Energy Storage and Applications

This article analyses the possibility of using Li-ion batteries removed from battery electric vehicles (BEVs) as short-term energy storage devices in a near-zero energy building (nZEB) in conjunction with a rooftop photovoltaic (PV) system. The technical and economic feasibility of this solution was compared to that of a standard commercial LIB (Lithium-Ion battery) BESS Battery Energy Storage System). Two generations of the same BEV model battery were tested to analyse their suitability for powering a building. The necessary changes to the setup of such a battery for building power supply purposes were analysed, as well as its suitability. As a result, analyses of profitability over the predicted life span and NPV (net present value) of SLEVBs (second-life BEV batteries) for building power were carried out. The study also conducted preliminary research on the effectiveness of such projects and their pros and cons in terms of security. The author calculates the profitability of a ready-made PV BESS with a set of SLEVBs, estimating the payback periods for such investments relative to electricity prices in Poland. The article concludes on the potential of SLEVBs to support self-consumption in nZEB buildings and its environmental impact on the European circular economy.

Methane, transported as Liquefied Natural Gas (LNG) at −163 °C, is becoming the leading fuel in the decarbonization of the maritime sector within the low-carbon fuels. More than 30 years of knowledge has allowed the development of an extensive offshore supply network that includes regasification plants to store and supply it to the grid, both onshore and offshore. This article first reviews the current state of the sector. Then, the operation of a typical onshore regasification plant and the heat transfer through the storage tanks that causes the generation of boil-off gas (BOG) are analyzed by means of two different methodologies. Finally, and based on the results obtained, the different improvements that can be implemented in this type of installation to improve its energy efficiency and insulation are established, such as, for example, an improvement of more than 4 W/m² by reinforcing the thickness of the materials of the tank dome.

This paper presents a comparative study of data-driven modeling approaches for vanadium redox flow batteries (VRFBs), utilizing Multiple Linear Regression (MLR) and Random Forest (RF) algorithms. Experimental voltage–capacity datasets from a 1 kW/1 kWh VRFB system were digitized, processed, and used for model training, validation, and testing. The MLR model, built using eight optimized features, achieved a mean error (ME) of 0.0204 V, a residual sum of squares (RSS) of 8.87, and a root mean squared error (RMSE) of 0.1796 V on the test data, demonstrating high predictive performance in stationary operating regions. However, it exhibited limited accuracy during dynamic transitions. Optimized through out-of-bag (OOB) error minimization, the Random Forest model achieved a training RMSE of 0.093 V and a test RMSE of 0.110 V, significantly outperforming MLR in capturing dynamic behavior while maintaining comparable performance in steady-state regions. The accuracy remained high even at lower current densities. Feature importance analysis and partial dependence plots (PDPs) confirmed the dominance of current-related features and SOC dynamics in influencing VRFB terminal voltage. Overall, the Random Forest model offers superior accuracy and robustness, making it highly suitable for real-time VRFB system monitoring, control, and digital twin integration. This study highlights the potential of combining machine learning algorithms with electrochemical domain knowledge to enhance battery system modeling for future energy storage applications.