System Scenarios Research Articles

Techno-economic dataset for energy market and capacity payment co-optimization in the Dominican Republic's power market.

The electric power industry has an impact on fossil fuel consumption, which must be considered in decarbonization strategies. Energy systems optimization modelling can be applied to evaluate policy scenarios in the power sector to accelerate energy transitions. These modelling tools need data to simulate different scenarios in the power system to clarify the design of energy policies. For this reason, collecting and processing technical and economic data is needed to guarantee quality input for the modelling tools. This article presents a dataset for an optimization model of the generation mix and the energy demand in the power system of the Dominican Republic to determine the capacity value of variable renewable energy (VRE), i.e., wind and solar, that can serve as an incentive for these technologies. While the data corresponds to the Dominican Republic's power system, the method of collecting and processing data can be implemented in other countries. The data collected is an open-access database of the independent system operator, the power sector regulator, and utilities, as well as websites and databases of international organizations.

2-D deployment of aerial base stations: A simulation model to provide voice communication

Unmanned aerial vehicles (UAVs) offer a potential alternative for providing voice services in areas where communication is disrupted due to natural disasters. These UAVs can be configured as aerial base stations (ABSs), enabling the deployment of a temporary communications network. However, communication networks based on ABSs pose several significant challenges. One of these challenges involves addressing interruptions or limitations in network coverage caused by natural disasters. In such situations, there is a high likelihood that users within the affected area may be unable to communicate due to a lack of coverage. This is a complex problem because it depends on factors, such as the mobile user locations, the characteristics of the air-to-ground channel, and geographical details of the area. In this work, we propose an optimization model to determine the placement of a set of ABSs within a limited disaster area that maximizes the probability of successful voice services (PSVSs). This optimization model integrates a network evaluation model that analyzes the wireless environment at a specific time. The network evaluation model utilizes two-ray and Rayleigh channel models, enabling the simulation of a worst-case scenario for wireless communication systems. We evaluate the proposed optimization model using the (1+1)-evolution strategy with a one-fifth success rule. We explore various parameter configurations to understand their impact on algorithm performance. This analysis helps identify the configuration of the optimization model that yields the maximum PSVSs. Simulation results indicate that by appropriately configuring the evolution strategy algorithm and comparing random ABS locations with those determined ABS locations by the evolution strategy algorithm, the PSVS can be enhanced by an average of 60%.

A Generalized Autonomous Power Plant Fault Detection Model Using Deep Feature Extraction and Ensemble Machine Learning

Ensuring operational reliability and efficiency in steam power plants requires advanced and generalized fault detection methodologies capable of addressing diverse fault scenarios in boiler and turbine systems. This study presents an autonomous fault detection framework that integrates deep feature extraction through Convolutional Autoencoders (CAEs) with the ensemble machine learning technique, Extreme Gradient Boosting (XGBoost). CAEs autonomously extract meaningful and nonlinear features from raw sensor data, eliminating the need for manual feature engineering. Principal Component Analysis (PCA) is employed for dimensionality reduction, enhancing computational efficiency while retaining critical fault-related information. The refined features are then classified using XGBoost, a robust ensemble learning algorithm, ensuring accurate fault detection. The proposed model is validated through real-world case studies on boiler waterwall tube leakage and motor-driven oil pump failure in steam turbines. Results demonstrate the framework’s ability to generalize across diverse fault types, detect anomalies at an early stage, and minimize operational downtime. This study highlights the transformative potential of combining deep feature extraction and ensemble machine learning for scalable, reliable, and efficient fault detection in power plant operations.

Intelligent Bayesian Neural Networks for Stochastic SVIS Epidemic Dynamics: Vaccination Strategies and Prevalence Fractions with Wiener Process

Real-time forecasting of infectious diseases is crucial for effective public health management, particularly during outbreaks. When infectious disease predictions are based on mechanistic models, they can guide resource allocation and help evaluate the potential effects of different interventions. However, accurately parametrizing these models in real time presents a challenge, as timely information on behavioral shifts, interventions and transmission pathways is often lacking. This investigation leverages the artificial neural networks with the Bayesian regularization (BR-ANN) backpropagation approach to examine the dynamical pathogen spread with Wiener process incorporation. The stochastic differential model is structured into susceptible, vaccinated, infectious and susceptible (SVIS) compartments. The Kloeden–Platen–Schurz (KPS) computing paradigm for the stochastic differential system is utilized to generate synthetic datasets by applying transformations to key factors, including the population recruitment ratio, transmission ratio of vulnerable individuals, natural death rate of the population, vaccination rate of vulnerable individuals, total population size, immune loss ratio of susceptible individuals, recovery rate and mortality rate from the disease among infected individuals. Random selection from the generated datasets is exploited for the training and testing procedures for constructing the BR-ANN networks. The significance of the proposed scheme for various stochastic SVIS system scenarios is endorsed by the comprehensive assessments of the BR-ANN approach that are conducted by means of extensive experimentations and comparison with the reference KPS solutions of the SVIS system in terms of MSE optimal performance plots, absolute errors, autocorrelation analysis, regression indices and error histograms.

Hydrogen production with grid-connected electrolysis: scenario-based analysis of the EU criteria for renewable fuels

Abstract Renewable hydrogen (H2) will play a pivotal role in the decarbonization of the energy and industrial sectors. However, during the transition to a clean energy system, the production of H2 with electrolysis runs the risk of increasing carbon dioxide (CO2) emissions if the electricity system is still partly based on fossil fuels. The European Union has set ambitious targets for the production of H2 and defined strict rules in delegated acts to the renewable energy directive, when H2 produced with public grid electricity can be counted as renewable. This paper analyzes two grid criteria central to these rules, renewable energy share and CO2 emission intensity, in several future scenarios of the European energy system. By uniquely focusing on the impact of H2 production from the perspective of EU grid criteria, this study offers a novel assessment of how these regulations interact with the evolving energy landscape. Fulfillment of the renewable H2 grid criteria strongly depends on the future build-out of renewable energy resources, electricity demand, and amount of domestically produced H2. In a scenario with ambitious renewable build-out until 2030, represented by current drafts of national energy and climate plans, many countries will meet the stated criteria. However, adding a high amount of domestically produced H2 partly cancels out this effect. In a scenario with reduced renewable build-outs, comparable to historically achieved renewable resource additions, many fewer countries achieve the grid criteria. Finally, net CO2 emission reductions are analyzed by comparing power sector emission changes with the opportunity emissions that result from fossil fuels replacements with H2. The results indicate that using H2 in CO2 intensive use cases can lead to emission reductions, even if grid criteria are below the thresholds defined in the delegated acts. However, reduced renewable energy expansion poses the risk of not achieving any emission reductions at all with the produced H2.

High-power high-performance radio-over-fiber link for minimalist wireless base station.

Broadband minimalist wireless base stations without energy-consuming electrical power amplifiers are the rosy scenario of the next-generation wireless communication systems. High-power radio-over-fiber (RoF) links, which are featured by large operation bandwidths, are regarded as the supporting technology for realizing such a vision. Nevertheless, the severe signal-to-noise ratio (SNR) deterioration induced by the second Brillouin scattering in high-power and long-distance RoF links must be first solved. Here, an approach to suppressing the secondary Brillouin scattering-induced SNR deterioration in high-power long-distance RoF links is proposed and demonstrated based on phase modulation driven by a sinusoidal signal. In the proof-of-concept experiment, the Brillouin threshold in a high-power RoF link involving a spool of single-mode fiber (SMF) with a length of 10 km is increased by more than 5 dB with the assistance of phase modulation. The SNR is improved by 31 dB when the optical power injected into the SMF is 22 dBm. In addition, the error vector magnitude (EVM) is optimized from 31.71% to 0.62% for transmitting a 256-QAM wireless communication signal with a center frequency of 5 GHz and a bandwidth of 6.75 MHz in this RoF link under the injected optical power of 22 dBm. Based on this high-power RoF link, a minimalist wireless base station utilizing a photodetector-direct-drive antenna is realized. This scheme is feasible for transmitting broadband wireless communication signals, where a 64-QAM orthogonal frequency division multiplexing (OFDM) wireless communication signal with a center frequency of 10 GHz and a bandwidth of 400 MHz has achieved high-quality transmission in this high-power RoF link under an injected optical power of 22 dBm.

Development and validation of spatial disorientation scenarios using virtual reality and motion simulator.

Spatial Disorientation (SD) can cause critical aviation accidents by adversely affecting the pilot's ability to perform a flight mission. One of the strategies to improve pilots' ability to deal with SD is to perform SD training using Virtual Reality and Motion Simulator (VRMS) system. However, there is still a lack of studies that investigated the application of VRMS for SD training. Thus, the purpose of this study is to develop and validate VRMS-based SD scenarios. Twenty-two male Air Force fighter pilots (11 in the low experienced group and 11 in the high experienced group) participated in a controlled experiment in which they performed the flight task under two conditions (SD flight, non-SD flight), wherein the SD flight condition included the induction of four distinct SD illusions. Findings showed that the developed SD scenarios in the VRMS system effectively induce SD. More specifically, there were differences in the influence of flight experience and SD condition on pilots' flight performance and workload. This study suggests that the VRMS system can demonstrate several types of SD scenarios effectively and has a huge potential to be utilized as an SD training tool to improve overall flight safety.

Processing and inspection of high-pressure microfluidics systems: A review.

In the field of microfluidics, high-pressure microfluidics technology, which utilizes high driving pressure for microfluidic analysis, is an evolving technology. This technology combines microfluidics and pressurization, where the flow of fluid is controlled by means of high-pressure-driven devices greater than 10 MPa. This paper first reviews the existing high-pressure microfluidics systems and describes their components and applications. Then, it summarizes several materials used in the microfabrication of high-pressure microfluidics chips, reviewing their properties, processing methods, and bonding methods. In addition, advanced laser processing techniques for the microfabrication of high-pressure microfluidics chips are described. Last, the paper examines the analytical detection methods employed in high-pressure microfluidics systems, encompassing optical and electrochemical detection methods. The review of analytical detection methods shows the different functions and application scenarios of high-pressure microfluidics systems. In summary, this study provides an efficient and advanced microfluidics system, which can be widely used in chemical engineering, food industry, and environmental engineering under high pressure conditions.

Comprehensive benefit optimization method for photovoltaic inverters participating in distribution network loss reduction by reactive compensation

When a large number of distributed photovoltaic (PV) systems are integrated into the distribution network, power flow becomes bidirectionally fluctuating, resulting in variable line losses. In the scenario of reverse flow, the increase in line loss is particularly significant. The bidirectional reactive power regulation of photovoltaic inverters is an effective approach to reduce losses in the distribution network. However, despite the benefits of reducing losses, reactive power regulation by photovoltaic inverters also incurs additional costs. Therefore, there is a need for research into a comprehensive benefit optimization method for inverters participation in reducing reactive power losses in distribution networks. Firstly, the cost quantification models for the investment, transformation, operation, and lifespan loss of the photovoltaic inverters involved in reactive power loss reduction are established. Secondly, the benefit quantification models for loss reduction and power factor improvement are developed. Thirdly, considering various operational scenarios of both photovoltaic systems and loads, a comprehensive benefit optimization method for photovoltaic inverters participating in reactive power loss reduction in distribution networks is proposed. Finally, through example analysis, the cost and benefit are calculated and fitted in different scenarios, and the optimization calculation is carried out. Compared to the scenario where the photovoltaic inverter operates at the maximum reactive power regulation capacity, the optimized comprehensive benefit is increased by 21.20%. The proposed method is validated to effectively enhance the comprehensive benefits of inverters participation in reactive power loss reduction.

Midwave infrared resonant cavity detectors with >70% quantum efficiency

We report resonant cavity infrared detectors with a peak wavelength of 4.54–4.58 μm that combine external quantum efficiency (EQE) exceeding 70% with spectral bandwidth 20–40 nm and ≤2% EQE at all non-resonance wavelengths between 4 and 5 μm. A 300-nm-thick absorber assures that most of the radiation propagating in the cavity produces photocurrent rather than parasitic loss. The cavity is formed by heterogeneously bonding a midwave infrared (MWIR) nBn detector chip to a GaAs/AlGaAs distributed Bragg reflector, etching away the GaSb substrate, forming mesas with diameter ≈100 μm, depositing a Ge spacer, and then depositing a single-period Ge-SiO2 top mirror. At all temperatures between 125 and 300 K, the responsivity at 150 mV bias exceeds 2.2 A/W and the EQE exceeds 61%. When the thermal background current for a realistic system scenario with f/4 optic that views a 300 K scene is derived from the observed EQE spectra, the resulting specific detectivity D* of 7.5 × 1012 cmHz½/W at 125 K operating temperature is 4.5 times higher than for a state-of-the-art broadband MWIR HgCdTe device. Simulations of the cavity performance indicate that EQE > 90% may be feasible following minimization of parasitic optical loss and maximization of the photocarrier collection efficiency. Potential applications include free space optical communication, chemical sensing, on-chip spectroscopy, and hyperspectral imaging.

Prospective life cycle assessment of climate and biodiversity impacts of meat-based and plant-forward meals: A case study of Indonesian and German meal options.

The emerging field of prospective life cycle assessment (pLCA) offers opportunities for evaluating the environmental impacts of possible future consumption shifts. One such shift involves a transition from meat-based to plant-forward diets, acknowledged to mitigate environmental impacts of the food system under present day conditions. Current diets are often meat intensive ("meat-based"), whilst "plant-forward" diets include mainly plant-based foods, encompassing flexitarian, vegetarian, and vegan diets. Here we illustrate the application of pLCA in a case study of meal options, implementing shared socio-economic pathway scenarios in the LCA background system to represent future production conditions. We assess the climate footprints and land-based biodiversity footprints of a typical meat-based meal in Germany and Indonesia compared to a plant-forward meal in both countries (i.e., four meals), now and in 2050. Our findings show that the plant-forward alternative maintains a lower impact per serving in all future scenarios. At the same time, the reduction in impact for the meat-based meals is more pronounced in future scenarios due to shifts in the agricultural system. Our findings highlight the importance of supply-side measures to produce lower-impact ingredients, complementing demand-side interventions to reshape food consumption. Results are further evaluated in cultural and nutritional contexts, highlighting the practical decision-making constraints faced by consumers. We find potential "leakage" effects in calories and nutrition when choosing a lower-impact, plant-forward meal. These leakage effects should be considered in future studies seeking to evaluate the environmental implications of meal substitutions in the context of broader dietary requirements.

The Role of Thermal Energy Storages in Future Smart Energy Systems

This paper conducts an in-depth energy systems analysis on the role of thermal energy storages in Denmark's transition to a fully decarbonized Smart Energy System. Using the EnergyPLAN software and national-scale energy system scenarios, the research examines how the use and impact of thermal energy storages evolves during this transition. Findings indicate that thermal energy storages play an important role in minimizing fuel consumption, curtailing losses, and in improving the overall energy-efficiency and balance of supply and demand. Initially, it primarily lowers fossil fuel use, potentially by 3 TWh per year. As renewable energy increases in the system, its main focus shifts towards reducing excess electricity via power-to-heat and conserving biomass, cutting up to 1 TWh of excess electricity annually through added flexibility. Variable system costs potentially decrease by 17-67 million EUR yearly, though economic feasibility depends on the phase of the transition when investment costs are included. In a future smart- and fully decarbonized system, the economic feasibility is heavily affected by energy prices along with other heat- and storage alternatives and flexible consumption. This leads to the novel understanding that the role of thermal energy storage changes along with the transition of the energy system.

Research on Directional Elements of Two-Terminal Weak-Feed AC Systems with a Negative Sequence Control Strategy

It has become a typical scenario in power systems that renewable energy power supply is connected to an AC system through flexible DC transmission. However, since both sides of the AC line are power electronic converters, the negative sequence suppression strategy will be put into the converters at both ends during the asymmetric fault, which causes fundamental changes in the fault characteristics of the system, which is reflected in the two-terminal weak-feed characteristics, leading to the decline of traditional protection performance and affecting the safe operation of the system. Therefore, this paper presents a directional element of a double-ended weakly fed AC system with a negative sequence control strategy. Firstly, the characteristics of the negative sequence impedance under the negative sequence suppression strategy are analyzed when the AC line has asymmetric faults. Secondly, the difference in negative sequence impedance amplitude is analyzed. Finally, the direction element is constructed by the method of de-wave trend analysis The proposed scheme can realize the rapid identification of fault directions at both ends. The simulation results show that the proposed scheme is suitable for a two-terminal weak-feed AC system and can operate reliably under 300 Ω transition resistance and 20 dB noise interference.

Robust Generalized Super‐Twisting Sliding Mode Control Design for Optimal Performance in Three‐Phase Grid‐Connected Photovoltaic Systems

ABSTRACTThis article introduces a third‐order super‐twisting sliding mode control (Gen‐STSMC) algorithm designed for the secure operation of a grid‐connected photovoltaic (PV) system. The improved method changes the traditional super‐twisting sliding mode control (STSMC) technique by incorporating both a linear term and a higher power term to reduce convergence time and chattering effects. This strategy generates the optimal control signals to regulate the output power of the PV system by compensating the state‐dependent uncertainties using the linear and growing term. The controller's stability analysis is conducted to demonstrate its rapid convergence compared to PI and conventional STSMC scheme. A comparative simulation study against PI and STSMC is conducted to showcase the controller's rapid convergence and robust performance in various grid‐connected PV system scenarios. Experimental tests are also performed to validate the controller's effectiveness in maintaining stable power generation in the presence of environmental fluctuations and DC link voltage variations.

Towards 6G vehicular networks: Vision, technologies, and open challenges

As an essential method to meet the communication requirements of intelligent transportation and automated driving, vehicular networks facilitate intelligent interaction and cooperation among vehicles and pedestrians, vehicles, infrastructures, and cloud platforms. Enabled by 4G and 5G communications, cellular vehicle to everything (C-V2X)-based vehicular networks have been widely applied in safety and efficiency improvement scenarios of intelligent transportation systems (ITS). However, the development of autonomous driving and ITS technologies requires the support of lower latency and more reliable communications to realize advanced driving applications such as platooning and sensor-sharing, which raises higher requirements for the performance of vehicular networks. Towards 2030 and beyond, 6G communications will construct intelligent connections for everything and provide the ultimate performance, thus 6G-enabled vehicular networks have been widely researched. This paper surveys and categorizes a comprehensive array of existing studies on the intersection of 6G and vehicular networks. Drawing insights from these studies, we delineate the evolutionary trajectory of 6G and vehicular networks, outlining a visionary perspective on their forthcoming development. Furthermore, we summarize and analyze the existing studies from the perspective of enabling technologies such as communication, intelligence, and security. Finally, by analyzing the characteristics of the existing works, we provide the future challenges and potential research directions for 6G vehicular networks.

Case Studies of Battery Energy Storage System Applications in the Brazilian Transmission System

This paper presents the preliminary results of studies aiming to use a battery energy storage system (BESS) in the Brazilian transmission system. The main objective of the BESS is to solve congestion problems caused mainly by the large increase in variable renewable generation in certain system areas. The studies were conducted based on actual forecasted system scenarios using a full representation of the electric grid available from the Brazilian system operator data base. In this work, only the steady-state modeling was considered as this may be the first stage in the assessment of a new technology. A qualitative economic comparison of the BESS application with other possible solutions to the congestion problems is also included.

Research on Animation VR System Scenario Considering High-Precision Line of Sight Tracking Algorithm

Research on Animation VR System Scenario Considering High-Precision Line of Sight Tracking Algorithm

Set pair analysis and system dynamics coupling approach for structure simulation and variation trend evaluation of water resources spatial equilibrium system

AbstractThe implement of water resources spatial equilibrium (WRSE) schemes is fundamental task of integrated water resources management in China, in which, the evaluation and simulation analysis of WRSE system is of great concern for understanding the overall variation and feedback characteristics of WRSE system. Therefore, we utilized the ordered degree, entropy and connection number coupling model to evaluate the variation of WRSE system, and also employed system dynamics (SD) and scenario simulation integrated method to reveal the feedback characteristics between different equilibrium variables and subsystems, and thus the set pair analysis‐SD based approach for structure simulation and variation trend evaluation of WRSE system was constructed. The application results in Anhui province, China demonstrated that, the overall variation of provincial spatial equilibrium situation of WRSE system presented obvious improving trend, 2009–2019, the index of ordered degree and connection number entropy in Hefei, increased from the minimum of 0.6434 (Grade 3, unequilibrium) to maximum of 0.9985 (Grade 1, equilibrium). Moreover, the future variation of WRSE system will display stable improving trend during 2020 to 2029, in which, annual water resources availability and water resources utilization efficiency will have significant influence on WRSE system. And the research findings can be favorable to formulate water resources development and utilization strategies.

Parametric Optimization Approach to Evaluate Dynamic Shading Within Double-Skin Insulated Glazed Units for Multi-Criteria Daylighting Performance in Tropics

This research aims to support the choice of an appropriate dynamic louver shading system (DL-SS) within double-skin facade insulated glazed units (DSF-IGUs) as a high-performance integrated window system (DSF-IGUs/DL-SS) that meets both thermal and energy performance via daylight availability under a tropical climate. The research framework has developed a multi-objective optimization method to achieve research objectives via optimizing two different scenarios of the proposed system. The first scenario was optimized for daylighting availability, meanwhile, the second scenario was optimized for energy and thermal performance. For each scenario, the best solutions are selected from respective Pareto fronts according to energy efficiency criteria, thermal comfort via enhancing daylighting availability. Based on the best options resulting from both optimizations, the final step involved comparing the results of all performance indicators in the best cases to select the best solution. Overall, based on the optimizing objectives, the ranking of the best cases varied based on giving priority to the improvement objective in the optimization process. For each scenario, the best solutions are selected from the respective Pareto fronts. Overall, ranking of the best cases varied based on giving priority to the improvement objectives. Optimizing DL-SS within DSF-IGUs while giving priority to improving energy and thermal comfort while maintaining daylighting at acceptable levels is more reasonable. Thus, the DSF-IGUs/DL-SS best-case resulting from the second optimization scenario was overcome all best cases and ranked first in energy and thermal comfort. Compared to the base case, the differences of total Predicted mean vote and percentage of dissatisfied for better thermal comfort achieved were -0.35% and -1.48% with an average decreased by 22.99% and 28.72%, respectively. The differences of total energy and cooling load for better energy performance reduced by -96.84 kwh/m2. and -86.88 kwh with an average decreased by 25.33% and 26.20%, respectively. Meanwhile, the total satisfied of spatial Daylight Autonomy for better daylighting distribution and better daylighting availability of useful daylighting illuminance improvement were improved by -5.54% and +24.76% with an average percentage variation increased by 6.25% and 36.87%, respectively.

Fault detection research on novel transfer learning-based method for cross-condition, cross-system and cross-operation in public building HVAC sensors

Transfer learning (TL) has the inspiring potential for artificial intelligence in heating, ventilation and air conditioning (HVAC) system with insufficient data labels. However, traditional TL-based methods are limited when applied across different conditions, systems, and operations.Unfortunately, public building HVAC systems encounter challenges related to data acquisition and richness, making it difficult to obtain data from similar HVAC systems conditions, scenarios and operations. It proposes a novel TL-based method that combines energy and mass balance constraint equation (EBCe) to diagnose the sensor faults in HVAC systems across different systems, conditions and operations.Firstly, it utilizes laboratory data as the source domain data and constructes EBCe based on the common physical laws of HVAC system to reduce the data differences between laboratory and public buildings. Then, an laplacian kernel domain-adaptive neural network (LkDaNN) is proposed to generalize more efficiently feature differences between the source domain data and target domain data. Finally, experiment analyzes the non-fault and four control-sensors fault under both cross-operation and non-cross operation conditions. The experimental results demonstrate that the EBCe-LkDaNN method achieves satisfactory fault detection and diagnosis (FDD) performance.The overall FDD accuracy of porposed method can reach 90.72 % and 88.64 % under different cross-operation, respectively. Practical application of the EBCe-LkDaNN strategy for HVAC sensor FDD are discussed at last.