Cover
Vol. 18 No. 2 (2022)

Published: December 31, 2022

Pages: 53-59

Original Article

Improvement of Wind Energy Systems by Optimizing Turbine Sizing and Placement to Enhance System Reliability

Abstract

Wind energy and its conversion is part of renewable energy resources as cheaper and cleaner energy today even though the initial cost varies from place to place. Most of the government sector always promotes renewable energy with a provision of subsidies as observed worldwide. Wind energy is an actual solution over costlier conventional energy sources. If it is not properly placed and the selection of turbine design is not up to the mark, then investments may require more time to acquire Net Profit Value called as NPV. This research work is focused on the development of mathematical models to optimize the turbine size and locations considering all constraints such as the distance between the turbines, hub height, and investment in internal road and substation cost. Particle-Swarm-Optimization is an intelligent tool to optimize turbine place and size. The database management system is selected as the appropriate data storage platform for before and after optimization simulation. Various plots and excel outputs of .net programming are addressed for the success of optimization algorithms for the purpose of wind turbine placement and WTG design is suggested to manage wind energy such that power system reliability has been improved and the same is monitored through the reliability indices.

References

  1. P. Asaah, L. Hao, J. Ji, “Optimal placement of wind turbines in wind farm layout using particle swarm optimization”, Journal of Modern Power Systems and Clean Energy, Vol. 9, Issue 2, pp. 367–375, 2021. doi:10.35833/MPCE.2019. 000087
  2. P. Sudta, N. Weerachayapornkul, W. Ongsakul, J. G. Singh, N. Sasidaran, “Optimal placement and sizing of DG based on single phase wind turbine generator in distribution system”, 2018 International Conference and Utility Exhibition on Green Energy for Sustainable Development (ICUE) , pp. 1–7, 2018. doi:10.23919/ICUE- GESD.2018.8635685.
  3. R. F. Latif, S. Irtiza Ali Shah, U. Rauf, “Analysis of wind turbine’s velocity deficit, recovery and output power losses using a hybrid cfd-jensen’s wake model scheme”, in: 2019. 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST), pp. 778– 788, 2019. doi:10.1109/IBCAST.2019.8667111.
  4. S. Paul, Z. H. Rather, “A new approach for selection of a suitable wind turbine for a wind farm”, in: 2016 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), pp. 1–6, 2016. doi:10.1109/PEDES.2016.7914351.
  5. P. P. Biswas, P. Suganthan, G. A. J. Amaratunga, “Optimal placement of wind turbines in a windfarm using l-shade algorithm”, in: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 83–88, 2017. doi:10.1109/CEC.2017.7969299.
  6. M. Y. Khan, M. Ali, S. Qaisar, M. Naeem, C. Chrysostomou, M. Iqbal, “Placement optimization for renewable energy sources: Ontology, tools, and wake models”, IEEE Access, Vol. 8, pp. 72781 – 72800, 2020. doi:10.1109/ACCESS. 2020.2984901.
  7. Z. Liu, B. Zou, J. Huang, X. Zhang, L. Wang, F. Wen, “Optimal planning of a virtual power plant with demand side management”, in: TENCON 2018, IEEE Region 10 Conference, pp. 0859–0864, 2018. doi:10.1109/TENCON.2018.8650125. .
  8. A. Arief, M. B. Nappu, “DG placement and size with continuation power flow method”, in: 2015 International Conference on Electrical Engineering and Informatics (ICEEI), pp. 579–584, 2015. doi:10.1109/ICEEI.2015.7352566.
  9. P. M. Sonwane, “Particle swarm Optimization: A Tool development: Case study of optimal capacitor placement”, 2015, International journal of Electronics communication and soft computing science and engineering, Vol. 4, Issue 3, 2015.
  10. A. Zaher, A. Cruden, C. Booth, B. Leithhead, “Database management for high resolution condition monitoring of wind turbines”, in: 2009 44th International Shirsath & Burade | 59 Universities Power Engineering Conference (UPEC), pp. 1–5, 2009.
  11. M. Lv, B. Duan, H. Jiang, D. Dong, “Application of knowledge graph technology in unified management platform for wind power data”, in: IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, pp. 1762–1766, 2020. doi:10.1109/IECON43393.2020.9255141
  12. E. Tercan, “Land suitability assessment for wind farms through best-worst method and GIS in balıkesir province of turkey”, Sustainable Energy Technologies & Assessments, Vol. 47, 2021. doi.org/10.1016/j.seta.2021.101491
  13. P. Sonwane, V. Shirsath, T. Varshney, “Optimal placement of shunt capacitor to enhance distribution system reliability”, in: IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), pp. 1–5, 2020. doi:10.1109/PEDES49360.2020.9379833
  14. V. Shirsath, R. Agrawal, “Optimization through wind modeling by means of mechanical design to enhance wind power generation and system reliability”, in: 2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE), pp. 1–5, 2020. doi:10.1109/ICRAIE51050.2020.9358353.
  15. D. Li, S. Miao, “Fitting the wind speed probability distribution with maxwell and power maxwell distributions: A case study of North Dakota sites”, Sustainable Energy Technologies and Assessments, Vol. 47, 2021. doi.org/10.1016/j.seta.2021.101446.
  16. Dragana Subotic, “Spatial Optimization for Wind Farm Allocation”, Thesis, The Netherlands, September, 2017.
  17. Bilal Naji Alhasnawi, Basil H. Jasim, “A New Coordinated Control of Hybrid Microgrids with Renewable Energy Resources Under Variable Loads and Generation Conditions”, IJEEE, Vol. 16, Issue 2, 2020. DOI:10.37917/ijeee.16.2.1