Technical Assessment of Wind Energy Potentials in Bangladesh

The goal of reducing carbon dioxide (CO2) emissions is a global collective challenge for the clean energy group, and it is reflected in Thailand's Renewable Energy Development Plan (AEDP) in 2018, which includes wind power as one of the six clean energy sectors. This study analysed the wind energy potential in Pattaya City, Bang Lamung District, Chonburi Province, in the Eastern Economic Corridor (EEC) Development Zone Plan, by utilizing data from weather stations and the Meteorological Department during the period of 2019-2021 at a height of 10 meters above the ground, comparing the areas of Pattaya Station, Chonburi Station, and Kao Si Chang Station. For the possibility of wind turbines with default heights ranging from 60 to 90 meters, three models are available: Bonus 1.3 MW, SWT-1.3-62, and SWT-2.3-82, as per the research results. The wind resources were found to be of Wind Power Class 1, with poor resource potential, and the prevailing wind direction in the study area was identified as northeast and west in Ko Sichang, southeast in Pattaya, and southwest and west in Chonburi. The Pattaya Station area was deemed unsuitable for wind farm establishment due to its low-wind power potential, as indicated by an economic analysis (public sector). It is not recommended for private or business sectors as it may not generate attractive returns or may result in losses. Based on the observations, the wind turbine with the highest AEP may not be the best choice for investment returns. This was demonstrated through an analysis of the SWT-2.3-82 VS wind turbine on Kaya Sira Hill, Koh Sichang, which had a CO2 Emission Reduction capacity of 2,154.24 tons CO2/GWh and an AEP of 3.366 GWh. Despite this, the LOCE of this model was not the lowest among the three models, and the LCOE and NPV of the SWT-1.3-62 were higher than the SWT-2.3-82 VS at all discount rate values. Moreover, the IRR of the SWT-1.3-62 were lower than the others, indicating that higher investments do not always lead to better returns.

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“…( 1) and Eq. ( 2) demonstrate how the shape of the Weibull distribution can be expressed [26][27][28][29][30]…”

Section: Frequency Distributionmentioning

confidence: 99%

Techno-Economic Assessment of Wind Energy in Urban Environments: A Case Study in Pattaya, Thailand

Chaihong

Taweekun

2023

ARFMTS

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“…The wind speed characteristics and the wind power potentials of any specified area can be estimated by using several statistical methods among which the widely acceptable technique is the Weibull distribution [22]. This method comprises different techniques to estimate the scale and shape parameters of Weibull based on the form of accessible data and the required level of complexity.…”

Section: Weibull Distribution Functionmentioning

confidence: 99%

“…Most of the new capacity should be provided by using solar energy with 54% and by using wind energy with 44 % capacity. However, when data from 2016 to 2018 is examined, it indicates that, to meet the target requirements for wind energy will be difficult [15,17,22]. However, Bangladesh has a limited number of wind power plants.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Wind Power Potentials in Chittagong, Bangladesh Using Weibull Distribution Function

Tarik Aziz Patwary,

Djamal Hissein Didane,

Sami Al-Alimi

et al. 2023

ARFMTS

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Wind resource assessment is a key stage in planning for a wind energy project to predict the output power. Therefore, a full understanding of the wind characteristics at the potential site is required. This study examines the potential wind power of twelve meteorological stations in the Chittagong division over a tenure of ten-year ranges from 2010 to 2019. The statistical analyzer as the Weibull two-parameter distribution method has been utilized to determine the characteristics of wind speed and wind energy potential at the selected locations. The "WRPLOT" software has also been used to display the wind direction and the corresponding wind speed direction. Among these stations, Ambagan has a high wind factor, ranging from 2.1 to 3.2 m/s. The highest wind speed for Ambagan station was recorded in April (3.26 m/s) and the lowest was in November (1.06 m/s) compared to other selected stations. The Weibull form parameter (k) and scale parameter (c) at Ambagan station had the highest values of 1.5 and 3.5 m/s, respectively. With additional analysis and observation of these parameters, it became possible to predict the location in Chittagong with the greatest wind energy potential. In terms of wind direction, the prevailing wind direction of all stations is generally in the south. Furthermore, the results concluded that all of the analyzed parameters considered for wind behavior were numerically higher for Ambagan in contrast to other stations of the Chittagong division. This study clearly indicates that the Ambagan station should be used to maximize the potential of wind energy to attain the future goals of sustainable development for Bangladesh.

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