Peak Current Research Articles

Hourly cooling demand prediction through a bottom-up model in London

ABSTRACT In the context of global warming, domestic cooling demand in the UK, particularly in London, is expected to rise significantly, posing challenges for an energy system traditionally focused on heating. This paper presents a high-resolutison, bottom-up model to predict hourly cooling demand in London by integrating building simulation, inventory data, and meteorological projections. The model categorizes domestic buildings into four main types and assigns different insulation levels based on building age, generating 12 distinct cooling demand profiles across London’s 33 boroughs. The model forecasts a 45% increase in cooling demand by 2050, with peak electricity demand for cooling potentially doubling residential electricity usage in 2020. This study further reveals significant geographical and temporal variations and analyses the impact of increased cooling demand on London’s electricity grid. The analysis of cooling duration curves highlights the low utilization of cooling equipment during non-peak periods, emphasizing the inefficiency in current systems. To mitigate peak demand, energy storage systems are proposed, with simulations suggesting that up to 50% of peak demand can be reduced, improving grid stability. The findings underscore the importance of not only accounting for future cooling demand but also optimizing equipment utilization and planning cooling infrastructure in urban areas.

Cloud to surface lightning activity in the Eastern Mediterranean Sea in the context of marine infrastructure safety

Lightning poses a significant threat to safety at sea and to marine infrastructure. Lightning protection systems are designed to withstand currents <200 kA; however, the theoretical limit of lightning intensity is 2–3 times higher. Lightning bolts with peak currents (PCs) greater than 100 kA, also known as Lightning Superbolts (LSBs) occur predominantly over the oceans, including the Eastern Mediterranean Sea. In this study we analyzed the cloud to sea surface lightning activity in the Israeli Mediterranean Exclusive Economic Zone (IMEEZ) based on two global lightning detection networks and Israeli national detection network. The main findings of this survey indicate that 0.4% (n = 557) of the lightning strikes in the IMEEZ have a PC strength significantly higher than 200 kA with median and average PCs of 248 and 298 ± 122 kA (±1STD), respectively. The spatial distribution of the LSBs in the IMEEZ is relatively homogeneous. Considering that seawater salinization and acidification may increase lightning intensity over the IMEEZ by 25% until 2050, it is clear that the risk to marine infrastructure in the region will increase and appropriate steps should be taken to upgrade their lightning protection systems to withstand a PC of 400 kA, which is one STD above the calculated mean.

A System Control and Waveform Acquisition Framework for the Kicker System at SHINE

A lumped kicker magnet system is being developed as part of the electron beam switchyard of the Shanghai High Repetition Rate X-Ray Free-Electron Laser (XFEL) and Extreme Light (SHINE) facility at Shanghai Advanced Research Institute (SARI), Chinese Academy of Sciences (CAS). SHINE is a linac-based facility with an energy of 8 GeV and a repetition rate of 1 MHz for multiple users. The kicker system is a critical element of the deflection system and determines the stability performance of the beamlines. To ensure autonomous and reliable operation in a harsh environment, a dedicated control system—consisting of a custom-designed programmable logic controller (PLC) and waveform acquisition system—has been developed based on the Experimental Physics and Industrial Control System (EPICS). The PLCsystem enables status acquisition, control implantations, and alarm handling, allowing for automated operation. The waveform acquisition system provides an intuitive demonstration and enables the real-time diagnosis of kicker waveforms. The results demonstrate a half quasi-sinuous waveform with a peak current of 11.5 A and a full width at half maximum of 218 ns at a repetition rate of 10 kHz. The detailed design, overall concept, achieved performance, and initial test results of two modular controls are provided for further demonstration.

TCAD-Based Analysis on the Impact of AlN Interlayer in Normally-off AlGaN/GaN MISHEMTs with Buried p-Region

With the growing demand for more efficient power conversion and silicon reaching its theoretical limit, wide bandgap semiconductor devices are emerging as a potential solution. For instance, Gallium Nitride (GaN)-based high-electron-mobility transistors (HEMTs) are getting more attention, and several structures for the normally off operation have been proposed. Adding an AlN interlayer in conventional AlGaN/GaN normally on HEMT structures is known to enhance the current density. In this work, the effect of an AlN interlayer in the normally off AlGaN/GaN MISHEMT with a buried p-region was investigated using a TCAD simulation from Silvaco. The added AlN interlayer increases the two-dimensional electron gas density, requiring a higher p-doping concentration to achieve the same threshold voltage. The simulation results show that the overall effect is a reduction in the device’s current density and peak transconductance by 21.83% and 44.4%, respectively. Further analysis of the current profile shows that because of the buried p-region and at high gate voltages, the current flows near the AlGaN/GaN interface and along the insulator/AlGaN interface. Adding an AlN interface blocks the migration of channel electrons to the insulator/AlGaN interface, resulting in a lower current density.

Enhancing Sulfinpyrazone determination via Electrochemistry: Development of a S-Doped CoFe2O4 Modified Sensor

Abstract An electrochemical sensor for the electrochemical determination of Sulfinpyrazone (SFP) was developed based on ferrite, a Sulphur-doped Cobalt Ferrite (S- CoFe2O4), functionalized glassy carbon electrode (GCE). SFP displayed remarkably increased electrochemical activity on the S-CoFe2O4 modified GCE as the modifier enhances the surface area and electrocatalytic activity. Characterization of the electrode modifier was carried out through XRD and SEM-EDS analyses. Cyclic voltammetry (CV) and square wave voltammetry (SWV) were utilized to explore SFP analysis. The optimum immersion time for the study was found to be 20 seconds. An enhanced oxidation of SFP was noticed at a pH of 7.4 for the investigation between the potential range of 0 to 1.0 V. The process displayed irreversible and diffusion-controlled nature. Under ideal experimental conditions, the oxidation peak current of SFP showed a direct proportionality to its concentration within the range of 1.0 to 10 µM, with a detection limit of 2.45 nM. This sensor effectively determines the determination of SFP in pharmaceutical products, yielding satisfactory results in terms of recovery, stability, and sensitivity. This approach creates opportunity for exploration in the electrochemical investigation of SFP. These findings suggest that the constructed sensor could be useful in pharmacokinetic studies and quality control labs.

Electrochemical Determination of Hydrazine Based on Modified Screen-Printed Carbon Electrode

Abstract A new strategy for the electrochemical sensing of hydrazine based on Ni-Fe layered double hydroxide nanosheets modified screen-printed carbon electrode (Ni-Fe LDH NSs/SPCE) was studied. The techniques such as differential pulse voltammetry (DPV), cyclic voltammetry (CV), and chronoamperometry were utilized to evaluate the application of Ni-Fe LDH NSs modified SPCE for hydrazine determination. The results from CV exhibit that Ni-Fe LDH NSs/SPCE can significantly increase the oxidation peak current of hydrazine and also reduce the required over-potential. The quantitative determination of hydrazine was performed by DPV technique. This sensor showed a linear response to hydrazine in the concentration range of 0.05 to 670.0 µM. The limit of detection for hydrazine based on the modified SPCE was 0.02 µM. Furthermore, the Ni-Fe LDH NSs/SPCE platform presented satisfactory recoveries and reliabilities in river and well water samples, indicating the application potential of this developed electrochemical sensor for hydrazine determination in water samples.

Development and Validation of a Capacitor–Current Circuit Model for Evaporation-Induced Electricity

Evaporation-induced electricity is a promising approach for sustainable energy generation which is particularly suited for off-grid and Internet-of-Things (IoT) applications. Despite significant progress, the mechanism of electricity generation remains debated due to complex factors. In this study, we introduce a simplified capacitor–current circuit model to describe the behavior of evaporation-induced electricity. The primary objective of this work is to provide a framework for understanding the transient and steady-state behavior of this phenomenon. We validated this model using experimental data from wood-based nanogenerators with citric acid modified microchannels. The fitting results revealed a steady-state current of approximately 9.832 μA and an initial peak current of 16.168 μA with a time constant of 621.395 s. These findings were explained by a hybrid model incorporating a capacitor and current source components, and subsequent discharge through internal resistance. This simplified model paves the way for better understanding and optimization of evaporation-induced electricity, highlighting potential improvements in device design for enhanced performance. While improving device performance is beyond the scope of this study, the insights gained from this model offer a foundation for future optimization and the enhanced performance of evaporation-induced electricity generation devices.

Influence of SiC MOSFET Drive Control Parameters on Short Circuit Characteristics

This paper focuses on the short-circuit characteristics of SiC MOSFETs. The SiC MOSFET, categorized as a third-generation wide bandgap power semiconductor, shows significant promise for use in high-voltage applications. Short-circuit faults are categorized into hard-switch short circuits and load short circuits. The drive parameters, including Gate Resistance, Gate-Source Voltage, and DC Bus Voltage, significantly affect the short-circuit characteristics. Increasing Gate Resistance can slow down the rise rate and peak value of short-circuit current, reducing the risk of device damage, although too much Gate Resistance will slow down the switching speed. Raising the Gate-Source Voltage increases the short-circuit current peak and accelerates the rise time, but too high a Gate-Source Voltage increases the risk of device damage. The DC Bus Voltage does not have a significant effect on the short-circuit current but primarily influences the Gate-Source Voltage. Studying the influence of drive parameters on short-circuit characteristics is crucial for optimizing design and improving system stability

Optimizing power grids: A valley-filling heuristic for energy-efficient electric vehicle charging.

The expansion of electric vehicles (EVs) challenges electricity grids by increasing charging demand, thereby making Demand-Side Management (DSM) strategies essential to maintaining balance between supply and demand. Among these strategies, the Valley-Filling approach has emerged as a promising method to optimize renewable energy utilization and alleviate grid stress. This study introduces a novel heuristic, Load Conservation Valley-Filling (LCVF), which builds on the Classical and Optimistic Valley-Filling approaches by incorporating dynamic load conservation principles, enabling better alignment of EV charging with grid capacity. We conducted a comprehensive analysis of the heuristic across five EV charging scenarios. In both the Original and Flexible scenarios, LCVF reduced energy demand by up to 10.65%, demonstrating its adaptability and effectiveness. Notably, in the 24-hour Availability scenario, LCVF achieved a reduction of over 20% in energy demand compared to CVF. These findings indicate that LCVF could play a crucial role in enhancing real-world EV charging infrastructure, boosting energy efficiency and grid stability. By integrating DSM strategies like LCVF, energy grids can better accommodate renewable energy sources, promoting more sustainable operations.

Two-stage multi-objective framework for optimal operation of modern distribution network considering demand response program.

To improve the inadequate reliability of the grid that has led to a worsening energy crisis and environmental issues, comprehensive research on new clean renewable energy and efficient, cost-effective, and eco-friendly energy management technologies is essential. This requires the creation of advanced energy management systems to enhance system reliability and optimize efficiency. Demand-side energy management systems are a superior solution for multiple reasons. Firstly, they empower consumers to actively oversee and regulate their energy consumption, resulting in substantial cost savings by minimizing usage during peak hours and enhancing overall efficiency. The Demand Response Program (DRP) and optimal power sharing have gained significant attention to provide technical and economic benefits, while they require an efficient operation framework. Therefore, a two-stage framework is proposed for multi-objective operation of a distribution network with several generation resources. The first stage applies DRP to maximize the distribution network operator's (DNO) profit by optimizing common incentive rate for all consumers participate in DRP and an individual curtailed power for each consumer. In addition to an individual incentive rate for each consumer participates in DRP which is a new solution in the field of demand side management. The second stage achieves optimal power sharing among generation resources, while considering multiple objectives and incorporating the modified load of the first stage. The multi-objective problem is formulated to reduce energy losses, voltage deviation, total operational cost, gas emissions, and maximize the voltage stability index. The problem is optimized using a combination of the technique for order of preference by similarity to ideal solution (TOPSIS) and the elephant herding optimization (EHO) technique. The framework is validated using a modified IEEE 33-bus that incorporates photovoltaic system, diesel generators, and wind generation system. The proposed framework based on an individual incentive rate DRP provides superior response compared to common incentive rate DRP which reduces the total energy losses by 38.13%, reduces the total generation cost by 9.468%, and reduces the emission by 5.9%.

Electrochemical Sensor Based on Co-MOF for the Detection of Dihydromyricetin in Ampelopsis grossedentata.

Dihydromyricetin (DMY), as the main active ingredient in Ampelopsis grossedentata, is a naturally occurring flavonoid that has attracted extensive attention for its multiple biological activities. For the quick and accurate measurement of DMY, a novel electrochemical sensor based on a glassy carbon electrode (GCE) modified with a cobalt metal-organic framework (Co-MOF) was proposed in this work. The Co-MOF was synthesized via a single-step hydrothermal process using Co(NO3)2·6H2O. Fourier infrared spectroscopy, X-ray photoelectron spectroscopy and scanning electron microscopy were used to study the morphology and structure of the synthesized Co-MOF. Utilizing differential pulse voltammetry and cyclic voltammetry methods, the effectiveness of DMY electro-oxidation on the Co-MOF/GCE was examined. The results showed that, in comparison to the bare GCE, the electro-oxidation peak current of DMY was considerably increased by the Co-MOF/GCE. The detection limit was 0.07 μM, and the peak current demonstrated two linear relationships in the ranges of 0.2-20 μM and 20-100 μM, with the linear equations of Ip (μA) = 0.4729c (μM) + 1.0822 (R2 = 0.9913) and Ip (μA) = 0.0939c (μM) + 8.4178 (R2 = 0.9971), respectively. The average DMY content in Ampelopsis grossedentata samples was measured to be 3.275 μM, with a good recovery of 108.27% and a relative standard deviation value of 3.46%. The proposed method is simple, rapid and sensitive and can be used for the determination of DMY in Ampelopsis grossedentata.

Predictive Energy Demand and Optimization in Metro Systems Using AI and IoT Technologies

Introduction: With the rapid urbanization of modern cities, metro systems have become indispensable for efficient mobility. However, the increasing demand for public transportation has led to rising energy consumption, posing significant challenges for operational sustainability. Current energy management strategies in metro networks rely on static models and centralized systems, which often fail to adapt to real-time fluctuations in energy demand, leading to inefficiencies and wasted resources. Methods: This paper proposes an innovative approach to optimizing energy demand in metro systems by integrating Artificial Intelligence (AI) and the Internet of Things (IoT). By leveraging real-time data collected from IoT sensors deployed throughout the metro network, we apply machine learning algorithms such as Random Forests and Neural Networks to dynamically predict energy demand. These predictions enable metro operators to adjust energy consumption in real-time, thus improving overall system efficiency and reducing operational waste. Our approach was validated using data from the Parisian metro system through extensive simulations. Results: The results of simulations demonstrate significant improvements in energy efficiency. Optimized energy demand management led to a reduction in wasted energy during metro operations, particularly through the utilization of regenerative braking systems. Conclusions: Our findings suggest that integrating AI and IoT technologies into metro systems significantly improves energy efficiency by enabling dynamic energy demand prediction and real-time adjustment of energy consumption. The proposed system is scalable and adaptable, making it suitable for application in metro networks globally, thereby enhancing energy efficiency and supporting sustainable transport initiatives.

Prussian blue-doped CaCO3 nanoparticle-labeled secondary antibodies for electrochemical immunoassay of interleukin-6 with migraine patients.

The sensitive and accurate identification of interleukin-6 (IL-6) in biological fluids is essential for assessing migraine due to its role in different physiological and pathological processes. In this study, we designed a simple and feasible electrochemical immunosensing method for the voltammetric measurement of IL-6. The electrochemical immunosensor was fabricated through covalent conjugation of anti-IL-6 capture antibodies on the glassy carbon electrode with a typical carbodiimide coupling method. Anti-IL-6 secondary antibodies were labeled on the surface of Prussian blue-doped CaCO3 nanoparticles (PBCaNP) via the epoxy-amino reaction. The assay was carried out with a sandwich-type immunoreaction. In the presence of target IL-6, the analyte was sandwiched between the capture antibody and detection antibody. Thereafter, the carried PBCaNP accompanying the secondary antibody could be detected by using square wave voltammetry (SWV). The voltammetric peak current was dependent on the concentration of target IL-6. Under optimum conditions, the electrochemical immunosensor exhibited good analytical properties, and allowed detection of IL-6 within a wide linear range from 0.1 to 1000 pg mL-1. The limit of detection was estimated to be 0.078 pg mL-1 of IL-6 at the 3sB criterion. An intermediate reproducibility of ≤10.59% was accomplished with batch-to-batch identification, and good anti-interference capacity against other biomolecules was achieved. Importantly, clinical human serum samples obtained from 15 migraine patients were analyzed with the developed electrochemical immunosensors, giving results well-matched with those obtained from the referenced enzyme-linked immunosorbent assay (ELISA) method.

Microscopic analysis of the destruction of passive film on stainless steel caused by sulfide in simulated cooling water

Abstract Sulfide often appears in circulating cooling water due to the presence of sulfate reducing bacteria and could affect corrosion behavior of cooling pipe metals such as stainless steel. Scanning Kelvin probe and scanning electrochemical microscope measurements, combined with electrochemical testing, were used to investigate the micro-electrochemical information of passive film and analyzed the influence of sulfide in simulated cooling water on corrosion resistance of stainless steel. Results showed that the presence of sulfide in water caused a negative shift in surface potential of stainless steel, an increase in surface potential difference, and an increase in local response current on the surface, resulting in a current peak that gradually increased over time. The analysis results of passive film composition showed that the presence of sulfide caused increase in the ratio of Fe/Cr and OH−/O2−, as well as the content of Cr(OH)3 and Fe(OH)3 in passive film, whereas caused a decrease of Cr2O3 content, and led to the formation of FeS2 in the passive film. These changes in the composition of the passive film made it easier for active sites to appear on the surface of stainless steel and enhanced the conductivity of the passive film and significantly reducing its protective performance.

Improving energy efficiency

This paper discusses strategies for improving energy efficiency, focusing on the adoption of high-efficiency electrical devices and minimizing unnecessary electricity consumption across residential, public, and industrial areas. The study highlights the importance of selecting energy-efficient devices, utilizing modern technologies in lighting systems, and optimizing the use of industrial motors and transformers. Additionally, it explores demand-side management (DSM) techniques such as load curve modification, energy storage, and the potential of new energy sources like wind, solar, and tidal power. The paper concludes by emphasizing the need for effective energy management through DSM to reduce energy consumption, enhance power quality, and achieve sustainable economic and environmental benefits.

Electrochemical Detection of Cadmium Using a Bismuth Film Deposited on a Brass Electrode

Cadmium is one of the most dangerous pollutants found in the environment, where it exists mainly due to human activities. High cadmium concentrations can cause serious problems, which is why the detection and determination of Cd is one of the most important tasks. Electroanalytical methods provide rapid and accurate results in the detection of cadmium in various solutions. In this study, the possibility of using a bismuth film electrode deposited on a brass surface and electroanalytical techniques for the detection of cadmium is investigated. The bismuth film was deposited on the surface of the brass electrode using a chronoamperometric technique. Cyclic voltammetry and electrochemical impedance spectroscopy were used to characterize the synthesized bismuth film electrode. The current peaks obtained by anodic square-wave stripping voltammetry under optimized conditions showed a linear relationship in the investigated concentration range of cadmium. The study of the interference of different cations (Cr3+, Mn2+, Zn2+, Ca2+, K+, Mg2+ and Na+) showed that the tested cations have no influence on the determination of Cd2+ ions in the investigated solution. This finding provides a good opportunity for the use of the synthesized electrode in real samples.

A low-cost disposal pencil electrode for cyclic voltammetry analysis

This study explores the use of disposable pencil electrodes in cyclic voltammetry (CV) and compares their performance to traditional glassy carbon electrodes. We conducted CV tests to evaluate different pencil grades, HB, 2B, 4B, 6B, 8B, and 10B along with glassy carbon to assess response time, peak current, and electrode stability during the analysis of potassium ferrocyanide. The results showed that the 4B pencil outperformed other grades and the glassy carbon electrode, with faster response times, higher peak currents, and greater stability. Notably, the 4B pencil's anodic and cathodic currents were higher at 25 mV/s scan rate. This work demonstrates that 4B pencils can serve as cost-effective and reliable alternatives to glassy carbon electrodes in electrochemical applications, offering an accessible option for potassium ferrocyanide analysis and broader electrochemical studies.

Optimal time recommendation model for home appliances: HSB living lab + dishwasher study

This study investigates the effectiveness of an Optimal Time Recommendation model (OTR) in encouraging citizens to shift the usage of their home appliances, such as dishwasher to off-peak hours. The research was conducted at the HSB Living Lab + in Gothenburg city, involving 74 participants from diverse social groups, including students, one-person households, couples, and families with kids. The study employed a mixed-methods approach, combining surveys, interviews, and data from self-reporting QR-code or iPad-based web-interface. Participants were provided with personalised recommendations generated by the OTR model, which considered factors such as energy demand, grid load, electricity pricing and level of CO2. The recommendations aimed to assist users in identifying the optimal time slots for operating their home appliances during off-peak, motivated by the lower price, lower CO2 emission or both. Results indicated a positive response from participants across all social groups. Most participants reported an increased awareness of their energy consumption patterns and a willingness to adopt delay shifting practices. However, some frictions and obstacles to adopt shifting time of the behaviour were highlighted as well. The findings from this case study contribute to the existing knowledge on flexibility and Demand-Side Management (DSM). These findings can inform home appliances producers to increase the delay start function usability, policymakers to emphasise the eco-design of the white goods, and researchers in developing effective strategies to encourage energy conservation practices on a larger scale.

Design and experimental study of a field-reversed configuration plasma thruster prototype

Abstract The field-reversed configuration (FRC) plasma thruster driven by rotating magnetic field (RMF), abbreviated as the RMF-FRC thruster, is a new type of electric propulsion technology that is expected to accelerate the deep space exploration. An experimental prototype, including diagnostic devices, was designed and constructed based on the principles of the RMF-FRC thruster, with an RMF frequency of 210 kHz and a maximum peak current of 2 kA. Under the rated operating conditions, the initial plasma density was measured to be 5 × 1017 m-3, and increased to 2.2 × 1019 m-3 after the action of RMF. The coupling efficiency of RMF was about 53%, and the plasma current reached 1.9 kA. The axial magnetic field changed in reverse by 155 Gauss, successfully reversing the bias magnetic field of 60 Gauss, which verifies the the formation of FRC plasma. After optimization research, it was found that when the bias magnetic field is 100 Gauss, the axial magnetic field reverse variation caused by FRC is the highest at 164 Gauss. The experimental results are discussed and strategies are proposed to improve the performance of the prototype.

Wavelet-Based Analysis of Motor Current Signals for Detecting Obstacles in Train Doors

Trains used in urban mass passenger transit often have side entrance doors through which passengers can rapidly enter and exit the train. These doors are typically electrically powered and automated. Many incidents have occurred in which a passenger is trapped and injured while passing through the doors as they are closing. Existing solutions rely on sensitive-edge sensors and current signal peak detection in the time domain to detect door obstructions. However, these methods have notable limitations: sensors are expensive, and sensor failure can result in safety risks, while time-domain signal analysis is prone to noise, potentially leading to false peak detection. The proposed efficient and cost-effective method enhances safety by implementing the torque control of a DC motor which limits the door closing force to prevent potential injuries. In addition, it reduces reliance on traditional edge sensors, which are prone to failure and may result in undetected obstructions. By using a robust time–frequency domain approach, the system ensures more accurate detection, minimizing potential injury risks. An obstruction of the door causes a corresponding change in the motor current. These changes can be detected by using the discrete wavelet transform to decompose the current signal. The norm and peak of the current are used as obstacle detection features, and appropriate threshold values are obtained from a simulation. The simulation results were validated through an experiment. The proposed novel system effectively detects forces between 100 and 200 N (indicating the presence of an object) within 0.3 s and complies with EN14752 safety standard. It can also differentiate between soft and hard objects trapped in train doors.