Energies, Volume 17, Issue 7 (April-1 2024) – 282 articles

The electric scroll compressor is driven by a built-in electric motor that rotates the scroll disk. It is known for its simple structure, adjustability, and high efficiency, making it highly promising for various applications. This paper reviews the current application and research status of electric scroll compressors. It covers topics such as the optimal design of scroll compressor profiles, scroll disk leakage sealing, and computer simulation optimization design methods. Additionally, the progress and development trends of vapor-injection scroll compressors (SCVIs) are discussed. This paper also presents the latest research progress on the application of the new refrigerant CO₂ in electric scroll compressors, along with its latest applications that align with sustainable development requirements. Finally, this paper concludes with recommendations for the application of electric scroll compressors and suggests future directions for research. Full article

This paper presents a study about the integration of Piezoelectric Energy Harvesting Systems (PE-EHSs) into building envelopes for powering Fiber Bragg Grating (FBG) sensors, enabling efficient and low-consumption monitoring with the objective of leveraging structural health monitoring (SHM). The research includes preliminary tests conducted in a real environment to validate the PE-EHS when fully integrated into a ventilated façade, capturing mechanical vibrations generated mainly by wind loads. Based on these activities, the final configuration of PE-EHSs is defined to provide a complete system for façade monitoring. This integrated system includes the piezoelectric generator (PEG), supercapacitor (SC), Power Conditioner Circuit (PCC), Fiber Optic Sensing (FOS) interrogator, and the IoT gateway transmitting measurement data within an Internet of Things (IoT) monitoring platform. This configuration is tailored to address the challenges related to the structural integrity of building envelopes. Results demonstrate a potential for a stand-alone solution in the façade sector but raise issues for certain limitations, requiring further investigation. In particular, the study emphasizes constraints related to the energy production of PE-EHSs for façade integration. It highlights the necessity to carefully consider these limitations within the broader context of their applicability, providing insights for the informed deployment of piezoelectric energy harvesting technology in building envelope monitoring. Full article

Italy has been defined as the “logistics platform” of the Mediterranean Sea. The Italian port system, with 11.6 million TEUs handled and 61.4 million passengers in 2022 (Assoporti data January–December 2022), is the key to fulfilling this title through adequate levels of reliability, safety, and sustainability. This contribution addresses port logistics and shipping, focusing on primary issues related to the energy sector with a specific focus on what can be observed in the Italian context. Specifically, the decarbonization of the maritime sector and related infrastructural problems (e.g., cold ironing or alternative fuels, where the uncertainty about resource availability and related costs do not allow for easy strategic planning by both the ship owner and the port authority), as well as policies such as the Emission Trading System (ETS), will be analyzed. All these issues, hereafter addressed with a systematic critical review of the existing literature and other relevant sources, could represent the driving force of the growth of the national port sector toward its competitiveness at a global scale. The review was performed through a wide search and analysis of studies published in well-known online research databases (Scopus, Web of Science, IEEE Xplore, ScienceDirect), sector studies, or specialized technical magazines. The review focuses on the results of each analyzed contribution rather than on the analysis method adopted with the final aim to identify useful hints and innovative ideas for further studies on the topic. Full article

This paper investigates optimizing the power exchange between electric vehicles (EVs) and the grid, with a specific focus on the DC-DC converters utilized in vehicle-to-grid (V2G) systems. It specifically explores using model predictive control (MPC) in synchronous boost converters to enhance efficiency and performance. Through experiments and simulations, this paper shows that replacing diodes with SIC MOSFETs in boost converters significantly improves efficiency, particularly in synchronous mode, by minimizing the deadtime of SIC MOSFETs during switching. Additionally, this study evaluates MPC’s effectiveness in controlling boost converters, highlighting its advantages over traditional control methods. Real-world validations further validate the robustness and applicability of MPC in V2G systems. This study utilizes TMS320F28379D, one of Texas Instruments’ leading digital signal processors, enabling the implementation of MPC with a high PWM frequency of up to 200 MHz. This processor features dual 32-bit CPUs and a 16-bit ADC, allowing for high-resolution readings from sensors. Leveraging digital signal processing technologies and advanced electronic circuits, this study advances the development of high-performance boost converters, achieving power outputs of up to 48 watts and output voltages of 24 volts. Electronic circuits (PCB boards) have been devised, implemented, and evaluated to showcase their significance in advancing efficient V2G integration. Full article

The article proposes to use machine learning as one of the areas of artificial intelligence to forecast the volume of biogas production from household organic waste. The use of five regression algorithms (Linear Regression, Ridge Regression, Lasso Regression, Random Forest Regression, and Gradient Boosting Regression) to create an effective model for forecasting the volume of biogas production from household organic waste is considered. Based on the comparison of these algorithms by MSE and MAE indicators, the quality of training and their accuracy during forecasting are evaluated. The proposed algorithm for creating a model for forecasting biogas production volumes from household organic waste involves the implementation of 10 main and 3 auxiliary steps. Their advantage is that they aid in the performance of component data analysis, which is carried out based on the method of reducing the dimensionality of the data set, increasing interpretability, and minimizing the risk of data loss. An analysis of 2433 data is was carried out, which characterizes the formation of biogas from food (FW) and yard waste (YW) according to four features. Data preparation is performed using the Jupyter Notebook environment in Python. We select five machine learning algorithms to substantiate an effective model for forecasting volumes of biogas production from household organic waste. On the basis of the conducted research, the main advantages and disadvantages of the used algorithms for building forecasting models of biogas production volumes from household organic waste are determined. It is found that two models, “Random Forest Regressor” and “Gradient Boosting Regressor”, show the best accuracy indicators. The other three models (Linear Regression, Ridge Regression, Lasso Regression) are inferior in accuracy and were not considered further. To determine the accuracy of the “Random Forest Regressor” and “Gradient Boosting Regressor” models, we choose the MSE and MAE indicators. The Random Forest Regressor model is found to be a more accurate model compared to the Gradient Boosting Regressor. This is confirmed by the fact that the MSE of the “Random Forest Regressor” model on the training data set is 7.14 times smaller than that of the “Gradient Boosting Regressor” model. At the same time, MAE is 2.67 times smaller in the “Random Forest Regressor” model than in the “Gradient Boosting Regressor” model. The MSE and MAE of both models are worse on the test data set, which indicates overtraining tendencies. The Gradient Boosting Regressor model has worse MSE and MAE than the Random Forest Regressor model on both the training and test data sets. It is established that the model based on the “Random Forest Regressor” algorithm is the most effective for forecasting the volume of biogas production from household organic waste. It provides MAE = 0.088 on test data and the smallest absolute errors in predictions. Further systematic improvement of the “Random Forest Regressor” model for forecasting biogas production volumes from household organic waste based on new data will ensure its accuracy and maintain competitive advantages. Full article

Operating nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) element data that contain information about the status of components, assets, and systems. Some of this information is in textual form where the occurrence of abnormal events or maintenance activities are described. Analyses of NPP textual data via natural language processing (NLP) methods have expanded in the last decade, and only recently the true potential of such analyses has emerged. So far, applications of NLP methods have been mostly limited to classification and prediction in order to identify the nature of the given textual element (e.g., safety or non-safety relevant). In this paper, we target a more complex problem: the automatic generation of knowledge based on a textual element in order to assist system engineers in assessing an asset’s historical health performance. The goal is to assist system engineers in the identification of anomalous behaviors, cause–effect relations between events, and their potential consequences, and to support decision-making such as the planning and scheduling of maintenance activities. “Knowledge extraction” is a very broad concept whose definition may vary depending on the application context. In our particular context, it refers to the process of examining an ER textual element to identify the systems or assets it mentions and the type of event it describes (e.g., component failure or maintenance activity). In addition, we wish to identify details such as measured quantities and temporal or cause–effect relations between events. This paper describes how ER textual data elements are first preprocessed to handle typos, acronyms, and abbreviations, then machine learning (ML) and rule-based algorithms are employed to identify physical entities (e.g., systems, assets, and components) and specific phenomena (e.g., failure or degradation). A few applications relevant from an NPP ER point of view are presented as well. Full article

This paper investigates the dynamic patterns in the small-signal behavior of power systems with wind power generation. The interactions between synchronous generators and wind generators are investigated. In addition, the impact of increased wind generation penetration on the damping and frequency of the synchronous generator’s electromechanical oscillations is addressed. Wind generators of three different technologies are considered throughout this study. Very detailed dynamic models of wind generators are used and detailed. Full article

Due to a rising share of renewable energy sources on the production side and electrification of transport and heating on the consumption side, the efficient management of flexibility in distribution networks is crucial for ensuring optimal operation and utilization of resources. Nowadays, the sensitivity-based approach is mainly used in medium-voltage (MV) networks for regulating voltage profiles with reactive power of distributed energy resources (DER). The main disadvantage of the simplified sensitivity-based method is its inaccuracy in case of a high deviation of the network voltage from the nominal values. Furthermore, it was also noted that despite the fact that the method is well described in the literature, there is a lack of systematic approach to its implementation in real-life applications. Thus, the main objective of this paper is to address this disadvantage and to propose an algorithm designed to calculate required consumer flexibility in near real-time to ensure distribution grid operation within operational criteria. In the first part of the paper, network state, including line loading and node voltages, is assessed to determine distribution network node capacity. By analyzing the sensitivity of network busbars to changes in consumption and production, our algorithm effectively identifies the most efficient nodes and facilitates strategic decision-making for resource allocation. We demonstrate the effectiveness of our approach through simulations of real-world distribution network data, highlighting its ability to enhance network flexibility and improve resource utilization. Leveraging sensitivity coefficients, the algorithm enables flexible consumption and production management across various scenarios, supporting the transition to a more dynamic and efficient power system. Full article

When a medium–low-speed (MLS) maglev train is braking, part of its regenerative braking (RB) power consumption may cause a significant rise in the positive rail (PR) voltage. For RB energy re-utilization, an RB energy feedback system (RBEFS) is a promising application, but there is still no specific research in the field of MLS maglev trains. From this perspective, this article focuses on identifying the PR voltage rise behavior and investigating the application of an RBEFS on the over-voltage inhibition. Some development trends of the MLS maglev train, including the DC 3 kV traction grid system and the speed being raised to 160~200 km/h, are also considered in the analyzed scenarios. At first, a modeling scheme of a detailed vehicle–grid electrical power model with the RBEFS is established. On this basis, the PR voltage rise characteristics are analyzed with consideration of three pivotal influencing factors: RB power, PR impedance and supply voltage level. Subsequently, to stabilize the PR voltage fluctuations, the influence rules of the RBEFS on the voltage rise and the mutual transient voltage influences under the operating status switching for multiple vehicles running on the same power supply section are analyzed. Full article

Renewable energies have become pivotal in the global energy landscape. Their adoption is crucial for phasing out fossil fuels and promoting environmentally friendly energy solutions. In recent years, the energy management system (EMS) concept has emerged to manage the power grid. EMS optimizes electric grid operations through advanced metering, automation, and communication technologies. A critical component of EMS is power forecasting, which facilitates precise energy grid scheduling. This research paper introduces a deep learning hybrid model employing convolutional neural network–long short-term memory (CNN-LSTM) for short-term photovoltaic (PV) solar energy forecasting. The proposed method integrates the variational mode decomposition (VMD) algorithm with the CNN-LSTM model to predict PV power output from a solar farm in Boussada, Algeria, spanning 1 January 2019, to 31 December 2020. The performance of the developed model is benchmarked against other deep learning models across various time horizons (15, 30, and 60 min): variational mode decomposition–convolutional neural network (VMD-CNN), variational mode decomposition–long short-term memory (VMD-LSTM), and convolutional neural network–long short-term memory (CNN-LSTM), which provide a comprehensive evaluation. Our findings demonstrate that the developed model outperforms other methods, offering promising results in solar power forecasting. This research contributes to the primary goal of enhancing EMS by providing accurate solar energy forecasts. Full article

The shales of the Taiyuan Formation and Shanxi Formation in the North China Basin have good prospects for shale gas exploration and development. In this study, Well KP1 at the northern margin of the North China Basin was used as the research object for rock mineral, organic geochemical, and elemental geochemical analyses. The results show that brittle minerals in the shales of the Taiyuan Formation and Shanxi Formation are relatively rare (<40%) and that the clay mineral content is high (>50%). The average TOC content is 3.68%. The organic matter is mainly mixed and sapropelic. The source rocks of the Taiyuan Formation and Shanxi Formation are mainly felsic, and the tectonic background lies in the continental island arc area. The primary variables that influenced the enrichment of organic materials during the sedimentary stage of the Taiyuan Formation were paleosalinity and paleoproductivity. Paleosalinity acted as the primary regulator of organic matter enrichment during the sedimentary stage of the Shanxi Formation. Full article

The decarbonization of European economies is an established reality that has been accelerating in recent years. The focus of EU policy is on the dynamic transformation of the energy balances of Member States, which most significantly impacts economies reliant on coal. In the context of emerging megatrends, this study sets out to determine the extent of changes occurring in the economies of European Union countries in relation to the Green Deal paradigm. The objective of this article is to introduce a comprehensive method developed by the authors for assessing the dynamics of energy transformation in the European Union countries under study. This method is divided into two phases. Initially, countries are classified according to the energy transformation dynamics matrix. Subsequently, the actual assessment of energy transformation dynamics is conducted using a novel composite indicator, the ETPI (Energy Transition Progress Index), based on analyses for 2022 and 2013 using Eurostat data. The results identify leaders in energy transformation, such as Sweden, Germany, Denmark, France, Italy, Spain, Austria, Finland, and the Netherlands, while highlighting significant challenges facing Poland and Bulgaria. Full article

To meet future load projection with the integration of renewable sources, the transmission system must be planned optimally. Thus, this paper introduces a comparative analysis and comprehensive methodology for transmission expansion planning (TEP), incorporating the combined effects of wind power, losses, N-1 contingency, a FACTS, and storage in a flexible environment. Specifically, the optimal placement of the FACTS, known as series capacitive compensation (SCC) devices, is used. The intraday constraints associated with wind power and energy storage are represented by the methodology of typical days jointly with the load scenarios light, heavy, and medium. The TEP problem is formulated as a mixed-integer nonlinear programming (MINLP) problem through a DC model and is solved using a specialized genetic algorithm. This algorithm is also used to determine the optimal placement of SCC devices and storage systems in expansion planning. The proposed methodology is then used to perform a comparison of the effect of the different technologies on the robustness and cost of the final solution of the TEP problem. Three test systems were used to perform the comparative analyses, namely the Garver system, the IEEE-24 system, and a real-world Colombian power system of 93 buses. The results indicate that energy storage and SCC devices lead to a decrease in transmission requirements and overall investment, enabling the effective integration of wind farms. Full article

Numerical calculations were performed to study the vortex-induced vibration (VIV) of a circular cylinder, which was elastically supported by springs of linear and cubic terms. These simulations were conducted at high Reynolds numbers ranging from 4200 to 42,000. To simulate the cylinder’s motion and the associated aerodynamic forces, Computational Fluid Dynamics were employed in conjunction with dynamic mesh capabilities. The numerical method was initially verified by testing it with various grid resolutions and time steps, and subsequently, it was validated using experimental data. The response of cubic nonlinearities was investigated using insights gained from a conventional linear vortex-induced vibration (VIV) system. This 2D study revealed that both the amplitude and frequency of vibrations are contingent on the flow velocity. The highest output was achieved within the frequency lock-in region, where internal resonance occurs. In the case of a hardening spring, the beating response was observed from the lower end of the initial branch to the upper end of the initial branch. The response displacement amplitude obtained for the linear spring case was 27 mm, whereas in the cubic nonlinear case, the value was 31.8 mm. More importantly, the results indicate that the inclusion of nonlinear springs can substantially extend the range of wind velocities in which significant energy extraction through vortex-induced vibration (VIV) is achievable. Full article

Sustainable development is the subject of many economic analyses, but so far no attempt has been made to identify the main mechanism of interdependence between sustainable energy development and sustainable economic development in the second decade of the 21st century. The particular role of energy in achieving the Sustainable Development Goals is due to the fact that the production, supply and use of energy underpin economic growth. The article fills this research gap and spawns both a better understanding of the essence of sustainable development as well as practical conclusions. The aim is to assess sustainable energy development and sustainable economic development in EU member states and to determine the correlation between the two in the EU. Substantive and formal methods were used to select diagnostic variables, including: the parametric method, the standardized sums method, and correlation analysis. The analysis period covers the years 2014–2021. The conducted research demonstrated a significant variation in the level of sustainable energy development and sustainable economic development among EU countries. Full article

The premise for formulating effective emission control strategies is to accurately and reasonably evaluate the actual emission level of vehicles. Firstly, the active subspace method is applied to set up a low-dimensional model of the relationship between CO₂ emission and multivariate vehicle driving data, in which the vehicle specific power (VSP) is identified as the most significant factor on the CO₂ emission factor, followed by speed. Additionally, acceleration and exhaust temperature had the least impact. It is inferred that the changes in data sampling transform the establishment of subspace matrices, affecting the calculation of eigenvector components and the fitting of the final quadratic response surface, so that the emission sensitivity and final fitting accuracy are impressionable by the data distribution form. For the VSP, the best fitting result can be obtained when the VSP conforms to a uniform distribution. Moreover, the Bayesian linear regression method accounts for fitting parameters between the VSP and CO₂ emission factor with uncertainties derived from heteroscedastic measurement errors, and the values and distributions of the intercept and slope α and β are obtained. In general, the high-resolution inventory of the carbon emission factor of the tested vehicle is set up via systematically analyzing it, which brings a bright view of data processing in further counting the carbon footprint. Full article

In this paper, in order to improve the fuel economy of the actual application of the engine under multi-operating conditions, an experimental study is carried out on a turbo-charged direct-injection engine based on different pre-chamber structures. The engine used for the study is a four-cylinder turbo-charged direct-injection gasoline engine with different structures of pre-chamber spark plugs. The operating conditions in this study include load characteristics at 2000 r/min and characteristic loads at different speeds, including 3000 r/min, 3200 r/min, and 3600 r/min. With stable BMEP or fully open throttle and pedal, the experiment was conducted by the spark angle scanning method to collect data of engine power, economy, and emission under each condition. It was found that the pre-chamber structure has a direct effect on engine performance, with a clear load demarcation line for its effect. Under the WOT condition, the power of pre-chamber ignition is 1.6% higher than that of conventional spark plugs; at the low load of 2 bar, the economy of pre-chamber ignition is degraded by 6%; at the medium load of 8 bar, the economy of the two is comparable; at the large load of 16 bar, the fuel economy proves advantageous. Compared with conventional spark plugs, the pre-chamber spark angle can be advanced by 2~3 °CA, and the pre-chamber ignition with separate ground electrodes is highly reliable. The emission levels of the pre-chamber spark plugs and conventional spark plugs are comparable at all loads. Full article

In this paper, a thermal storage system based on a phase change material is proposed and investigated. The system is composed of several tubes that cross a phase change material mass. A fluid flowing in the tubes charges and discharges the heat storage system. A mathematical model of the system has been developed, which provides the time and space distribution of velocity, temperature, and liquid phase-changing material concentration in a non-stationary regime. A hybrid solution method based on finite volumes and finite differences techniques has been employed for the model equations in the MATLAB environment. To the tubes, a rectangular cross section has been assigned. The performance of the system in terms of accumulated energy density and accumulated power density has been investigated by varying some geometric parameters. The considered geometric parameters influence the number of tubes per unit of system width, the tube hydraulic resistance, the amount of phase change material around each tube, the heat transfer surface of the tube, and the heat storage velocity. In the parametric analysis, peaks have been evidenced in the investigated performance parameters at different instants after the beginning of the heat storage. Full article

The modular multilevel converter (MMC) is a research hotspot in medium-voltage and high-voltage applications. The measurement offset error would cause an increase in the monitoring error of the submodule (SM) capacitance of the MMC, affecting the estimation accuracy of the SM capacitance monitoring. This paper proposes a capacitor monitoring strategy based on the offset error compensation, where two reasonable capacitor monitoring periods are selected in one fundamental period under the proposed voltage-balancing control (VBC) based on the virtual capacitor voltage (VCV) to compensate for the offset error impact on the capacitance monitoring. The proposed strategy can effectively eliminate the offset error impact on the capacitance monitoring, which ensures the accuracy of the SM capacitance monitoring in the MMCs. The effectiveness of the proposed monitoring strategy is confirmed by the simulations and experiments. Full article

Regional Integrated Energy Systems (RIESs) and Shared Energy Storage Systems (SESSs) have significant advantages in improving energy utilization efficiency. However, establishing a coordinated optimization strategy between RIESs and SESSs is an urgent problem to be solved. This paper constructs an operational framework for RIESs considering the participation of SESSs. It analyzes the game relationships between various entities based on the dual role of energy storage stations as both energy consumers and suppliers, and it establishes optimization models for each stakeholder. Finally, the improved Differential Evolution Algorithm (JADE) combined with the Gurobi solver is employed on the MATLAB 2021a platform to solve the cases, verifying that the proposed strategy can enhance the investment willingness of energy storage developers, balance the interests among the Integrated Energy Operator (IEO), Energy Storage Operator (ESO) and the user, and improve the overall economic efficiency of RIESs. Full article

Composition regulation of zeotropic mixture working fluid for a thermodynamic cycle is an effective way to improve energy conversion efficiency, which offers the potential to construct efficient, flexible and intelligent cycles. Current research on cycle construction of zeotropic mixture composition regulation still heavily relies on construction methods using pure working fluids, where the characteristics of flexible composition variations fail to be utilized. In this paper, the research progress of cycle construction methods and composition regulated structures are comprehensively reviewed, aiming to clarify the potential for enhancing a thermodynamic cycle based on composition regulation. The characteristics of different cycle construction methods are firstly summarized and compared. Then, the composition-regulated structures of a physical-based method and chemical-based method are introduced, and the composition regulation performance are also concluded. Finally, a future outlook on the cycle design and structure design is provided. The review results show that the combination of 3D construction method and superstructure/intelligences construction method has the potential to maximize the cycle performance, where the improvement of each thermal process and the optimization of complex cycles can be considered simultaneously. The composition regulation based on a passive physical method has the advantage of being readily applicable; however, the composition regulation range is limited. In addition, the distillation and hydrate method have a wider regulation range through extra energy input, where the trade-off between energy consumption and cycle performance improvement should be considered in the future. This study greatly assists in the design of thermodynamic cycles involving zeotropic mixture composition regulation and the corresponding composition regulation structures. Full article

In the current source photovoltaic grid-connected system, to prevent the DC-link inductor from incurring an opening circuit fault, it is necessary to include the overlap time in the switching signals. However, current error and serious harmonic distortion in the inverter-side and grid-side currents are generated, which will cause additional losses and reduce the power quality of the grid, so it is important to compensate for the current error caused by the overlap time. In this paper, the relationship between the nonlinear current errors caused by the overlap time and the AC-side voltage is analyzed. Then, the mathematical expression of the low-order harmonics with losses caused by the overlap time is derived. On this basis, a current error compensation method with a discrete filter of AC-side voltage is proposed. Finally, a simulation and experiment are carried out to verify the correctness and effectiveness of the theoretical analysis and compensation scheme presented in this paper. With an overlap time of 3 μs, the THD of the grid-side current decreases from 5.93% to 1.59% after compensation. Full article

The supercritical CO₂ power cycle driven by solar as a new generation of solar thermal power generation technology has drawn significant attention worldwide. In this paper, a cogeneration system derived from a supercritical CO₂ recompression Brayton cycle is proposed, by considering the recovery of waste heat from the turbine outlet. The absorption refrigeration cycle is powered by the medium-temperature waste heat from the turbine outlet, while the low-temperature waste heat is employed for heating, achieving the cascaded utilization of the heat from the turbine outlet. As for the proposed combined cooling, heating, and power (CCHP) system, a dynamic model was built and verified in MATLAB R2021b/Simulink. Under design conditions, values for the energy utilization factor (EUF) and exergy efficiency of the cogeneration system were obtained. Moreover, the thermodynamic performances of the system were investigated in variable cooling/heating load and irradiation conditions. Compared with the reference system, it is indicated that the energy utilization factor (EUF) and exergy efficiency are 84.7% and 64.8%, which are improved by 11.5% and 10.3%. The proposed supercritical CO₂ CCHP system offers an effective solution for the efficient utilization of solar energy. Full article

The laws of thermal degradation of the mixture of the heavy fraction of low-temperature coal tar and coal shale were investigated using dynamic thermogravimetry. The kinetic characteristics of the process were determined using various methods, including the Ozawa–Flynn-Wall, Friedman, non-parametric kinetics and Šesták–Berggren methods. It is shown that coal shale initiated changes in the kinetic parameters and decomposition rate of the heavy fraction of coal tar. It was found that a 13% content of coal shale in the mixture led to the maximum rate of weight loss of the heavy fraction of coal tar. A hydrodemetallization kinetic model of the mixture of the heavy fraction of low-temperature coal tar and coal shale is proposed. The kinetic parameters of the hydrodemetallization process were determined; in addition, the rate constants at various temperatures were estimated. The study shows that the distribution of trace elements in the hydrogenate from the initial mixture and in the hydrogenate from the solid residue was characterized by relatively low values of reaction rate constants. The maximum microelement distribution rate was achieved in the hydrogenate solid residue. Energy indicators of activation processes indicated that hydrodemetallization at low temperatures is advantageous from an energy point of view. Full article

To address the issue of traditional static evaluation models being unable to comprehensively analyze the performance of ultra-supercritical coal-fired units under varying loads, we propose a dynamic comprehensive evaluation model based on the improved Criteria Importance Through Inter-criteria Correlation (CRITIC) method and entropy weight method (EWM). The comprehensive performance evaluation index system of ultra-supercritical coal fired units is constructed by examining the boiler performance, turbine performance, plant power performance, environmental performance, and flexible performance of coal-powered units. The CRITIC and EWM methods are used to calculate the weights of the indicators, which are then combined with the static evaluation results. Using a dynamic comprehensive evaluation model, we analyze ultra-supercritical coal-fired units, taking into account time weight. This allows us to obtain the comprehensive dynamic real-time evaluation value of the units under different loads. The research indicates that the weight of the evaluation index is changed when using the dynamic comprehensive evaluation model of the improved CRITIC and EWM. The index with lower weight is increased by 6.2%, while the index with higher weight is decreased by 0.22%. This alteration in weight range can provide a more objective reflection of the relationship between evaluation indicators. This model offers significant advantages in improving evaluation accuracy, weight balance distribution, and generality. Full article

The theoretical impact of the electric vehicle (EV) market share growth has been widely discussed with regards to technical and socioeconomic aspects in recent years. However, the prospection of EV scenarios is a challenge, and the difficulty increases with the granularity of the study and the set of variables affected by user behavior and regional aspects. Moreover, the lack of a robust database to estimate fast-charging stations’ load curves, for example, affects the quality of planning, allocation, or grid impact studies. When this problem is evaluated on highways, the challenge increases due to the reduced number of trips related to the reduced number of charger units installed and the limited EVs range, which influence user anxiety. This paper presents a methodology to estimate the highway fast-charging station operation condition, considering regional and EV user aspects. The process is based in a block of traffic simulation, considering the traffic information and highway patterns composing the matrix solution model. Also, the output block estimates charging stations’ operational conditions, considering infrastructure scenarios and simulated traffic. A Monte Carlo simulation is presented to model entrance rates and charging times, considering the PDF of stochastic inputs. The results are shown for the aspects of load curve and queue length for one case study, and a sensibility study was conducted to evaluate the impact of model inputs. Full article

The most rapid and efficient method to transport natural gas from its source to its destination is through a pipeline network. The optimal functioning of control stations for natural gas pipelines depends on the use of electrical devices, including data loggers, communication devices, control systems, surveillance equipment, and more. Ensuring a reliable and consistent power supply proves to be challenging due to the remote locations of these control stations. This research article presents a case study detailing the design and dynamic modeling of a hybrid power system (HPS) to address the specific energy needs of a particular natural gas pipeline control station. The HOMER Pro 3.17.1 software is used to design an optimal HPS for the specified location. The designed system combines a photovoltaic (PV) system with natural gas generators as a backup to ensure a reliable and consistent power supply for the control station. Furthermore, it provides significant cost savings, reducing the cost of energy (COE) by USD 0.148 and the annual operating costs by USD 87,321, all while integrating a renewable energy fraction of 79.2%. Dynamic modeling of the designed system is performed in MATLAB/Simulink R2022a to analyze the system’s response, including its power quality, harmonics, voltage transients, load impact, etc. The experimental results are validated using hardware in the loop (HIL) and OPAL-RT Technologies’ real-time OP5707XG simulator. Full article

Due to the access of distributed energy and a new flexible load, the electrical characteristics of a new distribution network are significantly different from those of a traditional distribution network, which poses a new challenge to the original topology identification methods. To address this challenge, a hierarchical topology identification method based on regression analysis and knowledge reasoning is proposed for an active low-voltage distribution network (ALVDN). Firstly, according to the new electrical characteristics of bidirectional power flow and voltage jump caused by the ALVDN, active power is selected as the electric volume for hierarchical topology identification. Secondly, considering the abnormal fluctuation of active power caused by bidirectional power flow characteristics of distributed energy users, a user attribution model based on the Elastic-Net regression algorithm is proposed. Subsequently, based on the user identification results, the logic knowledge reflecting the hierarchical topology of the ALVDN is extracted by the AMIE algorithm, and the “transformer-phase-line-user” hierarchical topology of the ALVDN is deduced by a knowledge reasoning model. Finally, the effectiveness of the proposed method is verified by an IEEE example. Full article

Shale gas is an important supplement to the supply of natural gas resources and plays an important role on the world’s energy stage. The efficient implementation of hydraulic fracturing is the key issue in the exploration and exploitation of shale gas. The existence of bedding structure results in a distinct anisotropy of shale rock formation. The anisotropic behaviors of shale rock have important impacts on wellbore stability, hydraulic fracture propagation, and the formation of complex fracture networks. This paper briefly reviews previous work on the anisotropic mechanical properties of shale rock and their relation to hydraulic fracturing in shale reservoirs. In this paper, the research status of work addressing the lithological characteristics of shale rock is summarized first, particularly work considering the mineral constituent, which determines its physical and mechanical behavior in essence. Then the anisotropic physical and mechanical properties of shale specimens, including ultrasonic anisotropy, mechanical behavior under uniaxial and triaxial compression tests, and tensile property under the Brazilian test, are summarized, and the state of the literature on fracture toughness anisotropy is discussed. The concerns of anisotropic mechanical behavior under laboratory tests are emphasized in this paper, particularly the evaluation of shale brittleness based on mechanical characteristics, which is discussed in detail. Finally, further concerns such as the effects of bedding plane on hydraulic fracturing failure strength, crack propagation, and failure pattern are also drawn out. This review study will provide a better understanding of current research findings on the anisotropic mechanical properties of shale rock, which can provide insight into the shale anisotropy related to the fracture propagation of hydraulic fracturing in shale reservoirs. Full article

Microgrid optimization scheduling, as a crucial part of smart grid optimization, plays a significant role in reducing energy consumption and environmental pollution. The development goals of microgrids not only aim to meet the basic demands of electricity supply but also to enhance economic benefits and environmental protection. In this regard, a multi-objective optimization scheduling model for microgrids in grid-connected mode is proposed, which comprehensively considers the operational costs and environmental protection costs of microgrid systems. This model also incorporates improvements to the traditional particle swarm optimization (PSO) algorithm by considering inertia factors and particle adaptive mutation, and it utilizes the improved algorithm to solve the optimization model. Simulation results demonstrate that this model can effectively reduce electricity costs for users and environmental pollution, promoting the optimized operation of microgrids and verifying the superior performance of the improved PSO algorithm. After algorithmic improvements, the optimal total cost achieved was CNY 836.23, representing a decrease from the pre-improvement optimal value of CNY 850. Full article