The increased penetration of renewable energy sources in the distribution system affects the stability and efficiency of the system. To account for the intermittent nature of these sources, distribution network reconfiguration and the integration of custom power devices are important. This paper aims to identify the optimum location of photovoltaic systems and unified power quality conditioners in the distribution system considering economic and technical aspects. Three metaheuristic algorithms namely nondominated sorting genetic algorithm-II (NSGA-II), strength pareto evolutionary algorithm-2 (SPEA2) and multi-objective evolutionary algorithm based on decomposition (MOEA/D) were employed. Furthermore, three hybrid algorithms were developed by dividing the population into two parts. Multi-objective particle swarm optimisation (MOPSO) was applied in the upper part while NSGA-II, SPEA2 or MOEA/D was used in the lower part of the population resulting in three hybrid algorithms: MOPSO-NSGA II, MOPSO-SPEA2, MOPSO-MOEA/D. The simulation was performed on the IEEE-123 Node Test Feeder system using the OpenDSS and MATLAB environment. The performance of the proposed algorithms was compared according to their computation time and performance metrics such as pure diversity, generational distance and spacing. It was found that the hybrid algorithms enhance the convergence of the solutions to the true Pareto front. Combining SPEA2 or MOEA/D with MOPSO also reduced the complexity of the algorithms resulting in a lower simulation time.

1. S. Sharma, K. R. Niazi, K. Verma, T. Rawat, “Coordination of different DGs, BESS and demand response for multi-objective optmisation of distribution network with special reference to Indian power sector, ” Electrical Power and Energy Systems, vol 121,  pp. 1-10, 2020, doi: 10.1016/j.ijepes.2020.106074
2. W. L. Theo, J. S. Lim, W. S. Ho, H. Hashim, C. T. Lee, “Review of distributed generation (DG) planning and optimisation techniques: Comparison of numerical and mathematical modelling methods,” Renewable and Sustainable Energy Reviews, vol. 67, pp. 531-573, 2017, doi: 10.1016/j.rser.2016.09.063
3. N. Gupta, A. Swarnkar, K. R. Niazi, “Distribution network reconfiguration for power quality and reliability improvement using genetic algorithms,” Electrical Power and Energy Systems, vol. 54, pp. 664-671, 2014, doi: 10.1016/j.ijepes.2013.08.016.
4. M. Abdelaziz, “Distribution network reconfiguration using a genetic algorithm with varying population size,”Electric Power Systems Research, vol. 142, pp. 9-11, 2017, doi: 10.1016/j.epsr.2016.08.026
5. N. D. Kareila,”Distributed generation and role of UPQC-DG in meeting power quality criteria-a review,” Procedia Technology 21, pp. 520-525, 2015, doi: 10.1016/j.protcy.2015.10.042
6. J. Sarker, S. K. Goswami,”Optimal location of unified power quality conditioner in distribution system for power quality improvement,” International Journal of Electrical Power & Energy Systems, vol. 83, pp. 309-324, 2016, doi: 10.1016/j.ijepes.2016.04.007
7. Q. Qi, J. Wu, C. Long, “Multi-objective operation optimization of an electrical distribution network with soft open point,” Applied energy, vol. 208, pp. 734-744, 2017, doi: 10.1016/j.apenergy.2017.09.075
8. A. Eid, ”Allocation of distributed generations in radial distribution systems using adaptive PSO and modified GSA multi-objective optimizations,” Alexandria Engineering Journal, vol. 59, pp. 4771-4786, 2020, doi: 10.1016/j.aej.2020.08.04
9. M. J. Hadidian-Moghaddam, ”A multi-objective optimal sizing and siting of distributed generation using ant lion optimization technique,” Ain Shams Engineering Journal, vol. 9, pp. 2101-2109, 2018, doi: 10.1016/j.asej.2017.03.001
10. S. M. Arif, “Analytical hybrid particle swarm optimisation algorithm for optimal siting and sizing of distributed generation in smart grid,” Journal of Modern Power Systems and Clean Energy, vol. 8, no. 6, pp. 1221-1230, 2020, doi: 10.35833/MPCE.2019.000143
11. M. I. Akbar, S. A. A. Kazmi, O. Alrumayh, Z. A. Khan, A. Altamimi, M. M. Malik, “A novel hybrid optimization -based algorithm for the single and multi-objective achievement with optimal DG allocations in distribution networks,” IEEE Access, vol. 10, pp. 25669-25687, 2022, doi: 10.1109/ACCESS.2022.3155484
12. S. Nagaballi, R. R. Bhosale, V. S. Kale, “A hybrid fuzzy and PSO based optimum placement and sizing of DG in radial distribution system,” 2018 International Conference on Smart Electric Drives and Power System, 12-13 June 2018, Nagpur, India, doi: 10.1109/ICSEDPS.2018.8536057
13. S. Kaur, N. Kaur, “Application of optimisation techniques to mitigate voltage sag and swell by using real time values,” 2022 International Conference on Electronics and Renewable Systems (ICEARS), 16-18 March 2022, Tuticorin, India, doi: 10.1109/ICEARS53579.2022.9751971
14. A. Selim, S. Kamel, F. Jurado,”Optimal allocation of distribution static compensators using a developed multi-objective sine cosine approach,” Computers & Electrical Engineering, vol. 85, pp. 1-17, 2020, doi: 10.1016/j.compeleceng.2020.106671
15. S. Ganguly,” Multi-objective planning for reactive power compensation of radial distribution networks with unified power quality conditioner allocation using particle swarm optimization,” IEEE TRANSACTIONS ON POWER SYSTEMS, vol. 29, no. 4, pp. 1801-1810, 2014, doi: 10.1109/TPWRS.2013.2296938
16. T. Yuvaraj, K. Ravi, “Multi-objective simultaneous DG and DSTATCOM allocation in radial distribution networks using cuckoo searching algorithm,” Alexandria Engineering Journal, vol. 57, pp. 2729-2742, 2018, doi: 10.1016/j.aej.2018.01.001
17. S. Lakshmi, S. Ganguly,”Multi-objective planning for the allocation of PV-BESS integrated open UPQC for peak load shaving of radial distribution networks,” Journal of Energy Storage, vol. 22, pp. 208-218, 2019, doi: 10.1016/j.est.2019.01.011
18. K. R. Devabalaji, K. Ravi, “Optimal size and siting of multiple DG and DSTATCOM in radial distribution system using bacterial foraging optimization algorithm,” Ain Shams Engineering Journal, vol. 7, pp. 959-971, 2016, doi: 10.1016/j.asej.2015.07.002  
19. S. Naveen, K. S. Kumar, K. Rajalakshmi, “Distribution system reconfiguration for loss minimization using modified bacterial foraging optimization algorithm,” Electrical Power and Energy Systems,” vol. 69, pp. 90-97, 2015, doi: 10.1016/j.ijepes.2014.12.090.
20. T. T. Nguyen, T. T. Nguyen, B. Le, “Optimisation of electric distribution network configuration for power loss reduction based on enhanced binary cuckoo search algorithm,” Computers & Electrical Engineering, vol. 90, pp. 1-24, 2021, doi: 10.1016/j.compeleceng.2020.106893
21. M. N. M. Nasir, N. M. Shahrin, Z. H. Bohari, M. F. Sulaima, M.Y. Hassan, “A Distribution Network Reconfiguration Based on PSO: Considering DGs Sizing and Allocation Evaluation for Voltage Profile Improvement,” 2014 IEEE Student Conference on Research and Development, December 16-17, pp. 1-6, doi: 10.1109/SCORED.2014.7072981
22. A. S. Hassan, Y. Sun, Z. Wang, ”Multi-objective for optimal placement and sizing of DG units in reducing loss of power and enhancing voltage profile using BPSO-SFLA,” Energy Reports, vol. 6, pp. 1581-1589, 2020, doi: 10.1016/j.egyr.2020.06.013
23. P. Ramsami, R. T. F. Ah King, “Multi-objective optimisation of photovoltaic systems and unified power quality conditioners with simultaneous distribution network reconfiguration,” 2020 3^rd International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM), 25-27 November 2020, Balaclava, Mauritius, doi:  10.1109/ELECOM49001.2020.9297033
24. D.H. Wolpert, W.G. Macready, “No Free Lunch Theorems for Optimization,” IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, vol. 1, no. 1, pp. 67-82, 1997, doi: 10.1109/4235.585893
25. A. Elhossini, S. Areibi, R. Dony, “Strength Pareto Particle Swarm Optimization and Hybrid EA-PSO for Multi-Objective Optimization,” Evolutionary Computation, vol.18, no. 1, pp. 127-156, 2010, doi: 10.1162/evco.2010.18.1.18105.
26. K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, “A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182-197, 2002, doi: 10.1109/4235.996017
27. E.Zitler, M. Laumanns, L. Thiele, “SPEA2: Improving the Strength Pareto Evolutionary Algorithm,” Swiss Federal School of Technology, Zurich, Switzerland, TIK-Report 103, 27 September 2001, doi: 10.3929/ethz-a-004284029
28. Q. Zhang, H. Li. MOEA/D, “A Multiobjective Evolutionary Algorithm Based on Decomposition,” IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, vol. 11, no. 6, pp. 712-731, 2007, doi: 10.1109/TEVC.2007.892759  
29. A. Sundaram,”Combined Heat and Power Economic Emission Dispatch Using Hybrid NSGAII-MOPSO Algorithm Incorporating an Effective Constraint Handling Mechanism,” IEEE Access, vol. 8, pp. 13748-13768, 2020, doi:  10.1109/ACCESS.2020.2963887
30. M. Muller, K. G. Gutbrod, C. Ramshorn, R. Vogt, “Meteoblue Weather Phoenix” [online]. Available: https://www.meteoblue.com/en/weather/forecast/week/phoenix_united-states-of-america_5308655
31. Fuller, Y. Xu, “IEEE PES AMPS DSAS Test Feeder Working Group”. IEEE Power and Energy Society [online]. Available: http://sites.ieee.org/pes-testfeeders/resources/
32. P.  Radatz,  N. Kagan, C. Rocha, J. Smith, R. C. Dugan, “Assessing Maximum DG Penetration levels in a Real Distribution Feeder by using OpenDSS,” 17^th International Conference on Harmonics and Quality of Power (ICHQP), Belo Horizonte, Brazil, October 16-19, 2016, doi: 10.1109/ICHQP.2016.7783416
33. H. V. Padullaparthi, P. Chirapongsananurak, S. Santoso, J. A. Taylor, “Edge-of-Grid Voltage Control: Device Modeling, Strategic Placement, and Application Considerations,”  IEEE Power and Energy Technology Systems Journal, vol. 4, no. 4, pp. 106-114, 2017, doi: 10.1109/JPETS.2017.2750479
34. P. Dehghanian, S. H. Hosseini, M. Moeini-Aghtaie, A. Arabali, “Optimal sizing of DG units in power systems from a probabilistic multi-objective optimization perspective,” Electrical Power and Energy Systems, vol. 51, pp. 14-26, 2013, doi: doi.org/10.1016/j.ijepes.2013.02.014
35. Y. Zeng, Y. Sun, “Comparison of Multiobjective Particle Swarm Optimization and Evolutionary Algorithms for Optimal Reactive Power Dispatch Problem,” 2014 IEEE Congress on Evolutionary Computation, July 6-11, pp.258-265, 2014, doi: 10.1109/CEC.2014.6900260
36. D. Thomas, O. Deblecker, C. S. Loakimidis, “Optimal design and techno-economic analysis of an autonomous small isolated microgrid aiming at high RES penetration”,  Energy, vol. 116, no. 1, pp. 364-379, 2016,  doi: doi.org/10.1016/j.energy.2016.09.119
37. A. Chatterjee, R. Rayudu, “Techno-Economic Analysis of Hybrid Renewable Energy System for Rural Electrification in India,” 2017 IEEE Innovative Smart Grid Technologies-Asia (ISGT-Asia), Auckland, New Zeeland, December 4-7, 2017, doi: 10.1109/ISGT-Asia.2017.8378470
38. C. H. Lin, C. S. Chen, H.J. Chuang, C. S. Li, M. Y. Huang, “Optimal Switching Placement for Customer Interruption Cost Minimisation” 2006 IEEE Power Engineering Society General Meeting, Montreal, Que., Canada, June 18-22, 2006, doi: 10.1109/PES.2006.1708917
39. A. Elmitwally, A. Eladl, “Planning of multi-type FACTS devices in restructured power systems with wind generation,” Electrical Power and Energy Systems, vol. 77, pp. 33-42, 2016, doi: 10.1016/j.ijepes.2015.11.023
40. H. Bilil, G. Aniba, M. Maaroufi, “Multiobjective optimisation of renewable energy penetration rate in power systems,” Energy Procedia, vol. 50, pp.368-375, 2014, doi: 10.1016/j.egypro.2014.06.044
41. M. Barukcic, Z. Hederic, K. Miklosevic, “Multiobjective optimisation of energy production of distributed generation in distribution feeder,” ENERGYCON 2014, May 13-16, pp. 1325-1333, 2014, doi: 10.1109/ENERGYCON.2014.6850595     
42. H. Eskandari, C. D. Geiger, G. B. Lamont, “A Dynamic Population Sizing Approach for Solving Expensive Multiobjective Optimization Problems,” 4^th International Conference, EMO 2007, Matsushima, 5-8 March 5-8, 2007, pp. 141-155, doi: 10.1007/978-3-540-70928-2_14
43. M. Premkumar, P. Jangir, R. Sowmya, “MOGBO: A new multiobjective gradient-based optimizer fore real-world structural optimization problems,” Knowledge-Based Systems, pp. 1-40, 2021, doi: 10.1016/j.knosys.2021.106856
44. Chakraborty, A., Kar, A.K. Swarm Intelligence: A Review of Algorithms. Nature-Inspired Computing and Optimization, pp. 475-494, 2017 doi: 10.1007/978-3-319-50920-4_19

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