Effect of logistic management on power generation efficiency of offshore wind turbines - Het effect van logistiek management op de efficiëntie van energie productie van offshore wind turbines

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Delft University of Technology

FACULTY MECHANICAL, MARITIME AND MATERIALS ENGINEERING

Department Marine and Transport Technology Mekelweg 2 2628 CD Delft the Netherlands Phone +31 (0)15-2782889 Fax +31 (0)15-2781397 www.mtt.tudelft.nl

Specialization: Transport Engineering and Logistics Report number: 2016.TEL.8069

Title: Effect of logistic management on

power generation efficiency of offshore wind turbines

Author: H.A. (Jasper) Roosendaal

Het effect van logistiek management op de efficiëntie van energie productie van offshore wind turbines.

Assignment: Literature assignment

Confidential: No

Initiator (university): X. Jiang

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Delft University of Technology

FACULTY MECHANICAL, MARITIME AND MATERIALS ENGINEERING

Department Marine and Transport Technology Mekelweg 2 2628 CD Delft the Netherlands Phone +31 (0)15-2782889 Fax +31 (0)15-2781397 www.mtt.tudelft.nl

This report consists of 44 pages . It may only be reproduced literally and as a whole. For commercial purposes only with written authorization of Delft University of Technology. Requests for consult are only taken into

Student: H.A. (Jasper)

Roosendaal Assignment type: Literature assignment Supervisor (TUD): X. Jiang Creditpoints (EC): 10

Specialization TEL

Report number: 2016.TEL.8069 Confidential: No

Subject:

In Europe, more than 900 MW of offshore wind energy capacity has been installed in and around the North and Baltic seas. Jack-up vessels are generally deployed to install offshore wind turbines in shallow water up to 55m of water depth. As wind farm construction moves towards deeper waters, a heavy lift floating vessel operating on dynamical positioning mode can be used for installation. The advantages of offshore installation versus onshore turbines include the size of an offshore wind turbine is not limited by road or rail logistical constraints; the visual and noise annoyances of wind turbines can be avoided if the turbines are installed a sufficient distance from shore; vast expanses of uninterrupted open sea are available and the installations will not occupy land, interfering with other land uses. On the other hand, a higher capital investment is required for offshore wind turbines because of the costs associated with marinization of the turbine and the added complications of the foundation, support structure, installation and decommissioning. Moreover, offshore installations are less accessible than onshore installations, which raises the operations and maintenance costs and possibly increases the downtime of the machines.

The power generation efficiency is an important index to assess the feasibility of an offshore wind farm. It is usually evaluated in terms of produced energy against initial investment and /or lifetime cost. In this literature assignment, the student is demanded to review the development of Power generation efficiency evaluation of offshore wind turbines. The following aspects are required to be illustrated in the report:

 What are the main influential aspects / parameters of power generation of offshore wind turbine?  How to evaluate the power generation efficiency (in Europe and other places)? Associated rules /

standards/ EU projects, etc.

 How to improve the power generation efficiency of offshore wind turbines from a logistic management perspective? Using a simple model to explain your idea.

 Conclude and discuss what you have found and studied.

This report should be arranged in such a way that all data is structurally presented in graphs, tables, and lists with belonging descriptions and explanations in text.

The report should comply with the guidelines of the section. Details can be found on the website. If you would like to know more about the assignment, you may contact with Dr. X. Jiang through x.jiang@tudelft.nl. The professor,

The supervisor, X. Jiang

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Effect of logistic management on

power generation efficiency of

offshore wind turbines

Literature Assignment

ME2110 – Literature Assignment

Jasper Roosendaal (1508792)

Supervisor: Xiali Jiang

Date: 9 december 2016

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Preface

This literature assignment is one of the final steps in obtaining my MSc degree at the TU Delft. I started this literature assignment after a 4 months internship and one full-time month working at Heineken during the spring of 2016. The knowledge acquired during this research assignment focussing on supply chain definitely contributed to bringing this literature research to a deeper level

During the MSc program I have seen different parts of the transport and logistic sector. Additionally to the MSc program my visits to various companies such as Pon, Philips, SEW Eurodrive, APM Terminals, and Heineken helped me to generate a broad view on logistic management.

I realize that conducting a literature assignment would have never been possible without support of my supervisor Xiaoli Jiang. The weekly meetings promoted the consistency and the progress of this literature study and gave this research the right direction. Parallel to the supervision of Xiaoli I would like to thank Margot Kromhout for her support. She works as an construction engineer at Vattenfall and provided me with the practical background of this study.

The variation between the subjects Logistics and the Economics have made this research challenging and enlightening.

Delft, November 16th₂₀₁₆

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List of Figures

Figure 1: Evolution of water depth and distance from shore over time ... 3

Figure 2: Main support structure in relation to water depth ... 4

Figure 3: Principles of variation in LCOE ... 8

Figure 4: Cost of Energy split up in a hierarchy diagram (Camilla Thomson, 2015) ... 9

Figure 5: Life Cycle of a wind farm ... 9

Figure 6: CoE relational tree ... 10

Figure 7: The economics of wind systems ... 11

Figure 8: Trend LCOE in the past years (Vattenfal Conference, 2016) ... 12

Figure 9: Levelised cost of wind electricity over time, developed market average [USD/MWh] (Salvatore, 2013) ... 13

Figure 10: LCOE of renewable energy technologies and conventional power plants at locations in Germany in 2013 ... 13

Figure 11: Global LCOE of Energy in Q2 of 2013 ... 14

Figure 12: Typical breakdown of capital cost for large offshore wind farm ... 16

Figure 13: Capital Cost breakdown for offshore wind power projects, in shallow waters (Kaldellis, 2012) ... 16

Figure 14: Average investment cost per MW related to offshore wind farns in Horns Revn and Nysted . 17 Figure 15: Investment in offshore wind farms, million [EUR/MW] ... 18

Figure 16: Simplified overview of factors in power performance measurement (Lopez, 2014) ... 20

Figure 17: Typical wind power output with steady wind speed ... 21

Figure 18: Illustration of the Betz limit ... 22

Figure 19: The effect of Weibull shape factor on the probability density of wind velocity of some potential sites ... 23

Figure 20: OMCE concept of preventive maintenance control (Pieterman, 2011) ... 24

Figure 21: Failure and power production scenarios (Besnard, 2009) ... 26

Figure 22: General structure of the model with controllable options and uncontrollable external factors 28 Figure 23: Cost distribution of different vessel charter ... 30

List of Tables

Table 1: Offshore wind farm CAPEX cost breakdown ... 18

Table 2: Percent of average transmission losses of system (N. Barberis Negra, 2005) ... 23

Table 3: Comparison of vessel currently used for maintenance in the offshore wind industry ... 31

List of definitions and abbreviations

AEP – Annual Energy Production CAPEX – Capital Expenditures CF – Capacity Factor

ESWE – Expected Storage Wind Energy EWES – Expected Wind Energy Supplied GWEC – Global Wind Energy Council IEA – International Energy Agency

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O&M – Operations & Maintenance

OMCE – Operation and Maintenance Cost Estimator OPEX – Operational Expenditures

PGE – Power Generation Efficiency WACC – Weighted Average Cost of Capital

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1 Summary

Over the last decade, there has been a considerable growth in the offshore wind power capacity and the amount of electricity produced is rapidly increasing every year. Besides the rapid growth also the objectives of governmental institutions become more and more challenging.

The wind power industry today is faced with the challenge of becoming competitive and thus the Power Generation Efficiency is an important index to assess the feasibility of an offshore wind farm or to compare offshore wind to other forms of renewable energy. The advantages of offshore installation over onshore wind include the size of an offshore wind turbine is not limited by road or rail logistical constraints and the higher wind revenues to be obtained at sea. On the other hand, a higher capital investment is required for offshore wind turbines because of the costs associated with marinization of the turbine and the added complications of the foundation, support structure and installation.

The cumulative installed capacity of offshore wind power in the European Union has increased from 532 MW in the year 2003 to 6600MW in the end of 2013, which represents an annual growth of about 29%. In addition to the growth in installed capacity the offshore wind turbines are installed in increasing water depths and the distance from shore is also increasing. The transition to deeper waters requires a development from different perspectives.

This literature study focusses on the potential logistic improvements and builds up an understanding of the Power Generation Efficiency (PGE) of offshore wind farms. This is done by reviewing the existing literature on this topic. Since the Levelised Cost of Energy is the widely used term to express the PGE within the wind industry and other energy resources, this literature assignment focusses on determining the main influential parameters of LCOE.

Basically, the LCOE is formulated as the Cost of Energy divided by the Energy Produced. The Cost of Energy is again split up in the capital cost, operational cost and decommissioning cost the lifetime of an offshore wind farm. The Energy produced is subdivided in wind resources and losses.

On the first place the capital cost has the most impact on the LCOE. As part of the capital cost the installation of the turbine is the most significant parameter. The Energy produced influences the LCOE directly. Caused by downtime, which is indirectly caused by failures and maintenance. Of these two the failures the most significant parameter.

During the operational span of an offshore wind farm, a number of scheduled and unscheduled maintenance tasks have to be performed in order to keep the turbines operational and to sustain power generation. The preventive and reactive maintenance that impacts the availability of an offshore wind farms is directly decreased the LCOE. In addition to the maintenance strategies the lifting operations by using an external crane accounts for more a significant percentage of the overall O&M cost. Finally, in this literature report the effect of logistic management is concluded and recommendation stated for further research.

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2 Introduction

In the introduction the background information regarding the assignment is given. Also this chapter presents how the assignment is build up and explains the structure of the assignment and lists the objectives and scope. In addition to the structure also the approach is covered in this introduction.

Background information on Offshore Wind

2.1

The current installed capacity of offshore wind turbines connected to the grid in Europe is approximately 11,6 GW (Wind Europe, 2016). About 73% is installed in the North Sea and around 16% of the total installed wind capacity accounts to the UK (GWEC, 2015). Over the last decade, there has been a considerable growth in the offshore wind power capacity and the amount of electricity produced is rapidly increasing every year. For instance, the cumulative installed capacity of offshore wind power in the European Union has increased from 532 MW in the year 2003 to 6600MW in the end of 2013, which represents an annual growth of about 29%. (Shafiee, 2014) The annual growth of the offshore wind industry is sufficient compared to the solar energy sector, which has an annual market growth of 15% over 2016. On top of that the annual growth rate of the installed capacity of offshore wind is increasing versus the decreasing annual growth rate of solar energy. (Europe, 2016)

The Global Wind Energy Council expects a growth in the next four years to 70 GW installed capacity towards 2020. This tends to be in line with the targets set by the European Union. GWEC expects an installed capacity in the European water of 23.5 MW in 2020. To reach this the energy produced outside the European waters has to growth significantly. Steven Chu (Secretary of Energy 2009-2013 of the United States) announced during his time in the office a national strategy to achieve an installed capacity of 10 GW in 2020. The contribution of the United States is serious to achieve the 70GW in 2020 globally. (Beaudy-Losique, 2011)

This paragraph expresses a more global view on the offshore wind industry. China outperformed the EU in cumulative installations in 2015. These installations include both onshore and offshore installations. Currently, the Chinese offshore sector has 1 GW of projects in the construction phase. And globally is the largest market outside the European water. China accounts for approximately 8,4% of the global offshore wind market. However Europe is by far the largest market in the offshore wind industry, governments outside Europe are setting ambitious targets. Countries as Japan and South Korea starting to take off and have their first turbines in the water. In contrast to the Asian countries the American offshore wind energy is still in its early stages. At the moment the average offshore wind farm is around 43.3 km from shore with a depth of 27.1 meter. The average size of an offshore wind turbine is 4.2 MW. (GWEC, 2015)

The advantages of offshore wind turbine installation versus onshore turbines include the size of an offshore wind turbine is not limited by logistical constraints of road or rail. The visual and noise annoyances of wind turbines can be avoided when the turbines are installed at a sufficient distance from shore. The vast expanses of uninterrupted open sea are available and the installation will not occupy land or interfere with other land users. On the other hand, a higher capital investment is required for offshore wind turbines because of cost associated with marinisation of the turbine and the added complications of the foundation, support structure, installation and decommissioning. In addition, contractors have to transport the electricity to shore, which comes to a high cable costs and electricity losses. Moreover, offshore installations are less accessible than onshore installations, which raises the operations and maintenance costs and possibly increases the downtime of the machine. (IRENA, Renewable energy technologies: Cost analysis series, 2012)

Given aforementioned costs associated with installing turbines offshore, the most offshore wind farms are installed in the shallow waters. Actually, (Kaldellis, 2012) stated that on the other hand the average

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distance to shore was lower than 5 km, while today the distance from shore is almost 30 km. As earlier mentioned in this introduction different studies show a trend in increasing water depth and distance from shore, which is shown in Figure 1. This movement to deeper waters is not clarified in the literature yet, however the practical reason is likely to be that all available nearshore sites are depleted, the resistance of coastal residences is, combined with higher production due to higher winds offshore combined with decreasing installation costs for increasing turbine sizes. Deep offshore designs will need to adapt to these increased turbine sizes to achieve the optimal balance between power production and cost.

Figure 1: Evolution of water depth and distance from shore over time

Next to the shift of distance to shore and water depth, the development in types of wind turbine is recognisable. As shown in Figure 2 the main support structures are related to the water depth. The transition to deeper water causes a boost in technical development in types of wind turbines and its support structures. Currently, floating wind turbines are becoming a realistic option. The installation of these floating wind turbines is in maturation at the moment. Despite of the impact of technical development, in this literature focusses on the logistic point of view. Logistically, the impact of logistics will become bigger when offshore wind farms are located further from shore.

The Paris Agreement is a bridge between today's policies and climate-neutrality before the end of the century. Consequently in line with this agreement nine European countries have pledged to cooperate on future offshore wind farm project in the North Sea. The countries that are involved are; Netherlands, Germany Belgium, Luxembourg, France, Denmark, Ireland, Sweden and Norway. They collaborate on the planning and construction of offshore wind farms. This project was initiated by the Dutch minister of Economic Affairs Henk Kamp. He proposed to investigate in new technologies for generating renewable energy at the North Sea. The outcomes of this collaboration are in line with the literature study. The collaboration is expected to reduce the construction costs, as well as to help harmonise national regulations for grid management, subsidies and permits, and coordinate safety requirements. (Magazine, 2016)

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Figure 2: Main support structure in relation to water depth

The actual growth rate greater than 29% indicates that offshore wind is a reasonable alternative to other Energy sources. The industry today is faced with the challenge of becoming competitive and thus the Power Generation Efficiency is an important index to assess the feasibility of offshore wind energy. The volume and growth of offshore wind energy causes that the power generation efficiency becomes more and more important for the different stakeholders. This situation implies that the evaluation of costs incurred during development, installation and operation is one of the most pressing issues in this industry at the moment. Unfortunately, actual costs analyses suffer from less resilient input data and the application of simple methodologies (Ederer, 2014). Frequently, the power generation efficiency (PGE) is expressed in the underlying economical term as the Levelised Cost of Electricity (LCOE). For wind power the LCOE represents the sum of all cost of a fully operational wind power system over the lifetime of the project with financial flows discounted to the common year. The principal components of the LCOE of wind power systems include the capital costs, operation and maintenance costs divided by the annual energy production (AEP) (IRENA, 2012). The LCOE is mostly denoted in euros per Mega Watt hour [€/MWh]. Another advantage using the LCOE as an evaluation for the PGE is that is makes it easy to compare with between different wind project and other energy sources.

Scope

2.2

This literature assignment aims to develop an understanding of power generation efficiency of offshore wind farms by reviewing the existing literature on this topic. Since the Levelised Cost of Energy is the widely used term to express the PGE within the wind industry, this literature assignment focusses on determining the main influential parameters of LCOE. In addition the state-of-art of each of these parameters is evaluated and it is determined if and how much these parameters are influenced by operations and logistics. So, this literature review identifies the points of focus for the offshore wind business from a logistic management perspective. The main geographical focus in this literature assignment is Europe, however the status of the rest of the world is also inquired.

Approach

2.3

To review the development of power generation efficiency in terms of Levelised Cost of Energy the following aspects are illustrated:

 Why is LCOE the most widely used term to evaluate the Power Generation Efficiency and what is the state-of-art level of LCOE for offshore wind turbines?

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 What are the main influential parameters of LCOE of offshore wind turbines?

 On which parameters of LCOE does logistic management have an effect and how could this contribute to reduce the LCOE?

In this literature report the questions above will be answered. The main influential aspects or parameters will be treated during each chapter of this report. Even more important is how the PGE needs to be treated? What are the influences of subsidies on the LCOE of offshore wind farms? The influences of subsidies and the efficiency depending on the rules and regulations can be derived from the tenders done by the companies and judged by the governmental institutions? Furthermore, the resulting question of this research is how to improve the LCOE of offshore wind turbines from logistic management perspective? The parameters that are related to a logistic management perspective are researched in how to improve the LCOE. The parameters in the figure are involved in the logistic management of building and offshore wind farm. Obviously, other parts of the diagram are also involved to the logistics of building and operating the offshore wind farm. However (Camilla Thomson, 2015) stated that offshore wind is by some margin more expensive than onshore wind, nuclear and gas generation. It appears to be substantial to reduce costs significantly in 2020.

In this literature study some parameters are addressed as logistic issues. Parallel to the logistic issues treated in the core of this literature study, some parameters are addressed as technical issues. These parameters will not be treated in depth, because this study is written from an logistic point of view rather than a technical point of view.

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3 Levelised Cost of Energy

As earlier explained the reason for this assignment is to clarify the impact of logistics on the PGE. In the first part of this chapter the motivation for the application of the LCOE as an index for the PGE is clarified. In the second part of this chapter the state-of-art of the LCOE is further researched. How is the Levelised Cost of Energy expressed and what are the developments over time?

In a broader view, the renewable energy production is valued by the Levelised Cost of Energy. Approximately the total installed power generation accounts for about 6,500 GW. (Siemens, 2014). Siemens calculated that the expectations are that the total installed power production will almost double to 2030 to about 10,500 GW. In this growth the International Energy Agency assumes that more than 25% of the world’s energy consumption is generated by renewables and again 25% should come from wind in 2035. Concluding, around twenty years from now 656 GW is coming from wind power. To compare different energy resources and evaluate investment decision the LCOE is set as a suitable definition.

LCOE as index for PGE

3.1

This section is the backbone of this research. In the end all the parameters that influence the LCOE are threated. In the last chapter the parameters that are influenced by logistic management are highlighted. A clear explanation of the LCOE is crucial to discuss the impact of logistics on the Power Generation Efficiency.

Definition of LCOE 3.1.1

The Levelised Cost of Energy is one of the utility industry’s primary metrics for the cost of electricity produced by a generator. It is calculated by accounting for all of a system’s expected lifetime costs (including construction, financing, fuel, maintenance, taxes, insurance and incentives), which are then divided by the system’s lifetime expected power output (kWh) (Advisor, 2016). In this section the definition of LCOE is explained. In the literature this index (LCOE) is interpreted differently in some details. Technically the first question of this literature report is the LCOE the right index to analyse when it comes to elect the right energy source? The LCOE represents only the expected life-time of a power station. It is calculated as the ratio of the lifetime sum of discounted capital and operating costs, including fuel, divided by the lifetime sum of discounted electricity output. (Siemens, 2014). Another organisation, the Crown Estate, which is an independent commercial business that invests in sustainable assets, interviewed many companies in the business. Their collaboration with companies, organisations and other interested parties resulted in a validated assessment of the potential cost reduction for offshore wind power. The Crown Estate defined the LCOE as the sum of discounted lifetime generation costs (£) divided by the sum of discounted lifetime electricity output (MWh). They set the generation cost as the costs that includes all capital, operating, and decommissioning cost incurred by the generator/developer. Over the lifetime of the project, including the transmission costs. The discounted in the definition of the Lifetime cost and electricity output is the Weighted Average Cost of Capital (WACC) over the lifetime of the project.

Levelised Cost of Energy formula:

𝐿𝐶𝑂𝐸 =𝑃𝑟𝑒𝑠𝑒𝑛𝑡 𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝑇𝑜𝑡𝑎𝑙 𝐿𝑖𝑓𝑒𝑡𝑖𝑚𝑒 𝑐𝑜𝑠𝑡 (€) 𝐿𝑖𝑓𝑒𝑡𝑖𝑚𝑒 𝐸𝑛𝑒𝑟𝑔𝑦 𝑃𝑟𝑜𝑑𝑢𝑐𝑒𝑑 (𝑀𝑊ℎ)

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Generally the above given formula describes the idea of the LCOE. The reason the general formula is presented is that there is no official standard governing calculation of LCOE. There are several methodologies in use. Apparently the IEA method is the most generally used. In (Thomson, 2015) a list of ten sources are listed that are using the IES method. The detailed formula used is given here:

𝐿𝐶𝑂𝐸 = ∑ 𝐶𝑡+ 𝑂𝑡+ 𝐹𝑡+𝐷𝑡 (1 + 𝑟)𝑡 𝑇 𝑡 ∑ 𝐸𝑡 (1 + 𝑟)𝑡 𝑇 𝑡

In this detailed formula; C is the Capital Cost [€]; O is the operations and maintenance cost [€]; F is the fuel cost [€]; D is the decommissioning cost [€]; E is the electricity produced [MWh]; r is the discount rate [%]; and t is the year in which a cost occurs during the project lifetime [T]. Regarding a wind farm, during the power generation there is no fuel burned, so for Ft is zero for offshore wind farms. The indirect fuel

use for transport is associated with many activities within the other parameters in the lifetime cost part. (Thomson, 2015)

However this formula calculates a valuable index, this formula does not includes the environmental costs associated with energy production. Nowadays governmental institutions have begun to address these issues and try to quantify them in costs. These various financial instruments are not taken into account in this research.

For the index LCOE (Thomson, 2015) has built a structure for different assumptions, methods and uncertainties. These can be divided in four categories: variation in input data; variation in financial functions; variations in system boundaries; and variation in LCOE methodology.

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Figure 3: Principles of variation in LCOE

The report of life cycle costs from (Thomson, 2015) provides a brief explanation of the terms used in Figure 8:

 Cost Uncertainty: Fundamental issues around the definition of cost. This article states several questions around cost uncertainties; Is the actual cost of a component or system? Is it purely the materials used? Do the costs include labour costs and overhead of the business? These questions create the cost uncertainty.

 Time Frame: Wind farms are built in different years and therefore subject to economic and financial factors including currency, inflation and other financing terms.

 Locational Data: The costs associated with components is location dependent. Some countries have difficulties in comparisons with other countries. Most studies have typical values which other countries don’t use.

 Capacity Factor: Defined as the ratio between the energy production of the wind farm, compared to the maximum potential output in case the system is capable to operate the full capacity over the same time period. The value of the capacity factor can vary because of substantial inter-annual wind speed variation.

 Discount Rate: As mentioned before, the discount rate is weighted as the WACC (Weighted average cost of capital). The higher expected discount rate reduces the future costs while leaving the capital costs unchanged. This could be highly important when it comes to comparing Capital Cost with operating and Maintenance Cost.

 Risk Adjustment: When using the same discount rate for different technologies over time it ignores the risk differences.

 Currency and Year: There are substantial fluctuations in the currency values over time. Lot of studies in the offshore wind industry has been executed in the United Kingdom. This can have a big impact on the costs.

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 Taxation Rules and Rates: Most studies apply the IEA method.

 Scope of analysis: Different studies set different physical system boundaries. For example, is the grid connection included in the LCOE?

 Cost components Considered: There are cost associated with project development, which are detailed in most work. For example, decommissioning cost, which tend to be more uncertain than other costs. For example, nuclear differs with high decommissioning cost and more uncertainties than wind energy.

 Design life: Considerably a standard wind farm has a lifetime of 20 years. A shorter design will tend to raise the LCOE. This can cause higher O&M costs compared to the Capital Cost

 Scope of analysis: Different studies set different physical system boundaries for analysis: a single turbine, a farm including other infrastructure such as grid connection, or inclusion of ‘knock on effects’ elsewhere in the system – this is considered in detail in Section 3.

In addition to Figure 3 there are more subjects that needs to be researched to discuss the Power Generation Efficiency. Not only the parameters or the buttons that can be turned are relevant, the cost of energy or earlier called the LCOE is applied in many different scopes with a variation of different assumptions, methods and uncertainties that are not covered by only using the split up that is shown in Figure 4.

The PGE is not only evaluated in LCOE. The PGE can be expressed in the expected wind energy supplied (EWES). This is a useful index for a power system. The ESWE index also provides useful information in determining storage capacity when considering energy storage options, and battery charging and discharging patterns can be estimated using the hourly distribution of this index. (Karki, 2004). These indexes (EWES and ESWE) are not commonly used and therefore not chosen as a relevant index to measure the PGE.

Figure 4: Cost of Energy split up in a hierarchy diagram (Camilla Thomson, 2015)

Although the energy production is maximized by placing more turbines in the wind farm area, this also makes the lifetime cost rise. Hence, the design goals are conflicting, meaning there is not a single solution for the problem. The LCOE evaluates that development and the trade-off of this statement. The structure of the wind farm life cycle is illustrated in the diagram below.

Cost of Energy

Lifetime Cost

Capital Costs Wind Turbines Balance of Plant Operations & Maintenance Operations Spare Parts Decomissioning Energy Produced Availability Reliability

Efficiency Wind Turbines

Array Efficiency Wind Resources

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It is important to note that LCOE, and cost in general, is not the only important factor in the economics of electricity generation; investors will also look at overall return on investment, which requires estimates of revenue to be determined. In a market setting this is a complex exercise, and the source of much uncertainty and risk. The extent to which this uncertainty can be mitigated is a large determinant of whether a particular generating technology can be regarded as a viable investment. As such, LCOE alone is rarely used for actual investment decisions but it is regarded as a useful tool for policymaking, as long as the limitations are well understood. (Engineering, 2014) (Thomson, 2015)

In addition to the Cost of Energy split up made by (Thomson, 2015), (Valverde, 2014) has presented a similar relational tree that illustrates the Cost of Energy. This tree shows also the wind farm costs as a result of the Capital Cost and Operational Cost (CAPEX and OPEX). In Figure 6 the decommissioning cost are neglected. All the parameters are taken into account in this literature research. This article literally argues that the LCOE is the variable that is used to represent the efficiency of a project as a whole. Other financial parameters (IRR, NPV, etc.) are also interesting. These parameters decide whether a project is feasible or not purely from an economic perspective.

Another example which has almost the same structure as the previous two diagrams is the one constructed by the (IRENA, Renewable energy technologies: Cost analysis series, 2012).

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Figure 7: The economics of wind systems

Assessing the cost of a wind power system requires a careful evaluation of all of these components over the life of the project. In this hierarchical diagram the price of the turbine, foundation and the rotor diameter, and other physical characteristics are connected, because the price of a turbine depends on the physical characteristics of the turbine. In addition, to the price of the turbine also the costs of O&M are in relation to, for example the hub height and the rotor diameter.

State of art in LCOE

3.2

In this section the state-of-art of the Levelised Cost of Energy is analysed following existing literature. In addition, key questions in this section are; What are the relevant trends in LCOE of Offshore Wind Energy? What is the trend compared to other energy resources? What are the goals set by governmental institutions and are they realised?

First of all, there seems to be a huge decline in the level of the LCOE. Recently, a tender was won by the Danish Energy concern Dong. With a large gap between competitors Dong Energy won with 73 [€/MWh] without including the grid connection (87 [€/MWh] including grid connection). This price is 54% lower than it was in 2010. (Vries, 2016) expects that offshore wind in the near future will grow enormously and will eventually be able to compete with fossil fuel generated electricity when looking at the LCOE. This is immediately confirmed by the last bid won by Vattenfall (Weston, 2016). Vattenfall won the competition from six other developers and consortiums. Considerably they suggest that the job in Kriegers Flak can be done about 30% lower than Dong Energy bid for Borssele I and II in the Netherlands. In Figure 8 this above mentioned reinforcement in lowering the LCOE is shown. The tender Krieger Falk is not included in this figure. As earlier noticed, the winning bid prices in [€/MWh] largely depends on the distance to shore and the depth of the installation and other cost influencing factors like the grid connection. Nevertheless this decline in LCOE is noteworthy.

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Figure 8: Trend LCOE in the past years (Vattenfal Conference, 2016) The eight structural price-reducing factors are:

1. The produced offshore wind energy has grown in two years from 104 TWh to 180 TWh, resulting in a more industrialized process and lower risk perception.

2. Innovations made turbines generally better. (e.g. turbines bolted to their foundations instead of bonding with grout).

3. The number of competitive turbine suppliers has grown from two to five. 4. The power per turbine doubled from 3.5 to 7 MW.

5. The Dutch government has created an environment where real competition was possible, by publishing all relevant parcel information beforehand.

6. With the Energy Agreement (4,500 GW of offshore wind by 2023) a long-term goal was created, based on the ability and willingness of the companies to invest.

7. The designation of the transmission system operator Tennet which provides the grid connection, standardizes some difficult processes.

8. Larger wind farms generate the benefits of scale.

Despite of the fact that Figure 8 suggests that there is a decline in LCOE. Before 2014, the cost of electricity had more an increasing trend caused by higher capital cost (CAPEX).

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Figure 9: Levelised cost of wind electricity over time, developed market average [USD/MWh] (Salvatore, 2013)

The report written by the International Renewable Energy Agency (IRENA) expects that the total installed cost reduction can potentially reach the 15% in 2025. As well as many other resources IRENA argues that the most of the cost reduction will come from the installation cost. The will be achieved by the on-land assembly of the larger turbines, which will reduce the time of installation. (IRENA, THE POWER TO CHANGE: Solar and wind cost reduction potential to 2015, 2016)

The developments in the LCOE can largely be assigned to the capital costs. However the Capital Cost tripled the LCOE has only doubled. The capacity factor has been the counter factor for the overall lowering of LCOE. (Voormolen, 2014) conducted a research that took place in European countries. In the UK the LCO increased from 120 to 220 [€/MWh], while in other European countries the LCOE remained constant or even decreased (Germany and Belgium).

To give a clear expression of the LCOE, this section show a comparison to other energy resources. Although in 2014 the LCOE was estimated around

the 160 [€/MWh], which can be concluded from Figure 10 as well. Currently, in the end of 2016 the LCOE of energy has dropped for some winning tenders to the same level as brown coal displayed in this graph. Another picture of global LCOE for different energy sources can be seen in Figure 11. Noticable is the price for the renewable energy sources contrary to the conventional resources. The power plants using fossil fuel, hydro power, nuclear energy and biomass have an LCOE between the 50-100 [€/MWh]. However, big differences can be seen in offshore wind which ranges between 220-230 [€/MWh] globally. The conventional power plant seems not to get cheaper over time, in contrast to the renewable energy sources for which the LCOE is decreasing every year as earlier mentioned. Clearly the last two years there has been made a lot of progress regarding the LCOE of offshore wind energy.

Figure 10: LCOE of renewable energy technologies and conventional power plants at locations in Germany in 2013

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4 Lifetime Cost

As demonstrated in Chapter 3, the power generation efficiency of offshore wind turbines is mostly expressed in the Levelised Cost of Energy. In this chapter the Lifetime Cost of an offshore wind farm is split out. The Lifetime Cost of an offshore farm can be divided in three different segments, the Capital Cost, Operations & Maintenance Cost and the Decommissioning Cost. Firstly, the different sections of the lifetime cost will be introduced. Secondly, the impact of each section on the total of Lifetime Cost of Energy is given. Finally, the parameters which can be influenced by logistic management are highlighted. The wide range of presentations in terms of different currency, years, definition makes it hard to make direct comparisons of different sources and to build a clear state of art of the financials of the Offshore Wind Industry.

Capital Cost

4.1

Offshore wind energy is a capital intensive energy resource, which has no fuel cost during the operational stage. The installation cost of an offshore wind farm is dominated by the upfront capital cost (often referred to as CAPEX) for the wind turbines (including towers and installation). For offshore wind, capital cost is the dominant determinant of the LCOE. It typically accounts for 60-80% of the overall life cycle cost and is either expressed in terms of cost per unit capacity of wind farm [EUR/kW], as a total cost, or as a component of the LCOE [EUR/MWh].. An important factor is whether the grid connection is included in the capital cost or not. This has direct impact on the percentage capital cost of the total LCOE. (Camilla Thomson, 2015)

Breakdown of the installed capital cost for offshore wind 4.1.1

In literature different methods are used to subdivide the capital cost of an offshore wind farm. In this paragraph an overview of these different subdivisions is provided.

The capital cost of an offshore wind farm can be broken down into the following major categories according to (Institute, 2016):

- The turbine cost: Including blades, tower and transformer

- Civil works: Inter array cables and foundations, including construction cost for site preparation - Grid connection costs: This includes transformers and substations, as well as the connection to

the local distribution or transmission network

- Other capital cost: this includes the construction of buildings, control systems, project consultancy cost, etc.

Capital costs of offshore wind power projects can be divided into the following main categories according to: (Kaldellis, 2012)

 Cost of wind turbines (e.g. blades, rotor, tower, condition monitoring etc.).

 Cost of electrical infrastructure (underwater cables for collection of power and transmission to the grid, substations, transformers etc.).

 Cost of support structures.

 Cost of logistics and installation.

 Development and engineering costs (e.g. licensing procedures, permits, environmental impact assessment studies etc.).

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According to (Camilla Thomson, 2015), the capital cost can be broken down by a series of major cost item.

The following major cost items are: - The development of the project - Purchase of equipment

- Transportation - Site preparation - Installation

According to (IRENA, Renewable energy technologies: Cost analysis series, 2012) the capital investment costs are 3300-5000 USD/kW. According to (Ederer, Evaluating capital and operating cost efficiency of offshore wind farms: A DEA approach, 2014), on average, the specific capital cost is 2992 EUR/kW. Figure 12 shows an example for an early UK Round 3 offshore wind farm. This study, as most other studies, gives figures on a ‘farm’ level basis, and includes the costs of connecting the farm to the grid but excludes

interest during construction. As can be seen in the Figure, the most significant part of the capital cost is the turbine itself, which account for 45%, although the proportion is lower than for onshore turbines due as a result of the other significant cost elsewhere in the offshore wind farm.

Figure 13: Capital Cost breakdown for offshore wind power projects, in shallow waters (Kaldellis, 2012)

Later development stages sees capital costs tending to rise, with larger individual turbines and foundations (driven in part by the need for specialist installation vessels able to handle the weight and size), larger farms, deeper water and a greater distance to shore.

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The nature of offshore wind farms is such that, above and beyond the cost of the equipment itself, the cost of installation is more substantial than for onshore farms. The installation of a 4 MW turbine costs around 700,000 EUR per turbine, with 61% associated with installing the foundation, 22% the cabling within the array, and only 17% for installing the turbine itself. This accounts for around 20% of the capital costs, excluding the grid connection costs. The costs of vessels are a very substantial component of this cost.

Figure 14: Average investment cost per MW related to offshore wind farns in Horns Revn and Nysted

The investment cost of offshore wind energy also largely depends on weather and wave conditions, water depth and distance from the coast. The most detailed information on recent offshore installations comes from the UK and Sweden. The higher offshore capital costs are due to larger structures and complex logistics of installing the towers. The costs of offshore foundations, construction, installations and grid connection are significantly higher than onshore. (EWEA, 2009)

Most studies offer a range of costs, however as there is limited consistency in terms of how uncertainty in capital cost is reported, interpretation requires care. For example the IEA reports costs from a range of OECD countries from France to Belgium, while Crown Estate shows the range for a generic round 2 project. (Estate, 2012) It is clear however, that here is a substantial uncertainty about capital costs, particularly for more challenging sites.

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Figure 15: Investment in offshore wind farms, million [EUR/MW]

Another split up of the CAPEX is made by (Board, 2010). The cost component of the capex is categorized in: labour, material and other. In Table 1 a breakdown of the cost of offshore wind farm is expressed.

Table 1: Offshore wind farm CAPEX cost breakdown

Operations & Maintenance Cost

4.2

Although the operating costs are less significant than capital costs, they remain a key input to the LCOE, especially from a logistic management perspective. Before quantifying the yearly O&M costs, it is important to notify that the OPEX is not exactly the same as O&M. OPEX includes O&M, but also other annual operating expenses. It is estimated that O&M is about 50% of the total OPEX. (Voormolen, 2014). As a proportion of LCOE, operating costs account for 16-35% of the overall lifetime costs, with UK analysis in the range of 20-35%. The operating costs are higher in recent UK studies. Partially as a result of more experience with offshore wind operations and recognition of the challenge. (EWEA, 2009) Operating costs are expressed as fixed and/or variable components in a number of different ways:

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1. Fixed annual cost based on percentages of capital costs (%); a fixed annual cost per unit of capacity ([EUR/kW/year]);

2. Variable cost or Levelised Cost per unit of production ([EUR/MWh]).

The wide range of presentations makes direct comparison less straightforward. In general, operating costs for offshore wind are modest, but higher than onshore wind as a result of the challenges associated with accessing turbines. This challenge can be marked as a logistic management issue which are later discussed in more depth in chapter 6.

Breakdown of O&M Cost 4.2.1

There is a substantial variation in reported costs for operating costs: studies expressing O&M as a proportion of CAPEX suggest values around 3%, while estimated overall operating costs is around 140 ([EUR/kW/Year]). The estimated operating costs for a current round 2 scheme as being around the 180 [EUR/kW/Year] (round 2 scheme is expressed in (Estate, 2012), with half associated with operations and maintenance, just over 40% associated with grid connection charges and the balance being insurance costs. They also mentioned that unplanned maintenance costs will be around twice of the planned maintenance cost, mostly caused by the reduced availability of the turbines due to the inevitable waiting for improved weather circumstances before the maintenance can be performed. The introduction of higher capacity turbines with better energy capture and reliability and lower operating costs, has lead to as much as a 9% reduction in costs. (Young, 2015)

Decommissioning Cost

4.3

The decommissioning costs are largely neglected in studies. The discounted value is generally low, or costs are assumed to be equivalent to the salvage value of assets. (IEA, 2010) around 5% of the capital costs. Also the capital cost can be calculated per kW cost. The costs of removing the turbines and infrastructure above the seabed but ignore any residual value. It accounts for around 180 [€/kW] ‘surety bond’ to cover costs of decommissioning.

In October wind farm Lely is decommissioned as one of the first offshore wind farms in the world. Wind farm Lely was a 2MW nearshore site in the Ijsselmeer that supplied renewable energy for approx. 1200 households for over 22 years. The monopiles were retracted with the PVE-500M. This machine decommissions the foundations of offshore turbines. It has not been published what the total decommissioning cost over this project were.

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5 Energy produced

In this section the energy produced is treated by explaining the lower part of Figure 6 from chapter three. This results in a clear breakdown of the definition Energy produced. This chapter is divided in two sections: one on the Wind resource of the AEP, in which the potential of the wind as a source is discussed. The other part of this chapter is about the availability and efficiency of the offshore wind farms. Finally the influences of the logistic management on the AEP is discussed.

Wind resource

5.1

A reliable prediction of the wind resource is crucial to check whether the expansion of offshore wind energy is feasible or not. Different models can be applied to measure the control and stability of electric power systems is to maintain the balance between generated and consumed power. In the section of ‘ wind resource’ is investigated by the explanation of the p-curve, and what this all contains together wither the local wind conditions. All different factors that are influence the final output ‘ p-curve’ are displayed in Figure 16. Not all of these factors are threated in this section, because that would be more from a technical point of view rather then from the logistc point of view.

Figure 16: Simplified overview of factors in power performance measurement (Lopez, 2014)

The wind speeds fluctuate over time and the increasing use of wind turbines for power generation causes more fluctuations in the power production, mostly influenced by the size of the wind farms. The size and the geographical location of the turbine has effect on the final power production.

P-Curve 5.1.1

Figure 17 shows a sketch of how the power output from a wind turbine varies with steady wind speed. At very low wind speeds, there is insufficient torque exerted by the wind on the turbine blades to make them rotate. However, as the speed increases, the wind turbine will begin to rotate and generate electrical power. The speed at which the turbine first starts to rotate and generate power is called the cut-in speed and is typically around the 3.5 [m/s].

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Figure 17: Typical wind power output with steady wind speed

As the wind speed rises above the cut-in speed, the level of electrical output power rises rapidly as shown. However, typically somewhere around the 14 [m/s], the power output reaches the limit that the electrical generator is capable of. This limit to the generator output is called the rated power output and the wind speed at which it is reached is called the rated output wind speed. At higher wind speeds, the design of the turbine is arranged to limit the power to this maximum level and there is no further rise in the output power. How this is done varies from design to design but typically with large turbines, it is done by adjusting the blade angles so as to keep the power at the constant level. (Turbine Characteristics, 2016) This section is full approached from a technical point of view, so there is no logistics involved.

As the speed increases above the rate output wind speed, the forces on the turbine structure continue to rise and, at some point, there is a risk of damage to the rotor. As a result, a braking system is employed to bring the rotor to a standstill. This is called the cut-out wind speed and is around 25 [m/s] depending on the type of rotor for example.

The wind speed U is in [m/s], the density 𝜌 is in [kg/m3]_{and the rotor diameter d is in [m] then the}

available power is in Watts. The power µ, of the wind turbine is simply defined as the actual power delivered divided by the maximum potential power:

µ = 𝑃

1 2 𝜌𝑈3

𝜋𝑑2

4

There is a theoretical limit on the amount of power that can be extracted by a wind turbine from an airstream. It is called the Betz limit.

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Figure 18: Illustration of the Betz limit

Local Wind Conditions 5.1.2

Ascribed in previous sections, the offshore environment confronts challenges for the development of offshore wind farms. One of these challenges is posed by the local wind conditions. However these same conditions offshore also provide different potential advantages above the onshore atmosphere, like high wind speeds and no disturbance of surrounding inhabitants as there are none. An evident research and an accurate description of the offshore meteorological conditions is required before the wind far layout and turbine type can be determined. (Office, 2012).

The wind power forecasts are constructed with statistical models of wind farms, which are achieved with numerical weather prediction models. (Kariniotakis, 2006). The advanced development in the weather forecast, supports energy suppliers with the prediction of their supplies. The share of the renewable resources grows, and therefore the forecasted supply as well as the demand is becoming significant. The power output of these sources of energy varies largely and this mostly depends on the weather conditions. (Richardson, 2016). First far offshore atmospheric conditions relevant for wind energy are studied from a meteorological point of view, which should result in a comprehensive, accurate and implementable description of offshore atmospheric conditions for wind energy purposes. (Holtslag, 2016)

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Figure 19: The effect of Weibull shape factor on the probability density of wind velocity of some potential sites

Offshore wind speeds are further generally analysed by (G.C. Biswal, 2015) via the probability distributions, based on hourly data from different weather stations. This phenomena is called the Weibull distribution. The wind speed histograms for a certain station displays the preference for stronger wind speeds but does not show the likelihood of the winds to blow above a specified speed. The characteristics of a wind farm can be integrated with the wind energy as a potential. The output from the wind energy as a whole from the conversion system. By multiplying the p-curve by the Weibull distribution the Statistical Annual Energy Produced is created. This output caries the average wind speed

Losses

5.2

In the section losses two elements, ‘availability and efficiency’, are discussed that both have an effect on the AEP. In addition the influences of the wake effect is explained.

Efficiency 5.2.1

In this section the efficiency of a wind turbine is treated by splitting out the different components that have a direct impact on the efficiency.

Electrical Transmission Efficiency

Electrical losses are experienced between the low voltage terminals of each of the wind turbines and the wind farm Point of Connection, which is usually located within a wind farm switching station. This includes the electrical losses encountered when the wind farm is operational and which manifests as a reduction in the energy measured by an export meter at the point of connection. This is presented as an overall electrical efficiency, and is based on the long-term average expected production pattern of the wind farm. It is also necessary to consider the power that the wind farm consumes when the wind farm is not operational. (Wind Energy The Facts, 2016). The electrical losses for larger wind farms are around the 2.5%. (N. Barberis Negra, 2005)

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Turbine Performance

In an energy production calculation, a power curve supplied by the turbine supplier is used within the analysis. Most wind turbines will shut down when the wind speed exceeds a certain limit. High wind speed shutdown events can cause significant fatigue loading. A detailed description of the wind turbine cut-in and cut-out parameters are available, this is used to estimate the loss of production due to high wind hysteresis, by repeating the analysis using a power curve with a reduced cut-out wind speed. It is also necessary to adjust the turbine efficiency for any generic or site specific issues, which may mean that for a specific site the wind turbine will not perform in accordance with the supplied power curve. (Wind Energy The Facts, 2016)

Wake effect

Wind turbines extract energy from the wind and thus downstream creates a wake, where wind speed is reduced and the flow of are is turbulent. As the flow proceeds downstream, there is a spreading of the wake and the wake recovers towards free stream conditions. The wake effect is the aggregated negative influence on the energy production of the wind farm, which results from the changes in wind speed caused by the impact of the turbines on each other. It is important to consider wake effects during wind farm lay out design and optimisation, and to take wake effect from neighbouring wind farms and the possible impact of wind farms which will be built in the future into account.

Figure 20: OMCE concept of preventive maintenance control (Pieterman, 2011) Availability

5.2.2

The energy yield of a wind farm during a specific time period is estimated as a function of the installation's capacity facto (CF) and the rated power of the wind turbines. The technical availability, which is actually configured by the hours of operation of a given wind turbine or wind farm by considering the time period that the machine is kept out of operation due to scheduled maintenance or unforeseeable events determines the CF of a wind farm. (Besnard, 2009) Obviously, many days of downtime during a specific time period will reduce the availability of the wind farm and therefore its energy production and CF over that time period. So, that is the relation between the availability and the CF which will be further investigated in this section. However, the amount of energy loss will depend on when the downtime occurs, in example the impact of the availability on the energy performance of the plant will be much higher when the local wind potential is high. (Kaldellis, 2012) This is exactly the reason why operators prefer using the energy availability in percentages.

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Turbine availability

This factor defines the expected average turbine availability of the wind farm over the lifetime of the project. It represents, as a percentage, the factor which needs to be applied to the gross energy to account for the loss of energy associated with the amount of time the turbines are unavailable to produce electricity. Similar factors are needed for the 'Balance of Plant' availability, which relates to the electrical infrastructure of the site and 'Grid Availability', which relates to the availability of the grid over which power can be exported. (Wind Energy The Facts, 2016)

Preventive maintenance strategy

As wind turbines are placed offshore the accessibility may be significantly restricted to perform maintenance, thus in case of an acute failure potentially causing a considerable impact to the availability of the wind farm and in turn to the energy and economic performance of the whole project. (Pieterman, 2011) has adopt a preventive maintenance strategy to its location (e.g. mother vessel and workboats remain on-site). This strategy is modelled successfully with the OMCE-Calculator, further explained in this article and showed in the flow diagram in Figure 20. The tool results that long waiting times for suitable weather windows are a major contributor to the wind farm downtime leading to high revenue losses. He also argued that it can lead to a reduction of 1.4% in total O&M costs.

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Reactive maintenance to failures

This parameter is, as aforementioned, of special interest for offshore wind parks, especially during winter, due to bad weather conditions (high winds and huge waves suspend the ship departure, thus preventing maintenance and repair of the existing wind turbines). In certain conditions, dirt can form on the blades or, over time, the surface of the blade may degrade, which has a negative influence on the wind turbine performance. Also, ice can build up on a wind turbine. These influences can affect the energy production of a wind turbine. Extremes of weather can also affect the energy production of a wind farm. For example, extender storms of greater intensity. impact of wave forces and an shifting of the seabed in the form of scour and migrating sand waves caused by wave height. (Kimberly, 2012) (Wind Energy The Facts, 2016) The biggest reason for reactive maintenance is thus environmental. This is also stated by (Besnard, 2009), see Fout! Verwijzingsbron niet gevonden. below. This optimization model presents the maintenance schedule every day based on the present corrective maintenance tasks.

Figure 21: Failure and power production scenarios (Besnard, 2009)

In his conclusion (Besnard, 2009) stated that for this example 43% of the preventive maintenance cost could be saved. To establish a more grounded conclusion

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6 Logistic management on Offshore Wind Farms

In this chapter the potential influences of logistic management on the LCOE are described. In the previous chapters the LCOE’s main influential parameters were treated and to evaluate the impact of the logistics on the PGE those parameters that are significantly influenced by logistic management are highlighted in this section. However, firstly the definition of logistics used has to be clear to validate the impact correctly:

Logistics concerns the application of mathematical methods to describe the physical processes behind the transport process and the determination of the physical actions that are necessary to permit the transport to be accomplished as efficiently and effective as possible. (Lodewijks, 2016)

Logistics is an important competitive factor for the offshore wind industry. With a share of up to 20% of total cost, logistics expenses have a significant impact on the profitability of a wind farm. However, compared to other industries, the wind energy sector is still far away from transparency in logistics costs, which make the analysis performed in this literature assignment more difficult A particular challenge for the maritime supply chain results from the effects of disturbances such as the influence of weather conditions, which considerably complicate the planning of a holistic logistics concept. (Lange, 2016)

Logistic planning on O&M

6.1

In this section the impact of the logistics on the O&M is discussed. O&M activities represent a significant share of the expenses during the lifetime of offshore wind farms. When compared to onshore wind, O&M costs are increased for offshore, due to specialised vessels, shorter weather windows and rough environmental conditions, the occurrence of more failures, longer downtimes, decreasing availability and accessibility. Furthermore, the offshore environment involves personnel traveling to and from the offshore turbines, which increases equipment and labour costs as well as insurance costs. In general, O&M costs consist of labour costs, material costs, access vessels & lifting vessels costs, and revenue losses. (Dalgic Y. , 2013)

(Dalgic Y. , 2013) mentions in his report that the O&M cost per turbine per year varies around €40,000 to €250,000. These fluctuations in these cost are caused by different variables, for example logically the size of the turbines. He also compares the O&M costs with the onshore wind farms which limits to €38,000. A recent study done by (Kraemer, 2016) supports the same aspect indicating that the O&M cost is 2-6 times as high as onshore wind.

In addition (Dalgic Y. , 2013) mentions in his article that the cost of lifting operations by using an external crane accounted for more than 50% of the overall O&M costs. Complementary studies have been performed by (Iraklis Lazakis, 2013) and also by while (Dinwoodie, 2013) arguing that the vessel costs contribute the largest percentage of costs which is the key component of overall costs to control. This is a significant conclusion in order to the impact of logistic management on the LCOE. The cost of vessel has a significant impact on the O&M expenditures. From the operator point of view, better planning becomes urgent to stay competitive as a company and from a larger perspective for the offshore wind industry in general. This results in a conclusion that the influences of logistics is large. This is dominated by the costs of lifting operations using external cranes.

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Figure 22: General structure of the model with controllable options and uncontrollable external factors

It has been identified that the costs associated with transportation systems account for 73% of the total O&M costs. (Dalgic Y. , Advanced logistic planning for offshore wind farm operation and maintenance activities, 2015)

With regard to the total fuel cost, it can be recognised that the total fuel cost is not increasing proportional to the fleet size, but there are significant increases on the total fuel cost when the fleets are composed of only CTV-1 (Figure 4). Due to the fact that the CTV with higher fuel consumption is only available in these particular fleet compositions, there is no cheaper alternative to CTV-1. 42 Investigation of Optimum Crew Transfer Vessel Fleet for Offshore Wind Farm Maintenance Operations

Figure 4.

There are some studies done to optimise or reduce the operations and maintenance costs. A time domain Monte-Carlo approach is adopted while taking into consideration the climate parameters, failure characteristics of turbine components, the specification of crew transfer vessels, and the composition of vessel fleet. Through this extensive study, it is concluded the O&M related costs can be reduced significantly while the availability and the productivity of the turbines can be increased by optimising the use of the O&M vessel fleet in terms of fleet size and vessel capabilities. (Dalgic Y. , Investigation of Optimum Crew Transfer Vessel fleet for offshore wind farm maintenance operations, 2014)