Wind turbine performance analysis for energy cost minimization in Sourth and North of Oman

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Wind turbine performance analysis for energy cost minimization in Sourth and North of Oman

Abstract& overview of the main output of this research:

The use of wind energy worldwide has grown in recent years to reduce greenhouse gas emissions. Wind power is free, but the installation and maintenance of wind turbines remain very costly. The size of the installation of the wind turbine is not only determined by wind statistics at a given location but also by turbine infrastructure and maintenance costs. The payback time of the turbine is dependent on turbine energy costs. This study estimates the wind power generation capacity of Northern and Southern Oman and discusses the selection of the most economical, efficient, and reliable wind turbines in Oman. HOMER Pro Software was used in this paper to evaluate the wind energy in the north and south of Oman and to provide well‑informed guidance on the most suitable turbines for the power needs of each area. Six different standard wind turbines were measured and compared in terms of the cost of energy and performance. The simulation analysis reveals that the DW54 turbine is the best possible turbine to generate electricity in northern Oman at $0.119/kW. Due to the difference in the wind regime between the north and the south of Oman, the simulation showed that the Hummer H25.0–200 kW turbine is the best option for south Oman with power generation at $0.070/kW. The northern wind turbine plant can efficiently contribute to the decarbonization of the energy sector in Oman, with a potential reduction of CO2 emission of approximately 19,000 tons/year in comparison to natural gas and 28,000 tons/year in comparison to diesel. In the Southern Power Plant, carbon emissions are reduced by 18,000 and 12,000 tons/year compared to diesel and natural gas.

 

Source Image: Yassine Charabi,2020

Unmet electrical loads for different turbines

Conclusion

In this study, the feasibility of using wind energy as a source of power production was calculated by collecting and analyzing hourly data on wind regimes over a 1-year period. HOMER Pro software was used to calculate the C.O.E. production of six different wind turbines, in order to select the most suitable wind turbine for two distinct locations in the north and south of Oman. The study’s main findings can be summarized as follows:

  • DW54 turbine produced by Energy Wind Technology in Amersfoort, The Netherlands, would have the best performance for Oman’s northern regions and can generate the cheapest possible energy from wind at $0.119/kW.
  • H25.0–200  kW turbine manufactured by the AnhuiHummer Dynamo Company of Hefeit, China, gives the best C.O.E. production for the southern regions of Oman, and the lowest possible wind energy can be produced at $0.70/KW.• The difference of the wind regime between the northern and southern parts of Oman and the power curves of the turbines are the main reasons for the selection of two different wind turbines from different manufacturers.
  • The northern wind turbine plant is estimated to decrease CO2 emissions by around 19,000 tons per year, compared to natural gas, while diesel emissions by around by 28,000 tons per year.
  • The southern wind turbines have a potential carbon emission reduction of about 18,000 and 12,000 tons per year compared to diesel and natural gas.

The application of the turbine selection using the HOMER Model described in this paper determined that the H25.0–200  kW turbine selected for the southern parts of Oman has a C.O.E. that is 58.8% lower than theDW54  turbine that was selected for the northern parts of the country. The application of the method followed in this research by developers during the planning stage could significantly improve the financial performance of their investments. Similarly, such techniques could be added to tools such as WAsP to improve decision-making during the initial planning stage.

The full resech available from: https://www.researchgate.net/publication/343709239_Wind_turbine_performance_analysis_for_energy_cost_minimization

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