Energy Storage and Applications

The paper examines the experimental performance of air–rock bed, oil only, and oil–rock bed systems for storing heat suitable for cooking applications. The air–rock bed system is charged using hot air from a compressed air tank, while the oil–rock bed system employs a resistive heating element to heat a small volume of oil, which then circulates naturally. The charging process for the oil systems was controlled by adjusting funnel heights, and temperature measurements were taken using thermocouples connected to a data logger. Both systems can store thermal energy ranging from 4.5 kWh to 8 kWh and achieve temperatures between 150 °C and 300 °C, depending on supply temperatures. The simpler oil–rock bed allows for the direct boiling of water using the high temperature produced, and tests indicated comparable boiling times between systems. The findings suggest that these heat storage systems could enhance the advancement and integration of solar cookers, enabling more flexible cooking options.

The challenge of optimally controlling energy storage systems under uncertainty conditions, whether due to uncertain storage device dynamics or load signal variability, is well established. Recent research works tackle this problem using two primary approaches: optimal control methods, such as stochastic dynamic programming, and data-driven techniques. This work’s objective is to quantify the inherent trade-offs between these methodologies and identify their respective strengths and weaknesses across different scenarios. We evaluate the degradation of performance, measured by increased operational costs, when a reinforcement learning policy is adopted instead of an optimal control policy, such as dynamic programming, Pontryagin’s minimum principle, or the Shortest-Path method. Our study examines three increasingly intricate use cases: ideal storage units, storage units with losses, and lossy storage units integrated with transmission line losses. For each scenario, we compare the performance of a representative optimal control technique against a reinforcement learning approach, seeking to establish broader comparative insights.

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.