Wind Resource Map Research Articles

Renewable Energy Complementarity (RECom) maps – a comprehensive visualisation tool to support spatial diversification

Abstract. Maps showing the mean wind speed only give an inaccurate indication of the quality of locations for future wind power developments. Calculating the capacity factor and plotting that on a map gives a better indication of the expected mean power output, but the outcome depends on the turbine choice. In this article, we introduce a general step-by-step method for improved visualisation of potential wind power locations. First, the mentioned dependency on turbine choice is compensated for by putting the expected mean power output in relation to the expected mean power output of all other wind parks of the region. This relative capacity factor results in comprehensive wind resource maps and can be plotted for the situation today and also for a future scenario. Since the expected income of a potential wind park is the product of mean power output and mean market value, looking at the relative capacity factor only does not give the full picture. The mean market value is influenced by the merit order effect that is mainly driven by covariance with other wind parks and the capacity factor's relation to production at low-wind moments. A market value factor is introduced that captures the expected mean market value relative to other wind parks, based on a simplified power market model. Finally the Renewable Energy Complementarity (RECom) index is defined, combining the relative capacity factor and market value factor into a single index, resulting in RECom maps. This map can comprehensively show the revenue potential of different locations for potential future wind power developments.

Optimization and Evaluation of the Weather Research and Forecasting (WRF) Model for Wind Energy Resource Assessment and Mapping in Iran

This study aims to optimize the Weather Research and Forecasting (WRF) model regarding the choice of the best planetary boundary layer (PBL) physical scheme and to evaluate the model’s performance for wind energy assessment and mapping over the Iranian territory. In this initiative, five PBL and surface layer parameterization schemes were tested, and their performance was evaluated via comparison with observational wind data. The study used two-way nesting domains with spatial resolutions of 15 km and 5 km to represent atmospheric circulation patterns affecting the study area. Additionally, a seventeen-year simulation (2004–2020) was conducted, producing wind datasets for the entire Iranian territory. The accuracy of the WRF model was assessed by comparing its results with observations from multiple sites and with the high-resolution Global Wind Atlas. Statistical parameters and wind power density were calculated from the simulated data and compared with observations to evaluate wind energy potential at specific sites. The model’s performance was sensitive to the horizontal resolution of the terrain data, with weaker simulations for wind speeds below 3 m/s and above 10 m/s. The results confirm that the WRF model provides reliable wind speed data for realistic wind energy assessment studies in Iran. The model-generated wind resource map identifies areas with high wind (wind speed > 5.6 m/s) potential that are currently without wind farms or Aeolic parks for exploitation of the wind energy potential. The Sistan Basin in eastern Iran was identified as the area with the highest wind power density, while areas west of the Zagros Mountains and in southwest Iran showed high aeolian potential during summer. A novelty of this research is the application of the WRF model in an area characterized by high topographical complexities and specific geographical features. The results provide practical solutions and valuable insights for industry stakeholders, facilitating informed decision making, reducing uncertainties, and promoting the effective utilization of wind energy resources in the region.

Technoeconomic Analysis of a Wind Power Generation System and Wind Resource Mapping Using GIS Tools: The Case of Twelve Locations in the Commune of Evodoula, Cameroon

This paper performs a technoeconomic analysis of the wind power potential and evaluates the cost of wind power generation in Evodoula, comparing two methods, the conventional method and the uncertainty method based on a comparative spatiotemporal approach using the geographic information system (GIS) software tool. This study is based on satellite wind data measured at 10 m above ground level (AGL) over a 40-year period (1980-2019), by the meteorological service NASA (National Aeronautics and Space Administration)/Goddard Space Flight Center (GSFC). The main objectives are to obtain an appropriate design for a proven optimal location and to assess the viability of wind power at Evodoula. For this type of study, there is little literature available. The optimization of an onshore wind farm and deployment in the wind energy development interest area (ZIDEE) is carried out to obtain a minimum and parsimonious discounted cost production unitary (CPU) of the electricity produced. The results showed that in Okok, a location with a large energy deficit, an onshore wind farm with an electricity generation capacity of 12.5 MW with 5 NORDEX N100-2.5MW wind turbines would have a total energy production (TEP) of about 64.0254825 TWh and a selling price of electricity that would be 0.0034 CAD$/kWh, which is very low compared to the utility price (about 98% cheaper). The total cost of the wind farm would be about $11 million, with a net present cost of about $218 million. The annual profit generated by the wind farm would be over $6 billion. The return on investment (ROI) of the project is estimated at 2880.882%. The constructed onshore wind farm would avoid CO2 emission at over 11 MtCO2,eq/year as the energy generated is from the atmosphere. The wind farm would realize an average annual cash flow estimated at nearly $30 million after 20 years of operation. These savings would allow the installation of CO2 capture systems in conventional power plants. In addition, the analysis of uncertainties and risks was identified and quantified to estimate the confidence levels of the project development results. The risks have been assessed, and we recommend that the total uncertainty of the project is around 15%. The energy values in P75 and P90 are 10.08% and 19.22%, respectively, lower than the energy value in P50 of 11.60 GWh/An. Finally, the main policy recommendations for an inclusive design process are highlighted. The contribution of this study is to assist policy makers in making appropriate decisions in the development and implementation of energy and environmental policy in Cameroon and in many continental areas.

Mesoscale modelling for wind resource evaluation purposes. A test case in complex terrain

The aim of this work was the preliminary wind resource assessment of a complex terrain site, located in Bulgaria, using a virtual wind data series, taken from mesoscale modelling Weather Research and Forecasting system (WRF). The meso-micro scale coupling was carried out applying the virtual wind data series to the point where once a local wind measurement was conducted. This way, the comparison between the two data sets, virtual and local measurement, could be done. Using the mesoscale virtual information the characterization of the wind was done for the place of study. The microscale model Wind Atlas Analysis and Application Program (WASP) was used to make the wind resource mapping and the calculation of the estimated wind resource and annual production of energy, taking into account the defined configuration for the wind farm and the turbine model chosen. The obtained differences, in terms of the magnitude of the estimated resource and its own dominant orientation indicates that the application of mesoscale models in very complex topography terrains should be done with extreme care.

Optimal Wind Power Plant Layout Using Ant Colony Optimization

The optimal layout of wind power plants is very important as the arrangement of wind turbine generators (WTGs) profoundly affects the overall energy output of the wind power plant. To address this important issue, this research investigates the best layout for WTGs in wind power plants with different terrain features across three locations in Thailand using the Ant Colony Optimization (ACO) algorithm. The objective functions of maximizing the net annual energy production (AEP) and minimizing the wake losses were used to achieve the optimal wind power plant layout. Using the MERRA-2 database, computational fluid dynamics (CFD) wind flow modeling was performed to create 10x10 km2 microscale wind resource maps of locations characterized by flat, semi-complex, and complex terrains to install wind power plants. The CFD wind flow modeling yielded wind speeds of 5.00 to 5.76, 4.21 to 8.90, and 3.10 to 4.45 m/s for the flat, semi-complex, and complex terrains, respectively, making them feasible for utility-scale wind power plants. WTGs of multiple blade diameters, ranging from 90 to 126 m, with a nominal capacity of 2.5 MW at 100 m above ground-level hub heights, were used in this study. The Gamesa G126-2.5MW WTG with a 126 m blade diameter produces the highest net AEP of 14.3, 76.1, and 38.9 GWh/yr for the three terrains. Hence, this WTG was used to perform an ACO-based optimization to improve the electricity production of the wind power plants. Such studies are important to improve the efficiency of wind power plants, thus extracting the maximum kinetic energy possible from the winds and improving the economic viability of wind power plants.

Wind data modeling and energy mapping of the wind potential in the city of Douala (Cameroun)

This work helps in the implementation of wind energy projects in the city of Douala. Wind data used (speed and direction) were collected from January 1, 2020, to December 31, 2020, at a height of 10m. The use of wind speed distribution laws allowed us to obtain predictions of the available wind energy on-site at different altitudes. Then, the wind direction is established for the orientation of the wind turbines and the turbulence analysis is done to highlight the exploitable wind periods. The results show that harnessing wind energy is possible on site from 10 AM to 06 PM. The wind potential at 138 m height has an average speed of 8.15 m/s for an overall energy density of 749.78 W/m2 for roughness class 0, and an average speed of 3.7 m/s for an overall energy density of 69.118 W/m2 for roughness class 4. The wind turbines will be installed in front of the north-north-east direction between 15 and 25°. Finally, the energy mapping of the city's wind resources allows us to estimate the total energy available at a reference height.

An evaluation method for wake effect of wind farm group based on CFD-WRF coupled wind resource map

The wake effect of wind farm can reduce the incoming wind speed at the wind turbine located in the downstream direction, resulting in the decrease of global output. WRF model adopts a three-layer two-way nested grid division scheme to simulate the upper atmospheric circulation, obtain wind speed, wind direction and other data that can truly reproduce the fluid characteristics of the regional wind farm group. The boundary conditions and solution conditions of CFD model are set, and the computational fluid dynamics model of the region is obtained. WRF is coupled with CFD, and Fitch wake model is introduced into it. By introducing the drag coefficient of wind turbine into the calculation of wind speed and turbulent kinetic energy in CFD-WRF coupling model, the wind field characteristics and wake effect of wind farm are simulated online. Monte Carlo sampling method is used to obtain random wind resource data in CFD-WRF coupling model, and then the sampled data is used to calculate the group output of wind farms, and evaluate the impact of wake effect on wind farm treatment. The experimental results show that this method can effectively analyze the characteristic data of regional wind field, and the calculation time of RANS method is about 3 s. Due to the wake effect, the overall output and efficiency of wind field will be significantly reduced.

Featured Cover

The cover image is based on the Research Article Machine learning-based statistical downscaling of wind resource maps using multi-resolution topographical data by Myeongchan Oh et al., https://doi.org/10.1002/we.2718.

Wind power generation and appropriate feed-in-tariff under limited wind resource in central Thailand

The objective of this paper is to assess the wind energy resource in the central region of Thailand for wind power generation, along with analyzing the economic feasibility and appropriate feed-in-tariff (FIT) of a proposed 15 MW wind power plant. A microscale wind resource map was created using measured wind data, a computational fluid dynamics wind flow modeling and high-resolution topography databases. Five utility-scale wind turbine generators (WTG), with hub heights ranging from 80 to 120 m above ground level (agl), were used to estimate the annual energy production (AEP). Considering the available wind resource, the most appropriate WTG was identified, and a wind power plant layout was achieved to maximize the AEP as well as minimizing the wake losses. The maximum net AEP, capacity factor (CF), %AEP improvement, %wake loss reduction, and CO2eq emission avoidance were also analyzed. Several financial indices and the levelized cost of energy (LCOE) were analyzed on the basis of a cost–benefit analysis. The economic sensitivity of the costs was used to determine the most appropriate FIT for the project. Results reveal that the mean annual wind speed at 120 m agl in the central region of Thailand reaches 5.8 m/s. The net AEP, CF, %AEP improvement, %wake loss, and CO2eq emission avoidance for a 15 MW wind power plant are estimated at 41 GWh/year, 30%, 6%, 10% and 231 ktonnes CO2eq/year, respectively. The LCOE for a base case scenario is estimated at 0.093 US$/kWh, with a FIT of 0.195 US$/kWh. Finally, the results of this work can be used as guidelines for wind power project development in the central region of Thailand and in other regions of the world where the wind resource is low to moderate under current existing WTG technology.

NWP-CFD 축소화에 의한 육상풍력 자원지도 고해상도화

An accurate prediction of the wind energy production is essential in the initial design process of wind farms. Many researchers use meteorological data obtained from Numerical Weather Prediction (NWP) methods to predict wind energy production. The NWP method does not consider the influence of local topography and surface roughness on wind conditions. In Korea, most of onshore wind farms are installed in mountainous areas with complex topography, which makes the meteorological data obtained from only NWP method not applicable for the generation of wind resource map. The NWP-CFD coupled method, which uses the meteorological data from NWP method as the boundary condition of the CFD simulation, is adopted to overcome the limitations inherent in the NWP method. In this way, the spatial resolution of onshore wind resource map is downscaled to 100m-level considering the effects of the complex topography on the wind conditions.

A Comparative Study of Wind Characteristics Between South-Western and South-Eastern Thailand Using Different Wind Turbine Models

Globally, wind energy has proven to be one of the most sustainable sources of energy. Wind energy assessment plays a critical role on determining installation of wind turbines worldwide. This work presents the technical evaluation of wind energy potential using three available wind turbine models for prospective onshore wind farm in the southern Thailand at Krabi and Songkhla sites. Ten-minute interval wind data over a period of 3 to 4 years obtained from Weather Observing Station is utilized to observe the diurnal and monthly wind speed, as well as frequency distribution. WAsP program is applied for energy yield calculations and wind resource maps. Our results reveal that Krabi and Songkhla has the highest mean wind speed of 4.39 m/s in December and 3.91 m/s in February, respectively. The prevailing wind direction in Krabi and Songkhla are north-east and south-east, respectively. WAsP analyses show that the total net AEP for Krabi is 7163.782 MWh, 7762 MWh and 12731 MWh using 275 kW, 300 kW and 500 kW wind turbine models, respectively. Similarly, the total net AEP for Songkhla is 7116.63 MWh, 7775.245 MWh and 12390 MWh using 275 kW, 300 kW and 500 kW wind turbine models, respectively. The total capacity factor for Songkhla and Krabi is 29.1% and 28.3%, respectively. Our results indicate that Enercon E-40/5.40 500 kW wind turbine model produces the highest total gross AEP and total net AEP for Krabi and Songkhla sites. Besides, the Vergnet GEV MP-C 275 kW turbine model shows slightly higher capacity factor in case of both sites. The findings of this study reflect that small to medium size wind turbines can be utilized to generate electricity at the sites.

Machine learning‐based statistical downscaling of wind resource maps using multi‐resolution topographical data

AbstractThe need for high‐resolution wind resource maps is increasing with the increase in the supply and development of wind power. Many physical downscaling models have been developed and applied to make these maps. However, as the existing models require extensive computations and time, statistical models with higher efficiency are being studied. Statistical models such as regression and machine learning models can quickly calculate wind resource maps, but they have a problem of low accuracy. This study proposes a machine learning model with new topography‐derived variables to interpret the physical characteristics of the wind. As the shape of topography, which was unable to be interpreted in previous studies, can be considered with new derived variables, a significant performance improvement was identified. The analysis was conducted using 1 km Weather Research and Forecasting (WRF) results and ERA5 reanalysis data from South Korea. Two Weibull distribution parameter maps were calculated and used as input and output data. Three collections of derived variables were devised and compared. Therefore, the multi‐resolution topography data showed the highest improvements with approximately 15% reduction in root mean square error (RMSE) for both the linear regression and machine learning models. In particular, the land area showed a decrease of 20%. The best proposed models showed an RMSE of 7% and 8% for two Weibull parameters. The results are expected to serve as a reference for continuing research and utilization of statistical models.

Offshore wind resource mapping in Cambodia: Sensitivity assessment of the weather research and forecasting model

The objective of this paper is to assess the sensitivity of the weather research and forecasting (WRF) to three important parameters: nesting with nudging options, planetary boundary layer (PBL) options, and nudged variable options for Cambodian territory. Three tests are set up and carried out, and each of the test, intended for each parameter, is comprised of several experiments. All experiments are simulated for the same period of 15-day. Then the outputs of the WRF model are validated against measured wind data from four meteorological stations at 10 m above the ground level. The results show that the WRF is unlikely influenced by the nesting choices but more sensitive to the PBL options for wind speed simulation. In term of wind direction, the model is insensitive to any of the tested parameters. Through statistical and graphical analyses, the best experiments are found to be the two-way nesting with gridded nudging for nesting with nudging options, MYNN2.5 scheme for PBL options, and nudged wind components for nudged variable. With these optimal configurations, the model is then applied for simulations of higher vertical-level wind and for mapping the offshore wind resource in Cambodia. The offshore winds at 80 and 100 m above sea-level are found to be around 5–7 m/s over Cambodian EEZ.

Wind Energy Data Analysis and Resource Mapping of Dangla, Gojjam, Ethiopia

Energy is one of the most important factors in socio-economic development. The rapid increase in energy demand and air pollution has increased the number of ways to generate energy in the power sector. Currently, wind energy capacity in Ethiopia is estimated at 10,000 MW. Of these, however, only eight percent of its capacity has been used in recent years. One of the reasons for the low use of wind energy is the lack of accurate wind atlases in the country. Therefore, the purpose of this study is to develop an accurate wind atlas and review the wind resources using Wind Atlas Analysis and Application Program in Dangla so that the best sites used for the installation of wind farms can be easily identified. Wind climatology data, vector maps, and wind turbine data were used to model the Wind Atlas Analysis and Application Program. The wind data collected from National Meteorology Survey Agency over three years were used for the analysis. Besides, vector data for the survey area was prepared using Global Mapper V20.1. In this study, the Wind Atlas Analysis and Application Program tool was used to estimate wind resources. The wind resource map was developed at an altitude of 80 m above ground level, and a horizontal axis type wind generator called (i.e., GridStreamer Vestas V100 1.8 MW) was selected from the Wind Atlas Analysis and Application Program catalog to mimic the wind farm. The results obtained from the analysis of actual wind data showed that the minimum wind speed was 0.12083 m/s while the maximum wind speed was 9.96389 m/s. At a height of 10 m a.g.l., the mean wind speed and wind power density were 1.8 m/s and 9 w/m2, respectively. The most common wind direction was also discovered to be 210 degrees. The maximum wind speed and capacity factor were available around hills, according to the wind resource map, and the net annual energy output of the Dangla wind farm at a turbine height of 80 m a.g.l. was found to be 282.726 GWh (or, 32.27 MW). The farm’s capacity factor was also discovered to be 9.54 percent, indicating that the site is rated as low potential.

Visual cost of energy facilities: A comprehensive model and case study of offshore wind farms

The visual impact assessment of large facilities can be improved thanks to Visual Impact Maps (VIMs). A VIM can be a valuable predictor (numerical and graphical) of visual effects. VIMs are conceived to help with the analysis of the Landscape professional. Even before the design stage, VIMs provide important data that complement the set of starting technical or environmental criteria, such as wind resource, land use or flood maps. Additionally, they provide a numerical model for the Visual Cost Of Energy (VCOE). A set of visual indicators and a set of visual inventory layers properly combined result in a series of VIMs: a wide collection of information that depicts the visual effect that the development of any facility generates on the considered area. The production of VIMs can result in creation of a new computational and environmental consultation service that is available along the facility life cycle and that is useful for protection, planning, and management aims.

Evaluation of surface wind using WRF in complex terrain: Atmospheric input data and grid spacing

This study evaluates a numerical weather prediction model as a tool for wind resource assessment in complex terrain and how the simulations are affected by the selection of initial and boundary conditions and various grid resolutions. Two global reanalyses, ERA-Interim and ERA5, and four grid resolutions, 27 km, 9 km, 3 km and 1 km, have been considered. The simulations have been compared to hub-height wind measurements. The ERA5 forced simulations were found to provide improved wind speed results. The simulations show clear improvements in terms of lower error when the grid spacing is reduced from 27 km via 9 km to 3 km. From 3 km to 1 km, the error is not further reduced. However, the 1 km simulations tend to better reproduce the mean wind features and show defined areas with higher and lower wind speeds, providing useful information for wind resource mapping in complex terrain.

Offshore wind can power Canada

As a maritime nation, Canada is the home to high-quality offshore wind resources. The assessment of Canada's offshore wind resources has a strategic significance for realizing its low-carbon future. This study focused on high-resolution and high-accuracy estimates of offshore wind resources over Canada using satellite-derived wind maps, e.g., CCMP (Cross-Calibrated Multi-Platform) images from 1988 to 2017. The CCMP derived wind speed and density were bias-corrected by ocean surface wind measurements collected from 63 stations located along the seas/lakes in Canada from 1987 to 2018. A series of consistent offshore wind resource maps were produced and analyzed. The results generated from CCMP were then compared with previous assessment (i.e., Environment and Climate Change Canada (ECCC)'s Wind Atlas) and proved to perform better in simulating wind speeds. We estimated the technically available offshore wind area, gross power capacity and gross potential over Canada. For example, a gross potential production of 20.348, 20.441, and 20.376 PWh/year were calculated using three commercial offshore wind turbines, i.e. GE 3.6s, REpower 5 M, and V164–8.0 over the calculated total gross offshore area of 1.053 million km2. It far exceeds the Canada's electricity consumption (i.e., 0.641 PWh) in 2018. In addition, most of the potential power productions are distributed in the Canadian Atlantic and Pacific Oceans, Hudson Bay and St Lawrence, with the mean wind speeds increasing from 1988 to 2017. This study can help to assess offshore wind resources and provide decision-makers with some scientific evidences for developing offshore wind in Canada.

육상풍력 입지지도 I: 풍력자원 분류지도 개발 및 자원의 적합성 평가

In order to advance the onshore wind energy siting atlas, we developed the national wind resource classification map using ultra-high resolution wind resource maps and market economics models. The wind resource map was classified into fair and poor class suggesting presence or absence of economic feasibility, and good class in fair class was additionally recategorized with high priority areas. About 25.1% of the country had economic feasibility(fair class), of which 1.7% was superior to the result(good class). These amounts has been influenced by accuracy of capacity factor map as well as changes of Capital expenditure(CAPEX) and subsidy derived by system marginal prices up to -7.6%p ~ 12.6%p for fair class(-1.1%p ~ 2.7%p for good class). It had the greatest impacts on the subsidy change derived SMP and capacity factor than CAPEX. We also identified that with CAPEX affects on overcoming economic feasibility within limited capacity factor.

Wind energy resource assessment for Mukdahan, Thailand

ABSTRACT The generation of electricity from wind energy can reduce the fossil sources imported and carbon dioxide emissions. The wind is free, clean, and green energy. This study aims to present a wind energy assessment for Mukdahan site of Thailand. The wind speed at heights of 60, 90, and 120 m above ground level was measured from January 1st, 2012 to December 31st, 2017. The annual mean wind speeds were 3.59, 4.02, and 4.51 m/s at 60, 90, and 120 m AGL, respectively. The majority of wind directions were contributed by the monsoon for this region. The Wind Atlas Analysis and Application Program (WAsP) was used to predict the Annual Energy Production and Capacity Factor for five selected wind turbines in a capacity range of 0.85 − 3.30 MW. Moreover, the high-resolution wind resource map also indicated the high potential for wind around the site. The low cut-in speed and high hub-height wind turbines such as VestasV110-2.0 MW and V126-3.3 MW can produce higher energy output, capacity factor and better cost of energy, especially when the wind turbine site is located on the ridges and peaks of hills around the site. The best cost of energy was 0.030 USD/kWh, which was observed for the V110 wind turbine and the second best was 0.041 USD/kWh for the V126 wind turbine, especially when considering placement on the peak of a hill. The important data from this study can be developed for wind farm project installation and suitable wind turbine design for the whole local region.

Wind Resource Assessment and Economic Viability of Conventional and Unconventional Small Wind Turbines: A Case Study of Maryland

Annual mean wind speed distribution models for power generation based on regional wind resource maps are limited by spatial and temporal resolutions. These models, in general, do not consider the impact of local terrain and atmospheric circulations. In this study, long-term five-year wind data at three sites on the North, East, and West of the Baltimore metropolitan area, Maryland, USA are statistically analyzed. The Weibull probability density function was defined based on the observatory data. Despite seasonal and spatial variability in the wind resource, the annual mean wind speed for all sites is around 3 m/s, suggesting the region is not suitable for large-scale power generation. However, it does display a wind power capacity that might allow for non-grid connected small-scale wind turbine applications. Technical and economic performance evaluations of more than 150 conventional small-scale wind turbines showed that an annual capacity factor and electricity production of 11% and 1990 kWh, respectively, are achievable. It results in a payback period of 13 years. Government incentives can improve the economic feasibility and attractiveness of investments in small wind turbines. To reduce the payback period lower than 10 years, modern/unconventional wind harvesting technologies are found to be an appealing option in this region. Key contributions of this work are (1) highlighting the need for studying the urban physics rather than just the regional wind resource maps for wind development projects in the build-environment, (2) illustrating the implementation of this approach in a real case study of Maryland, and (3) utilizing techno-economic data to determine suitable wind harnessing solutions for the studied sites.