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

doi:10.21203/rs.3.rs-2828376/v1

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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

https://doi.org/10.21203/rs.3.rs-2828376/v1

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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.

wind energy potential

wind resource mapping

atmospheric modelling

levelized cost of electricity (LCOE)

Northeastern of Thailand

renewable energy

The global electricity price has rapidly increased due to the rising demand for energy consumers during the invasion situation and the restriction of the coronavirus pandemic. These circumstances prompted enhanced policies to forereach the development of clean energy, reduce dependency on fuel imports, and raise awareness of power generation and electrification from renewable resources instead of fossil fuels worldwide (Bolton and Hinson 2022), whereas the demand for energy is rising, the concern about greenhouse gas emissions, which cause climate change, global warming, and pollution crisis, has emerged as a commitment in global enterprises to reduce the CO₂ gas substantially from current levels and to limit the temperature increase further to 1.5°C in 2050 with the Net-zero target (Mohammadi et al. 2016; Carver 2021). Apparently, fossil-based energy is the main contributor to the global climate change crisis (Kassem et al. 2019). Moreover, the Climate Change Committee emphasized the necessity for the government to prioritize actions to reduce and import emissions sustainably (House of Commons Public Accounts Committee 2021).

Wind power is the highest power generation among all renewable energy technologies, with electricity generation rising to a record 273 TWh, and growth is 55% higher than that achieved in 2020. Rapid development has placed China with nearly 70% growth in wind production (Bojek 2022), whereas wind power installations in Thailand have gradually grown in line with a newly installed capacity of 322 MW. The extremely of global energy prices rapidly due to ongoing war and the unprecedented of Covid-19 pandemic, pushed forward the energy sector including in Southeast Asia to improve to these rapid changes sustainably (ASEAN Centre for Energy (ACE) 2022), activating the Thai energy department to emerge awareness of electricity prices and climate crisis solving by reinforcing the capacity of electricity production especially in wind energy to reach up to 1,538 MW and aim to reduce the greenhouse gases emission of 117.6 tons of CO₂ gases emission or 20% by 2030 (DEDE 2020). In addition, the Thai energy department provides financial support for producers to underwrite the viability of wind power enterprises in areas with low to moderate wind resources (Waewsak et al. 2019).

The evaluation of wind energy potential is the key to assessing the characteristics of natural wind over the local territory to appraise the wind speed and the produced energy. Notably, the influence of wind flow over an individual topography affects the characteristics of wind speed and wind shear, which strongly depends on the location of the local area (Mundu et al. 2022). The approach to examining wind energy potential is generally assessed by analyzing the measured data from wind masts installed at research sites. Another procedure is the simulation of wind flow models, which is performed by numerical methods, Computational Fluid Dynamics (CFD), and numerical weather prediction (NWP) models (Brower et al. 2012). Despite the wind energy potential estimation being a circumspect attempt for processing wind power plant projects, the high costs, including the planning, manufacture, and maintenance of wind masts installed over a long time period in all areas, must be considered. Accordingly, wind energy resource mapping has become a reasonable way to characterize local wind flow and estimate energy (Liu et al. 2022) to estimate the overall wind power plant project before rechecking the monitoring process.

The development of wind resource mapping in Thailand has grown slightly over the last decade, and earlier wind resource maps were created using the numerical weather prediction model of the MesoMap with a nominal spatial resolution of 1 km. The presented wind speed maps of the entire country indicate that the mean wind speed of the country was below 6 m/s (TrueWind Solutions 2001), corresponding to the assessment of wind energy based on the calculation of the Karlsruhe Atmospheric Mesoscale Model (KAMM) with a horizontal spatial resolution of 3×3 km2, which represents the high potential areas located in the mountainous areas in the southern, northeastern, and western parts of the central region (Janjai et al. 2014). Nonetheless, the overall wind energy potential map with a high resolution is too coarse to evaluate wind resources at the regional or provincial level. The high resolution of wind energy potential maps has become a crucial tool for assessing the local wind flow regime in wind power plant projects.

The evolution of high-resolution wind energy potential maps generally presented the wind energy potential in the south of the country. The investigation of wind energy resources at Ko Yai in Songkhla province, which generated the wind resource maps and produced energy by numerical and CFD model in WEST Toolkit and WindSim. The simulation results referred to the wind speed at Ko Yai as reaching a maximum of 5.18 m/s along the ridge of the mountainous area of the land due to the wind power density of those areas and the finest simulation of 20 MW wind power plant could produce the annual energy production about 33 GWh/year with a capacity factor of 21% (Waewsak et al. 2017). With a similar approach, the wind resource maps of the southernmost region of Thailand using CFD modelling to perform the wind resource maps with 50 meters of resolution. 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 CO₂ 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 five sites and accordingly avoid the greenhouse gas emissions of more than 80 kilotons of CO₂ gas emission /year (Niyomtham et al. 2022).

Regarding the wind energy assessment in the northeastern region of Thailand, the mean wind speed in Mukdahan province proffered at the elevation of 120 m (AGL) at approximately 4.51 m/s, which could produce the energy output of 6,979 MWh/year with 2.0 MW wind turbine (Polnumtiang and Tangchaichit 2021). The mean wind speed at a similar elevation in Kalasin province is 3.94 m/s with the distributed wind directions mainly from the East to the South, which could generate 2,747 MWh/year and share 15.68% of a capacity factor with a similar wind turbine (Polnumtiang and Tangchaichit 2018; Suksomprom et al. 2019).

Whether the northeastern region has significant potential energy resources caused by the wind characteristics flowing over the local area. The potential of harnessing wind energy in any location depends on the specific site. Noticeably that the emphasis knowledge on local wind resources is an imperative way to accomplish wind power plant projects (Pandeya et al. 2022) due to the local wind climatology is influenced by topography, could be mentioned that the wind flow is modified by local terrain, land and sea surface, and roughness of surface which depend on the location of each local area (Şahin and Türkeş 2020). While the previous research on the northeastern territory of the country mainly focuses in terms of measured monitoring with the small area around the wind masts. However, considering the high cost of the wind masts installations has limited the assessment of the wind energy potential in the northeastern region in the overall area (Liu et al. 2022).

Identification of the potential areas in the country needs to attain in response to power generation from renewable sources for solving the environment and climate crisis continually and sustainably whereas the available wind resource maps have been too coarse and deficient to assess the energy potential at the level of the district or provincial levels. Ostensibly that the wind energy potential of the northeastern region has not been evolving with high-resolution modelling. These limitations lead to the objective of this research, which is to develop the wind resource maps of the upper northeastern region in Thailand at the level of high-resolution simulation. By using the wind flow models were performed using a coupled mesoscale and microscale atmospheric model, the geophysical data and the topography data of the land use database in combination with the Wind Energy Simulation (WEST) toolkit. The 10 MW of wind power plants are delineated to specify the efficiency of the potential sites together with the computing of annual energy production, a capacity factor, levelized cost of energy and the reduction of CO₂ emissions gas of each site have been investigated in this research to identify the most appropriate wind power plants.

The methodology of this research is displayed in Fig. 1.

The methodology of this research comprises five main stages. The information of the research area including the wind climate data, the geophysical data, detail of the roughness surface, the land cover data and the wind masts location are prepared in the first process. Subsequently, the simulation of wind flow characteristics over the research area was performed by mesoscale and microscale of atmospheric modelling combined in the open source software, Wind Energy Simulation Toolkit (WEST). And the results from the simulation stage were applied for validation with observed data and visualized by Geographical Information System (GIS) software in the fourth stage. The wind resource results from the visualized process were classified to define the range of wind speed, the energy production, the reduction of CO₂ emission gases and the feasibility of wind power plants in the final process of the research. The summary of the research methodology is presented in Fig. 1.

2.1. Research Area

The research area is located in the center of the northeastern region of Thailand, which is comprised of three provinces including Khon Kaen, Maha Sarakham, and Roi Et provinces. The total area of the research is 24,477.12 square kilometres, which is varying from 15º24 to 17°5 north and the longitude is varying from 101°44 to 104º21 east (Land Use Development Department 2022). The topography of the study area is plateau terrain, as shown in Fig. 2 and Fig. 3. The highest elevation is 838 m (ASL), which is located in the west of Khon Kaen province.

2.2. Preparation of Data Input

In the mesoscale and microscale wind flow simulation with WEST, the atmospheric data based on the NCEP/FNL Operational Global Analysis database, which operationally every six hours by the resolution of 1 degree × 1 degree, covers ten years period from 2008 to 2018 (Research Data Archive 2020) and those atmospheric data have been statistically systematized by the frequency of occurrence of three parameters involving wind speed, wind direction, and atmospheric temperature are prepared for the wind resource maps simulation as the input data. (Waewsak et al. 2015) (Gasset and Yu 2021). And the detail of physical geography data, for instance, geophysical data, vegetation data or elevation of the ground surface are contained in the GenGeo database which is combined in the WEST. Whilst the topography and the detail of surface roughness applied for the visualization input data were received from the Land Use Land Cover (LULC) maps, the Land Development Department (LDD) (Land Development Department 2020) (NASA 2020)

2.3. Mesoscale and Microscale Modelling

In general, the wind flow model simulates the flow over obstacles, the roughness change, the construction, and the sheltering obstacles, the varied topography is influent the characteristics of wind flow (Meziane et al. 2021). Respecting wind flow modelling methods characterize the wind flow in the atmospheric boundary layer. The primary modelling simulated the wind flow on an environmental scale in the mesoscale modelling step, thus simulating the flow elaborately, to generate the speed and the produced energy in the microscale modelling step. In this research, the open-source software to approach the wind resource maps in the research area is Wind Energy Simulation Toolkit (WEST).

The Mesoscale Compressible Community (MC2) atmospheric model which solves the fully compressible Euler equation of motion based on an efficient Semi-Implicit Semi-Lagrangian time-stepping approach is simulated for primary modelling in mesoscale modelling together with the topography, the land cover, and geophysical datasets in WEST geodatabase. Therefore, the preparations of mesoscale results are applied as the microscale input data. Thus, the Ms-Micro microscale model which using linear models, such as the ones based on the Jackson and Hunt model or the RANS methods to simulate the flow over terrain and reproduce the wind speeds and wind power in boundary layer fields over terrains (Gasset et al. 2012).

The mesoscale modelling simulated the introductory model with 3 kilometers and 5 kilometers of spatial resolution. The statistic of mesoscale results is prepared as the microscale input data subsequently. While the delicate model was simulated with 100 meters and 200 meters of spatial resolution to cover the research area completely in microscale modelling.

2.4. Data Validation

The verification of the performance and accuracy of the wind flow simulations for the upper northeastern region of the country could identify the uncertainty of the prediction as the error of the wind flow model. The difference between measured and predicted wind speed could determine by the measured and predicted wind speed ratio (M/P) and the Percent Mean Relative Error (PMRE) as shown in the following Eq. (1) and Eq. (2). (Chancham et al. 2017)

$$Measured/Predicted=\frac{Observed wind speed \left(m/s\right)}{Predicted wind speed \left(m/s\right)}$$

$$PMRE=\frac{1}{n}\sum _{i=1}^{n}\left(\frac{{o}_{i}-{p}_{i}}{{o}_{i}}\right)\times 100\%$$

Where ${o}_{i}$is the observed wind speed (m/s) is, ${p}_{i}$is the predicted wind speed (m/s) and $n$ is the number of data of research period.

The results of mean wind speed obtained from mesoscale and microscale are compared with measured data, which is observed from the wind mast placed at 16.13°N, 102.7°E (Polnumtiang 2019) as presented the location of the wind mast in Fig. 4.

2.5. Wind Turbine Selection and Wind Energy Potential Estimation

The quantity of wind energy production generally depends on the wind speed distribution, the topography of the territory, the location of wind turbines and the wind turbine features (Meziane et al. 2021). In this research, the mean wind speed results from microscale wind resource maps at elevations of 120 m (AGL) of the research area, which was applied to determine the minimum of wind speed that starts the power generation of the wind turbine or mention the cut-in speed of the turbine (BRANZ Inc 2022).

Concerning Thailand's wind energy policies, the Very Small Power Producer (VSPP 10 MW) of 10 MW wind power plant scenarios (OERC 2022) was used to evaluate the energy production from wind turbine generators by calculating the Annual Energy Production (AEP) and Capacity Factor (CF) of each potential site. The Annual Energy Production (AEP) and Capacity Factor (CF) of the wind power plant can be calculated as Eq. (3) and Eq. (4).

$$Annual Energy Production =8760 {\int }_{{U}_{cut in}}^{{U}_{cut out}}{P}_{t}\left(u\right)f\left(u\right)du$$

$$Capacity Factor=\frac{AEP}{{N\times P}_{r}}\times 8760$$

where ${\text{U}}_{\text{c}\text{u}\text{t} \text{o}\text{u}\text{t}}$ and ${\text{U}}_{\text{c}\text{u}\text{t} \text{i}\text{n}}$ are the cut-out speed and cut-in speed of the wind turbine and 8,760 is the number of hours per year while ${\text{P}}_{\text{t}}\left(\text{u}\right)$, ${\text{P}}_{\text{r}}$ and $\text{N}$ are power curve, rated power and the number of wind turbine generators, serially (Polnumtiang and Tangchaichit 2018).

2.6. Feasibility of Wind Power Plant Project

The feasibility viability of wind power is varying across locations whereupon affecting the benefits and costs associated with a particular wind project (Ohunakin and Akinnawonu 2012). In this context, in the investigation of the costs for an overall wind power plant project in high potential site, the levelized cost of energy (LCOE) is computed following the given Eq. (5), for comparing the combination of investment costs, operation and maintenance cost, and fuel costs (Chrysochoidis-Antsos et al. 2020; Ranthodsang et al. 2020).

$$LCOE=\frac{\sum _{t=1}^{n}\frac{I+O\&M+F}{{\left(1+r\right)}^{r}}}{\sum _{t=1}^{n}\frac{E}{{\left(1+r\right)}^{r}}}$$

where $I$ is the initial cost in year $t$, $O\&M$ is the cost of operation and maintenance, $F$ is the fuel cost in year $t$, $E$ is the electricity generation, $r$ is the discount rate, and $n$ is the lifetime of the project (Waewsak et al. 2017) (Polnumtiang and Tangchaichit 2021).

The initial cost ($I$) including land cost, turbine price, installation cost and grid integration is 30% of wind turbine price.
The cost of operation, maintenance and repairing ($O\&M$) is 2% of wind turbine price.
The discount rate ($r$) is 7% (Bank of Thailand 2022)
The life of the system ($N$) is 20 years
Wind turbine price is 960 USD/kW (IRENA 2020)

3.1. Data Validation

Investigating the uncertainty of mesoscale and microscale wind flow modelling for the central northeastern region, the M/P ratio, PMRE, and a scatter plot of the predicted and the observed mean wind speeds are displayed following.

Table 1

The measured and predicted wind speed result from the wind mast and wind resource maps simulation.
Height (m, AGL)	Observed (m/s)	Mesoscale (m/s)		Microscale (m/s)
Height (m, AGL)	Observed (m/s)	Resolution Grid of 5 km	Resolution Grid of 3 km	Resolution Grid of 200 m	Resolution Grid of 100 m
60	3.79	2.82	2.691	2.720	2.806
90	4.32	2.986	2.857	2.880	2.975
120	4.66	3.103	2.973	2.993	3.101
Average	4.257	2.970	2.840	2.864	2.961

Table 2

The M/P Ratio and PMRE of the validation.
	Height (m, AGL)	Mesoscale (m/s)		Microscale (m/s)
	Height (m, AGL)	Resolution Grid of 5 km	Resolution Grid of 3 km	Resolution Grid of 200 m	Resolution Grid of 100 m
M/ P Ratio	60	1.344	1.408	1.393	1.351
	90	1.447	1.512	1.500	1.452
	120	1.502	1.044	0.993	0.965
Average M/ P Ratio		1.433	1.499	1.486	1.438
PMRE (%)	60	25.59%	29.00%	28.23%	25.96%
	90	30.88%	33.87%	33.33%	31.13%
	120	33.41%	36.20%	35.77%	33.45%
Average PMRE		30.23%	33.27%	32.71%	30.45%

n comparison between observed and simulated wind resource maps, the M/P ratio is in the range of 1.044 to 1.512 for mesoscale maps and the range of 0.965 to 1.5 for microscale maps, while the average M/P ratio for mesoscale and microscale shared 1.376 and 1.276 as displayed in Table 1, meaning that the measured mean speed is normally greater than simulated mean speed for both mesoscale and microscale modelling. In addition, the average PMRE for mesoscale and microscale wind resource maps are 31.49% and 31.32%, the detail of the validation of observed and simulated wind maps is presented in Table 2. Regarding the validated results of each size of grid modelling, the M/P ratio and PMRE of the grid resolution with 5 km and 100 m are less than the other grid models. Thus, those spatial resolutions are more appropriate for the evaluation of wind energy potential over the research area.

The scatter plot between observed and predicted mean wind speed obtained from wind mast and wind resource maps as presented in Fig. 5, referred that both mesoscale and microscale modelling is in the range of error of ± 20% for all total wind speed results. Considering the uncertainty of wind flow modelling, which could verify that the wind flow models are reasonable for wind resource assessment and wind power plant evaluation in the central northeastern area.

3.2. Mesoscale and Microscale of Wind Resource Maps

The simulation results from mesoscale modelling are presented the mean wind speed at elevations of 100 m and 120 m (AGL) in the central of northeastern, as shown in Fig. 6, and Fig. 7.

As the mesoscale modelling results, the highest mean wind speed is varying in range from 3.69 to 3.86 m/s, mostly occurring in the southern area of the research territory.

As the microscale results in Fig. 8, and Fig. 9 which were generated from the MS-Micro modelling with 100 m of grid resolution presented the highest mean wind speeds in the range of 3.67 to 3.72 m/s. The detail of wind speed results over the research area at 120 m (AGL) are displayed in Table 3 (Land Use Development Department 2022), indicating that the minimum wind speed over the center of the northeastern region is varying in from 1.52 to 2.49 m/s, it could mention the minimum wind speed that starts the power generation of the wind turbine is about 2 m/s.

The characteristics of wind flow over the central northeastern region are influenced by the local monsoon direction and the topography of those areas, since the profile of wind flows over obstacles of land cover, such as vegetation covering the soil surface, the other buildings, and the plain regions (Arshad and O’Kelly, 2019. Due to the friction or drag force exerted on the layers of the atmosphere near the ground, causing wind speeds to tend to decrease in highly rough areas (Brower et al. 2012). It could be remarked that the wind speed in the research area is influenced by the mountainous areas located in these regions as well.

Table 3

The mean wind speeds of each site at elevations of 120 m (AGL)
Province	Area size (km²)	Mean Wind Speed (m/s)	Wind Speed Range (m/s)
Khon Kaen	10,885.99	2.64	1.52–3.62
Maha Sarakham	5,291.68	2.82	2.49–3.29
Roi Et	8,299.45	2.68	2.26–3.42

3.3. Wind Turbine Selection

The results of the mean wind speed at 120 m (AGL) in Table 3, revealed that the mean wind speed is vary in a range of 2.64 to 2.82 m/s. Apparently that the minimum wind speed for energy generation by the wind turbine, mentioned that the cut in speed for the wind turbine generator in these territories is about 2 m/s. Among the commercial wind turbine generator with low cut-in speeds, four wind turbine models from Siemens Gamesa (Pierrot 2016, 2018a, b, c) are outstanding with the cut in speeds of 2 m/s, which is reasonable for wind power estimation in central the northeastern region. The specifications of the wind turbine generator have appeared in Table 4.

Table 4

Detail of wind turbine mechanical data for wind energy estimation in research region
Wind turbine	Rotor diameter (m)	Rated output (MW)	Hub height (m)	Cut-in speed (m/s)	Rated wind speed (m/s)	Cut-out speed (m/s)
G126-2.5 MW	126	2.5	85 to 129	2	10	25
G132-3.3 MW	132	3.3	84 to 134	2	25	11
G128-4.5 MW	128	4.5	81 to 140	2	27	12.5
G132-5.0 MW	132	5	95 to 140	2	27	13.5

3.4. Wind Energy Potential Estimation

The variations in wind speed can affect large variations in wind turbine power output and the number of appropriate wind turbines could give the highest amount of wind energy with the lowest number of turbines to minimize (Charabi et al. 2022). Since the wind speed results obtained from wind resource maps at the elevation of 120 m (AGL) were statistically systemized by the wind speed bin and frequencies of occurrence in a year, the computation of the annual energy production (AEP) and capacity factor (CF) of wind power plants under the condition of VSPP 10 MW of Thai energy policies are applied to identify the wind energy potential of each site in the research area.

Table 5

Annual energy production, capacity factor and the reduction of CO₂ gas emission of wind power plants in three provinces.
Site Name	Wind Turbine Generator	Rated power (MW)	Number of Wind Turbine	AEP (MWh/year)	Capacity Factor (%)	Reduction of CO₂ gas emission (tones of CO₂/MWh)
Khon Kaen	G126-2.5 MW	2.5	4	16,269.93	20.64%	5,255.19
	G132-3.3 MW	3.3	3	15,194.61	19.57%	4,907.86
	G128-4.5 MW	4.5	2	4,204.91	5.33%	1,358.19
	G132-5.0 MW	5	2	4,748.51	6.02%	1,533.77
Maha Salakham	G126-2.5 MW	2.5	4	17,804.16	22.58%	5,750.75
	G132-3.3 MW	3.3	3	16,891.05	21.75%	5,455.81
	G128-4.5 MW	4.5	2	4,607.10	5.84%	1,488.09
	G132-5.0 MW	5	2	5,252.52	6.66%	1,696.56
Roi Et	G126-2.5 MW	2.5	4	15,069.73	19.11%	4,867.52
	G132-3.3 MW	3.3	3	13,809.72	17.79%	4,460.54
	G128-4.5 MW	4.5	2	3,872.03	4.91%	1,250.67
	G132-5.0 MW	5	2	4,331.24	5.49%	1,398.99

As the AEP and CF results in Table 5, appeared that the greatest energy output was produced by a wind turbine with a G126-2.5 MW model in Maha Salakham site, followed by G132-3.3 MW in a similar site and G126-2.5 MW in Khon Kaen site, corresponding to the CF of wind power plants presented in the range of 4–22%. The capacity factor of each plant does not only depend on wind energy potential resources but also on the performance of the wind turbine. The higher the capacity factor referred to the more effective the electricity generation with wind turbines (Oyedepo et al. 2012; Quan and Leephakpreeda 2015). Thus, a capacity factor above 15% was used to assess the feasibility of a wind power plant in the next section.

The impact of greenhouse gas emissions on the atmosphere causes surface temperatures and environmental issues. These limitations of the climate crisis aim to require a reduction of CO₂ emissions worldwide (House of Commons Public Accounts Committee 2021). As an advantage circumstance of renewable energy sources, wind energy exists everywhere and can play a role to confine greenhouse gas emissions to avoid the climate crisis (Zhang et al. 2016). In this context, the current CO₂ emissions from electricity generation in Thailand are 0.323 kgCO₂/kWh compared to fossil fuels electricity generation (EPPO 2018). As the results in Table 5 presented, the wind power plants situated in Khon Kaen, Maha Salakham and Roi Et sites is varying in the range of 1,250.67 to 5,750.75 tons of CO2/MWh whilst the Maha Salakham site could avoid the largest of CO2 gas emissions into the atmosphere.

3.5. Feasibility of Wind Power plants project

Considering the wind power plant construction expenses, the overall cost could be determined from the LCOE. The wind power plants with a capacity factor above 15% were applied to assess the feasibility of wind power plant projects in the central of the northeastern region.

The LCOE is strongly inverse to the capacity factor. Obviously that the relation between the capacity factor of wind power and LCOE is evaluated based on different scenarios (Dehghani-Sanij et al. 2022). As postulated in Figure. 10, Maha Salakham site shared the minimum total cost with the G126-2.5 MW of wind turbine which is about 0.2, followed by the G132-3.3 MW model which was placed in a similar site.

Eventually, the important data from this research should be further reanalysis in terms of the impact of wind flow over buildings via computational fluid dynamics to judiciously optimize the best wind farm layout (Gomes da Silva et al. 2021).

Wind energy potential maps in the central northeastern region consisting of Khon Kaen, Maha Salakham, and Roi Et provinces are presented in this research. The mesoscale and microscale wind energy potential maps according to NCEP/FNL atmospheric database were performed to develop the high resolution of wind energy potential maps at elevations of 100 m and 120 m above ground level (AGL). The simulation results of mesoscale and microscale are validated with the observation from the installed wind mast along the centre of the northeastern region of the country. The mean wind speed at a height of 120 m (AGL) is varying in the 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 site, Maha Sarakham site and Roi Et site serially. The wind power estimation results revealed the performance of the wind potential resources generated by the wind turbine generators under the condition of VSPP 10 MW. The Maha Salakham site produced the greatest energy output with a G126-2.5 MW wind turbine, which could produce 17,804.16 MWh/year according to the capacity factor and LCOE value which exposed that Maha Salakham site shared the greatest CF and the cost of energy about 0.2 USD/kWh. It can be mentioned that the Maha Salakham site is appropriate for a wind power plant project. Nevertheless, the high wind potential territory as displayed in the red contour of wind energy potential maps has to additionally considered for the wind monitoring process and estimating the performance of other wind turbine models, the impact on the environment, the social acceptance, and the financial feasibility for commercial investments in future studies.

Acknowledgement The authors are grateful to the graduate school of Khon Kaen University for the research fund, and also thankful to Chana Chancham, Somphol Chiwamongkhonkarn, and Lattawan Niyomtham, Ph.D. researchers of the research center in energy and environment of Thaksin University (Phatthalung Campus) for their advisement, suggestion and all of the research tools and equipment. And thankful to Srichai Laopongcharoen, IT staff at the office of digital technology of Khon Kaen University for the high-performance computer (HPC) support for the computational process in this research.

Conflict of Interest The authors declare no conflict of interest.

Funding Information This research is supported by the The Graduate School Research Funding of Khon Kaen University for their financial support (Grant number 611JT211), and researchers of the research center in energy and environment of Thaksin University (Phatthalung Campus) for supporting this research with the climatological data, mapping datasets, and also the necessary tools for the wind map simulations.

Ethics approval/declarations Not applicable

Consent to participate Not applicable

Consent for publication Not applicable

Conflict of Interest The authors declare no conflict of interest.

Availability of data and material/ Data availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability No code was developed in this current study.

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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

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Abstract

Figures

1. Introduction

2. Methodology

2.1. Research Area

2.2. Preparation of Data Input

2.3. Mesoscale and Microscale Modelling

2.4. Data Validation

2.5. Wind Turbine Selection and Wind Energy Potential Estimation

2.6. Feasibility of Wind Power Plant Project

3. Results and Discussion

3.1. Data Validation

3.2. Mesoscale and Microscale of Wind Resource Maps

3.3. Wind Turbine Selection

3.4. Wind Energy Potential Estimation

3.5. Feasibility of Wind Power plants project

4. Conclusion

Declarations

References

Additional Declarations

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