Energies, Volume 16, Issue 24 (December-2 2023) – 209 articles

With the increasing popularization and application of the smart grid, the harm of the data silo issue in the smart grid is more and more prominent. Therefore, it is especially critical to promote data interoperability and sharing in the smart grid. Existing data-sharing schemes generally lack effective incentive mechanisms, and data holders are reluctant to share data due to privacy and security issues. Because of the above issues, a dynamic incentive mechanism for smart grid data sharing based on evolutionary game theory is proposed. Firstly, several basic assumptions about the evolutionary game model are given, and the evolutionary game payoff matrix is established. Then, we analyze the stabilization strategy of the evolutionary game based on the payoff matrix, and propose a dynamic incentive mechanism for smart grid data sharing based on evolutionary game theory according to the analysis results, aiming to encourage user participation in data sharing. We further write the above evolutionary game model into a smart contract that can be invoked by the two parties involved in data sharing. Finally, several factors affecting the sharing of data between two users are simulated, and the impact of different factors on the evolutionary stabilization strategy is discussed. The simulation results verify the positive or negative incentives of these parameters in the data-sharing game process, and several factors influencing the users’ data sharing are specifically analyzed. This dynamic incentive mechanism scheme for smart grid data sharing based on evolutionary game theory provides new insights into effective incentives for current smart grid data sharing. Full article

Traditional crude oil heating methods that use fossil fuels or electricity have the disadvantages of high consumption of nonrenewable resources, low energy utilization, and high carbon emissions. Therefore, it is urgent to develop green and sustainable crude oil heating technologies. In this paper, a solar synergistic sewage heat pump (SSHS) dual heat source crude oil temperature maintenance system is proposed. The system utilizes clean and sustainable solar energy to heat crude oil while combining sensible heat storage technology and the waste heat utilization technology of a sewage source heat pump to solve the unstable fluctuation of the solar heating problem. A simulation and analysis model is established to analyze the influencing factors of the SSHS, and the optimal operation scheme is provided. The results show that the efficiency of the solar collector decreases and the proportion of crude oil heating increases with an increase in the solar energy guarantee rate, while the unit flow rate of the pump has a large impact on the performance of the sewage source heat pump. In order to avoid energy waste, it is more appropriate to adopt a 30% guarantee rate and an A3 pump unit flow rate, under which the solar collector efficiency is 50.18%, the proportion of solar heating of crude oil is 47.16%, the average temperature of crude oil is 42.59 °C, and the COP of the sewage source heat pump is 4.65. Further increases in the COP of the wastewater source heat pump can be realized by increasing the temperature of the wastewater supply. The results of this study provide a valuable reference for the optimization of crude oil storage heating systems. Full article

A numerical model of a flue gas scrubber sump is developed with the aim of enabling optimization of the design of the sump in order to reduce energy consumption. In this model, the multiphase flow of the continuous phase, i.e., water, and the dispersed phase, i.e., air bubbles, is considered. The air that is blown in front of the agitators, as well as the influence of the flow field of the agitators on the distribution of the dispersed phase and the recirculation pumps as outlet, is modeled. The bubble Sauter mean diameter is modeled using the population balance model. The model is used to analyze operating parameters such as the bubble retention time, the average air volume fraction, bubble Sauter mean diameter, the local distribution of the bubble size and the amount of air escaping from the pump outlets at two operating points. The purpose of the model is to simulate the two-phase flow in the sump of the flue gas scrubber using air dispersion technology with a combination of spargers and agitators, which, when optimized, reduces energy consumption by 33%. The results show that the homogeneity of air is lower in the bottom part of the absorber sump and that the amount of air escaping through recirculation pipes equals 1.2% of the total air blown into the absorber sump. The escaping air consists mainly of bubbles smaller than 6 mm. Additional operating point results show that halving the magnitude of the linear momentum source lowers the air retention, as well as the average homogeneity of the dispersed air. Full article

One of the global ecological problems is the excessive carbon dioxide emissions generated by vehicles in the transport sector, including passenger transport. Therefore, the objective of this investigation was to develop a model that supports the prediction of vehicle variants that will be satisfactory to the customer in terms of: (i) quality level and (ii) environmental impact throughout the life cycle. This model was developed with the following techniques: TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), LCA (Life Cycle Assessment), SMARTER (Specific, Measurable, Achievable, Relevant, and Time-bound), Pareto–Lorenz, and the Multi-Criteria Decision Method rule (7 ± 2). A model test was carried out for production variants of the electric vehicle BEV (battery electric vehicle) for which the quality level and life cycle assessment were estimated. Vehicle quality levels ranged from 0.15 to 0.69, with a weight of 0.75. However, vehicle life cycle scores were estimated in the range of 0.25 to 0.57, with a weight of 0.25. Ultimately, the level of the vehicles’ LCA ranged from 0.18 to 0.62. As a result, it was shown that on the basis of various modifications of the quality level of vehicle variants and the corresponding environmental impacts throughout their life cycle, it is possible to predict the vehicle variant that is most satisfactory for the customer and, at the same time, environmentally friendly. The originality of the model relies on supporting the making of sustainable design decisions and the planning of vehicle improvement actions according to customer expectations. Therefore, the model can be used to analyse different types of vehicles by producers and dealers of these products. Full article

To study the pressure drop characteristics of hypervapotron, which was designed as a water-cooling structure in the divertor dome of the fusion reactor, the pressure drop tests of subcooled water were carried out in a vertically upward hypervapotron. To simulate the one-side radiant heating condition in the engineering application, the non-uniform heat fluxes were obtained by using the off-center electrically heating method. The system parameters were as follows: mass flux G = 2000–5000 kg·m⁻²·s⁻¹, inlet pressure p = 2–4 MPa, and equivalent one-side radiating heat flux q_e = 0–5 MW·m⁻². The effects of the parameters on the pressure drop were discussed in detail. It was observed that in the single-phase (SP) region, the pressure drop was little influenced by the inlet fluid temperature (T_b,in). However, in the subcooled boiling region, the pressure drop increased rapidly with the increasing T_b,in. A higher G leads to a high pressure drop. In the SP region, the influence of p on the pressure drop is not obvious, and the pressure drop decreased with the increasing q_e. The test data are used to evaluate the typical pressure drop correlation, and the results show that none of these correlations can predict the pressure drop well under the test conditions. Therefore, a new pressure drop correlation is proposed for subcooled water in a hypervapotron under high and non-uniform heat fluxes. The new correlation has a high prediction accuracy for the test data, and the mean relative error (MRE) and root mean square error (RMSE) are 0.72% and 4.33%, respectively. The test results have a reference value for the design of the water-cooling structure of the diverter. Full article

The increasing urbanization and growth of cities worldwide have led to a significant increase in energy demand. As a transition to a low carbon environment occurs, the role of renewable and sustainable energy systems in urban areas is benefiting industry and the environment alike. From this perspective, the Sustainable Development Goals (SDGs) have a lot to offer to the energy industry, particularly the integration of renewable and sustainable energy systems for environmental protection in cities. This study presents a comprehensive view that integrates technological, economic, political, and social challenges confronted with the effective implementation of renewable and sustainable energy in urban cities and proposes a solution agenda to overcome these hurdles with the aid of the SDGs. The weights for the challenges of adopting renewable and sustainable energy systems were determined using the Fuzzy Best-Worst Method. The SDGs were then ranked using the fuzzy TOPSIS technique to overcome predetermined challenges. The originality of this study lies in finding solutions to the determined challenges by adopting SDGs, emphasizing the need for integrated solutions that address energy-related concerns, and highlighting the role and importance of SDGs in environmental protection. The study highlights the importance of SDGs in promoting renewable energy integration in urban areas, with SDG 11 being the most crucial to mitigate harmful environmental occurrences related to energy-related issues in urban areas, followed by SDG 7 and SDG 13. Full article

The ion storage layer in electrochromic devices (ECDs) stores protons or lithium ions to provide electrochemical stability and extend cycle durability. This paper reports on the performance and stability of ECDs paired with various ion storage layers (NiO, V₂O₅, and IrO₂ films). The complementary ECD using a V₂O₅ ion storage layer presented the fastest response time, but the lowest optical contrast. In addition, the ECD using an IrO₂ ion storage layer proved the most effective as an ion storage layer, due to its high optical modulation ability capability and long-term stability. Chronoamperometry analysis revealed that IrO₂-based ECD (glass/IZTO/WO₃/liquid electrolyte/IrO₂/IZTO/glass) can be highly effective in modulating optical transmittance, as indicated by T = 61.5% (from T_bleaching (69.6%) to T_coloring (8.1%)) and switching times of 5.3 s for coloring and 7.3 s for bleaching at 633 nm. Full article

Fruit and vegetable processing comes 6th in terms of energy consumption in the agri-food industry. At the same time, 88.4% of the industry’s final energy consumption structure is thermal energy, which depends heavily on electricity consumption. In addition, fruit and vegetable processing has a significant impact on the environment due to consumption of significant amounts of water. Reducing these three indicators simultaneously would increase the efficiency of the process while improving environmental protection. This paper proposes neural models of thermal energy, electricity and water consumption for selected major fruit- and vegetable-processing plants in Poland. These models were the basis for formulating a multi-criteria optimization task. Optimization of thermal energy, electricity and water consumption was carried out using genetic algorithms. The optimization results in the sense of Pareto can be the basis for the use of sustainable technology in selected fruit- and vegetable-processing plants. Full article

In pursuit of Finland’s carbon neutrality objective by 2035, integrating renewable energy sources into the power grid is essential. To address the stochastic nature of these resources, additional sources of flexibility are required to maintain grid stability. Meanwhile, district heating network (DHN) operators in Finland are decommissioning fossil fuel-based combined heat and power plants (CHPs) and electrifying heating systems with heat pumps (HPs) and electric boilers. A techno-economic assessment and the optimized operation of DHN-connected HPs and electric boilers in providing ancillary balancing services were explored in this study. The primary goal was to maximize the potential revenue for DHN operators through participation in the day-ahead electricity market and frequency containment reserve (FCR) balancing markets. Three interconnected DHNs in the Helsinki metropolitan area were optimized based on 2019 data and each operator’s decarbonization strategies for 2025. HPs are expected to achieve the highest profit margins in the FCR-D up-regulation market, while electric boilers could generate substantial profits from the FCR-D down-regulation market. In contrast to other balancing markets studied, the FCR-N market exhibited limited profit potential. Sensitivity analysis indicated that spot electricity prices and CO₂ emission allowance prices significantly influence the profitability derived from balancing markets. Full article

Stratigraphic identification from wire-line logs and core samples is a common method for lithology classification. This traditional approach is considered superior, despite its significant financial cost. Artificial neural networks and machine learning offer alternative, cost-effective means for automated data interpretation, allowing geoscientists to extract insights from data. At the same time, supervised and semi-supervised learning techniques are commonly employed, requiring a sufficient amount of labeled data to be generated through manual interpretation. Typically, there are abundant unlabeled geophysical data while labeled data are scarcer. Supervised and semi-supervised techniques partially address the cost issue. An underutilized class of machine-learning-based methods, unsupervised data clustering, can perform consonant classification by grouping similar data without requiring known results, presenting an even more cost-effective solution. In this study, we examine a state-of-the-art unsupervised data clustering algorithm called piecemeal clustering to identify lithofacies from wire-line logs, effectively addressing these challenges. The piecemeal clustering algorithm groups similar wire-log signatures into clusters, determines the number of clusters present in the data, and assigns each signature to one of the clusters, each of which represents a lithofacies. To evaluate the performance, we tested the algorithm on publicly released data from ten wells drilled in the Hugoton and Panoma fields of southwest Kansas and northwest Oklahoma, respectively. The data consist of two major groups: marine and non-marine facies. The study herein is centered around addressing two fundamental research questions regarding the accuracy and practicality of the piecemeal clustering algorithm. The algorithm successfully identified nine distinct clusters in our dataset, aligning with the cluster count observed in previously published works employing the same data. Regarding mapping accuracy, the results were notable, with success rates of 81.90% and 45.20% with and without considering adjacent facies, respectively. Further detailed analysis of the results was conducted for individual types of facies and independently for each well. These findings suggest the algorithm’s precision in characterizing the geological formations. To assess its performance, a comprehensive comparative analysis was conducted, encompassing other data clustering algorithms, as well as supervised and semi-supervised machine learning techniques. Notably, the piecemeal clustering algorithm outperformed alternative data clustering methods. Furthermore, despite its unsupervised nature, the algorithm demonstrated competitiveness by yielding results comparable to, or even surpassing, those obtained through supervised and semi-supervised techniques. Full article

The role of prosumers is changing as they become active and empowered members of the grid by exchanging energy. This introduces bidirectional power flow and other challenges into the existing power systems, which require new approaches capable of dealing with the increased decentralization and complexity. Such approaches rely on game-theoretic models and mechanisms to analyze strategic decisions in competitive settings. More specifically, a coalitional game can encourage participants to trade energy with one another and obtain fair and sustainable outcomes. Therefore, the contents of this work address the coalitional game for sustainable energy trading, as well as the challenges associated with its application in power systems. This is achieved by identifying literature works that successfully implemented coalitional games in energy trading and management applications while providing an overview of solution concepts and discussing their properties and contributions to sustainability. Moreover, this work also proposes conditions that peer-to-peer energy trading should satisfy to be considered sustainable. Finally, a case study is presented to demonstrate how a coalitional game and various solution concepts can be successfully implemented to ensure the benefits and stability of cooperation in power systems. The weighted Shapley value is proposed to allocate profits among communities according to their level of sustainability. Full article

Steelmaking is one of the most energy-intensive industries, so improving control efficiency helps to reduce the energy used to produce a tonne of steel. Mutual influences between the phases of an electric arc furnace in available electrode movement control systems cause unproductive electrode movements as a reaction to the redistribution of currents among the phases of a three-phase power supply system due to changes in arc length in one of the phases. The nonlinearity of the characteristics of an electric arc furnace significantly complicates the ability to provide autonomous electrode movement control. The approach proposed in this paper, based on the formation of a matrix of mutual influences with variable coefficients, significantly improves the per-phase autonomy of the electrode movement control system. Nonlinear dependences of the mutual influence coefficients as a function of the current increment in the phase in which the disturbance occurred are obtained. Thus, it is possible to practically eliminate unproductive electrode movements in existing control systems by avoiding the traditional use of a dead zone, which reduces the control quality in the zone of small disturbances. The complex of experiments performed using the mathematical model demonstrate that the mutual influence improves the dynamic properties of the electrode movement system in certain operating modes. Full article

The coordination of pumped storage and renewable energy is regarded as a promising avenue for renewable energy accommodation. Considering wind power output uncertainties, a collaborative capacity optimization method for wind–pumped hydro storage hybrid systems is proposed in this work. Firstly, considering the fluctuation of wind power generation caused by the natural seasonal weather and inherent uncertainties of wind power outputs, a combined method based on the generative adversarial network and K-means clustering algorithm is presented to construct wind power output scenarios. Then, a multi-objective wind–pumped storage system capacity optimization model is established with three objectives consisting of minimizing the levelized cost of energy, minimizing the net load peak–valley difference of regional power grids, and minimizing the power output deviation of hybrid systems. An inner and outer nested algorithm is proposed to obtain the Pareto frontiers based on the strength of the Pareto evolutionary algorithm II. Finally, the complementarity of wind power and pumped storage is illustrated through an analysis of numerical examples, and the advantages of variable-speed pumped storage in complementary operation with wind power over fixed-speed units are verified. Full article

The number of electric vehicles (EVs) is growing exponentially, which presents the power grid with new challenges to turn their reliance to renewable energy sources (RESs). Coordination between the available generations from RESs and the charging time should be managed to optimally utilize the available generation from RESs. The dispatch scheduling of EVs can significantly reduce the impact of these challenges on power systems. Three different technologies can be used to manage the dispatch of EV batteries which are unregulated charging (UC), unidirectional grid-to-vehicle (G2V), and bidirectional vehicle-to-grid (V2G) technologies. This study aims to address the primary reason for EV owners’ disbelief in the accuracy of battery wear models, which is impeding their involvement in V2G technology. This paper introduces a novel accurate EV battery wear model considering the instantaneous change in the operation of the EV battery. Moreover, an effective musical chairs algorithm (MCA) is used to reduce everyday expenses and increase revenue for V2G technologies in a short convergence time with accurate determination of optimal power dispatch scheduling. The results obtained from these three strategies are compared and discussed. The salient result from this comparison is that V2G technology increases wear and reduces the battery lifespan in comparison with the UC and G2V. The yearly expenses of G2V are reduced by 33% compared to the one associated with the UC. Moreover, the use of V2G technology provides each EV owner with USD 3244.4 net yearly profit after covering the charging and wear costs. The superior results extracted from the proposed model showed the supremacy of V2G usage, which is advantageous for both EV owners and the power grid. Full article

Belt conveyors, owing to their simple construction, high reliability and relatively low energy consumption, are the basic means of transporting loose and granular materials. Currently, thanks to continuous development, belt conveyors can reach a length of up to several kilometres, and their belt width can be more than two meters. Such possibilities are achieved thanks to increasingly better belts and drives. However, the most common are short belt conveyors with a length of up to 40 m and belt widths of up to 1 m, frequently referred to as belt feeders. Apart from the mining industry, they are widely used in power engineering, metallurgy and other industries (chemical plants, trans-shipment ports, storage yards, etc.). The design of machines, including belt feeders, is based on calculations. Modern design in technology is based on advanced computational methods and the possibilities of computer technology. Multi-variant simulation calculations are necessary, especially in the case of belt feeders, where none of the devices—despite the use of typical elements and subassemblies—are a repeatable solution. Only this procedure guarantees the selection of rational solutions already at the early stages of design. Therefore, in this article, an analytical model of a typical belt feeder was developed and its stability and forces in the supports were determined. This allowed the development of an application for testing the stability of the belt feeder at the design stage or when introducing structural changes. Full article

The push for decarbonization of internal combustion engines (ICEs) has spurred interest in alternative fuels, such as hydrogen and ammonia. To optimize combustion efficiency and reduce emissions, a closer look at the intake system and in-cylinder flows is crucial, especially when a hard-to-burn fuel, such as ammonia is utilized. In port fuel injection ICEs, airflow within cylinders profoundly affects combustion and emissions by influencing the air–fuel mixing phenomenon. Adjusting intake port openings is an important factor in controlling the in-cylinder airflow. In previous experiments with a transparent cylinder, tangential and helical ports demonstrated that varying the helical port’s opening significantly impacts flow velocities, swirl ratios, and swirl center positions (SCPs). In this study, we used a particle image velocimetry technique to investigate how the tangential port’s opening affects intake and in-cylinder flows. Flow velocities were assessed at different planes near the cylinder head, evaluating streamline maps, turbulent kinetic energy (TKE), and SCPs. Under the given experimental conditions, swirl flows were successfully generated early in the compression stroke when the tangential port opening exceeded 25%. Our findings emphasize the importance of minimizing TKE and SCP variation for successful swirl flow generation in engine cylinders equipped with both tangential and helical ports. Full article

Thermoelectric cooling is an ideal solution for chip heat dissipation due to its characteristics of no refrigerant, no vibration, no moving parts, and easy integration. Compared with a traditional thermoelectric device, a thin-film thermoelectric device significantly improves the cooling density and has tremendous advantages in the temperature control of electronic devices with high-power pulses. In this paper, the transient cooling performance of a compact thin-film thermoelectric cooler with a horizontal structure was studied. A 3D multi-physics field numerical model with the Thomson effect considered was established. And the effects of impulse current, thermoelectric leg length, pulse current imposition time, and the size of the contact thermal resistance on the cooling performance of the device were comprehensively investigated. The results showed that the model achieved an active cooling temperature difference of 25.85 K when an impulse current of 0.26 A was imposed. The longer the length of the thermoelectric leg was, the more unfavorable it was to the chip heat dissipation. Due to the small contact area between different sections of the device, the effect of contact thermal resistance on the cooling performance of the device was moderate. Full article

This study presents a novel methodology for determining the apparent thermal diffusivity of subsoil in situ, employing two heat transfer models within the subsurface: one method is based on heat conduction caused by air temperature oscillations, while the other considers heat transmission via both conduction and convection due to groundwater flow. Differential equations were solved, and non-linear regression analysis was employed. This method has direct applications in various engineering and environmental domains, such as underground transmission lines, oil and gas pipelines, radioactive waste management, and geothermal systems, especially in the context of implementing horizontal geothermal collectors (HGC). The apparent thermal diffusivity value of 1.514 × 10⁻⁶ m² s⁻¹, within a 95% confidence interval spanning 1.512 × 10⁻⁶ m² s⁻¹ and 1.516 × 10⁻⁶ m² s⁻¹, was obtained from the section between 1.67 and 3.86 m depth in a research borehole located in Asturias, Northern Spain, using twenty-one temperature sensors. The method allowed for the calculation of the subsoil’s apparent thermal diffusivity up to a depth of 14.55 m. Full article

A light shelf is a type of natural daylight system that brings natural light from the outside into an indoor space through a reflector and a ceiling surface. The introduction of light shelves has led to studies evaluating their efficiency. However, past studies on light shelves did not consider the diversity of ceiling types when evaluating their performance. Therefore, this study derives fundamental data involving external light shelf designs by evaluating light shelf performance based on the ceiling type present using a light environment simulation method. This study analyzed the indoor illuminance distribution with Radiance to evaluate the performance according to light shelves and indoor space types. The results derived from this study are as follows: (1) In the case of a flat ceiling, the performance of an external light shelf can be improved by increasing its angle and width. However, adjusting the external light shelf angle to 30° during the middle of the season and 20° in winter is ineffective because natural light is not reflected by the ceiling surface. (2) The performance of a light shelf can be improved by increasing the slope and curvature of the ceiling types specified in this study. However, setting the light shelf angle to 30° during the middle season and to 30° and 20° in winter, when external natural light entering the indoor space is not reflected by the ceiling surface, is ineffective due to the low levels of daylight performance, regardless of the type of space. (3) To increase uniformity levels in gable ceilings and curved ceilings, it is advantageous to increase the number of reflections and diffusion areas on the ceiling’s surface due to the uniqueness of these ceiling shapes. Furthermore, the optimal external light shelf angle for these ceiling types differs from that of other types. (4) Regarding the appropriate external light shelf size according to a particular ceiling type, installing an angle-controllable external light shelf with a width of 1.2 m can improve daylight performance. Full article

Due to the advantages of fewer energy conversion stages and a simple structure, direct current (DC) microgrids are being increasingly studied and applied. To minimize distribution loss in DC microgrids, a systematic optimal control framework is proposed in this paper. By considering conduction loss, switching loss, reverse recovery loss, and ohmic loss, the general loss model of a DC microgrid is formulated as a multi-variable convex function. To solve the objective function, a top-layer distributed integral convex optimization algorithm (DICOA) is designed to optimize the current-sharing coefficients by exchanging the gradients of loss functions. Then, the injection currents of distributed energy resources (DERs) are allocated by the distributed adaptive control in the secondary control layer and local voltage–current control in the primary layer. Based on the DICOA, a three-layer control strategy is constructed to achieve loss minimization. By adopting a peer-to-peer data-exchange strategy, the robustness and scalability of the proposed systematic control are enhanced. Finally, the proposed distribution current dispatch control is implemented and verified by simulations and experimental results under different operating scenarios, including power limitation, communication failure, and plug-in-and-out of DERs. Full article

Solar power is a clean and sustainable energy source that does not emit greenhouse gases or other atmospheric pollutants. The inherent variability in solar energy due to random fluctuations introduces novel attributes to the power generation and load dynamics of the grid. Consequently, there has been growing attention to developing an accurate forecast model using various machine and deep learning techniques. Temporal attention mechanisms enable the model to concentrate on the critical components of the input sequence at each time step, thereby enhancing the accuracy of the prediction. The suggested GRU–temporal fusion transformer (GRU-TFT) model was trained and validated employing the “Daily Power Production of Solar Panels” Kaggle dataset. Furthermore, an innovative loss function termed DILATE is introduced to train the proposed model specifically for multistep and nonstationary time series forecasting. The outcomes have been subjected to a comparative analysis with alternative algorithms, such as neural basis expansion analysis for interpretable time series (N-BEATS), neural hierarchical interpolation for time series (N-HiTS), and extreme gradient boosting (XGBoost), using several evaluation metrics, including the absolute percentage error (MAE), mean square error (MSE), and root mean square error (RMSE). The model presented in this study exhibited significant performance improvements compared with traditional statistical and machine learning techniques. This is evident from the achieved values of MAE, MSE, and RMSE, which were 1.19, 2.08, and 1.44, respectively. In contrast, the machine learning approach utilizing the Holt–Winters method for time series forecasting in additive mode yielded MAE, MSE, and RMSE scores of 4.126, 29.105, and 5.3949, respectively. Full article

The aggravated global warming and energy crisis have greatly challenged the healthy and sustainable development of society worldwide. Improving energy efficiency is one of the vital ways to overcome the dilemma. Existing studies explore the impact of environmental regulation on energy efficiency; however, the potential impact of the environmental protection tax (EPT) on urban energy efficiency has received little attention. Using the panel dataset of 278 Chinese cities from 2011 to 2019, the unified efficiency index (UEI) based on a total non-radial directional distance function (TNDDF) is first used to calculate urban energy efficiency. A difference-in-differences (DIDs) model is conducted to explore the impact of the EPT policy on the urban UEI and its potential mechanisms. The findings indicate that: (1) The average UEI in cities experienced an uptrend and a downtrend during 2011–2019. The overall UEI levels were low, especially in Jiaxiaguan, Tianshui, and Huyang cities. (2) The EPT policy significantly increases energy efficiency for the heavily polluting cities by approximately 5.21% more than that of the non-heavily polluting cities. (3) Heterogeneity analysis shows that EPT has a better effect on improving UEI in higher-level economic and non-resource-based cities. (4) Mechanism analysis implies that EPT boosts the urban UEI by stimulating urban green technology innovation, upgrading the industrial structure, and introducing foreign direct investment. This study offers empirical evidence and implications for policymakers using EPT to achieve higher urban energy efficiency and sustainable targets. Full article

The exceptional performance of the wireless power transfer (WPT) system hinges on its resonant state. However, the capacitance drift caused by manufacturing tolerance and temperature will result in a state of detuning. In this manuscript, a PWM-controlled switched impedance (PCSI) topology that can express inductive and capacitive is proposed to eliminate line mismatches resulting from the above factors. Firstly, the PCSI topology is introduced, and its placement is determined based on the characteristics of the inductor–capacitor–capacitor series (LCC-S) network. Secondly, the working principle of the proposed topology is introduced. Finally, the simulation and experimental results show that the system could be restored to its resonant state by adjusting the PCSI topology. Under different values of resonant capacitors, the PCSI topology enhances the output power of the system by 40 W~150 W compared to the previous state, and the efficiency is increased by 9~13%. Full article

The worldwide adoption of Electric Vehicles (EVs) has embraced promising advancements toward a sustainable transportation system. However, the effective charging scheduling of EVs is not a trivial task due to the increase in the load demand in the Charging Stations (CSs) and the fluctuation of electricity prices. Moreover, other issues that raise concern among EV drivers are the long waiting time and the inability to charge the battery to the desired State of Charge (SOC). In order to alleviate the range of anxiety of users, we perform a Deep Reinforcement Learning (DRL) approach that provides the optimal charging time slots for EV based on the Photovoltaic power prices, the current EV SOC, the charging connector type, and the history of load demand profiles collected in different locations. Our implemented approach maximizes the EV profit while giving a margin of liberty to the EV drivers to select the preferred CS and the best charging time (i.e., morning, afternoon, evening, or night). The results analysis proves the effectiveness of the DRL model in minimizing the charging costs of the EV up to 60%, providing a full charging experience to the EV with a lower waiting time of less than or equal to 30 min. Full article

Modern ships are complex energy systems containing a large number of different elements. Each of these elements is simulated separately. Since all these models form a single system (ship), they are interdependent. The operating modes of some systems influence others, but at the same time, the work of all the systems should be aimed at fulfilling the basic functions of the ship. The work proposes a methodological approach to combining various systems of ships into a single complex model. This model allows combining models of ship systems of various levels (microlevel, macrolevel, metalevel, megalevel). The work provides examples of models of such multi-level energy systems. These are energy systems composed of an electric generator, a diesel engine, a propeller shaft, and algorithms used for operating the common parts of the ship’s electric power system and a piston wear process. Analytical, structural, numerical, and object-oriented models were made for these objects. Each of these particular models describes a limited class of problems, has characteristic properties, and a mathematical structure. The work shows how particular models can be interconnected using a set-theoretic description. Particular models are combined into macrolevel models, whose output parameters are quantities that are by no means related. The macrolevel models are interrelated using control models. Control models belong to the metalevel and allow for assigning settings and response thresholds to algorithms used in automation systems. Such a model (megalevel model) allows, ultimately, investigating the dynamics of the entire system as a whole and managing it. Full article

Hexagonal distributed embedded energy converters, also known as hexDEECs, are centimeter-scale energy transducers that leverage variable capacitance to generate electricity when their hyperelastic structure is dynamically deformed. To better understand, characterize, and optimize hexDEEC designs, a series of numerical methods and techniques were developed to model the hyperelastic mechanics of hexDEECs, electrostatic properties, and electricity generation characteristics. The numerical methods developed for the hyperelastic structural analysis were corroborated by empirical results from another study, and the models and equations for capacitance, electrostatic forces, and electrical potential energy were derived from fundamental electrostatic equations. These methods and techniques were implemented within the STAR-CCM+ multiphysics software Version 2020.3 (15.06.008) environment. Results from this analysis revealed methodologies and techniques necessary to model the energy converters, which will enable future exploration and optimization of more specific designs and corresponding applications. Full article

This paper presents a resilience-oriented energy management approach (R-OEMA) designed to bolster the resilience of networked microgrids (NMGs) in the face of extreme events. The R-OEMA method strategically incorporates preventive scheduling techniques for hydrogen (H2) systems, renewable units, controllable distributed generators (DGs), and demand response programs (DRPs). It seeks to optimize the delicate balance between maximizing operating revenues and minimizing costs, catering to both normal and critical operational modes. The evaluation of the R-OEMA framework is conducted through numerical simulations on a test system comprising three microgrids (MGs). The simulations consider various disaster scenarios entailing the diverse durations of power outages. The results underscore the efficacy of the R-OEMA approach in augmenting NMG resilience and refining operational efficiency during extreme events. Specifically, the approach integrates hydrogen systems, demand response, and controllable DGs, orchestrating their collaborative operation with predictive insights. This ensures their preparedness for emergency operations in the event of disruptions, enabling the supply of critical loads to reach 82% in extreme disaster scenarios and 100% in milder scenarios. The proposed model is formulated as a mixed-integer linear programming (MILP) framework, seamlessly integrating predictive insights and pre-scheduling strategies. This novel approach contributes to advancing NMG resilience, as revealed by the outcomes of these simulations. Full article

There is a significant demand for materials and energy throughout the manufacturing and construction of a solar power plant’s component parts. Electricity and fossil fuels are used in enormous quantities during the industrial processes in the photovoltaic power plant’s life cycle. It is then necessary to assess the energy needs, especially during production processes, to improve the efficiency of energy usage and management of natural resources from the global perspective. This will lead to a decrease in natural resource consumption and electricity demand. The main aim of this study was to assess the energy demand in the life cycle of the photovoltaic power plant and identify the most energy-intensive stages and components of this type of installation throughout its life cycle. The study of energy consumption in the whole life cycle was conducted for a 2 MW photovoltaic power plant situated in the northern region of Poland using the Life Cycle Assessment (LCA) methodology, particularly the Cumulative Energy Demand (CED) method. Two post-consumer management scenarios were investigated: recycling and landfilling. It was found that the life cycle of PV panels and the inverter station had the largest energy demand among all the components. This study revealed that, compared to recycling, the life cycle involving post-consumer management in the form of landfilling had a higher total energy demand of 4.09 × 10⁷ MJ. The results of this investigation validate the benefits of recycling. Thus, recycling ought to be commonplace to improve the environment. Full article

In the contemporary energy landscape, power generation comprises a blend of renewable and non-renewable resources, with the major supply of electrical energy fulfilled by non-renewable sources, including coal and gas, among others. Renewable energy resources are challenged by their dependency on unpredictable weather conditions. For instance, solar energy hinges on clear skies, and wind energy relies on consistent and sufficient wind flow. However, as a consequence of the finite supply and detrimental environmental impact associated with non-renewable energy sources, it is required to reduce dependence on such non-renewable sources. This can be achieved by precisely predicting the generation of renewable energy using a data-driven approach. The prediction accuracy for electric load plays a very significant role in this system. If we have an appropriate estimate of residential and commercial load, then a strategy could be defined for the efficient supply to them by renewable and non-renewable energy sources through a smart grid, which analyzes the demand-supply and devises the supply mechanism accordingly. Predicting all such components, i.e., power generation and load forecasting, involves a data-driven approach where sensitive data (such as user electricity consumption patterns and weather data near power generation setups) is used for model training, raising the issue of data privacy and security concerns. Hence, the work proposes Federated Smart Grid (FedGrid), a secure framework that would be able to predict the generation of renewable energy and forecast electric load in a privacy-oriented approach through federated learning. The framework collectively analyzes all such predictive models for efficient electric supply. Full article

The vast majority of research dedicated to enhancing the homogenous charge compression ignition (HCCI) low-temperature combustion system is focused on improving controllability, efficiency and emissions. This article aims to assess the impact of HCCI combustion on the operation of the piston ring system. Utilizing the measured pressures in the combustion chamber of a single-cylinder research engine operating in spark ignition (SI) and HCCI modes at various loads, simulations were carried out using an advanced ring pack model. This model integrates the gas flow, ring dynamics and ring mixed lubrication models. Simulations revealed that differences in the pressure above the piston between the HCCI and SI combustion significantly influence ring pack performance. The predicted energy losses due to the friction of piston rings against the cylinder liner are up to 5% higher in the HCCI engine than in the SI engine. This identified drawback diminishes the advantages of the HCCI engine resulting from higher thermal efficiency, and efforts should be made to minimize this negative impact. Full article