[1] The goal of this study is to quantify the world's wind power potential for the first time from data. Wind speeds are calculated at 80 m, the hub height of modern, 77-m diameter, 1500 kW turbines. Since relatively few observations are available at 80 m, the Least Square extrapolation technique is utilized and revised here to obtain estimates of wind speeds at 80 m given observed wind speeds at 10 m (widely available) and a network of sounding stations. Tower data from the Kennedy Space Center (Florida) were used to validate the results. Globally, ∼13% of all reporting stations experience annual mean wind speeds ≥ 6.9 m/s at 80 m (i.e., wind power class 3 or greater) and can therefore be considered suitable for low-cost wind power generation. This estimate is believed to be conservative. Of all continents, North America has the largest number of stations in class ≥ 3 (453), and Antarctica has the largest percent (60%). Areas with great potential are found in northern Europe along the North Sea, the southern tip of the South American continent, the island of Tasmania in Australia, the Great Lakes region, and the northeastern and northwestern coasts of North America. The global average 10-m wind speed over the ocean from measurements is 6.64 m/s (class 6); that over land is 3.28 m/s (class 1). The calculated 80-m values are 8.60 m/s (class 6) and 4.54 m/s (class 1) over ocean and land, respectively. Over land, daytime 80-m wind speed averages obtained from soundings (4.96 m/s) are slightly larger than nighttime ones (4.85 m/s); nighttime wind speeds increase, on average, above daytime speeds above 120 m. Assuming that statistics generated from all stations analyzed here are representative of the global distribution of winds, global wind power generated at locations with mean annual wind speeds ≥ 6.9 m/s at 80 m is found to be ∼72 TW (∼54,000 Mtoe) for the year 2000. Even if only ∼20% of this power could be captured, it could satisfy 100% of the world's energy demand for all purposes (6995–10177 Mtoe) and over seven times the world's electricity needs (1.6–1.8 TW). Several practical barriers need to be overcome to fully realize this potential.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2004JD005462

Evaluation of global wind power

Alternatives to grid extension for rural electrification: Decentralized renewable energy technologies in Vietnam

Wind energy in Vietnam: Resource assessment, development status and future implications

Optimization and assessment of floating and floating-tracking PV systems integrated in on- and off-grid hybrid energy systems

A bottom-up Integrated Resource Planning model is used to examine the economic potential of renewable energy in Vietnam's power sector In a baseline scenario without renewables, coal provides 44% of electricity generated from 2010 to 2030 The use of renewables could reduce that figure to 39%, as well as decrease the sector's cumulative emission of CO2 by 8%, SO2 by 3%, and NOX by 4% In addition, renewables could avoid installing 44 GW in fossil fuel generating capacity, conserve domestic coal, decrease coal and gases imports, improving energy independence and security Wind could become cost-competitive assuming high but plausible on fossil fuel prices, if the cost of the technology falls to 900 US$/kW

Economic Potential of Renewable Energy in Vietnam's Power Sector

The Wind Energy Resource Atlas of Southeast Asia covers four countries: Cambodia, Laos, Thailand, and Vietnam. The purpose of the atlas is to facilitate the development of wind energy both for utility-scale generation and for village power and other off-grid applications. Potential users of the atlas include government officials, international lending agencies and development institutions, and private developers. The atlas was made possible by the development in the past three years of a sophisticated new wind mapping system called MesoMap. This system uses a dynamical mesoscale weather model to simulate historical wind and weather conditions for a representative sample of days from 1984 to 1998. The data inputs include terrain elevations, land cover, and vegetation greenness on a 1 km grid scale, as well as meteorological data such as gridded reanalysis weather data, rawinsonde data, and sea surface temperature measurements. The results of the simulations are presented as color-coded maps of mean wind speed and wind power density, both annual and seasonal, and tabulated frequency distributions and wind rose charts.

Llc TrueWind Solutions

Papers

Wind energy resource atlas of Southeast Asia