Giuseppina Ciulla

This study investigates the application of the Positive Energy District paradigm to two existing and morphologically diverse urban districts in Rome: Testaccio and Valco San Paolo. The research aims to evaluate the feasibility and effectiveness of district-scale energy retrofitting strategies, integrating dynamic simulation tools to model current energy behavior and assess future scenarios. The methodology combines a range of interventions including envelope insulation, high-performance glazing, HVAC system upgrades, efficient lighting solutions, and large-scale photovoltaic deployment. Additionally, the study explores the potential benefits of energy storage systems, with particular focus on the optimal sizing of lithium-ion battery solutions to enhance local self-consumption and reduce grid dependency. Key performance indicators are used to analyze the alignment between renewable energy generation and district demand, as well as the interaction with the electrical grid. By calibrating simulation models with real thermophysical and consumption data, the research ensures methodological robustness and enables the replicability of the proposed approach in other urban contexts. The study offers a comprehensive framework for planners and policymakers seeking to support the decarbonization and resilience of urban districts through the implementation of PEDs. Future developments will focus on optimizing storage management, assessing the environmental impact of battery life cycles, and integrating PEDs within broader urban energy ecosystems.

Solid oxide fuel cells (SOFC) could facilitate the green energy transition as they can produce high-temperature heat and electricity while emitting only water when supplied with hydrogen. Additionally, when operated with natural gas, these systems demonstrate higher thermoelectric efficiency compared to traditional microturbines or alternative engines. Within this context, although digitalisation has facilitated the acquisition of extensive data for precise modelling and optimal management of fuel cells, there remains a significant gap in developing digital twins that effectively achieve these objectives in real-world applications. Existing research predominantly focuses on the use of machine learning algorithms to predict the degradation of fuel cell components and to optimally design and theoretically operate these systems. In light of this, the presented study focuses on developing digital twin-oriented models that predict the efficiency of a commercial gas-fed solid oxide fuel cell under various operational conditions. This study uses data gathered from an experimental setup, which was employed to train various machine learning models, including artificial neural networks, random forests, and gradient boosting regressors. Preliminary findings demonstrate that the random forest model excels, achieving an R² score exceeding 0.98 and a mean squared error of 0.14 in estimating electric efficiency. These outcomes could validate the potential of machine learning algorithms to support fuel cell integration into energy management systems capable of improving efficiency, pushing the transition towards sustainable energy solutions.

The combination/integration of renewable energy and storage systems appears to have significant potential, achieving high-energy results with lower costs and emissions. One way to cover the thermal needs of a building is through solar energy and its seasonal storage in the ground. The SMARTEP project aims to create an experimental area that provides for the construction of a road solar thermal collector directly connected to a seasonal low-temperature geothermal storage with vertical boreholes. The storage can be connected to a ground-to-water heat pump for building acclimatization. This system will meet the requirements of visual impact and reduction of the occupied area. Nevertheless, several constraints related to the radiative properties of the surfaces and the lack of proper thermal insulation have to be addressed. The project includes the study of several configurations and suitable materials, the set-up of a dynamic simulation model and the construction of a small-scale road thermal collector. These phases allowed for an experimental area to be built. Thanks to careful investigation in the field, it will be possible to identify the characteristics and the best operation strategy to maximize the energy management of the whole system in the Mediterranean area.

School buildings represent an energy-consuming sector of real estate where different efficiency actions are necessary. The literature shows how the design of a multi-energy system offers numerous advantages, however, there are problems related to the integration of cogeneration units with renewable energy sources due to the low flexibility of the first one and the high degree of uncertainty of the latter. The authors provide an alternative solution through the analysis of a case study consisting of a multiple energy system in three Sicilian schools, focusing on the system’s operational strategy, on the design and sizing of components and trying to exploit the energy needs complementarity of buildings instead of integrating the conventional energy storage systems. Not considering school activities in summer, it was decided to install a cogeneration unit sized on winter thermal loads, whereas the electricity demand not covered was reduced with photovoltaic systems designed to maximize production for seasonal use and with loads concentrated in the morning hours. The effectiveness of this idea, which can be replicated for similar users and areas, is proved by a payback time of less than 11 years and a reduction of 31.77% of the CO₂ emissions.