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Energy efficiency is an increasingly important dimension of household appliances, which is why they are labeled to indicate their energy consumption. In 2020, the European Union countries changed the labeling system from the previous system: ranging from A+++ to D, to the new system: ranging from A to G, assuming it would be more transparent for the consumer. The aim of the study was to find out the extent to which consumers are aware of the new labeling system, and the impact that the new labels have (compared to the previous ones) on the perception of household appliances and consumer decision-making. For this purpose, the survey was conducted on a nationwide representative Polish sample (n = 1054). The research was partly experimental, where the same appliances were presented with new and previous energy labels. The results showed that currently most people do not identify the new energy classes. Furthermore, products with the new labels are perceived as being less energy efficient in comparison with products with the previous labels, which shows that there is some confusion among consumers in terms of the new energy efficiency labeling system.

The conversion of chemical energy contained in organic matter into electricity has become an object of interest for many scientists worldwide. This technology is used in microbial fuel cells (MFC). Apart from generating electrical energy, these cells can be used simultaneously for wastewater treatment. Although the technology is constantly being improved, currently functioning microbial fuel cells cannot provide appropriate output parameters to use on an industrial scale. One of the barriers is so-called extracellular electron transfer, which in turn depends on the electrode type used, its material, shape, and size. According to current literature, carbon, graphite, stainless steel, and ceramics are the most frequently used electrode materials. However, more and more often, scientists are turning to other, unusual materials, the production of which uses the newest technologies, and one of them is graphene. This material is modified in different ways and connected with other materials, and the results of this seem to be very promising. Scientists manage to get a higher level of extracellular electron transfer and, hence, higher output parameters of the whole system. This article describes chosen technologies and attempts made by scientists worldwide to use graphene in MFC and their results.

The 2030 zero-net emission target in the E.U. demands a significant improvement in the energy performance of the building stock. This study analyses the adoption of connected thermostats and Home energy-management system solutions (HEMS) as an effective means to tackle the residential energy footprint. It reviews the main features of HEMS systems in terms of technology, cross-study performances, and the obstacles to widespread adoption; the study adopts the case-study methodology to examine the impact on the Italian real estate stock at a regional level. A matrix of adoption scenarios assesses the potential benefits of global residential energy savings, weighted by local climatic variations, dimension, number of single dwellings, and average primary energy reduction per household. Results demonstrate that all adoption scenarios dramatically reduce residential energy consumption, outperforming the E.U. targets for Italy by 2030.

Photovoltaic (PV) technology allows large-scale investments in a renewable power-generating system at a competitive levelized cost of electricity (LCOE) and with a low environmental impact. Large-scale PV installations operate in a highly competitive market environment where even small performance losses have a high impact on profit margins. Therefore, operation at maximum performance is the key for long-term profitability. This can be achieved by advanced performance monitoring and instant or gradual failure detection methodologies. We present in this paper a combined approach on model-based fault detection by means of physical and statistical models and failure diagnosis based on physics of failure. Both approaches contribute to optimized PV plant operation and maintenance based on typically available supervisory control and data acquisition (SCADA) data. The failure detection and diagnosis capabilities were demonstrated in a case study based on six years of SCADA data from a PV plant in Slovenia. In this case study, underperforming values of the inverters of the PV plant were reliably detected and possible root causes were identified. Our work has led us to conclude that the combined approach can contribute to an efficient and long-term operation of photovoltaic power plants with a maximum energy yield and can be applied to the monitoring of photovoltaic plants.

Accurate dynamic models of supercapacitors (SCs) are a basis for the design, control and exploitation of the hybrid energy storage systems for electric vehicles. This paper concerns a fractional model of SC impedance, based on the Cole–Cole equation describing relaxation in electric double layer. This article provides a new method of identifying the parameters of fractional order model of SC impedance, performed without disconnecting the SC module from the energy storage system. The test drive for this purpose needs only the respect a few simple recommendations. The article presents the conditions of the mentioned test drive that will ensure the frequency spectra of the recorded signals lying in the bandwidth necessary for the correct identification of the model parameters. These parameters are determined by means of the Nelder–Mead simplex optimization method. The results of the identification described by the time method coincide with those obtained in the frequency domain. It has been shown in the last part of the article that the real energy losses in these systems significantly exceed the losses determined only on the basis of the nominal capacity and series equivalent resistance (ESR), to which the SC catalogue data are usually limited. This paper also provides an auxiliary frequency criterion for the selection of SCs intended for energy storage systems of electric vehicles.

With regard to underground mining, methane is a gas that, on the one hand, poses a threat to the exploitation process and, on the other hand, creates an opportunity for economic development. As a result of coal exploitation, large amounts of coal enter the natural environment mainly through ventilation systems. Since methane is a greenhouse gas, its emission has a significant impact on global warming. Nevertheless, methane is also a high-energy gas that can be utilized as a very valuable energy resource. These different properties of methane prompted an analysis of both the current and the future states of methane emissions from coal seams, taking into account the possibilities of its use. For this reason, the following article presents the results of the study of methane emissions from Polish hard coal mines between 1993–2018 and their forecast until 2025. In order to predict methane emissions, research methodology was developed based on artificial neural networks and selected statistical methods. The multi-layer perceptron (MLP) network was used to make a prognostic model. The aim of the study was to develop a method to predict methane emissions and determine trends in terms of the amount of methane that may enter the natural environment in the coming years and the amount that can be used as a result of the methane drainage process. The methodology developed with the use of neural networks, the conducted research, and the findings constitute a new approach in the scope of both analysis and prediction of methane emissions from hard coal mines. The results obtained confirm that this methodology works well in mining practice and can also be successfully used in other industries to forecast greenhouse gas and other substance emissions.

This paper reports on the propagation of lightning overvoltage in a high-voltage direct current (HVDC) meshed grid. Since several topologies of meshed grids have been elaborated in the last decade, we used a common comprehensive reference test platform. The lightning impulse propagation was investigated with regard to the impact of surge arresters and the polarity of the lightning stroke concerning the DC line polarity (±500 kV). Various scenarios were considered, including a direct lightning strike to the DC+ conductor, to the tower, and to the shielding wire in the middle of the span, including backflash on the insulators. The influence of tower footing impedance on overvoltage levels at various nodes was assessed, depicting the critical value. A description of the models used in the simulations was provided. The main focus of the paper was on the wide-area propagation of the overvoltages in the meshed grid, at distant terminals and inside the feeders. An interesting observation was the effects of lightning at the far end of the analyzed grid, propagating through multiterminal and long-distance connections. The presented analysis, based on an exemplary meshed HVDC grid, underlines the importance of the insulation coordination studies and system security studies with respect to the localization of overvoltage protection systems.