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The vapour microturbine set's mathematical model has been updated to consider the partial admission of turbine stages. Experimental data from two distinct microturbine sets were used to verify the model. The model of the microturbine set was tested under varying operating conditions. Examples of a comparison between the experimental results and simulations are presented and analysed. It has been shown that, when simulating the off-design operation of multistage microturbines, not taking into account power losses due to partial admission may lead to significantly incorrect results. This conclusion does not apply to single-stage microturbines. [ABSTRACT FROM AUTHOR]

The energy harnessing from flow-induced vibrations (FIV) by an oscillating foil placed tandemly behind a circular cylinder (which serves as a vortex generator) is investigated. The foil is submerged in the wake produced by the fixed cylinder and could oscillate in the direction perpendicular to the incoming flow with single-degree freedom. The spacing ratio ranges from 1.0 to 5.0. The oncoming fluid velocity is U = 1–10 m/s, corresponding to the reduced velocity Ur = 3.81–38.08 and the Reynolds number Re = 9.58 × 103–9.58 × 104. Four harnessing damping ratios (ζharness = 0.0054–0.0216) are used to simulate the energy conversion conditions. The main conclusions are: (1) The optimal oscillation pattern related to the highest harnessed energy emerges as the spacing ratio close to 1.0. (2) The airflow energy converted by the foil is positively correlated with the harnessing damping ratio because the amplitude responses are similar at various harnessing damping ratios. A high velocity yields the highest harnessed power. (3) The harnessing efficiency of the foil could reach 48.89%, which is much more than that of an isolated flapping foil. [ABSTRACT FROM AUTHOR]

Surfactant-assisted spontaneous imbibition is an important mechanism in enhanced oil recovery by capillary pressure in low permeability and tight oil reservoirs. Though many experiments have been conducted to study the mechanism of enhanced oil recovery by surfactant-assisted spontaneous imbibition, the effects of surfactant type, concentration, and temperature have not been well studied. Using tight sandstone outcrop core samples with similar permeability and porosity, this paper experimentally studies surfactant-assisted spontaneous imbibition using three different surfactant types, i.e., sodium dodecylbenzene sulfonate (SDBS), cocamidopropyl betaine (CAB), and C12–14 fatty alcohol glycoside (APG). In addition to the type of surfactant, the effect of the surfactant concentration and the temperature is also investigated. The study results show that the ultimate oil recovery of spontaneous imbibition with formation water and denoised water is about 10%. Surfactant can significantly improve the oil recovery of spontaneous imbibition by reducing the interfacial tension between oil and water, emulsifying crude oil and improving oil mobility. APG showed better performance compared to SDBS and CAB, with a maximum oil recovery factor of 36.19% achieved with formation water containing 0.05% APG surfactant. Lower concentrations (0.05% APG) in the formation water resulted in a higher oil recovery factor compared to 0.1% APG. Increasing temperature also improves oil recovery by reducing oil viscosity. This empirical study contributes to a better understanding of the mechanism of surfactant-assisted spontaneous imbibition and enhanced oil recovery in tight oil reservoirs. [ABSTRACT FROM AUTHOR]

This paper proposes a new and simplified Nearest Vector Control (NVC) modulation technique for a grid-connected photovoltaic (PV) system using a Modular Multilevel Converter (MMC). Compared to the Nearest Level Control (NLC) technique, which defines three independent states for the three phases of medium to large four-wire multilevel converters, NVC offers a more coordinated behavior for three-wire converters. The proposed scheme is easy to implement, and it simplifies the understanding of using vectors when detecting the vector of the converter nearest to a given reference. Because it uses natural coordinates, namely, ab, bc and ca, the proposed method is easier to understand and more useful for further developments. Compared with earlier NVC methods, this approach offers full independence of the number of levels at the converter and it can readily accommodate changes in the number of levels, with no need for lookup tables or artificial coordinate transformations. The proposed NVC method was implemented on a 16-cell MMC used for PV generation and then it was compared to NLC, leading to a smaller and more consistent low-order harmonic distortion, requiring about the same complexity of implementation. Furthermore, in comparison to NLC, when applying the proposed NVC modulation, a behavior more insensitive to changes in the grid voltage was found, the most hazardous odd harmonics from the 5th to the 19th were reduced, and a consistent reduction of about 25 dB was achieved on the 5th and 7th harmonics. The newly proposed method is supported by simulations and experimental results with constant and sharply changing solar irradiance, leaving or removing the 100 Hz component of the MMC circulating currents. [ABSTRACT FROM AUTHOR]

Wind energy is a renewable resource with abundant reserves, and its sustainable development and utilization are crucial. The components of wind turbines, particularly the blades and various surfaces, require meticulous defect detection and maintenance due to their significance. The operational status of wind turbine generators directly impacts the efficiency and safe operation of wind farms. Traditional surface defect detection methods for wind turbines often involve manual operations, which suffer from issues such as high subjectivity, elevated risks, low accuracy, and inefficiency. The emergence of computer vision technologies based on deep learning has provided a novel approach to surface defect detection in wind turbines. However, existing datasets designed for wind turbine surface defects exhibit overall category scarcity and an imbalance in samples between categories. The algorithms designed face challenges, with low detection rates for small samples. Hence, this study first constructs a benchmark dataset for wind turbine surface defects comprising seven categories that encompass all common surface defects. Simultaneously, a wind turbine surface defect detection algorithm based on improved YOLOv5 is designed. Initially, a multi-scale copy-paste data augmentation method is proposed, introducing scale factors to randomly resize the bounding boxes before copy-pasting. This alleviates sample imbalances and significantly enhances the algorithm's detection capabilities for targets of different sizes. Subsequently, a dynamic label assignment strategy based on the Hungarian algorithm is introduced that calculates the matching costs by weighing different losses, enhancing the network's ability to learn positive and negative samples. To address overfitting and misrecognition resulting from strong data augmentation, a two-stage progressive training method is proposed, aiding the model's natural convergence and improving generalization performance. Furthermore, a multi-scenario negative-sample-guided learning method is introduced that involves incorporating unlabeled background images from various scenarios into training, guiding the model to learn negative samples and reducing misrecognition. Finally, slicing-aided hyper inference is introduced, facilitating large-scale inference for wind turbine surface defects in actual industrial scenarios. The improved algorithm demonstrates a 3.1% increase in the mean average precision (mAP) on the custom dataset, achieving 95.7% accuracy in mAP_50 (the IoU threshold is half of the mAP). Notably, the mAPs for small, medium, and large targets increase by 18.6%, 16.4%, and 6.8%, respectively. The experimental results indicate that the enhanced algorithm exhibits high detection accuracy, providing a new and more efficient solution for the field of wind turbine surface defect detection. [ABSTRACT FROM AUTHOR]

The prediction of building energy consumption is beneficial to utility companies, users, and facility managers to reduce energy waste. However, due to various drawbacks of prediction algorithms, such as, non-transparent output, ad hoc explanation by post hoc tools, low accuracy, and the inability to deal with data uncertainties, such prediction has limited applicability in this domain. As a result, domain knowledge-based explainability with high accuracy is critical for making energy predictions trustworthy. Motivated by this, we propose an advanced explainable Belief Rule-Based Expert System (eBRBES) with domain knowledge-based explanations for the accurate prediction of energy consumption. We optimize BRBES's parameters and structure to improve prediction accuracy while dealing with data uncertainties using its inference engine. To predict energy consumption, we take into account floor area, daylight, indoor occupancy, and building heating method. We also describe how a counterfactual output on energy consumption could have been achieved. Furthermore, we propose a novel Belief Rule-Based adaptive Balance Determination (BRBaBD) algorithm for determining the optimal balance between explainability and accuracy. To validate the proposed eBRBES framework, a case study based on Skellefteå, Sweden, is used. BRBaBD results show that our proposed eBRBES framework outperforms state-of-the-art machine learning algorithms in terms of optimal balance between explainability and accuracy by 85.08%. [ABSTRACT FROM AUTHOR]

Oil shale, as a vast potential resource, is considered an important alternative energy source, and its effective development and economic utilization are of significant importance in alleviating the contradiction between energy supply and demand. Presently, the in situ conversion technology for oil shale has gained significant global attention, with numerous extraction methods undergoing active research and development. One of these methods is the in situ conversion of oil shale based on the hydration reaction of calcium-oxide-based composite material (CaO-CM). This approach harnesses the heat produced by the reaction between CaO-CM and water as a heat source for the pyrolysis of oil shale. This paper conducted experiments to assess the feasibility of temperature associated with this method. The feasibility study mainly includes two aspects: First, it is necessary to investigate whether the temperature generated by the hydration reaction of CaO-CM can meet the temperature requirements for the pyrolysis of oil shale. Through pyrolysis experiments of Xinjiang oil shale, the minimum temperature required for oil shale pyrolysis was determined to be 330 °C. High-temperature and high-pressure reaction vessels were employed to explore the temperature generated by the hydration reaction of CaO-CM. The results show that with the increase in environment pressure, environment temperature, and reaction mass, the maximum temperature generated by the hydration reaction of CaO-CM also increases (reach 455.5 °C), meeting the temperature requirements for the pyrolysis of oil shale. Second, the study evaluates whether the hydration reaction of CaO-CM can induce pyrolysis hydrocarbons of the oil shale. Through the pyrolysis experiments of oil shale based on the hydration reaction of CaO-CM, the changes in the content of pyrolysis hydrocarbons (S2) in oil shale before and after pyrolysis are measured. The results show that under 10 MPa pressure, the content of pyrolysis hydrocarbons in the oil shale decreased from 40.96 mg/g to 0.08 mg/g after pyrolysis. This confirms the feasibility of the temperature conditions for the in situ conversion of oil shale based on the hydration reaction of CaO-CM. [ABSTRACT FROM AUTHOR]

In this paper, we develop a finite-difference time-domain (FDTD) model in which the time-evolving electrical conductivity of the air ionization plasma in DC voltage needed-plate setup is represented. Maxwell's equations are solved using the FDTD method, and the associated currents and discharge fields are computed over time and in three-dimensional space. The proposed model for the electrical conductivity is dependent on time, the applied DC voltage, and the gap length. The necessary data for developing the proposed model is obtained experimentally using a standard discharge needle, with its spherical tip measuring approximately 40 μ m in diameter. Once high voltage is applied, a steady state is achieved. The electrical conductivity σ (t) and its associated parameters are then calculated using nonlinear equations proposed to reproduce the experimentally obtained plasma behavior in the full-wave FDTD model. Voltage ranges from 4 kV to 9 kV, and gap distances are between 4 mm and 8 mm. [ABSTRACT FROM AUTHOR]

For breathing humans, the respiratory quotient (RQ = CO2 moles released/O2 mols consumed) ranges from 0.7 to 1.0. In Part I, the literature on the RQ was reviewed and Keeling's data on atmospheric CO2 and O2 concentrations (1991–2018) were used in the estimation of the global RQ as 0.47. A new interpretation of RQGlob is provided in Part II by treating the planet as a "Hypothetical Biological system (HBS)". The CO2 and O2 balance equations are adopted for estimating (i) energy-based RQGlob(En) and (ii) the CO2 distribution in GT/year and % of CO2 captured by the atmosphere, land, and ocean. The key findings are as follows: (i) The RQGlob(En) is estimated as 0.35 and is relatively constant from 1991 to 2020. The use of RQGlob(En) enables the estimation of CO2 added to the atmosphere from the knowledge of annual fossil fuel (FF) energy data; (ii) The RQ method for the CO2 budget is validated by comparing the annual CO2 distribution results with results from more detailed models; (iii) Explicit relations are presented for CO2 sink in the atmosphere, land, and ocean biomasses, and storage in ocean water from the knowledge of curve fit constants of Keeling's curves and the RQ of FF and biomasses; (iv) The rate of global average temperature rise (0.27 °C/decade) is predicted using RQGlob,(En) and the annual energy release rate and compared with the literature data; and (v) Earth's mass loss in GT and O2 in the atmosphere are predicted by extrapolating the curve fit to the year 3700. The effect of RQGlob and RQFF on the econometry and policy issues is briefly discussed. [ABSTRACT FROM AUTHOR]

In recent years, morphing wings have become not only a concept, but an aerodynamic solution for the aviation industry to take a step forward toward future technologies. However, continuously morphing airfoils became an interesting answer to provide green energy solutions. In this paper, the authors conducted experimental research on a continuously camber-morphing airfoil using the Particle Image Velocimetry (PIV) and Computational Fluid Dynamics (CFD) methods. The main objective of this work was to research a variety of morphing airfoils with different camber deflections. An average velocity distribution and turbulence distribution were compared and are discussed. The two-dimensional PIV results were compared to the CFD simulations to validate the numerical method's accuracy and obtain the aerodynamic coefficient's trends. A further comparison revealed that morphing airfoils have better aerodynamic performance than conventional airfoils for very low camber deflections and create substantial amounts of drag for significant camber deflections. [ABSTRACT FROM AUTHOR]

Since microgrids can effectively integrate renewable energy, energy storage devices, and controllable loads, this advantage promotes the rapid development and application of microgrid technology. However, with the high proportion of renewable energy access, only considering how energy is optimally distributed in microgrids can no longer meet the actual demand. How to aggregate user-side controllable loads to form regulation resources has become a research hotspot, and the users, as a passive party in the load scheduling process, should also be an important consideration in their perception of the use of electricity. First, a control model for temperature-controlled loads and a time-shift model for time-shiftable loads are developed. Then, the comprehensive electricity satisfaction model of users is established, and the two-layer optimal scheduling model of microgrids considering users' satisfaction is proposed, with users as the upper layer and microgrids as the lower layer, and the two-layer model is transformed into a single-layer model according to the KKT condition for solving. Finally, the effect of the weighting factor for satisfaction on the economy is discussed through the analysis of examples, which verifies the effectiveness of the two-layer model. [ABSTRACT FROM AUTHOR]

In this work, a simplified comprehensive three-dimensional numerical model is developed to study the effect of hydrogen production on co-gasification of biomass and low-density polyethylene (LDPE). CFD software AVL Fire 2020 inbuilt algorithms were employed to develop the gas phase while the solid phase was developed by user-defined FORTRAN subroutines. Solid hydrodynamics, fuel conversion, homogenous and non-homogenous chemical reactions, and heat transfer, including radiation, subroutines were defined and incorporated into AVL FIRE explicitly. Species concentrations of the syngas were analyzed for co-gasification of Beechwood and LDPE for three distinct types of bed materials (silica sand, Na-Y zeolite, and ZSM-5 zeolite). Then, the model is validated with experiment results available in the literature for a lab-scale fluidized bed reactor. The highest hydrogen production was observed in Na-Y zeolite followed by ZSM-5 zeolite and silica in both numerical and experimental analysis for the co-gasification of Beechwood and LDPE, providing a reasonable agreement between the numerical and the experimental results. Therefore, the current model predicts the enhancement of the quality of hydrogen-rich syngas through the application of co-pyrolysis within a fluidized bed reactor, incorporating a catalytic bed material. [ABSTRACT FROM AUTHOR]

With the large-scale use of wind turbines, the ability of the power system to resist frequency fluctuations has been greatly weakened, making the contradiction between frequency regulation supply and demand in the power system increasingly prominent. In order to ensure the frequency security of the power system, this paper conducts research on the primary frequency regulation (PR) backup configuration problem of a power system containing a high proportion of wind power. First, according to the dynamic properties of the speed regulator, a system frequency response model considering the limiting link is established. And the system frequency response model is transformed into a time-domain analytical function of PR reserve capacity and frequency maximum value. On this basis, a PR backup configuration model of a power system containing a high proportion of wind power is constructed with the optimization goal of minimizing system operating costs and taking into account the limiting link constraints. It is proposed to use the L-shape algorithm to decompose the model into main problems and sub-problems, which effectively reduces the solution complexity of the model. Finally, the correctness and effectiveness of this model are verified based on the improved IEEE 39-bus system. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are emerging as one of the pillars for achieving climate neutrality. They represent both a threat and an opportunity for the operation of the network. Used as flexible loads, they can favor the self-consumption of photovoltaic (PV) energy. This paper presents three EV charging tariff systems (TSs) based on the self-consumption of excess PV energy. The TS objectives are to increase the self-consumption rate (SCR) and thus indirectly decrease the charging cost of the EV users. Two of the proposed TSs correspond to an indirect control of EV charging. The third TS is a hybrid system where the charging power is controlled. The TS is designed using a series of rules that consider the momentary PV surplus and the charging power of each EV. The influence of the TS is simulated by considering real data from a PV collective self-consumption project in the Basque Country (Spain). The TS simulations performed with 6 months of data show a 13.1% increase in the SCR when applying the third TS, reaching an average of 93.09% for the SCR. In addition, the cost of EV charging is reduced by 25%. [ABSTRACT FROM AUTHOR]

Geothermal energy exploration, development, and research have been ongoing in Canada for several decades. The country's cold climate and the push to develop renewable energy sources have driven interest in geothermal energy. Despite this drive, regulatory complexities and competition with other relatively inexpensive energy sources with existing infrastructure have hindered development. As such, interest has grown and waned with changes in the energy economy over several decades, leaving many projects at a standstill. As of January 2023, there are currently no operational geothermal power projects in Canada. Many hot spring pool and spa complexes remain active, and Canada is a leading country in the installation of ground source heat pumps (GSHPs; also called geo-exchange systems). However, in the last decade, the interest in deep geothermal systems has renewed, with many new projects starting up across several provinces and territories. Moreover, projects that had shown limited progress for many years—such as Mount Meager in British Columbia—have begun to renew their development efforts. Research is also expanding within prominent research groups and universities. The areas of focus include both building upon previous studies (such as thermal gradients and the heat flow in sedimentary basins) and researching new methods and resources (such as GSHPs, closed-loop systems, integrated geothermal operations, and hybrid systems, including heat storage). The development is supported by federal, provincial, and territorial governments through grants and the development of regulatory frameworks. Although challenges still remain for Canada to develop its geothermal energy resources, several power, thermal, and co-production projects, ongoing research, funding, and regulatory acts are all moving forward to support geothermal development. This paper aims to study Canada's geothermal energy update in 2023 regarding the aspects mentioned above. [ABSTRACT FROM AUTHOR]

To reach climate neutrality by 2050, a goal that the European Union set itself, it is necessary to change and modify the whole EU's energy system through deep decarbonization and reduction of greenhouse-gas emissions. The study presents a current insight into the global energy-transition pathway based on the hydrogen energy industry chain. The paper provides a critical analysis of the role of clean hydrogen based on renewable energy sources (green hydrogen) and fossil-fuels-based hydrogen (blue hydrogen) in the development of a new hydrogen-based economy and the reduction of greenhouse-gas emissions. The actual status, costs, future directions, and recommendations for low-carbon hydrogen development and commercial deployment are addressed. Additionally, the integration of hydrogen production with CCUS technologies is presented. [ABSTRACT FROM AUTHOR]

With the continuous expansion of the scale of power generated by grid-connected renewable energy, the form and operation characteristics of power grids have undergone significant changes, and the power electronic characteristics of power systems are prominent, resulting in the frequent occurrence of wide-band oscillation problems when renewable energy power generation equipment is connected to a power grid. Oscillation has the characteristics of nonlinearity and has an oscillation frequency ranging from a few hertz to several thousand hertz or more, which seriously threatens the stable operation of power grids. This paper summarizes the wide-band oscillation events that occur worldwide under the background of renewable energy access to power grids, classifies the wide-band oscillation events according to the distribution of oscillation frequency bands, and sorts out the characteristics of various oscillations. From the perspectives of the source side and network side, the existing oscillation suppression measures are classified and prospected. [ABSTRACT FROM AUTHOR]

This paper investigates the asynchronous motors driven by a Three-Level Neutral-Point-Clamped Voltage Source Inverter (3L-NPC-VSI) and aims to achieve control without weight factors and reduce torque ripple. It puts forward a non-weighted two-stage Finite-Control-Set Model Predictive Control (FCS-MPC) strategy. First, a hierarchical optimization method is adopted to address the difficulty of setting weight factors in traditional FCS-MPC applications. The method offers stratified designs of three performance indices, voltage jump, common-mode voltage, and current tracking, obviating the need for weight factor setting and reducing the calculation load of predictions. Secondly, to further mitigate torque ripple, an optimal vector or vector combination is implemented at the current control layer by adhering to the principle of minimal current tracking error. During the selection of the optimal vector combination, the first vector of the combination is chosen to be the vector at the end of the present cycle. This ensures that there is at most one switch within each control period, reducing the switching losses of the two-stage FCS-MPC. Lastly, detailed simulation and experimental analyses are conducted to verify the feasibility and effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

The coupling of converters with resonant networks poses significant challenges for frequency tracking and power control in inductive power transfer (IPT) systems. This paper presents an implementation method that addresses these issues by dividing the system's operation into two distinct states: self-oscillating and power-injecting. Based on these states, a phase-closed loop is constructed. Within this closed loop, the phase tracking unit detects and tracks frequency drift, while the power regulating unit incorporates an integrator and adopts a control variable to adjust the output power by modifying the duration of the power injecting state. Meanwhile, the oscillating unit operates in the self-oscillating state. Operating in this manner, the system achieves self-oscillation and demonstrates the capability to effectively track and compensate for system variations within a single cycle. A verification prototype has been constructed, and it demonstrates that the converter within it completely decoupled from the resonant network. Experimental results validate that altering the control variable solely affects the duration of the power-injecting state, allowing for independent control of the output power. When the control variable changes from 2.0 V to 3.5 V, the output power changes from 178 W to 519 W while the self-oscillating state remains unchanged. Furthermore, the system accurately tracks frequency changes, even under significant variations in the coupling coefficient or load, without compromising the power injection state. When the air gap changes from 3 cm to 12 cm, the duration of the self-oscillating state changes from 22.1 μs to 26.3 μs, while the power injecting state remains unchanged. This approach exhibits a robust performance, particularly suitable for dynamic IPT systems sensitive to parameter variations. [ABSTRACT FROM AUTHOR]

In the realm of power systems, short-term electric load forecasting is pivotal for ensuring supply–demand balance, optimizing generation planning, reducing operational costs, and maintaining grid stability. Short-term load curves are characteristically coarse, revealing high-frequency data upon decomposition that exhibit pronounced non-linearity and significant noise, complicating efforts to enhance forecasting precision. To address these challenges, this study introduces an innovative model. This model employs complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to bifurcate the original load data into low- and high-frequency components. For the smoother low-frequency data, a temporal convolutional network (TCN) is utilized, whereas the high-frequency components, which encapsulate detailed load history information yet suffer from a lower fitting accuracy, are processed using an enhanced soft thresholding TCN (SF-TCN) optimized with the slime mould algorithm (SMA). Experimental tests of this methodology on load forecasts for the forthcoming 24 h across all seasons have demonstrated its superior forecasting accuracy compared to that of non-decomposed models, such as support vector regression (SVR), recurrent neural network (RNN), gated recurrent unit (GRU), long short-term memory (LSTM), convolutional neural network-LSTM (CNN-LSTM), TCN, Informer, and decomposed models, including CEEMDAN-TCN and CEEMDAN-TCN-SMA. [ABSTRACT FROM AUTHOR]

The Kingdom of Saudi Arabia is experiencing a surge in electricity demand, with power generation increasing 4 times in 25 years from 1990 to 2014. Despite the abundant primary renewable energy sources, the country has overlooked them in the past in national energy policies. However, in recent years, renewable energy has become a part of the Kingdom of Saudi Arabia's energy conservation policy due to climate changes, technological progress, economies of scale, and increased competitiveness in supply chains. The Saudi government has created the King Abdullah City for Atomic and Renewable Energy (KACARE) to develop national strategies for effectively utilizing renewable and nuclear energy. This paper reviews the current state of the art of the renewable energy technologies available on the market and evaluates the installation of renewable energy plants near Saudi Arabia's East Coast for a new town, focusing on technical rather than economic aspects. The paper provides a wide review of the possible technical solutions to exploit the producibility of different renewable sources, considering the challenging climate conditions typical of desert areas. The analysis of a real case study shows a high availability of wind and solar irradiance that allow a net energy production of 354 and 129 GWh, respectively. In addition, the comparison between a typical ground-mounted photovoltaic (PV) system and an emerging floating PV reveals that for the same installed power, occupied area, and environmental conditions, the latter has a 4% greater performance ratio due to the cooling effect of water. [ABSTRACT FROM AUTHOR]

This paper presents a topology optimization (TopO) method for conjugate heat transfer (CHT), with turbulent flows. Topological changes are controlled by an artificial material distribution field (design variables), defined at the cells of a background grid and used to distinguish a fluid from a solid material. To effectively solve the CHT problem, it is crucial to impose exact boundary conditions at the computed fluid–solid interface (FSI); this is the purpose of introducing the cut-cell method. On the grid, including also cut cells, the incompressible Navier–Stokes equations, coupled with the Spalart–Allmaras turbulence model with wall functions, and the temperature equation are solved. The continuous adjoint method computes the derivatives of the objective function(s) and constraints with respect to the material distribution field, starting from the computation of derivatives with respect to the positions of nodes on the FSI and then applying the chain rule of differentiation. In this work, the continuous adjoint PDEs are discretized using schemes that are consistent with the primal discretization, and this will be referred to as the "Think Discrete–Do Continuous" (TDDC) adjoint. The accuracy of the gradient computed by the TDDC adjoint is verified and the proposed method is assessed in the optimization of two 2D cases, both in turbulent flow conditions. The performance of the TopO designs is investigated in terms of the number of required refinement steps per optimization cycle, the Reynolds number of the flow, and the maximum allowed power dissipation. To illustrate the benefits of the proposed method, the first case is also optimized using a density-based TopO that imposes Brinkman penalization terms in solid areas, and comparisons are made. [ABSTRACT FROM AUTHOR]

The scientific community has shown considerable interest in Industry 4.0 due to its capacity to revolutionise the manufacturing sector through digitalisation and data-driven decision-making. However, the actual implementation of Industry 4.0 within complex industrial settings presents obstacles that are typically beyond the scope of mainstream research articles. In this paper, a comprehensive case-study detailing our collaborative partnership with a leading medical device manufacturer is presented. The study traces its evolution from a state of limited digitalisation to the development of a digital intelligence platform that leverages data and machine learning models to enhance operations across a wide range of critical machines and assets. The main business objective was to enhance the energy efficiency of the manufacturing process, thereby improving its sustainability measures while also saving costs. The project encompasses energy modelling and analytics, Fault Detection and Diagnostics (FDD), renewable energy integration and advanced visualisation tools. Together, these components enable informed decision making in the context of energy efficiency. [ABSTRACT FROM AUTHOR]

Variations in the coupling coefficient of loosely coupled transformers and dynamic loads have a significant impact on the overall performance of bidirectional inductive power transfer (BIPT) systems. However, a wide range of load and coupling coefficient variations are common in the actual charging process, which may cause the converter on both sides to operate in a hard switching state, resulting in switching noise, reduced efficiency, and potential safety concerns. In this paper, a triple-phase-shift control (TPSC) strategy is proposed to study the zero-voltage switching (ZVS) operating range and constant-voltage output (CVO) characteristics of the double-side-LCC (DS-LCC) topology. To ensure a CVO over the wide range of coupling coefficient variations, a dual-phase-shift control is introduced for AC voltage matching. Based on this, the third phase-shift angle control between the converters on both sides is introduced to ensure the ZVS realization. Meanwhile, the time-domain model is developed to analyze the rationality of the proposed third phase-shift angle and the ZVS operating range. Finally, the effectiveness of the proposed TPSC strategy is validated through a 1.5 kW experimental prototype with an air gap of 100–150 mm. [ABSTRACT FROM AUTHOR]

Transient stability of the electric power system still heavily rests on a timely and correct operation of the relay protection of individual power generators. Power swings and generator pole slips, following network short-circuit events, can initiate false relay activations, with negative repercussions for the overall system stability. This paper will examine the generator's underimpedance (21G) and out-of-step (78) protection functions and will propose a machine learning based classifier for supporting and reinforcing their decision-making logic. The classifier, based on a support vector machine, will aid in blocking the underimpedance protection during stable generator swings. It will also enable faster tripping of the out-of-step protection for unstable generator swings. Both protection functions will feature polygonal protection characteristics. Their implementation will be based on European practice and IEC standards. Classifier will be trained and tested on the data derived from simulations of the IEEE New England 10-generator benchmark power system. [ABSTRACT FROM AUTHOR]

Co-firing wood pellets with coal is an attractive alternative energy generation method with economic, social, and environmental benefits for the US energy generation sector. One way to sustainably use wood pellets for co-firing is to create consumer-supported green pricing programs (GPPs). Our study surveyed residents of five states (Alabama, New Jersey, New York, Pennsylvania, and Virginia) to investigate preferences for the attributes of a hypothetical GPP. The study applied the Best Worst Choice method, which employs the Best Worst Scaling (BWS) and binary choice (BC) task. The BWS analysis showed that residents of all five states most value the flexibility of contracts, the location of energy generation, and the reduction of carbon emissions as attributes of GPPs. The BC analysis, however, showed that residents are willing to pay a premium for length of contract, followed by reduction of carbon emissions and variability of payments. This study shows that the adoption of optimal GPP attributes can create real customer value. The success of GPPs will depend on increasing enrollment and public support; hence, the next step will be to increase awareness levels and green consciousness through sensitization in the form of public education exercises and media campaigns. Such measures will serve to inform and educate residents on the benefits of GPPs and lessen the gap between intrinsic value and willingness to pay for select attributes. [ABSTRACT FROM AUTHOR]

This manuscript presents a comprehensive review of recent advancements in electrical distribution networks, with a specific focus on the incorporation of direct current (DC) applications. The research aims to comprehensively address the current and future aspects of DC, spanning from the distribution level to the utilization level. The renewed interest in DC power systems has led to the investigation of several transitional challenges in recent years. A significant portion of these efforts has been dedicated to determining the feasibility of applying DC to specific use cases. Additionally, the literature has explored design considerations such as system architecture and voltage levels, the integration of DC into existing distribution networks, load flow (LF) computations, and the distinct safety concerns associated with DC power systems. In this paper, the various research endeavors are categorized, evaluated, and scrutinized to assess the current state of the transition from a purely alternating current (AC) distribution system to a solely DC or hybrid AC/DC distribution system. A bibliometric analysis is conducted, constructing a network of co-occurrence based on author-provided keywords, which reveals the primary research foci in this domain. The barriers hindering the widespread adoption of DC distribution systems and potential solutions are also discussed. Moreover, this article synthesizes ongoing efforts to address these obstacles and delineates future research directions by emphasizing the existing knowledge gaps. [ABSTRACT FROM AUTHOR]

We consider a model for the resilience analysis of interconnected critical infrastructures (ICIs) that describes the dependencies among the subsystems within the ICIs and their time-varying behavior. The model response is a function of uncertain inputs comprising ICIs design parameters and failure magnitudes of vulnerable elements in the system, etc. In this methodological paper, we present a systematic approach based on an innovative blend of methods to perform a sensitivity analysis for identifying the most relevant variables affecting the system resilience at different stages, during a disruptive event. The methods considered include the following: the use of the graphical representation of Cusunoro curves for a visualization of the impact of an input on the resilience metric and an understanding of whether the associated dependence is monotonic, increasing, or decreasing; the introduction of an ensemble of indicators related to different properties of the resilience metric to allow the prioritization of variable importance and avoid false negatives, meaning to regard a variable as non-influential when, instead, it plays a relevant role in the determination of the model response; the calculation of first-order variance-based sensitivity indices to have an appreciation on the relevance of interactions when inputs are independent; and a data approach to visually identify relevant second-order interactions. All the sensitivity methods considered are performed on a provided sample, and do not require additional model evaluations. They allow the analyst to post-process the data to extract, simultaneously, several desirable insights. The systematic approach proposed to apply these methods allows us to identify the model input variables and parameters that are not very relevant, while it enables the identification of the relevant ones which allows prioritizing interventions on the vulnerable elements of the system for its resilience at different stages during a disruptive event. Given the methodological nature of the work, a simplified infrastructure model describing an interconnected gas network and electric power grid is taken as case study: this allows us to show that the approach is straightforward to understand and implement, and the results obtained show the usefulness of the approach in providing meaningful insights that can be used by stakeholders and decision makers to inform strategies for the improvement of system resilience. By the application of the simplified ICIs model to the case study, it is shown that the approach can be straightforwardly implemented to identify the most relevant variables on system resilience and obtain the most important subsystems. The key factors which affect system resilience in multiple initial failures scenarios are found; this allows us to identify the key resilience improvement measurements, and their priorities. [ABSTRACT FROM AUTHOR]

To ensure the accuracy of state of charge (SOC) and state of health (SOH) estimation for battery packs while minimizing the amount of pre-experiments required for aging modeling and the scales of computation for online management, a decisive-cell-based estimation method with training-free characteristic parameters and a dynamic-weighted estimation strategy is proposed in this paper. Firstly, to reduce the computational complexity, the state estimation of battery packs is summed up to that of two decisive cells, and a new selection approach for the decisive cells is adopted based on the detection of steep voltage changes. Secondly, two novel ideas are implemented for the state estimation of the selected cells. On the one hand, a set of characteristic parameters that only exhibit local curve shrinkage with aging is chosen, which keeps the corresponding estimation approaches away from training. On the other hand, multiple basic estimation approaches are effectively combined by their respective dynamic weights, which ensures the estimation can maintain a good estimation accuracy under various load profiles. Finally, the experimental results show that the new method can quickly correct the initial setting deviations and have a high estimation accuracy for both the SOC and SOH within 2% for a series battery pack consisting of cells with obvious inconsistency. [ABSTRACT FROM AUTHOR]

This work deals with doubly fed induction generator (DFIG) modeling and stability when connected to weak AC grids. A detailed state-space model that includes the phase-locked loop (PLL) is developed. This work aims to determine the influence of the network's strength on DFIG stability through the short-circuit ratio (SCR). The critical values of the proportional control parameters of the grid-side and rotor-side converters (RSC and GSC), as well as PLL, which make the system unstable, are calculated for different SCR values. Finally, PSCAD/EMTDC dynamic simulations are used to validate the critical control parameters obtained by studying the eigenvalues of the DFIG state-space model regarding system stability. [ABSTRACT FROM AUTHOR]

Accurately forecasting energy metrics is essential for efficiently managing renewable energy generation. Given the high variability in load and renewable energy power output, this represents a crucial area of research in order to pave the way for increased adoption of low-carbon energy solutions. Whilst the impact of different neural network architectures and algorithmic approaches has been researched extensively, the impact of utilising additional weather variables in forecasts have received far less attention. This article demonstrates that weather variables can have a significant influence on energy forecasting and presents methodologies for using these variables within a long short-term memory (LSTM) architecture to achieve improvements in forecasting accuracy. Moreover, we introduce the use of the seasonal components of the target time series, as exogenous variables, that are also observed to increase accuracy. Load, solar and wind generation time series were forecast one hour ahead using an LSTM architecture. Time series data were collected in five Spanish cities and aggregated for analysis, alongside five exogenous weather variables, also recorded in Spain. A variety of LSTM architectures and hyperparameters were investigated. By tuning exogenous weather variables, a 33% decrease in mean squared error was observed for solar generation forecasting. A 22% decrease in mean absolute squared error (MASE), compared to 24-h ahead forecasts made by the Transmission Service Operator (TSO) in Spain, was also observed for solar generation. Compared to using the target variable in isolation, utilising exogenous weather variables decreased MASE by approximately 10%, 15% and 12% for load, solar and wind generation, respectively. By using the seasonal component of the target variables as an exogenous variable itself, we demonstrated decreases in MASE of 19%, 12% and 8% for load, solar and wind generation, respectively. These results emphasise the significant benefits of incorporating weather and seasonal components into energy-related time series forecasts. [ABSTRACT FROM AUTHOR]

As one of the most important and economically enhanced oil-recovery technologies, water flooding has been applied in various oilfields worldwide for nearly a century. Stratified water injection is the key to improving water-flooding performance. In water flooding, the water-injection rate is normally optimized based on the reservoir permeability and thickness. However, this strategy is not applicable after oilfields enter the ultra-high-water-cut period. In this study, an original method for optimizing water-flooding parameters for developing multi-layer sandstone reservoirs in the entire flooding process and in a given period is proposed based on reservoir engineering theory and optimization technology. Meanwhile, optimization mathematical models that yield maximum oil recovery or net present value (NPV) are developed. The new method is verified by water-flooding experiments using Berea cores. The results show that using the method developed in this study can increase the total oil recovery by approximately 3 percent compared with the traditional method using the same water-injection amounts. The experimental results are consistent with the results from theoretical analysis. Moreover, this study shows that the geological reserves of each layer and the relative permeability curves have the greatest influence on the optimized water-injection rate, rather than the reservoir properties, which are the primary consideration in a traditional optimization method. The method developed in this study could not only be implemented in a newly developed oilfield but also could be used in a mature oilfield that has been developed for years. However, this study also shows that using the optimized water injection at an earlier stage will provide better EOR performance. [ABSTRACT FROM AUTHOR]

Hydraulic fracturing is an essential technology in the development of coalbed methane reservoirs. Hydraulic fracturing can create a highly conductive fracture in the reservoir and increase its permeability. At present, the focus of coalbed methane reservoir fracturing optimization is gradually shifting to the fracturing scale. In the current development process, more and more coalbed methane blocks try to increase the fracturing scale to increase the gas production of coalbed methane wells. Field tests show that gas production of coalbed methane wells will increase to a certain extent with the increase of fracturing scale. However, the increase in the scale of fracturing also increases its cost. Therefore, the most economical fracturing scale is not necessarily the optimal fracturing scale for gas production. The field test usually pays more attention to the gas production effect, but the development of a coalbed methane field should pay more attention to the economic benefit, and the optimization of fracturing should take the economic benefit as the goal. Taking economic benefits as the starting point, this paper uses fracturing simulation to calculate the fracture extension under different geological conditions and different fracturing scales. It also uses numerical simulation to calculate the gas well productivity under different fracture extension conditions. The economic evaluation model was established to calculate the economic benefits under different fracturing scales, and the optimal fracturing scale was obtained. Finally, the typical maps of fracturing optimization under different geological conditions are formed. The optimization method of fracturing scale integrating economy, fracturing, and gas reservoir is realized. The research results have been successfully applied to the optimization scheme of Liulin block development, and very good results have been achieved. Because this method is targeted at different geological conditions, it can be used to guide the fracturing optimization of other coalbed methane blocks and has very important significance for the development and optimization of coalbed methane reservoirs. [ABSTRACT FROM AUTHOR]

The increase in the share of renewable energy sources (RES) leads to a growing need for sources or systems/actions to stabilize the national energy grid. Such stabilizing actions include market tools, such as prices and demand-side response (DSR) tools, as well as flexible energy sources (e.g., gas). In addition, energy storage, where pumped storage hydroelectricity (PSH) accounts for 90% of global storage capacity, plays an important role. Therefore, the authors presented a detailed analysis of PSH in the context of the dynamic growth of installed capacity in renewable energy sources. They analyzed the economic viability of this type of power plant, with a particular emphasis on operational costs, energy production, and revenue. The Młoty case study and market data, including historical data on various PSH, were presented and analyzed. This study uses copulas, simulation, and statistical analysis. The authors proved that market prices and arbitrage actions alone are not sufficient to achieve profitability of the investment; however, additional benefits, such as fees for available power, enable the achievement of economic profitability. The reason for this is the fact that one of the main goals of PSH is to serve as a power reserve. In addition, this paper presents the analysis of the utilization of existing PSH in the form of full pumping and energy generation cycles (charging and discharging storage). [ABSTRACT FROM AUTHOR]

This study proposes a novel double closed-loop robust control strategy based on a power switching affine model of three-phase voltage source converters (VSCs). The aim is to overcome the challenges posed by inaccurate mathematical models, complex controller configurations, indirect switching control, and performance degradation under circuit parameters uncertainty or load variation in conventional methods. These conventional methods rely on linearization models, duty ratio regulation, and pulse width modulation (PWM) technologies. The contributions of work are the following: (1) A two-dimensional (2D) power switching affine model is constructed without any approximation or averaging. (2) The proposed approach achieves direct switching control of three-phase VSCs, eliminating the need for complex rotation coordinate transformation, PWM, and phase locking loop (PLL), which are utilized in traditional control methods. (3) The rigor of the system stability analysis is enhanced based on the 2D power switching model compared to the existing three-dimensional (3D) current switching model. (4) A simple control structure with only two control parameters is employed to address circuit parameter uncertainties. The effectiveness and superiority of the proposed method is validated through simulation and experimental comparison results. [ABSTRACT FROM AUTHOR]

Despite the pressure of carbon emissions and clean energy, coal remains the economic backbone of many developing countries due to its abundant resources and widespread distribution. The stable supply of coal is also vital for the global economy and remains irreplaceable in the future global energy structure. China has been a major contributor to annual coal output, accounting for nearly 50% worldwide since 2014. However, despite implementing intelligent coal mining technology, China's coal mining industry still employs over 1.5 million underground miners, posing significant safety risks associated with underground mining operations. Therefore, the introduction of coal mining robots in underground mines is an urgently needed scientific and technological solution for upgrading China's and even the world's coal energy industry. The working face needs a shearer, hydraulic support, a scraper conveyor, and other equipment for coordination. The deep integration of intelligent technology with factors such as "humans, machines, the environment, and management" in the workplace is the core content of intelligent coal mines. This paper puts forward an advanced framework for robot technology systems in coal mining, including single robots, robotized equipment, robot crowds, and unmanned systems. The framework clarifies the common key technologies of coal mining robot research and development and the cross-integration with new technologies such as 5G, the industrial internet, big data, artificial intelligence, and digital twins to improve the autonomous and intelligent application of coal mining robots. By establishing a scientific and complete standard system for coal mining robots, we aim to achieve the customized research and development and standardized production of various types of robot. A specific analysis is conducted on the research progress of common key technologies such as the explosion-proof design, mechanical system innovation, power drive, intelligent sensing, positioning and navigation, and underground communication of coal mining robots. The current research and application status of various types of coal mining robots in China are summarized. A new direction for future coal mining robot research and development is proposed. Robotic mining systems should be promoted to enhance the overall intelligence level and efficiency of mining equipment. To develop human–machine environment-integrated robots to improve the autonomy and collaboration level of coal mining robots, the digital twinning of the entire mine robot system should be accelerated; the normalized operation level of coal mine robots should be improved; research on coal mining robots, shield support robots, and transportation robots should be performed; intelligence should be achieved in fully mechanized mining faces; and equipment shield support for fully mechanized mining faces should be provided. The practical process of implementing coal mining robotization is summarized in this paper, and the technical and engineering feasibility of the coal mining machine population is verified. [ABSTRACT FROM AUTHOR]

The rapid growth in computing and data transmission has significant energy and environmental implications. While there is considerable interest in waste heat emission and reuse in commercial data centers, opportunities in academic data centers remain largely unexplored. In this study, real-time onsite waste heat data were collected from a typical academic data center and an analysis framework was developed to determine the quality and quantity of waste heat that can be contained for reuse. In the absence of a comprehensive computer room monitoring system, real-time thermal data were collected from the data center using two arrays of thermometers and thermo-anemometers in the server room. Additionally, a computational fluid dynamics model was used to simulate temperature distribution and identify "hot spots" in the server room. By simulating modification of the server room with a hot air containment system, the return air temperature increased from 23 to 46 °C and the annual waste heat energy increased from 377 to 2004 MWh. Our study emphasizes the importance of containing waste heat so that it can be available for reuse, and also, that reusing the waste heat has value in not releasing it to the environment. [ABSTRACT FROM AUTHOR]

The growth of the geothermal industry demands the constant search of techniques with the aim of reducing exploration efforts whilst minimizing subsurface uncertainty. The exploration of geothermal resources is fundamental from the exploitation point of view, especially in those regions where this energy is not as widespread as the rest of renewable sources. This research shows how geoelectrical methods can contribute to the investigation and characterization of medium–low enthalpy geothermal resources until about 800 m of depth. A 2000 m long electrical-resistivity tomography profile was performed in a region of Southern Spain with previous evidence of moderate geothermal potential. Results of this geophysical campaign (together with a preliminary geological characterization) allowed for the obtainment of a 2D profile and a pseudo-3D model with extensive information about the subsoil in terms of geological composition and formations. The interpretation of geophysical results denotes the existence of a potential formation constituted by carbonate materials with thickness greater than 300 m, crossing different fractures. Once the ideal location for the geothermal exploitation is defined, the research evaluates the contribution of the possible energy source, deducing that the energy extraction in the potential fracturing area would be double that of the one in the vicinity of the site. [ABSTRACT FROM AUTHOR]

Permanent magnet motors (PMMs) have emerged as key components in numerous industrial applications due to their high efficiency, compact size, and robust performance characteristics. However, to attain optimal performance in PMM drives, accurately predicting and mitigating core losses is paramount. This paper aims to provide a comprehensive review of advancements and methodologies for enhancing the performance of PMM drives by integrating equivalent circuit models (ECMs) that account for core losses. Firstly, the significance of core losses in motor drives is underscored, alongside a survey of research endeavors dedicated to core loss reduction. Notably, emphasis is placed on mathematical models offering both swift computation and reasonable accuracy. Subsequently, the paper delves into the development of ECMs, focusing on approaches adept at capturing core loss effects across diverse operating conditions. Moreover, this paper explores the utilization of these improved ECMs in the design and control of PMMs to achieve enhanced performance. By integrating core loss considerations into design and control strategies, PMM drives can optimize efficiency, torque production, and overall system performance. In summary, this paper may consolidate the current state-of-the-art techniques for enhancing PMM performance through the integration of core-loss-aware ECMs. It highlights key research directions and opportunities for further advancements in this critical area, aiming to foster the development of more efficient and reliable PMM-based systems for a wide range of industrial applications. [ABSTRACT FROM AUTHOR]

To combat global environmental deterioration and energy scarcities, it is crucial to implement energy-saving upgrades for urban road lighting. Comparatively, LEDs have emerged as an advanced and eco-friendly lighting option due to their low energy consumption, excellent performance, high color rendering index, and prolonged lifespan. By incorporating solar cell technology, a smart LED street light controller based on small-scale integrated circuits was developed to enable intelligent control for various lighting needs such as dimming, timing, automatic detection, and sound and light control. Through circuit simulations and experimental outcomes, it has been validated that the controller's structure and performance parameters align with the design specifications. This design encompasses knowledge from diverse fields, including fundamentals of circuit and electronic technology, photovoltaic cell technology, power electronics, and sensor technology, showcasing robust engineering and practicality. Its utilization in the experimental course for second-year college students majoring in electrical engineering contributes to the grooming of professionals and expands the perspectives of future talents, enriching their application of knowledge and practical innovation capabilities. [ABSTRACT FROM AUTHOR]

The research problem of this paper is related to numerous open questions in the field of energy, its understanding, its use, and the challenges of the energy future. After the introductory part, in which a brief historical overview of energy literacy is provided, the paper focuses on energy literacy, its emergence, and the different approaches to its definition and measurement. The paper analyzes the energy literacy of students at the Faculty of Economics in Rijeka based on their cognitive, affective, and behavioral skills. Their knowledge about energy, energy processes, and the energy efficiency of the objects they encounter on a daily basis is examined. Their personal attitudes and values are examined through various forms of personal initiatives and active participation in energy sustainability projects. Finally, the habits and behavioral patterns that the respondents have in their daily lives and their tendencies to save energy resources and find energy-efficient solutions are examined. Despite some positive findings, the current results are not satisfactory and point to the creation of adequate public policies with a particular focus on education and the role of the corrective mechanisms of the state, but also the need for joint negotiations between policy makers, regulators, scientists, representatives of civil society, and the business community. The particular contribution of this work is reflected in being the first research of its kind conducted among Croatian economics students, as well as among Croatian students in general; in the creation of a research instrument that is conceptually consistent with the findings from the existing literature, but with an original set of questions within each energy literacy skill adapted to the cultural and sociological background of the respondents; and in a kind of progress from previous research by taking into account the quantitative aspect (in addition to the qualitative) assessment of the respondents' energy literacy. [ABSTRACT FROM AUTHOR]

This article presents an analytical solution for the evaluation of the thermal performance of packed bed sensible heat storage. The numerical model developed was tested for four different solid storage mediums. The thermal energy equation is solved numerically by deploying the finite difference method. The presented analytical solution is based on a novel mathematical approach. The numerical model was validated using the computer simulation package Comsol Multiphysics v5.3. Our numerical model results are in good agreement with the published experimental data, with an overall difference of ~10%. Hence, the numerical model is an efficient way of evaluating the thermal performance of packed bed thermal energy storage systems compared to other numerical strategies or computer simulation techniques. This proves that the novel analytical model has shown to be a reliable and broadly accurate approach to acquire the thermal performance of sensible heat storage. [ABSTRACT FROM AUTHOR]

Frequency response performance in power systems is becoming vulnerable due to the transition toward the higher penetration of renewable energy such as achieving carbon neutrality. In particular, reducing power system inertia energy as the asynchronous generation increases could result in violating the frequency stability constraint when a disturbance occurs in the power systems. In order to control the rapidly fluctuating frequency of the power system with low inertia, it is necessary to introduce fast frequency response resources such as a Battery Energy Storage System (BESS). This paper proposes a method to calculate the required capacity of BESS for compensating the frequency control performance of the power system using inertia energy. For calculating the required capacity of BESS, the inertia energy in the critical power system, where frequency control performance marginally satisfies frequency stability constraint, should be calculated. Also, the inertia energy in the evaluated power system having deficit inertia energy should be calculated. By comparing power systems that respond with different dynamics when the same disturbance occurs, the proposed calculation corresponds to the ratio of inertia energy deficiency based on critical power system inertia energy within the power imbalance. Through various case studies employing Korean power systems, the effectiveness of the inertia energy-based calculation method for the required BESS is verified by the fact that the BESS integrated power system marginally satisfies the frequency stability constraint. In these study cases, it is found that the instant response of BESS is very effective for compensating the frequency control performance of the low inertia power system. By applying the proposed method, it is also found that about 840 MW of BESS can achieve carbon neutrality in the Korean power system. [ABSTRACT FROM AUTHOR]

In order to study the temperature change law of a nitrogen pre-cooling LNG unloading pipeline, a three-dimensional numerical simulation of an LNG pipeline with a bellow expansion bend was conducted using Fluent software (2020 R2). This simulation involved progressively controlling the nitrogen injection temperature and flow rate. The results show that increasing the nitrogen flow rate can improve the pre-cooling rate and reduce the top–bottom temperature difference of the pipeline, but there is an optimal value. Under the same nitrogen injection velocity conditions, it was found that smaller pipe diameters result in smaller temperature differences between the top and bottom of the pipeline. However, due to the reduced cooling capacity of the nitrogen injection, this leads to a decrease in the pre-cooling rate. The top–bottom temperature difference of the pipeline is mainly related to the strength of the natural convection in the pipeline. The stronger the natural convection, the greater the temperature difference between the top and bottom. Gr and Gr/Re2 reflect the relative magnitude of the natural convection intensity and forced convection intensity in the pipe. The larger the Gr and Gr/Re2, the stronger the natural convection. Therefore, Gr and Gr/Re2 are positively correlated with the top–bottom temperature difference, and the variation trend of the top–bottom temperature difference can be judged by the values of Gr and Gr/Re2. [ABSTRACT FROM AUTHOR]

Advancements in new technologies, a reduction in CO2 emissions, and the rising demand for energy are causing a growth in the share of renewable energy sources. In distribution networks, an increasing number of distributed generators (DGs) makes the utility grid's protection complex and demanding. Vector surge and rate-of-change-of-frequency are the established anti-islanding protection methods, recognizing that the standard paradigm for protection, involving distributed generation, cannot be set only once but has to be continuously updated following the requirements and changes in the system. One of the requirements is active participation in the preservation of system frequency and voltage, which can be interrupted if the DG trips and disconnects from the utility grid. Anti-islanding protection and spurious tripping can be avoided by implementing new algorithms and techniques. This paper presents a novel protection scheme based on a voltage frequency differential. The proposed algorithm employs remote and local frequency measurements in such a manner that, for the occurrence of a frequency difference, it is assumed that the DG is in an islanding state. In this article, we demonstrate the feasibility of the algorithm through numerical analysis of grid events and laboratory testing emulating real grid-measured values. The test results show that the algorithm is resilient to false tripping for non-islanding events and more reliable than conventional methods in islanding detection. The algorithm can be set to low-frequency differential values, drastically reducing the non-detection zone in any DG type, regardless of its size and voltage level at the point of common coupling. Unlike standard anti-islanding methods, the algorithm supports the ability of the DG to fault-ride through demand. [ABSTRACT FROM AUTHOR]

Voltage and current measurements in high-voltage substations are fundamental for stable operation. Conventional instrument transformers (ITs) face challenges in gas-insulated switchgears (GISs), such as size, weight, accuracy limitations, and behavioral instability at abnormal voltages and currents. Non-conventional instrument transformers (NCITs) have emerged to address these issues, complying with International Electrotechnical Commission (IEC) standards and providing millivolt-level signals, enabling downsizing of GIS bays. The transition to digital substations, as mandated by IEC 61850-9-2, requires a shift from the conventional 110 V/5 A outputs to levels ranging from millivolts to volts. Electronic instrument transformers (EITs), compliant with the IEC 60044-7 and 8 standards, offer alternatives to conventional ITs with smaller sizes and wider frequency ranges. However, issues remain with EITs, including limited adoption, the necessity of separate power sources, and susceptibility to electromagnetic interference. Recent standards, transitioning to IEC 61869, focus on low-power instrument transformers (LPITs). Low-power voltage transformers (LPVTs) and low-power current transformers (LPCTs), designed with passive components, present potential solutions by directly connecting to merging units (MUs) for digital signal transmission. This review outlines the current status of various IT standards, covering conventional ITs, EITs based on IEC 60044-7 and 8, and LPITs based on IEC 61869-10 and 11. Advancements in sensor technology relevant to these standards are also explored. The paper provides insights into the evolving landscape of instrument transformers, addressing challenges and offering potential pathways for future developments in digital substations. [ABSTRACT FROM AUTHOR]

In this study, an optimized dual-layer configuration model is proposed to address voltages that exceed their limits following substantial integration of photovoltaic systems into distribution networks. Initially, the model involved segmenting the distribution network's voltage zones based on distributed photovoltaic governance resources, thereby elucidating the characteristics and governance requisites for voltages across distinct regions. Subsequently, a governance model for voltage limit exceedances, grounded in optimizing energy storage configurations, was formulated to mitigate photovoltaic power fluctuations by deploying energy storage systems. This model coordinates the reactive power output of photovoltaic installations with the active power consumption of energy storage systems, thereby augmenting voltage autonomy in the power grid. This study leveraged Karush–Kuhn–Tucker (KKT) conditions and the Big-M method to transform the dual-layer model into a single-layer linear model, thereby enhancing solution efficiency and precision. Finally, a simulation was carried out to demonstrate that the strategy proposed from this research not only achieves commendable economic efficiency, but also significantly improves the regional voltage effect by 28.7% compared to the optical storage capacity optimization model. [ABSTRACT FROM AUTHOR]

With the development of global clean energy and the implementation of carbon emission reduction policies, the direct purchase of electricity by large consumers has been increasingly promoted as a special form of electricity trading. Therefore, on the basis of the completion of low-carbon emission reduction targets in each country, how to rationalize the electricity purchase by large consumers in the electricity market so as to reduce their electricity purchase costs has become the main target of attention in each country. Currently, there are fewer studies in existing research on the direct electricity purchase strategy of large consumers under the consideration of the weight of consumption responsibility and risk. Based on this, this paper constructs a dual-game model for direct electricity purchase by large consumers based on the Stackelberg game and non-cooperative game theory. The concept of value at risk is further introduced, and the optimal strategy of direct electricity purchase by large consumers is proposed. The results of this study show that when market players make decisions on the purchase and sale of electricity, power suppliers will increase their biddings to obtain the highest returns, and large consumers can reduce the transaction costs by combining the medium- and long-term market with the spot market to purchase electricity. In the choice of electricity purchase market, with the increasing risk factor, large consumers shift from the risky spot market to the less risky medium- and long-term market and option market. This paper provides a reference for the issues of power suppliers' contract bidding and large consumers' electricity purchase strategy in the medium- and long-term contract transactions. [ABSTRACT FROM AUTHOR]