LIST OF FIGURES

LIST OF TABLES

My deep gratitude also goes to all colleagues at the EMO research team and the LHEEA for the friendly and pleasant working atmosphere. In particular, I am grateful to be part of the LHEEA team and system administrators.

I would like to say a heartfelt thank you to my parents, Mr. Abd Jamil and Mrs. Rodziah, my parents' in-law, Mr. Chin and Mrs. Selamah, for always believing in me and for their unconditional support, encouragement, sacrifice and prayers. Without their support and prayers, this work would not have been possible. I am also grateful to my sibling and sibling-in-law for their steady support and prayers. The Covid-19 pandemic had a significant impact on energy consumption worldwide, with global energy and electricity consumption falling respectively by 4% (Figure 1) and 0.9% (Figure 3) in 2020 [START_REF]Global Energy Review 2021[END_REF].

However, people across the world are putting in their best efforts to combat this pandemic.

As a result, the global economy is expected to improve in 2021. In particular, global energy consumption is expected to rebound by 4.6% relatively to 2020, and global electricity consumption is expected to increase by over 1000 TWh (4.5% increase) [START_REF]Global Energy Review 2021[END_REF]. Despite the decrease of global energy consumption due to the Covid-19 crisis, renewable energy demand has remained robust. Renewable energy production capacity grew by 10.3% (261 GW) in 2020 [2][36] and is expected to grow by a further 8% in 2021.

Figure 2 show the share of renewable and power capacity expansion between 2001 and 2019. The share of renewables in global electricity generation capacity increased to 28.6% in 2020, up from 26.9% in 2019 [START_REF] Irena | Renewable energy statistics 2021[END_REF]. In 2021, it is expected to reach 8300TWh in 2021 (29.6%) [START_REF]Global Energy Review 2021[END_REF].

Nowadays, renewable energy is developed for climate and energy supply security. The largest sources of greenhouse gas (GHG) emissions from human activities are transportation and burning fossil fuels for electricity generation [START_REF] Equinor | Annual report and Form 20-F[END_REF]. Therefore, to limit global warming, the reduction of the global greenhouse gas emission is vital. Renewable energy is typically local energy, exposed to seasonal and time-to-day changes (weather). Solar PV and wind energy are the most promising resources in the sector of renewable energy, and most importantly their lifecycle greenhouse-gases emissions intensity is an order of magnitude smaller than fossil fuels in terms of grams of CO2equivalent emissions per kilowatt-hour (CO2-eq/kWh) [START_REF] Equinor | Annual report and Form 20-F[END_REF]. Solar PV and wind are expected to put up two-thirds of renewables' growth. Renewable electricity generation (see Figure 4) from wind uniquely is set to grow by 275 TWh (17% year-onyear growth) in 2021, up from 175 TWh (12% year-on-year growth) in 2020 [START_REF]Global Energy Review 2021[END_REF]. Meanwhile, electricity generation from solar PV increased by 145 TWh (18% year-on-year growth) in 2021

(153 TWh (23% year-on-year growth) in 2020) [START_REF]Global Energy Review 2021[END_REF]. The global wind generating capacity has snowballed in Europe, Asia, and North America since the beginning of the twenty-first century. Table 1 shows the total wind energy capacity, offshore wind energy capacity and solar PV capacity for the year 2019 and 2020 worldwide, in Europe, in Asia and in the North America. Indicatively, global wind energy and solar PV is on track to record a significant increase in its capacity in 2020 from 2019 [START_REF] Archer | Evaluation of global wind power[END_REF]. As already mentioned, wind energy is a rapidly growing power generation source. At present, electricity is produced from wind energy using wind turbines. Even though wind farms may be costly to build, the cost of energy is highly competitive (because wind energy is massively available and free). Wind farms are also climate-friendly as the global lifecycle greenhouse-gases emissions produced by wind turbines is typically 11 -12 g CO2-eq per kWh of electricity [START_REF]Global wind report, Global Wind Energy Council[END_REF],

here as fossil fuels produce approximately 600 g CO2-eq per kWh for oil-based power generation and 1000 g CO2-eq per kWh for coal-based power generation [START_REF]Global wind report, Global Wind Energy Council[END_REF].

Offshore wind represents a significant energy resource. The estimate of mean global ocean wind power is 731 Wm -2 [START_REF] Capps | Global ocean wind power sensitivity to surface layer stability[END_REF], whereas it is 250 -320 Wm -2 onshore [11][42]. This is due to the very low surface roughness at sea. This advantage results in higher wind speed and greater wind power yield [START_REF] Possner | Geophysical potential for wind energy over the open oceans[END_REF]. The global offshore wind power was potentially generate more than 420 000 TWh per year [START_REF]Offshore wind outlook 2019[END_REF]. Also, offshore wind is less concerned by space constraints and conflicts of uses that could limit the deployment of onshore wind farms [START_REF] Archer | Evaluation of global wind power[END_REF]. The UK has an enormous amount of offshore wind capacity in Europe, with 45% of all installations. Germany is second with 34%, followed by Denmark (8%), Belgium (7%) and the Netherlands (5%) [START_REF]Offshore wind energy in Europe: Key trends and statistics[END_REF]. In France, despite that offshore wind energy development started 15 years ago, the first French offshore wind farm will only start producing by 2022. The French offshore wind farm capacity is expected to reach 3.5GW in the next 5 years. [START_REF]Wind Obsevatory: Analysis of the French wind power industry: market, jobs and challenges[END_REF].

The capacity factor (CF, in %) is a key metric to quantify the energy performance of a power generation source. It is defined as the ratio between the effective average power over a given period and the nominal power. In terms of energy, this corresponds to the ratio of the actual electrical energy produced by a system over a given period of time to the energy it would have produced if it had operated at its nominal power during the same period.

The capacity factor of offshore wind farm is in the range 39% to 60% [START_REF] Capps | Estimated global ocean wind power potential from QuikSCAT observations, accounting for turbine characteristics and siting[END_REF][10] to [START_REF] Umoh | Drivers for and Barriers to the Take up of Floating Offshore Wind Technology: A Comparison of Scotland and South Africa[END_REF], with a global fleet-wide average of 37% for operating wind farms [START_REF] Jamil | Investigation of the Capacity Factor of Weather-Routed Energy Ships Deployed in the Near-Shore[END_REF]. According to [START_REF] Irena | Future of wind: Deployment, investment, technology, grid integration and socio-economic aspects (A Global Energy Transformation paper[END_REF], the capacity factor of offshore wind will increase in the future, ranging from 36% to 58% in 2030 and 43% to 60% in 2050. As can be seen in Figure 5, capacity factors in the order of 60% may be achieved along the Atlantic coast from Western France to Estonia and in the Pacific (West Coast) [10][16].

To date, most offshore wind farms are bottom-fixed. This technology limits the deployment to shallow water. In order to address this issue, floating wind turbines have been developed and the first commercial floating wind farms have been installed. Hywind Scotland (56% CF) [START_REF] Equinor | Hywind Scotland remains the UK's best performing offshore wind farm[END_REF][85] and Kincardine are the biggest floating wind farms in the world. Hywind Scotland (see Figure 6) is sited 25 km far from shore, with 108 m water depth using SPAR buoy-type foundation technology. The wind farm consists of five 6MW Siemens Wind Turbines with a total installed capacity of 30MW [START_REF] Equinor | Hywind Scotland remains the UK's best performing offshore wind farm[END_REF].

The Kincardine (see Figure 7) wind farm is sited 15 km from shore at water depth ranging 60m to 80m using a semi-submersible foundation. The wind farm consists of one operating 2MW wind turbine and an additional five 9.5MW MHI Vestas (expected commissioning in 2021) with a total installed capacity of 50MW [START_REF]Offshore wind energy in Europe: Key trends and statistics[END_REF]. In the future, beating the records of Hywind Scotland and Kincardine wind farms (in the UK), Hywind Tampen (in Norway, commissioning in 2022) will have a total capacity of 88MW [9][69]. Even with floating offshore wind turbines, only the nearshore offshore wind energy potential can be harvested [START_REF] Capps | Estimated global ocean wind power potential from QuikSCAT observations, accounting for turbine characteristics and siting[END_REF][10] [START_REF] Jamil | Investigation of the Capacity Factor of Weather-Routed Energy Ships Deployed in the Near-Shore[END_REF][17] [START_REF] Babarit | Technoeconomic feasibility of fleets of far offshore hydrogen-producing wind energy converters[END_REF]. Indeed, grid-connection cost, moorings and installation cost and maintenance costs increase as the distance to shore and the water depth increases (see Figure 8) [START_REF] Abd-Jamil | A. Comparison of the capacity factor of stationary wind turbines and weather-routed energy ships in the faroffshore[END_REF] [START_REF] Mone | Cost of wind energy review[END_REF]. Furthermore, failures of offshore submarine power cables have lowered system availability. Despite the development of offshore wind farms, the number and cost of these incidents do not appear to be decreasing [START_REF] Mclaurin | Prevention of Offshore Wind Power Cable Incidents by Employing Offshore Oil/Gas Common Practices[END_REF]. Therefore, stationary grid-connected offshore wind turbines deployment is likely to be limited to nearshore. Mobile off-grid offshore wind energy conversion technologies are thus required to enable the exploitation of the far-offshore wind energy resource.

The energy ship is an example of such technology [START_REF] Babarit | Exploitation of the far-offshore wind energy resource by fleets of energy ships, Part A, Energy ship design and performance[END_REF]. In energy ships, electricity is produced by a water turbine attached underneath the hull of a ship propelled by the wind using sails. Since they are not grid-connected, energy ships must include an onboard energy storage system. It can be based on batteries, hydrogen, methanol, or others [START_REF] Babarit | Exploitation of the far-offshore wind energy resource by fleets of energy ships, Part A, Energy ship design and performance[END_REF].

A key advantage of energy ships is that being mobile, they may sail to the resource instead of having to wait for it [START_REF] Gilloteaux | Preliminary design of a wind driven vessel dedicated to hydrogen production[END_REF]- [START_REF] Clodic | Wind propulsion options for energy ships[END_REF].

Furthermore, their route schedules can be dynamically optimized [START_REF] Hagiwara | Weather routing of (sail-assisted) motor vessels[END_REF] taking into account weather forecast to maximize their capacity factor. Although the concept is obvious, to the best of our knowledge, this thesis is the first investigation of the capacity factor of weather-routed energy ships in the far offshore. Furthermore, in 2015, France enacted a law aiming at the self-sufficiency of its overseas territories for electricity supply by 2030 [START_REF] Loi | 2015-992 du 17 aout 2015 Relative à la transition énergétique pour la croissance verte[END_REF]. At first glance, offshore wind may appear as an appealing solution for the decarbonization of power generation for these islands. However, taking into account that the energy needs in islands are limited, "conventional" grid-connected offshore wind turbines may be challenging from an economic perspective because of infrastructure cost and the lack of economies of scale. Thus, energy ships may be a competitive alternative to offshore wind turbines for the power supply of islands. A study is needed to determine what capacity factor can be achieved for energy ship deployed near small islands.

For those reasons, in this thesis, the capacity factor of weather routed energy ship is investigated in the far offshore and near small islands. A sensitivity study also has been performed in order to investigate the sensitivity of the optimized capacity factor to the two main parameters; energy ship's sailing capability as function of the storage capacity aboard and unloading time; and weather routing optimization parameters.

Energy ship concept

The concept of an energy ship was first proposed in 1982 [START_REF] Salomon | Process of converting wind energy to elemental hydrogen and apparatus therefor[END_REF]. Since then, there have been several other design proposals (see Figure 9) [START_REF] Kim | Wind power generation with a parawing on ships, a proposal Energy[END_REF][21] [START_REF] Babarit | Exploitation of the far-offshore wind energy resource by fleets of energy ships, Part A, Energy ship design and performance[END_REF][29] [START_REF] Enercon | Enercon e-ship 1: A wind-hybrid commercial cargo ship Presentation at 4th Conference on Ship efficiency Hamburg[END_REF][32]- [START_REF] Meller | Wind-power linear motion hydrogen production systems[END_REF]. They differ by the choices of the used technologies and by the architecture of the ship. For wind propulsion for example (see Figure 9), it has been proposed to use kite wings (Kim & Park [20]), rigid sails (see Ouchi & Henzie [START_REF] Ouchi | Hydrogen generation sailing ship, conceptual design and feasibility study Proceeding of IEEE OCEAN 2017 conference[END_REF] and Meller [START_REF] Meller | Wind-power linear motion hydrogen production systems[END_REF]) and Flettner rotors (see Babarit et al. [START_REF] Babarit | Exploitation of the far-offshore wind energy resource by fleets of energy ships, Part A, Energy ship design and performance[END_REF]).

Design and mode of operation of the energy ship considered in this study

The energy ship design considered in this study is derived from that presented in [START_REF] Babarit | Exploitation of the far-offshore wind energy resource by fleets of energy ships, Part A, Energy ship design and performance[END_REF]. It consists of an 80 m long catamaran with four 30 m tall Flettner rotors, and two water turbines, at rated power 900 kW each. Figure 10 shows an artist impression of the proposed design. Its main characteristics are given in Table 2.

Figure 9 Picture of energy ship proposals Flettner rotors were selected against other wind propulsion options (soft sails, rigid sails, kites) because of their commercial availability, high lift capability and controllability (not an exhaustive list) [START_REF] Clodic | Wind propulsion options for energy ships[END_REF]. By using the Flettner rotors, the energy ship utilizes the Magnus Effect from the wind for the ship propulsion. The water turbines convert the kinetic energy of the ship into electricity, which is then stored in the onboard energy storage system for example the li-ion battery or convert into hydrogen or methanol form for storage.

A mathematical model of the energy ship has been developed in order to assess the performance of the energy ship (Velocity and Power Performance Program: VPPP) in previous study [START_REF] Gilloteaux | Preliminary design of a wind driven vessel dedicated to hydrogen production[END_REF] [START_REF] Babarit | Exploitation of the far-offshore wind energy resource by fleets of energy ships, Part A, Energy ship design and performance[END_REF]. The Flettner rotors are modelled through aerodynamic coefficients. The water turbine has been modelled using momentum theory. Ship resistance coefficients were obtained using the REVA software. In the first case study, the energy ship is deployed far-offshore in the North Atlantic ocean.

In this case, the energy ship is an autonomous wind energy converter that moves in fleets, Figure 11. Those fleets are escorted by tankers that would regularly collect the produced fuel (e.g.

Hydrogen). When their tanks are full, tankers are replaced by other empty tankers in order to ensure continuous operations. The full tanker sails to an on-shore terminal where the fuel is unloaded, stored and distributed. Once unloaded, the tanker sails back to the off-shore ocean to meet a fleet to replace an almost full tanker. The tanker would also act as a surveillance & control support vessel for the energy ship.

In the second case, the energy ship is used for the power supply of islands, see Figure 12.

The envisaged mode of operation is as follows. The island grid would be powered by a virtual power plant consisting in several batteries containers. This plant would be located in a port. Once a container would be empty, it would be loaded aboard an energy ship which would then set sail and start a charging cycle. Once the batteries would be charged, the energy ship would come back to the port, unload the filled batteries containers, load empty batteries containers, and start again a new charging cycle. In both cases (fuel production in the far-offshore and power supply of islands), the trajectory of the ships can be optimized using weather-routing (see section 2.4) in order to maximize energy production.

Research Objectives

There have been various energy ship concept proposed for far offshore wind energy exploitation in the past (see Figure 9) [19][22][32]to [START_REF] Meller | Wind-power linear motion hydrogen production systems[END_REF].The principle of operation is identical, but there are significant differences in the choice of technologies used and in the architecture of the ship. The concept of an energy ship was first proposed in 1982 [START_REF] Salomon | Process of converting wind energy to elemental hydrogen and apparatus therefor[END_REF]. The ship uses a wind propulsion system to move around. The ship is equipped with hydro-generators which produce electricity by moving the ship. The electricity is chemically transformed into hydrogen and stored onboard.

An energy ship must include the following sub-systems [START_REF] Babarit | Exploitation of the far-offshore wind energy resource by fleets of energy ships, Part A, Energy ship design and performance[END_REF]: the structure and hull of the ship, a wind propulsion system, hydro-generators, that is to say submerged turbines under the ship's hull, a unit of energy storage. On the basis of this study, the diagram of the energy ship is thus proposed in Figure 10 [21] to [START_REF] Clodic | Wind propulsion options for energy ships[END_REF]. To date, there has not yet been a study investigating the capacity factor of weather-routed energy ships in the far offshore or for the power supply of islands. This thesis aims at addressing this knowledge gap by producing optimized capacity factor using weather-routing for the proposed energy ship. The research objectives are thus:

1. Investigate the annual average capacity factor for a given energy ship design deployed in the North Atlantic Ocean; and compare to stationary floating wind turbines. This objective includes the development of a method for the performance assessment of a weather-routed energy ship.

2. Investigate the annual average capacity factor for a given energy ship design deployed in the nearshore; and compare to stationary floating wind turbines.

3. Assess the sensitivity of the capacity factor to the ship characteristics: onboard storage capacity, energy unloading time, velocity and power production polars.

4. Assess the sensitivity of the capacity factor to the parameters of the weather routing algorithm: number of initial optimization waypoints and search step angle.

Thesis Structure

The thesis is divided into five chapters.

Chapter 1 (this chapter) is the introduction. In a first part, it presents the global energy context. It also highlights the far-offshore wind energy potential. The second part presents the energy ship concept, characteristics, mode of operations and the case studies considered in this research. The rest of Chapter 1 sets out the aims and objectives of the research in this thesis.

Chapter 2 deals with the state of the art of wind energy harvesting (wind turbines). It focuses on their capacity factor depending on their deployment location. It includes the investigation of the capacity factor of 5MW stationary floating wind turbine which would be deployed far-offshore.

This chapter also presents an alternative concept for far-offshore wind energy conversion (sailing wind turbine) and its capacity factor.

Chapter 3 deals with the capacity factor optimization of the energy ship in the far-offshore using weather routing. The first part reviews existing ship weather routing methods and tools. This part also presents elaborations on the features and specifications of the QtVlm program. The second part presents the weather routed 1.6MW energy ships' capacity factor investigation. The capacity factor is compared to the capacity factor of stationary floating wind turbines. In addition, a sensitivity analysis to the two main parameters -the energy ship's sailing capability and numerical optimization parameters -is presented and in this chapter.

Chapter 4 presents the extension work of the weather routed 1.6MW energy ships' capacity factor investigation to the nearshore. This chapter considers two possible deployment locations:

the French archipelago of Saint-Pierre-et-Miquelon island and Ile de Sein island. This chapter also includes a statistical analysis of the energy produced by the energy ship, sailed distance, average boat speed, and true wind angle (TWA).

Finally, Chapter 5 is the conclusion of the thesis. It summarizes the key results and outlines perspectives for further research.

CHAPTER 2 CAPACITY FACTOR OF LAND-BASED, NEARSHORE AND FAR-OFFSHORE WIND TURBINES

In this chapter, the state-of-the-art of wind energy harvesting technology (wind turbines) is presented. Its capacity factor is discussed depending on its deployment location.

State-of-the-art of wind turbines

Wind energy development

Wind-based power is one of the renewable power sources that are expected to play a significant role in global decarbonization. According to previous resource assessments, the available wind energy in the atmosphere could potentially power the entire world [START_REF] Archer | Evaluation of global wind power[END_REF].

Global wind power capacity grew by 14% between 2019 and 2020 to 743GW with 93GW new added capacity for both onshore and offshore in 2020 [START_REF]Global wind report, Global Wind Energy Council[END_REF]. Global capacity is expected to reach 2000 GW global wind energy capacity by 2030, supplying up to 17-19% of global electricity, and reducing CO2 emissions by more than 3 billion tons per year. This projection includes both onshore and offshore wind farms. [38][39]. The expected total installed capacity of offshore wind by the end of 2030 is 64 GW (4.5 GW per year installation rate), providing around 250 TWh per year.

Vestas, GE Renewable Energy, Goldwind, Chinese Envision, Siemens Gamesa, Enercon are the largest wind turbine manufacturers ("OEMs") in in the world, having supplied 68% of global installed wind power capacity in 2020. Figure 13 shows their respective market share.

Vestas was the world's top provider of wind turbines in 2020, covering both onshore and offshore wind [40][46].

In 2020, the Asia Pacific are takes the lead in global wind power development with 60% new installed capacity in 2020, followed by North America (18%) and Europe (16%) [START_REF]Global wind report, Global Wind Energy Council[END_REF].

Looking forward, the wind farm technology in 2030, the development of wind industry technologies may bring up the turbine rating to 13 MW, with rotor diameter of 212 m and hub height of 128 m.

Relative to the capacity factor, in 2030, 46.7% capacity factor was estimated for typical installation site and exceed 50% capacity factor for best sites which discover better wind resources [START_REF] Hundleby | Unleashing Europe's offshore wind potential: A new resource assessment[END_REF].

Figure 14 shows the evolution of wind industry globally between 2001 to 2020. Following the scenario of 12% growth, the install capacity in wind industry could achieved up to 22 TW in 2050.

Figure 13 The world's top five rankings for wind turbine original equipment manufacturers ("OEMs") (Source: GWEC (2021 [START_REF]Global wind report, Global Wind Energy Council[END_REF])

Wind turbine technology

Historically, the earliest use of wind energy exists for boat navigation on the Nile River in 5000 BC. In the same era, windmills in China were also used to pump water. Essentially, wind power was utilized to produce mechanical power to pump water and grind cereals until the early twentieth century. Then, Poul LaCour, a Dane, constructed the first wind turbine that generated energy in 1891. During World Wars I and II, the Danish engineers improved the technology and employed it to tackle energy shortages. F.L. Wind Turbines, a Danish manufacturer was the one who manufactured the wind turbines [START_REF] Şahin | Progress and recent trends in wind energy[END_REF]. Since then, significant advancements in wind turbine design have been made. Modern technical advancements and improvements of a turbine and its components, in particular, have resulted in considerable increases in produced power output and efficiency. Nowadays, commercially available wind turbines range in size from a few kilowatts to many megawatts.

Modern wind turbines can be divided into two types:

1. Horizontal axis wind turbines (HAWTs)

2. Vertical axis wind turbines (VAWTs) HAWTs dominate the majority of the wind industry due to their greater efficiency and energy output in comparison to VAWTs.

The diameter of the turbine is among the most important parameter. The recent trend is toward large diameters, as longer blades sweep wind from a larger area and produce greater output energy. Table 3 shows the evolution of wind turbines size over time till 2020. Practically all modern wind turbines are designed according to the international standard IEC61400. Furthermore, wind turbine certification bodies such as the DNV-GL release their own amendments and additions to the IEC61400 standard [START_REF] Lennie | A Review of Wind Turbine Polar Data and its Effect on Fatigue Loads Simulation Accuracy[END_REF]. A wind turbine is composed by different components; the main components are listed and described as follows (see Figure 15) [START_REF] Kumar | Wind energy: Trends and enabling technologies[END_REF][48]:

1. Rotor -A rotor consists of large blades resembling an airplane wing. It converts the wind kinetic energy into the rotation of the rotor hub. Wind turbines have normally three blades.

Rotor blades can be very large in size.

2. Rotor hub -it connects the blades to the main shaft. It also contains the pitch drive.

3. Pitch drive -It is used to control the pitch of the blades. It changes the angle of attack of the blades with the goal of changing the rotation speed of the rotor. It is used to reduce the lift in high wind speed conditions. This is necessary to guarantee that the generator maintains a speed within an acceptable power system operation range of 1000-3600 RPM (revolutions per minute).

4. Drive train system -it is the part that transfers the energy from the rotor to the generator.

There are mainly three types of drive train system: geared, direct-drive and hybrid.

5. Nacelle -The housing of all the elements of the upper part of the wind turbine. The nacelle is located at the top of the turbine tower. It is attached to the rotor, and contains the main technical parts, such as the rotor shaft, gearbox, and generator. The main role of the nacelle is to protect the internal components of the wind turbine against the environment. The nacelle is connected to the tower with bearings and is able to rotate with respect to the wind direction in order to harness maximum wind energy. In addition, there are heaters/coolers fans inside the nacelle to control the temperature. To facilitate the access of operators to large wind turbines, the nacelle may include a helicopter-platform.

6. Gearbox -The turbine rotor typically has a speed of less than 100RPM, but most generators need 1000 to 3600 RPM to generate electricity. Thus, the gearbox converts low rotor speed into higher speeds in order to make the generator operational.

7. Generator -The generator converts the mechanical energy of the rotor into electrical energy. It is placed at the top of the tower, inside the nacelle.

8. Anemometerthe anemometer measures the wind speed, and the wind vane detects the wind direction. 9. Wind orientation control (Yaw control) -controls the rotor to face the wind direction and the yaw angle.

10. Access ladder -Although most modern wind turbines now include elevators to enable easy access to platforms within the towers and all the way to the nacelle, access ladders are still necessary in the event of an emergency lift breakdown or to access to interior tower section between platforms.

11. Tower and foundation -A tower is used to place the rotor at high altitudes in order to capture more wind energy. It is also capable to transfers the vertical and horizontal loads to the ground. The design and configuration depend on where the wind turbine is placed whether onshore or offshore (see Figure 16).

12. Grid connection -In order to reduce electric losses, a transformer converts the medium voltage from the wind turbine generator to high voltage.

Wind turbine power curve

A wind turbine operates from the cut-in wind speed and then increases its power output with increasing wind speed until the wind speed reaches the rated speed, at which point the turbine starts to run at its rated power [START_REF] Dupont | Global available wind energy with physical and energy return on investment constraints[END_REF]. Between the cut-in wind speed and the rated wind speed, the power is proportional to the cube of the wind speed. To avoid damage to the rotor, it is halted when the wind speed exceeds the cut-out speed. Typical values ranges are 3-4 m/s cut-in speed (vc), 11-17 m/s rated speed (vr) and 25 m/s cut-out speed (vf) [12][48].

Figure 17 shows the typical power curve of a wind turbine. The main elements are explained as follows (see) [START_REF] Dupont | Global available wind energy with physical and energy return on investment constraints[END_REF][48][50]:

1. Cut-in wind speed: the lowest wind speed for which the blades start to rotate and electrical energy is produced. The value of this speed is between 2 and 5 m/s.

2. Below cut-in: until the minimum wind speed is reached, the wind turbine is kept in standby. Once the wind speed reaches the cut-in speed, a start-up routine is carried out.

3. Non-rated region: in this region, the power increases with increasing wind speed it is proportional to the cube of the wind speed.

4. Nominal or rated wind speed: the minimum wind speed for which the maximum output power (rated power) is achieved. 5. Rated region: in this region, the wind turbine produces at rated power. The blade pitch is controlled in order to reduce loads and avoid overspinning of the rotor.

6. Cut-out wind speed: The wind speed for which the wind turbine stops producing electrical energy. As the speed increases above the rate output wind speed, the forces on the turbine structure continue to rise and, at some point, there is a risk of damage to the rotor. As a result, a braking system is employed to bring the rotor to a standstill. This is called the cut-out speed and is usually around 25 m/s.

Land based wind turbines

An onshore wind turbine is a category of turbine that is installed on land. Typically, it has 50-100 m tower height with a rotor diameter of 50-100m [START_REF] Kumar | Wind energy: Trends and enabling technologies[END_REF]. The general trend in wind turbine designs is to increase tower height and rotor blade length. The rotational speed of the rotor is typically 12-20 RPM, much lower than those installed during the 1980s, which operated at a 60 RPM [START_REF] Van Der Valk | Coupled Simulations of Wind Turbines and Offshore Support Structures: Strategies based on the Dynamic Substructuring Paradigm[END_REF]. As a result, modern turbines are capable of generating power at much lower wind speeds [START_REF] Kumar | Wind energy: Trends and enabling technologies[END_REF]. In the present day, storm control techniques enable wind turbines to operate even better during very high wind speed conditions. Onshore wind turbines are typically installed together into wind power plants, commonly known as wind projects or wind farms. The 2020 onshore wind energy capacity in Europe is presented in Figure 18.

Near shore/ far offshore wind turbines

Wind turbines installed beyond the coast are known as offshore power systems. The development of offshore wind energy has accelerated in the past decade. The advantages of the offshore environment are that, the wind is typically stronger and more sustained than inland [START_REF] Dupont | Global available wind energy with physical and energy return on investment constraints[END_REF][46], and offshore sites can accommodated larger power plants with larger wind turbines [START_REF] Kumar | Wind energy: Trends and enabling technologies[END_REF].

In Europe, the available offshore area for wind turbines deployment is 1,648,000 km², which constitutes 31.5% of the total area [START_REF] Dilara Gulcin Caglayan | The techno-economic potential of offshore wind energy with optimized future turbine designs in Europe[END_REF]. Since then, the capacity of the turbines and the size of offshore wind farms have been increasing.

Moreover, they are being installed in deeper waters further from the coast [START_REF] Bailey | Assessing Environmental Impacts of Offshore Wind Farms: Lessons Learned and Recommendations for the Future[END_REF].

In 2020, offshore wind has reached a total of 34,367 MW installed capacity globally.

Europe holds two-third out of the total offshore wind capacity, with a total of 24,920 MW (see Figure 19). It is followed by Asia with a total capacity of 9,418 MW [START_REF] Irena | Renewable energy statistics 2021[END_REF].

The challenges in offshore wind include the higher costs of the installation and operation of the wind turbines, specifically the foundation and the electrical system [START_REF] Irena | Offshore innovation widens renewable energy options: Opportunities, challenges and the vital role of international co-operation to spur the global energy transformation[END_REF]. The cost of the grid connection is also significantly higher offshore than onshore. Due to these higher costs, larger wind turbines are required to reduce the overall specific costs per installed kW [46][60]. Table 4 summarizes the advantages and the drawbacks of wind turbine deployment onshore and offshore. Offshore wind turbines are similar to that onshore. The only significant difference is the design of the foundations, which requires floating and/or other special foundations to account for underwater tower submergence [START_REF] Kumar | Wind energy: Trends and enabling technologies[END_REF]. There are two main types of offshore foundations that are bottom-fixed and floating wind turbines.

Bottom-fixed wind turbines

Figure 20 shows the various types of bottom-fixed foundations that may be used to deploy offshore wind turbines. These foundation types can be categorized based on the water depths in which they are used.

Shallow water depth (below 35 meter)

The majority of offshore wind turbines is currently installed in shallow water, which are waters depths up to 35 meters [START_REF] Van Der Valk | Coupled Simulations of Wind Turbines and Offshore Support Structures: Strategies based on the Dynamic Substructuring Paradigm[END_REF]. In this range of water depths, the most common type of foundation is the monopile. It is a long and large-diameter steel tubular structure that is hammered or vibrated into the seabed. Monopiles are the most widely utilized foundation type (more than 60% of wind turbines operating worldwide [START_REF] Sánchez | Foundations in Offshore Wind Farms: Evolution, Characteristics and Range of Use. Analysis of Main Dimensional Parameters in Monopile Foundations[END_REF]) due to their ease of manufacturing and installation [START_REF] Van Der Valk | Coupled Simulations of Wind Turbines and Offshore Support Structures: Strategies based on the Dynamic Substructuring Paradigm[END_REF]. 'XL-monopiles', with diameters up to 10 meters, are developed to expand their practicality to larger wind turbines and deeper water depths [START_REF] Van Der Valk | Coupled Simulations of Wind Turbines and Offshore Support Structures: Strategies based on the Dynamic Substructuring Paradigm[END_REF].

Gravity based foundation have also been used in shallow water depths. This type of foundation is simply laid on the seafloor and utilizes its own weight to support the wind turbine. Sand, rocks, or iron are commonly used to provide weight for stability. It is worth noting that the gravity-based foundation requires a solid bottom and can only be used in extremely shallow waters.

As a result, this type of offshore foundation is rarely utilized nowadays [START_REF]The European offshore wind industry -key trends and statistics[END_REF].

Medium water depth (approximately 35 to 80 meters)

Since the offshore wind industry is now moving toward installing larger wind turbines in deeper waters, shallow-water foundation options may become economically and/or technically unfeasible. As a result, the most widely utilized foundation installations at medium sea depths ranging from 30 meters to 80 meters [START_REF] Van Der Valk | Coupled Simulations of Wind Turbines and Offshore Support Structures: Strategies based on the Dynamic Substructuring Paradigm[END_REF] are as follows:

i. Tripod

Offshore foundations known as tripods are constructed with a central vertical tube attached to three-leg structured cylindrical steel tubes that form a broad base on the seabed [START_REF] Sánchez | Foundations in Offshore Wind Farms: Evolution, Characteristics and Range of Use. Analysis of Main Dimensional Parameters in Monopile Foundations[END_REF]. They are frequently connected to small diameter piles placed into the seabed. The broad base offers a solid foundation that can withstand significant overturning. Tripods account for around 5% of all offshore foundations currently in use [START_REF] Van Der Valk | Coupled Simulations of Wind Turbines and Offshore Support Structures: Strategies based on the Dynamic Substructuring Paradigm[END_REF].

ii. Jacket Jackets, the multi-membered structures are typically made up of three or four legs linked by bracing. This structure is normally deployed in the oil and gas industry; however, it has been optimized for the installation of offshore wind farms [START_REF] Sánchez | Foundations in Offshore Wind Farms: Evolution, Characteristics and Range of Use. Analysis of Main Dimensional Parameters in Monopile Foundations[END_REF].

Jackets have relatively high production costs since they are made up of several tubular components that are welded together at nodes. To anchor the structure to the bottom, the legs of the jacket linked to small diameter soil-piles or suction buckets [START_REF] Byrne | Foundations for offshore wind turbines[END_REF]. To anchor the structure to the bottom, the legs of the jacket linked to small diameter soil-piles or suction buckets [START_REF] Byrne | Foundations for offshore wind turbines[END_REF]. Jackets are less sensitive to wave loading than other foundation types [START_REF] Van Der Valk | Coupled Simulations of Wind Turbines and Offshore Support Structures: Strategies based on the Dynamic Substructuring Paradigm[END_REF].

Jacket type foundation has dimensions similarly to tripods, but due to its better adaptability to a variety of conditions and stability, they have become the second most widely used type, just behind monopiles [START_REF] Sánchez | Foundations in Offshore Wind Farms: Evolution, Characteristics and Range of Use. Analysis of Main Dimensional Parameters in Monopile Foundations[END_REF].

Floating wind turbines (Deep water: 80 meters and beyond)

Significant progress has been made in floating offshore wind in the recent years, including the commissioning of the world's first multi-units installation in 2017 (30 MW Hywind in Scotland). In 2018, several smaller demonstration projects were completed, including Floatgen (2 MW) in France and Hibiki in Japan (3 MW) [START_REF]Offshore wind outlook 2019[END_REF]. Table 5 shows the floating offshore wind turbines and wind farms in operation at the end of 2020. In addition, the 88 MW Hywind Tampen project in Norway, which will have 11 wind turbines, is set to begin commissioning in 2022 [65][69]. The facility was built specifically to supply energy to offshore oil and gas platforms.

Equinor also obtained approval in 2019 to develop a 200 MW floating offshore commercial wind farm off the coast of the Canary Islands, which is projected to be the world's largest floating offshore wind farm [START_REF]Offshore wind outlook 2019[END_REF] Table 5 In floating offshore wind, a floating platform and a platform anchoring system make the floating foundation. The platform has a transition piece to install the tower on top. Broadly, sparbuoy, semi-submersible, and tension leg platforms are the three primary types of floating foundations (see Figure 22). Many other variations exist, such as numerous turbines on a single platform and hybrid wind/wave floating systems [START_REF]Offshore wind outlook 2019[END_REF].

i. Floating spar buoy

A spar-buoy is a cylindrical buoy that floats vertically and is large yet slender. To make the construction stable, ballast is used to reduce the center of gravity below the center of buoyancy, and mooring-lines are used to anchor it to the seabed [START_REF] Van Der Valk | Coupled Simulations of Wind Turbines and Offshore Support Structures: Strategies based on the Dynamic Substructuring Paradigm[END_REF]. Hywind (see Figure 6), the world's first floating 2.3 MW wind turbine, was installed in Norway in 2009 and is supported by a spar-buoy [START_REF]Hywind: The world's first floating MW-scale wind turbine[END_REF].

ii. Semi-submersibles

Semi-submersible structures have a broad base and are partially submerged to provide a solid supporting foundation for the wind turbine [START_REF] Sánchez | Foundations in Offshore Wind Farms: Evolution, Characteristics and Range of Use. Analysis of Main Dimensional Parameters in Monopile Foundations[END_REF]. these anchoring lines known as tendons or tethers creates tension in the anchoring system [START_REF] Sadeghi | Tension leg platforms: An overview of planning, design, construction and installation[END_REF].

TLP was originally developed by the oil industry for its deep water offshore rigs then expanded in the floating offshore wind industry. The world's first floating wind turbine prototype was installed on a tension-leg platform by Blue H Technologies (small scale TLP with an 80 kW turbine). The 300-ton scale prototype was placed into the Adriatic Sea at a depth of 113 meters, 22 kilometers from the shore, to gather test data on wind and sea conditions [START_REF] Sadeghi | Tension leg platforms: An overview of planning, design, construction and installation[END_REF].

Capacity factor of wind turbines

The capacity factor (CF) is the ratio of the average delivered power to theoretical maximum power [START_REF] Ewea | Wind energy scenarios up to 2030[END_REF] :

𝐶 𝐹 = ∫ P(𝑡)𝑑𝑡 T 0 𝑇𝑃 𝑟𝑎𝑡𝑒𝑑 Eq. 1
Where 𝑃 is the delivered power, 𝑃 𝑟𝑎𝑡𝑒𝑑 is the maximum power, and T is the duration.

Figure 22 Various types of floating offshore wind turbines (Source: [START_REF]Hywind: The world's first floating MW-scale wind turbine[END_REF])

Semi-submersible Tension leg platform Floating spar buoy

The capacity factor can be computed for a single turbine, a wind farm consisting of several wind turbines or an entire country composed of hundreds of farms. Although geographical location determines in significant part the capacity factor of a wind farm, it is also a matter of turbine design.

Indeed, a large rotor combined with a small generator will take advantage of just about any wind and achieve a very high capacity factor, obviously at the cost of a low yearly energy output [START_REF] Irena | Renewable energy statistics 2021[END_REF].

The capacity factor of a wind farm and its profitability depends on whether it is adequately sized and sited. This is because the energy produced by a wind farm site depends on many factors, such as variation in wind speed distribution and wind turbine type. Also, taking into consideration the characteristics of speed like the cut-in velocity (vc), cut-out velocity (vf), rated velocity (vr), hub height, and the generator design [START_REF] Diyoke | A new approximate capacity factor method for matching wind turbines to a site: case study of Humber region[END_REF].

Land based wind-turbines

In the United Kingdom, the current average capacity factor of onshore wind turbines is 30% [53]. In mainland Europe, it is 24% [START_REF]Wind energy in Europe in 2019 -Trends and statistics accessible online at[END_REF]. IRENA has presented statistics of the global weightedaverage onshore capacity factor over year 2010 to 2019, see Figure 23. It shows that since 2016, the global onshore average capacity factor exceeds 30%. It reached 35.6% in 2019. The capacity factor for onshore wind is significantly higher than that of solar PV (18% in 2019) [START_REF] Irena | Renewable energy statistics 2021[END_REF].

Near shore wind turbines

Usually, in order to be cost effective, wind farms are installed at class 3 sites [START_REF] Dupont | Global available wind energy with physical and energy return on investment constraints[END_REF]. In [START_REF] Capps | Estimated global ocean wind power potential from QuikSCAT observations, accounting for turbine characteristics and siting[END_REF],

Capps & Zender showed that the average capacity factor for 5MW offshore wind turbines for locations characterized with class 3 wind speeds and water depth smaller than 200m is in range of 38 to 49%.

In practice, capacity factors of 40 to 50% have been reported for offshore wind farms [25][65].

They are significantly greater than land-based installations; thanks to higher wind speeds in open ocean areas in comparison to areas over land [START_REF] Possner | Geophysical potential for wind energy over the open oceans[END_REF]. Note that these capacity factors are for existing offshore wind farms that are located near-shore.

Figure 24 shows the global offshore average capacity factor over the period 2010 to 2019.

One can see that there is significant variability over the year. The highest capacity factors, over 45%, were obtained in 2013 and 2017. The lowest was obtained in 2014 (30.2%). Nevertheless, the capacity factor seems to be increasing, reaching values well over 40% over the last years. For 2020, the European Academy of Wind Energy expects that capacity factors will reach 44.6% for offshore wind in Europe [START_REF] Ewea | Wind energy scenarios up to 2030[END_REF]. As one can expected, the capacity factor varies significantly depending on the location of the wind turbine. Overall, Figure 25 shows very high capacity factors can be achieved offshore (especially far-offshore). The highest capacity factors (over 80%) could be obtained for wind turbines which would be deployed in the south of the southern hemisphere (between 40°S to 65°S).

On the other hand, very low capacity factors (10 to 30%) would be obtained along the equator line (between 10°N to 10°S). In the Northern hemisphere, high capacity factors (in the range 65 -75%) could be achieved for wind farms in the North of North Atlantic ocean.

Verification of the capacity factor of wind farms in the far-offshore

To confirm the results obtained by Dupont et al., an independent investigation of the capacity factor of stationary offshore wind turbines deployed in the far-offshore was carried out.

10m wind speed data for years 2015, 2016 and 2017 from the ERA-Interim dataset reanalysis was used [START_REF]European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset accessible[END_REF]. This dataset provides wind data every 6 h at a 0.75° × 0.75° precision. It was developed by the European Centre for Medium-Range Weather Forecasts (ECMWF).

The assessments of the capacity factor have been performed using a modified version of the QtVlm software. Originally, QtVlm is a free navigation and weather routing software designed for sailing boats. In collaboration with LHEEA, it has been modified in order to extend its capabilities to assess capacity factor of wind turbines and energy ships.

For the assessment of the capacity factor of a wind turbine, the methodology is as follows.

A grib-file containing the weather data is loaded in QtVlm. Then, a location is selected for the deployment of the wind turbine.

The wind turbine is modelled in QtVlm through its velocity polar plot and its power polar plot. QtVlm only accepts .pol format for the wind turbine performance input. As the wind turbine is assumed to be stationary, the polar plot for the velocity was set to zero. The polar plot for the power is shown in Figure 26. It was calculated using Eq.1 [START_REF] Sohoni | A Critical Review on Wind Turbine Power Curve Modelling Techniques and Their Applications in Wind Based Energy Systems[END_REF]:

𝑃 ̃(𝑣) = 𝑃 𝑟𝑎𝑡𝑒𝑑 { 0, 𝑖𝑓 𝑣 < v 𝑐 𝑣 3 -𝑣 𝑐 3 𝑣 𝑟 3 -𝑣 𝑐 3 , 𝑖𝑓 𝑣 𝑐 ≤ 𝑣 ≤ 𝑣 𝑟 1, 𝑖𝑓 𝑣 𝑟 ≤ 𝑣 ≤ 𝑣 𝑓 0, 𝑖𝑓 𝑣 ≥ 𝑣 𝑓
Eq. 1

Where:

• 𝑃 ̃ is the power produced by the wind turbine

• 𝑃 𝑟𝑎𝑡𝑒𝑑 is the rated power of the wind turbine

• 𝑣 is the wind velocity at the hub height

• 𝑣 𝑐 , 𝑣 𝑟 and 𝑣 𝑓 are respectively the cut-in, nominal and cut-out wind speeds.

The wind velocity at the altitude of the hub (90 m) is obtained from the wind data (which is given for 10 m altitude) using the power law profile: Where:

• H is the altitude of the wind data (10 m)

• z is the hub height (90 m)

• ∝ is the power law exponent. It is taken equal to 0.12 (open sea with waves) (source: [START_REF]Recommended practice DNV-RP-C205; Environmental conditions and environmental loads[END_REF])

At first, the capacity factor assessment was performed on a regular 20.0° x 20.0° global grid. Grid points located onshore were not considered in this assessment. Then, the assessment was refined for locations were the capacity factor appeared to vary rapidly.

The capacity factor was assessed for year 2015, 2016 and 2017 at 211 different locations covering the world's five oceans. Figure 27 shows the results for the average capacity factor.

Figure 28 focuses on the North Atlantic Ocean.

Figure 27 shows that the highest capacity factor (75 to 88%) are obtained between latitude 40°S and 60°S. Symmetrical, very high capacity factors may be achieved in the North of the oceans of the Northern hemisphere (69 to 80% in the North Atlantic ocean between 40°N and 60°N). On the other hand, the lowest capacity factors are concentrated near the equator line (0°) ranging from 30 to 45%. The greatest capacity factor is 88%, meanwhile the lowest capacity factor is 17%.

By comparing Figure 25 and Figure 27, one can see that there is a good agreement between the results of Dupont et al. [START_REF] Dupont | Global available wind energy with physical and energy return on investment constraints[END_REF] and the results obtained in this analysis.

Figure 27 Map of average capacity factor of theoretical floating offshore wind turbines for selected locations (Adapted from Dupont et al [START_REF] Dupont | Global available wind energy with physical and energy return on investment constraints[END_REF])

Figure 28 shows that, in the North Atlantic Ocean, the capacity factor varies significantly depending on the location of the wind turbine. Overall, it can be seen that it is primarily driven by the longitude, and secondly by the latitude. Wind turbines deployed northern than 45° N have capacity factors greater than 75% except in the West of the area (72% for wind turbine #12). Close to 45° N, the capacity factor varies from 64% to 79% depending on the latitude. It can be observed that the capacity factor decreases with getting closer to Europe (64% for wind turbine #16). The smallest capacity factors are obtained for the four wind turbines located on the most southern line (46% to 59%).

Annual variability in the North Atlantic ocean

Table 6 shows the annual variability of the capacity factor for the wind turbines hypothetically deployed in the North Atlantic Ocean (Figure 28). One can see that there are yearto-year variations which can be up to 7% in comparison the average over the three years (WT16).

Alwan et al. (2019) study

In [START_REF] Raphael Alwan | Preliminary study of sailing wind turbines for the harvesting of the far-offshore wind energy resource[END_REF], Alwan et al. have investigated a sailing wind turbine concept for harvesting far offshore wind resources. This concept consists in a floating barge equipped with a wind turbine, a keel and two propellers. It is neither moored nor grid-connected. The sailing wind turbine concept also includes an on-board energy storage system (e.g. batteries, hydrogen, etc.) as it is not gridconnected.

Figure 29 shows an artist impression of the sailing wind turbine design. Its main characteristics are given in Table 7. The concept is that the foundation's position can be controlled via the combined action of the propellers and the keel. In particular, the propellers can give the platform a forward velocity, thus enabling the generation of a lift force by the keel, which counteracts the drift force from the wind turbine.

A model was developed which enables the velocity and power performance of a sailing wind turbine to be estimated as a function of the environmental conditions.

Using the model, it is shown that there exist two operating regimes that can lead to positive net power production. They are the drifting regime and the sailing regime. In the drifting regime, the propellers are stopped. Nevertheless, thanks to the significant water resistance on the platform, the drift velocity is much smaller than the wind velocity. Thus, the apparent wind velocity is large, which leads to high levels of power production. However, a drawback of the drifting regime is that only downwind sailing conditions can be achieved in this regime.

In contrast, maintaining position and net power production can be achieved in the sailing regime. However, for a given wind speed, the power production is less than in the drifting regime because of the power consumed by the propellers.

The simplest mode of operation for a sailing wind farm is to use the sailing regime. Net power production of a sailing wind turbine at a true wind speed of 8 m/s is in order of 800 kW.

According to Alwan et al., capacity factors up to 36% could be achieved by sailing wind turbines operated in the North Atlantic Ocean. It is approximately half that of stationary wind turbines which would be deployed in the same area (Figure 28). However, note that 2 MW wind turbines (hub height 90 m) wind were considered in Alwan et al.'s study, whereas 5 MW wind turbines (hub height of 100 m) were used in Dupont et al. [START_REF] Dupont | Global available wind energy with physical and energy return on investment constraints[END_REF] and in our investigation. Thus, one can expect that the capacity factor of 5 MW sailing wind turbines could be greater. Moreover, as point out by Alwan et al., other exploitation strategies for the operation of sailing wind turbines may be developed, potentially resulting in significantly higher capacity factors.

Conclusion

Overall, in this chapter, we presented the state-of-the-art of wind energy technology.

Capacity factor of wind were discussed and compared for land based wind turbines and offshore wind turbines. Noticeably, the global average capacity has reached 35.6% for the land based wind turbines and 43.5% for offshore wind turbines in 2019.

We also investigated the capacity factor of stationary offshore wind turbines in the faroffshore and compared it with the investigation by Dupont et al. [START_REF] Dupont | Global available wind energy with physical and energy return on investment constraints[END_REF].

A good agreement is obtained. Results show that very high capacity factors (over 70%) can be achieved for wind turbines which would be deployed in the far offshore. Furthermore, the annual variability of 5MW floating wind farm at North Atlantic ocean was also investigated. Results

indicate that the year-to-year variation is in the order of a few percent.

Finally, we presented the alternative sailing wind turbine concept for far-offshore wind energy conversion which was proposed by Alwan et al. [START_REF] Raphael Alwan | Preliminary study of sailing wind turbines for the harvesting of the far-offshore wind energy resource[END_REF]. Its advantage is that it requires neither moorings nor grid-connection. However, its capacity factor is significantly smaller than that which could be obtained by moored stationary wind turbines.

CHAPTER 3 CAPACITY FACTOR OF WEATHER-ROUTED ENERGY SHIPS IN THE FAR-OFFSHORE

In this chapter, the capacity factor of energy ships which would be deployed in the faroffshore is investigated and compared to that of stationary floating wind turbines deployed at the same locations. The capacity factor is optimized using a modified version of the weatherrouting software QtVlm [27]. This chapter also presents the results of the sensitivity studies of boat performances (i.e polar speed and polar power plots) on the capacity factor and the energy production of the energy ship.

Ship weather-routing

Review of existing methods and tools

In contrast to stationary floating offshore wind turbine, energy ships are mobile.

Therefore, their trajectories (routes) can be optimized taking into account weather-forecast in order to maximize energy production [19][28]. This concept is called weather-routing [START_REF] Bowditch | The American Practical Navigator: An epitome of navigation. National Imagery and Mapping Agency[END_REF]. It is commonly used by offshore racers and in commercial shipping.

In this study, capacity factor is obtained using hindcast data. Thus, they correspond to perfect forecast. Errors and uncertainties in weather forecast can lead to smaller capacity factors in practice. However, this effect is expected to be limited as the routes are flexible and that they can be re-optimized as new weather forecast become available.

Based on marine weather forecast data and ship performances, ship weather routing calculates an optimal route at sea. The optimization criteria may correspond to maximum safety and crew comfort, minimum fuel consumption, minimum time underway, or any combination within defined weather and sea conditions [START_REF] Walther | Modeling and optimization algorithms in ship weather routing International[END_REF][73] [START_REF] Marie | Multi-objective optimisation of motor vessel route[END_REF]. Traditionally, the basis of the weather routing is the recommended route which is based on a review of weather and sea forecasts between the starting point and the endpoint (destination). It considers the vessel type, hull type, speed capability, safety considerations, cargo, and loading conditions, among several other criteria [START_REF] Roh | Determination of an economical shipping route considering the effects of sea state for lower fuel consumption[END_REF]. The vessel progress is monitored, and if bad weather or rough seas are predicted along the vessel present route, a diversion recommendation or weather advisory is sent to the user [START_REF] Bowditch | The American Practical Navigator: An epitome of navigation. National Imagery and Mapping Agency[END_REF]. By utilizing this method of initial route selection and constant progress monitoring for possible changes in projected weather and sea conditions along a route, it is possible to maximize both speed and safety of the voyages.

Matthew Fontaine Maury first introduced the concept of weather-routing [START_REF] Bowditch | The American Practical Navigator: An epitome of navigation. National Imagery and Mapping Agency[END_REF]. It started with the massive compilation of atmospheric and oceanographic data from ships' logbooks by Maury in the mid-19th century. Thus, the mariner had access to global climatology data consisting of ocean weather and currents for the first time. Towards the late 19th century, Maury used this information to construct seasonally recommended routes for sailing ships and early steam-powered vessels [START_REF] Bowditch | The American Practical Navigator: An epitome of navigation. National Imagery and Mapping Agency[END_REF].

Nowadays, weather routing and route optimization has been recognized in the shipping industry as an effective technique to assure ships safety, earn more economic advantage, and reduce environmental impact [START_REF] Dnvgl | Energy Management Study 2015[END_REF]. During the route planning process, ship captains frequently employ a weather routing tool to avoid potentially dangerous and harsh weather conditions, to limit the risk of ship/cargo damage and human injuries, to predict the expected arrival time (ETA), and many more [START_REF] Walther | Modeling and optimization algorithms in ship weather routing International[END_REF][72] [START_REF] Wang | Voyage Optimization Algorithms for Ship Safety and Energy-Efficiency[END_REF]. Based on current weather forecasts and ship performance models, a route optimization system uses optimization algorithms in computers to plan a ship sailing course and schedule in the most efficient way possible from a long-term perspective (until destination) [START_REF] Wang | A Three-Dimensional Dijkstra's algorithm for multi-objective ship voyage optimization[END_REF].

Figure 30 shows the usual route optimization technique: a grid of waypoints along a ship sailing area is first generated, and then a path searching method is used to find the best route based on specific end-user objectives [START_REF] Wang | Voyage Optimization Algorithms for Ship Safety and Energy-Efficiency[END_REF]. performance polar. In QtVlm, the weather data are provided in Gridded Binary (GRIB) format [START_REF]European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset accessible[END_REF] while boat performance polars are provided as text files (.pol).

A polar diagram describes how fast a sailing boat may go at different wind speeds (TWS) and in different angles to the wind (TWA). The true wind speed (TWS) is the actual speed of the wind as it passes the surface of the sea. While the true wind angle (TWA) is the angle between the boat's heading and the true wind direction (TWD).

Each type of boat has its specific polar diagram, which can computed using a velocity and power prediction program (VPPP) [START_REF] Babarit | Exploitation of the far-offshore wind energy resource by fleets of energy ships, Part A, Energy ship design and performance[END_REF] or obtained from sea-trials.

Route optimization algorithm

QtVlm uses the isochrones method to find the optimal route. The isochrone method was initially proposed by James in 1957 [START_REF] Walther | Modeling and optimization algorithms in ship weather routing International[END_REF]. Then Hagiwara [START_REF] Hagiwara | Weather routing of (sail-assisted) motor vessels[END_REF] proposed a modified isochrone method.

As defined by James, an isochrone is a set of connected points that a ship may reach in a certain amount of time by starting at one point and travelling in all possible directions within the time limit. These points are influenced by weather conditions such as wave direction and height. Based on the definition of the isochrone, the first isochrone visualizes the ship's speed characteristic. The characteristics are dependent on factors like the vessel's dimensions and contractual speed.

A perpendicular line to the tangent is determined from each point belonging to the first isochrone for the second isochrone (see Figure 32). The point of the second isochrone is defined by a segment of the line depicting the distance that the ship can reach within the next time limit.

The second isochrone is made up of a group of such connected points. Following that, the next isochrones are generated with the same method.

The original isochrone method was intended for manual use by navigators as an aid for route planning process. In 1989, Hagiwara had made an improvement to the original method and developed the modified isochrone method. The modified isochrone method formulates the ship routing problem as a discrete optimization problem for easier computer calculation [START_REF] Hagiwara | Weather routing of (sail-assisted) motor vessels[END_REF].

Figure 32 Construction of first and second isochrones [START_REF] Szlapczynska | Adopted isochrone method improving ship safety in weather routing with evolutionary approach[END_REF] The algorithm (see Figure 33) extends the basic isochrone algorithm by treating points obtained at the first iteration as initial points for the second iteration and so on.

To maintain a reasonable number of points, the search space is divided into a limited number of subsectors (see Figure 34). The number of subsections may vary depending on the chosen number of reference routes and the subsector width, which determines the accuracy of the result. The furthest point along the great circle route (GCR) connecting the departure point and the point under consideration are chosen to be part of the next isochrone within each subsector. The selected points are then treated as initial points for the next iteration, and the same procedure is repeated until the first point on an isochrone coincides with the destination.

The optimal path may be found by tracing it back.

Define arrival point X 2 (k) with maximum D 2 (i, j)

Compute ship speed, drift angle, engine power at each heading, C 0 at X 0 Calculate arrival point [X 1 (i)] at time t i (i = 1,2,…2m+1), ship heading from X 0 to X 1 (i) is memorized -Set of {X 1 (i)} defines isochrones at t 1

Calculate passage time, T n (k) between

X n (k) to X f Defined subsector S 2 (k) centered around great circle from X 0 to X f -Initial course C 2 (i, j) identifies the subsector S 2 (k) to be assigned to each X 2 (i, j)

Set up time interval, ∆t'

Repeat for X 3 (k), X 4 (k), X 5 (k), … X n (k) When X n (k) sufficiently approaches X f , ship navigated along rhumbline from X n (k) to X f -At this step X n (k) changed into [X n (i)] (i =

Nodes location optimization

The QtVlm software also includes the possibility to further improve the travel duration by optimizing the location of the nodes of the route determined by the modified isochrone method using the SIMPLEX method.

The SIMPLEX algorithm was developed by Nelder and Mead (1965). It is a popular deterministic method for solving nonlinear unconstrained optimization problem (see Figure 35) [START_REF] Hinnenthal | Robust Pareto Optimum Routing of Ships Utilizing Deterministic and Ensemble Weather Forecasts[END_REF]. The algorithm basic principle is as follows [START_REF] Hinnenthal | Robust Pareto Optimum Routing of Ships Utilizing Deterministic and Ensemble Weather Forecasts[END_REF]:

1. It requires n + 1 initial designs or starting points, i.e. for a 2-dimensional problem it starts with a triangle, a tetrahedron for a 3-dimensional problem, and a polytope (SIMPLEX) with n + 1 vertices for a n-dimensional problem. The algorithm terminates if the attainable improvement at successive optimization loops falls below a preset convergence limit.

The SIMPLEX algorithm is widely used in high dimensional optimization problems [START_REF] Hinnenthal | Robust Pareto Optimum Routing of Ships Utilizing Deterministic and Ensemble Weather Forecasts[END_REF]. Extensions have been developed for the SIMPLEX method. They include the capability to preserve an equable shape of the simplex or even some random capabilities to overcome the issue of convergence towards local optima.

QtVlm uses a combination of the isochrone and SIMPLEX methods for its routing optimization algorithm [83].

Weather-routing optimization of energy ships using QtVlm

Weather data

In this thesis, 10-meter wind speed data for the year 2015, 2016 and 2017 are used. It was obtained from the ERA-Interim dataset which was developed by the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis [START_REF]European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset accessible[END_REF]. The time step is 6 hours and the spatial resolution is 0.75° (approximately 80 km). It is coarse in comparison to other meteorological data. For example, the ERA5 dataset has a spatial resolution of 0.28° (approximately 31 km) and the ECMWF's operational high-resolution forecast (HRES) has a resolution of 0.25° [START_REF] Hersbach | Global reanalysis: Goodbye ERA-Interim, hello ERA5[END_REF].

In [START_REF] Ramon | What global reanalysis best represents near surface winds?[END_REF], five global reanalysis have been analyzed and compared in order to identify the most accurate dataset for the wind speed at the height of wind turbines' hub. The reanalysis are ERA5, ERA-Interim, the Japanese 55-year Reanalysis (JRA55), the Modern Era Retrospective Analysis for Research and Applications-2 (MERRA2), and the National Centers for Figure 35 Simplex optimization algorithm [START_REF] Hinnenthal | Robust Pareto Optimum Routing of Ships Utilizing Deterministic and Ensemble Weather Forecasts[END_REF] Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR)

Reanalysis 1 (R1). Table 9 shows the datasets' main characteristics. The accuracy of the surface and near-surface winds were evaluated by comparison to measurements at 77 instrumented tall towers. The study highlighted differences in DJF (December-January-February) mean wind speed, year-to-year variability, and long-term trends between the five global reanalysis datasets.

Particularly, the most significant discrepancies were encountered within continental areas.

Mean wind-speed differences can be partly explained by different representations of landsurface roughness and elevation at the various grid resolutions employed in the reanalysis models.

The verification process with the in-situ tall tower measurement wind offered insights into which reanalysis perform better than others on daily and seasonal time-scales. Amongst all five surface wind datasets plus the multi-reanalysis mean (MR), ERA5 shows the best results in terms of correlation and standard deviation. Indeed, the improvement in both correlation and variability with respect to the MR is statistically significant in 35% of tall tower sites, which is significantly higher than other reanalysis such as MERRA2 (9.1%) and ERA-Interim (1.3%) [START_REF] Ramon | What global reanalysis best represents near surface winds?[END_REF]. Neither JRA55 nor R1 is better than the MR in any of the tower locations. Overall, Ramon et al. concluded that the ERA5 near-surface wind dataset provides the most accurate estimations of mean wind speed and variability at turbine hub heights. Unfortunately, the ERA5 dataset was not available at the beginning of this thesis, which is why ERA-Interim was used.

In addition, in this thesis, the MERRA2 dataset and the ERA-INTERIM dataset wind datasets have been compared. The MERRA2 data was obtained through the website www.renewable.ninja. Figure 36 shows a comparison of the distribution of wind speed for the whole year 2017 at the exact location of 52°N 31°W. The mean wind speed for the MERRA2 dataset (11.18 m/s) is significantly greater than the ERA-INTERIM dataset (9.54 m/s). The maximum wind speed for MERRA2 also is 33.5 m/s, which is higher than that for ERA-INTERIM, 23.9 m/s. One may also note that the spatial resolution for the MERRA2 dataset is 0.5° latitude × 0.625° longitude (see Table 9). Thus, the data provided by MERRA2 is finer than ERA-INTERIM.

The significant differences between these two datasets indicate that there is a significant uncertainty on the results of the thesis. Thus, these results should be considered as preliminary estimates.

Figure 36 Wind distributions for MERRA2 and ERA-INTERIM dataset for year 2017

Energy ships and stationary wind turbines performances

As already mentioned, a preliminary design of an energy ship has been developed at LHEEA. It is an 80m long catamaran fitted with four (30m tall, 5m diameter) Flettner rotors [START_REF] Babarit | Exploitation of the far-offshore wind energy resource by fleets of energy ships, Part A, Energy ship design and performance[END_REF].

The mode of operation of the energy ship at far offshore is described in Chapter 1.

The performance of the energy ship is characterized by polar plots for its speed and power production. Those plots relate the speed of the boat (U) or the produced power to the true wind speed (TWS) and true wind angle (TWA). They were obtained using an in-house velocity and power performance program (VPPP) [START_REF] Babarit | Exploitation of the far-offshore wind energy resource by fleets of energy ships, Part A, Energy ship design and performance[END_REF]. The polar plots are shown in Figure 37. Five values for the true wind speed were considered ranging from 7.5 to 19.5 m/s. Note that the wind speed of the energy ship is at 10 m altitude.

The rated power of 1.6 MW for energy ship was chosen in order to allow a fair comparison to the 5MW wind turbine discussed in Chapter 2. Indeed, the 1.6 MW rated power is achieved for a true wind speed of 10.5 m/s at 10 m which corresponds to rated wind speed of 11.4 m/s at a nacell height of 90 m (which is a typical hub height for a floating offshore wind turbine).

Optimization criterion

In the standard version of QtVlm, the optimization criterion is the travel duration from the starting point A to the arrival point B. In this study, the aim is to optimize the energy production.

Therefore, a dedicated batch-mode version of QtVlm had to be developed in order to optimize the capacity factor over the route instead of the travel duration. The optimization algorithm defined for this version of QtVlm is shown in Figure 38.

QtVlm Route optimization algorithm Meteorological Data (ERA-Interim dataset)

Boat speed polar Power production polar

Energy ship performance model

Figure 38 Overview of energy ship route optimization system using QtVlm

Optimal route criterion

Capacity factor (CF)

Filling ratio (FR)

Energy Produced

Constraints

Storage capacity including energy unloading time

Land avoidance

The new optimization criterion is defined by:

𝐶 𝐹 = ∫ 𝑃 ̃(𝑡)𝑑𝑡 𝑇 0 (𝑇+𝑇 0 )𝑃 𝑟𝑎𝑡𝑒𝑑 (Eq. 2)
Where:

• 𝐶 𝐹 is the capacity factor

• 𝑇 is the route duration

• 𝑇 0 is the duration of the loading/unloading operations of the stored energy

• 𝑃 ̃ is the power produced by the energy ship (MWh)

• 𝑃 𝑟𝑎𝑡𝑒𝑑 is the rated power of the ship (MWh)

In the criterion, the duration T0 at the denominator is to account for the time necessary to unload the stored energy. This parameter is particularly useful to avoid the optimization converge to very short routes.

An important constraint to take into account in the optimization process is the limited energy storage capacity aboard the ship. Thus, we introduced the filling ratio 𝐹 that we define as the ratio of the energy stored in the energy reservoir 𝐸 = ∫ 𝑃 ̃(𝑡)𝑑𝑡 𝑇 0

to the reservoir capacity, 𝐸 𝑚𝑎𝑥 .

The reservoir capacity is N hours at rated power (𝐸 𝑚𝑎𝑥 = N𝑃 𝑟𝑎𝑡𝑒𝑑 ).

Thus, the filling ratio is:

𝐹 = ∫ 𝑃 ̃(𝑡)𝑑𝑡 𝑇 0 𝑁𝑃 𝑟𝑎𝑡𝑒𝑑 (Eq. 3)
To take into account the limited storage capacity, the produced power is set to 0 if the filling ratio reaches 1. If not, the produced power is obtained by interpolating in the power production polar plot (Figure 45) as function of the true wind speed and true wind angle at the ship location (𝑃 ̃= 𝑃(𝑇𝑊𝑆, 𝑇𝑊𝐴)) except during maneuvers. It is assumed that maneuvers (which correspond to events during which the axis of the ship crosses the axis of the wind) last for 15 minutes. During maneuvers, the produced power and ship velocity is reduced to 25% of the power and velocity in the polar plots.

Finally, the produced power 𝑃 ̃ is given by: 𝑃 ̃(𝑡) = { 0 𝑖𝑓 𝐹 ≥ 1 0.25𝑃(𝑇𝑊𝑆, 𝑇𝑊𝐴) during maneuver 𝑃(𝑇𝑊𝑆, 𝑇𝑊𝐴) otherwise (Eq. 4)

Optimization strategy

The optimization process requires the specifications of a starting point and an arrival point for the energy ship. It has been assumed that those points are one and the same point. This is because we assume that the energy ship meets at this location a platform or a tanker for unloading the stored energy. The mode of operations of the energy ship at far offshore was presented in chapter 1 (see Figure 11 and Figure 12). • The number of waypoints of the route. The locations of the waypoints are the optimization variables. They are optimized by QtVlm using the SIMPLEX algorithm in order to maximize the capacity factor (see Figure 35).

• The initial route direction: North, West, South or East.

• The initial Filling Ratio (FR).

Sensitivity analysis of the effect of the numerical parameters and initial filling ratio on the weather routing optimization results

The route optimization software QtVlm involves two main numerical parameters, the optimization search step angle and the number of initial optimization waypoints.

In this section, the effect of these parameters and initial filling ratio on the capacity factor is investigated. This sensitivity study was performed for the 1.6 MW rated power energy ship equipped with 168-hours storage capacity and 6-hours unloading time deployed in North Atlantic ocean in 2015. A small 10% initial filling ratio was selected to avoid long initial draft routes. Table 10 shows the optimization results for 6 values of the number of optimization waypoints and an 8° initial search step angle. One can see that the capacity factor increases with increasing number of waypoints. A capacity factor as high as 80% is achieved for 7 waypoints and more. As the simulation time increases with increasing number of waypoints, a combination of 8° initial search step angle and 7 waypoints was chosen as the optimal numerical parameters for the weather routing optimization of the energy ship in the far offshore.

Results and discussion

Optimized capacity factor of the energy ship

Let us first consider the case of an energy ship with starting point and destination at location N 54, 516660; W 27,551844. This point was selected because it is in the North Atlantic storm track (see Figure 40) which offers high density of wind resources [START_REF] Capps | Global ocean wind power sensitivity to surface layer stability[END_REF][6] [START_REF] Capps | Estimated global ocean wind power potential from QuikSCAT observations, accounting for turbine characteristics and siting[END_REF].

The optimization process was applied to calculate the optimized capacity factor over the three years of 2015, 2016 and 2017. The initial filling ratio was set to 10%. An 8° search step angle and 7 waypoints were selected for the optimization parameters. The storage capacity was set to 174-hour storage capacity (7 days). The unloading time was set to 6 hours.

The weather routing optimization results are shown in Table 11. Annual average filling ratio at the end of the routes % 77 76 70 One can see that the annual average capacity factor is very high. It consistently exceeds 80% for the three years. The average over the three years is 81% and it reaches 82% for the best the year of 2016 and 2017.

The best capacity factor achieved over one route is 99% for both year 2015 and 2016; and 97% for year 2017, which means that route optimization enabled the energy ship to sail in highly favorable conditions over the whole duration of the route. Nevertheless, the worst capacity factor over the three years is quite high (41%). This shows that despite the wind resource is very high in average, there happens to be some time when the wind is low.

Figure 41 shows the seasonal variability of the average capacity factor. One can see that the lowest monthly average capacity factors are achieved during the month of May to August.

Nevertheless, despite the low wind, the worst average capacity factor is still relatively high.

The average filling ratio over the three years is moderately high, 74% and the average route duration is 153 hours (approximately 6 days). However, these results need to be confirmed by running sensitivity studies for the effect of storage capacity on the capacity factor and route duration.

For comparison, the average capacity factor over years 2015, 2016 and 2017 of a stationary floating offshore wind turbine which would be deployed at the same location was performed. It is found to be also very high (over 80%).

Sensitivity to energy ship rated power

In this section, the effect of rated power on the capacity factor is investigated. It is expected that the capacity factor will increase with decreasing rated power, and vice-versa. Note that total energy production is expected to decrease with decreasing rated power.

Three rated powers were considered: 1 MW, 1.3 MW and 1.6 MW. The same velocity polar was used for the three configurations. The power production polar of configurations with rated power below 1 MW and 1.3 MW were derived from the polar of the 1.6 MW energy ship (Figure 37) by limiting the output power to the rated power (see Figure 44). In fact, in this study, the derivation of power production for 1 MW and 1.3 MW is the simplified way and considered as a conservative estimate to assess the effect on capacity factor for the energy ship. Thus, an individual derivation of boat performance for 1 MW and 1.3 MW using VPPP must be done in future.

The initial optimization waypoints were set to 7 and the search steps angle was set to 8°.

The storage capacity and unloading time of the energy ship was fixed to respectively 174 hours and 6 hours as it was shown in the previous section that it gives the best results. The resulting 3 years capacity factor, filling ratio and energy production are presented in Table 13.

As expected, the capacity factor increases with decreasing rated power. The greatest average capacity factor (84.3%) is obtained by the 1 MW energy ship. The 1.3 MW energy ship achieves a capacity factor of 81.3% which is only 0.1% greater than the 1.6 MW energy ship.

The greater capacity factor of the smaller rated power configurations comes at the cost of a significant reduction in annual energy production. Indeed, the energy production of the 1 MW energy ship is 35% smaller than that of the 1.6 MW energy ship, whereas its capacity factor is only Figure 45 shows the polars of the two considered energy ships. For Design #01, the 1 MW power production polar was derived from the 1.6 MW power production polar (see Figure 37) in order to allow a fair comparison between both designs.

One can see that the shape of the velocity and power production polar between both designs is significantly different. Both velocity polars look like butterfly curves, but the opening angle of the polar of Design #02 is smaller than that of Design #01. Design #02 also has the greatest boat velocity when sailing upwind, whereas the velocity polar of Design #01 looks more like general cardioids curves.

For both Design #01 and Design #02, the rated power produced is achieved at a 10.5 m/s true wind speed. However, one can see that the rated power is achieved for a wider range of true wind angles for Design #02 than for Design #01.

The optimized capacity factor was investigated for both designs. The storage capacity was fixed to 174-hour (7 days). A 6-hour unloading time was also used and the numerical optimization parameters were set to 8° search step angle with 7 initial number of waypoints. As in the other weather routing optimizations, the initial filling ratio was set to 10%. Table 15 shows the results. In average, the annual energy production and capacity factor of Design #01 are 18% greater than that of Design #02. This confirms that the shape of the velocity and power polars have a significant impact on energy performance of energy ships.

Conclusion

In this chapter, we investigated the capacity factor of energy ships that would be deployed far-offshore in the North Atlantic Ocean. It is optimized using weather-routing. We found that energy ships can achieve capacity factors exceeding 80%, which is similar to that of a 5MW stationary offshore wind turbine which would be deployed in the same area.

Sensitivity studies were also performed. It is found that storage capacity and velocity and power polars of the ship have a significant effect on the energy performance.

Energy ships could represent a relevant alternative for the power supply of these islands. In this scenario, the produced energy would be stored in batteries. The mode of operation of the energy ship at near shore has been described in Chapter 1. In summary, the island grids would be powered by virtual power plants consisting in several batteries containers. The plants would be located in porst. Once a battery container stationed at the port would be empty, it would be loaded aboard an energy ship which would then set sail and start a charging cycle (see Figure 12).

The trajectory of the charging cycle is optimized using weather-routing in order to charge the batteries as fast as possible. Once the batteries would be charged, the energy ship would come back to the port, unload the filled batteries containers, load empty batteries containers, and start again a new charging cycle.

Energy ship characteristics

As explained in chapter 3, the energy ship is modelled in QtVlm through its polar curves of velocity and power production. Three versions of the ship have been considered: one with 1 MW rated power, a second one with 1.3 MW rated power and a third one with 1.6 MW rated power.

Their velocity and power production polar curves are shown in Figure 37 and Figure 44 (in Chapter 3).

In comparison to hydrogen or methanol storage [START_REF] Gilloteaux | Preliminary design of a wind driven vessel dedicated to hydrogen production[END_REF][22], batteries have better round-trip efficiency, but their energy density is an order of magnitude smaller (approximately 2 kWh/kg for hydrogen storage vs 0.1 kWh/kg for battery storage). Therefore, in this chapter, only 12 to 48 hours of energy storage capacity have been considered.

Wind data

The same three years (2015, 2016, and 2017) global data of 10 m altitude wind speeds as in Chapter 3 is considered. The data being global, it covers the surrounding of the two considered islands: Saint-Pierre-et-Miquelon (SPM) and Ile de Sein (IDS).

Optimization method and strategy

The weather routing optimization is performed using the QtVlm software as presented and discussed in Chapter 3.

Statistical analysis data and method for energy production, sailed distance, boat speed and true wind angle (TWA) distribution

As the optimized trajectories of the energy ship appear to be complex and diverse. At first look, no typical pattern can be identified. Therefore, a statistical analysis has been implemented in order to determine whether there exist relationships patterns and trends in the data.

Thus, the statistical analysis was performed using the weather routing optimization results

for the 1. 16): The frequency distribution produced in this analysis is the relative frequency distribution.

The data in Dataset 1 were calculated for individual class intervals by dividing them by the average observed frequencies or data points. Each storage capacity has a different number of data points for each of 2015, 2016 and 2017. Thus, the average observed data points were used as a baseline.

Then the relative frequencies were visualized into a Histogram (for energy production distribution) and line graph (for the sailed distance over maximum distance). The frequencies were written in percentage (%).

Meanwhile, the frequency distribution for true wind angle (TWA) using Dataset 2 was produced using the joint frequency distribution. This method was used to analyze several occurrences simultaneously at each possible joint occurrence of two variables (energy ship power production and true wind angle). The contour diagram was produced to visualize the joint frequency distribution. All data calculations and diagrams were produced using Microsoft Excel software.

Sensitivity analysis of the effect of the numerical parameters on the weather routing optimization results

It is recalled that the route optimization software QtVlm involves two main numerical parameters:

a) The optimization search step angle: used as a first distance to initialize the first step of the simplex and to increase the search range b) The number of initial optimization waypoints: the points to be created by QtVlm in the optimization process to calculate and optimize the capacity factor based on the set filling ratio target. c) In this section, the effect of these parameters on the capacity factor is investigated.

The aim is to determine which parameters give the best results.

This sensitivity study was performed for the 1.6 MW rated power energy ship. Deployments in Saint-Pierre-et-Miquelon and Ile de Sein were both considered. The search step angles which were considered are: 0.5°, 1°, 2°, 4°, 6° or 8°. The number of initial optimization waypoints that were considered are: 2, 4, 6, 12 and 18 waypoints.

In this numerical parameter optimization, the default setting for the energy ship's storage capacity is 24 hours. The unloading time was set to 4 hours.

At the beginning of the sensitivity analysis, the analysis was started with 0.5° search step angle and 12 waypoints as the reference configuration. Then, the value of the search step angle was doubled up to 64°. The effect of the number of waypoints was also studies separately. Results show that the capacity factor depends significantly on the numerical parameters.

Saint-Pierre-et-Miquelon case study

Overall, the capacity factor appears to decrease with increasing number of initial optimization waypoints. This is unexpected as the optimization waypoints correspond to the variables of the optimization problem. It was thus expected that a greater number of optimization waypoints would allow more flexibility in the route, thus greater capacity factors. It indicates that the current 99 optimization method (initialization procedure + simplex) in QtVlm is not able to find the global maximum.

The other parameter of the optimization is the search step angle. For this parameter, Figure 47 shows that an 8° search step angle is optimal.

Ile de Sein case study

Figure 48 leads to the same conclusion for Ile de Sein as for Saint-Pierre-et-Miquelon. The capacity factor also decreases with increasing number of initial optimization waypoints. As for the Saint-Pierre-et-Miquelon case study, the greatest average capacity factor is obtained for 2 initial optimization waypoints. However, in this case study the optimal search step angle is 6° whereas it is 8° for Saint-Pierre-et-Miquelon.

In chapter 3, it was found that the optimal parameters for far offshore deployment are 8° search step angle and 7 waypoints. Therefore, it appears that the optimal numerical parameter depends on the deployment locations.

Sensitivity to the doubling of waypoints after the first optimization

In QtVlm, there is a possibility to double the number of waypoints after the first optimization. This means that, at the end of the first optimization, the number of initial optimization waypoints is doubled and that the optimization algorithm is re-run including the new waypoints.

A sensitivity analysis to this capability has been performed. The test case is the 1.6 MW energy ship with 24-hour storage capacity and 4-hour unloading time. The search step angle was set to 8° and the initial number of waypoints to 4. Indeed, even though a total of 2 initial waypoints was found to be the best setting in the previous section, it has been found to be too small in practice for the purpose of this analysis.

Table 17 shows the results of the optimized capacity factor of a 1.6 MW energy ship deployed at Saint-Pierre-et-Miquelon and Ile de Sein for the year 2015, 2016 and 2017 and the average optimized capacity factor for those three years. For Saint-Pierre-et-Miquelon (respectively Ile de Sein), doubling the number of waypoints increases the capacity factor from 53% to 55%

(respectively 48% to 50%). It represents an increment of 3.6% (respectively 4%). Furthermore, the average for a single route duration remains the same. Therefore, the difference is small. Since using this option increases significantly the simulation time, it was not activated for the remaining of this study.

Table 17 Comparison of optimized capacity factor (CF), filling ration (FR), and route duration with and without using the option to double the number of waypoints after the first optimization

Capacity factor of energy ships deployed at Saint-Pierre-et-Miquelon

In this section, the capacity factor of energy ships deployed at Saint-Pierre-et-Miquelon is investigated. The optimization parameters are set to two initial optimization waypoints and 8° search step angle as they give the best results according to Figure 47.

Table 18 shows the capacity factor over the three years of 2015, 2016 and 2017. The annual average capacity factor consistently exceeds 52% for the three years. The best annual capacity factor is obtained in 2017, 54%. Overall, the annual variability of the capacity factor appears to be limited (1 to 2%).

The average filling ratio is very high, 92.8%. The best capacity factor achieved over one route is 87% for each year. It corresponds to the maximum capacity factor which can be achieved taken into account the unloading time. The worst capacity factor is 11%. In comparison to other renewable power generation technologies such as solar photovoltaics or land-based wind energy, a capacity factor of 53% is high. It is of the same order of magnitude as that of currently operating offshore wind farms as discussed in Chapter 2.

However, it is almost 20% less than the capacity factor that was obtained for energy ships deployed in the North Atlantic ocean and with hydrogen storage. It can be explained by weaker winds and smaller energy storage capacity which cause shorter route duration and limits the weather-routing options.

Figure 49 shows the monthly average capacity factor. The lowest capacity factor (44%) is obtained in July of 44% due to low wind resource during that month as shown in Figure 50. Their number being limited, this issue is not expected to affect significantly the results.

Effect of rated power

A sensitivity study on the rated power was performed. It follows the same methodology as in Chapter 3. Three versions of the energy ship with rated power 1 MW, 1. It is expected that the capacity factor will increase with decreasing rated power, and vice-versa.

Note that the total energy production is expected to decrease with decreasing rated power.

Table 19 shows the average capacity factor for the 1 MW, 1.3 MW and 1.6 MW energy ship over the years (2015, 2016 & 2017. One can see that, as expected, the capacity factor decreases with increasing rated power. The greatest average capacity factor (58.8%) is obtained for the 1 MW energy ship. Regarding the average filling ratio, it exceeds 94% for all configurations. Thus, the weather routing enables an intensive use of the energy reservoir.

Regarding the annual energy production, even though the capacity factor decreases with increasing rated power, it appears to increase with increasing rated power. For instance, the energy production of the 1.6 MW energy ship is 22% greater than the 1 MW energy ship despite a 11% smaller in capacity factor. It is likely that even greater annual energy production may be achieved by further increasing the rated power.

Effect of storage capacity and unloading time

Table 20 shows the sensitivity of the capacity factor to storage capacity and unloading time.

The rated power is 1.6 MW. The numerical parameters are 8° for the search step and 2 waypoints. These results are surprising as one may have expected that greater storage capacity would lead to a greater number of weather-routing options, and thus a greater capacity factor and total energy production. One may note that the unloading time is also greater, but it does not change the theoretical maximum capacity factor (85.7%) for all the configurations. An explanation is that the optimization algorithm converges to a local maximum.

Regarding the filling ratio, the minimum is 91% obtained with 48-hour storage capacity and 8-hour unloading time. Thus, the filling ratio is high but is it comparable to the filling ratio of the energy ship deployed at far offshore.

Overall, one can see that the capacity factor and total energy production at Saint-Pierre-et-Miquelon is sensitive to the storage capacity and unloading time configuration.

Distribution of energy production

Figure 52 shows the distribution of the ratio of the energy delivered by the 1.6 MW energy ship to the maximum energy which could have been produced over the same period of time (rated power times duration of the route). One can see that, most of the time and for all storage capacities, the energy delivery is equal to the maximum energy. However, it also shows that the frequency of routes for which is the energy delivery is very small is relatively high. The other cases are relatively evenly distributed. These results could be explained by weaker wind or no wind available at certain time. This is particularly the case during the months of June to August (Summer) (see Figure 50).

One can see in Figure 52, as the storage capacity duration increases, the frequency of deliveries for which the amount of energy is equal to the maximum decreases (vice versa for the lowest energy delivery class).

Frequency distributions of sailed distance over maximum distance

Figure 53 shows the frequency distribution of the sailed distance (total distance travelled by the energy ship when arriving back to port). For the ships with the smallest storage capacities, one can see that, most of the time, the energy ship covers 80% of the maximum distance (which is defined as the distance that it could cover in the same amount of time if sailing at its maximum speed, e.g. 20 knots). For the energy ship equipped with 48-hour storage capacity, the distribution is broader. Through-out the year, most of time, this energy ship travels between 40% and 80% of its maximum distance.

Frequency distributions of True Wind Angle (TWA) in 2017

Figure 54 shows the frequency distribution of true wind angle power production of the 1.6 MW energy ship. It has been produced using dataset 2. As expected, one can see that the optimized routes correspond to routes for which the energy ship sails close to 90° true wind angle (beam reach). It can also be observed that the distribution is slightly distorted towards the upwind conditions, which means that, for optimized routes, the energy ship sails more often in upwind conditions than in downwind conditions.

No. of frequency :

Figure 54 Frequency of true wind angle (°) in response to produced energy over rated power of the 1.6 MW energy ship at Saint-Pierre-et-Miquelon

Comparison with a stationary offshore wind turbine

The capacity factor obtained by the 1.6 MW energy ship is compared to that of a stationary offshore wind farm. The capacity factor of the offshore wind turbine is obtained using the web application https://www.renewables.ninja. This application estimates the energy production of wind or solar farms at any location [START_REF] Staffell | Using bias-corrected reanalysis to simulate current and future wind power output[END_REF]. It uses the MERRA-2 weather data [START_REF]European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset accessible[END_REF].

Nine different locations were considered for the wind farm (indicated by the boxes in Figure 56). The wind farm area was bounded within 46.3° to 42. Table 21 shows the capacity factor for the 1.6 MW floating offshore wind turbines for 3 years (2015, 2016 & 2017) and its average over the 3 years.

One can see that the capacity factor varies from 61% to 65% depending on the location of the wind farm. The smallest capacity factor (62%) is obtained for location #1 which is one of the nearest to the energy ship start/end point. The greatest capacity factor 64.6% is obtained for locations #3 and #6 which are located in the northern part of the area, away from shore towards the middle of the North Atlantic Ocean.

The spatial average capacity factor of floating offshore wind farms is 63% which is 10% greater than the capacity factor obtained by the energy ship (53%).

Capacity factor of energy ships deployed at Ile de Sein case

In this section, we consider a second case study which is the deployment of an energy ship for the power supply of Ile de Sein (Figure 46). The capacity factor of the 1.6 MW energy ship was estimated for the three years of 2015, 2016 and 2017. The same optimization parameters as for Saint-Pierre-et-Miquelon were used (two initial optimization waypoints and 8° search step angle).

The storage capacity is 24 hours and the unloading time is 4 hours. The start/end point is N 48.044997°, W 5.146862°.

Table 22 shows the capacity factor over the three years. It shows that an average capacity factor of 49% can been achieved. It is 8% smaller than the average capacity factor obtained at Saint-Pierre-et-Miquelon (53%).

The year-to-year variability appears to be significant as the annual average capacity factor exceeds 50% both in 2015 and 2017, but falls to 45% in 2019. The best capacity factor over one route is 87%, corresponding to the achievable maximum taking account the unloading time. The worst capacity factor over one route (6%) is almost half that in Saint-Pierre-et-Miquelon (11%).

As for Saint-Pierre-et-Miquelon, the average filling ratio is very high (94.4%). This shows that the route optimization algorithm tends to converge towards solutions for which the batteries are fully charged when the ship comes back to port. Figure 59 shows the superimposition of the traces of all the optimized routes followed by the 1.6 MW energy ship in 2015, 2016 and 2017. It shows that the ship's trajectories cover a large part of West Ile de Sein. As in previous cases, the trajectories appear to be random which no typical pattern is can be identified. Thus, a statistical analysis on frequency distributions of power, sailed distance, speed and true wind angle (TWA) for all case studies will be presented in next subsections. The figure also shows that in a few cases, the ship is routed over land areas. This bug in the software shall be fixed in future work.

Effect of rated power

The same sensitivity study to rated power of the energy ship as for Saint-Pierre-et-Miquelon (see subsection 4.3.1.1) was performed. The results are shown in Table 23.

Similar to the Saint-Pierre-et-Miquelon case study, one can see that the energy production increases with increasing rated power. However, the capacity factor decreases. The maximum capacity factor of 55.3 % is obtained for the 1 MW energy ship, compared to only 48.5% (12% less) for the 1.6 MW energy ship. The effect of the rated power on the filling ratio appears to be limited as the average filling ratio exceeds 92% in all studied configurations. Results are shown in Table 24. As expected, and in contrast to Saint-Pierre-et-Miquelon, the average capacity factor increases with increasing storage capacity. Conversely, the filling ratio decreases, albeit the reduction is limited.

Frequency distributions of energy produced by the energy ship

Figure 60 shows the distribution of the ratio of the delivered energy to the maximum energy which could have been delivered over the same period of time. The distribution appears to be similar to that for Saint-Pierre-et-Miquelon: the two most frequent cases are that for which the delivered energy is equal to the maximum and that for which it close to 0. This result can be explained by weaker wind or no wind available at certain time especially in month of April to July (see Figure 63).

It also appears in Figure 60 that the frequency of energy deliveries for which the amount of energy is equal to the storage capacity decreases with increasing storage capacity (vice versa for the lowest energy delivery class). This effect was also observed for Saint-Pierre-et-Miquelon.

Frequency distributions of True Wind Angle (TWA) in 2017

Figure 62 shows the frequency distribution of true wind angle and power production of the 1.6 MW energy ship deployed at Ile de Sein. As expected, one can see that the optimized routes correspond to routes for which the energy ship sails close to 90° true wind angle (beam reach).

Comparison with a stationary offshore wind turbine

In this section, the capacity factor of the 1.6 MW energy ship at Ile de Sein is compared to that of a stationary offshore wind turbine that would be deployed in same area. The capacity factor of the offshore wind turbine is obtained using the same methodology and wind data as presented in the Saint-Pierre-et-Miquelon case study.

Nine locations are considered for the deployment of the floating offshore wind farm (indicated by the boxes in Figure 64). The wind turbine hub height is assumed to be 90 meters. Its rated power is 1.6 MW. Figure 63 shows the wind distribution and power curve of the floating wind turbine studied at Ile de Sein. The wind distribution was obtained from MERRA-2 weather data [START_REF]European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset accessible[END_REF].

No. of frequency : & 2017) and the average over the three years. The average capacity factor over the area reaches 63%. In addition, each wind turbine achieves a capacity factor over 60% except wind turbine #07 that achieves a capacity factor of 59%. It can be observed that the capacity factor decreases with approaching Ile de Sein. In comparison to Saint-Pierre-et-Miquelon, the average capacity factor is similar (62.7% in Ile de Sein vs 63.0% in Saint-Pierre-et-Miquelon). However, for the energy ship, the capacity factor is significantly smaller in Ile de Sein (49%) than in Saint-Pierre-et-Miquelon (53%). It shows that the capacity factor of an energy ship cannot be directly estimated from a wind turbine's capacity factor. Weather-routing appears to be necessary.

Conclusions

In this chapter, we investigated the capacity factor of an energy ship that would be deployed near shore in Saint-Pierre-et-Miquelon and Ile de Sein. The capacity factor of the energy ship is optimized using weather-routing.

We found that energy ships could have achieved an average capacity factor of 53% at Saint-Pierre-et-Miquelon and 49% at Ile de Sein for the three years of 2015, 2016 and 2017. It is smaller than the result of weather routing in the far offshore, which can be explained by lower wind speeds.

Indeed, Table 26 shows that the average true wind speed over 3 years at North Atlantic ocean is 12% greater than the average true wind speed in Saint-Pierre-et-Miquelon and Ile de Sein. A sensitivity study of the effect of the optimization parameters has been performed. Thus, the best numerical parameter is 8° search step angle and two waypoints. The option of doubling the waypoints after the first optimization can improve the results. However, it was not activated because the improvement is small in comparison to the increase in simulation time.

The sensitivity of rated power on the capacity factor and energy produced, three years was also analyzed. The results of capacity factor, filling ratio and total annual energy production for 1 MW, 1.3 MW and 1.6 MW energy ship deployed at Saint-Pierre-et-Miquelon and Ile de Sein were presented. It is found that when the rated power increases, the optimal capacity factors decreases.

In contrast, the produced energy increases with the increment of the rated power of the energy ship.

CHAPTER 5 CONCLUSION AND PERSPECTIVES

This chapter will conclude the thesis by summarizing the main results and then presenting some perspectives of future research to complete and improve this work.

Conclusion

This thesis aimed at addressing the knowledge gap for the capacity factor of weather-routed energy ships. The four objectives of the thesis are recalled here:

1. Investigate the annual average capacity factor for a given energy ship design deployed in the North Atlantic Ocean; and compare it to stationary floating wind turbines.

2. Investigate the annual average capacity factor for a given energy ship design deployed in the nearshore; and compare it to stationary floating wind turbines.

3. Assess the sensitivity of the capacity factor to the ship characteristics: onboard storage capacity, energy unloading time, and velocity and power production polars.

4. Assess the sensitivity of the capacity factor to the parameters of the weather routing algorithm: number of initial optimization waypoints and search step angle.

Objective 1 was addressed in chapters 2 and 3. A method has been developed to optimize the capacity factor of energy ships using a modified version of the weather-routing software qtVlm.

It was used to analyze and compare the capacity factors of energy ships to stationary offshore wind turbines that would be deployed far-offshore in the North Atlantic Ocean. We found that 1.6 MW energy ships that would have been deployed there during the years of 2015, 2016 and 2017 could have achieved capacity factors over 80%. This very high capacity factor is achieved thanks to weather-routing and also because the wind resource is very high in the North Atlantic Ocean.

Indeed, we confirmed in chapter 2 previous results, which indicate that the capacity factor of stationary floating offshore wind farms deployed there could exceed 69%.

Objective 2 was addressed in chapter 4, in which we investigated the capacity factor of a 1.6 MW energy ship that would be deployed near shore in Saint-Pierre-et-Miquelon (French archipelago) and Ile de Sein (French island). We found that energy ships could have achieved an average capacity factor of 53% at Saint-Pierre-et-Miquelon and 49% at Ile de Sein for the three years of 2015, 2016 and 2017. In comparison, the capacity factor of a floating wind turbine that would be deployed in the same areas is over 60%. The variation in capacity factor between the two systems is mainly due to the height of the wind turbine which allows access to greater wind speeds.

Objectives 3 and 4 were addressed in both chapters 3 and 4. The effect of the numerical parameters of the weather routing optimization on weather-routing has been studied, and it was determined that the best numerical parameters are 2 initial waypoints for a deployment nearshore 6 and 8° for the search step angle. For an energy ship deployed in the North Atlantic ocean, it was found that the capacity factor and energy production is sensitive to the storage capacity, unloading time configuration and the rated power of the energy ship. It was observed that as the energy ship's rated power increases, the capacity factor and filling ratio significantly decrease. In contrast, the energy produced increases with increasing rated power. In the nearshore, it was observed that the capacity factor decreases much more rapidly with increasing rated power than in the far offshore.

The sensitivity to storage capacity and unloading time was also investigated for the energy ship deployed at the near shore. Surprisingly, in Saint-Pierre-et-Miquelon, the shortest batteries storage capacity gave the best capacity factors. In contrast, in Ile de Sein, the best capacity factor would be obtained with the most prolonged batteries storage capacity. Overall, several results of the sensitivity studies indicate that the optimization method currently implemented in QtVlm conclusively converges to a local optimum, not a global optimum.

Future work

In order to fill the extensive knowledge gap for the capacity factor of weather-routed energy ships, there are still the following aspects to be studied in the future.

First, this thesis has provided the foundation of a method for the performance assessment of a weather-routed energy ship. However, the energy ship capacity factor investigation does not include the effect of sea conditions, turbine reliability, biofouling, and hazard avoidance. It is critical to consider these dynamic elements for real-world production. Thus, further sensitivity studies considering the dynamic elements of energy ships' performance are needed for future work.

Second, the optimized capacity factors of the energy ship obtained with different storage capacities were somewhat surprising. We may have expected that greater storage capacity would lead to a more significant number of weather-routing options and thus a better capacity factor.

Therefore, this dubiety the capability of the optimization method currently implemented in qtVlm to converge to the global optimum. Improved optimization methods should be developed.

Third, the weather-routing is highly dependent on weather data. In this thesis, MERRA2 The performance of the energy ship is characterized by polar plots for its speed and power production and this polar is use as an input for the energy ship capacity factor optimization.

In sub-function Routes ('Custom fields' configuration), the optimization criterion and its target value can be determined, and the polar of the energy ship's power production can be loaded (see Figure A1-3). Meanwhile, the energy ship's velocity polar can be loaded into the boat menu which has the configuration functions for all boat's setting. The efficiency of upwind and downwind can also be determined in the boat setting field (see Figure A1-4).

In the 'special LHEEA route' settings, the parameter used for the energy ship's capacity factor optimization, such as the numerical parameter (initial optimization waypoints), initial route target filling ratio and energy ship's sailing capability (energy ship power rating, storage capacity and unloading time) can be determined. The option to double the number of waypoints after the first optimization can also be selected in this 'special LHEEA route' settings (see Figure A1-3).

The boundary of the sailing area also can be specified in this section by specifying the minimum and maximum latitude and longitude. But the energy ship's In this sensitivity analysis, the default setting for the energy ship's sailing capability parameter as a function of the storage capacity aboard and unload time is fixed at 24 hours storage capacity and 4 hours unload time.

The sensitivity was measured by the average optimized capacity factor, using qtVlm weather routing software for three years of wind data, 2015, 2016 and 2017 presented in Chapter 2. The optimized capacity factor obtained for each year is then the average capacity factor over 3 years period is determined, presented and compared in the following section.

Sensitivity of initial optimization waypoints and search step angle

At the beginning of the sensitivity analysis, the analysis was started with 0.5° search step angle and 12 waypoints as the cluster of reference. Then, the value of search In Figure A2-1 one can see that for 2 initial optimization waypoints, as the search step angle increases, the average capacity factor also increases to a maximum of 52.8% at 8° search steps angle. However, this trend does not continue as the capacity factor dropped to 52.5% when the search step angle reached at 16° search step angle.

However, the absolute difference in capacity factor is slight, which is only 0.3%.

The maximum capacity factor for cluster of 4 initial optimization waypoints is The best average capacity factor obtained in Ile de Sein (49.3%) for a 1.6MW energy ship was slightly lower than the best average capacity factor obtained in Saint-Pierre-et-Miquelon (52.8%) absolute difference of 3.5%. Overall, one can see that the optimized capacity factor obtained by two initial optimization waypoints is more sensitive than the 18 and 36 waypoints, where minimal changes in the optimized capacity factor obtained by those number of waypoints. One can also note that subsequent sensitivity analysis limits 18 numbers of waypoints and 8° search step angle.

The limit is due to the identified pattern of optimized capacity factor obtained in sensitivity analysis for 1.6MW energy ship in Saint-Pierre-et-Miquelon. In Figure A2-3, the results show that for two initial optimization waypoints, as the search step angle increases, the average capacity factor also increases to a maximum of 55.6% at a 6° search steps angle. However, this trend does not continue as the capacity factor dropped to 55.1% when the search step angle reached an 8° search step angle.

The maximum capacity factor for another cluster of 4, 6 and 12 initial optimization waypoints are respectively 53.9%, 52.1%, 49.9% obtained by 8° search step angle.

Meanwhile, for 18 initial optimization waypoints, the maximum capacity factor of 47.8% obtained by a 6° search step angle.

Similarly, with the results obtained for the sensitivity study for 1.6MW energy ship, the decrement of average capacity factors can be seen for 1.3MW energy ship as the initial optimization waypoints increase. The highest average capacity factor for the 1.3MW obtained highest capacity factor is 2 initial waypoints. In other hand, the best optimization search step angle is between 6 and 8°. One would think more points initially in the route would be better, but that may be actually be counter-productive, because of the way the simplex works: it optimizes one point after another, so if the points n+1 and n-1 are too close the algorithm will not have enough space to work correctly. The Simplex algorithm used in QtVlm route's optimization process has been modified to use the custom fields accumulation parameters (see Figure 14) as a goal instead of best estimated time arrival.

This sensitivity analysis also shows that the optimal capacity factor is also significantly sensitive to the rated power of the energy ship. Thus, the sensitivity study of the energy ship's optimized capacity factor in response to the storage capacity and

Sensitivity of double waypoints after first optimization

In QtVlm batchmode version presented in Chapter 2, there is an option to double the number of waypoints after the first optimization. This means the indicated waypoints at initial optimization process will be doubled after first optimization.

A sensitivity analysis using this parameter has been done and the capacity factor assessed for energy ship 1.6MW and 1.3MW with same numerical parameter of 24 hours storage capacity and 4 hours unload time; 8° search step angle and 4 initial number of waypoints. Even though a two number of initial waypoint is the best parameter obtained in the sensitivity analysis, but 2 waypoints are too small for the purpose of this analysis, thus a 4 waypoints parameter is used. Furthermore, the optimized capacity factor obtained by 4 waypoints is considerably high. One may note that the unload time is also greater, but it does not change the theoretical maximum capacity factor, which is given by:

𝐶𝐹 𝑚𝑎𝑥 = 𝑁 𝑁 + 𝑇 0
Where we recall that N is the energy storage capacity in hours at rated power and T0 is the unload time. The proportion of storage capacity and unload time for all 4 clustered configurations meet the theoretical maximum capacity (85.7%).

To perform the sensitivity on energy ship's sailing capability parameter, the parameter of storage capacity and unloading time and the rated power of the energy ship were taken into consideration. Few set of optimal numerical parameter as function to search steps angle and optimization search step angle defined in previous section were chosen and set as the default setting. In this context, 3 set of search step angle and initial number of waypoints was chosen; they are 0.5° search step angle and 12 initial optimization waypoints, 8° search step angle and 2 initial optimization waypoint and 16° search step angle and 2 initial optimization waypoints. These numerical parameter clusters were chosen based on the referral cluster and the optimal cluster defined in the previous numerical parameter sensitivity study.

Note that all the tables shown the results for over the years 2015, 2016 and 2017 fixed for 4 different storage capacities and unload times.

Sensitivity of storage capacity and unload time

Table A2-3 shows the results of the optimized capacity factor for the 1.6 MW energy ship in both Saint-Pierre-et-Miquelon and Ile de Sein for the year 2015, 2016 and 2017 and the average of 3 years. One can note that the numerical parameter is set to 0.5° search step angle and 12 initial optimization waypoints.

One can see that, in Saint-Pierre-et-Miquelon, the highest three years averaged optimized capacity factor of 49.8% obtained by the smallest storage capacity of 6 hours and 1 hour unload time. Then followed by 12 hours storage capacity and 2 hours unload time (47.3%), 24 hours storage capacity and 4 hours unload time (47.3%), then 48 hours storage capacity and 8 hours unload time (47.3%).

Meanwhile, in Ile de Sein, the highest three years averaged optimized capacity factor of 44.2% was also obtained by the smallest storage capacity of 6 hours and 1 hour unload time followed by 12 hours storage capacity and 2 hours unload time (43.3%), 24 hours storage capacity and 4 hours unload time (39.5%) then 48 hours storage capacity and 8 hours unload time (37.9%).

Once can see, the optimized capacity factor reduced as the storage capacity increased. However, the filling ratios for all storage capacity at both deployment locations remain higher, more than 98%. However, the filling ratios for all storage capacity at both deployment locations remain higher, which are more than 93%. However, as in the other sensitivity analysis cases, the filling ratios for all storage capacity at both deployment locations remain higher, which are more than 93%. Overall, as one may have expected that greater storage capacity would lead to a greater number of weather-routing options and thus greater capacity factor. However, to that end, only four cases meet that expectation mentioned above which are used the numerical of 8° and 16° search step angle and two initial waypoints. All of them are 1.6MW and 1.3MW rated energy ship deployed at Ile de Sein. But, with the same parameter and rated power of the energy ship, the capacity factor decrease as the storage capacity increases.

In addition, in most cases, the optimized capacity factors for the energy ship were also decrease as the storage capacity increases. The result is rather surprising as one may note that the unload time is also greater as the storage capacity increases. But, it does not change the theoretical maximum capacity factor (85.7%), which is given by equation ( 8) explained in section 4.2.1. Looking into the relationship between the optimized capacity factor with the increment of the storage capacity, for the energy ship deployed at the Saint-Pierre-et-Miquelon, the capacity factor slightly reduced with the increased storage capacity and unloading time. In contrast, the increment of capacity factor was parallel with the increments of storage capacity and unloading time for the energy ship deployed at the Ile de Sein.

In Saint-Pierre-et-Miquelon, Table A2-9 shows the highest optimized capacity factor is obtained by a 1MW energy ship equipped with 6 hours of storage capacity with an average of 3 years capacity factor of 60.3%. With same rated energy ship, a slight Abstract : The energy ship is a relatively new concept for offshore wind energy harvesting. It consists of a windpropelled ship that generates electricity using water turbines attached underneath its hull. Since the energy ship is not grid-connected, the generated energy is stored aboard (for instance, using batteries or through conversion to hydrogen using an electrolyzer).

A key advantage of the energy ship is that it is mobile. Therefore, its trajectory can be optimized using weatherrouting in order to maximize energy production, which is the focus of this thesis. The analysis in the thesis is based on numerical simulations. The weather-routing software is a modified version of QtVlm in which the optimization objective has been replaced by the maximization of the energy production. The energy ship is modelled in the software by a velocity polar and a power production polar. The wind data is based on the ECMWF ERA-5 wind dataset. The energy ship concept is particularly well-suited for the harvesting of the wind energy conversion in the faroffshore. Therefore, the capacity factor of energy ships deployed in the North-Atlantic Ocean is investigated first.

Results show that a capacity factor of 70% can be achieved (annual average). It is similar to that of floating offshore wind farms which would be deployed in the same area. Energy ships may also be used for the power supply of islands and coastal communities. Therefore, the capacity factor of energy ships deployed nearshore is also investigated. Two case studies are considered: the island of "Ile de Sein" and the "Saint-Pierre-et-Miquelon" archipelagos. Results show that the capacity factor is in the order of 50%. In this case, it would be 10 to 20% smaller than that of offshore wind farms. The weather-routing optimization depends on physical (e.g. storage capacity, rated power) and numerical parameters. Sensitivity analyses are performed in order to understand their effect on energy production. Results show that the optimization algorithm in QtVlm tends to converge to local maxima. Therefore, future work should be directed towards the development of better optimization methods.

Figure 1 18 Figure 2 19 Figure 3 20 Figure 4 21 Figure 5 23 Figure 6 24 Figure 7 25 Figure 8 25 Figure 9 Figure 10 28 Figure 11 30 Figure 12 31 Figure 13 35 Figure 14 36 Figure 15 39 Figure 16 40 Figure 17 41 Figure 18 42 Figure 19 43 Figure 20 45 Figure 21 48 Figure 22 49 Figure 23 50 Figure 24 51 Figure 25 52 Figure 26 54 Figure 27 56 Figure 28 57 Figure 29 59 Figure 30 64 Figure 31 67 Figure 32 68 Figure 33 69 Figure 34 70 Figure 35 72 Figure 36 74 Figure 37 75 Figure 38 76 Figure 39 Figure 40 Figure 41 Figure 42 Figure 43 85 Figure 44 88 Figure 45 92 Figure 46 95 Figure 47 Figure 48 Figure 49 Figure 50 Figure 52 Figure 53 Figure 54 Figure 55 Figure 56 Figure 57 Figure 58 Figure 59 Figure 60 Figure 61 Figure 62 Figure 63 Figure 64 Figure A1- 2 Figure A1- 3 'Figure A1- 4 Figure A1- 5 Figure A2- 1 Figure A2- 2 Figure A2- 3 Figure A2- 4 Figure A2- 5

 1182193204215236247258259102811301231133514361539164017411842194320452148224923502451255226542756285729593064316732683369347035723674377538763940414243854488459246954748495052535455565758596061626364234512345 Figure 1 Evolution of total primary energy consumption and energy-related CO2 emissions, relative to 2019 [Source: IEA (2021)[2]] ............................................................... 18

Figure 1

 1 Figure 1 Evolution of total primary energy consumption and energy-related CO2 emissions, relative to 2019 [Source: IEA (2021)[2]]

Figure 2

 2 Figure 2 Renewable share of annual power capacity expansion [Souce: IRENA Renewable energy statistics (2021) [55]]

Figure 3

 3 Figure 3 Change in electricity consumption in 2020 and 2021 [Source: Global Energy Review 2021, IEA (2021) [2]]

Figure 5

 5 Figure 5 Average simulated capacity factors reflect the availability of the global offshore wind resources (Source: IEA, World Energy Outlook 2019 [65])

Figure 6 Figure 7 Figure 8

 678 Figure 6 Hywind Scotland floating offshore wind farm (Source: https://www.equinor.com/en/what-we-do/floating-wind/hywind-scotland.html)

Figure 11

 11 Figure 11 Energy ship's mode of operation for North Atlantic Ocean

Figure 12

 12 Figure 12 Mode of operation of energy ships for power supply of islands. The 6 -48 hours indicate the typical duration of the charging cycle for a full charge.

Figure 14

 14 Figure 14 Global wind industry evolution between 2001 to 2020 and expected trends up to 2050 (Source: IEA data and statistics)

Figure 15

 15 Figure 15 Wind turbine components (Source: [49])

Figure 16

 16 Figure 16 Wind turbine foundations (Source: [46])

Figure 17

 17 Figure 17 Power curve (Source: Dupont et al [12])

Figure 18

 18 Figure 18 Onshore wind energy capacity in Europe in 2020 (Source: [55][56])

Figure 19

 19 Figure 19 Offshore wind energy capacity for European countries in 2020 (Source: [55])

Figure 20

 20 Figure 20 Typical bottom-fixed offshore foundations (Source: EWEA [54])

Figure 21

 21 Figure 21 Windfloat Atlantic floating offshore wind farm (Source: https://www.offshoremag.com/renewable-energy/article/14188688/windfloat-atlantic-represents-major-offshorewind-milestone and [44])

Figure 23

 23 Figure 23 Global weighted-average capacity factor of onshore wind for corresponding year between 2010 and 2019 (Source: IRENA Renewable Cost Database [57]

Figure 24

 24 Figure 24 Global weighted-average capacity factor of offshore wind for corresponding year between 2010 and 2019 (Source: IRENA Renewable Cost Database [57])

Figure 26

 26 Figure 26 Zero-velocity (left) and wind turbine power production (right) polar contains data of true wind angle (TWA) and true wind speed (TWS) loaded in QtVlm for 5 MW wind turbine capacity factor assessment

Figure 28

 28 Figure 28 Tested locations for the wind turbines and average capacity factor over the three years of 2015, 2016 and 2017

Figure 31

 31 Figure 31 shows an example of the boat speed polar and the power production polar of a 1.6 MW energy ship. The boat speed polar shows the boat speed as function of the true wind angle while the power production polar shows the generated power as function of the true wind angle. Both polar are plotted for three true wind speeds (7.7 m/s, 9.3 m/s, 16.5 m/s).

Figure 31

 31 Figure 31 Example of 1.6 MW catamaran boat speed (left) and power production (right) polars

Figure 33

 33 Figure 33 Modified isochrones algorithm (Adopted from[START_REF] Hagiwara | Weather routing of (sail-assisted) motor vessels[END_REF] [START_REF] Hagiwara | Weather routing of (sail-assisted) motor vessels[END_REF]

Figure 34 2 . 3 . 4 .

 34234 Figure 34 Modified isochrone algorithm diagram adapted from[START_REF] Hagiwara | Weather routing of (sail-assisted) motor vessels[END_REF]

Figure 37

 37 Figure 37 Polar plots for the boat velocity (left, in knots) and power production (right, in kW) for the energy ship of 1.6 MW rated power (Design #01); 5 different true wind speeds (TWS) are shown in both plots ranging from 7.5 m/s (innermost curve) to 19.5 m/s.

  The weather routing optimization in this chapter uses a batch-mode version of QtVlm which generates the optimized route automatically. Apart from the boat performance curves (polars of velocity and power production as function of true wind speed and true wind angle, storage capacity, unloading time), it depends on four numerical parameters:• The search step angle (in degrees). It is used to initialize the first step of the simplex and to increase the search range. It is a distance where one minute latitude is equal to one NM. It has nothing to do with the boat position or boat heading. It represents how far away from a point of the route the simplex will initially look when optimizing the capacity factor over the route.

Figure 40 NorthFARWINDER

 40 Figure 40 North Atlantic Ocean Historical Storm and Hurricane Tracks (Source: NOAA, https://coast.noaa.gov)

Figure 42 showsFigure 42

 4242 Figure 42 shows the monthly average true wind speed at the location within North Atlantic ocean passed by the energy ship. Overall, the annual averages of true wind speed were 12 m/s for

Figure 43 also

 43 Figure 43 also shows the distribution of capacity factor as function of the delivery time for each route in 2015, 2016 and 2017. It can be observed that during the months with low wind, the route duration increases.

Figure 43

 43 Figure 43 Capacity factor at the end of each route plotted as function of the delivery time

Figure 44

 44 Figure 44 Polar plots of the speed (left, in knots) and power production (right, in kW) of a) 1MW and b) 1.3MW energy ship as function of the true wind direction (0-360°) and true wind speeds (TWS) ranging from 19.5 m/s (outer curve), 16.5 m/s, 13.5 m/s, 10.5 m/s and 7.5 m/s (innermost curve).

Figure 45

 45 Figure 45 Polar plots for the velocity (left, in knots) and power production (right, in kW) of proposed 1MW energy ship derived from a) Design #01 [21]for true wind speed of 7.5, 10.5, 13.5, 16.5, and 19.5 m/s (most outer curves)

Figure 46

 46 Figure 46 Locations of the Saint-Pierre-et-Miquelon and Ile de Sein Island (Source : Google Maps)

  6 MW rated power energy ship. The boat speed and power production polar diagram is shown in Figure 37. Four configurations of energy storage and unloading time (6-hour storage capacity and 1-hour unloading time, 12-hour storage capacity and 2-hour unloading time, 24-hour storage capacity and 4-hours unloading time, 48-hour storage capacity and 8-hour unloading time), and two deployment locations (Saint-Pierre-et-Miquelon and Ile de Sein) were considered. Based on the optimization results of qtVlm, two datasets were generated and analyzed. For each energy storage configuration and deployment location, Dataset 1 corresponds to a table of energy production, sailed duration and sailed distance at the end of each route travelled by the energy ship. It covers the three years of 2015, 2016 and 2017. It typically includes the number of data points depends on the size of the storage capacity as follows (see Table

Figure 47 shows

 47 Figure 47 shows the average capacity factor for the three years (2015, 2016 & 2017) as function of the search step angle and the initial number of waypoints at Saint-Pierre-et-Miquelon.

Figure 47 Figure 48

 4748 Figure 47 Sensitivity study on weather routing optimization criteria. The results of the 6 clustered initial optimization waypoints and 9 clustered optimization search steps angle for 1.6 MW energy ship in Saint-Pierre-et-Miquelon (average of 2015, 2016 & 2017) are presented. These results influence by 6 different initial waypoints of optimization that are set to 2, 4, 6, 12, 18 and 36; and 9 different optimization search steps.

Figure 49 Figure 50 Figure 51

 495051 Figure 49 Monthly average capacity factor for 1.6 MW energy ship deployed at Saint-Pierre-et-Miquelon in 2015, 2016 and 2017

  3 MW and 1.6 MW are considered. All ships are equipped with 24-hour storage capacity. The unloading time is 4 hours.The numerical parameters were fixed to 2 initial optimization waypoints and 8° search step angle.

Figure 51 1 . 6

 16 Figure 51 1.6 MW energy ship route traces for 24-hour storage capacity & 4-hour unloading time in 2015, 2016 and 2017 at Saint-Pierre-et-Miquelon

Figure 52 Frequency

 52 Figure 52 Frequency distribution of energy production for 1.6 MW energy ship in Saint-Pierre-et-Miquelon for 3 years (2015, 2016 & 2017)

Figure 53 Frequency

 53 Figure 53 Frequency distributions of sailed distance over maximum distance of 1.6 MW energy ship in Saint-Pierre-et-Miquelon for 3 years (2015, 2016 & 2017)

3 °

 3 North and 56.5° to 58.5° West approximately covering the area of navigation of the energy ship. The rated power of the offshore wind turbine was set to 1.6 MW. The hub height was set to 90 m. The wind distribution and the power curve of the floating wind turbine are shown in Figure 55.

Figure 55

 55 Figure 55 Wind distribution and power curve for the 1.6 MW stationary wind turbines over the three years of 2015, 2016 and 2017 in Saint-Pierre-et-Miquelon (Mean TWS = 10.28 m/s; Standard deviation = 4.92)

Figure 56 FARWINDER

 56 Figure 56 Tested locations for the 1.6 MW floating wind turbines and average capacity factor over the three years of 2015, 2016 and 2017 in Saint-Pierre-et-Miquelon

Figure 57

 57 Figure 57 indicates the monthly average capacity factor obtained by the 1.6 MW energy ship deployed at Ile de Sein. In contrast to the Saint-Pierre-et-Miquelon case, the lowest capacity factor is obtained in May, which corresponds to the month with the lowest wind resource as can be seen in Figure 58.

Figure 57 Figure 58 Figure 59

 575859 Figure 57 Monthly average capacity factor for 1.6 MW energy ship deployed at Ile de Sein in 2015, 2016 and 2017

Figure 60 Frequency

 60 Figure 60 Frequency distribution of energy production for 1.6 MW energy ship in Ile de Sein for 3 years (2015, 2016 & 2017)

Figure 61 Frequency

 61 Figure 61 Frequency distributions of sailed distance over maximum speed of 1.6 MW energy ship in Ile de Sein for 3 years (2015, 2016 & 2017)

Figure 62 Frequency

 62 Figure 62 Frequency of true wind angle (°) and power production of the 1.6 MW energy ship at Ile de Sein

Figure 63

 63 Figure 63 Wind distribution and power curve for the 1.6MW floating wind turbines over the three years of 2015, 2016 and 2017 in Ile de Sein (Mean TWS = 9.81 m/s; Standard deviation = 3.96)

Figure 64 FARWINDER

 64 Figure 64 Tested locations for the 1.6 MW floating wind turbines and average capacity factor over the three years of 2015, 2016 and 2017 in Ile de Sein

  datasets used in Renewable Ninja and ERA-INTERIM datasets provided by ECMWF were compared. In future work, other updated and quality-assured wind datasets, i.e., the ERA5 dataset from ECMWF, should be used in future weather-routing optimization of energy ships capacity factors. QtVlm menu has a configuration function, with all qtVlm settings. In configuration function, sub-function Routes ('General' configuration), the numerical parameter of initial search step angle used in the initial optimization process can be determined (see Figure A1-2). The search step angle is used as a first distance to initialize the first step of the simplex and to increase the search range. It is not a bearing but a distance where one minute latitude is equal to one NM. It has nothing to do with the boat position or boat heading. It represents how far away from a POI the simplex will initially look when optimizing the capacity factor over the route. The standard public version of QtVlm does not have this field, and instead compute the optimal value based on the neighboring points.

Figure A1- 1

 1 Figure A1-1 Route comparator display the result of the multi-routes optimization

Figure A1- 2

 2 Figure A1-2 QtVlm weather routing optimization configurations (General configurations)

Figure A1- 5

 5 Figure A1-5 Grib slot to enable QtVlm viewing grib files (weather data files)

  step angle were doubled up to 64° and assessed individually with 12 waypoints. Then the number of waypoints is decreased and increases accordingly and assessed individually to each search step angle to define the pattern of optimized capacity factor in response to the initial optimization waypoints and search step angle. Once the pattern has been identified the subsequent sensitivity analysis is limit to maximum of 18 numbers of waypoints and 8° search step angle.

Figure A2- 1

 1 Figure A2-1 and Figure A2-2 show the average capacity factor for the three years (2015, 2016 & 2017) for the sensitivity of search step angle and the initial number of optimization waypoints respectively at Saint-Pierre-et-Miquelon and Ile de Sein for 1.6MW energy ship.

51 .

 51 3% obtained by 32° search step angle, and 6 initial optimization waypoints are 50.1% obtained by 16° search step angle. For 12, 18 and 36 initial optimization waypoints, the capacity factor fluctuated as the search step angle increases. The maximum capacity factors respectively are 46.4%, 44.9% and 43.1%. However, these capacity factors are still cannot beat the best capacity factor obtained by 2 initial optimization waypoints and an 8° search step angle. The decrement of average capacity factors can be seen as the initial optimization waypoints increase. The highest and best average capacity factor for the 1.6MW energy ship in Saint-Pierre-et-Miquelon was 52.8%, obtained by 2 initial optimization waypoints and 8° search step angle. These results were used to define the pattern of optimized capacity factor in response to different numerical parameters. It shown that the highest optimized capacity factor (52.8%) for 1.6MW energy ship at Saint-Pierreet-Miquelon obtained by 2 initial optimization waypoints and 8° of optimization search step angle. Meanwhile, Figure A2-2 shows the same information for 1.6MW energy ship deployed at Ile de Sein. The results show that the capacity factor also decrease as the initial optimization waypoints increase with the highest average capacity factor, 49.3%, obtained by 2 initial optimization waypoints and 6° search step angle. The optimized capacity factor reduced when the optimization search step angle increased to 8°. The maximum capacity factor for another cluster of 4, 6, 12 and 18 optimization waypoints respectively are 47.6%, 44.9%, 41.4% and 39.9%.

Figure A2- 1

 1 Figure A2-1 Sensitivity study on weather routing optimization criteria. The results of the 6 clustered initial optimization waypoints and 9 clustered optimization search steps angle for 1.6MW energy ship in Saint-Pierre-et-Miquelon (average of 2015, 2016 & 2017) are presented. These results influence by 6 different initial waypoints of optimization that are set to 2, 4, 6, 12, 18 and 36; and 9 different optimization search steps.

Figure A2- 2

 2 Figure A2-2 Sensitivity study on weather routing optimization criteria. The results of the 5 clustered initial optimization waypoints and 6 clustered optimization search steps angle for 1.6MW energy ship in Ile de Sein (average of 2015, 2016 & 2017) are presented. These results influence by 5 different initial waypoints of optimization that are set to 2, 4, 6, 12, and 18; and 6 different initial optimization search steps.

Figure A2- 3

 3 Figure A2-3 Sensitivity study on weather routing optimization criteria. The results of the 5 clustered initial optimization waypoints and 6 clustered optimization search steps angle for 1.3MW energy ship in Saint-Pierre-et-Miquelon (average of 2015, 2016 & 2017)

Figure A2- 4

 4 Figure A2-4 Sensitivity study on weather routing optimization criteria. The results of the 5 clustered initial optimization waypoints and 6 clustered optimization search steps angle waypoints for 1.3MW energy ship in Ile de Sein (average of 2015, 2016 & 2017)

Figure A2- 5

 5 Figure A2-5 Average capacity factor for 1MW, 1.3MW, 1.6MW and 1.9MW energy ship in (top) Saint-Pierre-et-Miquelon and (below) Ile de Sein for 3 years (2015, 2016 & 2017)

Titre:

  Optimisation du facteur de capacité des voilier hydro-éolien pour la conversion de l'énergie éolienne en mer en utilisant le routage météo Mots clés : Énergie éolienne en mer, Voilier hydro-éolien, Facteur de capacité, Routage météorologique, Optimisation Résumé : Le navire à énergie est un concept relativement nouveau pour la récolte d'énergie éolienne offshore. Il s'agit d'un navire propulsé par le vent qui produit de l'électricité à l'aide de turbines hydrauliques fixées sous sa coque. Étant donné que le navire énergétique n'est pas connecté au réseau, l'énergie générée est stockée à bord (par exemple, à l'aide de batteries ou par conversion en hydrogène à l'aide d'un électrolyseur). Un avantage clé du navire énergétique est qu'il est mobile. Par conséquent, sa trajectoire peut être optimisée à l'aide d'un routage météorologique afin de maximiser la production d'énergie, ce qui est l'objet de cette thèse. L'analyse de la thèse est basée sur des simulations numériques. Le logiciel de routage météo est une version modifiée de QtVlm dans laquelle l'objectif d'optimisation a été remplacé par la maximisation de la production d'énergie. Le vaisseau énergétique est modélisé dans le logiciel par une polaire de vitesse et une polaire de production d'énergie. Les données de vent sont basées sur le jeu de données de vent ECMWF ERA-5. Le concept de navire à énergie est particulièrement bien adapté à la récolte de la conversion d'énergie éolienne dans le lointain au large. Par conséquent, le facteur de capacité des navires à énergie déployés dans l'océan Atlantique Nord est d'abord étudié. Les résultats montrent qu'un facteur de capacité de 70 % peut être atteint (moyenne annuelle). Elle est similaire à celle des parcs éoliens offshore flottants qui seraient déployés dans la même zone. Les navires à énergie peuvent également être utilisés pour l'alimentation électrique des îles et des communautés côtières. Par conséquent, le facteur de capacité des navires à énergie déployés à proximité du littoral est également étudié. Deux études de cas sont envisagées : l'île de « l'Ile de Sein » et l'archipel de « Saint-Pierre-et-Miquelon ». Les résultats montrent que le facteur de capacité est de l'ordre de 50 %. Dans ce cas, elle serait de 10 à 20 % inférieure à celle des parcs éoliens offshore. L'optimisation du routage météo dépend de paramètres physiques (par exemple, capacité de stockage, puissance nominale) et numériques. Des analyses de sensibilité sont effectuées afin de comprendre leur effet sur la production d'énergie. Les résultats montrent que l'algorithme d'optimisation dans QtVlm tend à converger vers les maxima locaux. Par conséquent, les travaux futurs devraient être orientés vers le développement de meilleures méthodes d'optimisation. Title : Optimization of the capacity factor of energy ships for far-offshore wind energy conversion using weather-routing Keywords : Offshore wind energy, Energy ship, Capacity factor, Weather-routing, Optimization

Table 1

 1 Total global energy capacity (Source: IRENA Energy Statistics 2021[START_REF] Irena | Renewable energy statistics 2021[END_REF]) ..[START_REF] Gilloteaux | Preliminary design of a wind driven vessel dedicated to hydrogen production[END_REF]

Table 2

 2 Main characteristics of the considered energy ship (Design #01) ................... 28

Table 3

 3 Development of wind turbine size onshore and offshore between 1985 and 2020 .................................................................................................................................................. 37

Table 4

 4 Advantages and disadvantages of onshore and offshore wind energy (Source: [59]) .......................................................................................................................................... 44

Table 5

 5

Commissioned floating offshore projects at mid-2021 (JAP: Japan; NOR: Norway; UK: United Kingdom; FRA: France; PRT: Portugal) [44][64][65]. ......................... 47 Table 6 Theoretical capacity factor for 5MW floating offshore deployed at 17 locations in North Atlantic ocean for 2015, 2016 and 2017. ................................................................... 58 Table 7 Design and characteristics of sailing wind turbines ........................................ 59 Table 8 Existing route optimization methods and algorithms ...................................... 65 Table 9 Summary of the basic characteristics of the five reanalysis datasets (Source: Ramon et al. (2019) [92]) ......................................................................................................... 73 Table 10 Sensitivity to weather routing numerical optimization parameters for the energy ship deployed in the North Atlantic Ocean for 2015. ................................................... 80 Table 11 Capacity factor of the 1.6MW energy ship equipped with 174-hour storage capacity and 6-hour unloading time and optimization parameter of 8° search steps angle and 7 initial waypoints ....................................................................................................................... 82

Table 12

 12 Capacity factor, filling ratio and energy production for 1.6 MW energy ship equipped with 3 configurations of storage capacity and unloading time in North Atlantic ocean for 3 years (2015, 2016 & 2017) .............................................................................................. 87 Table 13 Capacity factor, filling ratio and energy production for 1 MW, 1.3 MW and 1.6 MW energy ship in North Atlantic ocean for 3 years (2015, 2016 & 2017)...................... 90

Table 14

 14

Ship characteristics of two different designs of the energy ship. .................. 91

Table 15

 15 Comparison of optimized capacity factor, filling ratio and total energy produced for 1MW deployed at far offshore for 2 different boat performance polars. ........... 93 Table 16 Three-year average number of data points for two deployment locations available in Dataset 1 ............................................................................................................... 97 Table 17 Comparison of optimized capacity factor (CF), filling ration (FR), and route duration with and without using the option to double the number of waypoints after the first optimization ............................................................................................................................ 103 Table 18 Results for the optimization of the capacity factor of the 1.6 MW energy ship in Saint-Pierre-et-Miquelon; equipped with 24 hours storage capacity and 4 hours unloading time and optimization parameter of 8° search steps angle and 2 initial waypoints ............... 104

Table 19

 19

Capacity factor, filling ratio and total annual energy production for 1MW, 1.3MW and 1.6MW energy ship in Saint-Pierre-et-Miquelon for 3 years (2015, 2016 & 2017) ................................................................................................................................................ 107 Table 20 Capacity factor (%) of the 1.6 MW energy ship in Saint-Pierre-et-Miquelon with optimization parameter 8° search step angle & 2 number of waypoints and 4 storage capacity configurations. ......................................................................................................... 108 Table 21 Capacity factor for 1.6 MW floating offshore wind turbines in Saint-Pierre-et-Miquelon for 3 years (2015, 2016 & 2017) ........................................................................... 113 Table 22 Results for the optimization of the capacity factor of the 1.6 MW energy ship in Ile de Sein; equipped with 24 hours storage capacity and 4 hours unloading time and optimization parameter of 8° search steps angle and 2 initial waypoints .............................. 115 Table 23 Capacity factor, filling ratio and energy production for 1 MW, 1.3 MW and 1.6 MW energy ship in Ile de Sein for 3 years (2015, 2016 & 2017) .................................... 118 Table 24 Capacity factor (%) of the 1.6 MW energy ship in Ile de Sein with optimization parameter 8° search step angle & 2 number of waypoints ..................................................... 119 Table 25 Capacity factor for 1.6 MW floating offshore wind turbines in Ile de Sein for 3 years (2015, 2016 & 2017) .................................................................................................. 124 Table 26 Annual average true wind speed and route options at three deployment locations for energy ship ........................................................................................................ 125

Table A2 -

 A2 1 Summary of optimal numerical parameter for 1.6MW and 1.3MW rated energy ship with storage capacity of 24 hours including 4 hours unload time. ..................... 152 Table A2-2 Comparison of CF optimized with and without using the parameter double waypoint after first optimization ............................................................................................ 154 Table A2-3 Results for the optimization of the capacity factor (%) of the 1.6MW energy ship for optimization parameter 0.5° search step angle & 12 number of waypoints ............. 157 Table A2-4 Results for the optimization of the capacity factor (%) of the 1.6 MW energy ship for optimization parameter 8° search step angle & 2 number of waypoints .................. 159 Table A2-5 Results for the optimization of the capacity factor (%) of the 1.6 MW energy

	First and foremost, I would like to praise and thank God, the almighty, who has granted
	countless blessing, knowledge, and opportunity to the writer, so that I have been finally able to
	accomplish the thesis.
	This thesis would not have been possible without the inspiration, encouragement, guidelines
	and support of several individuals. I take this opportunity to give special thanks to the following
	people.

ship for optimization parameter 16° search step angle & 2 number of waypoints ................ 161 Table A2-6 Results for the optimization of the capacity factor (%) of the 1.3 MW energy ship for optimization parameter 0.5° search step angle & 12 number of waypoints ............. 163 Table A2-7 Results for the optimization of the capacity factor (%) of the 1.3 MW energy ship for optimization parameter 8° search step angle & 2 number of waypoints .................. 165 Table A2-8 Results for the optimization of the capacity factor (%) of the 1.3 MW energy ship for optimization parameter 16° search step angle & 2 number of waypoints ................ 168 Table A2-9 Capacity factor for 1MW, 1.3MW, 1.6MW and 1.9MW energy ship In Saint-Pierre-et-Miquelon for 3 years (2015, 2016 & 2017) ................................................... 172 Table A2-10 Filling ratio for 1 MW, 1.3 MW, 1.6 MW and 1.9 MW energy ship in Saint Pierre et Miquelon for 3 years (2015, 2016 & 2017) .................................................... 173 Table A2-11 Energy production for 1 MW, 1.3 MW, 1.6 MW and 1.9 MW energy ship in Saint Pierre et Miquelon for 3 years (2015, 2016 & 2017) ................................................ 174 Table A2-12 Capacity factor for 1 MW, 1.3 MW, 1.6 MW and 1.9 MW energy ship in Ile de Sein for 3 years (2015, 2016 & 2017) .......................................................................... 176 Table A2-13 Filling ratio for 1 MW, 1.3 MW, 1.6 MW and 1.9 MW energy ship in Ile de Sein for 3 years (2015, 2016 & 2017) ............................................................................... 177 Table A2-14 Energy production for 1 MW, 1.3 MW, 1.6 MW and 1.9 MW energy ship in Ile de Sein for 3 years (2015, 2016 & 2017) ...................................................................... 178 ACKNOWLEDGEMENT I would like to express my sincere gratitude to my main supervisor, Aurelien Babarit, for giving me the chance to join his research group and for his continuous guidance, inspiration, encouragement, and support. He has been a thoughtful and wonderful supervisor, and without him, this thesis would not have been completed or written. "Aurelien, thank you for valuable discussions and incredible feedback on my work. I have learned quite a lot on renewable energy from you." I would also like to extend my appreciation to my co-supervisor, Jean-Christophe Gilloteaux, for exciting and fruitful discussions, especially in wind turbines technologies. I am grateful for his unlimited support, especially when I desire a piece of advice on wind turbines. He was always open to me whenever I needed his advice on research or other issues.

  Last but not least, my deep and special appreciation goes to my beloved husband, Mohammad Zulkhairy, who has given me strength, support, encouragement, and inspiration.Thank you for your unconditional love and everything that comes to my life with you. I will never forget the patience, sacrifices, and suffering you endured in caring for our children, mainly when I focus entirely on my work. I also want to acknowledge my daughters, Aisyah Sofea, Ainul Mardhiah and my son, Az-Zubayr.Finally, I would like to thank everybody who has contributed either directly or indirectly to this thesis and express my apology that I could not mention personally one by one.

	DEDICATION
	CHAPTER 1
	"This thesis is specially dedicated; INTRODUCTION
	To my Husband, Mohammad Zulkhairy Chin 1.1. Motivation and Problem Studied
	To my beloved daughters & son, Aisyah Sofea, Ainul Mardhiah & Az-Zubayr
	1.1.1. State of The Art in Wind Energy
	Sincerely,
	Roshamida ABD JAMIL
	We lose both of you, but you'll always live inside of us."

To my parents, Abd Jamil & Rodziah To my parents-in-law, Chin & Selamah To my family & family-in-law also

To the memory of my late brother-in-law, Mohd Shukor Bin Chin, who always believed in my ability to succeed in the academic arena.

You are gone, but your belief & love has made this journey possible.

May this success accomplish your unfinished Master thesis and fulfil your dreams;

And to my late sister-in-law, Rosita Binti Chin Your strong-willed character always guides me to be strong and hold tight in anything I am doing.

Table 1

 1 Total global energy capacity (Source: IRENA Energy Statistics 2021 [55])

		Wind Energy Capacity	Offshore Wind Energy	Solar Photovoltaic (PV)
		(MW)	Capacity (MW)	Capacity (MW)
		2019	2020	2019	2020	2019	2020
	World	622 249	733 276	28 355	34 367	580 760	707 495
	Europe	196 311	207 743	22 031	24 920	140 320	161 145
	Asia	257 520	332 088	6 295	9 418	328 553	406 283
	North America	123 575	139 448	29	29	66 660	82 768

Figure 4 Renewable electricity generation growth by technology, 2019-2020 and 2020-2021 [Source: Global Energy Review 2021, IEA (2021) [2]] 1.1.2. Offshore wind energy resource: Capacity factor of wind turbines in the far-offshore

Table 2

 2 Main characteristics of the considered energy ship (Design #01)

	Figure 10 Artist's view of the considered energy ship design [22]
		Unit	Value
	Hull
	Length	m	80
	Breadth	m	31.7
	Draught	m	1.6
	Displacement	t	660
	Wind propulsion
	Type	-	Flettner rotors
	Number	-	4
	Rotor height	m	30

Table 3

 3

	Development of wind turbine size onshore and offshore between 1985 and 2020
	Year	Capacity (kW)	Rotor diameter (m)
	1985	50	15
	1989	300	30
	1992	500	37
	1994	600	46
	1998	1500	70
	2001	2000	72
	2002	2500	80
	2005	3600	120
	2010	7580	140
	2017	8000	164
	2020	12000	220

Table 4

 4 Advantages and disadvantages of onshore and offshore wind energy (Source:[START_REF] Bailey | Assessing Environmental Impacts of Offshore Wind Farms: Lessons Learned and Recommendations for the Future[END_REF])

	Onshore Turbines	Offshore Turbines
	Advantage	Drawbacks	Advantage	Drawbacks
	Cheap	Highly variable energy source ~25%) (capacity factor	Less variable energy ~40%) source (capacity factor	Expensive
				Increased operation
	One of the cheapest forms of renewable energy	Can endanger flying wildlife, such as birds and bats	Less turbines required to produce an equal amount of electricity	and maintenance costs caused by increased wear from wind and waves and
				difficult access
				Longer wait times
	Boost local economies	Noise and visual impact	Less visual impact and conflicts of use	required to correct any potential problems due to
				more limited access
		Inability to
		produce energy	Protects aquatic habitats
		year-round due to	by restricting access to
		reliance on optimal	certain waters
		wind conditions
			Excessive access to wind
			resources without
			landforms obstacle

Table 6

 6 Theoretical capacity factor for 5MW floating offshore deployed at 17 locations in North Atlantic ocean for 2015, 2016 and 2017.

	Turbine Latitude (N) Longitude (W)	2015	2016	2017	3 years average
	WT01	46.000000 °	-31.000000 °	0.76 (-1.30%)	0.78 (1.30%)	0.77	0.77
	WT02	46.000000 °	-21.500000 °	0.73	0.74 (1.37%)	0.73	0.73
	WT03	46.000000 °	-40.500000 °	0.79	0.81 (2.53%)	0.78 (-1.27%)	0.79
	WT04	52.000000 °	-31.000000 °	0.77 (-3.75%)	0.84 (5.00%)	0.79 (-1.25%)	0.80
	WT05	52.000000 °	-21.500000 °	0.78 (-1.27%)	0.82 (3.80%)	0.76 (-3.80%)	0.79
	WT06	52.000000 °	-40.500000 °	0.81 (1.25%)	0.78 (-2.50%)	0.80	0.80
	WT07	40.000000 °	-31.000000 °	0.62 (-1.59%)	0.64 (1.59%)	0.63	0.63
	WT08	40.000000 °	-21.500000 °	0.55	0.55	0.54 (-1.82%)	0.55
	WT09	40.000000 °	-40.500000 °	0.70 (1.45%)	0.70 (1.45%)	0.67 (-2.90%)	0.69
	WT10	58.000000 °	-31.000000 °	0.81 (3.85%)	0.77 (-1.28%)	0.76 (-2.56%)	0.78
	WT11	58.000000 °	-12.000000 °	0.81 (3.85%)	0.75 (-3.85%)	0.78	0.78
	WT12	58.000000 °	-50.000000 °	0.73 (1.39%)	0.74 (2.78%)	0.70 (-2.78%)	0.72
	WT13	34.000000 °	-31.000000 °	0.44 (-4.35%)	0.49 (6.52%)	0.45 (-2.17%)	0.46
	WT14	34.000000 °	-12.000000 °	0.55 (-6.78%)	0.62 (5.08%)	0.60 (1.69%)	0.59
	WT15	34.000000 °	-50.000000 °	0.56 (1.82%)	0.57 (3.64%)	0.53 (-3.64%)	0.55
	WT16	46.000000 °	-12.000000 °	0.69 (7.81%)	0.62 (-3.13%)	0.62 (-3.13%)	0.64
	WT17	46.000000 °	-50.000000 °	0.75 (2.74%)	0.74 (1.37%)	0.71 (-2.74%)	0.73
				Average wind farm capacity factor	0.69

Table 7

 7 Design and characteristics of sailing wind turbines

		Unit	Value
	Barge dimensions
	Width	m	40
	Height	m	10
	Draft	m	7.5
	Displacement	tonnes	12,000
	Propellers
	Diameter	m	6
	Number of Blades	-	3
	Keel
	Surface Area	m2	15
	Wind turbine dimensions
	Rotor Diameter	m	78

Figure 29 Artist view of a sailing wind turbine. (Source:[71])

Table 8

 8 Existing route optimization methods and algorithms Along with the development of optimization methods and algorithms, various weather routing software are now available on the market. They include PredictWind, Adrena, TimeZero, FastSeas and QtVlm (not an exhaustive list) [79] to [83]. In this thesis, the weather routing software QtVlm [27] developed by Meltemus [83] was used.QtVlm is a free navigation and weather routing software developed for sailing boats[27]. Its primary function is to calculate routings and routes based on weather data and boat

	Optimization Methods /Algorithms	Principle / Implementation	Advantages	Drawbacks
		-Pontryagin's maximum principle	Powerful and elegant mathematical	-Difficult to apply in
		-Optimization based on dynamic programming	approach	practice
		-minimize or maximize functionals often		-need a lot of calculation
		expressed as integrals, in order to find		time and memory space
	Calculus variations	-the optimization is achieved through variation extremals		for objective functions
		of the parameters that control the trajectory
		-Numerical Euler-Lagrange equations solver
		equivalent
		-Using Forward Dynamic Programming method	Can provide the solution giving the	-Extensively depends on
		based on Bellman's principle of optimality	global minimum to an objective	the fineness of the grid
		-Deterministically solve the optimization	function and can easily incorporate the	system used for the
		problem with one objective function and	constraints into the routing algorithm.	computation
		several constraints		-needs many grid points
	Dynamic programming			for the search routine to
				obtain an accurate
				solution, thus it uses a
				lot of calculation time
				and memory space
		-practical method of route planning process	Convenient to obtain the isochrones by	Not applicable for
		-treats the ship routing as a discrete	hand	computer programs due to
		optimization problem		"isochrones loops" -an
	Isochrones method	-work on a set of connected points dependent on weather factors, that a ship can reach within a		irregularity in shape of an isochrone
		given time limit starting from one point and
		going in all possible directions.

Table 9

 9 Summary of the basic characteristics of the five reanalysis datasets (Source: Ramon et al. (2019) [92])

	Name	ERA-Interim	ERA5	JRA55	MERRA2	R1
	Institution	ECMWF	ECMWF	JMA	NASA GMAO	NOAA/NCEP and NCAR
		1979 to	1980 to	1978 to	1980 to present	1948 to present
		present	present	present
	Period coverage	(Dicontinued August 2019)	(Expande d back to 1950 in
			August
			2019)
	Time resolution	6-hr	1-hr	6-hr	1-hr	6-hr
	Horizontal grid spacing	0.75° × 0.75°	0.3° × 0.3°	1.25° × 1.25°	0.5° latitude × 0.625° longitude	1.875° latitude × 2° longitude

Table 10

 10 Sensitivity to weather routing numerical optimization parameters for the energy ship deployed in the North Atlantic Ocean for 2015.

	8° INITIAL SEARCH			WAYPOINTS
	STEP ANGLE	2	4	6	7	12	15
		CAPACITY FACTOR (%)	73	78	78	80	80	80
		AVERAGED
		FILLING	52	71	72	73	76	83
	10%	RATIO (%)
	INITIAL	SINGLE
	FILLING RATIO	ROUTE DURATION	114	147	150	147	153	169
		(h)
		ENERGY
		PRODUCED	10247.06 10874.22 10881.24 11168.75 11219.05 11218.52
		(MWh)

Table 11

 11 Capacity factor of the 1.6MW energy ship equipped with 174-hour storage capacity and 6-hour unloading time and optimization parameter of 8° search steps angle and 7 initial waypoints

	Year	-	2015	2016	2017
	Annual average capacity factor	%	79	82	82
	Best capacity factor over one route	%	99	99	97
	Worst capacity factor over one route	%	40	44	39
	Average route duration	Hour (s)	162	156	142
	Longest route duration	Hour (s)	398.5	393.2	437.3
	Shortest route duration	Hour (s)	22.5	22.9	38.6
	Longest route distance	NM	3921.2	5010.4	5187.0
	Shortest route distance	NM	460.8	470.3	752.8

Table 12

 12 Capacity factor, filling ratio and energy production for 1.6 MW energy ship equipped with 3 configurations of storage capacity and unloading time in North Atlantic ocean for 3 years(2015, 2016 & 2017)

				NORTH ATLANTIC OCEAN
						1.6 MW
			Capacity factor (CF) (%)	Filling ratio (FR) (%)	Energy Produced (MWh)
			8°; 7 WPs	Average over 3 years	8°; 7 WPs	Average over 3 years	8°; 7 WPs	Average over 3 years
	87h storage	2015	78.72		83.22		11005.05
	capacity / 3h unload time	2016 2017	81.81 81.67	80.73	84.10 80.43	82.59	11468.89 11418.72	11297.56
	174h	2015	79.48		76.78		11115.88
	storage capacity /	2016	81.97	81.19	76.41	74.37	11492.27	11362.90
	6h unload	2017	82.11		69.93		11480.54
	time
	339h	2015	74.51		60.09		10427.42
	storage capacity /	2016	79.62	76.48	54.20	53.91	11172.39	10713.43
	12h unload	2017	75.32		47.43		10540.49
	time

Table 13

 13 Capacity factor, filling ratio and energy production for 1 MW, 1.3 MW and 1.6 MW energy ship in North Atlantic ocean

	for 3 years (2015, 2016 & 2017)
							NORTH ATLANTIC OCEAN
						1MW	1.3MW	1.6MW
					8°; 7	Average	8°; 7 WPs	Average	8°; 7	Average
					WPs	over 3		over 3 years	WPs	over 3 years
						years
		Capacity factor (CF)	%	2015 82.70 2016 85.59 2017 84.72	84.34	78.35 82.93 82.60	81.29	79.48 81.97 82.11	81.19
		Filling ratio (FR)	%	2015 81.40 2016 79.67 2017 77.37	79.48	80.24 78.69 71.60	76.84	76.78 76.41 69.93	74.37
	174-hour storage	Energy Production	MWh	2015 7227.39 2016 7486.53 2017 7404.61	7372.84	8901.7241 9430.3331 9385.2152	9239.09	11115.88 11492.27 11480.54	11362.90
	capacity / 6-hour unloading time	Annual routes options	-	2015 2016 2017	51 54 55	53	49 53 58	53	52 54 59	55
		Average single		2015	165		172		162
		route duration exclude unloading time	h	2016 2017	156 153	158	159 145	159	156 142	153
		Total annual		2015 8739.48		8739.83		8741.17
		route duration including unloading time	h	2016 8746.81 2017 8740.33	8742.21	8747.10 8740.66	8742.53	8762.57 8739.01	8747.58

Table 15

 15 Comparison of optimized capacity factor, filling ratio and total energy produced for 1MW deployed at far offshore for 2 different boat performance polars.

	Design #01	Design #02
		Average		Average
	8°; 7 WPs	over 3	8°; 7 WPs	over 3
		years		years

Table 16

 16 Three-year average number of data points for two deployment locations available in

			Dataset 1
		6-hour storage	12-hour storage	24-hour storage	48-hour storage
		capacity and 1-	capacity and 2-	capacity and 4-	capacity and 8-
		hour unloading	hour unloading	hours unloading	hour unloading
		time	time	time	time
	Saint-Pierre-et-Miquelon	828	410	207	102
	Ile de Sein	728	374	187	99
	Dataset 2 is more precise and extensive as it corresponds to data with a time sample of 5

minutes. It was obtained by gathering the raw data produced by QtVlm for each route of the year 2017, both for Saint-Pierre-et-Miquelon and Ile de Sein. In addition to power production at each time step includes the true wind speed, true wind angle, and boat speed. It contains 89,634 number of data points for Saint-Pierre-et-Miquelon and 90,185 number of data points for Ile de Sein.

Table 18

 18 Results for the optimization of the capacity factor of the 1.6 MW energy ship in Saint-Pierre-et-Miquelon; equipped with 24 hours storage capacity and 4 hours unloading time and optimization parameter of 8° search steps angle and 2 initial waypoints

	Year	-	2015	2016	2017
	Annual average capacity factor	%	52	52	54
	Best capacity factor over one route	%	87	87	87
	Worst capacity factor over one route	%	16	11	13
	Average route duration	Hour (s)	38.3	38.9	37.4
	Longest route duration	Hour (s)	140.5	212.9	173.4
	Shortest route duration	Hour (s)	10.1	9.6	8.5
	Longest route distance	NM	1892.4	1331.8	1551.4
	Shortest route distance	NM	147.6	198.6	180.1
	Average filling ratio at the end of the	%	92.5	93.4	92.6
	routes

Table 19

 19

				1 MW	1.3 MW	1.6 MW
				8°; 2 WPs	Average over 3 years	8°; 2 WPs	Average over 3 years	8°; 2 WPs	Average over 3 years
		Capacity	2015	59.94		55.81		52.39
		factor (CF)	2016	59.08	58.83	54.77	55.13	52.31	52.80
	24h storage capacity / 4h	(%) Filling ratio (FR) (%)	2017 2015 2016 2017	57.46 94.38 94.54 95.06	94.66	54.80 94.30 93.85 93.63	93.93	53.70 92.48 93.39 92.59	92.82
	unload	Total	2015 5233.43		6328.34		7311.22
	time	annual energy	2016 5174.75	5142.74	6237.78	6263.42	7317.95	7378.73
		production	2017 5020.05		6224.14		7507.00
		(MWh)

Capacity factor, filling ratio and total annual energy production for 1MW, 1.3MW and 1.6MW energy ship in Saint-Pierre-et-Miquelon for 3 years

(2015, 2016 & 2017)

Table 20

 20 Capacity factor (%) of the 1.6 MW energy ship in Saint-Pierre-et-Miquelon with optimization parameter 8° search step angle & 2 number of waypoints and 4 storage capacity configurations.

		Capacity factor (CF)	Filling ratio (FR)	Total annual energy
			(%)	(%)	production (MWh)
		8°; 2 WPs	Average over 3 years	8°; 2 WPs	Average over 3 years	8°; 2 WPs	Average over 3 years
	6h storage	2015 55.97		93.96		7808.28
	capacity / 1h	2016 54.21	53.76	94.00	94.45	7593.11	7513.39
	unload time	2017 51.10		95.38		7138.79
	12h storage	2015 54.52		94.02		7620.28
	capacity / 2h	2016 53.29	53.16	94.73	94.49	7454.48	7432.68
	unload time	2017 51.68		94.72		7223.27
	24h storage	2015 52.39		92.48		7311.22
	capacity / 4h	2016 52.31	52.80	93.39	92.82	7317.95	7378.73
	unload time	2017 53.70		92.59		7507.00
	48h storage capacity / 8h	2015 49.72 2016 52.48	50.97	91.55 93.06	91.12	6955.43 7360.10	7021.69
	unload time	2017 50.70		88.74		6749.54
	The obtained capacity factors are in the range 50 to 54%. The best capacity factor is
	obtained for 6-hour storage capacity and 1-hour unloading time. Both capacity factor and total
	annual energy production decrease with increasing storage capacity and unloading time. The
	annual energy production for 24-hour storage capacity and 6-hour unloading time is 7% less than
	that with 6-hour storage capacity and 1-hour unloading time.

Table 21

 21

	Capacity factor for 1.6 MW floating offshore wind turbines in Saint-Pierre-et-Miquelon
		for 3 years (2015, 2016 & 2017)
		Latitude (N)	Longitude (W)	2015	2016	2017 AVERAGE
	FWT01	46.3 °	58.5°	62.7% 62.4% 60.3%	61.8%
	FWT02	46.3 °	55.5°	63.4% 62.7% 62.0%	62.7%
	FWT03	46.3 °	52.5°	65.8% 64.1% 64.0%	64.6%
	FWT04	44.3 °	58.5°	62.2% 62.6% 63.5%	62.8%
	FWT05	44.3 °	55.5°	63.2% 63.4% 64.0%	63.5%
	FWT06	44.3 °	52.5°	64.5% 65.1% 64.3%	64.6%
	FWT07	42.3 °	58.5°	60.5% 63.2% 63.6%	62.4%
	FWT08	42.3 °	55.5°	61.1% 63.2% 62.4%	62.2%
	FWT09	42.3 °	52.5°	61.2% 63.1% 61.5%	61.9%
						63.0%

Table 22

 22 Results for the optimization of the capacity factor of the 1.6 MW energy ship in Ile de Sein; equipped with 24 hours storage capacity and 4 hours unloading time and optimization parameter of 8° search steps angle and 2 initial waypoints

	Year	-	2015	2016	2017
	Annual average capacity factor	%	51	45	50
	Best capacity factor over one route	%	87	87	87
	Worst capacity factor over one route	%	7	6	11
	Average route duration	Hour (s)	40.6	46.1	41.8
	Longest route duration	Hour (s)	337.5	401.4	215.3
	Shortest route duration	Hour (s)	10.3	5.8	10.8
	Longest route distance	NM	3051.6	1959.1	2303.8
	Shortest route distance	NM	208.4	51	227.8
	Average filling ratio at the end of the	%	94.5	93.6	94.9
	routes

Table 23

 23 In this section, the effects of storage capacity and unloading time are investigated. As for

		Capacity factor, filling ratio and energy production for 1 MW, 1.3 MW and 1.6
	MW energy ship in Ile de Sein for 3 years (2015, 2016 & 2017)
			1 MW	1.3 MW	1.6 MW
			8°; 2 WPs	Average over 3 years	8°; 2 WPs	Average over 3 years	8°; 2 WPs	Average over 3 years
		Capacity	2015 59.49%		54.99%		50.87%
		factor	2016 51.61%	55.34%	47.98%	51.07%	44.85%	48.50%
	24h storage capacity / 4h unload	(CF) Filling ratio (FR)	2017 54.93% 2015 95.33% 2016 93.20% 2017 95.36%	94.63%	50.23% 95.28% 93.70% 95.20%	94.73%	49.77% 94.54% 93.61% 94.94%	94.36%
	time	Total	2015 5198.18		6246.38		7110.62
		annual energy production	2016 4522.51 2017 4786.45	4835.71	5465.55 5706.59	5806.17	6287.91 6959.51	6786.01

Table 24

 24 Capacity factor (%) of the 1.6 MW energy ship in Ile de Sein with optimization parameter 8° search step angle & 2 number of waypoints

		Capacity factor (CF)	Filling ratio (FR)	Energy Produced (MWh)
		8°; 2 WPs	Average over 3 years	8°; 2 WPs	Average over 3 years	8°; 2 WPs	Average over 3 years
	6h storage	2015 52.16%		96.07%		7291.28
	capacity / 1h	2016 45.79%	47.78%	94.84%	95.59%	6374.22	6670.25
	unload time	2017 45.40%		95.87%		6345.26
	12h storage	2015 52.56%		95.15%		7346.57
	capacity / 2h	2016 46.16%	48.94%	94.92%	95.33%	6471.27	6847.07
	unload time	2017 48.10%		95.91%		6723.37
	24h storage	2015 50.87%		94.54%		7110.62
	capacity / 4h	2016 44.85%	48.50%	93.61%	94.36%	6287.91	6786.01
	unload time	2017 49.77%		94.94%		6959.51
	48h storage capacity / 8h	2015 53.53% 2016 48.08%	50.20%	93.14% 92.43%	92.56%	7441.51 6744.09	7013.52
	unload time	2017 49.00%		92.10%		6854.96

Table 25

 25 Capacity factor for 1.6 MW floating offshore wind turbines in Ile de Sein for 3 years(2015, 2016 & 2017)

		Latitude (N)	Longitude (W)	2015	2016	2017	AVERAGE
	FWT01	49.0 °	6.0°	66.6% 60.8%	61.4%	62.9%
	FWT02	49.0 °	8.0°	68.1% 63.0%	63.0%	64.7%
	FWT03	49.0 °	10.0°	68.9% 65.2%	64.7%	66.3%
	FWT04	48.0 °	6.0°	64.5% 58.4%	58.4%	60.4%
	FWT05	48.0 °	8.0°	66.7% 61.3%	60.3%	62.8%
	FWT06	48.0 °	10.0°	67.9% 63.0%	62.0%	64.3%
	FWT07	47.0 °	6.0°	62.3% 56.8%	56.6%	58.6%
	FWT08	47.0 °	8.0°	65.5% 60.0%	59.0%	61.5%
	FWT09	47.0 °	10.0°	66.9% 61.8%	60.6%	63.1%
							62.7%

Table 26

 26 Annual average true wind speed and route options at three deployment locations for energy ship

		Saint-Pierre-et-Miquelon	Ile de Sein	North Atlantic Ocean
		TWS (m/s)	No. Routes	TWS (m/s)	No. Routes	TWS (m/s)	No. Routes
	2015	9.58	206	10.23	196	11.42	253
	2016	9.75	204	9.71	175	11.49	264
	2017	9.78	211	9.68	191	10.89	248
	Average of 3 years	9.70	207	9.87	187	11.27	255

Table A2 -

 A2 1 Summary of optimal numerical parameter for 1.6MW and 1.3MW rated energy ship with storage capacity of 24 hours including 4 hours unload time.

	Rated Power	Deployment Area	Best Initial optimization waypoints	Best optimization search step angle (°)	Best optimized capacity factor (%)
	1.6MW	Saint-Pierre-et-Miquelon	2	8	52.8
		Ile de Sein	2	6	49.3
	1.3MW	Saint-Pierre-et-Miquelon	2	6	55.6
		Ile de Sein	2	8	51.1

Table A2 -

 A2 2 shows the results of the optimized capacity factor for a 1.6MW energy ship deployed at Saint-Pierre-et-Miquelon and Ile de Sein for the year 2015, 2016 and 2017 and the average optimized capacity factor for those three years. TableA2-2 also shows a less than 1% difference between averaged optimized capacity factor with and without using the option of double the number of waypoints after the first

optimization. It shows that the optimized CF remains 52% for 1.6MW in Saint-Pierre-Et-Miquelon and 48% in Ile de Sein. Meanwhile, for 1.3MW, optimized CF remains 55% in Saint-Pierre-Et-Miquelon and 50% in Ile de Sein. Even though the filling ratio

Table A2 -

 A2 2 Comparison of CF optimized with and without using the parameter double waypoint after first optimization

	2.2		1.6MW (Saint-Pierre-et-Miquelon) 24h storage capacity / 4h unload; 8° / 4WP (Starting time 03:00:00)	1.6MW (Ile de Sein) 24h storage capacity / 4h unload; 8° / 4WP (Starting time 03:00:00)	03:00:00) (Starting time 4h unload; 8° / 4WP 24h storage capacity / et-Miquelon) 1.3MW (Saint-Pierre-	03:00:00) (Starting time 4h unload; 8° / 4WP 24h storage capacity / 1.3MW (Ile de Sein)
			FR	CF	FR	CF	FR	CF	FR	CF
		2015	98.07%	52.50%	98.63%	52.56%	99.53%	54.89%	98.46%	52.85%
	DOUBLE WAYPOINT AFTER	2016	98.20%	52.19%	96.98%	45.63%	98.34%	56.07%	97.49%	47.57%
	FIRST OPTIMIZATION	2017	97.87%	52.42%	98.65%	47.64%	98.02%	54.96%	98.80%	51.04%
		AVERAGE 98.05%	52.37%	98.09%	48.61%	98.63%	55.31%	98.25%	50.49%
		2015	92.48%	52.39%	93.53%	50.04%	94.30%	56.69%	95.28%	54.99%
	WITHOUT DOUBLE WAYPOINT AFTER	2016	93.39%	52.31%	93.54%	45.05%	93.85%	54.77%	93.70%	47.98%
	FIRST OPTIMIZATION	2017	92.59%	53.70%	94.94%	49.77%	93.63%	54.80%	95.20%	50.23%
		AVERAGE 92.82%	52.80%	94.00%	48.29%	93.93%	55.42%	94.73%	51.07%

. Sensitivity study on the energy ship's sailing capability

  In addition, the energy ship capacity factor optimization results may also depend on the physical parameters of the energy ship sailing capability as function on storage capacity and unload time and the rated power production the energy ship. The sensitivity studies in this section consist of two parts. First part investigating the sensitivity of storage capacity and unload time to the optimized capacity factor of the energy ship, and second part investigate the results of similar output in response to the different rated power of the energy ship. This sensitivity also was measured by the optimized capacity factor of an energy ship. The method of optimization using qtVlm weather routing software and three years of wind data, 2015, 2016 and 2017 are as presented in Chapter 2.

	2.2.1. Data and input parameters

For the first part, the sensitivity study assessed 4 clustered storage capacity and unload time; that are set to 6 hours storage capacity and 1 hour unload time; 12 hours storage capacity and 2 hour unload time; 24 hours storage capacity and 4 hour unload time; and 48 hours storage capacity and 8 hours unload time.

Table A2

 A2

	-3 Results for the optimization of the capacity factor (%) of the 1.6MW energy ship for optimization parameter 0.5° search step
			angle & 12 number of waypoints
			Saint-Pierre-et-Miquelon			Ile de Sein
		Capacity factor (CF)	Filling ratio (FR)	Capacity factor (CF)	Filling ratio (FR)
			Average		Average		Average		Average
		0.5°; 12 WPs	over 3		over 3	0.5°; 12 WPs	over 3		over 3
			years		years		years		years
	6h storage	51,79%		99.44%		48.09%		99.22%
	capacity / 1h	49,83%	49,78%	99.25%	99.29%	42.24%	44.18%	99.24%	99.20%
	unload time	47,72%		99.19%		42.21%		99.15%
	12h storage	46,36%		99.30%		46.05%		99.38%
	capacity / 2h	48,28%	47.32%	99.16%	99.24%	41.55%	43.30%	99.07%	99.03%
	unload time	47,33%		99.27%		42.30%		98.65%
	24h storage	41,78%		99.49%		41.55%		98.9%
	capacity / 4h	43,58%	43.20%	99.20%	99.36%	37.35%	39.47%	98.8%	98.75%
	unload time	44,24%		99.38%		39.50%		98.5%
	48h storage	41,86%		99.41%		39.94%		98.52%
	capacity / 8h	40,81%	41.81%	99.19%	99.02%	37.63%	37.91%	97.39%	98.42%
	unload time	42,77%		98.45%		36.17%		99.36%

Table A2 -

 A2 [START_REF] Capps | Global ocean wind power sensitivity to surface layer stability[END_REF] shows the results of the optimized capacity factor for the 1.6 MW energy ship in both Saint-Pierre-et-Miquelon and Ile de Sein for the year 2015, 2016 and 2017 and the average of 3 years. One can note that the numerical parameter is set to an 8° search step angle and two initial optimization waypoints.

	As presented in Table A2-4, the optimized capacity factor obtained in Saint-
	Pierre-et-Miquelon shows that the highest three years averaged optimized capacity
	factor of 53.8% obtained by the smallest storage capacity of 6 hours and 1 hour unload
	time. Then followed by 12 hours storage capacity and 2 hours unload time (53.2%), 24
	hours storage capacity and 4 hours unload time (52.8%), then 48 hours storage capacity
	and 8 hours unload time (50.9%).
	Meanwhile, in Ile de Sein, the result shows fluctuated averaged optimized
	capacity factor. As might see, the highest optimized capacity factor, 50.2%, was
	obtained by 48 hours storage capacity and 8 hours unload time, followed by 12 hours
	storage capacity and 2 hours unload time (48.9%) and 24 hours storage capacity and 4
	hours unload time. The smallest optimized capacity factor of 47.8% was obtained by the
	smallest storage capacity, 6 hours storage capacity and 1 hour unload time. The filling
	ratios for all storage capacity at both deployment locations remain considerably high,
	which are more than 92%.

Table A2 -

 A2 [START_REF] Capps | Global ocean wind power sensitivity to surface layer stability[END_REF] Results for the optimization of the capacity factor (%) of the 1.6 MW energy ship for optimization parameter 8° search step angle & 2 number of waypoints

			Saint-Pierre-et-Miquelon			Ile de Sein
		Capacity factor (CF)	Filling ratio (FR)	Capacity factor (CF)	Filling ratio (FR)
			Average		Average		Average		Average
		8°; 2 WPs	over 3		over 3	8°; 2 WPs	over 3		over 3
			years		years		years		years
	6h storage	55.97%		93.96%		52.16%		96.07%
	capacity / 1h	54.21%	53.76%	94.00%	94.45%	45.79%	47.78%	94.84%	95.59%
	unload time	51.10%		95.38%		45.40%		95.87%
	12h storage	54.52%		94.02%		52.56%		95.15%
	capacity / 2h	53.29%	53.16%	94.73%	94.49%	46.16%	48.94%	94.92%	95.33%
	unload time	51.68%		94.72%		48.10%		95.91%
	24h storage	52.39%		92.48%		50.87%		94.54%
	capacity / 4h unload time	52.31% 53.70%	52.80%	93.39% 92.59%	92.82%	44.85% 49.77%	48.50%	93.61% 94.94%	94.36%
	48h storage	49.72%		91.55%		53.53%		93.14%
	capacity / 8h	52.48%	50.97%	93.06%	91.12%	48.08%	50.20%	92.43%	92.56%
	unload time	50.70%		88.74%		49.00%		92.10%

Table A2 -

 A2 [START_REF] Archer | Evaluation of global wind power[END_REF] shows the results of the optimized capacity factor for the 1.6 MW energy ship in both Saint-Pierre-et-Miquelon and Ile de Sein for the year 2015, 2016 and 2017 and the average of 3 years. The numerical parameter is set to 16° search step angle and two initial optimization waypoints in this sensitivity analysis.

	As shown, in Saint-Pierre-et-Miquelon, the highest three years averaged
	optimized capacity factor of 53.5% obtained by the smallest storage capacity of 6 hours
	and 1 hour unload time. Then followed by 12 hours storage capacity and 2 hours unload
	time (53.4%), 24 hours storage capacity and 4 hours unload time (52.5%), then 48 hours
	storage capacity and 8 hours unload time (50.2%).
	Meanwhile, in Ile de Sein, the optimized capacity factor obtained using 16°
	search step angle, and two initial optimization waypoints show a fluctuated result. One
	can see that the optimized capacity factor obtained for 12 hours storage capacity and 2
	hours unload time is 49% and 48 hours storage capacity and 8 hours unload is 49.9%.

The optimized capacity factors are higher than 12 hours storage capacity and 2 hours unload time (47.5%) and 24 hours storage capacity and 4 hours unload time (47.9%).

The highest three years averaged optimized capacity factor of 49.9% was obtained by the largest storage capacity of 48 hours and 8 hour unload time.

Table A2 -

 A2 5 Results for the optimization of the capacity factor (%) of the 1.6 MW energy ship for optimization parameter 16° search step angle & 2 number of waypoints

	Next, same with the sensitivity analysis done for the 1.6MW energy ship, the
	same sensitivity analysis was done for the 1.3MW energy ship. Table A2-6 shows the
	Saint-Pierre-et-Miquelon optimized capacity factor results with a set numerical parameter of 0.5° search step	Ile de Sein
	Capacity factor (CF) Average angle and 12 initial optimization waypoints.	Filling ratio (FR) Average	Capacity factor (CF) Average	Filling ratio (FR) Average
	16°; 2 WPs As shown in Table A2-6, in Saint-Pierre-et-Miquelon, the highest three-year over 3 years over 3 years 16°; 2 WPs 6h storage capacity / 1h 55.98% 53.46% 93.89% 94.38% 51.54% averaged optimized capacity factor of 52.6% was obtained by the smallest storage 53.73% 94.36% 45.21% capacity of 6 hours and 1 hour unload time. And then followed by 12 hours storage capacity and 2 hours unload time (50.7%), 24 hours storage capacity and 4 hours unload unload time 50.66% 94.88% 45.77% time (47.2%), then 48 hours storage capacity and 8 hours unload time (45.4%).	over 3 years 47.51%	96.28% 94.53% 96.41%	over 3 years 95.74%
	55.07% 53.03% Meanwhile, in Ile de Sein, the optimized capacity factor also shows a decreasing 93.23% 53.05% 12h storage capacity / 2h 53.43% 93.74% 93.73% 45.85%	49.02%	95.82% 94.03%	95.37%
	unload time pattern as the storage capacity and unloads time increase. One can see that the optimized 52.18% 94.25% 48.15%		96.27%
	52.92% capacity factor obtained for 6 hours storage capacity and 1 hour unload time obtained 93.07% 50.93% 24h storage capacity / 4h 52.51% 93.51% 52.90% 93.78% 44.54% the highest optimized capacity factor (46.6%). It then followed by 12 hours storage	47.96%	95.08% 93.55%	94.31%
	unload time capacity and 2 hours unload time (45.2%) and 24 hours storage capacity and 4 hours 51.70% 93.69% 48.41% unload time (41.1%). However, a small increment of the optimized capacity factor as		94.31%
	48h storage the storage capacity increased to 48 hours storage capacity and 8 hours unload time 49.18% 94.22% 52.96%		92.95%
	capacity / 8h (41.3%) which only marked a 0.2% difference with 24 hours storage capacity and 4 50.24% 93.70% 50.92% 94.76% 46.97% unload time 50.62% 92.13% 49.90% hours unload. The filling ratios for all storage capacity at both deployment locations	49.94%	93.12% 93.71%	93.26%
	remain higher which most are at 99%.

Table A2 -

 A2 [START_REF] Possner | Geophysical potential for wind energy over the open oceans[END_REF] Results for the optimization of the capacity factor (%) of the 1.3 MW energy ship for optimization parameter 0.5° search step angle & 12 number of waypoints

			Saint-Pierre-et-Miquelon			Ile de Sein
		Capacity factor (CF)	Filling ratio (FR)	Capacity factor (CF)	Filling ratio (FR)
		0.5°; 12 WPs	Average over 3 years		Average over 3 years	0.5°; 12 WPs	Average over 3 years		Average over 3 years
	6h storage	55.12%		99.40%		50.39%		99.41%
	capacity / 1h	52.55%	52.62%	99.53%	99.43%	44.38%	46.61%	99.18%	99.29%
	unload time	50.20%		99.35%		45.07%		99.29%
	12h storage	51.83%		99.29%		47.54%		99.57%
	capacity / 2h	50.45%	50.72%	99.44%	99.26%	42.76%	45.15%	99.40%	99.37%
	unload time	49.88%		99.06%		45.15%		99.15%
	24h storage	45.81%		99.22%		43.76%		98.89%
	capacity / 4h	47.40%	47.18%	99.23%	99.26%	38.76%	41.10%	98.94%	98.73%
	unload time	48.34%		99.34%		40.77%		98.35%
	48h storage	44.83%		99.24%		45.52%		99.80%
	capacity / 8h	43.75%	45.41%	99.77%	99.20%	40.28%	41.33%	98.42%	99.15%
	unload time	47.66%		98.60%		38.20%		99.22%

Table A2 -

 A2 7 shows the results of the optimized capacity factor for the 1.3 MW energy ship in both Saint-Pierre-et-Miquelon and Ile de Sein for the year 2015, 2016 and 2017 and the average of 3 years. The numerical parameter is set to an 8° search step angle and two initial optimization waypoints in this sensitivity analysis. As shown, in Saint-Pierre-et-Miquelon, the highest three years averaged optimized capacity factor of 56.5% obtained by the smallest storage capacity of 6 hours and 1 hour unload time. It then followed by 12 hours storage capacity and 2 hours unload time (55.7%), 24 hours storage capacity and 4 hours unload time (55.1%), then 48 hours storage capacity and 8 hours unload time (54.6%). Overall at Saint-Pierre-et-Miquelon, the optimized capacity factor decrease as the storage capacity increases. Contrary to Ile de Sein, the optimized capacity factor increases as storage capacity and unload time increase. The optimized capacity factor obtained for 48 hours and 8 hours unload time shows the highest value of 53% capacity factor. It then followed by 24 hours and 4 hours unload time (51.1%) and 12 hours and 2 hours unload time (51%). The lowest three-year averaged optimized capacity factor of 49.9% was obtained by the smallest storage capacity of 6 hours and 1 hour unload time.

Table A2 -

 A2 7 Results for the optimization of the capacity factor (%) of the 1.3 MW energy ship for optimization parameter 8° search step angle & 2 number of waypoints

			Saint-Pierre-et-Miquelon			Ile de Sein
		Capacity factor (CF)	Filling ratio (FR)	Capacity factor (CF)	Filling ratio (FR)
			Average		Average		Average		Average
		8°; 2 WPs	over 3		over 3	8°; 2 WPs	over 3		over 3
			years		years		years		years
	6h storage	59.07%		94.36%		54.01%		96.25%
	capacity / 1h unload time	57.16% 53.31%	56.51%	95.76% 95.84%	95.32%	47.36% 48.47%	49.95%	96.03% 95.92%	96.07%
	12h storage	58.15%		93.52%		54.68%		96.04%
	capacity / 2h unload time	55.41% 53.67%	55.74%	94.19% 95.19%	94.30%	47.86% 50.45%	51.00%	94.65% 96.52%	95.74%
	24h storage	55.81%		94.30%		54.99%		95.28%
	capacity / 4h unload time	54.77% 54.80%	55.13%	93.85% 93.63%	93.93%	47.98% 50.23%	51.07%	93.70% 95.20%	94.73%
	48h storage	56.18%		93.76%		56.29%		94.29%
	capacity / 8h unload time	54.92% 52.62%	54.57%	92.85% 91.28%	92.63%	50.81% 51.99%	53.03%	93.62% 92.99%	93.63%

Table A2 -

 A2 [START_REF]Wind Obsevatory: Analysis of the French wind power industry: market, jobs and challenges[END_REF] shows the results of the optimized capacity factor for the 1.3 MW energy ship in both Saint-Pierre-et-Miquelon and Ile de Sein for the year 2015, 2016 and 2017 and the average of 3 years. The numerical parameter is set to 16° search step angle and two initial optimization waypoints in this sensitivity analysis.As shown in Saint-Pierre-et-Miquelon, the optimized capacity factor obtained shown a decreasing pattern as the storage capacity and unload time increases. The optimized capacity factor obtained for 6 hours storage capacity and 1 hour unload time obtained the highest optimized capacity factor (56.3%), followed by 12 hours storage capacity and 2 hours unload time (56.1%) and 24 hours storage capacity and 4 hours unload time (55.1%). Even so, there is a small increment of 0.1% to the optimized capacity factor for the 48 hours storage capacity and 8 hours unload time (55.2%).Unlike the capacity factor obtained in Saint-Pierre-et-Miquelon, the optimized capacity factors obtained in Ile de Sein fluctuated. One can see that the highest optimized capacity factor was acquired by the largest storage capacity of 48 hours and 8 hours unload time, with a value of 52.3%. The second highest optimized capacity factor obtained for 12 hours storage capacity and 2 hours unload time, 51.2% and followed by 24 hours storage capacity and 4 hours unload, 50.1%. The lowest threeyear averaged optimized capacity factor of 49.9% was obtained by the smallest storage capacity of 6 hours and 1 hour unloads time. The filling ratios for all storage capacity at both deployment locations remain higher, which are more than 93%.

Table A2 -

 A2 8 Results for the optimization of the capacity factor (%) of the 1.3 MW energy ship for optimization parameter 16° search step angle & 2 number of waypoints 2 hour unloading time, 24 hours storage capacity 4 hour unloading time and 48 hours storage capacity 8 hour unloading time.

	hours storage capacity
			Saint-Pierre-et-Miquelon			Ile de Sein
		Capacity factor (CF)	Filling ratio (FR)	Capacity factor (CF)	Filling ratio (FR)
		16°; 2 WPs	Average over 3 years		Average over 3 years	16°; 2 WPs	Average over 3 years		Average over 3 years
	6h storage	59.24%		94.93%		53.04%		96.79%
	capacity / 1h	56.75%	56.32%	95.73%	95.58%	47.95%	49.87%	95.34%	96.19%
	unload time	52.98%		96.08%		48.63%		96.43%
	12h storage	57.92%		94.51%		53.98%		95.48%
	capacity / 2h	55.39%	56.07%	94.31%	94.40%	48.96%	51.18%	94.69%	95.32%
	unload time	54.90%		94.37%		50.60%		95.80%
	24h storage	56.10%		92.78%		52.97%		94.89%
	capacity / 4h	54.69%	55.12%	93.51%	93.47%	48.04%	50.58%	93.29%	94.50%
	unload time	54.57%		94.11%		50.72%		95.32%
	48h storage	54.09%		92.64%		55.23%		94.03%
	capacity / 8h	55.67%	55.23%	93.19%	93.08%	49.09%	52.34%	93.68%	93.66%
	unload time	55.94%		93.41%		52.71%		93.28%

Table A2 -

 A2 [START_REF] Musial | Offshore Wind Energy Resource Assessment for the United States[END_REF] Filling ratio for 1 MW, 1.3 MW, 1.6 MW and 1.9 MW energy ship in Saint Pierre et Miquelon for 3 years(2015, 2016 & 2017)

				SAINT-PIERRE-ET-MIQUELON
		1MW	1.3MW	1.6MW	1.9MW
		Filling ratio (FR)	Filling ratio (FR)	Filling ratio (FR)	Filling ratio (FR)
		8°; 2 WPs	Average over 3 years	8°; 2 WPs	Average over 3 years	8°; 2 WPs	Average over 3 years	8°; 2 WPs	Average over 3 years
	6h storage capacity / 1h unload time	95.92% 96.02% 96.78%	96.24%	94.36% 95.76% 95.84%	95.32%	93.96% 94.00% 95.38%	94.45%	92.01% 92.29% 92.53%	92.28%
	12h storage capacity / 2h unload time	94.82% 95.31% 94.96%	95.03%	93.52% 94.19% 95.19%	94.30%	94.02% 94.73% 94.72%	94.49%	90.84% 90.14% 91.68%	90.89%
	24h storage capacity / 4h unload time	94.38% 94.54% 95.06%	94.66%	94.30% 93.85% 93.63%	93.93%	92.48% 93.39% 92.59%	92.82%	88.61% 89.08% 90.02%	89.24%
	48h storage capacity / 8h unload time	94.18% 92.70% 93.76%	93.55%	93.76% 92.85% 91.28%	92.63%	91.55% 93.06% 88.74%	91.12%	90.43% 92.14% 91.77%	91.45%

Table A2 -

 A2 [START_REF] Shoaib | Assessment of wind energy potential using wind energy conversion system[END_REF] Energy production for 1 MW, 1.3 MW, 1.6 MW and 1.9 MW energy ship in Saint Pierre et Miquelon for 3 years(2015, 2016 & 2017)

				SAINT-PIERRE-ET-MIQUELON
		1MW	1.3MW	1.6MW	1.9MW
		Energy Produced	Energy Produced	Energy Produced	Energy Produced
		(MWh)	(MWh)	(MWh)	(MWh)
		8°; 2 WPs	Average over 3 years	8°; 2 WPs	Average over 3 years	8°; 2 WPs	Average over 3 years	8°; 2 WPs	Averag e over 3 years
	6h storage capacity / 1h unload time	5540.40 5278.93 4994.72	5271.35	6698.67 6504.34 6053.73	6418.91	7808.28 7593.11 7138.79	7513.39	8528.06 8291.47 7994.03	8271.1 9
	12h storage capacity / 2h unload time	5378.78 5315.07 5010.26	5234.70	6602.15 6310.42 6093.66	6335.41	7620.28 7454.48 7223.27	7432.68	8353.49 8145.28 7971.70	8156.8 2
	24h storage capacity / 4h unload time	5233.43 5174.75 5020.05	5142.74	6328.34 6237.78 6224.14	6263.42	7311.22 7317.95 7507.00	7378.73	8117.98 7913.40 7891.85	7974.4 1
	48h storage capacity / 8h unload time	5058.90 5203.70 5127.34	5129.98	6380.05 6258.66 5979.44	6206.05	6955.43 7360.10 6749.54	7021.69	8074.31 7819.52 8121.27	8005.0 4

Table A2 -

 A2 [START_REF] Dupont | Global available wind energy with physical and energy return on investment constraints[END_REF] Capacity factor for 1 MW, 1.3 MW, 1.6 MW and 1.9 MW energy ship in Ile de Sein for 3 years(2015, 2016 & 2017)

					ILE DE SEIN
		1MW	1.3MW	1.6MW	1.9MW
		Capacity factor (CF)	Capacity factor (CF)	Capacity factor (CF)	Capacity factor (CF)
			Average		Average		Average		Average
		8°; 2 WPs	over 3	8°; 2 WPs	over 3	8°; 2 WPs	over 3	8°; 2 WPs	over 3
			years		years		years		years
	6h storage capacity / 1h unload time	57.60% 50.96% 52.87%	53.81%	54.01% 47.36% 48.47%	49.95%	52.16% 45.79% 45.40%	47.78%	48.02% 42.04% 42.54%	44.20%
	12h storage capacity / 2h unload time	58.36% 52.48% 54.15%	55.00%	54.68% 47.86% 50.45%	51.00%	52.56% 46.16% 48.10%	48.94%	50.40% 42.42% 44.52%	45.78%
	24h storage capacity / 4h unload time	59.49% 51.61% 54.93%	55.34%	54.99% 47.98% 50.23%	51.07%	50.87% 44.85% 49.77%	48.50%	47.85% 41.57% 46.12%	45.18%
	48h storage capacity / 8h unload time	58.38% 52.99% 54.44%	55.27%	56.29% 50.81% 51.99%	53.03%	53.53% 48.08% 49.00%	50.20%	48.84% 45.38% 44.67%	46.30%
									177

Table A2 -

 A2 [START_REF] Jamil | Investigation of the Capacity Factor of Weather-Routed Energy Ships Deployed in the Near-Shore[END_REF] Filling ratio for 1 MW, 1.3 MW, 1.6 MW and 1.9 MW energy ship in Ile de Sein for 3 years(2015, 2016 & 2017)

					ILE DE SEIN
		1MW	1.3MW	1.6MW	1.9MW
		Filling ratio (FR)	Filling ratio (FR)	Filling ratio (FR)	Filling ratio (FR)
			Average		Average		Average		Average
		8°; 2 WPs	over 3	8°; 2 WPs	over 3	8°; 2 WPs	over 3	8°; 2 WPs	over 3
			years		years		years		years
	6h storage capacity / 1h unload time	96.99% 95.78% 96.85%	96.54%	96.25% 96.03% 95.92%	96.07%	96.07% 94.84% 95.87%	95.59%	95.22% 92.60% 95.06%	94.29%
	12h storage capacity / 2h unload time	95.75% 95.21% 96.26%	95.74%	96.04% 94.65% 96.52%	95.74%	95.15% 94.92% 95.91%	95.33%	94.49% 92.62% 94.19%	93.77%
	24h storage capacity / 4h unload time	95.33% 93.20% 95.36%	94.63%	95.28% 93.70% 95.20%	94.73%	94.54% 93.61% 94.94%	94.36%	94.54% 93.17% 91.23%	92.98%
	48h storage capacity / 8h unload time	93.18% 94.79% 95.28%	94.42%	94.29% 93.62% 92.99%	93.63%	93.14% 92.43% 92.10%	92.56%	91.80% 88.29% 89.43%	89.84%

Table A2 -

 A2 [START_REF] Smith | Offshore Wind Technologies Market[END_REF] Energy production for 1 MW, 1.3 MW, 1.6 MW and 1.9 MW energy ship in Ile de Sein for 3 years(2015, 2016 & 2017)

					ILE DE SEIN
		1MW	1.3MW	1.6MW	1.9MW
		Energy Produced	Energy Produced	Energy Produced	Energy Produced
		(MWh)	(MWh)	(MWh)	(MWh)
			Average		Average		Average		Average
		8°; 2 WPs	over 3	8°; 2 WPs	over 3	8°; 2 WPs	over 3	8°; 2 WPs	over 3
			years		years		years		years
	6h storage capacity / 1h unload time	5031.44 4463.96 4618.31	4704.57	6134.21 5393.17 5504.99	5677.46	7291.28 6374.22 6345.26	6670.25	7971.64 6996.50 7061.79	7343.31
	12h storage capacity / 2h unload time	5099.12 4597.67 4731.36	4809.38	6209.07 5451.36 5730.74	5797.06	7346.57 6471.27 6723.37	6847.07	8366.94 7060.50 7390.26	7605.90
	24h storage capacity / 4h unload time	5198.18 4522.51 4786.45	4835.71	6246.38 5465.55 5706.59	5806.17	7110.62 6287.91 6959.51	6786.01	7944.41 6921.95 7657.57	7507.98
	48h storage capacity / 8h unload time	5103.53 4644.52 4758.90	4835.65	6358.63 5790.10 5909.04	6019.26	7441.51 6744.09 6854.96	7013.52	8111.99 7558.81 7420.89	7697.23

Dupont et al. [START_REF] Dupont | Global available wind energy with physical and energy return on investment constraints[END_REF] estimated a capacity factor of 39% for floating offshore wind farms.

Nevertheless, the average capacity factor of the Hywind Scotland offshore wind farm throughout 2 years of operation is 54%, which is significantly greater [START_REF] Equinor | Hywind Scotland remains the UK's best performing offshore wind farm[END_REF].

Far-offshore

Dupont et al. (2017)'s study

Dupont et al. [START_REF] Dupont | Global available wind energy with physical and energy return on investment constraints[END_REF] seems to be the first at having performed an extensive assessment of the theoretical global wind potential, including an estimation of the capacity factor of floating wind farms which would be deployed in the far-offshore (see Figure 25).

They considered wind turbines of 120 m diameter offshore (5 MW wind turbines). They used 3 m/s for the cut-in speed, 25 m/s for the cut-out speed, and 11 m/s for the rated wind speed.

The wind speed distribution at 100 m hub height from the ERA interim dataset has been used. As the ERA interim dataset does not include the wind speed at hub height, the arithmetic mean of wind Figure 25 Global map of capacity factor estimates (Source: Dupont et al [START_REF] Dupont | Global available wind energy with physical and energy return on investment constraints[END_REF]) Numerous route optimization methods and algorithms are available to deal with route planning problems [START_REF] Walther | Modeling and optimization algorithms in ship weather routing International[END_REF] [START_REF] Wang | Voyage Optimization Algorithms for Ship Safety and Energy-Efficiency[END_REF].

Walther in [START_REF] Walther | Modeling and optimization algorithms in ship weather routing International[END_REF] presents a comprehensive comparative overview of existing optimization methods for ship weather routing. They include calculus of variations [START_REF] Bijlsma | On Minimal-Time Ship Routing[END_REF], 3D Dynamic programming (Shao, Zhou & Thong, 2012) [START_REF] Walther | Modeling and optimization algorithms in ship weather routing International[END_REF], dynamic programming method (Wit,1990) [START_REF] Walther | Modeling and optimization algorithms in ship weather routing International[END_REF], Iterative dynamic programming (Luus, 2000) [START_REF] Walther | Modeling and optimization algorithms in ship weather routing International[END_REF], isopone method (Klompstra et al.,1992) [START_REF] Walther | Modeling and optimization algorithms in ship weather routing International[END_REF], original isochrones method (James, 1957) [START_REF] Walther | Modeling and optimization algorithms in ship weather routing International[END_REF], modified isochrone method [START_REF] Hagiwara | Weather routing of (sail-assisted) motor vessels[END_REF] [START_REF] Szlapczynska | Adopted isochrone method improving ship safety in weather routing with evolutionary approach[END_REF], 3D modified isochrones method (Lin et al. 2013) [START_REF] Walther | Modeling and optimization algorithms in ship weather routing International[END_REF], Dijkstra's algorithm (Dijkstra, 1959;Padhy et al, 2008) [START_REF] Walther | Modeling and optimization algorithms in ship weather routing International[END_REF], real coded genetic algorithm (Maki et al.

2011) [START_REF] Walther | Modeling and optimization algorithms in ship weather routing International[END_REF], Pareto-optimized multi objective genetic algorithm [START_REF] Hinnenthal | Robust Pareto Optimum Routing of Ships Utilizing Deterministic and Ensemble Weather Forecasts[END_REF], multi objective evolutionary algorithm, SIMPLEX algorithm, DIRECT (Diving Rectangles) method and many more [START_REF] Walther | Modeling and optimization algorithms in ship weather routing International[END_REF]. They are summarized in Table 8 along with their advantages and drawbacks (not an exhaustive list).

Route optimization algorithm

Constraints

Traffic lanes

Emission control areas

Land avoidance

Shallow water areas

Meteorological data

Fuel consumption

Ship motion Ship fatigue damage

Ship performance model

Optimal route with objective

ETA

Minimum fuel consumption

Maximum ship safety

Figure 30 Overview of route optimization system (Source: [START_REF] Wang | Voyage Optimization Algorithms for Ship Safety and Energy-Efficiency[END_REF])

QtVlm starts the optimization process by creating a draft route which starts and comes back to the one and same point. The heading is set such as the true wind angle is 90° and the ship goes in the initial route direction (North, West, South or East) set in the QtVlm configuration.

A first point is created in that direction at a distance equal to one-hour sailing. Next, the return trip is generated and the capacity factor and filling ratio at the time of arrival are calculated.

If the filling ratio is less than the target initial filling ratio, QtVlm repeats the process, starting from the last point created, until the target initial filling ratio is reached. Then, all the waypoints are removed except the last one. Finally, waypoints are inserted regularly along the path to match the target number of waypoints parameter.

Last but not least, the draft route generated by this process is optimized using a SIMPLEX optimization algorithm as discussed in subsection 3.1.2.2. Figure 39 shows the example of the initially generated draft route (left) and the route after optimization (right). The draft route duration is 36.58 hours and the initial capacity factor is 59%. After the optimization, the route duration has reduced to 24.83 hours and the capacity factor has increased to 83%. The optimized data is saved in the route logbook and route comparator table. The data can be exported in .csv format.

The details of QtVlm setting and configurations are presented in Appendix 2.

Figure 39 Example of the draft route initially generated (left) by QtVlm and the route after optimization (right).

Sensitivity to storage duration and unloading time

In this section, the sensitivity of the capacity factor to storage capacity and unloading time is investigated.

According to Eq. 4, the maximum capacity factor is:

Where we recall that N is the storage capacity (in hours at rated power) and T0 is the unloading time (in hours). For 7 days storage capacity (174 hours) and 6 hours unloading time, the maximum capacity factor is 96.7%.

Three configurations of storage capacity and unloading time have been considered:

• 3.5-days (87-hours) storage capacity and 3-hour unloading time,

• 7-days (174-hours) storage capacity and 6-hour unloading time,

• 14-days (339-hours) storage capacity and 12-hour unloading time

The unloading time was set proportional to the storage capacity in order to keep the same maximum capacity factor for all the configurations.

Table 12 shows the capacity factor, filling ratio and the annual energy production production for the years 2015, 2016 and 2017; and for the three years average for each configuration.

Capacity factors are in the range 75 to 81%. The best average capacity factor is obtained by the energy ship equipped with 174hours storage capacity (81.2%). The worst capacity factor is obtained for the energy ship with the greatest storage capacity (339 h). This result is unexpected as one could expect that the greatest capacity factor would have been obtained for the energy ship with the greatest storage capacity. It can be explained by the fact that the optimization method in qtVlm may not always converge to the global optimum.

3.7% greater. Therefore, regarding the cost of energy, it is unlikely that the benefit of the greater capacity factor of the 1 MW energy ship outweighs the loss in revenues due to smaller energy production. This is even more the case for the 1.3 MW energy ship as it has almost the same capacity factor as the 1.6 MW energy ship but 20% less energy production. Those results indicate that an energy ship with 1.9 MW or even greater rated power may actually lead to better economic performance.

CHAPTER 4 INVESTIGATION OF THE CAPACITY FACTOR OF WEATHER-ROUTED ENERGY SHIPS DEPLOYED IN THE NEAR-SHORE

In this chapter, we explore the capacity factor of weather-routed energy ships deployed nearshore and with batteries energy storage. Two case studies are considered: the island of Ile de

Sein and the archipelago of Saint-Pierre-et-Miquelon.

Results show that weather routed optimization for 1.6 MW energy ship at nearshore can achieve an average capacity factor of 43% at Saint-Pierre-et-Miquelon and 39% at Ile de Sein for the three years of 2015, 2016 and 2017. Such capacity factors are similar to that of currently operating offshore wind farms.

This chapter also investigates the sensitivity of the optimization results on the numerical optimization parameters (number of initial optimization waypoints and search step angle). At present, power supply for these islands is based on fossil fuels (diesel generation), which is an issue because of GHG emissions (over 700 gCO2/kWh). Moreover, as the fuel is imported and as the size of the power plants are relatively small, the electricity generation cost is very high (of the. order of 509 €/MWh at Saint-Pierre-et-Miquelon according to the Commission de Régulation de l'Energie (CRE, French Energy Regulation Authority)) [START_REF]Délibération n°136/2018: Programmation pluriannuelle de l'energie pour l'archipel de Saint-Pierre[END_REF].

The sensitivity to storage capacity and unloading time effect on the capacity factor was also investigated. The results were also found to be sensitive to these parameters. Surprisingly, in Saint-Pierre-et-Miquelon, the shortest batteries storage capacity was found to give the best capacity factors. This indicates that the optimization method which is currently implemented in QtVlm converges to a local optimum and not the global optimum.

For the statistical analysis of the energy production, we found that the pattern and energy production trends were the same at both deployment locations, Saint-Pierre-et-Miquelon and Ile de Sein. It is shown that the energy ship produced maximum power for all storage capacity configurations at most of the delivery time. However, there is also a relatively high route frequency, which is close to zero energy production compared to other energy production classes. This result can be explained by weaker wind or no wind available at a particular time, especially in the Summertime.

Comparisons of the energy ship's capacity factor and stationary offshore wind turbines deployed at same area were performed. The average capacity factor of offshore wind turbines in both Saint-Pierre-et-Miquelon and Ile de Sein is 63%. It is 20% greater than that of energy ships. Other than route comparator, route logs displayed the data of true wind speed (TWS), true wind direction (TWD), true wind angle (TWA), apparent wind speed (AWS), apparent wind angle (AWA), capacity factor, filling ratio, and energy produced for specified time interval. The interval time can be specified with minimum of 5 minutes and the logs can be exported in CSV format.

APPENDIX 2 SENSITIVITY STUDIES TO OPTIMIZATION PARAMETERS

It is essential to conduct an in-depth study of all the optimization parameters.

Therefore, in this chapter, sensitivity studies have been performed to investigate the sensitivity of the optimized capacity factor of the energy ship in response to the two main parameters, the energy ship's sailing capability and numerical optimization parameter. This chapter consists of two parts of sensitivity analysis. The first part is the numerical optimization parameters as a function of the number of initial optimization waypoints and search step angle. In this part also present the analysis of effect of using double waypoint after first optimization option in QtVlm (presented in Chapter 1). The second part is the sensitivity of the energy ship's sailing capability as a function of the storage capacity aboard and unloads time and different rated power of the energy ship.

This chapter presents the sensitivity analysis results compare and defines the best and optimal parameter to obtain the optimal capacity factor of an energy ship. It is shown that the optimization with optimal parameters input makes it possible to significantly increase the optimized capacity factor obtained by the energy ship proposed in this thesis.

Sensivity to numerical parameters

Data and input parameters

The energy ship capacity factor optimization results may also depend on the numerical optimization parameters as a function of the initial optimization search step angle and initial optimization waypoints.

This sensitivity study involved 1.6MW and 1.3MW rated energy ship both respectively deployed at Saint-Pierre-et-Miquelon and Ile de Sein. Except for a single case of a 1.6MW energy ship in Saint-Pierre-et-Miquelon which extended the analysis increased significantly to more than 98% by doubled the waypoints than without double the waypoint after the first optimization, the filling ratio remains high, which more than 92% achieved.

Therefore, the difference is negligible. Since considering double the number of waypoint after first optimization cause slightly higher simulation time, the option of double the number of waypoints after the first optimization is not taken into account for the remaining energy ship's weather routing optimization in this study.

Thus, the decreases in optimized capacity factor cannot be explained by the increasing batteries capacities and the unload time. Therefore, the question of why the capacity factor decreases with increasing batteries capacity remains open and will be addressed in future work.

On the other point of view, this sensitivity study shows that the "optimal" capacity factor and energy ship route is also highly sensitive to the physical optimization parameters and the deployment location. Thus, the optimization method is currently implemented in QtVlm converges to a local optimum and not the global optimum.

Furthermore, in -depth studies need to be done to examine other factors that influence the increase and decrease of the optimized capacity factor of the energy ships.

Sensitivity of rated power of energy ship (1MW, 1.3MW, 1.6MW and 1.9MW)

The resulting 3 years averaged capacity factor for the sensitivity of initial optimization waypoints and search step angle presented in Section 4.2.1, one can see that the best average capacity factor for 1.3MW energy ship in Saint-Pierre-et-Miquelon and Ile de Sein are higher compared to the best average capacity factor for 1.6MW in same deployment locations.

Therefore, in this section a sensitivity study has been performed to investigate the sensitivity of the optimized capacity factor in response to the rated power of the energy ship.

This sensitivity study involved four rated powers of energy ship, 1MW, 1.3MW, 1.6MW and 1.9MW. The output was also measured by the optimized capacity factor obtained by the energy ship. The optimization of the capacity factor for each rated power energy ship are using qtVlm weather routing software loaded with energy ship's speed and power production polar and three years of wind data as presented in Chapter 2. The performance of each rated energy ship is characterized by polar plots for its speed and power production.

For the optimization, the numerical optimization parameter as a function of the initial optimization waypoints and search steps angle are fixed to 2 initial optimization waypoints and 8° search steps angle. These numerical parameters are chosen based on the previous sensitivity study on the numerical optimization parameter which obtained the optimum value of optimized capacity factor for the energy ship. The sensitivity analysis also carried out for all storage capacity and unloading times of the energy ship analyzed in previous section. & 2017). Overall, at both deployment locations, one can note that as the rated power increased, the optimized capacity factors obtained were significantly decreased. Further reduction is also can be seen in average capacity factor obtained by all storage capacities as the rated power increase to 1.6MW and 1.9MW. The average capacity factor obtained for a 1.6MW energy ship was ranging from 47.8% to 50.2%. Meanwhile, the average capacity factor obtained by 1.9MW energy ship was ranging from 44.2% to 46.3%. & 2017 and its average for the three years. The average filling ratio obtained by all rated energy ships equipped with all cluster storage capacity was also high compared with the filing ratio obtained by energy ship deployed in Saint-Pierre-et-Miquelon, ranging from 89% to 96%.

Additionally, the energy ship can produce average energy ranging from 4705MW per hour to 7343MW per hour. These amounts of energy production by the energy ships produced in Ile de Sein were slightly lower comparing to the energy production in Saint-Pierre-et-Miquelon. One can see also that, larger rated energy ship will produce more energy, but contrary with case in Saint-Pierre-et-Miquelon, the energy production were also grow bigger concurrent with the storage capacity and unloading time. This event also reflects the expansion of the average capacity factor presented in Table A2-12.