Mirko Morini

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.

Solar energy is considered one of the most affordable renewable resources for meeting current energy demands and mitigating environmental problems. However, the exploitation of solar energy is challenging because of both diurnal and seasonal variations. Power-to-hydrogen technologies can play a key role to counterbalance the variation of solar irradiance. Moreover, hydrogen-fueled gas turbines are considered promising technologies to decarbonize the electricity sector. To tackle these concerns, this paper presents a multigeneration energy system operated in island mode in which a hydrogen-fueled gas turbine is coupled with a solar photovoltaic plant, an electrolyzer, an absorption chiller, electric and thermal energy storage, as well as a hydrogen storage. Therefore, the energy system is 100% based on renewable energy. The sizes of the components are optimized by maximizing the exploitation of renewable energy sources, while the supply of electricity from the national grid must be null. Moreover, the effect of ambient conditions on the optimal sizing is also investigated by considering the thermal, cooling, and electrical energy demands of two case studies located in two different climatic zones. The paper demonstrates that the adoption of hydrogen-fueled gas turbines coupled with power-to-hydrogen technologies can effectively support the transition toward a clean energy supply. Moreover, this study provides a procedure for the optimal sizing of a multigeneration energy system fully based on solar energy, by also demonstrating that both photovoltaic (PV) panel area and hydrogen storage volume are feasible, if compared to the considered district layout.

In recent years, power-to-gas technologies have been gaining ground and are increasingly proving their reliability. The possibility of implementing long-term energy storage and that of being able to capture and utilize carbon dioxide are currently too important to be ignored. However, systems of this type are not yet experiencing extensive realization in practice. In this study, an overview of the experimental research projects and the research and development activities that are currently part of the power-to-gas research line is presented. By means of a bibliographical and sitographical analysis, it was possible to identify the characteristics of these projects and their distinctive points. In addition, the main research targets distinguishing these projects are presented. This provides an insight into the research direction in this regard, where a certain technological maturity has been achieved and where there is still work to be done. The projects found and analyzed amount to 87, mostly at laboratory scale. From these, what is most noticeable is that research is currently focusing heavily on improving system efficiency and integration between components.

Europe stated the ambitious target of becoming carbon neutral by 2050 to combat climate change and meet the requirements imposed by the Paris Agreement, and renewable energy has proved to be a promising solution for the decarbonization of many sectors. Nonetheless, their aleatory nature leads to grid unbalances due to the difference between supply and demand. Storage solutions are needed, and electrofuels become a key factor in this context: they are fuels produced from electricity, which leads to carbon-neutral fuels if it originates from renewable sources. These can constitute a key solution to store the surplus energy and to decarbonize the so-called hard-to-abate sectors. Electrofuel production technologies have not yet been fully developed, and, in this context, extensive study of the state-of-the-art of existing projects can be very useful for researchers and developers. This work researches the European projects funded by the Horizon 2020 Programme regarding electrofuel production. The projects were analyzed in-depth using specific features, and the results were presented.

Refrigeration is an essential part of the food chain. It is used in all stages of the chain, from industrial food processing to final consumption at home. In these processes, mechanical refrigeration technologies are employed, where compressors increase gas pressure from evaporation to condensation. In industrial refrigeration systems, twin-screw compressors represent the most widely used technology. A detailed mathematical model of a twin-screw compressor has been developed in Simulink® using differential equations for energy and mass balances to simulate the compression cycle that includes suction, compression and discharge phases. Gas pressure and enthalpy can be calculated as time functions during the cycle. However, the computational times obtained limit the possibility to extend the use of the model in the development of control strategies for the whole refrigeration plant in its real operating conditions. Therefore, the detailed model has been used to train a simplified model developed in Matlab®: the simulated mass flow rate, shaft power and the fluid discharge temperature have been employed to identify several geometrical and thermodynamic parameters of the simplified model. The latter relies on non-linear algebraic equations and, thus, requires a very short computational time. A limited performance dataset has been used to train the model, and a different dataset to test it: the results of the models have been compared, and small errors in mass flow rate, shaft power and fluid discharge temperature have been observed.

Hybrid energy plants (HEPs), which include both fossil fuel technologies and renewable energy systems, can provide an important step toward a sustainable energy supply. In fact, the hybridization of renewable energy systems with gas turbines (GTs), which are fed by fossil fuels allows an acceptable compromise, so that high fossil fuel efficiency and high share of renewables can be potentially achieved. Moreover, electrical and thermal energy storage systems increase the flexibility of the energy plant and effectively manage the variability of energy production and demand. This paper investigates the optimal sizing of a HEP, which combines an industrial GT, renewable energy systems, and energy storage technologies. The considered renewable energy system is a photovoltaic system (PV), while the energy storage technologies are electrical energy storage and thermal energy storage. Moreover, a compression chiller and a gas boiler (GB) are also considered. For this purpose, the load profiles of electricity, heating, and cooling during a whole year are taken into account for the case study of the Campus of the University of Parma (Italy). The sizing optimization problem of the different technologies composing the HEP is solved by using a genetic algorithm, with the goal of minimizing the primary energy consumption (PEC). Moreover, different operation strategies are analyzed and compared so that plant operation is also optimized. The results demonstrate that the optimal sizing of the HEP, coupled with the optimized operation strategy, allows high average cogeneration efficiency (up to 84%), thus minimizing PEC.

In recent years, the flexibility of energy systems has become essential due to the growing penetration of renewable energy sources. The producers and consumers can enhance this flexibility by enabling a given amount of power that they can produce or consume in every condition. This is made available to the grid operator to globally optimize the dispatch management and to stabilize the grid. However, this can interfere with the operation of production units such as cogeneration plants, which also have to meet thermal demand. Therefore, producers and consumers require smart controllers to comply with grid operator requests at any time. This paper proposes a robust control strategy based on Model Predictive Control, which manages distribution networks and production plants by considering the uncertainty of the requirements for flexibility from the grid operator. The simulation case study is the district heating network of a school complex supplied by a Combined Heat and Power plant and a Thermal Energy Storage tank. The robustness of the proposed optimization is investigated by simulating several scenarios with different degrees of uncertainty about the request for electricity from the grid operator. The results show that the plant operator is able to comply with the electricity requirements to different extents depending on the degree of uncertainty and on system design choices. These considerations make it possible to improve the plant design and production planning from the perspective of grid flexibility.