Wind flow intensity is proportional to the density of air, the overall area of the flow’s cross-section and the value of wind speed to the power of three. Because it is a function of the cube of the wind speed, wind power is extremely variable and fluctuates within very wide ranges.
Average annual specific wind energy – or energy that flows within one year through one square meter of a cross section – is an integral, or averaging, value. It is also contingent on wind speed frequency, that is, on how long during the year the wind blew at one speed or another.
Fig. 2.7. exemplifies how the total annual specific wind energy value is computed (the area encompassed by the curve of Wsp) in the wind conditions of the coast of the Barents Sea at the average annual wind speed of υ = 8 m/s. Because of the third-power relationship between wind power and wind speed, the biggest contribution to the total wind energy value is made not by the most frequently observed wind speeds and not even by the average wind speeds, but by speeds that exceed the latter by 1.7 to 1.9 times.
When one has all necessary data on the average annual wind speeds (Fig.2.1), the vertical wind profile (Fig. 2.2), and the wind speed frequency (Fig.2.4), one can derive an energy pattern of a particular wind flow in any location and at any wind elevation on the Kola Peninsula.
When assessing energy resources, consideration is usually given specifically to potential, technical and economic resources. Potential wind energy resources are understood to be the total energy of the movement of air mass as it travels over a given area within one year’s time.
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Fig. 2.7. Wind speed frequency t and distribution of specific annual energy Wsp
on the coast of the Barents Sea at υ equaling 8m/s
υ1 - speed most frequently observed;
υ2 - average wind speed;
υ3 – speed that contributes most into annual energy production
Technical wind energy resources refer to that part of the potential resources which can be put to use with the help of technical means available to date. They are determined with considerations allowing for the unavoidable losses when wind energy is converted into power.
According to the so-called ideal wind wheel theory, only that part of energy which passes through the wind wheel’s cross section can be converted into net work. Net energy maximum is reached when the wind energy conversion coefficient xmax equals 0.593. Today, the wind energy conversion coefficient offered by best national and foreign-made wind wheel models is capable of reaching values ranging between 0.45 and 0.48.
Furthermore, practice shows that existing wind energy converter models are not sophisticated enough to be able to employ the whole range of wind speeds. Under speeds below a minimal level, the operating capacity of the wind wheel is not sufficient to even overcome the friction load in the WEC’s assembly units. Wind energy is used to its current achievable maximum within the speed range spanning between the minimal operating speed and the design-based speed, where the WEC can develop its installed capacity. As wind power further intensifies, including reaching a maximum operating speed, the converter’s capacity is kept at a consistent level with the help of regulating devices. Finally, when wind speed exceeds a maximum operating speed, the WEC is taken out of operation to avoid damaging the installation.
The results of calculations of technical wind energy resources of the Kola Peninsula are shown in Table 2.1. The technical resources were estimated in accordance with four different areas of the peninsula, which had been defined by the different levels of a multi-year wind speed average υ at an elevation of ten meters (Fig. 2.1.). In the first area, the is υ less than 7 m/s; in the second, it fluctuates between 6 m/s and 7 m/s; in the third, the range is between 5 m/s and 6 m/s, and in the fourth it is between 4 m/s and 5 m/s. A design-based wind speed – a speed, at which a WEC develops its nominal capacity – was determined for every area based on 3,000 hours’ worth of supply of energy consumption per year out of the installed capacity. Table 2.1. demonstrates that if a “dense forest” of windmills is built in these areas at a step of ten wind wheel diameters from each other, then the total installed capacity of the WECs will reach around 120 million kW, while the annual power output - or the technical wind power resources – will total about 360 TWh.
These estimations are evidence that the Kola Peninsula has at its disposal enormous wind power resources; they exceed greatly the electric power demands that the region currently has. Application of the accessible part of these resources, and their inclusion into the peninsula’s economy, absolutely deserves attention as an objective to be pursued by the region.
Table 2.1.
Wind resources of the Kola Peninsula at the ground-air interface,
elevations within 100 m.
| Parameter, name | Area |
Total | ||||
| 1 | 2 | 3 | 4 | |||
| Average annual wind speed |
|
|
|
|
| |
| in the area, m/s |
|
|
|
|
| |
| at 10 m | 7.5 | 6.5 | 5.5 | 4.5 |
| |
| at 70 m | 9.6 | 8.6 | 7.5 | 6.5 |
| |
| Specific wind energy, |
|
|
|
|
| |
| MWh/(m2/year) |
|
|
|
|
| |
| at 10 m | 5.2 | 3.4 | 2.4 | 1.4 |
| |
| at 70 m | 10.7 | 7.8 | 5.2 | 3.4 |
| |
| Average annual specific |
|
|
|
|
| |
| wind power, kW/m2 |
|
|
|
|
| |
| at 10 m | 0.59 | 0.39 | 0.27 | 0.16 |
| |
| at 70 m | 1.22 | 0.89 | 0.59 | 0.39 |
| |
| Estimated wind speed, m/s |
|
|
|
|
| |
| at 10 m | 12.3 | 10.4 | 8.5 | 7.6 |
| |
| at 70 m | 15.7 | 13.8 | 11.6 | 11.0 |
| |
| WEC capacity per 1 km2 |
|
|
|
|
| |
| of the area, MW | 7.2 | 4.9 | 2.9 | 1.9 |
| |
| WEC annual output |
|
|
|
|
| |
| per 1 km2, million kWh | 21.6 | 14.7 | 8.7 | 5.7 |
| |
| Hours of installed |
|
|
|
|
| |
| capacity use per year | 3,000 | 3,000 | 3,000 | 3,000 |
| |
| Area size, thousands of km2 | 3.5 | 5.9 | 9.4 | 20.7 | 39.5 | |
| WEC capacity in the area, |
|
|
|
|
| |
| thousands MW | 25 | 29 | 27 | 39 | 120 | |
| Technical wind energy |
|
|
|
|
| |
| resources, TWh | 75 | 87 | 81 | 117 | 360 | |
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