On the Wind Resource Assessment along the Western Coast of Thailand

Niyomtham, Lattawan; Lertsathittanakorn, Charoenporn; Waewsak, Jompob; Gagnon, Yves

doi:10.1016/j.egypro.2017.10.387

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…The wind energy potential at 140 m (AGL) displayed the potential area in the mountainous region of Yala and Narathiwat provinces with wind speeds above 8.0 m/s, the optimization of wind power plant simulation could produce 690 GWh/year of electricity and avoid greenhouse gas emissions of 1.2 million tons of CO 2 gas emission /year (Waewsak et al 2019). Moreover, the high resolution of wind maps on the western coast of the country presented the highest potential for the coast of the Andaman Sea is located in the Thungkhangok district with an annual mean wind speed of 8.14 m/s which could generate annual energy of 51 GWh/year and 58% of a capacity factor for the 10 MW wind power plant simulation (Niyomtham et al 2017) in line with the optimized of WTGs modelling results. the optimized of WTGs modelling results based on WindSim indicated those areas could generate the energy production of 135 GWh/year when using a single wind turbine model for the ve sites and accordingly avoid the greenhouse gas emissions of more than 80 kilotons of CO 2 gas emission /year (Niyomtham et al 2022).…”

Section: Introductionsupporting

confidence: 57%

Mesoscale/Microscale modelling for evaluation of wind energy potential and cost energy in developing country using a high-resolution grid: A case study in the northeastern region of Thailand

Tawinprai

Polnumtiang

Suksomprom

et al. 2023

Preprint

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The high demand for electricity during the pandemic has increased electricity prices. Awareness of power generation from renewable sources has become a global commitment to improve environmental issues due to greenhouse gas emissions. Wind resource maps for the center of the northeastern region of Thailand are presented in this research. Wind energy potential maps were obtained by mesoscale (MC2) and microscale (Ms-Micro) wind flow modelling. The simulation results were validated using observations from the wind mast installed in the research area. Indicated that the mean wind speed at an elevation of 120 m (AGL) varies in range from 1.52 to 3.62 m/s, 2.49 to 3.29 m/s, and 2.26 to 3.42 m/s for Khon Kaen, Maha Sarakham and Roi Et site, respectively. The wind power estimation revealed that the Maha Salakham site could produce the largest energy of 17,804.16 MWh/year, in accordance with a capacity factor of approximately 21% and an LCOE value about 0.21 USD/kWh. Nevertheless, the information on wind potential, as displayed in this research, must be additionally considered for the monitoring and estimation of the performance of other types of wind turbines, the impact on the environment, and social acceptance in further research.

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Section: Introductionsupporting

confidence: 57%

Mesoscale/Microscale modelling for evaluation of wind energy potential and cost energy in developing country using a high-resolution grid: A case study in the northeastern region of Thailand

Tawinprai

Polnumtiang

Suksomprom

et al. 2023

Preprint

Self Cite

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“…For instance, wind-speed dynamics have been incorporated for project evaluation in the USA [22]. Likewise, wind resource assessment for Australia has been studied by Hallgren et al [23] and along the Western Coast of Thailand by Niyomtham et al [24]. Additionally, the assessment for wind and solar power in the European Union has been considered by Buttler et al [25].…”

Section: Related Workmentioning

confidence: 99%

Technical assessment of small-scale wind power for residential use in Mexico: A Bayesian intelligence approach

et al. 2020

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Nowadays, the global energy system is in a transition phase, in which the integration of renewable energy is among the main requirements for attenuating climate change. Wind power is a major alternative to supply clean energy; hence, its widespread penetration is being pursued in all end-use sectors. In particular, it is currently noteworthy to analyze the feasibility of deploying small-scale wind power technology to provide cleaner and cheaper energy in the residential sector. As a first step, a technical assessment must be carried out to provide crucial information to intensive energy consumers, providers of small-scale wind power technology, electric energy distribution utilities, and any other party, to help them decide whether or not to deploy small-scale wind turbines. With this aim, we propose to perform such an analysis using a suitable probabilistic paradigm to solve complex decisionmaking problems with uncertainty, namely Bayesian Intelligence, since wind resources and energy demands are intermittent variables, properly characterized by probability distribution functions. Then, the problem of determining the technical feasibility can be formulated as an investigation into whether or not small-scale wind turbine technology can produce enough energy to cover the excess demand of intensive energy residential consumers to get off high-priced tariffs. For this purpose, we introduce a novel model based on probabilistic reasoning to assess the suitability of small-scale wind turbine technology to produce the said energy, taking into consideration the availability of wind resources and the energy pricing structure. To demonstrate the usefulness and performance of the proposed model, we consider a case study of deploying 5 and 10 kW wind turbines and analyze the feasibility of their implementation in Mexico, where the energy pricing structure and scattered wind resource availability pose difficult challenges.

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“…In Thailand, researchers are interested in analyzing and calculating the possibilities in terms of topography, wind direction and economic feasibility. They are particularly interested in the southern region, which has high mountains and coastal plains with winds blowing from the southeast, creating winds strong enough to be analyzed and calculated [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Wind Potential Assessment for Songkhla, Thailand

Chayakorn Chaitammachok,

Juntakan Taweekun,

Kittinan Maliwan

2024

ARFMTS

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The analysis and evaluation of wind energy projects are important to understand their economic and environmental suitability for installation and operation. Using wind data from the Meteorological Department, Used WAsP software for calculating, simulating, and analyzing the Annual Energy Production (AEP) and then applied it to calculate the Levelized Cost of Electricity (LCOE) of wind turbines installed in the Songkhla province area. The selected areas are suitable for all three models of wind turbines in the simulation. From the wind data analysis, wind direct was found that in the east sector (sector 4), there is the highest wind frequency at the wind station. The simulation results from Laem son-on area and Hua khao area show suitable mean wind speeds for wind farm installations. In the wind turbine simulation, three models were compared: Bonus 300 kW MkIII, Bonus 1.3 MW, and PowerWind 56 900 kW. At the Songkhla cape area, the positions with the highest AEP were 132.485 MWh, 344.419 MWh, and 332.597 MWh, respectively. Hua Khao area, the positions with the highest AEP were 225.284 MWh, 586.303 MWh, and 540.045 MWh, respectively. In the LCOE calculations for all model of wind turbines, at both Laem Son- On and Hua Khao locations, the most cost-effective wind turbine is the Bonus 300 kW. It has an LCOE of $142.43/MWh and $83.76/MWh, respectively. Therefore, the most cost-effective location is Hua Khao.

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On the Wind Resource Assessment along the Western Coast of Thailand

Cited by 6 publications

References 5 publications

Mesoscale/Microscale modelling for evaluation of wind energy potential and cost energy in developing country using a high-resolution grid: A case study in the northeastern region of Thailand

Mesoscale/Microscale modelling for evaluation of wind energy potential and cost energy in developing country using a high-resolution grid: A case study in the northeastern region of Thailand

Technical assessment of small-scale wind power for residential use in Mexico: A Bayesian intelligence approach

Wind Potential Assessment for Songkhla, Thailand

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