Multiple microgrids (MGs) close to each other can be interconnected to construct a cluster to enhance reliability and flexibility. This paper presents a comprehensive and comparative review of recent studies on DC MG clusters’ control strategies. Different schemes regarding the two significant control aspects of networked DC MGs, namely DC-link voltage control and power flow control between MGs, are investigated. A discussion about the architecture configuration of DC MG clusters is also provided. All advantages and limitations of various control strategies of recent studies are discussed in this paper. Furthermore, this paper discusses three types of consensus protocol with different time boundaries, including linear, finite, and fixed. Based on the main findings from the reviewed studies, future research recommendations are proposed. Full article

The aim of the manuscript was to present the collective results of research on the profitability of using various renewable sources in Poland with the greatest development potential. In the paper, the economic parameters of various investment projects were determined and calculated, i.e., Net Capital Value (NPV), Internal Rate of Return (IRR) and the Period of Return on Invested Capital (PBT). The economic assessment of the use of RES technologies was supplemented with the assessment of environmental benefits. The ecological criterion adopted in the study was the assessment of the potential and costs of reducing greenhouse gas emissions as a result of replacing fossil fuels with renewable energy technologies. On the basis of the constructed economic model to assess the profitability of investments, it has been shown that the analyzed projects will start to bring, depending on their type and technical specification, measurable economic benefits in the form of a reduction in the amount of energy purchased on an annual basis and environmental benefits in the form of reduction of carbon dioxide emissions to the atmosphere. Moreover, the calculations show a high potential for the use of certain renewable sources in Poland, which contributes to the fulfillment of energy and emission obligations towards the EU. The analyzes and research of the Polish energy market with the use of the presented models have shown that the project is fully economically justified and will allow investors to make a rational decision on the appropriate selection of a specific renewable energy source for their investment. The presented economic models to assess the profitability of investments in renewable energy sources can be successfully used in other countries and can also be a starting point for a discussion about the direction of energy development. Due to the lack of collective, original and up-to-date research on the domestic market, the manuscript provides the reader with the necessary knowledge regarding the legitimacy of using renewable energy sources, investment and environmental profitability. Full article

Classic and intelligent techniques aim to locate and track the maximum power point of photovoltaic (PV) systems, such as perturb and observe (P&O), fuzzy logic (FL), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFISs). This paper proposes and compares three intelligent algorithms for maximum power point tracking (MPPT) control, specifically fuzzy, ANN, and ANFIS. The modeling of a single-diode equivalent circuit-based 3 kWp PV plant was developed and validated to achieve this purpose. Then, the MPPT techniques were designed and applied to control the buck–boost converter’s switching device of the PV plant. All three methods use the ambient conditions as input variables: solar irradiance and ambient temperature. The proposed methodology comprises the study of the dynamic response for tracking the maximum power point and the power generated of the PV systems, and it was compared to the classic P&O technique under varying ambient conditions. We observed that the intelligent techniques outperformed the classic P&O method in tracking speed, tracking accuracy, and reducing oscillation around the maximum power point (MPP). The ANN technique was the better control algorithm in energy gain, managing to recover up to 9.9% power. Full article

Process integrity, insufficient data, and system complexity in the automotive manufacturing sector are the major uncertainty factors used to predict failure probability (FP), and which are very influential in achieving a reliable maintenance program. To deal with such uncertainties, this study proposes a fuzzy fault tree analysis (FFTA) approach as a proactive knowledge-based technique to estimate the FP towards a convenient maintenance plan in the automotive manufacturing industry. Furthermore, in order to enhance the accuracy of the FFTA model in predicting FP, the effective decision attributes, such as the experts’ trait impacts; scales variation; and assorted membership, and the defuzzification functions were investigated. Moreover, due to the undynamic relationship between the failures of complex systems in the current FFTA model, a Bayesian network (BN) theory was employed. The results of the FFTA model revealed that the changes in various decision attributes were not statistically significant for FP variation, while the BN model, that considered conditional rules to reflect the dynamic relationship between the failures, had a greater impact on predicting the FP. Additionally, the integrated FFTA–BN model was used in the optimization model to find the optimal maintenance intervals according to the estimated FP and total expected cost. As a case study, the proposed model was implemented in a fluid filling system in an automotive assembly line. The FPs of the entire system and its three critical subsystems, such as the filling headset, hydraulic–pneumatic circuit, and the electronic circuit, were estimated as 0.206, 0.057, 0.065, and 0.129, respectively. Moreover, the optimal maintenance interval for the whole filling system considering the total expected costs was determined as 7th with USD 3286 during 5000 h of the operation time. Full article

This article presents laboratory and spatial numerical modeling of cemented paste backfill. The first part of the research concerned laboratory tests of a mixture of sand, water, and variable cement content (5%, 10%, and 15%). The density and curing time of the mixture were determined. Moreover, cylindrical samples with a diameter of 46 mm and a height of 92 mm were constructed, for which compressive and tensile strength were calculated after one, two, three, and four weeks. The second part of the research concerned 3D numerical modeling with the use of RS3 software. For the exploitation field with dimensions of 65 m × 65 m, a strip-mining method was designed. The main objective of the research was to determine the changes in displacements around the haulage room and transportation roadway located in the immediate vicinity of the exploitation field. For the first time in numerical modeling, a two-sided strip method was used for the four stages of mining the ore deposit where the post-mining space was filled with a cemented paste backfill. Based on this research, the compressibility coefficient was determined. Full article

This study investigated the impact of sonication parameters on the efficiency of the extraction of bioactive substances from hawthorn berries. The ultrasonic treatment was performed in two modes: continuous and pulse. In the pulse mode, the samples were sonicated with the following processor settings: 1 s on-2 s off. The effective ultrasonic processor times were 5, 10, and 15 min, and the total extraction times were 15 min, 30 min, and 45 min. The content of total polyphenols and total anthocyanins was determined by a spectrophotometric method. We show that the operating mode of the processor affects extraction efficiency, energy consumption and unit energy inputs. Extraction supported by a pulsating ultrasonic field allowed saving from 20% to 51% of energy with a simultaneous higher efficiency of the process. In addition, we show that the unit energy consumption in the pulsed mode was about 40% to 68% lower than the energy consumption in the case of continuous operation. Full article

The growth of populations and economy in Northern Cyprus has led to continuing utilization of fossil fuels as the primary source of electricity, which will raise environmental pollution. Thus, utilizing renewable energy, particularly solar energy, might be a solution to minimize this issue. This paper presents the potential of grid-connected solar PV power generation at Near East University Hospital (NEU Hospital), one of the largest and leading medical facilities in Northern Cyprus, to meet the energy demand during the daytime to reduce energy bills. For this purpose, the first objective of the study is to evaluate the solar energy potential as a power source for the NEU Hospital based on four datasets (actual measurement, Satellite Application Facility on Climate Monitoring (CMSAF), Surface Radiation Data Set-Heliosat (SARAH), and ERA-5, produced by the European Centre for Medium-range Weather Forecast). The results showed that the solar resource of the selected location is categorized as excellent (class 5), that is, the global solar radiation is within the range of 1843.8–2035.9 kWH/m². The second objective is to investigate the impact of orientation angles on PV output, capacity factor, economic feasibility indicators, and CO₂ emissions by using different PV modules. The results are compared with optimum orientation angles found by Photovoltaic Geographical Information System (PVGIS) simulation software. This objective was achieved by using RETScreen Expert software. The results demonstrated that the highest performance of the proposed system was achieved for orientation angles of 180° (azimuth angle) and −35° (tilt angle). Consequently, it is recommended that orientation angles, PV modules, and market prices are considered to maximize energy production and reduce electricity production costs. Full article

Climate change is becoming a global problem. In many countries, actions are taken with the main aim of reducing CO₂ emissions. The main action, especially in developed countries, is decarbonization. The European Union has become one of the organizations that plays a leading role in decarbonization of the economy. For this reason, renewable energy sources are being intensively developed in the EU countries. Solar energy with the use of PV installations is developing the fastest. Poland is one of the European leaders in photovoltaic development, and according to estimates for 2021–2025, it will continue to be. The aim of this study was to find out the opinions of people toward actions related to the decarbonization policy in Poland. These opinions were obtained through the prism of respondents’ attitudes toward energy produced by means of PV micro-installations. A questionnaire survey was used in this research. The survey was conducted using the CAWI (Computer-Assisted Web Interview) technique. To analyze the results of the study, a Kruskal–Wallis ANOVA test and U–Mann Whitney test were used. Responses were obtained from 633 people. The results obtained from the survey allowed us to draw conclusions, which include the following: (1) a lack of general conviction of respondents about the effectiveness of Poland’s decarbonization policy on reducing global CO₂ emissions, especially among those who show a higher willingness to use PV installations, (2) the willingness to use PV installations is motivated by economic rather than environmental benefits, (3) the need for more widespread public campaigns aimed at promoting the benefits of decarbonization and renewable energy sources, and (4) the finding that the respondents’ region of residence (with a different degree of insolation) mattered for the willingness to use PV installations. Full article

The solid electrolyte interphase (SEI) film plays a significant role in the capacity and storage performance of lithium primary batteries. The electrolyte additives are essential in controlling the morphology, composition and structure of the SEI film. Herein, fluoroethylene carbonate (FEC) is chosen as the additive, its effects on the lithium primary battery performance are investigated, and the relevant formation mechanism of SEI film is analyzed. By comparing the electrochemical performance of the Li/AlF₃ primary batteries and the microstructure of the Li anode surface under different conditions, the evolution model of the SEI film is established. The FEC additive can decrease the electrolyte decomposition and protect the lithium metal anode effectively. When an optimal 5% FEC is added, the discharge specific capacity of the Li/AlF₃ primary battery is 212.8 mAh g⁻¹, and the discharge specific capacities are respectively 205.7 and 122.3 mAh g⁻¹ after storage for 7 days at room temperature and 55 °C. Compared to primary electrolytes, the charge transfer resistance of the Li/AlF₃ batteries with FEC additive decreases, indicating that FEC is a promising electrolyte additive to effectively improve the SEI film, increase discharge-specific capacities and promote charge transfer of the lithium primary batteries. Full article

The paper presents the results and analysis of interdisciplinary research concerning electromagnetic field shielding, conductive polymers printed on textiles and numerical simulation using the finite element method (FEM). The use of conductive, layered textiles for shielding electromagnetic interference (EMI) has been proposed. After establishing the optimal conditions for deposition of polyaniline (PANI) and polypyrrole (PPy) on polyacrylonitrile (PAN) fabric, conductive composites were made by means of reactive inkjet printing. For this purpose, polyacrylonitrile (PAN) fabrics were coated with polyaniline or polypyrrole, obtained by chemical oxidation of aniline hydrochloride and pyrrole by ammonium peroxydisulfate. The morphology of the obtained coatings was observed using a scanning electron microscope (SEM). The conductive properties (surface resistance) of the fabrics were measured using the four-wire method, and the tests of the effectiveness of electromagnetic shielding were carried out using the waveguide method in the frequency range from 2.5 to 18 GHz. The results of experimental shielding effectiveness (SE) tests and numerical simulation showed that the composites of polyacrylonitrile with polyaniline PAN/PANI and polyacrylonitrile with polypyrrole PAN/PPy achieved very good and good EMI shielding efficiency, respectively. Moreover, the obtained measurement results were verified by numerical modeling with the use of FEM–ANSYS HFFS software. Full article

The injection performance of carbon dioxide (CO₂) for oil recovery depends upon its injection capability and the actual injection rate. The CO₂–rock–water interaction could cause severe formation damage by plugging the reservoir pores and reducing the permeability of the reservoir. In this study, a simulator was developed to model the reactivity of injected CO₂ at various reservoir depths, under different temperature and pressure conditions. Through the estimation of location and magnitude of the chemical reactions, the simulator is able to predict the effects of change in the reservoir porosity, permeability (due to the formation/dissolution) and transport/deposition of dissoluted particles. The paper also presents the effect of asphaltene on the shift of relative permeability curve and the related oil recovery. Finally, the effect of CO₂ injection rate is analyzed to demonstrate the effect of CO₂ miscibility on oil recovery from a reservoir. The developed model is validated against the experimental data. The predicted results show that the reservoir temperature, its depth, concentration of asphaltene and rock properties have a significant effect on formation/dissolution and precipitation during CO₂ injection. Results showed that deep oil and gas reservoirs are good candidates for CO₂ sequestration compared to shallow reservoirs, due to increased temperatures that reduce the dissolution rate and lower the solid precipitation. However, asphaltene deposition reduced the oil recovery by 10%. Moreover, the sensitivity analysis of CO₂ injection rates was performed to identify the effect of CO₂ injection rate on reduced permeability in deep and high-temperature formations. It was found that increased CO₂ injection rates and pressures enable us to reach miscibility pressure. Once this pressure is reached, there are less benefits of injecting CO₂ at a higher rate for better pressure maintenance and no further diminution of residual oil. Full article

The higher share of renewable energy sources in the electrical grid and the electrification of significant sectors, such as transport and heating, are imposing a tremendous challenge on the operation of the energy system due to the increase in the complexity, variability and uncertainties associated with these changes. The recent advances of computational technologies and the ever-growing data availability allowed the development of sophisticated and efficient algorithms that can process information at a very fast pace. In this sense, the use of machine learning models has been gaining increased attention from the electricity sector as it can provide accurate forecasts of system behaviour from energy generation to consumption, helping all the stakeholders to optimize their activities. This work develops and proposes a methodology to enhance load demand forecasts using a machine learning model, namely a feed-forward neural network (FFNN), by incorporating an error correction step that involves the prediction of the initial forecast errors by another FFNN. The results showed that the proposed methodology was able to significantly improve the quality of load demand forecasts, demonstrating a better performance than the benchmark models. Full article

Different fluid compositions have been considered as heat transfer fluids (HTF) for concentrating solar power (CSP) applications. In linear focusing CSP systems synthetic oils are prevalently employed; more recently, the use of molten salt mixtures in linear focusing CSP systems has been proposed too. This paper presents a comparative assessment of thermal oils and five four nitrate/nitrite mixtures, among the ones mostly employed or proposed so far for CSP applications. The typical medium-size CSP plant (50 MWe) operating with synthetic oil as HTF and the “solar salt” as TES was considered as a benchmark. In the first part of the paper, physical properties and operation ranges of different HTFs are reviewed; corrosion and environmental issues are highlighted too. Besides an extensive review of HTFs based on data available from the open literature, the authors report their own obtained experimental data needed to thoroughly compare different solutions. In the second part of the paper, the impact of the different HTF options on the design and operation of CSP plants are analyzed from techno-economic perspectives. Full article

Short-term residential load forecasting is the precondition of the day-ahead and intra-day scheduling strategy of the household microgrid. Existing short-term electric load forecasting methods are mainly used to obtain regional power load for system-level power dispatch. Due to the high volatility, strong randomness, and weak regularity of the residential load of a single household, the mean absolute percentage error (MAPE) of the traditional methods forecasting results would be too big to be used for home energy management. With the increase in the total number of households, the aggregated load becomes more and more stable, and the cyclical pattern of the aggregated load becomes more and more distinct. In the meantime, the maximum daily load does not increase linearly with the increase in households in a small area. Therefore, in our proposed short-term residential load forecasting method, an optimal number of households would be selected adaptively, and the total aggregated residential load of the selected households is used for load prediction. In addition, ordering points to identify the clustering structure (OPTICS) algorithm are also selected to cluster households with similar power consumption patterns adaptively. It can be used to enhance the periodic regularity of the aggregated load in alternative. The aggregated residential load and encoded external factors are then used to predict the load in the next half an hour. The long short-term memory (LSTM) deep learning algorithm is used in the prediction because of its inherited ability to maintain historical data regularity in the forecasting process. The experimental data have verified the effectiveness and accuracy of our proposed method. Full article

Data centres are a key infrastructure for the global digital economy, helping enable the EU “Digital Decade” by 2030. In 2015, data centres were estimated to consume 2.5% of EU electricity demand. In Ireland, the concentrated presence of data centres could consume 37% of national electricity demand by 2028. The uncertainty of data centre facility-level energy efficiency paired with the need to achieve a low-carbon economy pose significant challenge for generation and transmission network planning. This is the first paper to apply a model of technology diffusion with a national forecast of changes in Irish data centre electricity demand through more efficient liquid cooling. The methodology serves as a technology-agnostic resource for practitioners performing forecasts under uncertainty with limited information. Results suggest that technology adoption could lower national electricity demand by 0.81% if adopted by new plant from 2019 to 2028. Savings rise to 3.16% over the same period if adopted by new and existing data centres. Adoption would also lower related emissions by 4.70% and 23.04% over the same period across both scenarios, respectively. Results highlight substantial potential electricity and associated emissions savings available in the sector and suggest policy options to support a transition towards a low-carbon economy. Full article