IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i7p1013-d104901.html
   My bibliography  Save this article

Optimal Power Flow Using Particle Swarm Optimization of Renewable Hybrid Distributed Generation

Author

Listed:
  • Usama Khaled

    (Electrical Engineering Department, College of Engineering, King Saud University, P.O. Box. 800, Riyadh 11421, Saudi Arabia
    Electrical Engineering Department, Faculty of Energy Engineering, Aswan University, Aswan Governorate 81528, Egypt)

  • Ali M. Eltamaly

    (Electrical Engineering Department, Mansoura University, Mansoura 35516, Egypt
    Sustainable Energy Technology Center, King Saud University, Riyadh 11421, Saudi Arabia)

  • Abderrahmane Beroual

    (Ecole Centrale de Lyon, University of Lyon, Ampere CNRS UMR 5005, 36 avenue Guy Collongue, Ecully 69134, France)

Abstract

The problem of voltage collapse in power systems due to increased loads can be solved by adding renewable energy sources like wind and photovoltaic (PV) to some bus-bars. This option can reduce the cost of the generated energy and increase the system efficiency and reliability. In this paper, a modified smart technique using particle swarm optimization (PSO) has been introduced to select the hourly optimal load flow with renewable distributed generation (DG) integration under different operating conditions in the 30-bus IEEE system. Solar PV and wind power plants have been introduced to selected buses to evaluate theirs benefits as DG. Different solar radiation and wind speeds for the Dammam site in Saudi Arabia have been used as an example to study the feasibility of renewable energy integration and its effect on power system operation. Sensitivity analysis to the load and the other input data has been carried out to predict the sensitivity of the results to any deviation in the input data of the system. The obtained results from the proposed system prove that using of renewable energy sources as a DG reduces the generation and operation cost of the overall power system.

Suggested Citation

  • Usama Khaled & Ali M. Eltamaly & Abderrahmane Beroual, 2017. "Optimal Power Flow Using Particle Swarm Optimization of Renewable Hybrid Distributed Generation," Energies, MDPI, vol. 10(7), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:1013-:d:104901
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/7/1013/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/7/1013/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Antanas Žilinskas & Julius Žilinskas, 2013. "A hybrid global optimization algorithm for non-linear least squares regression," Journal of Global Optimization, Springer, vol. 56(2), pages 265-277, June.
    2. Niknam, Taher & Narimani, Mohammad rasoul & Jabbari, Masoud & Malekpour, Ahmad Reza, 2011. "A modified shuffle frog leaping algorithm for multi-objective optimal power flow," Energy, Elsevier, vol. 36(11), pages 6420-6432.
    3. Mohamed, Mohamed A. & Eltamaly, Ali M. & Alolah, Abdulrahman I., 2017. "Swarm intelligence-based optimization of grid-dependent hybrid renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 515-524.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mahmoud G. Hemeida & Salem Alkhalaf & Al-Attar A. Mohamed & Abdalla Ahmed Ibrahim & Tomonobu Senjyu, 2020. "Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO)," Energies, MDPI, vol. 13(15), pages 1-37, July.
    2. Pingping Yun & Yongfeng Ren & Yu Xue, 2018. "Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method," Energies, MDPI, vol. 11(12), pages 1-23, December.
    3. Zahir Sahli & Abdellatif Hamouda & Abdelghani Bekrar & Damien Trentesaux, 2018. "Reactive Power Dispatch Optimization with Voltage Profile Improvement Using an Efficient Hybrid Algorithm †," Energies, MDPI, vol. 11(8), pages 1-21, August.
    4. Diego Fernando Vargas-Sosa & Oscar Danilo Montoya & Luis Fernando Grisales-Noreña, 2023. "Efficient Integration of Photovoltaic Solar Generators in Monopolar DC Networks through a Convex Mixed-Integer Optimization Model," Sustainability, MDPI, vol. 15(10), pages 1-17, May.
    5. Yuttana Kongjeen & Krischonme Bhumkittipich, 2018. "Impact of Plug-in Electric Vehicles Integrated into Power Distribution System Based on Voltage-Dependent Power Flow Analysis," Energies, MDPI, vol. 11(6), pages 1-16, June.
    6. Muhammad Yousif & Qian Ai & Yang Gao & Waqas Ahmad Wattoo & Ziqing Jiang & Ran Hao, 2018. "Application of Particle Swarm Optimization to a Scheduling Strategy for Microgrids Coupled with Natural Gas Networks," Energies, MDPI, vol. 11(12), pages 1-16, December.
    7. Mohammed Hamouda Ali & Ali M. El-Rifaie & Ahmed A. F. Youssef & Vladimir N. Tulsky & Mohamed A. Tolba, 2023. "Techno-Economic Strategy for the Load Dispatch and Power Flow in Power Grids Using Peafowl Optimization Algorithm," Energies, MDPI, vol. 16(2), pages 1-29, January.
    8. José Adriano da Costa & David Alves Castelo Branco & Max Chianca Pimentel Filho & Manoel Firmino de Medeiros Júnior & Neilton Fidelis da Silva, 2019. "Optimal Sizing of Photovoltaic Generation in Radial Distribution Systems Using Lagrange Multipliers," Energies, MDPI, vol. 12(9), pages 1-19, May.
    9. Park, Sung-Won & Cho, Kyu-Sang & Hoefter, Gregor & Son, Sung-Yong, 2022. "Electric vehicle charging management using location-based incentives for reducing renewable energy curtailment considering the distribution system," Applied Energy, Elsevier, vol. 305(C).
    10. Saket Gupta & Narendra Kumar & Laxmi Srivastava & Hasmat Malik & Alberto Pliego Marugán & Fausto Pedro García Márquez, 2021. "A Hybrid Jaya–Powell’s Pattern Search Algorithm for Multi-Objective Optimal Power Flow Incorporating Distributed Generation," Energies, MDPI, vol. 14(10), pages 1-24, May.
    11. Mihai Sanduleac & Alexandru Sandulescu & Cristina Efremov & Constantin Ionescu & Ioan Catalin Damian & Alexandru Mandis, 2023. "Aspects of Design in Low Voltage Resilient Grids—Focus on Battery Sizing and U Level Control with P Regulation in Microgrids of Energy Communities," Energies, MDPI, vol. 16(4), pages 1-25, February.
    12. Samson Ademola Adegoke & Yanxia Sun, 2023. "Diminishing Active Power Loss and Improving Voltage Profile Using an Improved Pathfinder Algorithm Based on Inertia Weight," Energies, MDPI, vol. 16(3), pages 1-14, January.
    13. Gracita Batista Rosas & Elizete Maria Lourenço & Djalma Mosqueira Falcão & Thelma Solange Piazza Fernandes, 2019. "An Expeditious Methodology to Assess the Effects of Intermittent Generation on Power Systems," Energies, MDPI, vol. 12(6), pages 1-18, March.
    14. Aqsa Naeem & Naveed Ul Hassan & Chau Yuen & S. M. Muyeen, 2019. "Maximizing the Economic Benefits of a Grid-Tied Microgrid Using Solar-Wind Complementarity," Energies, MDPI, vol. 12(3), pages 1-22, January.
    15. Gonggui Chen & Zhengmei Lu & Zhizhong Zhang, 2018. "Improved Krill Herd Algorithm with Novel Constraint Handling Method for Solving Optimal Power Flow Problems," Energies, MDPI, vol. 11(1), pages 1-27, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Narimani, Mohammad Rasoul, 2012. "Reserve constrained dynamic optimal power flow subject to valve-point effects, prohibited zones and multi-fuel constraints," Energy, Elsevier, vol. 47(1), pages 451-464.
    2. Davoudkhani, Iraj Faraji & Dejamkhooy, Abdolmajid & Nowdeh, Saber Arabi, 2023. "A novel cloud-based framework for optimal design of stand-alone hybrid renewable energy system considering uncertainty and battery aging," Applied Energy, Elsevier, vol. 344(C).
    3. Amr Khaled Khamees & Almoataz Y. Abdelaziz & Makram R. Eskaros & Mahmoud A. Attia & Mariam A. Sameh, 2022. "Optimal Power Flow with Stochastic Renewable Energy Using Three Mixture Component Distribution Functions," Sustainability, MDPI, vol. 15(1), pages 1-21, December.
    4. Shabanpour-Haghighi, Amin & Seifi, Ali Reza, 2015. "Multi-objective operation management of a multi-carrier energy system," Energy, Elsevier, vol. 88(C), pages 430-442.
    5. Elattar, Ehab E., 2019. "Environmental economic dispatch with heat optimization in the presence of renewable energy based on modified shuffle frog leaping algorithm," Energy, Elsevier, vol. 171(C), pages 256-269.
    6. Fitiwi, Desta Z. & Olmos, L. & Rivier, M. & de Cuadra, F. & Pérez-Arriaga, I.J., 2016. "Finding a representative network losses model for large-scale transmission expansion planning with renewable energy sources," Energy, Elsevier, vol. 101(C), pages 343-358.
    7. Ghasemi, Mojtaba & Ghavidel, Sahand & Ghanbarian, Mohammad Mehdi & Gharibzadeh, Masihallah & Azizi Vahed, Ali, 2014. "Multi-objective optimal power flow considering the cost, emission, voltage deviation and power losses using multi-objective modified imperialist competitive algorithm," Energy, Elsevier, vol. 78(C), pages 276-289.
    8. Fahd A. Alturki & Emad Mahrous Awwad, 2021. "Sizing and Cost Minimization of Standalone Hybrid WT/PV/Biomass/Pump-Hydro Storage-Based Energy Systems," Energies, MDPI, vol. 14(2), pages 1-20, January.
    9. Li, Shuijia & Gong, Wenyin & Wang, Ling & Yan, Xuesong & Hu, Chengyu, 2020. "Optimal power flow by means of improved adaptive differential evolution," Energy, Elsevier, vol. 198(C).
    10. Özyön, Serdar & Temurtaş, Hasan & Durmuş, Burhanettin & Kuvat, Gültekin, 2012. "Charged system search algorithm for emission constrained economic power dispatch problem," Energy, Elsevier, vol. 46(1), pages 420-430.
    11. Younes, Mimoun & Khodja, Fouad & Kherfane, Riad Lakhdar, 2014. "Multi-objective economic emission dispatch solution using hybrid FFA (firefly algorithm) and considering wind power penetration," Energy, Elsevier, vol. 67(C), pages 595-606.
    12. Ali S. Alghamdi, 2022. "Optimal Power Flow in Wind–Photovoltaic Energy Regulation Systems Using a Modified Turbulent Water Flow-Based Optimization," Sustainability, MDPI, vol. 14(24), pages 1-27, December.
    13. Nguyen, Thang Trung, 2019. "A high performance social spider optimization algorithm for optimal power flow solution with single objective optimization," Energy, Elsevier, vol. 171(C), pages 218-240.
    14. Saket Gupta & Narendra Kumar & Laxmi Srivastava & Hasmat Malik & Alberto Pliego Marugán & Fausto Pedro García Márquez, 2021. "A Hybrid Jaya–Powell’s Pattern Search Algorithm for Multi-Objective Optimal Power Flow Incorporating Distributed Generation," Energies, MDPI, vol. 14(10), pages 1-24, May.
    15. Narimani, Mohammad Rasoul & Azizipanah-Abarghooee, Rasoul & Zoghdar-Moghadam-Shahrekohne, Behrouz & Gholami, Kayvan, 2013. "A novel approach to multi-objective optimal power flow by a new hybrid optimization algorithm considering generator constraints and multi-fuel type," Energy, Elsevier, vol. 49(C), pages 119-136.
    16. Majed A. Alotaibi & Ali M. Eltamaly, 2021. "A Smart Strategy for Sizing of Hybrid Renewable Energy System to Supply Remote Loads in Saudi Arabia," Energies, MDPI, vol. 14(21), pages 1-24, October.
    17. Panda, Ambarish & Tripathy, M., 2015. "Security constrained optimal power flow solution of wind-thermal generation system using modified bacteria foraging algorithm," Energy, Elsevier, vol. 93(P1), pages 816-827.
    18. Kong, Hui & Li, Zheng & Yu, Zhufeng & Zhang, Jun & Wang, Hongsheng & Wang, Jian & Gao, Dan, 2021. "Environmental and economic multi-objective optimization of comprehensive energy industry: A case study," Energy, Elsevier, vol. 237(C).
    19. Christopher M. Cotnoir & Balša Terzić, 2017. "Decoupling linear and nonlinear regimes: an evaluation of efficiency for nonlinear multidimensional optimization," Journal of Global Optimization, Springer, vol. 68(3), pages 663-675, July.
    20. Singh, Kuljeet & Hachem-Vermette, Caroline, 2021. "Economical energy resource planning to promote sustainable urban design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:1013-:d:104901. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.