Energies, Volume 16, Issue 22 (November-2 2023) – 222 articles

The increasing penetration of Distributed Energy Resources (DERs) in Distribution Systems (DSs) has motivated studies on Virtual Power Plants (VPPs). However, few studies have jointly assessed the sizing and economic attractiveness of VPPs from the entrepreneur’s perspective and the potential benefits and impacts on power systems while maintaining the scope to DSs. This study proposes a methodology for sizing VPPs and simulating their economic optimal dispatch and economic attractiveness with a focus on the entrepreneur’s viewpoint. In addition, it also evaluates VPPs’ potential benefits and impacts on a DS or Transmission System (TS) while considering the interface between the Distribution System Operator (DSO) and the Transmission System Operator (TSO). The methodology employs optimization to minimize the Net Present Cost (NPC) of the project, in relation to sizing the DERs, and to obtain the economic optimal dispatch of the BESSs that comprise the VPP. Moreover, a power flow analysis and probabilistic reliability assessment are used to evaluate the benefits and impacts on the power system. The methodology was applied to a case study involving Photovoltaic (PV) systems and Battery Energy Storage Systems (BESSs) used by aggregated medium voltage consumers, which configure Technical Virtual Power Plants (TVPPs) participating in Demand Response (DR) via incentives, with a network model of the Brazilian National Interconnected System (SIN) adapted from the 2030 Ten-Year Energy Expansion Plan (PDE) of the Energy Research Office (EPE), along with data from the Geographic Database of the Distribution Utility (BDGD). The results indicate the economic attractiveness of DERs according to the premises adopted and indicate improvements in TS reliability indexes with the possibility of TVPPs’ dispatch after transmission contingencies. Full article

This paper provides a review of the aerodynamic behavior of horizontal axis wind turbines operating in hazardous environmental conditions. Over the past decade, renewable energy use has accelerated due to global warming, depleting fossil fuel reserves, and stricter environmental regulations. Among renewable options, solar and wind energy have shown economic viability and global growth. Horizontal axis wind turbines offer promising solutions for sustainable energy demand. Since wind turbines operate in an open environment, their efficiency depends on environmental conditions. Hazard environmental conditions, such as icing, rainfall, hailstorms, dust or sand, insects’ collisions, increased humidity, and sea spray, result in degraded aerodynamic characteristics. The outcome of most studies has been that the airfoils’ lift is degraded, and at the same time, drag is increased when wind turbines operate under these conditions. The objective of this review is to improve our comprehension of these crucial aspects so they are taken into account when designing wind turbine blades, and it offers suggestions for future research paths. It serves as a valuable resource that can inspire researchers who are dedicated to enhancing the aerodynamic characteristics of horizontal axis wind turbines. Full article

Effective fault detection, classification, and localization are vital for smart grid self-healing and fault mitigation. Deep learning has the capability to autonomously extract fault characteristics and discern fault categories from the three-phase raw of voltage and current signals. With the rise of distributed generators, conventional relaying devices face challenges in managing dynamic fault currents. Various deep neural network algorithms have been proposed for fault detection, classification, and location. This study introduces innovative fault detection methods using Artificial Neural Networks (ANNs) and one-dimension Convolution Neural Networks (1D-CNNs). Leveraging sensor data such as voltage and current measurements, our approach outperforms contemporary methods in terms of accuracy and efficiency. Results in the IEEE 6-bus system showcase impressive accuracy rates: 99.99%, 99.98% for identifying faulty lines, 99.75%, 99.99% for fault classification, and 98.25%, 96.85% for fault location for ANN and 1D-CNN, respectively. Deep learning emerges as a promising tool for enhancing fault detection and classification within smart grids, offering significant performance improvements. Full article

An effective dead-time control strategy for the three-phase dual active bridge (3P-DAB) converter of a distribution system is studied to reduce the switching losses of power switches and improve the under-light-load power conversion efficiency. Because of the advantages of a dual-active bridge converter, such as an inherent zero-voltage switching (ZVS) capability without any additional resonant tank and a seamless bi-directional power transition, this is an attractive topology for bi-directional application. The 3P-DAB converter is apt for high-power applications such as aircraft due to an interleaved structure, which can reduce conduction losses. However, the design of the dead time depends on engineering experience and empirical methods. In order to overcome the conventional practicality of the dead-time design method, the effective control of dead time is proposed based on the theoretical analysis. In this paper, the overall explanation of the 3P-DAB converter is shown with operation principles. In addition, the dead-time effect of the 3P-DAB converter is examined and the practical variable dead-time control strategy is studied. Finally, experimental results validate the proposed variable dead-time control strategy using a 25 kW prototype 3P-DAB converter. Full article

In recent years supercritical CO₂ power plants have seen a growing interest in a wide range of applications (e.g., nuclear, waste heat recovery, solar concentrating plants). The Allam Cycle, also known as the Allam-Fetvedt or NET Power cycle, seems to be one of the most interesting direct-fired sCO₂ cycles. It is a semi-closed loop, high-pressure, low-pressure ratio, recuperated, direct-fired with oxy-combustion, trans-critical Brayton cycle. Numerical simulations play a key role in the study of this novel cycle. For this reason, the aim of this review is to offer the reader a wide array of modeling solutions, emphasizing the ones most frequently employed and endeavoring to provide guidance on which choices seem to be deemed most appropriate. Furthermore, the review also focuses on the system’s performance and on the opportunities related to the integration of the Allam cycle with a series of processes, e.g., cold energy storage, LNG regasification, biomass or coal gasification, and ammonia production. Full article

Regarding the existing evaluation methods for photovoltaic (PV) hosting capacity in the distribution system that do not consider the spatial distribution of rooftop photovoltaic potential and are difficult to apply on the actual large-scale distribution systems, this paper proposes a PV hosting capacity evaluation method based on the improved PSPNet, grid multi-source data, and the CRITIC method. Firstly, an improved PSPNet is used to efficiently abstract the rooftop in satellite map images and then estimate the rooftop PV potential of each distribution substation supply area. Considering the safety, economy, and flexibility of distribution system operation, we establish a multi-level PV hosting capacity evaluation system. Finally, based on the rooftop PV potential estimation of each distribution substation supply area, we combine the multi-source data of the grid digitalization system to carry out security verification and indicator calculation and convert the indicator calculation results of each scenario into a comprehensive score through the CRITIC method. We estimate the rooftop photovoltaic potential and evaluate the PV hosting capacity of an actual 10 kV distribution system in Shantou, China. The results show that the improved PSPNet solves the hole problem of the original model and obtains a close-to-realistic rooftop photovoltaic potential estimation value. In addition, the proposed method considering the photovoltaic potential in this paper can more accurately evaluate the rooftop PV hosting capacity of the distribution system compared with the traditional method, which provides data support for the power grid corporation to formulate a reasonable PV development and hosting capacity enhancement program. Full article

In recent years, there has been a growing interest in utilizing hydrogen as an energy carrier across various transportation sectors, including aerospace applications. This interest stems from its unique capability to yield energy without generating direct carbon dioxide emissions. The conversion process is particularly efficient when performed in a fuel cell system. In aerospace applications, two crucial factors come into play: power-to-weight ratio and the simplicity of the powerplant. In fact, the transient behavior and control of the fuel cell are complicated by the continuously changing values of load and altitude during the flight. To meet these criteria, air-cooled open-cathode Proton Exchange Membrane (PEM) fuel cells should be the preferred choice. However, they have limitations regarding the amount of thermal power they can dissipate. Moreover, the performances of fuel cell systems are significantly worsened at high altitude operating conditions because of the lower air density. Consequently, they find suitability primarily in applications such as Unmanned Aerial Vehicles (UAVs) and Urban Air Mobility (UAM). In the case of ultralight and light aviation, liquid-cooled solutions with a separate circuit for compressed air supply are adopted. The goal of this investigation is to identify the correct simulation approach to predict the behavior of such systems under dynamic conditions, typical of their application in aerial vehicles. To this aim, a detailed review of the scientific literature has been performed, with specific reference to semi-empirical and control-oriented models of the whole fuel cell systems including not only the stack but also the complete balance of plant. Full article

This study introduces novel stochastic distributed cooperative control (SDCC) in the context of island microgrids (MGs). A proportional resonant (PR) controller and virtual impedance droop control in stationary reference frames are employed in cooperation with distributed averaging secondary control optimized by the dragonfly algorithm (DA). The suggested approach demonstrates the capability to achieve mean-square synchronization for the voltage and frequency restoration of distributed generators (DGs) to ensure efficient active power sharing. Therefore, a sparse communication network has been used to avoid data congestion and reduce the need for extensive communication and information exchange. The proposed system offers an instinctive compromise between voltage regulation and reactive power sharing. A conventional centralized secondary control with PR droop control is simulated for performance evaluation and comparison purposes. In this study, empirical evidence is demonstrated to support the MG’s ability to confront communication failure and its ability to work reliably during plug-and-play operations. Full article

Solid-state and sol-gel syntheses were selected as easy and scalable methods to prepare a lithium-rich cathode material for lithium-ion batteries. Among the extended family of layered oxides, Li_1.2Ni_0.2Mn_0.6O₂ was chosen for its low nickel content and the absence of cobalt. Both synthesis methods involved two heating steps at different temperatures, 600 and 900 °C. The first step is needed to decompose the metal acetates, which were selected as precursors, and the second step is needed to crystallise the material. To obtain a material with well-defined defects, the rate of heating and cooling was carefully controlled. The materials were characterised by X-ray diffraction, SEM coupled with EDS analysis, and thermal analysis and were finally tested as cathodes in a lithium semi cell. The solid-state synthesis allowed us to obtain better structural characteristics with respect to the sol-gel one in terms of a well-formed hexagonal layer structure and a reduced Li⁺/Ni²⁺ disorder. On the other hand, the sol-gel method produced a material with a higher specific capacity. The performance of this latter material was then evaluated as a function of the discharge current, highlighting its good rate capabilities. Full article

Power batteries generate a large amount of heat during the charging and discharging processes, which seriously affects the operation safety and service life. An efficient cooling system is crucial for the batteries. This paper numerically simulated a power battery pack composed of 8 lithium-ion cells immersed in the coolant AmpCool AC-110 to study the effects of different coolants, different discharge rates, different coolant mass flow rates, different inlet temperatures and different inlet and outlet settings on the maximum temperature, the maximum temperature difference, the pressure drop, and the required pump power in the battery pack. Among the five coolants studied, W-E in water-based fluids has the best cooling effect, but because of high electric conductivity, it requires special considerations to avoid electric leakage. Increasing the mass flow rate of the coolant can significantly decrease T_max and ΔT_max, but when the mass flow rate is already high, the decrease is limited and not obvious. Both Δp and the required pump power increase as the mass flow rate increases, and the required pump power increases faster. The inlet temperature will affect the physical properties of the coolant, and choosing the appropriate inlet temperature can not only decrease ΔT_max, but also decrease Δp and the required pump power in the battery pack. The range of 25~27 °C of the coolant AC-110 inlet temperature is recommended. For different inlet and outlet settings, the two-inlet two-outlet setting used in Case 7 has the best cooling effect, and the results indicate uniform distribution is very important to decrease temperature. Full article

Natural gas is the most growing fossil fuel due to its environmental advantages. For the economical transportation of natural gas to distant markets, physical (i.e., liquefaction and compression) or chemical (i.e., direct and indirect) monetisation options must be considered to reduce volume and meet the demand of different markets. Planning natural gas supply chains is a complex problem in today’s turbulent markets, especially considering the uncertainties associated with final market demand and competition with emerging renewable and hydrogen energies. This review study evaluates the latest research on mathematical programming (i.e., MILP and MINLP) as a decision-making tool for designing and planning natural gas supply chains under different planning horizons. The first part of this study assesses the status of existing natural gas infrastructures by addressing readily available natural monetisation options, quantitative tools for selecting monetisation options, and single-state and multistate natural gas supply chain optimisation models. The second part investigates hydrogen as a potential energy carrier for integration with natural gas supply chains, carbon capture utilisation, and storage technologies. This integration is foreseen to decarbonise systems, diversify the product portfolio, and fill the gap between current supply chains and the future market need of cleaner energy commodities. Since natural gas markets are turbulent and hydrogen energy has the potential to replace fossil fuels in the future, addressing stochastic conditions and demand uncertainty is vital to hedge against risks through designing a responsive supply chain in the project’s early design stages. Hence, hydrogen supply chain optimisation studies and the latest works on hydrogen–natural gas supply chain optimisation were reviewed under deterministic and stochastic conditions. Only quantitative mathematical models for supply chain optimisation, including linear and nonlinear programming models, were considered in this study to evaluate the effectiveness of each proposed approach. Full article

This article examines the potential for wind and solar energy generation in the state of Amapá, Brazil, using ERA5 data from between 1991 and 2020. Key metrics considered include wind power density, capacity factor, photovoltaic potential, and concentrated solar power output. Analyses revealed pronounced wind speeds offshore during summer and in continental regions during spring. Solar irradiance was notably higher in the spring. Differences in wind potential were observed between northern and southern offshore areas. Concentrated solar power efficiency and photovoltaic potential were influenced by location and cloud cover, respectively. Overall, summer presents the best offshore wind energy potential, while spring is optimal for onshore solar energy in Amapá. This study underscores the importance of understanding local climatic patterns when planning energy installations in the region. Full article

Silicon carbide (SiC) MOSFETs tend to become one of the main switching elements in power electronics applications of medium- and high-power density. Usually, SiC MOSFETs are connected in parallel to increase power rating. Unfortunately, unequal current sharing between power devices occurs due to mismatches in the technical parameters between devices and the layout of the power circuit. This current imbalance causes different current stress upon power switches, raising concerns about power system reliability. For over a decade, various methods and techniques have been proposed for balancing the currents between parallel-connected SiC MOSFETs. However, most of these methods cannot be implemented unless the deviation between the technical parameters of semiconductor switches is known. This requirement increases the system cost because screening methods are extremely costly and time-consuming. In addition, most techniques aim at suppressing only the transient current imbalance. In this paper, a simple but innovative current balancing technique is proposed, without the need of screening any power device. The proposed technique consists of an open-loop system capable of balancing the currents between two parallel-connected SiC MOSFETs, with the aid of two active gate drivers and an FPGA, actively and independently of the cause. Experimental test results validate that the proposed open-loop method can successfully achieve suppression of current imbalance between parallel-connected SiC MOSFETs, proving its durability and validity level. Full article

A community-integrated energy system under a multiple-uncertainty low-carbon economic dispatch model based on the deep reinforcement learning method is developed to promote electricity low carbonization and complementary utilization of community-integrated energy. A demand response model based on users’ willingness is proposed for the uncertainty of users’ demand response behavior; a training scenario set of a reinforcement learning agent is generated with a Latin hypercube sampling method for the uncertainties of power, load, temperature, and electric vehicle trips. Based on the proposed demand response model, low-carbon economic dispatch of the community-integrated energy system under multiple uncertainties is achieved by training the agent to interact with the environment in the training scenario set and reach convergence after 250 training rounds. The simulation results show that the reinforcement learning agent achieves low-carbon economic dispatch under 5%, 10%, and 15% renewable energy/load fluctuation scenarios, temperature fluctuation scenarios, and uncertain scenarios of the number of trips, time periods, and mileage of electric vehicles, with good generalization performance under uncertain scenarios. Full article

Phase change materials (PCMs) are increasingly gaining prominence in thermal energy storage due to their impressive energy storage capacity per unit volume, especially in applications with low and medium temperatures. Nevertheless, PCMs have significant limitations regarding their ability to conduct and store heat, primarily due to their inadequate thermal conductivity. One potential solution for improving the thermal conductivity of PCMs involves the inclusion of nanoparticles into them. However, a recurring issue arises after several thermal cycles, as most nanoparticles have a tendency to clump together and settle at the container’s base due to their low interfacial strength and poor compatibility. To address this challenge, including surfactants such as sodium dodecylbenzene sulfonate (SDBS) has emerged as a prevalent and economically viable approach, demonstrating a substantial impact on the dispersion of carbon nanoparticles within PCMs. The foremost objective is to investigate the improvement of thermal energy storage by utilizing graphene nanoplatelets (GNP), which are dispersed in A70 PCM at various weight percentages (0.1, 0.3, 0.5, 0.7, and 1.0), both with and without the use of surfactants. The findings indicate a remarkable enhancement in thermal conductivity when GNP with surfactants is added to the PCM, showing an impressive increase of 122.26% with a loading of 1.0 wt.% compared to conventional PCM. However, when 1.0 wt.% pure GNP was added, the thermal conductivity only increased by 48.83%. Additionally, the optical transmittance of the composite containing ASG-1.0 was significantly reduced by 84.95% compared to conventional PCM. Furthermore, this newly developed nanocomposite exhibits excellent stability, enduring 1000 thermal cycles and demonstrating superior thermal and chemical stability up to 257.51 °C. Due to its high thermal stability, the composite NePCM is an ideal candidate for preheating in industrial and photovoltaic thermal (PVT) applications, where it can effectively store thermal energy. Full article

The basic malfunction of automatic transmissions (ATs) is oil flow through hydraulic precision pair clearances called an “internal leakage”, leading to difficulties in controlling the AT. There are no sufficiently accurate methods for assessing the impact of “internal leakage” on the AT technical condition in the course of operation. A proprietary hydraulic precision pair internal leak flow model has been proposed in the paper. The novelty of the model is applying electro-hydraulic controller precision pair clearance values as data that was determined through measurements involving an actual object with a specific AT operation period. The authors conduct variant tests of the model to determine the total AT hydraulic system controller leakage. Reduced oil viscosity (approx. 20%) results in internal leakage increasing by 25%. Significant wear of the controller’s precision pair and increased oil temperature (above 80 °C) lead to internal leakage increasing by more than 50% and oil pressure decreasing below the permissible value. Full article

At least 24 liquefied natural gas (LNG) rollover incidents have been reported since 1960. During rollover, because of the heat ingress through the tank walls, a stratified LNG may be suddenly homogenized while releasing massive amounts of vapor. It can result in an overpressure in the tank and significant amounts of potentially explosive LNG vapor being vented out. Both of these factors represent considerable hazards. Rollover is a physical mixing process in a single tank with two or more different cells of LNG of different compositions, temperatures, and densities that can manifest in large boil-off rates. It can exceed venting equipment capacities, and vapor pressure in tank increases rapidly and in extreme cases can lead to tank damage. This paper presents numerical approach for determination of time of rollover occurrence in storage tank. The presented model is based on the energy balance of the stratified cryogenic liquid and the gas phase as separate three thermodynamic systems in the storage tank. As a result of proposed model, for the adopted assumptions and cylindrical tank volume of 78,500 m³, the approximate time of the rollover occurrence was determined for two cases. In the first case, for heavier LNG, the rollover phenomenon will occur 193.25 h after the start of the calculations from the assumed initial conditions. In the second case, for light LNG with a higher initial liquid level in the tank, the rollover will occur after 150.25 h. Full article

Fossil fuel is a non-renewable fuel, and with the development of modern industry and agriculture, the storage capacity of fossil fuels is constantly decreasing. In this study, a systematic study and analysis were conducted on the combustion characteristics, engine performance, and exhaust emission characteristics of castor biodiesel–diesel blends and pure diesel fuel in different proportions at different speeds of a single-cylinder four-stroke diesel engine under constant load. The castor biodiesel required for the experiment is generated through an ester exchange reaction and mixed with diesel in proportion to produce biodiesel–diesel blends. The experimental results show that as an oxygenated fuel with a higher cetane number, the CO, HC, and smoke emissions of diesel and B80 blend fuel at 1800 rpm were reduced by 16.9%, 31.6%, and 68%, respectively. On the contrary, the NOx and CO₂ emissions increased by 17.3% and 34.6% compared to diesel at 1800 rpm. In addition, due to its high viscosity and low calorific value, the brake thermal efficiency and brake-specific fuel consumption of the biodiesel–diesel blends are slightly lower than those of diesel, but the biodiesel–diesel blends exhibit lower exhaust gas temperatures. Comparing B80 and diesel fuel at 1800 rpm, the BSFC of diesel at 1800 rpm is 3.12 kg/W·h, whereas for B80 blended fuel, it increases to 4.2 kg/W·h, and BTE decreases from 25.39% to 21.33%. On the contrary, B60 blended fuel exhibits a lower exhaust emission temperature, displaying 452 °C at 1800 rpm. Based on the experimental results, it can be concluded that castor biodiesel is a very promising clean alternative fuel with low waste emissions and good engine performance. Full article

Inverse Uncertainty Quantification (IUQ) has gained increasing attention in the field of nuclear engineering, especially nuclear thermal-hydraulics (TH), where it serves as an important tool for quantifying the uncertainties in the physical model parameters (PMPs) while making the model predictions consistent with the experimental data. In this paper, we present an extension to an existing Bayesian inference-based IUQ methodology by employing a hierarchical Bayesian model and variational inference (VI), and apply this novel framework to a real-world nuclear TH scenario. The proposed approach leverages a hierarchical model to encapsulate group-level behaviors inherent to the PMPs, thereby mitigating existing challenges posed by the high variability of PMPs under diverse experimental conditions and the potential overfitting issues due to unknown model discrepancies or outliers. To accommodate computational scalability and efficiency, we utilize VI to enable the framework to be used in applications with a large number of variables or datasets. The efficacy of the proposed method is evaluated against a previous study where a No-U-Turn-Sampler was used in a Bayesian hierarchical model. We illustrate the performance comparisons of the proposed framework through a synthetic data example and an applied case in nuclear TH. Our findings reveal that the presented approach not only delivers accurate and efficient IUQ without the need for manual tuning, but also offers a promising way for scaling to larger, more complex nuclear TH experimental datasets. Full article

As developed regions explore avenues to enhance their industries in order to become climate-neutral, numerous studies have identified distinct factors that may hinder the shift towards a low-carbon economy. The objective of our research was to pinpoint key barriers to adaptation to a low-carbon economy among small- and medium-sized enterprises (SMEs) of Central Europe from the viewpoint of the company’s structure. The aim was to examine whether attitudes towards barriers to adaptation to a low-carbon economy represent a key factor that prevents the faster and more effective uptake of such adaptations by SMEs. Both the industrial and service sectors were considered. A quantitative data collection method, CATI, was employed. Using our methodology, we applied a non-parametric testing procedure, specifically, the Kruskal–Wallis test, to compare more than two independent samples, together with the Mann–Whitney U test. Through this analysis, it was found that companies regard the uncertainty of return on investment and its payback period as the most serious barrier to adaption to a low-carbon economy. Meanwhile, the lack of cooperation with research institutions and universities is perceived as the least important barrier. Companies are critical of existing regulations for adaptation to the low-carbon economy, which do not provide incentives for companies, though sole traders consider this an insignificant barrier. The shift towards a low-carbon economy is one of the greatest challenges of the 21st century. Understanding the initial motivational variables can significantly contribute to the process of transition towards the use of renewable energy sources by companies, regardless of their size or sector. Full article

In the face of increasing irregular temperature patterns and climate shifts, the need for accurate power consumption prediction is becoming increasingly important to ensure a steady supply of electricity. Existing deep learning models have sought to improve prediction accuracy but commonly require greater computational demands. In this research, on the other hand, we introduce DelayNet, a lightweight deep learning model that maintains model efficiency while accommodating extended time sequences. Our DelayNet is designed based on the observation that electronic series data exhibit recurring irregular patterns over time. Furthermore, we present two substantial datasets of electricity consumption records from South Korean buildings spanning nearly two years. Empirical findings demonstrate the model’s performance, achieving 21.23%, 43.60%, 17.05% and 21.71% improvement compared to recurrent neural networks, gated-recurrent units, temporal convolutional neural networks and ARIMA models, as well as greatly reducing model complexity and computational requirements. These findings indicate the potential for micro-level power consumption planning, as lightweight models can be implemented on edge devices. Full article

The electric capacitance tomography (ECT) technique has been widely used in phase distribution reconstruction, while the practical application raised nonideal noise and other errors for cryogenic conditions, requiring a more accurate algorithm. This paper develops a new image reconstruction algorithm for ECT by coupling the traditional Landweber algorithm with the least square support vector regression (LSSVR) for cryogenic fluids. The performance of the algorithm is quantitatively evaluated by comparing the inversion images with the experimental results for both the room temperature working medium with the dielectric constant ratio close to cryogenic fluid and the cryogenic fluid of liquid nitrogen/nitrogen vapor (LN₂-VN₂). The inversion images based on the conventional LBP and Landweber algorithms are also presented for comparison. The benefits and drawbacks of the developed algorithms are revealed and discussed, according to the results. It is demonstrated that the correlated coefficients of the images based on the developed algorithm reach more than 0.88 and a maximum of 0.975. In addition, the minimum void fraction error of the algorithm is reduced to 0.534%, which indicates the significant optimization of the LSSVR coupled method over the Landweber algorithm. Full article

To enable highly automated manufacturing and net-zero carbon emissions, manufacturers have invested heavily in smart manufacturing. Sustainable and smart manufacturing involves improving the efficiency and environmental sustainability of various manufacturing operations such as resource allocation, data collecting and monitoring, and process control. Recently, a lot of artificial intelligence and optimization applications based on smart grid systems have improved the energy usage efficiency in various manufacturing operations. Therefore, this survey collects recent works on applications of artificial intelligence and optimization for smart grids in smart manufacturing and analyzes their features, requirements, and challenges. In addition, potential trends and further challenges for the integration of smart grids with renewable energies for smart manufacturing, applications of 5G and B5G (beyond 5G) technologies in the SG system, and next-generation smart manufacturing systems are discussed to provide references for further research. Full article

Fresh water is one of the prime necessities of a society; however, its availability is becoming a major concern with the increasing population. There are not enough sources of fresh water at present due to the high rate of population increase. Many regions worldwide face limited access to fresh water. Given economic limitations, there is an urgent need to create and market technologies enabling households to generate their fresh water. In areas with abundant solar energy and proximity to seawater or well-water sources, solar still technology, if developed and commercialized, offers a cost-effective solution for freshwater needs. Thus, the current study is focused on exploring the potential of solar stills for producing fresh water. A single-slope solar still is designed, fabricated and experimentally tested for the production of fresh water. The results of the analysis indicate a maximum production of 2.88 L/day with an energy efficiency of 52.42% and an exergetic efficiency of 7.04%. Overall, the current study reveals significant potential in utilizing solar stills for producing fresh water, which could be increased further if research is conducted on modifying its basic design to increase its productivity. Full article

Geothermal water (GW) resources are highly valued as clean, renewable energy sources. In this study, a comprehensive analysis of water chemistry and isotope data from 25 GW samples was conducted to gain insights into the hydrochemical characteristics and formation mechanisms of the GW in the northern Jinan region (NJR). Statistical analysis and hydrochemical methods were employed for relevant analysis. The findings reveal that the GW in the NJR exhibits high salinity, with an average total dissolved solids (TDS) concentration of 9009.00 mg/L. The major ions identified are Na⁺ and Cl⁻, with mean concentrations of 2829.73 mg/L and 4425.77 mg/L, respectively, resulting in a hydrochemical type of Cl⁻Na. The analysis of δ²H and δ¹⁸O isotopes indicates that the GW originates from atmospheric precipitation that undergoes deep cycling and interaction with older groundwater. The composition of ³H suggests that the GW in the NJR is a mixture of waters, while radiocarbon dating (¹⁴C) suggests that the recharge of the GW may have occurred in the late Pleistocene era. The GW in the NJR is classified as partially equilibrated waters. The temperature range of geothermal reservoirs is 57.13 to 99.74 °C. The hydrochemical components primarily result from water–rock interactions, including silicate weathering, cation exchange, as well as carbonate weathering and the dissolution of halite and gypsum. Moreover, taking into account the hydrogeological conditions, hydrochemistry, and isotope analysis, a conceptual model of the geothermal reservoir in the NJR was developed. The research findings serve as a valuable reference and foundation for the development and utilization of geothermal resources in the Jinan region. These originate from the Taiyi mountains in the south or the Taihang mountains in the west, and experience deep circulation and long runoff times. This study provides a reference for the sustainable development and utilization of regional geothermal resources. Full article

This article investigates the impact of loading on the hybrid powertrain of the FCAT-30 model, equipped with a proton-exchange-membrane fuel cell (PEMFC) and a nickel–metal hydride (NiMH) battery. This study involves analyzing structural component performance based on voltage and current measurements of the fuel cell, battery, and powertrain. Tests conducted under different load conditions reveal significant differences in battery current and fuel-cell voltage, highlighting the crucial role of the battery in the powertrain. External loading induces cyclic operation of the fuel cell, generating peak power. The energy balance analysis demonstrates that, under no-load conditions, the vehicle consumes 37.3% of its energy from the fuel cell, with a total energy consumption of 3597 J. Under load, the energy from the battery is significantly utilized, resulting in a constant fuel-cell share of approximately 19%, regardless of the vehicle’s load. This study concludes that the battery predominantly drives the powertrain, with the fuel cell acting as a secondary energy source. These findings provide valuable insights into the power distribution and energy balance in the hybrid powertrain. Using a load driving profile reduced the fuel-cell-stack energy contribution by 6.85% relative to driving without an external load. Full article

Furan tests provide a non-intrusive and cost-effective method of estimating the degradation of paper insulation, which is critical for ensuring the reliability of power grids. However, conducting routine furan tests can be expensive and challenging, highlighting the need for alternative methods, such as machine learning algorithms, to predict furan concentrations. To establish the generalizability and robustness of the furan prediction model, this study investigates two distinct datasets from different geographical locations, Utility A and Utility B. Three scenarios are proposed: in the first scenario, a round-robin cross-validation method was used, with 75% of the data for training and the remaining 25% for testing. The second scenario involved training the model entirely on Utility A and testing it on Utility B. In the third scenario, the datasets were merged, and round-robin cross-validation was applied, similar to the first scenario. The findings reveal the effectiveness of machine learning algorithms in predicting furan concentrations, and particularly the stacked generalized ensemble method, offering a non-intrusive and cost-effective alternative to traditional testing methods. The results could significantly impact the maintenance strategies of power and distribution transformers, particularly in regions where furan testing facilities are not readily available. Full article

The primary aim of this study was to assess the investment activity of basic local government units in the development of renewable energy sources co-financed by EU funds depending on CO₂ emissions and other socio-economic conditions in terms of regions of Poland in the years 2007–2020. Empirical studies aimed at the verification of the research hypothesis that “the greatest investment activity in local projects co-financed from EU funds related to the development of renewable energy sources is observed for local government units in regions with highest CO₂ emissions”. Empirical studies were conducted based on data from the Ministry of Investment and Economic Development in Poland, the Local Data Bank, and the National Centre for Emissions Management. Thus, the conducted analyses provide both cognitive and applicatory values for the establishment of an appropriate energy transition policy in individual regions of Poland, which may be implemented by local government authorities within the current financial framework. Data concerning CO₂ emissions at the regional level were estimated by applying the original disaggregation method as modified by the authors, which made it possible to fill the research gap resulting from the lack of data on emissions at the regional level. In order to show the regional diversification in investment activity of local government units in terms of renewable energy sources, its multi-faceted analysis was conducted by applying the Ward method. Clusters of regions with similar investment activity of local government units were described based on characteristics included in the typological classification (so-called active characteristics) and selected indexes showing CO₂ emission levels, as well as selected socio-economic indexes (so-called passive characteristics). Based on the empirical studies, the research hypothesis presented in this paper was negatively verified. Considering both multiannual financial frameworks, the EU financial support for the development of renewable energy sources was used primarily by local government units of a predominantly agricultural character, and less advanced in terms of their development but exhibiting conditions conducive to renewable energy development. Full article

The 21st century brings new challenges related to the rapid development of renewable energy sources. Increasingly ambitious climate targets adopted at the European and global level are stimulating an increase in the share of photovoltaic sources in electricity generation. Unfortunately, the intermittent supply of electricity with solar panels makes this energy much more difficult to use. The production of electricity only during the sunny period forces the need to collect it during the day and then use it at night or during unfavorable weather conditions. Therefore, energy storage facilities are important when producing energy from renewable sources. Their installation increases the flexibility of transmission systems and creates opportunities for stable operation with a large share of renewable energy sources. This article offers an economic evaluation of the use of energy storage for a photovoltaic farm under the conditions of using the prices of the Polish Power Exchange. The period from June 2020 to May 2023 was analyzed. The results in terms of productivity of PV installations from the village of Łęki and prices from the Commodity Energy Exchange in the same period were used. Analyzing the results, it can be seen that energy storage brings additional revenue, especially during periods with large spreads in the value of electricity prices. The use of energy storage also allows for more efficient use of energy from photovoltaic panels. The value of additional revenue from energy storage was particularly evident in 2022, when energy prices peaked. Full article

In this paper was analysed a wireless power transfer system (WPT) with multiple resonators supplying, for example, sensors or LED lighting. Energy is transferred simultaneously using a group of identical planar spiral circular coils acting as transmitters and receivers. These coils were arranged to form transmitting and receiving planes. The receivers were connected to independent power supply circuits of each, e.g., sensor or LED lighting. Higher power reliability and flexibility can be achieved by isolating these circuits. The proposed system was described and discussed. Taking into account the skin effect and mutual couplings, a theoretical analysis was made. A detailed analysis was made at the resonant frequency of the system. The system was modeled using a matrix equation and appropriate formulas. The calculations were verified experimentally for different loads and two distances between transmitters and receivers. The efficiency and receiver power were compared and discussed. The maximum efficiency was about 45% at the small distance between the planes. The maximum efficiency of the WPT system decreased more than two times to less than 20% when the distance between the coils was doubled. The results and discussion of the conducted analysis may provide valuable knowledge when designing this type of system. Full article