A quasi oppositional forensic based investigation algorithm for optimizing distributed generation placement and sizing in power distribution systems

. 2025 May 12 ; 15 (1) : 16471. [epub] 20250512

Status PubMed-not-MEDLINE Jazyk angličtina Země Anglie, Velká Británie Médium electronic

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid40355509

Grantová podpora
TN02000025 National Centre for Energy II
101139527 ExPEDite (European Union's Horizon Mission Programme)
CZ.10.03.01/00/22_003/0000048 European Union

Odkazy

PubMed 40355509
PubMed Central PMC12069694
DOI 10.1038/s41598-025-01378-4
PII: 10.1038/s41598-025-01378-4
Knihovny.cz E-zdroje

The optimal siting and sizing of DGs are vital for the efficient operation of both radial and microgrid distribution systems. From an operational perspective, minimizing real power loss, reducing voltage deviation, and improving voltage stability index are the three primary objectives considered in this study. This manuscript addresses these issues by proposing a novel quasi-oppositional forensic-based investigation (QOFBI) algorithm, an evolutionary meta-optimization technique designed to optimize the location and sizing of DGs under various operating conditions, while adhering to system constraints. The approach introduces a weighting factor-based multiobjective formulation, where optimal weighting factors are computed dynamically. This ensures a balanced approach to minimizing power loss, voltage deviation, and enhancing voltage stability. Extensive simulations were conducted on the IEEE 33-bus and IEEE 69-bus standard distribution systems, evaluating the impact of DG placement with varying power factors under operational constraints. The results demonstrate the superiority of the proposed approach in terms of faster convergence, reduced complexity, and improved performance compared to existing optimization methods. The QOFBI algorithm achieves a 94.44% reduction in active power loss, highlighting its robust performance across different operational scenarios. These findings underscore the potential of QOFBI as a highly effective tool for DG optimization in modern distribution systems, offering both operational efficiency and system reliability.

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Adefarati, T. & Bansal, R. C. Integration of renewable distributed generators into the distribution system: A review. IET Renew. Power Gener.10(7), 873–884. 10.1049/iet-rpg.2015.0378 (2016).

Ufa, R. A., Malkova, Y. Y., Rudnik, V. E., Andreev, M. V. & Borisov, V. A. A review on distributed generation impacts on electric power system. Int. J. Hydrogen Energy47(47), 20347–20361. 10.1016/j.ijhydene.2022.04.142 (2022).

Colmenar-Santos, A., Reino-Rio, C., Borge-Diez, D. & Collado-Fernández, E. Distributed generation: A review of factors that can contribute most to achieve a scenario of DG units embedded in the new distribution networks. Renew. Sustain. Energy Rev.59, 1130–1148. 10.1016/j.rser.2016.01.023 (2016).

Kumar, R., Singh, B. K., & Singh, B. (2024). A Critical Review of Distributed Generations Planning in Distribution Networks for Improved System Performances. Journal of The Institution of Engineers (India): Series B, 105(5), 1373–1427. 10.1007/s40031-024-01097-w

Guzmán-Henao, J. A., Bolaños, R. I., Montoya, O. D., Grisales-Noreña, L. F. & Chamorro, H. R. On Integrating and Operating Distributed Energy Resources in Distribution Networks: A Review of Current Solution Methods, Challenges, and Opportunities. IEEE Access10.1109/ACCESS.2024.3387400 (2024).

Mishra, A., Tripathy, M. & Ray, P. A survey on different techniques for distribution network reconfiguration. Journal of Engineering Research12(1), 173–181. 10.1016/j.jer.2023.09.001 (2024).

Rastgou, A. Distribution network expansion planning: An updated review of current methods and new challenges. Renew. Sustain. Energy Rev.189, 114062. 10.1016/j.rser.2023.114062 (2024).

Connolly, D., Lund, H., Mathiesen, B. V. & Leahy, M. A review of computer tools for analysing the integration of renewable energy into various energy systems. Appl. Energy87(4), 1059–1082. 10.1016/j.apenergy.2009.09.026 (2010).

Bansal, R. C. (2005). Optimization methods for electric power systems: An overview. International Journal of Emerging Electric Power Systems, 2(1). 10.2202/1553-779X.1021

Ehsan, A. & Yang, Q. Optimal integration and planning of renewable distributed generation in the power distribution networks: A review of analytical techniques. Appl. Energy210, 44–59. 10.1016/j.apenergy.2017.10.106 (2018).

Fettah, K. et al. A pareto strategy based on multi-objective optimal integration of distributed generation and compensation devices regarding weather and load fluctuations. Sci. Rep.14(1), 10423 (2024). PubMed PMC

Eltamaly, A. M. & Mohamed, M. A. A novel software for design and optimization of hybrid power systems. J. Braz. Soc. Mech. Sci. Eng.38, 1299–1315 (2016).

Adegoke, S. A., Sun, Y., Adegoke, A. S. & Ojeniyi, D. Optimal placement of distributed generation to minimize power loss and improve voltage stability. Heliyon10(21), e39298 (2024). PubMed PMC

Malika, B. K., Pattanaik, V., Sahu, B. K., Rout, P. K., Panda, S. & Bajaj, M. (2024). Optimal distributed generation and shunt capacitor bank placement in microgrid distribution planning for enhanced performance. Neural Comput. Appl. 1–26.

Etamaly, A. M., Mohamed, M. A. & Alolah, A. I. A smart technique for optimization and simulation of hybrid photovoltaic/wind/diesel/battery energy systems. In 2015 IEEE International Conference on Smart Energy Grid Engineering (SEGE), 1–6 (IEEE , 2015).

Chatterjee, S. et al. Optimal real-time tuning of autonomous distributed power systems using modern techniques. Front. Energy Res.11, 1055845 (2023).

Lone, R. A., Javed Iqbal, S. & Anees, A. S. Optimal location and sizing of distributed generation for distribution systems: An improved analytical technique. Int. J. Green Energy21(3), 682–700 (2024).

Hussein Farh, H. M., Al-Shamma’a, A. A., Qamar, A., Saeed, F. & Al-Shaalan, A. M. Optimal sizing and placement of distributed generation under N-1 contingency using hybrid crow search–particle swarm algorithm. Sustainability16(6), 2380 (2024).

Qi, H., Sharaf, M., Annuk, A., Ilinca, A. & Mohamed, M. A. A distributionally robust optimization scheduling model for regional integrated energy systems considering hot dry rock co-generation. CMES Comput. Model. Eng. Sci.140(2), 1387–1404 (2024).

Saravanan, C., Vengadachalam, N., Balakrishnan, P. & Sathyanarayanan, T. K. S. Enhancing distribution network efficiency with Andean condor algorithm-driven optimal placement of distributed generation and network reconfiguration. In Electric Power Components and Systems, 1–17 (2024).

Dharavat, N. et al. Optimal allocation of renewable distributed generators and electric vehicles in a distribution system using the political optimization algorithm. Energies15(18), 6698 (2022).

Farh, H. M. H. et al. Optimization and uncertainty analysis of hybrid energy systems using Monte Carlo simulation integrated with genetic algorithm. Comput. Electr. Eng.120, 109833 (2024).

Hamad, Y. K., Hussain, A. N., Lafta, Y. N., Al-Naji, A. & Chahl, J. Multi-objective optimization of renewable distributed generation placement and sizing for technical and economic benefits improvement in distribution system. IEEE Access12, 164226–164247. 10.1109/ACCESS.2024.3492119 (2024).

Ha, M. P., Huy, P. D. & Ramachandaramurthy, V. K. A review of the optimal allocation of distributed generation: Objectives, constraints, methods, and algorithms. Renew. Sustain. Energy Rev.75, 293–312. 10.1016/j.rser.2016.10.071 (2017).

Li, R., Wang, W., Chen, Z., Jiang, J. & Zhang, W. A review of optimal planning active distribution system: Models, methods, and future researches. Energies10(11), 1715. 10.3390/en10111715 (2017).

Abdmouleh, Z., Gastli, A., Ben-Brahim, L., Haouari, M. & Al-Emadi, N. A. Review of optimization techniques applied for the integration of distributed generation from renewable energy sources. Renew. Energy113, 266–280. 10.1016/j.renene.2017.05.087 (2017).

Kothari, D. P. Power system optimization. In 2012 2nd National conference on computational intelligence and signal processing (CISP), 18–21 (IEEE, 2012). 10.1109/NCCISP.2012.6189669.

Lin, J., Magnago, F., & Alemany, J. M. (2018). Optimization methods applied to power systems: Current practices and challenges. In: Classical and Recent Aspects of Power System Optimization, 1–18. 10.1016/B978-0-12-812441-3.00001-X

Soliman, S. A. H. & Mantawy, A. A. H. Modern Optimization Techniques with Applications in Electric Power Systems (Springer, New York, 2011). 10.1007/978-1-4614-1752-1.

Somefun, T., Popoola, O., Abdulkareem, A. & Awelewa, A. Review of different methods for siting and sizing distributed generator. Int. J. Energy Econ. Policy12(3), 16. 10.32479/ijeep.12803 (2022).

Kumar, M., Soomro, A. M., Uddin, W. & Kumar, L. Optimal multi-objective placement and sizing of distributed generation in distribution system: A comprehensive review. Energies15(21), 7850. 10.3390/en15217850 (2022).

Ismail, B. et al. A comprehensive review on optimal location and sizing of reactive power compensation using hybrid-based approaches for power loss reduction, voltage stability improvement, voltage profile enhancement and loadability enhancement. IEEE Access8, 222733–222765. 10.1109/ACCESS.2020.3043297 (2020).

Abu-Mouti, F. S. & El-Hawary, M. E. Optimal distributed generation allocation and sizing in distribution systems via artificial bee colony algorithm. IEEE Trans. Power Delivery26(4), 2090–2101. 10.1109/TPWRD.2011.2158246 (2011).

Truong, K. H., Nallagownden, P., Elamvazuthi, I. & Vo, D. N. A quasi-oppositional-chaotic symbiotic organisms search algorithm for optimal allocation of DG in radial distribution networks. Appl. Soft Comput.88, 106067. 10.1016/j.asoc.2020.106067 (2020).

Selim, A., Kamel, S., Alghamdi, A. S. & Jurado, F. Optimal placement of DGs in distribution system using an improved Harris Hawks optimizer based on single-and multi-objective approaches. IEEE Access8, 52815–52829. 10.1109/ACCESS.2020.2980245 (2020).

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

Kuyu, Y. Ç. & Vatansever, F. Modified forensic-based investigation algorithm for global optimization. Eng. Comput.38(4), 3197–3218. 10.1007/s00366-021-01322-w (2022).

Shaheen, A. M., Ginidi, A. R., El-Sehiemy, R. A. & Ghoneim, S. S. A forensic-based investigation algorithm for parameter extraction of solar cell models. IEEE Access9, 1–20. 10.1109/ACCESS.2020.3046536 (2020).

Chou, J. S. & Truong, D. N. Multiobjective forensic-based investigation algorithm for solving structural design problems. Autom. Constr.134, 104084. 10.1016/j.autcon.2021.104084 (2022).

Moradi, M. H. & Abedini, M. 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. 10.1016/j.ijepes.2011.08.023 (2012).

Georgilakis, P. S. & Hatziargyriou, N. D. Optimal distributed generation placement in power distribution networks: Models, methods, and future research. IEEE Trans. Power Syst.28(3), 3420–3428. 10.1109/TPWRS.2012.2237043 (2013).

Hung, D. Q. & Mithulananthan, N. Multiple distributed generator placement in primary distribution networks for loss reduction. IEEE Trans. Ind. Electron.60(4), 1700–1708. 10.1109/TIE.2011.2112316 (2011).

Malika, B. K., Pattanaik, V., Sahu, B. K. & Rout, P. K. Quasi-oppositional forensic-based investigation for optimal DG selection for power loss minimization. Process Integr. Optim. Sustain.7(1–2), 73–106. 10.1007/s41660-022-00277-9 (2023).

Sahoo, N. C. & Prasad, K. A fuzzy genetic approach for network reconfiguration to enhance voltage stability in radial distribution systems. Energy Convers. Manag.47(18–19), 3288–3306. 10.1016/j.enconman.2006.01.004 (2006).

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