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Multi-objective placement and sizing of DGs in distribution networks ensuring transient stability using hybrid evolutionary algorithm

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  • Nayeripour, Majid
  • Mahboubi-Moghaddam, Esmaeil
  • Aghaei, Jamshid
  • Azizi-Vahed, Ali

Abstract

Distributed generation (DG) units are increasing their popularity around the world. Considering the low inertia constant of DGs, the transient stability of them in the network is one of the major issues. In this paper, a new Pareto-based multi-objective problem is proposed for the placement and sizing of multiple micro-turbines in a distribution network to improve the transient stability index in addition to the losses and voltage profile. To calculate the transient stability index, the rates of fault occurrence in the different locations are considered. Also, the loads are modeled as both constant power and voltage dependent cases. In order to identify Pareto optimal solutions of the optimization problem, a novel hybrid evolutionary algorithm based on the Particle Swarm Optimization (PSO) and Shuffled Frog-Leaping (SFL) algorithm is presented. A 33-bus distribution test system is used to demonstrate the performance of the proposed method in DIgSILENT® PowerFactory software which can be used for practical applications in power systems.

Suggested Citation

  • Nayeripour, Majid & Mahboubi-Moghaddam, Esmaeil & Aghaei, Jamshid & Azizi-Vahed, Ali, 2013. "Multi-objective placement and sizing of DGs in distribution networks ensuring transient stability using hybrid evolutionary algorithm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 759-767.
  • Handle: RePEc:eee:rensus:v:25:y:2013:i:c:p:759-767
    DOI: 10.1016/j.rser.2013.05.016
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    2. Rezaee Jordehi, Ahmad, 2016. "Allocation of distributed generation units in electric power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 893-905.
    3. Theo, Wai Lip & Lim, Jeng Shiun & Ho, Wai Shin & Hashim, Haslenda & Lee, Chew Tin, 2017. "Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 531-573.
    4. Muttaqi, K.M. & Le, An D.T. & Aghaei, J. & Mahboubi-Moghaddam, E. & Negnevitsky, M. & Ledwich, G., 2016. "Optimizing distributed generation parameters through economic feasibility assessment," Applied Energy, Elsevier, vol. 165(C), pages 893-903.
    5. Syed Ali Abbas Kazmi & Muhammad Khuram Shahzad & Dong Ryeol Shin, 2017. "Multi-Objective Planning Techniques in Distribution Networks: A Composite Review," Energies, MDPI, vol. 10(2), pages 1-44, February.
    6. Jordehi, A. Rezaee, 2015. "Optimisation of electric distribution systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1088-1100.
    7. Xie, Shiwei & Hu, Zhijian & Wang, Jueying, 2020. "Two-stage robust optimization for expansion planning of active distribution systems coupled with urban transportation networks," Applied Energy, Elsevier, vol. 261(C).
    8. Nojavan, Sayyad & Majidi, Majid & Esfetanaj, Naser Nourani, 2017. "An efficient cost-reliability optimization model for optimal siting and sizing of energy storage system in a microgrid in the presence of responsible load management," Energy, Elsevier, vol. 139(C), pages 89-97.
    9. Ramdhan Halid Siregar & Yuwaldi Away & Tarmizi & Akhyar, 2023. "Minimizing Power Losses for Distributed Generation (DG) Placements by Considering Voltage Profiles on Distribution Lines for Different Loads Using Genetic Algorithm Methods," Energies, MDPI, vol. 16(14), pages 1-25, July.
    10. Wallisson C. Nogueira & Lina P. Garcés Negrete & Jesús M. López-Lezama, 2023. "Optimal Allocation and Sizing of Distributed Generation Using Interval Power Flow," Sustainability, MDPI, vol. 15(6), pages 1-24, March.
    11. Muhammad Usman Riaz & Suheel Abdullah Malik & Amil Daraz & Hasan Alrajhi & Ahmed N. M. Alahmadi & Abdul Rahman Afzal, 2024. "Advanced Energy Management in a Sustainable Integrated Hybrid Power Network Using a Computational Intelligence Control Strategy," Energies, MDPI, vol. 17(20), pages 1-53, October.
    12. Karabacak, Kerim & Cetin, Numan, 2014. "Artificial neural networks for controlling wind–PV power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 804-827.
    13. Sarimuthu, Charles R. & Ramachandaramurthy, Vigna K. & Agileswari, K.R. & Mokhlis, Hazlie, 2016. "A review on voltage control methods using on-load tap changer transformers for networks with renewable energy sources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 1154-1161.
    14. Pesaran H.A, Mahmoud & Huy, Phung Dang & Ramachandaramurthy, Vigna K., 2017. "A review of the optimal allocation of distributed generation: Objectives, constraints, methods, and algorithms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 293-312.

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