SYSTEMS AND METHODS FOR GENERATING RENEWABLE ENERGY

Abstract

A movable maritime vessel for generating, storing and transporting energy, the vessel comprising a hull, at least one sail configured to capture wind energy to move the vessel, and an energy generation system comprising a hydro generator, wherein the hydro generator is configured to generate energy from the movement of fluid, for example water, for example sea water, through the hydro generator.

Claims

1-38. (canceled)

39. A movable maritime vessel comprising: a hull; at least one sail configured to capture wind energy to move the vessel; and an energy generation system comprising a hydro generator, wherein the hydro generator is configured to generate energy from a movement of a fluid through the hydro generator.

40. The vessel of claim 39, further comprising an energy transformation system configured to receive and process energy generated by the energy generation system.

41. The vessel of claim 39, further comprising an energy storage system configured to store energy received from the energy generation system.

42. The vessel of claim 40, further comprising an energy storage system configured to store energy received from the energy transformation system.

43. The vessel of claim 40, wherein the energy transformation system comprises: i) a water purification plant for purifying liquid wherein the water purification plant comprises at least one of a filter and a desalination unit, the water purification plant being configured to output purified liquid via a first conduit; and ii) an electrolysis plant for electrolysing the liquid received from the water purification plant via the first conduit, and for outputting a first gas to atmosphere via a second conduit, and for outputting a second gas via a third conduit.

44. The vessel of claim 43, further comprising a liquefaction plant for liquefying gas received from the electrolysis plant via the third conduit and outputting the liquid via a fourth conduit.

45. The vessel of claim 44, wherein one of, any number of, or all of the water purification plant, the electrolysis plant and the liquefaction plant is partially or entirely powered by energy from the energy generation system.

46. The vessel of claim 44, further comprising an energy storage system comprising a cryogenic storage tank for storing the liquid received from the liquefaction plant via the fourth conduit.

47. The vessel of claim 46, wherein the energy storage system further comprises a fuel cell wherein the cryogenic storage tank is configured to output hydrogen gas boil off via a fifth conduit and wherein the fuel cell is configured to receive hydrogen gas boil off from the cryogenic storage tank via the fifth conduit.

48. The vessel of claim 43, further comprising an energy storage system comprising a compressor configured to receive gas from the electrolysis plant via the third conduit and a gas storage tank for storing the gas.

49. The vessel of claim 39, wherein the hydro generator is: i) directly attached to the hull; or ii) attached to the hull by a flexible or inflexible connector so that vessel tugs the hydro generator.

50. The vessel of claim 39, wherein the energy generation system further comprises at least one solar panel or an array of solar panels.

51. The vessel of claim 50, wherein the at least one solar panel or the array of solar panels is located on at least one upward facing surface of the hull and/or embedded in the at least one sail.

52. The vessel of claim 40, further comprising an energy storage system comprising a battery or an array of batteries configured to store the energy from any part of the energy generation system or the energy transformation system.

53. The vessel of claim 39, further comprising circuitry configured to communicate with a remote control system in order to navigate the vessel autonomously or using artificial intelligence.

54. The vessel of claim 53, wherein the circuitry is partially or entirely powered by energy from the energy generation system.

55. A method comprising the steps of: generating energy on a movable maritime vessel via an energy generation system; transforming the energy generated by the energy generation system on the vessel via an energy transformation system; storing, using an energy storage system, the energy received from the energy generation system or the energy transformation system on the vessel; and transporting the energy stored in the energy storage system to an endpoint on land.

56. The method of claim 55, wherein the movable maritime vessel comprises: a hull; at least one sail configured to capture wind energy to move the vessel; and the energy generation system comprising a hydro generator, wherein the hydro generator performs the generating of the energy from a movement of a fluid through the hydro generator.

57. The method of claim 56, further comprising: remotely routing, using a control system, the movable maritime vessel autonomously.

58. The method of claim 57, wherein the routing occurs using artificial intelligence.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0128] The present invention will now be described with reference to the accompanying drawings, in which:

[0129] FIG. 1 shows a vessel according to the invention;

[0130] FIG. 2 shows a schematic diagram of a system according to the invention;

[0131] FIG. 3 shows a first arrangement of a hull and hydro generator of a vessel according to the invention;

[0132] FIG. 4 shows a second arrangement of a hull and hydro generator of a vessel according to the invention;

[0133] FIG. 5 shows a third arrangement of a hull and hydro generator of a vessel according to the invention;

[0134] FIG. 6 shows a fourth arrangement of a hull and hydro generator of a vessel according to the invention;

[0135] FIG. 7 shows a system and method according to the invention; and

[0136] FIGS. 8 and 9 show exemplary technical specifications.

[0137] FIG. 10 shows a method of routing a movable maritime vessel to optimise energy generation according to the invention;

[0138] FIG. 11 shows a route discovery stage of a method of routing a movable maritime vessel to optimise energy generation according to the invention;

[0139] FIG. 12 shows graphs representing a branch and bound algorithm according to the invention;

[0140] FIG. 13 shows a graph of vessel speeds for various wind speeds and sailing angles according to the invention;

[0141] FIG. 14 shows a graph of wind speeds and directions for various locations according to the invention;

[0142] FIG. 15 shows a sigmoid curve relating vessel speed to utilisation for various vessel speeds according to the invention;

[0143] FIG. 16 shows graphs comparing energy generation for each of a greedy routing algorithm and a smart routing algorithm according to the invention;

[0144] FIG. 17 shows further graphs comparing energy generation for each of a greedy routing algorithm and a smart routing algorithm according to the invention;

[0145] FIGS. 18 to 33 show exemplary data according to the invention.

DETAILED DESCRIPTION

Movable Maritime Vessel

[0146] FIG. 1 shows a vessel 10 according to the invention. The vessel 10 is movable maritime vessel (e.g. a catamaran or trimaran) for generating, storing and transporting energy. The vessel 10 comprises a hull 12, a sail 14 (although there may be a plurality of such sails) configured to capture wind energy to move the vessel 10 and an energy generation system comprising a hydro generator 18.

[0147] The hydro generator 18 is configured to generate energy from the movement of fluid, for example sea water, through the hydro generator 18. Typically, the hydro generator will comprise a turbine (not shown).

[0148] In some embodiments, as well as the hydro generator 18, the energy generation system comprises at least one solar panel or an array of solar panels (not shown in FIG. 1).

[0149] The vessel 10 further comprises an energy transformation system configured to receive and process energy generated by the energy generation system, the energy generation system including the hydro generator 18. The energy transformation system comprises a water purification plant (or water treatment plant) 20 for purifying or treating liquid, for example sea water. The water purification plant 20 comprises at least one of a filter and a desalination unit (not shown in FIG. 1). The energy transformation system also comprises an electrolysis plant (or electrolyser) 22 for electrolysing liquid received from the water purification plant, and for outputting gas e.g. oxygen gas to atmosphere, and for outputting gas e.g. hydrogen gas to an energy storage system of the vessel as described below. In the embodiment shown in FIG. 1, the energy transformation system also comprises a liquefaction plant 24 for liquefying gas e.g. hydrogen gas received from the electrolysis plant and outputting liquid e.g. liquid hydrogen. As will be described below, there are embodiments where the energy transformation plant will not comprise a liquefaction plant 24

[0150] In FIG. 1, the vessel 10 further comprises an energy storage system configured to store energy received from the energy generation system and/or the energy transformation system. In the embodiment shown in FIG. 1, the energy storage system comprises a storage tank 26, for example a cryogenic storage tank, for storing liquid, for example liquid hydrogen, received from the liquefaction plant 24.

[0151] FIG. 2 shows a schematic diagram of a system 100 according to the invention, for example with the use of the vessel 10 shown in FIG. 1 and described above. The system 100 comprises an energy generation system 102, an energy transformation system 104 and an energy storage system 106.

[0152] In the embodiment shown in FIG. 2, the energy generation system 102 comprises a hydro generator 18 as described previously and at least one solar panel or an array of solar panels 26, although this is not essential. The at least one solar panel or array of solar panels 26 would typically be located on at least one upward facing surface of the hull and/or embedded in the at least one sail, although it will be understood that other locations could be employed.

[0153] In the arrangement shown in FIG. 2, the energy transformation system 104 comprises a water purification plant (or water treatment plant) 20 for purifying/treating liquid (as described previously), for example sea water. In the arrangement shown in FIG. 2, the water purification plant 20 comprises a filter 28 and a desalination unit 30, the water purification plant being configured to output purified liquid via a first conduit 32. The energy transformation system also comprises an electrolysis plant (or electrolyser) 22 as described previously for electrolysing liquid received from the water purification plant 20 via the first conduit 32, and for outputting gas e.g. oxygen gas to atmosphere via a second conduit 34, and for outputting gas e.g. hydrogen gas via a third conduit 36.

[0154] As well as the water purification plant 20 and electrolysis plant 22 described above, the energy transformation system 104 shown in FIG. 2 further comprises a liquefaction plant 24 as described previously for liquefying gas e.g. hydrogen gas received from the electrolysis plant 22 via the third conduit 36 and outputting liquid e.g. liquid hydrogen via a fourth conduit 38. However, the liquefaction plant 24 is not essential as will be described below.

[0155] In the arrangement shown in FIG. 2, the energy storage system 106 comprises a storage tank 26 as described previously, for example a cryogenic storage tank, for storing liquid, for example liquid hydrogen, received from the liquefaction plant 24 via the fourth conduit 36. However, in some embodiments, the energy transformation system 104 does not comprise a liquefaction plant 24. In such cases, the energy storage system 106 comprises a compressor (not shown) configured to receive gas, for example hydrogen gas, from the electrolysis plant 22 via the third conduit 36 and a storage tank for storing the gas.

[0156] In the embodiment shown in FIG. 2, the energy storage system 106 further comprises a fuel cell 40, for example a hydrogen fuel cell. The storage tank 26 is configured to output hydrogen gas boil off via a fifth conduit 42. The fuel cell 40 is configured to receive hydrogen gas boil off from the storage tank 26 via the fifth conduit 42.

[0157] FIGS. 3 to 6 show arrangements of hull 12 and hydro generator 18 of the vessel 10 according to the invention.

[0158] In FIG. 3, the hydro generator 18 is directly attached to the hull 12 by a flexible or inflexible connector 44.

[0159] In FIG. 4, the hydro generator 18 is attached to the hull 12 by a keel 46 of the vessel 10.

[0160] In FIG. 5, the hydro generator 18 is connected to the hull 12 by a flexible connector 48 and is towed by the vessel 10.

[0161] In FIG. 6, the vessel comprises a kite 50, such as a sea kite, that is connected to the hull by a flexible connector 52.

[0162] FIG. 7 depicts a system and method according to the invention. The vessels 10 according to the invention generate energy as described herein, transform the energy as described herein, store the energy as described herein and transport the energy to port. Typically, the vessels 10 are remotely routed, for example autonomously, for example by artificial intelligence

Method for Routing a Movable Maritime Vessel

[0163] FIG. 10 shows a method 1000 of routing a movable maritime vessel to optimise energy generation according to the invention. The method 1000 provides an algorithm for determining one or more optimal routes for the vessel along a journey of the vessel and is shown at a high level. The method 1000 may comprise five stages 1100, 1200, 1300, 1400, 1500. Alternatively, one or more of the stages 1100, 1200, 1300, 1400, 1500 may be combined.

[0164] The first stage 1100 of the method 1000 is an input stage. There may be three primary groups of inputs to the method 1000. These inputs may be related to the vessel itself and/or the journey of the vessel. For example, in the embodiment shown, the inputs include route options, boat properties, i.e., vessel properties, and weather data.

[0165] Examples of route options include the starting location of the vessel, i.e., where the vessel starts its journey, the ending location of the vessel, i.e., the desired destination of the vessel, and the start time of the journey, i.e., the planned start time.

[0166] Examples of boat properties include the number of discrete sailing angles of the vessel, the horizon over which to optimise, i.e., the number and length of the timesteps for which to determine an optimal set of possible routes for the vessel, the maximum wind speed that may be sailed into, and the utilisation hours of the vessel, i.e., the amount of energy capable of being stored by the vessel, for example, tank volume of the vessel in the case of storing energy as liquid hydrogen.

[0167] Examples of weather data include real-time and/or forecasted wind speed and wind direction data.

[0168] The second stage 1200 of the method 1000 involves exploring the possible routes that may be taken by the vessel over a given horizon, i.e., a predetermined number of subsequent timesteps, to inform a selection of the most optimal route of the vessel over a next timestep of the journey of the vessel.

[0169] The second stage 1200 involves exploring all possible routes from a current location of the vessel for a next timestep, for example, for a 3-hour time interval. This involves calculating the next position of the vessel, the utilisation of the vessel, and the net benefit, i.e., in terms of utilisation and energy generation, for each route. The most optimal routes for this timestep are taken forward, and all possible routes from this set of new locations are calculated for a subsequent timestep. These steps are repeated until N timesteps have been explored, allowing for a determination of a set of possible routes for the vessel for the next N timesteps.

[0170] By calculating the net benefit of each route, net scores may be assigned to routes over to individual timesteps, and an aggregate net score may be assigned to the possible routes for the vessel for the next N timesteps. When assigning net scores to each of the set of the possible routes over N timesteps, the weighting of the estimated utilisation, i.e., reward score, and the weighting of the penalty score in a net score function may dictate the behaviour of the vessel and the returned utilisation. The net score accrued by individual steps further into the future can also be down-weighted to account for uncertainty in weather forecasts.

[0171] The third stage 1300 of the method 1000 involves identifying the most optimal route to be taken by the vessel over a next timestep. The route from the determined set of possible routes over N timesteps with the highest net benefit, i.e., net score, is chosen.

[0172] The fourth stage 1400 of the method 1000 involves following the identified route and updating the location of the vessel after navigating the vessel according the identified route. Having chosen the best route, optimised over N timesteps, the vessel may be moved, i.e., navigated, to the end of the first timestep of that journey. Only a single timestep is used for routing the vessel as the weather forecast that informs the routing may change in the next timestep, and the next move should include information from the N+1th timestep.

[0173] The fifth stage 1500 of the method 1000 involves determining whether the vessel has completed its journey. After every location update of the vessel, it is checked to see if the vessel has reached the end of its journey, for example, whether the vessel has returned to port. If the vessel has not reached the end of its journey, for example, has not returned to port, the method 1000 may be looped until the vessel has reached the end of its journey.

[0174] In an embodiment, to be considered at port, the vessel must be within a 100 nautical mile radius of a predetermined end location of the journey and have at least 95% energy storage, for example, 95% of a full tank in the case of storing energy in the form of liquid hydrogen.

[0175] FIG. 11 shows the second stage 1200 of the method 1000, i.e., the route discovery stage of the method 1000.

[0176] As touched on above, the second stage 1200 of the method 1000 explores possible routes that the vessel could take over the following N timesteps. At the end of this process a set of possible routes optimised over N timesteps have been determined, and the rewards, penalties and net scores are calculated for each route.

[0177] In an embodiment, at the start of the second stage 1200, the possible sailing angles of the vessel relative to the wind are calculated, and the current wind speed is obtained. From this, the possible vessel speeds for each sailing angle, and the subsequent vessel location after one timestep, for example, 3 hours, of sailing is calculated.

[0178] These locations are then checked for two criteria. Firstly, whether the location is within a defined polygon (i.e., not on land), and secondly, whether the wind speed at the location at the end of the next timestep exceeds the maximum wind speed that the vessel can sail into.

[0179] Subsequently, a penalty is calculated for each route based on how far from the end location of the journey the location of the vessel at the end of the route is. This penalty increases in strength as the amount of energy stored by the vessel, for example, in a liquid hydrogen tank, fills. This penalty encourages the vessel to return to port as it becomes full. A reward score is calculated for each route using a sigmoid curve that relates vessel speed to utilisation.

[0180] The net score for each route is a combination of the reward score and the penalty score. In an embodiment, the final step of the second stage, or the first step of the third stage, is to estimate the best-case and worst-case utilisations, i.e., rewards, for each route over the N timesteps. Routes for which the best-case utilisation is worse than the route with the highest worst-case utilisation are pruned.

[0181] FIG. 12 shows graphs 2000 representing a branch and bound algorithm according to the invention. The estimation of best-case and worst-case utilisations for each route may be done using a known approach called the branch and bound algorithm. Employing the algorithm can filter down the set of possible routes being explored. As shown in the graphs, two routes are being investigated as options, A and B, with their utilisations over 5 future timesteps being explored. Estimate a worst-case scenario for the current best route (Route A) is that it only achieves 50% utilisation at every subsequent timestep. Estimate a best-case scenario for Route B is that it achieves 100% utilisation at every subsequent timestep. Doing this, it can be seen that Route B is never better than Route A, and so Route B can be discarded as an option.

[0182] FIG. 13 shows a graph 3000 of vessel speeds for various wind speeds and sailing angles according to the invention. FIG. 14 shows a graph 4000 of wind speeds and directions for various locations according to the invention. FIG. 15 shows a sigmoid curve 5000 relating vessel speed to utilisation for various vessel speeds according to the invention. The graph may include a predetermined high wind speed cut-off point 5100 at which the energy generation system of the vessel is turned off. FIGS. 13, 14 and 15 represent exemplary data that may be included in the one or more inputs to the method 1000, for example, for the second stage 1200, i.e., the route discovery stage.

[0183] FIG. 16 shows a map 6100 for routes of each of a greedy routing algorithm and a smart routing algorithm according to the invention graphs. FIG. 16 also shows graphs 6200 comparing energy generation for each of a greedy routing algorithm and a smart routing algorithm according to the invention. FIG. 17 shows further graphs 7000 comparing energy generation for each of a greedy routing algorithm (left) and a smart routing algorithm (right) according to the invention.

[0184] A greedy algorithm, the routing of which is reflected in the dashed line in the graphs of FIG. 16, is that which fails to consider a horizon of multiple timesteps when determining a route of the vessel to optimise energy generation, and instead attempts to maximise energy generation by considering the next timestep only. By comparison, a smart algorithm, such as method 1000, the routing of which is reflected in the solid line in the graphs of FIG. 16, is that which considers a horizon of five timesteps when determining a route of the vessel to optimise energy generation. As can be seen from the graphs, the greedy algorithm can be caught in an area with low wind speed which is avoided by the smart algorithm, and the smart algorithm fills its energy stores over ten timesteps ahead of its greedy equivalent. The graphs of FIG. 16 the wind speed (in knots) and direction the vessel is travelling in for the same journey with both the greedy and smart algorithms. The shading indicates the vessel's speed (red indicates low speed and green indicates high speeds). In the case of the greedy algorithm, it can be seen that the vessel finds itself in low wind speed regions with low utilisations. In the case of the smart algorithm, it can be seen that the vessel finds itself in high wind speed regions with high utilisations.

[0185] The present invention has been described above in exemplary form with reference to the accompanying drawings which represent a single embodiment of the invention. It will be understood that many different embodiments of the invention exist, and that these embodiments all fall within the scope of the invention as defined by the following claims.

Exemplary Results of the Invention

[0186] Trials performed managed to produce approximately six litres of green hydrogen over a two-hour test run. The energy yacht outperformed expectations and it was thought that it could have produced over ten times more of the renewable gas. This is equivalent to charging twenty smartphones.

[0187] The power of data has been used to find optimal weather conditions in which to route the vessels when at sea. This routing algorithm makes green energy mobile.

[0188] There has been a focus not only on ocean class vessels that create renewable energy, which is then converted into hydrogen, but also on using data to find optimal wind speeds to create, store and distribute hydrogen.

[0189] It was found that a flotilla of energy yachts using at least some aspects of the invention could achieve a load factor of 72.5 percent. This means that energy vessels according to at least some aspects of the invention are capable of working at full capacity roughly five days a week.

[0190] By comparison, verified load factors for wind turbines in the UK are 26.5 percent for onshore wind farms and 39.9 percent for offshore wind farms (operational 2-3 days per week) It has been found that routing, as described according to at least some aspects described herein, can continually optimise the course of the vessels to maintain the highest possible utilisation of the ship.

Routing Examples

[0191] The following examples investigate the impact of varying a single parameter at a time on the utilisation recorded by the vessel.

[0192] All examples ran for a minimum of 50 weeks from 1 Jan. 2020. The results discussed below show the utilisation for a single vessel running continuously over this time period, with 6 hours downtime between journeys. The default parameters were used for all other inputs other than the parameter in question. Because each parameter combination in each example is measured by a single run (i.e., one year's worth of operation) the results may be susceptible to local conditions particular to that run. These examples should therefore not be used to identify optimal parameter combinations, but rather to obtain a quick understanding of how this parameter may impact the behaviour of the vessel. A map 8000 of the boundaries of the region the routes ran in the example are shown in FIG. 18.

[0193] In an example that assesses the impact of increasing the number of time steps considered in the route optimisation, it has been observed that as the time horizon over which to determine a set of possible routes for the vessel to be optimise energy generation increases, the mean utilisation improves but to the detriment of the run time which increases (to over 1000 minutes for 7 timesteps). See graphs 9100, 9200 of FIG. 19. Accordingly, there may be little benefit in increasing a time horizon beyond four to six timesteps (12-18 hours in the case of 3-hour timesteps) as the utilisation improvements tails off beyond this range. This is shown It has also been observed that the utilisation of the vessel is better in winter months than when compared to summer. For the examples, a time horizon of 6 timesteps (18 hours) was selected as a good balance between optimal utilisation and run times. Graph 9300 of FIG. 19 shows the utilisation of individual journeys throughout the year for vessels operating with different number of horizons to optimise over. The vessel has noticeably poorer performance in the summer months when compared to winter. The greedy algorithm (i.e., chooses the best move available at the time) has much bigger variation in performance, particularly in summer months where it can often get stuck in low wind areas. By optimising over more timesteps, the vessel is able to avoid the low wind regions more successfully.

[0194] FIG. 20 shows a North Atlantic Ocean polygon 10000, a region for the routing of the vessel. In an example assessing the impact of starting location of the journey on the average annual utilisation, when observing the North Atlantic, it has been observed that an optimal greedy start location, close to the mid-North Atlantic, produces the highest utilisation (82%) whereas starting locations closer to Europe result in average annual utilisations of 72-77%. These results are shown in graph 11000 of FIG. 21. This variation is consistent with the heat map for 100 points 12000, FIG. 22, generated using the greedy algorithm but the utilisations are higher due to the smart algorithm (and the heat map is based on 52 journeys rather than a single vessel sailing in a year).

[0195] FIG. 23 shows a graph 13000 of utilisation across each individual journey for a vessel operating out of Penzance and a vessel operating from the optimal location found by the greedy algorithm. A noticeable difference between the two locations is that the utilisation starting in Penzance was much lower during spring, and that the lower utilisation season seems to be longer than it is in the North Atlantic.

[0196] In an example that assesses the impact of modifying storage hours, i.e., energy storage volume of the vessel, on average annual utilisation, it has been observed that as the maximum storage capacity of the energy generation decreases, the average utilisation of the vessel decreases. The default parameter used in all the examples is 120 storage hours (5 days at 100% utilisation). When this value is reduced, the mean annual utilisation decreases. This may be due to having longer offloading times relative to journey durations, and there being less opportunity for the vessel to find areas of high wind. 120 hours of storage means the vessel will do at least 8760/126=70 journeys vs 30 hours storage=8760/36=243 journeys. As the utilisation hours increase, the mean utilisation increases but tails off to around 86% beyond 200 hours. The offloading time has an increasingly smaller impact on the utilisation. These results are shown in graph 14000, FIG. 24.

[0197] Increasing the number of discrete sailing angles of the vessel should in theory improve the utilisation as you are creating more options for the vessel to choose from to enable it to follow an optimal path. In an example that assesses the impact of modifying the number of discrete sailing angles on average annual utilisation, it has been observed that the increase above 10 sailing angles evenly spaced between 50 degrees and 310 degrees can improve the utilisation by 1-2% but there will be a trade-off with runtime increasing as the number of angles increase. It is likely that a manually curated list of angles that provide optimal sailing directions would give greater gains in utilisation. These results are shown in graph 15000, FIG. 25.

[0198] In an example that assesses the impact of modifying the utilisation s-curve on average annual utilisation, it has been observed that the choice of s-curve (i.e., the ability of the turbines of the vessel to produce reasonable volumes of hydrogen at lower speeds) has a significant impact on the annual utilisation. Ramp-ups at low speeds can increase the utilisation by 5%. The default curve used in all examples uses a mid-point of 17 knots. These results are shown in graphs 16000, FIG. 26.

[0199] The impact of changing the lower bound on the mean utilisation and run time of the smart algorithm has been explored. The default parameter for the lower bound reward score was 0, i.e., routes are assessed with the assumption that the minimum utilisation in a time step is 0. This is quite conservativeincreasing the lower bound will reduce the solution space and reduce the run time but there is a risk that the global optimal solution will be eliminated from the solution space. In an example that assesses the impact of changing the lower bound on mean utilisation and run-time of the smart algorithm on average annual utilisation, it has been observed that increasing the lower bound will reduce the solution space and reduce the run time but there is a risk that the global optimal solution will be eliminated from the solution space. Results show that you can reduce the run time significantly by increasing the lower bound reward without it having a significant impact on the mean utilisation. These results are shown in graphs 17000, FIG. 27.

[0200] In an example that assesses the impact of increasing one of the parameters of the penalty function that is used to encourage the vessel to return to its starting point when it is nearly full, it has been observed that for an exponent parameter (exponent_param) of 0.5 in a penalty function scale_param*(tank_fill{circumflex over ()}exponent_param), increasing the exponent_param to high values (>1) results in a penalty function that encourages the vessel to explore for longer until the tank gets full whereas low values discourage the vessel to explore even when the tank is relatively empty. Increasing the exponent_param value results in the penalty function getting exponentially smaller for the same tank level. This effectively means that the vessel can travel further before it is encouraged back to the port. The results suggest that greater free range does not necessarily result in a noticeable improvement in the mean utilisation. These results are shown in graph 18000, FIG. 28.

[0201] In an example that assesses the impact of changing the other parameters of the penalty function that is used to encourage the vessel to return to its starting point when it is nearly full, it has been observed that for a scale parameter (scale_param) of 2 in a penalty function=scale_param*(tank_fill{circumflex over ()}exponent_param), increasing the scale_param increases the amplitude of the return to port signal. Results suggest that weaker signals (low values) result in a lower mean utilisation (e.g., the vessel reaches 100% tank capacity well before returning to port). Very strong signals (above 2) also appear to result in a lower utilisationperhaps as a result of the vessel being overly constrained to stay near port. The analysis suggests that this function can be tweaked to marginally improve the utilisation outcome (albeit avoiding low values for the penalty scale). These results are shown in graph 19000, FIG. 29.

[0202] In an example that assesses the impact of varying the discount factor on the average annual utilisation, it has been observed that for a total net score=Sum over N horizons(net*discount_factor**n), a large discount factor (e.g., 0.5) has a negative impact on the mean utilisation i.e., the algorithm is putting greater weight on the first time-steps and discounting routes that have better conditions at later timesteps. The use of a discount is a useful way of taking account of uncertainty in wind forecasts. The accuracy of forecasts will likely decrease with longer time horizons and therefore it is sensible to discount any forecast good wind conditions in the future to account for the uncertainty that they can be realised by the vessel. These results are shown in graph 20000, FIG. 30.

Other Data

[0203] In specific examples, it is assumed that the wind speed is constant for a 3-hour time period, however, it is likely that this is an oversimplification. If it is assumed that the distribution of wind speeds can be approximated as a normal distribution, alternative utilisations can be assessed. These results are shown in graphs 21000, FIG. 31.

[0204] Utilisations are possible for various vessel speeds. While the distribution of utilisations is still has a mode at 1, some of the wind speeds in this distribution can have utilisations as low as 0. Using the current assumptions, a vessel in 20 knot wind sailing at 110 degrees would have a utilisation of 1. These results are shown in graphs 22000, FIG. 32. However, the utilisation will vary depending on the size of the variation in wind speed (left graph, graphs 23000, FIG. 33). For a given sailing angle, new utilisation curves for different distributions of wind may be plotted (right graph, graphs 23000, FIG. 33). If there is significant variation in the wind then utilisations of 1 in a given time period are unlikely to be obtained.

Embodiments

[0205] 1. A movable maritime vessel for generating, storing and transporting energy, the vessel comprising: [0206] a hull; [0207] at least one sail configured to capture wind energy to move the vessel; and [0208] an energy generation system comprising a hydro generator, wherein the hydro generator is configured to generate energy from the movement of fluid, for example sea water, through the hydro generator. [0209] 2. The vessel of embodiment 1, further comprising an energy transformation system configured to receive and process energy generated by the energy generation system. [0210] 3. The vessel of embodiment 1 or embodiment 2, further comprising an energy storage system configured to store energy received from the energy generation system and/or the energy transformation system. [0211] 4. The vessel of embodiment 2 or embodiment 3, wherein the energy transformation system comprises: [0212] i) a water purification plant for purifying liquid, for example sea water, wherein the water purification plant comprises at least one of a filter and a desalination unit, the water purification plant being configured to output purified liquid via a first conduit; and [0213] ii) an electrolysis plant for electrolysing liquid received from the water purification plant via the first conduit, and for outputting gas e.g. oxygen gas to atmosphere via a second conduit, and for outputting gas e.g. hydrogen gas via a third conduit. [0214] 5. The vessel of embodiment 4, further comprising a liquefaction plant for liquefying gas e.g. hydrogen gas received from the electrolysis plant via the third conduit and outputting liquid e.g. liquid hydrogen via a fourth conduit. [0215] 6. The vessel of embodiment 4 or embodiment 5, wherein one of, any number of, or all of the water purification plant, electrolysis plant and liquefaction plant is partially or entirely powered by energy from the energy generation system. [0216] 7. The vessel of embodiment 5 or embodiment 6, wherein the energy storage system comprises a cryogenic storage tank for storing liquid, for example liquid hydrogen, received from the liquefaction plant via the fourth conduit. [0217] 8. The vessel of any one of embodiments 4 to 7, wherein the energy storage system comprises a compressor configured to receive gas, for example hydrogen gas, from the electrolysis plant via the third conduit and a gas storage tank for storing the gas. [0218] 9. The vessel of any one of embodiments 4 to 8, wherein the energy storage system further comprises a fuel cell, for example a hydrogen fuel cell, wherein the storage tank is configured to output hydrogen gas boil off via a fifth conduit and wherein the fuel cell is configured to receive hydrogen gas boil off from the cryogenic storage tank via the fifth conduit. [0219] 10. The vessel of any one of the preceding embodiments, wherein the hydro generator is: [0220] i) directly attached to the hull; or [0221] ii) attached to the hull by a flexible or inflexible connector so that the vessel tugs the hydro generator. [0222] 11. The vessel of any one of the preceding embodiments, wherein the energy generation system further comprises at least one solar panel or an array of solar panels. [0223] 12. The vessel of embodiment 11, wherein the at least one solar panel or array of solar panels is located on at least one upward facing surface of the hull and/or embedded in the at least one sail. [0224] 13. The vessel of any one of embodiments 3 to 12, wherein the energy storage system comprises a battery or an array of batteries configured to store energy from any part of the energy generation system and/or the energy transformation system. [0225] 14. The vessel of any one of the preceding embodiments, further comprising circuitry configured to communicate with a remote control system in order to navigate the vessel, for example autonomously and/or with the use of artificial intelligence. [0226] 15. The vessel of embodiment 14, wherein the circuitry is partially or entirely powered by energy from the energy generation system. [0227] 16. A method comprising the steps of: [0228] generating energy on a movable maritime vessel via an energy generation system; [0229] transforming the energy generated by the energy generation system on the vessel via an energy transformation system; [0230] storing the energy received from the energy generation system and/or the energy transformation system on the vessel by means of an energy storage system; and [0231] transporting the energy stored in the energy storage system to an endpoint on land. [0232] 17. The method of embodiment 16, wherein the vessel is a vessel according to any one of embodiments 1 to 15. [0233] 18. A control system for remotely routing a vessel according to any one of embodiments 1 to 15 and/or for executing a method according to embodiment 16 or embodiment 17, for example autonomously, for example by artificial intelligence. [0234] 19. A computer-implemented method for routing a movable maritime vessel to optimise energy generation along a journey, the movable maritime vessel capable of generating, storing and transporting energy, the method comprising: [0235] a) receiving one or more inputs associated with the journey; [0236] b) based on the received inputs, determining a set of possible routes for the vessel to be optimised over N timesteps of the journey, where N is a predetermined number; [0237] c) from the set of possible routes, identifying an optimal route to be taken by the vessel over a next timestep of the journey; [0238] d) navigating the vessel over the next timestep of the journey according to the identified optimal route; and [0239] e) after navigating the vessel over the next timestep of the journey, determining whether the vessel has completed the journey. [0240] 20. The method of embodiment 19, wherein step b) comprises: [0241] b1) based on the received inputs, determining a set of possible routes for the vessel over a next timestep; [0242] b2) assigning each of the possible routes for the vessel over the next timestep a net score, wherein the net score is a reward score for the respective route less a penalty score for the respective route; [0243] b3) filtering out possible routes for the vessel over the next timestep having a net score below a predetermined threshold; and [0244] b4) incrementing the timestep and repeating steps b1) through b3) N1 times for each of the routes not filtered out to determine a set of possible routes for the vessel to be optimised over N timesteps of the journey. [0245] 21. The method of embodiment 19, wherein step b) comprises: [0246] b1) based on the received inputs, determining a set of possible routes for the vessel over a next timestep; [0247] b2) for each of the possible routes for the vessel over the next timestep, calculating an expected position of the vessel at the end of the timestep, a reward score for the respective route and a penalty score for the respective route; [0248] b3) assigning each of the possible routes for the vessel over the next timestep a net score based on the reward score for the respective route and the penalty score for the respective route; [0249] b4) selecting one or more of the possible routes for the vessel over the next timestep based on their respective net scores; and [0250] b5) incrementing the timestep, and repeating steps b1) through b4) N1 times for each of the one or more selected possible routes for the vessel over the next timestep to determine a set of possible routes for the vessel to be optimised over N timesteps, wherein each of the set of possible routes for the vessel to be optimised over N timesteps is assigned an aggregate net score based on the sum of the net scores of each of its respective timestep routes. [0251] 22. The method of any one of embodiments 20 or 21, wherein the one or more inputs comprise real-time and/or forecasted wind speed and wind direction data and step b1) comprises: [0252] b1.1) based on the wind direction data, determining a set of possible sailing angles of the vessel; [0253] b1.2) based on the wind speed data, calculating possible vessel speeds for each route of the set of possible routes for the vessel over the next timestep, wherein each route for the vessel over the next timestep corresponds to one of the set of possible sailing angles of the vessel; [0254] b1.3) based on the vessel speeds, estimating a vessel location for each route of the set of possible routes for the vessel at the end of the next timestep; [0255] b1.4) based on the received inputs, filtering out those routes which result in the vessel location at the end of the next timestep being outside a region of interest, for example, where the wind speed at the location of the vessel at the end of the next timestep exceeds a maximum tolerable wind speed that the vessel can sail into. [0256] 23. The method of any one of embodiments 19 to 22, wherein step b) comprises assigning an aggregate net score to each route of the set of possible routes, wherein the aggregate net score for each route is a sum of reward scores for each timestep of the respective route less a sum of penalty scores for each timestep of the respective route. [0257] 24. The method of any one of embodiments 19 to 23, wherein step b) comprises estimating a best-case aggregate net score and a worst-case aggregate net score for each route of the set of possible routes for the vessel to be optimised over N timesteps of the journey, wherein the aggregate net score for each route is a sum of reward scores for each timestep of the respective route less a sum of penalty scores for each timestep of the respective route, and filtering out those routes for which the best-case aggregate net score is worse than the route with the highest worst-case aggregate net score. [0258] 25. The method of embodiment 24, wherein estimating the best-case aggregate net score and the worst-case aggregate net score for each route of the set of possible routes for the vessel to be optimised over N timesteps of the journey is based on a branch and bound algorithm. [0259] 26. The method of any one of embodiments 20 to 25, wherein the calculations of the penalty score for each timestep of a respective route and the reward score for each timestep of a respective route comprise a determination based on the one or more received inputs, for example, environmental data, for example, data relating to oceanic routes. [0260] 27. The method of any one of embodiments 20 to 26, wherein the vessel comprises an energy storage medium, and wherein the calculation of the penalty score for each timestep of a respective route comprises a determination based on how far the vessel will be from a predetermined end location of the journey at the end of the timestep, and wherein the strength of the penalty score increases as an amount of energy stored by energy storage medium of the vessel increases. [0261] 28. The method of any one of embodiments 20 to 27, wherein the vessel comprises an energy generation system, and wherein the reward score for each timestep of a respective route comprises a determination based on a sigmoid curve that relates vessel speed to a utilisation of an energy generation system of the vessel. [0262] 29. The method of any one of embodiments 20 to 28, wherein the reward score for each timestep of a respective route is scaled inversely proportional to an amount of time in the future the timestep is. [0263] 30. The method of any one of embodiments 21 or 23 to 25, wherein step c) comprises selecting a route from the set of possible routes for the vessel to be optimised over N timesteps that has the highest aggregate net score. [0264] 31. The method of any one of embodiments 19 to 30, wherein the vessel comprises an energy storage medium and/or an energy generation system, for example, a hydro generator. [0265] 32. The method of any one of embodiments 19 to 31, wherein the one or more inputs associated with a journey of the vessel comprises real-time and/or forecasted weather data, for example, wind data and/or wherein the one or more inputs associated with a journey of the vessel comprises one or more of journey properties, vessel properties, environmental data and weather data, and/or wherein the journey properties comprise one or more of journey starting location, journey ending location and journey starting time. [0266] 33. The method of embodiment 32, wherein the vessel properties comprise one or more of a number of discrete sailing angles, a number of timesteps over which to optimise route determination, N, a maximum tolerable wind speed that the vessel can sail into by the vessel, performance data of the vessel, for example, how well the vessel operates in particular wind and/or wave states, and an amount of energy capable of being stored by the vessel. [0267] 34. The method of any one of embodiments 32 or 33, wherein the environmental data comprises one or more of data relating to spot prices of liquid hydrogen at various locations, hazard data for oceanic routes, data relating to oceanic shipping channels, environmental data, for examples, satellite data relating to ice flows, data relating to oceanic piracy activity, data relating to marine life, for example oceanic whale routes, data relating to obstacles in the ocean, for example, icebergs or fallen shipping containers, automatic identification system (AIS) data, for example, data relating to other vessels in the ocean. [0268] 35. The method of any one of embodiments 32 to 34, wherein the weather data comprises one or more of real-time and/or forecasted wind speed, real-time and/or forecasted wind direction, and/or real-time and/or forecasted oceanic data, for example, wave data, for one or more regions of interest. [0269] 36. The method of any one of embodiments 19 to 35, wherein if it is determined that the vessel has not completed the journey, the method further comprises repeating steps a) through e). [0270] 37. The method of any one of embodiments 19 to 36, wherein step d) comprises transmitting instructions to the vessel to perform the identified optimal route over the next timestep. [0271] 38. The method any one of embodiments 19 to 37, wherein step e) comprises: [0272] e1) determining whether the vessel is within a predetermined radius, for example, a nautical mile radius, for example, a nautical 30-mile radius, 25-mile radius, 20-mile radius, 15-mile radius, 10-mile radius, 5-mile radius, 4-mile radius, 3-mile radius, 2-mile radius or 1-mile radius, of a predetermined end location of the journey; [0273] e2) determining whether an amount of energy stored by an energy storage medium of the vessel is above a predetermined percentage of an energy storage capacity of the energy storage medium; and [0274] e3) if both e1) and e2) are determined positively, determining that the vessel has completed the journey, and if one or both of e1) and e2) are determined negatively, determining that the vessel has not completed the journey. [0275] 39. The method of any one of embodiments 19 to 38, wherein the journey comprises a journey of the vessel from a predetermined start location to one of a set of predetermined end locations. [0276] 40. The method of any one of embodiments 19 to 39, wherein step a) comprises receiving an updated end location of the journey during the journey. [0277] 41. The method of any one of embodiments 19 to 40, wherein the energy generation comprises hydro energy generation of the vessel and/or wherein the energy generation comprises wind energy generation of the vessel, and/or wherein the energy generation comprises solar energy generation of the vessel, optionally wherein optimising energy generation comprises maximising hydrogen generation of the vessel for each timestep of the journey and/or wherein a timestep defines a period of time which may be varied in length across the journey. [0278] 42. The method of any one of embodiments 19 to 41, wherein a timestep defines a predetermined period of time, for example, approximately, exactly or less than 10 hours, approximately, exactly or less than 9 hours, approximately, exactly or less than 8 hours, approximately, exactly or less than 7 hours, approximately, exactly or less than 6 hours, approximately, exactly or less than 5 hours, approximately, exactly or less than 4 hours, approximately, exactly or less than 3 hours, approximately, exactly or less than 2 hours, or approximately, exactly or less than 1 hour, optionally wherein a timestep defines a period of time which may be varied in length across the journey. [0279] 43. The method of any one of embodiments 19 to 42, wherein the predetermined number, N, defines the number of time horizons over which to optimise the determination of a set of possible routes for the vessel. [0280] 44. A computer readable medium comprising instructions for causing a processor to execute instructions according to the method of any one of embodiments 19 to 43. [0281] 45. A computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the steps of any one of embodiments 19 to 43. [0282] 46. A trained model for executing step c) according to any one of embodiments 19 to 43, wherein step c) is performed by machine learning. [0283] 47. A control system for routing, for example, remotely, a vessel, the control system comprising one or more processors configured to perform the method of any one of embodiments 19 to 43, for example autonomously, for example by artificial intelligence. [0284] 48. A movable maritime vessel for generating, storing and transporting energy, the vessel comprising: [0285] a hull; [0286] at least one sail configured to capture wind energy to move the vessel; [0287] an energy generation system comprising a hydro generator, wherein the hydro generator is configured to generate energy from the movement of fluid, for example sea water, through the hydro generator; and [0288] a processor configured to communicate with the control system of embodiment 47 for routing the vessel.