The analysis was conducted for a location in southeastern Romania, in Tulcea County, at 45.27° N and 28.42° E. The data measured for the analysis included wind speed, wind direction, temperature, and air pressure28.

The distance between the measuring station and the analyzed location is approximately 500 m. Since the terrain has the same characteristics and the wind profile in the area seems to be the same at the measured point and at the analyzed location, as can be observed in Fig. 4, it was considered unnecessary to extrapolate the wind speed horizontally, but only vertically. The data were measured over the course of a year, from January 1, 2020, to December 31, 2020.

Assessing and analyzing wind energy resources is essential for the successful development of wind farm projects. Using meteorological data collected throughout 2020, this study undertakes a comprehensive evaluation of the wind energy potential within Romania’s southeastern region.

Location 3, illustrated in Fig. 3, is situated between two areas—locations 1 and 2—with high wind potential, where wind farms have been developed. One of these areas, location 2, hosts the Fantanele-Cogealac Wind Park, the largest onshore wind park in Europe, having a capacity of 600 MW. In this location, there are 2.5 MW wind turbines installed, while in location 1, the wind turbines have a power of 2 and 2.5 MW each. The purpose of the analysis is to evaluate the wind potential of location 3 and to determine the optimal power of the wind turbines that could be installed in this area. This approach is essential for the efficient and sustainable development of wind energy in the region (Fig. 4).

The dataset used in this analysis encompasses hourly average wind speed and direction measurements, collected at a standard height of 10 m above ground level. These parameters play a crucial role in evaluating the potential for wind energy capture in this region.

The analysis of wind energy potential was conducted using Python software, which can process a vast amount of data from CSV files30. Python, a versatile and powerful programming language, is widely used in data analysis and scientific computing due to its simplicity and the extensive availability of libraries. The analysis process involved reading the CSV files, which contained comprehensive wind speed and direction data, alongside other meteorological parameters. The utilization of Python’s data manipulation packages, particularly Pandas, played a crucial role in arranging this data into well-organized formats, enabling easy and effective analysis. In this context, Python was employed to assess wind potential, a crucial step in developing renewable energy infrastructure. Python facilitates data segmentation by seasons, allowing a deeper understanding of seasonal variations in wind energy production. Thus, more accurate predictions can be made about turbine efficiency in different weather conditions.

Using this dataset, we compute various essential parameters that are fundamental to the planning of wind energy projects. This includes the total annual number of operating hours for the assessed wind turbines, which allows us to estimate their annual energy production. We also calculate the capacity factor for these turbines, which is an important indicator of their efficiency and performance.

This study provides insights into the wind energy potential in Romania’s southeastern region by analyzing these parameters. It also offers essential insights for stakeholders and developers who aim to make well-informed decisions regarding wind farm investments in this region.

Figure 5 illustrates the diurnal temperature fluctuations observed on an hourly basis at the examined geographical site throughout the year 2020. The temperature data presented in this analysis demonstrate a year characterized by significant dynamism. On March 16th, the temperature reached a minimum of − 5.8 °C, marking the lowest recorded value. Conversely, on July 30th, the temperature peaked at 36.26 °C, making it as the highest recorded temperature of the year.

On the other hand, there were clear and obvious trends in atmospheric pressure, as shown in Fig. 6. During the summer months, there were few fluctuations observed, indicating a state of rather steady atmospheric conditions. Nevertheless, in the winter and spring seasons, there was a notable fluctuation in atmospheric pressure. On February 5th, the pressure dropped to its lowest point, measuring 987.9 Pa, while on January 10th, it peaked at 1040.9 Pa, demonstrating the extremes experienced in these seasons.

The notable fluctuations in temperature and pressure observed over the course of the year highlight the complex structure of the regional climate, making it an interesting subject needing further investigation and analysis.

The wind rose for the region being analyzed is illustrated in Fig. 7. The prevailing wind direction was obtained by binning the wind directions into a wind rose with 16 segments. According to the graphic, the dominant wind directions observed are west-southwest (WSW) and northwest (NW), cumulatively having a frequency of approximately 25%. On the other hand, the data indicates that SE and SSE winds demonstrate a higher frequency, accounting approximately 35% of the total occurrences. The analysis of wind patterns presented in this study offers significant insights into the meteorological conditions specific to the local area.

Several statistical distributions, including the commonly used Rayleigh and Weibull distributions, are essential for the characterization and analysis of wind resource data31. Among the various statistical methods available for modeling wind speed data, the Weibull distribution stands as an effective and reliable option32,33. The reason for its extensive implementation in the field of wind energy can be attributed to its capacity to accurately depict the characteristics of wind data34.

In this study, the wind speed potential at the selected location was evaluated using the Weibull probability density function. The statistical tool can be used to characterize the probability distribution of wind speeds. The probability density function of the measured wind speed was obtained by binning the data sets into 1 m/s wide intervals and calculation of data points percentage for each bin. The mathematical form of the Weibull distribution function is given by the following expression35:

In this context, the variable ‘v’ denotes the magnitude of wind speed, ‘k’ represents the shape parameter, and ‘c’ [m/s] represents the scale parameter associated with the Weibull distribution.

The form factor k and the scale factor c are established through Eqs. (2) and (3), respectively:

In the given expression, \(\overline{v}\) represents the average wind speed, σ is the standard deviation of the wind speed, and Γ denotes the gamma function. The gamma function, often recognized as an extension of the factorial function to complex numbers, is formally expressed as:

Using this probability distribution to the recorded wind speed data, researchers can obtain significant knowledge about features of the wind resource36,37. This information is crucial for the effective planning and implementation of wind energy systems in the examined location. The utilization of this statistical methodology significantly improves our capacity to effectively exploit renewable energy sources, thereby making a substantial contribution to the adoption of sustainable energy practices.

Wind speeds that can be used to generate energy are those that fall between the cut in wind speed and cut off wind speed. The power curve, which establishes a relationship between the power of the wind turbine and the wind speed, represents the power produced by the wind turbine at different wind speeds. The relationship between turbine power output and wind speed can be expressed mathematically as follows:

where vci, vr, and vco represent the cut in wind speed, nominal wind speed, and cut off wind speed for turbine protection, respectively.

The cut in wind speed of a wind turbine is the speed at which it begins to produce energy. If the wind speed is less than this, the turbine will not be able to produce electricity. When the wind speed is between the cut in wind speed and cut off wind speed, the wind turbine generates power according to the cubic relationship between wind speed and power. If the wind speed exceeds the maximum wind speed, the turbine is shut down.

For the analysis, six types of turbines were studied for three power categories, namely 1.5, 2, and 3 MW. Figure 8 presents the power curves for the analyzed turbines, which are: Sinovel—1.5 MW, AAER—1.5 MW, Vestas—2.0 MW, AAER—2.0 MW, Vestas—3.0 MW, and Sinovel—3.0 MW.

The characteristics of the wind turbines examined are displayed in Table 1.

Since wind speed measurement was not conducted at the operating height of the wind turbines, it is necessary to extrapolate the wind speed values to the height of the hub, which is also the height at which turbine manufacturers provide the power curves for these turbines. Since wind speed increases with altitude, the power law model was used to extrapolate the wind speed to the hub height of location according to the vertical wind profile relationship39.

where v: wind speed at the turbine hub height (m/s). h: turbine hub height (m). v : wind speed measured at the anemometer height (m/s). h : height of the anemometer (m). n: wind shear coefficient.

The concept of wind power potential refers to the theoretically available amount of wind power at the specific location. The first step in estimating the power potential of a wind site involves collecting wind data because the intermittency and variability of the wind make it challenging to predict its power potential accurately40. Despite all the analyses and modeling conducted to date, a precise estimation of wind potential at any location globally has not been achieved. Modeling the wind speed distribution involves fitting a known continuous function (such as Weibull) to match the histogram of collected wind speed data at the analyzed location. The available power from the wind, P , is given by 741:

where \(\rho_{a}\) is air density, \(f\left( v \right)\) is probability density function, variable A denotes the spatial area swept by the blades, and v represents the instantaneous wind speed.

The variation of air density in the troposphere as a function of altitude was determined by considering the variation of temperature and pressure with altitude42.

Pressure variation as a function of height was calculated with Eq. 8:

The variation of temperature with altitude based on the lapse rate was calculated using Eq. (9):

The equation for computing the variation of density as a function of height in the troposphere, considering changes in relative pressure and temperature changes, is:

The available wind energy Ea for a time of N hours is given by the relation 11:

The recoverable energy at the investigated site, based on the power curve characteristics of the wind turbine, is determined with the relation 1243:

where V is the cut-in speed of wind turbine; V is the rated wind speed of wind turbine; V is the cut-out wind speed of wind turbine.

The capacity factor allows us to determine the most suitable turbine for installation at the analyzed location, as follows:

The maximum annual energy Ei, that can be generated by the wind turbine is given by the relationship (14):

Carbon emission savings are calculated using the emission factor, \(e_{CO2}\), for the national electricity generation system. The annual emission savings, denoted as V (t CO /year), are calculated by multiplying the total annual energy output of the wind turbine, represented as E (MWh/year), by the emission factor \(e_{CO2}\) (t CO /MWh) for the reference scenario of the national electricity production system 44.…Read more by Paraschiv Lizica, Spiru, Galati, Romania, Paraschiv, Simona, Dunarea de Jos University of Galati