A Framework for the Selection of Optimum Offshore Wind Farm Locations for Deployment

Abstract

This research develops a framework to assist wind energy developers to select the optimum deployment site of a wind farm by considering the Round 3 available zones in the UK. The framework includes optimization techniques, decision-making methods and experts’ input in order to support investment decisions. Further, techno-economic evaluation, life cycle costing (LCC) and physical aspects for each location are considered along with experts’ opinions to provide deeper insight into the decision-making process. A process on the criteria selection is also presented and seven conflicting criteria are being considered for implementation in the technique for the order of preference by similarity to the ideal solution (TOPSIS) method in order to suggest the optimum location that was produced by the nondominated sorting genetic algorithm (NSGAII). For the given inputs, Seagreen Alpha, near the Isle of May, was found to be the most probable solution, followed by Moray Firth Eastern Development Area 1, near Wick, which demonstrates by example the effectiveness of the newly introduced framework that is also transferable and generic. The outcomes are expected to help stakeholders and decision makers to make better informed and cost-effective decisions under uncertainty when investing in offshore wind energy in the UK.

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energies
Article
A Framework for the Selection of Optimum Offshore
Wind Farm Locations for Deployment
Varvara Mytilinou 1, *, Estivaliz Lozano-Minguez 2ID and Athanasios Kolios 3ID
1Renewable Energy Marine Structures Centre for Doctoral Training, Cranfield University, Cranfield,
Bedfordshire MK43 0AL, UK
2Department of Mechanical Engineering and Materials—CIIM, Universitat Politècnica de València,
Camino de Vera s/n, 46022 Valencia, Spain; eslomin@upv.es
3Department of Naval Architecture, Ocean & Marine Engineering, University of Strathclyde, HD2.35,
Henry Dyer Building, 100 Montrose Street, Glasgow G4 0LZ, UK; athanasios.kolios@strath.ac.uk
*Correspondence: v.mytilinou@cranfield.ac.uk; Tel.: +44-1234-75-4631
Received: 23 April 2018; Accepted: 9 July 2018; Published: 16 July 2018


Abstract:
This research develops a framework to assist wind energy developers to select the optimum
deployment site of a wind farm by considering the Round 3 available zones in the UK. The framework
includes optimization techniques, decision-making methods and experts’ input in order to support
investment decisions. Further, techno-economic evaluation, life cycle costing (LCC) and physical
aspects for each location are considered along with experts’ opinions to provide deeper insight into the
decision-making process. A process on the criteria selection is also presented and seven conflicting
criteria are being considered for implementation in the technique for the order of preference by
similarity to the ideal solution (TOPSIS) method in order to suggest the optimum location that was
produced by the nondominated sorting genetic algorithm (NSGAII). For the given inputs, Seagreen
Alpha, near the Isle of May, was found to be the most probable solution, followed by Moray Firth
Eastern Development Area 1, near Wick, which demonstrates by example the effectiveness of the
newly introduced framework that is also transferable and generic. The outcomes are expected to
help stakeholders and decision makers to make better informed and cost-effective decisions under
uncertainty when investing in offshore wind energy in the UK.
Keywords:
multi-objective optimization; nondominated sorting genetic algorithm (NSGA);
multi-criteria decision making (MCDM); technique for the order of preference by similarity to
the ideal solution (TOPSIS); life cycle cost
1. Introduction
The future of wind energy seems to keep growing as 18 GW are expected to be deployed by 2020
in the UK, with potential for more ambitious targets after 2020. Thus, there is a substantial need to
reduce the cost of energy by identifying relevant cost reduction strategies in order to achieve these
goals. The future of the UK’s industry size strongly depends on these goals [
1
]. Significant price
increases in the overall cost of turbines, operations and maintenance have a direct impact on large-scale
wind projects, hence the wind energy industry is determined to lower the costs of producing energy
in all phases of the wind project from predevelopment to operations. Following the UK technology
roadmap, the offshore wind costs should be reduced to £100/MWh by 2020 [
2
]. According to [
1
] the
costs were stabilized at £140 per MWh in 2011. The UK’s Offshore Wind Programme Board (OWPB)
stated that the offshore wind costs dropped below £100/MWh when 2015–2016 projects achieved a
levelized cost of energy (LCOE) of £97 compared to £142 per MWh in 2010–2011, according to the
Cost Reduction Monitoring Framework report in 2016 [
3
]. Recently, in 2017, Ørsted (formerly DONG
Energies 2018,11, 1855; doi:10.3390/en11071855 www.mdpi.com/journal/energies

Energies 2018,11, 1855 2 of 23
Energy) guaranteed a £57.5/MWh building the world’s largest offshore wind farm in Hornsea 2,
according to [4].
Developers and operators of offshore wind energy projects face many risks and complex decisions
regarding service life cost reduction. In many cases, the manufacturers produce large volumes of parts
in order to deal with the issue via economies of scale. Also, project consents can be time-consuming and
difficult to obtain, however, all offshore wind farms were successfully completed regarding investment
and profit [
1
]. Ensuring a long-term and profitable investment plan can be challenging, with both
pre-consent and post-consent delays introducing considerable risks [
2
,
5
]. To this end, appropriate
planning studies should be conducted at the early development stages of the project in order to
minimize the investment risk. A breakdown of the key costs in an offshore wind farm can be found
in [
6
] while studying existing projects, the location of a wind farm and the type of support structure
have a great impact on the overall costs [7–9].
The aim of this paper is to develop a wind farm deployment framework, as illustrated in Figure 1,
for supporting investment decisions at the initial stages of the development of Round 3 offshore wind
farms in the UK by combining multi-objective optimization (MOO), life cycle cost (LCC) analysis and
multicriteria decision making (MCDM). The contribution to knowledge is in developing and applying
this novel and transferable framework that combines an economic analysis model by using LCC and
geospatial analysis, MOO by using nondominated sorting genetic algorithm (NSGA II), survey data
from real-world experts and finally MCDM by using a deterministic version and a stochastic expansion
of the technique for the order of preference by similarity to the ideal solution (TOPSIS). Also, a criteria
selection framework for the implementation of MCDM methods has been devised. The outcomes are
expected to provide a deeper insight into the wind energy sector for future investments.
Energies 2018, 11, x FOR PEER REVIEW 2 of 22
DONG Energy) guaranteed a £57.5/MWh building the world’s largest offshore wind farm in Hornsea
2, according to [4].
Developers and operators of offshore wind energy projects face many risks and complex
decisions regarding service life cost reduction. In many cases, the manufacturers produce large
volumes of parts in order to deal with the issue via economies of scale. Also, project consents can be
time-consuming and difficult to obtain, however, all offshore wind farms were successfully
completed regarding investment and profit [1]. Ensuring a long-term and profitable investment plan
can be challenging, with both pre-consent and post-consent delays introducing considerable risks
[2,5]. To this end, appropriate planning studies should be conducted at the early development stages
of the project in order to minimize the investment risk. A breakdown of the key costs in an offshore
wind farm can be found in [6] while studying existing projects, the location of a wind farm and the
type of support structure have a great impact on the overall costs [7–9].
The aim of this paper is to develop a wind farm deployment framework, as illustrated in
Figure 1, for supporting investment decisions at the initial stages of the development of Round 3
offshore wind farms in the UK by combining multi-objective optimization (MOO), life cycle cost
(LCC) analysis and multicriteria decision making (MCDM). The contribution to knowledge is in
developing and applying this novel and transferable framework that combines an economic analysis
model by using LCC and geospatial analysis, MOO by using nondominated sorting genetic algorithm
(NSGA II), survey data from real-world experts and finally MCDM by using a deterministic version
and a stochastic expansion of the technique for the order of preference by similarity to the ideal
solution (TOPSIS). Also, a criteria selection framework for the implementation of MCDM methods
has been devised. The outcomes are expected to provide a deeper insight into the wind energy sector
for future investments.
Figure 1. Main framework.
Figure 1. Main framework.

Energies 2018,11, 1855 3 of 23
The structure of the remaining sections of this paper starts with a literature review on related
studies for LCC analysis, turbine layout optimization, MCDM, and wind farm location selection in
the offshore wind energy sector. Next, the development of the proposed framework is documented.
The nondominated results for all zones will be analyzed and discussed followed by the prioritization
process from TOPSIS. Conclusions and future work are documented at the end of the paper.
2. Literature Review
The Crown Estate has the rights of the seabed leasing up to 12 nautical miles from the UK
shore and the right to exploit the seabed for renewable energy production up to 200 miles across its
international waters. In recent years, the Crown Estate has run three rounds of wind farm development
sites and their extensions. When the Crown Estate released the new Round 3 offshore wind site leases,
they provided nine large zones of up to 32 GW power capacity [
10
]. The new leases encourage larger
scale investments and consequently bigger wind turbines and include locations further away from the
shore and in deeper waters [2,5,11–13].
Currently, all Round 3 zones have been suggested and published according to reports by the
Department for Energy and Climate Change (DECC) and other stakeholders after the outcome of a
strategic environmental assessment [
14
]. It should be noted that new offshore and onshore electricity
transmission networks are needed in order to cover Round 3 connections up to 25 GW [
14
]. The Round
3 zones are the following; Moray Firth, Firth of Forth, Dogger Bank, Hornsea, East Anglia (Norfolk
Bank), Rampion (Hastings), Navitus Bay (West Isle of Wight), Atlantic Array (Bristol Channel), and
Irish Sea (Celtic Array). Every zone consists of various sites and extensions. Here, the five first zones
in the North Sea are investigated in order to demonstrate the proof of the developed framework’s
applicability. Each location faces similar challenges such as deep waters or long distances from the
shore, etc. as shown in Figure 2.
Only a few location-selection-focused studies can be found, and usually, the findings and the
formulation of the problems follow a different direction that this present study. For instance [
15
],
uses goal programming in order to obtain the optimum offshore location for a wind farm installation.
The study involves Round 3 locations in the UK and discusses its flexibility to combine decision-making.
The work integrates the energy production, costs and multicriteria nature of the problem while
considering environmental, social, technical and economic aspects.
For instance, the following literature presents cases in renewable energy where optimization has
been successfully applied by utilizing different algorithms. An approach that links a multi-objective
genetic algorithm to the design of a floating wind turbine was presented in [
16
]. By varying nine
design variables related to the structural characteristics of the support structure, multiple concepts
of support structures were modelled and linked to the optimizer. In [
17
], the authors provide a case
study for the optimization of the electricity generation mix in the UK by using hybrid MCDM and
linear programming and suggest a methodology to deal with the uncertainty that is introduced in the
problem by the bias in experts’ opinions and other related factors. In [
18
], a structural optimization
model for the support structures of offshore wind turbines was implemented by using a parametric
Finite Element Analysis (FEA) analysis coupled with a genetic algorithm in order to minimize the
mass of the structure considering multicriteria constraints.
LCC analysis evaluates costs, enabling suggestions in cost reductions throughout a project’s
service life. The outcome of the analysis provides pertinent information in investments and can
influence decisions from the initial stages of a new project [
19
]. In [
20
], a parametric whole life cost
framework for an offshore wind farm and a cost breakdown structure was presented and analyzed,
where the project is divided into five different stages; the predevelopment and consenting (C
P&C
),
production and acquisition (C
P&A
), installation and commissioning (C
I&C
), operation and maintenance
(C
O&M
), and decommissioning and disposal (C
D&D
) stage. The advantages and disadvantages of
the transition to offshore wind and an LCC model of an offshore wind development were proposed
in [
21
]. However, the study mainly focused on a simplified model and especially the operation and

Energies 2018,11, 1855 4 of 23
maintenance stage of the LCC analysis, and it was suggested that there could be a further full-scale
LCC framework in the future. In [
22
], a detailed failure mode identification throughout the service life
of offshore wind turbines was performed and a review of the three most relevant end-of-life scenarios
were presented in order to contribute to increase the return on investment and decrease the levelized
cost of electricity. However, there are limited studies that integrate a high fidelity of life cycle cost
(LCC) analysis into a multi-objective optimization (MOO) algorithm. LCC analysis gains more ground
over the years because of the increased uncertainty of wind energy projects throughout their service
life, including the cost of finance, the real cost of Operational Expenditure (OPEX) and the potential of
service life extension.
Energies 2018, 11, x FOR PEER REVIEW 4 of 22
life of offshore wind turbines was performed and a review of the three most relevant end-of-life
scenarios were presented in order to contribute to increase the return on investment and decrease the
levelized cost of electricity. However, there are limited studies that integrate a high fidelity of life
cycle cost (LCC) analysis into a multi-objective optimization (MOO) algorithm. LCC analysis gains
more ground over the years because of the increased uncertainty of wind energy projects throughout
their service life, including the cost of finance, the real cost of Operational Expenditure (OPEX) and
the potential of service life extension.
Figure 2. Round 3 offshore locations around the UK by using open source licensed geographic
information system QGIS.
MCDM is beneficial for policy-making through evaluation and prioritization of available
technological options because of their ability to combine both technical and non-technical alternatives
as well as quantitative and qualitative attributes in the decision-making process. A number of MCDM
methods are applicable to energy-related projects, however, TOPSIS was selected because of the wide
applicability of the method as can be found in literature and the connection of the method to
numerous energy-related studies such as [23–25]. It is common to combine stochastic and fuzzy
processes in order to deal with uncertain environments. In [23], Lozano-Minguez employed a
methodology on the selection of the best support structure among three design options of an offshore
Figure 2.
Round 3 offshore locations around the UK by using open source licensed geographic
information system QGIS.
MCDM is beneficial for policy-making through evaluation and prioritization of available
technological options because of their ability to combine both technical and non-technical alternatives
as well as quantitative and qualitative attributes in the decision-making process. A number of MCDM
methods are applicable to energy-related projects, however, TOPSIS was selected because of the wide
applicability of the method as can be found in literature and the connection of the method to numerous

Energies 2018,11, 1855 5 of 23
energy-related studies such as [
23
–
25
]. It is common to combine stochastic and fuzzy processes in
order to deal with uncertain environments. In [
23
], Lozano-Minguez employed a methodology on
the selection of the best support structure among three design options of an offshore wind turbine,
considering a set of qualitative and quantitative criteria. A similar study was reported by Kolios in [
26
],
extending TOPSIS to consider stochasticity of inputs.
Methods and techniques to cope with a high number of criteria and high dimensionality of
decision-making problems are available in the literature. The multiple criteria hierarchy process
(MCHP) [
27
–
29
] has been employed in order to deal with multiple criteria in decision-making processes.
MCHP is usually employed in combination with outranking MCDM methods. Further applications
can be found in [30,31].
In general, classifying criteria as either qualitative or quantitative is related to their nature and
fidelity of the analysis. The employed decision-making methods can be based on priority, outranking,
distance or combination of the three [
32
]. In [
23
], a decision-making study was conducted in three
fixed wind turbine support structure types considering both quantitative and qualitative criteria while
using TOPSIS. A decision-making study on floating support structures by combining both quantitative
and qualitative criteria was presented in [33].
The approach proposed here for the stochastic expansion of deterministic methods was based
in [
26
] that has reported the expansion of different deterministic methods, under the consideration that
input variables are modelled as statistical distributions (derived by fitting data collected for each value
in the decision matrix and weight vector), as shown in Figure 3. By using Monte Carlo simulations,
numerous iterations quantify results and identify the number of cases where the optimum solution
will prevail, i.e., there is a Piprobability that option Xiwill rank first.
Energies 2018, 11, x FOR PEER REVIEW 5 of 22
wind turbine, considering a set of qualitative and quantitative criteria. A similar study was reported
by Kolios in [26], extending TOPSIS to consider stochasticity of inputs.
Methods and techniques to cope with a high number of criteria and high dimensionality of
decision-making problems are available in the literature. The multiple criteria hierarchy process
(MCHP) [27–29] has been employed in order to deal with multiple criteria in decision-making
processes. MCHP is usually employed in combination with outranking MCDM methods. Further
applications can be found in [30,31].
In general, classifying criteria as either qualitative or quantitative is related to their nature and
fidelity of the analysis. The employed decision-making methods can be based on priority, outranking,
distance or combination of the three [32]. In [23], a decision-making study was conducted in three
fixed wind turbine support structure types considering both quantitative and qualitative criteria
while using TOPSIS. A decision-making study on floating support structures by combining both
quantitative and qualitative criteria was presented in [33].
The approach proposed here for the stochastic expansion of deterministic methods was based in
[26] that has reported the expansion of different deterministic methods, under the consideration that
input variables are modelled as statistical distributions (derived by fitting data collected for each
value in the decision matrix and weight vector), as shown in Figure 3. By using Monte Carlo
simulations, numerous iterations quantify results and identify the number of cases where the
optimum solution will prevail, i.e., there is a Pi probability that option Xi will rank first.
Figure 3. Stochastic expansion algorithm of deterministic Multi-Criteria Decision Making (MCDM)
methods.
In [26], during deterministic TOPSIS, the weights for each criterion were considered fixed, but
under stochastic modelling, statistical distributions were employed to best fit the acquired data of the
experts’ opinions. Perera [34] has presented a study that combines MCDM and multi-objective
optimization in the designing process of hybrid energy systems (HESs), using the fuzzy TOPSIS
extension along with level diagrams. In [35], MCDM under uncertainty is discussed in an application
where the alternatives’ weights are partially known. An extended and modified stochastic TOPSIS
approach was implemented using interval estimations.
In [26], the authors extend the previous MCDM study on the decision-making of an offshore
wind turbine support structure among different fixed and floating types. The decision matrix
includes stochastic inputs (by using data from experts) in order to minimize the uncertainties in the
study. In the same study, an iterative process has been included, and the TOPSIS method was
implemented. In [36], a study suggests a methodology for classification and evaluation of 11 available
offshore wind turbine support structure types while considering 13 criteria by using TOPSIS as the
decision-making method.
In [24], an expansion of MCDM methods to account for stochastic input variables was
conducted, where a comparative study was carried out by utilizing widely applied MCDM methods.
The method was applied to a reference problem in order to select the best wind turbine support
Figure 3.
Stochastic expansion algorithm of deterministic Multi-Criteria Decision Making
(MCDM) methods.
In [
26
], during deterministic TOPSIS, the weights for each criterion were considered fixed, but
under stochastic modelling, statistical distributions were employed to best fit the acquired data of
the experts’ opinions. Perera [
34
] has presented a study that combines MCDM and multi-objective
optimization in the designing process of hybrid energy systems (HESs), using the fuzzy TOPSIS
extension along with level diagrams. In [
35
], MCDM under uncertainty is discussed in an application
where the alternatives’ weights are partially known. An extended and modified stochastic TOPSIS
approach was implemented using interval estimations.
In [
26
], the authors extend the previous MCDM study on the decision-making of an offshore
wind turbine support structure among different fixed and floating types. The decision matrix includes
stochastic inputs (by using data from experts) in order to minimize the uncertainties in the study. In the
same study, an iterative process has been included, and the TOPSIS method was implemented. In [
36
],
a study suggests a methodology for classification and evaluation of 11 available offshore wind turbine
support structure types while considering 13 criteria by using TOPSIS as the decision-making method.

Energies 2018,11, 1855 6 of 23
In [
24
], an expansion of MCDM methods to account for stochastic input variables was conducted,
where a comparative study was carried out by utilizing widely applied MCDM methods. The method
was applied to a reference problem in order to select the best wind turbine support structure type for
a given deployment location. Data from industry experts and six MCDM methods were considered,
so as to determine the best alternative among available options, assessed against selected criteria in
order to provide a level of confidence to each option.
An electricity generation systems allocation optimization model is suggested in [
37
] for the case
of a disaster relief camp in order to minimize the total project cost and maximize the share of systems
that were assessed through a decision-making process and were prioritized accordingly. Bi-objective
integer linear programming and a decision-making method (VIKOR) were employed and the overall
model was applied to a hypothetical map.
A study performed in [
38
] uses a TOPSIS model by incorporating technical, environmental and
social criteria and finally combines the evaluation scores to develop a MOGLP (multi-objective grey
linear programming) problem in order to assess the decision-making of power production technologies.
The outcome of this work was the optimal mix of electricity generated by each option in the UK energy
market. In [
39
], a methodology for an investment risk evaluation and optimization is suggested
in order to mitigate the risks and achieve sustainability for wind energy projects in China. In this
study, Monte Carlo analysis and a multi-objective programming model are used so as to increase the
confidence in the planning of investment research and the sustainability of renewables in China.
In this study, NSGA II is employed because it is suitable for MOO problems with many objectives
and was further analyzed in previous studies in offshore wind energy applications in [
40
], where a
methodology was proposed to support the decision-making process at these first stages of a wind
farm investment considering available Round 3 zones in the UK. Three state-of-the-art algorithms
were applied and compared to a real-world case of the wind energy sector. Optimum locations were
suggested for a wind farm by considering only round 3 zones around the UK. The problem comprised
of techno-economic Life Cycle Cost related factors, which were modelled by using the physical aspects
of each wind farm location (i.e., the wind speed, distance from the ports and water depth), the wind
turbine size and the number of turbines.
3. Framework
3.1. Wind Farm Deployment Model
The wind farm deployment model implemented in this study couples the LCC analysis with
a geospatial analysis as described below. The LCC analysis of a project involves all project stages
described in Figure 4. In [
20
,
41
], a whole LCC formulation is provided, and this study integrates these
phases into the MOO problem. Assumptions and related data in the modelling of the problem
were gathered from the following references [
20
,
41
–
46
] based on which the present model was
developed. The LCC model described in [
20
] is used as a guideline in this study, and along with the
site characteristics and the problem’s formulation, the optimization problem is formed. The type of
foundation that was considered in the LCC model is the jacket structure as it constitutes a configuration
that can be utilized in a range of water depths allowing for the optimization process to be automated.
The total LCC is calculated as follows:
LCC = CP&C + CP&A + CI&C + CO&M + CD&D (1)
where
LCC: Life Cycle Cost
CI&C: Installation and Commissioning cost
CP&C: Predevelopment and Consenting cost
CO&M: Operation and Maintenance cost

Energies 2018,11, 1855 7 of 23
CP&A: Production and Acquisition cost
CD&D: Decommissioning and Disposal Cost
CAPEX = CP&C + CP&A + CI&C (2)
OPEX = CO&M (3)
CAPEX: Capital expenditure
OPEX: Operational expenditure
The power extracted is calculated for each site and each wind turbine respectively as:
P=1
2ACpρu3(4)
where
A: Turbine rotor area
ρ: Air density
Cp: Power coefficient
u: Mean annual wind speed of each specific site
The total installed capacity (TIC) of the wind farm dependents on the number of turbines and the
rated power of each of them, and is calculated for every solution:
TIC =PR×NWT (5)
where
PR: Rated power
NWT: Number of turbines
For each offshore location, a special profile was created including the coordinates, distance from
designated construction ports, annual wind speed and average site water depth, as listed in Table 1,
where data was acquired from [
45
]. Among various data, Table 1shows the locations that each of these
zones contains.
Energies 2018, 11, x FOR PEER REVIEW 7 of 22
Figure 4. Life cycle cost (LCC) breakdown [20].
The total LCC is calculated as follows:
LCC = CP&C + CP&A + CI&C + CO&M + CD&D (1)
where
LCC: Life Cycle Cost
CI&C: Installation and Commissioning cost
CP&C: Predevelopment and Consenting cost
CO&M: Operation and Maintenance cost
CP&A: Production and Acquisition cost
CD&D: Decommissioning and Disposal Cost
CAPEX = CP&C + CP&A + CI&C (2
)
OPEX = CO&M (3
)
CAPEX: Capital expenditure
OPEX: Operational expenditure
The power extracted is calculated for each site and each wind turbine respectively as:
=1
23
(4)
where
: Turbine rotor area
: Air density
Cp: Power coefficient
: Mean annual wind speed of each specific site
The total installed capacity (TIC) of the wind farm dependents on the number of turbines and
the rated power of each of them, and is calculated for every solution:
TIC = P
R × NWT
(5)
where
P
R: Rated power
NWT: Number of turbines
For each offshore location, a special profile was created including the coordinates, distance from
designated construction ports, annual wind speed and average site water depth, as listed in Table 1,
where data was acquired from [45]. Among various data, Table 1 shows the locations that each of
these zones contains.
Figure 4. Life cycle cost (LCC) breakdown [20].

Energies 2018,11, 1855 8 of 23
Table 1. Round 3 zones and sites, and specific data acquired from [45].
Site Index Zone Wind Farm Site Name Centre
Latitude
Centre
Longitude Port Distance from
the Port (km)
Annual Wind Speed
(m/s) (at 100 m)
Average Water
Depth (m)
0 Moray Firth Moray Firth Western
Development Area 58.097 −3.007 Port of Cromarty 123.691 8.82 44
1 Moray Firth Moray Firth Eastern
Development Area 1 58.188 −2.720 Port of Cromarty 157.134 9.43 44.5
2 Firth of Forth Seagreen Alpha 56.611 −1.821 Montrose 72.598 9.92 50
3 Firth of Forth Seagreen Bravo 56.572 −1.658 Montrose 91.193 10.09 50
4 Dogger Bank Creyke Beck A 54.769 1.908 Hartlepool and Tess 343.275 10.01 21.5
5 Dogger Bank Creyke Beck B 54.977 1.679 Hartlepool and Tess 319.949 10.04 26.5
6 Dogger Bank Teesside A 55.039 2.822 Hartlepool and Tess 447.124 10.05 25.5
7 Dogger Bank Teesside B 54.989 2.228 Hartlepool and Tess 380.788 10.04 25.5
8 Hornsea Hornsea Project One 53.883 1.921 Grimsby 242.328 9.69 30.5
9 Hornsea Hornsea Project Two 53.940 1.687 Grimsby 217.270 9.73 31.5
10 Hornsea Hornsea Project Three 53.873 2.537 Grimsby 310.521 9.74 49.5
11 Hornsea Hornsea Project Four 54.038 1.271 Grimsby 173.928 9.71 44.5
12 East Anglia (Norfolk Bank) East Anglia One 52.234 2.478 Great Yarmouth 92.729 9.5 35.5
13 East Anglia (Norfolk Bank) East Anglia One North 52.374 2.421 Great Yarmouth 81.104 9.73 45.5
14 East Anglia (Norfolk Bank) East Anglia Two 52.128 2.209 Great Yarmouth 74.559 9.46 50
15 East Anglia (Norfolk Bank) East Anglia Three 52.664 2.846 Great Yarmouth 124.969 9.56 36
16 East Anglia (Norfolk Bank) Norfolk Boreas 53.040 2.934 Great Yarmouth 143.464 9.53 31.5
17 East Anglia (Norfolk Bank) Norfolk Vanguard 52.868 2.688 Great Yarmouth 111.449 9.56 32

Energies 2018,11, 1855 9 of 23
For the distances from the ports calculation an open source licensed geographic information
system (GIS) called QGIS was used, which is a part of the Open Source Geospatial Foundation
(OSGeo) [
47
]. A list of ports was acquired from [
48
–
50
]. The QGIS maps of the offshore sites were
acquired from the official Crown Estate website [
51
] for QGIS and AutoCAD. The list contains
designated, appropriate and sufficient construction ports that are suitable for the installation,
manufacturing and maintenance for wind farms. New ports are to be built specifically to accommodate
needs of the offshore wind industry; however, this study takes into account a selection of currently
available ports around the UK. The distances were calculated assuming that the nearest port to the
individual wind farm is connected in a straight line. QGIS was also employed to measure and model
aspects of the LCC related to the geography and operations. The estimated metrics were integrated
into the configuration settings of the whole LCC.
Three layout configurations are considered. The lower and upper limits of a theoretical array
layout from [
52
] will be employed along with an extreme case. More specifically, in the lower limit case
(layout 1), the horizontal and vertical distance between turbines is 3 and 5 times the rotor diameter,
respectively. The turbine specifications used for the LCC model are listed in Table 2. In the upper limit
case (layout 2), 5 and 9 times the rotor diameter were considered horizontally and vertically. In the
extreme case (layout 3), the horizontal and vertical distance between turbines is 10 and 18 times the
rotor diameter. All different configurations are illustrated in Figure 5. The present work focuses on the
optimization of offshore wind farm locations considering the maximum wind turbine number that
can fit in the selected Round 3 locations according to three different layout configuration placements.
The wind farm is oriented according to the most optimal wind direction. Different layouts provide a
different maximum wind turbine number that can guide the optimization process to more detailed
calculations. The maximum number of wind turbines is determined by considering types of reference
turbines of 6, 7, 8 and 10 MW and by following three layout cases, as listed below in Figure 5, where D
is the diameter of each turbine.
Table 2. Turbine specifications.
Turbine Type Index Rated Power (MW) Rotor Diameter (m) Hub Height (m) Total Weight (t)
0 10 190 125 1580
1 8 164 123 965
2 7 154 120 955
3 6 140 100 656
Energies 2018, 11, x FOR PEER REVIEW 9 of 22
For the distances from the ports calculation an open source licensed geographic information
system (GIS) called QGIS was used, which is a part of the Open Source Geospatial Foundation
(OSGeo) [47]. A list of ports was acquired from [48–50]. The QGIS maps of the offshore sites were
acquired from the official Crown Estate website [51] for QGIS and AutoCAD. The list contains
designated, appropriate and sufficient construction ports that are suitable for the installation,
manufacturing and maintenance for wind farms. New ports are to be built specifically to
accommodate needs of the offshore wind industry; however, this study takes into account a selection
of currently available ports around the UK. The distances were calculated assuming that the nearest
port to the individual wind farm is connected in a straight line. QGIS was also employed to measure
and model aspects of the LCC related to the geography and operations. The estimated metrics were
integrated into the configuration settings of the whole LCC.
Three layout configurations are considered. The lower and upper limits of a theoretical array
layout from [52] will be employed along with an extreme case. More specifically, in the lower limit
case (layout 1), the horizontal and vertical distance between turbines is 3 and 5 times the rotor
diameter, respectively. The turbine specifications used for the LCC model are listed in Table 2. In the
upper limit case (layout 2), 5 and 9 times the rotor diameter were considered horizontally and
vertically. In the extreme case (layout 3), the horizontal and vertical distance between turbines is 10
and 18 times the rotor diameter. All different configurations are illustrated in Figure 5. The present
work focuses on the optimization of offshore wind farm locations considering the maximum wind
turbine number that can fit in the selected Round 3 locations according to three different layout
configuration placements. The wind farm is oriented according to the most optimal wind direction.
Different layouts provide a different maximum wind turbine number that can guide the optimization
process to more detailed calculations. The maximum number of wind turbines is determined by
considering types of reference turbines of 6, 7, 8 and 10 MW and by following three layout cases, as
listed below in Figure 5, where D is the diameter of each turbine.
Table 2. Turbine specifications.
Turbine Type Index
Rated Power (MW)
Rotor Diameter (m)
Hub Height (m)
Total Weight (t)
0
10
190
125
1580
1
8
164
123
965
2
7
154
120
955
3
6
140
100
656
Figure 5. Demonstrating different layouts, where D corresponds to the diameter of the turbine.
For the estimation of cabling length, which is required to calculate parts of the LCC related to
the spatial distribution of the wind turbines in the wind farm, the minimum spanning tree algorithm
is used. The location of the turbines is treated as a vertex of a graph, and the cabling represents the
edge that connects the vertices. Given a set of vertices, which are separated by each other by the
different layout indices, from Figure 5, the minimum spanning tree connects all these vertices without
creating any cycles, thus yielding minimum possible total edge length. This represents the minimum
cabling length of the particular layout.
Figure 5. Demonstrating different layouts, where D corresponds to the diameter of the turbine.
For the estimation of cabling length, which is required to calculate parts of the LCC related to the
spatial distribution of the wind turbines in the wind farm, the minimum spanning tree algorithm is
used. The location of the turbines is treated as a vertex of a graph, and the cabling represents the edge
that connects the vertices. Given a set of vertices, which are separated by each other by the different
layout indices, from Figure 5, the minimum spanning tree connects all these vertices without creating

Energies 2018,11, 1855 10 of 23
any cycles, thus yielding minimum possible total edge length. This represents the minimum cabling
length of the particular layout.
The way the length of the cables was calculated provides an approximation of the actual length.
In the presence of relevant actual data, the calculations of both the layouts and the LCC would provide
more realistic values. For instance, the cable length would be expected to be larger because of the
water depth and the burial of the cables for each turbine. For each cable, both ends will have to come
from the seabed to the platform, so at least twice the water depth should be added to each cable and
finally allow for some contingency length for installation.
The wind rose diagrams provided the prevailing wind direction, which sets the layout orientation.
The wind speeds, the wind rose graphs, and the coordinates of each location were obtained by FUGRO
(Leidschendam, The Netherlands) and 4COffshore (Lowestoft Suffolk, UK) [
45
,
53
]. All wind farm
sites were discovered to have dominant southwestern winds followed by western winds. For that
reason, the orientation of the layouts is assumed to be southwestern (as the winds are assumed to blow
predominantly from that direction). The wind rose graphs for each offshore site are determined by
data acquired from [
53
] and the grid points they created around the UK. The nearest grid point to the
offshore site is used.
An important factor to be considered is also the atmospheric stability. Although the different
layouts considered in this study may be affected by the atmospheric stability states, as it impacts the
layout’s wake recovery pattern, it was not considered in the framework. Also, the power curves and
their multiplicity in turbine type were not considered in this study because the aim is to devise and
demonstrate a generic and transferable methodology. It is suggested that both elements could be
further investigated in future studies to evaluate their effect in the derivation of the optimum solution.
In Figure 6, the example of Moray Firth zone (which includes Moray Firth Western Development
Area and Moray Firth Eastern Development Area 1) shows the positioning of the turbines depending
on the layout 1, 2 and 3 and the turbine size.
Energies 2018, 11, x FOR PEER REVIEW 10 of 22
The way the length of the cables was calculated provides an approximation of the actual length.
In the presence of relevant actual data, the calculations of both the layouts and the LCC would
provide more realistic values. For instance, the cable length would be expected to be larger because
of the water depth and the burial of the cables for each turbine. For each cable, both ends will have
to come from the seabed to the platform, so at least twice the water depth should be added to each
cable and finally allow for some contingency length for installation.
The wind rose diagrams provided the prevailing wind direction, which sets the layout
orientation. The wind speeds, the wind rose graphs, and the coordinates of each location were
obtained by FUGRO (Leidschendam, The Netherlands) and 4COffshore (Lowestoft Suffolk, UK)
[45,53]. All wind farm sites were discovered to have dominant southwestern winds followed by
western winds. For that reason, the orientation of the layouts is assumed to be southwestern (as the
winds are assumed to blow predominantly from that direction). The wind rose graphs for each
offshore site are determined by data acquired from [53] and the grid points they created around the
UK. The nearest grid point to the offshore site is used.
An important factor to be considered is also the atmospheric stability. Although the different
layouts considered in this study may be affected by the atmospheric stability states, as it impacts the
layout’s wake recovery pattern, it was not considered in the framework. Also, the power curves and
their multiplicity in turbine type were not considered in this study because the aim is to devise and
demonstrate a generic and transferable methodology. It is suggested that both elements could be
further investigated in future studies to evaluate their effect in the derivation of the optimum
solution.
In Figure 6, the example of Moray Firth zone (which includes Moray Firth Western Development
Area and Moray Firth Eastern Development Area 1) shows the positioning of the turbines depending
on the layout 1, 2 and 3 and the turbine size.
Figure 6. Moray Firth zone. A maximum number of wind turbines placed according to layout 1, layout
2 and layout 3 for the case of 10 MW turbine. In (a) Moray Firth, 10 MW turbines positioned in layout
1; (b) Moray Firth, 10 MW turbines positioned in layout 2; (c) Moray Firth, 10 MW turbines positioned
in layout 3.
3.2. Multi-Objective Optimization
The optimization problem includes eight objectives; five LCC-related objectives, based on [20],
which are the cost-related objectives to be minimized. The three additional objectives are the number
of turbines (NWT), the power that is extracted (P) from each offshore site and the total installed
capacity (TIC), which are to be minimized, maximized, and maximized, respectively.
More specifically, the LCC includes the predevelopment and consenting, production and
acquisition, installation and commissioning, operation and maintenance and finally
decommissioning and disposal costs. The power extracted is calculated by the specific mean annual
wind speed of each location along with the characteristics of each wind turbine both of which are
considered inputs.
The optimization problem formulates as follows:
Minimize CP&C, CP&A, CI&C, CO&M, CD&D, NWT, (−P), (−TIC) (6)
Figure 6.
Moray Firth zone. A maximum number of wind turbines placed according to layout 1,
layout 2 and layout 3 for the case of 10 MW turbine. In (
a
) Moray Firth, 10 MW turbines positioned
in layout 1; (
b
) Moray Firth, 10 MW turbines positioned in layout 2; (
c
) Moray Firth, 10 MW turbines
positioned in layout 3.
3.2. Multi-Objective Optimization
The optimization problem includes eight objectives; five LCC-related objectives, based on [
20
],
which are the cost-related objectives to be minimized. The three additional objectives are the number of
turbines (NWT), the power that is extracted (P) from each offshore site and the total installed capacity
(TIC), which are to be minimized, maximized, and maximized, respectively.
More specifically, the LCC includes the predevelopment and consenting, production and
acquisition, installation and commissioning, operation and maintenance and finally decommissioning
and disposal costs. The power extracted is calculated by the specific mean annual wind speed of each
location along with the characteristics of each wind turbine both of which are considered inputs.

Energies 2018,11, 1855 11 of 23
The optimization problem formulates as follows:
Minimize CP&C, CP&A , CI&C, CO&M , CD&D, NWT, (−P), (−TIC) (6)
Subject to 0 ≤site index ≤20,
0≤turbine type index ≤3
1≤layout index ≤3
50 ≤Number of turbines ≤maximum number per site
TIC ≤Maximum capacity of Round 3 sites based on the Crown Estate
Although the maximum number of turbines has been estimated by using QGIS, the maximum
capacity allowed per region was also considered, as specified by the Crown Estate, as listed in Table 3.
These were selected because of the possibility that the constraints might overlap in an extreme case
scenario. Therefore, both constraints were added to the problem in order to secure all cases.
Table 3. Maximum capacity of Round 3 wind farms, specified by the Crown Estate [1].
Zone Capacity (MW)
1. Moray Firth 1500
2. Firth of Forth 3465
3. Dogger Bank 9000
4. Hornsea 4000
5. East Anglia 7200
6. Rampion 665
7. Navitus Bay 1200
8. Bristol Channel 1500
9. Celtic Array 4185
TOTAL CAPACITY 32,715
The optimization part of the framework has been implemented in Python 3, employing library
‘platypus’ in Python [54].
3.3. Criteria Selection Process
For the MCDA, the criteria selection process follows the process illustrated in Figure 7:
1.
The first step is to create a mind map of the problem and the different aspects involved. Then, via
brainstorming, criteria that can potentially impact on the alternatives of the problem are listed.
2.
The second step is to perform an extensive literature review on the topic. It is vital that the
literature review is conducted in order to discover related studies and also confirm or reject ideas
that were found in the first step. During this process, it is possible to discover gaps that will help
to define the study more precisely and also discover criteria that were never considered before.
3.
Step three is about discussing ideas with subject matter experts and communicating to them the
aims and ideas of the project in order to obtain useful insights into the initial stages of the criteria
selection. Their expertise can confirm, discard or suggest new criteria according to their opinion.
Experts can also provide helpful data and confirm the value of the study.
4.
In step four, the strengths and weaknesses of the work and criteria should be identified, followed
by a preliminary assessment. The selected criteria should be clear and precise, and no overlaps
should be present (avoiding similar terms or definitions that can potentially include other criteria).
Each criterion should characterize and affect the alternatives in a different and unique way. None
of the criteria should conflict with each other. The criteria should now have a detailed description.
Their description and explanation should be unique to avoid confusion especially if the criteria
are sent to experts in the form of a survey.

Energies 2018,11, 1855 12 of 23
5.
Step five describes how to proceed with the study. Assigning values to the criteria can be done
either by calculating the values directly or by extracting them from the experts via a questionnaire.
In the latter case, additional data or opinions could be considered. Via a survey, experts could
either assign values or rate the criteria according to their knowledge and experience. Here, it is
important to note that for a different set of criteria, different approaches can be followed. For
example, in the case of criteria that need numerical values (and probably require calculations) that
no expert can provide on the spot, receiving replies is challenging. The experts should provide
their expertise in an easy and fast process. The definition of the criteria has to be very clear before
scoring, normally at a scale of 1 to 5 or otherwise. The calculations could lead to assigned values
for every criterion, but the experts could provide further insight regarding the importance of
those criteria and how much they affect the alternatives. In this case, the experts provide the
weights of the criteria, which is very useful in order to achieve higher credibility of the problem.
In some cases, it would be very useful to include validation questions in the survey. It would also
be useful to include questions in order to increase the validity of the problem, for example, to ask
for further criteria that were not considered in the study. Another example would be to include a
question about the perceived expertise of the experts that will answer the questionnaire. Hence,
their answers will be weighted and further credible.
6.
Step six is related to selecting a method for decision-making. In general, it is important to decide
quite early which method of the multicriteria analysis will be used. This is important because
different methods require different criteria and problem set up. In the case of hierarchy problems
and pairwise comparisons, the problem has to be set up differently, and the values need to be
set for every pair comparison. The important question here is how the outcomes are derived.
Having a picture of the total process and aims, objectives and results early enough can help to
speed up the process.
Energies 2018, 11, x FOR PEER REVIEW 12 of 22
can be followed. For example, in the case of criteria that need numerical values (and probably
require calculations) that no expert can provide on the spot, receiving replies is challenging. The
experts should provide their expertise in an easy and fast process. The definition of the criteria
has to be very clear before scoring, normally at a scale of 1 to 5 or otherwise. The calculations
could lead to assigned values for every criterion, but the experts could provide further insight
regarding the importance of those criteria and how much they affect the alternatives. In this case,
the experts provide the weights of the criteria, which is very useful in order to achieve higher
credibility of the problem. In some cases, it would be very useful to include validation questions
in the survey. It would also be useful to include questions in order to increase the validity of the
problem, for example, to ask for further criteria that were not considered in the study. Another
example would be to include a question about the perceived expertise of the experts that will
answer the questionnaire. Hence, their answers will be weighted and further credible.
6. Step six is related to selecting a method for decision-making. In general, it is important to decide
quite early which method of the multicriteria analysis will be used. This is important because
different methods require different criteria and problem set up. In the case of hierarchy problems
and pairwise comparisons, the problem has to be set up differently, and the values need to be
set for every pair comparison. The important question here is how the outcomes are derived.
Having a picture of the total process and aims, objectives and results early enough can help to
speed up the process.
Figure 7. Criteria selection framework.
3.4. Multicriteria Decision Making
Following the process of MOO and criteria selection, two versions of the MCDM method were
implemented (i.e., deterministic and stochastic TOPSIS) and were linked to the results of the previous
outcomes, as shown in Figure 1. A set of qualitative and quantitative criteria is combined in order to
investigate the diversity and outcomes obtained from different sets of inputs in the decision-making
process. Stochastic inputs are selected and imported in TOPSIS. All data were collected from industry
experts, so as to prioritize the alternatives and assess them against seven selected conflicting criteria.
The outcome of the method is expected to assist stakeholders and decision makers to support
decisions and deal with uncertainty, where many criteria are involved.
TOPSIS is depicted in Figure 8, initially proposed by Hwang et al. [55], and the idea behind it
lies in the optimal alternative being as close in the distance as possible from an ideal solution and at
the same time as far away as possible from a corresponding negative ideal solution. Both solutions
Figure 7. Criteria selection framework.
3.4. Multicriteria Decision Making
Following the process of MOO and criteria selection, two versions of the MCDM method were
implemented (i.e., deterministic and stochastic TOPSIS) and were linked to the results of the previous
outcomes, as shown in Figure 1. A set of qualitative and quantitative criteria is combined in order to

Energies 2018,11, 1855 13 of 23
investigate the diversity and outcomes obtained from different sets of inputs in the decision-making
process. Stochastic inputs are selected and imported in TOPSIS. All data were collected from industry
experts, so as to prioritize the alternatives and assess them against seven selected conflicting criteria.
The outcome of the method is expected to assist stakeholders and decision makers to support decisions
and deal with uncertainty, where many criteria are involved.
TOPSIS is depicted in Figure 8, initially proposed by Hwang et al. [
55
], and the idea behind it lies
in the optimal alternative being as close in the distance as possible from an ideal solution and at the
same time as far away as possible from a corresponding negative ideal solution. Both solutions are
hypothetical and are derived from the method. The concept of closeness was later established and led
to the actual growth of the TOPSIS theory [56,57].
Energies 2018, 11, x FOR PEER REVIEW 13 of 22
are hypothetical and are derived from the method. The concept of closeness was later established and
led to the actual growth of the TOPSIS theory [56,57].
Figure 8. TOPSIS methodology.
After defining  criteria and  alternatives, the normalized decision matrix is established. The
normalized value  is calculated from the equations below, where  is the -th criterion value for
alternative  (= 1, … ,  and = 1, … , ).


=




(7)
The normalized weighted values  in the decision matrix are calculated as follows:
=
(8)
The positive ideal  and negative ideal solution  are derived as shown below, where 
and  are related to the benefit and cost criteria (positive and negative variables).
={, … , }=|,|
(9)
={, … , }=|,|
(10)
From the -dimensional Euclidean distance,  is calculated below as the separation of every
alternative from the ideal solution. The separation from the negative ideal solution follows:


=







(11)


=






(12)
The relative closeness to the ideal solution of each alternative is calculated from:
=
+
(13)
After sorting the  values, the maximum value corresponds to the best solution to the problem.
A survey that considers all seven criteria was created and disseminated to industry experts, so
as to obtain the weights for the following MCDM study. In this case, experts provided their opinions
based on the importance of each criterion in the wind farm location selection process. In total, 13
experts (i.e., academics, industrial experts and university partners) with relative expertise responded
and rated the criteria according to their importance. The total number of 13 experts is considered
sufficient for this work because the overall number of offshore wind experts is very limited and their
engagement is challenging. The input data from the 13 experts were acquired through an online
survey platform where the perceived level of expertise was also provided. The assessments varied
between 2 and 5 (with 1 being a non-expert and 5 being an expert) with a mean value of 3.8 and a
standard deviation of 0.89.
Figure 8. TOPSIS methodology.
After defining
n
criteria and
m
alternatives, the normalized decision matrix is established.
The normalized value
rij
is calculated from the equations below, where
fij
is the
i
-th criterion value for
alternative Aj(j=1, . . . , mand i=1, . . . , n).
rij =fi j
q∑m
j=1f2
ij
(7)
The normalized weighted values vij in the decision matrix are calculated as follows:
vij =wiri j (8)
The positive ideal
A+
and negative ideal solution
A−
are derived as shown below, where
I0
and
I00 are related to the benefit and cost criteria (positive and negative variables).
A+=v+
1, . . . , v+
n=MAXjvi ji∈I0,MI Njvi ji∈I00  (9)
A−=v−
1, . . . , v−
n=MI Njvi j i∈I0,MAXjvi ji∈I00  (10)
From the
n
-dimensional Euclidean distance,
D+
j
is calculated below as the separation of every
alternative from the ideal solution. The separation from the negative ideal solution follows:
D+
j=sn
∑
i=1vij −v+
i2(11)
D−
j=sn
∑
i=1vij −v−
i2(12)
The relative closeness to the ideal solution of each alternative is calculated from:
Cj=
D−
j
D+
j+D−
j(13)

Energies 2018,11, 1855 14 of 23
After sorting the Cjvalues, the maximum value corresponds to the best solution to the problem.
A survey that considers all seven criteria was created and disseminated to industry experts,
so as to obtain the weights for the following MCDM study. In this case, experts provided their
opinions based on the importance of each criterion in the wind farm location selection process. In total,
13 experts (i.e., academics, industrial experts and university partners) with relative expertise responded
and rated the criteria according to their importance. The total number of 13 experts is considered
sufficient for this work because the overall number of offshore wind experts is very limited and their
engagement is challenging. The input data from the 13 experts were acquired through an online survey
platform where the perceived level of expertise was also provided. The assessments varied between
2 and 5 (with 1 being a non-expert and 5 being an expert) with a mean value of 3.8 and a standard
deviation of 0.89.
The implementation of the stochastic version of TOPSIS was modelled through Palisade’s software
@Risk 7.5. Specifically, for the stochastic implementation, the Monte Carlo simulations of @Risk were
combined with the survey data, providing the best distribution fit for each value to be used as inputs in
the decision matrix of TOPSIS. By separately conducting a sensitivity analysis among 100, 1000, 10,000
and 100,000 iterations, 10,000 iterations for a simulation were found to deliver satisfactory results
on acceptable computational effort requirements. Next, the stochastic approach is compared to the
deterministic one and finally, the outcomes are presented in the next section.
All criteria and the final decision-making matrices were scaled and normalized, respectively in
different phases of the process, while the seven criteria used in this study include both qualitative and
quantitative inputs. Combining these two types can help decision makers to define their problems in
a more reliable method. Next, both deterministic and stochastic approaches will be conducted and
compared. The criteria are listed in Table 4.
Table 4. List of criteria.
Criteria ID
1. Accessibility C1
2. Operational environmental conditions C2
3. Environmental Impact C3
4. Extreme environmental conditions C4
5. Grid Connection C5
6. Geotechnical conditions C6
7. LCOE C7
The criteria were selected based on literature and a brainstorming session with academic and
industrial experts. In the session, common criteria were consolidated in order to avoid double counting
and finally concluded to the ones used in the study. The criteria were selected such as to have both
a manageable number and to cover all aspects but at the same time not make the data collection
questionnaire too onerous.
More specifically, the criteria are defined and analyzed below:
1.
Accessibility: This criterion considers the accessibility of each wind farm by considering the
distance from the ports and the number of nearby wind farms. The distances were acquired
from the 4COffshore database [
58
]. The number of nearby wind farms was acquired from the
interactive map of 4COffshore [
45
]. In order to select the number of nearby farms, only the farms
that already produce energy and are located between the ports and the wind farm in question
were considered. The nearby wind farms and the distance from the ports were assessed from
1 to 9 (1 being not close to any wind farms and 9 being close to many wind farms) and 9 to 1
(9 being very close to the ports and 1 being extremely far from the shore) respectively for each
offshore site. The weighted values (equally weighted by 50–50) then were summed. This criterion
is qualitative, and it varies from 1 to 9 (1 being not at all accessible to 9 extremely accessible).

Energies 2018,11, 1855 15 of 23
This criterion is also considered positive in the MCDM process. Both in the deterministic and
stochastic processes, the values used are the same.
2.
Operational environmental conditions: This criterion considers the aerodynamic loads in the
deployment location. More specifically, the wind speed (m/s) in specific points (close to
each offshore sites) according to [
53
]. The criterion is quantitative and also positive. In the
stochastic and deterministic approach, the fitted wind distributions and the mean values were
used, respectively.
3.
Environmental impact: This criterion considers the structures’ greenhouse gas emissions during
the construction and installation phase. The amount of CO
2
equivalent (CO
2
e) emissions per kg
of steel was estimated relative to the water depth (maximum and minimum water depth were
measured in each location) and the distance from the ports. The support structure was assumed
to be the jacket structure. This criterion was calculated according to an empirical formula in [
23
],
and the water depth and distance from the ports were both considered in these calculations.
Finally, an index of the square of CO
2
equivalent (CO
2
e
2
) was considered from the two cases as a
value for each offshore site. This criterion is negative. The criterion is also quantitative, and for
the stochastic approach, a triangle distribution was considered. In the deterministic approach,
the mean value was used.
4.
Extreme environmental conditions: This criterion considers the durability of the structure due to
extreme aerodynamic environmental loads. Data were extracted from [
53
]. The wind distributions
that represent the probabilities above the cut off wind speed (i.e., approximately 25 m/s) were
considered. This criterion is quantitative and negative. For the stochastic approach, a triangle
distribution was considered. In the deterministic approach, the mean values were used.
5.
Grid connection: This criterion considers the possible grid connection options of a new offshore
wind farm (connection costs to existing or new grid points). The inputs of this criterion consider
the cost (£million) of connecting to nearby substations where other Rounds already operate,
extending existing ones or building new ones. In the national grid report that was created for
the Crown Estate in [
14
], the costs were calculated by considering more than one cases per
Round 3 location. In this study, the maximum and the minimum costs were considered, and a
uniform distribution was used as a stochastic input. In the deterministic approach, the mean
value is used. The criterion is quantitative and represented by the above cost values, and it is
considered negative.
6.
Geotechnical conditions: This criterion represents the compatibility of the soil of each of the
offshore locations for a jacket structure installation. Experts provided their input and rated the
offshore locations according to their soil suitability from 1 to 9 (1 being very unsuitable to 9 being
extremely suitable). This criterion is qualitative and positive. For the stochastic approach, a pert
distribution was considered. In the deterministic approach, the mean value was used.
7.
Levelized cost of electricity (LCoE): This criterion considers an estimation of the LCoE for each
offshore location (2015 £/MWh). The values were calculated according to the DECC simple
levelized cost of energy model in [
59
]. The calculations assumed an 8 MW size turbine. Jacket
structure and a range of water depths (maximum and minimum water depth measured in each
site) per offshore site. The criterion is quantitative and negative. In the stochastic approach,
the triangle distribution was used and in the deterministic, the mean value.
This study considered the criteria that have greater impact than others in the final decision-making
process by assigning weights derived from the insights of experts.
It should be noted that some aspects were excluded for this analysis as they do not appear to
affect the location selection process or they already included in the existing selected criteria and other
steps of the framework. Fisheries and aquaculture is a criterion that considers the positive effects of
the aquaculture and the fisheries around the wind farms. The criterion could be assessed according to
similar fisheries and aquacultures that seem to benefit from nearby wind farms. This information is

Energies 2018,11, 1855 16 of 23
hard to obtain systematically or does not meet the unique characteristics of the wind farm locations.
Regarding the environmental extensions, such as birds and fish, these were not considered in the
environmental impact criterion. The Department for Energy and Climate Change conducted a strategic
environmental assessment on the offshore sites for over 60m of water depth around the UK and the
Crown Estate identified possible suitable areas for offshore wind farm deployments aligned with
government policy and released the 3 Rounds [
11
,
13
]. Further, service life extension will not be
considered because of the nature of the problem. In order to consider life extension, a sample of
individual turbines is monitored, tested and investigated. There is no evidence whether there is a link
of life extension possibility to the offshore location. Finally, marine growth or artificial reefs will not be
included in the study because it does not reveal the uniqueness of the offshore sites. Marine growth
exists in all offshore structures.
4. Results and Discussion
The data obtained from the experts were analysed and used in MCDM both deterministically
and stochastically. The results from all locations (from all five zones) are provided and illustrated in
Figure 9as cost breakdown analysis. All 7 solutions shown and discussed were obtained from the
execution of the NSGA II, and they are equally optimal solutions, according to the Pareto equality.
The problem considered all 18 sites from the five selected Round 3 zones and the optimum results
minimize CAPEX, OPEX and C
D&D
, as shown in Figure 9. At the same time, the remaining objectives
are also optimized. All layouts were found to deliver optimal solutions, where layout 3 was found
only once with few turbines.
Energies 2018, 11, x FOR PEER REVIEW 16 of 22
4. Results and Discussion
The data obtained from the experts were analysed and used in MCDM both deterministically
and stochastically. The results from all locations (from all five zones) are provided and illustrated in
Figure 9 as cost breakdown analysis. All 7 solutions shown and discussed were obtained from the
execution of the NSGA II, and they are equally optimal solutions, according to the Pareto equality.
The problem considered all 18 sites from the five selected Round 3 zones and the optimum results
minimize CAPEX, OPEX and CD&D, as shown in Figure 9. At the same time, the remaining objectives
are also optimized. All layouts were found to deliver optimal solutions, where layout 3 was found
only once with few turbines.
All optimal solutions are listed in Table 5. The solution that includes Hornsea Project One and
layout 3 delivered the lowest costs of the optimal solutions. Although that was expected as it was
found that only 50 turbines were selected by the optimizer, the same solution is the second most
expensive per MW as shown in Figure 9. Moray Firth Eastern Development Area 1 could deliver the
lowest cost per MW. The three solutions of the Seagreen Alpha included both layouts 1 and 2. The
fact that Seagreen Alpha was selected three times shows the flexibility of multiple options for a
suitable budget assignment that the framework can deliver to the developers. The CD&D presents low
fluctuations for all solutions. In the range between £2 and £2.3 billion of the total cost, four solutions
were discovered, for the areas of Seagreen Alpha (twice), East Anglia One and Hornsea Project One.
Figure 10 illustrates the % frequency of the occurrences of the optimal solutions. Five locations were
selected from the 18 in total. Seagreen Alpha was selected three times more than the rest of the
optimum solutions.
Table 5. Numerical results for all zones.
Offshore Wind Farm
Site
Layout
Selected
Turbine
Size (MW)
NWT OPEX (£) CD&D [£] CAPEX [£] Total Cost [£]
Moray Firth Eastern
Development Area 1
layout 1 10 122 307,322,672 365,371,991 4,316,454,016 4,989,148,680
Seagreen Alpha
layout 2
6
70
115,563,086
365,329,300
1,821,862,415
2,302,754,802
Norfolk Boreas
layout 2
6
521
3,612,087,515
383,807,107
16,034,493,829
20,030,388,452
Seagreen Alpha
layout 1
7
59
97,590,070
363,801,519
1,806,818,815
2,268,210,405
Seagreen Alpha
layout 2
7
259
996,944,713
373,550,029
6,323,114,490
7,693,609,234
East Anglia One
layout 2
7
57
93,654,614
364,474,208
1,712,388,330
2,170,517,154
Hornsea Project One
layout 3
7
50
81,096,384
371,523,572
1,640,942,787
2,093,562,744
Figure 9. Cost breakdown per MW For all Pareto Front solutions for layout cases 1, 2 and 3.
02,000,000 4,000,000 6,000,000
Moray Firth Eastern Development Area 1,
Turbine 10 MW, 122 turbines, layout1
Seagreen Alpha, Turbine 6 MW, 70 turbines,
layout2
Norfolk Boreas, Turbine 6 MW, 521 turbines,
layout2
Seagreen Alpha, Turbine 7 MW, 59 turbines,
layout1
Seagreen Alpha, Turbine 7 MW, 259
turbines, layout2
East Anglia One, Turbine 7 MW, 57 turbines,
layout2
Hornsea Project One, Turbine 7 MW, 50
turbines, layout3
(£)
CD&D/MW OPEX/MW CAPEX/MW
Figure 9. Cost breakdown per MW For all Pareto Front solutions for layout cases 1, 2 and 3.
All optimal solutions are listed in Table 5. The solution that includes Hornsea Project One and
layout 3 delivered the lowest costs of the optimal solutions. Although that was expected as it was
found that only 50 turbines were selected by the optimizer, the same solution is the second most
expensive per MW as shown in Figure 9. Moray Firth Eastern Development Area 1 could deliver
the lowest cost per MW. The three solutions of the Seagreen Alpha included both layouts 1 and 2.
The fact that Seagreen Alpha was selected three times shows the flexibility of multiple options for
a suitable budget assignment that the framework can deliver to the developers. The C
D&D
presents
low fluctuations for all solutions. In the range between £2 and £2.3 billion of the total cost, four

Energies 2018,11, 1855 17 of 23
solutions were discovered, for the areas of Seagreen Alpha (twice), East Anglia One and Hornsea
Project One. Figure 10 illustrates the % frequency of the occurrences of the optimal solutions. Five
locations were selected from the 18 in total. Seagreen Alpha was selected three times more than the
rest of the optimum solutions.
Table 5. Numerical results for all zones.
Offshore Wind Farm
Site
Layout
Selected
Turbine
Size (MW) NWT OPEX (£) CD&D [£] CAPEX [£] Total Cost [£]
Moray Firth Eastern
Development Area 1 layout 1 10 122 307,322,672
365,371,991
4,316,454,016 4,989,148,680
Seagreen Alpha layout 2 6 70 115,563,086
365,329,300
1,821,862,415 2,302,754,802
Norfolk Boreas layout 2 6 521 3,612,087,515
383,807,107
16,034,493,829 20,030,388,452
Seagreen Alpha layout 1 7 59 97,590,070
363,801,519
1,806,818,815 2,268,210,405
Seagreen Alpha layout 2 7 259 996,944,713
373,550,029
6,323,114,490 7,693,609,234
East Anglia One layout 2 7 57 93,654,614
364,474,208
1,712,388,330 2,170,517,154
Hornsea Project One layout 3 7 50 81,096,384
371,523,572
1,640,942,787 2,093,562,744
Energies 2018, 11, x FOR PEER REVIEW 17 of 22
The output of MOO is used as an input to the MCDM process. The output of TOPSIS is a
prioritization of the alternatives (i.e., the five offshore sites). Two variations of TOPSIS (i.e.,
deterministic and stochastic) are employed. By combining those two methods, MOO and MCDM, the
best location is identified, and the decision maker’s confidence increases. These five locations were
selected to take part in the MCDM process in order to be further discussed and to obtain a ranking
of the locations using the stochastic expansion of TOPSIS. Following the process of TOPSIS, the
considered alternatives are listed in Table 6, which are all considered to be unoccupied and available
for a new wind farm installation for the purposes of the problem.
Figure 10. Percent of frequency of occurrences of optimal locations. Five sites were revealed by the
optimizer.
Table 7 shows the final decision matrix with the mean values for every alternative versus
criterion. The criteria and alternatives’ IDs were used for clarity and simplification. All qualitative
inputs were scaled from 1 to 9, as mentioned before. Table 8 shows the frequency of the experts’
preference per criterion and the normalized mean values of the weights extracted from them.
Table 6. List of alternatives.
Alternatives/Zones
Wind Farm Site Name
ID
Moray Firth
Moray Firth Eastern Development Area 1
A1
Firth of Forth
Seagreen Alpha
A2
Hornsea
Hornsea Project One
A3
East Anglia (Norfolk Bank)
East Anglia One
A4
East Anglia (Norfolk Bank)
Norfolk Boreas
A5
Table 7. Decision matrix.
Alternatives/Criteria
C1
C2
C3
C4
C5
C6
C7
A1
4.5
11.5
61,979,649,702
25.8
226
5.6
118.7
A2
4.5
10.4
31,984,700,386
25.8
157.5
6.4
129.2
A3
7
10.0
65,153,119,337
26.0
5939
6.4
114.2
A4
6
9.8
29,122,509,239
25.8
1859
6.7
114.5
A5
4.5
10.0
39,619,870,326
25.8
1859
6.7
114.2
Figure 10.
Percent of frequency of occurrences of optimal locations. Five sites were revealed by
the optimizer.
The output of MOO is used as an input to the MCDM process. The output of TOPSIS
is a prioritization of the alternatives (i.e., the five offshore sites). Two variations of TOPSIS
(i.e., deterministic and stochastic) are employed. By combining those two methods, MOO and MCDM,
the best location is identified, and the decision maker’s confidence increases. These five locations
were selected to take part in the MCDM process in order to be further discussed and to obtain a
ranking of the locations using the stochastic expansion of TOPSIS. Following the process of TOPSIS, the
considered alternatives are listed in Table 6, which are all considered to be unoccupied and available
for a new wind farm installation for the purposes of the problem.
Table 7shows the final decision matrix with the mean values for every alternative versus criterion.
The criteria and alternatives’ IDs were used for clarity and simplification. All qualitative inputs were
scaled from 1 to 9, as mentioned before. Table 8shows the frequency of the experts’ preference per
criterion and the normalized mean values of the weights extracted from them.

Energies 2018,11, 1855 18 of 23
Table 6. List of alternatives.
Alternatives/Zones Wind Farm Site Name ID
Moray Firth Moray Firth Eastern Development Area 1 A1
Firth of Forth Seagreen Alpha A2
Hornsea Hornsea Project One A3
East Anglia (Norfolk Bank) East Anglia One A4
East Anglia (Norfolk Bank) Norfolk Boreas A5
Table 7. Decision matrix.
Alternatives/Criteria C1 C2 C3 C4 C5 C6 C7
A1 4.5 11.5 61,979,649,702 25.8 226 5.6 118.7
A2 4.5 10.4 31,984,700,386 25.8 157.5 6.4 129.2
A3 7 10.0 65,153,119,337 26.0 5939 6.4 114.2
A4 6 9.8 29,122,509,239 25.8 1859 6.7 114.5
A5 4.5 10.0 39,619,870,326 25.8 1859 6.7 114.2
Table 8. Frequency of experts’ preference per criterion.
Rate (1–5) Criteria
C1 C2 C3 C4 C5 C6 C7
1 Not at all important 0 0 0 0 0 0 0
2. Slightly important 1 1 5 1 1 0 1
3. Moderately important 5 1 3 6 2 5 1
4. Very important 4 7 2 2 6 7 4
5. Extremely important 3 4 3 4 4 1 7
Normalized mean weights 0.138 0.153 0.121 0.138 0.150 0.138 0.161
Specifically for the calculation of C6 against alternatives in Table 7, input from three experts was
considered. Although the number of experts replying to the seven criteria was mentioned before
(i.e., 13), a different number of experts (i.e., 3) was involved in the estimation of the geotechnical
condition criterion in order to form the distribution from their answers. The reason that the number of
experts was not the same in the two procedures is that different expertise was required in both cases.
The geotechnical conditions can be better perceived by geotechnical engineers, and the total number of
experts is very specific and more difficult to engage with. Based on experts’ answers, the normalized
mean weights of the criteria are estimated by the frequency of experts’ preferences per criterion in
Table 8.
The results of both variations of TOPSIS are listed in Table 9. By implementation, the stochastic
variation reveals more quantitative information about the alternatives and assigns the probability
that an option will rank first, as shown in Figure 11. According to stochastic TOPSIS, the alternative
that involves Seagreen Alpha was the most probable solution, followed by Moray Firth Eastern
Development Area 1. Also, the former is three times more probable to be selected compared to the
latter. The probability of other options to be selected is significantly lower, and Hornsea Project One is
unlikely to be selected.

Energies 2018,11, 1855 19 of 23
Table 9.
Results of deterministic and stochastic Technique for the Order of Preference by Similarity to
the Ideal Solution (TOPSIS).
Alternatives Deterministic TOPSIS Stochastic TOPSIS
Score Rank Score Rank
A1 0.733 2 21.88% 2
A2 0.816 1 64.44% 1
A3 0.181 5 0.00% 5
A4 0.712 3 10.22% 3
A5 0.660 4 3.50% 4
Energies 2018, 11, x FOR PEER REVIEW 18 of 22
Specifically for the calculation of C6 against alternatives in Table 7, input from three experts was
considered. Although the number of experts replying to the seven criteria was mentioned before (i.e.,
13), a different number of experts (i.e., 3) was involved in the estimation of the geotechnical condition
criterion in order to form the distribution from their answers. The reason that the number of experts
was not the same in the two procedures is that different expertise was required in both cases. The
geotechnical conditions can be better perceived by geotechnical engineers, and the total number of
experts is very specific and more difficult to engage with. Based on experts’ answers, the normalized
mean weights of the criteria are estimated by the frequency of experts’ preferences per criterion in
Table 8.
Table 8. Frequency of experts’ preference per criterion.
Rate (1–5)
Criteria
C1
C2
C3
C4
C5
C6
C7
1 Not at all important
0
0
0
0
0
0
0
2. Slightly important
1
1
5
1
1
0
1
3. Moderately important
5
1
3
6
2
5
1
4. Very important
4
7
2
2
6
7
4
5. Extremely important
3
4
3
4
4
1
7
Normalized mean weights
0.138
0.153
0.121
0.138
0.150
0.138
0.161
The results of both variations of TOPSIS are listed in Table 9. By implementation, the stochastic
variation reveals more quantitative information about the alternatives and assigns the probability
that an option will rank first, as shown in Figure 11. According to stochastic TOPSIS, the alternative
that involves Seagreen Alpha was the most probable solution, followed by Moray Firth Eastern
Development Area 1. Also, the former is three times more probable to be selected compared to the
latter. The probability of other options to be selected is significantly lower, and Hornsea Project One
is unlikely to be selected.
Table 9. Results of deterministic and stochastic Technique for the Order of Preference by Similarity
to the Ideal Solution (TOPSIS).
Alternatives
Deterministic TOPSIS
Stochastic TOPSIS
Score
Rank
Score
Rank
A1
0.733
2
21.88%
2
A2
0.816
1
64.44%
1
A3
0.181
5
0.00%
5
A4
0.712
3
10.22%
3
A5
0.660
4
3.50%
4
Figure 11. Probability chart of the stochastic TOPSIS.
Figure 11. Probability chart of the stochastic TOPSIS.
In the survey, the experts were asked to make recommendations or leave comments about the
criteria in order to include their insight in future studies or the limitations section. As expected, most
experts made some recommendations that are worth considering in the next steps. Some experts
responded according to their understanding of the work that is carried out and the work that was
done before this study. Some of them pointed out factors that were already included in the study in
the modelling of the work or already included in the criteria given to them, for example, the grid
availability and the power prices.
The importance of the operational environmental conditions was pointed out and how critical
they think it is as it drives the wind farm’s maximum output and capacity factor. It was also stated
that the wind speed should be taken into account separately in the study. The geotechnical conditions
and the soil’s impact on the design (both substructure and transmission system) were also pointed
out. One expert made clear this should not be overlooked. The geotechnical conditions were studied
separately and finally incorporated into this study as explained above.
At the end of the survey, the experts were asked to include any other criteria that can affect
the location selection. One suggestion was to include the consenting process as it can be affected by
environmental reasons such as the protection of biodiversity. This problem was seen in a wind farm
due to Sabellaria reefs in the past. The ease and time of consent were also raised by another expert. It
was suggested that specific stakeholders should be asked to participate such as the Ministry of Defence,
air traffic, shipping, fishing, etc.
The government support mechanism came up in the comments a few times. It was also mentioned
that the government regulations for each location need to be checked, because in many cases it might
be a better decision to open the market in other continents. Also, the project financing and other
contracts for difference (CfD) opportunities were mentioned. On top of that, the access to human
resources was pointed out to show the impact of different locations.

Energies 2018,11, 1855 20 of 23
Also, it was mentioned that if floating support structures were considered in the study, then
the water depth and availability of relatively large and deep shipyards would be very important
constraints. In this case, floating structures were not considered, but they could be included in
the future.
The results of the study could also impact the criteria and the way these locations are selected
by the Crown Estate providing more informed and cost-efficient options for future developers.
Considerable actions are mandated on top of the development plans for minimizing investment,
developing the supply chain, securing consents, ensuring economic grid investment and connection,
and accessing finance [2,5].
5. Conclusions
The coupling of MOO with MCDM and expert surveys was demonstrated in this paper, as a
method to increase the confidence of wind energy developers at the early stages of the investment.
A set of locations from Round 3 and types of turbines were considered in the LCC analysis. By
employing NSGAII and two variations of TOPSIS, optimum solutions were revealed and ranked based
on experts’ preferences. In the current problem formulation, among the optimum solutions, Seagreen
Alpha was the best option, and Hornsea Project One was the least probable to be selected. From the
surveys, additional criteria and stakeholders were recommended by the participants, which will be
considered in the future.
The proposed methodology could also be applied to other sectors in order to increase investment
confidence and provide optimum solutions. For example, the installation of floating offshore wind
and wave devices could benefit from the framework where the optimum locations can be suggested
concerning cost and operational aspects of each technological need. The foundation in this study is
considered to be the jacket structure because the LCC is formulated accordingly. More LCC parametric
analyses can be investigated in the future for different types of structures.
Author Contributions:
V.M. carried out the research and documented the findings. E.L.-M., as an associate,
provided domain expertise in the scientific field of Multi-Criteria Decision Making and guidance in the
implementation of the related processes. A.K. provided overall guidance and quality assurance in the publication.
Funding:
This work was supported by Grant EP/L016303/1 for Cranfield University, Centre for Doctoral Training
in Renewable Energy Marine Structures (REMS) (http://www.rems-cdt.ac.uk/) from the UK Engineering and
Physical Sciences Research Council (EPSRC). Data underlying this paper can be accessed at https://doi.org/10.
17862/cranfield.rd.6292703.
Conflicts of Interest: The authors declare that there is no conflict of interest.
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