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UCD SCHOOL OF BIOSYSTEMS ENGINEERING
Life Cycle Assessment of a
Small Wind Farm
BSEN 40440 – Life Cycle Applications
Luke ...
1
Contents
Goal and Scope ...................................................................................................
2
Significant Issues.........................................................................................................
3
Life Cycle Assessment of a Wind
Turbine
Goal and Scope
The wind energy sector is Ireland’s strongest growing renewable e...
4
The results are intended for an audience who are adept in life cycle analysis and have a
good understanding of the wind ...
5
p = Air Density
A = Swept Area (m²)
V = Wind Speed (m/s)
Obviously the wind does not blow at the same speed constantly a...
6
While accurate activity data on the proportion of each of these materials going to landfill or
recovery was secured, the...
7
ConcreteIron Copper
Steel Aluminium
ConcreteGlass Fibre
Nacelle Rotor SubstructureInverter Cables
Wind Mill
Maintenance
...
8
Data Requirements
For data relating to raw materials, electricity generation and waste processes, pre-existing
processes...
9
Assumptions
The largest assumption made in this model is that all power generated from wind is utilized
by on-site proce...
10
encapsulate the renewable energy system which did not allow the recycling processes,
located in the disposal phase, to ...
11
Type and format of the report
The format of the report will strictly adhere to ISO 14040 standards including a goal and...
12
Life Cycle Inventory
Data Collection
Raw Material Inputs
Quantities of raw materials required per MW of electricity are...
13
Table 3: Distances of component transport from manufacturing plant to assembly site
Turbine Component Truck [France and...
14
Emissions to air, water, soil
GaBi is highly depended on for this aspect of the model. The pre-existing system and unit...
15
Table 7: Weibull Wind Speed Distribution
Wind Speed Power Coefficient Weibull Wind Hours
m/s Cp hr
0 0 0 0
1 0 0.039992...
16
Figure 3: Wind energy vs grid energy to satisfy energy demand
Validation of data
Table.8 shows a compilation of consist...
17
Relating data to the function unit
All material quantities and by extension all energy inputs have been scaled to the f...
18
Life Cycle Impact Assessment
General
This phase of the LCA, which assigns characterisation factors to the data compiled...
19
Table 9: Sources of significant environmental impact within the wind turbine's life cycle
Unit Process Global Warming R...
20
Assignment of LCI results to selected impact categories
Table.9 shows that each of the unit processes contributes in so...
21
Resulting data after characterization:
Table.10 shows the values given by the CML-2001 characterisation method while fi...
22
Normalisation
Normalisation is the expression of this profile relative to a given geographical region. In this
project ...
23
Despite having the highest amount of material input (71.1%), the “site” infrastructure ranks
only third on the list wit...
24
Life Cycle Interpretation
Significant Issues
Reiterating the goal of this project, the primary objective was to identif...
25
(Environmental Literacy Council, 2015). According to the Environmental Literacy Council
(2015) copper is also extremely...
26
Expected trend and Nacelle anomaly
For the most part the remaining processes in the life cycle appear to yield straight...
27
These graphs change according to wind speed, rotor efficiency and wind-hours for each
respective scenario. The data for...
28
supplement energy demand. In each scenario thereafter, acidification, resource depletion
and GWP emissions increase dra...
29
II. Sensitivity
The sensitivity analysis used to demonstrate the impact of grid emissions proves that the
model respond...
30
A second or third sensitivity analysis should have been carried out to analyse other portions
of the model however due ...
31
Critical Review
The purpose of this study was to apply the LCA technique to a wind turbine in Co. Dublin to
determine w...
32
Appendices
Appendix 1 - Raw material extraction and manufacturing
33
34
Appendix 2 – Turbine assembly and deconstruction
35
Appendix 3 – Turbine maintenance and use phase
At optimum wind speed of 10
m/s, site processes 100% powered
by wind ene...
36
Reference List
Abeliotis, K & Pactiti, D. (2014) ‘Assessment if the Environmental Impacts of a Wind Farm in Greece
duri...
37
Wilburn, D.R (2011) ‘Wind energy in the United States and materials required for the land-based
turbine industry: From ...
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LCA of a Small Wind Farm

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LCA of a Small Wind Farm

  1. 1. UCD SCHOOL OF BIOSYSTEMS ENGINEERING Life Cycle Assessment of a Small Wind Farm BSEN 40440 – Life Cycle Applications Luke Martin 5/26/2015
  2. 2. 1 Contents Goal and Scope .......................................................................................................................................3 Goal.....................................................................................................................................................3 Renewable Energy System to be studied............................................................................................4 Function of the system .......................................................................................................................4 Functional Unit....................................................................................................................................5 System Boundary ................................................................................................................................5 Allocation procedure ..........................................................................................................................6 LCIA methodology and impacts ..........................................................................................................6 Interpretation to be used....................................................................................................................6 Data Requirements.............................................................................................................................8 Assumptions........................................................................................................................................9 Value Choices......................................................................................................................................9 Limitations ..........................................................................................................................................9 Data Quality Requirements...............................................................................................................10 Type of Critical Review......................................................................................................................10 Type and format of the report..........................................................................................................11 Life Cycle Inventory...............................................................................................................................12 Data Collection..................................................................................................................................12 Data Calculation................................................................................................................................14 Validation of data..............................................................................................................................16 Relating data to the function unit.....................................................................................................17 Refining the system boundary..........................................................................................................17 Allocation..........................................................................................................................................17 Life Cycle Impact Assessment...............................................................................................................18 General..............................................................................................................................................18 Selection of impact categories, category indicators and characterization models..........................18 Assignment of LCI results to selected impact categories .................................................................20 Calculation of category indicator results:.........................................................................................20 Resulting data after characterization: ..............................................................................................21 Normalisation....................................................................................................................................22 Grouping ...........................................................................................................................................22 Data Quality Analysis ........................................................................................................................22 Life Cycle Interpretation .......................................................................................................................24
  3. 3. 2 Significant Issues...............................................................................................................................24 Evaluation .........................................................................................................................................28 Conclusions .......................................................................................................................................29 Limitations and Recommendations ..................................................................................................29 Critical Review.......................................................................................................................................31 Appendices............................................................................................................................................32 Appendix 1 - Raw material extraction and manufacturing...............................................................32 Appendix 2 – Turbine assembly and deconstruction........................................................................34 Appendix 3 – Turbine maintenance and use phase..........................................................................35 Reference List........................................................................................................................................36
  4. 4. 3 Life Cycle Assessment of a Wind Turbine Goal and Scope The wind energy sector is Ireland’s strongest growing renewable energy sector with 222 wind farms located on the island (IWEA, 2015). The popular adoption of this technology is largely due to the pursuit of low-carbon intensive energy forms and to ease reliance on fossil fuels (Martinez et al, 2008). “Sustainable energy is to provide the energy that meets the needs of the present without compromising the ability of future generations to meet their needs” (Ghenai, 2012). Based on the premise of utilising the kinetic energy of the wind to generate a clean form of electricity, wind power appears to be the ideal solution to this issue. However the technology has recently come under scrutiny due to questions raised about wind power’s relative sustainability when manufacture, transport and disposal processes are taken into account (Tremeac & Meanier, 2009). Considering that these turbines are made from a combination of metals, concrete and fibreglass, a considerable amount of energy derived from fossil fuels is required during these stages of its lifecycle. Life cycle analysis is a tool which can be utilized to determine the environmental impacts of all of the stages of a renewable energy system life span and facilitates a more accurate comparison of a RES with a conventional energy system. This study intends to apply this tool to a wind turbine based on data sought from a wind farm in north county Dublin and assess the emissions associated with its life cycle. Goal As stated in the introduction, a life cycle analysis is being carried out on a wind farm in Lusk Co. Dublin. This site was chosen for the sake of simplicity; the farm consists of one Enercon E-48 turbine and has a primary energy demand in the form of a food packaging plant. This study aims to determine three things; 1. Are there any stages in particular within the turbine’s life cycle which has considerable impacts on the environment? 2. How does a wind turbine’s associated environmental impacts compare to conventional grid electricity use? 3. When all aspects of this wind turbine’s life cycle are quantified and converted into the respective impact categories does it warrant the label of a “sustainable technology”?
  5. 5. 4 The results are intended for an audience who are adept in life cycle analysis and have a good understanding of the wind energy industry. The results are not intended to be used for comparative assertion given the author’s lack of experience in the area of LCA however, the results derived from this study may be compared to those in the literature in order to assess their accuracy. Renewable Energy System to be studied In order to assess the true sustainability of wind energy a site in Lusk Co. Dublin was selected for this study for two main reasons;  The wind farm consists of only one turbine, making the gathering of activity data and the scaling of the model easier for the inexperienced LCA practitioner.  A certain degree of familiarity is associated between the author and this site given that the wind mill can be seen from his house. This site is owned by a company called “Country Crest” a commercial farming company which recently expanded its business to include packaged and processed foods. The electricity generated from the on-site turbine is predominantly used to power these processes. When the wind is not blowing at sufficient speeds, the factory is powered by the Irish grid. By enlisting in the help of GaBi, it is expected that a cradle-to-grave analysis will be successfully executed. This expectation is made because a considerable amount of time will be saved by the use of this software seen as it does all the calculations for the user. Hence the study considers all stages of development of an “Enercon E48” turbine. Figure.2 has segregated the system into several phases including raw material extraction, truck and ship transport, component manufacture, turbine assembly, use phase, maintenance phase, decommissioning and final disposal of turbine parts. The only exception is the recycling phase; the reason for this phase’s omission will be discussed in the “limitations” section. Function of the system The function of the system is to convert kinetic energy derived from wind into rotational kinetic energy in a turbine and subsequently into usable electricity to power on-site functions. This is depicted in the following equation albeit a simplified version: 𝑃𝑤 = 1 2 ∗ 𝑝𝐴𝑉³ Where Pw= Wind Power Figure 1: Enercon E-48 turbine (Country Crest.ie, 2015)
  6. 6. 5 p = Air Density A = Swept Area (m²) V = Wind Speed (m/s) Obviously the wind does not blow at the same speed constantly and each turbine model has a unique range rotor efficiencies at different wind speeds known as the power coefficient. The model will include a number of scenarios which manipulate the inputs of this equation along with the power coefficient, with data derived from a Weibull wind frequency distribution specific to the site, in order to demonstrate the wind turbine’s effect on emissions by offsetting the use of the Irish grid. Functional Unit The functional unit chosen for this study is the production of one MW of electricity. The amount of goods packaged during the use phase has been manipulated to demand this amount of power. All material quantities have been scaled to this unit. System Boundary Figure.2 depicts the system boundary in green. This study begins with raw material extraction depicted as “metal” and “other material” extraction phases. This step can rely on pre-existing processes in the GaBi database. The raw materials emerge from their respective extraction phases as refined material ready to be manipulated into the desired parts. The next step involves transporting these refined materials to either the turbine manufacturer in Picardie, France or, in the case of concrete, directly to the assembly site in Dublin, Ireland. Transported materials are processed into the respective turbine components via a number of industrial processes such as casting, forging or stamping (Ghenai, 2012), summarised in the “manufacturing phase”. Following this phase, the finished wind mill parts are exported via truck and ship transport from Picardie to Dublin where the “assembly phase” takes place involving a crane process, an excavator process and a bolting/drilling process to build the wind mill. Adjacent to this phase is the “use phase”, shaded in green in figure.2. In scenarios with optimum wind conditions and frequencies (≈6-12 m/s) wind power can completely satisfy the 1MW power demand however any scenario outside this range requires the use of the Irish grid. The “Irish Grid” process is a pre-existing process within GaBi which is more than acceptable to use in this project as outlined in the life cycle inventory. The end-of-life phases are split into three phases in figure.3. The “decommissioning phase” involve the dismantling and sorting of turbine components into the respective raw materials of steel, iron, aluminium, copper, PVC and glass fibre. Following the methods of Tremeac & Meanier (2009), concrete is assumed to be covered over in top soil and left in the ground hence a landfill of concrete process should adequately represent the end-of-life activity of this material.
  7. 7. 6 While accurate activity data on the proportion of each of these materials going to landfill or recovery was secured, there was an issue with how the model dealt with recycling (depicted by broken green lines). This phase had to be omitted from the original system boundary due to time constraints. This point is further elaborated on in the “limitations section”. Allocation procedure According to Martinez et al (2008), allocation is not a major issue in wind turbine LCA’s and any possible impacts on final results are minimal; hence all processes within the system boundary are assumed to have only one function to avoid this issue. LCIA methodology and impacts The first two objectives of this study; the sustainability of a wind turbine and how it compares to the Irish grid will be assessed on their impacts towards global warming potential (GWP), resource depletion and water use. The third objective which investigates any processes with a particularly high impact to the environment may consider other impacts such as acidification or eutrophication impacts. CML 2001 was chosen as the characterisation method for this project based on the premise that it uses midpoints over endpoints. Endpoint impact information is considered more useful to policy-makers, especially those without a scientific background, as it expresses impacts in a form that is easier understood. For example Eco-indicator 99 expresses the impact of acidification processes as the amount of species extinct per m² per year. According to Bare et al (2000), the majority of LCA experts believe that extending impact categories as far as end point reduces the integrity of results because the availability of reliable data remains too limited. Considering the target audience of this report are adept in LCA and wind energy, it can be reasonably assumed that they have scientific backgrounds hence mid-point indicators are used to express process emissions in this study. While GaBi conveniently affords the user the opportunity of using multiple characterisation methods the CML 2001 method is considered to be particularly reliable in its characterisation methods especially with respect to European datasets. Interpretation to be used Following ISO14044:2006 standards; the interpretation will involve an analysis of the LCI and LCIA results in order to identify the key contributors within the wind turbine’s life cycle to the aforementioned impact categories. The interpretation will also assess the integrity of the methods used to obtain these results and will consider the potential drawbacks with the software, sampling errors and data quality.
  8. 8. 7 ConcreteIron Copper Steel Aluminium ConcreteGlass Fibre Nacelle Rotor SubstructureInverter Cables Wind Mill Maintenance Metal Extraction Phase Other Material Extraction Phase CopperIron PVC Manufacturing Phase Truck Transport Assembly Phase Site Infrastructure Truck and Ship Transport Wind Energy Irish Grid Energy Food Packaging (1MW demand) Tower Use Phase (Including upstream processes) Decommissioning Phase Steel Aluminium Glass Fibre PVC Truck Transport Recycling Phase Landfill Phase Credit for Plastic/Metal Recovery Plastic/Metal/ Concrete to Landfill Waste Flow Mass Flow Emission Flow Refined System Boundary Power Flow Figure 2: System boundary of the “Country Crest” wind farm, Lusk Co.Dublin. Original System Boundary
  9. 9. 8 Data Requirements For data relating to raw materials, electricity generation and waste processes, pre-existing processes from the GaBi database are acceptable. Seeking specific data on these aspects of the model are not likely to have a significant impact on the overall environmental emissions of the system. For example the GaBi database contains cradle-to-gate processes for all respective raw material extraction and refinement phases of the model. These pre-existing processes rely on data gained from global averages. This is acceptable at this point in the model as this phase is so far removed from the conversion technology itself. In a similar vein, the use of pre-existing processes are generally favoured over user-created ones for the majority of the manufacturing phase. There are a lot of emissions associated with these processes and the pre-existing processes are likely to represent the process in reality than a user created one, especially a user lacking experience in specific industry techniques such as casting or forging. There are a few occasions when either a user-created process or an edited existing process can be included in the model. Bolting and drilling for example, are assumed to have emissions associated primarily with the electricity they use hence this process will require little activity data, reducing the likelihood of error. In some cases, pre-existing processes are present which are based on regional averages. The French, German and Irish grids are all present in the GaBi database for example; and these processes rely on grid mix data from November 2014. Given the up-to-date accuracy of these electricity processes, the pre-existing “Irish Grid” process was selected to power site process in the absence of ideal wind speed as shown in figure.2. This process will have a significant impact on the results of the model. High precision, site-specific data is required for the wind-mill’s size specifications as such data will have a direct impact on the turbine’s ability to generate electricity. This data will be sought from the Enercon website. In addition, wind speed data for the site must be of high temporal and geographical accuracy as this parameter will also have a profound effect on the model. As outlined in assumptions, the most direct route is always chosen when compiling distance data. This will be calculated using the distance function in google maps. Pre-existing GaBi transport processes will used to represent material and goods transport while exact distances will be inserted into the model using parameters within these processes. Finally, specific data will be utilised within the end-of-life stages of the model to determine the proportions of material going to landfill or recycling. Pre-existing landfill and recycling processes will be applied when possible. In the event when a material-specific landfill process is not available, an existing landfill/recycling process will be manipulated to accept the material in question.
  10. 10. 9 Assumptions The largest assumption made in this model is that all power generated from wind is utilized by on-site processes. In reality, the turbine provides power for approximately 150 local households during optimum wind conditions. Since the main objectives of the study are focused on the renewable energy system itself, this assumption is acceptable as these households are well and truly outside the system boundary. For raw material and component transport, it is assumed that the most direct route suggested by Google maps is taken. In addition it is assumed the port nearest the terrestrial site (Dublin) is used for component import. Waste generated in the form of scrap during the “manufacturing phase” is assumed to be 100% recycled as the waste is generated on site and could theoretically be used in subsequent component manufacture. (e.g. the “steel bending and stamping” process has a 5% scrap parameter attached to it) Value Choices This study is concerned primarily with GHG emissions, water usage and energy usage. In the event that a process is identified with a formidable effect on another impact category such as acidification potential, that impact category will also be discussed in the interpretation phase. However upon making suggestions on how a process could minimize the turbine’s overall environmental impact, the first three impact categories will override the latter. Limitations Design and development of the wind turbine will be omitted from the study due the fact that this process itself does not make a significant physical contribution to emissions of this now mass produced technology in comparison to the other phases in the life cycle (Rebitzer et al, 2004). Specific data for the quantity of materials used in an Enercon E-48 turbine could not be located. The material quantities per MW of a wind turbine with a steel tower were taken from Wilburn (2011) to overcome this limitation. The main drawback with this substitution is that this data is based on American turbines however it offered the most precise data on the quantities of materials scaled to 1 MW hence was ideal for this study. Picking up on the omission of the recycling phase from the system boundary; the user did not anticipate how GaBi dealt with the recycling of materials. The omission of this process is not ideal as it does not come under the cut-off criteria of the model. A considerable amount of material was recyclable hence the recycling phase was likely to reduce the overall emissions associated with the wind turbine manufacture stage significantly. The pre-existing “credit for recycling” processes within the database only function when linked to the manufacturing phase earlier on in the model. This model used a hierarchical structure to
  11. 11. 10 encapsulate the renewable energy system which did not allow the recycling processes, located in the disposal phase, to link up with the manufacturing phase upstream. In order to overcome this issue, the entire model would have to have been redesigned. This was not possible due to time constraints, with the model deadline fast approaching. As a slight consolation, the recyclable portion of the waste was successfully diverted from landfill within the model. As a result, the contribution of the disposal phase to life cycle emissions has been muted slightly hence the results are still considered somewhat robust and should allow for the objectives of this project to be achieved albeit with higher uncertainty. Finally, the model is being created on an educational version of GaBi. This version has a limited number of unit and system processes available to the user. This will require the input of data gathered from industry and the literature. This information can be difficult to locate hence there are likely to be gaps in the model due to this. Data Quality Requirements Close to the energy conversion source, data is expected to be up-to-date, geographically relevant, technologically precise and relatively complete. Ideally manufacturer specific date will be required in and around the use phase. Background processes need not be as site specific. There are hundreds of LCA studies on wind turbines hence any datasets obtained from the respective LCA databases can be considered relatively robust. Hence In the event where data for a specific unit process or raw material cannot be obtained, LCA data for a similar process or material will suffice as opposed to omitting the parameter altogether. For example, for disposal and raw material phases it is acceptable to use non-specific data from a similar study as the results are unlikely to deviate significantly from results derived from site specific data. Failing to find a suitable substitute process (i.e. in the case of recycling), the system boundary will be altered to avoid the process’s inclusion. While the specific data in this circumstance is available, it is difficult to determine a way to incorporate it into the model hence a zero value is technically assigned. As highlighted in the limitations section, this zero value is significant in the sense that it contributes to the muting of landfill values. It is clear that if recycling had of been successfully included in the model, emissions from manufacturing should also have been muted slightly. Type of Critical Review The critical review will assess whether the results and interpretation of the LCA satisfied the goal and scope outlined by the author, whether there are any discrepancies or omissions in the data or whether the author gave a well-rounded view of the subject. Ideally the project should be reviewed by another LCA practitioner to ensure the absence of bias and personal errors one might not be aware of.
  12. 12. 11 Type and format of the report The format of the report will strictly adhere to ISO 14040 standards including a goal and scope; inventory analysis, impact assessment and interpretation and will be written to cater for an audience with a solid grounding in LCA and wind energy.
  13. 13. 12 Life Cycle Inventory Data Collection Raw Material Inputs Quantities of raw materials required per MW of electricity are acquired from Wilburn (2011). Table.1 shows the exact quantities of steel, concrete, iron, fibreglass, copper and plastic. Table 1: Raw Material Quantities (Wilburn, 2011) Material Proportion of turbine Mass per FU (kg/MW) Stainless Steel 20% 116,800 Concrete 71% 402,000 Cast Iron 4.4% 24,925 Fibreglass 1.9% 10,780 Aluminium 1.4% 8,100 Copper 0.5% 2,800 PVC 0.08% 500 Transport Table.2 shows the distances the various parts required for turbine assembly need to travel from the respective material merchants to the Enercon manufacturing facility in Picardie. The parts are assumed to be sourced from the merchant closest to the Enercon facility according to google maps. Within the model, these parts are represented by system processes hence all upstream transport emissions from extraction source to processing facility have been estimated and are included within the dataset. Parameter explorer will be utilized to investigate the significance of increasing these transport distances. Table 2: Refined material transport (google maps, 2015) Material Truck Transport per FU (km) Stainless Steel 184 Concrete 65 Cast Iron 184 Fibreglass 71 Aluminium 81 Copper 77 PVC 100 Table.3 displays the distances the turbine components are required to travel from Picardie to the assembly site in Lusk, Co. Dublin. Again, google maps are used to estimate distances and it is assumed that the most direct route was taken.
  14. 14. 13 Table 3: Distances of component transport from manufacturing plant to assembly site Turbine Component Truck [France and Ireland] (km) Ship (km) Tower 442 880 Nacelle 442 880 Rotor 442 880 Inverter 442 880 Cable 442 880 Concrete 100 0 End-of-life transport is summarised in table.4. Material for landfill is assumed to be exported to the nearby landfill at Balleally, Lusk while recyclable material is assumed to go to a sorting centre in Malahide, Co. Dublin. Table 4: End-of-life transport Waste Treatment Method Distance (km) Landfill 3 Recycling 15 Energy Inputs Industry averages derived from the GaBi database or from online sources are sufficient for the use of electrical and thermal energy in processes such as welding, casting and forging are utilised in this LCA. Table 5 shows the total energy required for each manufacturing stage along with the conversion process used in real life and the process used to mimic this in GaBi. Table 5: Energy use by model processes Component Manufacturing Process GaBi Process Energy required (MJ) Tower Forging, Rolling Steel Bending 13703 Electrical Nacelle Forging, Rolling Steel Bending, Cast Iron System Process 54342 Electrical Rotor Composite Forming Welding, Cast Iron System Process 17600 Electrical Inverter Forging, Rolling Copper bending, Aluminium die cast 28285 Electrical 15390 Thermal Cable Polymer extrusion Rod formation/ Assembly (us-o) 1440 Electrical 1264 Thermal Concrete Construction System Process (Diesel; Covered in Transport)
  15. 15. 14 Emissions to air, water, soil GaBi is highly depended on for this aspect of the model. The pre-existing system and unit processes within the database have associated emissions embodied within them. When a user-process is created the associated emissions have to be input individually into the process. While every effort was taken to ensure the accurate location and input of these emissions, this inevitably leads to uncertainty due to the scant availability of such data on the internet. Whenever possible, the practice of copying and editing an existing unit process within GaBi is preferred to creating a new one provided it is at least similar in some way to the process in real life. Waste Table.6 shows the proportion of each material which is available for recovery or destined for landfill. These proportions are taken from Martinez et al (2008) as precise activity data for this particular site was not readily available. Table 6: Proportion of materials for waste or recovery Material Recycling (%) Landfill (%) Stainless Steel 97 3 Concrete 0 100 Cast Iron 95 5 Fibreglass 48 52 Aluminium 35 65 Copper 28 72 PVC 72 28 Data Calculation The main data calculations associated with this model are related to the “use phase”. The function of this phase is to demonstrate the environmental effect of displacing fossil fuel electricity generation with wind energy. In this model, a pre-existing system process of the Irish grid mix is selected as the alternative power source to wind at this site. This process consists of electricity generation from a combination of natural gas, peat, coal and a small proportion of wind. When the wind is blowing at a velocity of 9 or 10 m/s, the turbine operates at its highest efficiency and can completely satisfy the on-site energy demands. The wind however, is a highly variable resource and does not blow consistently at these optimum speeds. Table.7 shows the three main variables which determine the power output of a wind turbine; wind speed, power coefficient and wind hours. The power coefficient data, unique to this E-48 turbine and was acquired from Enercon (2012).
  16. 16. 15 Table 7: Weibull Wind Speed Distribution Wind Speed Power Coefficient Weibull Wind Hours m/s Cp hr 0 0 0 0 1 0 0.039992 350.32992 2 0 0.075234 659.04984 3 0.17 0.101903 892.67028 4 0.35 0.117783 1031.77908 5 0.43 0.122525 1073.319 6 0.46 0.117466 1029.00216 7 0.47 0.105108 920.74608 8 0.48 0.088447 774.79572 9 0.5 0.070333 616.11708 10 0.5 0.05303 464.5428 11 0.45 0.038 332.88 12 0.39 0.025925 227.103 13 0.32 0.016862 147.71112 14 0.27 0.010466 91.68216 15 0.22 0.006205 54.3558 16 0.18 0.003515 30.7914 17 0.15 0.001905 16.6878 18 0.13 0.000987 8.64612 19 0.11 0.00049 4.2924 20 0.09 0.000233 2.04108 21 0.08 0.000106 0.92856 22 0.07 4.16E-05 0.364416 23 0.06 1.92E-05 0.168192 24 0.05 7.96E-06 0.0697296 25 0.05 2.95E-06 0.025842 The wind-hours data was calculated using hourly wind frequency data from the Dublin Airport weather station provided by Met Eireann (2015). The key to achieving the second objective of this project is to incorporate this data into the model. This was facilitated with the use of parameter explorer in which a number of wind speed scenarios were created to include each of the rows in table.6. Figure.7 illustrates an “electricity chooser” process which switches grid supply on and off pending on wind conditions. The current wind scenario in this figure is set to 10 m/s; hence the grid is making no contribution to the 3.6 MJ (1MW) of power demanded by the “site processes”.
  17. 17. 16 Figure 3: Wind energy vs grid energy to satisfy energy demand Validation of data Table.8 shows a compilation of consistency checks carried out within each plan in the model. There is one major outlier which will be discussed in the interpretation. Table 8: Mass Balance Consistency Check Process Mass In (kg) Mass Out (kg) Difference (%) Tower Manufacture 74800 74700 -0.13368984 Nacelle Manufacture 35000 35000 0 Rotor Manufacture 27700 27700 0 Inverter Manufacture 9600 9600 0 Cable Manufacture 3250 3250 0 Site Infrastructure 402000 452000 12.43781095 Site Maintenance 13650 13650 0 Electricity Generation 566000 566000 0 Turbine Decommission 566000 566000 0
  18. 18. 17 Relating data to the function unit All material quantities and by extension all energy inputs have been scaled to the functional unit of one MW of electricity produced by the tactical collection of activity data as outlined in table.6. The entire model is scaled to the “site processes” unit process in the use phase which creates a demand for 1 MW of electricity. Obviously the wind turbine only generates electricity when the wind is blowing at sufficient speeds so this factor is covered by inputting the Weibull distribution specific to this site (Table.7) as a parameter in the model. When the wind is not blowing at sufficient speed to satisfy the demand, power from the grid makes up the difference as shown in appendix.3. The emissions associated with the Irish grid have been modelled in order to demonstrate how these emissions are offset by wind energy at this site. Refining the system boundary As stated in the “limitations” section it was necessary to refine the system boundary to exclude the recycling phase of the model as outlined in figure.2. This was not down to a lack of available activity data, as is usually the case when refining a system boundary but down to an oversight made by the inexperienced user when designing the model. Due to time constraints it was not possible to rectify the model to include recycling. As previously stated, this will have a significant impact on the results however it does not render them useless. Allocation Allocation was avoided in two circumstances within the model by using two separate techniques. 1. Waste flows associated with scrap of various metals during the “manufacturing phase” were assumed to be 100% recycled. Despite this being an incorrect assumption to make, the effect that this assumption will have on the model is well below the cut-off criteria of the model; hence it will not have a significant effect on the overall results. 2. During the “disposal phase” allocation was avoided by system expansion. Here a “sorting” process was created which assigned exact proportions of waste materials to their respective end-of-life process based on activity data gained from Martinez et al (2008).
  19. 19. 18 Life Cycle Impact Assessment General This phase of the LCA, which assigns characterisation factors to the data compiled during the inventory phase, will be carried out by GaBi. While the program offers a variety of options to the user for completing this step it is necessary to select an appropriate characterisation method, taking the scope of the project into account. Selection of impact categories, category indicators and characterization models According to ISO standards, it is acceptable to select a category indicator anywhere along an environmental chain between intervention and endpoint (Guinee et al. 2002). Hence the aims set out in goal and scope play a large part in choosing the appropriate method. On the basis that the intended audience of this study are assumed to be scientifically proficient, a mid-point approach to characterisation of LCI emissions data is selected for the life cycle impact assessment. The use of mid-point indicators over end-points is considered to be more accurate as they are closer in the cause-and-effect chain to the source of emissions. Many LCA practitioners are of the opinion that the availability of data is too limited to extend an impact as far as an end-point such as the “amount of species killed per unit area per year” (Bare et al, 2000). Fundamentally, what the various impact assessment methods do is multiply the inventory results by the appropriate characterisation factors yielding the “environmental profile” which is then normalised (Guinee et al, 2002). The scope of this project only requires focus on global warming potential, resource depletion and water usage however acidification will also be included. Although a midpoint impact method is being deployed to characterise LCI data, ISO 14044 standards require that the potential endpoint impacts must be explicitly stated in the report. The endpoints associated with global warming potential are polar ice- cap melting, sea-level rise and alteration prevailing weather patterns. Resource depletion exhibits endpoints related to unsustainability with a decrease of these resources available to future generations. An endpoint in this category might look like “amount of persons per square metre unable to meet electricity requirements”. With an endpoint as speculative as this it is easy to see why LCA practitioners believe that the current records are not robust enough to extend an impact as far as endpoints (Bare et al, 2000). Water usage can result in endpoints associated with drought, crop failure and species extinction for example “species depleted per square meter per unit time” as the “EcoIndicator 99” characterisation method in GaBi illustrates. Table.9 details the key contributors within the life cycle of a wind turbine to each of these respective impact categories.
  20. 20. 19 Table 9: Sources of significant environmental impact within the wind turbine's life cycle Unit Process Global Warming Resource Depletion Acidification Water Usage Tower Manufacture Steel rolling/forging, material transport, electricity use Steel extraction, diesel/oil use during transport. Release of acidic gases during production/ combustion Cooling during industrial processes, emissions to water. Rotor Manufacture Cast iron/fibreglass processing, material transport, electricity use Iron/Ferro-metals extraction, diesel/oil use during transport. “ “ “ “ Nacelle Manufacture Steel/Cast iron casting, material transport, electricity use Steel/Iron extraction, diesel/oil use during transport “ “ “ “ Inverter Manufacture Copper/Aluminium processing, material transport, electricity use Copper/Aluminium extraction, diesel/oil use during transport “ “ “ “ Cable Manufacture PVC/Copper/Steel processing/casting, material transport, electricity use Plastic derivative/Copper/Ste el extraction, diesel/oil use during transport “ “ “ “ Site Infrastructure Cement mixing/concrete casting, material transport, electricity use Sand/Lime/Rock extraction, diesel/oil use during transport and earth works “ “ Water used in concrete mix, emissions to water Turbine Assembly Bolting/drilling/wel ding parts together. Crane and JCB use of diesel “ “ Emissions to water Turbine Maintenance Involves a combination of all the aforementioned manufacturing processes in scaled down form Lubricating oil, diesel oil use during transport and site maintenance visits “ “ Involves a combination of all the aforementioned manufacturing processes in scaled down form Turbine Deconstruction Electricity/diesel use in deconstruction, emissions from landfill Diesel use in transport and crane/JCB processes Release of acidic emissions during decomposition/ combustion Emissions from landfill
  21. 21. 20 Assignment of LCI results to selected impact categories Table.9 shows that each of the unit processes contributes in some way to each of the four impact categories chosen for this study. Characterisation factors will be assigned to each of the emissions associated with these processes to express results as an environmental impact quantity. This will be carried out using CML-2001. Calculation of category indicator results: Based on the fact that midpoint indicators are to be utilised to characterise GWP, resource depletion and water usage in this model, the CML-2001 characterisation method was deemed most appropriate for this study. This method created by scientists from Leiden University in The Netherlands, is the most up-to-date method available in the educational version of GaBi and was selected on the basis of its high ranking on Hauschild et al’s (2013) assessment of the most reliable midpoint impact characterisation methods. This model expresses GWP (100 year time horizon) in as a midpoint in terms kg of CO₂ equivalent. The CML characterisation factor is based on data from the Intergovernmental Panel on Climate Change (BRE, 2005). Resource depletion is related to the extraction of scarce minerals and fossil fuels and is expressed as “abiotic resource depletion” in units of kg Sb equivalent. This unit takes into account calculations of remaining reserves and the rate of extraction (BRE, 2005). Acidification potential is expressed as kg SO₂-eq. Endpoints of this category are attributed to acid rain and ecosystem impairment. Acidic gases such as NOx and SOx, released during the various life cycle stages can react with moisture in the atmosphere, resulting in acid rain. CML’s midpoint of “freshwater aquatic eco-toxicity”, measured in kg of dichlorobenzene equivalent (kg of DCB-eq) to indicate how toxic releases from the life cycle of the wind turbine can affect freshwater environments.
  22. 22. 21 Resulting data after characterization: Table.10 shows the values given by the CML-2001 characterisation method while figure.4 expresses these values as relative contributions. Table 10: Life cycle impact assessment values Global Warming (GWP100) Abiotic Resource Depletion Acidification Potential Freshwater Aquatic Eco- Toxicity Units kgCO₂-eq Kg Sb-eq Kg SO₂-eq kgDBC-eq Tower Manufacture 63,600 3.03 298 1080 Rotor Manufacture 36,900 0.9 130 189 Nacelle Manufacture 30,000 1.22 126 479 Inverter Manufacture 75,300 12.8 322 707 Cable Manufacture 6,750 8.36 29.9 189 Site Infrastructure 59,100 0.0611 137 94.5 Turbine Maintenance 14,300 2.99 67 220 Turbine Deconstruction 1800 0.000182 6.7 9.18 Other 49350 0.0289 77.1 41.5 Total 337,000 29.4 1190 3010 Figure 4: Relative contribution of major turbine components per impact category 0% 20% 40% 60% 80% 100% Global Warming (GWP100) Abiotic Resource Depletion Acidification Potential Freshwater Aquatic Eco- Toxicity 18.9 10.3 25.0 35.9 10.9 0.2 10.9 6.3 8.9 4.1 10.6 15.9 22.3 43.5 27.1 23.5 2.0 28.4 2.5 6.3 17.5 0.2 11.5 3.1 4.2 10.2 5.6 7.3 0.5 0.0 0.6 0.3 14.6 3.0 6.5 1.4 Tower Rotor Nacelle Inverter Cable Site Maintenance End of life Other
  23. 23. 22 Normalisation Normalisation is the expression of this profile relative to a given geographical region. In this project it is acceptable to use a region as broad as Europe or even the world for this purpose. This facilitates a clearer understanding of the magnitude of LCI results as they are related to a specific population and time frame. CML-2001 carries this out automatically hence all results are already normalised according to global (GWP) and European (Resource depletion, acidification and water pollution) standards. Grouping Figure.4 has an “other” category highlighted in a green colour. This consists of an aggregate of all electricity, thermal energy and transport processes utilised throughout the model. Following a gravity/pareto analysis it was observed that these processes contributed a negligible amount of emissions individually however when grouped together their impact could be significant especially in terms of GWP (fig.4). Data Quality Analysis Gravity Analysis Figure.5 shows a pareto analysis of the wind turbine life cycle ranking the cumulatively highest contributors to the left of the graph and the lowest contributors to the right. Superimposed on top of this graph are the relative material quantities for each component (orange line). 0 5 10 15 20 25 30 35 40 45 50 PercentageContributiontoemissions(%) Global Warming (GWP100) Abiotic Resource Depletion Acidification Potential Freshwater Aquatic Eco-Toxicity Figure 5: Pareto Analysis of the major turbine components
  24. 24. 23 Despite having the highest amount of material input (71.1%), the “site” infrastructure ranks only third on the list with a GWP contribution of 17.5%. The “tower” component ranks only second in all categories except for “freshwater eco-toxicity” despite having a 13.3% share of material inputs. The “inverter” production process ranks highest on the list despite having only a 1.7% share of material inputs. This process along with the “cable” manufacturing process ranks particularly highly in the abiotic resource depletion category. Uncertainty Analysis Taking GWP as an example, Table.9 compares the emissions calculated from this study to those in the literature. After a brief analysis it becomes evident that the overall emissions have been grossly underestimated in this study. Table 10: Comparative analysis with other wind turbine studies This Study (2015) Tremeac & Meunier (2009) Ghennai (2012) Crawford (2009) Abeliotis et al (2014) Unit kg CO₂-eq kg CO₂-eq kg CO₂-eq kg CO₂-eq kgCO₂-eq Total emissions 337,000 820,467 1,400,000 1,844,000 872,000 Turbine Assembly 311,000 705,111 1,200,000 N/A 928,300 Turbine Maintenance 13400 N/A N/A N/A 405 Turbine Deconstruction 1710 -48.88888889 13095 N/A -70,200 Functional Unit 1 MW 1 MW 1MW 1MW 1MW
  25. 25. 24 Life Cycle Interpretation Significant Issues Reiterating the goal of this project, the primary objective was to identify and analyse any aspects of the wind turbine’s life cycle which have a considerable impact on the environment. In order to address this, the analysis will follow the structure of the gravity analysis carried out in the LCIA. Secondly, the project set out to determine the benefits of offsetting grid use; and ultimately intends to conclude whether the construction of a wind turbine is a worthwhile investment in terms of saving on emissions. This will be explained using a scenario analysis outlining the gradual increase in emissions as the Irish grid supplements the energy demand. Inverter (and cable) manufacture: Aluminium and copper The gravity analysis carried out in figure.5 revealed that despite a minuscule share (1.7%) of the overall amount of input materials, the production of the inverter component of the turbine proved to be the costliest aspect of the life cycle in terms of emissions. At first glance this outlier appeared to be the result of experimental error however another study also noted that copper usage is particularly detrimental in terms of emissions. Figure.6 hones in further on the inverter process to reveal that a “copper from electrolysis” process and an “Aluminium ingot” process are responsible for the poor environmental performance of this component. Figure 6: Relative contribution to emission for Inverter manufacture Aluminium is responsible for the majority of acidification and GWP emissions. Despite being the third most common element on Earth (hence its low contribution to resource depletion), the extraction process for aluminium is extremely energy intensive 0 20 40 60 80 100 120 Acidification Abiotic resource depletion GWP Freshwater eco- toxicity PercentageContributionofemissions(%) DE: Copper mix (99,999% from electrolysis) PE EU-27: Aluminium ingot mix PE
  26. 26. 25 (Environmental Literacy Council, 2015). According to the Environmental Literacy Council (2015) copper is also extremely rare in its pure form (hence its high contribution to resource depletion) and the electrolysis process used to purify is extremely energy intensive. This observation can be reiterated by looking at the “cable” process in figure.5, which also has a resource depletion outlier attributable to copper use. Wilburn (2011) notes the importance of reducing the amount of copper in future wind turbines to improve their environmental appeal. Despite this Wilburn did not report as large an anomaly as noted in this study. Steel vs concrete This is where the experimental error comes in, the copper and aluminium processes used in Gabi (appendix.1_inverter manufacture) are cradle-to-gate processes involving purification of the two elements from electrolysis. Furthermore aluminium extraction requires large amounts of strip mining and heavy industrial processes to separate it from the mineral bauxite. The copper and aluminium used in industry is generally sourced from recycled scrap to eliminate these costly processes from the supply chain (Environmental Literacy Council, 2015), something this model failed to do. With a pound-for-pound environmental performance this low, it might go some way to explaining why some of the studies used to compare against (Tremeac & Meunier,2009, Abeliotis et al, 2014), omitted the modelling of the inverter component altogether. Green-washing? Moving on to the next largest emitter, the “tower” process, accounting for only 13.3% of the total amount of material yields the highest contribution overall to freshwater eco-toxicity. This process also contributes higher in all impact categories over “site infrastructure” which accounts for 71.1% of the total material inputs. Figure.7 identifies pre-cast concrete as the main component of site infrastructure while steel is the predominant component of the tower manufacture. Clearly steel has a more significant impact to the environment due to a complex production process along with the use of rarer earth materials however it maintains a much lower pound-for-pound environmental burden than aluminium or copper. Figure 7: Concrete vs Steel emissions contribution 0 5 10 15 20 25 30 35 40 45 GWP Acidification Resource Depletion Freshwater Eco- toxicity RelativeContibutiontoemissions (%) EU-27: Pre- cast concrete PE DE: Steel billet (100Cr6) PE
  27. 27. 26 Expected trend and Nacelle anomaly For the most part the remaining processes in the life cycle appear to yield straight-forward results. Figure.5 illustrates that low emissions coincide with low material inputs. The processes such as turbine maintenance, nacelle and rotor manufacturing have relatively less intensive production processes also, keeping emission low. End-of-life is not shown in this figure as the associated emissions were below the cut-off point in this instance. There is a slight blip in the maintenance stage which is also attributable to copper and aluminium replacement parts. The only anomaly worth investigating further is the nacelle’s relatively high contribution to freshwater eco-toxicity. Steel appears to be the main culprit according to figure.8 which seems to follow the trend set by this material in figure.7. Figure 8: Nacelle Freshwater contribution Wind energy compared to grid use Up until this point, there has been little mention of emissions associated with the Irish grid. This is due to the default scenario of the turbine operating at optimum conditions hence there was no input from the Irish grid. Besides saving money and increasing energy security the main reason for an Irish enterprise to invest in a wind turbine is to reduce their environmental impact during the generation of electricity. In order to provide an answer to this question figure.9 shows a comparison between wind and grid energy emissions. Flow s Diagram:Nacelle_Manufacture - Inputs/Outputs DE:Steelbillet(100Cr6)PE DE:Castironpart(automotive)PE<p-agg> FR:Electricitygridmix(productionmix) CML2001-Apr.2013,FreshwaterAquaticEcotoxicityPot.(FAETPinf.)[kgDCB-Equiv.] 450 400 350 300 250 200 150 100 50 0
  28. 28. 27 These graphs change according to wind speed, rotor efficiency and wind-hours for each respective scenario. The data for each scenario is available in table.7. Figure.9 displays a slight reduction in emissions as wind speed increases from 3-6 m/s representing less of a reliance on the grid. From 6-13 m/s the turbine is operating at optimum conditions and 100% of the energy demand is being supplied by wind power. The emissions associated with these speeds are the total emissions from the turbine life cycle. At 14-m/s onwards, the rotor efficiency reduces and the wind frequency lowers meaning the grid kicks in to 0 100 200 300 400 500 600 700 3 m/s 4 m/s 5 m/s 6 m/s 7 m/s 8 m/s 9 m/s 10 m/s 11 m/s 12 ms 13 m/s 14 m/s 15 m/s 16 m/s Relativecontributiontoemssions(%) Wind Speed scenario GWP Acidification Freshwater Eco- toxicity Resource Depletion 0 2000 4000 6000 8000 10000 12000 14000 16000 14 m/s 15 m/s 16 m/s 17 m/s 18 m/s 19 m/s 20 m/s 21 m/s Relativecontributiontoemissions(%) Wind Speed Scenario GWP Acidification Freshwater Eco-Toxicity Resource depletion Figure 9: Wind speed scenarios impact on emissions
  29. 29. 28 supplement energy demand. In each scenario thereafter, acidification, resource depletion and GWP emissions increase dramatically shooting up to between 10,000 and 14,000%. Figure.10 shows absolute values for the influence of wind speed on the four impact categories. At 13 m/s all emissions are purely from wind energy but from 15 m/s onwards, the emission values increase over 1000 times to that of wind energy. Table 11: Influence Irish grid has on emissions. Units 13 m/s 15 m/s 17 m/s 19 m/s 21 m/s 23 m/s GWP kg CO2- Equiv. 337296.6 1002049 3517279 13973946 65032255 359535158.8 Acidification pot. kg SO2- Equiv. 1192.054 3125.94 10443.2 40863.55 189401.5 1046164.071 Freshwater Eco-Tox kg DCB- Equiv. 3007.201 3358.496 4687.695 10213.63 37195.92 192829.0055 Resource Depletion kg Sb-eq 2957556 11119319 42001060 1.7E+08 7.97E+08 4413153312 Evaluation The results show favourable emissions data for the wind energy industry however there is a major issue with the results of this study which must be taken into account before conclusions can be made. I. Completeness As noted in table.10, this study has grossly underestimated life cycle emissions for a wind turbine and a functional unit 1MW of electricity. The results from this study are between 3-8 times lower than those calculated by recent studies on wind turbines using GWP as a reference. This error is most likely due to the over-simplification of the model. The nacelle for example consists of 1000 different parts in real life. This study attempts to quantify these parts by categorising them into their core material. This leads to the omission of a vast amount of processes, which adds to the overall emissions. In addition, the use of stamping and bending processes in replacement of forging and casting processes probably underestimates emissions also. Another inconsistency associated with this model is the improper modelling of recycling. Had the recycling phase been executed properly, the emissions for the likes of copper and aluminium would have been lowered significantly improving the integrity of the model. Referring back to table.10 once more, some of the studies have minus figures for end-of-life phase which represents credit for recycling.
  30. 30. 29 II. Sensitivity The sensitivity analysis used to demonstrate the impact of grid emissions proves that the model responds as it is supposed to with respect to changes in its parameters. Wind speed, rotor efficiency and wind hours all alter according to the site specific Weibull distribution outlined in table.7. Figure.9 and table.10 demonstrate how the emissions steadily rise as the three parameters used in the sensitivity analysis alter. III. Consistency Every effort was taken to ensure spatially and temporally accurate data was utilised whenever possible. Many of the pre-existing processes in GaBi allow the user to select geographically relevant processes. The Irish and French grid mixes for example are derived from data as recent as November 2014. Some of the production processes however were from the NREL database and hence were based on American data. With regards to impact categories, the CML-2001 method should have accurately normalised data to the relevant spatial standard for each impact category. Conclusions  Despite a significant degree of experimental error, hotspots could be identified in the wind turbine’s life cycle. Aluminium and copper proved to be among the most notorious material for emissions in GWP, resource depletion, water eco-toxicity and acidification due to their energy-intensive extraction processes and electrolysis processes used for purification.  Steel was the second highest emitter and this was more proportionate to the amount of this material required.  According to these results wind energy can definitely be considered a green technology when the entire life-cycle is taken into account.  This observation is marred by the fact that the model grossly underestimated overall emissions.  Substituting the overall GWP emissions from this study with those from Crawford (2009), the Irish grid yields emissions 195 times greater than total emissions from wind energy. Based on these figures, wind energy unequivocally deserves the title of a “green technology”. Limitations and Recommendations The key limitation with this study is that the model is not of a sufficient resolution to represent the life cycle of a wind turbine accurately. A key recommendation is that more accurate activity data be sought for the wind farm in Lusk Co. Dublin as well as on industry data on the wind turbine process itself. Secondly the layout of the model in GaBi requires a rethink in order to incorporate recycling phases appropriately.
  31. 31. 30 A second or third sensitivity analysis should have been carried out to analyse other portions of the model however due to a modelling over-sight this proved too difficult to perform. The oversight consisted of leaving transport and recycling processes out of the naming hierarchy applied to manufacturing phases. This meant that when a sensitivity analysis was attempted the respective transport processes were indecipherable meaning it was impossible to know which leg of the transport was being edited. To rectify this would have meant to go back and redevelop the model/ There was insufficient to perform this hence only one sensitivity analysis will suffice.
  32. 32. 31 Critical Review The purpose of this study was to apply the LCA technique to a wind turbine in Co. Dublin to determine whether wind energy is worthy of its title of a “green technology”. The aims were to highlight any significant processes in the life cycle with considerable environmental effects as well as demonstrating the amount of fossil fuel emissions offset by this technology. The project initially set out to cover all process cradle-to-grave however due to a modelling error, the system boundary had to be refined to exclude the recycling phase. This omission along with the rationale behind it was illustrated very clearly and explicitly. The system diagram makes good use of colour to illustrate clearly the various unit processes and how they link up. The life cycle inventory phase seems to be executed reasonably well with an abundance of tables outlining the inputs to the model. The activity is predominantly derived from secondary sources from industry analyses. All sources are clearly referenced. The model itself appears to be the biggest issue with the project, the user had difficulty executing the end-of-life phase hence credit for recycling cannot be included in the model. In addition, the model is too simplified hence not all emissions are accounted for. These errors are clearly documented. The LCIA phase adequately described the endpoint categories and stated which midpoints were to be used in the project. The CML-2001 characterisation method was selected on recommendation from a journal article. Results were compiled neatly on an innovative graph which clearly shows material inputs against impact categories. The interpretation highlighted aluminium and copper as the key impacts to the turbine life cycle. Some of these anomalous figures were attributed to modelling error however some studies in the literature back this observation up to suggest these material have a relatively large environmental impact. These sources were reference appropriately. Despite an abundance of experimental errors, the study can confidently conclude wind energy is a green technology; with emissions approximately 195 times lower than the Irish grid mix. Hence the objectives of the study were accomplished. Appropriate recommendations for improvements were made and screenshots of the model were included in the appendices, adding to the transparency of the project, leaving it open to a more honest interpretation.
  33. 33. 32 Appendices Appendix 1 - Raw material extraction and manufacturing
  34. 34. 33
  35. 35. 34 Appendix 2 – Turbine assembly and deconstruction
  36. 36. 35 Appendix 3 – Turbine maintenance and use phase At optimum wind speed of 10 m/s, site processes 100% powered by wind energy At a less efficient wind speed of 15 m/s, site processes only ~40% powered by wind energy
  37. 37. 36 Reference List Abeliotis, K & Pactiti, D. (2014) ‘Assessment if the Environmental Impacts of a Wind Farm in Greece during its Life Cycle’, International Journal of Renewable Energy Research. 4, no.3. Arvesen, A. Tveten, A.G. Hertwich, E.G. Stromman, A.H. (2010) ‘Life-cycle assessments of wind energy systems’, Industrial Ecology Programme, Tronheim, Norway. Building Research Establishment (BRE). (2005) Green Guide to Specification - BRE materials industry briefing Note 3a: Characterisation. Available from: http://www.bre.co.uk/greenguide/files/CharacterisationBriefingDocumentFinal.pdf [Accessed 24 April 2015]. Country Crest. (2015) Country Crest.ie. Available from: http://countrycrest.ie/Our-Green-Ethos [Accessed 15 April 2015]. Enercon. (2012) ENERCON Product overview. Available from: http://www.enercon.de/en- en/Produktuebersicht.htm. [Date Accessed: 27 Jan 2015] Environmental Literacy Council. (2015) Enviroliteracy.org. Available from: http://enviroliteracy.org/article.php/1029.html [Accessed 24 April 2015]. Ghenai, C. (2012). Life Cycle Analysis of Wind Turbine, Sustainable Development - Energy, Engineering and Technologies - Manufacturing and Environment, Prof. Chaouki Ghenai (Ed.), ISBN: 978-953-51-0165-9, InTech, Available from: http://www.intechopen.com/books/sustainable-development-energy-engineering- andtechnologies-manufacturing-and-environment/life-cycle-analysis-of-wind-turbine Guinee, J.B. Gorree, M. Heijungs, R. Huppes, G. Koning, A. van Oers, L. Sleeswijk, A.W. Suh, S. Udo de Haes, H.A. (2004) Handbook on Life Cycle Assesment. Kluwer Academic publishers, Dordrecht. Hauschild, M. Z. Goedkoop, M. Guinee, J. Heijungs, R. Huijbregts, M. Jolliet, O. Margni, M. De Schryver, A. Hmbert, S. Laurent, A. Sala, S. Pant, R. (2013) ‘Indentifying best existing practice for characteriization medeling in life cycle impact assessment’, Life Cycle Assess, 18, 683- 697. Iso14044. (2006) ‘Environmental management- Life cycle Assessment- Requirements and guidelines’, British Standard, UK. Martinez, E. Sanz, F. Pellegrini, S. Jimenez, E. Blanco, J. (2009) ‘Life cycle Assessment of a multi- megawatt wind turbine, Renewable Energy, 34, 667-673. Pennington, D., Potting, J., Finnveden, G., Lindeijer, E., Jolliet, O., Rydberg, T. and Rebitzer, G. (2004) 'Life cycle assessment Part 2: Current impact assessment practice', Environment international, 30(5), 721-739. Rebitzer, G., Ekvall, T., Frischknecht, R., Hunkeler, D., Norris, G., Rydberg, T., Schmidt, W.-P., Suh, S., Weidema, B. P. and Pennington, D. (2004) 'Life cycle assessment: Part 1: Framework, goal and scope definition, inventory analysis, and applications', Environment international, 30(5), 701-720. Tremeac, B. Meunier, F. (2009) ‘Life Cycle Analysis of 4.5 MW and 250 MW wind turbines’, Renewable and Sustainable Energy Reviews, 13, 2104-2110.
  38. 38. 37 Wilburn, D.R (2011) ‘Wind energy in the United States and materials required for the land-based turbine industry: From 2010 through 2030’, United States Geological Survey, Scientific Investigations report, 5036.

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