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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.

In this study, we propose a hybrid AI optimal method to improve the efficiency of energy management in a smart grid such as Renewable Energy Community. This method adopts a Time Delay Neural Network to forecast the future values of the energy features in the community. Then, these forecasts are used by a stochastic Model Predictive Control to optimize the community operations with a proper control strategy of Battery Energy Storage System. The results of the predictions performed on a public dataset with a prediction horizon of 24 h return a Mean Absolute Error of 1.60 kW, 2.15 kW, and 0.30 kW for photovoltaic generation, total energy consumption, and common services, respectively. The model predictive control fed with such predictions generates maximum income compared to the competitors. The total income is increased by 18.72% compared to utilizing the same management system without exploiting predictions from a forecasting method.

The photocatalytic reduction of CO2 into solar fuel is considered a promising approach to solving the energy crisis and mitigating the environmental pollution caused by anthropogenic CO2 emission. Some powder photocatalysts have been demonstrated as efficient, but their drifting properties, along with difficult separation (catalyst and product), make continuous mode reaction very challenging, particularly in the liquid phase. In order to make this process commercially viable and economically more efficient, we have developed a simple and scalable method for immobilizing TiO2 P25 over the surface of glass slides using an organic-based surfactant. Improved adhesion properties and the homogeneous dispersion of catalyst nanoparticles were achieved. A holder was designed with 3D printing technology in such a way that it can hold up to six slides that can be dipped simultaneously into the suspension or solution of desired materials for a uniform and homogeneous deposition. The resulting surfaces of the dip-coated materials (e.g., TiO2 P25) were further modified by adding metallic nanoparticles and thoroughly characterized via XRD, DRS UV–Vis, SEM, and SEM–EDX. Photocatalytic tests have been performed for two major applications, viz., hydrogen production via the photoreforming of glucose and the photoreduction of CO2 into different solar fuels. The latter tests were performed in a specially designed, high-pressure reactor with Ag/P25 supported catalysts, which exhibited about three times higher formic acid productivity (ca. 20 mol/kgcat h) compared to the dispersed catalyst, with enhanced stability and recoverability. It is to note that catalysts deposited on the glass slides can easily be recovered and the materials did not show any weight loss. To the best of our knowledge, the obtained formic acid productivity is highest among the published literature.

Methods to treat kinetic data for the biodegradation of different plastic materials are comparatively discussed. Different samples of commercial formulates were tested for aerobic biodegradation in compost, following the standard ISO14855. Starting from the raw data, the conversion vs. time entries were elaborated using relatively simple kinetic models, such as integrated kinetic equations of zero, first and second order, through the Wilkinson model, or using a Michaelis Menten approach, which was previously reported in the literature. The results were validated against the experimental data and allowed for computation of the time for half degradation of the substrate and, by extrapolation, estimation of the final biodegradation time for all the materials tested. In particular, the Michaelis Menten approach fails in describing all the reported kinetics as well the zeroth- and second-order kinetics. The biodegradation pattern of one sample was described in detail through a simple first-order kinetics. By contrast, other substrates followed a more complex pathway, with rapid partial degradation, subsequently slowing. Therefore, a more conservative kinetic interpolation was needed. The different possible patterns are discussed, with a guide to the application of the most suitable kinetic model.

The continuous increase in generation from renewable energy sources, marked by correlated forecast uncertainties, requires specific methodologies to support power system operators in security management. This paper proposes a probabilistic preventive control to ensure N-1 security in presence of correlated uncertainties of renewable sources and loads. By adopting a decoupled linear formulation of the AC load flow equations, the preventive control is decomposed into two subsequent linear programming problems, the former concerning the active power and the latter the voltage/reactive power-related issues. In particular, in the active control problem, the algorithm combines Third Order Polynomial Normal Transformation, Point Estimate Method, and Cornish–Fisher expansion to model the forecast uncertainties and characterize the chance constraints in the problem. The goal is to find the optimal phase shifting transformer tap setting, conventional generation redispatching, and renewable curtailment at the minimum cost to assure the probabilistic fulfillment of N and N-1 security constraints on branch active power flows. The second stage solves another linear programming problem, which aims to minimize the adjustments to generators’ set-point voltages to avoid violations at node voltages and branch-rated limits due to reactive power flows while meeting generator reactive power constraints. Simulations performed on an IEEE test system demonstrate the effectiveness of the proposed security control method in limiting the probability of violating security limits in N and N-1 state, including voltage/reactive power constraints, in presence of correlated uncertainties.

In this work we focus on carbon nitride materials to improve the conversion and productivity of the photoreduction of CO2, developing a coupled strategy to optimize materials and operating conditions. The metal-free polymeric catalyst, graphitic carbon nitride (g-C3N4), is a relatively novel material, characterized by a wide absorbance in the visible region and demonstrating a superior performance compared to the commercial titania P25 benchmark, the most used photocatalyst for this application. We used an innovative photoreactor operating at high pressures of up to 20 bar, which is unprecedented in photocatalytic applications where transparent windows are needed. This enabled a boost in the solubility of CO2 in water when operating the reactor as a tri-phase liquid/gas/solid device and improved the surface adsorption over the catalyst. The best productivity for HCOOH so far achieved with these catalysts at 8 bar, pH = 14 and by using Na2SO3 as a hole scavenger was ca. 370 g/h kgcat. Such productivity is several orders of magnitude higher than the literature values.

The Dystar’s Levafix Brilliant Red E-6BA dye was tested as model molecule and treated through three different Advanced Oxidation Processes (APOs): H2O2/UV, Fenton and Photo-Fenton reactions. Amount of oxidant, quantity of Fe catalyst, type of light source were evaluated and optimized to complete the degradation in the shortest time. The best performances were observed when using a low-power UV lamp directly immersed into the solution. The time required to degrade 100 ppm solution of dye (pH 7, 25 °C, 36 mg/L of catalyst, 1 equivalent of oxidant) was ca. 10 minutes for both Photo-Fenton and UV/H2O2 processes, compared with 160 minutes required to complete the degradation in dark conditions. The reaction time almost doubled when employing an external UV lamp, while both visible LED and solar light sources were comparable in terms of results (ca. 50 min), but the latter strictly depended on the weather conditions. To check the possible formation of harmful intermediates in vivo acute toxicity tests of treated samples were carried out using Daphnia magna specimens. Results of toxicity test showed the importance to minimize the residual amount of H2O2 into the treated solution, as it was individuated as the main cause of toxicity.

The possibility to exploit renewable sources for the production of bulk chemicals is attractive and bio-ethanol has been recently proposed as platform to produce hydrogen via steam reforming and bio-ethylene by dehydration. Another compound with huge industrial applications is ethylene oxide, which may be in principle obtained in two steps following the route bioethanol ( bio-ethylene ( bio-ethylene oxide. Recently, a one-pot synthesis has been proposed. Based on that, the to design from the grass roots a new production plant, including the reactive and purification sections, has been developed, to exploit it industrially after checking its economic sustainability. In this work, the first step for design, i.e. kinetic modelling and reactor design, will be presented and discussed. A reaction pathway with parallel and consecutive reactions has been hypothesized and the kinetic parameters for the five reactions drawn have been retrieved by regression of experimental literature data. The model employed preliminarily is a power law pseudo-homogeneous one, used for a first sizing of the reactor and basic assessment of the technology. Three shell&tube heat exchange reactors were implemented to control the exothermicity of the reaction, with simultaneous steam production. Three catalyst beds were used in such reactors (200, 500 and 2,000 kg) with intercooling. 99.5 % ethanol conversion and 84 % selectivity to ethylene oxide were achieved, with ca. 90 kmol/h productivity, starting from the bioethanol production of a commercial bio-refinery as preliminary criterion for sizing.

This paper summarizes the results of a power system stability analysis realized for the EU project OSMOSE. The case study is the electrical network of Sicily, one of the two main islands of Italy, in a scenario forecasted for 2050, with a large penetration of renewable generation. The objective is to establish if angle and voltage stabilities can be guaranteed despite the loss of the inertia and the regulation services provided today by traditional thermal power plants. To replace these resources, new flexibility services, potentially provided by renewable energy power plants, battery energy storage systems, and flexible loads, are taken into account. A highly detailed dynamical model of the electrical grid, provided by the same transmission system operator who manages the system, is modified to fit with the 2050 scenario and integrated with the models of the mentioned flexibility services. Thanks to this dynamic model, an extensive simulation analysis on large and small perturbation angle stability and voltage stability is carried out. Results show that stability can be guaranteed, but the use of a suitable combination of the new flexibility services is mandatory.

Solar irradiation data collected at the latitude of Milan city, near the 45th parallel North, and original activity data of some high-performing photocatalysts (i.e., commercial TiO2 P25, TiO2 prepared by flame spray pyrolysis, 0.2% wt/wt Au/P25) have been used to evaluate the feasibility and the efficiency of an ideal solar photoreactor for the CO2 photoreduction in liquid phase. The best theoretical performance was achieved with commercial bare P25 titania, despite the fact that it was the material with the widest band gap (3.41 eV vs. 3.31 for FSP and 3.12 for Au/P25). In that case the efficiency of energy storage was calculated as about 2% (considering the total irradiated solar energy) and ca 18% (considering only the UV fraction of solar irradiance). Most of the energy content of the products was stored as formic acid, which would return a productivity of about 640 kg/year kgcat under daylight solar irradiation considering the variance of the irradiance data. Bare FSP titania gave a less promising result, while Au/P25 ranked in the middle. A comparison between the proposed setup and a photoreactor irradiated with UV lamps powered through a wind turbine or solar panels, which allow for an indirect use of renewable energy sources also intended for energy storage purposes, unveil that the latter is many times less efficient than the hypothesized direct solar photoreactor, despite the fact that it could be a reasonable storage system for energy production peaks.