**3. Siting**

Locating and assessing the feasibility of a wind farm is one of the most critical elements in a wind farm business plan. Maximum energy extraction from the investment is dependent on where the site is located and where each individual turbine is positioned within that site. Wake interactions between wind turbines and nearby wind farms can substantially impact power output.

Wind farm developers expend significant resources collecting data for site assessments. Topography, surface roughness, the local wind profile, turbine types, power curves, munici‐ pal site restrictions and other data are collected and processed to maximize profitability. Wind Farmer and WindPRO are commonly used software packages delivering a range of services from wake modeling to visual impact studies. While much of the assessment is based on available data and numerical calculations, a portion of the analysis is dependent on user preferences. Depending on the investors and farm developers, varying levels of un‐ certainty may be accepted in different areas of the study. For example, a developer may choose to install a single meteorological tower for profiling of the wind resource at the cen‐ ter of the site under consideration to reduce costs. Another developer may choose to install two or three towers to drive down uncertainty caused by extrapolation of the recorded data across the area of the site. Some sites can have unique curtailment requirements depending on neighboring properties or bat and bird migration.

A variety of models have been developed to simulate wakes within a wind farm. The most widely used models include the:


Brower [13] summarizes these models and their differences. The WAsP software commonly used for analysis of wake within a wind farm by site assessment tools makes use of the Park model. By accounting for geographical and ground surface conditions, the variation in wind speed profiles can be estimated along with expected wake propagation intensity. This esti‐ mation allows for "micro-siting" or individual placement of turbines within a wind farm while minimizing wake losses.

It is evident that there are other external factors that contribute to the definition of these pro‐ files as they show irregularities and do not exhibit a smooth shape. It is expected that the 6 month averaged data has reduced the effects of short term, isolated fluctuations in wind speed, humidity, temperature, air density and inhomogeneous wake at the downstream tur‐ bine and so there is a consistent fluctuation in the wind speed under turbine alignment con‐ ditions. Seasonal and site specific wind conditions are likely to contribute to the small scale unpredictability of wake velocity deficit and turbulence intensity. Inter-turbine wake effects are quantifiable and are accounted for in all major wind farms projects. However, there is still a large amount of uncertainty and error in the modeling of wind turbine wakes and as‐ sociated power losses. The next section discusses wind farm siting and its importance in

Locating and assessing the feasibility of a wind farm is one of the most critical elements in a wind farm business plan. Maximum energy extraction from the investment is dependent on where the site is located and where each individual turbine is positioned within that site. Wake interactions between wind turbines and nearby wind farms can substantially impact

Wind farm developers expend significant resources collecting data for site assessments. Topography, surface roughness, the local wind profile, turbine types, power curves, munici‐ pal site restrictions and other data are collected and processed to maximize profitability. Wind Farmer and WindPRO are commonly used software packages delivering a range of services from wake modeling to visual impact studies. While much of the assessment is based on available data and numerical calculations, a portion of the analysis is dependent on user preferences. Depending on the investors and farm developers, varying levels of un‐ certainty may be accepted in different areas of the study. For example, a developer may choose to install a single meteorological tower for profiling of the wind resource at the cen‐ ter of the site under consideration to reduce costs. Another developer may choose to install two or three towers to drive down uncertainty caused by extrapolation of the recorded data across the area of the site. Some sites can have unique curtailment requirements depending

A variety of models have been developed to simulate wakes within a wind farm. The most

minimizing uncertainty in the planning stages of a wind project.

on neighboring properties or bat and bird migration.

widely used models include the:

**•** Park

**•** Modified Park

**•** Eddy Viscosity

**•** Deep-Array Wake Model

**3. Siting**

72 Advances in Wind Power

power output.

The addition of wakes in an array is difficult to model. A simple model for wake region overlap is shown in Johnson and Thomas [14]. The model indicates a 42% loss in power pro‐ duction for a turbine 3.75D downwind of the first and a 70% loss in power for a 3rd turbine 6.25D from the first and 2.5D from the second. However, experimental data, as summarized by Vermeer et al. [5], would indicate that the third turbine in the row sees little effect from the first, but is significantly affected by the second. It is concluded here that a turbine is only noticeably affected by the closest upstream machine. It is difficult to quantify the addition of wakes while siting a wind farm. Ideally turbines are spaced at distances great enough to negate wake effects; however, this is not always economically feasible due to the cost and availability of real estate in addition to the expense of laying cables and the interconnection of machines and substations. The staggering of turbines can be used to minimize effects but it is difficult to avoid interaction completely because of the conical nature of wind turbine wakes [15]. As a result, wind farms are typically arranged for maximum turbine spacing in the directions of the prevailing winds with closer spacing in the directions receiving less fre‐ quent winds. In general, the spacing in the prevailing wind direction ranges from 6-10 rotor diameters and 3-4 diameters in cross-wind directions. Figure 7 illustrates a wind rose with a distinctly dominant wind sector.

The study of wake is not restricted to inter-turbine relations. As the number of wind farms increase globally, the distance between wind farms has been gaining importance. Offshore wake from a small wind farm has been seen to propagate for 14 km [14] over the water. Christian and Hasager [16] used satellite imaging to study wake effects of two large wind farms, Horns Rev and Nysted, off the coast of Denmark. The images show a trail downwind of the farm that propagates for 20 km before near-neutral conditions are reached. Offshore wind farm wake dynamics have been considered to propagate farther than onshore due to less atmospheric turbulence; which is required for wind speed re‐ covery [9]. Without this turbulence, mixing of the wake area with the surrounding at‐ mosphere takes longer and can result in wake effects at a greater distance from the farm. Inter-farm effects for offshore is currently becoming a significant issue in Europe where planned offshore wind capacity has been growing. Corten and Brand [17] discussed the planned installation of 6 GW of capacity over 25 farms of offshore wind in a 10,000 square kilometer area. By the methods described in their work it has been concluded that an inter-farm loss of 5-14% is probable. This is substantial and raises many concerns especially in situations where wind farms are not owned and operated by the same com‐ pany and the possession of wind resources is debated.

**Figure 7.** Wind rose indicating percentage of wind direction probability. Data are for the upstream turbine over the six month data set for all power producing winds (3-25 m/s) [11].

Onshore wind farm wake propagation is reduced by complex terrain and vegetation. As stated above, onshore wake propagation has been measured up to 15 rotor diameters downstream of a turbine. While optimal wind turbine spacing has been studied [7, 15Bryony L.D.P and Cagan, J., An Extended Pattern Search Approach to Wind Farm Layout Optimization, Proceedings of ASME IDETC: Design Automation Conference, 2010, 1-10.] further work on the limit of minimum wind farm footprint to maximize prof‐ itability may be necessary.
