Maximizing the Generated Power of Wind Farms by Using Optimization Method

Document Type : Research paper

Authors

1 Ahl Al Bayt University, Karbala, Iraq.

2 Al-Manara College for Medical Sciences, Amarah, Iraq.

3 Medical Technical College, Al-Farahidi University, Baghdad , Iraq.

4 AL-Nisour University College, Baghdad, Iraq.

5 Department of Optical Techniques, Al-Zahrawi University College, Karbala, Iraq.

Abstract

In this study, the Particle Swarm Optimization (PSO) method was employed to optimize the anticipated energy yield of a wind farm. The architecture of a wind farm, including its location, height, and shadow reduction, is determined using the PSO algorithm based on the turbine height and rotor diameter. The proposed model presents two potential scenarios for the wind velocity and dispersion direction originating from a level wind location. The findings indicate that the optimization of the wind farm layout, encompassing factors such as location, height based on hub and rotor diameter of turbines, and maximum energy output, leads to a reduction in the shadow effect. This is in contrast to prior methodologies that optimized only one or two elements at a time. The wind farm's output power was observed to have a significant increase (ranging between 40% and 98%), despite having the same total number of wind turbines. This increase was attributed to the utilization of different hub heights and rotor diameters in comparison to the wind farm with different hub heights and rotor diameters, but the same number of wind turbines.

Keywords

Main Subjects

References

  1. Molajou, A. Afshar, M. Khosravi, E. Soleimanian, M. Vahabzadeh, and H. A. Variani, “A new paradigm of water, food, and energy nexus,” Environ. Sci. Pollut. Res., pp. 1–11, 2021.
  2. Molajou, P. Pouladi, and A. Afshar, “Incorporating social system into water-food-energy nexus,” Water Resour. Manage., vol. 35, pp. 4561–4580, 2021.
  3. Koruzhde and R. W. Cox, “The transnational investment bloc in us policy toward saudi arabia and the persian gulf,” Cl. Race Corporate Power, vol. 10, no. 1, 2022.
  4. Koruzhde, “The iranian crisis of the 1970s-1980s and the formation of the transnational investment bloc,” Cl. Race Corporate Power, vol. 10, no. 2, 2022.
  5. Koruzhde and V. Popova, “Americans still held hostage: a generational analysis of american public opinion about the iran nuclear deal,” Polit. Sci. Q., vol. 137, no. 3, pp. 511–537, 2022.
  6. Matani and A. Mali, “Blending methanol as a renewable fuel in automotive industries towards minimizing vehicular air pollution,” Int. J. Recent Technol. Eng., vol. 8, no. 3, pp. 5496–5498, 2019.
  7. R. Nejad, J. Keller, Y. Guo, S. Sheng, H. Polinder, S. Watson, J. Dong, Z. Qin, A. Ebrahimi, R. Schelenz, et al., “Wind turbine drivetrains: state-of-the-art technologies and future development trends,” Wind Energy Sci. Discuss., vol. 2021, pp. 1–35, 2021.
  8. H. Rajpar, I. Ali, A. E. Eladwi, and M. B. A. Bashir, “Recent development in the design of wind deflectors for vertical axis wind turbine: A review,” Energ., vol. 14, no. 16, p. 5140, 2021.
  9. Mousavi-Sarabi, M. Jadidbonab, and B. Mohammadi Ivatloo, “Stochastic assessment of the renewable–based multiple energy system in the presence of thermal energy market and demand response program,” J. Oper. Autom. Power Eng., vol. 8, no. 1, pp. 22–31, 2020.
  10. Azlan, J. Kurnia, B. Tan, and M.-Z. Ismadi, “Review on optimisation methods of wind farm array under three classical wind condition problems,” Renewable Sustainable Energy Rev., vol. 135, p. 110047, 2021.
  11. Jasemi and H. Abdi, “Probabilistic multi-objective optimal power flow in an ac/dc hybrid microgrid considering emission cost,” J. Oper. Autom. Power Eng., vol. 10, no. 1, pp. 13–27, 2022.
  12. Genus and M. Iskandarova, “Transforming the energy system? technology and organisational legitimacy and the institutionalisation of community renewable energy,” Renewable Sustainable Energy Rev., vol. 125, p. 109795, 2020.
  13. D. Leiren, S. Aakre, K. Linnerud, T. E. Julsrud, M.-R. Di Nucci, and M. Krug, “Community acceptance of wind energy developments: Experience from wind energy scarce regions in europe,” Sustainability, vol. 12, no. 5, p. 1754, 2020.
  14. Aravindhan, M. Natarajan, S. Ponnuvel, and P. Devan, “Performance analysis of shrouded invelox wind collector in the built environment,” Sci. Technol. Built Environ., vol. 28, no. 5, pp. 677–689, 2022.
  15. Shahdadi, B. ZM-Shahrekohne, and S. Barakati, “Analyzing impacts of facts devices in dealing with short-term and long-term wind turbine faults,” J. Oper. Autom. Power Eng., vol. 7, no. 2, pp. 206–215, 2019.
  16. Porté-Agel, M. Bastankhah, and S. Shamsoddin, “Windturbine and wind-farm flows: A review,” Boundary Layer Meteorol., vol. 174, no. 1, pp. 1–59, 2020.
  17. Fuglsang and H. A. Madsen, “Optimization method for wind turbine rotors,” J. Wind Eng. Ind. Aerodyn., vol. 80, no. 1-2, pp. 191–206, 1999.
  18. Ozgener and L. Ozgener, “Exergy and reliability analysis of wind turbine systems: a case study,” Renewable Sustainable Energy Rev., vol. 11, no. 8, pp. 1811–1826, 2007.
  19. Emami and P. Noghreh, “New approach on optimization in placement of wind turbines within wind farm by genetic algorithms,” Renewable Energy, vol. 35, no. 7, pp. 1559–1564, 2010.
  20. Pope, I. Dincer, and G. Naterer, “Energy and exergy efficiency comparison of horizontal and vertical axis wind turbines,” Renewable energy, vol. 35, no. 9, pp. 2102–2113, 2010.
  21. Asgari and M. Ehyaei, “Exergy analysis and optimisation of a wind turbine using genetic and searching algorithms,” Int. J. Exergy, vol. 16, no. 3, pp. 293–314, 2015.
  22. Ashuri, M. B. Zaaijer, J. R. Martins, G. J. Van Bussel, and G. A. Van Kuik, “Multidisciplinary design optimization of offshore wind turbines for minimum levelized cost of energy,” Renewable energy, vol. 68, pp. 893–905, 2014.
  23. M. Mortazavi, M. R. Soltani, and H. Motieyan, “A pareto optimal multi-objective optimization for a horizontal axis wind turbine blade airfoil sections utilizing exergy analysis and neural networks,” J. Wind Eng. Ind. Aerodyn., vol. 136, pp. 62–72, 2015.
  24. H. Fakehi, S. Ahmadi, and M. R. Mirghaed, “Optimization of operating parameters in a hybrid wind–hydrogen system using energy and exergy analysis: Modeling and case study,” Energy Convers. Manage., vol. 106, pp. 1318–1326, 2015.
  25. S. Shehata, K. M. Saqr, Q. Xiao, M. F. Shehadeh, and A. Day, “Performance analysis of wells turbine blades using the entropy generation minimization method,” Renewable Energy, vol. 86, pp. 1123–1133, 2016.
  26. Wang, A. Kolios, T. Nishino, P.-L. Delafin, and T. Bird, “Structural optimisation of vertical-axis wind turbine composite blades based on finite element analysis and genetic algorithm,” Compos. Struct., vol. 153, pp. 123–138, 2016.
  27. Ahmadi and M. Ehyaei, “Exergy analysis of a wind turbine,” Int. J. Exergy, vol. 6, no. 4, pp. 457–476, 2009.
  28. Bai, X. Ju, S. Wang, W. Zhou, and F. Liu, “Wind farm layout optimization using adaptive evolutionary algorithm with monte carlo tree search reinforcement learning,” Energy Convers. Manage., vol. 252, p. 115047, 2022.
  29. Guo, M. Zhang, B. Li, and Y. Cheng, “Influence of atmospheric stability on wind farm layout optimization based on an improved gaussian wake model,” J. Wind Eng. Ind. Aerodyn., vol. 211, p. 104548, 2021.
  30. Khanali, S. Ahmadzadegan, M. Omid, F. Keyhani Nasab, and K. W. Chau, “Optimizing layout of wind farm turbines using genetic algorithms in tehran province, iran,” Int. J. Energy Environ. Eng., vol. 9, pp. 399–411, 2018.
  31. Zahedi, A. Ahmadi, and M. Sadeh, “Investigation of the load management and environmental impact of the hybrid cogeneration of the wind power plant and fuel cell,” Energy Rep., vol. 7, pp. 2930–2939, 2021.
  32. Wang, D. Tan, and L. Liu, “Particle swarm optimization algorithm: an overview,” Soft Comput., vol. 22, pp. 387–408, 2018.
  33. Marini and B. Walczak, “Particle swarm optimization (pso). a tutorial,” Chemom. Intell. Lab. Syst., vol. 149, pp. 153–165, 2015.
  34. Nourani, N. Rouzegari, A. Molajou, and A. H. Baghanam, “An integrated simulation-optimization framework to optimize the reservoir operation adapted to climate change scenarios,” J. Hydrol., vol. 587, p. 125018, 2020.
  35. Kennedy and R. Eberhart, “Particle swarm optimization in: Proceedings of icnn95-international conference on neural networks, 1942–1948,” IEEE, Perth, WA, Australia, 1995.
  36. M. Lateef, A. I. Al-Tmimi, and O. I. Abdullah, “Design and implementation of wind energy analysis tool (weatb) in iraq,” in AIP Conf. Proc., vol. 2144, AIP Publishing, 2019.
Journal of Operation and Automation in Power Engineering
Volume 11, Special Issue (Open)
Sustainable Power Systems, Energy Management, and Global Warming
March 2023

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History

  • Receive Date: 27 June 2023
  • Revise Date: 10 September 2023
  • Accept Date: 11 September 2023

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