Energies

This review provides a comprehensive analysis of advanced control strategies and operational optimization of energy systems, focusing on heat pumps, with an emphasis on their role in enhancing energy efficiency and operational flexibility. The study concentrates on methods supported by artificial intelligence algorithms, highlighting Model Predictive Control (MPC), Reinforcement Learning (RL), and hybrid approaches that combine the advantages of both. These methods aim to optimize both the operation of heat pumps and their interaction with thermal energy storage (TES) systems, renewable energy sources, and power grids, thereby enhancing the flexibility and adaptability of the systems under real operating conditions. Through a systematic analysis of the existing literature, 95 studies published after 2019 were examined to identify research trends, key challenges such as computational requirements and algorithm interpretability, and future opportunities. Furthermore, significant benefits of applying advanced control compared to conventional practices were highlighted, such as reduced operational costs and lower CO₂ emissions, emphasizing the importance of heat pumps in the energy transition. Thus, the analysis highlights the need for digital solutions, robust and adaptive control frameworks, and holistic techno-economic evaluation methods in order to fully exploit the potential of heat pumps and accelerate the transition to sustainable and flexible energy systems.

The output voltages of photovoltaic panels typically fluctuate due to variations in environmental conditions, and therefore the use of a buck–boost inverter is essential. This article presents a novel buck–boost voltage-source inverter topology. The proposed inverter is transformerless and thus is smaller and lower-cost than isolated topologies. The topology consists of four switches but only two of them operate at a high switching frequency during each half-cycle, which significantly reduces switching losses and improves efficiency. Furthermore, a common-ground connection between the inverter input and output effectively suppresses leakage current by mitigating the common-mode voltage issue. The modulation strategy, circuit operation, and design guidelines are presented in detail. Simulation and experimental results at 500 W are also provided to verify the effectiveness of the proposed inverter topology.

Single- and two-stage anaerobic digestion (AD) of fallen tree leaves was conducted. The AD process was preceded by mechanical, chemical, and enzymatic pretreatment. The most efficient option was the use of sulfuric acid (1%) at 121 °C for 60 min, resulting in a reducing sugar yield of 28.2 g glucose/L. The highest methane yield for single-stage AD was achieved for the 1–2 mm leaf fraction, 1.5% H₂SO₄ at 121 °C for 90 min, at 115.54 dm³ CH₄/kg VS. For two-stage AD, 10.25 dm³ H₄/kg VS and 81.24 dm³ CH₄/kg VS were achieved for the variant fraction >2 mm, 1.5% H₂SO₄, 121 °C, 60 min. The AD process can be useful for utilizing fallen leaves. Therefore, fallen leaves from trees can be used as a renewable energy source.