IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v88y2011i5p1578-1585.html
   My bibliography  Save this article

Optimal location of Hybrid Flow Controller considering modified steady-state model

Author

Listed:
  • Ara, A. Lashkar
  • Kazemi, A.
  • Niaki, S.A. Nabavi

Abstract

This paper introduces a modified power flow model for Hybrid Flow Controller (HFC) as an energy flow controller. The existing power flow models for Hybrid Flow Controller are suitable only for conventional power flow analysis, and are not applicable for OPF and optimal location analysis of FACTS devices. In this paper, some modifications were applied to the existing models to promote the accuracy and improve their conformability on any power system and hence leading to a precise steady-state analysis. The modified model and the existing model are investigated using different IEEE test systems and the results are compared together. The optimization method is numerically solved using Matlab and General Algebraic Modelling System (GAMS) software environments. The solution procedure uses Mixed Integer Non-Linear Programming (MINLP) and Relaxed Mixed Integer Non-Linear Programming (RMINLP) to solve the optimal location and setting of HFC incorporated in OPF problem considering the total fuel cost, power losses, and the system loadability as objective functions for single objective optimization problem and improve the power system operation.

Suggested Citation

  • Ara, A. Lashkar & Kazemi, A. & Niaki, S.A. Nabavi, 2011. "Optimal location of Hybrid Flow Controller considering modified steady-state model," Applied Energy, Elsevier, vol. 88(5), pages 1578-1585, May.
    • Handle: RePEc:eee:appene:v:88:y:2011:i:5:p:1578-1585
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306-2619(10)00486-1
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Alsumait, J.S. & Sykulski, J.K. & Al-Othman, A.K., 2010. "A hybrid GA-PS-SQP method to solve power system valve-point economic dispatch problems," Applied Energy, Elsevier, vol. 87(5), pages 1773-1781, May.
    2. Niknam, Taher & Mojarrad, Hassan Doagou & Nayeripour, Majid, 2010. "A new fuzzy adaptive particle swarm optimization for non-smooth economic dispatch," Energy, Elsevier, vol. 35(4), pages 1764-1778.
    3. Sadegheih, A., 2009. "Optimization of network planning by the novel hybrid algorithms of intelligent optimization techniques," Energy, Elsevier, vol. 34(10), pages 1539-1551.
    4. Niknam, Taher, 2010. "A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem," Applied Energy, Elsevier, vol. 87(1), pages 327-339, January.
    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. Weng, Xuemeng & Xuan, Ping & Heidari, Ali Asghar & Cai, Zhennao & Chen, Huiling & Mansour, Romany F. & Ragab, Mahmoud, 2023. "A vertical and horizontal crossover sine cosine algorithm with pattern search for optimal power flow in power systems," Energy, Elsevier, vol. 271(C).

    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. Glotić, Arnel & Zamuda, Aleš, 2015. "Short-term combined economic and emission hydrothermal optimization by surrogate differential evolution," Applied Energy, Elsevier, vol. 141(C), pages 42-56.
    2. Vo, Dieu Ngoc & Ongsakul, Weerakorn, 2012. "Economic dispatch with multiple fuel types by enhanced augmented Lagrange Hopfield network," Applied Energy, Elsevier, vol. 91(1), pages 281-289.
    3. Guojiang Xiong & Jing Zhang & Xufeng Yuan & Dongyuan Shi & Yu He & Yao Yao & Gonggui Chen, 2018. "A Novel Method for Economic Dispatch with Across Neighborhood Search: A Case Study in a Provincial Power Grid, China," Complexity, Hindawi, vol. 2018, pages 1-18, November.
    4. Jiangtao Yu & Chang-Hwan Kim & Abdul Wadood & Tahir Khurshiad & Sang-Bong Rhee, 2018. "A Novel Multi-Population Based Chaotic JAYA Algorithm with Application in Solving Economic Load Dispatch Problems," Energies, MDPI, vol. 11(8), pages 1-25, July.
    5. Roche, Robin & Idoumghar, Lhassane & Suryanarayanan, Siddharth & Daggag, Mounir & Solacolu, Christian-Anghel & Miraoui, Abdellatif, 2013. "A flexible and efficient multi-agent gas turbine power plant energy management system with economic and environmental constraints," Applied Energy, Elsevier, vol. 101(C), pages 644-654.
    6. Abdelaziz, A.Y. & Ali, E.S. & Abd Elazim, S.M., 2016. "Implementation of flower pollination algorithm for solving economic load dispatch and combined economic emission dispatch problems in power systems," Energy, Elsevier, vol. 101(C), pages 506-518.
    7. Zou, Dexuan & Li, Steven & Wang, Gai-Ge & Li, Zongyan & Ouyang, Haibin, 2016. "An improved differential evolution algorithm for the economic load dispatch problems with or without valve-point effects," Applied Energy, Elsevier, vol. 181(C), pages 375-390.
    8. Yaşar, Celal & Özyön, Serdar, 2011. "A new hybrid approach for nonconvex economic dispatch problem with valve-point effect," Energy, Elsevier, vol. 36(10), pages 5838-5845.
    9. Secui, Dinu Calin, 2016. "A modified Symbiotic Organisms Search algorithm for large scale economic dispatch problem with valve-point effects," Energy, Elsevier, vol. 113(C), pages 366-384.
    10. Cai, Jiejin & Li, Qiong & Li, Lixiang & Peng, Haipeng & Yang, Yixian, 2012. "A hybrid FCASO-SQP method for solving the economic dispatch problems with valve-point effects," Energy, Elsevier, vol. 38(1), pages 346-353.
    11. Xiong, Guojiang & Shi, Dongyuan & Duan, Xianzhong, 2013. "Multi-strategy ensemble biogeography-based optimization for economic dispatch problems," Applied Energy, Elsevier, vol. 111(C), pages 801-811.
    12. Goudarzi, Arman & Swanson, Andrew G. & Van Coller, John & Siano, Pierluigi, 2017. "Smart real-time scheduling of generating units in an electricity market considering environmental aspects and physical constraints of generators," Applied Energy, Elsevier, vol. 189(C), pages 667-696.
    13. Jianzhong Xu & Fu Yan & Kumchol Yun & Lifei Su & Fengshu Li & Jun Guan, 2019. "Noninferior Solution Grey Wolf Optimizer with an Independent Local Search Mechanism for Solving Economic Load Dispatch Problems," Energies, MDPI, vol. 12(12), pages 1-26, June.
    14. Erickson Diogo Pereira Puchta & Priscilla Bassetto & Lucas Henrique Biuk & Marco Antônio Itaborahy Filho & Attilio Converti & Mauricio dos Santos Kaster & Hugo Valadares Siqueira, 2021. "Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller," Energies, MDPI, vol. 14(12), pages 1-20, June.
    15. Niknam, Taher & Firouzi, Bahman Bahmani & Ostadi, Amir, 2010. "A new fuzzy adaptive particle swarm optimization for daily Volt/Var control in distribution networks considering distributed generators," Applied Energy, Elsevier, vol. 87(6), pages 1919-1928, June.
    16. Alsumait, J.S. & Sykulski, J.K. & Al-Othman, A.K., 2010. "A hybrid GA-PS-SQP method to solve power system valve-point economic dispatch problems," Applied Energy, Elsevier, vol. 87(5), pages 1773-1781, May.
    17. 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.
    18. Yacine Labbi & Djilani Ben Attous, 2017. "A Hybrid Big Bang–Big Crunch optimization algorithm for solving the different economic load dispatch problems," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 275-286, June.
    19. Wang, Jun & Cao, Junxing & Yuan, Shan & Cheng, Ming, 2021. "Short-term forecasting of natural gas prices by using a novel hybrid method based on a combination of the CEEMDAN-SE-and the PSO-ALS-optimized GRU network," Energy, Elsevier, vol. 233(C).
    20. Bornapour, Mosayeb & Hooshmand, Rahmat-Allah & Parastegari, Moein, 2019. "An efficient scenario-based stochastic programming method for optimal scheduling of CHP-PEMFC, WT, PV and hydrogen storage units in micro grids," Renewable Energy, Elsevier, vol. 130(C), pages 1049-1066.

    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:eee:appene:v:88:y:2011:i:5:p:1578-1585. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.