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When using point-by-point data input with former series models for wind power prediction, the prediction accuracy decreases due to data distribution shifts and the inability to extract local information. To address these issues, this paper proposes an ultra-short-term wind power prediction model based on the Z-score (ZS), Dish-TS (DT), and Patch time series Transformer (PatchTST). Firstly, to reduce the impact of data distribution shift on prediction accuracy, ZS standardization is applied to both training and testing datasets. Additionally, the DT algorithm, which can self-learn the mean and variance, is introduced for window data standardization. Secondly, the PatchTST model is employed to convert point input data into local-level input data. Feature extraction is then performed using the multi-head attention mechanism in the Encoder layer and a feed-forward network composed of one-dimensional convolution to obtain the prediction results. These results are subsequently de-standardized using DT and ZS to restore the original data amplitude. Finally, experimental analysis is conducted, comparing the proposed ZS-DT-PatchTST model with various prediction models. The proposed model achieves the highest prediction accuracy, with a mean absolute error of 5.95 MW, a mean squared error of 10.89 MW, and a coefficient of determination of 97.38%. [ABSTRACT FROM AUTHOR]

Large-scale optimization (LSO) problems among photovoltaic (PV) and concentrated solar power (CSP) systems are attracting increasing attention as they help improve the energy dispatch efficiency of PV and CSP systems to minimize power costs. Therefore, it is necessary and urgent to systematically analyze and summarize various LSO methods to showcase their advantages and disadvantages, ensuring the efficient operation of hybrid energy systems comprising different PV and CSP systems. This paper compares and analyzes the latest LSO methods for PV and CSP systems based on meta-heuristic algorithms (i.e., Particle Swarm Optimization, Genetic Algorithm, Enhanced Gravitational Search Algorithm, and Grey Wolf Optimization), numerical simulation and stochastic optimization methods (i.e., Constraint Programming, Linear Programming, Dynamic Programming Optimization Algorithm, and Derivative-Free Optimization), and machine learning-based AI methods (Double Grid Search Support Vector Machine, Long Short-Term Memory, Kalman Filter, and Random Forest). An in-depth analysis and A comparison of the essence and applications of these algorithms are conducted to explore their characteristics and suitability for PV and CSP or hybrid systems. The research results demonstrate the specificities of different LSO algorithms, providing valuable insights for researchers with diverse interests and guiding the selection of the most appropriate method as the solution algorithm for LSO problems in various PV and CSP systems. This also offers useful references and suggestions for extracting research challenges in LSO problems of PV and CSP systems and proposing corresponding solutions to guide future research development. [ABSTRACT FROM AUTHOR]

Acquiring real-time status information of the distribution system forms the foundation for optimizing the management of power system operations. However, missing measurements, bad data, and inaccurate system models present a formidable challenge for distribution system state estimation (DSSE) in practical applications. This paper proposes a physics-informed graphical learning state estimation approach, to address these limitations by integrating power flow equations and GraphSAGE. The generalization ability of GraphSAGE for unknown nodes is used to perform inductive learning of measurement information. For unseen measurement points in the training set, the simulation proves that the proposed approach can still satisfactorily predict the state quantity. The training process is guided by power flow equations to ensure it has physical significance. Additionally, the possibility of applying the proposed approach to an actual distribution area is explored. Equivalent preprocessing of the three-phase voltage measurement data of the actual distribution area is conducted to improve the estimation accuracy of the transformer measurement points and simplify the computation required for state estimation. [ABSTRACT FROM AUTHOR]

Recently, thermal regulators based on adsorption in a heat pipe have been proposed. The advantage of these so-called "switchpipes" over similar approaches is their low on state thermal resistance. In this paper, we propose a methodology to calculate and adjust the activation temperature of such switchpipes. For this purpose, we use a mass balance-based model that considers both the heat transfer properties of the heat pipe itself, which depend on the amount of working fluid, and the adsorption equilibrium of the adsorbent used. This model can be used not only to describe the activation behavior of a given heat pipe but also to optimize the configuration of a heat pipe for specific operating conditions and to select appropriate adsorbents. In this paper, we also propose definitions for basic indicators of the activation properties of the heat pipe, such as the activation temperature and the activation temperature span. Finally, a simplified calculation method is presented that allows the selection of the correct adsorbent among all adsorbents with Type IV and Type V adsorption isotherms. [ABSTRACT FROM AUTHOR]

Low-temperature combustion (LTC) concepts, such as homogeneous charge compression ignition (HCCI) and partially premixed combustion (PPC), aim to reduce in-cylinder temperatures in internal combustion engines, thereby lowering emissions of nitrogen oxides (NOx) and soot. These LTC concepts are particularly attractive for decarbonizing conventional diesel engines using renewable fuels such as methanol. This paper uses numerical simulations and a finite-rate chemistry model to investigate the combustion and emission processes in LTC engines operating with pure methanol. The aim is to gain a deeper understanding of the physical and chemical processes in the engine and to identify optimal engine operation in terms of efficiency and emissions. The simulations replicated the experimentally observed trends for CO, unburned hydrocarbons (UHCs), and NOx emissions, the required intake temperature to achieve consistent combustion phasing at different injection timings, and the distinctively different combustion heat release processes at various injection timings. It was found that the HCCI mode of engine operation required a higher intake temperature than PPC operation due to methanol's low ignition temperature in fuel-richer mixtures. In the HCCI mode, the engine exhibited ultra-low NOx emissions but higher emissions of UHC and CO, along with lower combustion efficiency compared to the PPC mode. This was attributed to poor combustion efficiency in the near-wall regions and engine crevices. Low emissions and high combustion efficiency are achievable in PPC modes with a start of injection around a crank angle of 30° before the top dead center. The fundamental mechanism behind the engine performance is analyzed. [ABSTRACT FROM AUTHOR]

The dual-active hybrid full-bridge (H-FDAB) DC–DC converter has great potential in medium-voltage high-power photovoltaic power station applications by introducing a three-level bridge arm to increase the output voltage range. However, its mathematical model and optimum modulation schemes have not been fully explored. Under the traditional PI control, the H-FDAB DC–DC converter will produce significant reflux power, which will lead to a decrease in converter efficiency and output voltage fluctuation. On this basis, this paper proposes a reflux power optimization strategy for an H-FDAB DC-DC converter based on active disturbance rejection control (ADRC). Firstly, the structure and power characteristics of the H-FDAB DC–DC converter are analyzed, and the relationship among the reflux power, the transmission power, and the phase shift angle is derived. Secondly, to reduce the complexity of the control calculation, upon the foundation of dual phase-shifting modulation, the Karush–Kuhn–Tucker (KKT) condition is used to solve for the phase shift angle that corresponds to the minimum reflux power. Simultaneously, we develop an ADRC loop utilizing an extended state observer (ESO) for the real-time estimation of system states. We also consider the sudden changes in input voltage, load switching, and transmission power fluctuations caused by reflux power optimization strategies as system disturbances and compensate for them accordingly. Finally, the experiments conclusively validate the designed control strategy's correctness and feasibility. [ABSTRACT FROM AUTHOR]

Under the condition of grid voltage imbalance, the circulation of the bridge arm inside the modular multilevel converter (MMC) increases significantly, which leads to the aggravation of the distortion of the bridge arm current, and, thus, increases the system loss and reduces the power quality. To address this problem, this paper analyzes the mechanism of circulating current generation and proposes a circulating current suppression strategy based on a reduced-order generalized integrator (ROGI), which firstly uses the ROGI system to separate the second-harmonic positive- and negative-sequence components in the circulating current from the DC, and then converts the rotating coordinates of the circulating current's second octave component into the DC to be fed into the proportional–integral quasi-resonance (PIR) controller for suppression. A simulation model of a 23-level MMC inverter is built in MATLAB/Simulink, and the control strategy proposed in this paper is compared with the classical proportional–integral (PI) control in simulation experiments. The simulation results show that the amplitude of the circulating current fluctuation of the classical PI control is reduced from 90 A to 22 A, and the harmonic distortion rate of the bridge arm current is reduced from 32.56% to 5.57%; the amplitude of the circulating current fluctuation of the control strategy proposed in this paper is reduced from 90 A to 5.7 A, and the harmonic distortion rate of the bridge arm current is reduced from 20.2% to 1.13%, which verifies the effectiveness of the pro-posed control strategy. [ABSTRACT FROM AUTHOR]

The growing interest in renewable energy solutions for sustainable development has significantly advanced the design and analysis of floating offshore wind turbines (FOWTs). Modeling FOWTs presents challenges due to the considerable coupling between the turbine's aerodynamics and the floating platform's hydrodynamics. This review paper highlights the critical role of computational fluid dynamics (CFD) in enhancing the design and performance evaluation of FOWTs. It thoroughly evaluates various CFD approaches, including uncoupled, partially coupled, and fully coupled models, to address the intricate interactions between aerodynamics, hydrodynamics, and structural dynamics within FOWTs. Additionally, this paper reviews a range of software tools for FOWT numerical analysis. The research emphasizes the need to focus on the coupled aero-hydro-elastic models of FOWTs, especially in response to expanding rotor diameters. Further research should focus on developing nonlinear eddy viscosity models, refining grid techniques, and enhancing simulations for realistic sea states and wake interactions in floating wind farms. The research aims to familiarize new researchers with essential aspects of CFD simulations for FOWTs and to provide recommendations for addressing challenges. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a decision support system (DSS) to capture the complexity of the transition of a national energy system to net zero in the context of multiple sustainability themes. The paper proposes an integrated assessment framework that combines dynamic systems modeling, sustainability indicators, and multi-criteria decision analysis (MCDA) with direct stakeholder involvement. To illustrate the use of the DSS, the paper compares bundles of climate change policies that aim to decarbonize the road transport sector in Iceland. Eighteen scenarios and alternative development trajectories are defined for the Icelandic energy system based on a combination of three main driving forces. These are, firstly, economic development (three cases); secondly, changes in energy efficiency (two cases); and finally, three climate policy bundles aimed at increasing the share of electric vehicles. Based on the results from the integrated assessment framework, the performance scores of the climate policy bundles are compared across the following five sustainability themes: social impact; economic development; environmental impact; energy security; and technical aspects. The findings confirm that a different conclusion may be reached when multiple sustainability themes are applied in the selection of preferred policy bundles as compared to conventional techno-economic criteria. Banning the registration of fossil-fueled vehicles, combined with economic instruments, offers the best decarbonizing strategy to reach climate and energy policy goals simultaneously. [ABSTRACT FROM AUTHOR]

The PWM voltage pulses output by the inverter reaches the stator winding of the wind turbine generator through the cable. Due to the impedance mismatch between the motor and the cable, the voltage at the motor end rises under the action of the circuit wave process; on the other hand, electromagnetic oscillation occurs within the motor winding. Higher overvoltage is generated under the combined effect of both, which greatly accelerates the aging and breakdown of the insulation layer of the permanent magnet synchronous wind turbine. In this paper, a three-dimensional electromagnetic model of the permanent magnet synchronous wind turbine generator is established. The distribution circuit parameters of the stator winding of the permanent magnet machine are calculated using the finite element method, and its circuit model is based. The voltage distribution of the stator winding under different PWM excitations is investigated by simulation software, and the effects of the time of the rising edge and the pulse width of the PWM pulse on the stator winding voltage to ground and the turn-to-turn voltage distribution are studied. The results show that the effect of the rising edge time is larger when the rising edge time is shorter, the maximum voltage to ground occurs in the first coil, and the amplitude can be up to 1.5 times of the output voltage. When the rising edge time is longer, the maximum voltage to ground occurs in the end coil, and the amplitude is slightly higher than the output voltage. The trend of turn-to-turn voltage variation is similar for different rising edges; only the maximum turn-to-turn voltage amplitude is different. Pulse width has a small effect on overvoltage and only occurs when the pulse width is less than 10 μs. The research results of this paper are of great significance in revealing the aging and breakdown mechanism of the generator stator insulation, as well as the insulation coordination and design. [ABSTRACT FROM AUTHOR]

This paper presents a novel methodology for modeling hydropower plants (HPPs) with and without pumping capability in resource adequacy assessment studies. The proposed method is based on the premise that HPPs should maximize their contribution to system adequacy within their technical constraints by using the energy reserves in their upper reservoirs without significantly deviating from their market schedule. The approach of this paper differs from the conventional operating policies for incorporating HPPs into resource adequacy assessment studies, which either adhere to a fixed market schedule or perform peak shaving, and are inelastic to real-time events or do not resort to realistic temporal correlations between natural water inflows on upper reservoirs and the water discharge needs to cover demand peaks, respectively. The modeling approach focuses on large-reservoir HPPs with natural inflows and is generic enough to deal with both stations incorporating pumping capabilities and those without. It utilizes the state-of-the-art Monte Carlo simulation technique to form the availability of system assets and determine the loss of load incidents. The market schedule and level of reservoir fulfillment for the HPPs are retrieved from a cost-optimal power system simulation algorithm executed offline before the application of the resource adequacy assessment. The effectiveness of the proposed methodology is demonstrated through its implementation in a case study of a power system experiencing different levels of adequacy, comparing the obtained results with various traditional HPP modeling methods from the literature. [ABSTRACT FROM AUTHOR]

This paper deals with the mitigation of magnetic field levels produced by a wireless power transfer (WPT) system to recharge the battery of an electric vehicle (EV). In this work, an array of active coils surrounding the WPT coils is proposed as a mitigation technique. The theory and new methodological aspects are the focus of the paper. Magnetic field levels in the environment are calculated numerically without and with the presence of an array of active coils in a stationary WPT system for automotive applications. By the proposed mitigation method, the field levels beside the vehicle are significantly reduced and comply with the reference levels (RLs) of the ICNIRP 2010 guidelines for human exposure to electromagnetic fields and the magnetic flux density limits proposed by ISO 14117 for electromagnetic interference (EMI) in cardiac implantable electronic devices (CIEDs). [ABSTRACT FROM AUTHOR]

The installation of electric vehicle supply equipment (EVSE) increases demand and peak loads, potentially straining existing energy distribution infrastructure. Dispersed and inadequately planned placement of charging points (CPs) can disrupt the electrical grid, surpass contracted demand thresholds, and require infrastructure upgrades, thereby incurring unfeasible costs for Distribution System Operators (DSOs). In this context, it is necessary to recognize the role of business models in enabling effective electrification of the transportation sector. In response to these challenges, this paper introduces a novel e-mobility hub management strategy, tailored for implementation in the Brazilian context. The proposed strategy revolves around a microgrid configuration encompassing dispatchable and photovoltaic generation, a battery energy storage system (BESS), EVSE infrastructure, and local loads. Moreover, this centralized controller facilitates the implementation of dynamic pricing and demand-response mechanisms, integral to business models seeking to integrate EVSE into the distribution grid. To validate the efficacy of the proposed solution, hardware-in-the-loop (HIL) simulations of the microgrid system are conducted. These simulations, incorporating the centralized controller, serve as a tool for assessing system performance and viability before on-site equipment deployment. Finally, this paper concludes with the insights gleaned from test analysis and its discussion through a selection of the most expressive scenarios, including islanded and connected operation modes. [ABSTRACT FROM AUTHOR]

This paper reviews the application of Hall position sensors in control and position estimation of permanent magnet synchronous motors (PMSMs). Accurate rotor position and motor speed data are essential for the high-efficiency control of PMSMs in modern industry. Rotor position and motor speed can be measured by mechanical position sensors, which are costly and less reliable, or by rotor position observers, which are sensitive to system models and changes in motor parameters. This paper examines the benefits, limitations, challenges, and uses of low-resolution Hall position sensors in PMSM drives, presenting them as a cost-effective solution for achieving a balance between performance and expense. In addition, the paper discusses recent solutions to issues related to misplaced Hall position sensors and fault-tolerant control algorithms, and gives an outlook to future developments in this area. [ABSTRACT FROM AUTHOR]

The article describes the mechanism of oxidation of polyunsaturated fatty acid esters and presents the effect of temperature as one of the factors accelerating this process. The consequences of aging for ester-based biofuels are discussed extensively. The article presents the results of aging of FAMEs obtained from frying oil and of FAMEs derived from unrefined rapeseed oil to examine the influence of temperature as a factor accelerating aging. Acid, peroxide and anisidine values were determined for each sample; additionally, IR spectra were measured. Based on the results, it was confirmed that temperature has a significant effect on the degradation of FAMEs. These changes are best represented by the anisidine value and the change in IR spectra. The paper presents the relationship between acid, peroxide and anisidine values. This paper also highlights the beneficial effect of natural antioxidants in the form of β–carotene, which is found in FAMEs derived from unrefined rapeseed oil. [ABSTRACT FROM AUTHOR]

The global interest in environmental issues and sustainable energy has propelled extensive research in photovoltaic (PV) technologies. Brazil has emerged as one of the top ten solar energy producers and flexible PV suppliers in the world. In this context, organic photovoltaic cells (OPVs) have garnered attention due to their flexibility and ability to integrate into various surfaces, albeit facing challenges regarding lifespan and efficiency compared to silicon cells. This review examines the current state of research on OPVs and thin-film solar technologies, employing the systematic literature review methodology Proknow-C. The review includes an analysis of raw materials such as conductive polymers, fabrication processes including film deposition and encapsulation, and technological advancements that enhance the efficiency and stability of OPVs. Utilizing the Proknow-C methodology, a bibliographic portfolio was constructed to identify the current state of research in this field. Of 268 papers from major scientific databases, only 30 were deemed relevant to the theme, highlighting a significant research gap. This paper is a valuable resource for researchers, providing an updated overview and a foundation for future investigations in organic photovoltaics. The results emphasize the importance of materials such as conductive polymers and donor–acceptor molecules and the role of nanotechnology in advancing OPVs. Innovations in manufacturing techniques, such as inkjet and blade coating-based printing techniques, are shown to increase efficiency by providing precise control over film thickness and uniformity and reducing costs through lower material waste. Overall, this review highlights the necessity of further exploration and collaboration within the scientific community to address the challenges and propel advancements in organic photovoltaic cells. These advancements are crucial for transitioning to cleaner energy sources, reinforcing the ongoing significance of thin-film technologies in energy innovation. [ABSTRACT FROM AUTHOR]

The C4F7N/CO2 mixture is considered one of the most promising alternatives to sulfur SF6. Recent studies have shown that the addition of O2 to the C4F7N/CO2 mixture can suppress carbon precipitation following electric arc discharges. This paper conducts arc-burning experimental research on SF6, 10%C4F7N/90%CO2, and 10%C4F7N/85%CO2/5%O2 mixtures. Measurements were taken of the arc voltage and arc current under a 10 kA breaking current for these three gases. Additionally, the pressure at the nozzle throat during arc and cold flow conditions, as well as the pressure in the storage chamber, were measured. The post-arc current and Rate of Rise of Recovery Voltage (RRRV) for the three gases were calculated. The study also compared the solid precipitates in the gas medium after multiple arc-burning experiments. The results indicate that adding O2 to the C4F7N/CO2 mixture can increase the pressure at the nozzle throat during the arc phase, and the inclusion of O2 has minimal impact on the peak arc-extinguishing voltage and critical RRRV. It is hypothesized that a small amount of oxygen has a negligible effect on the thermal recovery properties of the C4F7N/CO2 mixture, while also inhibiting the precipitation of carbon following electric arc discharges. This research could provide a reference for developing and optimizing eco-friendly high-voltage circuit breakers. [ABSTRACT FROM AUTHOR]

In the ongoing debate about the feasibility of enforcing a transition to decentralized energy conversion systems, arguments are often presented that lack scientific rigor. Granted, the issue is multi-faceted and fundamentally multi-disciplinary, and possible solutions strongly depend on the selection of location as well as on local climate and demographics. Furthermore, decentralizing the final energy distribution leads to potential socio-economic considerations that involve value judgements. However, the most serious problem is that media have appropriated the topic and are often publishing opinion papers authored by non-specialists and even by representatives of interest groups. The present paper proposes an approach that is innovative on two counts: first, it treats "final energy" as any other commodity and therefore expands the field of investigation to the problems arising from the decentralization of a generic production line or technological chain; second, it argues that a method solidly rooted in Thermodynamics, the Extended Exergy Accounting, may be used to quantify the total amount of primary exergy resources requested by a decentralized strategy (as opposed to a centralized one), so that a comparison can be performed and discussed on a rational, unbiased and scientific basis. This is an introductory paper that reports some theoretical results of the method: realistic applications are perforce excluded because the idea is that the procedure must be drafted in such a way to be applicable to different socio-economic scenarios and locations and to remain valid under a broad range of boundary conditions. [ABSTRACT FROM AUTHOR]

Anaerobic digestion (AD) of food waste (FW) is considered an environmentally sustainable process that can divert the disposal of FW to landfill and prevent greenhouse gas (GHG) emissions in managing the FW. Although several studies have attempted to demonstrate the AD of FW, low methane yields and a high incidence of process instability have been reported due to the rapid generation and accumulation of volatile fatty acids (VFAs). This paper reviews the recent research and development with high variation in FW composition, such as the carbon-to-nitrogen (C/N) ratio and, consequently, the effect of its physicochemical composition on process performance and methane yields. The paper highlights the significance of optimizing the anaerobic co-digestion (AcoD) of FW with carbon-rich substrates such as garden waste (GW) and/or the addition of trace elements as strategies that can improve the process performance and methane yields from FW. This review focuses on the factors effecting the feasibility of food organics and garden organics (FOGO) as a substrate for methane production. The review also critically analyses the prospects of enhancement of biomethane yield by optimizations of the impactful parameters. The progress in research related to these methods and identifying existing limitations to efficient AD of FOGO are the key findings of this review. This review also assesses the impact of nanotechnology on the process performance of the digester. The integration of FO and GO in AD processes has demonstrated enhanced biogas yields, improved process stability, and better waste management outcomes compared to the digestion of either substrate alone. Despite these advantages, challenges such as feedstock variability, process optimization, and the need for advanced pretreatment methods remain. Addressing these issues through continued research and technological innovations will be crucial for maximizing the efficiency and scalability of AD systems. Moreover, the economic feasibility and policy frameworks supporting AD need further development to promote broader adoption. [ABSTRACT FROM AUTHOR]

This paper proposes firstly a Second Order Sliding Mode Control (SOSMC) based on a Super Twisting Algorithm (STA) (SOSMC-STA) combined with a Direct Field-Oriented Control (DFOC) strategy of a Five-Phase Induction Motor (FPIM). The SOSMC-STA is suggested for overcoming the shortcomings of the Proportional Integral Controller (PIC) and the Conventional Sliding Mode Controller (CSMC). Indeed, the main limitations of the PIC are the slower speed response, the tuning difficulty of its parameters, and the sensitivity to changes in system parameters, including variations in process dynamics, load changes, or changes in setpoint. It is also limited to linear systems. Regarding the CSMC technique, its limitation is the chattering phenomenon, characterized by the rapid switching of the control signal. This phenomenon includes high-frequency oscillations which induce wear and tear on mechanical systems, adversely affecting performance. Secondly, this paper also proposes a Loss Model Controller (LMC) for FPIM energy optimization. Thus, the suggested LMC chooses the optimal flux magnitude required by the FPIM for each applied load torque, which consequently reduces the losses and the FPIM efficiency. The performance of the optimized DFOC-SOSMC-STA based on the LMC is verified using numerical simulation under the Matlab environment. The analysis of the simulation results shows that the DFOC-SOSMC-STA guarantees a high dynamic response, chattering reduction, good precision, and robustness in case of external load or parameter disturbances. Moreover, the DFOC-SOSMC-STA, combined with the LMC, reduces losses and increases efficiency. [ABSTRACT FROM AUTHOR]

Medium–long-term photovoltaic (PV) output forecasting is of great significance to power grid planning, power market transactions, power dispatching operations, equipment maintenance and overhaul. However, PV output fluctuates greatly due to weather changes. Furthermore, it is frequently challenging to ensure the accuracy of forecasts for medium–long-term forecasting involving a long time span. In response to the above problems, this paper proposes a medium–long-term forecasting method for PV output based on amplitude-aware permutation entropy component reconstruction and the graph attention network. Firstly, the PV output sequence data are decomposed by ensemble empirical mode decomposition (EEMD), and the decomposed intrinsic mode function (IMF) subsequences are combined and reconstructed according to the amplitude-aware permutation entropy. Secondly, the graph node feature sequence is constructed from the reconstructed subsequences, and the mutual information of the node feature sequence is calculated to obtain the graph node adjacency matrix which is applied to generate a graph sequence. Thirdly, the graph attention network is utilized to forecast the graph sequence and separate the PV output forecasting results. Finally, an actual measurement system is used to experimentally verify the proposed method, and the outcomes indicate that the proposed method, which has certain promotion value, can improve the accuracy of medium–long-term forecasting of PV output. [ABSTRACT FROM AUTHOR]

The detection and deterrence of collusive agreements among firms, such as price-fixing cartels, remain pivotal in maintaining market competition. This study investigates the application of machine learning methodologies in the behavioral screening process for detecting collusion, with a specific focus on parallel pricing behaviors in the wholesale fuel market. By employing unsupervised learning techniques, this research aims to identify patterns indicative of collusion—referred to as collusion markers—within time series data. This paper outlines a comprehensive screening research plan based on the CRISP-DM model, detailing phases from business understanding to monitoring. It emphasizes the significance of machine learning methods, including distance measures, motifs, discords, and semantic segmentation, in uncovering these patterns. A case study of the Polish wholesale fuel market illustrates the practical application of these techniques, demonstrating how anomalies and regime changes in price behavior can signal potential collusion. The findings suggest that unsupervised machine learning methods offer a robust alternative to traditional statistical and econometric tools, particularly due to their ability to process large and complex datasets without predefined models. This research concludes that these methods can significantly enhance the detection of collusive behaviors, providing valuable insights for antitrust authorities. [ABSTRACT FROM AUTHOR]

Accurate and trustworthy forecasting of coal prices can offer theoretical support for the rational planning of coal industry output, which is of great importance in ensuring a stable and sustainable energy supply and in achieving carbon neutrality targets. This paper proposes a novel decomposition integration model, called VCNQM, to perform point and interval forecasting of coal price by a combination of variational modal decomposition (VMD), chameleon swarm algorithm (CSA), N-BEATS, and quantile regression. Initially, the variational modal decomposition is enhanced by the chameleon swarm algorithm for decomposing the coal price sequence. Then, N-BEATS is used to forecast each subsequence of coal prices, integrating all results to obtain a point forecast of coal prices. Next, interval forecasting of coal prices is achieved through quantile regression. Finally, to demonstrate the superiority of the VCNQM model's prediction, we make a cross-comparison about predictive performance between the VCNQM model and other benchmark models. According to the experimental findings, we demonstrate the following: after the decomposition by CSA-VMD, the coal price subseries' fluctuation is significantly weakened; using quantile regression provides a reliable interval prediction, which is superior to point prediction; the predicted interval coverage probability (PICP) is higher than the confidence level of 90%; the share power industry index and coal industry index have the greatest impact on coal prices in China; compared to these benchmark models, the VCNQM model's prediction errors are all reduced. Therefore, we conclude that when forecasting coal prices, the VCNQM model has an accurate and reliable prediction. [ABSTRACT FROM AUTHOR]

Geothermal energy is a unique energy source in the energy policy mix that would help the clean energy transition and energy independence, supporting the energy needs in heating and electricity. Although there have been studies on the opportunities and challenges of renewable energy, this paper is the first paper that concentrates on geothermal energy for three distinct countries, Italy, Belgium, and Iceland, for the first time. Using semi-structured interviews that will cover the stakeholders representing the quadruple helix (academia, citizens, policymakers, and industry), this paper aims to find the unique and common opportunities and barriers the geothermal sector has. Shared challenges include financial barriers, regulatory complexities, environmental issues, and the need for improvement in the social acceptability of geothermal energy. Despite these challenges, geothermal energy, a promising energy source for clean transition, could create opportunities like improved household welfare through combined uses in district heating and electricity and have the potential to generate employment opportunities. [ABSTRACT FROM AUTHOR]

The integration of the Internet of Things (IoT) with renewable energy technologies is revolutionizing modern power systems by enhancing efficiency, reliability, and sustainability. This paper examines the role of the IoT in optimizing the integration and management of renewable energy sources, such as solar and wind power, into the electrical grid. The IoT enables real-time monitoring, data analysis, and automation, facilitating advanced load management, demand response, and energy storage solutions. Key advancements in IoT technologies, including smart grids and energy management systems, are discussed, highlighting their impact on improving grid stability and promoting the use of renewable energy. The paper also finds some challenges such as data security, privacy, and the need for standardized communication protocols. Furthermore, it finds how the IoT optimizes electric vehicle performance through advanced battery management, real-time energy consumption monitoring, and improved interaction with the electrical grid. Future research directions emphasize the potential of the IoT to further enhance renewable energy integration through artificial intelligence and machine learning, driving the transition towards a more sustainable and resilient energy future. [ABSTRACT FROM AUTHOR]

To address the global warming issue, China is prioritizing the development of clean energy sources such as wind and solar power under its "dual carbon target". However, the expansion of these resources is constrained by their intermittency and the spatial and temporal distribution of wind and solar energy. This paper systematically reviews the evolution of wind and solar energy reserves, their development potential, and their current status in China from a geographical perspective. In conjunction with existing research, this paper anticipates future exploration in the realm of wind–solar complementary development or multi-energy complementary development, viewed through the lens of resource quantity. The anticipated findings are intended to furnish a theoretical foundation for further studies on the development and utilization of wind and solar energy resources within China. [ABSTRACT FROM AUTHOR]

In recent years, global efforts towards a future with sustainable energy have intensified the development of renewable energy sources (RESs) such as offshore wind, solar photovoltaics (PVs), hydro, and geothermal. Concurrently, green hydrogen, produced via water electrolysis using these RESs, has been recognized as a promising solution to decarbonizing traditionally hard-to-abate sectors. Furthermore, hydrogen storage provides a long-duration energy storage approach to managing the intermittency of RESs, which ensures a reliable and stable electricity supply and supports electric grid operations with ancillary services like frequency and voltage regulation. Despite significant progress, the hydrogen economy remains nascent, with ongoing developments and persistent uncertainties in economic, technological, and regulatory aspects. This paper provides a comprehensive review of the green hydrogen value chain, encompassing production, transportation logistics, storage methodologies, and end-use applications, while identifying key research gaps. Particular emphasis is placed on the integration of green hydrogen into both grid-connected and islanded systems, with a focus on operational strategies to enhance grid resilience and efficiency over both the long and short terms. Moreover, this paper draws on global case studies from pioneering green hydrogen projects to inform strategies that can accelerate the adoption and large-scale deployment of green hydrogen technologies across diverse sectors and geographies. [ABSTRACT FROM AUTHOR]

This paper reviews sintered silver (s-Ag) die-attach materials for wide band gap (WBG) semiconductor packaging. WBG devices that die-attach with s-Ag have attracted a lot of attention owing to their low energy loss and high temperature operation capabilities. For their practical operation, a reliability design should be established based on the failure of physics of the s-Ag die layer. This paper first focuses on the material characteristics of the s-Ag and tensile mechanical properties. Then, the s-Ag die-attach reliability is assessed with high-temperature storage, power cycling, and thermal shock tests. Each fracture mode was discussed by considering both the fracture surface analysis results and its mechanical properties. Finally, the effective reliability design parameters of the s-Ag die layer are introduced. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources (RES) into smart grids has been considered crucial for advancing towards a sustainable and resilient energy infrastructure. Their integration is vital for achieving energy sustainability among all clean energy sources, including wind, solar, and hydropower. This review paper provides a thoughtful analysis of the current status of the smart grid, focusing on integrating various RES, such as wind and solar, into the smart grid. This review highlights the significant role of RES in reducing greenhouse gas emissions and reducing traditional fossil fuel reliability, thereby contributing to environmental sustainability and empowering energy security. Moreover, key advancements in smart grid technologies, such as Advanced Metering Infrastructure (AMI), Distributed Control Systems (DCS), and Supervisory Control and Data Acquisition (SCADA) systems, are explored to clarify the related topics to the smart grid. The usage of various technologies enhances grid reliability, efficiency, and resilience are introduced. This paper also investigates the application of Machine Learning (ML) techniques in energy management optimization within smart grids with the usage of various optimization techniques. The findings emphasize the transformative impact of integrating RES and advanced smart grid technologies alongside the need for continued innovation and supportive policy frameworks to achieve a sustainable energy future. [ABSTRACT FROM AUTHOR]

The widespread use of static converters for controlling electrical machines and the concern for electrical power quality in industrial environments provide an opportunity for utilizing these devices to enhance the power quality. In this context, this work presents a back-to-back converter model for driving induction machines. The converter is designed to correct the power factor of the point common coupling (PCC), compensate for harmonic currents (acting as an active filter), and withstand voltage sags. The necessary control system models were developed, and an alternative implementation for these functions in the converter was proposed. The results demonstrate the technical feasibility of this solution, as the converter operated within its nominal limits by compensating for harmonics and reactive power. Moreover, the equipment showed resilience to severe voltage sags. The contribution of this paper focuses on the multifunctionality of the frequency converter for driving induction machines. It emphasizes the advantage of the inverter in improving power quality in industrial environments through reactive power compensation and harmonic current compensation, thus functioning as an active power filter. Additionally, it is worth highlighting its ability to handle voltage dips. In this regard, this paper contributes by providing an operational strategy for driving the induction machine during such transients. [ABSTRACT FROM AUTHOR]

In the context of rapid growth in renewable energy installations and increasingly severe consumption issues, this paper designs a 100% green electricity supplied zero-carbon integrated energy station. It aims to analyze its configuration focusing on the following three core features: zero carbon emissions, 100% green electricity supply, and a centralized–distributed system structure. It discusses equipment selection and provides models for configuring upstream green electricity resources, power generation, energy storage, transformer, and energy conversion. The study examines the synergy between lithium-ion battery storage and modular molten salt thermal storage, along with the virtual energy storage characteristics formed by thermal load inertia, supported by mathematical models. Based on the data from a green electricity system in an Eastern Chinese city and typical load profiles, the paper validates a specific configuration for a 100% green electricity supplied zero-carbon integrated energy station, confirming model accuracy and calculating the required scale of upstream green electricity resources. It proves that establishing an electro-thermal storage synergy system is crucial for addressing the significant fluctuations in renewable energy output. It also argues that leveraging thermal load inertia to create virtual storage can reduce the investment in energy storage system construction. [ABSTRACT FROM AUTHOR]

The use of mobile robots in substations improves maintenance efficiency and ensures the personal safety of staff working at substations, which is a trend in the development of technologies. Strong electric and solid magnetic fields around high-voltage equipment in substations may lead to the breakdown and failure of inspection devices. Therefore, safe operation range measurement and coordinated planning are key factors in ensuring the safe operation of substations. This paper first summarizes the current developments that are occurring in the field of fixed and mobile safe operating range sensing methods for substations, such as ultra-wideband technology, the two-way time flight method, and deep learning image processing algorithms. Secondly, this paper introduces path-planning algorithms based on safety range sensing and analyzes the adaptability of global search methods based on a priori information, local planning algorithms, and sensor information in substation scenarios. Finally, in view of the limitations of the existing range awareness and path-planning methods, we investigate the problems that occur in the dynamic changes in equipment safety zones and the frequent switching of operation scenarios in substations. Furthermore, we explore a new type of barrier and its automatic arrangement system to improve the performance of distance control and path planning in substation scenarios. [ABSTRACT FROM AUTHOR]

The increasing use of renewable energy sources introduces significant fluctuations in power generation, demanding enhanced regulatory capabilities to maintain the balance between power supply and demand. To promote multi-energy coupling and the local consumption of renewable energy, integrated energy systems have become a focal point of multidisciplinary research. This study models adjustable sources, networks, and loads within electric–thermal integrated energy systems as energy storage entities, forming virtual energy storage systems to participate in the optimization and scheduling of integrated energy systems. This paper investigates the modeling and control strategies of virtual energy storage systems within electric–thermal integrated energy systems. Initially, it introduces the definition, logical architecture, and technical connotations of virtual energy storage. Next, it models temperature-controlled loads as virtual energy storage systems and compares them with traditional energy storage systems, analyzing their characteristic differences and summarizing virtual energy storage system modeling methods and characteristic indicators. This paper then focuses on the specific applications of virtual energy storage systems in four typical scenarios. Finally, it explores the future development directions of virtual energy storage. [ABSTRACT FROM AUTHOR]

The operation envelope of distribution networks can obtain the independent p-q controllable range of each active node, providing an effective means to address the issues of different ownership and control objectives between distribution networks and distributed energy resources (DERs). Existing research mainly focuses on deterministic operation envelopes, neglecting the operational status of the system. To ensure the maximization of the envelope operation domain and the feasibility of decomposition, this paper proposes a modified hyperellipsoidal dynamic operation envelopes (MHDOEs) method for distribution networks based on adjustable "Degree of Squareness" hyperellipsoids. Firstly, an improved convex inner approximation method is applied to the non-convex and nonlinear model of traditional distribution networks to obtain a convex solution space strictly contained within the original feasible region of the system, ensuring the feasibility of flexible operation domain decomposition. Secondly, the embedding of the adjustable "Degree of Squareness" maximum hyperellipsoid is used to obtain the total p-q operation domain of the distribution network, facilitating the overall planning of the distribution network. Furthermore, the calculation of the maximum inscribed hyperrectangle of the hyperellipsoid is performed to achieve p-q decoupled operation among the active nodes of the distribution network. Subsequently, a correction coefficient is introduced to penalize "unknown states" during the operation domain calculation process, effectively enhancing the adaptability of the proposed method to complex stochastic scenarios. Finally, Monte Carlo methods are employed to construct various stochastic scenarios for the IEEE 33-node and IEEE 69-node systems, verifying the accuracy and decomposition feasibility of the obtained p-q operation domains. [ABSTRACT FROM AUTHOR]

During liquid hydrogen bunkering into a cryogenic tank, boil-off losses occur due to the high thermal gradient between liquid hydrogen and the warm surface of the tank. This leads to gaseous hydrogen release. Such losses constitute a significant drawback in using hydrogen as a fuel for maritime applications where bunkering operations are regularly carried out, thereby constituting a significant loss along the liquid hydrogen pathway. Due to the inherently low temperature of liquid hydrogen, boil-off losses are always present. Some boil-off losses cannot be eliminated because they are thermodynamically constrained or intrinsic to the system's design. Boil-off recovery methods can be implemented to capture the boil-off; however, those solutions come with an additional cost and system complexities. Hence, this paper investigates the feasibility of minimizing boil-off losses during the first bunkering of liquid hydrogen or refilling of liquid hydrogen in an empty cryogenic tank by first precooling the cryogenic tank surface to decrease the thermal gradient between the liquid hydrogen and the tank surface/wall. In this paper, different media for precooling a cryogenic tank are evaluated to assess the boil-off reduction potential and the associated costs in order to identify the most suitable solution. The assessment has been carried out based on analytical formulation. [ABSTRACT FROM AUTHOR]

Variable renewable energy sources (VRESs) are essential for decarbonizing the energy sector, but they introduce significant uncertainty into power grids. This uncertainty necessitates increased flexibility to ensure reliable and efficient grid operations, impacting both short-term strategies and long-term planning. Effective management of VRESs is particularly challenging for distribution system operators (DSOs) due to limited grid measurement and other data, complicating state estimation. This paper proposes a tractable framework that operates under low-observability conditions. The framework uses conservative linear approximations (CLAs) to manage grid constraints efficiently, requiring only the ranges of power injections typically available to operators. The objectives are twofold: first, to determine the amount and location of flexibility needed to prevent voltage violations and, second, to establish voltage measurement thresholds to trigger flexibility activation. Key contributions include the formulation of optimization problems to mitigate voltage issues, the introduction of flexibility provision triggered by voltage thresholds, and novel algorithms for determining flexibility and trigger points. The framework's efficacy is demonstrated on IEEE 33-bus and UK 124-bus test systems, showing it can effectively mitigate grid voltage problems despite limited data. [ABSTRACT FROM AUTHOR]

The incorporation of ejectors into heat pump and refrigeration cycles has been the subject of growing interest, largely due to their simple structure, high reliability, and cost-effectiveness. This paper investigates the recent advancements in novel design concepts of ejector-enhanced vapor compression heat pump and refrigeration cycles. An overview of novel single-stage and two-stage compression cycles utilizing a single or multiple ejectors is provided. First, the system setup, operational principles, description, and figures of the existing schemes are provided. Second, the main results, such as the coefficient of performance (COP), volumetric heating capacity and exergy destruction, are discussed. In conclusion, the paper presents a coherent summary of the current developments, future prospects, and the current knowledge gap. A plethora of research is present in developing theoretical systems with high efficiency. However, experimental tests for real-life implementations are limited. This review aims to provide the reader with an overview of recent theoretical and experimental studies. [ABSTRACT FROM AUTHOR]

The paper addresses rare issue in cable ampacity calculations, namely the presence of discontinuities along the routes. One which occurs in almost all cable installations is the presence of joints. In a standard cable rating analysis, the joints are ignored, mostly because of difficulties in building analytical models that represent the heat transfer phenomena within them. However, they can be a limiting part of the cable rating and, therefore, there is a need to model them correctly. This paper introduces an analytical algorithm for cable rating calculations in the presence of discontinuities with an emphasis on cable joints. New developments are illustrated by several numerical examples. [ABSTRACT FROM AUTHOR]

Accurate and reliable PV power probabilistic-forecasting results can help grid operators and market participants better understand and cope with PV energy volatility and uncertainty and improve the efficiency of energy dispatch and operation, which plays an important role in application scenarios such as power market trading, risk management, and grid scheduling. In this paper, an innovative deep learning quantile regression ultra-short-term PV power-forecasting method is proposed. This method employs a two-branch deep learning architecture to forecast the conditional quantile of PV power; one branch is a QR-based stacked conventional convolutional neural network (QR_CNN), and the other is a QR-based temporal convolutional network (QR_TCN). The stacked CNN is used to focus on learning short-term local dependencies in PV power sequences, and the TCN is used to learn long-term temporal constraints between multi-feature data. These two branches extract different features from input data with different prior knowledge. By jointly training the two branches, the model is able to learn the probability distribution of PV power and obtain discrete conditional quantile forecasts of PV power in the ultra-short term. Then, based on these conditional quantile forecasts, a kernel density estimation method is used to estimate the PV power probability density function. The proposed method innovatively employs two ways of a priori knowledge injection: constructing a differential sequence of historical power as an input feature to provide more information about the ultrashort-term dynamics of the PV power and, at the same time, dividing it, together with all the other features, into two sets of inputs that contain different a priori features according to the demand of the forecasting task; and the dual-branching model architecture is designed to deeply match the data of the two sets of input features to the corresponding branching model computational mechanisms. The two a priori knowledge injection methods provide more effective features for the model and improve the forecasting performance and understandability of the model. The performance of the proposed model in point forecasting, interval forecasting, and probabilistic forecasting is comprehensively evaluated through the case of a real PV plant. The experimental results show that the proposed model performs well on the task of ultra-short-term PV power probabilistic forecasting and outperforms other state-of-the-art deep learning models in the field combined with QR. The proposed method in this paper can provide technical support for application scenarios such as energy scheduling, market trading, and risk management on the ultra-short-term time scale of the power system. [ABSTRACT FROM AUTHOR]

The demand for low-carbon and energy-efficient building designs is urgent, especially considering that building energy consumption constitutes a significant part of global energy usage. Unlike small to medium-sized buildings such as residential and office spaces, large public buildings, like sports facilities, have unique usage patterns and architectural forms, offering more significant potential for energy-saving strategies. This review focuses on sports buildings, selecting 62 high-quality papers published in building science over the past 30 years that investigate low-carbon and energy-efficient research. Summarizing and synthesizing these papers reveals that current studies predominantly concentrate on four main areas: indoor air quality, ventilation, thermal environment, and energy consumption. Notably, many studies emphasize improving indoor thermal comfort and reducing energy consumption in sports buildings through measurements and evaluations of indoor thermal environments, temperature distributions, heat transfer phenomena, and energy consumption analyses. Key outcomes indicate that green technology innovations, such as energy substitution technologies, significantly enhance energy efficiency and reduce CO2 emissions. However, present research emphasizes singular energy-saving approaches, suggesting future directions could integrate comprehensive green technologies, life-cycle assessments, and applications of intelligent technologies and the Internet of Things (IoT). These enhancements aim to provide more effective and sustainable solutions for implementing green, low-carbon energy practices in sports buildings. The review emphasizes that in order to accomplish sustainable urban growth and achieve global carbon neutrality targets, a comprehensive approach involving technical innovation, legislative assistance, and extensive preparation is crucial. [ABSTRACT FROM AUTHOR]

This paper provides an analysis of challenges and available solutions for exterior insulated panels suitable for deep energy retrofits of existing building envelopes. The analysis covers a review of available technologies that provide flexible retrofit insulated panels suitable for multiple climates and building typologies. Moreover, the paper proposes a new design for insulated retrofit panels that account for the majority of identified technical risks including cost, architectural diversity, climate variations, structural concerns, moisture resilience, air sealing, and water sealing. Additionally, the proposed design can be easily installed with minimal disruption to the occupants. A series of parametric and optimization analyses is carried out to identify the optimal design specifications for insulated panels suitable for deep retrofits of existing US housing stocks. The analysis results show that the optimal design criteria for the insulated panels can reduce heating and cooling energy consumption by up to 80% and HVAC capacities by 70%. Moreover, the results indicate that these insulated panels are highly cost effective for retrofitting US housing units located in cold climates. [ABSTRACT FROM AUTHOR]

Due to their compact structure and low laying cost, three-core power cables are widely used for power distribution networks. The three-phases of such cables are distributed symmetrically with a 120° shift to each other. Phase current is an important parameter to reflect the operation state of the power system and three-core cable. Three-core symmetrical power cables use a common shield, leading to magnetic field cancelation outside the cable during steady operation. Thus, traditional magnetic-based current transformers cannot measure the phase current on three-core cable non-invasively. In order to measure the phase current more conveniently, a phase current measurement method for three-core cables based on a magnetic sensor is proposed in this paper. Nonlinear equations of a phase current and the magnetic field of a measuring point are constructed. The calculated magnetic field distribution of the three-core cable is verified using a finite element simulation. The effectiveness of the measurement method is further validated through experiments. This proposed method is able to conveniently detect the phase current of three-core power cables, which can help cable maintenance. [ABSTRACT FROM AUTHOR]

Chiller fault diagnosis plays a crucial role in optimizing energy efficiency within heating, ventilation, and air conditioning (HVAC) systems. The non-stationary nature of chiller fault data presents a significant challenge, as conventional methodologies often fail to adequately capture the relationships between non-stationary variables. To address this limitation and enhance diagnostic accuracy, this paper proposes an improved graph deviation network for chiller fault diagnosis by integrating the sparse cointegration analysis and the convolutional block attention mechanism. First, in order to obtain sparse fault features in non-stationary operation, this paper adopts the sparse cointegration analysis method (SCA). Further, to augment the diagnosis accuracy, this paper proposes the improved graph deviation network (IGDN) to classify fault datasets, which is a combination of the output of a graph deviation network (GDN) with a convolutional block attention mechanism (CBAM). This novel architecture enables sequential evaluation of attention maps along independent temporal and spatial dimensions, followed by element-wise multiplication with input features for adaptive feature optimization. Finally, detailed experiments and comparisons are performed. Comparative analyses reveal that SCA outperforms alternative feature extraction algorithms in addressing the non-stationary characteristics of chiller systems. Furthermore, the IGDN exhibits superior fault diagnosis accuracy across various fault severity levels. [ABSTRACT FROM AUTHOR]

Power consumers can obtain authoritative green environmental value certification through green electricity trading, which plays an important role in improving the production competitiveness of enterprises, especially for international product trade affairs. However, the credibility of green electricity transactions faces serious challenges in the enterprise green authentication affairs, especially the user's identity authentication, the traceability of green electricity transactions, and the standardization of green electricity transactions. Aiming to solve the certification and traceability problem of tradable green certificates, this paper proposes an integrated green certificate trading protocol, which solves its double-trading problem and helps to improve the credibility of renewable energy use. The main contribution is providing a solution based on the consortium blockchain technology to solve the main challenges mentioned above. The main solved scheme designs a series of protocols, which includes a purchase protocol, payment protocol, and non-transferable protocol. The whole process ensures the credibility, traceability, and non-transferability of green certificate trading. Multiple verification measures are adopted to address security and privacy challenges in green certificate management. Through security analysis, the protocol effectively defends against attacks such as double payments, transaction rollback, and transaction replays while ensuring users' privacy. [ABSTRACT FROM AUTHOR]

This paper navigates the critical role of hydrogen in catalyzing a sustainable energy transformation. This review delves into hydrogen production methodologies, spotlighting green and blue hydrogen as pivotal for future energy systems because of their potential to significantly reduce greenhouse gas emissions. Through a comprehensive literature review and a bibliometric analysis, this study underscores the importance of technological advancements, policy support, and market incentives in promoting hydrogen as a key energy vector. It also explores the necessity of expanding renewable energy sources and international cooperation to secure a sustainable, low-carbon future. The analysis highlights the importance of scalable and cost-effective hydrogen production methods, such as solar-thermochemical and photo-electrochemical processes, and addresses the challenges posed by resource availability and geopolitical factors in establishing a hydrogen economy. This paper serves as a guide for policy and innovation toward achieving global sustainability goals, illustrating the essential role of hydrogen in the energy transition. [ABSTRACT FROM AUTHOR]

This paper employs the instrumental variable method and the intermediary model to explore the influence of monocentric and polycentric spatial structures of innovation agglomeration on energy efficiency. This paper uses data from 2010 to 2021 in China, and also panel data regression models were used. The findings indicate that the monocentric spatial structure of provincial system innovation agglomeration is primarily concentrated in northeastern and western regions of China, whereas the polycentric spatial structure is mainly distributed in eastern coastal areas. A monocentric spatial structure inhibits the enhancement of energy efficiency, but there is an environmental paradox of excessive resource agglomeration. In contrast, a polycentric spatial structure and its strengthening tendency facilitate the improvement of energy efficiency. A monocentric spatial structure has an inhibitory impact on the improvement of energy efficiency in the three sectors of agriculture, industry, and construction, while a polycentric spatial structure aids it. A monocentric spatial structure further hinders the improvement of energy efficiency by impeding the upgrading of the industrial structure, while the upgrading of the industrial structure driven by the polycentric spatial structure plays a role in enhancing energy efficiency. The policy recommendations of this paper are intended to help coordinate the pace of innovation development between cities, adjust the spatial structure of innovation agglomeration between regions, and promote the upgrading of the comprehensive industrial structure, thereby improving overall energy efficiency. [ABSTRACT FROM AUTHOR]

Electrical machines for transportation applications are subjected to harsh environmental conditions during their operations. Partial discharge (PD), which is one of the main reasons for insulation failure, is greatly affected by ambient conditions (i.e., temperature, pressure, and humidity). Countless efforts are made for a comprehensive understanding of the physics of PD under variable environmental factors. This paper aims to review recent works addressing temperature, pressure, and humidity impact on PD activity. The main content of the paper is organized into three sections dealing with each environmental factor. In every section, relevant publications are reviewed considering the type of samples tested, voltage waveform applied, mutual effects, and the most common PD modeling strategies used. The applicability of the PD measurements for PD risk assessment is also discussed. Based on the review, the current progress in understanding the environmental effects on the PD inception mechanism and PD characteristics is presented and discussed in detail, and future research trends in this field are outlined. [ABSTRACT FROM AUTHOR]

With the rapid development of the semiconductor chip and precision machining industry, there is a growing demand for high-performance planar drive devices, leading to an increasing depth of research on planar motors. Variable reluctance planar motors, induction planar motors, permanent-magnet synchronous planar motors and DC planar motors are discussed in this paper along with their working principles and current research status. The theory of planar motors remains incomplete and immature despite the extensive research conducted by scholars and research institutions on crucial aspects like magnetic field analysis and electromagnetic force calculations. The objective of this paper is to provide a comprehensive review of the research and development status of planar motors in order to positively impact future advancements in this field. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of water injection management practices for complex and impactful onshore and offshore carbonate reservoirs. It delves into the fundamental aspects of waterflooding design, surveillance techniques, and monitoring methods tailored for the unique challenges posed by carbonate formations. Two case studies from the Permian Basin in Texas and two from Lula Field offshore Brazil and Agbami Field offshore Nigeria are examined considering scientific principles into practice to provide insights into best practices, lessons learned, and strategies to maximize the benefits derived from real noteworthy waterflood operations. The paper underscores the significance of rigorous reservoir characterization, including understanding reservoir architecture, heterogeneities, fracture networks, fluid communication pathways, and rock–fluid interactions. It emphasizes the crucial role of integrated multidisciplinary teams involving geologists, reservoir engineers, production engineers, and field operators in ensuring successful waterflood design, implementation, and optimization. Through the case studies, the paper highlights the importance of designing pattern configurations, well placements, and injection/production strategies to the specific reservoir characteristics, continually optimizing these elements based on surveillance data. It also stresses the necessity of comprehensive data acquisition, advanced analytics, numerical simulations, and frequent model updates for effective reservoir management and decision-making. The paper is impactful in terms of the lessons learned from the actual case studies, and how can these be implemented in actual field projects. Different case studies documented in the paper provide the challenges facing them and how different authors have addressed their problems in unique ways. The paper distills the information and important findings from a variety of case studies and provides succinct information that is of immense value as a reference. Important findings of these case studies are connected using creativity and are innovative as they introduce unique techniques and establish successful ideas to create new value in terms of maximizing oil recovery. Most importantly, this paper explores the application of innovative technologies, such as intelligent completions, 4D seismic monitoring, and water–alternating gas (WAG) injection, which can significantly improve waterflood performance in complex carbonate reservoirs. In summary, the paper provides a thorough understanding of the factors contributing to the success and failure of waterfloods in carbonate reservoirs through case studies based on factually and technically sound operations. It documents guidelines for optimizing waterflood performance and reducing or eliminating the potential for failures, reinforcing positive results in these challenging yet invaluable hydrocarbon resources. [ABSTRACT FROM AUTHOR]

Energy modelling plays a crucial role in assisting governmental and policymaking bodies to strategise long-term investments within the context of energy transition. Among the well-established open-source optimisation models, OSeMOSYS—the Open-Source Energy Modelling System—stands out. This paper introduces clicSAND, a novel user interface designed for OSeMOSYS, aimed at reducing the learning curve and supporting novice energy modelers in efficiently conducting long-term investment analyses. clicSAND, freely available and open-source, features a user-friendly Excel interface for data input, integrated solvers, and a visualisation dashboard for result interpretation. The outcomes, projected up to 2070, hold the potential to inform policy decisions and mobilise financial resources for sustainable development endeavors, such as ensuring affordable and secure energy supply and mitigating climate change impacts. This advancement not only democratises access to energy modelling tools but also empowers policymakers and stakeholders to conduct thorough long-term investment analyses with ease. This paper elaborates on clicSAND's key advantages, architecture, and functionalities. Additionally, it discusses the evolutionary journey from clicSAND 1.0 to 3.0, emphasising a commitment to continuous improvement and user-centric adaptation, thereby enhancing its utility and relevance. The inclusion of a South African case study, conducted during the EMP-A (Energy Modelling Platform for Africa) 2021 international capacity-building event, showcases clicSAND's efficacy in facilitating knowledge transfer and skill development among inexperienced users, while providing a tangible example of its application in addressing specific regional energy challenges and policy contexts. Finally, current applications and future extensions of the software are also presented. [ABSTRACT FROM AUTHOR]