Improving Reliability of Active Distribution Networks Using Probabilistic Assessment of Renewable Resource Units

Document Type : Original Article

Authors

1 Department of Electrical Engineering Urmia University Urmia, Iran

2 Department of Electrical Engineering, Faculty of Mechanics, Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

Abstract

Today, with the growth of the population and increasing trend in the use of electrical energy, the importance of the reliability and stability of the power grid has increased. The ever-increasing development of the power grid and subsequent blackouts of the power grid can lead to serious problems in the daily life and economy of a country. In addition to economic damages, power losses in the power network can lead to dissatisfaction and decreased consumer confidence in the power grid. This research has been carried out to check the application of the genetic algorithm to calculate reliability indices including SAIFI, SAIDI, etc., and its impact on enhancing the reliability of the standard IEEE 33 and 69 bus distribution networks. Additionally, this study explores the GA effectiveness in minimizing both active and reactive power losses. The simulation results in MATLAB, show the constructive effect of applying the GA, shedding light on its potential to optimize the distribution network reliability and minimize power losses, offering valuable insights for power system optimization and reliability improvement.

Graphical Abstract

Improving Reliability of Active Distribution Networks Using Probabilistic Assessment of Renewable Resource Units

Keywords

Main Subjects

References

  1. Rafiei M, Khooban M-H, Igder MA, Boudjadar JJIToC, Briefs SIE. A novel approach to overcome the limitations of reliability centered maintenance implementation on the smart grid distance protection system. 2019;67(2):320-4.
  2. Hamidan M-A, Borousan FJJoES. Optimal planning of distributed generation and battery energy storage systems simultaneously in distribution networks for loss reduction and reliability improvement. 2022;46:103844.
  3. Malekshah S, Rasouli A, Malekshah Y, Ramezani A, Malekshah AJAEJ. Reliability-driven distribution power network dynamic reconfiguration in presence of distributed generation by the deep reinforcement learning method. 2022;61(8):6541-56.
  4. Moradi A, Alinejad-Beromi Y, Parsa M, Mohammadi M. Optimal Locating and Sizing of Unified Power Quality Conditioner- phase Angle Control for Reactive Power Compensation in Radial Distribution Network with Wind Generation %J International Journal of Engineering. 2018;31(2):299-306. https://www.ije.ir/article_73121_c8cb4a22595db2c5ef1ff8e0f09f7522.pdf
  5. Aazami R, Dabestani S, Shirkhani M. Optimal Capacity and Location for Renewable-based Microgrids Considering Economic Planning in Distribution Networks %J International Journal of Engineering. 2023;36(12):2175-83. 10.5829/ije.2023.36.12c.06 https://www.ije.ir/article_179013_1a5c92f7f25b15c2034c256c2f63ba69.pdf
  6. Ebadi Zahedan A, Adabi F, Soleymani S. Frequency and Voltage Stability of Distribution Network using Photovoltaic Stabilization System and Battery Storage System. Journal of Electrical Engineering and Automation. 2022;4(4):257-76. https://doi.org/10.36548/jeea.2022.4.004 https://doi.org/10.36548/jeea.2022.4.004
  7. Wadi M, Baysal M, Shobole AJEPC, Systems. Reliability and sensitivity analysis for closed-ring distribution power systems. 2022;49(6-7):696-714.
  8. Xu W, Zeng S, Du X, Zhao J, He Y, Wu XJE. Reliability of active distribution network considering uncertainty of distribution generation and load. 2023;12(6):1363.
  9. Wu C, Pan R, Zhao XJIT. Reliability assessment of multi-state performance sharing systems with transmission loss and random shocks. 2022;54(11):1060-71.
  10. Nowdeh SA, Davoudkhani IF, Moghaddam MH, Najmi ES, Abdelaziz AY, Ahmadi A, et al. Fuzzy multi-objective placement of renewable energy sources in distribution system with objective of loss reduction and reliability improvement using a novel hybrid method. 2019;77:761-79.
  11. Naderipour A, Abdul-Malek Z, Nowdeh SA, Ramachandaramurthy VK, Kalam A, Guerrero JMJE. Optimal allocation for combined heat and power system with respect to maximum allowable capacity for reduced losses and improved voltage profile and reliability of microgrids considering loading condition. 2020;196:117124.
  12. Prakash S, Rangta V, Jayalakshmi N, Jadoun VK, editors. Optimal location of DGs in a distribution system for maintaining voltage profile and loss reduction using genetic algorithm. 2020 International Conference on Power Electronics & IoT Applications in Renewable Energy and Its Control (PARC); 2020: IEEE.
  13. Lin MH, Lin JH, El Haj Assad M, Alayi R, Seyednouri SRJJoRE, Environment. Optimal location and sizing of wind turbines and photovoltaic cells in the grid for load supply using improved genetic algorithm. 2023;10(2):9-18.
  14. Bhandari AS, Kumar A, Ram MJQ, International RE. Hybrid PSO‐GWO algorithm for reliability redundancy allocation problem with Cold Standby Strategy. 2024;40(1):115-30.
  15. Shahraeini M, Soltanifar R. A Complex Network-based Approach for Designing of Wide Area Measurement Systems in Smart Grids using Adam-Eve Like Genetic Algorithm %J International Journal of Engineering. 2024;37(2):298-311. 10.5829/ije.2024.37.02b.07 https://www.ije.ir/article_179915_5bc7599ef1b9938b2bf983b8c17dd0ad.pdf
  16. Guediri A, Guediri A, Touil S. Optimization Using a Genetic Algorithm Based on DFIG Power Supply for the Electrical Grid %J International Journal of Engineering. 2022;35(1):121-9. 10.5829/ije.2022.35.01A.11 https://www.ije.ir/article_138816_bb4c70fac692d9aaa5e9d017b8325c5a.pdf
  17. Hasanien HM, Alsaleh I, Tostado-Véliz M, Zhang M, Alateeq A, Jurado F, et al. Hybrid particle swarm and sea horse optimization algorithm-based optimal reactive power dispatch of power systems comprising electric vehicles. 2024;286:129583.
  18. Sandelic M, Peyghami S, Sangwongwanich A, Blaabjerg FJR, Reviews SE. Reliability aspects in microgrid design and planning: Status and power electronics-induced challenges. 2022;159:112127.
  19. Numan M, Baig MF, Yousif MJJoES. Reliability evaluation of energy storage systems combined with other grid flexibility options: A review. 2023;63:107022.
  20. Kornilov GP, Abdulveleev IR, Logunova OS, Kalandarov PI, Tursunov O, Kodirov D, et al., editors. Electric power supply of steel producing companies: Schematic design solutions to improve reliability of power grids. AIP Conference Proceedings; 2022: AIP Publishing.
  21. Veeramsetty V, Venkaiah C, Kumar DVJEs. Hybrid genetic dragonfly algorithm based optimal power flow for computing LMP at DG buses for reliability improvement. 2018;9:709-57.
  22. Gudla S, Kuda NJFU, Series: Electronics, Energetics. A reliable routing mechanism with energy-efficient node selection for data transmission using a genetic algorithm in wireless sensor network. 2023;36(2):209-26.
  23. Rajesh P, Naveen C, Venkatesan AK, Shajin FH. An optimization technique for battery energy storage with wind turbine generator integration in unbalanced radial distribution network. Journal of Energy Storage. 2021;43:103160.
  24. Ali MH, Soliman AMA, Adel AH. Optimization of Reactive Power Dispatch Considering DG Units Uncertainty By Dandelion Optimizer Algorithm. International Journal of Renewable Energy Research (IJRER). 2022;12(4):1805-18.
  25. Najjarpour M, Tousi B, Jamali S. Loss Reduction in Distribution Networks With DG Units by Correlating Taguchi Method and Genetic Algorithm. Iranian Journal of Electrical and Electronic Engineering. 2022;18(4):1.
  26. Najjarpour M, Tousi B, editors. Loss Reduction of Distribution Network by Optimal Reconfiguration and Capacitor Placement Using Cuckoo and Cultural Algorithms. 2023 8th International Conference on Technology and Energy Management (ICTEM); 2023: IEEE.
  27. Najjarpour M, Tousi B. Probabilistic Reactive Power Flow Optimization of Distribution System in Presence of Distributed Units Uncertainty Using Combination of Improved Taguchi Method and Dandelion Algorithm %J International Journal of Engineering. 2024;37(1):37-47. 10.5829/ije.2024.37.01a.04 https://www.ije.ir/article_180386_edf36144c5bcf22ae80132815dda4fb3.pdf
  28. El-wazery MS, Mabrouk OM, El sissy AR. Optimization of Ultrasonic-assisted Friction Stir Welded using Taguchi Approach %J International Journal of Engineering. 2022;35(1):213-9. 10.5829/ije.2022.35.01A.20 https://www.ije.ir/article_140306_0166e0c1be7b9da745f1e009af53c757.pdf
International Journal of Engineering
Volume 37, Issue 9
TRANSACTIONS C: Aspects
September 2024
Pages 1780-1789

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