Accurate predictions of photovoltaic generation are essential for effectively managing power system resources, particularly in the face of high variability in solar radiation. This is especially crucial in microgrids and grids, where the proper operation of generation, load, and storage resources is necessary to avoid grid imbalance conditions. Therefore, the availability of reliable prediction models is of utmost importance. Authors address this issue investigating the potential benefits of a machine learning approach in combination with photovoltaic power forecasts generated using weather models. Several machine learning methods have been tested for the combined approach (linear model, Long Short-Term Memory, eXtreme Gradient Boosting, and the Light Gradient Boosting Machine). Among them, the linear models were demonstrated to be the most effective with at least an RMSE improvement of 3.7% in photovoltaic production forecasting, with respect to two numerical weather prediction based baseline methods. The conducted analysis shows how machine learning models can be used to refine the prediction of an already established PV generation forecast model and highlights the efficacy of linear models, even in a low-data regime as in the case of recently established plants.

In Italy, the calculation of the energy needs of buildings has been mainly based on quasi-steady state calculation procedures. Nowadays, the increasing interest in more detailed energy analysis for high-efficiency buildings requires more accurate calculation methods. In this work, starting from the hourly data of UNI 10349, the downscaling of a typical meteorological year was carried out by applying different mathematical and physical models for the main climate variables considered in the energy balance of a building to be used in dynamic simulation tools. All results were validated with one-minute measurements observed at the ENEA Research Centre in Rome, Italy. The results showed an MBE% of 0.008% and RMSE% of 0.114% using the interpolation spline method for the temperature, while, for the global horizontal irradiance, applying the novel sinusoidal–physical interpolation method showed an MBE% of −0.4% and an RMSE% of 31.8% for the 1 min observation data. In this paper, an easily implemented novel model for downscaling solar irradiance for all sky conditions that takes into account the physical aspects of atmospheric phenomena is presented.

This paper presents the bi-dimensional modelling of a thermal energy storage (TES) facility, ideated, realized and tested by ENEA (Italy). This prototype enabled the thermocline storage with molten salts in an innovative geometry including two vertical channels to force the circulation through two spiral heat exchangers, realized respectively for charging and discharging phases. A thermo-physical model was built and tested for this particular geometry in order to analyse the temperature distribution along the radius. The numerical results well reproduced the experimental values, particularly if the start-up period is excluded from the simulation. An analytical solution was also produced to provide a short-cut methodology able to evaluate the thermocline (along the vertical axis) depending on both the time and the radius values (2D variation). Hence, the thermocline degradation was studied accurately by taking the radius value as sensitivity parameter. It was found that at the edges (r→1) the thermocline does not “degrade” and does not vary for times 10 times greater with respect to the centre of the tank (r→0). The numerical modelling can be useful for the process analysis, for scaling-up the thermal storage system and for evaluating the system reliability for industrial plants (Solar-to-Electricity) from a technical-economical point of view.

Parabolic trough concentrated solar power (CSP) plants are particularly promising renewable sources of energy, whose easy integration with thermal energy storage (TES) systems allows to mitigate the intermittency of electricity generation. Currently, molten nitrates, with two tanks arrangement, are mainly used for sensible heat accumulation. To reduce costs and make the CSP storage systems more manageable, single tank configurations have been proposed, where the cold and hot fluids are stored in the same container and separated because of their density difference. The aim of the present work is to study the storage performances presented by two novel ternary and quaternary mixtures, proposed within the European project IN POWER. An experimental campaign was preliminarily performed to investigate the fluids thermo-physical properties, and the obtained values were utilized as input data to model the discharge phase in a thermocline tank. The simulation results were compared with the ones acquired considering two commercial materials, namely, solar salt and Hitec XL®. Overall, considering same temperature ranges, higher discharging times are obtained for the quaternary and ternary mixtures, with the ternary presenting a smaller thermocline thickness than the solar salt while this parameter is the same considering the quaternary and Hitec XL®.

Hybridization of CSP plants with alternative energy sources (fuels) represents a means to improve flexibility of operation, power dispatchability and utilization factor of the plant. New generation CSP plants make use of molten salts as Heat Transfer Fluid (HTF) besides Thermal Energy Storage (TES) medium. Therefore, proper interfaces should be developed to effectively transfer the heat from the back-up source to the molten salt. This paper presents the results obtained in the experimental validation of an innovative gas-fueled Molten Salt Heater (MSH) prototype. The objective of this research is to validate the MSH design, where the specific properties of molten salts (compared to other HTFs, e.g., thermal oils) have to be taken into account. The developed reduced-scale MSH (90 kW thermal) consists of a heat exchanger with the molten salt flowing inside finned tubes cross-flowed with the hot flue gas generated in an upstream combustion chamber. LPG or a biogas-like mixture has been used as gas fuel. Experimental results have been obtained with two different molten salt mixtures: the “solar salt” binary mixture (NaNO₃/KNO₃, 60/40%w) typically used in CSP applications (up to 565 °C) and the ternary mixture known as Hitec XL^® containing sodium/potassium/calcium nitrates (NaNO₃/KNO₃/Ca(NO₃)₂, 15/43/42%w) characterized by lower freezing temperatures. Experimental tests have been carried out changing some operative parameters like the flow rate of the molten salt (0.45–0.94 kg/s), the inlet temperatures of the molten salt (303–445 °C) and of the hot gas (596–632 °C). For both molten salt mixtures, it was demonstrated that heat transfer correlations based on the Dittus-Boelter equation allow to predict experimental results with <10% deviation between experimental and theoretical values of the heat transfer coefficient.