Vietnam, with a coastline of 3260 km, is considered a country with huge offshore wind potential. In this paper, the author will preliminarily evaluate the net capacity factor of a 600 MW offshore wind farm if it is developed in offshore water zones in Vietnam, using wind turbines with a rated power of 10 MW (typical for the development trend of current wind power technology). As well as evaluate the change in capacity factor of this wind farm during the year. Does it bring any benefits to the power system in Vietnam or not?
According to the World Bank , Vietnam’s technical offshore wind power potential is about 475 GW in water zones 200 km from the coastline – Figure 1; or according to the research group of Vietnam, Ireland and Japan , Vietnam’s technical offshore wind power potential in the water zones from 0 – 185 km up to 600 GW. These figures have partly proved the huge potential of offshore wind power in our country.
Figure 1. Technical offshore wind potential in Vietnam 
Although the offshore wind potential in Vietnam is not as good as the offshore wind potential in the North Sea – Europe, the offshore wind characteristics in our country has a great advantage for arranging the wind farm layout – that is wind direction, with 2 main monsoon seasons following 2 main directions: Northeast and Southwest – Figure 2. Therefore, along with the power density as the offshore wind farms in the North Sea, the wind farm layout in Vietnam will be designed: reduce the distance between the turbines in the non-main wind direction and increase the distance between the turbines in the main wind direction, which will help to minimize the wake loss effect between the turbines, also helps to reduce the amount of marine cable connecting wind turbines and optimize the use of marine space. In this paper, the author illustrates choosing a wind turbine layout with the main distance between wind turbines of 4 x 18D: the distance in the non-main wind direction is 4D, in the main wind direction is 18D ( D – rotor diameter of the wind turbine). The offshore wind farm layout is also consistent with the power density trend of offshore wind farms in the world , .
Figure 2. Wind rose in Vietnam and in the North Sea – Europe 
In the bellow, the authors will analyze more detailed offshore wind power characteristics in Vietnam such as: the net capacity factor map, how the capacity factor of the wind farm changes over months in an operated year…
The input parameters for the calculation are as follows:
Figure 3 shows the calculation results of the net capacity factor map of a offshore wind farm with a total capacity of 600 MW in Vietnam. The coastal areas along the South Central Coast provinces have quite high capacity factor; Especially the coastal along the area near Binh Thuan province, the capacity factor is very high (~ 50%) and most of this sea area is quite shallow (seabed depth < 50 m), highly suitable for developing offshore wind farms using fixed foundations.
Figure 3. Net capacity factor map of 600 MW offshore wind farm in Vietnam 
Considering an offshore wind farm in the position symbol ( ) in Figure 3 with an annual average wind speed of 9.5 m/s, the calculation results of the monthly generated capacity factor are presented in Figure 4. It can be determined that: In the Northeast monsoon months (December – January – February), the offshore wind farm has a very high capacity factor, typically in January – the capacity factor is over 80%. Figure 5 shows the calculation of the generated power in the first half of January 2020 of the offshore wind farm based on simulation data of wind speed ERA5 : Most of the operating days during this time period, the generated power is quite stable and close to the rated power of the offshore wind farm.
Figure 4. Monthly capacity factor of 600 MW offshore wind farm 
Figure 5. Generated power of 600 MW offshore wind farm in the first half of January 2020 of Northeast monsoon in Vietnam 
According to recent studies  , with the application of predictive models based on artificial intelligence (AI), big data, scanning LiDAR, numerical weather prediction (NWP) model, the accuracy of wind power output forecasting has been improved. This also contributes to increasing the proportion of renewable energy sources in the power system, especially offshore wind power.
Therefore, if the offshore wind farms in the South Central Coast region are developed, they will partly support the power system because: the generated power is stable, the model of generated power forecasting is getting more and more accuracy and the monsoon season period when wind power reaches its best capacity factor coincide with the peak dry season in the South of our country – when hydropower is running out of water resources.
/ ESMAP, “Going Global: Expanding Offshore Wind to Emerging Markets,” World Bank, Washington DC, 2019.
/ V. D. Quang, Q. V. Doan, V. N. Dinh and N. D. Duc, “Evaluation of resource spatial-temporal variation, dataset validity, infrastructures and zones for Vietnam offshore wind energy”, Vietnam Journal of Science, Technology and Engineering 62(1), 3-16; DOI: 10.31276/VJSTE.62(1).03-16, 2020, 2020.
/ Musial, W., P. Beiter, D. Heimiller, and G. Scott, “Offshore Wind Energy Resource Assessment for the United States” (Technical Report, NREL/TP-5000-66599), National Renewable Energy Laboratory (NREL), Golden, CO (US), 2016. http://www.nrel.gov/docs/fy16osti/66599.pdf.
/ The Renewables Consulting Group LLC, “Analysis of Turbine Layouts and Spacing Between Wind Farms for Potential New York State Offshore Wind Development”, New York, 2019.
/ Global Wind Atlas (version 3.0), Technical University of Denmark (DTU). https://globalwindatlas.info.
/ EMD, “WindPRO 3.4 – User manual: Energy calculation”, 2020
 EVNPECC3, “Báo cáo kết quả đánh giá sơ bộ tiềm năng điện gió ngoài khơi Việt Nam”, 2021.
/ Copernicus Climate Change Service (C3S), https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview.
 IRENA, “Innovation landscape brief: Advanced forecasting of variable renewable power generation”, International Renewable Energy Agency, Abu Dhabi, 2020.
 L. Valldecabres and M. Kühn, “On the use of remote sensing measurements for very short-term forecasting of offshore wind power”, Offshore Wind R&D Conference 2018, 2018.
LE THANH VINH – RENEWABLE ENERGIES DEPARTMENT, POWER ENGINEERING CONSULTING J.S COMPANY 3.
DINH VAN NGUYEN – ANTS & MAREI CENTRE, UCC, IRELAND; AVSE GLOBAL MEMBER.