In this study, we present a comprehensive optimization framework employing the Multi-Objective Multi-Verse Optimization (MOMVO) algorithm for the optimal integration of Distributed Generations (DGs) and Capacitor Banks (CBs) into electrical distribution networks. Designed with the dual objectives of minimizing energy losses and voltage deviations, this framework significantly enhances the operational efficiency and reliability of the network. Rigorous simulations on the standard IEEE 33-bus and IEEE 69-bus test systems underscore the effectiveness of the MOMVO algorithm, demonstrating up to a 47% reduction in energy losses and up to a 55% improvement in voltage stability. Comparative analysis highlights MOMVO's superiority in terms of convergence speed and solution quality over leading algorithms such as the Multi-Objective Jellyfish Search (MOJS), Multi-Objective Flower Pollination Algorithm (MOFPA), and Multi-Objective Lichtenberg Algorithm (MOLA). The efficacy of the study is particularly evident in the identification of the best compromise solutions using MOMVO. For the IEEE 33 network, the application of MOMVO led to a significant 47.58% reduction in daily energy loss and enhanced voltage profile stability from 0.89 to 0.94 pu. Additionally, it realized a 36.97% decrease in the annual cost of energy losses, highlighting substantial economic benefits. For the larger IEEE 69 network, MOMVO achieved a remarkable 50.15% reduction in energy loss and improved voltage profiles from 0.89 to 0.93 pu, accompanied by a 47.59% reduction in the annual cost of energy losses. These results not only confirm the robustness of the MOMVO algorithm in optimizing technical and economic efficiencies but also underline the potential of advanced optimization techniques in facilitating the sustainable integration of renewable energy resources into existing power infrastructures. This research significantly contributes to the field of electrical distribution network optimization, paving the way for future advancements in renewable energy integration and optimization techniques for enhanced system efficiency, reliability, and sustainability.

Centre of Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab 140401 India

Department of Electrical and Electronics Faculty of Engineering Alberoni University Kapisa Afghanistan

Department of Electrical Engineering College of Engineering Taif University 21944 Taif Saudi Arabia

Department of Electrical Engineering Graphic Era Dehradun 248002 India

Electrical Engineering Department College of Engineering King Khalid University 61421 Abha Saudi Arabia

Electrical Engineering Department Laboratory Hamma Lakhdar University of El Oued 39000 El Oued Algeria

Electrical Engineering Department Laboratory Mohamed Khider University of Biskra b7000 Biskra Algeria

Graphic Era Hill University Dehradun 248002 India

Hourani Center for Applied Scientific Research Al Ahliyya Amman University Amman Jordan

Laboratoire de Recherche en Electrotechnique Ecole Nationale Polytechnique El Harrach 16200 Algiers Algeria

Laboratoire Des Systèmes Électriques École Nationale d'Ingénieurs de Tunis Université de Tunis El Manar Tunis Tunisie

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Zhang K, Zhou B, Or SW, Li C, Chung CY, Voropai NI. Optimal coordinated control of multi-renewable-to-hydrogen production system for hydrogen fueling stations. IEEE Trans. Ind. Applicat. 2021 doi: 10.1109/TIA.2021.3093841. DOI

Yang Y, SiJia ZL, Wang P, Huang L, Zhang Y, Ji C. Whether rural rooftop photovoltaics can effectively fight the power consumption conflicts at the regional scale: A case study of Jiangsu Province. Energy Build. 2024;306:113921. doi: 10.1016/j.enbuild.2024.113921. DOI

Mehleri ED, Sarimveis H, Markatos NC, Papageorgiou LG. A mathematical programming approach for optimal design of distributed energy systems at the neighbourhood level. Energy. 2012;44(1):96–104. doi: 10.1016/j.energy.2012.02.009. DOI

Yao L, Wang Y, Xiao X. Concentrated solar power plant modeling for power system studies. IEEE Trans. Power Syst. 2024;39(2):4252–4263. doi: 10.1109/TPWRS.2023.3301996. DOI

Gopiya-Naik S, Khatod D, Sharma MP. Optimal allocation of combined DG and capacitor for real power loss minimization in distribution networks. Int. J. Electr. Power Energ. Syst. 2013;53:967–973. doi: 10.1016/j.ijepes.2013.06.008. DOI

Sara M, Ahmed E, Tamou N, Badr BI. NA direct power control of a DFIG based-WECS during symmetrical voltage dips. Prot. Control. Mod. Power Syst. 2020;5(1):36–47. doi: 10.1186/s41601-019-0148-y. DOI

Ali A, Mohammad KS. Optimal DG placement in power markets from DG Owners’ perspective considering the impact of transmission costs. Electric Power Syst. Res. 2021;196:107218. doi: 10.1016/j.epsr.2021.107218. DOI

Lu Z, Wang J, Shahidehpour M, Bai L, Xiao Y, Li H. Cooperative operation of distributed energy resources and thermal power plant with a carbon-capture-utilization-and-storage system. IEEE Trans. Power Syst. 2024;39(1):1850–1866. doi: 10.1109/TPWRS.2023.3253809. DOI

Suresh MCV, Edward JB. A hybrid algorithm based optimal placement of DG units for loss reduction in the distribution system. Appl. Soft Comput. 2020;91:106191. doi: 10.1016/j.asoc.2020.106191. DOI

Bikash D, Mukherjee V, Debapriya D. Optimum DG placement for known power injection from utility/substation by a novel zero bus load flow approach. Energy. 2019;175(15):228–249. doi: 10.1016/j.energy.2019.03.034. DOI

Zheng S, Hai Q, Zhou X, Stanford RJ. A novel multi-generation system for sustainable power, heating, cooling, freshwater, and methane production: Thermodynamic, economic, and environmental analysis. Energy. 2024;290:130084. doi: 10.1016/j.energy.2023.130084. DOI

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. doi: 10.1016/j.asoc.2020.106234. DOI

Murty VVSN, Kumar A. Optimal placement of DG in radial distribution systems based on new voltage stability index under load growth. Int. J. Electr. Power Energ. Syst. 2015;69:246–256. doi: 10.1016/j.ijepes.2014.12.080. DOI

Nezhadpashaki MA, Karbalaei F, Abbasi S. Optimal placement and sizing of distributed generation with small signal stability constraint. Sustain. Energ. Grids Netw. 2020;23:100380. doi: 10.1016/j.segan.2020.100380. DOI

Yang C, Kumar Nutakki TU, Alghassab MA, Alkhalaf S, Alturise F, Alharbi F, Abdullaev S. Optimized integration of solar energy and liquefied natural gas regasification for sustainable urban development: Dynamic modeling, data-driven optimization, and case study. J. Clean. Product. 2024 doi: 10.1016/j.jclepro.2024.141405. DOI

Sultana U, Khairuddin AB, Aman MM, Mokhtar AS, Zareen N. A review of optimum DG placement based on minimization of power losses and voltage stability enhancement of distribution system. Renew. Sustain. Energ. Rev. 2016;63:363–378. doi: 10.1016/j.rser.2016.05.056. DOI

Ganguly, S., Sahoo, N.C., Das, D. Multi-objective planning of electrical distribution systems incorporating shunt capacitor banks. In 2011 International Conference on Energy, Automation and Signal, Bhubaneswar 1–6 (India, 2011). 10.1109/ICEAS.2011.6147146.

Ganguly S, Sahoo N, Das D. Multi-objective particle swarm optimization based on fuzzy-Pareto-dominance for possibilistic planning of electrical distribution systems incorporating distributed generation. Fuzzy Sets Syst. 2023;213:47–73. doi: 10.1016/j.fss.2012.07.005. DOI

Kayal P, Chanda C. Strategic approach for reinforcement of intermittent renewable energy sources and capacitor bank for sustainable electric power distribution system. Int. J. Electric. Power Energy Syst. 2016;83:335–351. doi: 10.1016/j.ijepes.2016.04.029. DOI

Mahesh K, Nallagownden P, Elamvazuthi I. Advanced pareto front non-dominated sorting multi-objective particle swarm optimization for optimal placement and sizing of distributed generation. Energies. 2016;9(12):982. doi: 10.3390/en9120982. DOI

Kaur N, Jain SK. Multi-objective optimization approach for placement of multiple DGs for voltage sensitive loads. Energies. 2017;10(11):1733. doi: 10.3390/en10111733. DOI

Saleh, A.A., Mohamed, A.-A.A., Hemeida, A.M., Ibrahim, A.A. Multi-objective whale optimization algorithm for optimal allocation of distributed generation and capacitor bank. In 2019 International Conference on Innovative Trends in Computer Engineering (ITCE), 459–465 (Aswan, Egypt 2019). 10.1109/ITCE.2019.8646352.

Ali, S.M., Mohamed, A.-A.A., Hemeida, A.M. A pareto strategy based on multi-objective for optimal placement of distributed generation considering voltage stability. In 2019 International Conference on Innovative Trends in Computer Engineering (ITCE) 498–504 (Aswan, Egypt, 2019), 10.1109/ITCE.2019.8646383.

Ali, S.M., Mohamed, A.-A.A., Hemeida, A.M. A pareto strategy based on multi-objective for optimal placement of distributed generation considering voltage stability. In 2019 International Conference on Innovative Trends in Computer Engineering (ITCE). 10.1109/itce.2019.8646383.

Gampa SR, Das D. Simultaneous optimal allocation and sizing of distributed generations and shunt capacitors in distribution networks using fuzzy GA methodology. J. Electric. Syst. Inf. Technol. 2019;6(1):1–18. doi: 10.1186/s43067-019-0003-2. DOI

Zeynali S, Rostami N, Feyzi M. Multi-objective optimal short-term planning of renewable distributed generations and capacitor banks in power system considering different uncertainties including plug-in electric vehicles. Int. J. Electric. Power Energy Syst. 2020;119:105885. doi: 10.1016/j.ijepes.2020.105885. DOI

Yang D, Jia J, Wu W, Cai W, An D, Luo K, Yang B. Optimal placement and sizing of distributed generators based on multiobjective particle swarm optimization. Front. Energy Res. 2021;9:770342. doi: 10.3389/fenrg.2021.770342. DOI

Markana A, Trivedi G, Bhatt P. Multi-objective optimization based optimal sizing & placement of multiple distributed generators for distribution network performance improvement. RAIRO-Oper. Res. 2021;55(2):899–919. doi: 10.1051/ro/2021045. DOI

Kamarposhti MA, Lorenzini G, Solyman AAAA. Locating and sizing of distributed generation sources and parallel capacitors using multiple objective particle swarm optimization algorithm. Math. Modell. Eng. Problems. 2021;8(1):10–24. doi: 10.18280/mmep.080102. DOI

Saki R, Rokrok E, Doostizadeh M, Abedini M. A compromise solution based on fuzzy decision making for multi-objective hourly planning in clustered microgrids considering uncertainty of renewable energy resources. Comput. Intell. Electric. Eng. 2021;12(4):57–72. doi: 10.22108/isee.2020.122174.1349. DOI

Taha HA, Alham MH, Youssef HKM. Multi-objective optimization for optimal allocation and coordination of wind and solar dgs, besss and capacitors in presence of demand response. IEEE Access. 2022;10:16225–16241. doi: 10.1109/ACCESS.2022.3149135. DOI

Alanazi A, Alanazi TI. Multi-objective framework for optimal placement of distributed generations and switches in reconfigurable distribution networks: an improved particle swarm optimization approach. Sustainability. 2023;15(11):9034. doi: 10.3390/su15119034. DOI

Huy THB, Doan HT, Vo DN, Lee K, Kim D. Multi-objective optimal power flow of thermal-wind-solar power system using an adaptive geometry estimation based multi-objective differential evolution. Appl. Soft Comput. 2023;149:110977. doi: 10.1016/j.asoc.2023.110977. DOI

Huy THB, Nallagownden P, Truong KH, Kannan R, Vo DN, Ho N. Multi-objective search group algorithm for engineering design problems. Appl. Soft Comput. 2022;126:109287. doi: 10.1016/j.asoc.2022.109287. DOI

Huy THB, Kim D, Vo DN. Multiobjective optimal power flow using multiobjective search group algorithm. IEEE Access. 2022;10:77837–77856. doi: 10.1109/ACCESS.2022.3193371. DOI

Huy THB, Nguyen TP, Mohd-Nor N, Elamvazuthi I, Ibrahim T, Vo DN. Performance improvement of multiobjective optimal power flow-based renewable energy sources using intelligent algorithm. IEEE Access. 2022;10:48379–48404. doi: 10.1109/ACCESS.2022.3170547. DOI

Truong BH, Nallagownden P, Truong KH, et al. Multi-objective search group algorithm for thermo-economic optimization of flat-plate solar collector. Neural Comput. Appl. 2021;33:12661–12687. doi: 10.1007/s00521-021-05915-w. DOI

Mirjalili S, Jangir P, Mirjalili SZ, Saremi S, Trivedi IN. “Optimization of problems with multiple objectives using the multi-verse optimization algorithm. Knowledge-Based Syst. 2017;134:50–71. doi: 10.1016/j.knosys.2017.07.018. DOI

Mirjalili S, Mirjalili SM, Hatamlou A. Multi-verse optimizer: A nature-inspired algorithm for global optimization. Neural Comput. Appl. 2016;27(2):495–513. doi: 10.1007/s00521-015-1870-7. DOI

Duan Y, Zhao Y, Hu J. An initialization-free distributed algorithm for dynamic economic dispatch problems in microgrid: Modeling, optimization and analysis. Sustain. Energy Grids Netw. 2023;34:101004. doi: 10.1016/j.segan.2023.101004. DOI

Shirkhani M, Tavoosi J, Danyali S, Sarvenoee AK, Abdali A, Mohammadzadeh A, Zhang C. A review on microgrid decentralized energy/voltage control structures and methods. Energy Rep. 2023;10:368–380. doi: 10.1016/j.egyr.2023.06.022. DOI

Pal A, Chakraborty AK, Bhowmik AR. Optimal placement and sizing of DG considering power and energy loss minimization in distribution system. Int. J. Electric. Eng. Inf. 2020;12(3):624–665.

Wang R, Zhang R. Techno-economic analysis and optimization of hybrid energy systems based on hydrogen storage for sustainable energy utilization by a biological-inspired optimization algorithm. J. Energy Storage. 2023;66:107469. doi: 10.1016/j.est.2023.107469. DOI

Li S, Zhao X, Liang W, Hossain MT, Zhang Z. A fast and accurate calculation method of line breaking power flow based on taylor expansion. Front. Energy Res. 2022 doi: 10.3389/fenrg.2022.943946. DOI

Jones AD, Underwood CP. A modelling method for building-integrated photovoltaic power supply. Build Serv Eng Res Technol. 2002;23:167–177. doi: 10.1191/0143624402bt040oa. DOI

Hou M, Zhao Y, Ge X. Optimal scheduling of the plug-in electric vehicles aggregator energy and regulation services based on grid to vehicle. Int. Trans. Electric. Energy Syst. 2017;27(6):e2364. doi: 10.1002/etep.2364. DOI

Rekioua, D., Matagne, E. Modeling of solar irradiance and cells. In Optimization of Photovoltaic Power Systems, Green Energy and Technology (Springer, London, 2012)

Nasef A, Shaheen A, Khattab H. “Local and remote control of automatic voltage regulators in distribution networks with different variations and uncertainties: Practical cases study. Electric Power Syst. Res. 2022;205:107773. doi: 10.1016/j.epsr.2022.107773. DOI

Shaheen AM, Elsayed AM, El-Sehiemy RA, Kamel S, Ghoneim SSM. A modified marine predators optimization algorithm for simultaneous network reconfiguration and distributed generator allocation in distribution systems under different loading conditions. Eng. Optim. 2021;54:1–22.

Abdelkader Boukaroura. Contribution to the modeling and optimization of distribution networks under uncertainties. Ph.D. dissertation, Larbi Ben M'hidi-Oum El Bouaghi University, 2021.

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

Chaotic self-adaptive sine cosine multi-objective optimization algorithm to solve microgrid optimal energy scheduling problems