IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i22p4242-d284402.html
   My bibliography  Save this article

Multi-Objective Optimization for Determining Trade-Off between Output Power and Power Fluctuations in Wind Farm System

Author

Listed:
  • Van-Hai Bui

    (Department of Electrical Engineering, Incheon National University, 12-1 Songdo-dong, Yeonsu-gu, Incheon 406-840, Korea)

  • Akhtar Hussain

    (Department of Electrical Engineering, Incheon National University, 12-1 Songdo-dong, Yeonsu-gu, Incheon 406-840, Korea
    Research Institute for Northeast Asian Super Grid, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Korea)

  • Woon-Gyu Lee

    (Department of Electrical Engineering, Incheon National University, 12-1 Songdo-dong, Yeonsu-gu, Incheon 406-840, Korea)

  • Hak-Man Kim

    (Department of Electrical Engineering, Incheon National University, 12-1 Songdo-dong, Yeonsu-gu, Incheon 406-840, Korea
    Research Institute for Northeast Asian Super Grid, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Korea)

Abstract

In this paper, a multi-objective optimization method is proposed to determine trade-off between conflicting operation objectives of wind farm (WF) systems, i.e., maximizing the output power and minimizing the output power fluctuation of the WF system. A detailed analysis of the effects of different objective’s weight values and battery size on the operation of the WF system is also carried out. This helps the WF operator to decide on an optimal operation point for the whole system to increase its profit and improve output power quality. In order to find out the optimal solution, a two-stage optimization is also developed to determine the optimal output power of the entire system as well as the optimal set-points of wind turbine generators (WTGs). In stage 1, the WF operator performs multi-objective optimization to determine the optimal output power of the WF system based on the relevant information from WTGs’ and battery’s controllers. In stage 2, the WF operator performs optimization to determine the optimal set-points of WTGs for minimizing the power deviation and fulfilling the required output power from the previous stage. The minimization of the power deviation for the set-points of WTGs helps the output power of WTGs much smoother and therefore avoids unnecessary internal power fluctuations. Finally, different case studies are also analyzed to show the effectiveness of the proposed method.

Suggested Citation

  • Van-Hai Bui & Akhtar Hussain & Woon-Gyu Lee & Hak-Man Kim, 2019. "Multi-Objective Optimization for Determining Trade-Off between Output Power and Power Fluctuations in Wind Farm System," Energies, MDPI, vol. 12(22), pages 1-18, November.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:22:p:4242-:d:284402
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/22/4242/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/22/4242/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shakoor, Rabia & Hassan, Mohammad Yusri & Raheem, Abdur & Wu, Yuan-Kang, 2016. "Wake effect modeling: A review of wind farm layout optimization using Jensen׳s model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1048-1059.
    2. Unai Elosegui & Igor Egana & Alain Ulazia & Gabriel Ibarra-Berastegi, 2018. "Pitch Angle Misalignment Correction Based on Benchmarking and Laser Scanner Measurement in Wind Farms," Energies, MDPI, vol. 11(12), pages 1-20, December.
    3. Ebrahimi, F.M. & Khayatiyan, A. & Farjah, E., 2016. "A novel optimizing power control strategy for centralized wind farm control system," Renewable Energy, Elsevier, vol. 86(C), pages 399-408.
    4. Gionfra, Nicolò & Sandou, Guillaume & Siguerdidjane, Houria & Faille, Damien & Loevenbruck, Philippe, 2019. "Wind farm distributed PSO-based control for constrained power generation maximization," Renewable Energy, Elsevier, vol. 133(C), pages 103-117.
    5. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2018. "Battery energy storage system size determination in renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 109-125.
    6. Alizadeh, M.I. & Parsa Moghaddam, M. & Amjady, N. & Siano, P. & Sheikh-El-Eslami, M.K., 2016. "Flexibility in future power systems with high renewable penetration: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1186-1193.
    7. Zhao, Haoran & Wu, Qiuwei & Hu, Shuju & Xu, Honghua & Rasmussen, Claus Nygaard, 2015. "Review of energy storage system for wind power integration support," Applied Energy, Elsevier, vol. 137(C), pages 545-553.
    8. José F. Herbert-Acero & Oliver Probst & Pierre-Elouan Réthoré & Gunner Chr. Larsen & Krystel K. Castillo-Villar, 2014. "A Review of Methodological Approaches for the Design and Optimization of Wind Farms," Energies, MDPI, vol. 7(11), pages 1-87, October.
    9. Ulazia, Alain & Sáenz, Jon & Ibarra-Berastegi, Gabriel & González-Rojí, Santos J. & Carreno-Madinabeitia, Sheila, 2019. "Global estimations of wind energy potential considering seasonal air density changes," Energy, Elsevier, vol. 187(C).
    10. Baohua Zhang & Weihao Hu & Peng Hou & Jin Tan & Mohsen Soltani & Zhe Chen, 2017. "Review of Reactive Power Dispatch Strategies for Loss Minimization in a DFIG-based Wind Farm," Energies, MDPI, vol. 10(7), pages 1-17, June.
    11. Jannati, M. & Hosseinian, S.H. & Vahidi, B. & Li, Guo-jie., 2016. "A significant reduction in the costs of battery energy storage systems by use of smart parking lots in the power fluctuation smoothing process of the wind farms," Renewable Energy, Elsevier, vol. 87(P1), pages 1-14.
    12. Feng, Ju & Shen, Wen Zhong, 2015. "Solving the wind farm layout optimization problem using random search algorithm," Renewable Energy, Elsevier, vol. 78(C), pages 182-192.
    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. Van-Hai Bui & Akhtar Hussain & Thai-Thanh Nguyen & Hak-Man Kim, 2021. "Multi-Objective Stochastic Optimization for Determining Set-Point of Wind Farm System," Sustainability, MDPI, vol. 13(2), pages 1-16, January.
    2. Soumendra Nath Sanyal & Izabela Nielsen & Subrata Saha, 2020. "Multi-Objective Human Resource Allocation Approach for Sustainable Traffic Management," IJERPH, MDPI, vol. 17(7), pages 1-16, April.

    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. Pollini, Nicolò, 2022. "Topology optimization of wind farm layouts," Renewable Energy, Elsevier, vol. 195(C), pages 1015-1027.
    2. Manisha Sawant & Sameer Thakare & A. Prabhakara Rao & Andrés E. Feijóo-Lorenzo & Neeraj Dhanraj Bokde, 2021. "A Review on State-of-the-Art Reviews in Wind-Turbine- and Wind-Farm-Related Topics," Energies, MDPI, vol. 14(8), pages 1-30, April.
    3. Davide Astolfi & Francesco Castellani, 2019. "Wind Turbine Power Curve Upgrades: Part II," Energies, MDPI, vol. 12(8), pages 1-20, April.
    4. Antonini, Enrico G.A. & Romero, David A. & Amon, Cristina H., 2018. "Continuous adjoint formulation for wind farm layout optimization: A 2D implementation," Applied Energy, Elsevier, vol. 228(C), pages 2333-2345.
    5. Antonini, Enrico G.A. & Romero, David A. & Amon, Cristina H., 2020. "Optimal design of wind farms in complex terrains using computational fluid dynamics and adjoint methods," Applied Energy, Elsevier, vol. 261(C).
    6. Ulku, I. & Alabas-Uslu, C., 2019. "A new mathematical programming approach to wind farm layout problem under multiple wake effects," Renewable Energy, Elsevier, vol. 136(C), pages 1190-1201.
    7. Guirguis, David & Romero, David A. & Amon, Cristina H., 2017. "Gradient-based multidisciplinary design of wind farms with continuous-variable formulations," Applied Energy, Elsevier, vol. 197(C), pages 279-291.
    8. Feng, Ju & Shen, Wen Zhong, 2017. "Design optimization of offshore wind farms with multiple types of wind turbines," Applied Energy, Elsevier, vol. 205(C), pages 1283-1297.
    9. Siavash Asiaban & Nezmin Kayedpour & Arash E. Samani & Dimitar Bozalakov & Jeroen D. M. De Kooning & Guillaume Crevecoeur & Lieven Vandevelde, 2021. "Wind and Solar Intermittency and the Associated Integration Challenges: A Comprehensive Review Including the Status in the Belgian Power System," Energies, MDPI, vol. 14(9), pages 1-41, May.
    10. Barra, P.H.A. & Coury, D.V. & Fernandes, R.A.S., 2020. "A survey on adaptive protection of microgrids and distribution systems with distributed generators," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    11. Ju Feng & Wen Zhong Shen, 2015. "Modelling Wind for Wind Farm Layout Optimization Using Joint Distribution of Wind Speed and Wind Direction," Energies, MDPI, vol. 8(4), pages 1-18, April.
    12. Tao, Siyu & Xu, Qingshan & Feijóo, Andrés & Zheng, Gang & Zhou, Jiemin, 2020. "Wind farm layout optimization with a three-dimensional Gaussian wake model," Renewable Energy, Elsevier, vol. 159(C), pages 553-569.
    13. Guirguis, David & Romero, David A. & Amon, Cristina H., 2016. "Toward efficient optimization of wind farm layouts: Utilizing exact gradient information," Applied Energy, Elsevier, vol. 179(C), pages 110-123.
    14. Nicolas Kirchner-Bossi & Fernando Porté-Agel, 2018. "Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake Model," Energies, MDPI, vol. 11(12), pages 1-26, November.
    15. Abdelsalam, Ali M. & El-Shorbagy, M.A., 2018. "Optimization of wind turbines siting in a wind farm using genetic algorithm based local search," Renewable Energy, Elsevier, vol. 123(C), pages 748-755.
    16. Li, Hongze & Sun, Dongyang & Li, Bingkang & Wang, Xuejie & Zhao, Yihang & Wei, Mengru & Dang, Xiaolu, 2023. "Collaborative optimization of VRB-PS hybrid energy storage system for large-scale wind power grid integration," Energy, Elsevier, vol. 265(C).
    17. Nicolas Kirchner-Bossi & Fernando Porté-Agel, 2021. "Wind Farm Area Shape Optimization Using Newly Developed Multi-Objective Evolutionary Algorithms," Energies, MDPI, vol. 14(14), pages 1-25, July.
    18. Manzoor Ellahi & Ghulam Abbas & Irfan Khan & Paul Mario Koola & Mashood Nasir & Ali Raza & Umar Farooq, 2019. "Recent Approaches of Forecasting and Optimal Economic Dispatch to Overcome Intermittency of Wind and Photovoltaic (PV) Systems: A Review," Energies, MDPI, vol. 12(22), pages 1-30, November.
    19. Dongmyoung Kim & Taesu Jeon & Insu Paek & Daeyoung Kim, 2022. "A Study on Available Power Estimation Algorithm and Its Validation," Energies, MDPI, vol. 15(7), pages 1-14, April.
    20. Shi, Jing & Xu, Ying & Liao, Meng & Guo, Shuqiang & Li, Yuanyuan & Ren, Li & Su, Rongyu & Li, Shujian & Zhou, Xiao & Tang, Yuejin, 2019. "Integrated design method for superconducting magnetic energy storage considering the high frequency pulse width modulation pulse voltage on magnet," Applied Energy, Elsevier, vol. 248(C), pages 1-17.

    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:12:y:2019:i:22:p:4242-:d:284402. 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.