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RESEARCH ARTICLE

Optimal reconfiguration of photovoltaic/wind-based distributed generation systems: A techno-economic analysis using AHP–GA

Manish Kumar Madhav1 ,  Krishna B. Yadav1 ,  Akshit Samadhiya2 ,  Ahmad Taher Azar3,4 ,  Saim Ahmed4* ,  Walid El-Shafai4,5 ,  Chakib Ben Njima6
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1 Department of Electrical Engineering, National Institute of Technology, Jamshedpur, Jharkhand, India
2 Department of Electrical and Electronics Engineering, School of Engineering and Technology, Sandip University, Nashik, Maharashtra, India
3 College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
4 Automated Systems and Computing Lab (ASCL), Prince Sultan University, Riyadh, Saudi Arabia
5 Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
6 University of Sousse, Sousse, Tunisia
IJOCTA, 026140049 https://doi.org/10.36922/IJOCTA026140049
Received: 30 March 2026 | Revised: 13 May 2026 | Accepted: 15 May 2026 | Published online: 24 August 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Renewable distributed generation (RDG), notably solar and wind energy, is increasingly integrated into distribution networks (DNs) to enhance sustainability, meet rising demand, and improve grid reliability. This study proposes a coordinated planning framework that jointly optimizes RDG siting/sizing and DN expansion using a hybrid Analytic Hierarchy Process–genetic algorithm (AHP–GA). Multi-objective goals—minimizing active power losses, reliability-related costs, and annual equipment investment—are scalarized using AHP-derived weights and solved through a chromosome-segmentation GA that reduces the search space and accelerates convergence. The method was validated on a modified IEEE 37-node radial distribution system, demonstrating substantial techno-economic benefits: total cost reductions exceeding 25%, approximately 75% reduction in active power losses, and about 40% improvement in reliability indices. A comparative assessment of the IEEE 69-bus system further underscored the approach’s efficiency, achieving an active power loss of 16.88 kW (a 92.50% reduction) and 112.67 s of CPU time, outperforming alternative metaheuristics. After the integration of DGs, both reliability indices improved significantly, resulting in reductions of 40.27% in System Average Interruption Duration Index (SAIDI) and 58.84% in Energy Not Served (ENS) annually. The results indicate that coordinated RDG–DN planning yields DNs that are more economical, reliable, and operationally robust than those from sequential or uncoordinated strategies.

Graphical abstract
Keywords
Genetic algorithm
Renewable distributed generation
Load flow
Analytic Hierarchy Process
Distribution system
Reliability indices
Funding
This study was funded by the Research, Development, and Innovation Authority (RDIA), Kingdom of Saudi Arabia, with grant number 13382-psu-2023-PSNU-R-3-1-EI.
Conflict of interest
The authors declare that they have no competing interests.
References
  1. Zhu D, Broadwater RP, Tam KS, Seguin R, Asgeirsson H. Impact of DG placement on reliability and efficiency with time-varying loads. IEEE Trans Power Syst. 2006;21(1):419-427. https://doi.org/10.1109/TPWRS.2005.860943
  2. Fergany AE. Optimal allocation of multi-type distributed generators using backtracking search optimization algorithm. Int J Electr Power Energy Syst. 2015;64:1197-1205. https://doi.org/10.1016/j.ijepes.2014.09.020
  3. Ismael SM, Abdel Aleem SHE, Abdelaziz AY, Zobaa AF. State-of-the-art of hosting capacity in modern power systems with distributed generation. Renew Energy. 2019;130:1002-1020. https://doi.org/10.1016/j.renene.2018.07.008
  4. Lee SH, Park JW. Optimal placement and sizing of multiple DGs in a practical distribution system by considering power loss. IEEE Trans Ind Appl. 2013;49(5):2262-2270. https://doi.org/10.1109/TIA.2013.2260117
  5. Georgilakis PS, Hatziargyriou ND. Optimal distributed generation placement in power distribution networks: models, methods, and future research. IEEE Trans Power Syst. 2013;28(3):3420-3428. https://doi.org/10.1109/TPWRS.2012.2237043
  6. Nekooei K, Farsangi MM, Nezamabadi H, Lee KY. An improved multi-objective harmony search for optimal placement of DGs in distribution systems. IEEE Trans Smart Grid. 2013;4(1):557-567. https://doi.org/10.1109/TSG.2012.2237420
  7. Hung DQ, Mithulananthan N. Multiple distributed generator placement in primary distribution networks for loss reduction. IEEE Trans Ind Electron. 2013;60(4):1700-1708. https://doi.org/10.1109/TIE.2011.2112316
  8. Kansal S, Kumar V, Tyagi B. Optimal placement of different type of DG sources in distribution networks. Int J Electr Power Energy Syst. 2013;53:752-760. https://doi.org/10.1016/j.ijepes.2013.05.040
  9. Kaur S, Kumbhar G, Sharma JD. A MINLP technique for optimal placement of multiple DG units in distribution systems. Int J Electr Power Energy Syst. 2014;63:609-617. https://doi.org/10.1016/j.ijepes.2014.06.023
  10. Sheng W, Liu KY, Meng X, Li Y. Optimal placement and sizing of distributed generation via an improved nondominated sorting genetic algorithm II. IEEE Trans Power Deliv. 2015;30(2):569-578. https://doi.org/10.1109/TPWRD.2014.2325938
  11. Heidari A, Agelidis VG, Kia M. Considerations of sectionalizing switches in distribution networks with distributed generation. IEEE Trans Power Deliv. 2015;30(3):1401-1409. https://doi.org/10.1109/TPWRD.2014.2385654
  12. Ganguly S, Yokota H. Distributed generation allocation on radial distribution networks under uncertainties of load and generation using genetic algorithm. IEEE Trans Sustain Energy. 2015;6(3):688-697. https://doi.org/10.1109/TSTE.2015.2406915
  13. Abdelsalam A, Zidan A, El-Saadany A. Optimal DG allocation in radial distribution systems with high penetration of non-linear loads. Electr Power Compon Syst. 2015;43(13):1487-1497. https://doi.org/10.1080/15325008.2015.1043601
  14. Narayanan K, Siddiqui SA, Fozdar M. Optimal placement of distributed generators in radial distribution system for reducing the effect of islanding. J Electr Eng Technol. 2016;11(3):551-559. https://doi.org/10.5370/JEET.2016.11.3.551
  15. Poornazaryan B, Karimyan P, Gharehpetian G, Abedi M. Optimal allocation and sizing of DG units considering voltage stability, losses and load variations. Int J Electr Power Energy Syst. 2016;79:42-52. https://doi.org/10.1016/j.ijepes.2015.12.034
  16. Pereira B, Martin G, da Costa R, Contreras J, Mantovani JRS. Optimal distributed generation and reactive power allocation in electrical distribution systems. IEEE Trans Sustain Energy. 2016;7(3):975-984. https://doi.org/10.1109/TSTE.2015.2512819
  17. Amohadi M, Firuzabad MF. Optimal placement of switching and protection devices in radial distribution networks to enhance system reliability using the AHP-PSO method. Turk J Electr Eng Comput Sci. 2019;27(1):181-196. https://doi.org/10.3906/elk-1806-130
  18. Kaur N, Jain SK. Multi-objective optimization approach for placement of multiple DGs for voltage sensitive loads. Energies. 2017;10(11):1733. https://doi.org/10.3390/en10111733
  19. Meena NK, Parashar S, Swarnkar A, Gupta N, Niazi KR. Improved elephant herding optimization for multiobjective DER accommodation in distribution systems. IEEE Trans Ind Inform. 2018;14(3):1029-1039. https://doi.org/10.1109/TII.2017.2748220
  20. Eltamaly AM, Al-Saud MS. Nested multi-objective PSO for optimal allocation and sizing of renewable energy distributed generation. J Renew Sustain Energy. 2018;10(3):035302. https://doi.org/10.1063/1.5028484
  21. Grisales-Noreña LF, Montoya DG, Ramos-Paja CA. Optimal sizing and location of distributed generators based on PBIL and PSO techniques. Energies. 2018;11(4):1018. https://doi.org/10.3390/en11041018
  22. HassanzadehFard H, Jalilian A. Optimal sizing and location of renewable energy-based DG units in distribution systems considering load growth. Int J Electr Power Energy Syst. 2018;101:356-370. https://doi.org/10.1016/j.ijepes.2018.03.038
  23. Bhullar S, Ghosh S. Optimal integration of multi distributed generation sources in radial distribution networks using a hybrid algorithm. Energies. 2018;11(3):628. https://doi.org/10.3390/en11030628
  24. El-Ela AAA, El-Sehiemy RA, Abbas AS. Optimal placement and sizing of distributed generation and capacitor banks in distribution systems using water cycle algorithm. IEEE Syst J. 2018;12(4):3629-3636. https://doi.org/10.1109/JSYST.2018.2796847
  25. Khoubseresht O, Shayanfar H. The role of demand response in optimal sizing and siting of distribution energy resources in distribution network with time-varying load: an analytical approach. Electr Power Syst Res. 2020;180:106100. https://doi.org/10.1016/j.epsr.2019.106100
  26. Manna DK, Goswami SK. Optimum placement of distributed generation considering economics as well as operational issues. Int Trans Electr Energy Syst. 2020;30(3):e12246. https://doi.org/10.1002/2050-7038.12246
  27. Madhav MK, Yadav KB. Economic analysis of isolator placement in the radial network for enhancement of reliability indicators. Electr Eng. 2024;106(1):891-904. https://doi.org/10.1007/s00202-023-02013-z
  28. Pesaran HM, Nazari-Heris M, Mohammadi-Ivatloo B, Seyedi H. A hybrid genetic particle swarm optimization for distributed generation allocation in power distribution networks. Energy. 2020;209:118218. https://doi.org/10.1016/j.energy.2020.118218
  29. Wang HJ, Pan JS, Nguyen TT, Weng S. Distribution network reconfiguration with distributed generation based on parallel slime mould algorithm. Energy. 2022;244:123011. https://doi.org/10.1016/j.energy.2021.123011
  30. Kawambwa S, Hamisi N, Mafole P, Kundaeli H. A cloud model based symbiotic organism search algorithm for DG allocation in radial distribution network. Evol Intell. 2022;15:545-562. https://doi.org/10.1007/s12065-020-00529-y
  31. Shaheen AM, Elsayed AM, Ragab A, El-Sehiemy AY. Equilibrium optimization algorithm for network reconfiguration and distributed generation allocation in power systems. Appl Soft Comput. 2021;98:106867. https://doi.org/10.1016/j.asoc.2020.106867
  32. Rafi V, Dhal PK, Rajesh M, Srinivasan DR, Chandrashekhar M, Reddy NM. Optimal placement of time-varying distributed generators by using crow search and black widow-hybrid optimization. Meas Sens. 2023;30:100900. https://doi.org/10.1016/j.measen.2023.100900
  33. Subbaramaiah K, Sujatha P. Optimal DG unit placement in distribution networks by multi-objective whale optimization algorithm and its techno-economic analysis. Electr Power Syst Res. 2023;214:108869. https://doi.org/10.1016/j.epsr.2022.108869
  34. Aishvarya N, Srivastava SK, Singh SN. Congestion management approaches in restructured power system: key issues and challenges. Electr J. 2020;33(3):106715. https://doi.org/10.1016/j.tej.2020.106715
  35. Madhav MK, Yadav KB. Optimum location of isolator in radial distribution system using genetic algorithm to improve the system reliability. In: Kumar J, Tripathy M, Jena P, eds. Control Applications in Modern Power Systems. Lecture Notes in Electrical Engineering. Vol 870. Singapore: Springer; 2022:177-188. https://doi.org/10.1007/978-981-19-0193-5_15
  36. Selim A, Akher MA, Aly MM, Kamel S, Seniyu T. Fast quasi-static time-series analysis and reactive power control of unbalanced distribution systems. Int Trans Electr Energy Syst. 2019;29(1):e2673. https://doi.org/10.1002/etep.2673
  37. Saaty TL. A scaling method for priorities in hierarchical structures. J Math Psychol. 1977;15(3):234-281. https://doi.org/10.1016/0022-2496(77)90033-5
  38. Shaik MA, Mareddy PL, Visali N. Enhancement of voltage profile in distribution system by reconfiguring with DG placement using equilibrium optimizer. Alex Eng J. 2022;61(5):4081-4093. https://doi.org/10.1016/j.aej.2021.09.063
  39. Dashtdar M, Najafi M, Esmaeilbeig M, Bajaj M. Placement and optimal size of DG in the distribution network based on nodal pricing reduction with nonlinear load model using the IABC algorithm. Sadhana. 2022;47(2):73. https://doi.org/10.1007/s12046-022-01850-1
  40. Veeramsetty V, Venkaiah C, Kumar DMV. Hybrid genetic dragonfly algorithm based optimal power flow for computing LMP at DG buses for reliability improvement. Energy Syst. 2018;9(3):709-757. https://doi.org/10.1007/s12667-017-0268-2
  41. Rezvanfara R, Hagha MT, Zarea K. Power-based distribution locational marginal pricing under high penetration of distributed energy resources. Int J Electr Power Energy Syst. 2020;123:106303. https://doi.org/10.1016/j.ijepes.2020.106303
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An International Journal of Optimization and Control: Theories & Applications, Electronic ISSN: 2146-5703 Print ISSN: 2146-0957, Published by AccScience Publishing
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