TECHNO-ECONOMIC ANALYSIS OF REPOWERING POTENTIAL IN
NORTH RHINE-WESTPHALIA, GERMANY
Dissertation in partial fulfilment of the requirements for the degree of
MASTER OF SCIENCE WITH A MAJOR IN WIND POWER PROJECT MANAGEMENT
Uppsala University Department of Earth Sciences, Campus Gotland
Werner Baak
8. September 2019
TECHNO-ECONOMIC ANALYSIS OF REPOWERING POTENTIAL IN
NORTH RHINE-WESTPHALIA, GERMANY
Dissertation in partial fulfilment of the requirements for the degree of
MASTER OF SCIENCE WITH A MAJOR IN WIND POWER PROJECT MANAGEMENT
Uppsala University Department of Earth Sciences, Campus Gotland
Approved by:
Supervisor, Andrew Barney
Examiner, Dr. Ola Eriksson
8. September 2019 iii
ABSTRACT
Germany is one of the pioneer countries in wind turbine technology. They installed many wind turbines during the last decades and are now confronted with a shortage of land suitable for new wind parks. Now, with an estimated wind turbine service life of 20-25 years whole wind parks are becoming obsolete and owners have to decide whether do decommission, repower or to continue the operation of their parks. The advantages of repowering as well as the bureaucratic hurdles are outlined and evaluated.
This thesis deals with the repowering potential in North Rhine-Westphalia and is analysing the technical and economical possibilities of repowering. The main objectives are to identify wind turbines eligible for repowering and also to develop repowering scenarios in order to determine their techno-economic feasibility. The designed steps of the methodology allow the census and the subsequent implementation of the results in WindPro and RETScreen.
iv
NOMENCLATURE
AEP Annual Energy Production CO2 Carbon Dioxide EEG Renewable Energies Act (Erneuerbaren-Energien-Gesetz) EPEX European Power Exchange HTq High Torque IDE Integrated Development Environment IRR Internal Rate of Return LANUV NRW North Rhine-Westphalia State Agency for Nature, Environment and Consumer Protection km Kilometre kW Kilowatt LTq Low Torque m Metre MW Megawatt MWh Megawatt hour NRW North Rhine-Westphalia O&M Operations and Maintenance PBT Payback Time PPA Power Purchase Agreement WAsP Wind Atlas Analysis and Application Program WTG Wind Turbine Generator y Year
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TABLE OF CONTENTS
ABSTRACT III NOMENCLATURE IV TABLE OF CONTENTS V LIST OF FIGURES VI LIST OF TABLES VII 1. INTRODUCTION 1 2. CURRENT STATE OF THE ART 4 2.1 RELEVANT LITERATURE 4 2.2 WIND POWER AND REPOWERING 5 2.2.1 HISTORICAL OVERVIEW 5 2.2.2 WIND ENERGY – MARKET AND TENDENCIES 6 2.2.3 REPOWERING 9 2.2.4 POLICIES AND BUREAUCRATIC HURDLES 10 3. DATA AND METHODOLOGY 14 3.1 DATABASE AND SOFTWARE USED 14 3.2 METHODOLOGICAL FRAMEWORK 15 3.2.1 DATA ANALYSIS 15 3.2.2 SELECTION OF RELEVANT WTGS 20 3.2.3 HYPOTHESIS FOR REPOWERING SCENARIO 22 3.2.4 TECHNICAL/ ECONOMIC ANALYSIS 23 3.2.4.1 Technical Analysis 23 3.2.4.2 Economic Analysis 29 4. RESULTS 38 4.1 RESULTS OF THE TECHNICAL ANALYSIS 38 4.2 RESULTS OF THE ECONOMIC ANALYSIS 44 5. DISCUSSION AND ANALYSIS 49 6. CONCLUSIONS 54 REFERENCES 55 APPENDIX 59 APPENDIX A 59 A1: LANUV DATA SET – DATE OF COMMISSIONING: 2004/2005 59 APPENDIX B 67 B1: LONG-TERM FORECAST (SPOT MARKET) 67 B2: SHORT-TERM FORECAST (SPOT MARKET) 68 B3: TENDERING PRICE FORECAST 69 APPENDIX C 70 C1: RESULTS OF WTGS SELECTION AND DETERMINATION OF WTG-MODELS/MANUFACTURER 70 vi
LIST OF FIGURES
Figure 1 Electricity Generation by Fuel in the European Union – 28, 1990 - 2016 ______6 Figure 2 Electricity Generation from Renewables by Source in the European Union, 1990 – 2016 ______7 Figure 3 WTGs in Germany [2000-2018], Adapted from BWE, 2018 ______8 Figure 4 Triple Electricity Yield at Half the Number of Installed Wind Turbines. ______10 Figure 5 Duration of Wind Power Projects in Germany.______13 Figure 6 Methodological Framework ______15 Figure 7 Map – Distribution of WTGs in NRW ______16 Figure 8 Hub Height Approximation ______17 Figure 9 No. of WTGs Installed Yearly ______19 Figure 10 Distribution of WTG Manufacturer ______19 Figure 11 Timeline for Selection of WTGs ______20 Figure 12 Distribution of Relevant WTGs Commissioned 2004 and 2005 ______21 Figure 13 AEP Calculation – Resources ______24 Figure 14 Enlargement of WTG Area ______26 Figure 15 Flowchart for Determination of New WTG Model ______28 Figure 16 Electricity Price Forecast (Yearly) Based on 'EPEX Spot' Price ______31 Figure 17 Electricity Price Forecast (monthly) Based on 'EPEX Spot' Price ______31 Figure 18 Electricity Price Forecast (monthly) Based on 'EEG - tendering procedure' price ______32 Figure 19 Capital Cost Breakdown for a Typical Onshore Wind Power System and Turbine ______35 Figure 20 Results – Wake Losses ______43 Figure 21 Results – Capacity Factor ______43 Figure 22 Results – Number of WTGs ______44 Figure 23 Detailed Analysis of Financial Viability for #KS Scenario 1 (1) ______47 Figure 24 Detailed Analysis of Financial Viability for #KS Scenario 1 (2) ______48 Figure 25 Boxplot Comparison Between Energy Atlas Method and Calculated AEP ______51 Figure 26 Sensitivity Analysis for #ML, Cumulative Cash Flow for Scenario 3 ______53
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LIST OF TABLES
Table 1 Literature Results – Keywords: wind power, repowering, Germany ______4 Table 2 Literature Results Sorted by Date______5 Table 3 Missing Data for Given Parameters ______17 Table 4 Power Distribution of Installed WTGs ______18 Table 5 Price List for Second Hand WTGs (Germany, 1000-200kW) ______36 Table 6 Financial Parameters (RETScreen) ______37 Table 7 Wind Farm Identifiers ______38 Table 8 WindPro Results ______40 Table 9 Selected Manufacturer and Model ______41 Table 10 Results – Electricity Production ______42 Table 11 Results – PBT ______45 Table 12 Results – IRR ______46 Table 13 Comparison of Electricity Production Based on Dimensionless Identifier (1) ______50 Table 14 Comparison of Electricity Production Based on Dimensionless Identifier (2) ______50 Table 15 Statistical Results of Energy Atlas Method and Calculated AEP ______51 Table 16 Sensitivity Analysis IRR Assets (+2.5€ Annual Growth Rate of Electricity Price) ______52
1
1. INTRODUCTION
That the world sees climate change as everyone’s problem and a global priority becomes undeniable. In 2015, 195 countries agreed for the first time at the Paris Climate Change Conference on a general, legally binding global climate agreement. The long-term goal was to limit the rise in the global average temperature to well below 2 °C compared to pre-industrial levels. In doing so, the participating states committed themselves to define projects in order to achieve the corresponding goals. The EU already made its contribution in March 2015 and is already working towards its target of reducing emissions by at least 40% by 2030 (Robbins, 2016).
As EU member, Germany set its own goals with the decree of the "Renewable Energies Act 2017" (Erneuerbare Energien Gesetz (EEG), 2017). The goal is to increase the share of electricity generated from renewable energies in gross electricity consumption by 2025 from 40% to 45%, from 55% to 60% by 2035 and finally to at least 80% by 2050 (cf. EEG, 2017). In March 2018, Germany tightened its targets and defined the required share of renewable energies in gross electricity generation to be increased to 65% by 2030 (Koalitionsvertrag, 2018). An exemplary behaviour is shown by Scandinavian region: Iceland and Norway already covered about 71,6% and 71,2% respectively of their electricity demand with renewable energies in 2017 (Eurostat, 2018).
Germany’s strong expansion in the wind energy sector brought them already closer to their objectives and the share of renewable energies in net electricity generation increased sharply from 33.5% (2016) to 38.2% (2017) (Fraunhofer, 2018). Still, much remains to be done and especially the low-carbon economy as well as the nuclear phase- out planned for 2022 leaves a gap which should be refilled by renewable energies (Appunn, 2018). As it stands, the best possible way to implement these political objectives, is to increase further the progress at technical and especially political levels.
However, the development of renewable energies and especially wind energy is fraught with difficulties. Although wind energy yields from a given area may increase with the improvement in technology, the areas economically and technologically feasible 2
for the development of wind energy are decreasing as existing areas are almost exploited and potential areas are already largely covered by wind turbines in 2010, which has been shown in a study by the Federal Environment Agency (Umweltbundesamt) (Lütkehus et al., 2013). The increasing competition of economic, political and social interests for the use of inland areas is also a difficulty, such as disputes in the communities where the turbines are planned. This indicates a recent study about the future of Wind energy expansion in Germany (Energiewende, 2018).
These circumstances raise the question of repowering and its potential for wind energy in Germany. If the building space is scarce, it makes sense to take a closer look on repowering already existing wind turbine generators (WTGs) and particularly on the repowering option of taking down old WTGs which are reaching their end-of-life and replacing them with newer and more productive models. This is accompanied by elementary questions, is a repowering project technically possible taking into account possible exclusion criteria? If so, how many WTGs are eligible for repowering? Thus, is repowering economical feasible? Depending on the wind turbines already installed and their operating strategies, the present thesis aims to demonstrate the full techno-economic potential of onshore wind energy in North Rhine-Westphalia (NRW), Germany. The consideration of North Rhine-Westphalia is therefore of particular interest, since more energy is converted and consumed than in all other German federal states and consequently NRW constitutes in this thesis the main subject of investigation, which is examined in order to evaluate its techno-economic repowering potential and to analyse its feasibility.
The overall structure of the study takes the form of six chapters in which Chapter 2 provides the reader with information about the current state of art, by examining the relevant literature, the actual wind market, repowering as well as policies and bureaucratic hurdles.
Chapter 3 describes the methodology and data processing methods in order to address the presented problems. The approach implies an analysis and filtration of the given data which is then used to feed the three hypothesis/scenarios. The final format of the data, can 3
be subsequently used for the applications WindPro and RETScreen with the goal to evaluate the feasibility of the cases.
In Chapter 4, the methodology is applied on 10 wind farms and the results are presented in tabular and graphical form.
Chapter 5 discusses and analysis the results in depth. Limitations and the problematic parametrisation are topic addressed to.
The final Chapter 6 recapitulates the findings and gives and calls for further studies.
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2. CURRENT STATE OF THE ART
2.1 Relevant Literature
For a coherent and comprehensive review of the literature an extensive research for the most relevant and consistent scientific publications has to be done. The digital databases used for this thesis are ‘ScienceDirect’, ’Google Scholar’, Uppsala University’s ‘Digitala Vetenskapliga Arkivet’ (DiVA), ‘International Association of Energy Economics’ (IAEE) and ‘SpringerLink’. Moreover, the libraries ‘Almedalsbiblioteket’ as well as the ‘Universitätsbibliothek Siegen’ are serving as a traditional but important source of information. The database specific search syntax e.g. AND or “(keyword)” are used to narrow the search in order to get better results. As Table 1 shows, the number of outputs decreases as the more specific the data queries are processed. The keywords which are used are variation of: wind power, repowering and Germany.
Table 1 Literature Results – Keywords: wind power, repowering, Germany
Number of Results Keyword ScienceDirect Google Scholar IAEE SpringerLink wind power 31,995 624,000 25,217 14,116 repowering 1,594 14,700 84 621 repowering wind 613 6,150 31 427 repowering wind Germany 339 3,170 16 209
A closer look at particular results shows that the relevance of the data is decreasing with its increasing age such as a discussion about shipbuilding, ‘Floating German Air Bases’, ‘Repowering of the Asturias’ and ‘Wind Tunnel Tests’ (Silley, 1934). Therefore, the year of the publication plays an important role when filtering the results. What can be clearly seen is that the information using the words ‘repowering, wind and Germany’ was published within the last 18 years (cf Table 2).
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Table 2 Literature Results Sorted by Date
Number of Results repowering wind Germany ScienceDirect Google Scholar IAEE SpringerLink 2015- 140 1,040 1 98 2010-2015 100 1,270 8 78 2006-2010 48 631 7 31 2000-2005 31 216 0 6
In 2019 alone 18 results are provided by ScienceDirect and 86 by Google Scholar, respectively. These results reflect a major interest in the topic. However, a setback can be constituted when adding the keyword ‘NRW’ or ‘North Rhine-Westphalia’ into the search queries. One relevant technical article concerning protection of species could be found. A systematic understanding of how repowering schemes for specific regions contributes to scientific research is still lacking and it is hoped that this research will contribute to a deeper understanding and will be an important contribution of the missing field.
2.2 Wind Power and Repowering
Historical overview
From the first historical sources, which originated from the year 644 A.D., to the current state of art, windmills passed through a great scientific development due to technological progress. The simple structures of bamboo sticks were replaced with solid wooden buildings and later with cast iron, whereas the windmill was no more rigid but flexible with a rotating tower cap, which turned with the windwheel. Later, in the end of the 19th century, the Danish professor Poul La Cour carried out decisive researches of how to generate electrical current by using wind power. The first La-Cour-Lykkegard turbines had a rotor diameter up to 20m and an output between 10-35kW. With time moving on, bigger and more effective turbines have been built. In 1942 the Project MAN- 6
Kleinhenz built a turbine with a rotor diameter of 130m and rated power 10,000kW (Hau, 2017).
Even if we experienced the fast development wind turbine industry, the market potential is far from having been fully exploited. New designs with higher efficiencies are still developed like the Vestas V164-8.8 with a rated power of 8.8MW which is deployed in Scotland (Rathi, 2017). Therefore, wind power can be considered as one of an important technological achievement when it involves renewable energy. The next chapter however, will give a deeper insight into the wind energy market in order give an approximate picture of the extent of its performance and potentials.
Wind Energy – Market and Tendencies
Wind power is one of the major sources of electricity generation. According to the International Energy Agency (IEA), in the year 2016, 302,894 GWh of energy were generated which represented 9,3 % of the total production in the European Union (EU). What can also be concluded from the data given in Figure 1 is that there has been a steady growth over the last 19 years of electricity produced by wind power, but going more slowly or even being constant since 2015. However, the traditional fuels like coal and nuclear have not experienced significant changes in their relative amount of energy produced in the last two decades.
Figure 1 Electricity Generation by Fuel in the European Union – 28, 1990 - 2016 (IEA, 2018) 7
Closer review of electricity generation from renewables reveals that the total sum rises from ca. 3100,00 GWh to approximately 800,000 GWh in the same time frame as presented above. Furthermore, it can be constituted that wind power has a share of more than 30% of the total energy generation (cf. Figure 2).
Figure 2 Electricity Generation from Renewables by Source in the European Union, 1990 – 2016 (IEA, 2018) The trends presented here for the EU are also valid for the world’s energy production. However, the total world energy generation is increasing continuously and does not show any hint of stagnation.
According to WWEA (2019) the worldwide total wind power capacity in 2019 will reach 600 GW with the 53.9 GW, which were added in 2018. The biggest share of installed wind turbines is in China with more than 200GW, followed by the USA. On the other hand, the total amount of cumulative capacity in the EU account for 178.826 GW and Germany alone has 59.311 GW (WindEurope, 2019a). But while the global market is growing rapidly, the European one ‘seems to lose track’, as reported by WWEA (2019).
One reason for this phenomenon could be the maturity of European market. Whereas China started the erection of wind turbines in 2005, supported by the ‘Renewable Energy Law of the People's Republic of China’ 2005, the German market, which is one of the 8
pioneer countries for wind power technology, began development already in the 90s (BMWi 2019). One of a major consequence of an established market is the technological obsolescence of the aged wind turbine installations, which raises the question of what to do with them. With regard to Germany, the number of new plants installed between 2000 and 2018 as well as the cumulative value for the same period is shown in Figure 3. What can be constituted is that to total aggregated number of installed WTGs is rising constantly and reached around 30,000, whereas the number of new installed WTGs reached its peak 2002 with approximative 2300, but shows the lowest value for year 2018 with around 750 installed WTGs. Nonetheless, it is expected that the present downward trend will be restored once again, considering the climate change and the goals set for Germany. According to these findings, it can be constituted that many WTGs have been built during the presented period and many will reach the end-of-life service in the following years. From this, it can be also concluded that there is a high potential of WTGs suitable for the repowering process as well as that the number of WTGs can be roughly determined for each year. What repowering is and for what it is used will be covered in the next chapter.
2500 35000
30000 2000 25000
1500 20000
1000 15000 10000 500 5000
0 0
2005 2018 2000 2001 2002 2003 2004 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
New WTGs Aggregated
Figure 3 WTGs in Germany [2000-2018], Adapted from BWE, 2018
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Repowering
Repowering is one of the three options which has to be taken into account by their operators when wind turbines reach their end of the service life. The other two are decommissioning and lifetime extension (Martínez et al., 2018). The latter is also known as partial repowering, revamping, refurbishment, enhancement or reactivation. Here, critical turbine components are changed and/or upgraded, while at the same time retaining the obligatory safety level. Moreover, the accompanying increase of operation and maintenance (O&M) cost have to be considered (Ziegler et al., 2018).
Repowering usually offers four major advantages. One of the biggest benefits is the ‘mapping’. Wind conditions as well as the surface quality and surroundings are already well known and therefore the data and additional acquisition of information, which was gained during the life time of the old WTGs, can be further used and built upon these. The second major advantage lies in the significantly higher electricity production respectively in the greater yield of new and modern plan technology. Considering a classic repowering project, an increase in production between factor 5 and 10 are achievable on the same area (BWE, 2018). That means that on the same space requirements the repowered WTGs are producing significantly more energy (Wind-turbine, 2017). The next advantage lies in a much higher local acceptance in the population due to the pre-existing interaction with existing plants. Residents as well as municipalities may have participated in e.g. financial form in the old plants and are looking forward to do so again. The fourth advantage is that general project costs can be reduced due to the existing infrastructure. Substation, access routes or cable lines are some of the cost driver which can be possibly used or only slightly expanded (Wind-turbine, 2017).
Repowering can raise the question of whether to increase the number of wind turbines, install the same amount or even maximise the potential and increase the number of wind turbines. Repowering however, usually reduces the number of units significantly. This is one of the critical aspects since the public opinion often focuses on the landscape or natural scenery. This is exemplary shown in Figure 4 (adapted from BWI 2018), which 10
is a possible outcome of a repowering project in Düngstrup (Germany) and where eight old WTGs (1.3 MW) were replaced by four modern WTGs of 3 MW each. The electricity yield was tripled by halving the number of WTGs.
Figure 4 Triple Electricity Yield at Half the Number of Installed Wind Turbines. Adapted from BWI, 2018Policies and Bureaucratic Hurdles A closer look however reveals that the German wind power market is going through one of its worst crises in history. According to Windguard (2019), only 86 WTGs were erected in the first half of 2019. That corresponds with a gross addition of 287 MW and is the least added power capacity in a half-year, since the introduction of EEG in 2000. Compared with the first six months of the previous year the number of new installations dropped by 82%. Taking into account the 51 decommissioned plants with a power capacity of 56 MW, the net installation results in 231 MW.
Schulz (2019) points out that several factors can be seen as driving forces for the actual economic misery of the wind industry. He argues, that one major mistake was that the from May 2017 introduced immature auction-based model, replaced the mature subsidy format for wind power projects. Anyone who wants to install wind turbines can now apply and bid at the auctions which are written out by the government. Each call for tender is funding only a certain number of projects and only the one who’s bid is the lowest is accredited with the contract by the responsible Federal Network Agency (Bundesnetzagentur). Moreover, the initially introduced special rule for the so-called 11
community wind parks i.e. projects financed mostly by small investors and locals should increase the general acceptance of wind parks. However, these forms of wind parks were initially allowed to compete in auctions even without a valid building permit and the time for realising the project was much longer than compared to the traditional companies. The result was that many community wind farms competed with dumping bids and experienced wind farm companies hardly had a chance winning the auctions due to the rule of prioritisation. Meanwhile, the community wind farms that were awarded the contract had many problems in finding banks that accepted their low construction costs and in getting the necessary permits. Projects were delayed or given up completely. In June 2018, thus, the government reacted, the community wind parks lost their privileges and the building permits were mandatory for participating in the auctions. Nevertheless, the incurred permit requests, complicated even more the total permitting process which is also caused by the burgeoning bureaucracy: According to the industry survey done by Quentin (2019), more than 1000 WTGs with a power capacity of 1000 MW are currently put on hold due to the enormous number of appeals. The concerns are mainly reasoned with reference to species protection and civilian and military airspace concerns. Even so, the total ‘permit jam’ has taken on huge dimensions during the last years and is reported to be greater than 10,000 MW (Zfk, 2018).
Furthermore, the present government of NRW under minster Armin Laschet wants to enforce the plans regarding the ‘decree for the planning and approval of wind turbines and guidance for the objectives and application’ (MBI.NRW 2018). This decree includes, that, among other things, the construction of wind turbines in the forest is to be restricted. In addition, a minimum distance to residential areas of 1,500 meters is planned. So far, distances of 600 to 800 meters are common. The new decree also emphasises the fundamental ban on the construction of wind turbines in landscape conservation areas Members of the opposition are criticising this approach and are arguing that the expansion on wind power will be massively hindered and that the changes were not legally binding. It could therefore come to legal proceedings. Nevertheless, the cabinet argues back that 12
the decree will increase the acceptance among the population and hence the regulations are to be passed before the summer break 2019 (Wolf, 2019).
Exactly the same political reality can also be applied when repowering. Especially the bureaucracy hinders a faster process and makes the permitting process lasting as long as the procedures when installing a new wind farm. When repowering a WTG, a new application including the licensing procedure must be in accordance with the German Federal Pollution Control Act (BImSchG). As part of the licensing process, species protection issues are examined to the same extent as is the case with new projects (Bulling, 2015). Additionally, it turned out that apart from the questions of species protection and general acceptance, especially the different interests of the actors involved in the project can be a major problem. Since old operators and old lessees often have a financial interest in a continued operation of the wind farm and disagree with the dismantling of the old plant, there are regularly conflicts that can lead to the failure of a repowering project (Dağaşan, 2015).
Coming back to the issue of the long permitting procedures, a survey by Pietrowicz (2017) may give a more detailed view on the topic. The results are based on the responses of 22 companies which are small, medium as well as large project developers in Germany and are illustrated in Figure 5. The total duration of wind power projects can be divided into four individual steps: preliminary review, planning, permit and execution. The execution process is surprisingly the shortest step with a mean of thirteen months, whereas the permitting process is the second longest one. Hence, the mean total duration is 57 months, which is nearly 5 years. However, it has to be added, that the average values for federal state NRW are slightly lower as compared to the average of the remaining ones.
These bureaucratic hurdles as well as the fact that by the end of 2020, all wind turbines will lose their entitlement to compensation under EEG, makes the wind power market rather unattractive for investors. The position paper ‘Efficient land use through repowering and continued operation of wind turbines’ by BWE (2018), identified different options how to market the electricity after the expiration of the EEG renumeration: - Sale on the power exchange; 13
- Long term power purchase agreements (PPA’s); - Marketing contracts with regional major consumers; - Direct marketing contracts with industrial customers; - Marketing of wind power in other sectors
Another solution could be the introduction of cross-sectoral carbon pricing. Since the emission of climate-damaging carbon dioxide (CO2) is currently only a minor part of the electricity price, clean, CO2-free technologies such as wind energy cannot exploit their greatest competitive advantage and are thus permanently structurally disadvantaged. The abolition of the electricity tax could be also implemented in order to reduce the burden on costumers (BWE, 2018).
Figure 5 Duration of Wind Power Projects in Germany. Adapted from Pietrowicz, 2017 14
3. DATA AND METHODOLOGY
The following chapter describes how the data used for evaluating the repowering potential in NRW is filtered and processed. The different repowering scenarios are presented and connected with the data gained, which subsequently can be used for the applications WindPro and RETScreen with the goal to evaluate the feasibility of the cases.
3.1 Database and Software Used
The database used in this thesis is provided by the North Rhine-Westphalia State Agency for Nature, Environment and Consumer Protection (LANUV NRW) in form of an asset/installation register which was requested and provided by a representative of ‘Department 37: coordination board for climate protection and climate change’. This register consists of a list enumerating all WTGs in NRW in a shapefile-format. The data has twenty parameters, including the location and position of the WTGs, manufacturer, model type, year of installation, capacity in kW and MW, hub height, diameter and total height. Moreover, the data set used is the most recent one, which will be soon published in the ‘Energy Atlas’, a tool representing the results of the analyses for solar energy, wind energy, bio energy, geothermal energy, hydropower and pumped storage. The file contains all turbines reported to LANUV NRW by January 2019 and commissioned by December 2018. The coordinate system used is ETRS 1989 UTM Zone 32N. All plants with an output of smaller than 30 kW are located at the postcode centre for data privacy reasons. All other plants are shown in their exact location. The total number of WTGs is 3660, however data gaps occur which are the result of the low quality of the information transmission between all involved parties (network operators, Federal Network Agency, municipalities and Land Survey Offices).
The shapefile is analysed and assessed with ArcGIS, a geographic information system software and also with RStudio, which is an integrated development environment (IDE) 15
based on the statistical programming language R. Furthermore, the shapefile is converted into an xlsx format in order to concomitantly allowing the usage of Microsoft Excel. WindPRO version 3.2 published by EMD International A/S is used for the technical analysis.
3.2 Methodological Framework
Figure 6 Methodological Framework
The presented methodological framework is depicted in Figure 6 and divided in four major steps: - Data Analysis, - Selection of Relevant WTGs Based on Determined Criteria, - Hypothesis for Repowering Scenario - and Economic/Technical Analysis.
Data Analysis
The data analysis of 3660 WTGs is performed on the data set described in Chapter 3.1 and includes the following results: 16
Distribution of WTGs
Figure 7 Map – Distribution of WTGs in NRW
The general distribution of the given WTGs is provided in Figure 7. It can be seen that in the administrative districts Münster and Detmold 1008 WTGs, respectively 1000 WTGs, are located. Köln hosts 645, Arnsberg 616 and lastly Düsseldorf 391 WTGs. A more detailed assessment is shown in Appendix A, where the distribution of WTGs is shown for the NRW districts.
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Missing Data
Table 3 Missing Data for Given Parameters
Year of Rotor Hub Height Total Manufacturer Model Type Installation Diameter Height 41 486 731 1888 312 455
Table 3 indicates the missing parameters for the given data set. It should be noted, that the missing data is overlapping and is not mandatory unique, what means that one WTG can have several parameters lacking. In order to overcome issues for the impacted WTGs, the online database The Wind Power (2019) is used to contribute to the full assessment of the information. This dataset is based on 1709 sources from developers, operators and owners and furthermore, on 109 different manufacturer and turbine resources.
The key parameters, which are of most interest are ‘Year of Installation’ and ‘Hub Height’. However, the latter parameter can have some data gaps. In those cases where the hub height data was incomplete, a method of shadow calculation is applied on the maps given by Google (n.d.) to approximate the unknown hub height:
Figure 8 Hub Height Approximation 18
The degree of accuracy is adversely affected by the angle of which the satellite pictures have been taken. Moreover, the angle α (cf. Figure 8) which indicates the position of the sun, can only be guessed due to the fact that the images have no accurate time and day lodged. Thus, the hub height (h) can be approximated by h=shadow length/cot(α). If the results are not in-line with the general hub heights given by the WTG manufacturer, the shadow length is compared with other lengths of known hub heights of WTGs in the close proximity. Comparing the relations between the shadow lengths, the hub height can be estimated more properly and an estimated height was determined for the WTGs lacking this data.
Data Evaluation
The distribution of the installed WTGs in NRW is outlined in Table 4 as well as the number of WTGs installed per year in Figure 9. A significant number of WTGs have a rated power between 1000 kW and 2000 kW. On the other hand, WTGs with a power of more than 3000 kW represent a small share of the total. From 1989 until 2018 the quantity of WTGs installed does not follow a linear trend but oscillates. In 2002, 328 WTGs were installed, whereas only 17 WTGs were installed in 2008.
Table 4 Power Distribution of Installed WTGs
Rated Power [kW] No. of WTGs <500 468 500-1000 978 1000-2000 1131 2000-3000 781 3000-4000 270 >4000 32 Total 3660
19
328
317
300
259
215
186
162
160
142
132
120
113
109
107
103
95
93
88
86
81
80
NO. OF WTGS OF NO.
73
71
55
49
45
20
17
10
3
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 YEAR OF INSTALLATION
Figure 9 No. of WTGs Installed Yearly
A variation of the existing amount of wind turbine manufacturers can be identified. More than 50% of the WTGs erected in NRW were produced by Enercon GmbH or Vestas Wind Systems A/S as depicted in Figure 10. General Electric, Nordex and Senvion SE constitute further 25%. However, many WTGs were built by manufacturer which are no longer active and were taken over by ‘big player’. E.g. Seewind or Südwind, which are now part of Nordex or on the other hand Micon, which was taken over by NEG Micon and ultimately by Vestas Wind Systems A/S. In addition to this, some manufacturers do not exist anymore or are insolvent -as in case of Fuhrländer.
115 59
1739 450 352 342 312 244 NO. OF WTGS OF NO. 47 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Enercon GmbH Vestas Wind Systems A/S General Electric Nordex No Data Senvion SE Others Siemens Fuhrländer
Figure 10 Distribution of WTG Manufacturer 20
Selection of Relevant WTGs
Figure 11 Timeline for Selection of WTGs
Figure 11 depicts the timeline created which is used to find the relevant WTGs that are determined using the following criteria: - The service life time of WTGs (20 years); - the starting date of the ‘Repowering Project’ (1st January, 2020); - the time needed for all essential steps until the commissioning of the repowered WTGs (4-5 years) (cf. Figure 5).
Applying the determination criteria on the 3660 WTGs, only 306 remain for further investigation. They are mapped and shown in Figure 12. It can be seen that the distribution of the WTGs which are eligible for repowering is not equally dispersed throughout NRW. However, a general lack of WTGs can be constituted for NRW’s south-south-eastern portions, where few turbines have been constructed as well as for the of the federal state’s most northern area.
The next step is the application of a distribution filter, where the 306 WTGs are checked to see whether they can be grouped into wind farms. The minimum requirement is however that at least 4 WTGs have to form a wind farm in order to have a reasonable 21
repowering capacity. According to Koshkin et al. (2019) a distance of 5-8 times the rotor diameter between wind turbines is accepted in the industry. Therefore, the minimum distance between the new, repowered wind farm and the nearest WTG adjacent to the park should be between 500m and 800m for a WTG model with a rotor diameter of 100m. Thus, the mean value of 650m is the minimum required distance, which has been chosen as repowering criteria. WTGs with a small power capacity are not taken into account due to inconsistency of the data and especially the problematic of localisation of the respective WTGs.
Figure 12 Distribution of Relevant WTGs Commissioned 2004 and 2005 22
The selection of the new manufacturer/model of the WTGs to be installed is based on 2 major criteria. Firstly, the installation date of nearby turbines is reviewed to find ones which have been constructed since 2015. This approach helps to ensure that a selected model's characteristics such as the rated power, hub height, etc. are appropriate for the given location's wind speeds and specification. Another determining factor is that this approach ensures that the most advanced technology possible is installed but since WTGs commissioned recently (in 2018 or 2019) are rare, their distribution is low and the data is missing, an installation date up to 2015 has been used. A more detailed selection description regarding the repowering criteria can be found in the next chapter under ‘repowering conditions’.
Hypothesis for Repowering Scenario
Considering the technological development of WTGs, the replacement of old with new ones can bring benefits such as the reduction of the total installed number. That implies also an improvement of aesthetic and landscape impacts (cf. Figure 2, Chapter 2.3 Repowering). Additionally, the repowering can clearly help to achieve national and regional objectives including the implementation of the national energy production by wind energy.
However, the number of installed WTGs in one place has to be within the legal frame and it has to be carefully weighed how high the impact on social, environmental and political concerns can potentially be. Therefore, a repowering scenario of a maximum of 2 times the current installed capacity is seen as feasible, while a higher rate could tip the balance on the previously stated factors. The factor of 1.5 times the installed capacity serves a balance and can be regarded as solution when a double installed capacity is not feasible but replacement of WTGs with the same power capacity is seen as suboptimal.
Based on the above-mentioned criteria, three different scenarios have been developed: - The total capacity of the repowered wind farm is the same as the end-of-life plants; 23
- The total capacity of the repowered wind farm is 1.5 times the size of the end-of- life plants; - The total capacity of the repowered wind farm is 2 times the size of the end-of-life plants.
Technical/ Economic Analysis
The following chapter describes how the previously presented scenarios are the analysed. For this analysis, the focus is put on two points of interest, which are the technical and economic analysis.
3.2.4.1 Technical Analysis
The technical analysis is done with assistance of the software WindPro and its “Energy Modules” in order to calculate the annual energy production (AEP) of the repowered wind farms and additionally gaining important results as follows: - Result PARK [MWh/y] - Result – 10% [MWh/y] - GROSS [MWh/y] - Wake loss [%] - Capacity factor [%] - Mean WTG result [MWh/y] - Full load hours [Hours/year] - Mean wind speed @hub height [m/s]
Furthermore, the mathematical operation for the AEP is done via the standard Park model using the “Wind Atlas Analysis and Application Program” (WAsP) and the “Wake Model N.O. Jensen (RISØ/EMD) Park 2 2018.” WAsP is regarded as an industry-standard program, which calculates the AEP based on wind distributions and wake losses at each turbine. The latter is one of two available wake models, but is considered more developed 24
and accurate. The AEP calculation done is also based on the resources presented in Figure 13.
Figure 13 AEP Calculation – Resources
The meteorological data used is called “EMD-ConWx Meso Data Europe” which is a dataset of a meso-scale model with a resolution of 0.03°x0.03°, approximately 3x3 km. However, an absolute variation of wind speeds of 10% - 15% can be expected compared to the “real” (recorded by a local mast) wind speeds (EMDWiki, 2013).
Additionally, it must be noted, that the nearest measurement point with respect to the wind park/wind farm has to be selected. The decision in favour of the selected meteorological data rests on the premise that it is the most accurate and precise one, because the distance between the eligible WTGs and the meteorological masts are, compared to the other available data sets, very small. That means, that compared to the other data, the density of the measurement points of the data ‘EMD-ConWx Meso Data 25
Europe’ in NRW, can be ranked highest, which consequently covers a larger area with real-time measurements and thus favours its selection.
The roughness lines and roughness areas are both associated with the “Corine Land cover 2012 - 100m grid” data set, which is operationally available for most areas in Europe. WindPro offers four selection options, of which two have the highest resolution (100m grid), but one (Corine Land cover 2012 - 100m grid) was more recently updated.
The elevation data “Germany Nordrhein-Westfalen Elevation Model - 5m grid” is considered to be very detailed and gives additional value to the measurements and calculations. This is the best possible option regarding the resolution and quality for the elevation data.
Repowering Conditions
After mapping and reproducing the estimated current state of the WTGs, the main repowering boundaries and criteria for the three presented scenarios have to be declared. For this reason, it is necessary to consider the following parameters: 1. WTG’s area (current and new) 2. Optimisation method 3. WTG number, type and hub height
(1) The area in which the repowering scenarios take should correspond to the existing project area. Where possible and applicable, however, the new WTG area is allowed to be increased on each opposing side by 200m (2 x 200m = 400m). That implies that surrounding conditions have to be checked whether the installation of WTGs is possible and regulations are not violated. Existing streets, buildings, nature reserves, etc. have to be included into the analysis and the needed distance between the objects and WTGs have to be respected, when increasing the present WTG area. If the conditions do not allow an increasing of the WTG’s area, the existing area is maintained and hence not enlarged. The 200m distance has been chosen on the basis, that for the repowering scenarios the WTG’s hub heights will most probably increase and therefore bigger spacing between WTGs is 26
more (for cases where the number of WTGs is equal or bigger than the present one) or less necessary (for cases where a reduction of quantity of needed turbines is required) in order to reduce wake losses and increase productivity. Moreover, 200m seems a reasonable length which is not unnecessarily large, but allows for extra flexibility in the positioning of the new WTGs. An example demonstrating this is shown in Figure 14 where on the left-hand side the current WTG area is represented by a rectangular form and the new one, on the right-hand side, is enlarged. The shape can vary and does not have to be a square.
Figure 14 Enlargement of WTG Area
(2) The optimisation methods which are used for the best positioning of the planned WTGs are “Fast energy layout” and “Full energy optimization”. These methods can be found in WindPro under the ‘Optimization’ module. The module automatically creates a wind farm layout which fits best in the predetermined WTG area to achieve the best energy output. This is why wake losses are kept as small as possible and consequently the production and the capacity factor are kept as high as the parametric input allows. In a pre- study, in which the optimisation methods were compared (1 WTG, 5WTGs, 10 WTGs and 20 WTGs) it has been constituted that if the number of WTGs for the wind farm is ≤10, the “Fast energy layout” can be accepted as equivalate substitute, since the discrepancy regarding the result is relatively small and can be neglected. In addition, the required calculation/computing time is shorter. 27
(3) For the selection of the new WTGs, which have to be installed according to the “hypothesis for repowering scenarios” presented in 3.2.3, the following list of criteria has to be met and is to be selected from the filtered LANUV database of 306 WTGs: - The new WTG model has to be in installed somewhere in a radius of 5000m from the nearest positioned WTG of the wind farm which is repowered. - The new WTG model was erected during or after 2015. - The power capacity of the new WTG model has to be higher than to the current one.
If that the criteria set results in more than one potential WTG model, the following priority applies: 1. Power capacity (highest) 2. Year of construction (newest) 3. Hub height (highest)
Furthermore, it holds that - the power capacity difference has to be significant (>500kW). If this is not the case, priority 2 applies; - the data for the WTG model has to be available in WindPro’s database; - if all 3 priorities cannot be applied, the WTG model used with the greatest frequency throughout the whole project is selected.
The flowchart in Figure 15 serves an as illustration of the operations described. 28
Figure 15 Flowchart for Determination of New WTG Model 29
The number of new WTGs is dependent on the power capacity of the selected model as well as on total power capacity of the existing wind park. For scenario 1 the following formula should be applied: ∑ 푃표푤푒푟 퐶푎푝푎푐푖푡푦 푁푢푚푏푒푟 표푓 푊푇퐺푠 = 푖푛푠푡푎푙푙푒푑 푃표푤푒푟 퐶푎푝푎푐푖푡푦푛푒푤푊푇퐺푀표푑푒푙
For scenario 2 and 3 the total sum of the installed capacity has to be adapted accordingly (increased factors of 1.5 and 2). The results are rounded towards the nearest integer whereas an individual evaluation is done to determine whether to round up or down for numbers with .5 endings. Consequently, the number of WTGs with a .5 ending, which are summed up in one wind park, are rounded up if - all other scenarios are rounded up or have integers; - one scenario is rounded up and the other is rounded down.
Accordingly, a scenario has to be rounded down if the contrary is present.
3.2.4.2 Economic Analysis
The economic analysis operations are done with help of the financial tool RETScreen Clean Energy Management Software (short RETScreen), which was developed by NRC (Natural Resource Canada). RETScreen determines a techno-economic feasibility of renewable energy projects and is able conduct energy performance analysis. Furthermore, it includes databases to support project evaluation with WTG models and their power curves. With respect to this study, the parameters payback time (PBT) as well as the internal rate of return (IRR) are considered to be of critical importance, for determining the feasibility of the examined wind parks. For this analysis, the following input is necessary:
WTG parameters
The WTG parameters have to be taken from the resulting ‘WTG model determination’ process mentioned above, while the capacity factor results from the steps 30
mentioned in Chapter 3.2.4.1, where the AEP calculation is done based on WindPro’s energy module.
Electricity export rate (€/MWh)
The electricity export rate is the price for which the generated electricity can be sold to the market and is primary given in €/MWh. Because the current and past prices for electricity are very volatile and due to its impact on the calculation, small price variances greatly affect the economic analysis.
Therefore, the forecast for the electricity price is done using different approaches, but are all based on historical data from a) the spot market price and from b) tendering processes. a) The first long-term prediction is built on the yearly real (cf. nominal) electricity price traded on the European Power Exchange (EPEX) between 2002 and 2019. The market price represents also the mean of the so called ‘day ahead auction’, where trading takes place one day before the electricity is delivered. In the German market, the bids for the auctions for the following day must be submitted by 12 noon. The results of the corresponding surcharges will be published at 12:40 every day and from 15:00 it is already possible to operate the so called ‘intraday trading’ for the following day. The prediction is calculated in Microsoft Excel using its Forecast Worksheet on the yearly average market price of electricity and is shown in Figure 16. For the relevant project period of 20 years (from 2025 – 2045) the average value calculated is approximately 26.3€/MWh, where each year a constant reduction of around 0.67€/MWh can be constituted (Appendix B). It should be noted that this forecast value is valid for the electricity price from all sources and not only from wind power. 31
Electricity Price Forecast 2020-2045 100.00
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Figure 16 Electricity Price Forecast (Yearly) Based on 'EPEX Spot' Price
The second prediction is made using the same master data but only using recent one-year data (May 2018 – May 2019) prediction to create the forecast. However, for this method only the nominal price is given (Figure 17). Excel’s forecast tool shows constant growth over the next forecasted 12 months and a monthly growth of approximately 1.3€/MWh can be derived from the Figure.
Electricity Price Forecast (monthly) 2020 60 50 40 30
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Figure 17 Electricity Price Forecast (monthly) Based on 'EPEX Spot' Price 32
b) The tendering procedure for determining the financial support for WTGs (onshore) according to the EEG is done in a 3-month interval and amounts to an average of approximately 54.4€/MWh, which also applies to the period from May 1, 2017 until May 1, 2019 and is taken as the base price for the further calculations. The forecast is presented in Figure 18 for a period of 9 months and is presumed to continue linearly until 2045. An increment of approximately 2.2€/MWh each tender cycle can be seen for the predicted period (Appendix B).
Electricity Price Forecast 2020 80 60 40
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Figure 18 Electricity Price Forecast (monthly) Based on 'EEG - tendering procedure' price
Summary of results
The presented methods in a) should be used to evaluate the dynamics of the market. On the one hand and in the long run, the electricity prices are falling and on the other hand and based on the short run, the electricity prices are rising each month by nearly twice the value of the long-term forecast predicted per year. The results found in b) indicate the possible base price but also give a tendency that the price increase should be considered in the short-term prediction. As it stands, the forecast in b) also is given more credibility, since the tendering process for the electricity generation is only based the production of WTGs. Therefore, even if it is a short-term forecast, the tendering scheme shows growth and for the moment there is no contrary reaction predictable. It should be noted that the 33
forecast relies entirely on continued government intervention which is largely unpredictable. In a) people will continue to purchase electricity but the price is dependent on many factors which, however, will not be covered in the context of this work.
Built on this argument and on the fact that there is no longer historical data for the German tendering scheme, the base price average, 54.4€/MWh, is shifted to the starting year of the projects in 2025 and a yearly uptrend of a certain percentage could be included. The growth in b), which is 8.8€/MWh p.a. (2.2€/MWh x 4) seems very high whereas the growth rate in a) is even higher with the value of 15.6€/MWh p.a. (1.3 x 12) and can be considered as a reductio ad absurdum. The negative factor of the long-term prediction, however, should also be treated with caution.
On the other hand, experts like Linkenheil (2017), reason that the power price will continue to rise at least until 2040. One of the reasons these experts state for this phenomenon is the “higher demand caused by the ongoing electrification of the heat and transport sectors” and “the increasing primary energy and CO2-certificate prices.”
As a result, no accurate and final conclusion can be drawn from the presented forecast. A variance analysis would be indicated in order to cover the different possible outcomes and to demonstrate the high financial impact electricity price has on the project. This analysis however is beyond the scope of this report and will be not executed. Varying parameters (-10%, -5%, +5%, +10%) could also be added as indicator of the electricity price. On the basis of the presented findings, the electricity base price of 54.4€/MWh is predicted to remain the average during the entire 20-year lifetime and is taken as electricity price for the total economic analysis. Nonetheless, and weighing the growth rate to a greater extent as compared to the long-term prediction, a yearly growth rate of 2.5% could be a possibility in view of the predictions made. In application, this means that the electricity price would grow by 1.36€/MWh in the first year of the project and by ~2.17€/MWh in the last year. The average electricity price would be 70.41€/MWh for the total period of 20 years. This will be discussed in Chapter 5, where a sensitivity analysis is done to evaluate the scope of an increasing electricity price. 34
Inflation rate, debt ratio, debt interest rate and debt term
The inflation rate prediction for Germany varies depending on source between 2% for 2021 (Bundesbank, 2018), 1.3% for the year 2020 (European Commission, 2019) and 1.9% for the same year according to Trading Economics (2019). However, a look into historical statistics, shows a sinusoidal movement ranging from 0.3% – 2.3% in the last two decades producing a descending or increasing trend of a maximum period of time of 3 years. As a result, the mean value of the specified values as well as the highest and lowest rate of the sinusoid can be attributed to the inflation rate for the project. Due to the big time period the two values cover, are they treated with some caution and thus they (2+1.3+1.9+0.3+2.3)% make only 2/5 or 40% of the input: = 1.56% 5
According Berkhout et al. (2019) and Kost et al. (2018) the debt ratio can be assumed to be around 80%, whereas the debt interest rate is considered to be 4%. On the other hand, the analyse reported by WindEurope (2019b) shows figures where the value of the debt ratio is attributed with up to 90% and a static trend for the next year is foreseen. Due to this trend, 90% are taken into account for the planned period of time. It should be noted that this share of 90% to 10% is also based on the fact that the state is the major money lender. Moreover, a debt term of 15 years is generally valid for wind park investments.
Capital expenditure (CAPEX) and operational expenditure (OPEX)
The investment costs for new WTGs ranges from 1500€/kW to 2000€/kW according to Berkhout et al. (2019). A more detailed analysis is done by Kost et al. (2018), where the investment costs are divided into main investments and additional investments. The main investment includes the nacelle, tower, rotor blades, transport and installation, whereas the additional items consist of the foundation, grid connection, transport, planning, etc. This can generally be applied for WTGs with a rated power of 3MW to 4MW and a hub height between 120m and 140m. The median main investment cost is approximately 1180€/kW, whereas the additional investment is estimated to be around 387€/kW, which sums up into a total of 1567€/kW. A capital cost breakdown of cost drivers can be seen in Figure 19, where specific percentages are given to different WTG 35
parts and installation procedures which are for their part necessary when projecting wind parks. The figure shows that the WTG accounts for nearly two thirds of the capital costs but it should be noted that the percentage shares have shifted since 2012 as compared to findings of Kost et al. (2018) as shown below (e.g. grid connection costs shifted from 11% to 5% or foundation costs from 16% to 5%).
Figure 19 Capital Cost Breakdown for a Typical Onshore Wind Power System and Turbine (Irena, I. R. E. A., 2012)
The German market places 75% of the project costs in the 'main' investment category. The foundation makes up 5%, the grid connection amounts to 5% while the remaining 15% include planning and miscellaneous costs (Kost et al. 2018). The uncertainty of cost reductions remains high. On the one hand, one can expect a continuous reduction of the costs over the next years, but on the other hand the market is not readily predictable.
Furthermore, the decommissioning costs of the old WTGs have to be taken into account as well as the income from the decommissioned WTGs. The report assumes that the decommissioned WTGs are sold on the second hand market. The following Table 5 reveals current sample data prices of used wind turbines (1000kW-2000kW and sold in Germany), which can be found at wind-turbine.com: 36
Table 5 Price List for Second Hand WTGs (Germany, 1000-200kW)
Power (kW) € €/kW 2000 170,000 85.0 1500 160,000 106.7 1500 70,000 46.7 2000 320,000 160.0 2000 325,000 162.5 1500 85,000 56.7 1500 65,000 43.3 1800 190,000 105.6
The average price for a used wind turbine is 95.8€/kW, which is based on 8 WTGs. In addition to this, it can be deduced that WTGs with a higher rated power can be generally sold for more per kW than lower (1500kW) rated ones. Since the WTGs located in NRW are expected to be on the lower boundary (nearer 1000kW), the selling price is set to 63.2€/kW which is two thirds the average value. Moreover, the following prognosis and assumptions are made: - Since the cost reduction cannot be predicated but shows, according to MacDonald (2011), a positive trend, the decommissioning costs are set as equal to the cost reduction of the investment cost.
According to this scheme, the CAPEX is calculated as follows: CAPEX = WTG*1567€/kW - A1 + A2 - C1, where WTG=new installed power capacity in kW, C1=63.2€/kW*total rated power of decommissioned wind park, A1=A2, A1=cost reduction and A2=decommissioning cost
Operational expenditures range on average between 52€/kW (Fraunhofer, I. E. E., 2018) up to 56€/kW per year (Kost et al. 2018). Here again a slight cost reduction is plausible, as market dynamics cause pricing pressure. Thus, the following rule can be applied: The more WTGs are installed, the higher the need of O&M services; the higher this need, the larger the choice of services providers; the higher the price competition between the providers; the lower the price. Therefore, the lower yearly cost of 52€/kW for operating and maintaining the WTGs is used. 37
Interim result
The financial figures, which are needed for the PBT and IRR calculations are hence included in Table 6.
Table 6 Financial Parameters (RETScreen)
Financial parameters Inflation rate 1.56% Project life 20y Debt ratio 90% Debt interest rate 4% Debt term 15y Electricity sale price' 54.4€/MWh Initial costs Incremental initial cost variable
Annual costs and debt payments O&M (savings) costs variable
The two variable cost parameters depend on figures from chapter 3.2.4.1, where the ‘Incremental initial cost’ is equal to the CAPEX and on the operations and maintenance (O&M) costs, which vary based on the power capacity installed. 38
4. RESULTS
This chapter presents the results obtained by the implementation of the processes and methodology described in chapter 3. On the basis of the previous approach of splitting technical and economic analysis in different parts and due to continuity, the same format is maintained here.
4.1 Results of the technical analysis
Following the technical analysis, 10 different wind farms that fit the given repowering criteria could be identified (cf. Table 7). The wind parks are hence named by their location and a reference name is given in order to facilitate cross-references.
Table 7 Wind Farm Identifiers
Reference No. of Wind park name Name WTGs Warendorf_Beckum #WB 5 Detmold_Höxter #DH 9 Heinsberg Gangelt #HG 9 Heinsberg Waldfeucht #HW 10 Hochsauerland_Meschede #HM 4 Köln_Simmerath #KS 7 Korschenbroich #KB 5 Kleve_Kerken #KK 4 Viersen_Willich #VW 4 Minden_Lübbecke #ML 5
For each wind park, a list with its unique attributes is created, which for their part can be located by the feature FID_, OJECTID_1 or LANUV_ID. The list contains, apart from the shapefile parameter’s, fields where the nearest WTG to the wind park is fixed by the FID (a system-managed value that uniquely identifies a record or feature (ArcGIS)). Furthermore, nearby WTG models which are eligible for the repowering model selection 39
process are listed, where the highlighted or identified one is also the model used for the repowering scenario 1 – 3. The number of WTGs to be installed is also notated (Appendix C). However, for some wind parks certain practical information and approaches should be explained:
The wind park #WB is made up of the WTGs with the FID_ 2777, 476, 477, 1258, 1259 and the model used for repowering process corresponds to the Enercon E-115 3MW. Here, other WTGs, which are not up for the repowering and hence do not fall under the repowering criteria are very near; to be more specific, the distance between FID_1259 and FID_ 3535 is only 610m.
Wind farm #HG is repowered with the WTG model Vestas V112- 3,075, which can be found under the FID_ 2925. The hub height of that model is noted to be 140m but a turbine having the same hub height cannot be found in WindPro. Thus, a hub height of 119m is used. The same applies for wind farm #HW.
#HM’s repowering model is Vestas V126-3.45 HTq-3,450, which is selected for use instead of LTq (low torque) model. This decision is based on the low wind conditions where a HTq (high torque) model normally operates at low-speed.
The WTGs of wind park #KB can be found under the FID_ 2209, 2210, 2211, 2212, 2213 in the LANUV data set. However, the given data seems not to correspond with the information according to The Wind Power (2019). The confusion of the model names Nordex N-77-1.5 and Südwind S-77-1.5 however could be retraced to the moment of the takeover of Südwind Energy AG by Nordex SE. Therefore, the calculations for the base case or status quo are made not using the model Südwind S-77-1.5 but on 2 times the model Nordex S-77-1.5 with the hub height of 80m and 3 times the same model with the hub height of 85m. It should be noted that there is no difference between these two models, but the name.
A similar approach has been taken for the park #VW, although this time both sets of information (LANUV and The Wind Power (2019)) are not consistent. The resulting assumption of the information given is that four Vestas V80-2,000 WTGs are installed 40
and their hub height, according to WindPro’s database, is 78m. Moreover, the chosen model of repowering (FID_ 3113) is 21km far away.
The location of wind park #ML is very near the border of NRW and the shows that the WTGs neighbouring the park are old and of low capacity. Here, the missing LANUV data and the method of measuring the length of the cast shadows have shown that the WTG type installed is very likely the model Enercon E66/15.66-1.5. The repowering model for this park can be found under FID_ 177, which is a Senvion MM92-2,05.
Table 8 WindPro Results
Calculated Calculated Reference production production per Wake Capacity Mean wind Name [MWh/y] WTG [MWh/y] loss [%] factor [%] speed @hh [m/s] #WB 10,593.3 2,118.7 3.8 29.5 6.6 #DH 16,584.8 1,842.8 10.8 24.7 6.4 #HG 43,685.9 4,854.0 5.7 27.7 6.8 #HW 34,900.2 3,490.0 8.1 22.1 6.6 #HM 23,583.5 5,895.9 5.1 35.1 7.1 #KS 19,286.1 2,755.2 19.8 15.7 6.3 #KB 15,912.2 3,182.4 7.2 24.2 6.1 #KK 8,016.9 2,004.2 1.7 22.9 5.9 #VW 13,668.4 3,417.1 6.8 20.9 5.9 #ML 13,044.1 2,608.8 7.5 19.8 6.2
The results of the relevant parameters (production, wake loss, capacity factor and mean wind speed at hub height) of the existing projects are calculated with the software WindPro and summarised in Table 8. The mean wind speed at hub height is mediocre and ranges between 5.9m/s up to 7.1m/s. The highest capacity factor can be found in #HM with 35.1% and the lowest can be found in #ML with 19.8%. The production as well as the wake loss (1.7% – 19.8%) does not show any strict, linear or homogenous dependency on the number of WTGs, which could have been expected. The production per WTG ranges between 1842.8 MWh/y up to 5895.9 MWh/y as well, due to different technologies used, whereas the total sum of all 62 WTG’s production is 199,275.4 MWh/y with a mean 41
capacity factor of 24.26% and mean wake loss of 7.65% at a mean wind speed at hub height of 6.39m/s.
The selected models as well as the heights and the mean wind speed at hub height for repowering scenarios 1 – 3 can be taken from Table 9 and do not change throughout the scenarios.
Table 9 Selected Manufacturer and Model
Reference Wind Turbine Height Mean wind Name Manufacturer/Model [m] speed @hh [m/s] #WB Enercon E-115-3,000 135 7.8 #DH Enercon E-101-3,050 135.4 7.8 #HG Vestas V112-3,075 119 7.2 #HW Vestas V112-3,075 119 7.2 #HM Vestas V126-3.45 HTq-3,450 149 8.2 #KS Vestas V112-3.3 Gridstreamer -3,300 140 7.7 #KB Enercon E-82 E2-2,300 138.4 7.1 #KK Enercon E-115 TES-3,000 135.4 7.1 #VW General Electric GE 2.5-120-2,500 120 6.7 #ML Senvion MM 92-2,050 78.5 6.4
The average hub height after repowering is 126.97m with an average wind speed of 7.32m/s. The wind park #HM, which is equipped with the WTG model Vestas V126-3.45 HTq-3,450, has the highest hub height as well as the highest wind speed. The wind farm #ML has the lowest hub height and has the lowest wind speed.
The results of the electricity production per year in MWh for all three scenarios can be found in Table 10. According with the hypothesis for the repowering scenarios, where the installed capacity is 1.5x and respectively 2x greater than in scenario 1, the production nearly reflects in total the estimated production compared with the base case. Hence, where the same capacity is installed (scenario 1 and base case), the production is increased by 118182.4MWh/y which corresponds with a factor of approximately 1.593. 42
Table 10 Results – Electricity Production
Reference Scenario 1 Scenario 2 Scenario 3 Name #WB 12,608.8 24,793.6 36,065 #DH 31,018.3 40,631.4 48,531 #HG 59,556 40,631.4 115,581.3 #HW 60,341 88,276 114,561.8 #HM 29,834.8 55,755.8 66,997.5 #KS 44,291.6 63,185.5 80,887.6 #KB 19,495.2 31,925 43,589 #KK 10,920.2 21,534.1 31,266.1 #VW 27,149.4 43,730.6 52,350.3 #ML 22,242.5 31,284.5 36,002.3 SUM 317,457.8 441,747.9 625,831.9
The production of Scenario 2 is 1.391 times bigger than in scenario 1, while scenario 3 has nearly the double annual production compared to scenario 1 at 1.971 times the production.
The wake losses are depicted in Figure 20. As expected, they increase with the number of installed WTGs (losses increasing from scenario 1 to scenario 3). Wind farms with only one WTG do not have any losses due to the undisturbed wind reaching the turbine. This also the reason why wind parks #WB and #KK do not show any losses for scenario 1. The average wake loss values are 3.77% for scenario 1, 6.56% for scenario 2 and 8.92% for scenario 3. The wake losses for the base case falls between scenarios 2 and 3 at 7.65%. 43
Wake losses 25
20
15 % 10
5
0 #WB #DH #HG #HW #HM #KS #KB #KK #VW #ML
Scenario 1 Scenario 2 Scenario 3 Base Case
Figure 20 Results – Wake Losses The calculated capacity factors present a contrary behavior, as compared with the wake losses, but linked to them. This means the higher the number of WTGs installed the higher the wake losses and the lower the capacity factor. These expectations are fulfilled as Figure 21 shows. The average capacity factor falls from 35.49% in scenario 1 to 34.51% in scenario 2 and even further to 33.44% in scenario 3. The base case is represented by a low capacity factor of 24.26%, which is 9.17 percent less than the worst repowering scenario.
Capacity Factor 50 45 40 35 30
% 25 20 15 10 5 0 #WB #DH #HG #HW #HM #KS #KB #KK #VW #ML
Scenario 1 Scenario 2 Scenario 3 Base Case
Figure 21 Results – Capacity Factor 44
Another important result of the analysis was the outcome of the number of WTGs to be installed for the specific scenarios as Figure 22 shows. It displays that for every wind farm, the number of installed WTGs in scenario 1 is lower than the base case and only for #VW and #ML is the number higher in scenario 2. The respective sum of turbines for each scenario is 33, 50 and 68, whereas the base case has only 6 less than the scenario with double the installed capacity.
A possible reduction of WTGs may result in the improvement of aesthetic and landscape impacts, which can be considered as very important factor the local communities (cf. 2.2.4 Policies and Bureaucratic Hurdles). If considering only this scenario 1 performs the best, whereas scenario 2 has only for two cases where a higher number of installed WTGs were built as compared to the base case. What is remarkable is, that scenario 3, even with doubled capacity, performs in three cases (#WB, #DH, #KK) better than the base case.
No. of WTGs 12 12 11 11 10 10 9 9 8 8 7 7 6 6 5 5 4 4 3 3 2 2 1 1 0 0 #WB #DH #HG #HW #HM #KS #KB #KK #VW #ML
Scenario 1 Scenario 2 Scenario 3 Base Case
Figure 22 Results – Number of WTGs
4.2 Results of the economic analysis
The results for the economic analysis can be divided into two major categories, where each has two sub-categories. As chapter 3 presented, the PBT (in years) and IRR are 45
decisive factors in determining the feasibility of the projects; however, the simple payback and equity payback are considered along with the IRR on assets and IRR on equity. The results can be seen in Table 11 and Table 12.
The simple payback, which is the PBT, ranges between 9.4y and 21.6y, with an average of 13.37y for scenario 1, 14.3y for scenario 2 and 14.93y for scenario 3. The two wind farms, #HM and #ML, present the lowest and highest value of PBT as well as equity payback (‘>project’ means that the payback time is more than the project time, what is 20 years). Moreover, the total average of the equity payback is 12.92 years, which is created from the three averages of 12.07y for the first, 12.74y for the second and 13.97y for the last scenario. The results attributed with ‘>project’ are not taken into consideration when calculating the average. An equity payback time of longer than the project duration also results for wind farm #ML throughout the scenarios and for wind farm #KB in scenario 2 and scenario 3. The feasibility of these two are highly questionable.
The IRR on equity is positive nearly throughout the scenarios and ranges between - 4.7% and 33.5%. The situation is different for the results of the IRR on assets. Here, 26 out of 30 values are negative. The lowest average of the IRR can be attributed to scenario 3, whereas the highest average is ascribed to scenario 1.
Table 11 Results – PBT
Reference Simple Payback Equity Payback Name Scenario 1 Scenario 2 Scenario 3 Scenario 1 Scenario 2 Scenario 3 #WB 9.6 10.2 10.7 3.7 4.6 5.7 #DH 13.3 13.8 14.8 15.4 15.8 16.9 #HG 14.2 14.7 15.1 16.3 16.8 17.2 #HW 13.9 14.7 15.4 16 16.8 17.4 #HM 9.4 10.5 11.1 3.4 5.2 6.9 #KS 13.4 14.6 15.6 15.5 16.7 17.7 #KB 17.4 18.2 19.1 19.4 >project >project #KK 11.7 12.4 13 9 11.8 15.1 #VW 12 12.8 12.9 9.9 14.2 14.9 #ML 18.8 21.1 21.6 >project >project >project
46
Table 12 Results – IRR
Reference Pre-tax IRR - assets Pre-tax IRR - equity Name Scenario 1 Scenario 2 Scenario 3 Scenario 1 Scenario 2 Scenario 3 #WB 1.60% 1% -0.03% 30.90% 26.40% 22.60% #DH -3.20% -3.7% -4.70% 10.40% 8.90% 6.10% #HG -4.20% -4.6% -5.10% 7.60% 6.40% 5.30% #HW -3.90% -4.6% -5.30% 8.40% 6.40% 4.80% #HM 2.00% 0.2% -0.64% 33.50% 23.90% 19.80% #KS -3.30% -4.5% -5.50% 10.00% 6.60% 4.20% #KB -7.00% -7.6% -8.20% 0.95% -0.34% -1.60% #KK -1.40% -2.2% -2.90% 16.60% 13.80% 11.30% #VW -1.70% -2.7% -2.80% 15.50% 12.00% 11.60% #ML -8.00% -9.6% -9.90% -1.10% -4.10% -4.70%
A more detailed insight about the results of the economic calculation and reported by RETScreen is exemplary shown on the model wind park #KS, which is in line with the average performance (Figure 23 and Figure 24) of all ten wind farms. While the first depicts the financial parameters, the costs and the viability, the latter presents the yearly cash flow throughout the project life. 47
Figure 23 Detailed Analysis of Financial Viability for #KS Scenario 1 (1) 48
Figure 24 Detailed Analysis of Financial Viability for #KS Scenario 1 (2)
49
5. DISCUSSION AND ANALYSIS
The LANUV data on which the report is based and which is partially fragmentary and not consistent, is not likely to be 100% correct even with the use of additional data and measurements. However, this possible inaccuracy is not necessarily significant for the repowering project, since this would be a systemic error running through the entire project, but on the other hand can involve a potentially higher risk that the repowering results are better than they actually should be.
Furthermore, the selection of the wind parks is questionable and the number of WTGs for one wind park could have been larger than the minimum used of 4 in order to be more consistent and eliminate high impacting rounding errors. E.g. the number of WTGs for a small wind park was calculated with exactly 1.5 and is rounded accordingly the presented scheme would affect more the total inaccuracy as compared to a wind farm with 10 WTGs, which is going to the same rounding procedure. This is the case e.g. for wind park #WB and #KK where scenario 1 recommends the construction of only 1 WTG.
The selection of the repowering model is justified by the assumption that prior studies and investigations have selected the most suitable model for that area and the local conditions. This is also the reason why it was beneficial to not use the same model for every repowering scenario, but it did in turn produce some drawbacks with regards to uniformity. This raises the question of whether a global view of the repowering problem would have been more accurate and the scenarios should have been applied to all 306 WTGs which were determined eligible for repowering.
The results of the technical approach with WindPro for the repowering scenarios can be viewed as a success. The electricity production as well as the capacity factor, which are decisive parameters, have been increased by approximately 62% and 11.23% respectively for the same installed capacity as found in the base projects.
Based on the technical aspects, it is hard to make a decision between the 3 presented scenarios. A parameter which can give direction regarding the quality of the scenarios can 50
be the electricity production, which on its own already includes the wake losses and capacity factor parameters. The sum of each production is presented so that the multiples of the base case are reported:
Table 13 Comparison of Electricity Production Based on Dimensionless Identifier (1)
Scenario 1 Scenario 2 Scenario 3 Base Case
Sum (MWh/y) 317,457.8 441,747.9 625,831.9 199,275.4 Multiple of Base Case 1.59306066 2.21677086 3.14053767 1
Since the values are still not comparable due to the different installed power capacity (cf. repowering scenarios) a further step must be taken. Consequently, the results must be levelized in such a way that the multiples of the base case are divided by their factor of capacity, meaning that scenario 2 is divided by 1.5 and scenario 3 by 2.0:
Table 14 Comparison of Electricity Production Based on Dimensionless Identifier (2)
Base Scenario 1 Scenario 2 Scenario 3 Case Sum (MWh/y) 317,457.8 441,747.9 625,831.9 199,275.4 Multiple of Base Case 1.59306066 2.21677086 3.14053767 1 Levelized to dimensionless identifier 1.59306066 1.47784724 1.57026883 #
The obtained identifier could be ranked, where scenario 1 has the highest value and is followed by scenario 3. Scenario 2 is placed third, which is a bit surprisingly considering the expected linear result. However, the presented approach should not be regarded as a strict and solid solution which ranks the scenarios but more as a small tool giving some tendencies.
Due to the absence of any real verification option for the obtained results, a comparison between the calculated AEP and the production based on the LANUV ‘Energy Atlas’ model is a possible comparison method in order to harvest some information about the accuracy. However, it has to be said, that the ‘Energy Atlas – wind yield calculator’ model is a very basic one, where WTG model specification as well as installed number of 51
WTGs are not defined as detailed as in WindPro. The boxplot in Figure 25 shows the AEP percentage share between ‘Energy Atlas’ and calculated values, whereas Table 15 represents some statistical key aspects regarding the boxplot. What can be deduced is that the calculations of the AEP done in WindPro are on average between 33,8% (Scenario 1) and 30,4% (Scenario 3) higher but tend to fit more the ‘Energy Atlas’ results with the increasing number of installed WTGs. This finding gives the impression that both models show relevant defects: while the one is very basic, the other one does not include all relevant losses, but wake losses.
Figure 25 Boxplot Comparison Between Energy Atlas Method and Calculated AEP
Scenario 1 Scenario 2 Scenario 3
Min. 0.557 0.520 0.566 1st Qu. 0.617 0.610 0.664 Median 0.661 0.664 0.689 Mean 0.662 0.663 0.696 3rd Qu. 0.701 0.709 0.728 Max. 0.797 0.838 0.873
Table 15 Statistical Results of Energy Atlas Method and Calculated AEP The financial results are compared to the technical ones, by far as attractive. Two wind farms stand out positively (#WB, #HM) while two others stand out negatively (#KB, 52
#ML). The amount of uncertainty in the financial input parameters is relatively high and has a significant impact on the IRR. On the one hand the debt ratio of 90% seems some percentage points over the normal 80-20 ratio. The assumption, however, is that the state is the main money lender. The debt term of 15 years is also dependent on the debt ratio, which also influences the financial feasibility to a varying degree. The prediction of the electricity price and the resulting 54.4€/MWh also have a high impact on the study. The results of a sensitivity analysis with the assumed annual growth of 2.5% over the projected year, gives a totally different result. A look at the IRR for assets, which was previously negative for nearly all scenarios (based on 54.4€/MWh) presents very differently as Table 15 shows:
Table 16 Sensitivity Analysis IRR Assets (+2.5€ Annual Growth Rate of Electricity Price)
Pre-tax IRR - assets Reference Scenario Scenario Scenario Name 1 2 3 #WB 7.20% 6% 5.40% #DH 2.10% 1.6% 0.60% #HG 1.20% 0.3% -0.02% #HW 1.50% 0.3% -0.22% #HM 7.60% 5.7% 4.70% #KS 2.00% 0.8% -0.15% #KB -1.50% -2.1% -2.80% #KK 4.00% 3.2% 2.40% #VW 3.70% 2.6% 2.50% #ML -2.60% -4% -4.30% AVERAGE 2.52% 1.46% 0.81%
The worst case calculated for a constant electricity price resulted for the wind farm #ML. Now even this case can be considered potentially viable when planning the repowering process as shown by the cumulative cash flow (Figure 26) for Scenario 3: 53
Figure 26 Sensitivity Analysis for #ML, Cumulative Cash Flow for Scenario 3
Together these results provide important insights into the highly sensitive financial parameters. It can be concluded that scenario 1 and 2 are worth looking into due to the positive IRR. All but scenario 3 is only worth for further inspections for two wind parks (#WB, #HM). Taking the IRR on assets on its own all but 2 wind parks (#ML and partly #KB) are potentially feasible, where only 5 of 30 results, regarding equity payback and IRR on equity, are reported negative or respectively higher than the projected time of 20 years. The repowering approach as well as the methodology used here, can generally be applied anywhere in Germany where onshore WTGs are installed. However, it must be kept in mind that the federal states do have different regulations and legislations and thus procedures what have been used in one state may not be implemented in the other state. Small scale observation shows that the technical approach can be even applied on a broader spectrum, meaning everywhere where the same weather conditions can be found, and therefore the same WTG models are or can be installed. On the contrary, the economical methodology has strict boundaries which lie in the country-specific legal framework. 54
In order to validate the obtained results, based on the economic model, a comparison between calculated and actual values would be a sensibel approach. The economic data of real wind farms, using the same WTG models as modelled in this thesis, could therefore yield useful insights regarding the validation process. However, the research for the requiered data has been proven very difficult as they are not available for public review and further requests were not answered. 55
6. CONCLUSIONS
The objective of this thesis was to investigate the techno-economic repowering potential of wind turbines in the German federal state North Rhine-Westphalia. A data analysis of 3660 WTGs was performed and 306 WTGs were found eligible for the repowering process for the period of 2025-2045. From these findings, 10 wind farms with a total of 62 WTGs were the object of further examinations. Three repowering scenarios were developed, where the new installed capacity was the same as the currently one, 1.5 times and 2 times as high. The technical portion of this review focused mainly on the annual energy production and was calculated and processed using the WindPro software. An approximation of the currently installed WTGs’ production and other key parameters had to be done and was consequently used as the base case in order to compare the results with. The results showed a high potential for technical improvements.
The economic analysis was performed utilising mainly the RETScreen software. A forecast of the necessary parameters was done in order to calculate the simple payback, the equity payback, the IRR on assets and the IRR on equity. The results largely indicate that they are not worth pursuing unless the electrical prices are relatively high. A limitation of the work performed can be found in the selection of the parameters for the economic analysis. The simple economic models used do not allow a substantial and sound prediction of several key economic figure, especially the electricity price. Additionally, NRW’s legislation regarding the construction of the chosen WTGs with their parameters (e.g. increased hub height) was not examined. The current political conditions and subsidy systems complicate the repowering process and thus, investors are not attracted to. Moreover, the poor returns are constituting another major criterion disfavouring potential investments into the wind and especially repowering.
The results of this study could be seen as a starting point, considering the repowering of potential for all, or only some, wind farms in NRW. However, a more detailed financial analysis has to be made in order to ensure its feasibility. The selection process of the WTG models could be adapted, whereas research about the possible hub heights for each region could give additional value. 56
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APPENDIX
APPENDIX A
A1: LANUV Data Set – Date of Commissioning: 2004/2005
61
0 0 100 100 120 100 100 120 120 118 120 120 120 121 121 99.5 99.6 88.5 99.7 99.6 99.7 99.7 99.7 88.6 99.7 99.8 99.8 99.6 99.8 99.8 99.8 99.5 99.5 99.6 99.6 133.2 133.2 99.75 99.75 134.5 134.5 97.84 120.5 119.3 Gesamth oeh 0 5 65 99 99 65 65 98 98 85 65 64 64 85 65 6 85 85 85 85 85 85 86 86 90 64.6 75.6 75.5 75.6 77.7 70.5 70.5 75.6 75.6 75.6 77.7 77.7 70.5 70.5 70.5 64.75 64.75 62.84 73.25 73.25 Naben hoehe 0 47 71 71 48 48 48 48 70 70 70 70 70 70 44 71 71 71 48 48 48 48 70 70 70 70 70 70 70 70 70 70 70 70 44 44 58.6 58.6 52.9 52.9 58.6 58.6 58.6 58.6 Durch messe Anlagentyp E‐66.18.70 E‐66.18.70 E‐48 E‐70 E4 2000 E‐70 E4 2000 E‐40.6.44 E‐58.10.58 E‐58.10.58 E‐48 E‐48 E‐48 E‐66.18.70 E‐66.18.70 E‐66.18.70 E‐66.18.70 NA E‐40.6.44 E‐48 E‐48 E‐40.6.44 E‐53 E‐53 E‐66.18.70 E‐66.18.70 E‐58.10.58 E‐58.10.58 E‐58.10.58 E‐66.18.70 E‐66.18.70 E‐70 E4 2000 E‐70 E4 2000 E‐48 E‐48 E‐48 E‐70 E4 2000 E‐66.18.70 E‐66.18.70 E‐66.18.70 E‐66.18.70 E‐66.18.70 E‐66.18.70 E‐58.10.58 E‐66.18.70 E‐66.18.70 Hersteller Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Enercon Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Energietra Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land 2 2 1 1 1 1 1 2 2 2 1 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 0.6 0.8 0.8 0.8 0.6 0.8 0.8 0.8 0.6 0.8 0.8 0.8 0.6 0.8 0.8 Leistun g_M 600 800 800 800 600 800 800 800 600 800 800 800 600 800 800 1800 1800 1800 1800 2000 2000 1000 1000 1800 1800 1800 1800 1000 1000 1000 1800 1800 2000 2000 2000 1800 1800 1800 1800 1800 1800 1000 1800 1800 Leistun g_K 2004 2004 2004 2005 2005 2004 2004 2005 2005 2005 2004 2004 2004 2005 2005 2005 2005 2005 2004 2004 2004 2004 2004 2004 2004 2005 2005 2005 2005 2005 2005 2005 2004 2004 2004 2004 2004 2004 2004 2004 2004 2005 2005 2004 Inbetriebn 0 0 0 0 0 0 0 0 90 40 20 70 70 80 580 080 090 790 350 590 140 7860 7670 649840 570 570 5706460 5707450 5 5709590 5709590 5708360 5708220 5707570 5707390 5706470 5706710 5708460 5707750 5706850 5707130 5732590 5732220 56959 5763 5763 5763 5763 5762 5763 572135 572119 572145 567249 57308 57306 57311 572317 572296 5730 572439 572412 57453 57453 5712450 5687320 5663940 5664400 UTM32_nord 7 6 st 41464 41486 489012 488781 444653 443203 456824 430643 430253 435969 436192 432685 432711 444127 444134 435650 443601 455074 454117 428586 428609 467811 433138 433478 433490 432661 432056 432269 447571 447235 446990 437446 442401 442515 442389 490054 489974 492073 489468 489397 476083 476259 482404 401449 UTM32_o g g Meinerzhagen Meinerzhagen Marsberg Marsberg Möhnesee Möhnesee Nachrodt‐Wiblin Lippstadt Lippstadt Lippstadt Brilon Ense Ense Finnentrop Möhnesee Möhnesee Möhnesee Möhnesee Möhnesee Möhnesee Möhnesee Möhnesee Bad Berleburg Anröchte Anröchte Wadersloh Wadersloh Wadersloh Sassenberg Sassenberg Sassenberg Sassenberg Sassenberg Beckum Beckum Sassenberg Hövelhof Hövelhof Borchen Borchen Borchen Borchen Paderborn Bad Wünnenber Ort 58540 58540 34431 34431 59519 59519 58769 59556 59556 59556 59929 59469 59469 57413 59519 59519 59519 59519 59519 59519 59519 59519 57319 59609 59609 59329 59329 59329 48336 48336 48336 48336 48336 59269 59269 48336 33161 33161 33178 33178 33178 33178 33100 33181 PLZ Märkische Märkische Soest Soest Hochsauer Hochsauer Soest Soest Soest Olpe Soest Hochsauer Soest Soest Soest Soest Soest Märkische Soest Soest Soest Soest Soest Soest Siegen‐ Warendor Warendor Warendor Warendor Warendor Warendor Warendor Warendor Warendor Warendor Warendor Paderborn Paderborn Paderborn Paderborn Paderborn Paderborn Paderborn Kreis Paderborn 5962036 5962036 5974032 5974032 5958024 5958024 5974028 5974028 5974028 5966012 5974012 5958012 5974012 5974032 5974032 5974032 5974032 5962044 5974032 5974032 5974032 5974032 5974004 5974004 5970004 5570048 5570048 5570048 5570036 5570036 5570036 5570036 5570036 5570008 5570008 5570036 5774024 5774024 5774012 5774012 5774012 5774012 5774032 5774040 GKZ Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Münster Münster Münster Münster Münster Münster Münster Münster Münster Münster Münster Detmold Detmold Detmold Detmold Detmold Detmold Detmold Detmold Planungsre Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Detmold Detmold Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Detmold Detmold Detmold Münster Münster Münster Münster Münster Münster Detmold Münster Münster Münster Münster Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Münster Arnsberg Arnsberg Arnsberg Detmold Arnsberg Detmold Arnsberg Arnsberg Arnsberg Regierungs 78 79 92 357 358 366 367 348 347 326 325 264 265 324 218 195 141 194 126 125 124 495 493 494 455 454 453 123 451 452 114 115 374 375 376 377 384 450 369 370 373 416 368 106 LANUV_ID 697 698 706 707 689 688 667 666 615 616 665 571 548 501 547 488 487 486 822 820 821 784 783 782 485 780 781 477 478 714 715 716 717 723 779 709 710 713 749 708 445 446 458 472 OBJECTID_1 696 697 705 706 688 687 666 665 614 615 664 570 547 500 546 487 486 485 821 819 820 783 782 781 484 779 780 476 477 713 714 715 716 722 778 708 709 712 748 707 444 445 457 471 FID_ 62 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
87 87 121 121 121 100 100 140 140 140 150 140 150 140 100 100 140 98.8 99.8 99.8 99.8 99.9 138.5 138.5 138.5 138.5 138.5 102.5 135.25 135.25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 80 80 80 80 95 95 67 74 74 74 65 65 100 100 100 105 105 100 100 100 100 100 100 100 100 61.4 66.8 64.8 66.8 70.5 0 0 0 0 0 0 0 0 0 0 0 82 82 82 90 80 80 80 90 90 90 90 66 80 77 77 77 52 52 52 77 77 66 70 66 44 44 77 77 77 77 77 77 70.5 70.5 56.6 NA NA NA NA NA 1.5 sl 1.5 sl MM82 MM82 MM82 MD77 MD77 MD77 V 90 2000 V 52 V 52 V 80 2000 V 80 2000 V 80 2000 V 90 2000 V 90 2000 V 90 2000 V 90 2000 V 52 V 66 1650 V 80 2000 E‐58.10.58 E‐66.15.66 E‐66.18.70 E‐40.6.44 E‐66.15.66 E‐40.6.44 E‐66 E‐66 NA NA NA NM 60.1000 NM 60.1000 NM 60.1000 FL MD 77 FL MD 77 FL MD 77 FL MD 77 FL MD 77 FL MD 77 Enercon Enercon Enercon Enercon Enercon Enercon Fuhrländer Fuhrländer Fuhrländer Fuhrländer Fuhrländer Fuhrländer General Electric General Electric NEG Micon NEG Micon NEG Micon REpower REpower REpower REpower REpower REpower Enercon Enercon Vestas Vestas Vestas Vestas Vestas Vestas Vestas Vestas Vestas Vestas Vestas Vestas Vestas NA NA NA NA NA Enercon Enercon Enercon Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1.5 1.8 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.8 1.8 1.8 1.8 1.8 1.5 1.5 1.5 0.6 0.6 0.6 0.8 0.6 1.65 0.85 0.85 0.85 600 600 850 850 850 600 800 600 1000 1500 1800 1500 1500 1500 1500 1500 1500 1500 1500 1500 1000 1000 1000 2000 2000 2000 1800 1800 1800 1800 1800 2000 2000 2000 2000 2000 2000 2000 2000 1650 2000 1000 2000 1500 1500 1500 2004 2004 2004 2005 2004 2004 2005 2005 2004 2004 2005 2005 2004 2005 2004 2004 2004 2005 2005 2005 2004 2004 2004 2004 2004 2005 2004 2004 2004 2004 2005 2004 2004 2004 2004 2004 2004 2004 2005 2004 2005 2005 2005 2004 2004 2004 0 0 0 0 0 0 10 30 80 60 20 780 440 4400 4040 1180 1380 1200 74960 74750 74530 707590 707670 682440 814300 814500 814080 5714430 5713870 5714440 5714050 569142 569151 5 568 5707 5707 5706340 57322 57319 5654140 5653860 5 5 5653630 574586 5724590 5724560 56999 57 57 57 574 5 5 5 574 574 574 575825 577036 577045 57677 57678 5704610 5704500 5711210 5707060 5681650 5681250 5681800 5681450 99507.775 56 429825 429865 430431 429761 423362 452144 452220 452474 452511 423106 460003 450316 443471 443072 430890 427825 428146 425005 425104 420576 420808 425305 444037 392568 392866 478710 417310 417426 417537 417127 508736 508736 508730 457369 457543 457352 502698 508979 508657 498355 498720 487075 487332 483274 411839 478732.95 g g g h Balve Balve Bestwig Meschede Meschede Meschede Meschede Möhnesee Ense Möhnesee Olpe Olpe Meschede Olpe Brilon Brilon Welver Welver Welver Welver Wickede (Ruhr) Wickede (Ruhr) Fröndenberg.Ru Beckum Beckum Oelde Selm Selm Sendenhorst Ladbergen Ladbergen Ladbergen Bad Wünnenber Bad Wünnenber Bad Wünnenber Rietberg Rietberg Rietberg Extertal Blomberg Dörentrup Dörentrup Extertal Petershagen Petershagen Petershagen 58802 58802 59909 59872 59872 59872 59872 59519 59469 59519 57462 57462 59872 57462 59929 59929 59514 59514 59514 59514 58739 58739 58730 59269 59269 59302 59379 59379 48324 49549 49549 49549 33181 33181 33181 33397 33397 33397 32699 32825 32694 32694 32699 32469 32469 32469 Olpe Olpe Soest Soest Hochsauer Hochsauer Hochsauer Hochsauer Hochsauer Soest Olpe Hochsauer Märkische Märkische Soest Soest Soest Soest Soest Soest Hochsauer Hochsauer Unna Unna Unna Steinfurt Steinfurt Steinfurt Warendor Warendor Warendor Warendor Gütersloh Minden‐ Gütersloh Gütersloh Minden‐ Paderborn Lippe Lippe Lippe Lippe Minden‐ Lippe Paderborn Paderborn 5966024 5966024 5974032 5974012 5958008 5958032 5958032 5958032 5958032 5974032 5966024 5958032 5962008 5962008 5974048 5974048 5974048 5974048 5974056 5974056 5958012 5958012 5978012 5978032 5978032 5566032 5566032 5566032 5570008 5570008 5570028 5570040 5754032 5770028 5754032 5754032 5770028 5774040 5766028 5766016 5766024 5766024 5770028 5766028 5774040 5774040 Arnsberg Arnsberg Arnsberg Arnsberg Regionalver Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Regionalver Regionalver Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Münster Münster Münster Münster Münster Münster Münster Detmold Detmold Detmold Detmold Detmold Detmold Detmold Detmold Detmold Detmold Detmold Detmold Detmold Detmold Münster Detmold Münster Münster Arnsberg Arnsberg Detmold Detmold Detmold Detmold Detmold Münster Münster Detmold Arnsberg Münster Detmold Detmold Detmold Arnsberg Arnsberg Detmold Arnsberg Detmold Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Detmold Detmold Arnsberg Münster Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg Arnsberg 858 728 859 860 862 863 726 727 942 941 864 604 602 597 596 590 589 544 545 546 547 559 560 580 581 1268 1267 1000 1011 1012 1043 1085 1089 1144 1178 1179 1181 1189 1266 1013 1044 1079 1080 1081 1082 1083 926 924 919 918 915 914 871 872 873 874 886 887 905 906 1135 1031 1136 1137 1139 1140 1029 1030 1480 1479 1201 1253 1259 1260 1291 1331 1333 1376 1403 1404 1406 1412 1478 1200 1261 1292 1326 1327 1328 1329 1330 1141 925 923 918 917 914 913 870 871 872 873 885 886 904 905 1134 1030 1135 1136 1138 1139 1028 1029 1479 1478 1200 1252 1258 1259 1290 1330 1332 1375 1402 1403 1405 1411 1477 1199 1260 1291 1325 1326 1327 1328 1329 1140 63
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 119 133 100 133 133 133 100 149 149 149 149 121 149 100 121 100 100 100 100 100 100 100 133 123.5 149.5 149.5 133.8 149.09 131.25
Wind Land EnerconWind Land EnerconWind Land AN BonusWind Land NA DeWindWind Land NA EnerconWind Land NA EnerconWind Land D4 46.600 NAWind Land E‐58.10.58 EnerconWind Land E‐70 E4 2000 0 EnerconWind Land 46 0 77 Enercon 58Wind Land 71 E‐66.20.70 0 Enercon 70 Wind Land NA E‐40.6.44 85 114 90 0 EnerconWind Land NA DeWind 70Wind Land E‐58.10.58 DeWindWind Land 44 E‐58.10.58 98 DeWindWind Land D6 62.1000 Enercon 58Wind Land 78 0 D6 62.1000 DeWind 58Wind Land D6 62.1000 0 62 DeWindWind Land 0 0 E‐66.18.70 62 DeWindWind Land 0 D6 62.1000 62 0 DeWind 0 Wind Land D6 62.1000 DeWind 70 0 Wind Land D6 62.1000 62 Enercon 0 Wind Land D6 62.1000 62 98 EnerconWind Land D6 62.1000 62 Enercon 0 Wind Land E‐66.18.70 62 Enercon 0 Wind Land E‐66.18.70 62 Enercon 0 Wind Land E‐40.6.44 Enercon 70 0 Wind Land E‐66.18.70 Enercon 70 0 Wind Land E‐66.18.70 98 EnerconWind Land 44 E‐66.18.70 98 Enercon 70Wind Land E‐66.18.70 Enercon 70Wind Land 78 114 E‐66.18.70‐3 Enercon 70Wind Land 114 E‐66.18.70‐3 Enercon 70Wind Land 70 114 E‐66.18.70 66.5 114.09 EnerconWind Land 114 E‐66.18.70 EnerconWind Land E‐40.6.44 98 Enercon 70Wind Land E‐66.18.70 Enercon 70Wind Land E‐70 E4 2000 86 EnerconWind Land 44 114 E‐66.20.70 Enercon 70Wind Land 71 E‐66.20.70 EnerconWind Land 78 E‐66.20.70 114 86 Enercon 70Wind Land E‐66.20.70 Enercon 70Wind Land E‐66.20.70 65 Enercon 70Wind Land E‐66.20.70 65 Enercon 70Wind Land E‐66.20.70 65 Enercon 70Wind Land E‐30 300 65 Enercon 70 E‐66.18.70 65 70 E‐66.20.70 65 30 E‐40 65 70 70 98.8 0 98 40 0 20042004 15002004 1500 1.5 2004 1500 1.5 2004 600 1.5 2005 10002004 2000 0.6 2004 1800 1 2004 2000 2 1.8 2004 6002004 1800 2 2004 1000 0.6 1.8 2004 10002004 1000 1 2004 1000 1 2004 1000 1 2004 1800 1 2004 1000 1 1.8 2004 10002004 1000 1 2004 1000 1 2004 1000 1 2004 1800 1 2004 1800 1 1.8 2004 600 1.8 2004 18002004 1800 0.6 1.8 2004 1800 1.8 2004 1800 1.8 2004 1800 1.8 2004 1800 1.8 2004 1800 1.8 2004 1800 1.8 2004 600 1.8 2004 18002004 2000 0.6 1.8 2004 20002004 2000 2 2004 2000 2 2004 2000 2 2004 2000 2 2004 2000 2 2004 2000 2 2004 2 3002004 1800 2 2004 2000 0.3 1.8 600 2 0.6 1808 1809 1654 Düsseldorf Düsseldorf 5154056 Kleve 47589 Uedem 309235 5732490 1807 1808 1653 Düsseldorf Düsseldorf 5154056 Kleve 47589 Uedem 308850 5732510 1806 1807 1652 Düsseldorf Düsseldorf 5154056 Kleve 47589 Uedem 308233 5732470 180318041805 1804 1805 1806 1649 Münster 1650 Düsseldorf Münster Düsseldorf 1651 Düsseldorf 5154056 Düsseldorf Kleve 5554024 Borken 5154056 Kleve 48619 47589 Heek Uedem 47589 Uedem 309835 366961 308557 5732070 5773670 5732100 1802 1803 1645 Düsseldorf Düsseldorf 5154056 Kleve 47589 Uedem 309365 5731190 1801 1802 1644 Düsseldorf Düsseldorf 5154056 Kleve 47589 Uedem 309681 5730720 156915701731 15701795 15711796 1732 1387 Münster1797 1796 1388 Münster1798 Regionalver 5562004 1797 1572 Recklingh Köln1799 Regionalver 44577 5562012 1798 1638 Castrop‐Rauxel Recklingh Münster1800 46284 1799 1639 Dorsten Münster Münster Köln 1800 1640 Köln Münster 385440 5554068 1801 1641 Borken Düsseldorf Düsseldorf 5554036 1642 Borken Düsseldorf 5711990 5370032 48691 5154056 Köln Heinsberg Regionalver Vreden Kleve 1643 52525 5170032 Düsseldorf Waldfeucht Wesel 48739 356324 Düsseldorf Legden 5154056 47589 Kleve Uedem 47495 5370032 5728860 Rheinberg Heinsberg 52525 Waldfeucht 47589 288263 Uedem 351305 370649 5659080 331008 5771410 309010 288450 5769890 5716210 5730570 5660390 309132 5730790 182018211822 18211823 18221824 1823 1666 Münster1825 1824 1667 Münster1826 Münster 1825 1668 Münster1827 Münster 5566036 1826 1669 Steinfurt Münster1828 Münster 5554028 1827 1670 48366 Borken Köln Laer1829 Münster 5554028 1828 1671 Borken Köln1830 46359 Heiden 5554004 1829 1672 Borken Köln1831 46359 Köln Heiden 1830 1673 Köln 48683 Köln Ahaus 1831 1674 Köln Köln 1832 1675 Köln 5334028 Köln Städteregi 391472 1676 52152 Köln Simmerath 5334028 358906 Köln Städteregi 1677 52152 Düsseldorf Simmerath 5334028 358429 Köln Städteregi Düsseldorf 5771300 52152 Simmerath 5334028 5741630 5154044 Köln Städteregi Kleve 362069 52152 Simmerath 5334028 5741600 Städteregi 311710 52152 Simmerath 5334028 Städteregi 5768430 46459 311857 Rees 52152 Simmerath 5334028 Städteregi 5613250 312049 52152 Simmerath 5613100 312093 5613430 312398 5613210 312728 320937.475 5739679.363 5613620 312799 5613810 5613590 15221539 1523 1540 1328 Köln 1351 Düsseldorf Düsseldorf 5154064 Köln Kleve 47652 Weeze 5358036 Düren 52441 Linnich 305595 5727390 310987 5654520 1819 1820 1665 Münster Münster 5558012 Coesfeld 48653 Coesfeld 372997 5753690 1481 1482 1270 Detmold Detmold 5770028 Minden‐ 32469 Petershagen 509024 5814160 1480 1481 1269 Detmold Detmold 5770028 Minden‐ 32469 Petershagen 508964 5814400 180918101812 18101813 18111814 1813 1655 Münster1815 1814 1656 Münster1816 Münster 1815 1658 Arnsberg1817 Münster 5554024 1816 1659 Borken Münster Regionalver1818 5914000 5554024 1817 1660 Hagen Borken Münster Münster 48619 Heek 1818 1661 Münster Münster 48619 58091 Heek 5566036 Hagen 1819 1662 Steinfurt Münster Münster 5566036 1663 48366 Steinfurt Münster Laer Münster 5566036 1664 48366 Steinfurt Münster Laer Münster 5566036 48366 Steinfurt Laer Münster 5566036 48366 Steinfurt 367200 Laer 5558012 400521 48366 Coesfeld 368112 Laer 5773520 48653 Coesfeld 5683740 390770 5773620 391144 5769930 391177 5769350 390884 372314 5770810 3911451832 5771220 1833 5751810 5771210 1834 1833 1834 1835 1678 Münster 1679 Münster Münster 1680 Düsseldorf Münster Düsseldorf 5566068 Steinfurt 5154064 Kleve 5554028 48607 Borken Ochtrup 46359 47652 Heiden Weeze 374671.355 5783797.355 359860 305587 5741860 5727690 64 0 0 0
99 99 100 100 100 100 100 149 121 121 121 121 121 121 121 121 121 121 133 133 100 100 149 149 100 149 100 100 100 133 133 99.5 99.5 99.5 99.5 99.5 99.6 99.6 99.5 138.5 138.5 138.5 133.8
Wind Land EnerconWind Land EnerconWind Land EnerconWind Land E‐66.18.70 EnerconWind Land E‐66.18.70 EnerconWind Land E‐66.18.70 Enercon 70Wind Land E‐66.18.70 Enercon 70Wind Land E‐66.18.70 65 Enercon 70Wind Land E‐66.18.70 65 Enercon 70Wind Land E‐66.18.70 65 Enercon 70Wind Land E‐66.18.70 65 Enercon 70Wind Land E‐66.18.70 65 Enercon 70Wind Land 114 E‐66.18.70 Enercon 70Wind Land E‐66.18.70 86 Enercon 70Wind Land E‐66.18.70 86 Enercon 70Wind Land E‐66.18.70 86 Enercon 70Wind Land E‐66.18.70 86 Enercon 70Wind Land E‐66.18.70 86 Enercon 70Wind Land E‐66.18.70 86 Enercon 70Wind Land E‐66.18.70 Enercon 70Wind Land 0 E‐66.20.70 86 Enercon 70Wind Land E‐58.10.58 86 Enercon 70Wind Land E‐66.18.70 86 Enercon 70Wind Land E‐40.6.44 86 Enercon 58Wind Land E‐40.6.44 98 Enercon 70Wind Land 70.5 E‐66.20.70 EnerconWind Land 44 E‐66.20.70 98 EnerconWind Land 44 E‐66.20.70 GeEnergy 70Wind Land 78 E‐66.20.70 GeEnergy 70Wind Land 78 E‐48 GeEnergy 70Wind Land 0 1.5 sl Enercon 70Wind Land 0 1.5 sl 114 EnerconWind Land 1.5 sl 114 EnerconWind Land E‐66.20.70 48 EnerconWind Land 77 E‐48 EnerconWind Land 77 E‐48 100 76 Enercon 70Wind Land 77 E‐48 100 EnerconWind Land 114 E‐58.10.58 100 EnerconWind Land E‐58.10.58 48 EnerconWind Land E‐58.10.58 48 Enercon 58Wind Land E‐58.10.58 76 48 Enercon 58Wind Land 70.5 E‐48 76 Enercon 58Wind Land 70.5 E‐48 76 Enercon 58Wind Land 70.5 E‐58.10.58 EnerconWind Land 70.5 E‐66.18.70 EnerconWind Land E‐48 48 Enercon 58 E‐66.18.70 48 70 75.6 E‐58.10.58 70 75.6 E‐66.18.70 98 70 48 58 98 70 70.5 75 98.8 20042004 18002004 1800 1.8 2004 1800 1.8 2004 1800 1.8 2004 1800 1.8 2004 1800 1.8 2004 1800 1.8 2004 1800 1.8 2004 1800 1.8 2004 1800 1.8 2004 1800 1.8 2004 1800 1.8 2004 1800 1.8 2004 1800 1.8 2004 1800 1.8 2004 1800 1.8 2004 1800 1.8 2004 2000 1.8 2004 10002004 1800 2 2004 1 600 1.8 2005 6002005 2000 0.6 2005 2000 0.6 2005 2000 2 2005 2000 2 2005 2 8002005 1500 2 2005 1500 0.8 1.5 2005 1500 1.5 2005 2000 1.5 2005 8002005 2 8002005 0.8 8002005 1000 0.8 2005 1000 0.8 2005 1000 1 2005 1000 1 2005 1 8002005 1 8002005 1000 0.8 2005 1800 0.8 2005 1 800 1.8 2004 18002004 1000 0.8 1.8 1800 1 1.8 185618571858 18571859 1858 1859 1706 Münster 1860 1707 Köln Münster 1708 Köln 5554036 1709 Borken Arnsberg Köln Regionalver 48739 Köln 5913000 Legden Dortmund 44359 Dortmund 5362020 Rhein‐Erft‐ 50374 Erftstadt 5362020 Rhein‐Erft‐ 50374 Erftstadt 390038 372209 5714130 5770840 340903 340642 5632320 5632460 183518361837 18361838 18371839 1838 1681 Köln1840 1839 1682 Köln1841 1840 1685 Köln1842 Köln 1841 1687 Köln1843 Köln 1842 1689 Köln1844 Köln 1843 1690 Münster1845 5370032 Köln Heinsberg 1844 1691 52525 Köln1846 5370032 Münster Waldfeucht Köln Heinsberg 1845 1692 52525 Köln1847 5370032 Waldfeucht Heinsberg 5566052 1846 1693 52525 Steinfurt Köln1848 5370032 Waldfeucht Köln Heinsberg 1847 1694 48629 52525 Köln Metelen1849 5370032 Waldfeucht Köln Heinsberg 287988 1848 1695 52525 Köln1850 Waldfeucht Köln 288079 1849 1696 Köln1851 5370032 Köln 5659790 Heinsberg 288185 1850 1697 52525 Düsseldorf1852 5370032 Waldfeucht Köln 5660000 Heinsberg Regionalver 288413 1851 1698 52525 5170012 Köln1853 5370032 Waldfeucht Wesel 5660220 Köln Heinsberg 376796 288519 1852 1699 52525 Köln1854 5370032 Waldfeucht 5659850 Heinsberg 1853 1700 52525 46499 Köln1855 5370032 Waldfeucht Hamminkeln 5780710 5659610 Köln Heinsberg 292981 1854 1701 52525 Köln 5370032 Waldfeucht Köln Heinsberg 293141 1855 1702 52525 Münster Waldfeucht 5661600 Köln 293216 1856 1703 Münster 5370032 Münster 334656 Köln 5661400 Heinsberg 293366 1704 52525 Düsseldorf 5370032 Münster Waldfeucht 5661120 Heinsberg Regionalver 293528 5554060 1705 52525 Borken 5170012 Münster1860 5730850 5370032 Waldfeucht Wesel 5661660 Heinsberg 293548 5558012 52525 Coesfeld1861 5370032 Münster Waldfeucht 46354 5661470 Heinsberg Südlohn 48653 52525 464991862 Coesfeld Waldfeucht Hamminkeln 5661170 292635 5554036 1861 Borken1863 292815 18621864 48739 5662080 Legden 292941 1863 1710 Arnsberg1865 341180 5661840 293170 1864 1711 Münster Regionalver 3561071866 5662230 378401 5913000 1865 1712 Dortmund Köln1867 5735700 Münster 44359 5661920 Dortmund 1866 1713 5758450 Köln1868 5754780 5554064 1867 1714 Borken Köln1869 371632 Köln 1868 1715 Arnsberg1870 46342 Köln Velen 1869 1716 Münster Regionalver 5770200 390879 Köln 5913000 1870 1717 Dortmund Münster 5358048 Münster 44359 Düren Dortmund 1871 1718 Münster 5714290 5358048 Münster Düren 5554060 1719 Borken Düsseldorf 52382 5358048 Münster Niederzier Düren Düsseldorf 5554060 1720 Borken Düsseldorf 52382 46354 5154028 Niederzier Düsseldorf Südlohn Kleve 5554060 362013 Borken 52382 390454 46354 5154028 Niederzier Südlohn Kleve1877 46354 47647 Südlohn 5755250 Kerken 5714250 323565 47647 Kerken 323829 1878 5646870 351247 324262 5646790 350696 1727 5758060 5645860 Köln 351627 317078 5757610 316823 5758180 Köln 5705340 5706080 5334008 Städteregi 52499 Baesweiler 303530 5643010 18711872 1872 1873 1721 Düsseldorf Düsseldorf 1722 Düsseldorf 5154028 Düsseldorf Kleve1878 5154028 Kleve1976 47647 Kerken1982 47647 1879 Kerken 1977 1983 1728 Münster 1827 Münster Münster 316601 1833 Münster Münster 316341 5554052 Borken Münster 5706170 5558012 Coesfeld 48624 5705520 Schöppingen 5566004 48653 Steinfurt Coesfeld 48341 Altenberge 380335 5774630 375688 391576 5754070 5770990 187318741875 1874 1875 1876 1723 Detmold 1724 Arnsberg Detmold 1725 Arnsberg Regionalver 5914000 5758004 Hagen Herford Regionalver 5914000 Hagen 32257 Bünde 58091 Hagen 58091 Hagen 468967 397913 397994 5787070 5686560 5686360 1876 1877 1726 Detmold Detmold 5758012 Herford 32049 Herford 481729 5774960 65 0 0 0 0 0 0 0 0 0 0 0 100 135 100 100 99.5 133.8 133.8 138.5 138.5 138.5 123.5 138.5 138.5 138.5 134.5 134.5 134.5 136.5 136.5 136.5 136.5 136.5 138.5 134.5 138.5 138.5 138.5 138.5 138.5 138.5 134.5 138.5 138.5 138.5 138.5 0 0 0 0 0 0 0 0 0 0 0 94 70 70 85 61 96 96 96 98 98 98 98 98 96 96 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 61.5 98.8 98.8 0 0 77 77 77 77 82 60 77 60 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 70 70 70 70 77 N 77 N 77 N 77 N 77 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl 1.5 sl MD77 E‐66.18.70 E‐66.18.70 E‐66.18.70‐3 E‐66.18.70‐3 NM 82.1500 NM 60.1000 1.5 sl NM 60.1000 Enercon Enercon Enercon Enercon Enron General Electric General Electric General Electric General Electric General Electric General Electric General Electric General Electric General Electric General Electric General Electric General Electric General Electric General Electric General Electric General Electric General Electric General Electric General Electric General Electric General Electric General Electric General Electric General Electric General Electric General Electric General Electric General Electric General Electric General Electric General Electric General Electric General Electric Micon NEG Micon NEG Micon REpower Nordex Nordex Nordex Nordex Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land Wind Land 1 1 1.8 1.8 1.8 1.8 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1800 1800 1800 1800 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 1500 1000 1500 1000 1500 1500 1500 1500 1500 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2004 2005 2004 2005 2005 2005 2005 2005 2005 0 520 20420 20690 21120 9780.95 575703 5770960 5771910 5770630 5771000 5769500 5769920 5739370 5756280 5739240 5739520 5741330 5742410 5741740 5741480 5741390 5754670 5739090 57 5635860 5635790 5635360 57 57 5751 5658540 5658700 5646370 5646290 5646040 5669640 5736370 5696950 5696890 5724820 5724980 5628730 5770720 5769980 5765080 5763910 573 5672631.837 5672841.837 5672951.837 5672941.837 0 8 2 203 231 610 0813 360155 350693 351124 351248 351433 352271 352383 356770 358782 357349 357366 358381 359156 359522 360241 360734 361830 356860 392658 392736 391897 391790 38095 38127 321190 298637 299030 299463 321315 321479 328 387381 287984 288472 317 317 307443 307788 38 298527 30767 331665 331182 331475 331729 357341.837 e h h h h Heiden Vreden Vreden Vreden Vreden Vreden Vreden Heiden Velen Heiden Heiden Heiden Heiden Heiden Heiden Heiden Velen Heiden Gescher Datteln Datteln Alpen Hamminkeln Alpen Alpen Nottuln Altenberge Altenberge Altenberge Altenberge Würselen Würselen Würselen Stolberg (Rhld.) Selfkant Selfkant Ãœbach‐Palenb Aldenhoven Aldenhoven Wachtendonk Wachtendonk Remscheid Korschenbroic Korschenbroic Korschenbroic Korschenbroic 46359 48691 48691 48691 48691 48691 48691 46359 46342 46359 46359 46359 46359 46359 46359 46359 46342 46359 48712 45711 45711 46519 46499 46519 46519 48301 48341 48341 48341 48341 52146 52146 52146 52224 52538 52538 52531 52457 52457 47669 47669 42897 41352 41352 41352 41352 Recklingh Recklingh Borken Borken Borken Borken Borken Borken Borken Borken Borken Borken Borken Borken Borken Borken Borken Borken Coesfeld Borken Borken Borken Steinfurt Steinfurt Steinfurt Steinfurt Wesel Wesel Wesel Wesel Heinsberg Heinsberg Heinsberg Düren Düren Städteregi Städteregi Städteregi Städteregi Rhein‐ Rhein‐ Kleve Kleve Remschei Rhein‐ Rhein‐ 5562008 5562008 5554028 5554068 5554068 5554068 5554068 5554068 5554068 5554028 5554064 5554028 5554028 5554028 5554028 5554028 5554028 5554028 5558032 5554064 5554028 5554016 5566004 5566004 5566004 5566004 5170004 5170012 5170004 5170004 5370024 5370024 5370028 5358004 5358004 5334036 5334036 5334036 5334032 5162020 5162020 5154060 5154060 5120000 5162020 5162020 Münster Münster Münster Münster Münster Münster Münster Münster Münster Münster Münster Münster Münster Münster Münster Münster Münster Regionalver Regionalver Münster Münster Münster Münster Münster Münster Münster Köln Köln Köln Köln Köln Köln Köln Köln Köln Regionalver Düsseldorf Düsseldorf Regionalver Düsseldorf Düsseldorf Düsseldorf Düsseldorf Düsseldorf Regionalver Regionalver Münster Münster Münster Münster Münster Münster Münster Münster Düsseldorf Münster Münster Münster Münster Münster Münster Münster Münster Münster Düsseldorf Düsseldorf Köln Köln Düsseldorf Münster Münster Köln Münster Köln Köln Düsseldorf Düsseldorf Münster Köln Köln Köln Münster Münster Düsseldorf Münster Köln Düsseldorf Düsseldorf Münster Düsseldorf Düsseldorf Münster 2002 2003 2004 2005 2006 2007 2008 2009 2010 1992 1994 1995 1996 1997 1998 1999 2000 2001 2119 2120 1841 1842 1884 1904 1905 1986 1987 1988 1989 1990 1991 1993 2013 2014 2015 2016 2053 2095 2097 2102 2117 2118 1835 2011 2012 1834 2115 2116 2117 2118 2119 2120 2121 2122 2123 2105 2107 2108 2109 2110 2111 2112 2113 2114 2212 2213 1989 1990 2028 2045 2046 2099 2100 2101 2102 2103 2104 2106 2126 2127 2128 2129 2157 2191 2193 2197 2210 2211 1985 2124 2125 1984 2114 2115 2116 2117 2118 2119 2120 2121 2122 2104 2106 2107 2108 2109 2110 2111 2112 2113 2211 2212 1988 1989 2027 2044 2045 2098 2099 2100 2101 2102 2103 2105 2125 2126 2127 2128 2156 2190 2192 2196 2209 2210 1984 2123 2124 1983 66 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
120 120 120 120 120 140 140 140 140 140 140 140 140 140 146.1 138.5 138.5 138.5 138.5 138.5 138.5 138.5 138.5 138.5 138.5 138.5 138.5
Wind Land NordexWind Land REpowerWind Land REpowerWind Land N 77 REpowerWind Land MM92 SüdwindWind Land MD77 SüdwindWind Land MD77 SüdwindWind Land S‐77 Südwind 92Wind Land 77 S‐77 100.1 SüdwindWind Land 77 S‐77 SüdwindWind Land 77 0 S‐77 SüdwindWind Land 0 S‐77 77 SüdwindWind Land 0 S‐77 77 GeEnergyWind Land 100 S‐77 77 SüdwindWind Land 100 S‐77 77 SüdwindWind Land 1.5 sl 100 77 SüdwindWind Land 100 S‐77 77 SüdwindWind Land 100 S‐77 77 SüdwindWind Land 100 S‐77 77 SüdwindWind Land 77 100 S‐77 SüdwindWind Land S‐70 100 77 Südwind 0 Wind Land S‐70 77 SüdwindWind Land 100 S‐70 77 SüdwindWind Land 100 S‐70 77 SüdwindWind Land 100 S‐70 70 SüdwindWind Land 100 S‐77 70 SüdwindWind Land S‐77 85 70 SüdwindWind Land S‐77 85 70 SüdwindWind Land S‐77 85 70 SüdwindWind Land S‐77 85 77 SüdwindWind Land S‐77 85 77 VestasWind Land S‐77 77 Vestas 0 Wind Land S‐77 77 Vestas 0 Wind Land 77 Vestas 0 Wind Land V 80 2000 77 Vestas 0 Wind Land V 80 2000 77 Vestas 0 Wind Land V 80 2000 80 77 Vestas 0 Wind Land V 80 2000 80 Vestas 0 Wind Land V 80 2000 100 80 Vestas 0 Wind Land V 80 2000 100 80 NAWind Land V 80 2000 100 80 NAWind Land V 80 2000 100 80 NAWind Land V 80 2000 100 80 NAWind Land 100 80 NAWind Land 100 NA 80 NAWind Land 100 NA NA 100 NA NA NA NA 0 NA 0 0 0 0 0 0 0 0 0 0 0 0 0 20052005 15002005 2000 1.5 2005 15002004 1500 2 1.5 2004 1500 1.5 2004 1500 1.5 2004 1500 1.5 2004 1500 1.5 2004 1500 1.5 2004 1500 1.5 2004 1500 1.5 2005 1500 1.5 2005 1500 1.5 2005 1500 1.5 2005 1500 1.5 2005 1500 1.5 2005 1500 1.5 2005 1500 1.5 2005 1500 1.5 2005 1500 1.5 2005 1500 1.5 2005 1500 1.5 2005 1500 1.5 2005 1500 1.5 2005 1500 1.5 2005 1500 1.5 2005 1500 1.5 2005 1500 1.5 2005 1500 1.5 2005 1500 1.5 2005 2000 1.5 2005 20002005 2000 2 2005 2000 2 2005 2000 2 2005 2000 2 2005 2000 2 2005 2000 2 2004 2000 2 2004 1500 2 2004 1500 2 1.5 2004 1500 1.5 2004 1500 1.5 2004 1500 1.5 2004 1500 1.5 1500 1.5 1.5 231123122313 23122314 23132315 2314 2237 Köln2316 2315 2238 Köln 2316 2239 Köln Köln 2317 2240 Köln Köln 2241 Köln Köln 2242 Münster 5358048 Köln Düren 5358024 Münster Köln Düren 52382 5358024 Niederzier Düren 5554008 Borken 524282324 5358024 Jülich Düren 524282325 5358024 46395 Jülich Düren Bocholt 524282326 Jülich 2325 524282327 324038 Jülich 23262328 2327 2250 5646630 Düsseldorf2419 Düsseldorf 308521 2328 2251 Düsseldorf2420 328869 5154060 Düsseldorf Kleve 308572 2329 2252 Düsseldorf2421 5154060 5645510 Düsseldorf Kleve 308736 2420 2253 Düsseldorf 5745560 2422 5154060 5645980 Düsseldorf 47669 Kleve 308879 2421 2254 Wachtendonk Düsseldorf2423 5154060 5645840 Düsseldorf 47669 Kleve 2422 2369 Wachtendonk Köln2424 5154060 5644920 47669 Kleve 2423 2370 Wachtendonk Köln2425 314363 47669 2424 2371 Wachtendonk Köln2426 Köln 314595 47669 2425 2372 Wachtendonk Köln2427 5701510 Köln 314719 2426 2373 Köln2465 5700370 Köln 316740 2427 2374 Köln2466 5370008 5700130 Köln Heinsberg 316848 2428 2375 52538 Köln2467 5370008 Gangelt 5696880 Köln Heinsberg 2466 2376 52538 Köln2468 5370008 Gangelt 5696670 Köln Heinsberg 2467 2377 52538 Köln2469 5370008 Gangelt Köln Heinsberg 2468 2423 52538 Köln2470 5370008 Gangelt Köln Heinsberg 2469 2424 52538 Köln2471 5370008 Gangelt Köln Heinsberg 2470 2425 290270 52538 Köln 5370008 Gangelt Köln Heinsberg 2471 2426 290529 52538 Köln 5370008 Gangelt Köln Heinsberg 5658340 2472 2427 290652 52538 Köln 5370008 Gangelt Köln Heinsberg 5657750 2428 290762 52538 Köln 5370004 Gangelt Köln Heinsberg 5658120 2429 291013 41812 Köln 5370004 Erkelenz Köln Heinsberg 5658540 291202 41812 5370004 Erkelenz Köln Heinsberg 5657650 291395 41812 5370004 Erkelenz Köln Heinsberg 5658310 291619 41812 5370004 Erkelenz Heinsberg 5658020 291781 41812 5370004 Erkelenz Heinsberg 310858 5657650 41812 5370004 Erkelenz Heinsberg 310984 5657310 41812 Erkelenz 5655160 311088 5655450 311191 5655110 311295 5655310 311448 5655060 311596 5654890 5655170 226222632303 22632304 22642305 2304 2175 Köln2306 2305 2176 Köln2307 2306 2229 Köln2308 Köln 2307 2230 Köln2309 Köln 2308 2231 Köln2310 Köln 2309 2232 Köln 5362008 Köln Rhein‐Erft‐ 50129 2310 2233 Bergheim Köln 5362008 Köln Rhein‐Erft‐ 50129 2311 2234 Bergheim Köln 5358024 Köln Düren 2235 Köln 5358024 Köln Düren 2236 Düsseldorf 52428 5358024 Jülich Köln Düren Düsseldorf 339373 52428 5358024 5154032 Jülich Köln Düren Kleve 339499 524282317 5358024 Jülich Düren 5653580 524282318 5358004 Jülich Düren 47624 Kevelaer 5653810 524282319 5358004 Jülich Düren 2318 524572320 Aldenhoven 308347 2319 524572321 Aldenhoven 308347 2320 2243 Münster 5646080 308477 2321 2244 Münster 3053542323 Münster 5645700 308746 307797 2322 2245 Münster Münster 5644970 308939 5554008 308088 2246 Borken 5717160 Münster Münster 5645350 5646050 5554008 2324 2247 Borken Düsseldorf Münster 46395 5645150 5646200 Düsseldorf Bocholt 5554008 Borken 46395 5154052 Bocholt Kleve 5554008 2249 Borken Düsseldorf 46395 Düsseldorf Bocholt 46395 5154060 47638 Bocholt Kleve Straelen 328900 47669 Wachtendonk 329131 5745330 330352 5745570 330535 311031 311728 5743970 5744780 5700400 5699230 22132239 2214 2240 2121 Düsseldorf Düsseldorf 2149 Köln 5162020 Rhein‐ 41352 Köln Korschenbroich 331337 5370012 5672601.837 Heinsberg 52511 Geilenkirchen 297648 5654370 2322 2323 2248 Düsseldorf Düsseldorf 5154052 Kleve 47638 Straelen 311111 5700020 67
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 110 110 110 110 138.5 64.65
Wind Land NAWind Land NAWind Land NAWind Land EnerconWind Land NAWind Land NA NAWind Land NA E‐58.10.58 NAWind Land NA NAWind Land NA 58Wind Land NA REpowerWind Land NA 0 NAWind Land 0 NA 0 77 NAWind Land NA MD77 0 EnerconWind Land NA 100 0 70 NAWind Land 70 General ElectricWind Land NA 1.5 sl E‐58.10.58 75 70 General ElectricWind Land 77 NA 1.5 sl 75 70 EnerconWind Land 29.3 75 Gamesa 58Wind Land NA 0 75 GamesaWind Land 50 77 E‐40 GamesaWind Land 0 0 77 NA GamesaWind Land 0 0 NA GamesaWind Land 0 0 NA GamesaWind Land 0 0 NA 40 GamesaWind Land NA GamesaWind Land 0 NA 0 GamesaWind Land 0 NA 0 REpowerWind Land NA 0 Vestas 0 Wind Land NA 0 Enercon 0 Wind Land MD77 0 Enercon 0 Wind Land 0 Enercon 0 Wind Land V 90 2000 NA 0 Enercon 0 NA 0 0 77 E‐40.6.44 90 0 0 E‐53 0 0 0 0 44 0 0 0 53 0 0 73
20042004 15002005 1500 1.5 2004 1500 1.5 2004 1000 1.5 2004 20002004 2000 1 2004 2000 2 2004 2000 2 2004 2 3002005 1500 2 2005 0.3 600 1.5 2005 6002005 1000 0.6 2005 1500 0.6 2005 1500 1 1.5 2004 1500 1.5 2004 600 1.5 2004 8502004 0.6 850 0.85 2005 850 0.85 2004 850 0.85 2004 850 0.85 2004 850 0.85 2004 850 0.85 2004 850 0.85 2005 850 0.85 2005 1500 0.85 2005 2000 1.5 2004 8002004 2 6002004 0.8 600 0.6 800 0.6 0.8
263826492650 26392651 26502652 2651 2622 Düsseldorf2653 Regionalver 2652 2639 5170036 Detmold Wesel 2653 2640 Detmold Detmold 2654 2641 46514 Detmold Detmold Schermbeck 5762020 2642 Höxter Detmold Detmold 5762020 2643 Höxter Detmold Detmold 37671 5762020 Höxter Höxter Detmold 37671 354617 5762020 Höxter Höxter 37671 5762020 Höxter Höxter 5724280 37671 Höxter 37671 Höxter 520415 520708 5741170 520830 5741120 520829 5740930 520626 5740710 5740940 2614 2615 2594 Düsseldorf Düsseldorf 5166028 Viersen 47918 Tönisvorst 324956 5690780 26122613 2613 2614 2592 Detmold 2593 Düsseldorf Detmold Düsseldorf 5166028 5758024 Viersen Herford 47918 32584 Tönisvorst Löhne 324735 479118 5690920 5788080 26102611 2611 2612 2590 Detmold 2591 Düsseldorf Detmold Düsseldorf 5154036 5762008 Kleve Höxter 37688 47533 Beverungen Kleve 519676 5722200 303527 5744730 2609 2610 2589 Detmold Detmold 5762008 Höxter 37688 Beverungen 519685 5722340 27612777 2762 2778 2770 Arnsberg 2792 Münster Arnsberg Münster 5974020 Soest 5570008 Warendor 59269 59590 Beckum Geseke 428277 470238 5732310 5718790 260326042605 26042606 26052607 2606 2583 Düsseldorf2608 Düsseldorf 2607 2584 Düsseldorf 5166036 Düsseldorf Viersen 2608 2585 Düsseldorf 5166036 Düsseldorf Viersen 2609 2586 47877 Düsseldorf Willich 5166036 Düsseldorf Viersen 2587 47877 Münster Willich 5166036 Viersen 2588 47877 Düsseldorf Willich Münster Düsseldorf 47877 Willich 5154008 Kleve 5558016 Coesfeld 48249 331268 Dülmen 46446 Emmerich am Rh 331547 5681180 331554 311354 5681520 331824 5681230 5749090 380599 5681520 2655 5753850 26562657 26562668 26572669 2658 2645 Detmold2751 2669 2646 Detmold2760 Detmold 2670 2647 Detmold Detmold 5762020 2752 2665 Höxter Köln Detmold 5762020 2761 2666 Höxter Köln 37671 5762020 Höxter 2757 Höxter Münster 37671 Köln Höxter 2769 Arnsberg Münster 37671 Köln Höxter Arnsberg 5570016 Warendor 5362036 5974004 48317 Rhein‐Erft‐ Soest Drensteinfurt 50259 Pulheim 5374012 Oberbergi 520482 51647 Gummersbach 520703 59609 Anröchte 5741370 520636 415081 5741320 408892 5741520 5735180 339813 5658020 449337 5653780 5707630 247224732474 24732602 2474 2475 2430 Köln 2603 2431 Köln 2432 Düsseldorf Köln Düsseldorf 2582 Düsseldorf 5158032 Köln Düsseldorf Mettman 42555 5154036 Velbert Kleve 5370004 Heinsberg 41812 5370004 Erkelenz Heinsberg 47533 Kleve 41812 Erkelenz 368504 311635 5693060 311792 303831 5654840 5655130 5744800 2654 2655 2644 Detmold Detmold 5762020 Höxter 37671 Höxter 520423 5740940 68
APPENDIX B
B1: Long-Term Forecast (Spot Market)
Year Spot Market Price Forecast(Spot Market Price) 2002 29.55 2003 38.19 2004 36.6 2005 59.09 2006 61.25 2007 46.89 2008 77.16 2009 45.95 2010 51.9 2011 57.54 2012 46.63 2013 40.81 2014 34.77 2015 33.13 2016 29.8 2017 34.24 2018 44.26 2019 37 37 2020 36.33213622 2021 35.66427245 2022 34.99640867 2023 34.32854489 2024 33.66068111 2025 32.99281734 2026 32.32495356 2027 31.65708978 2028 30.98922601 2029 30.32136223 2030 29.65349845 2031 28.98563467 2032 28.3177709 2033 27.64990712 2034 26.98204334 2035 26.31417957 2036 25.64631579 69
2037 24.97845201 2038 24.31058824 2039 23.64272446 2040 22.97486068 2041 22.3069969 2042 21.63913313 2043 20.97126935 2044 20.30340557 2045 19.6355418
B2: Short-Term Forecast (Spot Market)
Date Spot Market Price Forecast(Spot Market Price) May-18 32.61 Jun-18 42.09 Jul-18 49.24 Aug-18 55.89 Sep-18 53.85 Oct-18 52.11 Nov-18 56.62 Dec-18 46.13 Jan-19 47.08 Feb-19 41.87 Mar-19 28.43 Apr-19 35.21 May-19 37.38 37.38 Jun-19 36.19692031 Jul-19 38.00823912 Aug-19 39.81955793 Sep-19 41.63087675 Oct-19 43.44219556 Nov-19 45.25351437 Dec-19 47.06483318 Jan-20 48.876152 Feb-20 50.68747081 Mar-20 52.49878962 Apr-20 54.31010844 May-20 56.12142725 70
B3: Tendering Price Forecast
Date Tendering Price Forecast(Tendering Price) 01/05/2017 57.06806956 01/08/2017 42.83861579 01/11/2017 38.19851489 01/02/2018 47.3377495 01/05/2018 57.27974704 01/08/2018 61.61862455 01/10/2018 62.55371756 01/02/2019 61.06495844 01/05/2019 61.31845611 61.31845611 01/08/2019 63.78423967 01/11/2019 66.01391509 01/02/2020 68.2435905
71
APPENDIX C
C1: Results of WTGs Selection and Determination of WTG-Models/Manufacturer
72
No. of WTs E‐82 E2 2 3.8 5.1 2.6 7.5 7.8 11.315.0 11.7 15.7 3075 3000 2350 3050 2350 3000 3075 2400 2300 Power Cap kW Power Cap kW Power Cap kW No. of WTs E‐115 No. of WTs N117‐ ‐ V 4.9 6.5 5.9 1.4 2.1 2.7 3.3 2015 2018 2017 2017 2015 2017 2017 2017 2018 Year Year Year No. of WTs E‐115 No. of WTs E No. of WTs ‐ ‐ V 3.8 5.0 5.9 1.7 2.6 3.5 2.5 140 119 120 103.9 135.4 135.48 138.38 149.08 138.38 No. of WTs E No. of WTs E No. of WTs Height Height Height 4100 6150 8200 7650 11475 15300 18000 2700036000 8.8 11.7 8.8 11.7 Type E‐92 E‐115 Installed Cap Type E‐101 E‐92 E‐115 Installed Cap Type V112 V112 ‐ 3075 N117.2400 E‐82 E2 Installed Cap 3717 3130 2925 2903 3467 4281 Scenario 1 Scenario 2 Scenario 3 Scenario 1 Scenario 2 Scenario 3 Scenario 1 Scenario 2 Scenario 3 Nearby WT Nearby WT Nearby WT 2649‐2398 2654‐3617 2419‐1712 2377‐5008 3535‐1259 2692‐476 FID FID FID T T T 610m 2265m 1696m 1583m 1057m 1003m Dist. Near W Dist. Near W Dist. Near W 114 115 2646 2647 2642 2644 2645 2374 2375 2376 2792 1011 1012 2639 2640 2641 2369 2370 2371 LANUV_ID LANUV_ID LANUV_ID 477 478 2657 2658 2653 2655 2656 2425 2426 2427 2778 1259 1260 2650 2651 2652 2420 2421 2422 #DH #HG #WB OBJECTID_1 OBJECTID_1 OBJECTID_1 476 477 2656 2657 2652 26532654 2655 2654 2643242224232424 24232425 2424 2372 2373 3457m 2322‐1843 3464 5008 3191 2426 2427 2428 2377 2777 1258 1259 2649 2650 2651 2419 2420 2421 FID_ FID_ FID_ 73
4.6 3.5 2.3 3450 3300 2400 1500 3450 2350
Power Cap kW Power Cap kW No. of WTs V126 V 4.6 7.5 3.5 6.0 8.9 2.3 15.0 11.3 11.9 2018 2016 2017 2014 2018 2014 Year Year No. of WTs N117.2400 No. of WTs No. of WTs E92 V V V 5.9 8.8 8.0 4.2 6.4 8.5 5.3 149 140 120 100 116 104 11.7 10.7 No. of WTs No. of WTs No. of WTs Height Year Power Cap kW Height Height 8000 18000 27000 36000 12000 16000 14000 21000 28000 V126 V112 Type N117.2400 Installed Cap Type Vensys77 V117 Installed Cap Type E‐92 Installed Cap 195 2994 2925 V112 140 2015 3075 3497 3080 Scenario 1 Scenario 2 Scenario 3 Scenario 1 Scenario 2 Scenario 3 Scenario 1 Scenario 2 Scenario 3 Nearby WT Nearby WT 1824‐3080 1829‐2994 1325‐923 FID FID 2557m 2303m 3217m Dist. Near WT FID Nearby WT Dist. Near WT Dist. Near WT 1670 1671 1672 1694 1695 1696 1698 1699 1700 1701 1080 1082 1081 1673 1674 1675 1676 LANUV_ID LANUV_ID 1825 1826 1827 1845 1846 1847 1849 1850 1851 1852 1327 1329 1328 1828 1829 1830 1831 #KS #HM #HW OBJECTID_1 OBJECTID_1 1824 1825 1826 1844 1845 1846 1848 1849 1850 1851 1326 1328 13251327 1326 10791827 1828 1829 1830 5289 184118421843 1842 1843 1844 1691 1692 1693 3457m 4513m 2424‐1843 1797‐1848 2008 FID_ FID_ FID_ OBJECTID_1 LANUV_ID 74
3000 2500 2050 3300 2300
Power Cap kW Power Cap kW 4.8 6.4 1.3 2.0 2.7 3.2 2018 2016 2017 2016 2016 Year Year No. of WTs E‐115 GE 2.5‐120 N N 5.2 7.0 5.5 7.3 1.2 1.8 2.4 3.5 3.7 134 120 78.5 138.38 2016 2300 138.38 135.48 No. of WTs No. of WTs No. of WTs MM92 Height Year Power Cap kW Height Year Power Cap kW Height Height 7500 3.3 4000 6000 8000 8000 7500 1125015000 4.9 6.5 12000 16000 11250 15000 Installed Cap No. of WTs E‐82 Type E‐115 TES Type E‐82 N131/3300 Installed Cap Type E‐82 GE 2.5‐120 Installed Cap Type MM92 Installed Cap 177 3157 3113 3528 Scenario 1 Scenario 2 Scenario 3 Scenario 1 Scenario 2 Scenario 3 Scenario 1 Scenario 2 Scenario 3 Scenario 1 Scenario 2 Scenario 3 Nearby WT Nearby WT 3445‐1872 1952‐1869 1939‐2606 2450‐2603 1475‐1479 FID FID 3324m 2927m 1308m 1775m 649m Dist. Near WT FIDDist. Near WT Nearby WT FID Nearby WT Dist. Near WT Dist. Near WT 1721 1722 2583 2584 2585 2586 1266 1267 1268 1269 1270 LANUV_ID LANUV_ID 1872 1873 2604 2605 2606 2607 1478 1479 1480 1481 1482 #DK #ML #VW OBJECTID_1 OBJECTID_1 221122122213 2212 2213 2214 2119 2120 2121 18691870 1870 1871 1719 1720 2399m 2683‐1720 3445 1871 1872 2603 2604 2605 2606 1477 1478 1479 1480 1481 22092210 2210 2211 2117 2118 2905m 1958‐2209 3157 FID_ OBJECTID_1 LANUV_ID FID_ FID_ FID_ OBJECTID_1 LANUV_ID