CORDIS

Executive Summary:
LEANWIND was awarded to a consortium of 31 participants (52% from industry) from 11 countries and is led by University College Cork, Ireland. The diverse team brings together experts from multiple sectors including oil and gas, maritime, shipping and offshore wind industries with representatives across the supply-chain including developers, utilities, turbine suppliers, vessel owners, shipbuilding, classification societies and academics. The project received funding of €9.98million from the European Commission and has a total value of €14.78million. LEANWIND commenced in December 2013 and ran for 4 years. The final event was held in Amsterdam, November 2017.

The European Union has a long-term commitment to reducing greenhouse gas emissions by 80-95% compared to 1990 levels by 2050. This has important implications for the current energy system and will require greater uptake of clean technologies such as wind energy in order to achieve these goals. The LEANWIND project began in December of 2013, at which time the Levelised Cost Of Energy (LCOE) for offshore wind energy was €140/MWh. Over the lifetime of the project, this cost has plummeted, with Vattenfall’s offshore wind price bid in 2016 for the Kriegers Flak project setting a record LCOE forecast of €40/MWh surpassing 2020 targets of €100/MWh.

However, there is still work to be done to achieve further cost reductions and cost parity with conventional energy generation. This fall in forecasted LCOE will increase price competition as developers are put under increasing pressure to match these forecasts. New markets in East Asia and North America still need to achieve these targets using the lessons learned by the existing industry. In addition, challenges are presented by future sites located further from shore, in harsher conditions and deeper waters. Larger turbines and projects also mean different equipment requirements and new logistics and maintenance issues. It is expected that LEANWIND results will particularly contribute to optimisation for future farms and, alongside applied research in years to come, guarantee the future of offshore wind within our energy mix.

The primary LEANWIND objective was to provide cost reductions across the Offshore Wind Farm (OWF) lifecycle and supply-chain through the application of lean principles and the development of state-of-the-art technologies and tools. “Lean thinking” is the dynamic, knowledge driven, and end-user focused process through which, an industry continuously seeks to eliminate wasteful stages and streamline processes with the goal of creating value. These principles have been adopted by many other industries however their application is a novel development in the offshore wind industry.

Taking a whole system perspective, the lean paradigm is applied in LEANWIND to the three critical project stages: installation, O&M and decommissioning. The project addressed the logistic challenges of deploying, installing and operating wind turbines (WT) ranging from 5-10MW in transitional water depths (40-60m) to deep sites (>60m) using fixed or floating substructures. The transport, logistical and maintenance operations associated with these structures are considered with novel approaches to vessel design, sub-structure alterations and O&M strategies that reduce both the CAPEX and OPEX costs. The innovations developed within the project have been rigorously validated where possible and assessed for their cost benefit. This will facilitate market uptake, thereby contributing to the competitiveness of the sector and to the creation of new employment opportunities within the industry.
Project Context and Objectives:
LEANWIND was awarded to a consortium of 31 participants (52% from industry) from 11 countries and is led by University College Cork, Ireland. The diverse team brings together experts from multiple sectors including oil and gas, maritime, shipping and offshore wind industries with representatives across the supply-chain including developers, utilities, turbine suppliers, vessel owners, shipbuilding, classification societies, academics, and other industry representatives. The project received funding of almost €10million from the European Commission and has a total value of €14.8million. LEANWIND commenced in December 2013 and ran for 4 years.

The European Union has a long-term commitment to reduce greenhouse gas emissions by 80-95% compared to 1990 levels by 2050. This has important implications for the current energy system. Wind power plays a crucial role in reaching the EU’s renewable goals. The offshore wind industry in Europe is, in fact, moving fast to a mainstream supplier of low-carbon electricity [1]. It has grown exponentially in recent years and is expected to cover up to 23% of EU’s electricity demand to 2030 [2]. Today, wind energy already meets 11% of the EU’s power demand, with high penetration levels in several countries (e.g. Denmark (42%), Spain (20%), Germany (13%) and UK (11%)). The wind energy sector represents over 300,000 jobs and generates €72 billion in annual turnover [3]. This unprecedented growth is due to an increased competitiveness of the sector due to several factors, such as the reduction in the cost of capital, industrial expansion and technological development3. The LEANWIND project began in December of 2013, at which time the Levelised Cost Of Energy (LCOE) for offshore wind energy was €140/MWh. Over the lifetime of the project, this cost has plummeted, surpassing 2020 targets of €100/MWh. Vattenfall’s offshore wind price bid of €49.9/MWh in 2016 for the Kriegers Flak project set a record LCOE forecast of €40/MWh [4].

However, there is still work to be done to actually achieve and maintain the expected cost reductions and ensure the cost-competitiveness of offshore wind in the energy sector. The anticipated fall in LCOE has and will increase price competition as developers are under pressure to match these forecasts. New markets in East Asia and North America still need to achieve these targets using the lessons learned by the existing industry. In addition, challenges are presented by future sites located further from shore in harsher conditions and deeper waters. Larger turbines and projects also mean different equipment requirements and new logistics and maintenance issues. Advances in both turbine foundation technology; operations and maintenance strategies and technologies; and the vessels to construct and service these wind farms are required. Industry are also facing the almost unknown costs of decommissioning the first offshore wind farms. This is a relatively new practice with no established methods or procedures, and a key area for potential cost reductions. It is expected that LEANWIND results will contribute further optimisation particularly for future farms and, alongside applied research in years to come, guarantee the future of offshore wind within our energy mix.

The primary LEANWIND objective is to provide cost reductions across the OWF lifecycle and supply-chain through the application of lean principles and the development of state-of-the-art technologies and tools. “Lean thinking” is the dynamic, knowledge driven, and end-user focused process through which people in a defined enterprise continuously eliminate wasteful stages and streamline processes with the goal of creating value [5]. Key principles include:
1) Identify what the customer needs
2) Track, reduce or eliminate wasteful stages in and between processes
3) Seek continuous improvement
4) Approach improvements from a whole system perspective
The “Lean" principles were originally developed by Toyota to optimize the processes of manufacturing industries; these principles of optimization and efficiency have subsequently been adopted by many other industries to remove wasteful stages and streamline processes. The application of lean principles is a novel development in the offshore wind industry. Taking a whole system perspective, the lean paradigm is applied in LEANWIND to each of the critical project stages from Installation, Operation and Maintenance (O&M) to Decommissioning, supporting efficient holistic strategies for the development of an OWF. Efficiencies have been sought at 3 levels to consider the needs of different industry stakeholders: 1) strategic project planning and management level, 2) tactical project operations level and 3) specific technological or procedural level.

LEANWIND bridges two themes addressing key targets of both the European Wind and Transport Technology Platforms, with a focus on reducing the cost of offshore wind. Innovative logistics including transport and erection techniques are key research objectives identified by the EWI to achieve wind energy targets [6]. Therefore, the project particularly addressed industry transport, logistics and equipment needs in the short and medium term. The primary objectives identified by the call include:
- Streamlining the deployment and installation of large-scale turbines and both fixed and floating substructures.
- Helping meet the increased demand for purpose-built installation and servicing vessels and equipment.
- Improving maintenance activities including strategy optimisation and developing novel access systems and equipment.
- Reducing the need for on-site maintenance through remote presence and condition monitoring systems.
- Optimising full supply-chain logistics including on-land transport links for large offshore structures.
- Developing new business models at European level for large offshore systems.
- Identifying industry specific safety procedures for installation and maintenance activities.

LEANWIND specifically addresses the logistical challenges of deploying, installing and operating large scale wind turbines (WT) (in the range 5-10MW) in transitional water depths (30-60m) and deep water (>60m) using fixed foundations as well as floating structures, which are relevant to longer term wind farm prospects. The transport, logistical and maintenance operations associated with these structures would be addressed through novel approaches to vessel design, vessel management, sub-structure alterations and O&M strategies in order to reduce both the CAPEX and OPEX cost. The following summarises the key aims:
• supply comprehensive analysis of the industry challenges, facilitating effective development of relevant solutions;
• design novel adaptations for fixed and floating substructures and a substructure selection framework to minimise costs and installation time;
• streamline the deployment and installation of large-scale turbines and both fixed and floating substructures with improved installation processes e.g. optimising vessel deck usage and developing efficient processes for turbine erection and technology that facilitates quicker and/or safer loading, transport or ballasting operations for substructures;
• develop a holistic supply-chain logistics model to optimise scenarios, increasing efficiency and reducing bottlenecks. This includes individual modules applicable to port logistics, transport, vessel chartering etc.; a Geographical Information System (GIS) transport model; and a decision-making model for port layout/configuration to improve planning of on-land logistics;
• construct a full lifecycle financial model capable of considering CAPEX and installation, OPEX, decommissioning activities and costs in detail as well as risk and lifecycle assessment;
• develop a range of models and provided recommendations for optimised O&M strategies for representative existing and planned farms, which will help reduce costs and improve efficiency. This includes a strategic decision-support tool; a dynamic-scheduling model; and a risk-based framework model;
• assess Reliability, Availability and Maintenance (RAM) methodologies, existing software tools and suitable modelling approaches to identify WT’s critical components and develop selected failure/degradation models to provide input to the O&M tools, facilitating strategy optimisation and the cost-time benefits of reliability-centred maintenance.
• fabricate and test a remote presence device and Condition Monitoring Software (CMS) to reduce the need for human intervention and maintenance costs;
• deliver purpose-built installation and servicing vessel concepts, meeting the increased demand;
• undertake tank and field testing activities to validate and assess the benefits of selected project innovations and procedures e.g. remote presence device, gravity based substructure, floating substructure and offshore operations;
• develop and showcase vessel simulation technologies to assess novel design concepts and replicate deployment and O&M activities, mitigating the risks associated with new strategies;
• identify industry specific safety and training procedures for installation and O&M;
• assess business models at European level for large offshore systems to encourage existing and new sources of investment;
• evaluate the benefits of optimised procedures and technical solutions with a combined financial and logistics OWF model, resulting in recommendations for wind farm development;
• assess the non-technical positive and adverse impacts of project innovations from environmental, societal and economic perspectives;
• provide recommendations for future growth and development in the business and policy landscape to adequately support the industry.

The LEANWIND innovations were rigorously tested and validated where possible and assessed for their cost benefit to industry. The project would also evaluate the applicability of the innovations to industry in order to facilitate market uptake of developed innovations and ensure there are immediate cost reductions seen by industry. As the above illustrates, this was an ambitious scope of work seeking to produce a large range of novel solutions that can improve existing practices and set standards in order to help industry meet their LCOE aspirations and maintain cost reductions as the industry develops. The following presents the results achieved and their potential impact including the wider socio-economic implications.

References
[1] BVG Associates and WindEurope, (2017). Unleashing Europe’s offshore wind potential: A new resource assessment.
[2] EEA, (2009). Europe’s onshore and offshore wind energy potential.
[3] WindEurope, (2017). Wind energy today.
[4] WindEnergyUpdate, (2016). Europe’s new record offshore LCOE forecast at 40 euros/MWh. Retrieved July 28, 2017, from http://newenergyupdate.com/wind-energy-update/europes-new-record-offshore-lcoe-forecast-40-eurosmwh
[5] Shili Sun, (2011). The Strategic Role of Lean Production in SOE’s Development. International Journal of Business and Management, Vol. 6, No. 2; February 2011, p 160.
[6] TPWindSecretariat , “Wind European Industrial Initiative Team 2010-2012 Implementation Plan”, May 2010, p. 6 and “Wind European Industrial Initiative Team 2013-2015 Implementation Plan”, February 2013, v. 3.
Project Results:
Installation - substructures
Considering the challenges associated with the new generation of OWFs, LEANWIND aimed to improve the overall costs of developing an OWF by optimising fixed substructure concepts considering transitional water depths from 40-60m. Work focused on optimising the design and material consumption as well as transportation strategies and design modifications to reduce installation time. In addition, the project developed a floating concept for deep water (>60m) to meet future industry needs.

In order to complete the scope of the described works, a series of uniform relevant design cases were identified. The relevant design cases are outlined in Table 1 and cover most of the parameter space for consented and planned wind farms in European waters. In addition, a reference 8MW turbine was developed for cases 1 and 2, the characteristics of which were published in the Journal of Physics Conference Series [1]. The NREL 5MW turbine specifications were used for the site 3 floating platform. Details are available at www.leanwind.eu.

For fixed foundations, the technical work was broken down into gravity based concepts and steel structures, which were investigated using a variety of numerical tools, combined with some physical model testing. For gravity based concepts, buoyant structures were considered as these can be floated into position before ballasting, without the need for expensive heavy lift vessels. This study included conceptual engineering, detailed analysis, supply-chain studies and economic modelling. The study on steel structures investigated innovations for both extra-large (XL) monopiles and jacket structures to enable more efficient design, construction and deployment, which would facilitate cost reductions.

Gravity Base Concept: A GBF is a concrete-based structure which is ballasted with a high density aggregate and relies on its self-weight to resist overturning moments. GBFs have been used extensively in the Baltic Sea, a calm sea with shallow waters. The use of concrete for these foundations has several benefits, including reducing exposure to volatile steel prices and removing the need for sea bed piling. However, due to the heavy weight of GBFs, their installation and transportation usually requires heavy lift vessels and cranes. Therefore, buoyant GBFs have been proposed as an alternative to the conventional lifted structures to negate the need for costly transportation vessels and introduce a more cost-effective foundation design.

The self-buoyant GBF is floated and towed to the offshore site, where it is filled with ballast and lowered to the seabed using standard tugs. However, in order for the foundation to remain stable during float-out, transit and ballasting, the hydrodynamic stability of the foundation during transit should be significantly enhanced. The overall stability of the foundation during the various deployment phases depends on its geometrical attributes such as the shape of the substructure, the relative height of various segments of the foundation, the diameter of contact area at the water level and the arrangement of internal ballasting chambers in the base. [2] A parametric study on a GBF was carried out as part of LEANWIND, in order to reduce material consumption and weight of the foundation. This was performed by conducting a geometrical optimisation while maintaining structural stability under offshore design loads. The following summarises the results:
− Achieving limited initial draft is an important consideration in the feasibility of buoyant GBFs, as it imposes significant restrictions on the choice of departure ports.
− The ballasting operation proves to be the most sensitive stage, as the ballast content significantly changes the metacentric height of the foundation, and hence its stability.
− In order to maintain stability throughout the ballasting operation, chambered ballasting reservoirs are required to limit the free surface effects.
− The height and number of compartments in the ballast reservoir are critical factors in determining hydrodynamic stability. It is not advised to continue ballasting above the height of compartments.
− It should be noted that the internal compartments add significant extra weight to the foundation, leading to considerable increase in the initial draft.
− Increasing the base diameter is beneficial in reducing the initial drafts, as it significantly increases the displaced volume of water and enhances stability during float-out. This is particularly true if ballast height does not exceed the height of dividing compartments, as beyond this point, larger base diameters produce larger free surface effects; thus decreasing the metacentric heights at a more rapid rate.
− Increasing the height of the foundation limits the extent of variation of the metacentric height, and can be helpful in avoiding sudden fluctuations in the stability of the foundation.
− However, increasing the height also increases the initial draft due to the increased total weight of foundation. The effect is much more pronounced when the height of lower chamber increases, as the extra weight of compartment walls play an important role in the excessive initial draft.
− The feasibility of buoyant GBFs is not merely a technical engineering decision. Availability of suitable infrastructure is likely to be the critical factor in determining whether the buoyant GBFs are viable and cost-effective options for the offshore wind industry. This question was addressed using the LEANWIND logistics optimisation models and results presented in [3].
Figure 4 is a schematic illustration of the Gravity Base Foundation (Source: Gavin and Doherty Geosolutions)
GBF Field Trial: In addition to this study, data from the Canary Islands Oceanic Platform (PLOCAN) [http://www.plocan.eu/index.php/en/about-us/whoweare/description] research infrastructure was used in the GBF design phase in LEANWIND. The PLOCAN platform supports a research laboratory and consists of a cuboid gravity structure built in 2016 using the same construction methods and technology as the GBF concept proposed by ACCIONA during LEANWIND. Since the prototype platform was built for offshore deployment, an innovative monitoring system was designed and installed to measure the fluid-structure and soil-structure interactions. Figures 5.1 and 5.2 illustrate the configuration selected for the installation of the PLOCAN platform (Source: PLOCAN). This system was used to validate theoretical models implemented within the design phase of the GBF and to measure wave actions on the structure. It was inferred that the system can provide valuable data for approximately two years after the installation period by measuring both incident waves (vertical wall sensor) and sub-bottom pressure (bottom pressure through the porous gravel bed bellow the structure). A total of 24 sensors were installed, 12 in vertical walls and 12 at the bottom slab. It is expected that the data gathered during 2017-2018 will show correlations between wave height and wave pressure on both vertical and sub-bottom position, allowing a better understanding of the safety coefficient used in different failure modes on those structures. Another aspect covered by this sea trial has been learning from the transport and installation process for this type of structure from the temporary gravel base construction at port up to the final ballasting process and scour protection stage.

During the deployment process, several problems arose and were solved, allowing the final ballasting process to be reached on 30th of November 2016. The main lessons-learned during this process were:
• due to a small leakage between internal ballast cells (concrete cold joints), a temporary steel floater system needed to be installed to gain positive GM (>0.5m) during towing and ballasting operations. In future, this problem can be solved by applying a proper joint treatment between bottom slab and vertical walls during fabrication;
• simulation activities of the ballasting process provide a valuable tool to determine and optimize the number and size of installation vessels (the operation was performed with only two tugs during the ballasting sequence instead of the four used in similar operations).

Monopile: Monopiles are large-diameter steel tubes driven or drilled into the soil that transfer axial and lateral load to the stronger subsoil to support the WT. They are the most common foundation type in the offshore industry, preferred due to their routine design procedures and relatively quick installation time. Figure 1 shows the installation of a monopile in Nordsee OWF in Germany with Jack-up Vessel Innovation (Source: DEME (GeoSea Maintenance NV)). However, their popularity is diminishing as wind farms are planned further offshore and in deeper water. A new generation of XL monopiles with increased diameters (up to 10mm) are required to make them suitable for deployment in future sites. New installation vessels and driving equipment are required for monopiles with a diameter greater than 7m. Also, current offshore monopile design guidelines are mainly based on design principles of the oil & gas industry, which were developed for slender piles (diameters 1 to 2m). In order to realise the full potential of XL monopiles, advanced design methods, standards and guidelines tailored to the requirements of the offshore wind sector could produce leaner designs and potential cost savings. Currently as the conventional ratio of pile diameter to plate thickness increases, this leads to a rise in the pile lateral resistance and increases the possibility of plates buckling during driving. Taking into account this extra resistance in the geotechnical design of XL monopiles can lead to leaner designs with smaller plate thickness, reducing the amount of steel used and providing cost savings.

As part of LEANWIND, a comparative study was conducted to evaluate the accuracy of conventional p-y methods for reliable prediction of the lateral capacity of XL monopiles in dense sand deposits. In the absence of full-scale test results, finite element (FE) modelling of the XL monopiles is believed to be the most accurate indicator of their behaviour in the field, and has been used as the basis of comparison. Plaxis 3D FEA software was employed for the purpose of modelling XL monopiles. In order to confirm that the traditional methods underestimate the capacity of XL monopiles, a comparative study was undertaken using the numerical FEA approach. The results of this study were used to make comparisons between deflections predicted using analytical versus numerical approaches. The API results were obtained by modelling the monopile geometry and associated loads in LPile, with turbine loads and a soil profile that were the same as those introduced for the LEANWIND project. Figure 2 shows the FE modelling of a monopile foundation in Plaxis 3D (Source: Gavin and Doherty Geosolutions.

The results of this study showed the API method results in larger deflections compared to the Plaxis approach. It shall also be noted that as the pile size increases, the difference in deflection prediction becomes larger. This confirms that application of the numerical models (instead of the conventional API analytical approach) results in more economical designs in large diameter monopiles and that cost saving benefits become more significant as the pile diameter increases.

Jacket structures are suitable for supporting relatively large Offshore Wind Turbines (OWT) installed in deep water (>40 m). Loads are transferred to the piles through axial behaviour of the slender members of the lattice. The relatively small diameter of members categorises the structure as a transparent support structure, with less significant hydrodynamic loads. Piles can be pre-driven or driven through the pile sleeves once the structure is positioned correctly on the sea-bed. These are also axially loaded piles, reducing the need for scour protection, when compared to monopile foundations. The wide cross-section at the sea-bed provides satisfactory resistance against overturning moments. Jacket foundations also provide a stiffer support structure for their weight, which is approximately in the range of 600 tonnes. This makes them ideal for deep water sites with extreme environmental conditions. Jackets can be fully assembled before float-out installation, and hence reduce the amount of offshore installation required. Suction caisson technology has been used in the oil and gas sector for several decades. Thousands of suction caissons have been installed as foundations and anchors for various facilities around the world. The loading conditions for the wind sector are dramatically different, but this technology still has huge scope to facilitate rapid installations. Suction caissons can be used to assist levelling of a traditional GBF or to support a jacket or tripod structure. Care must be taken to ensure that the resulting structure is capable of resisting the geotechnical tension loads.

LEANWIND participants developed a jacket foundation concept and suction buckets. The jacket foundation can be floated out to the installation site by conventional tugs, where it can be fixed to the seabed after controlled submergence. The suction buckets can potentially be used as floatation cylinders during transport of the jacket to the installation site. On arrival, the cylinders can be flooded and used as suction buckets to fix the jacket foundation to the seabed. This will particularly help to reduce cost by facilitating easier transport and reducing transport and lifting capacity requirements during installation. The designers believe physical scale model testing and validation of numerical modelling will be required to encourage the uptake of this output by the industry. The dimensional data of the jacket solution has been used to specify the vessels required for transportation and installation of this innovative concept.

As part of the development process, a broad evaluation of two float-out systems was carried out, based on the 5MW Jacket NREL-FAST. These included a vertically and a horizontally floating jacket from port to site. The vertical solution required only a single orientation of the float-out system, whereas the horizontal solution needed a float-out system able to manage the two orientations (horizontal at port and vertical at site) and a more complicated installation process to upend at site. Therefore, the project chose a vertically floating jacket. A concrete ballast on top of the floatation cylinders was considered to aid stability. Several ballast weights and configurations were evaluated to determine the best cases in terms of stability and efficiency for further analysis. The final geometries were checked in terms of structural and geotechnical performance and the integrity of the structure under water was also analysed. Figure 6 illustrates the LEANWIND Float-out jacket foundation (Source: EDF)

Floating foundations become an efficient option in water depths beyond 60m, when bottom-fixed designs are no longer economically viable and the site is deep enough to allow for efficient mooring. The main challenges encountered in the implementation of floating foundations is to maintain stability, an acceptable range of displacements, an efficient mooring and at the same time avoid costly designs, installation and maintenance. However, significant technological advancements are required to reduce investment and O&M costs for floating offshore wind solutions to achieve a LCOE comparable with other renewable sources. The most commonly investigated concepts in floating offshore foundations are ballast-stabilised floaters (i.e. spar buoy), buoyancy-stabilised floaters (i.e. semi-submersible), and mooring-stabilised floaters (i.e. tension leg platforms).

During LEANWIND, after carrying out a state-of-the-art study and a risk ranking exercise, semi-submersible floating technology was selected as the most suitable for the specific conditions and extreme environment present at the proposed case-study test site no. 3 (a location on the west coast of Ireland). The LEANWIND semi-submersible platform is a floating substructure for OWTs, designed for a 5MW WT to operate in water depths greater than 50 metres. The structure is made from steel and uses a 3-point catenary mooring lines. This relatively simple platform design is easy to manufacture, has a shallow draft, excellent hydrodynamic properties and a good platform-weight to turbine-rating ratio compared to competing designs.

A complete basic design of the platform was performed accounting not only the geometrical design and a weight calculation, but also the basic design of required auxiliary systems, mooring and anchoring systems. Additionally, an innovative tailored WT controller has been developed and benchmark simulation results were obtained. The dynamic behaviour and design consistency of the LEANWIND semi-submersible platform has been extensively evaluated both in numerical simulations and through the experimental basin test campaign. The physical tank tests were performed by University College Cork in the Lir National Ocean Test Facility on a 1:36 scale model for representative transportation, installation, operational and survival conditions including exposure to waves equivalent to a height of 32m at full scale. These tests demonstrated the ease of transportation and installation due to the self-stability of the platform along with the draft conditions achieved during these operations. They also illustrated the robustness of the solution developed and largely validated its technical viability.

Installation – vessels
New installation vessel concepts for the offshore wind sector could help address the current bottle-neck in vessel availability due to competition with other industries. Increased supply could reduce costs and designs tailored to offshore wind farm operations and components could increase efficiency and provide further savings. As vessels are a primary expense, this is a key area for optimisation particularly considering the new transport and installation challenges raised by larger turbines and farms as well as future farms further from shore in deeper waters.

Vessel technical limitations are primarily the main dimensions and the vessel stability, as the positioning of heavy cargo items influences the static stability of the vessel. Other important limitations are related to the jacking capacity of the jack-up vessel (JUP), i.e. the maximum elevated weight of JUP vessels, deck strength, size of components, size of sea fastening, gangway position for installation, crew accommodation constraints, propulsion package, and safety considerations. Regarding crane operations and lifting capacity of an installation vessel, the main limitations are the lifting capacity, which needs to be based on the heaviest possible parts to be lifted, and crane geometry, i.e. minimum clearance in order to avoid clashes between primary and secondary cranes. LEANWIND conducted an industry survey which identified the key vessel requirements and where optimisation could best be achieved. This is described in a public report [4] and results included:
• improved vessel design for less restrictive weather limitations e.g. increased maximum sea state for jacking operations and increased maximum crane operating wind speed;
• increased deck payload and usable area
• increased number of turbines loaded per trip;
• increased transit speed;
• increased jacking speed and decreased leg-preload duration.

Several types of WT and foundation installation vessels currently operate in the offshore wind market. These include lift-boats, jack-up barges, self-propelled installation vessels (SPIVs) and heavy-lift vessels (HLVs). Lift-boats, jack-up barges and SPIVs are collectively referred to as self-elevating vessels due to their characteristic feature of raising the entire hull above the waterline. SPIVs are also called Turbine Installation Vessels (TIVs) because they are used almost exclusively for these operations. During the project, the various types of installation vessels described above were reviewed and assessed using a set of criteria/design goals agreed in consultation with industry experts including:
1. Increased accessibility / operational uptime in sites further offshore [30%]
2. Ability to load, transport and install future larger turbines or sub-assemblies [20%]
3. Ability to load, transport and install future larger foundations [30%]
4. Ability to operate in deeper waters [5%]
5. Incorporation of generic sea fastenings into vessel design [10%]
6. Use of modern equipment handling tools [5%]

This process identified three concepts for further assessment:
− FTIV (Foundation Transport and Installation Vessel). This is floating type installation vessel and thus offers a larger depth range for the installation of wind farms. The vessel has less restrictions on stability while transiting due to the low centre of gravity compared with jack-up type platforms. However, the vessel has very large motions, especially at the point of hook load. There are also concerns regarding the viability of maintaining high accessibility with offshore lifts. These lifts will be hundreds of tonnes, which may be problematic for a floating platform.
− FTIJ (Foundation Transport and Installation Jack-up). This is a jack-up unit, which is the preferred type for wind farm installation activities based on industry feedback. The FTIJ is used for installation of foundations. For the LEANWIND 8MW turbine, both the mass (in excess of 1500 tonnes) and the dimensions of the largest jacket foundations limit the transport to 3-4 at one time. This restriction is further limited by the crane capabilities available within the current market: a crane lift capacity of more than 2000 tonnes is required to allow for dynamic factors during an offshore lift. Therefore, the FTIJ concept would thus not be cost effective, nor would it add value to the operations of current and near future wind farm sites.
− WTIJ (WT Transport and Installation Jack-up). This is also a jack-up unit and was identified as the concept that could best address future industry needs, carrying larger WTs (8-10MW). Design could also be optimised to maximise the number of turbines per transit, showing the most significant cost-reduction potential. Therefore, the WTIJ was thus taken forward to the detailed design stage and is described below.

The following highlights key features of the final installation vessel design:
• pure LNG propulsion system (first and only concept);
• capable of operating in most of the wind farm sites identified by the industry for future extension, without significant restrictions on operations due to the environmental parameters;
• capable of carrying and mounting 8 pieces of 8MW (or 7 pieces of 10MW) wind turbines (for 10MW subject to design features);
• capable of installing 32 wind turbines with 4 visits without refuelling;
• capable of installing wind turbines with 1500-ton main crane or to install both wind turbines and monopile foundations (4 pcs) with 2000-ton main crane;
• capable of operating under higher wind speed conditions via high wind boom lock system for installation of suspended weights;
• Environmental Regularity Number = (99, 99, 98, 98, 84) for dynamic positioning operations with existing propulsion and thruster system;
• 6000 m2 free main deck area optimized for fast installation;
• 70-person capacity (crew + technicians).
• Suitable lifting solutions, such as the boom lock system and leg-encircling cranes have been adopted into the vessel designs within LEANWIND project.
• The design is based on Lloyd’s Register Rules for offshore units and IMO, MODU Code 2009.
Figure 8 is a 3D Model of the WTU final design with 8MW turbines (Source: Delta Marine Co.).

To determine the WTIJ’s cost-reduction potential, LEANWIND undertook an economic assessment. Using a sample 100 turbine wind farm, the study compared the effective installation cost per turbine using vessels currently available versus the LEANWIND WTIJ. The installation cost considered the vessel charter rate (which is linked to the build cost and the expected returns for the vessel). This was adjusted taking into account the different sample farm’s completion based on the number of turbines carried in each transit. The time required for installation was estimated based on transit and installation along adjusted to account for weather constraints. Results are illustrated in Figure 7 (Source: GeoSea Maintenance and Lloyd’s Register). The LEANWIND WTIJ proved the most cost-effective offering over 3% savings compared with the next best solution, which is significant in terms of vessel costs. This concept also meets future industry needs that can help maintain the forecast savings to date.

Simulation was used to investigate the feasibility of the innovative installation vessel concept. These simulations included the operation of heavy lifting equipment. In particular, the following operations were integrated into the simulator training sessions:
• deck stowage plan and optimisation
• crane lift (instructor controlled)
• use of DP and special features in combination with the two operations mentioned above
Training in the simulator supports Health and Safety (H&S) improvement, since personnel will be able to gain experience with the new technology, equipment and procedures in a sheltered environment without the risk of injuries and damage. The installation vessel simulator training activities were showcased at a stakeholder dissemination event in November 2017. Figure 9 provides an impression of a simulator training session (Source: Maersk Training 2015).

Operation and Maintenance – strategies, tools and technologies
Operation and Maintenance (O&M) comprises a significant proportion (estimates typically range from 20-25%) of the overall cost of an offshore wind farm. The industry [5] have identified key areas where optimisation could improve efficiency and reduce costs including:
− Optimise O&M logistics and strategies for a given site from a long-term planning perspective and on a short term/day-to-day basis to maximise energy production at the lowest cost.
− Adopt strategies and develop technologies to reduce the need for manned interventions and corrective maintenance.
− Consider the implications of far-shore sites
− Consider the challenges associated with new technologies
− Improve accessibility by overcoming the lack of suitable, purpose-built vessels and develop new access systems to increase safe personnel transfers beyond 1.5m significant wave height (Hs).
The project produced a range of tools and technologies to address these research priorities.

LEANWIND has developed several tools with the objective of optimizing the O&M strategies and providing decision-support. Optimization of O&M implies finding an optimal maintenance effort considering direct O&M costs and wind farm availability. The overall problem involves both strategic decision problems relating to long-term planning and tactical and operational decision problems with shorter planning horizons. Optimizing O&M is very complex and multi-faceted, and the tools developed address different aspects of the overall problem and use a variety of approaches. The following decision-support tools have been developed to address this problem and will be described below:
• The O&M strategy model: a simulation tool for strategic decision support, in particular for optimizing maintenance logistics;
• The risk-based model: a framework using Bayesian networks and simulation techniques to provide strategic decision support relating to the times and methods for repairs, inspections, and Condition Monitoring;
• Dynamic routing and scheduling framework: a set of optimisation models for operational and tactical decision support relating to vessel logistics.

O&M Strategy model
The LEANWIND O&M strategy model is a strategic decision-support tool that simulates the maintenance activities of an OWF over a given number of years using a discrete-event Monte Carlo simulation approach to estimate key performance parameters, such as wind farm availability and O&M costs. The model varies key risk factors such as turbine failures and weather, generating a time series per iteration using markov chain modelling. The use of the O&M strategy model has been demonstrated in three case studies with relevant decision problems for an OWF owner/operator:
1) Timing of jack-up vessel charter periods for pre-determined heavy maintenance campaigns;
2) Selecting the size and composition of the Crew Transfer Vessel (CTV) fleet;
3) Timing of annual service (predetermined preventive maintenance) campaigns.
The study was published in Sperstad et. al. (2016) [6]. Results substantiate that optimising the jack-up charter strategy and CTV fleet composition both offer substantial economic potential for the wind farm owner/operator. The findings for jack-up vessel charter periods (decision problem 1) indicate that pre-chartering jack-up vessels for a set of campaign periods is a competitive strategy when compared to the conventional "fix-on-failure" strategy of chartering jack-up vessels as soon as the need arises.

Using a Monte Carlo simulation approach provides insight into the risks and uncertainties associated with choosing a strategy. For instance, the selection of charter periods for jack-up vessel campaigns is associated with much larger variability than selection of the other decision problems considered. This implies a lower certainty for a wind farm operator that the expected best solution actually turns out to be the most profitable for that particular wind farm over the years it is operational, and hence jack-up vessel campaigns carry a higher risk.

The O&M Strategy model is a high-level model that captures several aspects of the O&M of the wind farm, and "system effects" such as the interactions between different maintenance tasks, logistics and weather conditions. As such it can also identify the risk of selecting sub-optimal strategies if solving each decision problem in isolation instead of viewing different decision problems as a whole. For instance, it was shown how choosing less costly CTVs in isolation would seem like an optimal solution to decision problem 2. However, considered simultaneously with the timing of annual services (decision problem 3), it was found that with more expensive and robust vessels, one could concentrate the annual service campaign in the summer months where the expected downtime losses are lowest.

The risk-based O&M framework can be used to optimise the timing and methods for repairs, inspections, and CM for deteriorating components, minimising the total expected O&M costs. The theoretical basis for the risk-based model is the Bayesian pre-posterior decision analysis. A computational framework has been developed, which consists of two decision models used for estimating the frequency/probability of inspection, repair, and failure in each time step. The decision models are based on probabilistic models for deterioration, inspections, CM, and repairs for all decision rules and parameters. For simple decision rules, Bayesian networks are used directly to estimate the probabilities of inspection, repairs, and failures. The result is exact. For decision rules using the probability of failure as the decision parameter, simulations are used to estimate probabilities, and Bayesian networks are used for decision-making within simulations. To find the optimal decision rules and parameter values, the probabilities of inspections, repairs, and failures are combined with expected specific costs of inspections, repairs, and failures, including lost revenue. The influence of vessel and jack-up strategy can be included indirectly in the expected specific costs.

The risk-based approach was applied in a case-study to blade maintenance. A Markov deterioration model was developed based on inspection data from a database. The specific costs of inspections, repairs, and failures were estimated based on weather data and assumptions on durations and weather thresholds. The risk-based O&M model was then used to find the optimal inspection method, optimal decision rules for inspections and repairs, and to estimate the value of CM. Without CM, simple decision rules with equidistant inspections resulted in the lowest costs. When CM was included, the advanced decision rules considering all information were found to give the lowest costs.

The dynamic routing and scheduling framework is a set of algorithms for optimising the maintenance logistics at OWFs. The decision problems addressed are related to the scheduling of maintenance activities (corrective and preventive) and the routing of CTVs. The framework primarily considers operational decision problems, with a planning horizon of 1–7 days, but also considers tactical decision problems with a planning horizon of one month. Three optimization frameworks were proposed:
• Routing and scheduling of preventive maintenance for multiple OWFs and multiple O&M bases. The first model is a mathematical model for selecting the optimum route configuration developed to minimise the total cost comprising travel costs and technician costs. It is intended for operational decision support and considers a 3–7 days planning horizon;
• A framework for dynamic routing and scheduling of preventive and corrective maintenance, integrating a tactical and operational scheduling model. First, the model generates a schedule for preventive maintenance over a planning horizon of approximately one month; this is then used as a starting point for a more detailed routing and scheduling model that is run for each day, considering both corrective and preventive maintenance. This approach is dynamic in the sense that the previous schedules from the tactical model are updated based on new information on a daily basis in the operational model;
• A stochastic vessel routing optimisation model for corrective and preventive maintenance. This model takes into account uncertainties in parameters such as the travel time of each vessel and the length of the weather windows allowing safe access to the turbines. The planning horizon is one day. It is dynamic in the sense that it provides proactive plans designed to be robust to uncertain conditions rather than reactive repair plans.

These algorithms form the theoretical foundation for the development of advanced decision-support tools. They could be used by OWF operators to decide which technicians and vessels should visit which turbines the following day, optimising for cost and thereby reducing the LCOE compared to the current practice for routing and scheduling.

Reliability-based tools for WTs have been developed using advanced RAMS methodologies, existing software tools and suitable modelling approaches. Based initially on RAMS methodologies, the WT’s critical components were identified. Their failure/degradation models were then analysed and developed. Given the lack of data available, analysis was composed from multiple sources including data from industry partners, literature surveys and databases. Firstly, an extensive study based on the FMECA approach was performed, providing failure rates and downtime periods for existing wind farms, as well as a criticality ranking based on different sources. A classical statistical analysis was developed for the whole data set available. Then, a raw trend analysis, based on nominal power classification (NPC), was performed in order to predict failure rates and downtimes for 5 MW and 8 MW WTs. Finally, after comparison with the analysis results not considering NPC, further database refinement was performed as necessary. The presence of false (abnormal) data in the NPC database has made engineering judgement indispensable in extracting a coherent data subset. The application of this methodology is illustrated by an example, focusing on large WTs with rated power 7-8 MW [7].

In addition to the RAMS methodologies, a web-based tool with interactive characteristics was developed for the early design phase of the WT based on multi-objective optimization and reliability modelling. The WT is modelled as a system of components, and can be used by a designer to optimize a WT, combining different design options and O&M scenarios for time-based preventive maintenance. This reliability-based design tool estimates the total unavailability considering the components as a series system, with the option of introducing redundancy using components in parallel configuration. The estimated unavailability considers downtimes due to failures and maintenance, and can be transformed to lost revenue using data on wind speeds.

WT components (structural, mechanical and electrical/electronic) are subject to wear, fatigue and stresses that result in degradation. Considering the deterioration of components over time, can more effectively support decision-making when considering O&M strategies. However, modelling the failure and degradation process for various components is a very demanding depending on a comprehensive understanding of the conditions and limitations on wind farm sites, as well as previous experience in the industrial and wind power sector. Several degradation modes and mechanisms exist for various types of failures/damages in each item/component of the WT. In addition, difficulties due to interaction between degradation modes, interaction between items of a component, interaction between components, assumptions and the priorities for the estimation of the degradation rate and their remaining useful life must be considered.

In the LEANWIND project two types of structural degradation models were described:
i) physics based degradation/damage model (deterministic and stochastic);
ii) state-space/data-driven damage model.
In the case of mechanical and electrical components, both the physics-based and statistical/data-driven modelling approaches for degradation (fault diagnosis and RUL prognosis) of WTs were considered. The first is more demanding, requiring long experimental periods and the application of material physics and mathematics, whilst the second one requires adequate operating data and is more suitable to model the WT components. Therefore, a number of different approaches for fault detection and RUL estimation can be selected as best suited in each case. Artificial Neural Networks (ANN) may be preferable for gearbox RUL estimation, whereas electrical current signal analysis may give the best results for fault detection. Similarly, the best results for prognostics in rotating machinery can be obtained using multiple sensors, digital signal processing, and machine learning techniques. Finally, in the case of ball bearings, RUL estimation can be based on Support Vector Machine (SVM) and Dynamic Bayesian Networks (DBNs).

Specific deterioration models for the WT’s gearbox, rotating mechanisms, and bearings were analysed, various techniques for fault diagnosis (in data acquisition, data cleaning, data analysis and condition prediction) and RUL prognosis were discussed, and a case-study related to the main bearing was developed. The models can be used as modules and/or their results input to the O&M Strategy model.

Condition monitoring can predict turbine failure and identify the root cause, allowing pre-planning of O&M activities which ultimately reduces costs. In the case where it is identified that the root cause of a failure is non-critical, it may be possible to intervene from shore, or plan O&M activities into the existing service schedule. For a CM system, there are four main stages to transform the initial data acquisition to a diagnosis/prognosis conclusion. They are pre-treatment of the input data, feature extraction, detection of possible failures, and hypothesis discrimination. After a complete state-of-the-art analysis on WT diagnosis and prognosis methodologies, international standards and internet based programming tools, LEANWIND developed a web service with scripting and data management capacities as an IDPS (Integrated Diagnosis and Prognosis System). This mixes the latest industry applicable methodologies for fault diagnostics and prognostics, with the latest advances in web services, putting together the best knowledge of the industry and the best digital technology in a unique software piece. The new web service supports dataset management, and models execution over these datasets. It also allows the configuration of several methodologies. Besides the development of the web service, which can be used by any other application over the http standard protocol, a WebApp has been developed (client–server software application which the client runs in a web browser), which allows access to all features of the service without the need to develop new software.

As part of the data acquisition development for LEANWIND, a remote presence solution was proposed. The original concept was a remotely controlled robot inside the turbine nacelle that acts as a sensor platform. To enable the system to observe different parts of the turbine or the same part from multiple angles, the system is intended to move on a rail. The concept of remote presence can also be extended to include remote repair using interaction tools. After some iterations, a pilot prototype was developed consisting mainly of parts 3D printed in PLA plastic. Figure 10 shows the pilot prototype installed in wind turbine. (Source: Norsk Automatisering)) The sensors included in the prototype were two USB cameras used to observe the environment and for visual inspections; a thermographic camera to see heat signatures of the equipment; a microphone to measure sound; and sensors to measure internal and external temperature. The system was installed in a WT at a local wind test centre at the end of June 2015 and has since been operational and collecting data.

The various solutions developed for O&M optimization described above each consider a limited part of a complex optimization problem. To increase the level of detail and capture additional, combined use of the models has been demonstrated in four case studies.
1. Integration of deterioration models and risk-based decisions in the O&M strategy model: this presents three approaches for integration of deterioration models and risk-based maintenance strategies in the O&M Strategy model:
• ‘Loose integration’ approach, where the O&M model can simulate condition based maintenance using high level performance data. Inputs include the probability of detection of a failure, the probability distribution for the pre-warning time, and the failure rate. These parameters are estimated based on a deterioration model and a risk-based strategy using simulations. This approach is simple to implement, but the distribution of events in time is not correct.
• ‘Full integration’ approach, where the deterioration model and risk-based strategies are implemented directly in the model. This gives correct distribution of events in time, but increases the computation time and requires access to the O&M model source code.
• “Bayesian network-based” approach, where Bayesian networks are used to estimate the probability distribution of time to fail and the conditional probability distribution of the pre-warning time for potential failure. To use this approach, a new module for the O&M Strategy model must be developed. Computation of the input for this module is performed using a stand-alone tool based on Bayesian networks, and gives the correct distribution of events in time.
The first two integration approaches were demonstrated using the O&M Strategy model. Only minor differences in maintenance costs and wind farm availability were found. The concept for the third approach was demonstrated using the deterioration model and risk-based strategies developed in a case-study on WT blades used for demonstrating the risk-based O&M model.

2. Cost-benefit analysis of CMS
This case-study presents a cost-benefit analysis of CM systems performed using the O&M Strategy model, based on high-level performance data of a CM system supplied by an industrial partner. The CM system was assumed to cover the gearbox and the main bearing, and would (with specified probabilities) give early warning, late warning, or no warning before failure. A warning would initiate the preparations of a repair, and the turbine could continue to run, until the turbine was about to fail, or the repair was initiated. Both corrective and preventive repairs were assumed to require jack-up vessels, and the only benefit of condition based maintenance was due to reduced downtime, although the presented methodology could include different repair methods and costs. However, even with these conservative assumptions, a clear benefit of CM was seen, which would increase with the underlying failure rate. Both fix-on-failure jack-up vessel charter strategies and strategies with predetermined campaigns would benefit from condition based maintenance. Potentially, the application of more advanced jack-up vessel charter strategies could give even larger benefit.

3. Main bearing fault diagnosis and RUL prognosis
This case-study concerns Fault diagnosis and RUL prognosis based on CM measurements. The architectures for three models related to main bearing fault diagnosis and RUL prognosis are proposed: the first follows a physics-based approach including information from several sensor types, and the other two follow a data-driven approach using temperature-vibration and vibration measurements respectively. The potential application of each approach is based on the specific availability of the required data in each model. The data-driven model, based solely on vibration monitoring, was implemented and further demonstrated using vibration time series (data provided from an industrial partner) from sensors mounted near the main bearing of a WT in operation. Due to the limited amount of data, although the fault initialisation was detected, a further evaluation of the method is needed to draw safe conclusions regarding the potential of the method for the RUL prognosis.

4. Reduction of mobilization costs for risk-based O&M model
The motivation for this case-study was the limitations of the risk-based O&M model with regard to modelling of costs. The risk-based O&M model can be used for identifying optimal strategies for CM, inspections, and repairs given expected costs of inspections, preventive repairs and failures per event. For the case-study concerning WT blades, the expected costs were found based on weather data, O&M access limits, assumptions concerning repair phases and durations, and WT data. But it was assumed that a jack-up needed to be mobilized for each blade exchange, where in reality it could in some cases be possible to use the same vessel for several repairs within the same lease period, resulting in lower mobilization costs per repair, and less lost revenue, if a vessel is already under mobilization when a failure occurs.

To remove this limitation concerning sharing of vessels, a simple simulation based tool was developed for the assessment of expected costs per failure. The simulation tool can model various jack-up strategies and contracts including job-based and time-based contracts for fix-on-failure strategies, as well as time-based campaigns. The job-based contracts can have different flexibility with regard to whether it is possible to add tasks to the job list after the vessel has been ordered: always, before it arrives on site, or never. The higher the flexibility, the lower the expected costs per failure, due to less mobilized vessels and less downtime. Compared to the base case with no sharing of vessels, 44% lower specific failure costs were found in the case-study. For the time-based contracts, the lease period agreed upon when ordering the vessel could be extended, but 30% in demurrage rate was added to the day rates after the agreed rental period. For this type of contract, 35% lower specific failure costs compared to the baseline was found.

O&M vessel concept and access methods
As well as improved strategies through the aforementioned tools and technologies, further cost savings can be realised through the use of innovative O&M service vessels and improved access methods. The increasing distance from shore has led wind farm developers and operators to push the frontiers of vessel design and access logistics as they face growing challenges of moving equipment and personnel to locations which are often hostile. Increased distance from shore means frequent trips back to port are no longer an option and access becomes far more weather dependent. Growing distances from shore have made it necessary for developers to consider vessels capable of remaining at sea for long periods in order that technician time at site is efficiently utilised. A degree of multi-functionality also seems unavoidable when vessels are expected to remain at site for longer periods; however, some in the industry believe that abandoning the concept of vessel specialisation will ultimately increase costs. The main challenges identified for service vessels are:
• reducing motion and improving operational efficiency to increase accessibility in larger sea states;
• increasing fuel efficiency;
• reducing seasickness and its detrimental effect on maintenance crew;
• establishing optimum vessel size and hull form type for varying distances from shore.

LEANWIND developed a novel O&M vessel concept. The overall target was to increase weather windows (through reduced vessel RAOs, thus reducing the vessel heave/roll/pitch response), comfort of crew and higher work efficiency (by reducing sea sickness and staying injury free during an extreme event). A similar assessment process applied to the installation vessel was followed for the LEANWIND O&M vessel. Selection was based on a qualitative method of listing goals for the vessels based on the challenges set by the industry and assessing these for a number of vessel concepts. The main design goals [with weighting] are shown below for selection of the O&M vessel:
1. CAPEX & OPEX [30%]
2. Comfort and Endurance [20%]
3. Time reduction and optimised man-hours [30%]
4. Ability to operate in an offshore environment [10%]
5. Multi-purpose capability [10%]

The assessment resulted in selection of one concept design for the O&M vessel. The following highlights key features of the vessel:
• pure LNG propulsion system (first and only concept);
• capable of operating in all regions of Emission Control Areas (ECA);
• enhanced access to transition piece via motion compensated gangway;
• capable of using general electrical network via backup battery during maintenance operation to avoid excessive fuel consumption and emissions;
• more than 30 days operation time with the help of battery backup unit;
• helideck to facilitate personnel/crew transfer between main land and the vessel, which allows long term activities at offshore;
• 50 technician capacity;
• 10 container capacity aft deck container storage area and additional covered storage area;
• Environmental Regularity Number = (99, 99, 99, 99, 2) for dynamic positioning operations with existing propulsion and thruster system;
• motion compensated gangway for safe transfer of crew/personnel which can be operated at Hs = 2.5 m;
• the main crane of the unit is capable of lifting 15 tonnes with a maximum reach of 15m to the side of the vessel;
• two daughter crafts with a capacity of 8 personnel each. These daughter vessels are completely enclosed for safe launching.
The design of the unit was based on Lloyd’s Register Rules for offshore units and IMO, MODU Code 2009. Figure 12 is a D3 model of the O&M Vessel final design (Source: Delta Marine Co.)

As for the installation vessel, design and training simulations of O&M vessel activities were carried out. The development of the simulator based design and training tools was motivated by industry requirements including tools supporting:
• assessment of vessel suitability for mission requirements;
• assessment of manoeuvring and station keeping performance;
• optimisation of deck layout, and gangway and crane positions;
• optimisation of bridge layout including field of vision;
• optimisation of gangway motion compensating concept (3-DOF vs. 6-DOF);
• tuning of DP system;
• tuning gangway motion compensation control system (integrated with DP control system);
• assessment of required propulsion system configuration, thrust allocation strategies and required power;
• assessment of operational limits (determination of weather windows for safe operation);
• design, optimisation and validation of operational procedures;
• training of navigators and gangway operators in operational and communication procedures;
• validation and demonstration of feasibility of innovative O&M vessel designs.

In order to fulfil these expectations, a simulator set-up was developed based on the simulator system SimFlex 4.0. The O&M vessel simulator design and training facility was showcased at a final stakeholder show case event in November 2017 demonstrating a fully realistic service operation which was repeated for various environmental constraints e. g. for different sea states, wind speeds, visibility conditions etc. Figure 13 show a trainee in the LEANWIND GangWay operator station (Source: FORCE Technology, 2017)

Access system testing improving O&M cost
The safe access of technicians to the offshore structures of an OWF is one of the major challenges in the offshore wind industry. New access techniques and vessels are constantly being developed with the aim of achieving safe transfers in wave heights up to 3m. The means of access often depends on the workforce requirements, the distance to shore, the prevailing weather conditions etc. Four major means of access can be distinguished: helicopter: winching (technicians are lowered one by one by means of a cable) or landing on helipad (offshore substation, offshore vessel); gangway: floating vessel (requiring motion compensation) or jack-up vessel (fixed gangway connection); bump & jump: fender friction only or additional access aid; man-basket.

Currently, most of the offshore transfers are performed by “bump & jump”, as most farms are still within the reach of a CTV. This is still the most economical and flexible mean of access. For the next generation of wind farms (>40km offshore), more Service Operation Vessels (SOV) will are deployed and technicians will remain offshore for longer periods (one or two weeks). In this case, transfers can be performed via a heave compensated gangway or a daughter craft. While the LEANWIND O&M vessel considers transfers from an SOV, in August 2017, an offshore trial with CTVs was carried out by GeoSea Maintenance on the C-Power wind farm, 30km off the Belgian coast. This study aimed to evaluate the performance of three different types and sizes of CTVs using the bump & jump access method. In order to assess the workability motions were measured on board while wave conditions were monitored by a buoy in the farm vicinity. The key objective of these trials was a comparative evaluation of:
• the motions during transit;
• the maximum wave height (and other limiting parameters) for safe boat landing;
• crew comfort at sea during idle time and transit time.
Results provided a better understanding of CTV’s workability when it comes to transferring people through bump & jump. Mapping each vessel’s limits allows for more reliable forecasts of weather downtime given an OWF location and wave climate. Figure 11 shows a camera shot on board CTV Phantom during bump & jump technician transfer (Source: GeoSea Maintenance NV).

Risk Assessment and safety of O&M access methods
The transfer of personnel to an offshore structure is the highest safety risk activity in the offshore wind industry, due to the frequency and the severity of the action. Independent of the access methodology, training the people working in an offshore environment is essential. A risk assessment of the different access methods was performed, by determining the main hazards and defining required measures to eliminate or reduce the risk. From a safety perspective, the walk-to-work principle (gangway access) is the preferred option. On the other hand, this requires a suitable platform (OSV, jack-up vessel), which comes with an expensive dayrate compared to CTV’s. LEANWIND also produced a public report examining the H&S issues and required personnel skills related to project innovations and the wider industry [8]. It is a comprehensive analysis of the existing situation regarding H&S issues in the offshore wind industry, in terms of regulatory framework and relevant guidelines from key players of the industry, availability of H&S specific accident databases, and the risk levels of critical accident scenarios. The report also assesses selected innovation categories that have been examined in the framework of the LEANWIND project, in terms of their effect on H&S issues. This includes worker access systems, lifting arrangements, and novel vessel concepts. Furthermore, the report presents an overview of existing regulations and requirements regarding training. The report identifies gaps that need to be filled to cover the actual competencies required in the wind industry and proposes training requirement guidelines that will improve the overall level of safety for workers in OWFs.

Logistics and supply-chain
One of the key LEAN principles is to approach improvements from a whole system perspective and an optimised supply-chain is an important factor in decreasing the costs of offshore wind energy. BVG Associates estimate that improving the supply-chain could contribute to a 9% reduction in LCOE [9]. Modelling is a safe and cost-effective way to evaluate and optimise operations. However, there is a lack of comprehensive decision-support tools, detailed enough to provide insight into the effects of technological innovations and novel strategies. They can reduce costs by identifying potential savings and fostering effective decision-making for a wide range of stakeholders. Therefore, two core activities of LEANWIND has been developing a holistic set of logistics optimisation models and a full lifecycle financial cost model.

The logistics models consider multiple decision variables e.g. ports, vessel fleets etc., ranking an optimum set of supply-chain configurations, while the financial model assesses the impact of strategic decisions and technologies on costs and time in detail. The financial and logistics models can be used independently but are also designed to be complementary. They are all state of the art tools that when used together, can optimise the entire supply-chain (prior to/post port; at port; to/from site) and simulate the full wind farm lifecycle (installation, O&M and decommissioning). The tools were developed to:
• improve existing and develop alternative strategies for installation, O&M and decommissioning at representative current, mid-term and future-term sites;
• consider the added value of project innovations e.g. novel substructure and vessels.

The logistics models comprise nine tools as follows:
Prior to port tool. The term prior to port includes all activities that occur up to the parts arriving at the offshore wind support port. Present trends show that WT manufacturers are considering building new facilities in coastal locations with waterside access. This is due to the fact that as turbine size increases, transportation of larger components on public roads is impractical. However, currently manufacturing still rests with industry located far from shore. Therefore, on-land transportation constitutes an important phase in the supply-chain. Logistic planning is key to reducing current and future costs.

The LEANWIND prior to port tool seeks the optimal arrangement of supply chain (suppliers, manufacturers/plants, and warehouses (ports)) and schedule from the production of turbine parts to delivery at port. Developing this tool involved:
1. Analysis of current European resources: in order to understand the transportation requirements for the various components involved and gather data on the locations of component manufacturing sites, the transport networks and the potential ports.
2. Analysis of the on-land transportation segments including their limitations and identifying opportunities to increase efficiency. It was found that due to the lack of harmonized road transport regulations (even within Europe), there are a diverse range of on-land transportation restrictions including physical limitations due to infrastructure capacity; physical obstacles and lack of suitable number or capacity of transport equipment for the increasingly large components as well as the number of farms being constructed at European level. The holistic approach of the LEANWIND logistics tools is needed to remove wastage and improve efficiency and face the new demands of the wind energy supply-chain.
3. Since the information concerned is a collection of spatial data, a GIS is the preferred solution to collate and visual the information. An open-source GIS software package called QGIS was chosen to present the database of European resources compiled. The GIS tool includes the three key datasets mentioned: manufacturing locations for the various components of an OWF, the locations of suitable ports for deployment of OWTs and the main transportation networks that link these two sets of points. The tool allows the user to search the database to identify ports with suitable infrastructure and plot transport routes, determining travel distance.

The GIS tool and port selection model (described below) were applied to allow the selection of a suitable port for the industrial implementation of self-buoyant GBFs. The aim is to assess the existing infrastructure and the improvements required. The study concludes that port depth, the availability of heavy load quays and large storage arrays could be bottlenecks to the manufacturing of these foundations [3]. This study is also a key illustration of the importance of taking a whole system perspective, examining the interaction required between technical developments and the associated logistics to achieve cost-reductions.

Port selection tool: Information was gathered to define the technical requirements of ports from several offshore wind energy stakeholders. This included ports already involved in the industry and ports under development with manufacturing facilities planned as part of the overall port capability. Discussions identified the most important criteria to support OWF logistics. Secondary sources and industry examples were examined to clarify each criterion and to investigate the implications on port design. An Analytical Hierarchy Process (AHP) based decision-making model was proposed to aid stakeholders in selecting the most suitable base for an OWF at a particular phase of its lifecycle based on the following port suitability criteria group:
• port’s physical characteristics: Including the seabed suitability, quay length, port depth, quay load bearing capacity, and component handling capabilities;
• port’s connectivity: Including distance to wind farm and key component suppliers as well as the road networks and heliports;
• port layout: Including the storage area, component fabrication facility, components repair facilities and component recycling facilities.

This model was applied to the LEANWIND Design Case site 1. This found that a trade-off is necessary between port costs and distance, when cost is a priority. Therefore, the most suitable port may not be the best option for decision makers. In this way, the model serves as a managerial tool, tackling strategic challenges. The full study is available on the LEANWIND website and is among the first studies that has systematically assessed the port requirements for the offshore wind industry [10].

This model was further developed to include a port layout optimisation tool for the installation phase. As the industry moves to larger WTs, ports will require bigger lay-down areas and specialised heavy lift equipment. This poses unique technical challenges and requires efficient design of ports and infrastructure to streamline the unloading, storage, assembly and loading of components prior to offshore installation. Optimising port layout is not unique to the offshore wind industry and the containerisation of cargo has immensely assisted port managers in maximising the use of available space for cargo handling. However, the size and variable dimensions of the WT components require a different approach since:
• unlike the container ports, where there are several equal areas i.e. zones, in which the containers are stacked, these areas may not be equal in an offshore wind port since the components vary significantly in their size; and
• while the area in most container ports has a regular shape, this might not be the case as for offshore wind, leading to irregularities.
As well as optimising for space, this model arranges components to minimise transportation distance between different areas, thereby reducing costs. This tool was applied to the layout of the Port of Arderiser, a real-case potential offshore wind port in Scotland, UK.

Port to site/Site to port: Vessel resources were identified as the most expensive components for the installation and O&M phase. To reduce cost, optimization tools were developed for both the installation and O&M phases. A literature survey showed that only a few studies exist of the logistic challenges related to the maritime supply-chain for the installation phase. These mainly consider the installation scheduling problem but no studies were found that explicitly study the resource management problem. The LEANWIND project developed the Installation Vessel Optimizer (LIVO) for this purpose. A study was conducted to illustrate how the model can be applied for LEANWIND Design Case 1. [11].

For the O&M phase, some studies exist that consider the resource management problem, and also several that involve simulation models for evaluating best O&M strategies and costs. However, there are few that involve the use optimisation techniques. LEANWIND developed a mathematical model for the resource management problem using a heuristic optimisation. This method has the advantage over current proposed methods, as it can be used to solve larger problems in more detail. The model does not guarantee an overall optimal solution, but will within reasonable computational time, provide a local optimum. It has been implemented in a number of studies. [11-12]
The decision-support systems for the installation and O&M phases are primarily intended to propose an optimal combination of vessel resources and corresponding infrastructure. However, they can also be used for analysis such as the following:
• setting competitive time charter rates of vessels;
• analyse the cost-benefit of using vessels/helicopters with different characteristics, e.g. higher operational wave height limits, higher transit speed;
• analyse the cost-benefit of offshore station concepts e.g. mother vessel concepts;
• indicate which installation/O&M strategies/activities are most promising;
• calculate potential cost savings of fewer turbine failures e.g. to justify investment in more expensive and more robust WTs or a CMS.

For decommissioning, as this phase has not matured within the offshore wind industry, work has mostly been a qualitative analysis. However, an integrated dismantling model was developed to analyse the site to port and port to disposal/recycling site legs of the decommissioning phase.

While the above tools are independent, they can be used partially or fully together depending on the decision problem. The integrated framework has been tested on the LEANWIND Design Case site 1 [13]. The GIS interface captures the key supply-chain information and plays a key role in assisting the users to visualise the supply-chain solutions.

Financial Analysis and Recommendations
While the logistics models focus on ranking an optimum set of supply-chain configurations, the financial model assess the impact of strategic decisions and technologies on costs and time in detail. A review of existing technology showed that there are no models that could be employed for these purposes in the detail and scope required for a whole-system and full lifecycle evaluation. The financial model comprises a central input/output file (Excel) that links three independent modules (MATLAB), which simulate installation, O&M and decommissioning activities. The user initiates the model by entering the required data through the Excel Graphical User Interface (GUI) and selecting the start button. The modules can also be run individually in order to focus analysis on a specific aspect.

The phase modules allow for a detailed assessment of strategies and technologies. They are all probabilistic, employing Monte Carlo simulation to consider unknown stochastic elements such as weather and component failures. Given the uncertainty of inputs (e.g. procedures and costs), the LEANWIND studies drew on the experience of project participants to supply financial figures and technical assumptions. They have been validated against existing farms and/or inter-model comparison where possible; through studies and estimates in the current literature; and sensitivity studies.

The model produces a project timeline as well as a comprehensive breakdown of CAPEX, OPEX and DECEX (decommissioning) costs, which are used to determine key financial indicators including LCOE, Net Present Value (NPV) and Internal Rate of Return (IRR). The model has also been designed to facilitate further analysis of project finance risks and identify ways to reduce the investment risk profile; consider the impact of the primary contractual arrangements on specific risks (such as vessel or weather delays) and their impact on cost; and produce the outputs required for lifecycle analysis to consider the environmental impact of a given scenario.

The Installation Module can simulate the installation of the turbine, foundation (fixed or floating), substation and foundation, as well as inter-array and export cabling. Different operations are associated with the installation of each asset. The model considers the vessels available and simulates activities considering an hourly time-series of weather for a given farm location, which varies per iteration to consider the potential impact of this key risk factor. The module generates the schedule of activities, recording the sequence of events, the time spent carrying out each activity and any delays. The final output includes a detailed breakdown of the duration and costs of activities including the CAPEX of assets; pre-installation transport from the manufacturer to the supply port (not included in the time series); the charter rate and fuel for vessels; survey and monitoring; port activities; and other balance of plant costs e.g. onshore works.

The installation module was validated using a number of case studies including: C-Power Phase 1, a small scale 30 MW OWF located on Thornton Bank in the North Sea, 30 km from the Belgium coastline. Results were found to closely correlate with the €153 million quoted for this farm, only 4.67% less [14]. Furthermore, sensitivity analysis was conducted to ensure the model worked as expected and to assess the impact of key variables on costs. An increase in CAPEX and the number of turbines have the most severe impact on cost. Figure 14 illustrates the trend in results (Source: UCC).

The OPEX module is the O&M strategy model described in section 1.3.3. This was developed from the pre-existing NOWIcob model. Utilising Monte Carlo simulation, the model simulates maintenance operations over the lifetime of a farm project using an hourly time-series. Offshore maintenance operations are highly weather dependent; therefore, weather data is generated per simulation using the markov chain modelling technique. Other stochastic variables include failure rates. Probability distributions can also be specified for the mobilisation time of chartered vessel, the lead time of spare parts, the direct repair time of maintenance tasks, and the pre-warning time for condition-based maintenance tasks. Several input parameters, both decision variables (choice of vessel mix) and uncontrollable variables (e.g. failure rates), can be changed to assess their impact on performance parameters, such as the availability of the wind farm and the cost of energy. Running multiple iterations for each case, the model presents the results as histograms estimating probability distributions. More detailed descriptions of the functionalities of the tools are found in (Hofmann and Sperstad 2013) [15] and (Hofmann et al. 2015) [16]. Information about the model and related work can also be found online (http://www.sintef.no/en/projects/nowicob-norwegian-offshore-wind-power-lifecycle-c/).

During its years of development, the O&M Strategy model has undergone extensive validation activities, including real wind farm projects and in collaboration with industrial LEANWIND participants. Industrial studies include a project with a Norwegian offshore wind developer for the investment decision of the Dudgeon offshore wind farm. Dedicated validation collaborations within LEANWIND include a study undertaken with the logistics O&M optimisation model. This compared their assessment of an undisclosed offshore wind farm project with results from an industry-grade tool currently used by an LEANWIND industrial partner and its affiliate (offshore wind farm owners/developers) [17]. These LEANWIND models have also previously been used together and benchmarked against other state-of-the-art O&M models [18].

Decommissioning Module: Yttre Stengrund and Vindeby are the only farms to be decommissioned to date. Given the lack of experience, there are no established methods and no real data is available about the actual cost of decommissioning, with a range of estimates available in the literature. An OWF has to be removed from the sea at the end of the lifetime. Re-powering or upgrading may be undertaken to extend a farm lifetime, but this will still involve aspects of decommissioning. Dismantling may also be necessary if the turbine is no longer functional due to damage, technical problems or withdrawal/expiry of the approval. While every farm will produce a decommissioning plan at the consenting phase, regulations as well as the optimal methods and equipment may change over the project lifetime. Therefore, this is an important area for research and potential cost-savings.

Currently, the industry agrees that decommissioning will be performed similarly to installation, but in the reverse order. With this in mind, LEANWIND has focused on optimising the efficiency of deploying OWTs and particularly their substructures. However, this assumption does not consider potentially faster methods e.g. demolition when the turbine/foundation are not intended for re-use or that components may not be in suitable condition for reverse-engineering. It also does not consider other areas for potential optimisation, such as the supply-chain during this phase. Therefore, research has been done to assess the different decommissioning options and logistical requirements (paper in preparation). In addition, the decommissioning module of the financial model allows the user to consider a variety of strategies and their associated costs and duration.

The module scope includes dismantling the turbine and foundation. Inputs include the component (e.g. blades, nacelle, gearbox etc.) and order in which they are dismantled; component materials and weight; operation durations; up to three destination ports; landfill or recycling centre locations; number of technicians; vessels available etc. The model derives an estimation of decommissioning costs, salvage revenue and the time taken. Given the lack of experience in this phase, the model also facilitates a simplified method to calculate a) DECEX (decommissioning cost) as a percentage of CAPEX or installation costs, and b) salvage revenue based on the estimated market value of the steel.

Validation of this model is difficult given the lack of industry experience. However, the model was run using the installation module case-studies for consistency. Costs were expected to fall within the range estimated by DNV GL of €200,000-€600,000/MW [19]. Results for the C-Power OWF were €513,000 per MW. While at the upper limit, this and other validation studies correlated well with the estimates. The high result may also indicate that costs could be greater than current industry expectations. Sensitivity analysis ensured the model worked as expected e.g. increasing the number of vessels and technicians available reduced the time required to complete activities etc. Sensitivity analysis also highlighted areas for optimisation:
• the cost of additional resources could outweigh time saved. Therefore, the optimal balance should be determined for a specific scenario;
• the impact of operational restrictions and durations is relative to the site location e.g. harsher conditions will increase the importance of optimising activities and perhaps investing in vessels with greater operational capabilities;
• feeder vessels will be less useful further from shore where they would require longer transit windows. Without feeder vessels, activities took longer but cost less. Therefore, the decision will depend on the priorities of the owner and the specific site;
• economies of scale were evident when increasing the farm size or the turbine capacity.

Risk and Business Models: To complement the LEANWIND financial model, a number of studies and tools were produced that propose novel business models for current and future industry needs; analyse risk; and assess the LCA of technologies assessed in the financial model.

Modern supply-chains face increased exposure to risks because of their complexity and globalisation due to the lack of visibility and control. The development of Decision Support Systems (DSS) can help companies in assessing their supply-chain risks and choosing suitable mitigation measures. Therefore, a two-stage supply-chain risk profile reduction support system was developed that determines not only strategies but also tactics, including contingency plans to mitigate risks. This model was adapted to utilise Monte Carlo simulations and function with @Risk software, which can take the outputs of the LEANWIND Financial model and further analysis a project’s risk profile.

This DSS consists of a decisions tree which evolved to include a matrix formulation that extends the matrix formulation of the Bayes’ formula. The DSS was applied to a real-world application of an offshore wind supply-chain and validated through this study and a collection of expert judgements from a focus group. It showed that for a supply-chain characterised by a medium exposure to risks, the risk-profile-minimising strategy is Engineering, Procurement and Construction (EPC), followed by multi-contracting and project alliance. Furthermore, sensitivity analysis suggests that multi-contracting could be more effective than EPC for an OWF characterised by low exposure to risks. This DSS improves and extends previous systems employing the supply-chain risk management process:
1) Proposing a method for estimating probabilities from expert judgements;
2) Considering the relationships among risks and mitigation measures;
3) Modelling a selection of mitigation measures to find the lowest supply-chain risk profile.
Future improvements to this DSS include an extension of the model based on real-option theory and the use of fuzzy numbers in the pairwise comparison matrices employed for determining parameters from expert judgements. There are currently some problems concerning fuzzy-pairwise comparisons and these will need to be fully resolved before implementing this strategy.

A Lifecycle Assessment (LCA) allows the environmental impacts and sustainability of any innovative construction process to be evaluated and compared to conventional technologies. An LCA tool was developed as part of the financial model to calculate the environmental impacts of a project. This considers inputs from the various phase modules including but not limited to: distance travelled by each vessel; the time spent performing certain operations; the volumes and weight of the materials included in the primary project assets; and the whether the materials will be sent to recycling and salvage or as waste. In addition to this tool, LEANWIND conducted an assessment of three of the substructure concepts. The results are under the potential impact section.

Analysis: The logistics and financial models can be used independently or in conjunction to provide input in the FEED stage decision-making. Working in isolation, both models have their own advantages. Optimising the supply-chain is a complex problem and beyond the human capacity to evaluate the number of possibilities in order to find the optimal cost efficient solution. However, they do not include substantial detail given they must consider multiple combinations of input options in order to determine an optimal scenario. In contrast, the financial model utilises Monte Carlo simulation to consider multiple iterations of a project lifecycle, accounting for the uncertainty of weather, component failures and cost inputs etc. It can also include a larger level of detail and thereby assess the impact of strategic choices on cost and time efficiency. While scenarios can be optimised using the simulation models, this is extremely time-consuming. In addition, the logistics models provide cost estimates of key aspects of the on-land and offshore supply-chain including aspects not considered in the financial model (transport to port, warehouses and storage). Through combined use, the optimisation models determine the key supply-chain configurations and the financial models examine the top ranking options in further detail. Together, they can obtain the most economically viable and time efficient solutions.

LEANWIND applied the logistics and financial models to a number of case-studies to a) evaluate the LEANWIND project innovations and b) provide final recommendations for cost-reductions to the offshore wind industry. LEANWIND selected a set number of scenarios to be run for the LEANWIND Design Case sites, which are representative of current and future OWF sites. Industry project participants were consulted to determine the scenarios of most interest; for technical validation of the proposed studies; and to gather inputs. Secondary sources were also consulted. Analysis of the LEANWIND foundations comprised 7 case-studies grouped by LEANWIND site including: the LEANWIND GBF (CS1) and a novel GBF (in collaboration with an external project) (CS2) compared to a conventional piled jacket (CS3) at Site 1; the LEANWIND jacket floated-out to site (CS4) and the LEANWIND floating platform – the semi-submersible (CS5) compared to a conventional piled jacket (CS6) at Site 2; and the LEANWIND floating platform at site 3 (CS7). Figure 15 illustrates the overall results for the foundations.

At site 1, the LEANWIND GBF (CS1) results in a 13.48% less LCOE when compared to the conventional jacket, while the Novel GBF (CS2) is 21.35% less.
- For installation, the savings for the CS1 & 2 are a combination of the cheaper material costs (concrete versus steel for the jacket foundation) and the ability to be floated to/from site, only requiring tugs rather than heavy-lift vessels. There are additional time and cost savings for CS2 as the turbine is pre-installed, reducing the time required offshore.
- As each scenario considered the same site, turbine and therefore farm production, O&M costs are fairly similar across the technologies. There is a slight difference due to the different failure rates applied to the foundations considering their different propensity to scour.
- CS2 proved to be the cheapest and fastest to decommission. Although it requires the most time to remove the foundation offshore and requires a dredger plus 3 tugs, the turbine is dismantled at port. CS3 performs second in terms of duration and cost, but best in terms of salvage revenue with the largest amount of steel. When revenue is offset against costs, the jacket proves the best option with only marginally more time required than CS2. CS1 performs the worst in terms of cost and time. Even though the foundation is floated to shore with tugs, the turbine is dismantled offshore requiring expensive heavy-lift vessels and the foundation takes considerably longer than the jacket to break down offshore.

At site 2, the LEANWIND substructures (CS4 and CS5) show cost-saving potential when compared to the current market conventional jacket (CS6). The LEANWIND jacket results in a 13.39% reduction in the overall LCOE, while the semi-submersible achieves a saving of 17.85%. When reviewing results, it must be noted that the LEANWIND semi-submersible platform was not designed for this site or for an 8MW turbine so the results are driven by a number of assumptions in the inputs. In addition, the O&M strategy is based on a number of simplified assumptions and requires further experience with floating platforms.
- Both the LEANWIND concepts are cheaper to install than the current industry standard (jacket) at this site. The LEANWIND semi-submersible (CS5) results in the cheapest and fastest installation as it is floated out with tugs, does not require seabed preparation and has a pre-installed turbine. The LEANWIND jacket (CS4) is the second cheapest and fastest to install at this site, again due to the fact that it can be floated to site. The turbine installation; however, adds time and cost. Both LEANWIND concepts result in lower material costs, requiring less steel than the conventional jacket concept.
- The same O&M strategy and parameters were applied for the two fixed structures and therefore results in similar costs. The LEANWIND semi-submersible (CS5) resulted in cheaper O&M but it should be noted that the strategy was based on simplified assumptions and a structure failure rate was not considered. O&M for floating platforms may be more complex and hazardous than for fixed structures. While this scenario assumes that O&M can be undertaken on site, transferring crew and replacing parts on a moving, floating structure will have many practical and H&S issues that need to be further examined and tested in real world conditions to determine the feasibility.
- The LEANWIND semi-submersible platform proves the fastest and cheapest to decommission due to the brief time required offshore as the turbine is dismantled at port. Decommissioning is also likely to require simple Anchor Handling Tug Supply (AHTS) vessels and tugs rather than an expensive jack-up vessel. As expected, the semi-sub has the lowest salvage revenue compared with the LEANWIND floating jacket (CS4) and the pre-piled jacket (CS 6). The LEANWIND floating jacket also results in lower decommissioning costs and time than the conventional jacket structure at this site, which is in line with the assumed shorter time required for anchor versus pile removal offshore.

The LEANWIND semi-submersible was designed for site 3. Despite low decommissioning costs, the O&M at site 3 for a floating turbine results in an extremely high LCOE of 0.59€/kWh.
- At Site 3, the installation of six floating turbines took almost 3 years. This was primarily due to the particularly harsh conditions at this location. Installation costs per MW were between 0.94-4.48 times higher than costs estimated for the other case studies.
- The floating foundation at site 2 (CS5) using a traditional O&M strategy (i.e. onsite using a jack-up vessel for major repairs rather than towing to port for maintenance) shows similar potential savings as the LEANWIND floating jacket (CS4). Due to the water depth at site 3 a jack-up vessel is not feasible and the O&M strategy involves towing the turbines to and from short for major replacements. Due to the lack of weather windows, this results in a very low energy-based availability of 49% and a high LCOE.
- While more expensive than at site 2 due to the harsh conditions, decommissioning costs continue to be lower per MW for floating versus fixed foundations. This is due to the short time required to detach and transport the foundations. This also only requires an anchor handling vessel and tugs and the turbine can be dismantled at port.

The Remote Presence eliminates the need to travel to an OWF and to access turbines, both of which are restricted by the uncertainties of weather. With the inclusion of Remote Presence, the average AEP increases by an average of 27.4 GWh across a selection of scenarios simulated. The LEANWIND installation and O&M vessel concepts were also evaluated. The installation vessel provided a reduction in time offshore compared to the next best industry alternative vessel at the far from shore site (Site 2). This supports the economic analysis previously described by concept developers. The LEANWIND SOV was also found to reduce OPEX costs and increase availability at the far from shore site (Site 2) in comparison to the next best industry vessel. However, due to limitations in available date, nearshore environmental conditions were used to represent Site 2. This will limit the impact of these technologies in more extreme conditions and it is suggested that further studies are carried out to look at incrementally more extreme site conditions, representative of more exposed far from shore sites, to get a clearer understanding of the benefits of these vessels.

In addition to the scenarios considered to assess project innovations, further case-studies (19 in total) were run to determine overall general potential time and cost saving recommendations for future wind farms. These include the following conclusions:
- Concrete provides material cost savings to the total CAPEX of a project. However; while steel is more costly, it can provide better salvage revenue at project end.
- Seabed preparation such as creating foundation beds for GBF adds cost and time.
- Elimination of piling operations e.g. use of suction buckets for seabed-fixing has been shown to provide key time and cost savings for both nearshore and far-shore sites.
- Reducing the need for HLVs has also been identified as a critical area for cost and time savings. For both near and far-shore sites, and independent of foundation material, float-out installation methodologies provide significant savings in installation costs. However, there is only limited time savings when compared to the craned installation of the same float-out concepts by HLV.
- Turbine installation was considered at site 1 with a variety of turbine assembly and installation strategies. As the lifts offshore reduce, time and costs decrease.
- The pre-installation of the turbine on the float-out GBF greatly reduced costs and time, predominately due to the elimination of HLVs and offshore lifts.
- Due to the advantages seen with float-out installation methods and minimal seabed preparation, the floating platform technology appears to be the most efficient in terms of time and cost for far shore sites such as Site 2. It should be noted that for all cases a similar discount rate was applied which would not reflect the higher risk perception for the floating platform. It also does not take into account technical feasibility of the foundation at that site. However, the results do show a promising opportunity for floating technologies should they become a standardised, proven foundation in the industry.
- The O&M strategy for floating technology is assumed to be the same as for fixed foundations, however from a health and safety perspective this is unlikely to be the case without major adaptations or new technologies. The impact of tow-back O&M strategy is simply not feasible from a weather window or downtime perspective at Site 3.
- Remote presence in all cases has been shown to provide savings in OPEX at all sites based on the assumptions used to represent the technology. There is a clear opportunity for this technology to impact the O&M costs of the industry.
- For the first time, simple decommissioning strategies were modelled and in all cases, analysis showed higher decommissioning costs to those anticipated in literature and project plans.
- The steel foundations provide a salvage revenue to offset the dismantling costs and can be the shortest time to remove assuming piles can remain in situ. It is not yet clear whether this will be a feasible option. The suction bucket technology; therefore, was designed to be pressurised and decommissioned in reverse installation, which provides advantages over other foundation technologies for both environmental reasons and for ease-of-removal.
Figure 16 summarises some of the key potential cost savings that can be made.

From the outset, LEANWIND conducted a comprehensive analysis of current challenges across the project life-cycle and potential solutions were produced as described above. The project also examined the non-technical challenges to determine the business and policy landscape required for the successful implementation of solutions. The recommendations proposed include:
Business models
Larger turbines and arrays bring complexities in the technologies and supply-chains. Based on real-world evidence from recent OWF projects, such as London Array and secondary data sources, analysis has identified that the innovative use of three purchasing and supply management practices [make–or–buy decisions; contract forms; and local-to-global sourcing decisions] can address current and future supply-chain needs.
Regulation & Legislation
- Collaboration among offshore wind developers of all EU member states and national authorities as well as relevant stakeholders is needed to achieve efficiencies in on-land and port infrastructure activities. Government incentives are required to encourage collaboration.
- Further studies are needed not only to assess the merits of the UK’s zone appraisal and planning for offshore wind development, but also to evaluate the options and benefits of having similar approaches in other European countries.
- Consideration should be given to the applicability of current emissions regulations to offshore wind installation vessels operating in ECA, as such vessels follow very different routines to normal shipping.
- There is a need to address the wide variety of (often competing) regulations relating to vessel operations at a regional, national and EU level.
- Standardisation of O&M activities and knowledge sharing would improve efficiency and lead to common EU best practices, which ultimately reduces wasteful processes.
Health & Safety
- To minimise H&S hazards, developers need to consider existing H&S risk assessment criteria at the early stages of wind farm design.
- An online information platform should be established for existing and potential suppliers to the offshore wind industry, detailing all the necessary offshore wind requirements in terms of standards and licences to provide visibility of the industry’s expected working standards.
- Sharing information on H&S and cross-sector/cross-border learning are suggested to compile offshore wind industry specific H&S regulations. These should consider current and future technologies.
- A guideline to safe and acceptable working hours for offshore wind crew should be created at an EU level to ensure that the requirements of year-round operations are met with no increase in risk to crew safety.
Training
- There is a need to develop offshore wind specific (e.g. diving skills) training programmes that include both H&S and technical training. Further information is needed about the possibility of cross-border standards.
- Some degree of standardisation and a common EU framework are required for training of escort drivers and traffic directors. Further information is required to assess the viability of introducing elements of offshore wind component transportation in such training courses.
- Virtual reality training facilities should be examined as an alternative to training facilities with real equipment.
- Cooperation is needed among schools, employers, universities etc. to ensure there are more suitably qualified graduates and to address skills gaps.
Environmental
- Waste management plans are needed for on-land operations.
- Flood risk assessment and prevention measures should be promoted in any new port development.
- Decommissioning programmes or plans outlining available recycling options for all offshore wind components should be produced.
- Further study into the impact of altered sedimentation during installation operations is required to ensure minimal impact on marine life.
- Understanding and minimising negative impacts of O&M activities on the environment is a necessary part of a wider goal to reduce greenhouse gas emissions. There is also currently a lack of understanding of the environmental effects of O&M activities.

References:
[1] Desmond, C., Murphy, J., Blonk, L. and Haans, W. (2016) 'Description of an 8 MW reference wind turbine', Journal of Physics: Conference Series, 753(9), 092013 (16pp). doi:10.1088/1742-6596/753/9/092013
[2] Attari, A., Doherty, P., Reig Amoros, E., & Paulotto, C. (2015). Design drivers for buoyant gravity-based foundations. Journal of Wind Energy. http://doi.org/10.1002/we.1953
[3] Akbari, Negar, Azadeh Attari, Lucy Cradden and Paul Doherty (November 2015). A GIS-based approach for port selection and bottleneck identification for the deployment of Self-Buoyant Gravity Based Foundations, EWEA Paris conference.
[4] LEANWIND consortium, (2015). Key design parameters and criteria related to installation and maintenance vessels design; their layouts, crane operations and access systems. Available at http://www.leanwind.eu/wp-content/uploads/GA_614020_LEANWIND_D3.2.pdf
[5] Strategic Research Agenda / Market Deployment Strategy (SRA/MDS), European Wind Energy Technology Platform, 2014, available online at http://www.windplatform.eu/fileadmin/ewetp_docs/Documents/reports/TPWind_SRA.pdf; ECOWindS- Newsletter for The European Clusters for Offshore Wind Servicing, 1, 2014; and Mike Newman, ORE Catapult, ‘Operations and maintenance in offshore wind: key issues for 2015/2016,’ September 2015.
[6] Sperstad, I. B.; McAuliffe, F. D.; Kolstad, M.; Sjømark, S., (2016). Investigating key decision problems to optimise the operation and maintenance strategy of OWFs, Energy Procedia, vol. 94, pp. 261-268.
[7] LEANWIND Consortium (2015). Optimised maintenance and logistic strategy models. Confidential report. Executive summary available at: http://www.leanwind.eu/wp-content/uploads/LEANWIND_D4.2_Executive-Summary.pdf
[8] LEANWIND consortium, (2017). Health & Safety risk control measure and required personnel skills, Available at http://www.leanwind.eu/wp-content/uploads/GA_614020_LEANWIND_D6.3_ExecutiveSummary.pdf
[9] G. Hundleby, (November 2016). The supply-chain’ s role in LCOE reduction.
[10] LEANWIND consortium, (2015). Ports suitability assessment for offshore wind development - Case studies report. Available at http://www.leanwind.eu/wp-content/uploads/GA_614020_LEANWIND_D5.3V3.pdf
[11] LEANWIND consortium, (2016). Mathematical optimisation models and methods for transport systems. Confidential Report, Executive Summary available at http://www.leanwind.eu/wp-content/uploads/LEANWIND-D5-6-Exe-Summary-Final.pdf
[12] Magne Nonås, Lars, Elin E. Halvorsen-Weare, Magnus Stålhane, (2015). Finding cost-optimal solutions for the maritime logistic challenges for maintenance operations at OWFs, EWEA Offshore Wind Conference.
[13] LEANWIND consortium, (2016). Holistic supply-chain optimisation model. Confidential Report, Executive Summary available at http://www.leanwind.eu/wp-content/uploads/GA614020_LEANWIND_D5.7_Executive_Summary.pdf
[14] C-Power. Retrieved 08/201/2017 from http://www.c-power.be/index.php/project-phase-1/overview
[15] Hofmann, M., Sperstad, I.B. (2013), NOWIcob – A Tool for Reducing the Maintenance Costs of OWFs, Energy Procedia, vol. 35, pp 177-186
[16] Hofmann, M.; Sperstad, I.B.; Kolstad, M.L. (2015), Technical documentation of the NOWIcob tool (for NOWIcob version 3.2) report no. TR A7374, SINTEF Energy Research, Trondheim.
[17] LEANWIND consortium, (2017). D7.2 Case study validation of combined economic and logistics tools. Confidential Report, Executive Summary available at www.leanind.eu/results/
[18] Sperstad, I. B.; Stålhane, M.; Dinwoodie, I.; Endrerud, O.-E. V.; Martin, R.; Warner, E. (2017): Testing the robustness of optimal access vessel fleet selection for operation and maintenance of offshore wind farms. Ocean Engineering, vol. 145, pp. 334–343. Available at http://www.sciencedirect.com/science/article/pii/S0029801817305280
[19] Chamberlain, K. (2016). Offshore Operators Act on Early Decommissioning, New Energy Update, Retrieved 09/10/2016 from http://newenergyupdate.com/wind-energy-update/offshore-operators-act-early-decommissioning-data-limit-costs
Potential Impact:
DNV-GL’s 2014 cost-reduction manifesto asserts that the following three strategies could collectively contribute to a 25% drop in LCOE:
Do it right - focus on reducing risk and preventing mistakes
Do it better – improve the efficiency of existing processes
Do it differently – implement alterative and innovative ways of doing things [1]
This reflects the “LEAN” philosophy of the project, which sought to optimise or remove wasteful activities across the lifecycle; streamline flow between project stages; and enhance quality. LEANWIND has successfully provided a large range of novel solutions that can improve existing practices and set standards in order to help industry meet their LCOE aspirations and maintain cost reductions as the industry develops. This conclusion summarises the potential impact of the project and how LEANWIND innovations will drive cost reductions in offshore wind.

Offshore monopiles dominate the wind farm sector at present sites, with many developers promoting their use for upcoming projects. However, recognising the industry trend for larger diameter monopiles in deeper water, XL monopile studies, LEANWIND committed significant research resources into developing optimum design methods (by modelling more realistic soil springs and suggesting more efficient use of Finite Element modelling) to reduce monopile sizes and steel tonnage. The benefits of leaner and more efficient design approaches are clear to the entire industry, leading to significant CAPEX cost reductions. Implementing these new design approaches on offshore wind projects could immensely reduce steel tonnage below the mudline. Over the next five years, as we strive for cost parity with conventional sources of energy, these optimised monopile design methods have a critical role to play. Industry partners are already using these design methods for real project optimisation and monopile design on projects across Europe.

Considering sites in transitional water depths (40m) LEANWIND means of improving Gravity Base Foundation (GBF) design and the efficiency of installation. The focus of this research has been on the detailed design of novel buoyant concrete structures, including geotechnical, structural and hydrodynamic analysis. A suite of parametric studies has been undertaken to define the optimum geometric configurations for buoyant gravity structures – this is particularly useful to assist in FEED stage engineering design. Research on geometrical optimisation of these structures has contributed to lowering material consumption by designing lighter yet equally stable foundations, which brings about savings in manufacturing costs. Furthermore, significant savings in transportation and installation costs result from eliminating expensive jack-up vessels by towing and ballasting gravity based foundations. Utilising the LEANWIND logistics and financial models, the LEANWIND GBF resulted in a 13.48% reduction in LCOE compared to the next best technology, a piled jacket foundation. In addition to economic analysis, a GBF platform was built using the same construction methods and technology as the LEANWIND GBF concept. The platform was deployed and key lessons-learned through an innovative monitoring system installed to measure the fluid-structure and soil-structure interactions. It is expected that this research will be further developed by industry partners in future projects. Demonstration of an optimised prototype could be a next step.

For deeper waters further from shore (60m) the integrated jacket foundation and suction bucket design developed in LEANWIND. This introduces cost-savings including a light weight design requiring less steel. There are particularly significant reductions during the transportation and installation phase due to the foundation being floated out rather than using expensive installation vessels. LEANWIND financial assessments calculated a potential 13.39% saving in LCOE compared to a piled jacket at the same site. Further research is expected to validate the design.

LEANWIND market analysis indicates that there is high potential for floating offshore wind energy. Given the interest in building wind farms in deeper water, more insight into the design principles of floating foundations will be instrumental in near future. LEANWIND’s development of a semi-submersible platform helps meet these demand of sites in depths at and above 100m where limited foundation types are applicable. Extensive research efforts in scale testing, numerical modelling and optimisation of anchor geometry, cable attachment and mooring configuration of the semi-submersible floating platform, has provided better understanding of performance and design of this relatively innovative concept. This output is valuable to facilitate future risk-reduced testing of concepts. Two case-studies were run to determine the cost-benefit of this concept. While the study based in 100m water depth resulted in a high LCOE, this was due to the extreme weather conditions at the site considered. When assessed at a more benign site (60m from shore), the semi-submersible achieved a saving of 17.85%. This was mainly due to the reduced material costs versus fixed foundations, as well as the savings in time and vessel costs during installation and decommissioning as the turbine is a) pre-installed and disassembled on shore and b) does not require expensive heavy lift vessels. It should be noted that due to the lack of experience in floating wind technologies, these studies are based on simplified assumptions and further research is required to determine the feasibility of this concept. However, testing has provided initial validation the structural design and a patent application has been submitted. The software developed to optimise the anchor geometry is currently being trialled in a floater project.

A LEANWIND 8MW reference turbine was developed. Considering potential project impact, this provides a reference turbine design for the R&D community so that technologies and methods can be meaningfully compared. The reference turbine has already been used in this manner within the LEANWIND project in the design of foundations, vessels, port layout and O&M strategy development.

Considering the growing need for clean energy and the growing size of WTs, the LEANWIND installation vessel design was optimized in terms of economy (size and number of turbines carried at once – 8*8MW turbines), environment (pure LNG propulsion system), and operation (ability to carry and install large turbines via special propulsion and lifting systems and optimized deck arrangement). The vessel can also be optimised to carry 10MW turbines, future-proofing against industry advances and the associated logistical complexities and financial burdens. Furthermore, the vessel can operate in most of the wind farm sites identified by the industry for future extension without significant restrictions on operations due to the environmental parameters. As well as meeting future industry requirements in capabilities, project assessment confirmed the anticipated time and cost-savings of this concept. However as described in section 1.3.5 due to limitations in the data available, further research at sites with more extreme conditions is needed to better quantify the potential impact.

The LEANWIND O&M vessel concept meets current and future challenges as farms are located further from shore. It includes substantial personnel capacity and a helideck to allow the transfer of crew, facilitating long-term activities offshore. The vessel design also prioritised increasing weather windows through reduced vessel RAO’s; improving the comfort of crew to foster higher work efficiency; and facilitating safe crew transfer via a motion compensated gangway at wave heights of 2.5m Hs and safe launching for the two daughter crafts. In addition, the vessel design was optimized in terms of economy (backup battery to run during maintenance work to avoid both fuel consumption and emission from vessel) and environment (pure LNG propulsion system). These aspects address many of the key industry requirements identified by the project market assessment. Financial assessment confirms the cost-saving potential of this project innovation at sites far from shore, but as with the installation vessel, further research in sites with more extreme conditions is required to assess the time and cost-benefits.

The vessel concept developers aim to incorporate the general advancement of knowledge gathered during concept design into their services and aim to further develop the concepts towards commercialisation in the future. Through this exploitation of project results, LEANWIND will directly help to address the market bottleneck for suitable and efficient vessels, providing purpose-built OWF concepts that address the offshore wind industry’s specific requirements as well as the needs of future sites further from shore in more extreme conditions.

LEANWIND O&M research has produced novel tools and technologies focused on making activities lean and efficient, ultimately helping to achieve and maintain the expected drop in LCOE. The O&M strategy model has produced a number of recommended areas for strategic optimisation e.g. efficient chartering of jack-up vessels, optimising the preventive maintenance season, creating the best CTV fleet in terms of cost and capabilities, the use of condition monitoring to reduce the need for offshore maintenance etc. The strategy has already been used by an industry player to validate their own tool and assess a real wind farm project under consideration. The dynamic scheduling model addresses the relatively new area of day-to-day planning tools specific to OWFs, promoting efficient scheduling of technicians, vessel routing etc. This was presented at the second LEANWIND stakeholder showcase and developers have been approached by industry expressing interest in collaborating on its future improvement. The development of a risk-based framework for O&M as well as the RAMS methodology assessment, degradation modelling, CM software and the remote presence prototype allowed LEANWIND to illustrate the potential benefits of monitoring systems and adopting risk and reliability-centred approaches. These can reduce the need for costly offshore trips and minimise the impact of failures by facilitating maintenance before failure occurs. The project illustrated how these tools could be implemented in an integrated way to capture a level of detail unprecedented in existing tools and systems. This will facilitate optimal decision-making at both the planning and operational stages, which in turn would reduce costs.

Considering design, a turbine controller for a semi-submersible platform was developed which presents a modification to the turbine control system to reduce loading on the system. It thus has the potential to reduce the occurrence of failures and extend turbine lifetime. Additionally, RAMS methodology and degradation modelling led to the development of a reliability-based design tool to ensure that turbine reliability is a key focus of the design stage, thus reducing O&M costs once operational.

Looking at the potential impact of the specific technologies, the cost-benefits of condition monitoring and the remote presence system have been demonstrated by the O&M strategy model in isolation and as part of the full financial cost model. Several companies have shown interest in the remote presence concept, which is being commercialized as the React™ solution by the Norwegian company emip a.s. a spin-off from NA.AS. The LEANWIND CM software presents a unique solution, mixing the latest methodologies of fault diagnostic and prognostic recommended by international standards, with the latest advances in web services. The CEANI research group that developed this software have been working in this field in close contact with industry from 2006. They intend to further develop the software to include new pre-programmed software pieces and improve the user’s interface with Artificial Intelligence based advice. In parallel, CEANI expect to sign agreements with some end users, in order to validate the software using a broad variety of significant industrial test cases.

The field trials involving the gravity-based foundation for the PLOCAN platform; the remote presence prototype; and access system testing have provided vital lessons regarding installation and deployment methods; data acquisition via remote presence; and the workability of different types and sizes of CTVs when using the bump-and-jump method of access. The direct industry involvement has facilitated immediate learning with regard to potential risks and cost-savings.

Vessel simulator tools were developed to a) validate the installation and O&M vessel concepts; b) demonstrate the use of simulation in optimising related operations; and c) show the potential of using virtual reality in training. The simulator tools allow for the trialling of concepts and training of crew in a controlled environment before entering into the real world where cost and risk are far greater. The deck simulators were showcased using the LEANWIND vessel concepts and demonstrating a variety of operations in different sea-states to industry participants in November 2017. This illustrated the potential of using simulation to reduce time and ultimately costs prior to deployment as well as mitigate Health and Safety risks, particularly for new/un-trialled technologies. The tools developed will be made available to industry through the relevant partners’ commercial services from 2018.

The logistics and financial models have been used in collaboration to assess the likely impact of the various project outputs. The financial models also provided general recommendations for potential cost-savings focusing on future wind farm sites and technologies. By facilitating effective decision-support, it is also expected that the models themselves could have a significant impact. By providing a whole-system perspective the logistics and financial models are relevant for a wide range of stakeholders across the supply-chain and at every stage of the life-cycle. For example:
- Manufacturers - to guide their facility location policy and optimise the movement and storage of parts; Ports - to assess the impact of upgraded facilities and capabilities;
- A technology developer - to assess the impact of the innovation on costs and time;
- A wind farm developer - to support planning by comparing various strategies at each phase e.g. the impact of size and type of vessel fleet on project finances;
- An OWF operator - to investigate the impact of a specific change in circumstances during operations e.g. if a new type of vessel becomes available in the O&M phase;
- A project investor or insurance companies - to analyse the possible financial outcomes or key risk factors of a given project or projects;
- Policy makers and funding bodies - to undertake broader cost-benefit analysis to determine areas with most potential for innovation and support;
- Academics/research - to analyse OWF project finances, technical innovations and trends.

The holistic set of logistics models are all innovative and state-of-the-art within offshore wind farm logistics planning and are mainly optimization based. This means that they automatically search through the extremely large solution space looking for the optimal solution. Thus, they enable the logistic planners to:
- be more efficient in the planning process, spending time evaluating near-optimal solutions; and
- produce better and more reliable logistics solutions by validating expert planner's subjective opinions with an objective analytical/mathematical approach.
The logistics models have been validated where possible with real case-studies and industry expertise. A number of the logistics model developers will be ready for collaboration with industry (subject to licence agreements) from 2018. Some developers have already been in contact with several major industry actors with an interest in using the tools beyond the lifetime of the project. Currently one of the tools is being advanced (directly funded from one of these actors) and new research projects initiatives (both national and EU funded) are underway that directly build upon the LEANWIND work.

The full lifecycle financial model is a probabilistic simulation tool that has facilitated the assessment of proposed methods and technologies for realising cost saving in an offshore wind project. The model has also provided general recommendations for potential cost-savings at representative current and future sites. It is state-of-the-art as there is currently no other model that can provide this level of detailed analysis across a project lifecycle. The model itself has the potential to accelerate and standardise the RD&P stage of a project and thus, ultimately result in cost reductions. The financial model developers have also been contacted by a number of stakeholders for future collaborations using the model including some of the largest offshore wind developers, offshore floating platform designs, shipping/vessel developers and wind industry representatives. Their requests included the analysis of a new vessel design for WT installation; a new WT platform and installation method; decommissioning analysis to aid in risk assessment for large portfolios of wind farms and installation analysis. This is in addition to industry participation in the validation of the financial model. The LEANWIND financial analysis of innovations already included collaboration with industry, featuring analysis of a novel float-out GBF with an external industry stakeholder. It is likely that in the future, the model developers will conduct research on behalf of users due to the complex and detailed nature of the full cost model. However, specific modules such as the O&M strategy model may be made available subject to a licence agreement.

Project modelling and testing, field trials and demonstrations have validated innovations and facilitated direct industry-led learning about potential cost savings and risk reduction. A number of projects and proposals will further develop the work of LEANWIND, and a substantial amount of interest has been expressed by industry in continuing to learn from project innovations e.g. the O&M, financial and logistics models. A wealth of knowledge has also been collated and disseminated via the LEANWIND project through public reports, side-events, three stakeholder workshops, conference presentations and posters. The project has produced 17 peer-reviewed publications, 8 of which are open access, and there are a large number currently in preparation based on the final project results. Details are available on the project website www.leanwind.eu.

At every stage, LEANWIND sought to ensure research is applicable to industry and undertook studies to assess results and facilitate market uptake as far as possible. As outlined above, it is expected that a number of the project innovations, e.g. the remote presence device, will be further developed and reach the status of commercial products/solutions. To facilitate uptake of project results, LEANWIND evaluated the existing market impact and market creation potential of technical innovations (substructures and vessels), as well as the possible non-technical impacts and viability of results in relation to policy, environmental, H&S etc. The output will help to streamline Research, Development and Planning (RD&P) activities by allowing assessment of the commercial potential of the technology. This will avoid time wasted in the future development of non-viable concepts and will allow the potential of competing solutions to be compared on a more equitable basis.
- This market survey sought to assess whether the LEANWIND innovations met the challenges considered most important to the industry. Results were disseminated in a poster and are expected to be published in a paper in 2018. The study revealed a high degree of correlation between designers’ and respondents’ priorities e.g. larger installation vessels that can install larger components and are more environmentally efficient. The correlations suggest that LEANWIND innovations will have high potential.
- Life Cycle Analysis (LCA) was conducted on LEANWIND innovations to assess their environmental and non-technical impacts on the local environment and communities. This found that both of the steel LEANWIND solutions perform well relative to their competitors (based on published studies). In the case of the jacket foundation, its impacts are found to be considerably lower than those for a similar sized foundation for a similar water depth. This is probably due to the lower impacts of the floater/suction bucket design. Only one other study on GBFs was identified and it found comparable results to the LEANWIND solution. The full report is public and available at www.leanwind.eu.

LEANWIND has developed state-of-the-art technologies and tools as well as recommendations that can provide costs reductions across the OWF lifecycle and supply-chain. The innovations seek to promote the use of lean principles, removing unnecessary and optimising required activities; enhancing quality and minimising risk; and offering alternative procedures and technologies. The novel solutions delivered by LEANWIND meet key industry needs and will support continued cost reductions, particularly for the more extreme sites of the future. This will help to guarantee the competitiveness of offshore wind and the EU’s leadership in this sector.

References
DNV-GL. (2014). Offshore Wind - A Manifesto For Cost Reduction.

List of Websites:
Website: www.leanwind.eu
Contact: leanwind@ucc.ie