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2000
Volume 18, Issue 8
  • ISSN: 2352-0965
  • E-ISSN: 2352-0973

Abstract

Introduction

With the widespread integration of distributed photovoltaics into the power grid, challenges such as voltage fluctuations and increased deviations have arisen, alongside heightened demands for power quality and economic dispatch.

Methods

This study tackles the complexities of integrating distributed photovoltaic systems into the power grid, encompassing network flow variations, node voltage fluctuations, and risks of exceeding limits while also considering scheduling costs. A comprehensive control strategy is proposed, integrating the coordinated output of reactive power adjustable devices, energy storage systems (ESS), and transformers. Subsequently, a multi-objective optimization mathematical model is formulated with the aims of minimizing network loss, operation cost, and voltage deviation. An enhanced Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is introduced, leveraging a ring neighborhood topology and specialized crowding distance measurement to establish a stable network in both decision and target spaces, devoid of any niche parameters.

Results

This approach eliminates subjective influences while ensuring rationality. Tests on the IEEE 30-node network example demonstrate that the algorithm can generate a large number of Pareto optimal solutions, highlighting its advantages in the application of reactive power optimization.

Conclusion

The method proposed in this paper can fully utilize the dispatchable equipment in the power grid, effectively reducing network losses and voltage deviations while maintaining low operating costs.

© 2025 Bentham Science Publishers
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2024-08-06
2025-11-15

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References

  1. PoudelS. MukherjeeM. SadnanR. ReimanA.P. Fairness-aware distributed energy coordination for voltage regulation in power distribution systems.IEEE Trans. Sustain. Energy20231431866188010.1109/TSTE.2023.3252944
     [Google Scholar]
  2. GandhiO. Rodríguez-GallegosC.D. ZhangW. ReindlT. SrinivasanD. Levelised cost of PV integration for distribution networks.Renew. Sustain. Energy Rev.202216911292210.1016/j.rser.2022.112922
     [Google Scholar]
  3. Hadi AbdulwahidA. Al-RazganM. FakhruldeenH.F. Churampi ArellanoM.T. MrzljakV. Arabi NowdehS. MoghaddamM.J.H. Stochastic multi-objective scheduling of a hybrid system in a distribution network using a mathematical optimization algorithm considering generation and demand uncertainties.Mathematics20231118396210.3390/math11183962
     [Google Scholar]
  4. AlanaziM. AlanaziA. AboRasK.M. GhadiY.Y. Multiobjective and coordinated reconfiguration and allocation of photovoltaic energy resources in distribution networks using improved clouded leopard optimization algorithm.Int. J. Energy Res.2024202411910.1155/2024/7792658
     [Google Scholar]
  5. MerajS.T. YuS.S. RahmanM.S. HasanK. Hossain LipuM.S. TrinhH. Energy management schemes, challenges and impacts of emerging inverter technology for renewable energy integration towards grid decarbonization.J. Clean. Prod.202340513700210.1016/j.jclepro.2023.137002
     [Google Scholar]
  6. GoliV.P. GangulyS. Cost-benefit analysis of multiple shunt compensators allocation in pv-integrated distribution networks for profit maximization.Electr. Power Compon. Syst.2024202411510.1080/15325008.2024.2309226
     [Google Scholar]
  7. ChowdhuryA. RoyR. MandalK.K. Enhancement of technical, economic and environmental benefits of rdn using jaya algorithm considering renewables uncertainties.Electr. Power Compon. Syst.2024202411510.1080/15325008.2024.2303066
     [Google Scholar]
  8. FilhoG.L. CorrêaH.P. VieiraF.H.T. Distributed reactive power injection-based approach for minimization of losses in electrical networks considering heuristic algorithms and voltage deviation.Energies20231619676110.3390/en16196761
     [Google Scholar]
  9. LakshmiS. GangulyS. Coordinated operational optimization approach for PV inverters and BESSs to minimize the energy loss of distribution networks.IEEE Syst. J.20221611228123810.1109/JSYST.2020.3041787
     [Google Scholar]
  10. AdibA. PintoJ.O.P. ChinthavaliM.S. GA-based voltage optimization of distribution feeder with high-penetration of DERs using megawatt-scale units.Energies20231613484210.3390/en16134842
     [Google Scholar]
  11. CaiY. LuoW. Coordinated active and reactive power operation of multiple dispersed resources for flexibility improvement.Front. Energy Res.202311113376810.3389/fenrg.2023.1133768
     [Google Scholar]
  12. GodhaN.R. BapatV.N. KorachagaonI. Ant colony optimization technique for integrating renewable DG in distribution system with techno-economic objectives.Evol. Syst.202213348549810.1007/s12530‑021‑09416‑y
     [Google Scholar]
  13. GengQ. LuX. LiuS. SuX. HuoY. Hierarchical scheduling algorithm design of active distribution network based on multi-microgrid system.Appl. Nanosci.20231342901291010.1007/s13204‑021‑02230‑7
     [Google Scholar]
  14. KumarP.G.A. JeyanthyP.A. DevarajD. Hybrid multi-objective method based on ant colony optimization and firefly algorithm for renewable energy sources.Sustain Comput: Inform Syst20223610081010.1016/j.suscom.2022.100810
     [Google Scholar]
  15. LiuH. LiuS. GaiX. LiuY. YanY. SunL. Optimal allocation of distributed generators in active distribution network considering TOU price.Int. J. Antennas Propag.2023202311610.1155/2023/7471214
     [Google Scholar]
  16. KaushikE. PrakashV. GhandourR. Al BarakehZ. AliA. MahelaO.P. ÁlvarezR.M. KhanB. Hybrid combination of network restructuring and optimal placement of distributed generators to reduce transmission loss and improve flexibility.Sustainability2023156528510.3390/su15065285
     [Google Scholar]
  17. M’dioudM. BannariR. ElkafaziI. A novel PSO algorithm for DG insertion problem.Energy Syst202215127
     [Google Scholar]
  18. SawB.K. KumarB. BohreA.K. Optimal allocation of renewable DGs with optimal reconfiguration using adaptive PSO in distribution network.Lect Notes Elect Eng202281245546810.1007/978‑981‑16‑6970‑5_34
     [Google Scholar]
  19. ZhengC. ZhouH. ZhengD. LinZ. ZhangX. An active distribution network fault location method based on improved multi-universe algorithm.Power Syst Protect Cont202351169179
     [Google Scholar]
  20. ChenD. ShiY. XuW. Multi-objective optimization method for location and capacity of a distribution network with distributed photovoltaic energy based on an improved FPA algorithm.Power Syst Protect Cont2022507120125
     [Google Scholar]
  21. IslamJ. MerajS.T. MasaoudA. MahmudM.A. NazirA. KabirM.A. HossainM.M. MumtazF. Opposition-based quantum bat algorithm to eliminate lower-order harmonics of multilevel inverters.IEEE Access2021910361010362610.1109/ACCESS.2021.3098190
     [Google Scholar]
  22. XinY. YiJ. ZhangK. ChenC. XiongJ. Offline selective harmonic elimination with (2N+ 1) output voltage levels in modular multilevel converter using a differential harmony search algorithm.IEEE Access2020812159612161010.1109/ACCESS.2020.3007022
     [Google Scholar]
  23. ShengL. QianS.Q. YeY.Q. WuY.H. An improved immune algorithm for optimizing the pulse width modulation control sequence of inverters.Eng. Optim.20174991463148210.1080/0305215X.2016.1250894
     [Google Scholar]
  24. ChenG. ZhangA. ZhaoC. ZhangZ. Optimal placement and capacity of combined DGs and SCs in radial distribution networks based on PSO-OS algorithm.IAENG Int. J. Comput. Sci.20214814
     [Google Scholar]
  25. CaiY. LiuJ. GaoN. Research on reactive power optimization control method for distribution network with DGs based on improved second-order oscillating PSO algorithm.J. Contr. Sci. Eng.2023202311710.1155/2023/5813277
     [Google Scholar]
  26. WangL. ChengZ. ZengS. LiL. LiT. ZhangK. Active and reactive power coordination optimization method of distribution network based on improved PSO-GA algorithm.2021 IEEE 5th Conference on Energy Internet and Energy System Integration (EI2), 2021 Taiyuan, China10.1109/EI252483.2021.9713604
     [Google Scholar]
  27. ZuluE. HaraR. KitaH. An efficient hybrid particle swarm and gradient descent method for the estimation of the hosting capacity of photovoltaics by distribution networks.Energies20231613520710.3390/en16135207
     [Google Scholar]
  28. WuY. LiuJ. WangL. AnY. ZhangX. Distribution network reconfiguration using chaotic particle swarm chicken swarm fusion optimization algorithm.Energies20231620718510.3390/en16207185
     [Google Scholar]
  29. FengF. YanX. ZhengB. YinX. ZhouM. JiangX. Bi-level optimization of distribution network for hybrid energy storage system of storage battery and hydrogen storage.Xitong Fangzhen Xuebao20223414051416
     [Google Scholar]
  30. (a ChenS. Research on reactive power optimization of distributed photovoltaic power distribution based on improved PSO algorithm.J Phys Conf Ser202223061012008
     [Google Scholar]
  31. (b KennedyJ. EberhartR. Particle swarm optimizationProceedings of ICNN'95 - International Conference on Neural Networks, Perth, WA, Australia, 1995, pp. 1942–1948,10.1109/ICNN.1995.488968
     [Google Scholar]
  32. LiX. Niching without niching parameters: particle swarm optimization using a ring topology.IEEE Trans. Evol. Comput.200914150169
     [Google Scholar]
  33. YueC. QuB. LiangJ. A multi-objective particle swarm optimizer using ring topology for solving multimodal multi-objective problems.IEEE Trans. Evol. Comput.201822580581710.1109/TEVC.2017.2754271
     [Google Scholar]
  34. DebK. PratapA. AgarwalS. MeyarivanT. A fast and elitist multiobjective genetic algorithm: NSGA-II.IEEE Trans. Evol. Comput.20026218219710.1109/4235.996017
     [Google Scholar]
  35. RathnayakeU. Migrating storms and optimal control of urban sewer networks.Hydrology20152423024110.3390/hydrology2040230
     [Google Scholar]
  36. RathnayakeU.S. TanyimbohT.T. Optimal control of combined sewer systems using SWMM 5.0.WIT Trans. Built Environ.2012122879610.2495/UW120081
     [Google Scholar]
/content/journals/raeeng/10.2174/0123520965315165240724062059
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Voltage Regulation for PV-integrated Power Grid using Improved Multi-Objective Particle Swarm Optimization
Recent Advances in Electrical & Electronic Engineering 18, 1335 (2025); https://doi.org/10.2174/0123520965315165240724062059
/content/journals/raeeng/10.2174/0123520965315165240724062059
/content/journals/raeeng/10.2174/0123520965315165240724062059
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  • Article Type:     Research Article
Keyword(s): distributed power supply; energy storage systems; multi-objective particle swarm optimization; reactive power optimization; ring neighborhood topology; Voltage Regulation

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