Emerg Elec Power Syst 2019;20:1–14. https://doi.org/10.1515/ijeeps-2018-0239.Search in Google Scholar
12. Manas, M, Saikia, BJ, Baruah, DC. Optimal distributed generator sizing and placement by analytical method and fuzzy expert system: a case study in Tezpur University, India. Technol Econ Smart Grid Sustain Energy 2018;3:1. https://doi.org/10.1007/s40866-018-0038-9.Search in Google Scholar
13. Dehghani, M, Montazeri, Z, Malik, O. Optimal sizing and placement of capacitor banks and distributed generation in distribution systems using spring search algorithm. Int J Emerg Elec Power Syst 2020;21:1–9. https://doi.org/10.1515/ijeeps-2019-0217.Search in Google Scholar
14. Basetti, V, Chandel, AK, Subramanyam, K. Power system static state estimation using jade-adaptive differential evolution technique. Soft Comput 2018;22:7157–76. https://doi.org/10.1007/s00500-017-2715-3.Search in Google Scholar
15. Sudhakar, AV, Karri, C. Bio inspired algorithms in power system operation: a review. In: 2017 international conference on recent trends in electrical, electronics and computing technologies (ICRTEECT). Warangal, India: IEEE; 2017:113–9 pp.10.1109/ICRTEECT.2017.18Search in Google Scholar
16. Vedik, B, Shiva, C, Harish, P. Reverse harmonic load flow analysis using an evolutionary technique. SN Appl Sci 2020;2:1–11. https://doi.org/10.1007/s42452-020-03408-4.Search in Google Scholar
17. Hayyolalam, V, Kazem, AAP. Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems. Eng Appl Artif Intell 2020;87:103249. https://doi.org/10.1016/j.engappai.2019.103249.Search in Google Scholar
18. Amirsadri, S, Mousavirad, SJ, Ebrahimpour-Komleh, H. A levy flight-based grey wolf optimizer combined with back-propagation algorithm for neural network training. Neural Comput Appl 2018;30:3707–20. https://doi.org/10.1007/s00521-017-2952-5.Search in Google Scholar
19. Mirjalili, S. Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 2016;27:1053–73. https://doi.org/10.1007/s00521-015-1920-1.Search in Google Scholar
20. Release, M. The mathworks. Natick, Massachusetts, United States: Inc.; 2013. p. 488.Search in Google Scholar
21. Veeramsetty, V, Venkaiah, C, Kumar, DV. Hybrid genetic dragonfly algorithm based optimal power flow for computing lmp at dg buses for reliability improvement. Energy Syst 2017:1–49.10.1007/s12667-017-0268-2Search in Google Scholar
22. IEX, d. IEX-Market Data; 2017. [online]. https://www.iexindia.com/marketdata [Accessed 02 Nov 2020].Search in Google Scholar
23. Veeramsetty, V, Chintham, V, DM, VK. Lmp computation at dg buses in radial distribution system. Int J Energy Sect Manag 2018;12:364–85. https://doi.org/10.1108/ijesm-03-2017-0002.Search in Google Scholar
24. Goldberg, DE, Holland, JH. Genetic algorithms and machine learning. Mach Learn 1988;3:95–9. https://doi.org/10.1023/a:1022602019183.10.1023/A:1022602019183Search in Google Scholar
25. Eberhart, R, Kennedy, J. A new optimizer using particle swarm theory. In: Micro machine and human science, 1995. MHS’95. Proceedings of the sixth international symposium on. Nagoya, Japan: IEEE; 1995:39–43 pp.10.1109/MHS.1995.494215Search in Google Scholar
26. Ryan, TP. Statistical methods for quality improvement. New Jersey, US: John Wiley & Sons; 2011.10.1002/9781118058114Search in Google Scholar
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