A large deployment of energy storage solutions will be required by the stochastic and non-controllable nature of most renewable energy sources when planning for higher penetration of renewable electricity into the energy mix. Various solutions have been suggested for dealing with medium- and long-term energy storage. Hydrogen and ammonia are two of the most frequently discussed as they are both carbon-free fuels. In this paper, the authors analyse the energy and cost efficiency of hydrogen and ammonia-based pathways for the storage, transportation, and final use of excess electricity from an offshore wind farm. The problem is solved as a linear programming problem, simultaneously optimising the size of each problem unit and the respective time-dependent operational conditions. As a case study, we consider an offshore wind farm of 1.5 GW size located in a reference location North of Scotland. The energy efficiency and cost of the whole chain are evaluated and compared with competitive alternatives, namely, batteries and liquid hydrogen storage. The results show that hydrogen and ammonia storage can be part of the optimal solution. Moreover, their use for long-term energy storage can provide a significant, cost-effective contribution to an extensive penetration of renewable energy sources in national energy systems.

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Solid oxide fuel cells (SOFC) have developed to a mature technology, able to achieve electrical efficiencies beyond 60%. This makes them particularly suitable for off-grid applications, where SOFCs can supply both electricity and heat at high efficiency. Concerns related to lifetime, particularly when operated dynamically, and the high investment cost are however still the main obstacles toward a widespread adoption of this technology. In this paper, we propose a hybrid cogeneration system that attempts to overcome these limitations, in which the SOFC mainly provides the baseload of the system. Introducing a purification unit allows the production and storage of pure hydrogen from the SOFC anode off-gas. The hydrogen can be stored, and used in a proton exchange membrane fuel cell (PEMFC) during peak demands. The SOFC system is completed with a battery, used during periods of high electricity production. We propose the use of a mixed integer-linear optimization framework for the sizing of the different components of the system, and particularly for identifying the optimal trade-off between round-trip efficiency and investment cost of the battery-based and hydrogen-based storage systems. The proposed system is applied and optimized to two case studies: an off-grid dwelling, and a cruise ship. The results show that, if the SOFC is used as the main energy conversion technology of the system, the use of hydrogen storage in combination with a PEMFC and a battery is more economically convenient compared to the use of the SOFC in stand-alone mode, or of pure battery storage. The results show that the proposed hybrid storage solution makes it possible to reduce the investment cost of the system, while maintaining the use of the SOFC as the main energy source of the system.@en

Shipping is responsible for approximately the 90% of world trade leading to significant impacts on the environment. As a consequence, a crucial issue for the maritime industry is to develop technologies able to increase the ship efficiency, by reducing fuel consumption and unnecessary maintenance operations. For example, the marine fouling phenomenon has a deep impact, since to prevent or reduce its growth which affects the ship consumption, costly drydockings for cleaning the hull and the propeller are needed and must be scheduled based on a speed loss estimation. In this work a data driven Digital Twin of the ship is built, leveraging on the large amount of information collected from the on-board sensors, and is used for estimating the speed loss due to marine fouling. A thorough comparison between the proposed method and ISO 19030, which is the de-facto standard for dealing with this task, is carried out on real-world data coming from two Handymax chemical/product tankers. Results clearly show the effectiveness of the proposal and its better speedloss prediction accuracy with respect to the ISO 19030, thus allowing reducing the fuel consumption due to fouling.

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The shipping industry is today increasingly concerned with challenges related with sustainability. CO $$:2$$ emissions from shipping, although they today contribute to less than 3% of the total anthropogenic emissions, are expected to rise in the future as a consequence of increased cargo volumes. On the other hand, for the 2 $$^\circ $$ C climate goal to be achieved, emissions from shipping will be required to be reduced by as much as 80% by 2050. The power required to propel the ship through the water depends, among other parameters, on the trim of the vessel, i.e. on the difference between the ship’s draft in the fore and the aft of the ship. The optimisation of the trim can, therefore, lead to a reduction of the ship’s fuel consumption. Today, however, the trim is generally set to a fixed value depending on whether the ship is sailed in loaded or ballast conditions, based on results performed on model tests in basins. Nevertheless, the on-board monitoring systems, which produce a huge amount of historical data about the life of the vessels, lead to the application of state of the art data analytics techniques. The latter can be used to reduce the vessel consumption by means of optimising the vessel operational conditions. In this book chapter, we present the potential of data-driven based techniques for accurately predicting the influence of independent variables measured from the on board monitoring system and the fuel consumption of a specific case study vessel. In particular, we show that gray-box models (GBM) are able to combine the high prediction accuracy of black-box models (BBM) while reducing the amount of data required for training the model by adding a white-box model (WBM) component. The resulting GBM model is then used for optimising the trim of the vessel, suggesting that between 0.5 and 2.3% fuel savings can be obtained by appropriately trimming the ship, depending on the extent of the range for varying the trim.

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In this paper the authors investigate the problems of predicting the fuel consumption and of providing the best value for the trim of a vessel in real operations based on data measured by the onboard automation systems. Three different approaches for the prediction of the fuel consumption are compared: White, Black and Gray Box Models. White Box Models (WBM) are based on the knowledge of the physical underling processes. Black Box Models (BBMs) build upon statistical inference procedures based on the historical data collection. Finally, the authors propose two different Gray Box Model (GBM) which are able to exploit both mechanistic knowledge of the underlying physical principles and available measurements. Based on these predictive models of the fuel consumption a new strategy for the optimisation of the trim of a vessel is proposed. Results on real world operational data show that the BBM is able to remarkably improve a state-of-the-art WBM, while the GBM is able to encapsulate the a-priory knowledge of the WBM into the BBM so to achieve the same performance of the latter but requiring less historical data. Moreover, results show that the GBM can be used as an effective tool for optimising the trim of a vessel in real operational conditions.

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In this paper authors investigate the problem of predicting the fuel consumption of a vessel in real scenario based on data measured by the onboard automation systems. The goal is achieved by exploiting three different approaches: White, Black and Gray Box Models. White Box Models (WBM) are based on the knowledge of the physical underling processes. Black Box Models (BBMs) build upon statistical inference procedures based on the historical data collection. Author proposal is a Gray Box Model (GBM) which is able to exploit both mechanistic knowledge of the underlying physical principles and available measurements. Results on real world data shows that the BBM is able to remarkably improve a state-of-the-art WBM, while the GBM is able to encapsulate the a-priory knowledge of the WBM into the BBM so to achieve the same performance of the latter but requiring less historical data.

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