A novel methodology for the assessment of wave energy options at early stages

DOCTORAL THESIS 2023 A NOVEL METHODOLOGY FOR THE ASSESSMENT OF WAVE ENERGY OPTIONS AT EARLY STAGES PABLO RUIZ-MINGUELA SUPERVISORS: PROF JESÚS MARIA BLANCO ILZARBE & DR VINCENZO NAVA ? A novel methodology for the holistic assessment of wave energy technologies at early design stages AUTHOR: José Pablo Ruiz Minguela SUPERVISORS: Prof Jesús María Blanco Ilzarbe Dr Vincenzo Nava A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy Bilbao, May 2023 (cc)2023 JOSE PABLO RUIZ MINGUELA (cc by-sa 4.0) In loving memory of my dear mother Lucía (1929-2020) “Pygmies placed on the shoulders of giants see more than the giants themselves” Friar Diego de Estella (1524 – 1578) vii ABSTRACT Increasing the share of electricity generation from renewable sources is key to ensure a fully decarbonised energy system and fight against climate change. Wave energy is an abundant and powerful resource but at the same time, the least developed of all renewable energy technologies. It is discouraging that despite the considerable efforts the international research community has made over the last decades, wave energy technologies have once and again failed to achieve the desired design convergence to support their future market growth. Traditional approaches mainly focused on assessing technology maturity have proven insufficient to ensure that wave energy technologies achieve their technical, economic and social goals. To meet the high sector expectations, this research proposes a systematic approach from the outset of technology development that ensures traceability of requirements, creates fair performance assessments and applies sound innovation strategies to overcome the remaining technological challenges. The common evaluation framework is based on sound Systems Engineering principles. It encompasses the external context, system requirements and evaluation criteria. This step of the methodology creates a prioritisation of the various wave energy attributes for the qualitative assessment of wave energy technologies. The analysis of the external context provides an understanding of the factors influencing the development of wave energy technologies and the corresponding impact on system requirements. The identification of the market application, key drivers and stakeholders’ groups provides an excellent foundation for the objective assessment of wave energy technologies against the systems requirements. This framework avoids any inconsistency with the formulation of system requirements and can be applied to different levels of technology maturity. It provides flexibility for adapting it to rapidly changing market conditions or stakeholder priorities and can be expanded to focus the analysis on specific wave energy sub-systems. Besides, it grasps the qualitative aspects related to the stakeholder expectations that higher-level metrics such as LCOE cannot provide. On the other hand, the proposed novel approach guides design decisions along the development process for the adequate management of risk and uncertainty. To this purpose, the holistic assessment developed through this research comprises the evaluation at intermediate development stages and the projection of future costs when the technology viii has been sufficiently replicated. This step of the methodology facilitates wave energy technology selection and benchmarking at different levels of maturity in a controlled manner. The fair assessment of wave energy technology performance creates awareness of potential technology gaps throughout the various development stages. It facilitates the selection of the most suitable option for a particular market application and enables benchmarking of technologies across different markets. Additionally, it offers a tool for exploring uncertainties, drawing attention to the cost estimate accuracy and identifying potential learnings from the beginning of technology development. The innovation strategies proposed in this research deliver valuable information for focusing innovation efforts on areas having the highest influence on technology performance. The methods include the analysis of structural patterns in the wave energy system architecture and the identification of technical trade-offs and corresponding inventive principles. This final step of the methodology results in the identification of promising concepts worth exploring. Incorporating effective innovation strategies into wave energy development helps to manage system complexity, enhance the understanding of causality within the system, and channel innovation toward useful improvements. It substitutes the conventional trial-anderror method based on expert judgement and engineering compromise. Moreover, it provides a predictable technique to deal with problems based on past knowledge and proven principles, bringing efficiency into the process. The practical implementation of this methodology to various illustrative cases of hypothetical wave energy systems, public reference models and state-of-the-art technologies has produced promising results. While the findings of this research do not focus on a specific concept that can deliver the necessary step change, the thesis provides a holistic and structured approach to assessing the potential of innovative archetypes. Furthermore, future work could expand and adapt this novel methodology for the assessment of wave energy options to other possible settings. RESUMEN El aumento de la proporción de electricidad generada a partir de fuentes renovables es clave para garantizar un sistema energético totalmente descarbonizado y luchar contra el cambio climático. La energía de las olas es un recurso abundante y potente, pero, al mismo tiempo, es la menos desarrollada de todas las tecnologías renovables. Resulta desalentador que, a pesar de los considerables esfuerzos que los investigadores internacionales han realizado en las últimas décadas, las tecnologías de captación no hayan conseguido lograr la deseada convergencia de diseño para sustentar su futuro crecimiento comercial. Las metodologías convencionales centradas principalmente en evaluar la madurez de la tecnología han demostrado ser insuficientes para garantizar que las tecnologías undimotrices alcancen sus objetivos técnicos, económicos y sociales. Para cumplir con las altas expectativas del sector, esta investigación propone un enfoque sistemático desde el inicio del desarrollo de la tecnología que garantiza la trazabilidad de los requisitos, crea evaluaciones de desempeño objetivas y aplica estrategias de innovación sólidas para superar los retos pendientes. El marco de evaluación común se basa en los principios sólidos de la Ingeniería de Sistemas. Abarca el contexto externo, los requisitos del sistema y los criterios de evaluación. Este paso de la metodología crea una priorización de los diversos atributos de un sistema de energía undimotriz para la evaluación cualitativa de las tecnologías de energía de las olas. El análisis del contexto externo proporciona una comprensión de los factores que influyen en el desarrollo de dichas tecnologías y el impacto correspondiente en los requisitos del sistema. La identificación de la aplicación de mercado, los factores clave y los grupos de interés proporciona una base sólida para la evaluación objetiva de las tecnologías de energía de las olas frente a los requisitos de los sistemas. Este marco evita cualquier inconsistencia en la formulación de los requisitos del sistema y se puede aplicar a diferentes niveles de madurez tecnológica. Proporciona flexibilidad para adaptarlo a las condiciones del mercado o prioridades de las partes interesadas rápidamente cambiantes además de poderse extender para centrar el análisis en subsistemas específicos de energía de las olas. Asimismo, capta los aspectos cualitativos relacionados con las expectativas de los grupos de interés que métricas de alto nivel como el LCOE no pueden proporcionar. Por otro lado, el enfoque novedoso propuesto orienta las decisiones de diseño a lo largo del proceso de desarrollo para una adecuada gestión del riesgo y la incertidumbre. Para ello, la evaluación holística desarrollada a través de esta investigación comprende la evaluación en etapas intermedias de desarrollo y la proyección de costes futuros tras haber replicado suficientemente la tecnología. Este paso de la metodología facilita la selección y CONTENTS xvi 5.5 Conclusions ................................................................................................................... 125 CHAPTER 6 ESTIMATING FUTURE TECHNOLOGY COSTS ................................. 127 6.1 Overview ........................................................................................................................ 127 6.2 Methods and Tools ...................................................................................................... 128 6.2.1 Propagation of Error or Uncertainty ............................................................... 128 6.2.2 Technological Learning ...................................................................................... 129 6.3 Future Costs of Wave Energy .................................................................................... 131 6.3.1 Background .......................................................................................................... 131 6.3.2 Current Cost and Performance ........................................................................ 132 6.3.3 Cost Escalation..................................................................................................... 136 6.3.4 Projection of Future Costs ................................................................................. 138 6.4 Practical Implementation ........................................................................................... 139 6.4.1 Case Study: Reference Model 5 ......................................................................... 139 6.4.2 Cost and Performance of the 50-Unit Farm ................................................... 141 6.4.3 Cost Escalation to Account for Uncertainties ................................................ 142 6.4.4 Projecting the Future Cost of Mature Technology ....................................... 144 6.5 Conclusions ................................................................................................................... 146 CHAPTER 7 OVERCOMING THE CHALLENGES ...................................................... 149 7.1 Overview ........................................................................................................................ 149 7.2 Methods and Tools ...................................................................................................... 150 7.2.1 Design Structured Matrix (DSM) ..................................................................... 150 7.2.2 Theory of Inventive Problem Solving (TRIZ) ................................................ 151 7.3 Innovation Strategies ................................................................................................... 156 7.3.1 Background .......................................................................................................... 156 7.3.2 Design Structure of Wave Energy Systems ..................................................... 157 7.3.3 Establishing Priorities of TRIZ Inventive Principles .................................... 160 7.4 Practical Implementation ........................................................................................... 163 7.4.1 Learning from Failed Technologies ................................................................. 163 7.4.2 Promising Concepts Worth Exploring ............................................................ 171 7.5 Conclusions ................................................................................................................... 177 CHAPTER 8 OVERALL CONCLUSIONS ........................................................................ 179 8.1 Overview ........................................................................................................................ 179 CONTENTS xvii 8.2 Summary of Findings .................................................................................................. 179 8.2.1 Common framework of wave energy system requirements and metrics.. 180 8.2.2 A fair assessment of wave energy technology performance throughout the development process ........................................................................................................ 183 8.2.3 Innovation strategies to improve the cost-effectiveness of wave energy .. 186 8.3 Recommendations for Future Research .................................................................. 187 REFERENCES .............................................................................................................................. 189 APPENDICES .............................................................................................................................. 215 Appendix A: Survey of External Forces .............................................................................. 215 Appendix B: Prioritisation Matrices ................................................................................... 217 Appendix C: List of TRIZ 39 Technical Parameters ........................................................ 222 Appendix D: Contradiction Matrix .................................................................................... 225 Appendix E: List of TRIZ 40 Inventive Principles ............................................................ 226 Appendix F: RM5 Breakdowns ............................................................................................ 232 PUBLICATIONS ......................................................................................................................... 239 This page intentionally left blank xix FIGURES AND TABLES List of Figures Figure 1.1: The three perspectives of successful innovation (adapted from [7]). ................ 2 Figure 1.2: Summary of thesis structure. ..................................................................................... 8 Figure 2.1: Global distribution of annual mean wave power in kW/m [25]. ...................... 13 Figure 2.2: Global distribution of wave power seasonal variability [25]. ............................. 13 Figure 2.3: Milestones of wave energy development: Early history (1799-1970). .............. 14 Figure 2.4: Milestones of wave energy development: Age of Enlightenment (1970-1990). ........................................................................................................................................................... 15 Figure 2.5: Milestones of wave energy development: Contemporary age (1990-2020). ... 15 Figure 2.6: Classification according to the device location. ................................................... 16 Figure 2.7: Classification according to device orientation (adapted from [49])................. 17 Figure 2.8: Classification according to device working principle. ........................................ 18 Figure 2.9: Types of reaction points. .......................................................................................... 20 Figure 2.10: Alternative PTO Configurations. ......................................................................... 20 Figure 2.11: Domains of the design world (adapted from[84]). ............................................ 25 Figure 2.12: Representation of dependencies in a multiple-domain model (adapted from [87]). ................................................................................................................................................. 26 Figure 2.13: Requirements and Metrics in the Systems Engineering V-model. ................. 27 Figure 2.14: Technology Readiness Levels and IEC Stages. ................................................... 35 Figure 2.15: Evaluation Areas included in the Evaluation and Guidance Framework [142]. ........................................................................................................................................................... 37 Figure 2.16: Various system boundaries for a wave energy assessment. ............................. 38 Figure 2.17: Example of a hierarchy of wave energy metrics (adapted from [142]). ......... 39 Figure 2.18: Design freedom, knowledge and related costs (adapted from [64]). ............. 41 Figure 3.1: An example of a three-level decision hierarchy. .................................................. 48 Figure 3.2: The House of Quality. ............................................................................................... 50 Figure 3.3: The six dimensions of PESTLE analysis. ............................................................... 55 FIGURES AND TABLES xx Figure 3.4: Wave energy stakeholder groups. ........................................................................... 57 Figure 3.5: Key Drivers for Utility-scale Generation ............................................................... 61 Figure 3.6: Key Drivers for Remote Community Generation ................................................ 62 Figure 3.7: Political concerns for the wave energy stakeholders ........................................... 63 Figure 3.8: Economic Concerns for the Wave Energy Stakeholders. ................................... 64 Figure 3.9: Social Concerns for the Wave Energy Stakeholders. ........................................... 65 Figure 3.10: Technological Concerns for the Wave Energy Stakeholders. .......................... 66 Figure 3.11: Legal Concerns for the Wave Energy Stakeholders. .......................................... 67 Figure 3.12: Environmental Concerns for the Wave Energy Stakeholders. ........................ 68 Figure 3.13: Relative importance of SD for the application market. .................................... 69 Figure 3.14: Relative importance of SH for the application market. .................................... 72 Figure 4.1: Wave Energy System, External Systems and Context (adapted from [62]). ... 76 Figure 4.2: Octopus diagram. ....................................................................................................... 77 Figure 4.3: Function hierarchy in a FAST diagram ................................................................. 78 Figure 4.4: Degrees of simultaneity/replaceability of logic operators (adapted from [159]). ........................................................................................................................................................... 79 Figure 4.5: Approach to building Wave Energy System Requirements. .............................. 81 Figure 4.6: Aggregation of MOE. ................................................................................................ 84 Figure 4.7: Fundamental relationship between the CF and the wave energy level (adapted from [217], Supplemental Information). ................................................................................... 85 Figure 4.8: Lifecycle of the wave energy system and entities. ................................................ 86 Figure 4.9: Octopus diagram for the operation phase (a) and rest of phases (b). .............. 87 Figure 4.10: FAST diagram for the Wave Energy System....................................................... 88 Figure 4.11: Aggregation of MOP. .............................................................................................. 90 Figure 4.12: Illustrative relationship between the LF and efficiency. ................................... 93 Figure 4.13: Strategies to minimise failures (adapted from [228])........................................ 94 Figure 4.14: Aggregation of TPM. ............................................................................................... 95 Figure 4.15: Relative importance of SRs for the application market. ................................... 97 Figure 4.16: Sensitivity of Global Merit to MOE Utility for each market application: (a) Utility-scale generation; (b) Powering remote communities. ................................................ 98 Figure 4.17: Relative importance of FR for the application market. ..................................... 99 Figure 4.18: MOE Sensitivity for Utility-scale Generation: (a) Convert wave energy; (b) Operate when needed; (c) Reduce upfront costs; (d) Prevent business risks. .................. 100 FIGURES AND TABLES xxi Figure 4.19: Sensitivity of Global Merit to MOP Utility for each market application: (a) Utility-scale generation; (b) Powering remote communities. ............................................. 101 Figure 4.20: Relative importance of DPs for the application market. ................................ 102 Figure 5.1: Maximisation (a) and minimisation (b) value functions. ................................ 107 Figure 5.2: Saturation (a) and constraint (b) value functions. ............................................. 108 Figure 5.3: Optimisation (a) and avoidance (b) value functions. ........................................ 109 Figure 5.4: Deriving threshold metrics from benchmark data ............................................ 109 Figure 5.5: Metric exhibiting a decreasing performance behaviour (lower is better). .... 113 Figure 5.6: Metric exhibiting an increasing performance behaviour (higher is better). . 113 Figure 5.7: Commercial Attractiveness (CA). ......................................................................... 115 Figure 5.8: Technical Achievability (TA). ............................................................................... 116 Figure 5.9: Cone of uncertainty and DD levels....................................................................... 117 Figure 5.10: Value functions for the six benchmark cases.................................................... 120 Figure 6.1: Probability representation of uncertainty and main statistical properties. ... 129 Figure 6.2: Cost reduction pathways with cumulative experience at three different Learning Rates (5%, 10% and 20%) .......................................................................................... 130 Figure 6.3: The proposed 3-step approach for estimating the future cost of an emerging wave energy technology at different stages of technology development, with an illustrative LCOE estimate and uncertainty at each stage. ....................................................................... 131 Figure 6.4: Standard cost and performance breakdown for an illustrative commercial project (adapted from [156], [227], [257], [258]). ................................................................. 132 Figure 6.5: Schematic of the RM5 floating OWSC. ............................................................... 140 Figure 6.6: 50-unit farm array layout (not drawn to scale). ................................................. 140 Figure 6.7: Breakdown of costs for the RM5 farm. Left: percentage of total lifetime costs; Right: distribution of OPEX costs. ............................................................................................ 142 Figure 6.8: Uncertainties of the high-level components in the LCOE equation. Note that LCOE uncertainty is propagated and not simply added. ..................................................... 144 Figure 6.9: Learning Rates (LR) of the high-level components in the LCOE equation. Note resulting LR for the LCOE is propagated and not simply added. ....................................... 145 Figure 6.10: Emerging technology cost trajectories with three distinct levels of uncertainty (U) and learning capacity (L). The numbers ①②③ relate to methodological steps, depicted in Figure 6.3. ................................................................................................................. 147 Figure 7.1: Example of system graph (left) and corresponding DSM (right). .................. 150 Figure 7.2: Types of interconnections and corresponding DSM representations............ 151 FIGURES AND TABLES xxii Figure 7.3: The TRIZ approach to problem-solving. ............................................................. 152 Figure 7.4: Finding contradictions in the matrix. .................................................................. 154 Figure 7.5: Hierarchical representation of the wave energy system.................................... 158 Figure 7.6: Block diagram of the wave energy system with interactions ........................... 158 Figure 7.7: Pelamis P2 device, pictured at the European Marine Energy Centre, 2011. . 163 Figure 7.8: Block diagram for the 3 MW Pelamis P2 farm. .................................................. 164 Figure 7.9: Mocean Blue X testing (left) and Blue Horizon artistic impression (right). . 165 Figure 7.10: Block diagram for the 3 MW Blue Horizon 250 farm..................................... 165 Figure 7.11: WaveBob 1:4 device tests (left) and full-scaled design (right). ...................... 167 Figure 7.12: Block diagram for the 3 MW WaveBob farm. .................................................. 168 Figure 7.13: CorPower C4 hull (left) and schematic (right). ................................................ 169 Figure 7.14: Block diagram for the 3 MW CorPower C4 farm. ........................................... 169 Figure 7.15: Pneumatics or hydraulics and corresponding inventive operators. ............. 172 Figure 7.16: Use of pneumatics or hydraulics (a) Onshore OWC device [304]; (b) NoviOcean device [303].............................................................................................................. 172 Figure 7.17: Dynamism and corresponding inventive operators. ....................................... 174 Figure 7.18: Replace the working principle and corresponding inventive operators. ..... 174 Figure 7.19: Dynamism and replacing working principle (a) NREL’s FlexWEC [306] ; (b) PNNL’s FMC-TENG device [309]. ........................................................................................... 175 Figure 7.20: Change properties and corresponding inventive operators. .......................... 176 Figure 7.21: Change properties (a) NREL’s Variable Geometry OSWC [311]; (b) WEPTOS [312]; (c) CorPower C4 [37]. ..................................................................................................... 177 Figure 7.22: Preliminary action and corresponding inventive operators. ......................... 177 FIGURES AND TABLES xxiii List of Tables Table 2.1: System breakdown for wave energy technologies. ................................................ 19 Table 2.2: Design domains according to different authors. ................................................... 24 Table 2.3: Estimation of the assessment effort. ......................................................................... 42 Table 3.1: Gradation scale for pairwise comparisons [102]. .................................................. 49 Table 3.2: Random Index, RI [102]............................................................................................. 49 Table 3.3: Application market characterisation. ...................................................................... 54 Table 3.4: Wave energy drivers. .................................................................................................. 55 Table 3.5: Wave energy stakeholders. ........................................................................................ 58 Table 3.6: Importance rating scale [204]. .................................................................................. 72 Table 4.1: Special cases for weighted power mean m=2 [210] ............................................... 80 Table 4.2: Generalised conjunction-disjunction. Values of d [159] ..................................... 80 Table 4.3: Stakeholder roles and expectations. ......................................................................... 82 Table 4.4: Stakeholder Requirements and Metrics. ................................................................. 83 Table 4.5: Functional Requirements and Metrics. ................................................................... 89 Table 4.6: Technical Requirements and Metrics. ..................................................................... 92 Table 4.7: IEC Technology Classes [226]. ................................................................................. 93 Table 4.8: Mapping of Technical Requirements (TRs) to Design Parameters (DPs). ....... 96 Table 4.9: Ranking of Stakeholder Requirements (SRs). ........................................................ 97 Table 4.10: Ranking of Functional Requirements (FRs). ........................................................ 99 Table 4.11: Ranking of Design Parameters (DPs). ................................................................. 103 Table 5.1: Technical Difficulty (adapted from [249])............................................................ 116 Table 5.2: Illustrative benchmark cases. .................................................................................. 118 Table 5.3: Stakeholder Requirements and Utility. ................................................................. 119 Table 5.4: Qualitative assessment of MOE. ............................................................................. 121 Table 5.5: Global Merit (GM) of wave energy option for the application markets. ........ 121 Table 5.6: Wave energy attractiveness. .................................................................................... 123 Table 5.7: TA for the MOE in the utility-scale generation market. .................................... 124 Table 5.8: TA for the MOP in the utility-scale generation market. .................................... 124 Table 5.9: Degree of difficulty factors for FR. ......................................................................... 125 FIGURES AND TABLES xxiv Table 6.1: Suggested contingencies and lognormal properties of uncertainty ranges normalised by mode (adapted from [270]). ............................................................................ 137 Table 6.2: Case study specifications .......................................................................................... 141 Table 6.3: Uncertainty categories, associated standard deviation and 80% confidence intervals. ......................................................................................................................................... 143 Table 6.4: Component-based LR, uncertainty and standard deviation ............................. 145 Table 7.1: TRIZ 39 Technical Parameters ............................................................................... 153 Table 7.2: TRIZ Separation Principles and Inventive Principles......................................... 154 Table 7.3: TRIZ 40 Inventive Principles .................................................................................. 155 Table 7.4: DSM model for the block diagram from Figure 7.6. ........................................... 159 Table 7.5: Top-10 inventive principles for the utility market – improving a positive feature. ......................................................................................................................................................... 161 Table 7.6: Top-10 inventive principles for the utility market – minimising the impact of a worsening feature. ........................................................................................................................ 162 Table 7.7: Top-10 inventive principles for the utility market – both objectives. .............. 162 Table 7.8: DSM model for the 3 MW Pelamis P2 farm ......................................................... 164 Table 7.9: DSM model for the 3 MW Blue Horizon 250 farm ............................................. 166 Table 7.10: DSM model for the 3 MW WaveBob farm ......................................................... 168 Table 7.11: DSM model for the 3 MW CorPower C4 farm .................................................. 170 Table 7.12: Summary of Complexity Scores. .......................................................................... 170 Table 7.13: Impact of the DP conflicts (blue=high; red=low). ............................................ 171 Table 8.1: Summary of the Novel Methodology. .................................................................... 180 Table A.1: System Drivers for Utility-scale Generation. ....................................................... 217 Table A.2: System Drivers for Remote Community Generation. ....................................... 217 Table A.3: System Drivers to Stakeholders (SHs) for Utility-scale Generation. ............... 218 Table A.4: System Drivers to Stakeholders (SHs) for Remote Community Generation. 218 Table A.5: Stakeholders to Stakeholder Requirements (SRs) for Utility-scale Generation. ......................................................................................................................................................... 219 Table A.6: Stakeholders to Stakeholder Requirements (SRs) for Remote Community Generation. .................................................................................................................................... 219 Table A.7: Stakeholder Requirements to Functional Requirements (FRs) for Utility-scale Generation. .................................................................................................................................... 220 Table A.8: Stakeholder Requirements to Functional Requirements (FR) for Remote Community Generation. ............................................................................................................ 220 FIGURES AND TABLES xxv Table A.9: Functional Requirements to Design Parameters (DPs) for Utility-scale Generation. ................................................................................................................................... 221 Table A.10: Functional Requirements to Design Parameters (DPs) for Remote Community Generation. ............................................................................................................ 221 Table A.11: List of TRIZ 39 Technical Parameters. ............................................................... 222 Table A.12: Contradiction Matrix. ........................................................................................... 225 Table A.13: List of TRIZ 40 Inventive Principles. .................................................................. 226 Table A.14: Detailed Breakdown of Cost and Performance (adapted from [282]) ......... 232 Table A.15: Propagation of Uncertainties and Corresponding Costs. ............................... 234 Table A.16: Component-based Learning and Future Cost Projections. ............................ 236 NOMENCLATURE xxxii n i No. of installation trips per device n s No. of service trips per device O Plant operator p shape factor of the value function P Rated power r Discount rate r ij QFD relationship matrix coefficients s i Suitability S Area of the hydrodynamic object t Tolerance of the value function t c Cycle time t l Logistic time t t Travel time t w Waiting time T Target performance U Uncertainty level v(x) Value function w Importance weightings W Ocean waves W k+ Weightings for the IPs when the aim is to improve a positive feature W kWeightings for the IPs when the aim is to improve a worsening feature X Cumulative experience y Project lifetime Y Future cost of the technology NOMENCLATURE xxxiii Greek Letters δ Uncertainty λ Failure rate λ max Maximum eigenvalue of the judgement matrix in AHP µ Mean η d Delivery Efficiency η t Transformation Efficiency ρ Repair rate σ Standard deviation ω Wave Frequency This page intentionally left blank 1 CHAPTER 1 INTRODUCTION 1.1 Overview This chapter begins with an introduction to the social, technical and commercial landscape and the underlying challenges that motivate this research (section 1.2). The research goal and objectives are described in section 1.3. Section 1.4 summarises the main contributions of the thesis. Finally, the thesis structure is presented in section 1.5. 1.2 Motivation and Problem Statement Nations all over the world are setting ambitious decarbonisation targets as a means to fight climate change [1]. However, despite the need to increase the share of electricity generation from renewable sources, ocean energy, and particularly wave energy, remains a largely untapped resource [2]. Wave energy is abundant, predictable, widely distributed and indigenous for many populations living in coastal areas [3]. Together with tidal stream, wave energy has the potential to satisfy up to 10% of the global electricity demand by 2050 [4]. All in all, the path to developing effective wave energy technologies has been poised with many challenges [5]. Designing wave energy technologies is a long and intricate process implying many decisions. In an early stage, multiple design parameters must be assigned, which significantly influence its ultimate cost and performance expectations [6]. Failure of the wave energy sector to meet those expectations has more than once delayed the industrial development of wave energy [3]. Hence, the engineering challenge is to create robust devices that harness wave energy efficiently, reliably and cost-effectively while also surviving the roughest seas. For this purpose, the technology development process should gradually replace initial assumptions with knowledge since these uncertainties represent a significant risk. Furthermore, any successful innovation must contain three essential features, namely social desirability, technical feasibility and commercial viability [7]. Although these “If you cannot make knowledge your servant, make it your friend” Baltasar Gracián (1601 – 1658) INTRODUCTION 2 criteria may not be developed simultaneously, all must be present incidentally to ensure a thriving business. Social desirability explores whether the innovation will meet real user needs, in other words, if we are solving the right problem. On the other hand, the technical feasibility and commercial viability investigate our capability to deliver the innovation and its profitability in the market respectively, that is, if we are solving the problem right. At the intersection of the three lenses lies the optimum design space for successful innovation. Figure 1.1: The three perspectives of successful innovation (adapted from [7]). Right now, social desirability is quite favourable for wave energy. The transition to a sustainable and resilient carbon-neutral economy is no longer a political decision but an ample social demand. With world energy consumption estimated to rise considerably over the next decades, international instability (e.g. Ukraine war) and high energy prices, increasing security of supply and reducing fossil fuel dependence are becoming powerful drivers [8]. Wave energy can play a broad role in attaining UN Sustainable Development Goals [9] by providing affordable and clean energy (Goal 7), creating jobs in coastal regions (Goal 8), promoting energy security (Goal 9), reducing CO 2 (Goal 13) and protecting ecosystems (Goal 14). Additionally, the need for a vigorous forward-looking recovery from the harm inflicted by Covid-19 may revive interest in wave energy development [10]. Lastly, with a high penetration of renewable energies in the energy system, wave energy can provide significant value in balancing the grid due to its complementarity to other renewable energy sources such as wind and solar [4]. Many wave energy concepts have been developed over the last 30 years. Various technologies are in different development stages, but none have achieved commercial readiness [11]. The great diversity of archetypes can explain why the maturity of wave energy technologies is still relatively low. However, the limited number of technologies deployed in the water has shown harnessing wave energy is technically feasible [12]. Due INTRODUCTION 3 to the wide variety of wave energy technologies and the strong dependence of their performance on the sea conditions in which they are tested, it is extremely difficult to objectively assess the relative merits of the markedly different designs. The ocean is a ruthless environment wherein technologies must demonstrate long-term reliable performance to compete with more mature alternatives. At present, commercial viability is the main missing innovation factor for wave energy technology success. The business case of wave energy is made upon the cost of producing energy. To demonstrate an attractive business case proposition, wave energy technology developers are expected to gather significant evidence. This is especially challenging since the development process for wave energy technology is particularly costly and lengthy [13]. To achieve system cost and performance requirements, early technological development is essential. According to several experts, the conceptual design phase determines around 70–80% of the product lifecycle costs [14] [15] [16]. The logical conclusion is that decisions made during the early stages of product development are far more important than those made later on. Too little time spent on conceptual design can result in a lack of understanding of the problem's requirements and an insufficient ability to generate novel concepts. This might result in a design being developed that cannot perform well enough to be a viable commercial solution, wasting time and resources [17]. It is disappointing that many wave energy companies have moved through the technology readiness levels, reaching the pre-commercial scale, just to realise they fail to meet their targets. Therefore, it is highly advisable to have clear guidance on the potential of wave energy technologies from the early stages of design. Common methodologies mainly focused on assessing technology maturity have proved inadequate to ensure wave energy technologies reach their technical, economic and social goals. Hence, a rigorous development process of wave energy technologies is needed to help regain investor confidence, improve the social perception of the sector’s potential and provide compelling evidence to drive technical decisions. Many industrial sectors (e.g. automotive, aerospace, and oil & gas) have successfully applied Systems Engineering methods to develop innovative products meeting very diverse and demanding customer needs. For instance, Muller and Falk [18] illustrate the contribution of Systems Engineering to oil & gas with concrete case studies from subsea production. Discouragingly, their application in wave energy is still limited and fragmented. INTRODUCTION 4 1.3 Research Objectives The ultimate research goal of this thesis is to develop a novel methodology for the holistic assessment of wave energy systems from the early stages of technology development based on the application of sound Systems Engineering principles. This systematic design approach aims to: 1. Build a common framework that ensures traceability and consistency of wave energy system requirements and metrics. 2. Create fair performance assessments of wave energy technologies to objectively guide design decisions throughout the development process. 3. Apply sound innovation strategies to suggest promising concepts that can improve the cost-effectiveness of wave energy technologies. The research goal will be achieved through the following specific objectives: • Review the existing methods applicable to the specification and assessment of wave energy technologies. • Analyse the external forces influencing decisions related to the conception, development and operation of wave energy systems. • Propose a standard set of stakeholder, functional and technical requirements for wave energy systems. • Guarantee the traceability of system requirements throughout the entire wave energy design process. • Establish a hierarchy of metrics and corresponding aggregation methods. • Develop value functions to facilitate the qualitative assessment of wave energy. • Allocate design targets and uncertainty ranges to benchmark wave energy technology performance along the intermediate development stages. • Improve the accuracy, consistency, and usefulness of projected cost predictions for emerging wave energy technologies. • Visualise potential problems in the functional allocation of wave energy system capabilities to the physical embodiment. • Identify the most impactful trade-offs for wave energy systems and corresponding inventive principles. • Implement the novel approach in a performance assessment and innovation tool developed in Excel. • Apply this assessment methodology to illustrative cases of hypothetical wave energy systems, public reference models and state-of-the-art technologies. INTRODUCTION 5 1.4 Contributions The main contributions of this thesis are outlined below. 1.4.1 Analysis of external forces influencing the development of wave energy technologies Understanding wave energy requirements is critical to the creation of any successful technology. However, wave energy development cannot be separated from the larger context in which the technology is intended to operate, because multiple external forces influence its conception, development, and operation. The two fundamental elements that constitute this broad environment are external drivers and stakeholder groups. External drivers are closely related to the intended market use, whereas stakeholder groups express technological performance expectations. Each intended market application may call for a different combination of external drivers. In turn, ranking those external drivers to each stakeholder group will ultimately dictate the importance of the wave energy requirements. External drivers are identified and ranked for two market applications based on the Analytic Hierarchy Process (AHP) method. Similarly, wave energy stakeholders are elicited and prioritised regarding the external drivers using a Quality Function Deployment (QFD) approach for the same application markets. This ranking can be easily customised to local contexts and expanded to new wave energy application markets. 1.4.2 Hierarchical formulation of wave energy requirements and metrics The formulation of requirements aims to create a systematic overview of the purposes underpinning the search for solutions. Requirements that bind a solution space are hierarchical and interrelated. Initially, the wave energy specification includes all necessary and prioritised Stakeholder Requirements (SRs) that are compatible with the technical, financial and risk constraints. Upon completion, the next step is to define the Functional Requirements (FRs). FRs define what the system must do to achieve the SRs without addressing how the system should accomplish them. Last but not least, Technical Requirements (TRs) specify the issues related to the technology needed for the successful implementation of the system in physical components. Systems engineering is driven by the need to satisfy requirements. Thus, for Systems Engineering to be successful, evaluating and validating those requirements is equally crucial. Verification and validation are processes based on evidence used to evaluate if a system fulfils the specification of requirements. They rely on metrics and data. A QFD INTRODUCTION 6 approach has been used to collect and rank stakeholder, functional, and technical requirements common to wave energy market applications. This framework guarantees the seamless traceability of design information for each stage of the design process together with a three-level hierarchy of metrics. 1.4.3 Assessment of wave energy technology performance The result of the performance evaluation for a wave energy concept offers an estimate of how close or distant the technology is from reaching its techno-economic objectives. It is essential to understand that most estimates of wave energy are based on projected data. The assessment method introduces risks due to the reliance upon projected figures, which can be significant depending on the stage of technological progress, the amount of innovation, the quality of the assumptions, and the evaluation detail. The projected accuracy of the estimations will increase as development proceeds, resulting in a decrease in the uncertainty range. A qualitative assessment of the Global Merit of a wave energy technology is enabled by an aggregation method of metrics based on the Logical Scoring of Preference (LSP) theory. Besides, two new concepts are introduced for performance benchmarking of wave energy technologies. Commercial Attractiveness (CA) enables not only the selection of the best wave energy alternative for a certain market application but also the comparison of technologies across various market applications. The concept of Technical Achievability (TA) provides a method to assess the ability of technologies under development to achieve the system requirements, based on the unmet performance and the Degree of Difficulty (DD). The DD is defined by technology maturity and fundamental limits. 1.4.4 Method to project future costs of emerging wave energy technologies Direct LCOE computation is highly inappropriate for prototype technologies. Assessing the affordability of emerging technologies needs a future projection of costs with a reference to the mature technology and a first-of-a-kind commercial deployment. Starting from the current breakdown of wave energy costs, the suggested approach allocates uncertainty bands depending on the estimation accuracy used to determine the first-of-a-kind cost of the commercial technology. After installing a certain capacity through several commercial projects, component-based learning rates are then used to estimate the LCOE of the mature technology. This method counters the human propensity to over-optimism in preliminary estimates, which produces highly unrealistic LCOE values for commercial technology. It offers a tool that may be used to investigate uncertainties, concentrate efforts on the accuracy of cost projections, and identify any lessons that might have been learned during the early stages of technological development. INTRODUCTION 7 Statistical propagation of uncertainties is achieved by combining uncertainties from multiple sources into the final LCOE metric. Besides, a disaggregated technique is utilised to consider individual learning effects at the component level, resulting in more accurate cost reduction estimates for technologies under development that lack historical data. 1.4.5 Innovation strategies to overcome technical challenges The development of cost-effective wave energy systems is a difficult endeavour due to the size of the solution space, which calls for innovative technologies or designs. Many technical challenges remain unresolved and incremental innovation alone cannot fill the gap between the current techno-economic estimates and the medium-term policy targets established for wave energy. A standard representation of wave energy subsystems and their interfaces is presented based on the Design Structured Matrix (DSM) method. DSM is a tool to support the wave energy system improvement, helping visualise potential problems that can lead to major changes in later phases, longer integration time, and greater uncertainties and risks. Structured innovation methods are applied to point out potential innovation strategies. The TRIZ problem-solving approach has permitted the identification of the most impactful trade-offs and corresponding inventive principles having the greatest impact on the initial Stakeholder Requirements (SRs). Inventive principles suggested can be used to overcome the main technology showstoppers and recurrent challenges. 1.5 Thesis Structure The remainder of the thesis is structured into seven chapters to address the research goal and objectives, as shown in Figure 1.2. The following descriptions briefly outline the content of each chapter. STATE OF THE ART 14 more than 3,000 applications have been filed worldwide, and this number has not yet stopped growing. The European Marine Energy Centre (EMEC) lists 256 concepts on its website [29]. Besides, the ELBE project identified 87 companies in 2021, 60% still in the early phase of development [30]. One reason for the large diversity of concepts is the wave energy resource’s high temporal and geographical variability. The history of wave energy has undergone a cyclic process of optimism, setback and reassessment [5]. In the early years of wave energy development, many concepts were proposed. Progress was slow and inconsistent as inventors lacked a complete understanding of the complex hydrodynamic interactions. Figure 2.3 illustrates the main milestones from this early period, which ended with the first commercial application of wave energy, a navigation buoy from Japanese commander Yoshio Masuda, considered the father of modern wave energy technology [31]. Figure 2.3: Milestones of wave energy development: Early history (1799-1970). The oil crisis of 1973 triggered a significant change in the renewable energy scenario, drawing attention to wave energy. A scientific paper published in 1974 by Stephen Salter [32] became a landmark for the research community. This was the time for the first pioneers of the hydrodynamic theory and maximum power absorption, the first National funded concepts and the first scientific conferences. In this period, many concepts of wave energy technologies were proposed whose design sought to maximise the annual power generation. Figure 2.4 illustrates the main milestones from this period. STATE OF THE ART 15 Figure 2.4: Milestones of wave energy development: Age of Enlightenment (1970-1990). More recently, concerns about climate change, the security of energy supply and an increase in energy prices renewed the interest in other renewable sources, and more precisely in wave energy. The available R&D funding in this period increased steadily from the first preliminary actions started in 1991 to the most recent programmes. Fruit of this European and National support, a wealth of prototypes was developed, and a small portion was demonstrated at sea. Survivability concerns mainly drove the design of wave energy technologies. International conferences, cooperation and standardisation facilitated sharing of good practices and promoted consensus in the sector. Figure 2.5 illustrates the main milestones from the contemporary age. Despite the considerable efforts, the only grid-connected project is the Mutriku Wave Power Plant, which has been continuously operating since 2011 and delivered more than 2.7 GWh. Figure 2.5: Milestones of wave energy development: Contemporary age (1990-2020). STATE OF THE ART 16 Despite the increased efforts over the last decades, harnessing wave energy continues to fox the best engineering minds. Failure of the wave energy industry to deliver on the initial expectations of investors has once and again delayed its commercial-scale development [3]. Although technologies have not reached full maturity, there is still significant activity in wave energy development around the world, including in the United Kingdom, Europe, the United States, Australia, Japan, China, and India. Further R&D is needed to explore and identify the best solutions and to achieve convergence in design. 2.2.3 Technology Classification The design of effective wave energy devices is a complex endeavour that brings into play a large set of decisions. Many design parameters, such as the size and deployment position or the extraction principle, must be selected at an early stage. Even though wave harnessing concepts are so diverse, technologies can be classified according to three main criteria: device location, orientation and working principle. To begin with, the classification based on the device location and distance to the coast distinguishes among three generations of devices (see Figure 2.6). This classification was adopted by the European thematic network WaveNet [33]. • Onshore (first generation). Devices which are fixed to or embedded in shorelines, from where the electricity is easily transmitted. These are less energetic locations due to energy loss as the waves reach the shore. Examples include Mutriku [34] and SSG [35]. • Nearshore (second generation). Floating or bottom-mounted devices installed in shallow waters (10-40 m). Devices must be placed beyond the breaker zone to avoid any survivability issues. Performance might be sensitive to tidal range. Examples include WaveRoller [36] and CorPower C4 [37]. • Offshore (third generation). Floating or submerged devices deployed in deep waters. They benefit from the much larger energy resource but also imply higher costs of seakeeping and energy transmission to shore. Examples include Mocean [38] and SBM S3 [39]. Figure 2.6: Classification according to the device location. STATE OF THE ART 17 The second classification considers the device size and orientation concerning the dominant direction of the incident wavefront (see Figure 2.7). This classification originated in the work by Budal and Falnes in 1975 and was later extended by Falnes and Hals [40]. • Terminator (T). A device with has a larger dimension in the direction across the predominant wave crests. The main dimension is larger than one wavelength. Examples include Wave Dragon [41] and CycWEC [42]. • Attenuator (A). A device with a larger dimension aligned with the direction of the predominant wave propagation. Examples include Pelamis [43] and Anaconda [44]. • Point Absorber (PA). A device with small dimensions relative to the incident wavelength and able to absorb energy from all directions. Examples include OPTPB3 [45] and AWS [46]. • Quasi Point Absorber (QPA). An axisymmetric device with relatively large dimensions compared with the wavelength. The primary dimension is between a PA and a Line Absorber (i.e. the aggrupation of T & A). Examples include OE Buoy [47] and Wello [48]. Figure 2.7: Classification according to device orientation (adapted from [49]). The great diversity of concepts has motivated a third classification of devices. This time, devices are classified according to their working principle. The nine groupings are based on recent classification efforts of [2], [3], [50] and [51]. • Oscillating Water Column (see Figure 2.8-a): Partially submerged structures open below the sea level and with air trapped above the water surface. Incoming waves make oscillate the water surface within the device, moving the air like a piston. Examples include Mutriku [34] and OE Buoy [47]. • Hinged Contour (see Figure 2.8-b): Devices with two or more separate bodies that move relative to each other as a wave passes them. Energy is extracted from STATE OF THE ART 18 the reaction between the individual components. Examples include Pelamis [43] and Mocean [38]. • Buoyancy (see Figure 2.8-c): Energy is extracted from the motion induced as waves pass the relatively small buoyant bodies. Examples include OPT-PB3 [45] and CorPower C4 [37]. • Oscillating Wave Surge (see Figure 2.8-d): Devices which extract energy from wave surges and the movement of water particles within them. Examples include WaveRoller [36] and WavePiston [52]. • Overtopping (see Figure 2.8-e): Devices which are essentially reservoirs that waves fill with water. The water is then returned to the sea via a turbine. Examples include SSG [35] and Wave Dragon [41]. • Submerged Pressure Differential (see Figure 2.8-f): Submerged devices in which a pressure differential is created as the wave passes above due to the sea level fluctuation. The alternating pressure is used to generate energy. Examples include AWS [46] and mWave [53]. • Bulge Wave (see Figure 2.8-g): Submerged tubular devices filled with pressurised seawater and moored to the seabed. The passing wave causes pressure variations creating a bulge that travels along the length of the tube and is used to generate energy. Examples include SBM S3 [39] and Anaconda [44]. • Inertia (see Figure 2.8-h): Devices that use the motion of the waves to rotate, swing or precess an inertial mass. Examples include Wello [48] and ISWEC [54]. • Lift Force (see Figure 2.8-i): The passing waves produce lift on a hydrofoil creating a torque at the main shaft of rotation. Examples include CycWEC [42] and LiftWEC [55]. Figure 2.8: Classification according to device working principle. STATE OF THE ART 19 2.2.4 Main Subsystems An overview of the key subsystems that require consideration for wave energy systems is provided in [56], [57] and [58]. According to these sources, the WECs can be grouped into five main subsystems, Reaction System, Power Take-Off, Hydrodynamic System, Power Transmission and Control, leading to many combinations. The large variety of wave energy concepts makes it challenging to analyse all possible decompositions and to produce a generic and manageable system breakdown. Thus, the standard approach adopted in the sector is to define the high-level breakdown concerning the various functions the device must fulfil. The taxonomy of subsystems described below is mainly derived from [56] and [59]. Table 2.1: System breakdown for wave energy technologies. Function Subsystems Capture energy Hydrodynamic System (HS) Provide reaction point Reaction Body (RB) Convert energy Power Take-Off (PTO) Store and condition energy Storage and Power Conditioning (SC) Deliver energy Transmission System (TS) Maintain position Station Keeping (SK) Control operation Instrumentation and Control (IC) Hydrodynamic System (HS). This term describes the device structure and mechanisms directly interacting with the waves, which can be either floating or submerged. It is therefore the primary wave absorption system. The HS is connected to the RB and the PTO for the active transfer of forces and motions. Reaction Body (RB). It is the structure that provides a reaction point for the PTO and/or support for the HS. Three main reaction types can be identified (see Figure 2.9): • Fixed reference: A static coupling to the Seabed or a dynamic one through the SK. In the latter, the RB has a large mass to emulate a fixed reference avoiding the need to adjust to the tidal range. • Self-reference: In this case, the HS reacts against another HS without needing a physical RB. • Inertial reference: The RB is somehow encapsulated within the HS and reacts against it. Examples are a pendulum, sliding or rotating mass, trapped water and gyroscope. The RB mass is smaller than that of the HS. STATE OF THE ART 20 Figure 2.9: Types of reaction points. Power Take-Off (PTO). This system converts the mechanical energy extracted from the waves into a useful form, generally electricity. Several alternative configurations have been proposed involving a combination of fluid, mechanical and electrical power flows (see Figure 2.10). For each primary energy conversion stage, different commercial solutions exist (see [60], [61]): • Air Turbine: Wells turbine, Dennis-Auld turbine, Impulse turbine, Bi-radial turbine. • Hydro Turbine: Pelton turbine, Kascheme turbine, Francis turbine. • Hydraulic System: Hydraulic ram, Hydraulic pump. • Mechanical Transmission: Gearbox drive, Rack and pinion drive, Ball screw drive. • Direct Drive: Linear generator, Ball screw generator, Electroactive polymers, Triboelectric nanogenerators (TENGs). Figure 2.10: Alternative PTO Configurations. STATE OF THE ART 21 Storage and Power Conditioning (SC). The instantaneous wave power absorbed by the PTO fluctuates broadly between individual waves and wave groups. When present, this optional subsystem aims to avoid excessive peaks, allow a smooth output and improve the power quality. Depending on the WEC configuration, it can be placed at different points of the transformation chain (see Figure 2.10) and can make use of either fluid power (Accumulator), mechanical power (Flywheel), electrical power (Battery, Capacitor, Inverter) or a combination of them. Transmission System (TS). This is the method by which energy is transferred to shore. It generally involves aggregation, export and grid connection. Although topologies vary, electricity transmission from individual devices to an onshore substation requires interarray cables connected to a collection point, which is likely to involve step-up transformation and isolation switchgear and an export cable. Station Keeping (SK). This system maintains the device in position relative to the seabed. It can be either rigid (foundation) or compliant (mooring). The former is more likely to be used nearshore (i.e. shallow water), whereas the latter is more appropriate for offshore locations (i.e. deep water). Mooring systems are comprised of one or more lines and an anchoring system. In turn, mooring lines can be slack, taut or combined. Instrumentation and Control (IC). Hardware and software systems to safeguard the device and optimise its performance under a range of operating conditions. They comprise sensors, data acquisition, communication, and data transfer equipment to implement control actions. 2.3 Systems Engineering 2.3.1 A Systematic Problem-solving Approach Systems Engineering (SE) has a relatively short history. The first documented use of this term dates to Bell Telephone Laboratories in the early 1940s [62]. Developed at Bell Labs in the following decade, SE was further refined during the successful NASA Apollo programme in the 1960s. Since then, it has evolved into a formal discipline that can be adapted to various types of product developments. SE uses a system thinking approach to analyse engineering problems. The individual outcome of such efforts is the engineered system. A system can be defined as an interacting combination of elements to accomplish a defined objective [63]. Fundamental to SE is the notion of the system life cycle [64]. The life cycle of a product begins with the identification of a need. It extends through conceptual and preliminary design, detailed design and development, manufacture and installation, operation and maintenance, decommissioning and finally disposal or recycling. STATE OF THE ART 22 The need for SE arises with the increase in the complexity of engineered systems. SE is a holistic, top-down approach to understanding stakeholder needs, exploring opportunities, documenting requirements, and synthesising, verifying, validating and evolving solutions while considering the complete problem [63]. The ambiguity in defining the requirements and the lack of proper planning are the major factors that drive the need for a SE approach [65]. SE hinges upon several fundamental principles. Among them, five of the most important ones are [66]: • Abstraction. SE is based on the idea that the purpose of design is not to produce a concrete solution but to create an abstract entity called a system. Such a system can then be materialised through several different solutions. • Decomposability. A system can be broken down into separate elements (modularisation) that may cover several layers (hierarchy). These elements have an integrative architecture. • Pluralism. The system can be addressed from complementary points of view, which must be organised in ways that permit the sharing of complex knowledge. • Alignment. SE concerns both the product and the way the design is organised. Developing a solution requires aligning design processes and product structure. • Incremental improvement. Design organisation is based on “routines” that can be codified, generalised, learned and re-cycled from one project or team to another. SE is about both design and decision-making [66]. The success of any complex engineering project depends upon four main activities: • Identifying and evaluating alternatives, • Managing uncertainty and risk, • Designing quality into a system, and • Dealing with project management issues. The first activity is critical as it defines the probability of success, whilst the rest help the engineer to avoid any errors. A Systems Engineer needs to understand that decisions must be made with the best information available at the time, and therefore there are always subject to some degree of uncertainty. SE approaches and methods have been successfully applied in many industrial sectors (e.g. automotive, aerospace, oil & gas) to develop innovative products meeting very diverse and demanding stakeholder requirements. Several standards have been developed for SE such as [67], [68], and [69]. Through the years, the initial practice-based SE has been enriched with a plethora of theoretical approaches, tools and models in different SE schools worldwide [70]. Among STATE OF THE ART 23 the many methodologies used, the SE approaches can be grouped into three categories according to their primary focus: • Generic design methodologies such as Systematic Design [16], [71], and Axiomatic Design [72]; • Process-oriented methodologies such as Concurrent Engineering [73] and Design Structure Matrix [74]; and finally • Design methodologies to achieve concrete goals such as DfX [75], QFD [76], FMEA [77] and TRIZ [78]. Abstract models are replacing the traditional document-based SE as the primary means of retaining and communicating information. Model-based Systems Engineering (MBSE) enhances the ability to capture, analyse, share, and manage the data associated with the specification of a product [63]. MBSE helps to identify issues early in the system definition, thus improving system quality and lowering both the risk and cost of system development. As introduced in CHAPTER 1, initial ideas or expectations about the engineering system are built on a relatively insecure information basis at an early stage [79]. Frequently, neither the problem nor the solution field is particularly well-known. Therefore, a systematic and well-structured process should underpin the search for solutions and selection. 2.3.2 The Concept of Design Domains The design of a new product is an endeavour that involves a mix of creativity, technical skills and decision-making. No matter where an innovative concept may come from, its realisation should always be the outcome of a thorough design process. To that purpose, organising the design information is critical. Design involves an interplay between what the engineer wants to achieve and how this need is satisfied. However, there is no single commonly acknowledged sequence of steps in engineering systems design. The concept of design domains helps systematise this process by creating boundary lines between different design activities [72]. Design domains provide engineers with an improved way of arranging design information to facilitate better SE [80]. They help to organise information on requirements and to discriminate it from the information associated with design solutions. The systematic presentation of information stimulates the search for solutions and facilitates identifying and combining essential solution characteristics [71]. Ultimately, this framework avoids quantum leaps from the initial requirements to the physical realisation that are ad hoc, inefficient, ineffective, and often lead to cost and schedule overruns [81]. Design domains structure information in particular ways to accommodate their own needs. Much attention should be paid to the consistency of information within and across domains. Each design domain has an associated model, which acts as a framework for STATE OF THE ART 30 included by combining utility analysis with SBD methods. To apply utility-based decisions in SBD, designers create a utility function that weights each concept’s attribute. Within each attribute, the concept is given an interval score. The interval score allows the designers to account for the span of possible values given the imprecision of conceptual design. Multi-criteria analysis methods inform the decision-making process for selecting solutions to complex engineering problems, mainly when alternative solutions can be heterogeneous. Many methods have been developed to solve different types of decision problems. However, the decision maker is faced with the arduous task of selecting an appropriate decision support tool [100]. One way to address this task is to look at the modelling effort (i.e. required input data) and the granularity of outcomes (i.e. feasible solution, partial or complete ranking). Multi-Attribute Utility Theory (MAUT) [101] is used at the highest modelling effort when a representation of the perceived utility for every selection criterion can be built. Analytical Hierarchy Process (AHP) methods [102] use pairwise comparisons between criteria and options at a medium scale of the modelling effort. Finally, at the lowest end of the modelling effort, Data Envelopment Analysis (DEA) [103] is mostly used for performance evaluation or benchmarking, where no subjective inputs are required. Solving a real problem using a linear approach is seldom achievable. The SE approach can be applied iteratively to move towards an acceptable solution to a problem within a larger cycle of stakeholder value [63]. The evaluation is repeated at increasing levels of technological maturity as the concept progresses from an initial idea to a thoroughly tested and proven system. This iterative risk-based analysis method for product development is formalised in SE through the spiral model [85] and the Stage-Gate model [104]. Over the years, SE has developed many tools and techniques for risk management, such as FMEA [77], FTA [105], Fuzzy Logic [106], Bayesian Analysis [107] and Monte Carlo simulation [108]. If a SE approach is established early in the project, the system metrics achieved at any stage are compared to the design goals and improvements implemented, if necessary, to achieve these goals. 2.3.6 Application of SE Methods to Wave Energy Wave energy technology is a clear example of a complex engineering product, whose development is inevitably multidisciplinary. So far, wave energy development experience shows that excellence in each discipline is a necessary but not sufficient condition to achieve a viable product. SE provides a framework for a holistic approach that might allow progress towards a successful wave energy technology [109]. The need for a more comprehensive systems perspective on the development of wave energy technologies was also highlighted in a recent workshop on identifying future emerging technologies in the ocean energy sector [60]. The report points out that some practical aspects neglected at an early stage can become a problem if taken up at a later stage. Therefore, technology developers should move from a sequential to a system design STATE OF THE ART 31 process. To overcome failures previously experienced in the sector, an integrated systems approach is required to develop wave energy systems; subsystems cannot be developed in isolation. Similarly, sector experts have recognised SE principles as a way to accelerate marine energy research [110]. Survey results recommended focusing on common components to enable affordable ways to harvest marine energy and not on specific technologies. Experts also suggested proving that a system works reliably, checking its functionality in the early project stages and consequently focusing on end-user requirements. As presented in section 2.2, wave energy technologies span a broad design space. The variety of concepts makes it extremely difficult to identify common design approaches. Moreover, there is little published work on the specific design methods used in developing these devices since most technology developers are private companies. Even though some companies seem aware of existing SE methods, it is a strikingly recent phenomenon (only documented in the last 10-year timeframe). Also, the application of SE might have been limited and fragmented, since these technology developers have not been free from suffering expensive, high-risk, slow, rigid and discontinued technology developments. A small fraction of references to activities carried out during the environmental analysis can be found in the literature: • Bull et al. [111] presented the context diagram used to define the external systems that directly influence the success of a grid-connected wave energy farm. This list identifies the factors that are out of the control of the external systems and the farm (i.e. political, social, and economic climate). It is pointed out that the overarching context can influence the external systems and the farm’s success. However, the SDs are not explicitly analysed. • Sandberg et al. [112] analysed the critical factors to the commercial viability of WECs in off-grid luxury resorts and small utilities using PESTLE tools and Porter’s five competitive forces. Factors like the available wave resource, distance from shore, existing infrastructure, power demand, supply chain logistics, alternative energy sources and current cost of energy were found to have significant impacts. • de Andres et al. [113] carried out a similar analysis to reveal the risks and uncertainties facing large-scale grid-connected wave and tidal energy projects. This work showed that although the political, economic and social aspects have great importance, the technological barriers are key to attracting investors. • PNNL and NREL are conducting a three-year project to review the grid value for marine energy development at scale on an intermediateto long-term horizon. Grid values are arranged into three categories: marine energy's spatial or locational aspects, temporal or timing factors, and specific applications to capture the most situational benefits [114]. STATE OF THE ART 32 • H2020 DTOceanPlus project presented a summary of non-technical barriers and enablers to wave and tidal stream commercialisation in its public deliverable D8.1 [115]. The factors listed from literature sources comprise private and public financing, insurance, continued cost reduction, supportive consenting and regulation, infrastructure, standards and certification, innovation, cross-sectoral interlinkages, and ethical and environmental concerns. Attributes that characterise the System Drivers (SDs) are fairly covered for wave energy, but unfortunately, there is no reference to how these SDs interact with each other and are prioritised. Regarding the stakeholder analysis, the review of the literature reveals very diverse classifications of stakeholders for marine energy projects, such as: • Isakhanyan and Wilt [116] identify six main stakeholder groups, namely Designers & developers, Governments & public authorities, Partner companies, Financial institutions, Knowledge institutes, and Environmental organisations • The FP7 EQUIMAR project [117] considers stakeholders during the entire project lifecycle. At the initial stages of project development, owners, developers, suppliers, employees, the government, unions, and individuals or whole communities located near or in the vicinity have a crucial influence. When operational, creditors and end energy users can be included as well. Stakeholders are then grouped into four categories: Statutory consultees, Strategic stakeholders, Community stakeholders, and Symbiotic stakeholders. • More recently, in [118], twenty-six wave energy stakeholders are identified, who are grouped into four categories: Highest-level stakeholders, Core stakeholders, First-tier suppliers, and Low-tier suppliers. Despite the underpinning research that assists in identifying wave energy stakeholders, stakeholder prioritisation has not been carried out systematically. Stakeholder mapping techniques, usually based on two or three dimensions (e.g. power, interest and urgency), have been used in other sectors to determine the priority of identified stakeholders [119] [120]. The elicitation of Stakeholder Requirements (SRs) largely depends on the type of market being addressed. As explained in subsection 2.3.2, the environmental domain accounts for the factors linked to the added value to the intended market. Both Wavebob [121] and utility company PG&E [122] mention using SE to reflect end-user needs and develop toplevel requirements. At the time of writing, the Wave-SPARC project [123] has produced the most comprehensive analysis of the wave energy stakeholder domain. Wave-SPARC has delivered a complete and agnostic formulation of a utility-scale wave energy project through SE and stakeholder analysis. The analysis of stakeholders’ needs in [118] led to STATE OF THE ART 33 seven high-level SRs and a total of 33 low-level SRs. Costs and risks are identified as two of the high-level requirements. The other five categories in the high-level SRs contain a mixture of benefits (reliable for grid operations), opportunities (benefit society, deployable globally) and risks (acceptability and safety). SRs are not ranked/weighted according to their relative importance. To rank SRs, Jahanshahi et al. [124] applied the Delphi method to assess the economic requirements and their relative importance for developing wave and tidal energy technologies based on the expert's judgment. Operational costs and revenue were ranked as the most important criteria from the experts' points of view. Pre-operation costs and investment, incentives, profitability and externalities were ordered in the next priorities, respectively. It is worthwhile noting that both the incentives and externalities are System Drivers and thus should belong to the environmental domain. Further research efforts should be devoted to the development of a more integrated and objective approach to stakeholder analysis for various potential markets of wave energy technologies. The functional analysis in SE has the objective of defining the functional architecture of the system and characterising its functional behaviour. Functional Requirements (FRs) are the bridge between the stakeholders and technical teams and shall be specified at each stage of the system lifecycle. • Wavebob [121] defined operational scenarios right through from transportation, assembly, installation and commissioning to operation, maintenance, support and decommissioning. More recently, Babarit et al. [118] identified six lifecycle stages for a wave energy farm: Engineering, Procurement, Construction, Installation, Operations, and Disposal. • French [125], [126] proposed a systematic approach for the conceptual design of WECs during its operational phase, identifying the functions, selecting those having an important bearing on cost, and trying to find ways of performing those functions economically. The design of WECs is exemplified through the analysis of possible combinations of three main functions: provide a working surface, provide a reaction force, and extract power. • The University of Uppsala has applied a systems approach to develop ways to harness wave energy which considers manufacturing, maintenance and compatibility with the natural environment early in the design process [127]. These criteria are not generally used for down-selecting a concept from a set of solutions that achieve the desired functionality. • Technology developer Martifer [128] implemented a SE approach to systematically select candidate architectures and to define FRs for system design and development. Similarly, the utility company PG&E [122] developed a set of functional block diagrams to identify functional relationships between system infrastructure segments and external systems in the WaveConnect project. [129] STATE OF THE ART 34 described the functions performed by the OWC power plant to convert wave power into electricity. • Partial coverage of FRs can be found in [130], where FRs are formulated in the context of wave energy conversion, but only for the mooring system, and [131], who has produced a comprehensive landscaping report for Wave Energy Scotland (WES) on FRs for WEC controls. Innosea [132] presents a functional analysis of the submergence system for a Spar OWC in the form of an octopus diagram, exposing the elements interacting with the system, and the main functions (service and constraint). The functional analysis results in a set of functional specifications, showing the expected system functions, the judgement criteria, the levels of these criteria, and the flexibility. • Bull et al. [133] present a full taxonomy of FRs for a wave energy farm. The five top-level functions identify what the wave energy farm must do to meet its mission. The subfunctions below the top levels further decompose the top-level functions (e.g. WEC or electrical substation). These subfunctions identify the unique aspects that must be achievable to satisfy the higher-level function. Further breakdown is given to subfunctions in the form of sub-subfunctions, further focusing on the needed details (e.g. PTO within a WEC). At each level, functions are mapped to capabilities through MOPs. The analysis of FRs for wave energy systems is reasonably well covered in the literature. There is also a growing awareness of the need to define functional performance measures to judge the success of wave energy technologies. Although this is very positive, there is still the need for methods that establish the relative importance of FRs and their interactions. The technical analysis deals with the lower-level functions allocated to the system’s physical architecture [65], which depend on the design solution. Hence, there is little information on the Technical Requirements (TRs) used to take design decisions and sizing components. • Scharmann [134] presents a comprehensive functional analysis, technical breakdown and mapping of system requirements to the main cost centres of a particular WEC, i.e. rotor, PTO, substructure, installation and maintenance operations. • Wavebob [121] and Waves4Power [135] are two examples of technology developers where system decomposition and functional allocation have also been documented. In the case of Wavebob, this process was mainly driven by reliability concerns. • Several standards and guidelines have been produced to assist in the development of the TRs and assessment of technical performance: EMEC guidelines for Grid Connection [136], as well as IEC design requirements [137], power performance requirements [138] and power quality requirements [139]. STATE OF THE ART 35 Finally, the identification of manufacturing risks begins at the earliest stages of technology development and continues vigorously throughout each stage of system design. Unfortunately, there are no references in the literature to the development of Manufacturing Requirements (MRs) specific to WEC devices. Manufacturing Readiness Levels (MRLs) are commonly used to measure progress on the effectiveness of producing specific components and assemblies [140]. EMEC has produced some guidelines for the Manufacturing, Assembly and Testing of Marine Energy Conversion Systems [141]. This document does not contain a list of MRs, but it could be used to inspire the development of MRs. 2.4 Technology Performance Assessment 2.4.1 An Evolving Framework Evaluation of technology performance is a continuous activity that should occur at all development stages [63]. A commonly agreed evaluation framework can bring significant benefits for all wave energy stakeholders, including increased clarity, consistency and direction in the development [142]. Early design decisions based on objective criteria are key to lowering development uncertainties, cost and time. Traditionally, the evaluation of wave energy technologies has heavily hinged on the Technology Readiness Levels (TRLs). The TRL scale was initially formulated at NASA in 1974 (seven levels) and formally defined as it stands today (nine levels) in 1989 [143]. The TRL concept was conceived to assist in the development of space technologies and enable more effective communication on the maturity level of emerging technologies. However, TRLs only assess the maturity and risks within the wave energy development process rather than its quality, technical or economic performance. Figure 2.14: Technology Readiness Levels and IEC Stages. STATE OF THE ART 36 Several TRL definitions specific to wave energy have been proposed [144], [145]. It is usual in wave energy to group the systematic TRL development in stages. A device or subsystem must fulfil stage-gate criteria at the end of each stage before passing to the next development stage. The most common framework consists of five stages. It was initially proposed at HMRC to mitigate financial and technical risks during the development of buoyant devices [146], later adopted as best practice by IEA-OES [147] and FP7 EQUIMAR [59], and finally recommended by IEC [148]. Figure 2.14 presents the TRL scale and its correlation with IEC stages. The first attempt to derive a proper performance assessment of wave energy technologies was proposed by Nielsen [19]. Suggestions included ratios such as the Capture Width, Energy to Volume or Mass, Power Take-Off Efficiency, Capacity Factor and Capital Cost to Energy. Later, the European project EQUIMAR [59] added other assessment figures to these metrics such as the Operating Cost, Availability Factor and Levelised Cost of Energy (LCOE). In 2009, EMEC introduced some guidelines for functional performance measures of marine energy conversion systems, such as reliability, maintainability and survivability [149]. Evaluation methodologies based on the LCOE have been at the centre of wave energy technology development. LCOE combines two relevant stakeholder requirements in a single metric: lifetime costs and energy production. This is why Carcas et al. [150] examine the key performance metrics that underpin LCOE (i.e. CAPEX, OPEX, Yield, Reliability, Cost of finance, Survivability, Durability and Project size). Furthermore, the LCOE assessment method is akin to well-known cost-benefit analyses [71]. The reversed LCOE engineering [145] is a methodology to explore the limits of the WEC’s technical parameters. In this approach, an LCOE target is set and the upper-cost limits for the main subsystems of the WEC are obtained. Learning rates due to factors such as production volume and automation can also be considered to assess whether the cost limits for a subsystem can be reached from current costs. This methodology relies on prior knowledge of allocating cost centres to the physical realisation. It helps existing prototypes to improve their commercial attractiveness but does not guarantee stakeholder value is maximised. Since 2014, the United States has been developing and applying a holistic and quantitative techno-economic assessment metric system to identify technology weaknesses and strengths, ultimately advancing technology towards their market applications [133]. This de-risking approach applies to all WEC systems that are currently under development and to the novel systems invented in the project. System performance is measured through the Technology Performance Levels (TPLs) metric. The development of the TPL assessment criteria, methods and tools was first introduced by Weber [151], further developed in [152], and practically applied and enhanced in the Wave-SPARC project [123]. The latest version of the TPL Assessment Tool can be accessed online at NREL’s website [153]. STATE OF THE ART 37 The list of requirements developed in Wave-SPARC serves as the components of the TPL metric [133]. The seven capabilities groups meet the seven high-level SRs and constitute the ultimate metrics a utility-scale wave energy project must satisfy. The lowest level system capabilities in the TPL method are scored and progressively aggregated following a mathematical calculation. There are three ways of combining the lowest level scores: arithmetic mean, geometric mean and multiplication with normalisation. The overall score is calculated from scores for the seven high-level capabilities arranged in three categories (weighted average of individual geometric means). However, this approach requires expert assistance to perform the assessment due to the scoring complexity. In the public version of the tool, the weighting of the different criteria is fixed. The TPL assessment cannot be adapted to changing market conditions or stakeholders’ expectations, which will incidentally hinder the traceability of system requirements across domains. Since 2016, WES has promoted the development of performance metrics and tools for ocean energy technologies via workshops with a broad international cross-sector input [154]. Similarly, the Water Power Technologies Office [155] has contributed to gaining an international consensus by compiling a list of existing Ocean Energy performance metrics for the farm level, the wave energy device, and its main subsystems (e.g. structure, PTO, control, mooring). As mentioned before, the concept of staged development is inherent to performance assessment. IEA-OES is promoting the adoption of an international evaluation and guidance framework for ocean energy technologies based on this concept [142]. Stages are loosely related to the TRL scale; at each stage gate, an evaluation of the relevant metrics is done. Figure 2.15: Evaluation Areas included in the Evaluation and Guidance Framework [142]. STATE OF THE ART 38 The first-of-kind implementation of this framework has been produced in the EU H2020funded DTOceanPlus suite of design tools for ocean energy systems [156]. Assessments are grouped into four main categories, namely SPEY (System Performance and Energy Yield), RAMS (Reliability, Availability, Maintainability and Survivability), SLC (System Lifetime Costs), and ESA (Environmental and Social Acceptance). These assessments feed into a Stage-Gate tool for the overall assessment of ocean energy technologies. As can be appreciated above, performance requirements are moving from merely evaluating energy production and cost to a more comprehensive assessment. The selection at the intermediate stages of system design contributes to reducing risks. The iteration at low TRLs until the desired performance is achieved will contribute to the analysis of the solution space and the production of more cost-effective designs. Equally, the evaluation is evolving from analysing the basic wave energy subsystems involved in the power conversion to complete wave energy farms including multiple devices, and the balance of plant or installation and maintenance activities. Discouragingly, most novel wave energy concepts are still focusing their efforts on optimising power capture, leaving out of the initial design considerations other essential performance requirements and subsystems that later become expensive “add-ons” [60]. Experience in very diverse engineering sectors has shown that the early stages of technology development are crucial to meet cost and performance expectations [157] since engineering problems are built at the concept stage. 2.4.2 Assessment Criteria Hierarchy Wave energy technologies require assessment criteria that can be applied at different system levels of aggregation. Hence, a subsystem must be set in the context of a device and, in turn, placed in the context of a wave farm to assess that subsystem’s impact on global performance [142]. Figure 2.16 illustrates several frames of reference of wave energy technologies, including the external environment to consider the installation of the wave farm in a specific deployment site and the commercial aspects of the wave energy project. Figure 2.16: Various system boundaries for a wave energy assessment. Full traceability of assessment criteria is needed to ensure consistency in the same way requirements are traced throughout both the system hierarchy and design domains. To do so, the key design parameters of the technical solution should be selected to calculate the TPMs; the TPMs in turn considered to compute the MOPs; the MOPs taken to determine the MOEs; lastly, the MOEs can be aggregated into a final figure of merit that distils the STATE OF THE ART 39 wave energy technology suitability. This fundamental hierarchy of assessment criteria ensures a holistic evaluation that captures the metrics’ different levels of detail and granularity. Functional relationships can be established by analysing the different design domains. FAST diagrams [158] can be used to develop the hierarchy of requirements and corresponding metrics. Technology performance should be evaluated using different metrics depending on the system boundaries (see Figure 2.16). It is worth noting that the three first levels of the assessment hierarchy can be further expanded by repeating this mapping process for each subsystem, assembly or component. The only thing to consider is that system’s TRs will become the subsystem’s FRs, thus creating the need for an additional layer [81]. This way the traceability of both metrics and requirements is satisfactorily maintained. Figure 2.17 presents the evaluation areas considered in IEA-OES Task 12 [142]. It shows how high-level metrics can be combined with lower-level technology-agnostic ones until reaching a single affordability metric. Figure 2.17: Example of a hierarchy of wave energy metrics (adapted from [142]). For consistency, all assessment criteria should be selected so they are at the same level of detail and cover the full extent of technology requirements. Metrics should not be strongly correlated to each other to provide insight into different characteristics of the technical solution or alternatives being assessed and to avoid overlap or double accounting of criteria. Trade-offs can be captured and evaluated when metric scores for an embodiment are related to critical design parameters. Value functions shall be used to characterise the fundamental relationships between assessment criteria. 2.4.3 Aggregation Structure Another important aspect to consider in analysing the functional relationships is the aggregation logic of the assessment criteria. This page intentionally left blank 47 CHAPTER 3 UNDERSTANDING THE WAVE ENERGY CONTEXT 3.1 Overview This chapter provides an awareness of the broader context and its potential impact on system requirements and dependencies to ensure that wave energy technologies can fulfil stakeholders’ expectations. Section 3.2 introduces the specific methods and tools used in this step of the methodology. Initially, AHP is used in the environmental domain to prioritise System Drivers (SD). The QFD tool with Chen normalisation is then used to link SD to Stakeholders (SH) and provide importance ratings to wave energy requirements in the different domains. Section 3.3 develops the wave energy context, which comprises multiple external forces that have no direct interaction with the wave energy system, but may influence decisions related to its conception, development and operation. The identification of the market application, key drivers and stakeholders’ concerns offers a solid foundation for objectively evaluating wave energy options versus system requirements. Section 3.4 describes the practical implementation of this step. An anonymous survey was designed to prioritise the external forces. Results obtained from the consultation to wave energy representatives are presented for the application markets, key drivers and stakeholder groups listed in section 3.3. Finally, section 3.5 summarises the chapter and discusses some partial findings from this novel methodology that might be of interest to the wave energy sector. “For me context is the key; from that comes the understanding of everything” Kenneth Noland (1924 – 2010) UNDERSTANDING THE WAVE ENERGY CONTEXT 48 3.2 Methods and Tools 3.2.1 Analytic Hierarchy Process (AHP) Complex engineering problems often require a set of interdependent and competing criteria. The Analytic Hierarchy Process (AHP) is a valuable tool that provides a systematic approach to support multi-criteria decision-making. Developed by Saaty in 1980 [102], AHP captures subjective and objective aspects of an engineering problem by breaking down decisions into a series of pairwise comparisons and combining them into a single scale. Furthermore, AHP includes an effective technique to check the evaluation’s consistency, hence reducing the bias in the final decision. Since its emergence, it has become one of the more widely used multi-criteria analysis methods. AHP is formalised in four main steps. The two last steps are optional but highly recommended to confirm the robustness of the results. Step 1: Decompose the decision problem into a hierarchy of sub-problems. It starts by decomposing the decision problem into a hierarchy of sub-problems. The overall goal, criteria and attributes are arranged into different hierarchical levels as illustrated in Figure 3.1. The decision problem goal sits at the top of the hierarchy. The second level consists of several primary criteria of equal importance. If appropriate, a third level can be added. Figure 3.1: An example of a three-level decision hierarchy. Step 2: Perform pairwise comparisons and establish priorities. Decision criteria are placed in an mxm squared matrix, and two criteria are compared each time to determine which one is more important. Whenever the criteria in rows are more important than the ones in columns, the 9-point gradation scale [102] shown in Table 3.1 is used to quantify the comparison, a ij . Otherwise, the reciprocal value is assigned, a ji = 1/a ij . UNDERSTANDING THE WAVE ENERGY CONTEXT 49 Table 3.1: Gradation scale for pairwise comparisons [102]. Importance Definition Explanation 1 Equal Factors contribute equally to the objective 3 Moderate One factor is slightly favoured over another 5 Strong One factor is strongly favoured over another 7 Very strong Evidence exists for a factor dominance 9 Extremely strong Highest possible validity of a factor 2, 4, 6, 8 Intermediate values For a compromise between the above values Step 3: Synthesise judgements to obtain a set of weights. Based on each criteria’s priority, the overall ranking is developed by normalising the judgement matrix. The relative importance, w i , is calculated as follows:    =   ∑       ,  = 1 , 2 , … ,  ;  = 1 , 2 , … ,  (1)   = ∑         ,  = 1 , 2 , … ,  (2) Step 4: Evaluate and check the consistency of judgements. Finally, the degree of consistency among the pairwise comparisons is measured by computing the Consistency Index and Consistency Ratio [102]. The Consistency Index (CI) is calculated as  =   −   − 1 (3) where λ max is the maximum eigenvalue of the judgement matrix. CI is then compared with that of a Random Index (RI). The ratio derived, CI/RI, is termed the Consistency Ratio (CR). A CR below 0.1 is deemed satisfactory. Table 3.2: Random Index, RI [102]. Factors 1 2 3 4 5 6 7 8 9 10 RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 Until now, AHP has only been applied in wave energy to rank technology options concerning techno-economic criteria (e.g., energy capture, cost, reliability, environmental friendliness, adaptability) in a single step [61]. To limit the subjectivity of and dependence on expert judgements, AHP will be used in the environmental domain to prioritise System Drivers (SDs) at the outset of this novel methodology. UNDERSTANDING THE WAVE ENERGY CONTEXT 50 3.2.2 Quality Function Deployment (QFD) QFD [76] is another well-known design tool developed in Japan by the end of the 1960s, being first documented at the Kobe shipyards of Mitsubishi Heavy Industries in 1972. It is used to translate the Voice of the Customer (VoC) into system requirements employing a series of matrices called the House of Quality (HoQ). System requirements initially consisted of just customer needs and technical requirements, but they can equally be functions, design parameters or critical process variables. Furthermore, QFD matrices can be linked in a waterfall manner to ensure the complete traceability of the requirements. Figure 3.2: The House of Quality. QFD is formalised in 6 main steps: Step 1: Determine input requirements and relative importance ratings (What). In the proposed methodology, AHP is adopted to prioritise initial factors, that is, System Drivers (SDs). Step 2: Benchmark input requirements (Now vs What). This step aims to determine how the requirements are currently satisfied. Even though the wave energy system is a new design, there will always be a competitive product that is intended to meet the same need. This step creates an awareness of what already exists and facilitates assigning target values to these requirements. Please refer to CHAPTER 5 for further details on target allocation. Step 3: Generate output requirements (How). The output requirements restate the design problem in the corresponding domain. The Functional Analysis and System Technique (FAST) can be used to identify the output requirements [158]. UNDERSTANDING THE WAVE ENERGY CONTEXT 51 Step 4: Fill in the relationship matrix (What vs How). The relationship matrix is the centre part of the HoQ and is used to relate the input and output requirements. This way the priorities of the input requirements can be translated into the relative importance ratings of output requirements (Step 6). To do so, the relationships traditionally expressed in qualitative symbols (e.g.,  strong,  medium, and  weak) are converted into numerical coefficients (e.g., 9-3-1). Step 5: Complete the correlation matrix (How vs How). The correlation matrix placed over the “roof” of the HoQ is added to highlight interrelationships between output requirements. Positive relationships represent supporting requirements, whilst negative linkages help identify conflicts and trade-offs. Qualitative symbols (e.g., +, −) or numerical ratings (e.g., 1, −1) are used to describe these relationships. Step 6: Determine relative importance ratings (How Much). The absolute level of importance of the output requirement, w j , is obtained by summing the relative importance of the input requirements, d i , multiplied by the quantified numerical coefficients, r ij . The relative importance rating,   , is then computed as:   =    ∙       ,  = 1 , 2 , … ,  ;  = 1 , 2 , … ,  (4)    =   ∑       (5) where n and m are the number of input and output requirements, respectively. Some authors have proposed normalisation models to determine the relative importance ratings, including the correlation matrix. Chen’s approach [181] aims to overcome other models’ limitations that produce unreasonable results. In this method, the numerical coefficients, r ij , are normalised according to the following equation:    =  ∑ !  !   "   ∑  ∑ !  !   "       , ∈ $ 1 , − 1 % (6) where c kj are the number ratings of the correlation matrix. In wave energy, QFD has been applied to assess the potential of wave energy innovations defined by its functions, without any normalisation and in a single step [182]. The QFD tool with Chen normalisation will link SDs to Stakeholders (SHs) and assign importance ratings to wave energy requirements in the different domains. UNDERSTANDING THE WAVE ENERGY CONTEXT 52 3.3 The Wave Energy Context 3.3.1 Background The engineering complexity and the wide variety of wave energy concepts require a comprehensive development approach [60]. Hence, defining the full set of requirements for the design problem from the start is paramount to developing a successful wave energy technology [62]. Furthermore, an early understanding of the overarching context and its potential impact on system requirements and dependencies will provide a solid basis for developing wave energy technologies that meet stakeholders’ expectations [183]. Attention to context is not new to Systems Engineering (SE), but its consideration has increased hand in hand with the sophistication of engineering problems. The system context comprises multiple external forces that have no direct interaction with the wave energy system but may influence decisions related to its conception, development and operation [62]. A structural view of the system should consider the multiple value dimensions of the technology (or system drivers) together with the various stakeholders interested in the technology [184]. Drivers that are associated with a stakeholder group are often called concerns. As introduced in section 2.3.6, the most comprehensive analysis of the wave energy requirements has been produced within the Wave-SPARC project [111]. This work led to a complete and agnostic formulation for a utility-scale wave energy farm through SE and stakeholder analysis. However, the definition of system context is only partially addressed. The authors present a context diagram used to define the external systems that can directly influence the success of a grid-connected wave energy farm. It is pointed out that this overarching context can influence the design of the technology, but these factors are not explicitly analysed. On the other hand, Sandberg et al. [112] investigated the various external forces acting in the system context of wave energy for off-grid applications. They acknowledged that the external factors may not affect the viability of grid-connected systems in the same way but did not analyse this impact. Despite the existence of research to assist in the identification of wave energy stakeholders, such as [116], [117], [118] and [185], as far as we are aware, there is no public reference to assist in the prioritisation of stakeholders in the wave energy sector. The knowledge gained from analysing the overarching context comprising the market application, key drivers and stakeholders’ concerns provides a solid basis for objectively evaluating wave energy technologies against the systems requirements. UNDERSTANDING THE WAVE ENERGY CONTEXT 53 3.3.2 Wave Energy Markets The intended market application drives the development of innovations since new technologies are created to address existing or unexploited market opportunities and problems [186]. Knowledge about future markets is vital at all stages of the innovation process [187]. Therefore, defining the target market(s) is the first logical step to characterising the overarching context. Wave energy devices are used to transform the motion of the ocean and waves into any usable form of energy. However, the primary product for wave energy is likely to be electricity generation due to the important contribution of this energy carrier to the decarbonisation of the global energy system [115]. Although some technology developers are interested in other products such as freshwater (through desalination) or hydrogen (through electrolysis), they mostly conceive wave energy technologies for electricity production. Owing to its size, large-scale grid-connected electricity generation is the most attractive market for wave energy technologies [188]. Wave energy presents a great opportunity to meet international decarbonisation targets. However, integrating wave energy technologies into the utility-scale market is challenging since these emerging technologies must struggle to compete in cost with more mature renewable energies such as wind or solar. Alternatively, non-utility markets may present an appealing option for wave energy technologies to be exploited at a smaller scale in a less competitive setting. In particular, islands and other off-grid markets could provide a stepping stone supporting the deployment of wave energy technologies while providing environmentally friendly energy to coastal communities. These territories experience a much distinct reality than their continental fellows and may require bespoke solutions [189]. Consumers mainly depend on exchanges with mainland or fossil fuel-based generation; they pay high electricity prices compared to mainstream markets and are more vulnerable to fluctuations in the tariff. Other niche applications for wave energy systems have been proposed, given their colocation nature, potential synergies and cost savings [188]. Among them, it is worth mentioning the energy supply to offshore oil & gas platforms, marine aquaculture and ocean observation and navigation [190]. However, this chapter will not investigate these markets because of their lesser size, great variety of requirements and lack of consistent information to characterise them. Table 3.3 summarises the main features of the two power markets analysed in this chapter: utility-scale generation and powering remote communities. UNDERSTANDING THE WAVE ENERGY CONTEXT 54 Table 3.3: Application market characterisation. Id Market Characteristics M1 Utility-scale generation • Attractive but also very competitive • WEC design is mainly driven by this market • Increasing demand for renewable electricity • Legal obligations to meet decarbonisation targets M2 Powering remote communities • A narrower span of competition (sometimes just one option - diesel) • Low energy security and quality • Consumers vulnerable to price fluctuation and high energy costs • Simplified market and regulatory conditions 3.3.3 System Drivers (SDs) Wave energy drivers are an essential part of the context where the wave energy system operates. Drivers are exogenous forces outside the system boundaries that can constrain, enable or alter the design solution [80]. The context includes the political, economic, social, technological, legal and environmental factors. The existence of favourable conditions in the intended market will undoubtedly stimulate the development of wave energy technologies. PESTLE analysis is a standard tool used by companies to track the context they are operating or are planning to launch a new project, product or service [191]. This tool can be combined with SWOT 1 analysis to provide an excellent framework to investigate wave energy drivers from many different angles and dimensions [192]. PESTLE is an acronym which encompasses six dimensions (see Figure 3.3) and in its expanded form stands for: • P for Political. Political drivers determine the extent to which a government may influence a specific industry. • E for Economic. Economic drivers comprise factors that directly impact economic viability. • S for Social. Social drivers scrutinise social trends and attitudes. • T for Technological. Technological drivers pertain to key knowledge and technologies that affect the industry. • L for Legal. Legal drivers include regulations that affect the business environment. • E for Environmental. Environmental drivers allude to factors determined by the surrounding natural environment in which the wave energy system is placed. 1 Strengths, Weaknesses, Opportunities and Threats UNDERSTANDING THE WAVE ENERGY CONTEXT 55 Figure 3.3: The six dimensions of PESTLE analysis. Attributes that characterise wave energy drivers are fairly covered in literature such as [51], [112], [113], and [193]. Table 3.4 provides a summary of wave energy drivers per the main category. Table 3.4: Wave energy drivers. Id Driver Attributes SD1 Political • Favourable policies (e.g. energy security, sustainability, job creation) • Market support mechanisms • Political stability and low bureaucracy SD2 Economic • Access to finance, credit & insurance • Energy price and/or volatility SD3 Social • Growing energy demand • Social acceptance SD4 Technological • Technology maturity and certification • Infrastructure readiness • Supply chain availability SD5 Legal • Simplified procedures (e.g. consenting, environmental assessment) • Standards and regulation SD6 Environmental • Stricter protection (e.g. pollution, natural disasters, climate change) • Suitable site and resource conditions A survey of wave energy representatives was conducted to prioritise wave energy drivers and to establish the importance ranking of wave energy drivers for each application market. Respondents were asked to grade the political, economic, technological, legal and environmental factors using a Likert scale, with one being the highest importance and six the lowest. More details about the practical implementation can be found in section 3.4. UNDERSTANDING THE WAVE ENERGY CONTEXT 62 3.4.2.2 Powering remote communities Likewise, the distribution of priorities per external driver and the most frequently ranked factors are presented in Figure 3.6. This time, the focus is on applying wave energy technologies in a remote community generation market. Figure 3.6: Key Drivers for Remote Community Generation It can be observed that the Social drivers now have the top priority with 26% of responses, whereas the Legal drivers score last. The Economic and Political drivers are ranked second. The level of agreement in the responses is not so marked for all drivers as for the utilityscale generation. This means that the prioritisation of Political (20% of responses), Technological (21%) and Environmental (25%) drivers may be sensitive to the sample size. The distribution of responses is much flatter for these three drivers. UNDERSTANDING THE WAVE ENERGY CONTEXT 63 Prioritisation of Political drivers has a higher degree of uncertainty as it can get a higher rank (1) or a much lower rank (5) with minor changes in the responses. 3.4.3 Drivers Interrelationship with Stakeholder Groups 3.4.3.1 Political factors The importance distribution of Political concerns per wave energy stakeholder group and the most frequently ranked stakeholders are presented in Figure 3.7. Figure 3.7: Political concerns for the wave energy stakeholders While the Government clearly shows up in the first position with 70% of responses, the EPCI contractor and O&M provider are the least important stakeholders in terms of political concerns. These last two drivers have also received fewer responses (15% and 18%, respectively). They could step up one position accounting for the sample’s margin of error, which is insufficient to alter the overall prioritisation. Lenders display the most significant UNDERSTANDING THE WAVE ENERGY CONTEXT 64 degree of uncertainty. They can either be ranked 4, 5 or 6 with minor changes in the responses. 3.4.3.2 Economic factors The importance distribution of Economic concerns per wave energy stakeholder group and the most frequently ranked stakeholders are presented in Figure 3.8. Figure 3.8: Economic Concerns for the Wave Energy Stakeholders. The Owner stands out in the first position with 51% of responses. There is a high level of agreement in prioritising stakeholders according to Economic factors. Given the margin of error in the sample, the only uncertainty is for the Regulators who could step up to the same position as the Consumers and the Pressure groups. However, as Regulators score in the last position with fewer responses (15%), this does not change the overall ranking. UNDERSTANDING THE WAVE ENERGY CONTEXT 65 3.4.3.3 Social factors The importance distribution of Social concerns per wave energy stakeholder group and the most frequently ranked stakeholders are presented in Figure 3.9. Figure 3.9: Social Concerns for the Wave Energy Stakeholders. In this case, Consumers are ranked first according to Social factors with 33% of responses. There is a firm agreement concerning the importance of Pressure groups (46%), the Owner (33%) and Regulators (28%). However, the Government can swap from the third to the first position considering the margin of error in the sample. Finally, the EPCI contractor, Lenders and the O&M provider get fewer responses (16-20%). Their ranking, however, is unaffected by this level of uncertainty. UNDERSTANDING THE WAVE ENERGY CONTEXT 66 3.4.3.4 Technological factors The importance distribution of Technological concerns per wave energy stakeholder group and the most frequently ranked stakeholders are presented in Figure 3.10. Figure 3.10: Technological Concerns for the Wave Energy Stakeholders. As per the Economic drivers, the Owner jumps again into the first position, but in this case with the highest number of responses (74%). There is a high level of agreement in prioritising stakeholders according to Technological factors. Given the margin of error in the sample, the only uncertainty is that the Government could step up to the same position as Regulators. However, this does not change the overall ranking as the Government has fewer responses (15%). Finally, Pressure groups and Consumers close this ranking. UNDERSTANDING THE WAVE ENERGY CONTEXT 67 3.4.3.5 Legal factors The importance distribution of Legal concerns per wave energy stakeholder group and the most frequently ranked stakeholders are presented in Figure 3.11. Figure 3.11: Legal Concerns for the Wave Energy Stakeholders. Regulators present the highest priority with 49% of responses and Consumers with the lowest with 30% of responses. There is a significant level of agreement in the ranking of stakeholders despite the margin of error in the sample. The only uncertainty remains with the position of the Owner, which can be swapped from four to one. UNDERSTANDING THE WAVE ENERGY CONTEXT 68 3.4.3.6 Environmental factors Finally, the importance distribution of Environmental concerns per wave energy stakeholder group and the most frequently ranked stakeholders are presented in Figure 3.12. Figure 3.12: Environmental Concerns for the Wave Energy Stakeholders. Pressure groups and Regulators share the first position with 38% and 34% of responses, respectively. Lenders are ranked last. The overall ranking is not sensitive to the margin of error except for the Government, which can take either the third or fourth position. However, the Government accounts for fewer responses (26%) than the Consumers (28%), which make the obtained prioritisation still reliable. UNDERSTANDING THE WAVE ENERGY CONTEXT 69 3.4.4 Prioritisation of SDs According to the survey results, the ranking of wave energy drivers considerably differs between the two application markets. Economic factors are the primary motivations for developing utility-scale generation projects, whereas Social factors drive the remote community generation market. This result is in line with the market characterisation presented in section 3.3.2 and the qualitative feedback collected from the consultation to wave energy representatives. In other words, utility-scale generation is a very competitive market, whilst the social demand for clean energy and public acceptance drive powering remote communities. The application of AHP provides more granularity to compare this outcome. The weights resulting from pairwise comparisons are reliable since the Consistency Ratio yields a satisfactory value below 0.1 in both cases. As shown in Figure 3.13, the Economic, Political and Technological factors are significant drivers in the utility-scale generation, accounting for almost 85% of the total ratings. However, in powering remote communities, more drivers come into play. Economic, Political and Technological factors are still important, but Social factors dominate. Altogether, they account for 92% of the total ratings. Figure 3.13: Relative importance of SD for the application market. Wave energy utility-scale projects are not at the commercial stage, as they require technological development to demonstrate the necessary reliability and cost-effectiveness. This circumstance creates important barriers to accessing the required financial and insurance support. Hence, public funding is needed to develop wave energy to the point that the private sector can pick it up. In this sense, a key political driver is long-term revenue support from governments. Additionally, investment decisions may hinge on available political targets concerning climate change, energy transition and security of UNDERSTANDING THE WAVE ENERGY CONTEXT 70 supply. Consequently, utility-scale development of wave energy technologies will largely depend on attractive economic support and a favourable political framework. In contrast, the maturity of existing wave energy technologies may be sufficient to provide energy at a smaller scale in a remote community project. Remote communities already bear a high energy cost, opening the way to make wave energy technologies competitive with other energy sources. In this sense, the growing energy demand and social acceptance of the local communities are crucial drivers. Moreover, local populations are much more engaged and have a closer appreciation of nearby environmental and economic benefits. The economic and political factors score second in a remote community market application, and the social concerns highly determine them. Technological factors are second for the utility-scale projects and third for the remote community generation. This reflects that technology maturity is somehow assumed to be in place before any significant technology roll-out can be conceived. Besides, grid infrastructure may be critical but needs stronger pushes in the economic, political and social drivers since it is out of the hands of the technology developers. Surprisingly, the legal and environmental factors are considered to have minor importance for both markets, and the social factors score last in the utility-scale market when it is the major motivation for a remote community market. Legal and environmental aspects are generally perceived as barriers instead of drivers. Many procedures are partially in place and data gathered on the potential environmental impacts are limited or poorly validated because of the short deployment times of current technologies. It will be important to address these uncertainties in the future. However, it is considered that if wave technology is proven to work, then the legal and permitting side will eventually follow. Factors relating to competing uses of resource areas might also impact decision-making. Stricter environmental protection will speed up the transition to renewables for energy companies. The legal factor is usually equalised once the political factor is in place and sets the legal environment. The previous results point out that each application market is a central issue but also that drivers are somehow interlinked. Finance is connected to a suitable political framework. Limited support will delay technology maturity, but if the technology is proven, the legal side will follow. The political factors will contribute to setting the legal framework. Environmental concerns may be the motivator for political and social factors. Finally, job creation is a political aspect but can also improve social acceptance. 3.4.5 Prioritisation of SHs In the stakeholder domain, the wave energy problem is expressed in terms of stakeholders’ expectations. The different importance ranking of these stakeholders for each key driver and market application will hence determine the system requirements for developing wave energy technologies that are tailored to each specific use. UNDERSTANDING THE WAVE ENERGY CONTEXT 71 According to the survey results, two broad clusters of stakeholders arise. On the one hand, the Owner, Lenders, EPCI contractor and O&M provider are the most important actors for the Economic and Technological factors. On the other hand, the Government, Regulators, Pressure groups and Consumers are mainly connected to Political, Social, Environmental and Legal factors. This result is in line with the few references in the literature [111], [112] and the qualitative feedback collected from the consultation to wave energy representatives. Political factors are of primary concern to the Government and Regulators. It is worth noting that although the Political drivers are directly steered by the Government and Regulators, the Pressure groups and the Consumers also have a certain degree of influence on the Government. Economic and Technological factors share a similar profile of stakeholders’ concerns. The ranking starts with the Owner followed by the Lenders, EPCI contractor and O&M provider. However, the Government is slightly more concerned with Economic drivers than Technological ones. At the current stage of development, Economic and Technological drivers are crucial for both Owners and Lenders as their return on capital is at stake. EPCI contractors and O&M providers will try to reduce their exposure due to technology immaturity. Social and Environmental drivers are more connected to the public and therefore are vital for the Government, Regulators, Pressure groups and Consumers. These four stakeholders score high for the Environmental drivers. However, Consumers and Pressure groups stand out in the case of Social drivers. Lastly, Legal factors are driven by those who can support and define the boundaries of the legal framework, namely Regulators, the Government and Pressure Groups. These results show that the Owner and the Government are lead players in the two stakeholder clusters. We have seen earlier Economic and Political factors dominate utilityscale generation, whilst powering remote communities is mainly motivated by Social drivers. Accordingly, it can be inferred that the development of wave energy technologies will be primarily influenced by the needs of the Owner and the Government for utilityscale and remote community projects, respectively. This research has some limitations, as pointed out during the discussion of the key drivers of wave energy projects. The prioritisation of concerns contains a certain degree of uncertainty due to the sample size and corresponding margin of error. A different sample can reduce Lenders’ concerns about Political factors and Environmental factors. Similarly, Regulators, the Government and the Owner can have greater concerns about the Economic, Social and Legal factors, respectively. The qualitative responses in the wave energy representatives’ survey help to establish the interrelationship of SHs with SDs. In the proposed method, QFD is adopted to prioritise survey results into SH weights for each application market and SDs. The use of QFD FORMALISING SYSTEM REQUIREMENTS 78 4. Verify the structure of the FAST diagram by starting at the lowest-level functions on the right and asking the question, “Why is this function included?” The function to the immediate left of the function being considered should answer this question. Responses to each question can be single, multiple (using AND connector) or optional (using OR connector). Figure 4.3: Function hierarchy in a FAST diagram 4.2.2 Logical Scoring of Preference (LSP) The aggregation concept is a common feature of multi-criteria analysis methods. Even though tools such as AHP or QFD can be used to derive weightings for the various evaluation criteria, combining the lower-level evaluation criteria into an aggregated score is not a simple task. For instance, the TPL methodology [133] introduces three different ways of combining the lowest level scores (i.e. arithmetic mean, geometric mean and multiplication with normalisation) and four degrees of flexibility ranging from high flexibility to none. The Logical Scoring of Preference (LSP) method proposed by Dujmovic [159] is used here to capture the underlying functional relationships and add more granularity to the aggregation step by allowing the definition of the degree of simultaneity of the requirements to be combined from the total disjunction to full conjunction [209]. Conjunction in LSP means that the output utility is predominantly affected by the value of the smallest input, calling for simultaneous high input values. The geometric and harmonic means, respectively, are examples of conventional operators that provide increasing levels of simultaneity. Conversely, disjunction means that the output utility allows the replaceability of low-value inputs. The square mean is an example of partial replaceability. Neutrality, which is the perfect balance between conjunction and disjunction, is denoted in LSP by the weighted arithmetic mean. When combining FORMALISING SYSTEM REQUIREMENTS 79 mandatory and optional inputs or sufficient and optional inputs, conjunctive or disjunctive partial absorption is used, respectively. The intensity of the simultaneity or replaceability can be continuously adjusted by selecting different operators as shown in Figure 4.4. Weightings in LSP are adjusted using QFD [76]. Figure 4.4: Degrees of simultaneity/replaceability of logic operators (adapted from [159]). Following this approach, the evaluation criteria can be aggregated sequentially into higher hierarchical levels accounting for the degree of simultaneity of the different attributes until the final overarching merit is obtained. The overall suitability can be interpreted as the qualitative degree of satisfaction with all specified requirements. This suitability, s 0 , is computed from the next level of evaluation criteria, s i , as follows: & ' = (    ∙ & )     *  ) ;        = 1 ,  = 1 , 2 , … ,  (7) where m is the number of evaluation criteria, w n are their weightings, and d is a coefficient that depends on the degree of simultaneity. Values of d range from −∞ for pure conjunction to +∞ for pure disjunction. Special cases of weighted power mean for m=2 are shown in Table 4.1. FORMALISING SYSTEM REQUIREMENTS 80 Table 4.1: Special cases for weighted power mean m=2 [210] Aggregator s 0 d Maximum + ( &  , & - ) +∞ Square mean /   &  - +  - & - - 2 Arithmetic mean   &  +  - & - 1 Geometric mean ( &  ) 1 2 ∙ ( & - ) 1 3 0 Harmonic mean 1   / &  +  - / & - −1 Minimum  ( &  , & - ) −∞ Additional values of d are provided in Table 4.2 for other alternatives of partial conjunction and disjunction. The ‘Andness’ column represents the degree of conjunction. Table 4.2: Generalised conjunction-disjunction. Values of d [159] Aggregator Symbol Andness m=2 m=3 m=4 m=5 Extreme disjunction D 0.0000 +∞+∞+∞ +∞ Very strong disjunction D++ 0.0625 20.630 24.300 27.110 30.090 Strong disjunction D+ 0.1250 9.521 11.095 12.270 13.235 Medium strong disjunction D+- 0.1875 5.802 6.675 7.316 7.819 Medium disjunction DA 0.2500 3.929 4.450 4.825 5.111 Medium weak disjunction D-+ 0.3125 2.792 3.101 3.318 3.479 Weak disjunction D0.3750 2.018 2.187 2.302 2.384 Square mean S 0.3768 2.000 - - - Very weak disjunction D-- 0.4375 1.449 1.519 1.565 1.596 Arithmetic mean A 0.5000 1.000 1.000 1.000 1.000 Very weak conjunction C-- 0.5625 0.619 0.573 0.546 0.526 Weak conjunction C0.6250 0.261 0.192 0.153 0.129 Geometric mean G 0.6667 0.000 - - - Medium weak conjunction C-+ 0.6875 −0.148 −0.208 −0.235 −0.251 Medium conjunction CA 0.7500 −0.720 −0.732 −0.721 −0.707 Harmonic mean H 0.7726 −1.000 - - - Medium strong conjunction C+- 0.8125 −1.655 −1.550 −1.455 −1.380 Strong conjunction C+ 0.8750 −3.510 −3.114 −2.823 −2.606 Very strong conjunction C++ 0.9375 −9.060 −7.639 −6.689 −6.013 Extreme conjunction C 1.0000 −∞−∞−∞ −∞ FORMALISING SYSTEM REQUIREMENTS 81 4.3 Wave Energy System Requirements 4.3.1 Background The previous chapter analysed the external forces influencing wave energy technology development due to the key role they play in establishing further requirements. The hierarchical formulation of wave energy requirements is built upon these results. QFD is used to produce traceable mappings between the environmental, stakeholder, functional and technical domains as represented in Figure 4.5. The Stakeholder Requirements (SRs) are translated into several prioritised Functional Requirements (FRs) and Design Parameters (DPs) that the wave energy system should meet. This way, the functional analysis produces a complete and unambiguous definition of the design problem space. Figure 4.5: Approach to building Wave Energy System Requirements. Once the critical system properties are established in the form of wave energy system requirements, evaluation criteria are assigned to offer a credible means by which to assess various design options. Metrics linked to the SRs are usually referred to as Measures of Effectiveness (MOEs). Measures of Performance (MOPs) are used to gauge the FRs of a design solution, whilst Technical Performance Measures (TPMs) are used to demonstrate the successful delivery of the TRs. This hierarchy of evaluation criteria ensures a holistic assessment that captures different levels of detail and granularity in the metrics. To carry out this analysis, it is necessary to delimit the scope of the wave energy system. Most commonly, technology developers identify the system of reference with their Wave Energy Converter (WEC), whereas suppliers consider it to be one of its main constituents, such as the Power Take-Off (PTO) or the mooring system. However, it is more appropriate and unbiased to designate the wave energy farm as the baseline system for the global assessment of technologies since this is the final product that can meet the market need for sustainable, affordable, and secure energy. Moreover, this definition is fully consistent with the system analysis conducted by Babarit et al. for wave energy [118]. FORMALISING SYSTEM REQUIREMENTS 82 4.3.2 Stakeholder Requirements (SRs) The mission statement of a wave energy system is presented in [118] for a utility market application. This overarching goal is reformulated and generalised here to other electricity generation markets as follows: “The wave energy farm converts ocean wave energy into consumable power” Starting with this mission statement, the roles and expectations of the different stakeholder groups have been structured from various literature sources such as [116], [117], [118], and [211]. They are summarised in Table 4.3. Table 4.3: Stakeholder roles and expectations. Id Stakeholder Roles Expectations SH1 Owner Initiate the project and design the farm Provide equity Set return on investment targets Manage project risks Sell electricity to consumers Competitive profitability Low project risks Access to affordable credit Stability of policy framework Assess performance levels Competitive cost of electricity Predictable generation Match consumer demand SH2 Lenders Provide debt Set interest rate Assess financial risk Low revenue risks Maintain reputation SH3 EPCI contractor Manage farm construction and installation Provide insurance during construction Select suppliers Manage end-of-life recycling Select the best components and systems Avoid cost overruns and delays Well-understood and manageable risks SH4 O&M provider Provide spare parts and services Perform (un)scheduled maintenance Provide insurance during the operation Select service suppliers Reliability of assets during the project's lifetime Avoid cost overruns and delays Well-understood and manageable risks Safety at sea SH5 Government Develop and implement sectoral policies Review compliance Provide investment and generation incentives Economic development Efficient use of public resources Compliance with regulation Socio-economic benefits SH6 Regulators Establish permitting requirements Review project use of ocean space Provide concession Compliance with regulation Maintain reputation SH7 Pressure groups Lobby for or against the project Improve the well-being of the community Acceptable environmental impact No affection for other activities Socio-economic benefits SH8 Consumers Set power quality requirements Purchase generated electricity Competitive cost of electricity Predictable generation FORMALISING SYSTEM REQUIREMENTS 83 Positive social and economic impacts Underlying all stakeholders’ expectations, there is the need to make wave energy competitive and acceptable for the targeted market, or expressed in another form, wave energy must address the energy trilemma, namely energy security, sustainability and affordability [212]. With this in mind, Stakeholder Requirements (SRs) and Measures of Effectiveness (MOEs) have been identified through an iterative process of distilling stakeholders’ expectations until arriving at the condensed list as shown in Table 4.4. Table 4.4: Stakeholder Requirements and Metrics. Id Stakeholder Requirement (SR) Measure of Effectiveness (MOE) SR1 Convert wave energy into consumable power Capacity Factor (CF) [59] SR2 Operate when needed Availability Factor (AF) [142] SR3 Reduce upfront costs Capital Expenditure (CAPEX) [59] SR4 Reduce annual costs Operational Expenditure (OPEX) [59] SR5 Prevent business risks Fixed Charge Rate (FCR) [213] It is worthwhile noting that the way SRs are elicited greatly facilitates the definition of MOEs. A closer look at the upper system metrics reveals parallelism with the simplified LCOE equation [214]. 567 = 897: × <= + 697: 8 , 766 × 9 × < × 8< (8) where • CAPEX, Capital Expenditure, represents all capital costs associated with the farm development, manufacturing, installation and decommissioning at the end of the project life. • FCR, Fixed Charge Rate, is the annual return, i.e. the fraction of CAPEX which is needed to meet investor revenue requirements. • OPEX, Annual Operating Expenditure, include all routine maintenance, operations, and monitoring activity. • 8,766 is the average total hours in a year. • P, Rated Power, is the nominal installed capacity of the farm. • CF, Capacity Factor, is the gross annual power generated by the wave energy farm as compared to its rated output at 100% availability. • AF, Availability Factor, is the percentage of the time that the wave energy farm is available to provide energy to the grid. By convention, the zero production periods (i.e the wave resource lies below or above certain limits) are counted against the CF but not against the AF. FORMALISING SYSTEM REQUIREMENTS 84 As can be seen, the numerator accounts for the annuitized lifetime costs and the denominator is the net energy production per year. In the proposed method, QFD is used to prioritise the SRs. To maintain traceability, the importance ranking of SRs for each application market was obtained in connection to the SHs. The same importance rating scale previously shown in Table 3.6 is used to derive SH– SR relationships. Additionally, LSP is used to aggregate the MOEs sequentially accounting for the degree of simultaneity of the different attributes until a final measure of suitability is obtained, which can be interpreted as the global degree of satisfaction of the SRs. Figure 4.6 presents the aggregation logic of the MOEs into this Global Merit (GM). The weights above each arrow, w i , represent the relative importance ratings of the SRs. The Geometric mean (G) and Arithmetic mean (A) operators were chosen to combine attributes with a multiplicative and additive nature, respectively. Figure 4.6: Aggregation of MOE. LCOE is the most common highest-level metric used to assess wave energy options [142]. However, the reader should bear in mind that the GM of a wave energy option might differ from the preference obtained using the numerical LCOE values since the aggregation logic also accounts for the relative importance expressed by the stakeholders, the underlying degree of simultaneity and the flexibility allowed to the various requirements, all of them qualitative aspects. CAPEX and OPEX vary largely for prototype technologies. Based on the OceanSET Third Annual Report [205], a CAPEX of €5m per MW and an OPEX of €500,000 (i.e. 10% of CAPEX) can be considered as threshold values for a zero utility, respectively. The simplified LCOE expression uses the Fixed Charge Rate (FCR): <= =  $ 1 − ( 1 +  ) A B % (9) FORMALISING SYSTEM REQUIREMENTS 85 where  is the discount rate and C is the project lifetime in years. For pre-demonstration projects with a maximum lifetime of 10 years, the discount rate can be as high as 15% [215] leading to a maximum FCR of 20% (zero utility). On the other hand, mature technologies with long lifetimes (>25 years) can achieve an FCR of just 5% with low discount rates of 3% (i.e. very low borrowing and inflation rates). The CF will generally increase with the higher wave energy flux. Figure 4.7 presents an illustrative plot of the upper CF bound for various wave energy levels, based on estimates of Babarit et al. [216] for eight different WECs at five sites along the Atlantic coast of Europe. This reference is useful to set the maximum CF utility at 50%. Figure 4.7: Fundamental relationship between the CF and the wave energy level (adapted from [217], Supplemental Information). Finally, according to the World Energy Council’s Performance of Generating Plant Committee [218], 80% of the gap in the best achievable AF is due to suboptimal O&M management practices. This is supported by OceanSET reporting an average AF of 78% for 13 wave energy projects [205]. Moreover, Greaves and Iglesias [3] identified an operational availability threshold of 75% for marine renewable energy devices. 4.3.3 Functional Requirements (FRs) Functional Requirements (FRs) are the bridge between the stakeholders and technical teams, and they should be elicited in all phases of the system lifecycle [81]. Functional analysis is used to identify what functions the wave energy system should perform, their logical structure and interactions to satisfy SRs efficiently. Whilst the engineering system exists only for its usage, all life phases must be considered since they add important constraints to the system design. Figure 4.8 shows the typical FORMALISING SYSTEM REQUIREMENTS 86 lifecycle of a wave energy system and the independent entities to which it is physically or virtually linked, that is the External Systems. Stages have been adapted from [118]. Figure 4.8: Lifecycle of the wave energy system and entities. The construction phase encompasses all manufacturing, transport and assembly activities performed onshore. Similarly, the end-of-life phase includes reusing, recycling or safely disposing of the parts that make up the wave energy system. The installation, maintenance and retrieval phases comprise the offshore transport. Additionally, maintenance involves inspection, repair, or replacement [219]. Minor repairs can be performed on-site. For major repairs and replacements, the wave energy system might be brought to shore and may require specific industrial processes, as for the construction and end-of-life phases. Finally, the operation is the most important phase in the system lifecycle since it is the only one that directly adds value to the end-users. The operation phase includes the standby, normal, malfunction and survival modes of the wave energy devices. Firstly, the external analysis of the wave energy farm is carried out to provide a general overview of the service functions of the wave energy system. The Octopus diagram is used to display the interactions of the wave energy system with the external systems. During its operational phase (Figure 4.9a), the wave energy system interacts with two External Systems, namely the Waves and the Point of connection where the converted energy is consumed. Accordingly, the primary function of a wave energy system is stated as follows: F p : Convert wave energy into consumable power This primary function is precisely elicited as the mission statement presented earlier. The remaining operational functions are secondary: F s1 : Operate when needed F s2 : Control energy capture F s3 : Transfer loads to the seabed F s4 : Reduce the severity of environmental threats F s5 : Avoid risks to receptors FORMALISING SYSTEM REQUIREMENTS 87 These secondary functions connect the wave energy system with the Operator, Seabed, Ocean Environment and Receptors, respectively. (a) (b) Figure 4.9: Octopus diagram for the operation phase (a) and rest of phases (b). The rest of the phases, which add constraints to the system design, have been merged into a single diagram for convenience (Figure 4.9b) leading to three additional secondary functions. F s6 : Manufacture by industrial processes F s7 : Install by service vessels F s8 : Maintain by service vessels These new functions connect the wave energy system with the Industrial processes (F s6 ) and Service vessels (F s7 and F s8 ). Note that F s4 and F s5 are present in all life phases of the system. For the internal functional analysis, the FAST diagram is used to translate the high-level functions into lower-level functions that must be performed by the wave energy system. Figure 4.10 presents the functional decomposition of the wave energy system into FRs (first level) and TRs (second level). The service functions from the external analysis and the SRs are included for the sake of traceability. It can be noted that the resultant FAST diagram organises the functions into consistent levels of detail and engineering domains. Service functions mainly belong to the functional domain (F s5 , F s6 , F s7 , F s8 ), but also some to the technical domain (F s2 , F s3 , F s4 ) and even the stakeholder domain (F s1 ). FORMALISING SYSTEM REQUIREMENTS 94 environmental loads. A third strategy involves using higher safety factors to increase design margins as depicted in Figure 4.13. Figure 4.13: Strategies to minimise failures (adapted from [228]). Minimising total downtime (FR5) demands using near maintenance port (TR11), increasing weather accessibility (TR12) and avoiding unplanned delay time (TR13). These requirements are characterised by Travel time (t t ), Waiting time (t w ) and Logistic time (t l ) respectively. In turn, t w depends on the site accessibility and the service time required to perform the maintenance operation. Again, the three TPM are partially replaceable and thus the neutral Arithmetic mean (A) is used to combine them. Manufacturing by industrial processes (FR6) requires employing mature manufacturing processes (TR14) and manufacturing in large quantities (TR15). These requirements are characterised by the Cycle time (t c ) and the Unit cost (UC m ). These two attributes are more replaceable than the perfect balance and thus the weak disjunction (D-) is used for their aggregation. UC m depends on the investment costs incurred for the manufacturing tooling, the variable cost of manufacturing each unit and the number of units. In general, replicative processes have higher investment costs and lower variable costs [229]. The Unit cost is given by Equation (11) with displays a characteristic hyperbolic form. F  =  G  +  H (11) where C f is the fixed cost, C v is the variable cost and m is the number of units. Installing and retrieving by service vessels (FR7) demands using low-cost vessels (TR16) and reducing the number of vessel trips (TR17). These requirements are characterised by the Install vessel charter cost (UC i ) and the No. of trips per device (n t ). The multiplicative nature requests a combination with the Geometric mean (G). Likewise, maintaining by service vessels (FR8) requires using low-cost vessels (TR18) and reducing the maintenance frequency (TR19). These requirements are characterised by Service vessel charter cost (UC s ) and the No. of trips per device (n t ). The multiplicative nature also calls for aggregation through the Geometric mean (G). Long-term agreements FORMALISING SYSTEM REQUIREMENTS 95 for one or several years can significantly reduce vessel charter rates compared with the spot market [230]. The no. of trips per device depends on the MTTF. Surviving the harsh environment (FR9) needs transferring loads to the seabed (TR20), reducing the severity of threats (TR21) and detecting conditions above the threshold (TR22). These requirements are characterised by the Maximum permissible foundation load (F f ), the Load shedding capability (L s ) and the Detection level (DL). Load shedding and detection require a certain degree of simultaneity and the Geometric mean (G) operator is used to combine them. The resulting utility is aggregated through the Arithmetic mean (A) as they can compensate for each other. Last but not least, avoiding risks to receptors (FR10) demands reducing the environmental pressure (TR23). The EIS metric is directly matched to the Farm density (FD) as the principal stressor. Figure 4.14 summarises the aggregation logic for the different TPMs. Figure 4.14: Aggregation of TPM. FORMALISING SYSTEM REQUIREMENTS 96 4.3.5 Design Parameters (DPs) The large number of TRs results in a QFD matrix difficult to manage. To begin with, ranking a multitude of requirements is simply beyond human cognitive capability. Besides, the analysis of the information contained in the QFD matrix becomes much more challenging. Finally, TRs may not be independent, as can be observed in the list of TPMs which share some commonalities. To avoid this problem, TRs are mapped to the design parameter space. Design Parameters (DPs) are used in Axiomatic Design [84] to characterise the physical attributes of a system. Given that one DP can be shared by two or more TRs, the technical domain analysis can be greatly simplified. This transformation is also supported by factor analysis [231], a mathematical technique for simplifying the relationship among a large number of correlated variables by a lower number of underlying variables called factors. DPs should be selected so they are independent of one another. To enforce factor independence, a selection from TRIZ technical parameters [232] was considered. The 39 technical parameters identify the most widely used and important features of technical systems (see Appendix C: List of TRIZ 39 Technical Parameters). Altshuller extracted these a priori characteristics after studying over 400,000 worldwide patents [233]. DPs should be also defined at the same level of abstraction as FRs. To avoid, as far as possible, coupled designs, the same number of DPs and FRs should be considered. A list of 10 DPs from the relevant common parameters from TRIZ has been mapped to the TRs as shown in Table 4.8. Table 4.8: Mapping of Technical Requirements (TRs) to Design Parameters (DPs). Id Design Parameters TRIZ no. Technical Requirements DP1 Area of moving object 5 TR1, TR23 DP2 Strength 14 TR2, TR10, TR20 DP3 Duration of action by moving object 15 TR8 DP4 Loss of energy 22 TR5, TR7 DP5 Loss of time 25 TR11, TR12, TR13 DP6 Quantity of substance 26 TR15, TR16, TR18 DP7 Adaptability 35 TR3, TR9, TR21 DP8 Device complexity 36 TR4, TR6 DP9 Difficulty of detecting and measuring 37 TR22 DP10 Productivity 39 TR14, TR17, TR19 FORMALISING SYSTEM REQUIREMENTS 97 4.4 Practical Implementation 4.4.1 Prioritisation of SRs The practical implementation of the proposed methodology yields the relative importance of SRs plotted in Figure 4.15. The complete outcomes of the QFD ranking for the two market applications can be consulted in Appendix B: Prioritisation Matrices. Figure 4.15: Relative importance of SRs for the application market. It can be observed that the SRs have a relatively similar importance for the two application markets under consideration, with a variability below 10%. The conversion of wave energy into consumable power (SR1), the continuous operation (SR2) and the reduction of annual costs (SR4) have a greater influence on remote community generation. Alternatively, the utility-scale generation market puts more emphasis on the prevention of business risks (SR5) and the reduction in upfront costs (SR3). This qualitative assessment assigns weights above the average importance rating (20%) to SR1 and SR5 for both markets, but with a reversed ranking as presented in Table 4.9. It is worth noting that the standard deviation of weightings for the five SRs is below 3%. Table 4.9: Ranking of Stakeholder Requirements (SRs). Rank Utility-scale Remote community 1 Prevent business risks Convert energy into consumable power 2 Convert energy into consumable power Prevent business risks 3 Reduce upfront costs Operate when needed 4 Operate when needed Reduce annual costs 5 Reduce annual costs Reduce upfront costs FORMALISING SYSTEM REQUIREMENTS 98 SRs prioritisation reinforces the conclusions drawn from the analysis of the wave energy context in the previous chapter. The Economic concerns of the Owner have the greatest influence on the importance ratings of the SRs for the utility-scale market, whereas the Political concerns from the Government mainly drive the SRs for the remote community generation. The aggregation of SRs' utility into global merit provides an additional perspective. Figure 4.16 shows the impact of changes in one MOE utility while the rest maintain the highest score for each application market. (a) (b) Figure 4.16: Sensitivity of Global Merit to MOE Utility for each market application: (a) Utility-scale generation; (b) Powering remote communities. In both market scenarios, the conversion of wave energy into consumable power (SR1: CF) has the greatest influence on the Global Merit followed by operating when needed (SR2: AF). When deriving the Global Merit, the logical preference operators used for combining the MOEs have a stronger influence than their corresponding weightings. The Geometric mean penalises low utility values. This logical operator is applied twice consecutively to combine SR1: CF and SR2: AF. Then it follows the reduction in upfront costs (SR3: CAPEX) and prevention of business risks (SR5: FCR). Their influence is however swapped for each application market. Finally, the less sensitive MOE to changes in the utility is the annual costs (SR4: OPEX). This is due to the fact the utility is combined through the Arithmetic mean, which is a neutral operator. OPEX has the least influence until it reaches medium utility, where it becomes more predominant, particularly for the utility-scale generation market. FORMALISING SYSTEM REQUIREMENTS 99 4.4.2 Prioritisation of FRs Figure 4.17 depicts the results from the practical implementation of the methodology in the functional domain. Likewise, the complete outcomes of QFD ranking for the two market applications can be consulted in Appendix B: Prioritisation Matrices. Figure 4.17: Relative importance of FR for the application market. FRs have relatively equal importance for the two application markets under consideration, with a variability lower than 9%. The functions contributing to each SR follow the same pattern as before. However, we can appreciate that capturing (FR1) and transforming (FR2) wave energy, minimising total downtime (FR5) and surviving the harsh environment (FR9) are the most relevant requirements, all of them above the average importance rating (10%) for both markets. The full ranking of FRs is shown in Table 4.10. Table 4.10: Ranking of Functional Requirements (FRs). Rank Utility-scale Remote community 1 Survive the harsh environmental Capture energy from waves 2 Capture energy from waves Survive the harsh environmental 3 Minimise total downtime Minimise total downtime 4 Transform into energy Transform into energy 5 Avoid risks to receptors Maximise total uptime 6 Maximise total uptime Avoid risks to receptors 7 Manufacture by industrial processes Maintain by service vessels 8 Maintain by service vessels Deliver energy to point of consumption 9 Deliver energy to point of consumption Manufacture by industrial processes 10 Install by service vessels Install by service vessels FORMALISING SYSTEM REQUIREMENTS 100 Surviving the harsh environment (FR9) is ranked first for the utility-scale market followed by capturing wave energy (FR1), whereas these FRs are swapped for the remote community generation. It is worth noting that the standard deviation of weightings is slightly above 2% indicating that this ranking may be altered with small changes in the stakeholder preference. The proposed method differs from the TPL scoring methodology [133] as the latter considers that most of the capabilities have equal influence. For instance, the same weights are assigned to equivalent pairs of requirements FR6 and FR7, FR2 and FR3, and FR4 and FR5. The traceability of design information and requirements through the different domains offers a more objective way to account for those differences without assuming either a flat distribution or any other arbitrary distribution of weights. (a) (b) (c) (d) Figure 4.18: MOE Sensitivity for Utility-scale Generation: (a) Convert wave energy; (b) Operate when needed; (c) Reduce upfront costs; (d) Prevent business risks. FORMALISING SYSTEM REQUIREMENTS 101 The aggregation of FRs’ utility into stakeholder value provides additional insights. Figure 4.18 shows the impact of changes in one MOP utility while the rest maintain the highest score for the utility-scale market. Due to the low variation of weightings, the changes in utility are insignificant between the two applications considered. Capturing energy from waves (FR1: C wn ), minimising total downtime (FR4: MTTR), manufacturing by industrial processes (FR6: CAPEX) and surviving the harsh environment (FR9: SURV) have the greatest influence in their respective MOE. However, FR4 and FR9 have the widest utility variation as a result of the logical operator chosen (harmonic mean and strong conjunction respectively). Figure 4.19 depicts the sensitivity of Global Merit to each FR for both market scenarios. In line with the ranking of FRs, the installation by service vessels (FR7) has a lesser impact on the Global Merit. Low utility values for transforming (FR2) and delivering (FR3) energy have a bigger impact on the Global Merit than manufacturing (FR6). The impact of capturing wave energy is kept between manufacturing (FR6) and maintenance (FR8) for a wider range of utility values, particularly for the remote community generation which has a higher weighting. Finally, low utility values for minimising downtime (FR5), surviving the harsh environment (FR9), avoiding risks to receptors (FR10) and maximising uptime (FR4) have the greatest influence on the Global Merit. As the utility of these MOPs increases, maintenance (FR8) becomes more penalising for the Global Merit. As can be seen in Figure 4.19, the sensitivity to low MOP utility is longer maintained for the remote community generation. (a) (b) Figure 4.19: Sensitivity of Global Merit to MOP Utility for each market application: (a) Utility-scale generation; (b) Powering remote communities. FORMALISING SYSTEM REQUIREMENTS 102 4.4.3 Prioritisation of DPs As said before, the large number of TRs results in a QFD matrix difficult to manage. The ranking of 23 requirements is simply beyond the human cognitive capability and, if it were possible, the interpretation would become much more challenging. Supported by the stakeholder and functional domain results, it can be assumed that the importance of the weightings would be minor and that the two markets considered will yield quite similar relative importance. Moreover, logical operators which require higher conjunction will influence more the Global Merit. Taking as reference the sensitivity analysis of FRs with values greater than 0.3 (Figure 4.19), we can anticipate that the following TPMs will be key for achieving high merit: • The Unit service vessel cost and Number of trips for REPEX. • Load shedding and Detection level for SURV. • The Farm density for EIS. • Travel, Waiting and Logistic times for MTTR. • Technology class, Load shedding and Safety factor for MTTF. • Maximum permissible load for Cwn. Figure 4.20 shows the results from the practical implementation of the methodology for the mapping of Design Parameters (DPs). The complete outcomes of the QFD ranking for the two market applications can be consulted in Appendix B: Prioritisation Matrices. Figure 4.20: Relative importance of DPs for the application market. Again, DPs have relatively equal importance for the two application markets under consideration, with a variability lower than 4%. The top priority DPs, all of them above the average importance rating (10%), are the Strength (DP2), Area of moving object (DP1), Adaptability (DP7), Loss of energy (DP4) and Quantity of substance (DP6). The FORMALISING SYSTEM REQUIREMENTS 103 full ranking of FRs is shown in Table 4.11, which remains the same for the two market applications. Table 4.11: Ranking of Design Parameters (DPs). Rank Utility-scale & Remote community 1 Strength 2 Area of moving object 3 Adaptability 4 Loss of energy 5 Quantity of substance 6 Device complexity 7 Productivity 8 Difficulty of detecting 9 Loss of time 10 Duration of the action We can observe that the three top-ranked design parameters, namely Strength (DP2), Area of moving object (DP1) and Adaptability (DP7), are related to the primary stakeholder requirement, converting wave energy into consumable power (SR1). Moreover, they also contribute to operating when needed (SR2) and preventing business risks (SR5). This result is consistent with Figure 4.16 in section 4.4.1 since these were the most sensitive MOE. Loss of energy (DP4) is linked to transforming (FR2) and delivering (FR3) energy, both of which are connected to SR1. Finally, Quantity of substance (DP6) contributes to manufacturing (FR6), installing (FR7) and maintaining (FR8), which are linked with the other two stakeholder requirements. 4.5 Conclusions The creation of a standard framework for wave energy technologies constructed around the notion of design domains assists in the organization of data on requirements and metrics to make system validation and verification easier. This common framework can be applied to different levels of system aggregation, technology maturity and markets ensuring a consistent and fully traceable assessment. By repeating the domain mapping process and adding additional layers to the requirements hierarchy, traceability offers flexibility to adapt this framework to rapidly changing market conditions and stakeholder priorities or to focus the analysis on particular wave energy sub-systems, assemblies or components. The LSP technique allows for greater granularity in the formulation of the aggregation logic and enables the seamless combination of mandatory, sufficient and optional metrics. This method can amalgamate disparate attributes and criteria expressed by a variety of GUIDING THE DESIGN DECISIONS 110 way of illustration, in [145], the maximum CAPEX of a wave energy technology with a certain annual energy production is determined to reach the target LCOE. In this allocation, respecting the theoretical or fundamental limits that cannot be surpassed is paramount. For instance, physical limits for power capture, such as the Budal upper bound [224] and the maximum capture width [238], should be respected. Underperformance in an intermediate metric can create an issue at a higher level and, therefore, should be thoroughly checked. However, it may be compensated by other metrics with better scores at the same level, due to the existence of multiple solutions in the allocation for group criteria targets. Allocation of target values will significantly depend on the specific technology option being considered. However, target values for the technology's most innovative aspects should preferably be above the maximum benchmark values. Otherwise, investors may not be willing to take the risk. On the other hand, target values not essential for the innovation can be within the achievable range from the technology spectrum known. A threshold value can be suggested whenever guidance on the possible maximum and minimum range of values can be obtained from the state-of-the-art. According to Chebyshev’s inequality [239], no more than 1/k 2 of the benchmark values can be k or more standard deviations (σ) away from the mean (μ) for any probability distribution and any constant k greater than 1. This probabilistic statement can be written as follows: 9 ( | : − S | ≥ UV ) ≤ 1 U - (16) with X being the performance variable under consideration with mean value μ and standard deviation σ. The probability that benchmark values lie outside the interval (μ - k×σ, μ + k×σ) does not exceed 1/k 2 (see Figure 5.4). For example, to identify a threshold value within 75% of benchmarks, the value of k = 2 can be obtained by solving 0.75 = 11/k 2 . Literature review such as [240] is quite helpful to synthesise the range of values that might be considered for key assessment criteria. Practical Capture Width Ratios (CWR) for heaving devices result in a mean value of 17.5% and a standard deviation of 12%. Assuming k = 2, the suggested threshold CWR should be set at 41.5%. Other useful literature sources provide relationships between the availability and resource level [241], the absorbed power and displaced volume [242], or the steel mass and total WEC volume [243]. In the lack of benchmarks in the existing literature, commercial values in known akin applications could also be considered reference values to establish the targets. GUIDING THE DESIGN DECISIONS 111 5.3 Assessment of Wave Energy Capabilities 5.3.1 Background Staged development processes are employed in many engineering sectors to ensure that technologies are developed in a controlled manner, therefore managing risk and uncertainty [244]. A staged development process defines suitable evaluation metrics that should be monitored throughout technology development, and thresholds for these metrics that must be met to demonstrate successful progress [245]. With clear evaluation metrics, progress can be quantified, and the development process guided to produce the desired outcome. Moreover, by identifying the weakest and strongest areas of the technology, the development efforts can be allocated more appropriately, and more costeffective designs can be produced through various design iterations. The previous chapter presented a common evaluation framework for wave energy technologies based on three levels of metrics. Satisfaction of wave energy requirements is expressed at different hierarchical levels through MOEs, MOPs and TPMs. Furthermore, aggregating system requirements into a final figure, or Global Merit (GM), enables a qualitative assessment of the overall suitability of the wave energy technology. Evaluation of technology performance is inherently a continuous activity [63]. As the wave energy technology matures, however, the purpose of this assessment will shift from strategic evaluation and feasibility studies to funding authorisation, budgeting and project control. Notably, most wave energy assessments carried out to date have been based on projected data and were not derived from direct open-sea deployment experience [246]. The reliance on projected figures leads to further uncertainties in the assessment process, which can be substantial depending on the stage of technology development, the degree of innovation, the data quality of assumptions, and the level of detail in the assessment. To the best of our knowledge, the quantification of the assessment uncertainty is a topic that has not been addressed in wave energy. The earlier the stage of technology development is, the lower the accuracy can be achieved during the evaluation due to the limited knowledge. Many evaluation areas may not have been adequately addressed at the initial maturity level where the concept is formulated (i.e. TRL1). They will require taking numerous assumptions leading to significant uncertainties. However, the accuracy of these estimates will be progressively refined in subsequent development stages. Thus, the uncertainty band will narrow. The value assigned to the assessment criteria of a wave energy technology should be supported by evidence of the activities carried out at each development stage [142]. For instance, the H2020-funded DTOceanPlus project proposes a series of activities a technology developer must complete at each main development stage [156]. GUIDING THE DESIGN DECISIONS 112 The state-of-the-art values in wave energy or any other closed-related sector application could be used as a reference for a holistic evaluation. Nevertheless, until it becomes feasible to collect practical evidence on the metrics, they will be obviously taken as control values; if the technology solution diverges much from its target, the overall performance might be compromised. It can be easily inferred that the larger the gap from the expected targets, the greater the challenges ahead, which can compromise the technological feasibility for market entry. The development trajectory must ensure that each identified challenge is addressed at the earliest stage since the same performance gap will be harder to overcome at the next TRL. 5.3.2 Performance Ratio (PR) System performance needs to be measured against a specified reference to provide a quantitative assessment. QFD considers a particular step to benchmark how the system requirements are currently satisfied. Besides, awareness of best practices in wave energy helps to assign acceptable, achievable and desirable ranges for system requirements, as mentioned in section 5.2.2 for the capture width [240]. These target values enable benchmarking of the relative performance of wave energy technologies in a quantitative manner. Evaluation criteria targets divide technology performance into two separate regions. There is a region of acceptable performance where the technology meets or exceeds the specified reference for the corresponding metric. By contrast, unacceptable performance occurs when the technology falls short concerning this reference value [247]. Any wave energy developer aims to reach the acceptable performance region for all mandatory metrics. Notwithstanding the metric under consideration, evaluation criteria can present two different performance behaviours. Whereas some metrics in the evaluation hierarchy must decrease to meet the established target (see Figure 5.5), other metrics display an increasing performance pattern (see Figure 5.6). M1 to M5 stands for measured values at each development stage. GUIDING THE DESIGN DECISIONS 113 Figure 5.5: Metric exhibiting a decreasing performance behaviour (lower is better). Let us define the Performance Ratio (PR) to overcome this opposing behaviour. For metrics that exhibit decreasing performance (i.e., lower is better), the PR i is calculated as follows: 9=  = E  D  (17) where T i and M i are the target and measured performance values, respectively, for the evaluation criteria i. Typical examples of this category of metrics are the Levelized Cost of Energy (LCOE), Mean Time to Repair (MTTR) and Waiting time (t w ). Alternatively, for metrics that show an increasing performance pattern (i.e., higher is better), the PR i is calculated by reversing this quotient, which accounts for the percentage that the measured performance exceeds the target value. 9=  = D  E  (18) Some examples of this category of metrics are the Availability Factor (AF), Mean Time To Failures (MTTF) and Relative bandwidth (B r ). Figure 5.6: Metric exhibiting an increasing performance behaviour (higher is better). The outcome of performance benchmarking for a wave energy concept estimates how close or far the technology is to achieving its previously established technical goals. A PR i ≥ 1 means that the wave energy technology is in the acceptable performance region for the evaluation criteria i. Conversely, a PR i < 1 denotes an unacceptable performance for this evaluation criteria. GUIDING THE DESIGN DECISIONS 114 Technologies with all mandatory requirements in the acceptable performance region can be benchmarked regarding their Commercial Attractiveness (CA). Otherwise, the Technical Achievability (TA) should be investigated. 5.3.3 Commercial Attractiveness (CA) Commercial Attractiveness (CA) is a broad concept encompassing various aspects ranging from economic profitability to stakeholder acceptability and size of the market opportunity. In wave energy, CA has been defined as the ratio of the target LCOE value to the calculated one to explore concepts beyond the existing technologies [248]. Note that this ratio fits perfectly within the generic PR definition from Eq. (22) & (23), but in this case applied to the Levelised Cost of Energy (LCOE), which is the most common high-level affordability metric. The assessment of CA is also mentioned in the International Evaluation Framework for Ocean Energy Technologies [142], this time comprising both the cost of energy and sustainability aspects such as environmental and social acceptance. The guideline, however, neither provides any metric for sustainability nor a procedure for the computation of the CA. To take into consideration the qualitative aspects beyond mere affordability (i.e. stakeholders’ preference), the proposed methodology will define CA as the product of the Global Merit (GM), derived from the qualitative assessment, and the Performance Ratio (PR), resulting from the quantitative estimations of the LCOE, whenever PR ≥ 1. The previous statement can be written as follows: If PR ≥ 1 8 = XD × 9= ; else 8 = 0 (19) The Geometric mean (G) operator is chosen to combine these attributes to prevent compensation. This definition has the advantage of enabling an objective comparison of wave energy technologies in various markets presenting dissimilar energy prices and responding to different stakeholder demands and priorities. Although CA is mainly a useful concept for comparing the affordability of wave energy systems, it can be equally applied to the partial evaluation of lower-level design attributes in wave energy technologies, such as MOEs, MOPs or TPMs. It only requires substituting the GM for the partial utility of the performance metric under consideration resulting from the QFD analysis. Figure 5.7 exemplifies the concept of CA for assessing two illustrative wave energy options. Whereas the single quantitative assessment will rank Option 2 on top of Option 1, the qualitative assessment reverses this order of preference. The hatched area (PR < 1) highlights the need to improve some wave energy capabilities. GUIDING THE DESIGN DECISIONS 115 Figure 5.7: Commercial Attractiveness (CA). 5.3.4 Technical Achievability (CA) For wave energy technologies that cannot meet one or more of the mandatory requirements and, therefore, technological improvements are needed, the Technical Achievability (TA) concept is introduced. It measures the technology development risk, time or effort to meet the target performance. This concept is particularly useful when guiding technologies with long development times such as wave energy. TA has been formulated in [248] for power performance and subsystem cost metrics. Improvement factors and learning rates are used to assess the degree of effort needed. Likewise, the reverse LCOE engineering method [6] was proposed to explore the limits of the technical parameters of wave energy technologies. This is a unidimensional analysis in which all partial evaluation criteria are fixed. The cost reduction is investigated to achieve a PR = 1. This methodology proposes an alternative but more comprehensive definition that can be used to assess wave energy performance at any hierarchical level. The TA definition has been adapted from [249], where it is used to support decisions of new defence technologies through their development lifecycle based on performance assessment. TA combines the Performance Ratio (PR) and Degree of Difficulty (DD) as shown in Equation (20). In this expression, DD effectively measures the risk probability, whilst the unmet performance (1 − PR) measures the risk severity or importance. GUIDING THE DESIGN DECISIONS 116 E8 = 9= 1 + ( 1 − 9= )  ZZ (20) Table 5.1 presents the DD levels and their corresponding numerical values. The risk levels are based on [249]. However, the assigned numerical values have been resized to a 9-point scale for consistency with the QFD ranking methods. The lower bound (0) indicates no risk in meeting the performance requirement, and success is guaranteed. Conversely, the upper bound (9) means that it is impossible to meet this requirement. Intermediate levels denote different degrees of difficulty. Table 5.1: Technical Difficulty (adapted from [249]). Level Degree of Difficulty (DD) Value 1 Very low uncertainty (certain feasibility) 0 2 Moderate uncertainty 1 3 High uncertainty 3 4 Very high uncertainty (fundamental breakthrough) 9 Figure 5.8 illustrates four achievability curves for different DD levels. For instance, the TA of one technology with very low uncertainty and PR = 0.6 (point a) is analogous to a technology with a PR = 0.94 (point c) and very high uncertainty, which requires a fundamental breakthrough. Similarly, a technology with very high uncertainty but the same PR = 0.6 (point b) will severely decrease its TA to 0.13. Figure 5.8: Technical Achievability (TA). Assigning the DD level to the system requirements of a wave energy technology under development may seem entirely subjective and challenging. Despite the difficulties, too little time spent in the early design phases can lead to gaps in understanding the problem GUIDING THE DESIGN DECISIONS 117 requirements, limited opportunities for novel concept generation and wasted time and money developing a concept that cannot perform well enough to become a viable solution [17]. In practical terms, the ability of new technology to meet its performance targets will depend on its innovation capability and it is limited by fundamental limits (ideality). In the early stages, emerging technologies will have significant improvement potential. In contrast, mature technologies in the later development stages will have limited improvement potential. Thus, DD indicates the Learning Rate (LR) needed to achieve a PR = 1. Different learning mechanisms have been described in the literature, as will be further discussed in CHAPTER 6. However, in the context of technology development, technological learning refers to the rate at which new knowledge is effectively acquired to improve its performance. As technology development progresses, new knowledge is acquired, the sources of variability for the various evaluation criteria are pinned down, and the uncertainty of the estimates is narrowed. This phenomenon is known as the “cone of uncertainty”. Defined initially for software development [250], this concept has been used in Project Management for decades to describe uncertainty reduction as engineering systems evolve. Figure 5.9: Cone of uncertainty and DD levels. At the concept stage, the initial estimate is based on minimal information. This estimate is a rough order of magnitude, whose variance can be as much as 100% depending on the source of evidence. Then the variance will be progressively diminished in subsequent GUIDING THE DESIGN DECISIONS 118 phases until the technology is finally deployed and there is no uncertainty remaining. The cone of uncertainty delimits the upper and lower bounds for five development stages as illustrated in Figure 5.9. All the estimations with PR < 1 that lie within the cone of uncertainty would be assigned a low DD. However, the same estimation should increase its DD if the PR does not improve. For instance, a PR = 0.7 can be assigned a DD level 1 at concept design (Stage 1) but increased to 3 in the design phase (Stage 2) or even rated 9 for later stages. The innovation capability is limited as the technology matures. Therefore, the PR should be penalised with a higher DD at later design stages. Conversely, an early TRL opens the room for improvements through innovation. Weber [151] expresses the same underlying idea in the generic WEC development trajectories displayed over a TRL-TPL matrix. Fundamental system changes are only feasible and affordable at low TRLs. Cost reduction and improved performance for mature technologies are mainly limited to learning by doing and economies of scale. 5.4 Practical Implementation 5.4.1 Benchmark Cases The practical implementation of the proposed methodology is showcased with six illustrative cases of hypothetical wave energy technologies. These benchmark cases are defined with an identical installed capacity (1 MW) but different combinations of MOE, leading to a plurality of LCOE values. The numerical values for the different evaluation criteria are summarised in Table 5.2. The LCOE is calculated using Equation (8), presented in the previous chapter. Table 5.2: Illustrative benchmark cases. Eval Criteria (MOE) Case 1 Case 2 Case 3 Case 4 Case 5 Case 6 P (MW) 1 1 1 1 1 1 CF (%) 30 25 50 40 20 25 AF (%) 95 97 99 98 92 85 CAPEX (M€) 1 1.2 3 3 1.9 3.5 OPEX (k€) 45 92 150 210 114 140 FCR (%) 8 10 9.4 10.2 11 9.3 LCOE (€/MWh) 50 100 100 150 200 250 Case 1 represents a high-performing wave energy technology in all evaluation criteria. It leads to the lowest LCOE of 50 €/MWh, which could compete in cost terms with traditional energy sources even without additional subsidies. GUIDING THE DESIGN DECISIONS 119 Case 2 and Case 3 involve two wave energy options that reach the same LCOE of 100 €/MWh through alternative performance paths. Whereas Case 2 has a moderately lowcapacity factor coupled with competitive lifetime costs, Case 3 displays the highest net energy production but also carries high CAPEX and OPEX costs. Depending on the innovation potential of these technologies, they could have scope for further energy cost reduction. Case 4 explores a wave energy technology that cannot compensate for the high lifetime costs despite the significant net energy production. Hence, Case 4 leads to an LCOE of 150 €/MWh. The EU’s SET Plan implementation plan for Ocean Energy [226] establishes a target LCOE of 150 €/MWh by 2030 and 100 €/MWh by 2035 for wave energy technologies. However, the two last benchmark cases have an LCOE beyond the EU’s SET Plan implementation plan targets. Case 5 has a very low-capacity factor and moderately high costs, which results in an LCOE of 200 €/MWh. Finally, Case 6 has the highest investment costs and lowest availability resulting in the least affordable option, which leads to the highest LCOE of 250 €/MWh. 5.4.2 Global Merit (GM) A value function is defined for each MOE to compare the different wave energy options. The function is normalised considering maximum (1) and minimum (0) utility values as shown in Table 5.3. Maximum and minimum bounds to the MOE have been assigned examining wave energy literature, as described in section 4.3.2. Table 5.3: Stakeholder Requirements and Utility. MOE Min = 0 Max = 1 Value Function CF (%) 0% ≥50% Maximisation type, Convex AF (%) ≤75% 100% Maximisation type, Concave CAPEX (M€) ≥5 M€ 0 M€ Minimisation type, Concave OPEX (k€) ≥500 k€ 0 k€ Minimisation type, Convex FCR (%) ≥20% ≤5% Constraint type CF is modelled with a maximisation type value function. It has a slightly convex shape: this reflects the increasing difficulty of improving utility as the CF gets closer to its maximum value. The neutral point is set to 17.5%, the average value reported for heaving point absorbers in [240]. Figure 5.10-a) depicts the function and the values for the six benchmark cases. APPENDICES 222 Appendix C: List of TRIZ 39 Technical Parameters Free access at https://onlinelibrary.wiley.com/doi/10.1002/9780470684320.app1 [232]. Table A.11: List of TRIZ 39 Technical Parameters. No. Title Explanation 1 Weight of moving object The mass of the object, in a gravitational field. The force that the body exerts on its support or suspension 2 Weight of stationary object The mass of the object, in a gravitational fi eld. The force that the body exerts on its support or suspension, or on the surface on which it rests. 3 Length of moving object Any one linear dimension, not necessarily the longest, is considered a length. 4 Length of stationary object Same. 5 Area of moving object A geometrical characteristic described by the part of a plane enclosed by a line. The part of a surface occupied by the object OR the square measure of the surface, either internal or external, of an object. 6 Area of stationary object Same. 7 Volume of moving object The cubic measure of space occupied by the object. Length x width x height for a rectangular object, height x area for a cylinder, etc. 8 Volume of stationary object Same. 9 Speed The velocity of an object; the rate of a process or action in time. 10 Force Force measures the interaction between systems. In Newtonian physics, force = mass x acceleration. In TRIZ, force is any interaction that is intended to change an object’s condition. 11 Stress or pressure Force per unit area. Also, tension. 12 Shape The external contours, appearance of a system. 13 Stability of the object’s composition The wholeness or integrity of the system; the relationship of the system’s constituent elements. Wear, chemical decomposition, and disassembly are all decreases in stability. Increasing entropy is decreasing stability. 14 Strength The extent to which the object is able to resist changing in response to force. Resistance to breaking. 15 Duration of action by a moving object The time that the object can perform the action. Service life. Mean time between failure is a measure of the duration of action. Also, durability. APPENDICES 223 No. Title Explanation 16 Duration of action by a stationary object Same. 17 Temperature The thermal condition of the object or system. Loosely includes other thermal parameters, such as heat capacity, that affect the rate of change of temperature. 18 Illumination intensity Light flux per unit area, also any other illumination characteristics of the system such as brightness, light quality, etc. 19 Use of energy by moving object The measure of the object’s capacity for doing work. In classical mechanics, Energy is the product of force x distance. This includes the use of energy provided by the super - system (such as electrical energy or heat.) Energy required to do a particular job. 20 Use of energy by stationary object Same. 21 Power The time rate at which work is performed. The rate of use of energy. 22 Loss of energy Use of energy that does not contribute to the job being done. See 19. Reducing the loss of energy sometimes requires different techniques from improving the use of energy, which is why this is a separate category. 23 Loss of substance Partial or complete, permanent or temporary, loss of some of a system’s materials, substances, parts or subsystems. 24 Loss of information Partial or complete, permanent or temporary, loss of data or access to data in or by a system. Frequently includes sensory data such as aroma, texture, etc. 25 Loss of time Time is the duration of an activity. Improving the loss of time means reducing the time taken for the activity. ‘Cycle time reduction’ is a common term. 26 Quantity of substance/the matter The number or amount of a system’s materials, substances, parts or subsystems which might be changed fully or partially, permanently or temporarily. 27 Reliability A system’s ability to perform its intended functions in predictable ways and conditions. 28 Measurement accuracy The closeness of the measured value to the actual value of a property of a system. Reducing the error in a measurement increases the accuracy of the measurement. 29 Manufacturing precision The extent to which the actual characteristics of the system or object match the specified or required characteristics. 30 External harm affects the object Susceptibility of a system to externally generated (harmful) effects. 31 Object - generated harmful factors A harmful effect is one that reduces the efficiency or quality of the functioning of the object or system. These APPENDICES 224 No. Title Explanation harmful effects are generated by the object or system, as part of its operation. 32 Ease of manufacture The degree of facility, comfort or effortlessness in manufacturing or fabricating the object/system. 33 Ease of operation Simplicity: The process is not easy if it requires a large number of people, large number of steps in the operation, needs special tools, etc. ‘Hard’ processes have low yield and ‘easy’ process have high yield; they are easy to do right. 34 Ease of repair Quality characteristics such as convenience, comfort, simplicity, and time to repair faults, failures or defects in a system. 35 Adaptability or versatility The extent to which a system/object positively responds to external changes. Also, a system that can be used in multiple ways for under a variety of circumstances. 36 Device complexity The number and diversity of elements and element interrelationships within a system. The user may be an element of the system that increases the complexity. The difficulty of mastering the system is a measure of its complexity. 37 Difficulty of detecting and measuring Measuring or m onitoring systems that are complex, costly, require much time and labour to set up and use, or that have complex relationships between components or components that interfere with each other all demonstrate ‘difficulty of detecting and measuring’. Increasing cost of measuring to a satisfactory error is also a sign of increased difficulty of measuring. 38 Extent of automation The extent to which a system or object performs its functions without human interface. The lowest level of automation is the use of a manually operated tool. For intermediate levels, humans program the tool, observe its operation, and interrupt or re-program as needed. For the highest level, the machine senses the operation needed, programs itself and monitors its own operations. 39 Productivity The number of functions or operations performed by a system per unit time. The time for a unit function or operation. The output per unit time, or the cost per unit output. APPENDICES 225 Appendix D: Contradiction Matrix Free access at https://www.triz.co.uk/learning-centre-innovation-materials [232]. Table A.12: Contradiction Matrix. Weight of moving object Weight of stationary object Length of moving object Length of stationary object Area of moving object Area of stationary object Volume of moving object Volume of stationary object Speed Force (Intensity) Stress or pressure Shape Stability of the object's composition Strength Duration of action of moving object Duration of action of stationary object Temperature Illumination intensity Use of energy by moving object Use of energy by stationary object Power Loss of Energy Loss of Substance Loss of Information Loss of Time Quantity of substance Reliability Measurement accuracy Manufacturing precision Object-affected harmful factors Object-generated harmful factors Ease of manufacture Ease of operation Ease of repair Adaptability or versatility Device complexity Difficulty of detecting and measuring Extent of automation Productivity 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 Weight of moving object 1 + 15, 8, 29,34 29, 17, 38, 34 29, 2, 40, 28 2, 8, 15, 38 8, 10, 18, 37 10, 36, 37, 40 10, 14, 35, 40 1, 35, 19, 39 28, 27, 18, 40 5, 34, 31, 35 6, 29, 4, 38 19, 1, 32 35, 12, 34, 31 12, 36, 18, 31 6, 2, 34, 19 5, 35, 3, 31 10, 24, 35 10, 35, 20, 28 3, 26, 18, 31 1, 3, 11, 27 28, 27, 35, 26 28, 35, 26, 18 22, 21, 18, 27 22, 35, 31, 39 27, 28, 1, 36 35, 3, 2, 24 2, 27, 28, 11 29, 5, 15, 8 26, 30, 36, 34 28, 29, 26, 32 26, 35 18, 19 35, 3, 24, 37 Weight of stationary object 2 + 10, 1, 29, 35 35, 30, 13, 2 5, 35, 14, 2 8, 10, 19, 35 13, 29, 10, 18 13, 10, 29, 14 26, 39, 1, 40 28, 2, 10, 27 2, 27, 19, 6 28, 19, 32, 22 19, 32, 35 18, 19, 28, 1 15, 19, 18, 22 18, 19, 28, 15 5, 8, 13, 30 10, 15, 35 10, 20, 35, 26 19, 6, 18, 26 10, 28, 8, 3 18, 26, 28 10, 1, 35, 17 2, 19, 22, 37 35, 22, 1, 39 28, 1, 9 6, 13, 1, 32 2, 27, 28, 11 19, 15, 29 1, 10, 26, 39 25, 28, 17, 15 2, 26, 35 1, 28, 15, 35 Length of moving object 3 8, 15, 29, 34 + 15, 17, 4 7, 17, 4, 35 13, 4, 8 17, 10, 4 1, 8, 35 1, 8, 10, 29 1, 8, 15, 34 8, 35, 29, 34 19 10, 15, 19 32 8, 35, 24 1, 35 7, 2, 35, 39 4, 29, 23, 10 1, 24 15, 2, 29 29, 35 10, 14, 29, 40 28, 32, 4 10, 28, 29, 37 1, 15, 17, 24 17, 15 1, 29, 17 15, 29, 35, 4 1, 28, 10 14, 15, 1, 16 1, 19, 26, 24 35, 1, 26, 24 17, 24, 26, 16 14, 4, 28, 29 Length of stationary object 4 35, 28, 40, 29 + 17, 7, 10, 40 35, 8, 2,14 28, 10 1, 14, 35 13, 14, 15, 7 39, 37, 35 15, 14, 28, 26 1, 10, 35 3, 35, 38, 18 3, 25 12, 8 6, 28 10, 28, 24, 35 24, 26, 30, 29, 14 15, 29, 28 32, 28, 3 2, 32, 10 1, 18 15, 17, 27 2, 25 3 1, 35 1, 26 26 30, 14, 7, 26 Area of moving object 5 2, 17, 29, 4 14, 15, 18, 4 + 7, 14, 17, 4 29, 30, 4, 34 19, 30, 35, 2 10, 15, 36, 28 5, 34, 29, 4 11, 2, 13, 39 3, 15, 40, 14 6, 3 2, 15, 16 15, 32, 19, 13 19, 32 19, 10, 32, 18 15, 17, 30, 26 10, 35, 2, 39 30, 26 26, 4 29, 30, 6, 13 29, 9 26, 28, 32, 3 2, 32 22, 33, 28, 1 17, 2, 18, 39 13, 1, 26, 24 15, 17, 13, 16 15, 13, 10, 1 15, 30 14, 1, 13 2, 36, 26, 18 14, 30, 28, 23 10, 26, 34, 2 Area of stationary object 6 30, 2, 14, 18 26, 7, 9, 39 + 1, 18, 35, 36 10, 15, 36, 37 2, 38 40 2, 10, 19, 30 35, 39, 38 17, 32 17, 7, 30 10, 14, 18, 39 30, 16 10, 35, 4, 18 2, 18, 40, 4 32, 35, 40, 4 26, 28, 32, 3 2, 29, 18, 36 27, 2, 39, 35 22, 1, 40 40, 16 16, 4 16 15, 16 1, 18, 36 2, 35, 30, 18 23 10, 15, 17, 7 Volume of moving object 7 2, 26, 29, 40 1, 7, 4, 35 1, 7, 4, 17 + 29, 4, 38, 34 15, 35, 36, 37 6, 35, 36, 37 1, 15, 29, 4 28, 10, 1, 39 9, 14, 15, 7 6, 35, 4 34, 39, 10, 18 2, 13, 10 35 35, 6, 13, 18 7, 15, 13, 16 36, 39, 34, 10 2, 22 2, 6, 34, 10 29, 30, 7 14, 1, 40, 11 25, 26, 28 25, 28, 2, 16 22, 21, 27, 35 17, 2, 40, 1 29, 1, 40 15, 13, 30, 12 10 15, 29 26, 1 29, 26, 4 35, 34, 16, 24 10, 6, 2, 34 Volume of stationary object 8 35, 10, 19, 14 19, 14 35, 8, 2, 14 + 2, 18, 37 24, 35 7, 2, 35 34, 28, 35, 40 9, 14, 17, 15 35, 34, 38 35, 6, 4 30, 6 10, 39, 35, 34 35, 16, 32 18 35, 3 2, 35, 16 35, 10, 25 34, 39, 19, 27 30, 18, 35, 4 35 1 1, 31 2, 17, 26 35, 37, 10, 2 Speed 9 2, 28, 13, 38 13, 14, 8 29, 30, 34 7, 29, 34 + 13, 28, 15, 19 6, 18, 38, 40 35, 15, 18, 34 28, 33, 1, 18 8, 3, 26, 14 3, 19, 35, 5 28, 30, 36, 2 10, 13, 19 8, 15, 35, 38 19, 35, 38, 2 14, 20, 19, 35 10, 13, 28, 38 13, 26 10, 19, 29, 38 11, 35, 27, 28 28, 32, 1, 24 10, 28, 32, 25 1, 28, 35, 23 2, 24, 35, 21 35, 13, 8, 1 32, 28, 13, 12 34, 2, 28, 27 15, 10, 26 10, 28, 4, 34 3, 34, 27, 16 10, 18 Force (Intensity) 10 8, 1, 37, 18 18, 13, 1, 28 17, 19, 9, 36 28, 10 19, 10, 15 1, 18, 36, 37 15, 9, 12, 37 2, 36, 18, 37 13, 28, 15, 12 + 18, 21, 11 10, 35, 40, 34 35, 10, 21 35, 10, 14, 27 19, 2 35, 10, 21 19, 17, 10 1, 16, 36, 37 19, 35, 18, 37 14, 15 8, 35, 40, 5 10, 37, 36 14, 29, 18, 36 3, 35, 13, 21 35, 10, 23, 24 28, 29, 37, 36 1, 35, 40, 18 13, 3, 36, 24 15, 37, 18, 1 1, 28, 3, 25 15, 1, 11 15, 17, 18, 20 26, 35, 10, 18 36, 37, 10, 19 2, 35 3, 28, 35, 37 Stress or pressure 11 10, 36, 37, 40 13, 29, 10, 18 35, 10, 36 35, 1, 14, 16 10, 15, 36, 28 10, 15, 36, 37 6, 35, 10 35, 24 6, 35, 36 36, 35, 21 + 35, 4, 15, 10 35, 33, 2, 40 9, 18, 3, 40 19, 3, 27 35, 39, 19, 2 14, 24, 10, 37 10, 35, 14 2, 36, 25 10, 36, 3, 37 37, 36, 4 10, 14, 36 10, 13, 19, 35 6, 28, 25 3, 35 22, 2, 37 2, 33, 27, 18 1, 35, 16 11 2 35 19, 1, 35 2, 36, 37 35, 24 10, 14, 35, 37 Shape 12 8, 10, 29, 40 15, 10, 26, 3 29, 34, 5, 4 13, 14, 10, 7 5, 34, 4, 10 14, 4, 15, 22 7, 2, 35 35, 15, 34, 18 35, 10, 37, 40 34, 15, 10, 14 + 33, 1, 18, 4 30, 14, 10, 40 14, 26, 9, 25 22, 14, 19, 32 13, 15, 32 2, 6, 34, 14 4, 6, 2 14 35, 29, 3, 5 14, 10, 34, 17 36, 22 10, 40, 16 28, 32, 1 32, 30, 40 22, 1, 2, 35 35, 1 1, 32, 17, 28 32, 15, 26 2, 13, 1 1, 15, 29 16, 29, 1, 28 15, 13, 39 15, 1, 32 17, 26, 34, 10 Stability of the object's composition 13 21, 35, 2, 39 26, 39, 1, 40 13, 15, 1, 28 37 2, 11, 13 39 28, 10, 19, 39 34, 28, 35, 40 33, 15, 28, 18 10, 35, 21, 16 2, 35, 40 22, 1, 18, 4 + 17, 9, 15 13, 27, 10, 35 39, 3, 35, 23 35, 1, 32 32, 3, 27, 16 13, 19 27, 4, 29, 18 32, 35, 27, 31 14, 2, 39, 6 2, 14, 30, 40 35, 27 15, 32, 35 13 18 35, 24, 30, 18 35, 40, 27, 39 35, 19 32, 35, 30 2, 35, 10, 16 35, 30, 34, 2 2, 35, 22, 26 35, 22, 39, 23 1, 8, 35 23, 35, 40, 3 Strength 14 1, 8, 40, 15 40, 26, 27, 1 1, 15, 8, 35 15, 14, 28, 26 3, 34, 40, 29 9, 40, 28 10, 15, 14, 7 9, 14, 17, 15 8, 13, 26, 14 10, 18, 3, 14 10, 3, 18, 40 10, 30, 35, 40 13, 17, 35 + 27, 3, 26 30, 10, 40 35, 19 19, 35, 10 35 10, 26, 35, 28 35 35, 28, 31, 40 29, 3, 28, 10 29, 10, 27 11, 3 3, 27, 16 3, 27 18, 35, 37, 1 15, 35, 22, 2 11, 3, 10, 32 32, 40, 25, 2 27, 11, 3 15, 3, 32 2, 13, 25, 28 27, 3, 15, 40 15 29, 35, 10, 14 Duration of action of moving object 15 19, 5, 34, 31 2, 19, 9 3, 17, 19 10, 2, 19, 30 3, 35, 5 19, 2, 16 19, 3, 27 14, 26, 28, 25 13, 3, 35 27, 3, 10 + 19, 35, 39 2, 19, 4, 35 28, 6, 35, 18 19, 10, 35, 38 28, 27, 3, 18 10 20, 10, 28, 18 3, 35, 10, 40 11, 2, 13 3 3, 27, 16, 40 22, 15, 33, 28 21, 39, 16, 22 27, 1, 4 12, 27 29, 10, 27 1, 35, 13 10, 4, 29, 15 19, 29, 39, 35 6, 10 35, 17, 14, 19 Duration of action by stationary object 16 6, 27, 19, 16 1, 40, 35 35, 34, 38 39, 3, 35, 23 + 19, 18, 36, 40 16 27, 16, 18, 38 10 28, 20, 10, 16 3, 35, 31 34, 27, 6, 40 10, 26, 24 17, 1, 40, 33 22 35, 10 1 1 2 25, 34, 6, 35 1 20, 10, 16, 38 Temperature 17 36,22, 6, 38 22, 35, 32 15, 19, 9 15, 19, 9 3, 35, 39, 18 35, 38 34, 39, 40, 18 35, 6, 4 2, 28, 36, 30 35, 10, 3, 21 35, 39, 19, 2 14, 22, 19, 32 1, 35, 32 10, 30, 22, 40 19, 13, 39 19, 18, 36, 40 + 32, 30, 21, 16 19, 15, 3, 17 2, 14, 17, 25 21, 17, 35, 38 21, 36, 29, 31 35, 28, 21, 18 3, 17, 30, 39 19, 35, 3, 10 32, 19, 24 24 22, 33, 35, 2 22, 35, 2, 24 26, 27 26, 27 4, 10, 16 2, 18, 27 2, 17, 16 3, 27, 35, 31 26, 2, 19, 16 15, 28, 35 Illumination intensity 18 19, 1, 32 2, 35, 32 19, 32, 16 19, 32, 26 2, 13, 10 10, 13, 19 26, 19, 6 32, 30 32, 3, 27 35, 19 2, 19, 6 32, 35, 19 + 32, 1, 19 32, 35, 1, 15 32 13, 16, 1, 6 13, 1 1, 6 19, 1, 26, 17 1, 19 11, 15, 32 3, 32 15, 19 35, 19, 32, 39 19, 35, 28, 26 28, 26, 19 15, 17, 13, 16 15, 1, 19 6, 32, 13 32, 15 2, 26, 10 2, 25, 16 Use of energy by moving object 19 12,18, 28,31 12, 28 15, 19, 25 35, 13, 18 8, 35, 35 16, 26, 21, 2 23, 14, 25 12, 2, 29 19, 13, 17, 24 5, 19, 9, 35 28, 35, 6, 18 - 19, 24, 3, 14 2, 15, 19 + - 6, 19, 37, 18 12, 22, 15, 24 35, 24, 18, 5 35, 38, 19, 18 34, 23, 16, 18 19, 21, 11, 27 3, 1, 32 1, 35, 6, 27 2, 35, 6 28, 26, 30 19, 35 1, 15, 17, 28 15, 17, 13, 16 2, 29, 27, 28 35, 38 32, 2 12, 28, 35 Use of energy by stationary object 20 19, 9, 6, 27 36, 37 27, 4, 29, 18 35 19, 2, 35, 32 - + 28, 27, 18, 31 3, 35, 31 10, 36, 23 10, 2, 22, 37 19, 22, 18 1, 4 19, 35, 16, 25 1, 6 Power 21 8, 36, 38, 31 19, 26, 17, 27 1, 10, 35, 37 19, 38 17, 32, 13, 38 35, 6, 38 30, 6, 25 15, 35, 2 26, 2, 36, 35 22, 10, 35 29, 14, 2, 40 35, 32, 15, 31 26, 10, 28 19, 35, 10, 38 16 2, 14, 17, 25 16, 6, 19 16, 6, 19, 37 + 10, 35, 38 28, 27, 18, 38 10, 19 35, 20, 10, 6 4, 34, 19 19, 24, 26, 31 32, 15, 2 32, 2 19, 22, 31, 2 2, 35, 18 26, 10, 34 26, 35, 10 35, 2, 10, 34 19, 17, 34 20, 19, 30, 34 19, 35, 16 28, 2, 17 28, 35, 34 Loss of Energy 22 15, 6, 19, 28 19, 6, 18, 9 7, 2, 6, 13 6, 38, 7 15, 26, 17, 30 17, 7, 30, 18 7, 18, 23 7 16, 35, 38 36, 38 14, 2, 39, 6 26 19, 38, 7 1, 13, 32, 15 3, 38 + 35, 27, 2, 37 19, 10 10, 18, 32, 7 7, 18, 25 11, 10, 35 32 21, 22, 35, 2 21, 35, 2, 22 35, 32, 1 2, 19 7, 23 35, 3, 15, 23 2 28, 10, 29, 35 Loss of substance 23 35, 6, 23, 40 35, 6, 22, 32 14, 29, 10, 39 10, 28,24 35, 2, 10, 31 10, 18, 39, 31 1, 29, 30, 36 3, 39, 18, 31 10, 13, 28, 38 14, 15, 18, 40 3, 36, 37, 10 29, 35, 3, 5 2, 14, 30, 40 35, 28, 31, 40 28, 27, 3, 18 27, 16, 18, 38 21, 36, 39, 31 1, 6, 13 35, 18, 24, 5 28, 27, 12, 31 28, 27, 18, 38 35, 27, 2, 31 + 15, 18, 35, 10 6, 3, 10, 24 10, 29, 39, 35 16, 34, 31, 28 35, 10, 24, 31 33, 22, 30, 40 10, 1, 34, 29 15, 34, 33 32, 28, 2, 24 2, 35, 34, 27 15, 10, 2 35, 10, 28, 24 35, 18, 10, 13 35, 10, 18 28, 35, 10, 23 Loss of Information 24 10, 24, 35 10, 35, 5 1, 26 26 30, 26 30, 16 2, 22 26, 32 10 10 19 10, 19 19, 10 + 24, 26, 28, 32 24, 28, 35 10, 28, 23 22, 10, 1 10, 21, 22 32 27, 22 35, 33 35 13, 23, 15 Loss of Time 25 10, 20, 37, 35 10, 20, 26, 5 15, 2, 29 30, 24, 14, 5 26, 4, 5, 16 10, 35, 17, 4 2, 5, 34, 10 35, 16, 32, 18 10, 37, 36,5 37, 36,4 4, 10, 34, 17 35, 3, 22, 5 29, 3, 28, 18 20, 10, 28, 18 28, 20, 10, 16 35, 29, 21, 18 1, 19, 26, 17 35, 38, 19, 18 1 35, 20, 10, 6 10, 5, 18, 32 35, 18, 10, 39 24, 26, 28, 32 + 35, 38, 18, 16 10, 30, 4 24, 34, 28, 32 24, 26, 28, 18 35, 18, 34 35, 22, 18, 39 35, 28, 34, 4 4, 28, 10, 34 32, 1, 10 35, 28 6, 29 18, 28, 32, 10 24, 28, 35, 30 Quantity of substance/the matter 26 35, 6, 18, 31 27, 26, 18, 35 29, 14, 35, 18 15, 14, 29 2, 18, 40, 4 15, 20, 29 35, 29, 34, 28 35, 14, 3 10, 36, 14, 3 35, 14 15, 2, 17, 40 14, 35, 34, 10 3, 35, 10, 40 3, 35, 31 3, 17, 39 34, 29, 16, 18 3, 35, 31 35 7, 18, 25 6, 3, 10, 24 24, 28, 35 35, 38, 18, 16 + 18, 3, 28, 40 13, 2, 28 33, 30 35, 33, 29, 31 3, 35, 40, 39 29, 1, 35, 27 35, 29, 25, 10 2, 32, 10, 25 15, 3, 29 3, 13, 27, 10 3, 27, 29, 18 8, 35 13, 29, 3, 27 Reliability 27 3, 8, 10, 40 3, 10, 8, 28 15, 9, 14, 4 15, 29, 28, 11 17, 10, 14, 16 32, 35, 40, 4 3, 10, 14, 24 2, 35, 24 21, 35, 11, 28 8, 28, 10, 3 10, 24, 35, 19 35, 1, 16, 11 11, 28 2, 35, 3, 25 34, 27, 6, 40 3, 35, 10 11, 32, 13 21, 11, 27, 19 36, 23 21, 11, 26, 31 10, 11, 35 10, 35, 29, 39 10, 28 10, 30, 4 21, 28, 40, 3 + 32, 3, 11, 23 11, 32, 1 27, 35, 2, 40 35, 2, 40, 26 27, 17, 40 1, 11 13, 35, 8, 24 13, 35, 1 27, 40, 28 11, 13, 27 1, 35, 29, 38 Measurement accuracy 28 32, 35, 26, 28 28, 35, 25, 26 28, 26, 5, 16 32, 28, 3, 16 26, 28, 32, 3 26, 28, 32, 3 32, 13, 6 28, 13, 32, 24 32, 2 6, 28, 32 6, 28, 32 32, 35, 13 28, 6, 32 28, 6, 32 10, 26, 24 6, 19, 28, 24 6, 1, 32 3, 6, 32 3, 6, 32 26, 32, 27 10, 16, 31, 28 24, 34, 28, 32 2, 6, 32 5, 11, 1, 23 + 28, 24, 22, 26 3, 33, 39, 10 6, 35, 25, 18 1, 13, 17, 34 1, 32, 13, 11 13, 35, 2 27, 35, 10, 34 26, 24, 32, 28 28, 2, 10, 34 10, 34, 28, 32 Manufacturing precision 29 28, 32, 13, 18 28, 35, 27, 9 10, 28, 29, 37 2, 32, 10 28, 33, 29, 32 2, 29, 18, 36 32, 23, 2 25, 10, 35 10, 28, 32 28, 19, 34, 36 3, 35 32, 30, 40 30, 18 3, 27 3, 27, 40 19, 26 3, 32 32, 2 32, 2 13, 32, 2 35, 31, 10, 24 32, 26, 28, 18 32, 30 11, 32, 1+ 26, 28, 10, 36 4, 17, 34, 26 1, 32, 35, 23 25, 10 26, 2, 18 26, 28, 18, 23 10, 18, 32, 39 Object-affected harmful factors 30 22, 21, 27, 39 2, 22, 13, 24 17, 1, 39, 4 1, 18 22, 1, 33, 28 27, 2, 39, 35 22, 23, 37, 35 34, 39, 19, 27 21, 22, 35, 28 13, 35, 39, 18 22, 2, 37 22, 1, 3, 35 35, 24, 30, 18 18, 35, 37, 1 22, 15, 33, 28 17, 1, 40, 33 22, 33, 35, 2 1, 19, 32, 13 1, 24, 6, 27 10, 2, 22, 37 19, 22, 31, 2 21, 22, 35, 2 33, 22, 19, 40 22, 10, 2 35, 18, 34 35, 33, 29, 31 27, 24, 2, 40 28, 33, 23, 26 26, 28, 10, 18 + 24, 35, 2 2, 25, 28, 39 35, 10, 2 35, 11, 22, 31 22, 19, 29, 40 22, 19, 29, 40 33, 3, 34 22, 35, 13, 24 Object-generated harmful factors 31 19, 22, 15, 39 35, 22, 1, 39 17, 15, 16, 22 17, 2, 18, 39 22, 1, 40 17, 2, 40 30, 18, 35, 4 35, 28, 3, 23 35, 28, 1, 40 2, 33, 27, 18 35, 1 35, 40, 27, 39 15, 35, 22, 2 15, 22, 33, 31 21, 39, 16, 22 22, 35, 2, 24 19, 24, 39, 32 2, 35, 6 19, 22, 18 2, 35, 18 21, 35, 2, 22 10, 1, 34 10, 21, 29 1, 22 3, 24, 39, 1 24, 2, 40, 39 3, 33, 26 4, 17, 34, 26 + 19, 1, 31 2, 21, 27, 1 2 22, 35, 18, 39 Ease of manufacture 32 28, 29, 15, 16 1, 27, 36, 13 1, 29, 13, 17 15, 17, 27 13, 1, 26, 12 16, 40 13, 29, 1, 40 35 35, 13, 8, 1 35, 12 35, 19, 1, 37 1, 28, 13, 27 11, 13, 1 1, 3, 10, 32 27, 1, 435, 16 27, 26, 18 28, 24, 27, 1 28, 26, 27, 1 1, 4 27, 1, 12, 24 19, 35 15, 34, 33 32, 24, 18, 16 35, 28, 34, 4 35, 23, 1, 24 1, 35, 12, 18 24, 2 + 2, 5, 13, 16 35, 1, 11, 9 2, 13, 15 27, 26, 1 6, 28, 11, 1 8, 28, 1 35, 1, 10, 28 Ease of operation 33 25, 2, 13, 15 6, 13, 1, 25 1, 17, 13, 12 1, 17, 13, 16 18, 16, 15, 39 1, 16, 35, 15 4, 18, 39, 31 18, 13, 34 28, 13 35 2, 32, 12 15, 34, 29, 28 32, 35, 30 32, 40, 3, 28 29, 3, 8, 25 1, 16, 25 26, 27, 13 13, 17, 1, 24 1, 13, 24 35, 34, 2, 10 2, 19, 13 28, 32, 2, 24 4, 10, 27, 22 4, 28, 10, 34 12, 35 17, 27, 8, 40 25, 13, 2, 34 1, 32, 35, 23 2, 25, 28, 39 2, 5, 12 + 12, 26, 1, 32 15, 34, 1, 16 32, 26, 12, 17 1, 34, 12, 3 15, 1, 28 Ease of repair 34 2, 27 35, 11 2, 27, 35, 11 1, 28, 10, 25 3, 18, 31 15, 13, 32 16, 25 25, 2, 35, 11 1 34, 9 1, 11, 10 13 1, 13, 2, 4 2, 35 11, 1, 2, 9 11, 29, 28, 27 1 4, 10 15, 1, 13 15, 1, 28, 16 15, 10, 32, 2 15, 1, 32, 19 2, 35, 34, 27 32, 1, 10, 25 2, 28, 10, 25 11, 10, 1, 16 10, 2, 13 25, 10 35, 10, 2, 16 1, 35, 11, 10 1, 12, 26, 15 + 7, 1, 4, 16 35, 1, 13, 11 34, 35, 7, 13 1, 32, 10 Adaptability or versatility 35 1, 6, 15, 8 19, 15, 29, 16 35, 1, 29, 2 1, 35, 16 35, 30, 29, 7 15, 16 15, 35, 29 35, 10, 14 15, 17, 20 35, 16 15, 37, 1, 8 35, 30, 14 35, 3, 32, 6 13, 1, 35 2, 16 27, 2, 3, 35 6, 22, 26, 1 19, 35, 29, 13 19, 1, 29 18, 15, 1 15, 10, 2, 13 35, 28 3, 35, 15 35, 13, 8, 24 35, 5, 1, 10 35, 11, 32, 31 1, 13, 31 15, 34, 1, 16 1, 16, 7, 4 + 15, 29, 37, 28 1 27, 34, 35 35, 28, 6, 37 Device complexity 36 26, 30, 34, 36 2, 26, 35, 39 1, 19, 26, 24 26 14, 1, 13, 16 6, 36 34, 26, 61, 16 34, 10, 28 26, 16 19, 1, 35 29, 13, 28, 15 2, 22, 17, 19 2, 13, 28 10, 4, 28, 15 2, 17, 13 24, 17, 13 27, 2, 29, 28 20, 19, 30, 34 10, 35, 13, 2 35, 10, 28, 29 6, 29 13, 3, 27, 10 13, 35, 1 2, 26, 10, 34 26, 24, 32 22, 19, 29, 40 19, 1 27, 26, 1, 13 27, 9, 26, 24 1, 13 29, 15, 28, 37 + 15, 10, 37, 28 15, 1, 24 12, 17, 28 Difficulty of detecting and measuring 37 27, 26, 28, 13 6, 13, 28, 1 16, 17, 26, 24 26 2, 13, 18, 17 2, 39, 30, 16 29, 1, 4, 16 2, 18, 26, 31 3, 4, 16, 35 30, 28, 40, 19 35, 36, 37, 32 27, 13, 1, 39 11, 22, 39, 30 27, 3, 15, 28 19, 29, 39, 25 25, 34, 6, 35 3, 27, 35, 16 2, 24, 26 35, 38 19, 35, 16 18, 1, 16, 10 35, 3, 15, 19 1, 18, 10, 24 35, 33, 27, 22 18, 28, 32, 9 3, 27, 29, 18 27, 40, 28, 8 26, 24, 32, 28 22, 19, 29, 28 2, 21 5, 28, 11, 29 2, 5 12, 26 1, 15 15, 10, 37, 28 + 34, 21 35, 18 Extent of automation 38 28, 26, 18, 35 28, 26, 35, 10 14, 13, 17, 28 23 17, 14, 13 35, 13, 16 28, 10 2, 35 13, 35 15, 32, 1, 13 18, 1 25, 13 6, 9 26, 2, 19 8, 32, 19 2, 32, 13 28, 2, 27 23, 28 35, 10, 18, 5 35, 33 24, 28, 35, 30 35, 13 11, 27, 32 28, 26, 10, 34 28, 26, 18, 23 2, 33 2 1, 26, 13 1, 12, 34, 3 1, 35, 13 27, 4, 1, 35 15, 24, 10 34, 27, 25 + 5, 12, 35, 26 Productivity 39 35, 26, 24, 37 28, 27, 15, 3 18, 4, 28, 38 30, 7, 14, 26 10, 26, 34, 31 10, 35, 17, 7 2, 6, 34, 10 35, 37, 10, 2 28, 15, 10, 36 10, 37, 14 14, 10, 34, 40 35, 3, 22, 39 29, 28, 10, 18 35, 10, 2, 18 20, 10, 16, 38 35, 21, 28, 10 26, 17, 19, 1 35, 10, 38, 19 1 35, 20, 10 28, 10, 29, 35 28, 10, 35, 23 13, 15, 23 35, 38 1, 35, 10, 38 1, 10, 34, 28 18, 10, 32, 1 22, 35, 13, 24 35, 22, 18, 39 35, 28, 2, 24 1, 28, 7, 10 1, 32, 10, 25 1, 35, 28, 37 12, 17, 28, 24 35, 18, 27, 2 5, 12, 35, 26 + Worsening Feature Improving Feature APPENDICES 226 Appendix E: List of TRIZ 40 Inventive Principles List of 40 inventive principles and 160 elementary operators based on the extensive experience of TRIZ application in industrial companies [315]. Table A.13: List of TRIZ 40 Inventive Principles. No. Inventive Principle Inventive Operator 1 Segmentation a) Divide the object into independent objects or parts. b) Design the object to be sectional or dismountable. c) Increase the object’s degree of fragmentation or segmentation: reduce size up to granules and powder, microand nano-level, molecules and atoms. d) Divide the function of the object or system into independent sub-functions. e) Divide the process steps into sub-steps, make two or more process steps instead of one. 2 Leaving out / Trimming a) Take out or remove the disturbing parts or substances from the system. b) Check which system components, parts or substances can be omitted. c) Take out or remove the disturbing functions from the system. Check which functions can be omitted. d) Take out or remove one of the process steps. e) Extract or single out the only one necessary part, substance, property or function from the system. 3 Local quality a) Change the uniform structure or properties of an object to a non-uniform. b) Change the uniform structure or properties of surrounding medium (external environment) to non-uniform. c) The various parts of the object should fulfil different functions. d) Each part of the object should function under conditions which are most suitable for its operation. e) Different parts of the object can have opposite properties (e.g. one part hot, another part cold). 4 Asymmetry a) Replace the symmetrical shape or property of an object with one that is asymmetrical. b) If the object is already asymmetrical, increase its degree of asymmetry. c) Convert the asymmetrical shape or property of an object back to symmetrical one. 5 Combining a) Combine identical objects in space to perform parallel operations. b) Combine functions or process steps in time to perform parallel or contiguous operations. c) Combine similar objects with different characteristics, properties or parameters. d) Combine different objects complementing each other and enhancing positive properties. e) Combine objects with competing, alternative or opposing properties (e.g. caustic and acid). 6 Universality a) Make a part or object universal, performing multiple functions, and thus eliminate unnecessary objects. b) Make a process universal, for example suitable for different substances, conditions, operations, etc. 7 Nesting / Integration a) Place an object inside another one, which, in turn, is placed inside a third object and so on (Nested Doll principle). b) An object is passed through the cavities in another object. APPENDICES 227 No. Inventive Principle Inventive Operator c) Telescopic objects or systems. 8 Anti-weight a) Compensate the object’s weight by counterweight. b) Compensate the object’s weight by merging it with another object that provides a lifting force /buoyancy (e.g. floating object or hot-air balloon). c) Compensate the object’s weight by interaction with another medium (e.g. by means of aerodynamic or hydrodynamic forces). d) Use gravitational force or centrifugal force. 9 Prior counteraction of harm a) If it is necessary to perform an action with both harmful and useful effects, b) counteraction measures against harm must be taken in advance. c) If the object will be under working stress, create beforehand stress in direction which is opposite the undesirable working stress. Thus, the working stress can be compensated. d) If the object will be exposed to high temperatures, cool it beforehand to avoid overheating. e) Use rigid constructions, highly stable structures (e.g. honeycomb) to withstand extreme operating conditions like high temperature, high pressure, high volume. 10 Prior useful action f) Perform the required action or useful function in advance, either fully or partially. g) Pre-arrange the objects so they can come into action at the most convenient position and without losing time. h) Perform part of the process step or operation beforehand. 11 Preventive measure / Cushion in advance a) Compensate the low reliability of an object by preparing emergency countermeasures in advance. b) Increase process reliability by preparing emergency countermeasures in advance. 12 Equipotentiality a) Change the working conditions so that an object doesn’t have to be raised or lowered. b) Avoid changes of potential energy in the system. c) Avoid strong fluctuations of process parameter, peaks and valleys in energy d) consumption, thermal shocks, etc. 13 Inversion a) Instead of currently used action, carry out the inversed action with opposite direction or properties (e.g. heating instead of cooling, downwards instead upwards, etc). b) Make moving parts of the object fixed, and the fixed parts movable. c) Turn the object or process upside down. d) Perform the process or its phases in the reversed order. Change sequence of operations. e) Change properties or action mode of the external environment to the opposite (e.g. moving to fixed, high pressure to vacuum, etc). 14 Sphericity and rotation a) Replace rectilinear parts or forms with curved, ball-shaped forms or structures. b) Use balls, rollers, spheres, domes or spirals. Apply cylindrical, conical or multi-conical configurations. c) Provide rotary motion of parts, substances or force fields. Replace a linear motion of objects or substances with rotation. d) Use vortex flows and swirling motion for cyclonic separation, cooling or heating. e) Use centrifugal and Coriolis forces. APPENDICES 228 No. Inventive Principle Inventive Operator 15 Dynamism a) Make an object, external environment or process adjustable to enable optimal performance parameter at each stage of operation. b) Divide an object into elements whose position changes relative to one another. Make object movable and adaptive. c) If a process is rigid or inflexible, make it adaptive. d) Use adaptive and flexible elements like joints, springs, elastomers, fluids, gases, magnets/electromagnets. e) Change static force fields to movable or dynamics fields, which change in time or in structure. 16 Partial or excessive action a) If it is difficult to obtain exactly 100% of a desired effect, then obtain slightly more or slightly less. The problem may be considerably easier to solve. b) If it is difficult to obtain the optimal or exact amount of substance, apply an excessive amount. Remove surplus substance by using additional force or energy field. c) If it is difficult to obtain the optimal or exact action (force or energy field), apply an excessive action. Compensate surplus action by using protective shield. 17 Shift to another dimension a) Change the straight line to a 2D or 3D curve, or plane form or movement to the three-dimensional. b) Reduce object size or dimensions to mini-, microor nano-level. c) Use a multi-layered or multi-storey structure of objects or processes. d) Tilt the object, lay it on its side, use reversed side or internal surfaces (hollows). e) Increase contact area between objects or substances from the contact along a line or on a surface to interaction in 3D-space. 18 Mechanical vibration a) Cause an object to oscillate or vibrate. b) If oscillation already exists, change, or increase its frequency (even up to the ultrasonic). c) Use the resonant frequency of an object and self-oscillations. d) Use piezo-electric vibrators instead of mechanical ones. e) Combine ultrasonic oscillations with other fields: ultrasonic and electromagnetic vibrations; ultrasonic with heat source; ultrasonic with capillary effect. 19 Periodic action a) Replace a continuous action with a periodic or pulsed one. b) If an action is already periodic, change its frequency, amplitude, and mean value. c) Use pauses between impulses to perform additional actions. The frequencies of all periodic actions should be matched or intentionally mismatched. d) Avoid or use resonance. The frequencies of the periodic action should be matched or intentionally mismatched to the natural frequency of one of the objects. e) Apply mutually exclusive periodic actions alternately. Separate contradictory properties in time. 20 Continuity of useful action a) Carry on a process continuously (without pauses). b) All parts of an object or equipment should operate at full load. c) Eliminate all idle and intermittent actions or work. 21 Skipping / Rushing through a) Perform a process, or individual stages at very high speed to skip destructible or hazardous operations. b) Increase dramatically the speed or power in a process that may result in new useful properties of the system. APPENDICES 229 No. Inventive Principle Inventive Operator 22 Converting harm into benefit a) Utilize harmful factors or negative environmental effects to obtain a positive effect. b) Remove a harmful factor by combining it with another harmful factor. c) Amplify a harmful action to such a degree that it is no longer harmful. 23 Feedback and automation a) Introduce feedback to improve a process or action. b) If feedback already exists, change it (e.g. its magnitude or influence). c) Increase a degree of automation and controllability of the system, use adaptive feedback control and artificial intelligence. d) Utilize information and data processing. 24 Mediator a) Introduce an intermediate object to transfer or carry out an action. b) Merge one object temporarily with another intermediate object that can be easily removed. c) Use an intermediary process or process step. 25 Self-service / Use of resources a) Make the object serve itself and carry out supplementary and repair operations. b) Utilize waste resources, energy, or substances. c) Use available environmental resources: substances, energy, space, information, and data. 26 Copying and modelling a) Use simple inexpensive copies instead of unavailable, expensive, fragile objects. b) Replace an object or process with its optical copies (graphical images, threedimensional images, holograms). c) If visible optical copies are already used, move to infrared, ultraviolet, X-ray copies, optical or radio shadows. d) Use digital models and computer simulations. e) Use virtual reality, computer augmented reality, etc. 27 Disposability / Cheap short-living objects a) Use cheap short-living objects or substances. b) Replace an expensive object by a multiple inexpensive one, forgoing certain qualities (e.g. longevity). c) Use one-way disposable or temporary objects. d) Create cheap short-living objects from available resources, such as waste, water, air, environment, etc. 28 Replacement of the mechanical working principle a) Replace the mechanical working principle by electric, magnetic, or electromagnetic one. b) Use optical working principle (e.g. IR, UV, Laser, LED). c) Use an acoustic or sound system (e.g. ultrasonic, infrasonic, etc). d) Use thermal, chemical, olfactory (smell) or biological system. e) Use electromagnetic fields in conjunction with ferromagnetic particles, magnetic or electro-rheological fluids. 29 Pneumatic or hydraulic constructions a) Use gas or liquid as working elements, for example gas and liquid flows, aeroand hydrostatics or dynamics, hydro-reactive systems, etc. b) Replace solid parts by gas or liquid (e.g. inflatable elements, air cushion, parts filled with liquids under pressure). c) Use negative pressure, partial vacuum, and vacuum chambers. d) Use fluidisation of powders, dusts or granulates in the air flow, for example in the fluidised bed. e) Use fluids and gases for heat and energy transfer: heat pipe, heat exchanger, vortex cooler tube, shock waves, cavitation, etc. APPENDICES 230 No. Inventive Principle Inventive Operator 30 Flexible shells or thin films a) Replace traditional constructions with those made of flexible shells or thin films. b) Isolate the object or parts from its environment using flexible shells or thin films. c) Use piezoelectric foils. d) Apply flexible brushes for guiding, cleaning, vibration damping. e) Use membranes, membrane operations and processing. 31 Porous materials a) Make an object or its surface porous, or add porous elements (inserts, covers, etc). Utilize objects with hollow spaces or cavities. b) If an object is already porous, fill the pores with a useful substance. c) Utilize capillary and micro-capillary effects in porous materials. d) Use the filler in combination with physical effects (e.g. ultrasound, electromagnetic field, temperature differences, osmosis, etc). e) Use structured porosity, like honeycombed structure, pipes or canals, capillaries on the molecular level. 32 Changing colour a) Change the colour of an object or its external environment. b) Change the degree of transparency of an object or its external environment. c) Use coloured additives to observe an object or process which is difficult to see. d) If such additives are already being used, add luminescent traces or other tracer elements. 33 Homogeneity a) Make objects interacting with a given object of the same material, or material with identical properties. b) The interacting objects should have similar properties such as size, weight, temperature, optical or magnetic properties, etc. c) Homogeneous or uniform distribution of material or properties (temperature, concentration, viscosity, etc). 34 Discarding and restoring a) Reject or modify (discard, dissolve, evaporate, etc) a part of an object after it has completed its function or become useless. b) Restore any part of an object which has become exhausted or depleted directly in operation. c) Generate object or material just on time and on site, that can be more efficient and less expensive. 35 Transformation of the physical and chemical properties a) Change an object’s aggregate state (e.g. solid to liquid or liquid to gas - or vice versa). b) Change the object’s concentration or consistency. c) Change other relevant physical properties or operational conditions (pressure, density, hardness, viscosity, conductivity, magnetism, etc), separately or together. d) Change the object’s temperature. e) Change other chemical properties or operational conditions (formulation, pH, solubility, etc), change process chemistry. 36 Phase transitions a) Use phenomena accompanying the phase transitions of a substance (e.g. the emission or absorption of heat energy, density or volume changes, etc). b) Use the second-order phase transitions: shape memory of metals and polymers, transition beyond the Curie point in ferromagnetic substances, conversion of a crystalline structure, etc. APPENDICES 231 No. Inventive Principle Inventive Operator 37 Thermal expansion and contraction a) Use thermal expansion or contraction of materials (solids, fluids or gases). b) Use constructions made of multiple materials with different coefficients of thermal expansion (e.g. bi-metals). c) Use heat shrinkable materials (e.g. heat shrinkable tubing). d) Use thermo-mechanical shape memory of metals and polymers. 38 Strong oxidants a) Replace common air with oxygen-enriched air. b) Replace oxygen-enriched air with pure oxygen. c) Expose air or oxygen to ionising radiation, use ionized oxygen. d) Raise the ozone level. Replace ozonized (or ionized) oxygen with ozone. e) Use other strong or extreme oxidants. 39 Inert environment a) Replace the normal environment with an inert one. b) Carry out the process in inert atmosphere of (e.g. helium or argon). c) Carry out the process in a vacuum. d) Use inert, protective or antioxidant coatings or additives. e) Use foams or foamed substances to protect or isolate objects. 40 Composite materials a) Replace a homogeneous, uniform material with a composite one (e.g. carbon-fibre composite, laminates, etc). b) Take advantage of the anisotropic properties of the composite materials, like mechanical, electrical, thermal. c) Use additives to provide specific properties to the composites (e.g. fire retardant additives in polymer matrix composites). d) Use materials with composite microstructure, controllable by external field. e) Use a composition of materials in different aggregate states (e.g. mixture of liquid and gas). This page intentionally left blank 239 PUBLICATIONS The PhD process requires actively disseminating the findings of the research in the form of reports, conference communications, peer-reviewed journal publications or books. In this chapter, the main research outcomes related to the subject of this thesis are listed. JOURNAL PAPERS P. Ruiz-Minguela, D. R. Noble, V. Nava, S. Pennock, J. M. Blanco, H. Jeffrey. "Estimating Future Costs of Emerging Wave Energy Technologies". Sustainability, vol 15, nº 1, p. 215, Dec. 2022. https://doi.org/10.3390/su15010215 P. Ruiz-Minguela, J. Blanco, V. Nava, H. Jeffrey. “Technology-Agnostic Assessment of Wave Energy System Capabilities”. Energies, vol. 15, nº 7, p. 2624, Apr. 2022. https://doi.org/10.3390/en15072624 P. Ruiz-Minguela, V. Nava, J. Hodges y J. Blanco, “Review of Systems Engineering (SE) Methods and Their Application to Wave Energy Technology Development”, Journal of Marine Science and Engineering, vol. 8, nº 10, p. 823, Oct. 2020. https://doi.org/10.3390/jmse8100823 CONFERENCE PAPERS P. Ruiz-Minguela, J. M. Blanco, V. Nava. “Successful innovation strategies to overcome the technical challenges in the development of wave energy technologies”. Proceedings of the 15 th European Wave and Tidal Energy Conference, 3-7 September 2023, Bilbao (Spain) – Publication pending. P. Ruiz-Minguela, J. M. Blanco, V. Nava. “On the relevant, realistic and effective criteria for wave energy technology assessment – a dialogue with EWTEC2019 paper ID 1426”. Proceedings of the 14 th European Wave and Tidal Energy Conference, 5-9 September 2021, Plymouth (UK). P. Ruiz-Minguela, J. M. Blanco, V. Nava. “Novel Methodology for Holistic Assessment of Wave Energy Design Options”. Proceedings of the 13th European Wave and Tidal Energy Conference, 1-6 September 2019, Naples (Italy). REPORTS AND BOOK CHAPTERS P. Ruiz-Minguela, V. Nava, J. M. Blanco. “External Forces Influencing the Development of Wave Energy Technologies for Power Markets”. Zenodo: Geneva, Switzerland, Feb. 2022. https://doi.org/10.5281/zenodo.6168328 PUBLICATIONS 240 Hodges J., Henderson J., Ruedy L., Soede M., Weber J., Ruiz-Minguela P., Jeffrey H., Bannon E., Holland M., Maciver R., Hume D., Villate J-L, Ramsey T., “An International Evaluation and Guidance Framework for Ocean Energy Technology”, IEA-OES 2021. https://www.ocean-energy-systems.org/documents/47763-evaluation-guidance-oceanenergy-technologies2.pdf RESEARCH PROJECTS SEETIP OCEAN: Support to SET Plan Implementation Working Group and European Technology and Innovation Platform for Ocean Energy. EU Horizon Europe, no 101075412, 2022-2025. https://cordis.europa.eu/project/id/101075412 VALID: Verification through Accelerated testing Leading to Improved wave energy Designs. EU H2020, no 101006927, 2019-2021. https://cordis.europa.eu/project/id/101006927/results ETIP OCEAN: European Technology and Innovation Platform for Ocean Energy. EU H2020, no 727483, 2019-2021. https://cordis.europa.eu/project/id/727483/results DTOceanPlus: Advanced Design Tools for Ocean Energy Systems Innovation, Development and Deployment. EU H2020, no 785921, 2018-2021. https://cordis.europa.eu/project/id/785921/results OPERA: Open Sea Operating Experience to Reduce Wave Energy Cost. EU H2020, no 654444, 2016-2019. https://cordis.europa.eu/project/id/654444/results J. Pablo Ruiz Minguela A novel methodology for the holistic assessment of wave energy technologies at early design stages PhD Thesis, May 2023 Supervisors: Prof Jesús María Blanco Ilzarbe and Dr Vincenzo Nava Universidad del País Vasco / Euskal Herriko Unibertsitatea Energy Engineering Department Plaza Ingeniero Torres Quevedo, 1 E-48013 Bilbao TECNALIA, Basque Research and Technology Alliance (BRTA) Offshore Renewable Energy Group Energy, Climate and Urban Transition Unit Parque Científico y Tecnológico de Bizkaia, Astondo Bidea, Edificio 700 E-48160 Derio Open Access This PhD thesis is distributed under the terms of the Creative Commons Attribution-ShareAlike 4.0 License (https://creativecommons.org/licenses/bysa/4.0/), which allows users to distribute, remix, adapt and build upon the material in any medium or format, provided credit is given to the original author and any adaptations are shared under the same terms.