| Peer-Reviewed

Hybrid Heuristic Technique for Optimal Distributed Generation Integration in Distribution Systems

Received: 8 May 2021     Accepted: 7 June 2021     Published: 16 June 2021
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Abstract

Integration of Distributed Generations (DGs) in distribution systems receives great attention nowadays due to its numerous benefits, the most important of which are reducing the overall power losses and improving voltage profile in distribution systems. In order to enhance the performance of the network, the DG units must be installed at optimal placement and sizing. Solution techniques for DG placement rely on various optimization methods. In this paper, a hybrid heuristic technique is proposed to solve the optimization problem for a single DG unit using two heuristic tests performed in two stages. In the first stage, a sensitivity test is used to determine the candidate location for DG placement. Then in the second stage, the optimal size is identified using a curve fitting test. A comprehensive analysis is performed in order to validate the results of the proposed technique. Both techniques have been tested on IEEE 33-bus and 69-bus radial distribution test systems. The obtained results show that although the comprehensive analysis can achieve slightly greater power loss reduction and voltage profile improvement, it requires a large number of tests that is proportional to the size of the distribution system. On the other hand, the proposed technique can achieve comparable results using a small fixed number of tests for any system, which means that this technique reduces the solution search space i.e., the computational demand and convergence time, while maintaining satisfactory results.

Published in International Journal of Data Science and Analysis (Volume 7, Issue 3)
DOI 10.11648/j.ijdsa.20210703.15
Page(s) 82-88
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Distributed Generation, Optimization, Radial Network, Fitting Curve

References
[1] Electricity distribution system losses non-technical overview, Sohn Associates, 2009.
[2] T. Ackermann, G. Andersson and L. Söder, "Distributed generation: A definition," Electr. Power Syst. Res, vol. 57, no. 3, pp. 195-204, 2001.
[3] Abu-Mouti, Fahad Saad. Radial distribution feeders compensation using distributed generation. ProQuest, 2009.
[4] Darfoun, Mohamed. "Improving the Voltage Profile and Minimizing the Power Loss of the Houn (11kV) Distribution Network." International Conference on Technical Sciences (ICST2019). Vol. 6. 2019.
[5] Sedighizadeh, M., and A. Rezazadeh. "Using genetic algorithm for distributed generation allocation to reduce losses and improve voltage profile." World Academy of Science, Engineering and Technology 37. 1 (2008): 251-256.
[6] Kashyap, Mohan, Ankit Mittal, and Satish Kansal. "Optimal placement of distributed generation using genetic algorithm approach." Proceeding of the Second International Conference on Microelectronics, Computing & Communication Systems (MCCS 2017). Springer, Singapore, 2019.
[7] Ogunsina, Adeseye Amos, et al. "Optimal distributed generation location and sizing for loss minimization and voltage profile optimization using ant colony algorithm." SN Applied Sciences 3.2 (2021): 1-10.
[8] Hussain, Israfil, and Anjan Kumar Roy. "Optimal distributed generation allocation in distribution systems employing modified artificial bee colony algorithm to reduce losses and improve voltage profile." IEEE-International Conference on Advances In Engineering, Science And Management (ICAESM-2012). IEEE, 2012.
[9] Darfoun, Mohamed A., and Mohamed E. El-Hawary. "Multi-objective optimization approach for optimal distributed generation sizing and placement." Electric Power Components and Systems 43. 7 (2015): 828-836.
[10] Moeini, A., et al. "Disco planner flexible DG allocation in MV distribution networks using multi-objective optimization procedures." 2010 12th International Conference on Optimization of Electrical and Electronic Equipment. IEEE, 2010.
[11] Abu-Mouti, F. S., and M. E. El-Hawary. "Heuristic curve-fitted technique for distributed generation optimisation in radial distribution feeder systems." IET Generation, Transmission & Distribution 5.2 (2011): 172-180.
[12] Anwar, Adnan, and H. R. Pota. "Loss reduction of power distribution network using optimum size and location of distributed generation." AUPEC 2011. IEEE, 2011.
[13] Mehta, Pankita, Praghnesh Bhatt, and Vivek Pandya. "Optimal selection of distributed generating units and its placement for voltage stability enhancement and energy loss minimization." Ain Shams Engineering Journal 9. 2 (2018): 187-201.
[14] Hajizadeh, Amin, and Ehsan Hajizadeh. "PSO-based planning of distribution systems with distributed generations." International Journal of Electrical and Electronics Engineering 2. 1 (2008): 33-38.
[15] Baran, Mesut E., and Felix F. Wu. "Optimal capacitor placement on radial distribution systems." IEEE Transactions on power Delivery 4. 1 (1989): 725-734.
Cite This Article
  • APA Style

    Mohamed Darfoun, Huda Hosson. (2021). Hybrid Heuristic Technique for Optimal Distributed Generation Integration in Distribution Systems. International Journal of Data Science and Analysis, 7(3), 82-88. https://doi.org/10.11648/j.ijdsa.20210703.15

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    ACS Style

    Mohamed Darfoun; Huda Hosson. Hybrid Heuristic Technique for Optimal Distributed Generation Integration in Distribution Systems. Int. J. Data Sci. Anal. 2021, 7(3), 82-88. doi: 10.11648/j.ijdsa.20210703.15

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    AMA Style

    Mohamed Darfoun, Huda Hosson. Hybrid Heuristic Technique for Optimal Distributed Generation Integration in Distribution Systems. Int J Data Sci Anal. 2021;7(3):82-88. doi: 10.11648/j.ijdsa.20210703.15

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  • @article{10.11648/j.ijdsa.20210703.15,
      author = {Mohamed Darfoun and Huda Hosson},
      title = {Hybrid Heuristic Technique for Optimal Distributed Generation Integration in Distribution Systems},
      journal = {International Journal of Data Science and Analysis},
      volume = {7},
      number = {3},
      pages = {82-88},
      doi = {10.11648/j.ijdsa.20210703.15},
      url = {https://doi.org/10.11648/j.ijdsa.20210703.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20210703.15},
      abstract = {Integration of Distributed Generations (DGs) in distribution systems receives great attention nowadays due to its numerous benefits, the most important of which are reducing the overall power losses and improving voltage profile in distribution systems. In order to enhance the performance of the network, the DG units must be installed at optimal placement and sizing. Solution techniques for DG placement rely on various optimization methods. In this paper, a hybrid heuristic technique is proposed to solve the optimization problem for a single DG unit using two heuristic tests performed in two stages. In the first stage, a sensitivity test is used to determine the candidate location for DG placement. Then in the second stage, the optimal size is identified using a curve fitting test. A comprehensive analysis is performed in order to validate the results of the proposed technique. Both techniques have been tested on IEEE 33-bus and 69-bus radial distribution test systems. The obtained results show that although the comprehensive analysis can achieve slightly greater power loss reduction and voltage profile improvement, it requires a large number of tests that is proportional to the size of the distribution system. On the other hand, the proposed technique can achieve comparable results using a small fixed number of tests for any system, which means that this technique reduces the solution search space i.e., the computational demand and convergence time, while maintaining satisfactory results.},
     year = {2021}
    }
    

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  • TY  - JOUR
    T1  - Hybrid Heuristic Technique for Optimal Distributed Generation Integration in Distribution Systems
    AU  - Mohamed Darfoun
    AU  - Huda Hosson
    Y1  - 2021/06/16
    PY  - 2021
    N1  - https://doi.org/10.11648/j.ijdsa.20210703.15
    DO  - 10.11648/j.ijdsa.20210703.15
    T2  - International Journal of Data Science and Analysis
    JF  - International Journal of Data Science and Analysis
    JO  - International Journal of Data Science and Analysis
    SP  - 82
    EP  - 88
    PB  - Science Publishing Group
    SN  - 2575-1891
    UR  - https://doi.org/10.11648/j.ijdsa.20210703.15
    AB  - Integration of Distributed Generations (DGs) in distribution systems receives great attention nowadays due to its numerous benefits, the most important of which are reducing the overall power losses and improving voltage profile in distribution systems. In order to enhance the performance of the network, the DG units must be installed at optimal placement and sizing. Solution techniques for DG placement rely on various optimization methods. In this paper, a hybrid heuristic technique is proposed to solve the optimization problem for a single DG unit using two heuristic tests performed in two stages. In the first stage, a sensitivity test is used to determine the candidate location for DG placement. Then in the second stage, the optimal size is identified using a curve fitting test. A comprehensive analysis is performed in order to validate the results of the proposed technique. Both techniques have been tested on IEEE 33-bus and 69-bus radial distribution test systems. The obtained results show that although the comprehensive analysis can achieve slightly greater power loss reduction and voltage profile improvement, it requires a large number of tests that is proportional to the size of the distribution system. On the other hand, the proposed technique can achieve comparable results using a small fixed number of tests for any system, which means that this technique reduces the solution search space i.e., the computational demand and convergence time, while maintaining satisfactory results.
    VL  - 7
    IS  - 3
    ER  - 

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Author Information
  • Electrical Engineering Department, College of Engineering Technology, Houn, Libya

  • Electric and Computer Engineering, University of Nebraska, Lincoln, NE, USA

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