Journal of Green Energy Research and Innovation
Quarterly

Optimizing Reactive Power for DG Units to Minimize Power System Losses Using Stochastic Modeling

Document Type : Research article

Authors

Majid Najjarpour 1
Behrouz Tousi 2
Amir Hossein Karamali 1

1 Department of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.

2 Electrical Engineering Department, Faculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran.

https://doi.org/10.61186/jgeri.1.4.35
Abstract
In recent decades, because of some main and principle world problems such as increasing the population, global warming, climate changes, and fossil fuel sources reduction, the using of renewable energies has impressively increased that can solve and reduce the caused problems by traditional power plants, and also can control power system the important indexes such as losses, voltage drop, transferring capacity. Reactive power has an important role in controlling and minimizing of losses, the optimal distribution of reactive power in presence of Distributed generation (DG) units in distribution networks is an important and key problem. In this paper, for uncertainties modelling of DG units and optimizing the reactive power, the statistical-quality based Taguchi method and Genetic algorithm are used, respectively.  The simulation of this paper is checked and done in MATLAB and MINITAB using IEEE 57-bus standard network, and simulation results show 5.5 MW reduction of the distribution network losses.

Graphical Abstract

Optimizing Reactive Power for DG Units to Minimize Power System Losses Using Stochastic Modeling

Highlights

 

  • Optimal distribution of reactive power in presence of DG units
  • Genetic algorithm
  • Minimize Power System Losses
  • statistical-quality based Taguchi method

 

Keywords

Genetic algorithm
Wind turbine
Orthogonal arrays
Optimal reactive power distribution

 

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The ethical issues, including plagiarism, informed consent, misconduct, data fabrication and/or falsification, double publication and/or submission, redundancy, have been completely observed by the authors.

 

Credit Authorship Contribution Statement

Majid Najjarpour: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Roles/Writing - original draft. Behrouz Tousi: Supervision. Amirhossein Karamali: Writing-review & editing.

 

Bibliography

 Majid Najjarpour was born in 1997 in Urmia, West Azerbaijan, Iran. He received his Diploma and Pre-university degrees from Ferdowsi High School Tabriz in Tabriz in 2014 and 2015, respectively all in Mathematics and Physics fields. Winning the title of the first person of the Mathematical Olympiad in East Azerbaijan Province, Iran in 2011 and Member of Tabriz and Mathematics House and B.Sc. and M.Sc. degrees from Urmia University in Urmia in 2019 and 2021, respectively all in Electrical Engineering. He is currently working towards a Ph.D. degree in the Department of Electrical Engineering at Iran University of Science and Technology (IUST) in Tehran, Iran since Sep.2021 he was ranked first in M.Sc. and was accepted without exams by using the quota of talented students in M.Sc. and Ph.D. His field of interest includes Power System Protection, Distribution Systems Protection, and Automation.

 Behrouz Tousi received the B.Sc. degree in Electronic Engineering from University of Tabriz, Tabriz, Iran. He received the M.Sc. and Ph.D. degrees both in Electric Power Engineering from Amirkabir University of Technology, Tehran, Iran, in 1995 and 2001, respectively. He is now a Professor at Faculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran. His research interests include analysis and applications of power electronics and electric power system studies.

 Amirhossein Karamali was born in 2000 in Mahallat city, Iran. He completed his bachelor's degree in Electrical Engineering in 2022 and is currently pursuing a master's degree in Power Electronics and Electric Machines at Iran University of Science and Technology (IUST). His research focuses on the application of power electronic converters in power systems, with the goal of advancing renewable energy technologies for a sustainable future.

 

Citation

M. Najjarpour, B. Tousi, and A. H. Karamali, "Optimizing Reactive Power for DG Units to Minimize Power System Losses Using Stochastic Modeling," Journal of Green Energy Research and Innovation, vol. 1, no. 4, pp. 35-46, 2024.

 

  1. A. A. A. Mohamed, Y. S. Mohamed, A. A. El-Gaafary, and A. M. Hemeida, "Optimal Power Flow using Moth Swarm Algorithm," Electric Power Systems Research, vol. 142, pp. 190-206, 2017.
  2. I. Rahman, and J. Mohamad-Saleh, "Hybrid Bio-Inspired Computational Intelligence Techniques for Solving Power System Optimization Problems: A Comprehensive Survey," Applied Soft Computing, vol. 69, pp. 72-130, 2018.
  3. H. R. Nikzad, and H. Abdi, "A Robust Unit Commitment Based on GA-PL Strategy by Applying TOAT and Considering Emission Costs and Energy Storage Systems," Electric Power Systems Research, vol. 180, 106154, 2020.
  4. L. Chinmoy, S. Iniyan, and R. Goic, "Modeling Wind Power Investments, Policies and Social Benefits for Deregulated Electricity Market–A Review," Applied energy, vol. 242, pp. 364-377, 2019.
  5. W. Zhang, Y. Xu, Z. Y. Dong, Y. Wang, and R. Zhang, "An Efficient Approach for Robust SCOPF Considering Load and Renewable Power Uncertainties," 2016 Power Systems Computation Conference (PSCC), pp. 1-7, 2016.
  6. M. Ebeed, A. Alhejji, S. Kamel, and F. Jurado, "Solving the Optimal Reactive Power Dispatch using Marine Predators Algorithm Considering the Uncertainties in Load and Wind-Solar Generation Systems," Energies, vol. 13, no. 17, p. 4316, 2020.
  7. F. Gami, Z. A. Alrowaili, et al., "Stochastic Optimal Reactive Power Dispatch at Varying Time of Load Demand and Renewable Energsy Resources using an Efficient Modified Jellyfish Optimizer," Neural Computing and Applications, vol. 34, no. 22, pp. 20395-20410, 2022.
  8. M. H. Ali, A. M. A. Soliman, et al., "A Novel Stochastic Optimizer Solving Optimal Reactive Power Dispatch Problem Considering Renewable Energy Resources," Energies, vol. 16, no. 4, p. 1562, 2023.
  9. R. Jamal, J. Zhang, et al., "Solution to the deterministic and stochastic Optimal Reactive Power Dispatch by integration of solar, wind-hydro powers using Modified Artificial Hummingbird Algorithm," Energy Reports, vol. 9, pp. 4157-4173, 2023.
  10. S. Karimi, S. Hosseini-Hemati, and A. Rastgou, "Stochastic Multi-Objective ORPD for Active Distribution Networks," Sustainable Energy Technologies and Assessments, vol. 57, 103235, 2023.
  11. M. Yan, M. Shahidehpour, et al., "A Convex Three-Stage SCOPF Approach to Power System Flexibility with Unified Power Flow Controllers," IEEE Transactions on Power Systems, vol. 36, no. 3, pp. 1947-1960, 2020.
  12. N. Mezhoud, and M. Amarouayache, "Multi-Objective Optimal Power Flow Based Combined Non-Convex Economic Dispatch with Valve-Point Effects and Emission Using Gravitation Search Algorithm," Journal of Applied Research in Electrical Engineering, vol. 2, no. 1, pp. 26-36, 2023.
  13. M. Gholami, and M. J. Sanjari, "Optimal Operation of Multi-Microgrid System Considering Uncertainty of Electric Vehicles," International Journal of Engineering, vol. 36, no. 8, pp. 1398-1408, 2023.
  14. B. Y. Qu, Y. S. Zhu, et al., "A Survey on Multi-Objective Evolutionary Algorithms for the Solution of the Environmental/Economic Dispatch Problems," Swarm and Evolutionary Computation, vol. 38, pp. 1-11, 2018.
  15. S. Li, W. Gong, L. Wang, X. Yan, and C. Hu, "Optimal Power Flow by Means of Improved Adaptive Differential Evolution," Energy, vol. 198, 117314, 2020.
  16. S. Mardanimajd, S. Karimi, and A. Anvari Moghaddam, "Voltage Stability Improvement in Distribution Networks by Using Soft Open Points," International Journal of Electrical Power & Energy Systems, vol. 155, 109582, 2024.
  17. R. Leng, Z. Li, and Y. Xu, "Two-stage Stochastic Programming for Coordinated Operation of Distributed Energy Resources in Unbalanced Active Distribution Networks with Diverse Correlated Uncertainties," Journal of Modern Power Systems and Clean Energy, vol. 11, no. 1, pp. 120-131, 2023.
  18. R. Elazab, M. Ser-Alkhatm, M. A. A. Adma, and K. M. Abdel-Latif, "A Two-Stage Stochastic Programming Approach for Planning of Svcs in PV Microgrids under Load and PV Uncertainty Considering PV Inverters Reactive Power using Honey Badger Algorithm," Electric Power Systems Research, vol. 228, 109970, 2024.
  19. B. R. Prusty, and D. Jena, "A Critical Review on Probabilistic Load Flow Studies in Uncertainty Constrained Power Systems with Photovoltaic Generation and a New Approach," Renewable and Sustainable Energy Reviews, vol. 69, pp. 1286-1302, 2017.
  20. H. Nosratabadi, M. Mohammadi, and A. Kargarian, "Nonparametric Probabilistic Unbalanced Power Flow with Adaptive Kernel Density Estimator," IEEE Transactions on Smart Grid, vol. 10, no. 3, pp. 3292-3300, 2018.
  21. F. J. Ruiz-Rodriguez, J. C. Hernandez, and F. Jurado, "Iterative Harmonic Load Flow by using the Point-Estimate Method and Complex Affine Arithmetic for Radial Distribution Systems with Photovoltaic Uncertainties," International Journal of Electrical Power & Energy Systems, vol. 118, 105765, 2020.
  22. J. C. Hernandez, F. J. Ruiz-Rodriguez, F. Jurado, and F. Sanchez-Sutil, "Tracing Harmonic Distortion and Voltage Unbalance in Secondary Radial Distribution Networks with Photovoltaic Uncertainties by an Iterative Multiphase Harmonic Load Flow," Electric Power Systems Research, vol. 185, 106342, 2020.
  23. Y. Pan, L. Zhang, Z. Li, and L. Ding, "Improved Fuzzy Bayesian Network-Based Risk Analysis with Interval-Valued Fuzzy Sets and D–S Evidence Theory," IEEE Transactions on Fuzzy Systems, vol. 28, no. 9, pp. 2063-2077, 2019.
  24. C. Zhang, H. Chen, et al., "An Interval Power Flow Analysis Through Optimizing-Scenarios Method," IEEE Transactions on Smart Grid, vol. 9, no. 5, pp. 5217-5226, 2017.
  25. E. Acar, G. Bayrak, et al., "Modeling, Analysis, and Optimization under Uncertainties: A Review," Structural and Multidisciplinary Optimization, vol. 64, no. 5, pp. 2909-2945, 2021.
  26. E. Farhat, S. Kamel, A. M. Atallah, and B. Khan, "Optimal Power Flow Solution Based on Jellyfish Search Optimization Considering Uncertainty of Renewable Energy Sources," IEEE Access, vol. 9, pp. 100911-100933, 2021.
  27. A. Faramarzi, M. Heidarinejad, B. Stephens, and S. Mirjalili, "Equilibrium Optimizer: A Novel Optimization Algorithm," Knowledge-Based Systems, vol. 191, 105190, 2020.
  28. D. K. Panda, and S. Das, "Smart Grid Architecture Model for Control, Optimization and Data Analytics of Future Power Networks with More Renewable Energy," Journal of Cleaner Production, vol. 301, 126877, 2021.
  29. H. Heidari, and M. Tarafdar Hagh, "Optimal Reconfiguration of Solar Photovoltaic Arrays using a Fast Parallelized Particle Swarm Optimization in Confront of Partial Shading," International Journal of Engineering, vol. 32, no. 8, pp. 1177-1185, 2019.
  30. M. Papadimitrakis, N. Giamarelos, et al., "Metaheuristic Search in Smart Grid: A Review with Emphasis on Planning, Scheduling and Power Flow Optimization Applications," Renewable and Sustainable Energy Reviews, vol. 145, 111072, 2021.
  31. W. Peng, A. Maleki, M. A. Rosen, and P. Azarikhah, "Optimization of a Hybrid System for Solar-Wind-Based Water Desalination by Reverse Osmosis: Comparison of Approaches," Desalination, vol. 442, pp. 16-31, 2018.
  32. D. Wang, D. Tan, and L. Liu, "Particle Swarm Optimization Algorithm: an Overview," Soft computing, vol. 22, no. 2, pp. 387-408, 2018.
  33. M. E. Meral, and D. Çelík, "A Comprehensive Survey on Control Strategies of Distributed Generation Power Systems under Normal and Abnormal Conditions," Annual Reviews in control, vol. 47, pp. 112-132, 2019.
  34. P. Rajesh, C. Naveen, A. K. Venkatesan, and F. H. Shajin, "An Optimization Technique for Battery Energy Storage with Wind Turbine Generator Integration in Unbalanced Radial Distribution Network," Journal of Energy Storage, vol. 43, 103160, 2021.
  35. H. Yu, and W. D. Rosehart, "An Optimal Power Flow Algorithm to Achieve Robust Operation Considering Load and Renewable Generation Uncertainties," IEEE Transactions on Power Systems, vol. 27, no. 4, pp. 1808-1817, 2012.
  36. K. M. D. Puspitasari, J. Raharjo, A. S. Sastrosubroto, and B. Rahmat, "Generator Scheduling Optimization Involving Emission to Determine Emission Reduction Costs," International Journal of Engineering, vol. 35, no. 8, pp. 1468-1478, 2022.
  37. M. Najjarpour, and B. Tousi, "Loss Reduction of Distribution Network by Optimal Reconfiguration and Capacitor Placement using Cuckoo and Cultural Algorithms," 8th International Conference on Technology and Energy Management (ICTEM), pp. 1-5, 2023.
  38. M. Najjarpour, and B. Tousi, "Probabilistic Reactive Power Flow Optimization of The Distribution System in The Presence of Distributed Units Uncertainty Using the Combination of Improved Taguchi Method and Dandelion Algorithm," International Journal of Engineering, vol. 37, no. 1, pp. 37-47, 2024.
  39. M. Najjarpour, B. Tousi, and S. Jamali, "Loss Reduction in Distribution Networks with DG Units by Correlating Taguchi Method and Genetic Algorithm," Iranian Journal of Electrical & Electronic Engineering, vol. 18, no. 4, 2022.
  40. B. Gasbaoui, and B. Allaoua, "Ant Colony Optimization Applied on Combinatorial Problem for Optimal Power Flow Solution," Leonardo Journal of Sciences, vol. 14, pp. 1-17, 2009.
Journal of Green Energy Research and Innovation
Volume 1, Issue 4
Autumn 2024
Pages 35-46

  • Receive Date 09 March 2024
  • Revise Date 18 April 2024
  • Accept Date 21 April 2024
  • Publish Date 01 December 2024

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