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Optimizing the distributed generators integration in electrical distribution networks: efficient modified forensic-based investigation

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Abstract

As a result of various loads, including critical installations (industries, nuclear facilities, etc.), electrical distribution networks (EDNs) must operate safely and sustainably in order to overcome problems such as high power losses and voltage drops, which must be addressed with the most efficient location and capacity of distributed generators (DGs). In order to address this purpose, the proposed research introduces a robust modified forensic-based investigation (mFBI) optimization method that is demonstrated first time to produce the optimum allocation of DGs in EDNs for minimizing power losses and voltage deviations. Moreover, the analytical hierarchy process approach is employed to generate the most applicable weighting factors of the multi-objective function (MOF). Validation and demonstration of the newly developed mFBI technique is conducted by studying the impact of DG integration on 118 IEEE EDN nodes and real Delta-Egypt EDNs. Additionally, an in-depth comprehensive analysis has been carried out between the novel mFBI and 7 recent proposed optimizers, considering the Wilcoxon sign rank test that is used to verify the significant nature of the results. The numeric results best demonstrate the advantage and utility of incorporating the MOF approach and the superior mFBI technique in the EDN to derive an efficient optimum solution.

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References

  1. Abou El-Ela AA, El-Sehiemy RA, Kinawy A, Mouwafi MT (2016) Optimal capacitor placement in distribution systems for power loss reduction and voltage profile improvement. IET Gener Transm Distrib 10(5):1209–1221. https://doi.org/10.1049/iet-gtd.2015.0799

    Article  Google Scholar 

  2. Aprilia E, Meng K, Zeineldin HH, Hosani MA, Dong ZY (2020) Modeling of distributed generators and converters control for power flow analysis of networked islanded hybrid microgrids. Electric Power Syst Res 184:106343. https://doi.org/10.1016/j.epsr.2020.106343

    Article  Google Scholar 

  3. Arabic Republic of Egypt, Ministry of Electricity and Renewable Energy, E. E. H. C. A. report. (2020). General Annual Report English version 2019–2020. http://www.moee.gov.eg/english_new/report.aspx

  4. Arabic Republic of Egypt, M. of E. and R. E. (2020). Renewable Global Status Report. http://www.nrea.gov.eg/

  5. Awad NH, Ali MZ, Suganthan PN (2017) Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems. IEEE Congress Evolut Comput 2017:372–379. https://doi.org/10.1109/CEC.2017.7969336

    Article  Google Scholar 

  6. Babu BK, Maheswarapu S (2019) New hybrid multiverse optimisation approach for optimal accommodation of DGs in power distribution networks. IET Gener Transm Distrib 13(13):2673–2685. https://doi.org/10.1049/iet-gtd.2018.5763

    Article  Google Scholar 

  7. ChithraDevi SA, Lakshminarasimman L, Balamurugan R (2017) Stud Krill herd algorithm for multiple DG placement and sizing in a radial distribution system. Eng Sci Technol Int J 20(2):748–759. https://doi.org/10.1016/j.jestch.2016.11.009

    Article  Google Scholar 

  8. Chou J-S, Nguyen N-M (2020) FBI inspired meta-optimization. Appl Soft Comput 93:106339. https://doi.org/10.1016/j.asoc.2020.106339

    Article  Google Scholar 

  9. Das G, De M, Mandal KK (2021) Multi-objective optimization of hybrid renewable energy system by using novel autonomic soft computing techniques. Comput Electr Eng 94:107350. https://doi.org/10.1016/j.compeleceng.2021.107350

    Article  Google Scholar 

  10. Devabalaji KR, Yuvaraj T, Ravi K (2018) An efficient method for solving the optimal sitting and sizing problem of capacitor banks based on cuckoo search algorithm. Ain Shams Eng J 9(4):589–597. https://doi.org/10.1016/j.asej.2016.04.005

    Article  Google Scholar 

  11. Dixit M, Kundu P, Jariwala HR (2017) Incorporation of distributed generation and shunt capacitor in radial distribution system for techno-economic benefits. Eng Sci Technol Int J 20(2):482–493. https://doi.org/10.1016/j.jestch.2017.01.003

    Article  Google Scholar 

  12. Dokeroglu T, Sevinc E, Kucukyilmaz T, Cosar A (2019) A survey on new generation metaheuristic algorithms. Comput Ind Eng 137:106040. https://doi.org/10.1016/j.cie.2019.106040

    Article  Google Scholar 

  13. El-Fergany A (2015) Optimal allocation of multi-type distributed generators using backtracking search optimization algorithm. Int J Electr Power Energy Syst 64:1197–1205. https://doi.org/10.1016/j.ijepes.2014.09.020

    Article  Google Scholar 

  14. Essallah S, Khedher A, Bouallegue A (2019) Integration of distributed generation in electrical grid: optimal placement and sizing under different load conditions. Comput Electr Eng 79:106461. https://doi.org/10.1016/j.compeleceng.2019.106461

    Article  Google Scholar 

  15. Fathy A, Yousri D, Abdelaziz AY, Ramadan HS (2021) Robust approach based chimp optimization algorithm for minimizing power loss of electrical distribution networks via allocating distributed generators. Sustain Energy Technol Assess 47:101359. https://doi.org/10.1016/j.seta.2021.101359

    Article  Google Scholar 

  16. Ganguly S, Samajpati D (2017) Distributed generation allocation with on-load tap changer on radial distribution networks using adaptive genetic algorithm. Appl Soft Comput 59:45–67. https://doi.org/10.1016/j.asoc.2017.05.041

    Article  Google Scholar 

  17. Gil-González W, Garces A, Montoya OD, Hernández JC (2021) A mixed-integer convex model for the optimal placement and sizing of distributed generators in power distribution networks. Appl Sci 11(2):627. https://doi.org/10.3390/app11020627

    Article  Google Scholar 

  18. Hashim FA, Houssein EH, Mabrouk MS, Al-Atabany W, Mirjalili S (2019) Henry gas solubility optimization: a novel physics-based algorithm. Futur Gener Comput Syst 101:646–667. https://doi.org/10.1016/j.future.2019.07.015

    Article  Google Scholar 

  19. Hashim FA, Hussain K, Houssein EH, Mabrouk MS, Al-Atabany W (2021) Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Appl Intell 51(3):1531–1551. https://doi.org/10.1007/s10489-020-01893-z

    Article  MATH  Google Scholar 

  20. Hassan A, Al-Abdeli YM, Masek M, Bass O (2022) Optimal sizing and energy scheduling of grid-supplemented solar PV systems with battery storage: sensitivity of reliability and financial constraints. Energy 238:121780. https://doi.org/10.1016/j.energy.2021.121780

    Article  Google Scholar 

  21. Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst 97:849–872. https://doi.org/10.1016/j.future.2019.02.028

    Article  Google Scholar 

  22. Hemeida AM, Bakry OM, Mohamed A-AA, Mahmoud EA (2021) Genetic algorithms and satin bowerbird optimization for optimal allocation of distributed generators in radial system. Appl Soft Comput 111:107727. https://doi.org/10.1016/j.asoc.2021.107727

    Article  Google Scholar 

  23. Hemeida MG, Alkhalaf S, Mohamed A-AA, Ibrahim AA, Senjyu T (2020) Distributed generators optimization based on multi-objective functions using manta rays foraging optimization algorithm (MRFO). Energies 13(15):3847. https://doi.org/10.3390/en13153847

    Article  Google Scholar 

  24. Hemeida MG, Ibrahim AA, Mohamed A-AA, Alkhalaf S, El-Dine AMB (2021) Optimal allocation of distributed generators DG based Manta Ray Foraging Optimization algorithm (MRFO). Ain Shams Eng J 12(1):609–619. https://doi.org/10.1016/j.asej.2020.07.009

    Article  Google Scholar 

  25. Hota AP, Mishra S, Mishra DP (2022) Active power loss allocation in radial distribution networks with different load models and DGs. Electric Power Syst Res 205:107764. https://doi.org/10.1016/j.epsr.2021.107764

    Article  Google Scholar 

  26. Houssein EH, Hashim FA, Ferahtia S, Rezk H (2021) An efficient modified artificial electric field algorithm for solving optimization problems and parameter estimation of fuel cell. Int J Energy Res 45(14):20199–20218. https://doi.org/10.1002/er.7103

    Article  Google Scholar 

  27. Houssein EH, Saad MR, Hashim FA, Shaban H, Hassaballah M (2020) Lévy flight distribution: a new metaheuristic algorithm for solving engineering optimization problems. Eng Appl Artif Intell 94:103731. https://doi.org/10.1016/j.engappai.2020.103731

    Article  Google Scholar 

  28. Hussain K, Mohd Salleh MN, Cheng S, Shi Y (2019) Metaheuristic research: a comprehensive survey. Artif Intell Rev 52(4):2191–2233. https://doi.org/10.1007/s10462-017-9605-z

    Article  Google Scholar 

  29. IEEE (2018) Standard for interconnection and interoperability of distributed energy resources with associated electric power systems interfaces-2018. In: IEEE Std 1547-2018 (Revision of IEEE Std 1547-2003). https://doi.org/10.1109/IEEESTD.2018.8332112

  30. International Atomic Energy Agency (2017) Industrial applications of nuclear energy, IAEA nuclear energy series No. NP-T-4.3. IAEA. https://www.iaea.org/publications/10979/industrial-applications-of-nuclear-energy

  31. Jamian JJ, Mustafa MW, Mokhlis H (2015) Optimal multiple distributed generation output through rank evolutionary particle swarm optimization. Neurocomputing 152:190–198. https://doi.org/10.1016/j.neucom.2014.11.001

    Article  Google Scholar 

  32. Jamil Mahfoud R, Sun Y, Faisal Alkayem N, Haes Alhelou H, Siano P, Shafie-khah M (2019) A novel combined evolutionary algorithm for optimal planning of distributed generators in radial distribution systems. Appl Sci 9(16):3394. https://doi.org/10.3390/app9163394

    Article  Google Scholar 

  33. Jin J, Rothrock L, McDermott PL, Barnes M (2010) Using the analytic hierarchy process to examine judgment consistency in a complex multiattribute task. IEEE Trans Syst Man Cybern Part A Syst Hum 40(5):1105–1115. https://doi.org/10.1109/TSMCA.2010.2045119

    Article  Google Scholar 

  34. Kennedy J, Eberhart R (1995) Particle swarm optimization. Proc ICNN’95 Int Conf Neural Netw 4:1942–1948. https://doi.org/10.1109/ICNN.1995.488968

    Article  Google Scholar 

  35. Liu Z, Wen F, Ledwich G (2011) Optimal siting and sizing of distributed generators in distribution systems considering uncertainties. IEEE Trans Power Deliv 26(4):2541–2551. https://doi.org/10.1109/TPWRD.2011.2165972

    Article  Google Scholar 

  36. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

  37. Mohamed AW, Hadi AA, Mohamed AK, Awad NH (2020) Evaluating the performance of adaptive gaining sharing knowledge based algorithm on CEC 2020 benchmark problems. IEEE Congress Evolut Comput (CEC) 2020:1–8. https://doi.org/10.1109/CEC48606.2020.9185901

    Article  Google Scholar 

  38. Mohamed Imran A, Kowsalya M (2014) Optimal size and siting of multiple distributed generators in distribution system using bacterial foraging optimization. Swarm Evol Comput 15:58–65. https://doi.org/10.1016/j.swevo.2013.12.001

    Article  Google Scholar 

  39. Moradi MH, Abedini M (2012) A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems. Int J Electr Power Energy Syst 34(1):66–74. https://doi.org/10.1016/j.ijepes.2011.08.023

    Article  Google Scholar 

  40. Moradi MH, Abedini M (2016) A novel method for optimal DG units capacity and location in microgrids. Int J Electr Power Energy Syst 75:236–244. https://doi.org/10.1016/j.ijepes.2015.09.013

    Article  Google Scholar 

  41. Nguyen TP, Tran TT, Vo DN (2019) Improved stochastic fractal search algorithm with chaos for optimal determination of location, size, and quantity of distributed generators in distribution systems. Neural Comput Appl 31(11):7707–7732. https://doi.org/10.1007/s00521-018-3603-1

    Article  Google Scholar 

  42. Nguyen TP, Vo DN (2018) A novel stochastic fractal search algorithm for optimal allocation of distributed generators in radial distribution systems. Appl Soft Comput 70:773–796. https://doi.org/10.1016/j.asoc.2018.06.020

    Article  Google Scholar 

  43. Prakash P, Meena DC, Malik H, Alotaibi MA, Khan IA (2022) A novel analytical approach for optimal integration of renewable energy sources in distribution systems. Energies 15(4):1341. https://doi.org/10.3390/en15041341

    Article  Google Scholar 

  44. Quadri IA, Bhowmick S, Joshi D (2018) A comprehensive technique for optimal allocation of distributed energy resources in radial distribution systems. Appl Energy 211:1245–1260. https://doi.org/10.1016/j.apenergy.2017.11.108

    Article  Google Scholar 

  45. Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248. https://doi.org/10.1016/j.ins.2009.03.004

    Article  MATH  Google Scholar 

  46. Saha S, Mukherjee V (2016) Optimal placement and sizing of DGs in RDS using chaos embedded SOS algorithm. IET Gener Transm Distrib 10(14):3671–3680. https://doi.org/10.1049/iet-gtd.2016.0151

    Article  Google Scholar 

  47. Sanjay R, Jayabarathi T, Raghunathan T, Ramesh V, Mithulananthan N (2017) Optimal allocation of distributed generation using hybrid Grey Wolf Optimizer. IEEE Access 5:14807–14818. https://doi.org/10.1109/ACCESS.2017.2726586

    Article  Google Scholar 

  48. Selim A, Kamel S, Alghamdi AS, Jurado F (2020) Optimal placement of dgs in distribution system using an improved harris hawks optimizer based on single- and multi-objective approaches. IEEE Access 8:52815–52829. https://doi.org/10.1109/ACCESS.2020.2980245

    Article  Google Scholar 

  49. Sharma S, Bhattacharjee S, Bhattacharya A (2016) Quasi-oppositional swine influenza model based optimization with quarantine for optimal allocation of DG in radial distribution network. Int J Electr Power Energy Syst 74:348–373. https://doi.org/10.1016/j.ijepes.2015.07.034

    Article  Google Scholar 

  50. Sivakumar K, Jayashree R, Danasagaran K (2021) Efficiency-driven planning for sizing of distributed generators and optimal construction of a cluster of microgrids. Eng Sci Technol Int J 24(5):1153–1167. https://doi.org/10.1016/j.jestch.2021.02.015

    Article  Google Scholar 

  51. Sultana S, Roy PK (2014) Multi-objective quasi-oppositional teaching learning based optimization for optimal location of distributed generator in radial distribution systems. Int J Electr Power Energy Syst 63:534–545. https://doi.org/10.1016/j.ijepes.2014.06.031

    Article  Google Scholar 

  52. Sultana S, Roy PK (2016) Krill herd algorithm for optimal location of distributed generator in radial distribution system. Appl Soft Comput 40:391–404. https://doi.org/10.1016/j.asoc.2015.11.036

    Article  Google Scholar 

  53. Tolba MA, Rezk H, Al-Dhaifallah M, Eisa AA (2020) Heuristic optimization techniques for connecting renewable distributed generators on distribution grids. Neural Comput Appl. https://doi.org/10.1007/s00521-020-04812-y

    Article  Google Scholar 

  54. Tolba MA, Tulsky VN (2021) Integration of DGs optimally to enhance the voltage profile and stability index of distribution grid. In: Proceedings of the 3rd 2021 international youth conference on radio electronics, electrical and power engineering, REEPE 2021. https://doi.org/10.1109/REEPE51337.2021.9387986

  55. Tolba MA, Zaki Diab AA, Vanin AS, Tulsky VN, Abdelaziz AY (2018) Integration of renewable distributed generation in distribution networks including a practical case study based on a hybrid PSOGSA optimization algorithm. Electric Power Comp Syst. https://doi.org/10.1080/15325008.2018.1532470

    Article  Google Scholar 

  56. Truong KH, Nallagownden P, Elamvazuthi I, Vo DN (2020) A quasi-oppositional-chaotic symbiotic organisms search algorithm for optimal allocation of DG in radial distribution networks. Appl Soft Comput 88:106067. https://doi.org/10.1016/j.asoc.2020.106067

    Article  Google Scholar 

  57. Tulsky VN, Tolba MA, Radwan AA, Foly OM, Diab AAZ (2017) Measurement and analysis of an electric power distribution system with optimal reactive power compensation for improving the power quality. Case study: Middle Egypt region. In: Proceedings of the 2017 IEEE Russia section young researchers in electrical and electronic engineering conference, ElConRus 2017. https://doi.org/10.1109/EIConRus.2017.7910881

  58. Tulsky VN, Vanin AS, Tolba MA, Sharova AY, Diab AAZ (2016) Study and analysis of power quality for an electric power distribution system—case study: Moscow region. In: Proceedings of the 2016 IEEE North West Russia section young researchers in electrical and electronic engineering conference, EIConRusNW 2016. https://doi.org/10.1109/EIConRusNW.2016.7448281

  59. Wilcoxon F (1992) Individual comparisons by ranking methods. Springer, pp 196–202. https://doi.org/10.1007/978-1-4612-4380-9_16

    Book  Google Scholar 

  60. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82. https://doi.org/10.1109/4235.585893

    Article  Google Scholar 

  61. Yammani C, Maheswarapu S, Matam SK (2016) A Multi-objective Shuffled Bat algorithm for optimal placement and sizing of multi distributed generations with different load models. Int J Electr Power Energy Syst 79:120–131. https://doi.org/10.1016/j.ijepes.2016.01.003

    Article  Google Scholar 

  62. Zhang D, Fu Z, Zhang L (2007) An improved TS algorithm for loss-minimum reconfiguration in large-scale distribution systems. Electric Power Syst Res 77(5–6):685–694. https://doi.org/10.1016/j.epsr.2006.06.005

    Article  Google Scholar 

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Acknowledgements

The researcher [Mohamed A. Tolba] is funded by a full scholarship [Mission 2019/20] from the Ministry of Higher Education of Egypt. But, no funding was received for this work by the mentioned ministry of Egypt or any another organization/foundation.

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Authors and Affiliations

  1. Reactors Department, Nuclear Research Center, Egyptian Atomic Energy Authority (EAEA), Cairo, 11787, Egypt

    Mohamed A. Tolba

  2. Faculty of Computers and Information, Minia University, Minia, Egypt

    Essam H. Houssein

  3. National Center for Radiation Research and Technology (NCRRT), Egyptian Atomic Energy Authority (EAEA), Cairo, 11787, Egypt

    Ayman A. Eisa

  4. Faculty of Engineering, Helwan University, Cairo, 11795, Egypt

    Fatma A. Hashim

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  1. Mohamed A. Tolba
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  3. Ayman A. Eisa
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  4. Fatma A. Hashim
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Contributions

MAT: Conceptualization, Methodology, Formal analysis, Data curation, Writing—original draft, Writing—review & editing. EHH: Supervision, Methodology, Software, Formal analysis, Visualization, Writing—review & editing, Project Administration. AAE: Investigation, Visualization, Writing—review & editing. FAH: Conceptualization, Software, Data curation, Writing—review & editing. All authors read and approved the final paper.

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Correspondence to Mohamed A. Tolba.

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Tolba, M.A., Houssein, E.H., Eisa, A.A. et al. Optimizing the distributed generators integration in electrical distribution networks: efficient modified forensic-based investigation. Neural Comput & Applic 35, 8307–8342 (2023). https://doi.org/10.1007/s00521-022-08103-6

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