21.2.1 Rotor

The rotor is made up of the hub and blades.

21.2.2 Nacelle

The nacelle houses the electricity generating equipment including gearbox (if geared), generator, foundation, cover, yaw system (a bearing system that allows the wind turbine to change direction to face the wind), and controls.

21.2.3 Tower

The hub height of turbines has increased significantly in recent decades with the tallest turbines reaching over 150 m. As such the turbine tower makes up a large proportion of the mass of the turbine. Typical materials are structural steel, which is rolled and welded into tower sections, or concrete.

21.2.4 Foundation

The foundation of wind turbines can change significantly, depending on the installation location. Onshore foundation designs include: tensionless pier, a cast-in-place concrete ring around 3–5 m in diameter and up to 10 m deep; anchor deep, a 2 m thick concrete ring supported by up to 20 steel anchors up to 15 m deep; and gravity spread, a broad steel-reinforced concrete disk up to 20 m in diameter [10]. Offshore designs include: gravity-based, using mass to prevent the turbine from tipping over; monopile, consisting of a single, hollow steel pile driven into the sea bed; tripod, consisting of a braced Y-frame and three, smaller piles into the sea bed; and floating, consisting of a floating ballast submerged and moored to the sea bed [11].

21.2.5 Balance of Systems

The balance of system comprises all of the other components and installations to allow the wind system to operate. This includes inverters (if the turbine puts out DC electricity), electrical control systems, operational buildings and roads, spare equipment (e.g., replacement blades) grid interconnection, and energy storage (if required).

21.3.1 Cumulative Energy Demand

CED is an impact metric that “represents the direct and indirect energy use, including the energy consumed during the extraction, manufacturing and disposal of the raw and auxiliary materials.” [13, p. 2189] Certain environmental energy flows are not accounted, as such the wind flowing through the turbine is not included in the CED for wind-generated electricity.

We may define CED on the basis of either nameplate capacity (to give CEC) or lifetime electricity generation (to give LCEC). Mathematically, we may say

$\mathrm{CEC}\left[\frac{M{J}_{\mathrm{p}}}{W_{\mathrm{p}}}\right]=\frac{\mathrm{CED}}{K}$ (21.1)

(21.1)

where K is the nameplate capacity of the device and

$\mathrm{LCEC}\left[\frac{kW{h}_{\mathrm{p}}}{kW{h}_{\mathrm{e}}}\right]=\frac{\mathrm{CED}}{E}$ (21.2)

(21.2)

where E is the energy delivered by the device over its lifetime.

21.3.2 Energy Payback Time

Energy payback time (EPBT) is the amount of time that an energy technology takes to deliver the amount of energy required over its life cycle [14]. Mathematically, we may define this as

$\mathrm{EPBT}\left[\mathrm{years}\right]=\frac{\mathrm{CED}}{\dot{E}}$ (21.3)

(21.3)

where $\dot{E}$ is the energy delivered by the device annually.

21.3.3 Fractional Reinvestment

The fractional reinvestment, f, defines the amount of electricity that an industry composed of devises with a certain EPBT must invest in deploying new devices to maintain a certain growth rate [15,16] (It should be noted that here EPBT is defined using a quality correction factor to directly compare electricity production with the energy investments, which we denote with a subscript e, where ${\mathrm{EPBT}}_{\mathrm{e}}=CE{D}_{\mathrm{e}}/E_{\mathrm{e}}$.). Mathematically we can define this as

$f[\%]=r{\mathrm{EPBT}}_{\mathrm{e}}$ (21.4)

(21.4)

where r is the industry growth rate in percent per year.

If $f\mathrm{>100\%}$, the industry is running in deficit, if $f\mathrm{<100\%}$, the industry can provide surplus electricity to society. Currently the global photovoltaic industry has a fractional reinvestment, $f_{PV}\mathrm{=90\%}$ meaning that only 10% of electricity production by the PV industry is available for other uses within society.

21.4.1 Literature Search

Searches were made of a number of publication types including peer-reviewed journals, industry reports, reports by national agencies, e.g., the US Department of Energy (DOE) and other work, e.g., conference papers and doctoral theses. The search terms included the “wind,” with the following phrases: “embodied energy”; “cumulative energy demand”; “life cycle inventory”; “life cycle assessment”; “energy payback time”; “net energy ratio” (NER); “energy yield ratio” (EYR); “energy return on investment”; and “EROI.”

The initial search produced 120 items published since 2012. These were then passed to the screening process.

21.4.2 Literature Screening

A number of criteria were used to screen the initial results (1) the study should be in English; (2) the study should be original research or should reference data used; (3) the study should give data on wind turbine technologies; (4) the study should give numeric data on net energy metrics, e.g., CED, or net energy ratio (NER); and (5) the study should give sufficient information on assumptions and system boundaries to allow for harmonization. Cross-referenced estimates were also eliminated. The studies remaining after screening are presented in Table 21.1 along with data from previous meta-analyses [7,8,15].

21.4.3 Harmonization of Study Boundaries and Data

A number of methods were used to allow comparison of results: Data given in terms of primary energy was changed to electricity equivalents using conversion factors given in the study. If no conversion factor was given, a standard conversion factor of 30% was used. If data was given in terms of an energy intensity, i.e., energy inputs per unit of electricity produced, e.g. [MJ (kW h_e)⁻¹], this was converted to per unit capacity inputs by either: using the capacity factor, i.e., the ratio of the average power output to nameplate capacity of the system; or using the total lifetime electricity production of the system; or using the annual electricity production of the system and the lifetime of the system, if no lifetime was given, the system was assumed to have a nominal lifetime of 25 years.

21.5.1 Capital Energetic Costs (CEC)

Capital costs include the energy requirements to extract and process all raw materials, manufacture, and install the capital equipment including any site preparation and grid interconnection. Units of measurement for CEC are primary energy inputs per unit of nameplate capacity [MJ_p (W_p)⁻¹].

Fig. 21.3 shows the distribution in estimates of CEC for the various wind technologies and analysis methods. This presentation does not account for changes in these values over time but instead shows the distribution across all studies over the more than 40 year period. The boxes represent 25-50-75 percentiles and whiskers plot minimum and maximum values. Generally there is a large min–max range in the data, with much smaller interquartile range. Onshore wind tends to have a lower CEC than offshore wind (due to the added costs of offshore deployment). Input–output analysis tends to produce higher estimates with a larger range, whereas hybrid analyses produced the lowest range and median value.

21.5.2 Life-Cycle Energy Costs (LCEC)

LCEC includes all of the energy inputs over the full life cycle of the system, including end-of-life, normalized by the total lifetime electricity output from the system. The unit of measurement is primary energy per unit of electricity production [kW h_p (kW h_e)⁻¹]. Unlike the financial metric LCOE, no discounting of inputs and outputs has been made.

Fig. 21.4 shows the life-cycle energy requirements for onshore and offshore technologies and all of the different analysis methods. Similarly to CEC we find a wide range in the data. Again onshore has a lower median value than offshore and process analysis methods produce a lower variation in value.

21.5.3 Harmonization

During the harmonization procedure, parameter inputs that impact the calculation of performance metrics are substituted for standard values. We will harmonize the capacity factor and turbine lifetime to analyze the effect on LCEC. In Fig. 21.5A we see the unharmonized values of LCEC computed using the data in the studies and ranked from smallest to largest. The corresponding distribution is shown in Fig. 21.6. We first replace the value for capacity factor used in the study, with a standard value of 25% (representing the global median value, as shown in Fig. 21.2) and recalculate LCEC, as shown ranked in Fig. 21.5B. As can be seen in the new distribution in Fig. 21.6 this step actually increased the min–max and interquartile range, and slightly increased the median value. We then replaced the turbine lifetime used in the study, with a nominal value of 25 years. The recalculated values for LCEC can be seen in Fig. 21.5C with the corresponding distribution again shown in Fig. 21.6. The min–max range has now decreased (though still not below the unharmonized range), however, the interquartile range has decreased below unharmonized.

21.5.4 Components

Many of the studies provide a breakdown of CED by different components. This data is presented in Table 21.2 with distributions presented in Fig. 21.7. Again there is a large distribution to the values. The tower contributes the highest median value to the overall CED (23%). Transport has the highest range in values, which is greatly influenced by both distance and the size of the system. Disposal presents an interesting case. Many studies give energy credits for recycling of turbine materials (primarily steel), leading to a negative value, as much as 50% of the overall value.

21.5.5 Trends in Parameters

The distributions presented in Figs. 21.3–21.7, did not account for all physical attributes of the turbines or studies. For instance we might expect that larger turbines might have a lower CEC or that a turbine built today would have a lower CED than the equivalent turbine built 10 years ago.

To assess the relationship between CEC and turbine power rating, we present Fig. 21.8. CEC decreases as turbine rating increases.

We also expect that as installed capacity increases the industry decreases the cost of producing wind power systems. The energy learning curve for wind is depicted in Fig. 21.9 with a power curve fitted to the data. The learning rate is defined as the percent reduction in cost per doubling of installed capacity. The learning rate for the wind industry is approximately 5%.

21.5.6 Net Energy Trajectory of the Global Wind Industry

Combining data on CED and EPBT (including learning effects, see Section 21.5.5), as well as global wind industry capacity factors 21.2 and growth rate of installed capacity (see Fig. 21.1), we can determine the fractional reinvestment in each year. Combining these annual values we can develop the net energy trajectory for the global wind industry, as shown in Fig. 21.10 (It is worth noting that these values are based on a capacity factor of 25%. In reality offshore wind farms tend to have higher capacity factors (more like 35%–40%), so are likely to have shorter EPBT and correspondingly lower fractional reinvestment.).

As can be seen both the onshore and offshore wind industries are operating with low fractional reinvestment. The onshore industry currently has a growth rate of around 16% year⁻¹ and wind turbines have an EPBT_e of just over 0.3 years, giving $f_{\mathrm{ON}}=5\%$. The offshore wind industry is currently growing more rapidly at a rate of around 40% year⁻¹ and has a higher EPBT (since less offshore capacity has been installed) giving a higher fractional reinvestment, $f_{\mathrm{OFF}}=15\%$.