Carbon Capture and Utilization (CCU) is a viable solution to valorise the CO₂ captured from industrial plants’ flue gas, thus avoiding emitting it and synthesizing products with high added value. On the other hand, using CO₂ as a reactant in chemical processes is a challenging task, and a rigorous analysis of the performance is needed to evaluate the real impact of CCU technologies in terms of efficiency and environmental footprint. In this paper, the energetic performance of a DME and methanol synthesis process fed by 25% of the CO₂ captured from a natural gas combined cycle (NGCC) power plant and by the green hydrogen produced through an electrolyser was evaluated. The remaining 75% of the CO₂ was compressed and stored underground. The process was assessed by means of an exergetic analysis and compared to post-combustion Carbon Capture and Storage (CCS), where 100% of the CO₂ captured was stored underground. Through the exergy analysis, the quality degradation of energy was quantified, and the sources of irreversibility were detected. The carbon-emitting source was a 189 MW Brayton–Joule power plant, which was mainly responsible for exergy destruction. The CCU configuration showed a higher exergy efficiency than the CCS, but higher exergy destruction per non-emitted carbon dioxide. In the DME/methanol production plant, the main contribution to exergy destruction was given by the distillation column separating the reactor outlet stream and, in particular, the top-stage condenser was found to be the component with the highest irreversibility (45% of the total). Additionally, the methanol/DME synthesis reactor destroyed a significant amount of exergy (24%). Globally, DME/methanol synthesis from CO₂ and green hydrogen is feasible from an exergetic point of view, with 2.276 MJ of energy gained per 1 MJ of exergy destroyed.

Modeling and predicting the long-term performance of Li-ion batteries is crucial for the effective design and efficient operation of integrated energy systems. In this paper, we introduce a comprehensive semi-empirical model for Li-ion cells, capturing electrothermal and aging features. This model replicates the evolution of cell voltage, capacity, and internal resistance, in relation to the cell actual operating conditions, and estimates the ongoing degradation in capacity and internal resistance due to the battery use. Thus, the model articulates into two sub-models, an electrothermal one, describing the battery voltage, and an aging one, computing the ongoing degradation. We first propose an approach to identify the parameters of both sub-models. Then, we validate the identification procedure and the accuracy of the electrothermal and aging models through an experimental campaign, also comprising two real cycle load tests at different temperatures, in which real measurements collected from real Li-ion cells are used. The overall model demonstrates good performances in simulating battery characteristics and forecasting degradation. The results show a Mean Absolute Percentage Error (MAPE) lower than 1% for battery voltage and capacity, and a maximum absolute error on internal resistance that is on par with the most up-to-date empirical models. The proposed approach is therefore well-suited for implementation in system modeling, and can be employed as an informative tool for enhancing battery design and operational strategies.

Accurate forecasting of renewable generation is crucial to facilitate the integration of RES into the power system. Focusing on PV units, forecasting methods can be divided into two main categories: physics-based and data-based strategies, with AI-based models providing state-of-the-art performance. However, while these AI-based models can capture complex patterns and relationships in the data, they ignore the underlying physical prior knowledge of the phenomenon. Therefore, in this paper we propose MATNet, a novel self-attention transformer-based architecture for multivariate multi-step day-ahead PV power generation forecasting. It consists of a hybrid approach that combines the AI paradigm with the prior physical knowledge of PV power generation of physics-based methods. The model is fed with historical PV data and historical and forecast weather data through a multi-level joint fusion approach. The effectiveness of the proposed model is evaluated using the Ausgrid benchmark dataset with different regression performance metrics. The results show that our proposed architecture significantly outperforms the current state-of-the-art methods. These findings demonstrate the potential of MATNet in improving forecasting accuracy and suggest that it could be a promising solution to facilitate the integration of PV energy into the power grid.