scholarly journals Assessment of wind energy potential ofthe Murmansk Regionand performance of Kola wind farm

Vestnik MGTU ◽  
2020 ◽  
Vol 23 (4) ◽  
pp. 376-386
Author(s):  
V. F. Beley ◽  
G. V. Kotsar
Keyword(s):  
Power Generation ◽  
Wind Energy ◽  
Electricity Generation ◽  
Wind Farm ◽  
Reactive Power ◽  
Energy System ◽  
Energy Potential ◽  
Murmansk Region ◽  
Wind Energy Potential ◽  
Wind Activity

The paper gives an analysis of perspectives for development of wind power generation, information on the share of wind energy in electricity generation in the world and in Russian Federation is provided as well. Data on gross, technical, economic potential of wind energy of Russia and the Murmansk Region has been presented. When calculating the gross wind energy potential of the region, data from the last 10 years of observations carried out at 37 meteorological stations have been used. The territory of the region has been divided into 6 distinctive zones, based on the wind activity. Gross energy potential has been calculated for each zone: for the heights of 10, 50, 100, and 150 m. Gross wind energy potential of the region at the height of 150 m has thus been estimated at 23,090 billion kWh. The Murmansk Region's 201 MW Kola wind farm, which consists of 57 Siemens Gamesa SG 3.4-132 wind turbines with a unit capacity of 3.465 MW, is to be constructed by 2021 under the direction of Enel Green Power. Wind energy potential and annual power generation of the Kola wind farm have been assessed. The difference between the obtained results and calculations of Enel Group's specialists amounts to less than 15 %. For the cases of relocation of Kola wind farm to different wind zones, the annual power generation of the wind farm has been estimated. It has been determined, that in case of Kola wind farm's relocation to the zone with the highest wind activity its annual electricity generation could be increased up to 1.5 times. A model of the Kola energy system has been developed in NEPLAN software, its validity has been proven. The calculations of the wind farm's operation modes show that voltage levels of the system nodes and powerflows are within the boundaries defined by normative documents. The effectiveness of reactive power regulation of the wind farm has been shown.

WIND ENERGY POTENTIAL FOR POWER GENERATION OF A LOCAL SITE IN GUSAU, NIGERIA

2010 ◽  
Vol 11 (1-4) ◽  
pp. 99-116 ◽  
Author(s):  
Oluseyi O. Ajayi ◽  
R. O. Fagbenle ◽  
James Katende ◽  
Joshua O. Okeniyi ◽  
O. A. Omotosho
Keyword(s):  
Power Generation ◽  
Wind Energy ◽  
Energy Potential ◽  
Local Site ◽  
Wind Energy Potential

Assessment of wind energy potential and characteristics in Qatar for clean electricity generation

2021 ◽  
pp. 0309524X2110438
Author(s):  
Carlos Méndez ◽  
Yusuf Bicer
Keyword(s):  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Rose ◽  
Energy Potential ◽  
Wind Power Density ◽  
Wind Speeds ◽  
Wind Energy Potential

The present study analyzes the wind energy potential of Qatar, by generating a wind atlas and a Wind Power Density map for the entire country based on ERA-5 data with over 41 years of measurements. Moreover, the wind speeds’ frequency and direction are analyzed using wind recurrence, Weibull, and wind rose plots. Furthermore, the best location to install a wind farm is selected. The results indicate that, at 100 m height, the mean wind speed fluctuates between 5.6054 and 6.5257 m/s. Similarly, the Wind Power Density results reflect values between 149.46 and 335.06 W/m2. Furthermore, a wind farm located in the selected location can generate about 59.7437, 90.4414, and 113.5075 GWh/y electricity by employing Gamesa G97/2000, GE Energy 2.75-120, and Senvion 3.4M140 wind turbines, respectively. Also, these wind farms can save approximately 22,110.80, 17,617.63, and 11,637.84 tons of CO2 emissions annually.

scholarly journals Managing Wind Power Generation via Indexed Semi-Markov Model and Copula

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4246 ◽  
Author(s):  
Guglielmo D’Amico ◽  
Giovanni Masala ◽  
Filippo Petroni ◽  
Robert Adam Sobolewski
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Wind Energy ◽  
Output Power ◽  
Wind Turbines ◽  
Wind Farm ◽  
Energy Systems ◽  
Wind Farms ◽  
Energy System ◽  
Spatial Dependencies

Because of the stochastic nature of wind turbines, the output power management of wind power generation (WPG) is a fundamental challenge for the integration of wind energy systems into either power systems or microgrids (i.e., isolated systems consisting of local wind energy systems only) in operation and planning studies. In general, a wind energy system can refer to both one wind farm consisting of a number of wind turbines and a given number of wind farms sited at the area in question. In power systems (microgrid) planning, a WPG should be quantified for the determination of the expected power flows and the analysis of the adequacy of power generation. Concerning this operation, the WPG should be incorporated into an optimal operation decision process, as well as unit commitment and economic dispatch studies. In both cases, the probabilistic investigation of WPG leads to a multivariate uncertainty analysis problem involving correlated random variables (the output power of either wind turbines that constitute wind farm or wind farms sited at the area in question) that follow different distributions. This paper advances a multivariate model of WPG for a wind farm that relies on indexed semi-Markov chains (ISMC) to represent the output power of each wind energy system in question and a copula function to reproduce the spatial dependencies of the energy systems’ output power. The ISMC model can reproduce long-term memory effects in the temporal dependence of turbine power and thus understand, as distinct cases, the plethora of Markovian models. Using copula theory, we incorporate non-linear spatial dependencies into the model that go beyond linear correlations. Some copula functions that are frequently used in applications are taken into consideration in the paper; i.e., Gumbel copula, Gaussian copula, and the t-Student copula with different degrees of freedom. As a case study, we analyze a real dataset of the output powers of six wind turbines that constitute a wind farm situated in Poland. This dataset is compared with the synthetic data generated by the model thorough the calculation of three adequacy indices commonly used at the first hierarchical level of power system reliability studies; i.e., loss of load probability (LOLP), loss of load hours (LOLH) and loss of load expectation (LOLE). The results will be compared with those obtained using other models that are well known in the econometric field; i.e., vector autoregressive models (VAR).

Wind resource assessment and wind farm modeling in Mossobo-Harena area, North Ethiopia

2020 ◽  
pp. 0309524X2092540
Author(s):  
Addisu Dagne Zegeye
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Turbine ◽  
Power Density ◽  
Energy Production ◽  
Wind Farm ◽  
Ground Level ◽  
Energy Potential ◽  
Mean Power ◽  
Wind Energy Potential

Although Ethiopia does not have significant fossil fuel resource, it is endowed with a huge amount of renewable energy resources such as hydro, wind, geothermal, and solar power. However, only a small portion of these resources has been utilized so far and less than 30% of the nation’s population has access to electricity. The wind energy potential of the country is estimated to be up to 10 GW. Yet less than 5% of this potential is developed so far. One of the reasons for this low utilization of wind energy in Ethiopia is the absence of a reliable and accurate wind atlas and resource maps. Development of reliable and accurate wind atlas and resource maps helps to identify candidate sites for wind energy applications and facilitates the planning and implementation of wind energy projects. The main purpose of this research is to assess the wind energy potential and model wind farm in the Mossobo-Harena site of North Ethiopia. In this research, wind data collected for 2 years from Mossobo-Harena site meteorological station were analyzed using different statistical software to evaluate the wind energy potential of the area. Average wind speed and power density, distribution of the wind, prevailing direction, turbulence intensity, and wind shear profile of the site were determined. Wind Atlas Analysis and Application Program was used to generate the generalized wind climate of the area and develop resource maps. Wind farm layout and preliminary turbine micro-sitting were done by taking various factors into consideration. The IEC wind turbine class of the site was determined and an appropriate wind turbine for the study area wind climate was selected and the net annual energy production and capacity factor of the wind farm were determined. The measured data analysis conducted indicates that the mean wind speed at 10 and 40 m above the ground level is 5.12 and 6.41 m/s, respectively, at measuring site. The measuring site’s mean power density was determined to be 138.55 and 276.52 W/m2 at 10 and 40 m above the ground level, respectively. The prevailing wind direction in the site is from east to south east where about 60% of the wind was recorded. The resource grid maps developed by Wind Atlas Analysis and Application Program on a 10 km × 10 km area at 50 m above the ground level indicate that the selected study area has a mean wind speed of 5.58 m/s and a mean power density of 146 W/m2. The average turbulence intensity of the site was found to be 0.136 at 40 m which indicates that the site has a moderate turbulence level. According to the resource assessment done, the area is classified as a wind Class IIIB site. A 2-MW rated power ENERCON E-82 E2 wind turbine which is an IEC Class IIB turbine with 82 m rotor diameter and 98 m hub height was selected for estimation of annual energy production on the proposed wind farm. 88 ENERCON E-82 E2 wind turbines were properly sited in the wind farm with recommended spacing between the turbines so as to reduce the wake loss. The rated power of the wind farm is 180.4 MW and the net annual energy production and capacity factor of the proposed wind farm were determined to be 434.315 GWh and 27.48% after considering various losses in the wind farm.

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