impact of weather windows in offshore wind farm operation and maintenance expenses

Impact of Weather Windows in Offshore Wind Farm Operation and

Maintenance expenses

Tiago Rocha

Instituto Superior Técnico, Avenida Rovisco Pais 1, 1049-001 Lisboa, Portugal

Corresponding author’s E-mail: [email protected]

October 2014

Abstract

Offshore wind (OW) is an emerging technology in the wind energy conversion system, where wind resources are stronger and more

consistent, in terms of their availability, than land-based wind resources. The present article intents to analyse the impact of weather windows

for this emergent technologies operation and maintenance (O&M) expenses. Up to 25-30% of the total cost of energy from OW generation

is associated to O&M tasks, where the biggest share of the cost belongs to downtime. According to this, an O&M strategy focused in

maximizing the yield and minimizing the downtime of wind turbine can be a key factor to validate this type of investments.

In order to achieve an optimal strategy, focused in reduce O&M expenses, a tool has been developed using MATLAB software. Then it

was implemented to obtain the O&M expenditures for a future offshore wind farm (OWF) that will be located off the coast of Portugal, in

Viana do Castelo.

From the case study it can be concluded that OWF average monthly accessibility for 1 hour weather window will vary from 3% to 79%

depending on mission requirements and time of the year. This results in an O&M cost, for the techno-economics information admitted, of

23.05 €/MWh, corresponding to a Net Present Value (NPV) and Internal Rate of Return (IRR) for the investment of 4.78 M€ and 9.19%,

respectively. These are good economical values although, they can be improved, through an optimization strategy, in order to lower the O&M expenses to 9.14 €/MWh, getting a NPV and IRR of 14.2 M€ and 10.49%, respectively.

Keywords: Weather Window; Operation and Maintenance; Availability; Downtime; Accessibility; Offshore Wind Farm.

1. Introduction

The increasing demand of electrical energy along with the

increase of prices of fossil fuels and the emissions of

greenhouse gases, induced an investment in renewable

energy resources. European Union’s (EU) renewable energy

policy intent to achieve, inside of EU, a 34% renewable

electricity supply in 2020 and a 100% by 2050 [1].

Wind energy will be one of the pillars to achieve

energetic sustainability in the EU. It can be produced both

onshore and offshore, with an expected installation of 282

GW in 2020, from which, up to 1/3 will be in an offshore

environment [2].

Despite the difficulties, it is actually possible to install

OWF using not only fixed structures, used in shallow waters

or onshore, but also using floating platforms. This

innovation allows to eliminate technical constraints related

to water depth. An example of this achievement is the

WindFloat, an offshore wind turbine of 2 MW located near

shore, at Póvoa de Varzim in Portugal. This type of OW

systems implies, at least for now, more demanding and

expensive technology, but also a larger effort in O&M tasks,

induced by its accessibility condition.

OWF accessibility, defined as the percentage of time that

the farm can be accessed, is one of the key elements to ensure

a high level of its availability, i.e. high percentage of time in

which the farm is able to produce energy. This accessibility

will be influenced not only by the meteorological conditions

at the location but essentially by the requirement of the

weather window (WW). This can be defined as the

occurrence of meteorological conditions that allow the crew

access to the farm, perform the O&M tasks and return to the

shore.

The availability of an OWF is a function of the adopted

O&M strategy, its accessibility and the theoretical

availability, which in the other hand is a function of

reliability, effective maintenance and serviceability, as show

in the figure 1.

Serviceability

(ease of service)

Accessibility

Theoretical

availability

Fig. 1. Model of obtaining the accessibility of an OWF [3].

Reliability

(failures/year)

O&M

strategy

Availability

Maintainability

(ease of repair)

Impact of Weather Windows in Offshore Wind Farm Operation and Maintenance expenses

2 Rocha T.

Despite the fact that the availability achieves high values

for onshore wind farms, these values decrease as the distance

to shore increases, as can be seen by the figure 2, using a boat

as a vehicle.

According to [5] it is possible to achieve availability

values for OWF above 98%. This study considered 10 wind

turbines, located near shore, at Odder in Denmark, with high

values of accessibility and an active O&M activities.

For the present article, the set of O&M tasks inherent to a

sustainable use of the OWF can be categorized in 2

maintenance categories [6]:

Preventive maintenance – corresponds to a

systematic control and monitoring operations, with

the objective of avoiding failures. This kind of

operation has a reasonably constant effort during the

lifetime of the farm;

Corrective maintenance – operation aimed at

restoring, correcting or recovering the productive

capacity of the turbine. Its major effort appear at the

beginning and at the end of the OWF lifetime.

The effort related with O&M tasks will depend

on the repair time, which can be defined as the

period of time from the beginning of the failure

until the system returns to its nominal

performance. This repair time can be divided in

three stages:

1. Logistic time – Includes all the preparatory

phase to repair the wind turbine, which include

organizing the repair crew, obtaining the

necessary material and waiting for the arrival of

the vessels;

2. Waiting time – Amount of time in which it is not

possible to access the OWF, due to bad weather

conditions. This is the main responsible for the

downtime of the farm, reaching the 90% of total

downtime in the present case study;

3. Mission time – This time includes the travel time

to the turbine, the repair time and the travel time

back to shore.

From what was showed in the present chapter, it has

become clear the increase of O&M effort as the OWF moves

from Onshore to Offshore environment. As a consequence, it

is essential to build a tool that allows us studying the impact

of the weather windows in O&M expenses. Then this tool

will be used in the study of the future OWF, located off the

coast of Viana do Castelo, Portugal.

2. Techno-economic model of corrective O&M

Besides the graphs that will be showed at the chapters 4

and 5, the techno-economic model of corrective O&M,

together with the model of the chapter 3 will allow to obtain

the values to perform the economic analysis and evaluate the

impact of weather windows in O&M expenses, presented in

the chapters 6 and 7, respectively.

The scheme in the figure 3, represents the structure of the

techno-economic model of corrective O&M built using the

MATLAB software.

As it can be seen, the model of figure 3 is composed of

three sub-models: Manipulation model, Technical O&M

model and economical O&M model. These perform roughly

the following steps:

1. Receives the meteorological data and inserts them

into the Manipulation model to perform its

formatting, categorization and interpolation;

2. The manipulated data is transferred to the Technical

O&M model along with OC, resulting the waiting

time to obtain a WW as a function of MD and another

information that will not be used by the Economic

model, but allow to get the graphs of the figures 11

to 14;

3. The values of waiting time to obtain a WW as a

function of MD are inserted into the Economic O&M

model together with technical and economical

specifications;

4. From the Economic O&M model results the average

annual expenses and energy produced by OWF, from

which can be easily obtained the average annual

expenses of corrective O&M per energy produced.

Reliability

Maintainability

(ease of repair) Serviceability

(ease of service)

Theoretical

availability

O&M

strategy ibility

Availability

Maintainability

(ease of repair) Serviceability

(ease of service)

Theoretical

availability

O&M

strategy ibility

Availability

Reli

abili

ty

100 80 60 400

70

80

90

100

Accessibility [%]

Av

aila

bil

ity

[%

]

Availability as a function of accessibility

(onshore) (near shore) (offshore) (remote offshore)

Fig. 2. Availability of OWF as a function of its accessibility [4].

Fig. 3. Scheme of techno-economic model of corrective O&M.

Technical

O&M

Model

Manipulation

Model

Data

Operational conditions

Technical specifications

Economical

specifications

Expenses &

Energy

gerada

Accessibility

for each WW

Number of WW as a

function of

its duration

Manipulated

data

Economic

O&M

Model

Average annual

cost of corrective

O&M in €/MWh


3 Rocha T.

In the next three subchapters the operations mode of each

sub-model will be explained.

2.1. Manipulation model

The manipulation model is the smallest of the three

models, and its function is to manipulate weather data,

performing roughly and briefly the following steps:

1. Model receives weather data in csv format and

transforms it to mat format;

2. After transforming the weather data, the model

proceeds to its interpolation from 3 hour time

steps to 1 hour time step.

After being transformed and interpolated the values are

inserted into the technical O&M model.

2.2. Technical O&M model

The technical O&M model is responsible for the selection

of data received from the last model performing the

following steps:

1. Receives the manipulated weather data and the

operation conditions (OC) specifications;

2. Verifies the OC requirements (table 10) and computes

the periods that allow to access the OWF;

3. The model performs three separate operations:

Computes the accessibility values for each

month. After having the monthly accessibility,

the model computes the trimestral and annual

accessibility.

Determines the number and duration of each

weather window. Later on the model selects the

weather window by trimester and makes the

average of the last 21 years, as shown in the

figures 13 and 14.

The model determines the beginning and the

end of each WW. These values will be used to

compute the average waiting time to obtain a

weather window as a function of mission

duration, as presented in the figures 15 and 16.

Finally these values will be used by the

economic model.

The average waiting time to obtain a weather window will

be used in the Economic O&M model.

2.3. Economic O&M model

This model is characterized by being the largest of the

three sub-models. It contains a large set of information

regarding the technical and economic specifications and

average waiting time to obtain a weather window as a

function of its duration. This will be used to compute the

average annual O&M expenses and the energy produced by

the OWF.

In order to explain the operating mode of this model a

brief description of the techno-economic specification is

required.

2.3.1. Technical specifications

Technical specifications describe, in a precise way, the

information regarding the materials and the procedures

introduced in the construction of the algorithm.

In order to evaluate the impact of the failures in the

corrective O&M expenses, a failure mode and effect analysis

must be done, according to table 1 [8][10][14][16][17][18].

Table 1.

Failure Mode and Effects Analyses for a generic wind turbine.

System

Prob. of

failure

[%/year]

Maintenance

category

Repair

time

[h]

Logistic

time

[h]

Gearbox 0.213 3 3 8

0.013 2 10 48

0.226 1 50 160

Generator 0.065 3 3 8

0.026 2 10 48

0.039 1 50 160

Blade 0.014 3 3 8

0.014 2 10 48

0.041 1 50 160

0.001 1 40 500

Pitch mechanism

0.075 3 3 8

0.075 2 10 48

Control system

0.105 3 3 8

0.105 2 10 48

Shaft & bearing

0.001 3 3 8

0.009 1 40 500

Electrical system

0.243 3 3 8

0.022 2 10 48

Yaw system

0.005 1 50 160

0.13 3 3 8

0.068 2 10 48

Mechanical brake

0.002 1 40 500

0.04 3 3 8

0.01 2 10 48

For the maintenance categories and repair strategies

introduced in the code, it was used a simplified model of

[10], [15] and [19], presented in the tables 2 and 3.

Table 2.

Action Sequence for each Maintenance Category Type

Maintenance

Category Seq. Task

Average

duration

[h]

WW

HS

[m]

U10

[m/s]

1 1 Transit to

site

Cruising

speed × Distance

to farm

2.5 12

2 Remove moorings

24 0.9 10

3 Tow wind

turbine to

shipyard

Drag

speed × Distance

to farm

2.5 12

4 Repair at

port Table 1 - -

5 Tow wind turbine to

site

Cruising speed × Distance

to farm

2.5 12


4 Rocha T.

6 Connect moorings

24 0.9 10

7 Transit to shipyard

Cruising speed ×

Distance

to farm

2.5 12

2 1 Transit to site

Cruising speed ×

Distance

to farm

2.5 12

2 Accessing the turbine

0,5 0.9 10

3

Putting

loads on the

platform

1 0.9 10

4 Repair at

site Table 1 0.9 10


Cruising

speed × Distance

to farm

2.5 12

3 1 Transit to site

Cruising speed ×

Distance

to farm

2.5 12

2 Accessing the turbine

0.5 0.9 10

3 Repair at

site Table 1 0.9 10


Cruising speed ×

Distance to farm

2.5 12

Table 3.

Operational characteristics of each vessel.

Vessel Maintenance

Category

Cruising

speed

[km/h]

Drag

speed

[km/h]

Average crew

[nº de

technicians]

AHTS 1 24 7.4 4.01

Windcat 2 & 3 48 - 2.01

In order to generate electricity, three wind turbines, with

a power of 8 MW each, with the power curve equal to that

in figure 4, were considered.

2.3.2. Economic specifications

The economic specifications illustrate the information

regarding the costs of materials and vessels, among other

values inserted into the Economic O&M model, as shown

tables 4 to 6 [8][20][23][24][25][33].

Table 4.

Materials cost for each maintenance category

Maintenance category Average materials costs

[% of investment cost]

1 0.03%

2 0.8 %

3 7 %

Table 5.

Vessels costs

Vessel MOB/DEMOB

cost [€] Rest cost [€/day]

AHTS 150 000 57 000

Windcat with crane - 2 500

Windcat without crane - 2 000

Table 6.

Feed-in tariff, technician cost and investment cost

Feed-in tariff 168 €/MWh

Technician cost 80 €/h

Investment cost 4 M€/MW

The values presented in the figure 4 and in the tables 1 to

6 will be used, along with technical specifications to

compute the generated energy, downtime and O&M

expenses, according to the next subchapter.

2.3.2. Calculation procedure for the economic model

The calculation procedure will be specified here, taking

into account the follow assumptions:

Vessels are always ready at the shipyard;

In the maintenance categories 2 and 3 (table 2),

it will be used the OC#1 for all the tasks, instead

of using OC#2 for transit.

To compute the annual expenditures and energy

generated, the model must perform roughly the following

steps:

1. For each failure type, in the table 1, the model

associates a sequence of actions, according to

table 2, resulting in the logistics time and

mission duration;

2. Uses the waiting time to obtain a WW as a

function of its mission duration, from the

technical O&M model, the logistic time and

mission duration in order to obtain the total

downtime associated to each failure;

3. Simultaneously the model computes the

expenses of each failure, according to the tables

4 to 6, resulting the crew, materials and vessel

costs;

4. Obtains the corrective O&M costs for each

failure, adding the values to point 3 and 5;

5. To each failure it will be assigned its probability

(table 1), resulting in the average annual costs

and downtime per failure;

0 4 8 12 16 20 240

2000

4000

6000

8000

Wind Speed [m/s]

Pow

er [

kW]

Wind turbine power curve

Fig. 4. Power curve for a wind turbine V164-8MW

Source: Vestas catalogue


5 Rocha T.

6. Computes the gross average hourly generated

energy1, in the last 21 years, using the power

curve of the figure 4 and the wind speed at 80 m,

nacelle height, as well as the energy losses due

to downtime, resulting in the net average hourly

generated energy;

7. The revenues losses will be computed by

multiplying the energy losses by the feed-in

tariff, presented in the table 6;

8. Computes the availability of the OWF by the

different between annually hours and downtime,

dividing by annually hours;

9. Computes the corrective O&M costs as a

function of energy generated by dividing

corrective O&M (at the point 4) by the net

generated energy (at the point 6).

After computing the corrective O&M costs, it will be

added to preventive O&M cost, obtained in the next chapter,

in order to get all O&M costs of the OWF.

3. Techno-economic model of preventive O&M

The preventive O&M tasks are performed in a

predetermined time interval or according to the criteria

indicators of the integrity of the material, aiming to mitigate

situations of unavailability of the turbine [12].

The evaluation of the costs due to preventive maintenance

tasks, for the present case study, will be carried out using a

strategy of time based maintenance (TBM). This is a

maintenance operation performed according to established

intervals of time, without performing the monitoring of the

equipment [13].

In order to proceed with the analyses of the preventive

O&M expenses, a set of the techno-economic values are

needed. These values include information regarding the

characteristics of the mission, materials, crew and vessel

costs for one wind turbine, according to table 7

[21][25][26][27][28].

Table 7.

Techno-economics specifications for preventive O&M.

Average mission duration 28 hours

Average number of technicians 3.5 technicians

Vessel Windcat

Average cost per technician 80 €/hour

Average material costs 16 500 €

Rental cost of the vessel 2 000 €/day

OWF accessibility + 25 %

3.1. The construction of the Model

The average annual expenditure regarding the preventive

O&M task will be achieved through the implementation

of a less complex model, presented in the figure 5,

compared to the model presented in Chapter 2.

1 This energy it’s compute for a 100% availability, i.e. no failures.

This model is composed of three types of expenses

referring to crews, vessel and materials. It performs the

following steps:

1. Crews expenses are obtained by the

multiplication of mission duration, number of

technicians and cost per technician;

2. Vessel rent is obtained by the multiplication of

MD and the daily rental cost of the vessel;

3. Obtaining the sum of all expenses together with

the downtime, which is equal to 25 % of the

mission duration due to OWF accessibility;

4. After obtained the expenses and downtime, the

model receives the annual energy produced,

from Techno-economic model of corrective

O&M, and computes the average annual

expenses of preventive O&M per energy

produced in €/MWh.

4. Characterization of the study area

The location of the OWF is, along with capital

expenditure, the element with the greatest influence on the

profitability of a wind energy investment. Its location will

have a direct influence in the amount of energy produced, as

well as the accessibility of the farm. This led to the choice of

the coast of Viana do Castelo in Portugal, with the

characterization presented in the table 8.

Table 8

Geographic characterization of the case study.

Distance to

shipyard [km] Depth [m]

Pilot plant

Latitude Longitude

10 35 41º42’57.6” -8º57’25.2”

MD

× Rental cost of

the vessel

Average

anual energy

genereted

Material costs

MD ×

Number of

technicians ×

Cost per

technician

Sum of the

expenditures

Expenditures

&

Downtime

Fig. 5. Schematization of the Techno-economic model of preventive O&M.

Average annual cost of

preventive O&M in

€/MWh


6 Rocha T.

This location was chosen mainly due to its high wind

resource, together with a nearby grid connection and a lower

depth that allowed to install the mooring system in an

economical sustainable way, since its prices strongly

increase with the depth [7].

4.1. Meteorological characterization

In order to proceed the meteorological characterization, a

set of data, containing significant wave height (HS), wind

speed at 10 meters (U10) and the respective directions, are

needed. For that purpose it was requested to BMT Argoss

EU Shelf hindcast which was responsible for the

computation, validation and calibration of the data received.

The wind and wave data records were obtained with a

precision of 0.01 m, in intervals of 3 hours, covering a period

of 21 years. This data will be used as an accessibility

restriction.

The wind data will be used not only as an accessibility

restriction, but also to compute the amount of energy

produced by the wind turbine. For this purpose, the wind

speed must be extrapolated to the height of the nacelle.

𝑈(𝑍)

𝑈(𝑍𝑅)=

ln (𝑍

𝑍0)

ln (𝑍𝑅𝑍0

) (1)

Where:

U(Z) – Wind speed at nacelle height [m/s];

U(ZR) – Wind speed recorded [m/s];

Z – Nacelle height [m];

ZR – Recorded wind height [m];

Z0 – Roughness of the soil, admitted as 3×10-4 m.

Table 9 presents a light synthesis of the meteorological

condition of the case study where it can be seized the good

values of wind and wave energy resources.

Table 9

Synthesis of the case study meteorological data.

U80 [m/s] U10 [m/s] Hs [m]

Maximum 30.33 25.28 13.21

Average 8.20 6.84 2.40

Minimum 0.88 0.73 0.15

Standard deviation 3.88 3.23 1.48

In order to get a more precise characterization of the

meteorological conditions a set of histograms and a

probability distribution function will be presented for the

wind and wave resources in the figures 6 to 9.

From figures 6 to 9, it is possible to verify the strong wind

resource, its low variation seasonally and the very low

probability of occurrence wind speed above 24 m/s, which is

the cut-down wind speed of the turbine (see chapter 2). This

location has also a good wave energy resource, strongly

influenced by seasonality, which will be one of the main

factors of inaccessibility of the farm, been more restricted in

winter and less in summer.

5. Weather window

A weather window is defined as the occurrence of

meteorological condition that allows crew’s access to the

farm, in order to perform the O&M tasks and return to shore.

This means that the duration of the weather window must be

bigger or equal to the mission duration (MD), according to

the scheme of figure 10.

0 5 10 15 20 250

0.02

0.04

0.06

0.08

0.1

0.12

0.14

Wind speed [m/s]

Pro

bab

ilit

y o

f o

ccu

rren

ce

Probability distribution function of U10

Fig. 6. Weibull distribution for U10 for the case study (λ = 7.739;

k = 2.2347).

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0

5

10

15

20

25

Influence of seasonality in U10

Win

d s

pee

d [

m/s

]

Fig. 7. Influence of seasonality in wind speed at 10 meters for the case study.

0 2 4 6 8 10 120

0.1

0.2

0.3

0.4

0.5

Wave height [m]

Pro

bab

ilit

y o

f o

ccu

rren

ce

Probability distribution function of Hs

Fig. 8. Weibull distribution of HS for the case study (λ = 2.7079; k = 1.7465).

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

0

2

4

6

8

10

12

Wav

e h

eig

ht

[m]

Influence of seasonality in Hs

Fig. 9. Influence of seasonality in wave height for the case study.


7 Rocha T.

The duration and frequency of occurrence of a weather

window will depend on the combination of meteorological

conditions and the type of O&M task. Each type of O&M is

associated to a number of constraints, namely maximum

values of HS and U10 that allows access to the OWF.

In order to proceed this case study, the following (OC)

were defined [8]:

OC type 1 – Includes minor O&M activities to

be done offshore using a workboat, for example

the Windcat;

OC type 2 – This category contains major O&M

activities to be performed onshore. In this case,

the wind turbine will be towed to shore using a

large vessel, for example the AHTS.

For the present article only the HS and the U10 were

admitted as accessibility’s restriction due to the lack of

available information. However, there are other factors that

might influence OWF accessibility, as the wave period and

daylight.

Each type of OC will be associated with one weather

window, presented in table 10, taking into account the work

made by [9][10][11].

Table 10

Weather window for each type of OC.

OC Weather window

Hs maximum [m] U10 maximum [m/s]

# 1 0.9 10

# 2 2.5 12

From table 10 it is possible to realise that OC of the type

1 are more demanding than OC of the type 2, which will

result in different values of accessibility, number of annual

weather windows and waiting times to obtain a weather

window.

5.1. Accessibility

Since OWF accessibility depends on meteorological and

OC, it is possible to conclude that each OC will have a

different accessibility value. Figures 11 and 12 present the

monthly accessibility values for a 1 hour weather window

for OC of type 1 and 2, respectively.

As expected, the OWF presents lower values of

accessibility for OC #1 than OC # 2, with a mean annual

accessibility of 8% and 54%, respectively. Once these values

were obtained for 1 hour weather window, the accessibility

values for these two OC will be lower than presented, since

mission durations are larger than 1 hour.

5.2. Average annual number of weather windows

The previous chapter showed the influence of the OC in

the OWF accessibility, without taking into account the

influence of the mission duration. The graphs presented in

figures 13 and 14 show the average annual number of

weather windows as a function of its duration. This takes

into account that one weather window of 6 hours contains

two weather window of 3 hours.

The figures allow us to analyse the influence of mission

duration in obtaining the number of WW. As an example, for

OC #1 an increase of mission duration from 3 to 6 hours will

decrease the annual number of WW from 218 to 102.

Waiting for a WW

Yes

WW found

WW duration it’s greater

than MD?

No

Mission

begins

Turbine

available

Fig. 10. Scheme to obtain a weather window.

Failure occurrence

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

0

20

40

60

Acc

essi

bil

ity

[%

]

Monthly accessibility for OC #1

Fig. 11. Monthly accessibility for OC of type 1.

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0

20

40

60

80

100

Monthly accessibility for OC #2

Acc

essi

bil

ity

[%

]

Fig. 12. Monthly accessibility for OC of type 2.

3 6 9 12 15 18 21 240

50

100

150

200

250Average annual number of WW for OC #1

Duration of WW [hour]

Nu

mb

er

of

WW

1º Trimester

2º Trimester

3º Trimester

4º Trimester

Fig. 13. Number of weather windows as a function of mission duration.


8 Rocha T.

Despite providing information on the annual number of

WW as a function of its duration, figures 13 and 14 do not

provide any information regarding the waiting time to obtain

a WW. This will be presented in the next chapter.

5.3. Waiting time to obtain a weather window

The waiting time for a WW is associated with OWF

accessibility, since low accessibility values will lead to

larger values of waiting time, and vice-versa. This will be

one of the key factors in O&M costs, since high values of

waiting time will lead to larger costs on crew and vessels, as

well as a greater amount of energy not produced, when the

wind turbine is unavailable and there is enough wind

resource to do it.

The figures 15 and 16 show the waiting time to obtain a

WW as a function of mission duration, for the OC type 1 and

2, respectively, from which can be confirmed the more

demanding OC #1, where there is larger waiting time to

obtain a WW, than OC #2. For example, to obtain a WW of

20 hours for the OC #1 it is necessary to wait, on average,

865 hours against the 102 hours of OC #2.

6. Techno-economic analysis of the case study

This chapter presents a techno-economic analysis of the

case study, whose characteristics were presented in the

chapters 2 and 3. From this analyses will result capital values

to validate OWF investments, namely the average annual

energy produced, expenditures regarding O&M tasks and the

NPV of the investment.

Table 11 presents the average annual values of the

downtime, availability, capacity factor, energy loss, O&M

expenses and the produced energy, per turbine. From this it

is possible to elaborate the performances analysis of future

OWF, presented in the table 12.

Table 11.

Average annual techno-economic characterization regarding the O&M tasks, per turbine.

Corrective

O&M tasks

Preventive

O&M tasks Total

Downtime [h]

Logistics 49.6 0.0 49.6

Waiting 714.5 7.0 721.5

Travel 6.5 0.6 7,1

Repair 18.8 27.4 46.2

Total 789.4 35.0 824.4

Availability [%] 91.0 99.6 90.6

Capacity factor [%] 37.5 41.1 37.3

Loss of production

[MWh] 2 602.6 115.4 2 718

Expenses [€]

Material costs 47 055 16 500 63 555

Crews costs 2 202 7 840 10 042

Vessels costs 69 333 4 000 73 333

Revenue losses 437 263 19 389 456 652

Total 555 853 47 729 603 582

Energy production

[MWh] 26 187

Table 12.

Average annual techno-economic characterization of the future OWF.

Energy [MWh]

Loss of production 8 155

Energy production 78 561

Cash flow [€]

O&M expenses 1 810 740

Gross revenues 14 568 300

Net revenues 12 757 560

Expenditures [€/MWh] 23.05

From the tables 11 and 12 it is possible to analyse relevant

values and take the following conclusions:

OWF accessibility of 90.6%. Similar value

obtained by [29] and [30]. However, this value

might be lower, since it was only considered as

a limiting element of accessibility the HS and

U10, together with the lack of failure modes

concerning the floating platform and electrical

cables;

A capacity factor of 37.3%. A value that lies

within the values obtained by [30] and the values

indicated by [31] and [32] for the study area;

3 6 9 12 15 18 21 240

250

500

750

1000

1250

1500

1750

Duration of WW [hour]

Nu

mb

er o

f W

W

Average annual number of WW for OC #2

1º Trimester

2º Trimester

3º Trimester

4º Trimester

Fig. 14. Number of weather windows as a function of mission duration.

0 5 10 15 20 250

3000

6000

9000

12000

15000

Mission duration [hour]

Wai

tin

g t

ime

[ho

ur]

Waiting time for a WW as a function of mission duration for OC #1

5% shorter waiting times

Average waiting time

5% greatest waiting times

Fig. 15. Waiting time to obtain a WW as a function of mission duration

for OC #1.

0 10 20 30 40 500

250

500

750

1000

1250Waiting time for a WW as a function of mission duration for OC #2

Mission duration [hour]

Wai

tin

g t

ime

[ho

ur]

5% shorter waiting times

Average waiting time

5% greatest waiting times

Fig. 16. Waiting time to obtain a WW as a function of mission duration for OC #2.


9 Rocha T.

An unavailability of 8,4 %, which causes a loss

of production of 8155 MWh per year,

corresponding to a revenue loss of 1.37 M€ per

year, for a feed tariff of 168 €/MWh;

The total downtime of the OWF is 2473.2 hours

per year, 96% of which corresponds to

corrective O&M;

The waiting time represents 91% of downtime

due to corrective maintenance;

From the total of 1.81 M€ regarding the O&M

expenditures, 92% corresponds to corrective

O&M;

Revenue losses represents 78% of the costs due

to corrective O&M;

An annual energy produced by the OWF of

78561 MWh, which represents a gross revenue

of 14.57 M€;

O&M costs of 23.05 €/MWh, representing 14%

of levelized cost of energy, according to [10] and

[33]. This O&M costs are similar to those

obtained by [34], [35] and [36]. However, the

O&M expenditures could amount to 30 €/MWh,

according to [35] and [36], with the introduction

of the new failure modes and new accessibility’s

restriction;

With the average annual values of revenues and

expenditures, it was possible to realize a viability analyses

of the investment through the calculation of the NPV and the

IRR of the investment. To elaborate the economic analyses,

the values of the table 13 will be used [25][37][38][39].

Table 13.

Data used in economic viability analysis.

Lifetime 20 Years

Investment cost 96 M€

Average annual revenue 14.6 M€

Average annual expenditure 1.8 M€

WACC 8.5 %

Inflation rate 2.23 %

Tax 23 %

The investment has a NPV of 4.78 M€ and an IRR of

9.19%. Despite the fact that these are positive economic

values, the viability of the investment may be compromised

with the introduction of the new failure modes and new

accessibility restrictions.

7. Impact of weather windows

The present chapter will analyse the impact of weather

windows in OWF O&M expenses. Beginning with a graph

that represents its influence in the OWF accessibility, taking

into account the last 21 years of data and a weather window

of 1 hour.

Figure 17 illustrates the variation of OWF accessibility as

a function of wind speed (in red), the wave height (in blue)

and combination of both of these resources (in green). Note

1 The four values are: wind speed for Oc#1, wind speed for Oc#2, wave height for

Oc#1 and wave height for Oc#2.

that, for the red and blue line, only the values of wind speed

and wave height, respectively, will be considered to compute

the accessibility values.

Comparing the values of figure 16, with the values

obtained in the figures 8 and 9, it is possible to conclude that,

for the OC used in this case study, the limitation with greater

influence on the accessibility is HS. As an example, the

OC#1 presents an average accessibility value of 8%,

according to the figure 8, which represents an average

accessibility of 9% and 83% inherent to HS and U10,

respectively according the figure 17.

After having illustrated the influence of each resource in

the OWF accessibility, it will be presented their influence in

the O&M expenditures, according to each OC, in the figures

18 and 19. Note that to compute the variation of the O&M

expenses, only one of each four values1 presented of the two

figures will change, while the other three will remain

constant.

0 2.5 5 7.5 10 12.5 15 17.5 200

20

40

60

80

100

Wind speed [m/s] and Wave height [m]

Acc

essi

bil

ity

[%

]

Variation of accessibility as a function of U10 and Hs

Wind speed

Wave height

Both U10 & Hs

Fig. 17. Variation of OWF accessibility as a function of U10 and HS.

0 4 6 8 10 12 14 16 18 200

20

30

40

50

60

Wind speed [m/s]

O&

M e

xp

ense

s [€

/MW

h]

Variation of O&M expenses as a funtion of U10 for OC#1 and OC#2

OC#1

OC#2

Fig. 19. Variation of O&M expenditures as a function of U10 for both type of

OC.

0 1.5 3 4.5 6 7.5 9 10.5 12 13.5 150

10

20

30

40

50

Wave height [m]

O&

M e

xp

ense

s [€

/MW

h]

Variation of O&M expenses as a funtion of Hs for OC#1 and OC#2

OC#1

OC#2

Fig. 18. Variation of O&M expenditures as a function of HS for both type of

OC.

1 2 3 4 5 6 7 8 9 10


10 Rocha T.

Through the analyses of figures 18 and 19, the following

conclusions can be drawn:

There are minimum values from which it is

impossible to access the OWF. Namely, 0.5 m

of HS and 3 m/s of U10 for OC#1 and 0.3 m of

HS and 2 m/s of U10 for OC#2;

In economic terms, there are minimum values,

from which the O&M expenses will be greater

than 30% the LCOE1. This values are 0.65 m of

HS and 2.5 m/s of U10 for OC#1, and 0.4 m of HS

and 2 m/s of U10 for OC#2;

Maximum values, from which an increase the

ceiling of accessibility by one unit will result in

a O&M expenses reduction smaller than

0.01 €/MWh. Namely, 6 m of HS and 12 m/s of

U10 for OC#1, and 5.5 m of HS and 6 m/s of U10

for OC#2;

OC#1 has a greater influence on the variation of

the maintenance costs than OC#2. Justified by

the number of failures and duration of weather

windows associated to OC#1.

7.1. Optimization strategy

This chapter presents solutions that can lead to a

reduction of O&M costs. The values presented in the table

14 show the quantitative summary of each optimization

strategy [10][15][40].

Table 14.

Optimization strategies regarding the O&M tasks.

Strategy Main strategy

change

O&M

expenses

[€/MWh]

Case study - 23.05 12.8 -

Improve vessel

system for OC#1

Increase max

HS from 0,9 m

to 1,5 m

11.98 13.6

Did account

for improvement

vessels.

Use a CBM strategy

Eliminate the

waiting time

associated to corrective

O&M.

Detection rate: 50%

14.83 13.4

Analysis

didn’t account for

investment

and operational

costs. Didn’t

account for costs due to

false alarms.

Improve mooring

systems

Reduce mooring’s

engagement and disengagement

time to 8 horas.

18.55 13

Analysis

didn’t

account costs of the new

moorings.

The implementation of the three strategies presented in

the table 14 will allow to obtain an availability of the OWF

of 97.7%, an O&M expenses of 8.62 €/MWh and a net

revenue of 13.8 M€. Using this last value, together with

some values of the table 13, results in a NPV of 14.2 M€ and

an IRR of 10.49%, which are good economical values

according [41].

1 Taking into account a LCOE of 168 €/MWh, which represent an O&M cost of

50 €/MWh.

8. Conclusion and recommendations of future work

The present article allows to obtain the following

conclusions inherent to the various analyses and studies

conducted:

Floating platform technologies, as the

WindFloat, allow eliminating technical

constraints related to water depth;

Availability decreases with increasing distance

from the shore, due to accessibility;

It is possible to achieve availability levels of

98% for some OWF;

WW characteristics represent a strong influence

into OWF accessibility, where an increase of

accessibility requirement will lead to a

significant decrease in accessibility;

Corrective O&M induces a larger downtime and

expenditures than preventive O&M;

Waiting time for a WW is the activity that

contributes most to corrective O&M downtime

and expenses;

Case study presents a O&M expenses of

23.05 €/MWh, which can be reduced to

8.62 €/MWh applying optimization strategies;

The investment in the present case study, have

an NPV of 4.78 M€ and an IRR of 9.19%, values

that can be increased to 14.2 M€ with an IRR of

10.49%.

Despite the fact that this article provides reliable techno-

-economic values, supported by several authors, obtaining

accurate values for O&M costs, requires the elaboration of

experimental and theoretical studies related to case study.

This studies should be done in order to analyse and obtain

values, with the least errors possible regarding the techno-

-economics specifications.

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impact of weather windows in offshore wind farm operation and maintenance expenses

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