IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i4p463-d94798.html
   My bibliography  Save this article

Power Loss Analysis for Wind Power Grid Integration Based on Weibull Distribution

Author

Listed:
  • Ahmed Al Ameri

    (Groupe de Recherche en Electrotechnique et Automatique GREAH Lab., University of Le Havre, 76600 Le Havre, France)

  • Aouchenni Ounissa

    (Laboratoire de Maitrise des Enregies Renouvelables, Faculty of Technology A. Mira University, 06000 Bejaia, Algeria)

  • Cristian Nichita

    (Groupe de Recherche en Electrotechnique et Automatique GREAH Lab., University of Le Havre, 76600 Le Havre, France)

  • Aouzellag Djamal

    (Laboratoire de Maitrise des Enregies Renouvelables, Faculty of Technology A. Mira University, 06000 Bejaia, Algeria)

Abstract

The growth of electrical demand increases the need of renewable energy sources, such as wind energy, to meet that need. Electrical power losses are an important factor when wind farm location and size are selected. The capitalized cost of constant power losses during the life of a wind farm will continue to high levels. During the operation period, a method to determine if the losses meet the requirements of the design is significantly needed. This article presents a Simulink simulation of wind farm integration into the grid; the aim is to achieve a better understanding of wind variation impact on grid losses. The real power losses are set as a function of the annual variation, considering a Weibull distribution. An analytical method has been used to select the size and placement of a wind farm, taking into account active power loss reduction. It proposes a fast linear model estimation to find the optimal capacity of a wind farm based on DC power flow and graph theory. The results show that the analytical approach is capable of predicting the optimal size and location of wind turbines. Furthermore, it revealed that the annual variation of wind speed could have a strong effect on real power loss calculations. In addition to helping to improve utility efficiency, the proposed method can develop specific designs to speeding up integration of wind farms into grids.

Suggested Citation

  • Ahmed Al Ameri & Aouchenni Ounissa & Cristian Nichita & Aouzellag Djamal, 2017. "Power Loss Analysis for Wind Power Grid Integration Based on Weibull Distribution," Energies, MDPI, vol. 10(4), pages 1-16, April.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:4:p:463-:d:94798
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/4/463/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/4/463/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liu, Hui & Tian, Hong-Qi & Chen, Chao & Li, Yan-fei, 2010. "A hybrid statistical method to predict wind speed and wind power," Renewable Energy, Elsevier, vol. 35(8), pages 1857-1861.
    2. Soroudi, Alireza & Amraee, Turaj, 2013. "Decision making under uncertainty in energy systems: State of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 376-384.
    3. Warid Warid & Hashim Hizam & Norman Mariun & Noor Izzri Abdul-Wahab, 2016. "Optimal Power Flow Using the Jaya Algorithm," Energies, MDPI, vol. 9(9), pages 1-18, August.
    4. Ekren, Orhan & Ekren, Banu Y., 2010. "Size optimization of a PV/wind hybrid energy conversion system with battery storage using simulated annealing," Applied Energy, Elsevier, vol. 87(2), pages 592-598, February.
    5. Qingwu Gong & Jiazhi Lei & Jun Ye, 2016. "Optimal Siting and Sizing of Distributed Generators in Distribution Systems Considering Cost of Operation Risk," Energies, MDPI, vol. 9(1), pages 1-18, January.
    6. Mohsin Shahzad & Ishtiaq Ahmad & Wolfgang Gawlik & Peter Palensky, 2016. "Load Concentration Factor Based Analytical Method for Optimal Placement of Multiple Distribution Generators for Loss Minimization and Voltage Profile Improvement," Energies, MDPI, vol. 9(4), pages 1-21, April.
    7. Lahaçani, N.A. & Aouzellag, D. & Mendil, B., 2010. "Contribution to the improvement of voltage profile in electrical network with wind generator using SVC device," Renewable Energy, Elsevier, vol. 35(1), pages 243-248.
    8. Ran Li & Huizhuo Ma & Feifei Wang & Yihe Wang & Yang Liu & Zenghui Li, 2013. "Game Optimization Theory and Application in Distribution System Expansion Planning, Including Distributed Generation," Energies, MDPI, vol. 6(2), pages 1-24, February.
    9. Lahaçani, N. Aouzellag & Aouzellag, D. & Mendil, B., 2010. "Static compensator for maintaining voltage stability of wind farm integration to a distribution network," Renewable Energy, Elsevier, vol. 35(11), pages 2476-2482.
    10. Antonio Colmenar-Santos & Severo Campíez-Romero & Lorenzo Alfredo Enríquez-Garcia & Clara Pérez-Molina, 2014. "Simplified Analysis of the Electric Power Losses for On-Shore Wind Farms Considering Weibull Distribution Parameters," Energies, MDPI, vol. 7(11), pages 1-30, October.
    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. Kashif Sohail & Hooman Farzaneh, 2022. "Model for Optimal Power Coefficient Tracking and Loss Reduction of the Wind Turbine Systems," Energies, MDPI, vol. 15(11), pages 1-19, June.
    2. Varadharajan Sankaralingam Sriraja Balaguru & Nesamony Jothi Swaroopan & Kannadasan Raju & Mohammed H. Alsharif & Mun-Kyeom Kim, 2021. "Techno-Economic Investigation of Wind Energy Potential in Selected Sites with Uncertainty Factors," Sustainability, MDPI, vol. 13(4), pages 1-31, February.
    3. Zbigniew Kłosowski & Łukasz Mazur, 2023. "Influence of the Type of Receiver on Electrical Energy Losses in Power Grids," Energies, MDPI, vol. 16(15), pages 1-22, July.
    4. Arman Oshnoei & Rahmat Khezri & Mehrdad Tarafdar Hagh & Kuaanan Techato & SM Muyeen & Omid Sadeghian, 2018. "Direct Probabilistic Load Flow in Radial Distribution Systems Including Wind Farms: An Approach Based on Data Clustering," Energies, MDPI, vol. 11(2), pages 1-19, February.
    5. Sadik Kucuksari & Nuh Erdogan & Umit Cali, 2019. "Impact of Electrical Topology, Capacity Factor and Line Length on Economic Performance of Offshore Wind Investments," Energies, MDPI, vol. 12(16), pages 1-21, August.

    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. Qingwu Gong & Jiazhi Lei & Jun Ye, 2016. "Optimal Siting and Sizing of Distributed Generators in Distribution Systems Considering Cost of Operation Risk," Energies, MDPI, vol. 9(1), pages 1-18, January.
    2. Chen, Jun & Rabiti, Cristian, 2017. "Synthetic wind speed scenarios generation for probabilistic analysis of hybrid energy systems," Energy, Elsevier, vol. 120(C), pages 507-517.
    3. Zahraee, S.M. & Khalaji Assadi, M. & Saidur, R., 2016. "Application of Artificial Intelligence Methods for Hybrid Energy System Optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 617-630.
    4. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    5. Seyed Siavash Karimi Madahi & Andrija T. Sarić, 2020. "Multi-Criteria Optimal Sizing and Allocation of Renewable and Non-Renewable Distributed Generation Resources at 63 kV/20 kV Substations," Energies, MDPI, vol. 13(20), pages 1-22, October.
    6. Yasemin Merzifonluoglu & Eray Uzgoren, 2018. "Photovoltaic power plant design considering multiple uncertainties and risk," Annals of Operations Research, Springer, vol. 262(1), pages 153-184, March.
    7. Selin Kocaman, Ayse & Abad, Carlos & Troy, Tara J. & Tim Huh, Woonghee & Modi, Vijay, 2016. "A stochastic model for a macroscale hybrid renewable energy system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 688-703.
    8. Tascikaraoglu, Akin & Sanandaji, Borhan M. & Poolla, Kameshwar & Varaiya, Pravin, 2016. "Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform," Applied Energy, Elsevier, vol. 165(C), pages 735-747.
    9. repec:wut:journl:v:2:y:2013:id:1085 is not listed on IDEAS
    10. Askarzadeh, Alireza & Rezazadeh, Alireza, 2013. "Artificial bee swarm optimization algorithm for parameters identification of solar cell models," Applied Energy, Elsevier, vol. 102(C), pages 943-949.
    11. Méndez-Gordillo, Alma Rosa & Cadenas, Erasmo, 2021. "Wind speed forecasting by the extraction of the multifractal patterns of time series through the multiplicative cascade technique," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    12. Maleki, Akbar & Ameri, Mehran & Keynia, Farshid, 2015. "Scrutiny of multifarious particle swarm optimization for finding the optimal size of a PV/wind/battery hybrid system," Renewable Energy, Elsevier, vol. 80(C), pages 552-563.
    13. Marcin Rabe & Dalia Streimikiene & Yuriy Bilan, 2019. "The Concept of Risk and Possibilities of Application of Mathematical Methods in Supporting Decision Making for Sustainable Energy Development," Sustainability, MDPI, vol. 11(4), pages 1-24, February.
    14. Raji Atia & Noboru Yamada, 2016. "Distributed Renewable Generation and Storage System Sizing Based on Smart Dispatch of Microgrids," Energies, MDPI, vol. 9(3), pages 1-16, March.
    15. Salameh, Tareq & Ghenai, Chaouki & Merabet, Adel & Alkasrawi, Malek, 2020. "Techno-economical optimization of an integrated stand-alone hybrid solar PV tracking and diesel generator power system in Khorfakkan, United Arab Emirates," Energy, Elsevier, vol. 190(C).
    16. Guoqiang Sun & Wenxue Wang & Yi Wu & Wei Hu & Zijun Yang & Zhinong Wei & Haixiang Zang & Sheng Chen, 2019. "A Nonlinear Analytical Algorithm for Predicting the Probabilistic Mass Flow of a Radial District Heating Network," Energies, MDPI, vol. 12(7), pages 1-20, March.
    17. Omaji Samuel & Nadeem Javaid & Mahmood Ashraf & Farruh Ishmanov & Muhammad Khalil Afzal & Zahoor Ali Khan, 2018. "Jaya based Optimization Method with High Dispatchable Distributed Generation for Residential Microgrid," Energies, MDPI, vol. 11(6), pages 1-29, June.
    18. Busiswe Skosana & Mukwanga W. Siti & Nsilulu T. Mbungu & Sonu Kumar & Willy Mulumba, 2023. "An Evaluation of Potential Strategies in Renewable Energy Systems and Their Importance for South Africa—A Review," Energies, MDPI, vol. 16(22), pages 1-27, November.
    19. Niu, Tong & Wang, Jianzhou & Zhang, Kequan & Du, Pei, 2018. "Multi-step-ahead wind speed forecasting based on optimal feature selection and a modified bat algorithm with the cognition strategy," Renewable Energy, Elsevier, vol. 118(C), pages 213-229.
    20. Tao Xu & He Meng & Jie Zhu & Wei Wei & He Zhao & Han Yang & Zijin Li & Yuhan Wu, 2021. "Optimal Capacity Allocation of Energy Storage in Distribution Networks Considering Active/Reactive Coordination," Energies, MDPI, vol. 14(6), pages 1-24, March.
    21. Caputo, Antonio C. & Federici, Alessandro & Pelagagge, Pacifico M. & Salini, Paolo, 2023. "Offshore wind power system economic evaluation framework under aleatory and epistemic uncertainty," Applied Energy, Elsevier, vol. 350(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:4:p:463-:d:94798. 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.