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There are many promising renewable energy (RE) technologies that could help increase the contribution of RE in energy supply but which are not yet commercially available. The development rate of new RE technologies depends on many factors, such as Research and Development (R&D) efforts and policies. This study focuses on comparing China’s efforts regarding the development of new RE technologies (e.g., wave and tidal, binary geothermal power, floating solar, micro hydro, osmotic energy, floating offshore wind and vertical axis wind turbines) with those of the European Union (EU). For this purpose, we collected data from publications and databases and analysed several indicators: e.g., the development of renewable electricity generation and capacity, demonstration projects, investments in R&D and patent applications. The results show that China has become a big player globally for mainstream renewable electricity (hydropower, wind and solar PV). This development is due to China’s industrial policy and prioritization of effectiveness over cost efficiency. The main developments in China occurred in the 2010s, while the EU was a frontrunner in the 2000s. For the newer or less mainstream technologies, the application in China is still low, compared to the EU, except for floating solar, where China is a lead player. Regarding patent applications, China has shown a higher application amount compared to the EU since 2006. However, only a small share of China’s patents are valid internationally. We conclude that China has emerged as a big player in mainstream renewable energy technologies over the last decade. In regard to new renewable energy technologies, China is predominantly involved in solar energy and, in comparison to the EU, less in other new technologies (e.g., binary geothermal systems and ocean energy).

Vertical-axis wind turbines (VAWTs) have a long history, with a wide variety of turbine archetypes that have been designed and tested since the 1970s. While few utility-scale VAWTs currently exist, the placement of the generator near the turbine base could make VAWTs advantageous over tradition horizontal-axis wind turbines for floating offshore wind applications via reduced platform costs and improved scaling potential. However, there are currently few numerical design and analysis tools available for VAWTs. One existing engineering toolset for aero-hydro-servo-elastic simulation of VAWTs is the Offshore Wind ENergy Simulator (OWENS), but its current modeling capability for floating systems is non-standard and not ideal. This article describes how OWENS has been coupled to several OpenFAST modules to update and improve modeling of floating offshore VAWTs and discusses the verification of these new capabilities and features. The results of the coupled OWENS verification test agree well with a parallel OpenFAST simulation, validating the new modeling and simulation capabilities in OWENS for floating VAWT applications. These developments will enable the design and optimization of floating offshore VAWTs in the future.

Autonomous electric vehicles (AEVs) have garnered increasing attention in recent years as they hold significant promise for transforming the transportation sector. However, despite advances in the field, effective vehicle drive control remains a critical challenge that must be addressed to realize the full potential of AEVs. This study presents a novel approach to AEV drive control for concurrently generating a suitable speed profile and controlling the vehicle drive speed along a planned path that takes into account various driving circumstances that mimic real-world driving. The designed strategy is divided into two parts: The first part presents a proposed speed planning algorithm (SPA) that works on developing an adequate speed profile for vehicle navigation; first, the algorithm uses an approach for identifying sharp curves on the predefined trajectory; secondly, based on the dynamic properties of these curves, it generates an appropriate speed profile to ensure smooth vehicle travel across the entire trajectory with varying velocities. The second part proposes a new back-stepping control technique with a space vector modulation (SVM) strategy to control the speed of an induction motor (IM) as a traction part of the AEV. A load torque observer has been designed to enhance the speed-tracking task, while the system stability has been proven using Lyapunov theory. Through a series of experiments and simulations using MATLAB/Simulink software and the dSPACE 1104 real-time interface, we demonstrate the effectiveness of the SPA combined with the back-stepping control technique and highlight its potential to advance the field of AEV technology. Our findings have important implications for the design and implementation of AEVs and provide a foundation for future research in this exciting area of study.

The automotive sector has been experiencing a rapid development in recent times. Modern trends aim at the progress of so-called sustainable, eco-friendly vehicles, in many cases associated with advanced technologies and assistance systems. It is increasingly common to see cars with electric, hybrid, gas or hydrogen propulsion systems. Such approaches can create a transformation of manufacturing processes towards the growth of new types and fields of technologies and systems in the automotive industry. The manuscript discusses the subject of vehicles with various types of power supply, i.e., propulsion system. The investigation was executed with different respondent groups whereby the analysis was performed predominantly by using two merging criteria such as place of residence and gender of respondents. Following the research conducted, we state that given a variety of variables, the majority of the respondents are convinced that, prospectively, electric and hybrid vehicles in all probability will be the prevailing types of vehicles occurring on the roads of the Czech Republic. However, it is also important to analyze the situation of the current access to energy resources for manufacturing of both kinds of vehicles and, above all, their utilization. Thus, the preferences of drivers are key issues, but it is also necessary to take into consideration the production capacity and investment profitability regarding the purchase of vehicles with modern types of propulsion system.

Due to the lack of historical output data of new wind farms, there are difficulties in the scheduling and planning of power grid and wind power output scenario generation. The randomness and uncertainty of meteorological factors lead to the results of traditional scenario generation methods not having the ability to accurately reflect their uncertainty. This article proposes a RAC-GAN-based scenario generation method for a new wind farm output. First, the Pearson coefficient is adopted in this method to screen the meteorological factors and obtain the ones that have larger impact on wind power output; Second, based on the obtained meteorological factors, the Grey Relation Analysis (GRA) is used to analyze the meteorological correlation between multiple wind farms with sufficient output data and new wind farms (target power stations), so that the wind farm with high meteorological correlation is selected as the source power station. Then, the K-means method is adopted to cluster the meteorological data of the source power station, thus generating the target power station scenario in which the cluster information serves as the label of the robust auxiliary classifier generative adversarial network (RAC-GAN) model and the output data of the source power station is considered as the basis. Finally, the actual wind farm output and meteorological data of a region in northeast China are employed for arithmetic analysis to verify the effectiveness of the proposed method. It is proved that the proposed method can effectively reflect the characteristics of wind power output and solve the problem of insufficient historical data of new wind farm output.

Biomass gasification is a relevant option to produce a gaseous fuel, it faces, however, several barriers regarding its quality for energetic applications. Therefore, in this study, air-steam and O2-enriched air mixtures were used as gasification agents during the gasification of residual biomass from eucalyptus to improve the producer gas quality. The steam addition promoted an increase in CO2 and H2 concentrations, whilst decreasing the CO and CH4 concentrations. The steam addition had no evident impact on the lower heating value of the dry producer gas and a positive effect on gas yield and the H2:CO molar ratio, attaining the later values up to 1.6 molH2∙mol−1CO. The increase in O2 concentration in the gasification agent (φ) promoted an increase in all combustible species and CO2 concentrations. The lower heating value of the dry producer gas underwent an increase of 57%, reaching a value of 7.5 MJ∙Nm−3drygas, when the φ increased from 20 to 40 %vol.O2,dryGA. The gas yield had a significant decrease (33%) with φ increase. This work showed that the addition of steam or O2 during air gasification of residual biomass improved producer gas quality, overcoming some of the barriers found in conventional air gasification technology.

The perovskite solar cell has recently gained momentum within the renewable energy industry due to its unique advantages such as high efficiency and cost-effectiveness. However, its instability remains a challenge to its commercialization. In this study, a singlet fission material, namely tetracene, is coupled with the perovskite solar cell to simulate its effect on the solar cell. The amount of thermalization loss and the temperature of the perovskite layer are simulated and analyzed to indicate the mechanism’s effectiveness. We found that coupling the tetracene layer resulted in a drastic reduction in thermalization loss and a slower slope in perovskite layer temperature. This indicates that tetracene would stabilize the perovskite solar cell and minimize its potential losses. The thickness of the solar cell layers is also analyzed as a factor of the overall effectiveness of singlet fission on solar cells.

Microalgal biomass is a promising alternative and renewable substrate for bioenergy production. The main problem for its commercial application is to obtain and keep a high level of production by providing microalgae with appropriate conditions for growth. The aim of this study was to determine optimal culture conditions such as temperature, photoperiod, and pH. The amount of biomass by gravimetry, optical density by spectrophotometry, and productivity were analyzed. Suitable values of cultivation parameters allowed for the increased growth and biomass productivity of Arthrospira platensis (4.24 g·L−1), Chlamydomonas reinchardtii (1.19 g·L−1), Chlorella vulgaris (2.37 g·L−1), and Dunaliella salina (4.50 g·L−1) and optical density for Ch. reinchardtii and C. vulgaris. These species had maximum biomass productivity of 0.72, 0.12, 0.36, and 0.77 g·L−1·d−1, respectively. Productivity was determined by cultivation temperature and for Ch. reinchardtii also by pH.

A chilled water system transports cooling functionality from refrigerators to users via heating, ventilation, and air conditioning (HVAC) systems. This paper investigated an optimal control strategy to regulate the volume flow rate of each user branch in a chilled water system, considering the minimum resistance operation to reduce energy consumption. An artificial neural network (ANN) was adopted to establish the nonlinear relationship between the volume flow rate of each user branch, pump frequency, and valve opening of each user branch. An optimal control strategy for a chilled water HVAC system is proposed in this article, according to the pump-valve combined control (PVCC) principle and an ANN model, i.e., pump-valve combined control using an artificial neural network model (PVCC-ANN). A series of tests were conducted to collect data to train the ANN model and analyze the performance of the PVCC-ANN in an experimental chilled water system. The results show that the trained ANN model has good prediction performance. A minimum resistance operation can be achieved to control the volume flow rate of each user branch independently by using the PVCC-ANN model. Moreover, the proposed PVCC-ANN method shows good energy-saving performance in chilled water systems, which can be attributed to the minimum resistance operation. Taking the energy consumption of the pump’s constant frequency operation as the reference, the energy saving rate using the PVCC-ANN is between 14.3% and 58.6% under 10 operating conditions, as reported in this paper.

Outdoor air conditioning systems (ACS) are used as autonomic systems as well as in combined outdoor and indoor ACS of the variable refrigerant flow (VRF) type, with variable speed compressors (VSC) as their advanced version. Methods for determining the optimal value of refrigeration capacity and providing the maximum rate of the summarized annual refrigeration energy generation increment, according to its needs at minimum compressor sizes and rational values, are applied to reveal the reserves for reducing the designed (installed) refrigeration capacity, thus enabling us to practically achieve maximum annual refrigeration energy generation as the primary criterion at the second stage of the general design methodology previously developed by the authors. The principle of sharing the total thermal load on the ACS between the ranges of changeable loads for outdoor air precooling, and a relatively stable load range for further processing air are used as its basis. According to this principle, the changeable thermal load range is chosen as the object for energy saving by recuperating the excessive refrigeration generated at lowered loading in order to compensate for the increased loads, thereby matching actual duties at a reduced designed refrigeration capacity. The method allows us to determine the corresponding level of regulated loads (LRL) of SRC and the load range of compressor operation to minimize sizes.

Earth–rock dams are typical soil–rock mixtures with high heterogeneity. Mastering the effect of dam heterogeneity on seismic wavefields is the premise of accurately detecting hidden risks in dams. In this paper, based on the soil–rock mixture characteristics of actual dams, a soil–rock mixture model that can reflect the heterogeneity of dams is established through local segmentation and reassignment of random disturbances. The influence of local area size on model heterogeneity is described. The seismic wavefield in a soil–rock mixture dam is numerically simulated through a staggered-grid finite-difference algorithm with second-order accuracy in time and sixth-order accuracy in space. Then, the effect of dam heterogeneity on effective wavefields is analyzed. The results show that the heterogeneity of the earth–rock dam can lead to scattered waves in the seismic wavefield, and the scattered waves are mainly generated by Rayleigh surface waves. In the seismic record, scattered waves with strong energy appear in the region below the surface waves. The scattered wave energy is weak and close to that in the homogeneous media in the region above the surface waves. As the rock content in the dam increases, the scattering of seismic wavefields and the energy of scattered waves weaken gradually. The scattered waves generated by the heterogeneity of the dam significantly impact the reflected longitudinal wave and converted wave but, affect the reflected shear wave less. The numerical simulation results are consistent with the acquired seismic wavefield from the field test, proving the effectiveness of the numerical simulation for the seismic wavefield propagation characteristics of the earth–rock dam.

Fibrous materials are often used in the manufacturing of fire protection devices such as fire curtains. Their optimization and improved performance is still a topic of interest. The present work aims to develop and test a new combination of fibers arranged in various 2D and 3D patterns with coatings. For this purpose, basalt fibers were added into a glass fiber fabric, and wires of a shape memory material (SMM) were inserted into the fabric to create air pockets induced by temperature. In fire curtains, the base structure is a 2D basket pattern, and all combinations were tested with and without a waterborne polyurethane (WPU) coating with inorganic materials. Three different tests were selected to characterize the thermal behavior: fire resistance, ignitability, and smoke production. Fiberglass proved to be the best material to provide thermal resistance in fire curtains, with the outer surface temperature of the fabric below 650 °C at the end of the tests. The SMM wires provided good protection during the initial stages of the test, but a combination of excessive deformation and reduced strength of the fabric resulted in a sudden failure of the structure. Basalt fibers contribute to a reduction of smoke production. It was observed an improvement of up to 10% in the thermal capacity between 1MIX2 (glass fibers fabric with coating, MIX2) and the best commercial curtain evaluated, Commercial3 (glass and steel fibers fabric with coating).

In recent years, photovoltaic energy has become a popular alternative to traditional fossil fuels due to its renewable and sustainable nature, and the incentives provided by countries to encourage research and development in this field have accelerated the implementation of photovoltaic roofs. One important factor in maximizing the efficiency of solar panels is their orientation to harness maximum irradiation with minimum roof area. In this article, an optimizer-supported east–west-oriented solar power plant installation is experimentally presented to utilize more solar energy with less area compared to wide-area south orientation. Furthermore, an analysis of different angle placements is simulated by PVSOL and PVGIS to test the robustness of the presented approach, and the pros/cons of the features of the east–west and south orientation are discussed comparatively. The obtained results are promising for the ideal placement of solar systems along with the use of optimizers to further enhance their performance, and for the minimization of the economic return of such an investment.

Internet of Things (IoT) and mobile edge computing (MEC) architectures are common in real-time application scenarios for improving the reliability of service responses. Energy conservation (EC) and energy harvesting (EH) are significant concerns in such architectures due to the self-sustainable devices and resource-constraint edge nodes. The density of the users and service requirements are further reasons for energy conservation and the need for energy harvesting in these scenarios. This article proposes decisive task scheduling for energy conservation (DTS-EC). The proposed energy conservation method relies on conditional decision-making through classification disseminations and energy slots for data handling. By classifying the energy requirements and the states of the mobile edge nodes, the allocation and queuing of data are determined, preventing overloaded nodes and dissemination. This process is recurrent for varying time slots, edge nodes, and tasks. The proposed method is found to achieve a high data dissemination rate (8.16%), less energy utilization (10.65%), and reduced latency (11.44%) at different time slots.

Since carbonate reservoirs develop pores and fractures and have a complex formation pressure system, overflow and even blowout seriously threaten the exploration and development of these kinds of reservoirs. According to the overflow characteristics of fractured-vuggy carbonate reservoirs, a field monitoring and identification method for overflow has been developed. This method is based on the top-down logic framework for early overflow identification, combined with optimized monitoring parameters. The DBSCAN clustering algorithm is used to identify abnormal logging parameters, and thus, a probability weight coefficient of overflow (K) indicated by the abnormal engineering parameters of a gas well can be calculated. K is divided into four early warning response levels of overflow, and the overflow control operation can be made according to the different levels of early warning response. Based on this method, an integrated software system for field monitoring and identification of early overflow is designed. This software system integrates the functions of data collection and management, overflow risk parameter calculation, overflow early warning, and overflow simulation analysis. Finally, according to a case analysis of overflow prediction for a fractured-vuggy carbonate reservoir in Well XX, this method’s prediction stability and accuracy are demonstrated, indicating that it can be used for the overflow early warning in engineering practice.

This article provides a detailed analysis of issues related to the complications while drilling in hydrate-bearing rocks of permafrost areas. The goal of the paper is to develop recommendations for preventing gas occurrence while drilling gas hydrate deposits and to eliminate gas leakiness of the intercasing space of the well. The results of modeling the effect of drilling mud injection on the temperature field of the well are presented. It is revealed that the most significant role is played by the injection rate of drilling mud and its temperature. The recommended flow rate of the process fluid should be within 0.30–0.45 m3/s, and its temperature should not exceed 20 °C. Controlling the parameters of drilling mud and its flow rate allows for avoiding intensive gas occurrence while drilling in gas hydrates. The presence of gas hydrates may be the cause of gas leakiness of the intercasing space in the permafrost area. One of the ways to eliminate leakiness is colmatation (clogging). A method of preventing leaks in the intercasing space of the gas well is the use of colmatating solution. An aqueous solution of sodium silicate with the addition of 2% polymer is used as a colmatating composition.

Sustainable energy development requires environment-friendly energy-generating methods. Pricing system constraints influence the efficient use of energy resources. Real-Time Pricing (RTP) is theoretically superior to previous pricing systems for allowing demand response (DR) activities. The DR approach has been useful for correcting supply–demand imbalances as technology has evolved. There are several systems for determining and controlling the DR. However, most of these solutions are unable to control rising demand or forecast prices for future time slots. This research provides a Real-Time Pricing DR model for energy management based on deep learning, where the learning framework is trained on demand response and real-time pricing. The study data in this article were taken from the Australian Energy Market Operator (AEMO), and the learning framework was trained over 17 years of data to forecast the real future energy price and demand. To investigate the suggested deep learning-based dynamic pricing strategy, two prediction instances are addressed: actual–predicted demand and actual–predicted price. We estimated pricing and demand outcomes using long short-term memory (LSTM), which were then greatly improved by architectural changes in the model. The findings showed that the suggested model is suitable for energy management in terms of demand and price prediction.

Increasing access to power, enhancing clean cooking fuels, decreasing wasteful energy subsidies, and limiting fatal air pollution are just a few of the sustainable development goals that all revolve around energy (E). Energy-specific sustainable development objectives were a turning point in the global shift towards a more sustainable and just system. By understanding energy resources, markets, regulations, and scientific studies, the country can progress more quickly towards a sustainable economy (SE). Investment in renewable energy industries is hampered by institutional obstacles such as market-controlled procedures and inconsistent supporting policies. Power plant building is currently incompatible with existing transmission and distribution networks, posing significant risks to investors. Deep neural networks (DNN) are specifically investigated in this article for energy demand forecasting at the individual building level. Other relevant information is supplied into fully connected layers along with the convolutional output. A single customer’s power usage data were used and analyzed for the final fuel and electricity consumption by various energy sources and consumer groups to test the DNN-SE technique. The energy intensity and labor productivity indexes for several economic sectors are displayed. A wide range of economic activities are examined to determine their impact on environmental pollution indicators, greenhouse gas emissions, and other air pollutants. A more effective and comprehensive energy efficiency strategy should be implemented to lower emission levels at lower prices. Research-based conclusions must be enhanced to help policymaking. The results of the experiment using the proposed method show that it is possible to predict 98.1%, grow at 96.8%, meet 98.5% of electricity demand, use 97.6% of power, and have a renewable energy ratio of 96.2%.

The search for charging electric vehicles using renewable energy sources and ensuring the stability of the electrical system has been growing. This has led to the development of charging stations that integrate photovoltaic systems, energy storage systems in batteries, and charging stations. The storage system allows for applications that increase the reliability of the electrical network. The objective of this work is to study, based on real operational scenarios, the impacts, and criteria of energy quality of a charging solution containing a photovoltaic system, a storage system, and a charging station. In the solution, the batteries are lead-carbon type and photovoltaics have priority in energy supply, using excess generation to charge the batteries. In this aspect, the work presents relevant contributions to the study of energy quality. An improvement in energy quality is pointed out with the use of batteries, proving that even in the most diverse operating situations, such as partial shading of the photovoltaic modules, with 20% shading relative to the nominal power, charging is not impacted and reductions in distortions are achieved, with a 50% reduction in voltage imbalance, when the storage system is a voltage reference and reference to the microgrid.

The appropriate design of the power oscillation damping controllers guarantees that distributed energy resources and sustainable smart grids deliver excellent service subjected to big data for planned maintenance of renewable energy. Therefore, the main target of this study is to suppress the low-frequency oscillations due to disruptive faults and heavy load disturbance conditions. The considered power system comprises two interconnected hydroelectric areas with heavy solar energy penetrations, severely impacting the power system stabilizers. When associated with appropriate controllers, FACTs technology such as the static synchronous series compensator provides efficient dampening of the adverse power frequency oscillations. First, a two-area power system with heavy solar energy penetration is implemented. Second, two neuro-based controllers are developed. The first controller is constructed with an optimized particle swarm optimization (PSO) based neural network, while the second is created with the adaptive neuro-fuzzy. An energy management approach is developed to lessen the risky impact of the injected solar energy upon the rotor speed deviations of the synchronous generator. The obtained results are impartially compared with a lead-lag compensator. The obtained results demonstrate that the developed PSO-based neural network controller outperforms the other controllers in terms of execution time and the system performance indices. Solar energy penetrations temporarily influence the electrical power produced by the synchronous generators, which slow down for uncomfortably lengthy intervals for solar energy injection greater than 0.5 pu.

Cointegration theory has been recently proposed for condition monitoring and fault detection of wind turbines. However, the existing cointegration-based methods and results presented in the literature are limited and not encouraging enough for the broader deployment of the technique. To close this research gap, this paper presents a new investigation on cointegration for wind turbine monitoring using a four-year SCADA data set acquired from a commercial wind turbine. A gearbox fault is used as a testing case to validate the analysis. A cointegration-based wind turbine monitoring model is established using five process parameters, including the wind speed, generator speed, generator temperature, gearbox temperature, and generated power. Two different sets of SCADA data were used to train the cointegration-based model and calculate the normalized cointegrating vectors. The first training data set involves 12,000 samples recorded before the occurrence of the gearbox fault, whereas the second one includes 6000 samples acquired after the fault occurrence. Cointegration residuals—obtained from projecting the testing data (2000 samples including the gearbox fault event) on the normalized cointegrating vectors—are used in control charts for operational state monitoring and automated fault detection. The results demonstrate that regardless of which training data set was used, the cointegration residuals can effectively monitor the wind turbine and reliably detect the fault at the early stage. Interestingly, despite using different training data sets, the cointegration analysis creates two residuals which are almost identical in their shapes and trends. In addition, the gearbox fault can be detected by these two residuals at the same moment. These interesting findings have never been reported in the literature.

Uncertainties are widely present in the design and simulation of aero-engine combustion systems. Common non-probabilistic convex models are only capable of processing independent or correlated uncertainty variables, while conventional precise probabilistic sensitivity analysis based on ideal conditions also fails due to the presence of uncertainties. Given the above-described problem, an imprecise p-box sensitivity analysis method is proposed in this study in accordance with a multi-dimensional parallelepiped model, comprising independent and correlated variables in a unified framework to effectively address complex hybrid uncertainty problems where the two variables co-exist. The concepts of the correlation angle and correlation coefficient of any two parameters are defined. A multi-dimensional parallelepiped model is built as the uncertainty domain based on the marginal intervals and correlation characteristics of all parameters. The correlated variables in the initial parameter space are converted into independent variables in the affine space by introducing an affine coordinate system. Significant and minor variables are filtered out through imprecise sensitivity analysis using pinching methods based on p-box characterization. The feasibility and accuracy of the method are verified based on the analysis of the numerical example and the outlet temperature distribution factor. As indicated by the results, the coupling between the variables can be significantly characterized using a multi-dimensional parallelepiped model, and a notable difference exists in the sensitivity ranking compared with considering only the independence of the variables, in which input parameters (e.g., inlet and outlet pressure, density, and reference flow rate) are highly sensitive to changes in the outlet temperature distribution factor. Furthermore, the structural parameters of the flame cylinder exert a secondary effect.

The intensification of countries’ growth causes the depletion of natural resources, biodiversity degradation, ecological imbalances, damage, and disasters. The aggravation of ecological issues requires the development of mechanisms for simultaneous achievement of economic, social, and ecological goals. The energy sector is the core direction of economic decarbonization. Therefore, green economic growth requires economic development due to the extension of innovative technologies for renewable energies and relevant investment for that. The study aims to test the hypothesis on the impact of green field investment on green economic growth. The object of the research was countries in the European Union (EU) for 2006–2020. This study applied the Malmquist-Luenberger Global Productivity Index to estimate green economic growth. It considers the resources available for the production process in the country (labor, capital, energy), the desired outcome (gross domestic product) and undesirable results (emissions to the environment) of this process. The study applied the Tobit model to test the hypothesis. The findings confirm the spatial heterogeneity of green economic growth among the EU countries. The asymmetry in technological efficiency and progress limits the efficacy of green innovations. At the same time, the obtained data confirm the research hypothesis. It is shown that along with green investments, economic openness and the efficiency of public governance have a positive effect on the green economic growth of countries. The findings highlight the importance of attracting green investments to increase green innovations in renewable energy, which boost green economic growth. This study explored the linear and direct effects of green investment on the green economic growth while eliminating the transmission impact of other mediating factors. It should be noted that further research should analyze the nonlinear impact of green investment on the green economic growth and the mediating effect, which could be caused by other variables (corruption, governance efficiency, green innovations, etc.).

Overhead lines that are exposed to the outdoors are susceptible to faults such as open conductors on weak points and disconnection caused by external factors such as typhoons. Arcs that occur during disconnection generate energy at a high heat of over 10,000 °C, requiring swift fault shut-off. However, most conventional fault detection methods to protect electrical power systems detect an overcurrent; thus, they can only detect faults after the line is disconnected and the cross-section of the line that generates the arc discharge makes contact with another line or the ground, causing a high risk of fire. Furthermore, in the case of ground faults owing to the disconnection of overhead lines, the load and the grounding impedance are not parallel. Therefore, in the case of the fault current not exceeding the threshold or a high impedance fault due to the high grounding impedance of the surrounding environment, such as grass or trees, it is difficult to determine overhead line faults with conventional fault detection methods. To solve these issues, this paper proposes an AI-based open conductor fault detection method on overhead lines that can clear the fault before the falling open conductor line comes into contact with the ground’s surface so as to prevent fire. The falling time according to the height and span of the overhead line was calculated using a falling conductor model for the overhead line, to which the pendulum motion was applied. The optimal input data cycle that enables fault detection before a line–ground fault occurs was derived. For artificial intelligence learning to prevent wildfires, the voltage and current signals were collected through a total of 432 fault simulations and were wavelet-transformed with a deep neural network to verify the method. The proposed total scheme was simulated and verified with MATLAB.

The co-combustion of dry coal and sludge was carried out in a self-built laboratory scale fluidized bed furnace. We used FTIR spectroscopy, high-resolution gas chromatography, high-resolution mass spectrometry, and XRF to capture and dynamically observe the flue gas fly ash and bottom ash. The sludge sample was taken from a municipal sewage treatment plant and its mixing ratio was below 20%. The temperature distribution of the furnace was observed and the emissions of the main gaseous pollutants such as SO2, NOx, and dioxins were measured in the combustion process. The heavy metal contents of fly ash and bottom ash were later detected to analyze the migration characteristics. The results showed that all of the gaseous pollutants measured increased linearly with the sludge mixing ratio. However, it could also meet the 1 metal content of bottom ash and fly ash did not change much. Through the elaboration of the above points, this study can provide guidance for future research on the co-combustion of coal boiler sludge and suggest favorable conditions for the co-combustion of coal boiler sludge.

Inspired by pigeon feathers, the drag-reducing contribution of spanwise grooves was studied. Surface topography of the wing feather was scanned by an instrument of white light interference. Three types of grooves of triangle, rectangle, and trapezoid were adopted based on the unsymmetric microstructures found on the feather surface. Numerical simulations were conducted to analyze drag-reducing mechanisms. According to the simulation results, the rectangular groove reduced the wall shear stress more efficiently but with greater additional pressure drag, while the triangular groove was the opposite. For the trapezoidal groove similar to the feather structure, drag reduction was the best out of the three. Wind tunnel experiments for the trapezoidal groove were performed by using a cylindrical model and large-area plate. Drag reduction was confirmed from the cylindrical model at a series of velocities from 15 m/s to 90 m/s (about 16% at velocity of 30 m/s and about 8.5% at velocity of 60 m/s). Drag reduction was also obtained from the plate model at a velocity range of 30 m/s to 75 m/s (about 19% at the velocity of 60 m/s), which worked for a wide range of velocity and was more meaningful for the application.

Energy management for multi-home installation of solar PhotoVoltaics (solar PVs) combined with Electric Vehicles’ (EVs) charging scheduling has a rich complexity due to the uncertainties of solar PV generation and EV usage. Changing clients from multi-consumers to multi-prosumers with real-time energy trading supervised by the aggregator is an efficient way to solve undesired demand problems due to disorderly EV scheduling. Therefore, this paper proposes real-time multi-home energy management with EV charging scheduling using multi-agent deep reinforcement learning optimization. The aggregator and prosumers are developed as smart agents to interact with each other to find the best decision. This paper aims to reduce the electricity expense of prosumers through EV battery scheduling. The aggregator calculates the revenue from energy trading with multi-prosumers by using a real-time pricing concept which can facilitate the proper behavior of prosumers. Simulation results show that the proposed method can reduce mean power consumption by 9.04% and 39.57% compared with consumption using the system without EV usage and the system that applies the conventional energy price, respectively. Also, it can decrease the costs of the prosumer by between 1.67% and 24.57%, and the aggregator can generate revenue by 0.065 USD per day, which is higher than that generated when employing conventional energy prices.

The geological storage of carbon dioxide (CO2) is the most suitable option to achieve a large-scale and low-cost CO2 emissions worldwide, but the selection of favorable areas for the geological storage is the main issue. In this study, evaluation indicators were selected considering four aspects, namely geological conditions, storage potential, hydrogeological conditions, and engineering conditions, based on drilling, geophysical, and geochemical data from the Beibu Gulf Basin. The analytical hierarchy process and the fuzzy comprehensive evaluation method were used to evaluate the suitability of this basin for geological storage of CO2. The results suggested that: (1) the Beibu Gulf Basin is suitable for CO2 geological storage, and the evaluation grade is the highest rating level; (2) considering the techno-economic feasibility, it is necessary to select the target area for CO2 geological storage with suitable burial depth, superior reservoir conditions, proximity to the CO2 collection device, and a high degree of exploration.

This work investigates the surface-barrier photoelectric properties of Au-palladium-n-GaP structures. Research into the visible spectrum region, under the action of both linearly polarized and natural radiation, provides us with new information about the height of the barrier, the interface m-s section, and the GaP band structure. SBs based on GaP (p- and n-type) are helpful for researchers in developing advantageous structures for creating various photovoltaic devices—photodetectors for fiber-optic control of energy systems or possible structures for solar energy. Despite many years of research, issues concerning the band structure of semiconductors based on the phenomenon of photoelectroactive absorption in such surface-barrier structures’ m-s remain urgent in the creation of new high-performance devices. Such structures may also be interesting for creating solar energy systems. They create a thin insulating dielectric layer (usually an oxide layer) in solar cells on SBs between the m and the semiconductor substrate. The advantage of solar cells based on m dielectric semiconductor structures is the strong electric field near the surface of the semiconductor that usually has a direction favoring the collection of carriers created by short-wavelength light. Diffusion of impurities usually results in crystal defects in the active region. There are no such defects in the studied elements. This is also the difference between solar cells on m dielectric structures and elements with diffusion in p-n junctions. We studied the PS of Au-Pd-n-GaP nanostructures to determine the height of the potential barrier qφBo and obtained accurate data on the zone structure of the n-GaP. The PS of nanostructured Au-Pd-n-GaP structures was studied in the visible region of the spectrum. Essential information about the semiconductor’s potential barrier parameters and band structure was obtained. The intermediate Pd nanolayer between Au and GaP has specific effects on the Au-Pd-n-GaP nanostructure, which are of considerable practical and scientific significance for future needs.

Passenger cars account for the largest share of GHG emissions in the road sector. However, given that the number of heavy-duty vehicles registered is lower but accounts for about a quarter of GHG emissions in the road sector, it is necessary to reduce carbon dioxide (CO2) emissions by improving the fuel efficiency of heavy-duty vehicles. However, experiments using dynamometers during the vehicle development process consume a lot of time and cost. Conversely, simulations can quantitatively analyze the sensitivity of parameters and accelerate optimization. Therefore, in this study, we modeled a heavy-duty vehicle using an AVL Cruise simulation and analyzed the effects of payload, air drag coefficient, and rolling resistance on fuel economy, CO2 emission, and the valid window ratio among the moving average window (MAW) for three driving routes. When the average vehicle speed was higher, the effect of the air drag coefficient on fuel economy was high. Additionally, when the average vehicle speed was lowered, the effect of the reduced rolling resistance on improving fuel efficiency was higher than that of the reducing air drag. Thus, the fuel efficiency improvement rate according to each 10% decrease in rolling resistance was higher by 2.2%, on average, in the low average speed route. Additionally, it was confirmed that the valid window ratio was high when driving in a section with a high vehicle speed first. Thus, the valid window ratio was almost 100% in the test of the route conditions starting from the highway section.

The development of a machine-learning method with high accuracy, high generalization, and strong robustness for evaluating battery health states is essential in the field of battery health management. In this work, the data-driven stacking regressor (SR) method with a two-layer diagnostic framework was proposed to estimate the state of health (SOH) and predict the remaining useful life (RUL). Five individual estimators were merged in the first layer, including bagging, gradient boosting regression (GBR), support vector regression (SVR), Hist-GBR, and AdaBoost, and linear regression (LR) was used in the second layer to construct the SR model. The SR model produces highly accurate results without the requirement of excessive parameter adjustment. Fifteen batteries from the NASA dataset were used for our experiments, resulting in rather low values of average root mean square error (ARMSE) and relative error (RE) for the SOH estimation and RUL predictions of the different batteries, demonstrating the superiority of the SR model.

The advent of energy communities will revolutionize the energy market. However, exploiting their full potential requires innovations in the structure and management of low-voltage grids. End users shall be aggregated within microgrids, where their physical interaction is possible and coordinated operation of power sources and energy storage systems can be achieved. Moreover, meshed network topologies will enable multiple paths for the power flow. The combination of smart control and meshed networks can dramatically improve microgrid performance in terms of power quality, efficiency, and resilience to transients and faults. Ubiquitous control of the power flow becomes possible, as well as active fault clearing and isolation of subgrids without tripping circuit breakers. This paper proposes a control approach that pursues such goals without requiring modification of control and communication hardware implemented in commercial inverters. Instead, a revision of control firmware, integrated with local measurements, allows retrofitting existing plants to improve microgrid operation. Further improvements may derive from the installation of community power sources and energy storage systems, which can extend microgrid operation to pursue demand response and islanding. The potential of the proposed control methods is demonstrated by simulation considering a standard microgrid under different operating conditions.

The present study focused on the analysis of a new geometrical modification of the conventional zig-zag channel for Printed Circuit Heat Exchangers. The research was carried out using OpenFOAM and Salome software, which were used for the CFD analysis and the construction of the computational domain. For the development of the study, three types of channel geometries were defined: a modified zig-zag channel, a conventional zig-zag channel, and a straight channel. The results show that the modified zig-zag channel achieves better thermal hydraulic performance compared to that of the conventional zig-zag channel, evidenced by a 7.6% increase in the thermal performance factor. The modified zig-zag channel proposed in the research caused a 1.5% reduction of the power consumption of supercritical Brayton cycle compressors. Additionally, the modified zig-zag channel achieves a maximum efficiency of 49.1%, which is 1.5% higher compared to that of the conventional zig-zag channel. The above results caused a 20.9% reduction of the operating costs of the supercritical Brayton cycle. This leads to a 5.9% decrease in the cost associated with using the PCHE compared to that of the conventional zig-zag channel. In general, the new geometric characteristics proposed for the conventional zig-zag channel minimize the high loss of the hydraulic performance without significantly compromising its heat transfer capacity. The geometric analysis of the proposed new zig-zag channel geometry was limited to evaluating the influence of the bend angle of 20–30°. Therefore, a more detailed geometric optimization process involving other geometric parameters of the channel is still needed. Future research will be focused on addressing this approach.

Moving bed heat exchangers (MBHE)s are used in industrial applications including waste heat recovery and the drying of solids. As a result, energy balance models have been developed to simulate the heat transfer between a moving bed and the gas phase. Within these energy balance models, phase change of components within the gas phase has not been considered as the liquefaction or desublimation of the gas phase does not occur in typical industrial applications. However, available energy balance models for cryogenic CO2 capture (CCC) have only focused on fixed packed beds. The development of a suitable energy balance model to predict the energy duties for MBHEs that include phase change would be beneficial for CCC applications. This work investigated the development of moving bed energy balance models for the design of moving bed columns that involve phase change of CO2 into frost, using existing models for MBHEs and fixed-bed CCC capture. The models were evaluated by comparison with available moving bed experimental work and simulated data, predicted energy duty requirements and bed flow rates from the suggested moving bed CCC models to maintain thermal equilibrium. The comparisons showed a consistent prediction between the various methods and closely align with the available experimental and simulated data. Comparisons of energy duty and bed flow rate predictions from the developed energy balance models with simulated cases for an oil-fired boiler, combined cycle gas turbine (CCGT) and biogas upgrading showed energy duty requirements for the gas phase with a proximity of 0.1%, 20.8%, and 3.4%, respectively, and comparisons of gas energy duties from developed energy balance models with energy duties derived from experimental results were compared with a proximity of 1.1%, 1.1% and 0.6% to experimental results for CO2 % v/v concentrations of 18%, 8% and 4%.

After the economic shock caused by COVID-19, with relevant effects on both the supply and demand for energy assets, there was greater interest in understanding the relationships between key energy prices. In order to contribute to a deeper understanding of energy price relationships, this paper analyzes the dynamics between the weekly spot prices of oil, natural gas and benchmark ethanol in the US markets. The analysis period started on 23 June 2006 and ended on 10 June 2022. This study used the DMCA cross-correlation coefficient in a dynamic way, using sliding windows. Among the main results, it was found that: (i) in the post-pandemic period, oil and natural gas were not correlated, in both short- and long-term timescales; and (ii) ethanol was negatively associated with natural gas in the most recent post-pandemic period, especially in short-term scales. The results of the present study are potentially relevant for both market and public agents regarding investment diversification strategies and can aid public policies due to the understanding of the interrelationship between energy prices.

Similar to many energy projects, the evaluation of investments in natural gas projects is influenced by technical and economic uncertainties. These uncertainties include natural resource characteristics, production, decline laws, prices, taxes, benchmark yield, and so on. In China, conventional natural gas is still the dominant energy source. The investors are mainly large state-owned energy companies. Therefore, it is necessary to include the technical and economic uncertainties, as well as the investment decision and optimization problems of the enterprises in a unified analytical framework. To this end, this paper innovatively constructs such a framework. Using numerical simulations of approaches, the process of investment decision optimization by companies based on technology assessment and price forecasting is visualized in detail. The results suggest that the investment decision of the enterprise needs to consider technical and economic uncertainties in an integrated manner. It also needs to combine the business strategy and social responsibility of the enterprise in order to construct the objective function. With the availability of data, the framework and its algorithms can be used for practical evaluation of investment plans and decision supports for conventional natural gas projects. The framework can also integrate the analytical perspective of the macroeconomic and political environment to bring in a more comprehensive range of uncertainties.

Short-term hydrothermal scheduling problem plays an important role in maintaining a high degree of economy and reliability in power system operational planning. Since electric power generation from fossil fired plants forms a major part of hydrothermal generation mix, therefore their emission contributions cannot be neglected. Hence, multi-objective short term hydrothermal scheduling is formulated as a bi-objective optimization problem by considering (a) minimizing economical power generation cost, (b) minimizing environmental emission pollution, and (c) simultaneously minimizing both the conflicting objective functions. This paper presents a non-dominated sorting disruption-based oppositional gravitational search algorithm (NSDOGSA) to solve multi-objective short-term hydrothermal scheduling (MSHTS) problems and reveals that (i) the short-term hydrothermal scheduling problem is extended to a multi-objective short-term hydrothermal scheduling problem by considering economical production cost (EPC) and environmental pollution (EEP) simultaneously while satisfying various diverse constraints; (ii) by introducing the concept of non-dominated sorting (NS) in gravitational search algorithm (GSA), it can optimize two considered objectives such as EPC and EEP simultaneously and can also obtain a group of conflicting solutions in one trial simulation; (iii) in NSDOGSA, the objective function in terms fitness for mass calculation has been represented by its rank instead of its EPC & EEP values by using the NS approach; (iv) an elite external archive set is defined to keep the NS solutions with the idea of spread indicator; (v) the optimal schedule value is extracted by using fuzzy decision approach; (vi) a consistent handling strategy has been adopted to handle effectively the system constraints; (vii) finally, the NSDOGSA approach is verified on two test systems with valve point loading effects and transmission loss, and (viii) computational discussion show that the NSDOGSA gives improved optimal results in comparison to other existing methods, which qualifies that the NSDOGSA is an effective and competitive optimization approach for solving complex MSHTS problems.

In the study of solar greenhouses, microclimate, soil, and back walls have an important influence on the greenhouse thermal environment because of their good heat storage and release characteristics. The transpiration of crops makes indoor humidity increase sharply, which is the main factor affecting indoor humidity distribution. Therefore, it is of great significance to grasp the microclimate change law of solar greenhouses and study the coupling effect of thermal and humidity environment. In this paper, based on computational fluid dynamics (CFD), a three-dimensional model of the thermal and humidity environment of a solar greenhouse is established, and the indoor temperature and humidity distribution under the influence of soil, crops, and back walls are considered. The CFD model initialization uses binary fitting functions to fit the temperature distribution of soil, back wall, and air. The distribution law of the temperature field and relative humidity field of the solar greenhouse under three different working conditions is simulated, that is, the insulation is uncovered and the ventilation window is closed during the day (G1), the insulation is uncovered and the ventilation window is opened during the day (G2), and the insulation is put down and the ventilation window is closed at night. (G3). The results show that the simulation results are in good agreement with the actual results under the three working conditions, and this paper can provide a reference for the improvement of the greenhouse structure and environmental regulation.

Stochastic simulations enable researchers to incorporate uncertainties beyond numerical discretization errors in computational fluid dynamics (CFD). Here, the authors provide examples of stochastic simulations of incompressible flows and numerical solutions for validating these newly emerging stochastic modeling methods. A numerical scheme is constructed for finding solutions to stochastic parabolic equations. The scheme is second-order accurate in time for the constant coefficient of the Wiener process term. The stability analysis of the scheme is also provided. The scheme is applied to the dimensionless heat and mass transfer model of mixed convective non-Newtonian nanofluid flow over oscillatory sheets. Both the deterministic and stochastic energy equations use temperature-dependent thermal conductivity. The stochastic model is more general than the deterministic model. The results are calculated for both flat and oscillatory plates. Casson parameter, mixed convective parameter, thermophoresis, Brownian motion parameter, Prandtl number, Schmidt number, and reaction rate parameter all impact the velocities, temperatures, and concentrations shown in the graphs. Under the influence of the oscillating plate, the results reveal that the concentration profile decreases with increasing Brownian motion parameters and increases with increasing thermophoresis parameters. The behavior of the velocity profile for the deterministic and stochastic models is provided, and contour plots for the stochastic model are also displayed. This article aims to provide a state-of-the-art overview of recent achievements in the field of stochastic computational fluid dynamics (SCFD) while also pointing out potential future avenues and unresolved challenges for the computational mathematics community to investigate.

Nowadays, sustainability-related issues have attracted growing attention due to fossil fuel depletion and environmental concerns. Considering many cities have gradually replaced taxis with electric vehicles (EVs), to reduce greenhouse gas emissions and traditional energy consumption, this paper studies the optimization strategy of battery swapping for electric taxis (ETs), and it is not only to ease congestion in the battery swapping station (BSS) but also for electric taxis to address their range anxiety and maximize their benefits. Firstly, based on the road network, the Dijkstra algorithm is adopted to provide the optimal path for ETs to BSSs with the minimum energy consumption. Then, this paper proposes the optimization objective function with minimum cost, which contains the battery service cost based on the battery’s state of charge, waiting cost caused by waiting for swapping battery in BSSs and the carbon emission reduction benefit generated during ETs driving to BSSs, and uses a mixed-integer linear programming (MILP) algorithm to solve this function. Finally, taking the Leisure Park of Laoshan City in Beijing as an example, the numerical simulation is carried out and the proposed battery swapping strategy is efficient to alleviate the congestion of BSSs and maximize the total benefit of ETs, and the cost based on the proposed strategy is 14.21% less than that of disorderly swapping.

Microseismic monitoring has become a well-known technique for predicting the mechanisms of rock failure in deeply buried energy exploration, in which noise has a great influence on microseismic monitoring results. We proposed an improved microseismic denoising method based on different wavelet coefficients of useful signal and noise components. First, according to the selection of an appropriate wavelet threshold and threshold function, the useful signal part of original microseismic signal was decomposed many times and reconstructed to achieve denoising. Subsequently, synthetic signals of different types (microseismic noise, microseismic current, microseismic noise current) and with various signal-to-noise ratios (SNRs, −10~10) were used as test data. Evaluation indicators (mean absolute error μ and standard deviation error σ) were established to compare the denoising effect of different denoising methods and verify that the improved method is more effective than the traditional denoising methods (wavelet global threshold, empirical mode decomposition and wavelet transform–empirical mode decomposition). Finally, the proposed method was applied to actual field microseismic data. The results showed that the microseismic signal (with different types of noise) could be fully denoised (car honk, knock, current and construction noise, etc.) without losing useful signals (pure microseismic), suggesting that the proposed approach provides a good basis for the subsequent evaluation and classification of rock burst disasters.

The paper deals with an optimization of a magnetic circuit of the field exciter designed to control magnetorheological fluid (MRF) in a hybrid soft–rigid jaw gripper. The case discussed includes sealing of the MRF inside a cushion made of thermoplastic polyurethane (TPU). The shear stress distributions in the MRF upon magnetic field excitation have been analyzed for various permanent magnet, yoke, and air gap dimensions. In the developed numerical model of the magnetic field exciter, the geometry of the considered domain was parameterized. As part of the simulation study, more than 4600 variants of the magnetic circuit were analyzed, for which the shear stress distribution in the MRF inside the cushion was determined. The numerical model has been implemented in the Ansys Electronics Desktop 2020 finite element method (FEM) package. Research was focused on finding dimensions of the magnetic circuit that ensure the desired distribution of the shear stress in the MRF inside the cushion. The undeformed and deformed by axial plunging of the pin cushions geometries have been analyzed. The evaluation criteria were the achievement of the highest possible value of the shear stress and the uniformity of its distribution in the given cross-sectional area of the MRF inside the cushion. The main objective of the analysis was to design the magnetic field exciter for application in the jaw pads of a gripper using MRF cushions. Through research, a suitable configuration tailored to the needs of the application was proposed.

Waste heat air-source heat pump water heater (WH-ASHPWH) systems have been widely used to provide water heating in buildings due to their advantages of high efficiency and low environmental impact. It is important to determine reasonable operating conditions for WH-ASHPWH systems to improve their heating efficiency, especially for systems using industrial waste heat. In this study, a semiempirical parametric model of a WH-ASHPWH system was established to simulate heating performance under different working conditions and determine the optimum operating range of the system. A new discrete model for a tube-in-tube condenser was also generated. Taking the equipment of a pharmaceutical factory as an example, the heat exchanger model was first calibrated; then, the global model was used to study the effects of different air-side and condenser-side waste heat conditions on the heating performance of the system. According to the simulation results, the coefficient of performance (COP) reached a maximum value of 4.5 under the optimum working conditions. The deviation between the COP simulation data and experimental values was less than 8.28%, and the simulation time was less than 8 min. These metrics indicate that the model is reasonable and efficient.

With the start of the intelligent age, textiles are no longer limited to safety protection, warmth, and aesthetic purposes. They have become intelligent textiles, which combine functionality, intelligence, and information technology to adapt to the era and enrich our lives, such as wearable textiles and energy harvesting electronics. However, the limited stretchable smart textiles and complex fabrication methods have largely hindered their development. Here, a mass-manufactured 3D stitching double weave fabric-based elastic triboelectric nanogenerator (3DWE-TENG) is developed. Based on its stable electrical output performances and rapid response to external tensile strain, it can be used for energy harvesting and self-powered sensing simultaneously through both the lining layer and the exterior layer. With an advanced 3D structural design and using the improved woven method, 3DWE-TENG can be stretched to 300% and achieves a stable mechanical structure, breathability, and excellent flexibility. Furthermore, it also has low costs, wearable comfortability, and high fabricating efficiency due to the mature woven technique and the common yarns used in the fabric. This work provides more opportunities for stretchable power sources and self-powered sensors with applications in wearable electronics.

The Brazilian Amazon is one of the main tropical wood-producing regions in the world, where exploration and industrial processing are among its main economic activities. Wood is characterized as a material consisting mainly of compounds with a high degree of polymerization and molecular weight such as cellulose, hemicellulose and lignin, in addition to other compounds such as ash and extractives. This chemical complexity of wood brings with it a wide possibility of chemical and thermochemical processing aiming at the production of bioproducts and biofuels. In this context, it is essential to know the physicochemical properties and thermal behavior of wood species from the Amazon biome to add value to the product, reducing waste and maximizing the species used. This work presents an investigation into the physicochemical and thermogravimetric characteristics of 21 species of wood from the Amazon, in addition to the determination of the higher heating value (HHV) of each one of them, focusing on the energy use of the biomass under analysis. The samples showed a high lignin content, varying between 26.8% and 33.9%, with a standard deviation of 1.7% and an average of 30.0%. The Trattinnickia sp. had the highest lignin content (33.86 ± 0.13%). The cellulose content varied from 31.3% to 55.9%, with a standard deviation of 7.3% and an average of 41.74%. The Ruizterania albiflora had the highest cellulose content (55.90 ± 1.20%). For the hemicellulose content, the variation ranged from 8.6% to 17.0%, with a standard deviation of 2.6% and an average of 12.38%. The samples that showed the highest HHVs were Ocotea sp. (18.588 ± 0.082 MJ kg−1) followed by Ferreiraa spectabilis (18.052 ± 0.157 MJ kg−1).

The cooling capacity needed by ultra-low temperature apparatus cannot be reached economically with a single vapor compression refrigeration cycle due to the constraint of the high compressor pressure ratio. The auto-cascade refrigeration cycle is a good alternative. In this work, a novel concept that applies the principle of the auto-cascade refrigeration cycle to store cold energy is conducted. The environment-friendly refrigerants of R600a/R290/R170 zeotropic mixtures are used to study the performance of the modified auto-cascade refrigeration cycle (MACRC) as an alternative for cold-energy applications. The simulation results show that a cooling capacity of 500 W can be provided below −60 °C. The mixture with a mass fraction of 0.25/0.35/0.40 yields a COP of 0.695 and an exergy efficiency of 0.262 at −66 °C. The performance of the MACRC system was investigated at an ambient temperature of 20 to 40 °C for indoor small-scale applications. It is concluded that the performance would be improved by decreasing the ambient temperature. The results of the work should be helpful for the design and optimization of auto-cascade systems.

This work performs an analysis of plasma-assisted non-premixed H2-air flames in Y-shaped micro combustors in the presence of field emission dielectric barrier discharge (FE-DBD) plasma actuators. The combustion, flow, and heat transfer characteristics are numerically investigated, and the effect of sinusoidal plasma discharges on combustion performance is examined at various equivalence ratios (φ). A coupled plasma and chemical kinetic model is implemented, using a zero-dimensional model based on the solution of the Boltzmann equation and the ZDPlasKin toolbox to compute net charges and radical generation rates. The estimated body forces, radical production rates, and power densities in the plasma regions are then coupled with hydrogen combustion in the microchannel. Plasma-assisted combustion reveals improvements in flame length and maximum gas temperature. The results demonstrate that FE-DBDs can enhance mixing and complete the combustion of unreacted fuel, preventing flame extinction. It is shown that even in cases of radical and thermal quenching, these plasma actuators are essential for stabilizing the flame.

Forecasting the electrical load is essential in power system design and growth. It is critical from both a technical and a financial standpoint as it improves the power system performance, reliability, safety, and stability as well as lowers operating costs. The main aim of this paper is to make forecasting models to accurately estimate the electrical load based on the measurements of current electrical loads of the electricity company. The importance of having forecasting models is in predicting the future electrical loads, which will lead to reducing costs and resources, as well as better electric load distribution for electric companies. In this paper, deep learning algorithms are used to forecast the electrical loads; namely: (1) Long Short-Term Memory (LSTM), (2) Gated Recurrent Units (GRU), and (3) Recurrent Neural Networks (RNN). The models were tested, and the GRU model achieved the best performance in terms of accuracy and the lowest error. Results show that the GRU model achieved an R-squared of 90.228%, Mean Square Error (MSE) of 0.00215, and Mean Absolute Error (MAE) of 0.03266.

Distribution system state estimation (DSSE) has been introduced to monitor distribution grids; however, due to the incorporation of distributed generations (DGs), traditional DSSE methods are not able to reveal the operational conditions of active distribution networks (ADNs). DSSE calculation depends heavily on real measurements from measurement devices in distribution networks. However, the accuracy of real measurements and DSSE results can be significantly affected by false data injection attacks (FDIAs). Conventional FDIA detection techniques are often unable to identify FDIAs into measurement data. In this study, a novel deep neural network approach is proposed to simultaneously perform DSSE calculation (i.e., regression) and FDIA detection (i.e., binary classification) using real measurements. In the proposed work, the classification nodes in the DNN allow us to identify which measurements on which phasor measurement unit (PMU), if any, were affected. In the proposed approach, we aim to show that the proposed method can perform DSSE calculation and identify FDIAs from the available measurements simultaneously with high accuracy. We compare our proposed method to the traditional approach of detecting FDIAs and performing SE calculations separately; moreover, DSSE results are compared with the weighted least square (WLS) algorithm, which is a common model-based method. The proposed method achieves better DSSE performance than the WLS method and the separate DSSE/FDIA method in presence of erroneous measurements; our method also executes faster than the other methods. The effectiveness of the proposed method is validated using two FDIA schemes in two case studies: one using a modified IEEE 33-bus distribution system without DGs, and the other using a modified IEEE 69-bus system with DGs. The results illustrated that the accuracy and F1-score of the proposed method are better than when performing binary classification only. The proposed method successfully detected the FDIAs on each PMU measurement. Moreover, the results of DSSE calculation from the proposed method has a better performance compared to the regression-only method, and the WLS methods in the presence of bad data.

Wind energy is one of the most relevant clean energies today, so wind turbines must have good health and be reliable in operation. Current wind turbines have slender and elastic structures that can be easily damaged through vibrations and compromise their health; therefore, vibration monitoring is essential to ensure safe operation. Here, we present a method for simple wind turbine vibration monitoring in the laboratory by means of an accelerometer placed on a weathervane under different scenarios, with recording of different amplitudes of vibrations caused at a constant speed of 10 km/h. The variables, trends, and data captured during vibration monitoring were then used to implement a prediction system of synthetic failure using machine learning methods such as: Medium Trees, Cubic SVN, Logistic Regression Kernel, Optimized Neural Network, and Bagged Trees, with the last demonstrating an accuracy of up to 0.87%.