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CRANFIELD UNIVERSITY
THOMAS BRINDLEY
SINGLE SCORE RISK INDICATOR FOR RENEWABLE ENERGY PROJECTS
SCHOOL OF ENGINEERING ADVANCED MECHANICAL ENGINEERING
MSc Academic Year: 2013 - 2014
Supervisor: Dr Athanasios Kolios June 2014
CRANFIELD UNIVERSITY
School of Engineering Advanced Mechanical Engineering
MSc
Academic Year 2013 - 2014
THOMAS BRINDLEY
Single Score Risk Indicator for Renewable Energy Projects
Supervisor: Dr Athanasios Kolios
June 2014
This thesis is submitted in partial fulfilment of the requirements for the degree of Advanced Mechanical Engineering
© Cranfield University 2014. All rights reserved. No part of this publication may be reproduced without the written permission of the
copyright owner.
i
ABSTRACT
Single score risk indicators will enable various renewable energy projects to be
simultaneously evaluated with the aim of selecting projects with minimum risk
for further development. This thesis explores a number of different multi-criteria
decision making analysis methods for optimum allocation of risk resources.
A comprehensive risk register for the offshore renewable wind energy sector is
performed using failure mode and effects analysis. These risks are separated
by project phase and categorised using PESTLE analysis.
A Fuzzy TOPSIS program is generated to optimise a selection of 18 risks
chosen to be ranked against 9 engineering criteria. 5 Decision makers with
backgrounds in management, risk and offshore engineering provide linguistic
responses to the risk matrix.
Keywords: Fuzzy TOPSIS, FMECA, MCDM, Risk Analysis, PESTLE
iii
ACKNOWLEDGEMENTS
I would like to thank Willis Insurance and Dr Athanasios Kolios for their
guidance in writing this report.
I would also like to thank my colleagues who have provided support throughout
this thesis and academic year.
iv
TABLE OF CONTENTS
ABSTRACT ......................................................................................................... i ACKNOWLEDGEMENTS................................................................................... iii LIST OF FIGURES ............................................................................................. vi LIST OF TABLES ............................................................................................... vi LIST OF EQUATIONS ....................................................................................... vii GLOSSARY ...................................................................................................... viii 1 INTRODUCTION ......................................................................................... 1
1.1 Background ........................................................................................... 1
1.2 Motivation .............................................................................................. 1 1.3 Aims and Objectives.............................................................................. 1 1.4 Thesis Structure .................................................................................... 2
1.5 Limitations ............................................................................................. 4 2 LITERATURE REVIEW – Sections 1- 4 ...................................................... 4
2.1 Section 1 - Stakeholders ....................................................................... 4 2.1.1 What are stakeholders? .................................................................. 4
2.1.2 Stakeholder PESTLE Analysis........................................................ 5 2.2 Decision Makers .................................................................................. 13
2.2.1 What are Decision makers? .......................................................... 13 2.2.2 Expert specialities utilised ............................................................. 13
2.3 Section 2 - Alternatives ....................................................................... 13
2.3.1 What are Alternatives? ................................................................. 14
2.3.2 Alternatives PESTLE Analysis ...................................................... 14 2.3.3 Alternatives selected for the risk matrix ........................................ 38
2.4 Criteria ................................................................................................. 41 2.4.1 What are Criteria? ......................................................................... 41 2.4.2 Criteria Identification ..................................................................... 41 2.4.3 Risk Matrix .................................................................................... 44
2.5 Section 3 - MCDA Selection ................................................................ 46 2.5.1 AHP .............................................................................................. 46
2.5.2 MAFMA ......................................................................................... 48 2.5.3 FMEA ............................................................................................ 54 2.5.4 Fuzzy Logic Methods .................................................................... 56
2.5.5 TOPSIS ........................................................................................ 57 2.5.6 MCDA Evaluation ......................................................................... 60
2.6 Section 4 – Preference Elicitation ....................................................... 60 3 Methodology – Section 5 ........................................................................... 63
3.1 Theory ................................................................................................. 63 3.1.1 Fuzzy TOPSIS method ................................................................. 63
3.2 Computation ........................................................................................ 66 3.2.1 User Input ..................................................................................... 66 3.2.2 Calculations .................................................................................. 67
3.2.3 Outputs ......................................................................................... 69 3.2.4 Obtaining results ........................................................................... 69 3.2.5 Questionnaire ............................................................................... 69
3.3 Validation and verification ................................................................... 70
v
3.3.1 Case Study 1 ................................................................................ 70
3.3.2 Case Study 2 ................................................................................ 71 4 Results ...................................................................................................... 74
4.1 Fuzzy TOPSIS output with closeness coefficient ................................ 74 5 Sensitivity analysis – Section 6 ................................................................. 76
5.1 Weight stability Intervals ...................................................................... 76
5.1.1 Scenario 1 – Safety Conscious ..................................................... 76 5.1.2 Scenario 2 – Eco-Environmental .................................................. 78 5.1.3 Scenario 3 – Socio-economic ....................................................... 79 5.1.4 Scenario 4 - Engineering Longevity .............................................. 81
5.2 Sensitivity in alternative ratings ........................................................... 82
5.2.1 Individual change .......................................................................... 83 5.2.2 Group change - Cost criteria ......................................................... 84
5.2.3 Group change - Benefit criteria ..................................................... 86 5.2.4 Group change - Solution ............................................................... 88
6 Analysis and Discussion............................................................................ 89 6.1 Analysis ............................................................................................... 89
6.1.1 Program structure ......................................................................... 89 6.1.2 Decision maker Responses .......................................................... 89
6.2 Discussion ........................................................................................... 92 6.2.1 General ......................................................................................... 92 6.2.2 Program ........................................................................................ 93
7 Conclusion – Section 7.............................................................................. 95
7.1 Further Work ....................................................................................... 96 REFERENCES ................................................................................................. 97 APPENDICES ................................................................................................ 100
Appendix A Literature Review ................................................................. 100 Appendix B Methodology ......................................................................... 105 Appendix C Results ................................................................................. 121
Appendix D Digital Information ................................................................ 139
vi
LIST OF FIGURES
Figure 1-1 ........................................................................................................... 2 Figure 2-1 ......................................................................................................... 10 Figure 2-2 ......................................................................................................... 17 Figure 2-3 ......................................................................................................... 49
Figure 6-1 ......................................................................................................... 92 Figure 7-1 ....................................................................................................... 101 Figure 7-2 ....................................................................................................... 106 Figure 7-3 ....................................................................................................... 107
Figure 7-4 ....................................................................................................... 108 Figure 7-5 ....................................................................................................... 108 Figure 7-6 ....................................................................................................... 109
Figure 7-7 ....................................................................................................... 109 Figure 7-8 ....................................................................................................... 110 Figure 7-9 ....................................................................................................... 110 Figure 7-10 ..................................................................................................... 111
Figure 7-11 ..................................................................................................... 112 Figure 7-12 ..................................................................................................... 113
Figure 7-13 ..................................................................................................... 114 Figure 7-14 ..................................................................................................... 115
LIST OF TABLES
Table 2-1 .......................................................................................................... 12 Table 2-2 .......................................................................................................... 16 Table 2-3 .......................................................................................................... 45
Table 2-4 .......................................................................................................... 48 Table 2-5 .......................................................................................................... 56
Table 3-1 .......................................................................................................... 70 Table 3-2 .......................................................................................................... 71
Table 3-3 .......................................................................................................... 70 Table 3-4 .......................................................................................................... 71 Table 3-5 .......................................................................................................... 71 Table 3-6 .......................................................................................................... 71 Table 3-7 .......................................................................................................... 71
Table 3-8 .......................................................................................................... 72 Table 3-9 .......................................................................................................... 72 Table 3-10 ........................................................................................................ 72 Table 3-11 ........................................................................................................ 72 Table 3-12 ........................................................................................................ 72
Table 3-13 ........................................................................................................ 73
Table 3-14 ........................................................................................................ 73
vii
Table 3-15 ........................................................................................................ 73
Table 3-16 ........................................................................................................ 73 Table 3-17 ........................................................................................................ 73 Table 3-18 ........................................................................................................ 73 Table 4-1 .......................................................................................................... 74 Table 4-2 .......................................................................................................... 75
Table 5-1 .......................................................................................................... 78 Table 5-2 .......................................................................................................... 79 Table 5-3 .......................................................................................................... 80 Table 5-4 .......................................................................................................... 82 Table 5-5 .......................................................................................................... 84
Table 5-6 .......................................................................................................... 86 Table 5-7 .......................................................................................................... 87
Table 7-1 ........................................................................................................ 122 Table 7-2 ........................................................................................................ 123 Table 7-3 ........................................................................................................ 125 Table 7-4 ........................................................................................................ 126
Table 7-5 ........................................................................................................ 129 Table 7-6 ........................................................................................................ 130
Table 7-7 ........................................................................................................ 133 Table 7-8 ........................................................................................................ 134 Table 7-9 ........................................................................................................ 137
Table 4-10 ...................................................................................................... 138
LIST OF EQUATIONS
Equation 2-8 ..................................................................................................... 55
Equation 2-1 ..................................................................................................... 58 Equation 2-2 ..................................................................................................... 58
Equation 2-3 ..................................................................................................... 58
Equation 2-4 ..................................................................................................... 58
Equation 2-5 ..................................................................................................... 59 Equation 2-6 ..................................................................................................... 59 Equation 2-7 ..................................................................................................... 59 Equation 3-1 ..................................................................................................... 63 Equation 3-2 ..................................................................................................... 64
Equation 3-3 ..................................................................................................... 64 Equation 3-4 ..................................................................................................... 64 Equation 3-5 ..................................................................................................... 65 Equation 3-6 ..................................................................................................... 65 Equation 3-7 ..................................................................................................... 65
Equation 3-8 ..................................................................................................... 65
viii
GLOSSARY
MCDM Multi-criteria Decision Makers
LCOE Levelized Cost of Energy
FMEA/ PC-FMEA/ LC-FMEA Failure Mode and Effects Analysis/ Priority cost FMEA/ Life Cost FMEA
EU European Union
PESTLE Political, Economic, Social, Legal and Environmental
MCDA Multi-criteria decision analysis
GIB Green Investment Bank
RES Renewable Energy Sector
OWF Offshore wind farm
DECC Department of Energy and Climate Change
HAWT Horizontal Axis Wind Turbine
VAWT Vertical Axis Wind Turbine
ROI Return on Investment
IMF International Monitory Fund
CO2 Carbon Dioxide
GHG Green House Gases
MW/ GW Mega Watts/ Giga Watts
R&D Research and Development
RE Renewable Energy
NREL National Renewable Energy Laboratory
NERC National Environmental Research Council
CAPEX Capital Expenditure
OPEX Operational Expenditure
FEED Front End Engineering Design
ALARP As Low as Reasonably Practical
CI Consistency Index
ix
AHP/ FAHP Analytic Hierarchal Process/ Fuzzy Analytic hierarchal process
TOPSIS Technique for Order Preference subject to Similarity to Ideal Solution
RPN Risk Prioritisation Number
O S D Occurrence, severity, detection
DM Decision Maker
MAFMA Multi-Attribute Failure Mode Analysis
MTTF Mean Time To Failure
MTBF Mean Time Between Failure
PIS/ FPIS Positive Idea Solution Fuzzy PIS
NIS/ FNIS Negative Idea Solution Fuzzy NIS
CRTF Cost reduction taskforce
MAUT
Multi-attribute Utility Theory
DSS
Decision support system
1
1 INTRODUCTION
1.1 Background
Risk analysis is used in industry as method of balancing the predicted
profitability with the risk of investment. The evaluation method characterises the
costs and problems likely encountered within a project and compares with the
probability of success.
One subject area used for the evaluation of projects is a field call multi-criteria
decision making analysis (MCDM). This topic comprises of a range of
optimisation methods for risk and criteria.
1.2 Motivation
Renewable energy has become more popular in the wake of international
mandates to reduce carbon dioxide emissions in the EU. A number of varying
designs and intended locations have led to the requirements for the use of a
MCDM analysis to evaluate a number of different designs for funding further
development.
1.3 Aims and Objectives
This project aims to identify new criteria to be used in conjunction with current
FMECA methodology for evaluating project risks and failure modes. This is then
applied to a MCDM analysis optimisation method ranking the failure modes to
optimise resource allocation. Risk ranking will then be used to create a single
metric that can be used to evaluate the predicted success of an offshore
renewable energy project.
A risk register is required to evaluate the extensive range of risks throughout the
project (separated by phase).
2
1.4 Thesis Structure
Figure 1-1 shows the structure of the thesis, separated into seven sections.
Figure 1-1
[1]
The offshore wind renewable energy sector will be used for the implementation
of the selected MCDA method. ‘Regional data’ and ‘other’ from Figure 1-1 have
3
been combined to form a PESTLE analysis which will be used to evaluate the
second and third tier in this design methodology. Selection of MCDA method
will include a review of current techniques utilised in the energy sector
(including where appropriate worked examples illustrating the methodology).
Introduction
Literature review
Section 1:
Stakeholders
Decision Makers
Section 2:
Alternatives
Criteria
Section 3:
Selection of Multi-criteria decision analysis Method
Section 4:
Preference Elicitation
Methodology
Results
Section 5:
Model Application
Sensitivity Analysis
Section 6:
Weight Stability Intervals
Sensitivity In Alternative Ratings
4
Analysis and Discussion
Conclusion and Further Work
Section 7:
Consensus
Proposal for Further Work
References
Appendices
1.5 Limitations
The project is only concerned with the offshore wind energy sector. Examples of
offshore risks are utilised to prove the validity of the proposed methodology
(examples from industry are not considered in the evaluation). The project will
not involve any dynamic study of risk.
2 LITERATURE REVIEW – Sections 1- 4
2.1 Section 1 - Stakeholders
2.1.1 What are stakeholders?
A stakeholder is a person, group or organisation with a vested interest in an
organisation.
One of the key principles in stakeholder identification is the premise of
inequality, their influence and priorities greatly impact their contribution (or
hindrance) to a project. Offshore wind energy has a large variety of
stakeholders with varying agendas. The stakeholder analysis is performed from
the viewpoint of the organisation implementing change.
5
Since stakeholders are not equal, each of the stakeholders must be analysed
with respect to their position on the project (goal) and their impact influence.
The stakeholders have a large range of criteria objectives which requires a
multi-criteria analysis to be carried out within each sector. The comprehensive
PESTLE analysis (Political, Economic, Social, Technological, and Legal and
Environmental factors) provides a framework to categorise stakeholders thus
enabling multi-criteria analysis.
Stakeholders exist for each of these headings; investors, legislators,
environmentalists and engineers etc. The pestle analysis is used to establish a
framework for risk management with organisations following BS ISO
31000:2009 (section 4.3.1 part a) [2]. Sections 4.3.6 and 4.3.7 discus risk
consolidation which involves categorising numerous sources of risk into a single
risk measure (risk evaluation 5.4.4).
Section 4.3.7 relates to external stakeholders, which are likely to be the
shareholders and board members who would have the final decision for funding
projects, this is the area this report will investigate how a single risk indicator
can be used to expedite informed decision making.
2.1.2 Stakeholder PESTLE Analysis
2.1.2.1 Political
Arguably the largest stakeholder in the production of offshore wind energy is the
UK government, who pledged to make Britain a world leader in offshore
renewable energy ahead of the EU directive for renewable energy. Other
member states of the EU are also stakeholders for the offshore wind industry,
due to attempts to diversify sources of renewable energy.
The Kyoto protocol binds countries in the UN (an international stakeholder) to
meeting targets for the reduction of greenhouse emissions by 2020, Article 2 of
the Kyoto Protocol to the United Nations Framework Convention on Climate
6
Change states that countries should promote the development and use of novel
forms of renewable energy [3].
The crown estate owns a majority of land within 12 miles of the British coast
and as such offshore installations within this area must with the crown estate
regulations and specifications. This enables the crown estate to influence
decisions in the operation of all wind energy projects operating within their
territory.
The government backed scheme for the Green Investment Bank (GIB) has
committed £461million to two of Britain’s largest wind farms (31st March 2014)
[4]. Government commitment to the development of the offshore energy sector
is critical for the industry to receive commercial funding; otherwise the high
CAPEX required to enter the market would lead to lower competitors, a high
LCOE, resulting in a low profit margin. Once the infrastructure is in place
offshore energy will become more economically viable. The coalition
government has pledged to work on this in 2010 [5].
The current economic climate has influenced the government’s position on
funding allocated to the RES, a reduction of 10% - £400m from the annual
budget was approved by the DECC and HM treasury in May 2013 [6]. The
national debt is held by the IMF which is an international organisation to
underwrite the risk of the British economy. The IMF exercises control over a
nation’s expenditure, recommending courses of action and so is an indirect
quasi-Political/ Economical stakeholder in Renewable energy.
The high capital expenditure for the development of offshore wind farms
requires government subsidies to lower the LCOE. Offshore wind is much more
reliable (than onshore) and is comprised of 3 mainstream designs; HAWT,
VAWT and Helical VAWT. The numbers of industrial stakeholders for these
designs vary, but the most widely accepted design is the HAWT.
2.1.2.1.1 Local government involved with offshore renewable energy
7
Operational OWF sites; Cumbria, Morray Firth, Northumberland, Merseyside,
Suffolf, Essex, Kent, Lincolnshire, Denbighshire, Conwy, Norfolk, North
Yorkshire, Dumfries and Galloway.
Commissioned OWF sites; East Anglia, Teesside and the Irish sea (multiple
currently undefined councils).
Figure 2-1 and Table 2-1 contain some key political stakeholders identified by
George Read (2013) [7] and ‘Best practice guidelines for offshore consultancy’
[8] correspondingly.
2.1.2.2 Economic
Economics stakeholders mostly consist of government funding agencies, banks
and insurance agencies. An insurance broker underwrites risks involved with
offshore wind energy and so require in depth knowledge of the risk environment
in order to manage financial implications. Willis Insurance is a significant
stakeholder in the renewable energy sector and was the third largest insurance
broker in the world when measured by revenues in 2013 [9].
Commercial banks are concerned with the return on investment (ROI) of the
industrial sectors they make loans to. High risks to reward industries are not
usually permitted loans or if they are, they have high interest rates applied to
offset the risk of company bankruptcy.
The green investment bank was set up to fund emerging technology to reduce
emissions from Britain in order to keep to the EU environmental initiative to cut
the CO2 production by 34% by 2020. Another initiative by the GIB is ‘15% of all
energy consumed generated from green sources by 2020’, the bank has a total
of £3.8bn to invest with 80% priority to accomplishing its key objectives [10].
There are a few evaluation criteria that all offshore wind projects are required to
meet in order to secure funding from the GIB, these are;
- Creating a reduction in GHG emissions
- Improving the efficiency of renewable energy capture.
8
- To protect or improve the natural environment.
- To protect or encourage biodiversity.
- Promotion of environmental sustainability – for example creating an
offshore electrical grid [10].
Offshore technology must be economically competitive with the current
technology in the market to be fully accepted by the consumer. The bottom line
for the average consumer is the price of electricity and so the consumer will be
only interested in a low cost alternative electrical generation. The overall
financial power the consumers have on the implementation of renewable energy
means the consumer is a stakeholder in their own right. Some companies have
approached this issue with surveys to find out the social (and hence financial
intention) response to developing the renewable energy sector.
The high LCOE for the offshore wind sector has been recognised by the DECC
on the renewable ‘roadmap’, they have responded with the creation of the
offshore wind cost reduction taskforce (CRTF) to highlight the actions required
to reduce cost down to £100/ MWh by the 2020 deadline. The taskforce was
directed by the RenewableUK Chairman: Andrew Jamieson, and found that the
target was attainable if 28 changes were made to the sector [11].
Figure 2-1 contains some key economical stakeholders identified by George
Read (2013) [7].
2.1.2.3 Social
National and international initiatives have seen increasing support for 'low
carbon' technologies. Since global warming and the effects of emissions have
been widely publicised, there has been an increase in the desire for recycled or
‘Green products’. This intention extends beyond the food industry and is starting
to influence a social demand for sustainable energy production.
Environmentalist organisations are examples of social organisations that are
stakeholders in offshore RE. Recreational clubs and societies that operate on or
near to proposed OWF sites are also stakeholders.
9
Onshore and offshore wind turbines have faced opposition from the ‘Not in My
Back Yard’ - NIMBY group with reasons ranging from damaging the aesthetics
of the area, to the disruption of the architecture of the area. Their potential for
the disruption of development requires them to be included in the stakeholder
analysis.
Figure 2-1 and Table 2-1 both contain social stakeholders identified by George
Read (2013) [7] and ‘Best practice guidelines for offshore consultancy’ [8]
respectively.
2.1.2.4 Technological
The technological stakeholders in offshore wind are composed of both
regulating bodies and technology providers in the subject field. A list of
operators, developers and manufacturers are shown below.
Development of technology
UK Technology: VESTAS, REpower, Siemans.
Irish Technology/ emerging companies: GE Wind Energy.
Manufacture
The Leshy Energy, Scottish and Southern, E.ON Renewables, DONG Energy,
SSE Renewables, Vattenfall, Centrica, Siemans, RWE NPower Renewables,
Scira Offshore, EDF-EN.
Operators/ Owners
Centrica, DONG Energy, SKM, Blyth offshore windfarm Ltd, SSE Renewables,
Vattenfall, E.ON Renewables, Masdar, NWP offshore Ltd, Statoil (50%)
Statkraft (50%), EDF-EN, Barrow offshore, Stadtwerke München, Gwynt y Môr.
10
Figure 2-1 shows a breakdown of stakeholders through PESTLE analysis by
George Read [7]:
Figure 2-1
2.1.2.5 Legal
The European commission is one of the main stakeholders for the production of
offshore wind. They produce the directives which the member states must
legally abide by. Regulatory bodies provide legally binding directives for the
implementation; one example is the use of health and safety.
Figure 2-1 contains some legal stakeholders for the offshore energy sector
identified by George Read (2013) [7].
11
Legal stakeholders for the offshore industry include; the Marine Management
Organisation (MMO), Infrastructure Planning Commission (IPC) [12] and
various law firms specialising in corporate and copyright law.
2.1.2.6 Environmental
The energy sector accounts for 27% of the total CO2 production in Britain and
in the year 2010, 156 metric tonnes of carbon dioxide equivalent [13] were
produced. The drive to reduce emissions relies heavily on the ability to de-
carbonise electrical generation in the UK. The government has set an initiative
to cut the production of CO2 by 2/3 by 2030, since the CO2 producing plants
will be decommissioned, the renewable energy sector is expected to be able to
support 30% of electrical generation by 2020 to be on target. Currently
accounting for 3GW the offshore wind sector is expected to reach a mean
predicted production of 18GW with an interquartile range of 18GW by 2020.
NERC (National Environmental Research Council) is a stakeholder for the
environmental sector and progressively monitors the introduction of renewable
energy devices have on marine and offshore wildlife. The meteorological
society is an important stakeholder who helps determine high yield areas for
offshore wind.
Figure 2-1 and Table 2-1 both contain environmental stakeholders identified by
George Read (2013) [7] and ‘Best practice guidelines for offshore consultancy’
[8] respectively.
12
Table 2-1
[8]
2.1.2.7 Force Majeure of Economic, Social, or Political Developments
Change in government policy in the near term is unlikely, since the green
agenda enforced by the European Union. However change is possible if this
connection is broken. The next EU vote is in 2014, and if it passes then we can
guarantee no changes in policy until 2019. The British government has its own
independent set of targets for environmental protection and will safeguard the
recycling business even if the connection with the EU is broken. Independent
member state targets can be abandoned without consequence providing they
are greater than the EU targets.
Scottish independence could ensure that the green agenda is maintained since
the SNP energy policy on renewables is a key agenda for the party. However
fluctuation in confidence of the Scottish currency could affect costing
projections.
13
2.2 Decision Makers
2.2.1 What are Decision makers?
Decision Makers are administrators who are able to rate alternatives against a
range of criteria within their speciality.
Decision makers are used in multi-criteria decision analysis to optimise the
performance, allocation of funding or time to a part product or system. Decision
makers have different levels of experience in their speciality; therefore including
more decision makers increases the accuracy of the ratings. The decision
makers were chosen based on their experience in; risk, offshore technology or
the renewable energy sector.
2.2.2 Expert specialities utilised
Contribution Breakdown;
Insurance: 0
Risk: 1
Offshore expert: 2
Engineering Project Managers: 2
A total of 5 decision makers contributed to rating the alternatives and criteria.
2.3 Section 2 - Alternatives
The alternatives identified will be used to demonstrate the Fuzzy MCDM
method separated by; design construction, and operation and decommissioning
phases of an offshore wind farm project.
14
2.3.1 What are Alternatives?
Alternatives are the options available for optimisation; they could be
manufacturers, contractors, etc. The identification of risks and failure modes will
provide the list of alternatives to be used in the fuzzy MCDM method.
2.3.2 Alternatives PESTLE Analysis
2.3.2.1.1 Political
2.3.2.1.1.1 International
The impact assessment proposal for a directive of the European parliament and
of the council amending Directive 2009/28/EC on the promotion of use of
energy from renewable energy sources prevents membership countries from
creating a biofuel sector for the Renewable Energy Directive that exceeds 10%
of the targets set for 2020 (European Commission (2013) [14]). This is an
attempt to reduce the greenhouse emissions that are released in combustible
fuels; this also prevents edible crop land from being used for the production of
fuel. The Renewable Energy Directive sets out to produce 20% of its energy
from renewable energy sources by 2020 [15]. This initiative is designed to
reduce foreign energy dependency, cut greenhouse emissions and create an
innovation revolution in the energy sector, hence reducing unemployment in the
EU.
The European Commission (2013) [14] report (page 6) evaluated the progress
and capacity to achieve the EU 2020 targets show that only one of the
alternative energy sources is on target (Photovoltaic cells). Alternative energy
sectors show lower than predicted progress for onshore/ offshore wind, biofuels
and biomass. The wind energy sector is expected to contribute 213GW of
electrical generation in member states with 44GW to be produced from offshore
wind energy along with 169GW from onshore wind. A total of 140TWh of
electricity is planned to be generated from offshore wind power across the EU.
The 2013 report postulates that infrastructure difficulties and reduction in
national effort will prevent the objective being accomplished.
15
A policy brief released by the European University Institute titled ‘A new EU
technology policy towards 2050: which way to go? [16]’ looks into the effect
both strong and weak carbon prices will have on the future of European Energy
policy. There are 3 options available;
1. To continue and extend the 2020 policies through to 2050.
2. In the event of a strong carbon price, the current technologies will be
extended along with the creation of a common platform for open
information exchange to support investors and their decision making.
3. In a weak carbon market, optimisation will be performed to find the most
cost effective portfolio of RE technology.
In the event of each of the three scenarios, offshore wind energy will likely play
a large role in energy policy through till 2050 due to its popularity over onshore
wind energy both in terms of energy efficiency and aesthetics. Offshore
electrical grid development will be one of the key infrastructure problems faced
in meeting 2020 targets.
2.3.2.1.1.2 National
The British government wishes to lead the way in reducing greenhouse
emissions and has set an independent target of 30% reduction of carbon
dioxide emissions of the 1990 level by the year 2020 [5]. It plans to accomplish
this through a diverse portfolio of renewable energy sources including the
promotion of marine energy. The government also pledged to produce an
offshore electricity grid in order to encourage private investment in offshore
energy.
2.3.2.1.2 Economical
The oil and gas reserves in the North Sea provided the UK with shelter from the
economic sanctions applied to countries without energy security by Russia (and
other oil producing nations) up until the end of the 20th century. Renewable
energy in the form of offshore renewable has the potential to extend this
independence until 2050 (at which point a transition to a hydrogen economy
using 4th gen nuclear power stations is expected). If the government supports
16
the creation of a supply chain to service the offshore wind industry, the UK
economy could benefit between £6 and 8 billion of annual revenue with 70 000
jobs created by 2020 [17]. The change in design and size of OWF has the
greatest impact on the economic viability of the sector; this is addressed in
section 2.2.2.4. A cost based sensitivity analysis on offshore wind energy by
NREL evaluated how changes in turbine design affected the cost of energy. The
results are shown below in Table 2-2;
Table 2-2
[18]
Figure 2-2 (Matthias Finger (2013) [19]) depicts of the state of the energy
industry in 2011, and shows that while ranked joint 4th with respect to overall
investment capital, wind energy has the second highest corporate funding
behind Hydrogen/ Fuel Cell technology. His report highlights how corporate
funding provides 70% of the overall R&D budget for non-nuclear low carbon
renewable energy. This would suggest that wind energy has a considerable
stake in the competitive energy market and filtering mechanisms need to be
adopted to select highest performing designs and most optimum design
solutions.
17
Figure 2-2
[19]
The academic paper [20] highlights how investment in offshore renewable
energy must initially be backed by the government in order for private
investment to follow. It concludes that the lack of private investment in offshore
technology is attributable to a combination of; the infancy of the sector creating
a high risk - low ROI environment, combined with the economic downturn (in
general investment) caused by the recession.
Competing renewable energy sectors pose a risk to offshore wind funding, for
example hydropower provides a more reliable source of energy with higher
energy density; however the variation in design and efficiency places high ROI
on potential investors (a similar limitation found in offshore wind).
The levelized cost of energy (LCOE) for offshore wind is significantly higher
than the current blend of energy sources. This poses a risk to the future
implementation of the technology since consumers want to minimise the
financial implications of switching to sustainable energy sources. The LCOE
influences the levels of; subsidies afforded to the industry from the government,
and taxation applied to commercial venders to encourage economic viability.
18
2.3.2.1.3 Social
Social response to offshore wind generation has been quite varied. Some of the
key concerns expressed by the public are identified in the journal
‘Understanding public responses to offshore wind power’ (Haggett, C. (2011)
[21]) and are condensed into 5 concepts; Visual Impact, Local context and
place attachment, the disjuncture between local and global, Relationships with
outsiders and finally Planning and participation.
Social risks can stem from project implementation phase in the form of; Human
Error, poor vertical and horizontal collaboration within the company, reduced
communication in the project, retaining/ risk of losing skilled employees,
complacency in job leading to risk of non-detection of failures/ faults, and finally
disruption to teamwork social collaboration.
2.3.2.1.4 Technological
Offshore turbine technology can be separated into 5 subsections; Foundation,
Tower, Blades, Drivetrain and (Grid and substation). Technology used for
onshore wind has been applied to the offshore environment to perform a
preliminary analysis. Offshore technology used for oil and gas (such as
foundation structures for oil drilling rigs) can provide a wealth of experience
(and risks) for developments of increasing water depth and distance from shore.
2.3.2.1.4.1 Foundation
Most (96%) of the current offshore wind energy is extracted from locations not
exceeding 40m, leading to the almost universal use of monopile foundations.
This is expected to expand to include the use of jacket foundations in the next 5
years with implementation planned for two sites in Fife, Scotland (Higgins, P.
and Foley, A. (2014) [22]). These new platforms encounter new forms of risks to
19
implementation and maintenance which can be adapted from known risks in oil
and gas industry.
2.3.2.1.4.2 Tower
Specialist coatings and standards have been developed for the towers of
offshore turbines to minimise the corrosive effect of the salt water, in an effort to
reduce the risk associated with material failure (see Chapter 2.3.3). Current
limitations to the size of tower sections prevent the tower from being
constructed from a single piece and so sections of 30 -40m are transported to
the area of deployment [22]. As the infrastructure develops it is hoped that the
single tower structures can be transported to deployment – this will reduce the
strength requirements (by minimising stress concentration points where the
sections are combined) and help minimise risk associated with material failure.
A key legislation directive specifies a 22m gap between the minimum height of
the blade and the peak yearly water height. This minimises the risk associated
with collision by enabling small craft clearance from the blades [22].
2.3.2.1.4.3 Aerofoil
The lift generated by an aerofoil intensifies as the length increases and so the
blade diameter is directly proportional to the power output. Current designs
allow for diameters up to 120m with a majority of currently installed turbines
around or below 66m. The largest blade diameter planned for the UK is 171m
and will be manufactured by Samsung heavy industries for deployment in Fife,
Scotland [22]. These improvements in materials, size and output power
increase the number of both known and unknown risks. The cost sensitivity
analysis results shown in Table 2-2 (Chapter 2.3.2.1.2) highlight the economic
benefits arising from the increase in size of offshore turbines.
Advances in the manufacture of turbine blades in the areas of reinforcement
and self-healing materials are increasing the design life span and reducing the
risks involved in transportation. 3D printing of the aerofoils is predicted to
reduce costs and increase competition in the energy market.
20
2.3.2.1.4.4 Drivetrain
Improvements in the design and materials used for offshore wind turbines
include the use of superconducting materials to reduce the mass of the
generator and increase efficiency. This however is balanced against the
increase in cost and risks resulting from the requirements of coolant required for
the superconducting materials. The prototype for this new design is expected to
be commercially available in 2020 [23].
2.3.2.1.4.5 Grid and substation
Substations are electrical transformers that are based on the sea bed with costs
up to £50 million; the risk of failure can have high cost and environmental
consequences. They are used to transform electrical power from DC to AC thus
preventing loss from line resistance in the grid. Both the grid and substation can
exert negative electro-magnetic effects on the surrounding environment [24].
2.3.2.1.5 Legal
Consent or planning permission can create legal risks when delays in the
consent affect the economic viability.
Creation of network of conservation sites from [25] states that offshore
conservation sites need to be defines such that offshore technology cannot
build on sites within a given distance (especially when drilling monopiles into the
seabed [26]).
2.3.2.1.6 Environmental
A report compiled to find the ecological risks associated with offshore wind [27]
found that the construction phase in deployment had the highest ecological
damage risk. The report finds that securing the foundations produced high
levels of noise pollution when the monopile foundation was used (since it is
21
required to be drilled into the seabed). It was highlighted that other foundations
were available which would have much less of an impact on the environment
(such as gravity based support structures). The impact to the wildlife from
stressors varies with age, where 1% of younger marine wildlife may be injured,
and 1% of the developed cod vulnerable to injury or even death. Seasonal
changes in wildlife populations led to periods of increased risk, the Ecological
Risk Assessment concluded that maintenance (such as cable trenching) be
postponed until June (if planned to start in December) to minimise distress to
spawning marine wildlife.
Another study researched the affects the offshore wind farms (OWF) had on the
surroundings [26] and found that monopiles caused disruption to the sediment
and substrate along with changing currents in the local area. It also highlighted
how marine bird populations could reduce from impacts with the OWF, Noise
and vibration from the turbine is unsettling for fish and marine mammals. These
are considered environmental risks of offshore wind.
It was highlighted that the foundations provided artificial structures favourable to
some aquatic species. A study was conducted on the seal (one of these aquatic
species) by attaching GPS units to seals and monitoring their interaction with
OWFs [28] which proved that the structures were beneficial for this marine
mammal.
22
2.3.2.2 Comprehensive Risk Register separated by project phase
2.3.2.2.1 Planning/ Design
PESTLE breakdown of risk register
Political Economical Social Technological Legal Environmental
Greater levels
of onshore
turbine
commissioning
[29]
Greater level of
optimisation
during FEED
[29]
Encourage Vertical
Collaboration [29]
Increase project
design life [29]
Improvements in
jacket design and
design standards
[29]
Greater level of
geophysical and
geotechnical
surveying [29]
Global
recession and
uncertainty of
future economy
[21]
Fishing communities –
incentive schemes [21]
Engineering design
uncertainty [21]
Availability of
design standards
and certification
guidelines [21]
Unknown
environmental
impacts (force
majeure) [21]
Commercial and
recreational boating [21]
Supply chain [21]
Insurer risk [21] Emergency [21] services Reliability
23
(component &
system) [21]
CAPEX [30] Tourism [21] Grid connection
[21]
UN not
supporting
future
renewable
energy
developments
[21]
Feasibility [30] Public acceptance [21] Design variability
based on depth
and conditions [21]
Environmental
impact
assessments [21]
Social groups being
ignored/not being
involved [21]
Fragmented
industry (no widely
accepted
configuration) [21]
Commitment to
legally bound
renewable targets
[21]
Strategic
environmental
assessments [21]
Current high
cost of
technology [21]
Project Complexity and
communication [31]
System efficiency
on array scale
development [21]
Licensing [21]
Government cut
backs in
spending for
Financial – Low
return on
investment
Offshore structures less
visible and so there is
less size restriction
Foundations,
turbines, grid
Planning
permission [21]
Wind, wave and
current – impact on
structure and
24
renewables [21] (ROI) [31] based on planning
restrictions (from NIMBY
campaign) [30]
connection [31] activities [31]
Local content –
requirement for
x% of final
product sourced
locally [30]
Procurement
difficulties for
sourcing
materials [30]
Feasibility [30] Overlooking details
of legislation [21]
Corrosion of
structure changing
the localised
composition of the
seawater – and
effect on wildlife
[30]
Loss of
management or
staff through
political
reasoning
(internal
company
politics) [30]
Drop in demand for
electricity [30]
Superior
technology making
offshore wind
redundant – e.g.
significant
breakthrough in
solar efficiency [30]
Copyright
infringement [30]
Subsurface
conditions –
geohazards, scour,
accretion [31]
25
Forbid contracts
with foreign
companies –
forbid
techniques
used [30]
Price of
electricity in
current market
[30]
Human Error Units of
measurement (SI
or Metric) [30]
Standards required
for country of
installation [30]
1/3 generation
capacity from
renewable mix
in offshore wind
(British target)*
[30]
Introduction of
multi‐variable
optimisation of
array layouts
[29]
Public’s willingness to
pay more for energy to
reduce emissions [30]
Corrosion [30]
Transferability of
knowledge from
similar industries
[21]
Standard
Industry Risk
Encourage Horizontal
Collaboration [29]
Array cable system
design for
Widen range of
working conditions
Protected marine
life migration
26
Register
template [29]
redundancy [29] for support
structure
installation [29]
patterns [30]
Shout about
success ‐ push
good news case
studies [29]
Incentivise early
site
investigation
and FEED work
[29]
Public image – linking to
popularity [30]
Step change in
wake modelling
science and
certainty [29]
Standardisation of
support structure
selection and
design [29]
Wake effect on
coastline (based
on proximity to
shoreline) [30]
20% final
energy
production - EU
targets for
2020* [30]
Initial funding –
Private and
government
incentives [30]
effects on employment
(other than the purely
economic) [32]
Delay [30] Improvements in
array cable
standards and
client spec [29]
27
2.3.2.2.2 Construction
PESTLE breakdown of risk register
Political Economical Social Technological Legal Environmental
Shout about
success ‐ push
good news case
studies [29]
Instigate step‐
change in WTG
manufacturing
quality [29]
Encourage Vertical
Collaboration [29]
Standardise site
investigation
technical
requirements [29]
Standardise
Contract Forms
[29]
Standardisation of
offshore
transmission
assets [29]
Health and safety
of the workforce
(both at sea and
associated land
areas), other users
of the sea, and
local communities
and members of
the public [32]
Environmentalists
causing delays [21]
Social groups Acute noise-
28
delaying/stopping
a project [21]
related impacts
during construction
phase (driving,
drilling and
dredging
operations) [24]
Difference in
regional political
support within the
UK [21]
Incentivise early
site investigation
and FEED work
[29]
Effects of
environmental
changes on local
residents
(including visual,
noise and traffic)
[32]
Primary industries
in place to supply
necessary parts.
[30]
Compliance with
relevant standards
from country of
operation [30]
Generation of
polluted sediments
during construction
and their re-
suspension [24].
Effect on leisure
pursuits [32]
Human Error [30] Units of
Measurement (SI
or Imperial) [30]
Disturbance during
exploration,
construction and
maintenance [24]
Job creation in Overheads for Communication Delay [30] Manufacturing
29
local area and
positive secondary
employment –
rejuvenating
communities [30]
delay in
construction phase
[30]
problems –
multinational
companies/
contracts may
have problems
with
miscommunication
[30]
carbon footprint to
be mitigated over
working lifespan
[30]
Encourage
Horizontal
Collaboration [29]
Improvements in
workshop
verification testing
[29]
Transportation of
parts kept to a
minimum to reduce
component carbon
footprint. [30]
30
2.3.2.2.3 Installation
PESTLE breakdown of risk register
Political Economical Social Technological Legal Environmental
Shout about
success ‐ push
good news case
studies [29]
Cost of hiring
specialist marine
vehicles for
installing turbine
[30]
Encourage Vertical
Collaboration [29]
Optimisation of
array cable
installation
vessels, tools and
methods [29]
Standardise
treatment of
Uncontrollable
Risk [29]
Improvements in
range of lifting
conditions for
blades [29]
Visual Impact [32] Delay [30] Check contract
times and location
specified in
contract including
overhead costs
[30]
Noise pollution [30]
Encourage
Horizontal
Collaboration [29]
Improvements in
the installation
process for space‐
Improvements in
weather
forecasting [29]
31
frames [29]
Archaeological
heritage [32]
Heavy lifts,
collision
(installation,
visiting or passing
vessels) and
damage [31]
health and safety
of the workforce
(both at sea and
associated land
areas), other users
of the sea, and
local communities
and members of
the public [32]
Turbine installation
disruption [32]
Transport – marine
aviation,
accommodation.
[31]
Water quality and
pollution incidents
during installation
and maintenance
[32]
Human Error [30] Improvements in
range of cable
installation working
Health and safety
– working at
height, confined
Carbon footprint
with transportation
[30]
32
conditions [29] space, electrical
and mechanical
working, structural
failings, fire, vessel
transfer,
evacuation and
rescue, diving [31]
Anchoring &
mooring [21]
Short-term
environmental
damage [21]
33
2.3.2.2.4 Operation
PESTLE breakdown of risk register
Political Economical Social Technological Legal Environmental
Shout about
success ‐
push good
news case
studies [29]
Instigate step‐
change in WTG
design for
reliability / O&M
[29]
Encourage
Vertical
Collaboration
[29]
Improvements in
personnel
access from
transfer vessel to
turbine [29]
Improvements
in personnel
transfer from
land base to
turbine location
[29]
Improvements in weather forecasting
[29]
Impact of anchorage, or the ‘artificial
reef effect’[24]
Number of
offshore
turbines
contributing to
national grid
[30]
Effects on
fisheries and
other users of
the sea [32]
Sea and air
navigation.
[32]
Interfaces –
land, port,
marine, aviation
[31]
Security Threats
– physical,
cyber [31]
Electromagnetic interference and
temperature rise from subsea power
transmission cables[24]
Competitiveness
with traditional
energy sources
[30]
Encourage
Horizontal
Collaboration
[29]
Autonomous
health
monitoring [30]
Warning lights
for low flying
aircraft. [30]
Impact - Sedimentary, biological,
visual, fishing, navigation [31]
34
OPEX [30] Improved
monitoring for
vibration
reduction in
structure [30]
Designated
areas and
proximity of
protected areas
[32]
Marine habitats and benthic (seabed)
communities [32]
Bathymetry, sediment transport
paths, bedforms, scouring, mixing,
turbidity. Changes in wave and tidal
current characteristics [32]
Fish resources, migration patterns,
nursery areas [32]
Marine mammals – distribution,
disturbance, displacement, impacts
of noise and vibration [32]
Noise, vibration, lighting [32]
Collision from wildlife [30]
Long-term environmental damage
[21]
35
2.3.2.2.5 Maintenance
PESTLE breakdown of risk register
Political Political Political Political Political Political
Communication to
upper level
management [30]
Instigate step‐
change in WTG
design for reliability
/ O&M [29]
Encourage Vertical
Collaboration [29]
Improvements in
personnel access
from transfer
vessel to turbine
[29]
Improvements in
personnel transfer
from land base to
turbine location
[29]
Improvements in
weather
forecasting [29]
Human Error [30] Weld/ Material
defect [30]
Vibration Damage
[30]
OPEX [30] Complacency in
job leading to
missed risks [30]
Increase in
autonomous
sensing for failure
[30]
Changes to
regulation
governing working
at heights [30]
Environmental
impact detection
more closely
monitored [30]
Introduction of
turbine condition‐
based
Encourage
Horizontal
Improvements in
jacket condition
Subsidence [30]
36
maintenance [29] Collaboration [29] monitoring [29]
Instigate step‐
chain in
Investment Risk
[29]
Teamwork [30] Change in system
affecting overall
time spent (on
repairs) in the
offshore structure.
[30]
Health and safety
law being updated
(learning from
incidents) [30]
Storm [30]
Indirect
environmental
damage [21]
37
2.3.2.2.6 Decommissioning
PESTLE breakdown of risk register
Political Political Political Political Political Political
Shout about
success ‐ push
good news case
studies [29]
Budgeting for
successful removal
over the planned
lifecycle. [30]
Encourage Vertical
Collaboration [29]
Similar to
installation
(technologically)
adjusted for time.
[30]
Compliance with
shipping and
disposal laws [30]
Cost in material
decomposition
(carbon fibre or
glass fibre-epoxy),
Transportation [30] Change in strength
of currency [30]
Evaluation of
lifetime vs energy
production [30]
Reduction in
recoverable scrap
metal value
relative to
purchase. [30]
Encourage
Horizontal
Collaboration [29]
Development of
new restrictions
regarding disposal
[30]
Impacts during
physical
decommissioning
(particularly with
the use of
explosives) [24]
This is not an exhaustive analysis.
38
2.3.3 Alternatives selected for the risk matrix
A risk matrix is a table that enables decision makers to compare alternatives
(risks) against a range of criteria. A selection of risks shall be selected from the
risk register and used to represent how mitigation of failure modes (risks) can
be optimised through a MCDA method.
A key risk in the design and implementation phase is in copyright infringement
along with standards. Legislation for the design and implementation of
engineering projects in European countries require that the work must be
performed under specific standards to be insured. These standards ensure that
the design and particularly the implementation of the project adheres to a strict
health and safety protocol and ensures that the product will have either minimal
local impact (ALARP) or sufficient strength to withstand certain extreme
scenarios within its design parameters.
Health and safety policy is constantly changing and adapting with new offshore
incidents. This ensures that the risk to human life is minimised when new risks
are discovered (lowering force majeure). This change in policy can drastically
affect the design of offshore structures, and while offshore turbines are not
manned all year round they required to facilitate the needs of the maintenance
staff. Upgrading an existing offshore structure to comply with changing
standards can be very expensive and so this is an important risk to consider.
The economic principle of supplying the sustainable energy is based on fixed or
increasing market wholesale price for the project to be profitable. Therefore
fluctuations (especially decreases) in the wholesale cost of energy can
significantly affect the bottom line of this industry. Variations in the cost of
carbon based fuel have a huge impact on the investment and competitiveness
of renewable technology. Shale gas is expected to lead to a drop in energy
prices over the coming decade and will likely affect the market for offshore wind
assuming emissions taxes (and Carbon Capture and Storage technology costs)
are not able to offset these changes to the wholesale energy price.
39
The EU has legally binding objectives to be met by 2020 and 2050 with regard
to the use and contribution of renewable energy. Member states also have their
own objectives and policies (usually exceeding the EU general objective).
Changes in national political policy for RE, brought about by the public’s
influence over politicians and reluctance to increasing energy costs, will impact
both commercial and government investment of the RE sector and so presents
a risk to the financial stakeholders invested in offshore wind.
Carbon friendly technologies (or technologies with increased efficiency) are
entitled to tax subsidies which encourage commercial competition by ensuring a
profit margin for emerging technologies. The government benefits from this by
meeting greenhouse gas reduction targets from the EU. (Higgins, P. and Foley,
A. (2014)) [33] Stated that the UK policy on government subsidies for wind
energy shall increase from £135/ MWh to £140/ MWh until 2019. This increase
is designed to promote the construction of further wind energy farms to meet
the EU Directive 2009/28/EC. Without these subsidies the initial development
and production of offshore wind turbines would not be viable. Changes to this
policy in the future will affect the profit margin of the venture.
Material failure is a component of all offshore structures which is attributed to
harsh environmental conditions coupled with the high cost of using corrosion
proof materials. Material failure can occur from faults in the manufacture
(welding) process, from the corrosive action of the salt water or from vibration at
the natural frequency of the support material. The corrosive failure mode is
linked to the design life and will be considered as a separate failure mode.
Delay can occur throughout the project lifespan; this can lead to financial and/
or non-financial consequences. Delay can be seen as consequence of deadline
mismanagement and is an important risk to monitor to ensure the project is
progressing according to schedule.
A reduction in Carbon emissions within the transport sector is required by
Directive 2009/28/EC. This has resulted in the formation of a carbon footprint
40
that is attached to all products throughout the manufacture and deployment
phases (recycling in decommissioning can offset the value).
There is a high emphasis to minimise the carbon footprint attached to the
production of renewable energy equipment such that the carbon footprint per
kilowatt hour is far below competing RE technologies. This can be achieved by
creating the infrastructure to produce the components of the turbines closer to
deployment location. This will boost the local economy and create jobs in the
energy sector.
Global warming is having an adverse effect on the predictability of
environmental conditions. This means that changes to the weather (and
climate) can produce excessive loading beyond the designed specifications
leading to failure.
Communication breakdown within the hierarchal structure of the company can
lead to top tier management uncourting important stakeholders that could
hinder the progress of the project. Therefore miscommunication and
management must be addressed as (separate) serious risks that can hinder
project completion.
Automated detection systems are often used in the maintenance phase to
monitor structural and performance characteristics and the failure of such
systems will significantly impact the level of associated risk.
Force majeure is the term denoted to risks that are unknown and so can be
considered project failure uncertainty. This can have high financial implications
despite an exhaustive failure mode analysis of the project.
Impacts from aerial wildlife can damage the turbines reducing their
performance; this can also affect the number and species of aerial wildlife in the
area. This can be caused by changes in migration patterns from global
warming. The effect of marine life migration must also be considered; however
this risk shall be performed separately as it’s a different cause of disruption.
41
Energy storage and transfer are major risks in offshore wind technology. An
article from Risktec (RISKworld, (2014) [34]) identifies cabling as the most
significant failure, resulting in the highest number of insurance claims from
offshore wind.
Supply chain problems can impose a significant risk to the development of
offshore wind installations. This can be compounded when there are few
manufacturers that are able to provide the specialized components leading to
waiting lists and contributing to project delay.
2.4 Criteria
2.4.1 What are Criteria?
To enable evaluation of a project for the optimisation of a part, product or
system, the parameters of optimisation are required to filter the performance
characteristics of the options available (alternatives). These parameters form
the criteria used in MCDA. Criteria are the conditions which the alternatives can
be effectively compared with one another.
2.4.2 Criteria Identification
The number of criteria that are required is dependent on the types of failures
expected for the project. This method aims to categorise predicted risk
scenarios to form the criteria. This will help identify the most critical failure
modes.
Failure mode and effects analysis uses three main criteria used to develop a
risk prioritisation number; these are occurrence, severity and detection. The
main aim is to link the probability of occurrence with the severity or
consequence of failure, the detection criteria is added to account for risk of non-
detection. Each of these factors has its own rating scale, all denoted by a 1-10
scale. This scaling system has been criticised due to information about the
contribution of each factor being lost in the calculation of the risk prioritization
number. Some methods chosen to combat the crisp scaling system have
42
included variations of fuzzy number ranges to prevent the repetition of RPN
values.
There has been much criticism on the subject of criteria used to develop a
representative and cumulative risk number for a part, component or system.
The three criteria used in FMEA were determined to enable sufficient
information in the operational lifespan for maintenance to be able to perform
their role effectively. The goal of this project is to determine the role additional
criteria can have on the overall communication of failure and risk information.
Failures in a system will impact different areas of the business with varying uses
for the information required. For example the maintenance crew need to know
the specifics of what parts to replace and when this needs to be accomplished
by, the management require information on cost of replacement/ repair and how
to manage/ optimise the funding necessary to keep the system at optimal
performance. Insurance requires estimations on the cost of failure and the cost
associated with downtime caused by failure (as well as indirect costs such as
customer satisfaction/ loss in customer purchase).
2.4.2.1 Case Study
A few of the key criteria (common to offshore operation) shall now be explored
using the deep-water horizon accident in 2010 as a case study.
The deep-water horizon Gulf of Mexico accident in 2010 started with an initial
topside gas explosion which killed 11 people.
“The accident involved a well integrity failure, followed by a loss of hydrostatic
control of the well. This was followed by a failure to control the flow from the
well with the blowout preventer (BOP) equipment, which allowed the release
and subsequent ignition of hydrocarbons. Ultimately, the BOP emergency
functions failed to seal the well after the initial explosions.” [35]
43
This resulted in an oil spill estimated at 4.9 million barrels into the sea; the spill
was finally halted after 87 days due to the remote location and great depth of
the seabed. BP was fined $32.7 billion for damages to coastlines and sea life,
$14 billion was spent on the response from BP with further commitments to
monitor environmental damage.
There was a direct loss of profit from the spill and clean-up, and an indirect cost
to the shareholder price as the company’s reputation was damaged causing a
reduction in customer demand. This contributes to the criteria associated with
failure in terms of direct and indirect financial cost. The loss of life associated
with the incident is a critical criterion that needs to be accounted for from a
health and safety perspective. One way to prevent future failure is to build
redundancy options into the system. This reduces the risk at this level however
will only be cost effective at high risk parts of the system.
In summary from the case study the following criteria have been shown to be
applicable to the offshore environment; Direct/ indirect cost of failure, Failure
impact on environment, Fatality associated with failure and Redundancy/
Mitigation.
Risk to business can be categorised by financial and non-financial. Financial
risk has been covered in the ‘cost of failure’ criteria; non-financial risk needs to
be defined and quantified.
The report by CFO Research (2012) [36] explains how non-financial risk can be
separated into 3 separate phases; Performance and competitiveness,
Information Management and (legal, liability and compliance).
These non-financial risks take many forms and are composed of but not limited
to; political, Operational, economic climate, vendor/ supplier failure to meet
company performance criteria, company reputation, ability to retain experienced
staff, loss of private competitive (or personal) data, environmental restrictions
(bad weather or disasters), compliance with standards and legislation, copyright
infringement and customer perception.(CFO Research. (2012)) [36].
44
Detectability is a criterion that enables the maintenance staff to convey the
difficulty in performing checks on certain failure modes. This can lead to the use
of automated detection systems as a method of redundancy in maintenance.
The operational lifespan of a turbine is dependent on an effective maintenance
scheme along with the intended design life. The cumulative effect of failure
modes can cause a reduction in the operational lifespan and so the operational
lifespan of wind turbines must be compared with their intended design life to
identify causes of force majeure.
2.4.3 Risk Matrix
In conclusion we have a total of 18 risks and 9 criteria that shall be used to
populate the risk matrix;
45
Criteria Alternatives
1 Component failure 1 Bird impacts
2 Design life 2 Carbon footprint
3 Detectability 3 Change in electricity costs
4 Direct/ Indirect cost of
failure 4 Change in environmental conditions
5 Fatalities 5 Change in government position on
subsidies
6 Impact on environment 6 Change in Health and safety
7 Redundancy/ Mitigation 7 Change in RE Policy
8 Risk to business - non-
financial 8
Communication to upper level
management
9 Tonnes of CO2 avoided
per year 9 Delay
10 Energy storage and transfer
11 Failure of automated detection
systems
12 Force Majeure
13 Human Error
14 Legislation
15 Marine life migration
16 Material failure
17 Miscommunication
18 Supply chain problems
Table 2-3
46
2.5 Section 3 - MCDA Selection
Changes in consumer desire and increased competition have caused
manufacturers specialise (in reliability, durability, cost, and other criteria) within
their respective fields. The creation of new products requires input from these
supporting manufacturers (alternatives) and stakeholders (defining criteria) to
compete financially with rivals.
Optimisation of alternatives and the stakeholder specified criteria must be
performed to ensure maximum performance within a specified budget. The
influence or ‘weight’ of each of the stakeholders (and their criteria) must be
accounted for against the desired goal of project completion.
2.5.1 AHP
2.5.1.1 Literature
The analytic hierarchal process determines relative importance of criteria
required to achieve the goal, utilising the pair-wise ranking process for criteria
and alternatives.
The pair-wise ranking consists of comparing the relative importance of two
criteria. This ranking system is based on a 1- 9 scale, where 9 indicates high
relative importance and 1 indicates equal importance (R.W. Saaty, (1987) [37]).
Inconsistencies in response is one of the limitations to this method, therefore a
consistency index (CI) is calculated to check the variation of the individual’s
responses. A CI of 0 would suggest that there is no variance and the person
has no difficulty in ranking all criteria. The AHP method is flexible, allowing a
variance of 0.1, meaning that controlled levels of uncertainty are allowable in
the ranking of criteria.
To illustrate the process of AHP an example was generated for the purchase of
an eco-friendly vehicle. The decision maker ranked the relative importance of 4
criteria (environmental friendliness, motorway performance, engine type and
47
price) against one another. The results of the example are shown in Appendices
A.1.1.
2.5.1.2 Analytic hierarchy process method
To make a decision in an organised way to generate priorities we need to decompose the decision into the following steps.
1. State the goal of the project and place on the highest tier.
2. Identify alternatives and assign to the lowest tier, state the criteria that the alternatives are to be measured against and assign to intermediate tier.
3. Perform pairwise comparisons on each of the alternatives with respect to
the criteria on the tier above.
4. Normalise the pairwise comparison of the criteria and then average with respect to each element to find the associated weighting.
5. Create scale for each of the alternatives utilising the maximum or minimum values dependent on ideal solution, then normalise scale. Multiply normalised scale by the associated weighting for each criterion to give the overall score for each alternative with respect to criteria.
6. Sum the overall scores for each alternative and rank maximum to minimum.
7. Perform consistency calculation on weighted criteria to check CI is less than 0.1.
2.5.1.3 Limitations
The relative weighting of criteria to achieve the goal will change when
conducted by different individuals; this will also create variance in the
consistency index. The synergetic productivity from teamwork should positively
influence (minimise) the consistency index.
The AHP process does not account for rank reversal – the concept that
critical criteria can change with time (especially in an emergency). Additional
criteria will require re-calculation to ensure that the CI does not change (note
that the RI increases with increasing criteria allowing for a small deviation in
48
pair-wise comparisons with respect to new criteria). The relative increase in
deviation in consistency allowable is shown in Saaty's Consistency Index Table
(Table 2-4).
Table 2-4
[38]
If consistency is maintained when adding new criteria, the rank of the
criteria should not change dramatically (assuming that the new criteria are not
dependant on current criteria).
The main limitation to the AHP process is the requirement for pairwise
comparison with previous criteria/ alternatives when new criteria/ alternatives
are added. This can be costly with projects involving large arrays of both
alternatives and criteria.
2.5.2 MAFMA
2.5.2.1 MAFMA Literature
M.Braglia (Feb, 2000) [39] identified a few shortcomings of the FMEA method:
- Implementation timing
- Creating unbiased teams
- Coordination
- Doesn’t consider important criteria (cost etc.)
- Linguistic variation – need for fuzzy definitions
49
In a review of previous literature the MAFMA paper found that cost evaluation
should be carried out simultaneously and not in parallel. This was based on the
understanding that the criteria would be weighted later on in the AHP method.
Simple multiplication of risk criteria (o, s and d) was not determined to be
representative of the relative importance manufacturers assigned to each of the
evaluation criteria. Quantification of failure modes was found to be difficult
especially when the criteria were linked (for example product failure eroding
customer satisfaction).
Multi-attribute Failure Mode Analysis utilises the Analytic Hierarchal Process
setting out the alternatives as the base level, the second tier represents the
range of criteria from the risk prioritisation number (occurrence, severity and
detectability) and combines with the predicted cost. The second level can be
made more inclusive if other criteria are wished to be considered also. The top
tier comprises of the cause of failure selection and is represented as the project
goal in AHP. The 3 tier system is used in order to select the type of failure the
product will most frequently encounter. Each of the criteria can be weighted in
order of preference; these weightings are then used to find the least disruptive
alternative for selection. The method utilises the strengths of the AHP method
where both qualitative and quantitative information can be compared either
individually or combined.
Figure 2-3
50
[40]
Each of the failure criteria (occurrence, severity and detection) was assigned
ranges of applicability for each of the scores. Occurrence was defined as the
mean time between failures and was quantifies on a time-scale ranging from 3
months to 10 years. Detectability was determined on a 1 to 10 scale and
categorised by; visibility, detectability (both directly and using automated
sensors) and time between inspection. Finally severity was determined using a
1 to 10 scale where an accident requiring more than 3 days off work was ranked
highly (legal stipulation from both government agency and insurance coverage).
This method of restricting the definition of each of the criteria was determined to
eliminate confusion in linguistic terminology while avoiding the use of fuzzy
logic.
The use of the AHP system to quantify criteria weighting enables a sensitivity
analysis to be performed. The sensitivity analysis shows how the variation in the
decision maker’s response to criteria weighting will affect the combined risk
ranking.
G. Carmignani (2009) [41] further develops the use of the AHP method to
prioritise high RPN scores and correlate to cost mitigation due to corrective
actions. This is called the priority cost failure mode and effects analysis.
The main aims of the academic paper were set out to characterise the three
original criteria, differentiate between equal values and correlate cost to all
risks.
In order to characterise o, s and d, the scale (traditionally based on a 1-10
system) needs to be changed. For severity the scale is linked to the economic
impact associated with the severity of the incident, it is hence calculated as the
hours of lost production multiplied by the potential hourly profit. This method of
cost based quantification enables domino affects to be identified with greater
ease.
The occurrence scale of 1-10 is replaced with a frequency figure relating
to a fixed quantity of time for example 1000hrs of operational time. Detection is
51
calculated from the cost of implementing a maintenance system multiplied by
the associated effectiveness. An advantage term is then calculated by
deducting the total potential loss with modification from the total current loss
associated with the failure mode.
This cost of implementation (calculated from the mean hourly cost
multiplied by the hours consumed by each action) is then deducted from the
advantage term to find the total profitability of pre-emptive intervention. The four
criteria are then used to create a graph of total current loss verses potential
profitability, this gives a visual representation to high priority cases and
highlights economic potential for prioritisation.
2.5.2.2 Limitations
Limitations highlighted to the method include variations in sensitivity
through the decision makers rating, the method is highly sensitive to variations
in cost estimates and highlights the need to verifiable facts and figures used.
This can be seen to be problematic from the point of view of the decision
makers (who inevitably have varying experiences of expected costs); this is a
fundamental problem in the field of multi-criteria decision making. The term
profitability needs to be concisely defined since it can relate to profitability
before and after tax etc. If equal weighting are applied to the three values for
occurrence, severity and detection, then this new method doesn’t provide a
sensitivity analysis and increase the sensitivity of the cost criteria in the model.
The method of evaluation of the PC-FMEA is of the same form as
analytic hierarchal process. This method is also reflected in the research carried
out by Braglia (2000). The alternatives are represented by system (or product)
faults, and are prioritised based on the 4 evaluation criteria to reach the goal of
selecting what faults to correct within strict budget limitations.
2.5.2.3 LC-FMEA Literature
Rhee S. J and Ishii, K (2003) [42] produced the academic paper ‘Using cost
based FMEA to enhance reliability and serviceability’. This aimed to assign cost
52
based quantification to the FMEA methodology in the hopes to make the
severity of the failure mode (denoted by the risk prioritisation number) easier to
understand over a range of different departments.
Some of the shortcomings identified in the paper include; Detectability
register should be separated by design phase such that confusion over the
ability to detect and the risk of non-detection can be eliminated. The three
scales used to determine the RPN are independent of one another and
therefore distances between their products will not supply any additional
information for prioritisation.
The proposal of a Life-Cost based FMEA has a more rigorous framework
of identifying failure in the associated phase of operation (design, manufacture
or installation). A cycle of continued design modifications is performed prior to
manufacture utilising an experienced manufacture team to identify design flaws
and work with the design team (before mass scale production) with the intention
that the design can be adapted for ease of manufacture. The data gathered at
this phase is used to from the origin and detection inputs to the method. This
however can have its own limitations the most apparent is the requirement for a
product to be in the design phase (or have extended knowledge of design and
implementation) to carry out the methodology effectively. In other words it
cannot be implemented on existing manufacture to improve performance.
The method outputs a term called failure cost which relates to the
associated cost of detection and reparative measures to return the part or
system back to optimum performance. The failure cost is broken down into
labour cost, material cost and opportunity cost (downtime loss of potential
earnings). To determine the sensitivity to input criteria, a sensitivity analysis is
performed using Monty Carlo simulation with triangular distribution (5% lower
bound, 50% mean and 95% upper bound).
Unfortunately both methods (MAFMA and PC-FMEA) inherit the limitations
highlighted in AHP section 1.1.1. Some of the most prevalent limitations are;
53
- When new faults or alternative failure modes are discovered, the process
of pairwise comparisons and alternative evaluations must be recalculated
(although he extensive support from software available somewhat
mitigates this problem).
- The failure modes must be evaluated and quoted for different modes of
operation since rank reversal is not possible in AHP.
- Failure modes that are dependent on one another are difficult to
categorise and evaluate using this format.
The life cost based FMEA differs from PC-FMEA in the way the cost term is
calculated, where LC-FMEA chooses to focus on the cost of a failure mode to
represent risk severity and PC-FMEA aims to use the cost calculations to find
the maximum benefit from preventative maintenance to optimise fund allocation.
LC-FMEA uses a much less complicated method of deriving the input terms
(based on MTBF and MTTF data), which places the methodology much closer
to the original FMEA calculation. This has advantages over PC-FMEA and
MAFMA such that limitations within the AHP structure are omitted. Hybrid
methodologies between these methods may produce a much more flexible and
reliable form for FMEA.
Sachdeva, A., Kumar, D. and Kumar,P. (2009) [43] broadens some of the terms
to include other factors for example severity can be broken down into
maintainability, spare parts and cost. The paper aims to improve the confidence
in each of the O S and D terms such that the FMEA method can be improved.
The terms are broken down into 6 components and given scores on a 1 to 9
scale. These 6 scores are then combined to be used in the final evaluation to
give a fully representative RPN.
2.5.2.4 Limitations
A limitation to this method is its similarity to the previous FMEA method where
the products can still be repeated (since crisp numbers are used).
54
2.5.3 FMEA
2.5.3.1 Literature
FMEA stands for Failure mode and effects analysis. It is utilised in evaluating
the performance of parts, components or systems in both working life and
design phase. FMECA is an adaption to the original FMEA method and
incorporates a critical assessment. This critical assessment is used to create a
risk prioritisation number (RPN), and evaluates the subject (part, component or
system) based on the criteria; severity, occurrence and detection. The higher
the RPN number, the more imminent corrective attention is required. The O S D
system translates qualitative terminology into quantitative information to be
used in the calculation and implementation of resources. This combination of
criteria is very general and applicable to a wide range of topics. This project is
concerned with deriving a suitable range of criteria to more effectively evaluate
offshore wind power in the renewable energy sector.
Tables 4, 5 and 6 from [44] show the standardised scales that severity,
occurrence and detection are calibrated to respectively (these are included in
appendices A.1.2).
The academic paper: A. Hadi-Vencheh, M. Aghajani (2013) [45]
researches extensively the effect of incorporating the relative weighting system
used in TOPSIS (see section 1.1.2) with fuzzy logic (to aid decision makers)
such that relative importance of occurrence, severity and detectability can be
determined. In the long term this can provide information on how to optimise
funding to reduce the RPN of each failure mode in the system.
A fuzzy number is one which accounts for a range of values. The
distribution and occurrence of this range is referred to as the membership
function. The membership function is normalised with respect to the mode.
Alpha-level sets are the range of values determined for the linguistic fuzzy
terms used to derive relative weighting. The alpha-level sets can be set at any
value between 0 and 1.
55
2.5.3.2 Method:
1. Define the content of the part, component or system being analysed.
Identify all possible methods of failure or hindrance and the environment
in which they occur.
2. Characterise the optimum operational conditions of part, component or
system, such that a state of failure can be easily identifiable.
3. Characterise the three values of occurrence, severity and detectability
with respect to repair, maintenance, logistics, etc.
4. Calculate the risk prioritisation number for the system failures.
5. Plan and implement steps to mitigate areas of high risk.
6. Create an effective maintenance schedule to reduce failure probability.
US MIL-STD-1629A [46] states that the risk prioritisation number can be
calculated as:
tC Pm
Equation 2-1
Where Cm is the criticality number for failure mode m, β is the conditional
probability of loss, α is the failure mode ration, λp is the part failure rate and t is
the time scale.
British Standard defines the calculation of the RPN as Severity multiplied
by probability of occurrence. This can be extended to incorporate the criteria of
detectability to ensure that the maintenance schedule is not underdeveloped in
the design phase.
2.5.3.3 Limitations
Liu, H., Liu, L., Liu, N. (2013) [47] identified of 11 shortcomings of FMEA (in a
review of academic papers relating to MCDM analysis) which are shown in
Table 2-5.
56
Table 2-5
[47]
Some of the key considerations from this list are; the relative importance of O, S
and D terms, the individuality of the RPN, Interdependencies of failure modes
and the variations in risk evaluations.
2.5.4 Fuzzy Logic Methods
Fuzzy methods for multi-criteria decision making are used to enable decision
makers (experts in the field of study) to characterise linguistic terms into a
numerical range consisting of a lower bound, average, and an upper bound.
The fuzzing of classical (crisp) values creates a level of security for failure
modes or consequences unseen in the evaluation prior to operation, and
decreases the sensitivity to DM input. Using fuzzy numbers also increases the
57
range of possible outcomes from the analysis – solving one of the core
problems identified in FMECA.
Fuzzy numbers combine vague descriptions and uncertainty with subjective
investor preference and expert knowledge to enable multi-criteria decision
making analysis [48].
2.5.5 TOPSIS
2.5.5.1 Literature
TOPSIS is the technique for order of preference by similarity to ideal solution.
The method involves calculating two values; the positive ideal solution
(maximum benefit and minimum cost = A*), along with the negative ideal
solution (minimum benefit with maximum cost = A-). The MCDM methodology
was created by Hwang and Yoon in1981 [49].
TOPSIS aims to translate qualitative and quantitative information into a
geometrical problem, optimising criteria with respect to weighting (cost to
benefit ratio).
The consistency index function from the Analytic Hierarchal Process can
be used to find if the decision makers used are consistent with their response to
the criteria weighting. This can be achieved through the pair wise comparison
process shown in chapter 2.5.1.2.
The degree of separation is calculated next, this relates the normalised
alternatives to both the positive and negative ideal solutions. This is used to find
the relative (geometric) closeness to ideal solution such that the alternatives
can be ranked by preference. The alternatives are ranked in descending order –
the higher the rank, the more suitable to the objective the alternative is.
58
2.5.5.2 Method
Step 1 – Normalize the decision matrix
Create a decision matrix consisting of the m alternatives and n criteria, and
normalise using the formula below
m
j
ij
ij
ij
f
fr
1
2
Equation 2-2
Where fij is the ith criterion function for the alternative Aj (j=1,..., m; i=1,..., n).
Step 2 – Create the weighted normalized decision matrix vij
ijiij rwv
Equation 2-3
Where wi is the ith criterion (or attribute) weight and
n
i iw1
.1
Step 3 - Calculate ideal and negative-ideal solutions
Isolate the benefit criteria from the cost criteria and assign maximum and
minimum alternative values respectively. Thus the ideal solutions (A∗) and the
negative-ideal solutions (A−) are calculated as:
)''|(min),'|(max,..., **
1
* IivIivvvA ijjijjn
Equation 2-4
)''|(max),'|(min,...,1 IivIivvvA ijjijjn
Equation 2-5
Where I’ represents benefit criteria, and I’’ represents cost criteria.
59
Step 4 – Distance measures
Using the n-dimensional Euclidean distance, the distance between each
alternative and the ideal solution is given as:
n
i
iijj vvD1
2** )(
Equation 2-6
Similarly for the negative-ideal solution:
n
i
iijj vvD1
2)(
Equation 2-7
Step 5 - Calculate closeness coefficient (to the ideal solution)
The relative distance between the alternative aj with respect to A∗ is given by:
)( *
*
jj
j
jDD
DC
Equation 2-8
Step 6 - Rank the Alternatives
Utilise the closeness coefficient to rank the alternatives in decreasing order.
Highlight the most optimum solution identified by the MCDA method (the
maximum C*j value).
2.5.5.3 Limitations
One of the limitations to TOPSIS is the high sensitivity to fluctuations in cost
which is valued highly (since it can convey the risk in a universally accepted
scale) and considered equal to the combination of all other benefits. In some
60
industries (such as aerospace, and nuclear energy), safety criteria outweigh the
cost implications and as such this method is less applicable.
2.5.6 MCDA Evaluation
The AHP example highlighted the strengths of using weighted criteria to allow
prioritisation of resources – something fundamental to balancing stakeholders
with varying project influence. MAFMA took this further and incorporated the
three evaluation criteria from FMEA with cost in series (as opposed to parallel).
This enables both engineering analysis and cost based evaluation (for
management) to be carried out simultaneously. AHP and MAFMA do not allow
for rank reversal and are hence inappropriate for evaluating offshore
engineering; where varying weather conditions can change the weighting of the
criteria and hence cause rank reversal.
FMECA provides the base criteria used for universal engineering evaluation
using quantitative rating scales. These scales have been proven ineffective in
translating the relative state (repetition in RPN) and hence priority of resources.
This requires the use of fuzzy number theory to increase the quantity of
possible number combinations, allowing the result to be representative. TOPSIS
combines the use of weighted criteria with cost evaluation and allows rank
reversal to take place, and therefore is the most useful MCDA method for
offshore wind application. Using fuzzy numbers for the weighting will prevent
number repetition and hence a Fuzzy TOPSIS MCDA method shall be utilised
for the offshore wind example.
2.6 Section 4 – Preference Elicitation
Preference elicitation is the process of evaluating decision maker response.
DSS are systems that support decision makers and offer an array of
tools to scrutinise their decision. Two commonly used methods for preference
elicitation are derived explicitly and implicitly which relate to absolute
measurement and pair-wise comparison respectively [50].
61
Explicit preference elicitation is based on absolute scales to enable the
decision makers to understand the direct consequence of decision. Implicit
elicitation uses pair-wise comparisons to find relative weights (to better
apprehend preference) and is subjective to decision maker’s personal
experience. Implicit evaluation makes the DM think more carefully about how
the alternatives interconnection, however require more comparisons to be made
in relation to the explicit method.
One method for passive pattern recognition (used in targeted
advertisements) collects previous decisions to provide a preference profile. This
can be applied to explicit responses to evaluate consistency in ranking by
comparing answers specified with the profile predicted results in for future
ranking. This study requires only one response from each of the decision
makers, therefore as a preliminary analysis passive pattern recognition will not
be considered.
The criticality analysis of FMECA utilises explicit elicitation for ranking
failure modes. Conversely MAUT, MAFMA and AHP use implicit pair-wise
comparisons to rank alternatives.
TOPSIS and Fuzzy TOPSIS both use explicit elicitation; the offshore wind
energy sector has many criteria for a range of purposes and fluctuating
numbers of risks, and so implicit evaluation is not suitable for the reasons
highlighted in chapter 2.5.1.3.
63
3 Methodology – Section 5
3.1 Theory
3.1.1 Fuzzy TOPSIS method
Fuzzy TOPSIS uses triangular fuzzy numbers to translate qualitative information into
quantitative range as an alternative to the use of ‘crisp’ numbers in TOPSIS.
Fuzzy triangular numbers are comprised of a lower bound, middle bound and upper
bound.
When normalising the fuzzy decision matrix with respect to cost, the minimum cost
term is the numerator and the triangular fuzzy number is inversed such that (min
cost/ max cost) = lower bound etc.
It is common practice to use scales consisting of 7 independent linguistic terms,
increasing the number of linguistic terms does not significantly improve the
confidence in the combined ratings. As such 7 terms have been specified for both
alternative and criteria scales. The fuzzy logic triangular scale proved to be most
universally accepted in literature and hence was selected for the fuzzy calculations.
The Fuzzy TOPSIS Method is given below;
A triangular fuzzy number has 3 terms (α1, α2, α3) and is defined by the membership
function µα(x);
.
,
,
,0
,
,
,0
)(
3
32
21
1
32
3
32
1
x
x
x
x
x
x
x
Equation 3-1
Step 1: Select evaluation criteria and form a team of decision makers.
64
Step 2: Allocate appropriate linguistic terms to the criteria weighting and alternative
ranking scales.
Step3: Average the criteria weights (wj) to find the average fuzzy weight of criterion
(Cj), and then aggregate the decision maker’s ratings (rij) into a matrix under to the
alternative Ai and criterion Cj.
n
w
C
n
j
j
j
1
Equation 3-2
Step 4: Build the fuzzy decision and criteria weight matrices, and then normalise with
respect to the criteria designation of either benefit (Equation 3-3) or cost (Equation 3-
4) to get the normalised fuzzy decision matrix (rij).
;,,,***
Bjc
c
c
b
c
ar
j
ij
j
ij
j
ij
ij
ij
ij cc max* If j ϵ B;
Equation 3-3
;,,, Cja
a
b
a
c
ar
ij
j
ij
j
ij
j
ij
iji
j aa min If j ϵ C;
Equation 3-4
Step 5: Multiply Cj by rij to find the weighted normalised fuzzy decision matrix.
Step 6: Specify the Fuzzy positive and negative ideal solutions either from the
criteria weighting, using zero and one, or the maximum and minimum possible
values from the decision maker ratings rij.
Step 7: Find the separation between the FPIS/ FNIS and the weighted normalised
fuzzy numbers.
65
Let α = (α1, α2, α3) and β = (β1, β2, β3) be two different fuzzy triangular numbers, the
distance between α and β is defined as;
].)()()[(3
1),( 2
33
2
22
2
11 d
Equation 3-5
Step 8: Sum both separation values (FPIS and FNIS) independently for each
alternative (equations 3-6 and 3-7 respectively), and then use to find the closeness
coefficient (equation 3-8).
n
j
dd1
** ),( , ,,.....2,1 mi
Equation 3-6
n
j
dd1
),( , ,,.....2,1 mi
Equation 3-7
,*dd
dCC
,,.....2,1 mi
Equation 3-8
Step 9: Use the score from the closeness coefficient to rank the alternatives in
descending order.
66
3.2 Computation
3.2.1 User Input
The excel program is based on a linked cell format. This enables the program to
quickly output new answers when new data is entered. The program was designed
to allow a maximum of 25 decision makers, 30 criteria and 20 alternatives (a risk
matrix of 600 inputs); however this can be tailored to the client’s specifications at a
later date and will be used in this report for proof of concept.
The program requires input of the criteria weightings from the decision makers (see
Figure 7-3) along with the associated cost or benefit indicator in the form of C or B
respectively (Figure 7-4).
Next the alternatives are ranked against one another with respect to the criteria, in
each of the 30 matrices representing the 30 possible criteria. The standard format
was to list the alternatives in the rows and the decision makers in the columns
(Figure 7-5).
Triangular fuzzy numbers were used to translate the qualitative ratings into
quantitative information to be used the calculation phase. This enabled a higher
range of outcomes compared with normal ‘crisp’ values.
There is the possibility for the user to change the linguistic terms. This was achieved
through the use of named matrices in the calculations tab in conjuncture with a
lookup function which searches the linguistic terms stated by the decision makers in
the two tables (rating and weighting scales).
Two examples were completed to demonstrate the flexibility in this regard (see
chapter 3.3: validation). Separate tables were generated for the weighting of the
criteria and rating of the alternatives. This enables the user to change the scaling of
the fuzzy numbers used to suit their specific style without changing the outcome. The
two scaling systems used for examples 1 and 2 are shown in Figures 7-6 and 7-7
respectively. These scales can however be changed (provided fuzzy triangular
numbers are used) to suit the user if the scales provided are not satisfactory. The
67
tables are named datasets in the calculation phase and lookup functions are used to
perform calculations, enabling change in scale.
While the alteration is only a factor of 10 between the scales, alternative scale
systems can be generated without changing the outcome providing these changes
are applied only to the ratings scale. The weightings scale is not normalised and so
will change the outcome when calculating the distances between fuzzy numbers. To
enable the user to control the fuzzy scale used for weighting, input cells were added
to the user interface and linked to the fuzzy positive and negative ideal solutions in
the calculations tab. This means that when changing the weighting scale, the user
needs to change the FPIS and FNIS to the maximum and minimum values
expressed in the new weighting scale.
In summary, the following inputs are required from the user;
3.2.1.1 Compulsory inputs;
- Criteria Weighting.
- Define the criteria optimisation process; either cost or benefit criteria.
- Enter the ratings of alternatives against criteria specified by the decision
makers
3.2.1.2 Optional inputs;
- Enter any changes to the alternative rating scale
- Enter any changes to criteria weighting scale and hence update FPIS and
FNIS inputs accordingly.
An image of the format of the tool is included in Appendix B.1.1. The red boxes
indicate inputs, green indicate criteria and grey are the solution; all calculations are
carried out on a separate sheet that is hidden from view/ modification.
3.2.2 Calculations
The calculations are generated in a separate tab that will be locked off on completion
of the project.
68
Initially the program generates the fuzzy triangular number from the linguistic term
specified for criteria weighting. This is then averaged to find the average fuzzy
number for weighting, shown in Figure 7-8.
The calculations required extrapolation of this data and for simplicity of calculation
and programming the index function was used to transpose the matrix (swapping
rows with columns). This is an effective and more efficient form of using the look up
function described for the scaling earlier. Figure 7-9 shows the new format for the
decision matrix.
The transposed matrix is then used for the generation of the fuzzy triangular
numbers for each linguistic term respectively. In the same manner as for the criteria,
the average fuzzy rating is calculated for the alternatives for each criterion. This
phase required error management such that lookup functions would be able to
search data in the calculation phase easily (the IFERROR operator was used to
achieve this in conjunction with the blank cell function “”). Figure 7-10 shows an
extract from the averaged fuzzy ratings for the alternatives. The criteria optimisation
designation (minimum or maximum ideal solution) is shown in the cost/ benefit
column.
The normalisation procedure was then performed where the maximum or minimum
value (for each criterion) was extracted based on whether the criterion was
designated a B (benefit) or C (Cost) term in the User interface respectively. This was
then used to normalise the fuzzy numbers using the fuzzy TOPSIS normalisation
formula for cost or benefit. The max/ min column uses the benefit or cost allocation
to find the largest part of the maximum fuzzy number ( *
jc ) or smallest part of the
minimum fuzzy number (
ja ) respectively. The normalised results are shown in
Figure 7-11.
The weighted normalised fuzzy decision matrix was then calculated by multiplying
the average fuzzy weighting (figure 7-8) with the normalised fuzzy decision matrix
(Figure 7-11). This is shown in Figure 7-12.
The fuzzy positive idea solution is usually set at 1 and fuzzy negative ideal solution
at 0 and this remains true for both the examples; however the option to change them
69
has been added to the user interface to allow changes in the criteria weighting scale.
Figure 7-13 shows the ideal solutions and the calculation of the distance values
between the ideal solutions.
These are then summed for to find the closeness coefficient using the formula from
fuzzy TOPSIS method. The results are then sorted into descending order and the
alternatives ranked for the output.
3.2.3 Outputs
The output from the calculations tab are sorted according to their closeness
coefficient and ranked accordingly. The closeness coefficient is included to highlight
the difference between the alternatives; other alternatives can be useful if key
stakeholders object to the top ranked solution. The output is shown in the user
interface (Figure 7-14).
3.2.4 Obtaining results
Combining FMEA and Fuzzy TOPSIS enables the system failure modes to be
ranked (with respect to the criteria weighting) to find the highest risk alternative. The
literature review revealed 18 potential failure modes that can affect the performance
of offshore wind turbines. This was not an exhaustive list, however were chosen to
demonstration the implementation of the method. Criteria were chosen to represent
a wide array of failure modes and were designated either a cost or benefit term
indicating the type of optimisation to be performed on the criteria.
To enable evaluation of the failure modes against the designated criteria, a
questionnaire was created for the input of the evaluation data by decision makers.
3.2.5 Questionnaire
The decision makers from chapter 2.2 were asked to fill out a questionnaire to
compare the relationship the risk alternatives has with the criteria allocated. The
questionnaire can be found in Appendices B.1.2.
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3.3 Validation and verification
To ensure the program completes the intended task, two case studies were taken
from literature for validation of program.
3.3.1 Case Study 1
Source: Extensions of the TOPSIS for group decision-making under fuzzy
environment by Chen-Tung Chen [51]
The numerical example provided in this paper, envisaged using fuzzy TOPSIS to
help filter potential employees to a software company. This paper was chosen for
two reasons;
1. It uses different scales for the weighting of criteria and rating of alternatives.
2. All of the criteria used in the example are for a benefit optimisation (testing the
ability of the program to optimise on one type of criteria).
The benefit criteria chosen are as follows; Emotional Steadiness, Oral
communication skills, personality, past experience and finally self-confidence.
Tables (3-1) – (3-6) are extracts from the program and show the inputs taken from
the example;
Enter Attribute Weighting by decision maker (linguistic value)
DM1 DM2 DM3 DM4
G1 H VH MH
G2 VH VH VH
G3 VH H H
G4 VH VH VH
G5 M MH MH
Table 3-1
DM ratings for alternatives against criteria
G1 DM1 DM2 DM3
X1 MG G MG
X2 G G MG
G2 DM1 DM2 DM3
X1 G MG F
X2 VG VG VG
X3 MG G VG
Table 3-3
71
Table 3-2
X3 VG G F
G3 DM1 DM2 DM3
X1 F G G
X2 VG VG G
X3 G MG VG
Table 3-4
G4 DM1 DM2 DM3
X1 VG G VG
X2 VG VG VG
X3 G VG MG
Table 3-5
G5 DM1 DM2 DM3
X1 F F F
X2 VG MG G
X3 G G MG
Table 3-6
The scale used for weighting of the criteria and rating of the alternatives can be
found in Appendix B.1.2 (Figure 7-7).
These inputs were then processed and gave the following output;
Output
CC Rank
0.765332 X2
0.701821 X3
0.638032 X1
Table 3-7
These were then compared with the results from the academic paper which were;
CC1 = 0.62, CC2 = 0.77, CC3 = 0.71. There is a small degree of separation between
the results which may have been caused by rounding errors in their solution;
however the rank of the three alternatives remains unchanged.
3.3.2 Case Study 2
Source: Constructing Project selection using Fuzzy TOPSIS approach by Yong-tao
Tan, Li-yin Shen et al [52].
A company is researching a number of subcontractors using a list of 9 criteria to
evaluate them. The criteria in order are as follows; Profitability, difficulty, relationship
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with owner, need for work, resources and capabilities, keenness of competitors,
competitors’ competitiveness, project execution risk and finally financial risk. Criteria
2, 6, 7, 8 and 9 are allocated for cost criteria optimisation, whereas 1, 3, 4 and 5 are
assigned to benefit optimisation.
The criteria are assigned the weightings shown in table (3-8) using a fuzzy rating
scale with a range between 1 and 0 (Appendix B.1.2).
Enter Attribute Weighting by decision maker (linguistic value)
DM1 DM2 DM3 DM4
G1 H H VH
G2 M MH M
G3 H H MH
G4 H MH M
G5 VH H H
G6 M MH MH
G7 H H H
G8 H MH MH
G9 MH H H
Table 3-8
G2 DM1 DM2 DM3
X1 MG F MG
X2 F F MG
X3 MG F F
Table 3-9
G3 DM1 DM2 DM3
X1 F F F
X2 F F MP
X3 F F MG
Table 3-10
G4 DM1 DM2 DM3
X1 F F MG
X2 G MG G
X3 MG MG MG
Table 3-11
G5 DM1 DM2 DM3
X1 G MG MG
X2 F F F
X3 MG MG MG
Table 3-12
73
The ratings for the alternatives are entered separately on tables (9 through 17)
respectively.
DM ratings for alternatives against criteria
G1 DM1 DM2 DM3
X1 VG VG G
X2 F F MG
X3 F MG F
Table 3-17
The scale used to rate the alternatives is on Figure7-6, Appendix B.1.2.
The program was able to calculate the closeness criteria and hence rank the
alternatives which are shown in table (3-18).Using the input data above and the
fuzzy TOPSIS method, the excel program derived the following solutions:
Output
CC Rank
0.544246 X3
0.496368 X1
0.466551 X2
Table 3-18
The academic paper listed the solutions as CC1 = 0.496, CC2 = 0.467 and CC3 =
0.544. This proves that both the calculations and ranking formula are able to perform
the calculations over an array of different inputs to a high degree of accuracy.
G6 DM1 DM2 DM3
X1 G F MG
X2 MG MG MG
X3 F F F
Table 3-13
G7 DM1 DM2 DM3
X1 G MG MG
X2 G MG F
X3 F F F
Table 3-14
G8 DM1 DM2 DM3
X1 MG MG MG
X2 F F MG
X3 F F F
Table 3-15
G9 DM1 DM2 DM3
X1 MG F F
X2 F MG MG
X3 F F F
Table 3-16
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4 Results
Expert responses can be found in appendix C.
4.1 Fuzzy TOPSIS output with closeness coefficient
Table 4-1 links the designation code for the alternatives with the corresponding
linguistic alternative from the questionnaire.
X1 Legislation
X10 Carbon footprint
X2 Human Error
X11 Change in environmental conditions
X3 Delay
X12 Marine life migration
X4 Change in Health and safety
X13 Communication to upper level management
X5 Material failure
X14 Bird impacts
X6 Volatility in wholesale energy costs
X15 Force Majeure
X7 Change in government position on subsidies
X16 Failure of automated detection systems
X8 Change in RE Policy
X17 Energy storage and transfer
X9 Failure of information transfer
X18 Supply chain
Table 4-1
The following table is the output from the Fuzzy TOPSIS program with respect
to the five decision makers. The closeness coefficient is used to rank the terms
in descending order. The most critical failure mode identified related to the
damage or change to wildlife caused by marine migration followed by the supply
chain problems associated with the wind turbines.
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Output
Closeness Coefficient
Rank Corresponding Linguistic term
1 0.254152 X12 Marine life migration
2 0.246818 X18 Supply chain
3 0.236966 X8
Change in Renewable Energy Policy
4 0.231433 X10 Carbon footprint
5 0.206862 X5 Material failure
6 0.206277 X1 Legislation
7 0.205712 X17 Energy storage and transfer
8 0.203197 X13
Communication to upper level management
9 0.199203 X6 Volatility in wholesale energy costs
10 0.188499 X14 Bird impacts
11 0.188377 X7
Change in government position on subsidies
12 0.179115 X16
Failure of automated detection systems
13 0.1692 X9 Failure of information transfer
14 0.150115 X15 Force Majeure
15 0.144312 X11
Change in environmental conditions
16 0.136967 X3 Delay
17 0.13542 X2 Human Error
18 0.106854 X4 Change in Health and safety
Table 4-2
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5 Sensitivity analysis – Section 6
TOPSIS has the advantage of assigning weights to the evaluation criteria
enabling the project managers to optimise resource allocation based on
personal preference parameter prioritisation. The data displayed in the results
section is not representative of the Renewable Energy sector as a whole and so
can only be considered as semi-quantitative information. This means that a
larger decision maker sample is required in order to reach a group consensus.
Figure 1-1 shows where a sensitivity analysis should be performed.
5.1 Weight stability Intervals
The results were collected from 5 decision makers which is a small sample, this
would suggest high sensitivity within decision maker responses; we are
interested in the correlation between a single DM’s input and its effect on the
output.
The criteria have a linear relationship with the closeness coefficient and hence
this would suggest that maximising or minimising the criteria would change the
ranking of alternatives that have high ratings. Large changes in single criteria
are unlikely to occur, however large changes in groups of criteria weight would
suggest a societal change in priority. Therefore a sensitivity analysis shall focus
on the cost of implementation potential future policies will have based on the
current ranking.
5.1.1 Scenario 1 – Safety Conscious
An example could include a society with a high value of life. Resources would
be concentrated on minimising human interaction with OWFs by increasing
reliance on automated maintenance systems. This would result in the weighting
associated with redundancy/ mitigation (G7) and fatalities (G2) would be
maximised.
The combined affect this change would have on the prioritisation of resources
relative to the initial findings are shown below;
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Rank Number
G2 and G7 Maximised
Original rank position
Current rank Rank Change
1 X12 X18 1
2 X18 X12 -1
3 X8 X8 0
4 X10 X10 0
5 X5 X5 0
6 X1 X13 2
7 X17 X1 -1
8 X13 X6 1
9 X6 X17 -2
10 X14 X16 2
11 X7 X14 -1
12 X16 X7 -1
13 X9 X15 1
14 X15 X9 -1
15 X11 X2 2
16 X3 X11 -1
17 X2 X3 -1
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18 X4 X4 0
Total rank change: 9
Table 5-1
A safety conscious society would impose the second largest cost of
implementation from change in risk rank.
5.1.2 Scenario 2 – Eco-Environmental
Another example could be of a society who becomes extremely eco-
environmentally friendly; this would influence the risk weighting of all
environmental criteria – maximising emphasis on detectability (G6) and tonnes
of carbon dioxide avoided per year (G9). Similarly these design parameters are
considered below;
Rank Number
G1, G6 and G9 Maximised
Original rank position
Current rank
Rank Change
1 X12 X18 1
2 X18 X12 -1
3 X8 X10 1
4 X10 X8 -1
5 X5 X1 1
6 X1 X13 2
7 X17 X5 -2
8 X13 X17 -1
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9 X6 X6 0
10 X14 X14 0
11 X7 X7 0
12 X16 X16 0
13 X9 X9 0
14 X15 X11 1
15 X11 X15 -1
16 X3 X2 1
17 X2 X3 -1
18 X4 X4 0
Total rank change: 7
Table 5-2
Scenario 2 has the second lowest rank change, demonstrating a lower cost of
execution than both scenarios 1 and 4.
5.1.3 Scenario 3 – Socio-economic
The third condition could involve a business orientated society where business
image and profitability is most crucial. This would maximise non-financial risk to
business (G3) and Direct/ Indirect cost of failure (G5);
Rank Number
G3 and G5 Maximised
Original rank position
Current rank
Rank Change
80
1 X12 X12 0
2 X18 X18 0
3 X8 X8 0
4 X10 X10 0
5 X5 X17 2
6 X1 X5 -1
7 X17 X1 -1
8 X13 X13 0
9 X6 X6 0
10 X14 X14 0
11 X7 X7 0
12 X16 X16 0
13 X9 X9 0
14 X15 X15 0
15 X11 X11 0
16 X3 X3 0
17 X2 X2 0
18 X4 X4 0
Total rank change: 2
Table 5-3
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The rank change score of 2 indicates that there is little change to the current
model and therefore a low cost of implementation.
5.1.4 Scenario 4 - Engineering Longevity
The final scenario considered is the future where the lifespan of the turbine is
most important, by maximising the priority to component failure (G4) and design
life (G8);
Rank Number
G4 and G8 Maximised
Original rank position
Current rank
Rank Change
1 X12 X8 2
2 X18 X10 2
3 X8 X12 -2
4 X10 X18 -2
5 X5 X5 0
6 X1 X13 2
7 X17 X17 0
8 X13 X1 -2
9 X6 X16 3
10 X14 X6 -1
11 X7 X7 0
12 X16 X14 -2
13 X9 X9 0
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14 X15 X15 0
15 X11 X3 1
16 X3 X11 -1
17 X2 X2 0
18 X4 X4 0
Total rank change: 10
Table 5-4
Scenario 4 had the highest rank change, with 10 of the alternatives changing
position. This high rank change suggests that the current solution and this new
proposed scenario is vastly different, indicating a high cost of implementation.
While each of these four scenarios is plausible, a combination of these factors
is likely to influence the future direction of the wind energy sector.
5.2 Sensitivity in alternative ratings
The method used for calculating the weighted normalised fuzzy matrix produces
a linear relationship between changes in criteria weight and the closeness
coefficient.
A sensitivity analysis on how small changes in alternative ratings influence the
closeness coefficient will produce a more complete picture of the importance of
individual ratings. This information will help identify the recommended number
of decision makers required to classify risks/ failure modes and their relative
priority in the offshore wind sector.
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5.2.1 Individual change
The normalisation process is very sensitive to changes. Equations 3-3 and 3-4
are used to perform optimisation on the averaged fuzzy ratings matrix. When
the criterion is specified as cost, the minimum aij (the smallest part of the fuzzy
number) with respect to that criterion becomes crucial in ascertaining the
complexity of the correlation between input and output. If the change in rating is
not significant then the change in output will be limited to the alternative
changed.
An example would be;
X3, G1, DM1 - increased from P to MP
Original Rank
Position Current Rank
Closeness
coefficient Rank Change
X12 X12 0.2542 0
X18 X18 0.2468 0
X8 X8 0.237 0
X10 X10 0.2314 0
X5 X5 0.2069 0
X1 X1 0.2063 0
X17 X17 0.2057 0
X13 X13 0.2032 0
X6 X6 0.1992 0
84
X14 X14 0.1885 0
X7 X7 0.1884 0
X16 X16 0.1791 0
X9 X9 0.1692 0
X15 X15 0.1501 0
X11 X11 0.1443 0
X3 X3 0.1362(01) 0
X2 X2 0.1354 0
X4 X4 0.1069 0
Total Rank Change: 0
Table 5-5
The change in the closeness coefficient is highlighted in the table. The original
score from the results section was 0.136967 compared with the altered rating
giving a new CC value of 0.136201 (a minimal change in the individual term –
as predicted). Rank changes can occur when individual changes are made,
however this depends on the initial proximity to other CC values.
5.2.2 Group change - Cost criteria
However if the change in rating creates a new averaged fuzzy aij value below all
other alternative ratings, the outcome for all values in the criterion will be
altered. For a rating modification to be considered large, the separation from
other DMs in the same criterion must be 2 or higher (dependent on a low
variance in decision maker response). A significant alternative is one in which a
small change will result in a change in rank of the maximum (cij) or minimum
(aij) average fuzzy number.
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Rank Number
DM1 – G3 – X12 reduced from MP to P
Original rank position
Current rank
Closeness Coefficient
Rank Change
1 X12 X12 0.254152 0
2 X18 X18 0.246818 0
3 X8 X8 0.236966 0
4 X10 X10 0.231433 0
5 X5 X5 0.206862 0
6 X1 X1 0.206277 0
7 X17 X17 0.205712 0
8 X13 X13 0.203197 0
9 X6 X6 0.199203 0
10 X14 X7 0.188377 1
11 X7 X16 0.179115 1
12 X16 X14 0.188499 -2
13 X9 X9 0.1692 0
14 X15 X15 0.150115 0
15 X11 X11 0.144312 0
16 X3 X3 0.136967 0
17 X2 X2 0.13542 0
18 X4 X4 0.106854 0
86
Total Rank Change: 2
Table 5-6
The initial average aij value for cost criteria 3, alternative number 12, was 0.064.
The decision maker 1 response was changed from MP to P resulting in the
average aij term to reduce to 0.046. This led to decreases in all 18 closeness
coefficients; averaging a 1.72%, and ranging from 0.30% to 7.54% compared
with the initial result. The change in CC resulted in a rank change, moving two
alternatives up and two down in their rank.
Two rank changes can result from a change to a single rating in the decision
matrix.
5.2.3 Group change - Benefit criteria
Conversely if the change in average fuzzy number breaches the initial
maximum value (Cij) and the criterion optimisation designation is for benefit,
then similarly the values of all normalised alternative fuzzy numbers within that
criterion will change accordingly.
The example for the benefit criterion is given in Table 5-7.
X15, G7, DM1 increase from MG to VG
Original Rank
Position Current Rank
Closeness
coefficient Rank Change
X12 X12 0.254 0
X18 X18 0.2461 0
X8 X8 0.2364 0
87
X10 X10 0.2313 0
X5 X5 0.2061 0
X1 X1 0.2057 0
X17 X17 0.2052 0
X13 X13 0.2025 0
X6 X6 0.199 0
X14 X7 0.1881 1
X7 X14 0.1879 -1
X16 X16 0.1782 0
X9 X9 0.1689 0
X15 X15 0.1514 0
X11 X11 0.1442 0
X3 X3 0.1366 0
X2 X2 0.1349 0
X4 X4 0.1065 0
Total Rank Change: 1
Table 5-7
The alternative chosen created an original cij value of 0.96; the increase in the
rating value gave the new cij value of 0.98. This correspondingly changed the
rank order by one place. The lack of benefit criteria prevented any more
influential examples being used. The small change in final rank despite an
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increase of 2 places in rating indicates a low variance within the ranking of the
criteria.
5.2.4 Group change - Solution
One solution to get around the dilemma of high sensitivity is to increase the
number of decision makers in the analysis. This will reduce the sensitivity to
large changes in alternative ratings by a single decision maker, by lowering the
overall contribution of each of the ratings. This will also enable a higher level of
confidence in the risk rating, since it is expected that each of the alternative
responses will fall in a narrow range – outputting a normal distribution with a low
variance (an inversely proportionate relationship between variance and
confidence).
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6 Analysis and Discussion
6.1 Analysis
6.1.1 Program structure
The examples used for validation of the TOPSIS program both used
combinations of linguistic risk ratings that precluded any of the average fuzzy
numbers for alternatives from containing zero terms. The results from the 5
decision makers highlighted a fault in the rating scales. When poor and very
poor correlation ratings were allocated by all decision makers, zero terms in the
averaged fuzzy rating scales prevented the normalisation process for cost
optimised criteria (this can be seen in Equation 3-4 in chapter 3).
In order to circumvent this error, the scale was changed such that the minimum
coefficient of the fuzzy number was at most 1% of the peak value in the scale.
This prevented a zero coefficient from occurring in the criteria normalisation
while having little effect on the overall results.
This problem was limited to the rating of alternatives since the criteria weighting
is not normalised, and so the limits of the criteria are linked to the FPIS and
FNIS scales.
6.1.2 Decision maker Responses
The results from the decision makers were analysed, giving the ranking shown
in Table 4-12. The largest risk was interference from or to marine life migration,
this was unexpected (volatility in wholesale energy costs was expected to be
the highest, but subsequently ranked 9/18). One of the reasons why this risk
was selected as the largest potential hindrance to the installation and operation
of OWFs could be the overwhelming number of government and non-
government stakeholders for the protection of offshore wildlife.
The second highest risk was from the supply chain, this can be expected since
delays or failure to produce the equipment is highly problematic in developing
90
industries; where alternative companies for sourcing parts/ reducing costs are
scarce. Change in renewable energy policy ranked third, indicating the influence
politics has on the future development of the sector. This could also have been
influenced by modern developments in shale gas (in the UK) which could
promise to bring down carbon fuel costs (and reduce the political need for
energy independence – through renewables).
Carbon footprint was ranked 4th which was expected. There is an increasing
interest in minimising carbon dioxide emissions and so carbon taxes are applied
to all products which either directly or indirectly expelled CO2 during their
production and transportation. Material Failure is an always present risk when
planning for offshore development and was ranked 5th accordingly.
Improvements in both design and material behaviour aim to make the failure of
these structures more predictable, to ensure adequate maintenance.
Legislation is required in the production of all engineering sectors to ensure the
product is built within strict limits for allowable risks. This is to prevent damage
to the environment or injury to humans operation on or around the structures.
This was ranked 6th above energy storage and transportation (which is currently
under construction with the aim of promoting commercial offshore installations).
This project aims to improve communication between the engineers (to convey
risks) to upper level management (and accountants). This project risk was
ranked 8th overall by the decision makers.
Volatility in wholesale energy costs was expected to rank higher since it directly
influences the (already narrow) profit margin to make the sector feasible.
Current high cost of energy may keep the production of offshore wind viable into
the near future; however changes in alternative energy source costs may harm
future investment in the sector.
The risk of bird impacts had two sides to it; environmental from loss of bird life
and structural damage to the aerofoils. As such it was collaboratively ranked
10th place, ahead of change in government position on subsidies at 11th.
91
Failure of automated detection systems slightly outranked failure of information
transfer most likely from the reliance of the detection systems where visual
inspection is difficult. Information transfer should be maintained if the correct
standards are adhered to.
Force majeure (unknown risk) and change is environmental conditions ranked
14th and 15th respectively. Delay, human error and change in health and safety
were the three lowest rated risks associated with offshore wind. Each of these
terms were expected to rank higher, however delay is more of a generic term
and hence more specific causes for delay (problems with supply chain) are
rated higher. Human error is currently being reduced to a minimum with designs
re-checked before sent for production, and is controlled autonomously and
hence reducing human interaction. Changes to health and safety is likely
however only in the medium to long term, if human interaction is designed out of
the maintenance process, health and safety (for maintenance) will no-longer be
important. There is little impact changes in policy can have on the final 5 risks
ranked (and hence their position in the ranking).
It is important to remember that these surveys capture a snapshot in time for
predicted risks faced by offshore wind. The information provided should be re-
generated at all phases of the project, to ensure the appropriate action for the
risk faced at any particular time.
Figure 6-1shows the trend in the output data, a linear regression line has been
added to indicate the relationship between rank and closeness coefficient. The
linear relationship shows that there is little to no skew in the output data,
suggesting that changes to the inputs should have a proportionate impact on
the ranking of the alternatives.
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Figure 6-1
6.2 Discussion
6.2.1 General
This thesis has explored concepts from failure mode and effects analysis and
applied them to a multi-criteria decision making method to enhance problem
solving for risk in the offshore wind energy sector.
The validity of the results for use in the offshore industry would require further
input from relevant stakeholders identified in the literature review. These include
but are not limited to; Academic institutions, insurance brokers, banking,
government officials and a wider array of experts from the offshore renewable
sector.
The selection of risks and failure modes extracted from the risk register were
able to prove that optimisation of resource allocation to risks through the use of
multi-criteria analysis is possible. The next step in the process of method
validation to enable a group consensus will require the method being applied in
a range of scenarios (for example as a cost optimisation tool for banking or a
benefit analysis for insurance brokers). The ability to reach group consensus is
0
0.05
0.1
0.15
0.2
0.25
0.3
0 5 10 15 20
Clo
sen
ess
Co
eff
icie
nt
Rank
Variation in closeness coefficient with respect to rank
Distribution of End Results
Linear (Distribution of EndResults)
93
dependent on the flexibility of the program to meet the requirements of a range
of industries.
6.2.2 Program
Both the criteria and alternatives were chosen to be representative for
application to multiple phases in offshore wind. In reality, these should be
broken down and evaluated as separate individual phases to ensure risks are
not overlooked or under evaluated.
The results expressed in this thesis are an indication of how a full scale
evaluation of how multi-criteria analysis can be applied to the ranking and
prioritisation of risks and failure modes in offshore wind. The number and type
of failure modes/ risk can be changed to suit the project phase. Some
evaluation criteria are required in all areas of project implementation (such as
costs). The variation in rank of these common criteria should provide a basis to
correlate different risk criteria metrics to assign relative importance when
evaluating the combined project risk.
Kutlu, A. C. and Ekmekçioğlu, M. (2012) [53], provided an analysis using a
combination of FAHP and fuzzy TOPSIS to evaluate the three risk criteria used
in failure mode and effects criticality assessment. The closeness coefficients
produced from this study were of the same order of magnitude as the results
obtained in this study. It was decided that AHP would not be used within this
thesis based on limitations preventing rank reversal and the increase in the time
taken for decision makers to rate on an implicit scale system. This study
contained more than twice the number of alternatives and it was concluded that
AHP works best from a small number of comparisons.
Sachdeva, A. and Kumar, D. and Kumar,P. (2009) [54], doubled the number of
evaluation criteria used in FMECA to enable comparisons with cost, safety and
spare parts for a more in-depth evaluation of the resources available to improve
the maintenance phase. Direct explicit scales were used where the MTBF was
94
separated into duration in terms of months to characterise the occurrence rate.
This study decided not to restrict the ranges for the evaluation criteria to be as
inclusive of varying aspects of the risk evaluation as possible. The sensitivity
analysis (chapter 5) attempted to provide insight into the variation in potential
futures and the effect on the cost of implementation based on current ranking.
This found that designing for both long (wind turbine) life and increased
restrictions on safety would create the greatest cost of implementation relative
to the two other potential future scenarios. These future scenarios link to the
additional criteria used in [54].
The program was designed for application in the insurance industry. The next
step to be performed for the program is to ascertain the maximum number of
alternatives that are expected for any particular phase of a project (since
projects are insured by project phase).
95
7 Conclusion – Section 7
The project aimed to implement a program capable of taking multiple inputs and
creating a verifiable and representative single metric for renewable energy
projects to be compared against one another.
The project goal is achievable by finding common risk areas throughout the
renewable energy industry (such as cost, design life, energy yield, etc.) and
comparing the top ranked alternatives within each of the energy sectors
respectively. The difference between these energy sectors will always induce
some ambiguity in risk caused by the lack of compatibility of risk evaluation
criteria.
A variety of risks were found in each of the project phases, and the
understanding/ experience of the decision makers changed was indicated by
variance in response. Selecting criteria for the evaluation of risks was difficult
and was hence generalised for simplicity. The evaluation criteria chosen were
not ideal for all risks faced in OWFs; therefore further analysis by project phase
is required.
Assigning project risk is a dynamic process, in which risk results are only
applicable for a limited time step (before external changes influence the risk
rank). This constant rank change is one of the key reasons why TOPSIS was
used in preference to AHP (which does not allow for rank reversal).
The use of fuzzy TOPSIS enabled a far greater range of outcomes effectively
eliminating the problems related to number repetition. The cause of repetition
stemmed from the use of crisp numbers to generate the RPN in the FMECA
method (which was criticised in the literature review). The ability to select new
criteria and apply a relative weighting in TOPSIS solved the difficulties in the
generation of a representative outcome for resource allocation (an extension to
the three criteria used to generate the RPN). The use of explicit linguistic values
to rate the alternatives minimised the decision maker’s response time (which
lowers the project cost).
96
The normalisation process enabled changes in the numerical range of the
scales which were used to translate the qualitative linguistic ratings into
quantitative fuzzy numbers for the alternatives. It was found that changes to the
quantitative scales for the weighting of criteria could be achieved by allowing
the user to assign the fuzzy positive/ negative ideal solutions the same limits as
the new weighting scale. These changes make the program more flexible and
hence applicable to bespoke applications.
7.1 Further Work
One future prospect would obtain decision maker results for risk matrices
generated by project phase and measure the cumulative total project risk.
Application of the decision tool to a real world problem will find the accuracy of
the program and characterise its usefulness. When applying the method to a
real world problem, taking several surveys (from decision makers) over the
project lifespan will help identify the dynamic change in risk priorities.
Another dynamic risk analysis could be obtained from asking the decision
makers to rank the alternatives with respect to changing socio-economic
conditions to find the project sensitivity to the business operating climate.
Further work should include running tests involving larger arrays of alternatives,
criteria and decision makers to calculate if there is a 1/n effect on the average
fuzzy rating (where n=number of DMs) relationship between changing a single
rating by one linguistic value. This would enable optimisation to find the
minimum number of DMs required for ambiguities in responses not to
significantly skew the data.
97
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APPENDICES
Appendix A Literature Review
A.1 MCDM Methods
A.1.1 AHP Example
10
1
Figure 7-1
10
2
A.1.2 FMECA Tables
Table 4, [44]:
10
3
Table 5, [44]:
10
4
Table 6, [44]:
10
5
Appendix B Methodology
B.1.1 Fuzzy TOPSIS program
Fuzzy TOPSIS User Interface
10
6
Figure 7-2
10
7
Figure 7-3
10
8
Figure 7-4
Figure 7-5
10
9
Figure 7-6
Figure 7-7
11
0
Figure 7-8
Figure 7-9
11
1
Figure 7-10
11
2
Figure 7-11
11
3
Figure 7-12
11
4
Figure 7-13
11
5
Figure 7-14
11
6
B.1.2 Questionnaire
This evaluation is for the offshore
wind renewable energy sector.
Using the scales provided, please
complete the table comparing the
criteria with the risks (alternatives).
Scale for the weighting of criteria:
Qualitative weighting
Linguistic term
VL Very Low
L Low
ML Moderately Low
M Medium
MH Moderately High
H High
VH Very High
Criteria Relative Importance
Weighting Benefit or cost
criteria
Impact on environment Cost
Fatalities Cost
Risk to business - non-financial
Cost
Component failure Cost
Direct/ Indirect cost of failure Cost
Detectability Benefit
Redundancy/ Mitigation Benefit
Design life Benefit
Tonnes of carbon dioxide avoided per year
Benefit
11
7
Scale for the ranking of alternatives:
Qualitative rating of
risks
Correlation with
criteria
VP Very Poor
P Poor
MP Moderately Poor
F Fair
MG Moderately Good
G Good
VG Very Good
List of alternatives (from risk register);
Legislation Carbon footprint
Human Error Change in environmental
conditions
Delay Marine life migration
Change in Health and safety Communication to upper level
management
Material failure Bird impacts
Volatility in wholesale energy
costs Force Majeure
Change in government position
on subsidies
Failure of automated detection
systems
Change in RE Policy Energy storage and transfer
Failure of information transfer Supply chain
11
8
Risk matrix
Impact on
environment Fatalities
Risk to
business -
non-
financial
Component
failure
Direct/
Indirect
cost of
failure
Detectability Redundancy/
Mitigation
Design
life
Tonnes
of CO2
avoided
per
year
Legislation
Human Error
Delay
Change in
Health and
safety
Material failure
Volatility in
wholesale
11
9
energy costs
Change in
government
position on
subsidies
Change in RE
Policy
Failure of
information
transfer
Carbon
footprint
Change in
environmental
conditions
12
0
Marine life
migration
Communication
to upper level
management
Bird impacts
Force Majeure
Failure of
automated
detection
systems
Energy storage
and transfer
Supply chain
12
1
Appendix C Results
C.1 Decision Maker 1
C.1.1 Risk matrix
Impact on environment
Fatalities
Risk to business
- non-financial
Component failure
Direct/ Indirect cost of failure
Detectability
Redundancy/ Mitigation
Design life
Tonnes of
carbon dioxide avoided per year
Legislation VG F MG F G F F F G
Human Error MP G G G G MP MP P P
Delay P P MG MG P P MG MP
Change in Health and
safety
P VG G G F F P P P
Material failure P MG G G MG F MG G P
Volatility in wholesale
energy costs
MG VP G P P MP P MP MG
Change in government position on subsidies
MG P G P F P MP P G
12
2
Change in RE Policy
G P F VP F VP F MP VG
Failure of information
transfer
G MG MG G G F MP F F
Carbon footprint
G P F VP F F VP VP VG
Change in environmental
conditions
G MP MG VP MP F VP MP MP
Marine life migration
F VP MP VP VP F VP P P
Communication to upper
level management
G G MG MG F F F G MG
Bird impacts MG P MP P P MG F P VP
Force Majeure G F G P F MP MG P P
Failure of automated detection systems
P G G G G MP G G P
Energy storage and
transfer
G MP MP VP G VP F MP VG
Supply chain
P P VG F VG F F P MP
Table 7-1
12
3
C.1.2 Criteria weighting
Criteria Relative Importance
Weighting Benefit or cost criteria
Impact on environment H Cost
Fatalities H Cost
Risk to business - non-financial MH Cost
Component failure M Cost
Direct/ Indirect cost of failure M Cost
Detectability M Benefit
Redundancy/ Mitigation MH Benefit
Design life M Benefit
Tonnes of carbon dioxide avoided per year MH Benefit
Table 7-2
C.2 Decision maker 2
C.2.1 Risk matrix
Impact on
environment Fatalities
Risk to business
- non-
Component failure
Direct/ Indirect cost of
Detectability
Redundancy/ Mitigation
Design life
Tonnes of carbon dioxide
12
4
financial failure avoided per year
Legislation G F G F MG F F MG G
Human Error P G G G MG P MP F P
Delay MP MP MG F MG MP MP MG MP
Change in Health and
safety
MP VG G MG MG F MP P P
Material failure P G MG MG MG F F G P
Volatility in wholesale
energy costs
G VP G P P F P MP MG
Change in government position on subsidies
G P G P MG P P MP G
Change in RE Policy
G P F VP MP VP F P G
Failure of information
transfer
G MG F G MG F P F F
Carbon footprint
G MP F VP F F VP P G
Change in environmental
conditions
MG MP G VP MP F VP P P
Marine life migration
F VP P VP P F P P P
12
5
Communication to upper
level management
G MG F MG F F F G MG
Bird impacts G MP P P MP G F P VP
Force Majeure G F F VP F MP G P P
Failure of automated detection systems
P MG MG G MG MP MG G P
Energy storage and
transfer
G P P VP MG VP MP MP G
Supply chain
P P G MG VG F MG MP MP
Table 7-3
12
6
C.2.2 Criteria Weighting
Criteria Relative Importance
Weighting Benefit or cost criteria
Impact on environment VH Cost
Fatalities H Cost
Risk to business - non-financial H Cost
Component failure M Cost
Direct/ Indirect cost of failure MH Cost
Detectability M Benefit
Redundancy/ Mitigation MH Benefit
Design life M Benefit
Tonnes of carbon dioxide avoided per year M Benefit
Table 7-4
12
7
C.3 Decision Maker 3
C.3.1 Risk matrix
Impact on environment
Fatalities
Risk to business - non-financial
Component failure
Direct/ Indirect cost of failure
Detectability
Redundancy/ Mitigation
Design life
Tonnes of carbon dioxide avoided per year
Legislation G G VP MP VP F VP VP G
Human Error VP VG F F VP G VP F VP
Delay VP VP VG VP VP VP VP VP G
Change in Health and safety
VP VG P F VP P VP VP VP
Material failure VP F F VG VG P P G VP
Volatility in wholesale energy costs
VP VP VP VP VG VP VP VP VP
12
8
Change in government position on subsidies
VP VP G VP VP VP VP P P
Change in RE Policy
VP VP G VP VP VP VP P P
Failure of information transfer
VP F P G G G VP G VP
Carbon footprint VG VP VP VP VP VP VP G VG
Change in environmental conditions
P MG G VG VG VG VP VG VG
Marine life migration
G VP VP MP VP VP VP VP VP
Communication to upper level management
VP VP P P MP P F P P
Bird impacts F P P G G VP VP F VP
12
9
Force Majeure VP VP G G G G G G VP
Failure of automated detection systems
G P G VG VG VG MP MP MP
Energy storage and transfer
G MP G P P P P P F
Supply chain P P G G G MP P G G
Table 7-5
13
0
C.3.2 Criteria Weighting
Criteria Relative Importance Weighting Benefit or cost criteria
Impact on environment L Cost
Fatalities MH Cost
Risk to business - non-financial H Cost
Component failure VH Cost
Direct/ Indirect cost of failure H Cost
Detectability H Benefit
Redundancy/ Mitigation L Benefit
Design life M Benefit
Tonnes of carbon dioxide avoided per year VH Benefit
Table 7-6
C.4 Decision Maker 4
13
1
C.4.1 Risk matrix
Impact on environment
Fatalities
Risk to business - non-financial
Component failure
Direct/ Indirect cost of failure
Detectability
Redundancy/ Mitigation
Design life
Tonnes of carbon dioxide avoided per year
Legislation G F MG F MG F F MG G
Human Error P MG G G MG P MP F P
Delay MP MP MG F MG p MP MG MP
Change in Health and safety
MP VG G MG MG F MP P P
Material failure P G MG MG MG MP F G P
Volatility in wholesale energy costs
G VP G MG P F P MP MG
Change in government position on subsidies
G P MG MP MG P P MP VG
Change in RE Policy
G P F VP MP VP F P VG
13
2
Failure of information transfer
G MG F G MG F P F F
Carbon footprint
G MP F VP F MG VP P G
Change in environmental conditions
MG MP G VP MP F VP P MP
Marine life migration
MG VP MP VP P F P P P
Communication to upper level management
G MG F MG F F F G G
Bird impacts G MP MP P MP G F P VP
Force Majeure G F F VP F P G P P
Failure of automated detection systems
P MG MG G MG MP MG G P
Energy storage and
G P P VP MG VP MP MP G
13
3
transfer
Supply chain P P G MG VG F MG MP P
Table 7-7
13
4
C.4.2 Criteria Weighting
Criteria Relative Importance Weighting Benefit or cost criteria
Impact on environment H Cost
Fatalities M Cost
Risk to business - non-financial M Cost
Component failure ML Cost
Direct/ Indirect cost of failure M Cost
Detectability MH Benefit
Redundancy/ Mitigation MH Benefit
Design life M Benefit
Tonnes of carbon dioxide avoided per year
MH Benefit
Table 7-8
C.5 Decision Maker 5
C.5.1 Risk matrix
13
5
Impact on environment
Fatalities
Risk to business - non-financial
Component failure
Direct/ Indirect cost of failure
Detectability
Redundancy/ Mitigation
Design life
Tonnes of carbon dioxide avoided per year
Legislation VG F MG F G F F F G
Human Error MP G G F G MP F F F
Delay F F MG MG P P MG MP
Change in Health and safety
F VG G G F F P P P
Material failure
P MG G G MG F MG G P
Volatility in wholesale energy costs
MG VP G P P MP P MP MG
Change in government position on subsidies
MG P G P F P MP P G
13
6
Change in RE Policy
G P F VP F VP F MP VG
Failure of information transfer
G MG MG G G F MP F F
Carbon footprint
G P F VP F F VP VP VG
Change in environmental conditions
G MP MG VP MP F VP MP MP
Marine life migration
F VP MP VP VP F VP P P
Communication to upper level management
G G MG MG F F F G MG
Bird impacts MG P MP P P MG F P VP
Force Majeure
G F G P F MP MG P P
Failure of automated detection
P G G G G F G G P
13
7
systems
Energy storage and transfer
G MP MP VP G VP F MP VG
Supply chain P P VG F VG F F P MP
Table 7-9
13
8
C.5.2 Criteria weighting
Criteria Relative Importance Weighting Benefit or cost criteria
Impact on environment MH Cost
Fatalities ML Cost
Risk to business - non-financial MH Cost
Component failure M Cost
Direct/ Indirect cost of failure M Cost
Detectability M Benefit
Redundancy/ Mitigation M Benefit
Design life M Benefit
Tonnes of carbon dioxide avoided per year
H Benefit
Table 7-10
139
Appendix D Digital Information
CD 1 contains electronic copies of all the software and documents used in the
thesis.
