Costanza Saletti

Airports are significant consumers of energy, primarily due to the extensive use of fuels for both aircraft and ground vehicles, such as ground support equipment. Increased efforts are being made to defossilize the aviation sector by replacing fossil fuels with sustainable alternatives. Among these, hydrogen presents a promising option. For aircraft, hydrogen could be utilized in its liquid form due to its higher energy density, which is essential for effective storage and use in aviation applications. However, a notable challenge with liquid hydrogen is the inevitable boil-off that occurs during storage and distribution. This boil-off results in gaseous hydrogen, which, rather than being wasted, could be repurposed to power ground vehicles at airports. This approach offers a viable alternative to the electrification of ground support equipment, contributing to the overall defossilization strategy of the aviation sector. This paper examines the feasibility of this scenario by evaluating the potential for replacing the conventional fuels used by ground vehicles with the gaseous hydrogen boiled off from liquid hydrogen storage. The analysis is conducted within the context of a small airport, providing insights into the practical implications and benefits of integrating hydrogen into airport energy systems. The findings suggest that utilizing boiled-off hydrogen for ground vehicles not only enhances energy efficiency but also supports the broader goal of reducing carbon emissions in the aviation industry.

Buildings are complex assets, characterized by environments and uses that change over time, variable occupancies, and long life cycles. They have high operational costs, mostly due to their energy requirements, and account for 30% to 40% of global greenhouse gas emissions. Consequently, substantial effort has been made to forecast their energy needs, with the scope of optimizing their economic and environmental impact. In this regard, the available literature focuses mainly on short-term modeling through the implementation of sets of physics-based equations (i.e., white-box), functional relationships between input and output variables (i.e., black-box), or a combination of both (i.e., grey-box). On the other hand, more research is required on long-term forecast models with the aim of reducing the energy needs. Within this context, this article presents an original automatic procedure for forecasting the energy needs of buildings in short- and long-term time horizons. This is accomplished by scaling an unknown facility from a similar facility that is already known and by executing a black-box approach based on machine learning algorithms. The proposed method is implemented in real case studies in Italy, predicting the energy needs (i.e., heating, cooling, and electricity) of Sant’Anna Hospital in Ferrara using the historical data of Ca’ Foncello Hospital in Treviso. The results show an adjusted coefficient of determination above 0.7 and an average error below 10% for all the energy vectors, demonstrating a feasible forecast performance with a low training set-to-test set ratio.

The need to reduce greenhouse gas emissions is leading to an increase in the use of renewable energy sources. Due to the aleatory nature of these sources, to prevent grid imbalances, smart management of the entire system is required. Industrial refrigeration systems represent a source of flexibility in this context: being large electricity consumers, they can allow large-load shifting by varying separator levels or storing surplus energy in the products and thus balancing renewable electricity production. The work aims to model and control an industrial refrigeration system used for freezing food by applying the Model Predictive Control technique. The controller was developed in Matlab^® and implemented in a Model-in-the-Loop environment. Two control objectives are proposed: the first aims to minimize total energy consumption, while the second also focuses on utilizing the maximum amount of renewable energy. The results show that the innovative controller allows energy savings and better exploitation of the available renewable electricity, with a 4.5% increase in its use, compared to traditional control methods. Since the proposed software solution is rapidly applicable without the need to modify the plant with additional hardware, its uptake can contribute to grid stability and renewable energy exploitation.