POTENTIAL FINANCIAL COST AND FEASIBILITY OF OFFSHORE WIND POWER IN GERMANY
Nils Goettner
A Thesis Submitted to the University of North Carolina Wilmington in Partial Fulfillment of the Requirements for the Degree of Master of Business Administration
Cameron School of Business
University of North Carolina Wilmington
2010
Approved by
Advisory Committee
Peter Schuhmann Vince Howe
Christopher F. Dumas Chair
Accepted by
______Dean, Graduate School
TABLE OF CONTENTS
TABLE OF CONTENTS...... II
ABSTRACT...... IV
ACKNOWLEDGEMENTS...... V
LIST OF TABLES...... VI
LIST OF FIGURES ...... VII
CHAPTER 1 ...... 1
INTRODUCTION ...... 1
CHAPTER 2 ...... 4
LITERATURE REVIEW ...... 4
Historical Review...... 4
Economic Efficiency of Wind Energy...... 7
Parameters of Wind Power Economics...... 8
Wind Power Generation Cost ...... 11
Comparison of Wind Power with Conventional Power Generation...... 13
External Costs ...... 16
Offshore Wind Power ...... 18
Outlook and Planning for Offshore Wind Power in Germany ...... 22
CHAPTER 3 ...... 25
OBJECTIVES AND RESEARCH QUESTIONS ...... 25
ii
CHAPTER 4 ...... 26
DATA AND METHODOLOGY...... 26
Data Collection Process ...... 26
Data Description ...... 27
Methodology...... 29
CHAPTER 5 ...... 31
RESULTS AND DISCUSSION...... 31
Descriptive Statistics and Discussion ...... 31
Correlation Statistics and Discussion...... 34
Multiple Regression Analysis and Discussion...... 35
Equation Model and Potential Cost Drivers ...... 39
Model Application to German Offshore Wind Farm Plans...... 41
CHAPTER 6 ...... 43
CONCLUSIONS AND RECOMMENDATIONS ...... 43
Final Conclusion ...... 43
Recommendation ...... 47
REFERENCES ...... 49
APPENDIX...... 57
iii
ABSTRACT
This study investigates the financial feasibility of offshore wind energy projects in
Germany. The investment costs necessary for German offshore wind projects are compared to similar costs for German onshore wind projects and to conventional energy investment costs. A review of the literature finds that wind power generation is almost cost-competitive with conventional energy production in Europe and will likely become increasingly competitive with coming improvements in wind turbine technology and rising fossil fuel prices. Offshore wind farms have access to higher wind speeds but incur higher investment costs. German offshore wind projects face higher challenges than such projects in other countries due to legal restrictions that push projects farther out to sea into deeper waters.
The data set contains information for a mix in offshore wind farms located worldwide and onshore wind farms located in Germany. Multiple regression analysis is used to estimate potential offshore wind farm investment costs per annual energy output. Results indicate that distance to shore and water depth are important cost drivers for offshore wind projects and that offshore wind farms in other countries are cost-competitive with onshore ones. The regression model is used to estimate investment costs for planned offshore wind energy pilot projects in
Germany. Results indicate that offshore investment costs average almost 30 percent higher than onshore investment costs; however, one-third of the twenty-two offshore projects have estimated investment costs within the range of onshore investment costs. Furthermore, it is likely that offshore investment costs will decrease further due to coming improvements in offshore turbine efficiency and the learning-by-doing and scale economies resulting from offshore industry expansion from pilot to commercial scale.
iv
ACKNOWLEDGEMENTS
My thanks go to the chair of the committee, Dr. Chris Dumas. He did a great job by guiding my thesis project over such a long distance (4253.82 miles) without any loss of efficiency. I still remember our discussions via skype.
Special thanks go to my wife, Kirsten. For many hours, she took care of our new born daughter, Pia Marie, so that I might have the research and writing time necessary to complete this project.
v
LIST OF TABLES
Table Page
1. Example data for calculating unit cost of wind-generated power...... 11
2. Example data or calculating unit cost of conventional power ...... 14
3. External costs of energy sources in Germany, 2005...... 17
4. German offshore wind farm planning...... 23
5. Descriptive statistics for all (onshore/ offshore together) wind farm locations...... 31
6. Descriptive statistics for offshore wind farm locations...... 32
7. Descriptive statistics for onshore wind farm locations ...... 32
8. Correlation statistics for wind farm data set ...... 34
9. Multiple regression model with all variables...... 36
10. Multiple regression model (date and offshore variables excluded) ...... 37
11. Multiple regression model with quadratic elements (capacity and date)...... 38
12. Potential investment costs for German offshore wind farms in pilot phase ...... 42
vi
LIST OF FIGURES
Figure Page
1. Global cumulative wind power capacity, 1990 – 2008 (in MW)...... 6
2. Annual installed capacity by region 2004 – 2008...... 6
3. World top 5 cumulative and new installed wind power capacity in 2008 ...... 7
4. Calculated costs per kWh of wind-generated power ...... 12
5. The present value costs of wind-produced power...... 12
6. Current cost comparison: conventional power plants vs. wind power, 2010 ...... 14
7. Sensitivity analysis of power generation costs to fossil fuel prices...... 15
8. Forecast cost comparison: Electricity generating costs in the EU, 2015/ 2030..... 16
9. Total offshore wind power installed by end 2008...... 18
10. Operating and planned offshore wind farms in Europe as of end 2008...... 19
11. Average share of investment cost for onshore and offshore locations...... 21
vii
CHAPTER 1
INTRODUCTION
The starting gun was fired in November 2009. The first German offshore pilot wind farm
ALFA VENTUS began operations, and twelve 5-megawatt wind turbines began to generate electricity and feed it into the power grid. Offshore wind power generation has the potential to play an important role in providing a secure power supply for the future. In addition, it has a positive side effect of providing relatively pollution-free electricity. Is this just a vision that will ultimately prove unsuccessful because the technical challenges are too high and projects are not financially feasible? Or is it possible that the German offshore wind energy sector will boom like the German onshore wind energy sector ten years ago? If so, wind power may help Germany conserve raw materials and the natural environment while securing its wealth and standard of living.
The trend for all forms of renewable energies is positive worldwide, and wind power is an important element of the renewable energy mix. For example, the EU installed more wind power than any other electricity generating technology 2008. [17] There are two primary reasons for the current interest in renewable energy sources such as wind power. First, western countries wish to reduce their high dependence on fossil fuels (especial oil and natural gas) and avoid a trend of rising dependency in the future. Countries face the prospect of escalating fossil fuel prices in the future, as fossil fuels reserves are finite, world demand for dwindling stocks is growing, and over 90 per cent of reserves are located in countries with unstable political systems.
The potential for sudden oil price shocks was emphasized on July 11 th 2008, when West Texas
Intermediate (WTI) brand oil reached its all time high price of $ 147.27 per barrel. In contrast, as
recently as 2001, oil prices ranged between $20 and $30 at the New York Mercantile Exchange
(NYMEX). Second, deepening political concern has emerged with regard to climate change and energy. Countries may need to reduce use of fossil fuels in order to combat the threat of global warming caused by carbon dioxide emissions. The EU was one of the first regions in the world to set binding targets to limit global climate change in 2007. One target is that 20 per cent of its energy supply should come from renewable resources by 2020. To meet this target, more than one-third of European electrical demand will need to come from renewable energy sources, and wind power is expected to deliver 12 to 14 per cent (180 Gigawatts) of the total demand. [12]
Until recently, the growth of the wind power industry was driven by onshore projects.
However, there is growing interest in the development of offshore wind farms. The offshore wind sector has a very rich potential and may be necessary for the EU to have a realistic chance of achieving greenhouse gas reduction targets. [19] Questions about the technical efficiency of offshore wind power and its potential costs relative to other energy sources need to be answered if wind energy is to play a leading role in providing a steady supply of indigenous, green power.
This thesis presents an analysis of the financial aspects of offshore wind power generation with a focus on Germany. Chapter 2 provides a literature review that begins with a historical review of wind power generation. The efficiency of wind energy is discussed, followed by a description of the different cost components of wind power generation with an example calculation of wind power generation costs. Next, wind power generation costs and external costs are compared with the corresponding costs of conventional power sources. Chapter 2 then focuses on offshore wind power its cost components in relation to onshore wind power. The final section of Chapter 2 provides an outlook for offshore wind power and describes specific challenges for Germany. A Table summarizes German plans for offshore wind power.
2
Following a brief summary of thesis objectives and key research questions in Chapter 3,
Chapter 4 describes the data selection process, provides a description of the data sample, and describes the statistical regression model used to analyze the data.
Chapter 5 provides descriptive statistics for the data and presents the results of the regression analysis. Descriptive statistics for onshore and offshore wind energy facilities are compared. Results of a correlation analysis used to identify any multicollinearity in the regressor variables and the implications of multicollinearity are discussed. Alternative regression models are tested to identify the one that best fits the data. The final regression model is identified, its parts defined, and relationships among its variables discussed. Chapter 5 concludes with an application of the regression model to the problem of calculating the potential costs of German offshore wind facilities for policy planning purposes.
Chapter 6 present conclusions, including an evaluation of key research questions and a summary of the technical feasibility and financial potential of offshore wind power generation in
Germany. Finally, recommendations are provided concerning the development of offshore wind energy in Germany and future research in this important area of energy policy.
3
CHAPTER 2
LITERATURE REVIEW
Historical Review
The power of the wind was first harnessed to generate electricity by using a wind mill at the end of the 19 th century. In the winter of 1887/ 88, the American Charles F. Brush (1849 -
1929) built the first fully automatic wind turbine in Cleveland, Ohio, with a capacity of 12 kilowatts (kW). Another important pioneer was the Dane, Poul la Cour (1846 – 1908), who discovered that wind turbines are more efficient if they have fewer blades and, as a result, spin faster.[4] Both Brush and la Cour developed wind turbines for household electricity supply in rural areas of their respective countries. At first, the small market for rural wind turbines grew steadily, but after a centralized electricity grid extended into rural areas, wind energy lost importance. However, there were exceptions periods of high fuel prices, such as during WW II.
The change came following the oil price shock of 1973, which highlighted the dependency of the western economies on oil imports. A political discussion began concerning potential solutions to the oil import problem, including increased energy efficiency and use of renewable energy sources. The first successes in using small wind turbines occurred at the end of the 1970s in
Denmark and in California (USA). These relatively small wind turbines each produced 50 - 100 kW of power. In both countries the government supported the technological development of wind turbines with special financial aid or tax breaks. But further development stopped after the
California government cut tax credits for wind turbines in the mid-1980s. By 1987, 15,000 wind turbines with a total capacity of 1,400 megawatts (MW) had been installed in California. In the
1980s other western countries focused on wind energy research programs and built large
experimental wind turbines, each capable of producing 1 - 4 MW. The results were average and accompanied by setbacks. In Germany, for example, the GROWIAN project produced the largest wind turbine in the world, rated at 3 MW, in 1983, but it soon exhibited many technical problems after operating only a short time. The project was abandoned without any reliable results. The engineers did not have any experience building a wind turbine of that size, and knowledge was limited at that time. [24, Ch. 2]
Since the 1990s, renewable energy has received new support as governments have begun to consider policies for reducing the negative impacts of global warming due to carbon dioxide emissions resulting from the burning of fossil fuels. As a result, there was renewed interest in electricity generation by wind turbines beginning in the late 1990s. At the forefront of the movement were European countries like Germany, Denmark and Spain. Wind power development was encouraged at this time by a combination of new wind turbine technologies and state subsidies. [33, pp. 25-29]
Since 2004, the rate of wind energy deployment reflects a worldwide boom. Global installed capacity increased from 40 Gigawatts (GW) at the end of 2003 to more than 120 GW at the end of 2008, an average annual growth rate of nearly 25 per cent. In 2008, growth in total worldwide installation reached a new all-time high - 27 GW. [16, p. 3]
Figure 1 shows the trend in global cumulative wind power capacity for the last 20 years.
Europe’s historical dominance in this market is evident.
5
Figure 1: Global cumulative wind power capacity, 1990 – 2008 (in MW)
140000
120000
100000
80000
60000 Megawatts
40000
20000
0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Rest of the w orld 1304 1354 1477 1590 1848 2324 2628 2883 3700 3916 4470 7133 8150 10940 13248 18591 26102 37587 55843 EU 439 629 844 1211 1683 2497 3476 4753 6453 9678 12887 17315 23098 28491 34372 40500 48031 56535 64948
Source: GWEC/ EWEA [16]
Fifty-five per cent of the world’s capacity was installed in Europe by the end of 2008, and European companies had a global market share of 66 per cent in 2007. [16, p. 3] However, in the last two years countries outside Europe have gained in importance. Figure 2 shows the annual installed capacity by region for the last 5 years (2004-2008), with North America, Asia and
Europe vying for market leadership in 2008. North America and Asia are new growth markets for wind energy deployment. These regions are dominated by the USA, China and India, countries with tremendous energy demands.
Figure 2: Annual installed capacity by region 2004 – 2008
9000
8000
7000
6000
5000
4000 Megawatts 3000
2000
1000
0 Europe North Asia Pacific Latin Africa & America Region America & Middle East Caribbean 2004 2005 2006 2007 2008 Source: GWEC [15; 21; 22; 23]
6
Figure 3 gives an overview of cumulative and new installed wind power capacity of the top five countries worldwide in 2008, the year in which the USA replaced Germany as number one in cumulative wind power capacity. Clearly, growth in the wind power sector is a world- wide trend.
Figure 3: World top 5 cumulative and new installed wind power capacity in 2008
Cumulative wind power capacity in 2008 New installed wind power capacity in 2008
30000 9000 8000 25000 7000 20000 6000 5000 15000 4000 Megawatts Megawatts 10000 3000 2000 5000 1000
0 0 USA Germany Spain China India USA China India Germany Spain
Source: GWEC [23] Source: GWEC [23]
Economic Efficiency of Wind Energy
This section gives an overview of the general economic parameters determining the cost- effectiveness of wind energy. It is followed by a consideration of the competitiveness of wind power in comparison to conventional electricity generation. This discussion defines the position wind power within the general context of power generation cost and indicates potential growth opportunities in the future.
7
Parameters of Wind Power Economics
The main parameters determining the cost-effectiveness of wind power are: [16, Ch. III.1 and 24, Ch. 19]
- investment costs
- operation and maintenance (O&M) costs
- electricity production
- turbine lifetime
- discount rate
Investment or capital costs consist of the following components: Wind turbine (ex- works), foundation, electric installation, grid connection, consultancy, land, financial costs and road construction. These cost components can vary significantly between projects. On average, wind turbine cost as a share of total investment cost is approximately 76 per cent, followed by grid connection cost, accounting for around 9 per cent, with the third largest component, foundation cost, amounting to about 7 per cent. The other cost components account for only minor cost shares, totalling around 8 per cent of total investment costs. The total investment cost per kW typically varies from around €1100 to €1400 per kW, with an average of €1225 per kW.
These costs are based on data from International Energy Agency (IEA) and are stated in 2006 prices. Other authors also mention values within this range [26, p. 181 and 27, pp. 326-328].
Operation and maintenance (O&M) costs include the following cost components:
Insurance, regular maintenance, administration, land rent, power from the grid, repair and spare parts. Some O&M cost components can be estimated relatively easily. For example, the costs of insurance, regular maintenance and land rent can usually be determined from standard contracts
8
written for terms that cover a considerable share of wind turbine expected lifetime. However, cost for repairs and spare parts are much more difficult to predict. Although all cost components tend to increase as the turbine gets older, costs for repair and spare parts are particularly influenced by turbine age, starting low and increasing over time.
Generally O&M costs are estimated to be around 1.2 to 1.5 euro cents per kilowatt hour
(kWh) of wind power produced over the total lifetime of a turbine, based on experiences in
Germany, Spain, UK and Denmark. Another author mentioned O&M costs of around 2 euro cent per kWh for a 1.5 MW turbine in Germany. [24, p. 842] Current O&M costs calculations for newer turbines are showing costs of around 0.6 to 0.7 euro cent per kWh in Germany and
Denmark. [16, p. 205] O&M costs constitute a substantial share of the total annual costs of a wind turbine. For a modern turbine, on average O&M costs typically comprise around 20 – 25 per cent share of the annual revenues of power generation.
These O&M cost estimates are based on limited data from a relative young wind power industry. Currently only a few turbines have reached their life expectancy of 20 years. These turbines are much smaller than those currently available on the market. As a result, there is still uncertainty in O&M cost estimates, especially near the end of a turbine’s lifetime. However, the general trend indicates declining costs as technology has evolved from older, smaller turbines to newer, lager ones. [16, p. 207]
Electricity production depends to a great extent on wind conditions at the turbine location and operational reliability of the turbine. Today, reliability of wind turbines is no longer a critical issue, as modern wind turbines achieve reliability rates of minimum 98 per cent. [18, p. 55]
However, wind conditions remain a key concern, as wind is the “fuel” used by the turbine to generate electricity. Generally, modern wind turbines operate within a wind speed range from 4
9
to 25 m/s, with maximum power output achieved at wind speeds between 12 and 14 m/s, depending on turbine type and configuration. Once maximum power output is achieved, it remains for higher wind speeds up to the safety shutdown. [26, p. 23] The common way of describing the wind regime at a particular location is the use of mean wind speed. It is important to know the altitude of measurement, because wind speed increases with altitude by 1 to 2 per cent per meter. [28, p. 23] Typically, the hub height of the turbine is used. But mean wind speed is just one reference value for a potential location. Also important is the distribution of wind speed. In many cases, a location with high variability in wind speed will generate more power than one with less variability. The frequency of strong winds is important because energy output of wind turbines is proportional to the third power of wind speed. [5] The wind regime at a station can also be described using the number of “full load hours”, calculated as the turbine’s annual energy production divided by its rated power. A higher full load hours rating at a given site indicates higher potential wind energy production at the site. Full load hours can range from
1500 for low wind inland areas, to around 2300 for medium wind inland areas, to 2900 for coastal areas. It is typically assumed that offshore areas produce 4000 full load hours. As a reference, the theoretical maximum value of full load hours is 8,760 hours per year. [16, p. 208 and 26, p. 39]
Choosing the best turbine size for a particular location is a complex planning process with many parameters to consider. Site characteristics will place a physical limit on the number, type and capacity of turbines. [33, p. 39] It is often incorrect to assume that a turbine with a higher power rating is necessarily more economical than a turbine with a lower rating. For cost- effectiveness, it is important to minimize the cost per kWh, and in some cases a smaller turbine is the better choice. [18, p. 53]
10
Turbine lifetime describes the time over which a turbine is expected to be in operation.
The minimum duration usually is 20 years, in accordance with most technical design criteria.
Discount rate or interest rate is a parameter used to evaluate the costs of capital. It is an important factor, because wind power is capital-intensive--approximately 75 to 80 per cent of total power production costs are related to capital costs. Interest rates can be a source of considerable uncertainty, as they vary considerably over time and between countries. [16, pp.
207-208]
Wind Power Generation Cost
The total cost per kWh (unit cost) is calculated by discounting and annualizing investment and O&M costs over the lifetime of the turbine and then dividing them by the annual electricity production. The unit cost of generation is thus calculated as an average cost over the turbine’s lifetime. Data based on calculation by European Wind Energy Association (EWEA).
[16, Ch. III.1; 18, Ch. 1.7]; [24, Ch. 20]
Table 1: Example data for calculating unit cost of wind-generated power
Parameter Comment Wind turbine New land-based, medium-sized (1.5 to 2 MW) Investment costs €1100 – 1400/ kW, on average €1225/kW, stated in 2006 prices O&M costs Average of 1.45 euro cent/ kWh, over lifetime Lifetime of turbine 20 years Discount rate Range from 5 – 10 per cent / year, for basic calculations 7.5 per cent Others Economic analyses are carried out on a simple net economic basis. Taxes, depreciation and risk premiums are not taken into account. All calculations are based on 2006 prices.
11
Figure 4: Calculated costs per kWh of wind-generated power
Source: Riso DTU [18 and 32]
Figure 4 illustrates calculated costs per kWh for wind-generated power as a function of the wind regime at the chosen site. Wind regime is represented by the number of full load hours.
Costs range from approximately 0.07 to 0.1 €/ kWh at sites with low average wind speeds to approximately 0.05 to 0.065 €/ kWh at windy coastal sites, with an average of approximately
0.07 €/ kWh at a wind site with medium wind speeds.
Figure 5: The present value costs of wind-produced power
Source: Riso DTU [18 and 32]
12
Figure 5 illustrates the effect of discount rates on the present value of wind power costs.
Assuming that the installed cost of wind turbines are €1225/ kW, the present value care shown as a function of wind speed (full load hours) and discount rate. Present value costs range between
0.06 and 0.08 €/ kWh at medium wind locations. In low wind areas, the costs are significantly higher at around 0.08 to 0.11 €/ kWh, while the production costs range between 0.05 and 0.07 €/ kWh in coastal areas.
Comparison of Wind Power with Conventional Power Generation
The cost of conventional power production is primarily determined by the following four components: [16, Ch. III.6 and 18, Ch. 1.9]
- fuel cost
- cost of CO 2 emissions (as given by the European Trading System for CO 2)
- operation and maintenance (O&M) costs
- capital cost, including planning and site work
Table 2 presents example unit cost results for conventional power generation based on calculations by EWEA. [16, pp. 248-250] A high proportion, 40 to 60 per cent, of the total cost of conventional power production is related to fuel and O&M costs; this share is considerably higher than the corresponding share for wind-generated power.
13
Table 2: Example data or calculating unit cost of conventional power
Parameter Comment Power plant (1) Coal-fired power plant 2010 and (2) natural gas heat & power plant 2010 Fuel cost Coal €1.6/ GJ and natural gas €6.05/ GJ linked to a oil price of $59/ barrel in 2010 (according to the IEA’s World Energy Outlook 2007) Cost of CO 2 Around €25/ton Lifetime of plants 40 years Discount rate 7.5 per cent Others Calculations are carried out in 2006 prices
Figure 6 shows the cost-comparison results for the reference cases: the cost of power generated at conventional power plants is lower than the cost of wind-generated power under the given assumptions with relatively low fuel prices. Wind-generated power at a European inland site is approximately 33 per cent more expensive than natural gas- and coal-generated power.
Regulation costs emerge for integrating variable wind power into the overall power system. At present, costs are on average 0.003 – 0.004 €/ kWh. They are expected to increase with higher levels of wind power penetration.
Figure 6: Current cost comparison: conventional power plants vs. wind power, 2010
0 ,0 8
0 ,0 7
0 ,0 6
0 ,0 5 Regulation 0 ,0 4 CO 2 25 € / t B a s ic 0 ,0 3 EUROkWh per
0 ,0 2
0 ,0 1
0 ,0 0 Coal Natural Gas Wind costal Wind inland
Source: Riso DTU [18 and 32]
14
Figure 7 shows the results of a sensitivity analysis of energy generation costs to fossil fuel prices. The assumptions are: (1) a doubled natural gas price compared to the reference, which is equivalent to an oil price of $118/ barrel in 2010, (2) an increase in coal price by 50 per cent, and (3) an increase in the price of CO 2 to €35/ ton from €25/ ton in 2008. As shown, the competitiveness of wind-generated power increases significantly. Costs at the inland site become lower than generation costs for the natural gas plant and only around 10 per cent more expensive than the coal-fired plant. At coastal sites, wind power produces the cheapest electricity.
Figure 7: Sensitivity analysis of power generation costs to fossil fuel prices
0 ,1 2
0 ,1 0
0 ,0 8 Regulation 0 ,0 6 CO 2 35 €/ t Ba s ic
EURO per kWh EURO per 0 ,0 4
0 ,0 2
0 ,0 0 Coal (50%) Natural Gas Wind costal Wind inland (100% )
Source: Riso DTU [18 and 32]
These cost comparisons show that wind power is close to being cost-competitive with conventional power generation. In Denmark, as a fairly cheap wind power country, this milestone is almost achieved. [16 and 24, p. 872] If fossil fuel prices increase in the future as many forecast, wind generated power will soon achieve competitiveness in other countries, as well. For example, Figure 8 shows the International Energy Agency (IEA) outlook for electricity generating costs based on conventional energy sources in the EU for 2015 and 2030. [25]
15
Figure 8: Forecast cost comparison: Electricity generating costs in the EU, 2015/ 2030
0 ,1 2 0 0 ,1 1 3 0 ,1 0 1 0 ,1 0 0 0 ,0 8 2 0 ,0 7 9 0 ,0 7 5 0 ,0 8 0 0 ,0 6 8
0 ,0 6 0
0 ,0 4 0 EURO per kWh EUROper 0 ,0 2 0
0 ,0 0 0 Coal Natural Gas Wind
2 0 1 5 2 0 3 0
Source: IEA World Energy Outlook 2008
Furthermore, in contrast to the rising costs of fossil fuel-based energy, wind energy cost per KWh may decrease in the future as a result of turbine price reductions based on learning curve and mass production effects and on technology improvements such as those enabling larger turbine capacity. [16, pp. 209-211] For example, at present, state-at-the-art turbines have a rated power of 5 megawatts, but 7 megawatt turbines are under development, and the trend is moving toward turbines of 10 megawatts for the near future. [16, p. 73]
External Costs
By definition, external costs are those costs incurred in activities which may cause damage to a wide range of receptors, including human health, natural ecosystems and the built environment, and yet are not reflected in the price paid by consumers. [16, p. 531] In general, conventional energy generation using fossil fuels (coal, lignite, peat, oil and natural gas) produces relatively high external costs (natural gas is the least damaging of the fossil fuels) and renewable energy produces relatively low external costs. [16, p. 368] Table 3 presents external
16
cost estimates for different energy sources for Germany in 2005. Data are provided by German
Federal Environmental Agency 1 (UBA). [8]
Table 3: External costs of energy sources in Germany, 2005
Energy source of electricity generation External cost External cost (cent/ kWh) (cent/ kWh) (70 € /ton CO 2) (20 € /ton CO 2) Lignite 8.7 3.1 Hard Coal 6.8 2.4 Heating Oil 6.1 2.3 Natural Gas 3.9 1.2 Wind power 0.1 Fossil energy sources (Average) 7.0 Source: UBA
External costs are often neglected in traditional cost-effectiveness calculations. If estimated external costs are added to the current cost basis of conventional power generation, conventional power loses its apparent cost advantage in comparison to wind generated power.
But estimating external costs is a complex process involving many uncertainties, especially in the case of estimating the eventual magnitude and timing of climate change effects. As a result, studies find a wide range of 0.01 to 0.25 €/ kWh for external costs from conventional power generation. For nuclear power generation, the range of estimates is even larger, from less than
0.01 to 200 €/ kWh. In contrast, for power generation by renewable energies studies show a strong consensus of very low external costs. [8] For wind power, estimated external costs are less than 0.005 €/ kWh. [26, p. 94]
1 German original title: Umweltbundesamt (UBA)
17
Offshore Wind Power
This section is focused on the offshore wind power sector with a description of the main differences between onshore and offshore locations. As result the reader gets an impression about opportunities and as well challenges of offshore wind power.
Offshore wind energy has not shared in the strong growth of the onshore wind power market in recent years. At the end of 2008, just 1,471 MW of capacity was located offshore which was equal to approximately 1 per cent share of total installed wind power capacity. [18, p.
61] The development of offshore wind power has mainly occurred in northern European countries around the North Sea and the Baltic Sea. The following figures show current installed offshore wind power capacity and the planed enlargement of offshore wind farms in the near future.
Figure 9: Total offshore wind power installed by end 2008
Total: 1471 MW
Netherlands; 247; Denm ark; 409; 1 7 % 2 8 % Sw eden; 133; 9%
Belgium ; 30; 2%
Ireland; 25; 2% U K; 591; 39% Finland; 24; 2% Germ any; 12; 1%
Source: EWEA [18]
18
Figure 10: Operating and planned offshore wind farms in Europe as of end 2008
Total: 37744 MW Netherlands; 2834; 8 %
Spain; 1976; 5% Belgium ; 1745; 5% Sweden; 3312; 9% Ireland; 1603; 4% UK; 8756; 23% N orw ay; 1554; 4%
Finland; 1330; 4% Denm ark; 1276; Germ any; 10928; 3 % 2 9 % France; 1070; 3%
Ita ly; 8 2 7 ; 2 % Poland; 533; 1%
Source: EWEA [18]
The future trend indicates a strong increase of offshore installed wind power capacity.
[19] But the conditions necessary for successful onshore and offshore wind power are not the same. The two wind power sectors have differences in terms of wind regime and operational conditions. The main advantage of offshore location is higher, more constant and more predictable wind speed. As a result, the amount of generated power (full-load hours) per turbine of the same size is higher for offshore locations relative to onshore. [33, p. 43] However, the distance to the coast typically must be at least 10 km to tap the full potential of offshore wind power, which can be 30 to 40 per cent greater than onshore. [24, p. 702] The disadvantage for offshore wind power is higher investment cost and possible higher O&M cost relative to onshore wind [24, Ch. 19.4 and 16, Ch. III.2]. According to recent studies, these costs may be 50 per cent higher for offshore wind. The main component of additional investment cost is the cost of the structural foundation. Foundation costs depend on both the water depth and the type of foundation. In an extreme case of water depth beyond 40 m, cost of foundation can rise to the
19
same level as expenditure for the turbine. Electrical infrastructure is the second important cost driver. These are expenditures for grid connection between turbines and the centrally located transformer station and from the station to shore by main cable. As example values, costs of hauling a high-voltage cable add up to around 250 € per meter and for medium voltage (internal grid) costs are between 100 and 150 € per meter. [24, p. 689] Some offshore projects in the planning stage involve distances of more than 100 km to shore. In Denmark and Germany, existing legal requirements relieve wind farm investors from some foundation costs related to power grid connection. The expenditures for the transformer station and the main transmission cable to the coast must be paid by transmission cable operators in the respective area. [18, p. 65]
The costs of offshore wind turbines themselves can differ from the costs of similar onshore turbines. Offshore turbines can be built with a lower tower which saves money.
However, because offshore turbines operate in a harsher marine environment, they require more substantial technical maintenance, which increases cost relative to onshore turbines. Altogether, the raw production costs are roughly similar for the two types of turbines. But manufacturers must bear a higher risk for warranty and service of offshore turbines which increases the final sales price of offshore turbines. In summary, currently the price of offshore turbines is around 10 to 20 per cent higher than the price of offshore turbines; however, the offshore turbine market is still in an early development stage in which scale economies have not been fully realized. Large scale production of offshore turbines may lower their cost relative to onshore turbines. [24, p.
843]
Current operational offshore wind farms show investment costs per MW in a range from
1.2 million € per MW to 2.7 million € per MW. On average investment costs for a new offshore wind farm are in the range of 2.0 to 2.2 million € per MW for near-shore, shallow water facility.
20
[18, pp. 63-64] Figure 11 summarizes graphically the differences in investment cost components between onshore and offshore locations. Another source gives similar cost shares. [7, p. 22]
Figure 11: Average share of investment cost for onshore and offshore locations
Average share of investment cost onshore Average share of investment cost offshore
Miscellaneous Miscellaneous 9% 7% Foundation Foundation Turbine Turbine 21% 7% 76% 49%
Electrical Infrastructure Electrical 10% Infrastructure 21% Total cost: 1.227 mil € Total cost: 1.680 mil €
Source: EWEA [16 and 18] Source: EWEA [16 and 18]
In general, offshore (O&M) costs are composed of the same elements as onshore O&M costs, but with a different weighting. Currently, offshore O&M costs are highly uncertain due to the lack of experience. Preliminary results are available for the first operational offshore projects, but the majority of these endeavors are “pilot projects”. Start-up problems and improvised workflows limit the ability to generalize results from these projects. This will change as procedures and workflows are optimized. Current estimates indicate that offshore O&M costs are higher than onshore O&M costs, by as much as 30 to 50 percent, increasing the O&M cost component of total investment costs by 3.5 to 4.5 percent. [26, p. 185] Reductions in offshore
O&M costs will require reductions in scheduled maintenance and repairs. Access is limited to offshore wind turbines due to greater variability in weather and wave conditions. In order to avoid long shutdown periods, offshore turbines must be designed in ways that are easy to maintain and that include system redundancy where possible. The target is to maximize the mean
21
time between failures. Currently, the expected operational availability of offshore turbines is 92 per cent, 6 per cent lower than for onshore turbines. [24, pp. 699, 847-849]
Outlook and Planning for Offshore Wind Power in Germany
Offshore wind power technology is in an early stage of development. As well, the land- based facilities for structure assembly, maintenance and operation are under construction or in planning. Different concepts have been proposed concerning ways to secure operational availability of offshore wind turbines under varying economic circumstances. Currently, offshore projects are divided into two main categories. The first is a pilot phase, and the majority of wind farms are currently in this stage. In the first phase, a smaller number of turbines are installed to gain experience. After evaluation, a second expansion phase will follow, in which the wind farm will expand to the total capacity. The current planning for a large offshore wind farm is a total capacity of 1000 megawatts or more, which is similar to the rated power of a conventional power plant. Crucial reasons for such large wind farms are potential economies of scale in turbine production, facility construction, and farm-to-shore long-distance cabling. [33, p. 43]
Many European countries are planning to build offshore wind farms (Figure 10). The most advanced plan is Denmark’s project “Energy 21”. [2 and 24, p. 712] Germany has one main disadvantage when developing offshore wind farms: the entire German North Sea coast is a nature reserve within which commercial use is not allowed. [19] For this reason, near-shore locations are blocked in Germany. In other European countries the majority of the current capacity is installed in relatively shallow waters (under 20m water depth) and less than 20 km from the cost, in order to minimize the extra costs of foundations and sea cables. In Germany projects are sited well out to sea, which means they are in deeper waters and with a longer
22
distance to shore. These factors increase the cost of German wind farms. [33, p.43]
Consequently, German offshore projects are based on larger turbines, of 5 MW or more, in order to achieve profitability. Following Table shows plans for German offshore wind farms. Data are provided by Wind Energy Agency Bremerhaven, Germany2 (WAB).
Table 4: German offshore wind farm planning
Pilot phase Pilot phase Final Final total Water Turbine Total cap. turbine capacity by Distance depth Name Location no. 3.6/ 5 MW no. 3.6/ 5 MW (km) (m) Status Borkum West North Sea 12 43.2/ 60 208 748.8/ 1040 43 30 approved Sankbank 24 North Sea 80 288/ 400 980 3528/ 4900 100 30 to 40 approved BARD Offshore I North Sea 80 288/ 400 320 1152/ 1600 87 39 to 41 approved Dan Tysk North Sea 80 288/ 400 300 1080/ 1500 45 23 to 31 approved Borkum Riffgrund West North Sea 80 288/ 400 458 1648.8/ 2290 40 30 to 35 approved Borkum Riffgrund North Sea 77 277.2/ 385 180 648/ 900 34 23 to 29 approved Nordsee Ost North Sea 80 288/ 400 250 900/ 1250 30 19 to 24 approved Butendiek North Sea 80 288/ 400 80 288/ 400 35 16 to 22 approved Windpower Delta Nordsee North Sea 48 172.8/ 240 80 288/ 400 40 28 to 32 approved Amrumbank West North Sea 80 288/ 400 80 288/ 400 35 21 to 25 approved Nördlicher Grund North Sea 80 288/ 400 400 1440/ 2000 86 23 to 40 approved Global Tech I North Sea 80 288/ 400 320 1152/ 1600 75 39 to 41 approved Hochsee WP Nordsee North Sea 80 288/ 400 240 864/ 1200 75 39 approved Gode Wind North Sea 20 72/ 100 224 806.4/ 1120 45 26 to 35 approved Meerwind North Sea 80 288/ 400 270 972/ 1350 53 22 to 32 approved Hochsee WP He Dreiht North Sea 80 288/ 400 119 428.4/ 595 75 39 authorization Borkum Riffgat North Sea 44 158.4/ 220 44 158.4/ 220 15 16 to 20 authorization Offsh WP Austerngrund North Sea 80 288/ 400 80 288/ 400 87 40 authorization Offsh WP Deutsche Bucht North Sea 80 288/ 400 80 288/ 400 87 40 authorization Uthland North Sea 80 288/ 400 80 288/ 400 49 25 authorization Vento Tec Nord I North Sea 80 288/ 400 200 720/ 1000 132 41 authorization Vento Tec Nord II North Sea 80 288/ 400 200 720/ 1000 104 41 authorization Offsh WP Nordergründe North Sea 25 90/ 125 25 90/ 125 13 2 to 18 authorization Kriegers Flak Baltic Sea 80 288/ 400 80 288/ 400 32 29 to 42 approved Baltic I Baltic Sea 21 75.6/ 105 21 75.6/ 105 15 16 to 19 approved Arkona Becken Südost Baltic Sea 80 288/ 400 80 288/ 400 34 23 to 36 approved GeoFreE Baltic Sea 5 18/ 25 5 18/ 25 20 21 approved Vento Tec Ost 2 Baltic Sea 80 288/ 400 80 288/ 400 40 40 approved Beltsee Baltic Sea 76 273.6/ 380 76 273.6/ 380 14 23 to 26 authorization Arcadis Ost 1 Baltic Sea 70 252/ 350 70 252/ 350 17 40 authorization Arcadis Ost 2 Baltic Sea 25 90/ 125 25 90/ 125 39 35 authorization Sky 2000 Baltic Sea 50 180/ 250 50 180/ 250 20 21 authorization Source: WAB [31, pp. 26 -27]
2 German original title: Windenergie Agentur Bremerhaven (WAB)
23
The successful implementation of an offshore wind farm project is not just a question of economics. Other aspects, such as impact on the environment, are also involved. Environmental impacts on humans may include noise and scenic view obscuration, while impacts on animals include alteration of ocean bottom during foundation construction and bird disturbance caused by rotating turbine blades. Another important aspect is the position of government and its energy policy. A secure energy supply is a base for economic development and stability. A government can influence the shares of alternative energy sources in its energy mix through policy action.
For example, in the 1960s research on electricity generation by nuclear power was highly supported by governments. Otherwise nuclear power generation wouldn’t be so inexpensive. To promote forms of renewable energies, many European governments have guaranteed fixed feed- in prices by law. Feed-in prices are guaranteed prices that energy producers will be paid for the energy they produce. A master plan was provided by the German Renewable Energy Sources
Act 3 (EEG) which in many cases was copied by other nations and adjusted for unique national characteristics. In Germany the feed-in tariff for offshore wind power was increased in January
1st 2009. In addition to feed-in price guarantees for renewable power producers, the government will need to play a role in redesigning the power grid system. Here, government coordination may be needed to facilitate the transition from the old power plant-based grid structure to a new grid design that efficiently incorporates renewable energies. Because an energy grid is a shared infrastructure system with significant public good characteristics, the market mechanism by itself may not be able to solve this challenge.
3 German original title: Erneuerbare Energien Gesetz (EEG)
24
CHAPTER 3
OBJECTIVES AND RESEARCH QUESTIONS
The preceding Chapters of this thesis have provided an introduction to the issues involved in the development of offshore wind power in Germany and have reviewed the existing literature to obtain foundational knowledge to support the analysis presented in the following
Chapters. This thesis focuses on estimating the potential market costs of offshore wind power generation in Germany. Any forms of state subsidy are not included. Key results include estimates of the potential financial costs and feasibility of offshore wind power in Germany.
Specifically, this thesis seeks to answer the following three research questions:
(1) What are the potential costs of offshore wind energy production in Germany, and
what are the key drivers of these costs?
(2) What are the relative costs of wind energy production across alternative offshore
locations in Germany?
(3) Is offshore wind energy production in Germany cost-competitive with onshore wind
energy production in Germany?
CHAPTER 4
DATA AND METHODOLOGY
Data Collection Process
The initial goal of the thesis project was to collect data on investment costs and annual
O&M costs for each wind farm. Generally, investment cost data were available, but obtaining
O&M cost data was more problematic. The majority of offshore data were obtained from offshorewindenergy.org, which provides wind farm data for farms in North-West Europe.
Missing data were found by a combined direct internet search of wind farm name and wind farm provider. For onshore locations, the web pages of wind farm developers and operators were important sources. These firms often provided information on their wind farm projects via links on their company web pages. However, only a few firms provided all required data on their websites. Therefore, a second internet search was carried out by using wind farm names as criteria. Some additional information was obtained, but many gaps still exist. Individual wind farm companies were called and emailed to request O&M cost data, but in many cases there was no response or just a reference to data security which prohibited data transfer. As result, the scope of the thesis was narrowed to the analysis of investment costs and O&M cost are not part of analysis. For offshore wind farms, investment costs comprise the highest proportion of total costs.
For onshore wind farms, only those locations with total farm capacity of 9 MW or more and individual turbine capacity (rated power) of 1 MW or more were included in the sample.
These criteria for onshore farm size and turbine capacity were used to ensure that onshore facilities in the sample produce power for sale, rather than for internal use only, and use the
larger, more efficient, modern technology turbines. Onshore farms with these characteristics are better comparable with offshore locations that produce power for sale and use larger, modern turbines. The price per unit of power production of wind turbines used by onshore farms has declined in recent years, as a result of learning curve effects and increases in turbine rated power.
[16, p. 210] Therefore, including older, lower-rated onshore wind turbines in the analysis could bias the comparison of present-day onshore farm costs with present-day offshore farm costs because the investment costs of older onshore farms can be higher in comparison to current requirements. All onshore wind farms are located in Germany. For offshore locations, wind farm data from all countries were included in the data set due to the limited number of wind farms worldwide.
Data Description
Data were collected for more than 235 different wind farms (offshore: 33; onshore: 202).
[34] The cleaned data set 4 contains information for 140 wind farms; offshore 29 and onshore
111. The variables used in the investment cost analysis are defined in this section.
The dependent variable for the analysis, variable “Y”, is wind farm capital costs per unit of annual energy output, measured in units of Euro per kWh. Capital costs are the investment costs for a wind farm, measured in million Euros, adjusted to 2003 price level by using German
PPI for industrial products. Capital costs range between 5.3 and 494.5 (Offshore: 5.3 to 494.5; onshore 8.9 to 84.5). Annual energy output is the calculated index amount based on a 100 per cent wind year. Annual energy output is measured in gigawatt hour (GWh) (equal to one million kWh) and ranges from 3.8 to 801. (Offshore: 3.8 to 801; onshore 12.96 to 130)
4 Cleaned data set is listed in Appendix A
27
Variable “Y” is the result of division of capital cost by annual energy output for a wind farm.
Variable “Y” has a mean of 0.695 Euro per kWh and ranges from 0.23 to 1.6.
Several independent variables are used to explain the variation in dependent variable
“Y”. Independent variable “Number of Turbines” measures the number of wind turbines per farm and ranges between 2 and 91.
Independent variable “Turbine Capacity” is the average amount of energy production per turbine, the result of dividing total wind farm energy production capacity by the number of turbines on the farm. “Turbine Capacity” is measured in units of megawatts (MW), and the range is between 0.45 and 5.
Variable “Construction Date” indicates the final year of wind farm construction and ranges between 1991 and 2009. In some multiple regression analyses, years are converted into 1,
2, 3 etc. to obtain values with similar orders of magnitude across variables.
Variable “Mean Wind Speed” measures average wind speeds at the wind farm location in meters per second; “Mean Wind Speed” ranges between 5.3 and 9.9. Mean wind speeds for onshore locations were determined through use of German Weather Service 5 (DWD) annual mean wind speed maps. [10 and 11] The results were checked by wind farms with official wind speed data close to the evaluated location with hub height taken into account. In many cases evaluated wind speed was lower by up to 0.9 meter per second. In these cases the value was adjusted to close this gap. The mean wind speed for offshore wind farm locations was determined by consulting the European Wind Atlas. [13]
Variable “Distance to Shore” is measured in kilometers to the approximate centroid of the wind farm geographic footprint. For onshore locations the value is set to zero. “Distance to
Shore” ranges between 0 and 46.5.
5 German original title: Deutscher Wetterdienst (DWD)
28
Variable “Water Depth” is measured in meters at the approximate centroid of the wind farm geographic footprint. For an onshore location the value is set to zero. “Water Depth” ranges between 0 and 40.
Variable “Offshore” is an indicator variable that differentiates between onshore and offshore locations. If the value of “Offshore” is one, then the wind farm is located offshore, and if it is zero, then the wind farm is located onshore.
Methodology
Multiple regression analysis is used to investigate key thesis questions. Although estimation of offshore wind farm investment costs is the focus of the thesis, the regression analysis is carried out with a sample that includes both onshore and offshore locations. One reason to include onshore wind farms is that exclusive use of the relatively low number of offshore wind farms in operational mode in the regression analysis would reduce its statistical reliability. Another reason is that the development of one combined model facilitates a comparison of investment costs across onshore and offshore locations. The multiple regression model is of the following form:
Yi = β0 – β1X1i – β2X2i + β3X3i + β4X4i + β5X5i + β6X5i + β7X7i + e i,
where for wind farm “i”, dependent variable “Y” is wind farm Investment Cost per unit of annual energy output, independent variables X 1 - X 7 are “Number of Turbines”, “Turbine
Capacity”, ”Construction Date”, “Mean Wind Speed”, “Distance to Shore”, “Water Depth”, and
29
“Offshore”, β0 - β7 are parameters to be estimated in the analysis, and e is a random error term.
The analysis is performed using Microsoft Excel 2007 software, version 12.0, January 2007.
Correlation analysis is used to check for multicollinearity among the independent variables in the regression model.
30
CHAPTER 5
RESULTS AND DISCUSSION
This Chapter presents descriptive statistics for model variables and the results of correlation and multiple regression analyses. In all tables a simplified labeling is used for the independent variables.
Descriptive Statistics and Discussion
Table 5 shows descriptive statistics for all wind farm locations (both onshore and offshore) together. To assess any differences between onshore and offshore Locations, Table 6 and Table 7 present descriptive statistics for onshore and offshore locations separately.
Table 5: Descriptive statistics for all (onshore/ offshore together) wind farm locations
Y Number Capacity Date Wind Speed Distance Depth Offshore (€ per kWh) (MW) (m/s) (km) (m)
Mean 0.6954 14.4286 1.9023 2002.4357 6.8543 2.1411 2.4554 0.2071 Standard Error 0.0134 1.2071 0.0645 0.2656 0.0847 0.5350 0.5303 0.0344 Median 0.7 10 1.8 2002 6.5 0 0 0 Mode 0.7 7 1.5 2001 6.4 0 0 0 Standard Deviation 0.1583 14.2829 0.7629 3.1422 1.0022 6.3308 6.2748 0.4067 Sample Variance 0.0251 204.0021 0.5821 9.8735 1.0044 40.0785 39.3725 0.1654 Kurtosis 11.3142 10.9378 6.6406 0.8226 0.6031 22.1559 13.2616 0.1362 Skewness 2.1914 3.0940 2.1673 0.0526 1.1884 4.3403 3.4156 1.4610 Range 1.37 89 4.55 18 4.6 46.5 40 1 Minimum 0.23 2 0.45 1991 5.3 0 0 0 Maximum 1.6 91 5 2009 9.9 46.5 40 1 Sum 97.35 2020 266.32 280341 959.6 299.75 343.75 29 Count 140 140 140 140 140 140 140 140
Source: Author’s calculation
Table 6: Descriptive statistics for offshore wind farm locations
Y Number. Capacity Date Wind Speed Distance Depth (€ per kWh) (MW) (m/s) (km) (m)
Mean 0.6945 27.9310 2.5448 2003.6207 8.5759 10.3362 11.8534 Standard Error 0.0616 4.6878 0.2354 0.8898 0.1136 1.9584 1.6617 Median 0.58 25 2.3 2005 8.5 7 8.5 Mode 0.55 30 2 2008 8.5 10 5 Standard Deviation 0.3315 25.2444 1.2674 4.7915 0.6116 10.5465 8.9486 Sample Variance 0.1099 637.2808 1.6063 22.9581 0.3740 111.2294 80.0782 Kurtosis 1.0468 0.2028 -0.1095 0.4998 -0.0155 4.0072 2.3391 Skewness 1.2923 1.0479 0.2527 -0.9952 -0.0470 1.9439 1.6045 Range 1.37 89 4.55 18 2.7 45.75 36.5 Minimum 0.23 2 0.45 1991 7.2 0.75 3.5 Maximum 1.6 91 5 2009 9.9 46.5 40 Sum 20.14 810 73.8 58105 248.7 299.75 343.75 Count 29 29 29 29 29 29 29 Source: Author’s calculation
Table 7: Descriptive statistics for onshore wind farm locations
Y Number Capacity Date Wind Speed (€ per kWh) (MW) (m/s)
Mean 0.6956 10.9009 1.7344 2002.1261 6.4045 Standard Error 0.0058 0.5611 0.0413 0.2357 0.0412 Median 0.7 9 1.65 2002 6.4 Mode 0.7 9 1.5 2001 6.4 Standard Deviation 0.0609 5.9114 0.4352 2.4831 0.4341 Sample Variance 0.0037 34.9446 0.1894 6.1658 0.1884 Kurtosis 2.2589 5.7389 28.5054 0.4480 0.6337 Skewness -1.1380 2.1099 3.9415 0.8327 0.0212 Range 0.35 36 4 11 2.3 Minimum 0.46 2 1 1998 5.3 Maximum 0.81 38 5 2009 7.6 Sum 77.21 1210 192.52 222236 710.9 Count 111 111 111 111 111 Source: Author’s calculation
32
The mean of dependent variable “Y” (investment cost per unit of annual energy output) is more or less the same with a value of 0.69 in all three tables. However, the standard deviation of
“Y” for offshore locations is five times higher than the standard deviation for onshore locations.
Other interesting observations are that the offshore minimum value of “Y” is half the onshore value, and the offshore maximum value is twice the onshore value. When considering these differences, it should be recalled that wind farms are pilot projects, onshore ones are on a more developed stage.
The “Number of Turbines” per wind farm is different across locations as well. The mean value offshore is 28, which is much higher than the onshore mean of 11 turbines. However, new offshore wind farms are in project phase one, which means that the final number of turbines is not yet installed. Plans call for many offshore wind farms to install 100 or more turbines. In
Germany onshore wind farms face limits on the number of turbines per farm due to space limitations and legal restrictions.
Offshore “Turbine Capacity” has a mean value of 2.54, higher than the mean value for onshore wind farms of 1.73. Offshore “Turbine Capacity” has a standard deviation three times the onshore standard deviation of 0.43. However, note that onshore turbines have to satisfy a minimum capacity requirement of 1 MW to be included in the data base, whereas offshore turbines do not have to meet this requirement and have a minimum value of 0.45.
The mean value of “Construction Date” is 2002 for all wind farms together and for onshore locations only; however, the offshore mean shows a younger date of 2003/ 04. A lager difference is shown for the minimum level of date which is for offshore 1991 and onshore 1998.
The main reason is the required minimum of 1 MW capacity for onshore turbines. This turbine size was first available at the end of the 1990s.
33
“Mean Wind Speed” offshore is 8.6 m/s, 34 percent higher than “Mean Wind Spend” onshore at 6.4 m/s. Standard deviations are similar across locations. An interesting point is that the maximum level of “Mean Wind Speed” onshore, 7.6 is close to the minimum level of 7.2 offshore. The main advantage of offshore wind farm locations is higher wind speed.
“Distance to Shore” for offshore wind farms ranges from 0.75 to 46.5 kilometers, with a mean value of 10.3 kilometers.
“Water Depth” for offshore wind farms ranges from 3.5 to 40 meters, with a mean value of 11.9 meters.
Correlation Statistics and Discussion
Table 8: Correlation statistics for wind farm data set
Y Number Capacity Date Wind Distance Depth Offshore Speed Y 1 Number -0.1797 1 Capacity 0.0563 0.1975 1 Date -0.1181 0.3207 0.6698 1 Wind Speed -0.0221 0.5681 0.5587 0.3689 1 Distance 0.2704 0.4709 0.6132 0.3747 0.7107 1 Depth 0.2645 0.3352 0.6210 0.3324 0.7289 0.8331 1 Offshore -0.0028 0.4849 0.4320 0.1934 0.8812 0.6640 0.7683 1 Source: Author’s calculations
Table 8 provides an overview of linear correlations between variables. While correlations between the independent variables and the dependent variable “Y” are desirable for the purpose of explaining variation in “Y” using a multiple regression model, correlations among independent variables are undesirable, as they can lead to the spurious conclusion that a model fits the data well when in fact it does not.
34
In Table 8 it is obvious that the variables “Mean Wind Speed”, “Distance to Shore”,
“Water Depth” and “Offshore” are strongly and positively correlated with one another. The highest correlation exists between “Mean Wind Speed” and indicator variable “Offshore”, a correlation of 0.88. This is comprehensible, as wind speed is commonly higher at offshore locations relative to onshore locations.
The second highest correlation is between “Distance to Shore” and “Water Depth”, a correlation of 0.83. This is to be expected as an increase in offshore distance typically leads to an increase in water depth.
Another interesting relationship is the moderately strong and positive correlation between
“Turbine Capacity” and “Construction Date”, with a correlation of 0.69. The variable
“Construction Date” is used as a proxy variable for technological development into the model, but because increased turbine capacity is a major form of technological development over time, it is not surprising that “Construction Date” and “Turbine Capacity” are correlated.
Multiple Regression Analysis and Discussion
The purpose of the regression analysis is, first, to develop a model for forecasting “Y”
(investment cost per unit of annual energy output) for planned wind farms, and second, to identify potential “cost driver” components of “Y”. For the purpose of forecasting, the model should have a high value of adjusted R-square, and for identifying cost drivers, independent variables should be statistically significant.
Table 9 presents the results of an initial multiple regression for all variables, with the variable “Construction Date” replaced by integer numbers, one instead of 1991, two for 1992, etc. The statistical significance of the overall model is very good based on the F-test criteria--the
35 value of F is high as well as a very low level of significance F. The adjusted R-square statistic indicates that 26 per cent of the total variation in “Y” is explained by this model.
The average expected error (standard error) in predicting “Y” with this model is plus or minus 0.1357 Euro per kWh at the average values of the independent variables (Variable “Y” has a mean of 0.695 Euro per kWh and ranges from 0.23 to 1.6.).
Table 9: Multiple regression model with all variables
Regression Statistic ANOVA Multiple R 0.549465151 df SS MS F significance F R Square 0.301911952 Regression 7 1.052006805 0.150286686 8.15541367 3.09845E-08 Adjusted R 2 0.264892131 Residual 132 2.432475337 0.018427843 Standard
Error 0.135749193 Total 139 3.484482143
Observations 140
Coefficients Standard t-stat p-value lower 95% upper 95% lower 99% upper 99% Error Intercept 0.908256074 0.174832275 5.195013748 7.57003E-07 0.562420557 1.254091591 0.451316497 1.365195651 Number -0.00274575 0.00110898 -2.47592901 0.014554845 -0.00493943 -0.00055208 -0.00564417 0.000152661 Capacity -0.03082772 0.026796308 -1.15044653 0.252039884 -0.08383346 0.022178026 -0.10086224 0.039206796 Distance 0.011526 0.003708312 3.108152531 0.00230608 0.004190592 0.018861407 0.001833999 0.021218 Depth 0.011361341 0.004228917 2.686583713 0.008147053 0.002996124 0.019726557 0.000308692 0.022413989 Wind Speed -0.00375223 0.029430995 -0.12749229 0.898744791 -0.06196964 0.054465191 -0.08067273 0.073168277 Offshore -0.16204682 0.072031183 -2.24967599 0.026125218 -0.30453162 -0.01956203 -0.35030667 0.026213029 Date -0.00867879 0.005410482 -1.60406973 0.111088718 -0.01938126 0.002023678 -0.02281956 0.005461982 Source: Author’s calculations
The t statistics indicate that the intercept, “Distance to Shore”, “Water Depth” and
“Offshore” are statistically significant at the alpha equals 5 percent level or better. “Construction
Date” is almost significant at the 10 percent level. “Mean Wind Speed” and “Turbine Capacity” are not significant at the 10 percent level.
36
The main reason why “Mean Wind Speed” and “Turbine Capacity” are not significant could be multicollinearity. There is high correlation between “Mean Wind Speed” and
“Offshore” and as well between “Turbine Capacity” and “Construction Date”.
Alternative regression models were developed in an attempt to alleviate multicollinearity among the independent variables. The alternative regression models reduce multicollinearity by dropping one or more of the independent variables. The following Table shows the summary output for the multiple regression that best fit the data. The variables “Construction Date” and
“Offshore” were excluded from this best-fitting model.
Table 10: Multiple regression model (date and offshore variables excluded)
Regression Statistic ANOVA Multiple R 0.517968137 df SS MS F significance F R Square 0.26829099 Regression 5 0.934855165 0.186971033 9.826581948 5.09263E-08 Adjusted R 2 0.240988415 Residual 134 2.549626977 0.019027067 Standard
Error 0.137938635 Total 139 3.484482143
Observations 140
Coefficients Standard t-stat p-value lower 95% upper 95% lower 99% upper 99% Error Intercept 1.124976524 0.125862735 8.93812232 2.75093E-15 0.876041967 1.37391108 0.79609505 1.453857997 Number -0.00334771 0.001066493 -3.13898515 0.002085637 -0.00545704 -0.00123837 -0.00613447 -0.00056094 Capacity -0.04316656 0.02067085 -2.08828177 0.038665078 -0.0840499 -0.00228322 -0.09717984 0.010846724 Distance 0.013144859 0.003710195 3.542902476 0.000545237 0.00580674 0.020482977 0.003450056 0.022839661 Depth 0.00730502 0.00379282 1.926012879 0.056220518 -0.00019652 0.014806558 -0.00260568 0.017215724 Wind speed -0.05037463 0.020326579 -2.4782643 0.014445083 -0.09057707 -0.0101722 -0.10348833 0.002739063 Source: Author’s calculations
The F test indicates that the overall model is highly statistically significant. The adjusted
R-square value indicates that 24 per cent of the total variation in “Y” is explained by this model.
The average expected error (standard error) in predicting “Y” with this model is plus or minus
37
0.1379 Euro per kWh The variables “Number of Turbines” and “Distance to Shore” are significant at the 1 percent level, “Turbine Capacity” and “Mean Wind Speed” are significant at the 5 percent level, and “Water Depth” is significant at the 10 percent level.
Furthermore, quadratic terms were used to test for possible nonlinear effects of independent variables. The model with the highest value of adjusted R-square includes the squared independent variables “Turbine Capacity” and “Construction Date”. The following table shows the summary output:
Table 11: Multiple regression model with quadratic elements (capacity and date)
Regression Statistic ANOVA Multiple R 0,704873626 df SS MS F significance F R Square 0,496846829 Regression 9 1,731253904 0,192361545 14,26340295 7,9797E-16 Adjusted R 2 0,462013148 Residual 130 1,753228239 0,013486371 Standard
Error 0,116130836 Total 139 3,484482143
Observations 140
Coefficients Standard t-stat p-value lower 95% upper 95% lower 99% upper 99% Error Intercept 1,531252187 0,187879925 8,150163908 2,64149E-13 1,159554229 1,902950145 1,040100765 2,022403609 Number -0,00045743 0,001047069 -0,436865829 0,662933435 -0,00252893 0,001614072 -0,00319465 0,002279795 Capacity -0,32904662 0,073708706 -4,464148671 1,72518E-05 -0,47487048 -0,18322276 -0,52173424 -0,136359001 Capacity sqr 0,057631437 0,013130794 4,389029224 2,33416E-05 0,031653733 0,083609142 0,023305215 0,091957659 Distance 0,004820805 0,003428042 1,406285309 0,162025268 -0,00196117 0,011602775 -0,00414070 0,013782313 Depth 0,011254037 0,00365201 3,081600609 0,002513885 0,004028971 0,018479102 0,001707035 0,020801038 Wind Speed -0,01648634 0,025320204 -0,651114046 0,516122147 -0,06657934 0,033606655 -0,08267784 0,049705161 Offshore -0,18924152 0,062792652 -3,013752703 0,003102907 -0,31346928 -0,06501377 -0,35339265 -0,025090404 Date -0,05571018 0,0204816 -2,720011084 0,007421134 -0,09623058 -0,01518978 -0,10925271 -0,002167647 Date sqr 0,002200262 0,000769013 2,861150967 0,004920429 0,000678862 0,003721662 0,000189926 0,004210598 Source: Author’s calculations
38
The F test indicates that the overall model is highly statistically significant. The adjusted
R-square value indicates that 46 per cent of the total variation in “Y” is explained by this model.
The intercept and the variables “Turbine Capacity” (linear and square), “Water Depth”,
“Offshore” and “Construction Date” (linear and square) are statistically significant at the alpha equals 1 percent level or better. “Number of Turbines,” “Distance to Shore” and “Mean Wind
Speed” are not significant at the 10 percent level.
Despite the high adjusted R-square value, this regression is not used as the baseline model. A deeper analysis shows that the quadratic trend is mainly caused by four 5 MW turbine data points, which have high variation in cost. At the moment, these data points should be considered with caution because the 5 MW turbine type is the newest technology and operates mainly at offshore locations. For higher certainty, more data on 5 MW turbines are necessary to identify any change in the generally linear downward trend in historical costs.
Equation Model and Potential Cost Drivers
For forecasting purposes, a linear regression model including all independent variables
(Table 9) is presented below. The base unit Euro is converted into Euro Cent (c €), and values are rounded after the second decimal place.
Y = 90.83 – 0.27 X 1 – 3.08 X 2 + 1.15 X 3 + 1.14 X 4 – 0.38 X5 – 16.2 X 6 – 0.87 X 7
X1: Number of turbines X2: Turbine capacity
X3: Distance to shore X4: Water depth
X5: Wind speed X6: Offshore location dummy
X7: Construction Date
39
Equation coefficient 90.83 is the expected value of “Y” when all X’s are equal to zero,
Coefficient – 0.27 is the expected change in “Y” for a one unit change in X1, while holding the values of all other X’s constant, and the remaining coefficients have similar interpretations.
Based on a second multiple regression analysis (Table 10), the question of potential cost drivers is discussed. The variables “Number of Turbines”, “Turbine “Capacity” and “Mean Wind
Speed” have negative coefficients, which means that an increase in any one of them will reduce the value of Y. Recalling that the value of “Y” is capital cost ( €) per annual energy output
(kWh), “Mean Wind Speed” has a negative impact on “Y” because with higher wind speed a turbine can generate more power and therefore a higher annual energy output. This higher energy output reduces cost per kWh. The same explanation is valid for “Turbine Capacity” because with an increase of rated power per turbine, the turbine generates more power and therefore lowers the proportional cost as well. Although it is possible that a higher rated power per turbine could increase cost, leaving the net impact on variable “Y” ambiguous, prices have declined over the last ten years, which means that an increase in rated power has occurred in combination with a decrease in price. [16, p. 210] A possible explanation for the negative impact of turbine number on cost per kWh could be returns to scale in the “Number of Turbines” per wind farm. For example, as fixed facility transformer or transmission cable costs are spread across a larger number of turbines, cost per turbine declines. Another reason could be discounted turbine prices offered by the turbine manufacturer for larger orders.
The remaining variables “Distance to Shore” and “Water Depth” have positive coefficients, which means that an increase in either of them leads to an increase in Y. The
40 reasons for this relationship are higher complexity of the foundation with greater water depth and a higher complexity for grid connection with greater distance from shore.
Model Application to German Offshore Wind Farm Plans
In this section, the potential investment cost per kWh (variable “Y”) for future (planned)
German offshore wind farms is estimated using the regression equation model of the previous section. The data for German offshore wind farms are taken from Table 4, Chapter 2. Notice that the predictive power of equation model is limited by the range of the available data for the model variables. As a result, just the pilot phase can be analyzed because in the final phase the majority of wind farms install high numbers of turbines, which is out of the range of the available data.
Ten wind farms are dropped from the list because their distance to shore is unusually high, and the model may not predict as well for these extreme cases. For estimation purposes, “Mean Wind
Speed” is set to 9 m/s for all North Sea locations and to 8 m/s for all Baltic Sea locations, which are conservative estimates. For forecasting purposes, “Construction Date” is set to 20; this means projects are assumed to be constructed in 2010. The mean value for distance to shore is 32 km and for water depth 27 m. Wind farm investment cost per kWh (“Y”) is calculated for two different turbine capacities 3.6 MW and 5 MW. State-of-the-art turbine capacity is 5 MW, the preferred capacity for German offshore locations. The following Table shows the results of calculating Y using the regression equation model. Estimated investment cost per kWh (“Y”) for offshore wind farms in the pilot phase has a mean value of 94.68 c€/ kWh for wind farms with
3.6 MW turbines (with a range of 62.32 to 124.41 c€/ kWh) and a mean of 90.37 c€/ kWh for farms with 5 MW turbines (with a range of 58.01 to 120.1).
41
Table 12: Potential investment costs for German offshore wind farms in pilot phase
Water Wind Turbine Distance depth speed Y (3.6 MW) Y (5 MW) Name No. (km) (m) (m/s) (c €/ kWh) (c €/ kWh) Borkum West 12 43 30 9 123,13 118,82 Dan Tysk 80 45 27 9 103,65 99,34 Borkum Riffgrund West 80 40 33 9 104,74 100,43 Borkum Riffgrund 77 34 26 9 90,67 86,36 Nordsee Ost 80 30 22 9 80,70 76,39 Butendiek 80 35 19 9 83,03 78,72 Windpower Delta Nordsee 48 40 30 9 109,96 105,65 Amrumbank West 80 35 23 9 87,59 83,28 Gode Wind 20 45 31 9 124,41 120,1 Meerwind 80 53 27 9 112,85 108,54 Borkum Riffgat 44 15 18 9 68,61 64,3 Uthland 80 49 25 9 105,97 101,66 Offshore WP Nordergründe 25 13 10 9 62,32 58,01 Kriegers Flak 80 32 36 8 99,34 95,03 Baltic I 21 15 18 8 75,20 70,89 Arkona Becken Südost 80 34 30 8 94,80 90,49 GeoFreE 5 20 21 8 88,69 84,38 Vento Tec Ost 2 80 40 40 8 113,10 108,79 Beltsee 76 14 25 8 67,18 62,87 Arcadis Ost 1 70 17 40 8 89,35 85,04 Arcadis Ost 2 25 39 35 8 121,10 116,79 Sky 2000 50 20 21 8 76,54 72,23 Source: Author’s calculations based on table 4
42
CHAPTER 6
CONCLUSIONS AND RECOMMENDATIONS
Conclusions
The planned development of large offshore wind farms in Germany is the result of renewed thinking concerning how best to secure renewable power supplies for the future.
Interest is motivated by the twin policy goals of reducing high dependency on imported fossil fuels (oil and natural gas) and reducing the threat of global warming caused by carbon dioxide emissions. Import dependency is increasingly risky for western countries due to escalating prices caused by reductions in fossil fuel reserves, rapidly growing demand in developing countries, and the location of over 90 per cent of reserves in countries with unstable political situations.
A literature review of wind power economics finds that wind generated power is almost cost competitive against conventional power production by coal and gas. The trend of rising fossil fuel prices likely will lead to a cost advantage for wind power in the future. This advantage is already achieved if external costs are included in cost calculations. In general, wind power generation is capital-intensive; approximately 75 to 80 per cent of total power production costs are related to investment (capital) costs. In contrast to the onshore wind power sector, offshore wind power sector is in an early stage of development characterized by pilot wind farm projects.
As a result, the experience and data necessary to predict precisely offshore wind generation costs are limited, especially for the case for offshore O&M costs. Preliminary estimates forecast 50 per cent higher investment and O&M costs for offshore wind power projects relative to onshore ones. The main cost drivers are distance to shore and water depth, as these factors determine foundation structure and electrical infrastructure costs. However, offshore wind energy’s
advantage is higher mean (and less variable) wind speed, which could provide significantly more power relative to onshore locations.
In recent years the boom in the wind power sector was driven exclusively by onshore projects. Currently, the best locations are the windy coastal onshore sites, but these locations are extremely limited due to the presence of existing coastal development and land use regulations.
The potential move to offshore locations offers access to an area of higher wind speed but also new technical challenges and the question of financial feasibility. Especially in the case of
Germany, offshore plans have the highest challenges and hold high risks because projects are located farther offshore relative to potential wind farm locations off the coasts of other countries.
As part of the decision-making process, potential investors and policy-makers need to forecast the costs of offshore projects and define the risks involved. This thesis makes a contribution to the estimation of the potential financial costs and feasibility of offshore wind power in Germany and should support the decision-making process. The thesis was guided by the three key research questions presented below along with brief findings based on the thesis analysis:
(1) What are the potential investment costs of offshore wind energy production in
Germany, and what are the key drivers of these costs? A multiple regression model
applied to German offshore wind farm data indicates a mean value of 94.68 c€/ kWh for
wind farms fitted with 3.6 MW turbines and a mean value of 90.37 c €/ kWh for 5 MW
turbines. Key cost drivers are “Distance to Shore” and “Water Depth”.
44
(2) What are the relative costs of wind energy production across alternative offshore
locations in Germany? Relative costs range between 62.32 and 124.41 c€/ kWh for 3.6
MW turbines. Costs decrease with 5 MW turbines, ranging from 58.01 to 120.1 c €/ kWh.
(3) Is offshore wind energy production in Germany cost-competitive with onshore wind
energy production in Germany? A comparison of mean values (“Y”) shows that
investment cost for offshore wind energy production (90.37 c €/ kWh) in Germany is
almost 30 percent higher than onshore investment cost (69.56 c €/ kWh). However, the
range of offshore investment costs (58.01 to 120.1 c €/ kWh) overlaps the range of
onshore investment costs (46 to 81 c€/ kWh), which means that some offshore projects
(one-third of the offshore locations in the sample) are cost-competitive with onshore
ones.
In summary, the potential investment costs of proposed offshore wind farms in Germany were estimated based on the results of a multiple regression model developed using offshore wind farm data from all countries. Estimated mean investment costs for offshore wind farms in
Germany (mean 90.37 c€/ kWh) are almost 30 percent higher than actual mean investment costs for onshore wind farms in Germany (mean 69.56 c€/ kWh). The advanced onshore technology and experience coupled with the unique challenges for German offshore projects such as longer distance to shore and deeper water relative to other offshore wind farm areas are responsible for this gap. Results also indicate that the variation in offshore investment costs is much larger than the variation in onshore costs, which is an indication of higher financial uncertainty for offshore projects at their current stage of development. However, investment costs for some offshore
45 wind facilities are within the range of similar costs for onshore facilities. This indicates that the higher power output produced by stronger winds offshore may compensate for higher offshore foundation and transmission facility costs at some locations. Furthermore, the German offshore cost estimates are based on pilot phase projects that are of smaller scale relative to planned, final phase projects. For final stage projects, a much larger number of turbines is planned, and returns to scale arising from spreading fixed transformer and power transmission cable costs across more turbines may reduce investment costs per unit of power produced. In addition, turbines with higher power ratings that are currently under development and that may be deployed efficiently only at offshore locations due to higher wind requirements may increase the relative cost- effectiveness of offshore farms. As well, it may be possible to access even stronger winds at offshore locations by raising turbine hub/tower heights above those being used in pilot phase farms.
Although this thesis focuses on the investment costs associated with offshore wind farms, the O&M costs of wind farm facilities are also important determinants of wind farm profitability and the cost-effectiveness of offshore farms relative to onshore farms. Unfortunately, the available data on wind farm O&M costs were not sufficient to support analysis of these costs at this time. Incorporating O&M costs in the analysis would be a valuable extension of this research.
Future research should attempt to incorporate cost data from offshore wind farms as they grow larger and transition from pilot phase to commercial-scale facilities. Important changes in cost structure may result from scale economies arising from larger facilities. As the industry expands and more data become available, additional variables that would provide greater detail on wind farm cost structure could be considered, such as turbine hub height, rotor diameter,
46 tower foundation type, and power grid connection type. Important future research questions related to farm size include: What are the engineering and environmental constraints on wind farm size? What additional types of costs arise as farm size increases? What is the optimal offshore wind farm size for Germany; what wind farm size is cost-efficient?
State subsidies for offshore wind farms in Germany could play a decisive role in the industry’s short-run financial feasibility. The German offshore wind power sector is in an early stage of development and faces unique challenges related to site locations that are farther from shore and at greater water depths than offshore sites in other countries, creating additional uncertainty and risk for potential investors. An offshore wind power feed-in tariff subsidy offers investors a risk premium and encourages them to invest. The German Renewable Energy
Sources Act (EEG) of 2009 sets the German offshore feed-in tariff subsidy at 15c € per kWh, which is more than 60 per cent higher than the onshore subsidy.
Recommendation
In spite of higher mean investment cost per annual energy output for German offshore wind farm locations relative to onshore locations, it is recommended that construction of German offshore wind farms continue and that at least some existing farms be expanded to commercial scale. In other countries, offshore wind farms are competitive with onshore farms. While German offshore farms typically occupy relatively high-cost locations farther from shore and in deeper water compared to offshore farms in other countries, new, larger, and more efficient turbines and large facility scale may reduce costs for German offshore farms. Expansion should be done in small, incremental steps, as is the current German policy. As expansion occurs, it is important to gather the information and experiences needed to identify and find solutions for unexpected
47 problems, optimize processes, and assess costs, especially O&M costs at various scales of operation. Support from the German Renewable Energy Sources Act (EEG) of 2009 in the form of a higher feed-in tariff for offshore-generated wind power should encourage potential investors to fund the expansion of wind farm projects that will be necessary to assess profitability at commercial scales.
48
REFERENCES
1. Danish Energy Agency (DEA),
2. DEA (October 2005) “Offshore Wind Power”, Publikationer/Vedvarende_energi_UK/havvindmoellerapp_GB-udg.pdf> 3. Danish Wind Industry Association (DWIA), 4. DWIA (June 2003) “History of Wind Turbines”, pictures/index.htm> 5. DWIA (June 2003) “Wind Speeds and Energy”, tour/wres/enrspeed.htm> 6. Deutsche Energie-Agentur (DENA), Offshore, 7. DENA (October 2001) “Concerted Action on Offshore Wind Energy in Europe Resources Economics” (EU short report), fileadmin/offshore/documents/Wirtschaftlichkeit/EU-Kurzbericht_Concerted_ Action_on_Offshore_Wind_Energy_in_Europe__Resources_Economics.pdf> 8. DENA (April 2007) “Externe Kosten kennen – Umwelt besser schützen” Umweltbundesamt (UBA), documents/Wirtschaftlichkeit/externekosten.pdf > 9. Deutscher Wetterdienst (DWD): 10. DWD (July 2008) “Annual Mean Wind Speed Map, Germany”, bvbw/generator/Sites/DWDWWW/Content/Oeffentlichkeit/KU/KU1/KU12/Klimagut achten/Windenergie/Download__Karte__D__80m,templateId=raw,property=publicat ionFile.pdf/Download_Karte_D_80m.pdf> 11. DWD (July/ August 2008), Downloads, maps and explanations: bvbw/appmanager/bvbw/dwdwwwDesktop?_nfpb=true&_pageLabel=dwdwww_resu lt_page&gsbSearchDocId=315948> 12. European Commission, Climate Action, eu_action/index_en.htm> and uri=CELEX:52008DC0030:EN:NOT> 13. European Wind Atlas (1989) by Risø National Laboratory, Roskilde, Denmark.: 14. European Wind Energy Association (EWEA) 15. EWEA (February 2006) “PR Global Statistics 2005 (GWEC)”, /fileadmin/ewea_documents/documents/statistics/gwec/stats2005.pdf> 16. EWEA (2009) “Wind Energy – The Facts”, London and Sterling (VA; USA): Earthscan 17. EWEA (February 2009) “Wind now leads EU power sector”, News Release, _February_2009_Wind_now_leads_EU_power_sector.pdf> 18. EWEA (March 2009) “The Economics of Wind Energy”, fileadmin/ewea_documents/documents/publications/reports/ Economics_of_Wind_Main_Report_FINAL-lr.pdf> 19. EWEA (September 2009) “Oceans of Opportunity”, /ewea_documents/documents/publications/reports/Offshore_Report_2009.pdf> 20. Global Wind Energy Council (GWEC) 50 21. GWEC (2007) “Global Wind 2006 Report”, 22. GWEC (May 2008) “Global Wind 2007 Report”, 2 nd ed., 23. GWEC (2009) “Global Wind 2008 Report”, < http: //www.gwec.net/fileadmin/documents/Global%20Wind%202008%20Report.pdf> 24. Hau, Erich (2008) “Windkraftanlagen”, 4 th ed., Heidelberg: Springer-Verlag Berlin 25. International Energy Agency (IEA) 26. Jarass, L.; Obermair, G.; Voigt, W. (2009) “Windenergie”, 2 nd ed., Heidelberg: Springer-Verlag Berlin 27. Kaltschmidt, M.; Streicher, W.; Wiese, A. (2006) “Erneuerbare Energien”, Heidelberg: Springer-Verlag Berlin, Ch. 6 “Stromerzeugung aus Windenergie” 28. Möller, J. (2003) “Möglichkeiten und Grenzen der Nutzung erneuerbarer Energieträter am Beispiel des Landes Brandenburg”, /dokserv?idn=971436673&dok_var=d1&dok_ext=pdf&filename=971436673.pdf> 29. Mukund R. Patel (1999) “Wind and Solar Power Systems”, Boca Raton (Florida): CRC Press LLC 30. Windenergie Agentur Bremerhaven (WAB) 31. WAB (June 2007) “Offshore Windenergie, Das Magazin”, agentur.de/deutsch/PDFs/OffshoreMagazin_reduced.pdf> 32. Risø DTU, National Laboratory for Sustainable Energy, Technical University of Denmark: 51 33. Szarka, J. (2007) “Wind Power in Europe”, New York: Palgrave Macmillan 34. Sources of data sample: http://www.ceere.org/rerl/projects/support/mwwg/minutes/Experiences%20from%20 Middelgrunden%2040%20MW%20Offshore%20Wind%20Farm,%20Larsen,%20Oct 05.pdf> pdf/geschaeftsbericht_ws-bs_2007.pdf> 2008.pdf> 52 2006.pdf> 53 1_1238748888.pdf> 1_1238748834.pdf> GmbH-Co-KG-1_1238748772.pdf> GmbH-Co-KG-1_1238749086.pdf> 54 T/360168vatt/5965811xou/902656oper/903724wind/P0289268.pdf> 55 AEE8-642B24C8EA4E}/print.pdf> 56 APPENDIX Appendix A: Cleaned Data Set for Regression Analysis Value of Y No. of Turbine Distance Water Mean Construction Offshore Turb. capacity to shore Depth Wind Date Lfd. speed No Name (€/ kWh) (MW) (km) (m) (m/ s) (Year) 1 Horns Rev 0,45 80 2 17 9 9,7 2002 1 2 Ronland 0,36 8 2,15 1 5 8,5 2003 1 3 Samsoe 0,42 10 2,3 3,50 20 7,8 2002 1 4 Middelgrunden 0,55 20 2 3 4,5 7,2 2000 1 5 Nysted 0,42 72 2,3 10 7,25 9,1 2003 1 6 Tuno Knob 0,77 10 0,5 6 4 7,8 1995 1 7 Vindeby 0,9 11 0,45 1,5 4 7,8 1991 1 8 Horns Rev II 0,62 91 2,3 30 12 9,9 2009 1 9 Yttre Stengrund 0,43 5 2 5 8 8,5 2001 1 10 Utgrunden 0,46 7 1,5 8 8,5 8 2000 1 11 Bockstigen-Valor 1,23 5 0,5 3 6 8 1998 1 12 Lillgrund 0,6 48 2,3 10 7 9 2008 1 13 Q7 - WP 0,88 60 2 23 22 9 2008 1 14 Egmond aan Zee 0,52 36 3 10 20,5 8,5 2006 1 15 Lely 1,38 4 0,5 0,75 7,5 7,8 1994 1 16 Kentish Flats 0,57 30 3 8,5 5 8,7 2005 1 17 Scroby Sands 0,55 30 2 2,3 6 8,5 2005 1 18 Blyth 0,55 2 2 0,8 8,5 8 2000 1 19 Beatrice 1,27 2 5 25 40 9,1 2007 1 20 Barrow in Furness 0,49 30 3 7 22 8,5 2006 1 21 Burbo 0,59 25 3,6 6,4 4,5 9,1 2007 1 22 North Hoyle 0,5 30 2 6 15 8,5 2003 1 23 Lynn & Inner Dowsing 0,61 54 3,6 5 9,5 8,5 2008 1 24 Robin Rigg 0,89 60 3 9,5 10 9,1 2008 1 25 Gunfleet Sands 0,64 30 3,6 6 10 9,1 2009 1 26 Rhyl Flats 0,58 25 3,6 8 9,5 8,5 2009 1 27 Arklow Bank Pilot Phase 0,23 7 3,6 10 3,5 8,5 2003 1 28 Alpha Ventus 1,08 12 5 46,5 30 9 2007 1 29 Thornton Bank Phase I 1,6 6 5 27 25 9 2008 1 30 Altlüdersdorf 0,74 9 1,5 0 0 6,1 2003 0 31 Beerfelde 0,74 7 1,5 0 0 6 2002 0 32 Dirlammen 0,76 8 1,3 0 0 6,4 2000 0 33 Hanstedt-Wriedel 0,7 11 1,5 0 0 7 2001 0 34 Hohengüstow 0,72 7 1,5 0 0 6,5 2003 0 35 Holßel 0,64 21 1,0 0 0 7,2 1999 0 36 Keyenberg/ Holzweiler 0,71 9 1,3 0 0 6,1 2001 0 37 Krempel I 0,62 11 1,3 0 0 6,9 1999 0 38 Spessart 0,7 9 1,5 0 0 5,6 2002 0 39 Friedland 0,69 6 1,5 0 0 6,9 2002 0 40 Jülich 0,66 6 1,5 0 0 6,5 2005 0 41 Meppen Helte 0,72 6 1,8 0 0 6,4 2000 0 42 Meppen Teglingen 0,71 6 1,8 0 0 6,4 2001 0 43 Bunderhee 0,61 9 2,3 0 0 7,1 2006 0 44 Börger 0,67 5 1,8 0 0 6,5 2003 0 45 Ablaß 0,77 12 1,5 0 0 5,4 1999 0 46 Willenscharen-Brokstedt 0,69 12 1,7 0 0 6,4 1999 0 47 Sitten 0,78 7 1,5 0 0 5,6 1999 0 48 Klettwitz 0,76 38 1,7 0 0 6,1 1999 0 49 Kostebrau 0,71 6 1,7 0 0 6,1 2000 0 50 Tarnow 0,73 9 1,5 0 0 6 2000 0 51 Bliesdorf-Ketzin 0,78 21 1,8 0 0 6,3 2000 0 52 Puschwitz 0,81 10 2,0 0 0 6,4 2001 0 53 Heinersdorf 0,75 9 1,5 0 0 6,7 2001 0 54 Heynitz 0,76 11 1,8 0 0 6,3 2002 0 55 Zerre 0,76 5 2,0 0 0 6,4 2002 0 56 Beeskow-Neuendorf 0,73 10 2,0 0 0 6,4 2002 0 57 Beeskow-Hufenfeld 0,73 9 2,0 0 0 6,5 2003 0 58 Lüdersdorf - Parstein 0,7 15 1,5 0 0 6,5 2004 0 59 Sallgast 0,76 12 2,0 0 0 6,5 2004 0 60 Brunsbüttel 0,73 10 2,0 0 0 6,9 2004 0 61 Iven 0,6 11 3,0 0 0 6,8 2008 0 62 Moorhusen 0,69 15 1,5 0 0 6,5 2001 0 63 Christinendorf 0,46 7 2,0 0 0 6,5 2009 0 64 Mahlwinkel-Nord 0,69 16 2,0 0 0 6,4 2008 0 65 Alttrebbin-Birkholz 0,68 9 1,7 0 0 6,5 2004 0 66 Uckermark 0,68 8 1,5 0 0 6,2 1999 0 67 Saubusch 0,58 14 1,5 0 0 6,5 2000 0 68 Wittstedt 0,72 7 1,5 0 0 6,5 2000 0 69 Wulfshagen 0,8 6 2,0 0 0 6,8 2000 0 70 Zinndorf 0,51 9 1,7 0 0 6,2 2000 0 71 Altmark 0,75 20 1,5 0 0 6,3 2001 0 72 Borne III Welbsleben 0,72 8 1,3 0 0 6,4 2001 0 73 Borsum-Ahlerstedt 0,74 25 1,8 0 0 6,4 2000 0 74 Lüdersdorf 0,57 10 2,0 0 0 6,3 2001 0 58 75 Wansleben 0,66 8 1,5 0 0 6,2 2002 0 76 Dubener Platte 0,56 19 1,5 0 0 6,6 2003 0 77 Zitz-Warchau 0,72 20 1,5 0 0 5,6 2003 0 78 Offshore WT B’haven 0,57 2 5 0 0 7,6 2008 0 79 Heldrungen 0,65 5 1,8 0 0 6,2 2004 0 80 Dornstedt 0,7 10 1,8 0 0 6,4 2001 0 81 Quenstedt 0,7 8 1,5 0 0 6,4 1999 0 82 Klostermoor 0,71 8 1,5 0 0 6,8 1999 0 83 Bredenborn 0,7 10 1,7 0 0 6,2 2003 0 84 Büttstedt 0,7 8 1,8 0 0 6,4 2003 0 85 Gehlenberg 0,7 12 1,8 0 0 6,3 2000 0 86 Quenstedt 0,7 8 1,5 0 0 6,4 1999 0 87 Thedinghausen 0,67 7 1,6 0 0 6 2003 0 88 Weenermoor 0,69 8 1,5 0 0 7 1998 0 89 Westerberg 0,68 9 2,0 0 0 5,3 2003 0 90 Wittstedt 0,71 7 1,5 0 0 6,5 2001 0 91 Wulfshagen 0,8 6 2,0 0 0 6,8 2000 0 92 Werl 0,69 5 1,8 0 0 6,7 2002 0 93 Issum 0,71 9 1,0 0 0 5,9 2003 0 94 Rheurdt 0,7 10 1,1 0 0 5,9 2003 0 95 Baumberge 0,72 8 1,7 0 0 6,4 2003 0 96 Hohenfelde 0,67 9 1,5 0 0 6,1 2005 0 97 Kleisthöhe 0,7 7 2,3 0 0 6,2 2006 0 98 Nadrensee 0,67 13 2,0 0 0 6,8 2006 0 99 Nechlin 0,67 15 1,7 0 0 6,2 2003 0 100 Neuenfeld, Uckermark 0,61 14 1,5 0 0 6,2 2001 0 101 Randowhöhe I 0,66 17 1,7 0 0 6,2 2003 0 102 Shönfeld 0,72 14 2,0 0 0 6,9 2006 0 103 Sonnenberg 0,65 33 2,0 0 0 6,3 2005 0 104 Süderland 0,71 11 2,2 0 0 7,4 2008 0 105 Wolfsmoor 0,67 22 2,0 0 0 6,1 2008 0 106 Meerberg 0,68 6 1,5 0 0 6 2000 0 107 Schliekum II 0,64 5 2,0 0 0 6,5 2004 0 108 Scholen 0,71 7 1,8 0 0 5,9 2004 0 109 Gehrden 0,71 5 2,0 0 0 5,4 2005 0 110 Bassum-Ahlbringh 0,69 7 1,5 0 0 5,4 2001 0 111 Grapzow 0,68 10 2,0 0 0 7,2 2004 0 112 Großenehrich 0,68 11 2,0 0 0 6,8 2004 0 113 Köthen 0,69 17 2,3 0 0 6,7 2005 0 114 Berglicht 0,7 9 1,5 0 0 6,6 2002 0 115 Meyenburg 0,75 8 1,8 0 0 6,7 2004 0 59 116 Thüle 0,77 7 2,0 0 0 5,8 2002 0 117 Neudersum 0,76 13 1,8 0 0 7,1 2002 0 118 Rhede 0,73 17 1,8 0 0 7,1 2001 0 119 Uetze 0,73 21 1,5 0 0 6,1 2002 0 120 Reinsfeld/ Hinzert-Pölert 0,72 9 1,5 0 0 6,2 2002 0 121 Schönfeld 0,7 12 2,0 0 0 7,5 2009 0 122 Looft 0,53 6 1,7 0 0 6,7 2001 0 123 Puls 0,78 10 1,7 0 0 6,4 1999 0 124 Seelow 0,74 9 2,0 0 0 6,2 2002 0 125 Halsdorf 0,69 10 1,0 0 0 6,3 2001 0 126 Düren 0,75 6 1,5 0 0 6 2002 0 127 Neuen 0,7 10 1,5 0 0 6,7 2002 0 128 Waltersdorf 0,72 12 2,0 0 0 6,3 2003 0 129 Heilenbach 0,73 7 2,0 0 0 7,1 2004 0 130 Haselünne 0,68 13 1,5 0 0 6,3 1999 0 131 Grüppenbühren 0,65 6 1,7 0 0 6 2000 0 132 Lahn 0,61 7 2,0 0 0 6,4 2000 0 133 Oldenbroker Feld 0,56 8 1,8 0 0 6,5 2001 0 134 Sustrum/ Renkenberge 0,8 32 1,5 0 0 6,2 1998 0 135 Flomborn/ Stetten 0,77 12 1,0 0 0 5,6 1999 0 136 Emlichheim 0,71 21 1,5 0 0 6,3 2000 0 137 Brake 0,67 5 2,0 0 0 6,4 2002 0 138 Fehnland 0,64 11 1,8 0 0 6,9 2001 0 139 Warburg 0,73 16 2,0 0 0 6,7 2001 0 140 Wietmarschen-Ohne 0,71 12 1,5 0 0 6,5 2001 0 60