IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i14p4291-d595305.html
   My bibliography  Save this article

Performance Enhancement of Proposed Namaacha Wind Farm by Minimising Losses Due to the Wake Effect: A Mozambican Case Study

Author

Listed:
  • Paxis Marques João Roque

    (Faculty of Engineering and the Built Environment, Department of Mechanical and Mechatronics Engineering, Tshwane University of Technology, Private Bag X680, Pretoria 0001, South Africa)

  • Shyama Pada Chowdhury

    (Faculty of Engineering and the Built Environment, Department of Electrical Engineering, Tshwane University of Technology, Private Bag X680, Pretoria 0001, South Africa)

  • Zhongjie Huan

    (Faculty of Engineering and the Built Environment, Department of Mechanical and Mechatronics Engineering, Tshwane University of Technology, Private Bag X680, Pretoria 0001, South Africa)

Abstract

District of Namaacha in Maputo Province of Mozambique presents a high wind potential, with an average wind speed of around 7.5 m/s and huge open fields that are favourable to the installation of wind farms. However, in order to make better use of the wind potential, it is necessary to evaluate the operating conditions of the turbines and guide the independent power producers (IPPs) on how to efficiently use wind power. The investigation of the wind farm operating conditions is justified by the fact that the implementation of wind power systems is quite expensive, and therefore, it is imperative to find alternatives to reduce power losses and improve energy production. Taking into account the power needs in Mozambique, this project applied hybrid optimisation of multiple energy resources (HOMER) to size the capacity of the wind farm and the number of turbines that guarantee an adequate supply of power. Moreover, considering the topographic conditions of the site and the operational parameters of the turbines, the system advisor model (SAM) was applied to evaluate the performance of the Vestas V82-1.65 horizontal axis turbines and the system’s power output as a result of the wake effect. For any wind farm, it is evident that wind turbines’ wake effects significantly reduce the performance of wind farms. The paper seeks to design and examine the proper layout for practical placements of wind generators. Firstly, a survey on the Namaacha’s electricity demand was carried out in order to obtain the district’s daily load profile required to size the wind farm’s capacity. Secondly, with the previous knowledge that the operation of wind farms is affected by wake losses, different wake effect models applied by SAM were examined and the Eddy–Viscosity model was selected to perform the analysis. Three distinct layouts result from SAM optimisation, and the best one is recommended for wind turbines installation for maximising wind to energy generation. Although it is understood that the wake effect occurs on any wind farm, it is observed that wake losses can be minimised through the proper design of the wind generators’ placement layout. Therefore, any wind farm project should, from its layout, examine the optimal wind farm arrangement, which will depend on the wind speed, wind direction, turbine hub height, and other topographical characteristics of the area. In that context, considering the topographic and climate features of Mozambique, the study brings novelty in the way wind farms should be placed in the district and wake losses minimised. The study is based on a real assumption that the project can be implemented in the district, and thus, considering the wind farm’s capacity, the district’s energy needs could be met. The optimal transversal and longitudinal distances between turbines recommended are 8D o and 10D o , respectively, arranged according to layout 1, with wake losses of about 1.7%, land utilisation of about 6.46 Km 2 , and power output estimated at 71.844 GWh per year.

Suggested Citation

  • Paxis Marques João Roque & Shyama Pada Chowdhury & Zhongjie Huan, 2021. "Performance Enhancement of Proposed Namaacha Wind Farm by Minimising Losses Due to the Wake Effect: A Mozambican Case Study," Energies, MDPI, vol. 14(14), pages 1-22, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4291-:d:595305
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/14/4291/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/14/4291/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fei, Zhao & Tengyuan, Wang & Xiaoxia, Gao & Haiying, Sun & Hongxing, Yang & Zhonghe, Han & Yu, Wang & Xiaoxun, Zhu, 2020. "Experimental study on wake interactions and performance of the turbines with different rotor-diameters in adjacent area of large-scale wind farm," Energy, Elsevier, vol. 199(C).
    2. Ge, Mingwei & Zhang, Shuaibin & Meng, Hang & Ma, Hongliang, 2020. "Study on interaction between the wind-turbine wake and the urban district model by large eddy simulation," Renewable Energy, Elsevier, vol. 157(C), pages 941-950.
    3. Abraham, Aliza & Hong, Jiarong, 2020. "Dynamic wake modulation induced by utility-scale wind turbine operation," Applied Energy, Elsevier, vol. 257(C).
    4. Chiyori T. Urabe & Tetsuo Saitou & Kazuto Kataoka & Takashi Ikegami & Kazuhiko Ogimoto, 2021. "Positive Correlations between Short-Term and Average Long-Term Fluctuations in Wind Power Output," Energies, MDPI, vol. 14(7), pages 1-15, March.
    5. Scheaua Fanel Dorel & Goanta Adrian Mihai & Dragan Nicusor, 2021. "Review of Specific Performance Parameters of Vertical Wind Turbine Rotors Based on the SAVONIUS Type," Energies, MDPI, vol. 14(7), pages 1-23, April.
    6. Sedighi, Hamed & Akbarzadeh, Pooria & Salavatipour, Ali, 2020. "Aerodynamic performance enhancement of horizontal axis wind turbines by dimples on blades: Numerical investigation," Energy, Elsevier, vol. 195(C).
    7. Regodeseves, P. García & Morros, C. Santolaria, 2020. "Unsteady numerical investigation of the full geometry of a horizontal axis wind turbine: Flow through the rotor and wake," Energy, Elsevier, vol. 202(C).
    8. Muhammad Shahzad Nazir & Fahad Alturise & Sami Alshmrany & Hafiz. M. J Nazir & Muhammad Bilal & Ahmad N. Abdalla & P. Sanjeevikumar & Ziad M. Ali, 2020. "Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend," Sustainability, MDPI, vol. 12(9), pages 1-27, May.
    9. 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.
    10. Longo, Riccardo & Nicastro, Patricia & Natalini, Matteo & Schito, Paolo & Mereu, Riccardo & Parente, Alessandro, 2020. "Impact of urban environment on Savonius wind turbine performance: A numerical perspective," Renewable Energy, Elsevier, vol. 156(C), pages 407-422.
    11. Ould Moussa, Mohamed, 2020. "Experimental and numerical performances analysis of a small three blades wind turbine," Energy, Elsevier, vol. 203(C).
    12. Sun, Haiying & Yang, Hongxing, 2020. "Numerical investigation of the average wind speed of a single wind turbine and development of a novel three-dimensional multiple wind turbine wake model," Renewable Energy, Elsevier, vol. 147(P1), pages 192-203.
    13. Qian, Yaoru & Wang, Tongguang & Yuan, Yiping & Zhang, Yuquan, 2020. "Comparative study on wind turbine wakes using a modified partially-averaged Navier-Stokes method and large eddy simulation," Energy, Elsevier, vol. 206(C).
    14. Sun, Haiying & Gao, Xiaoxia & Yang, Hongxing, 2020. "A review of full-scale wind-field measurements of the wind-turbine wake effect and a measurement of the wake-interaction effect," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    15. Alain Ulazia & Gabriel Ibarra-Berastegi & Jon Sáenz & Sheila Carreno-Madinabeitia & Santos J. González-Rojí, 2019. "Seasonal Correction of Offshore Wind Energy Potential due to Air Density: Case of the Iberian Peninsula," Sustainability, MDPI, vol. 11(13), pages 1-22, July.
    16. Dhiman, Harsh S. & Deb, Dipankar & Foley, Aoife M., 2020. "Bilateral Gaussian Wake Model Formulation for Wind Farms: A Forecasting based approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    17. Zhenzhou Shao & Ying Wu & Li Li & Shuang Han & Yongqian Liu, 2019. "Multiple Wind Turbine Wakes Modeling Considering the Faster Wake Recovery in Overlapped Wakes," Energies, MDPI, vol. 12(4), pages 1-14, February.
    18. Raymond Byrne & Davide Astolfi & Francesco Castellani & Neil J. Hewitt, 2020. "A Study of Wind Turbine Performance Decline with Age through Operation Data Analysis," Energies, MDPI, vol. 13(8), pages 1-18, April.
    19. Wilkie, David & Galasso, Carmine, 2020. "Impact of climate-change scenarios on offshore wind turbine structural performance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    20. Ouammi, Ahmed & Dagdougui, Hanane & Sacile, Roberto & Mimet, Abdelaziz, 2010. "Monthly and seasonal assessment of wind energy characteristics at four monitored locations in Liguria region (Italy)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(7), pages 1959-1968, September.
    21. Chaouachi, Aymen & Covrig, Catalin Felix & Ardelean, Mircea, 2017. "Multi-criteria selection of offshore wind farms: Case study for the Baltic States," Energy Policy, Elsevier, vol. 103(C), pages 179-192.
    22. Guo, Peng & Chen, Si & Chu, Jingchun & Infield, David, 2020. "Wind direction fluctuation analysis for wind turbines," Renewable Energy, Elsevier, vol. 162(C), pages 1026-1035.
    23. Fant, Charles & Gunturu, Bhaskar & Schlosser, Adam, 2016. "Characterizing wind power resource reliability in southern Africa," Applied Energy, Elsevier, vol. 161(C), pages 565-573.
    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. Yanfang Chen & Young Hoon Joo & Dongran Song, 2022. "Multi-Objective Optimisation for Large-Scale Offshore Wind Farm Based on Decoupled Groups Operation," Energies, MDPI, vol. 15(7), pages 1-24, March.
    2. Muhammad Nabeel Hussain & Nadeem Shaukat & Ammar Ahmad & Muhammad Abid & Abrar Hashmi & Zohreh Rajabi & Muhammad Atiq Ur Rehman Tariq, 2022. "Micro-Siting of Wind Turbines in an Optimal Wind Farm Area Using Teaching–Learning-Based Optimization Technique," Sustainability, MDPI, vol. 14(14), pages 1-24, July.
    3. Muhammad Nabeel Hussain & Nadeem Shaukat & Ammar Ahmad & Muhammad Abid & Abrar Hashmi & Zohreh Rajabi & Muhammad Atiq Ur Rehman Tariq, 2022. "Effective Realization of Multi-Objective Elitist Teaching–Learning Based Optimization Technique for the Micro-Siting of Wind Turbines," Sustainability, MDPI, vol. 14(14), pages 1-24, July.

    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. Kaldellis, John K. & Triantafyllou, Panagiotis & Stinis, Panagiotis, 2021. "Critical evaluation of Wind Turbines’ analytical wake models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    2. Wen, Jiahao & Zhou, Lei & Zhang, Hongfu, 2023. "Mode interpretation of blade number effects on wake dynamics of small-scale horizontal axis wind turbine," Energy, Elsevier, vol. 263(PA).
    3. Zhang, Huan & Ge, Mingwei & Liu, Yongqian & Yang, Xiang I.A., 2021. "A new coupled model for the equivalent roughness heights of wind farms," Renewable Energy, Elsevier, vol. 171(C), pages 34-46.
    4. Wang, Tengyuan & Cai, Chang & Wang, Xinbao & Wang, Zekun & Chen, Yewen & Song, Juanjuan & Xu, Jianzhong & Zhang, Yuning & Li, Qingan, 2023. "A new Gaussian analytical wake model validated by wind tunnel experiment and LiDAR field measurements under different turbulent flow," Energy, Elsevier, vol. 271(C).
    5. Sun, Haiying & Qiu, Changyu & Lu, Lin & Gao, Xiaoxia & Chen, Jian & Yang, Hongxing, 2020. "Wind turbine power modelling and optimization using artificial neural network with wind field experimental data," Applied Energy, Elsevier, vol. 280(C).
    6. Yuan Li & Zengjin Xu & Zuoxia Xing & Bowen Zhou & Haoqian Cui & Bowen Liu & Bo Hu, 2020. "A Modified Reynolds-Averaged Navier–Stokes-Based Wind Turbine Wake Model Considering Correction Modules," Energies, MDPI, vol. 13(17), pages 1-19, August.
    7. Hornshøj-Møller, Simon D. & Nielsen, Peter D. & Forooghi, Pourya & Abkar, Mahdi, 2021. "Quantifying structural uncertainties in Reynolds-averaged Navier–Stokes simulations of wind turbine wakes," Renewable Energy, Elsevier, vol. 164(C), pages 1550-1558.
    8. Ziyu Zhang & Peng Huang & Haocheng Sun, 2020. "A Novel Analytical Wake Model with a Cosine-Shaped Velocity Deficit," Energies, MDPI, vol. 13(13), pages 1-20, June.
    9. Ma, Hongliang & Ge, Mingwei & Wu, Guangxing & Du, Bowen & Liu, Yongqian, 2021. "Formulas of the optimized yaw angles for cooperative control of wind farms with aligned turbines to maximize the power production," Applied Energy, Elsevier, vol. 303(C).
    10. Gao, Xiaoxia & Chen, Yao & Xu, Shinai & Gao, Wei & Zhu, Xiaoxun & Sun, Haiying & Yang, Hongxing & Han, Zhonghe & Wang, Yu & Lu, Hao, 2022. "Comparative experimental investigation into wake characteristics of turbines in three wind farms areas with varying terrain complexity from LiDAR measurements," Applied Energy, Elsevier, vol. 307(C).
    11. Ge, Mingwei & Gayme, Dennice F. & Meneveau, Charles, 2021. "Large-eddy simulation of wind turbines immersed in the wake of a cube-shaped building," Renewable Energy, Elsevier, vol. 163(C), pages 1063-1077.
    12. Eidi, Ali & Ghiassi, Reza & Yang, Xiang & Abkar, Mahdi, 2021. "Model-form uncertainty quantification in RANS simulations of wakes and power losses in wind farms," Renewable Energy, Elsevier, vol. 179(C), pages 2212-2223.
    13. Zhao, Shuang & Wang, Jianwen & Han, Yuxia & Liu, Zhen, 2022. "Research on the rotor speed and aerodynamic characteristics of a dynamic yawing wind turbine with a short-time uniform wind direction variation," Energy, Elsevier, vol. 249(C).
    14. Angelo Algieri & Pietropaolo Morrone & Sergio Bova, 2020. "Techno-Economic Analysis of Biofuel, Solar and Wind Multi-Source Small-Scale CHP Systems," Energies, MDPI, vol. 13(11), pages 1-21, June.
    15. Sun, Chong & Tian, Tian & Zhu, Xiaocheng & Hua, Ouyang & Du, Zhaohui, 2021. "Investigation of the near wake of a horizontal-axis wind turbine model by dynamic mode decomposition," Energy, Elsevier, vol. 227(C).
    16. Fan, Xiantao & Ge, Mingwei & Tan, Wei & Li, Qi, 2021. "Impacts of coexisting buildings and trees on the performance of rooftop wind turbines: An idealized numerical study," Renewable Energy, Elsevier, vol. 177(C), pages 164-180.
    17. He, Ruiyang & Yang, Hongxing & Sun, Haiying & Gao, Xiaoxia, 2021. "A novel three-dimensional wake model based on anisotropic Gaussian distribution for wind turbine wakes," Applied Energy, Elsevier, vol. 296(C).
    18. Li, Li & Huang, Zhi & Ge, Mingwei & Zhang, Qiying, 2022. "A novel three-dimensional analytical model of the added streamwise turbulence intensity for wind-turbine wakes," Energy, Elsevier, vol. 238(PB).
    19. Zahra Sefidgar & Amir Ahmadi Joneidi & Ahmad Arabkoohsar, 2023. "A Comprehensive Review on Development and Applications of Cross-Flow Wind Turbines," Sustainability, MDPI, vol. 15(5), pages 1-39, March.
    20. Cao, Lichao & Ge, Mingwei & Gao, Xiaoxia & Du, Bowen & Li, Baoliang & Huang, Zhi & Liu, Yongqian, 2022. "Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines," Applied Energy, Elsevier, vol. 323(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:14:y:2021:i:14:p:4291-:d:595305. 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.