Electric Fluctuations Research Articles

Properties of tsunami-generated electromagnetic variation observed on islands.

Electrically conductive seawater, moving in an ambient magnetic field, generates electromagnetic (EM) variations. Tsunamis are significant contributors to this phenomenon, inducing observable electric and magnetic fluctuations at seafloor and coastal observatories. While understanding of these occurrences in open oceans is robust, knowledge regarding their observation on islands remains limited. This article seeks, through the use of numerical experimentation, to enhance our understanding of tsunami-generated EM (TGEM) variations observed on islands. Utilizing simulations involving conical islands, we identify three key insights regarding EM intensity normalized by the height of incident tsunamis: (i) increased ocean depth surrounding the island amplifies tsunami EM signals, particularly for periods shorter than 20 min; (ii) magnetic field strength at the island is approximately comparable to that observed at the seafloor in the absence of the island when the island radius is smaller than 6 km; and (iii) electric field intensity at the island notably surpasses that observed at the seafloor, especially with smaller island radii ([Formula: see text] 6 km). Additionally, we establish that employing the ratio of island radius to tsunami wavelength near the island coast facilitates the derivation of empirical functions for this phenomenon.This article is part of the theme issue 'Magnetometric remote sensing of Earth and planetary oceans'.

Collaborative Optimization of Multi-regional Integrated Energy System Based on Improved Beluga Whale Optimization Algorithm

With the installed capacity of the renewable energy power generation is growing at a high speed, a large number of the renewable energy is connected to the grid, the energy problem has been solved to a large extent, but the ensuing problems can not be ignored, the first is the consumption of the renewable energy, which is followed by the stability problem of the power system, and a multi-regional integrated energy system (MRIES) is constructed to solve the problem. In view of the wind power uncertainty, the absorption treatment is carried out by the equipment layer and the optimization layer respectively. A hybrid energy storage system (HESS) is introduced in the equipment layer to suppress the influence of the wind power uncertainty on the system stability. A combination of the convolutional filtering algorithm, the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise algorithm and the fuzzy control algorithm is introduced in the optimization layer to develop the charging and discharging strategy of the HESS. With the strategy, the impact of the electric power fluctuations on the power system during the optimization process can be reduced largely. Then, on the basis of fully considering the energy coupling in different equipments, a collaborative optimization model of the MRIES is constructed. And the integrated demand response model considering the time-of-use electric price is introduced in the load side. Finally, the improved Beluga optimization(IBWO) algorithm is developed to optimize the model. The optimization results show that the IBWO algorithm plays a good optimization effect both in the participation of energy supply equipments and the economy, plays a collaborative optimization role in the MRIES and ensures the stability of the whole power system.

Integrating Evolutionary Game-Theoretical Methods and Deep Reinforcement Learning for Adaptive Strategy Optimization in User-Side Electricity Markets: A Comprehensive Review

With the rapid development of smart grids, the strategic behavior evolution in user-side electricity market transactions has become increasingly complex. To explore the dynamic evolution mechanisms in this area, this paper systematically reviews the application of evolutionary game theory in user-side electricity markets, focusing on its unique advantages in modeling multi-agent interactions and dynamic strategy optimization. While evolutionary game theory excels in explaining the formation of long-term stable strategies, it faces limitations when dealing with real-time dynamic changes and high-dimensional state spaces. Thus, this paper further investigates the integration of deep reinforcement learning, particularly the deep Q-learning network (DQN), with evolutionary game theory, aiming to enhance its adaptability in electricity market applications. The introduction of the DQN enables market participants to perform adaptive strategy optimization in rapidly changing environments, thereby more effectively responding to supply–demand fluctuations in electricity markets. Through simulations based on a multi-agent model, this study reveals the dynamic characteristics of strategy evolution under different market conditions, highlighting the changing interaction patterns among participants in complex market environments. In summary, this comprehensive review not only demonstrates the broad applicability of evolutionary game theory in user-side electricity markets but also extends its potential in real-time decision making through the integration of modern algorithms, providing new theoretical foundations and practical insights for future market optimization and policy formulation.

Two-Stage Optimization Scheduling of Integrated Energy Systems Considering Demand Side Response

This study proposes a two-level optimization scheduling method for multi-region integrated energy systems (IESs) that considers dynamic time intervals within the day, addressing the diverse energy characteristics of electricity, heat, and cooling. The day-ahead scheduling aims to minimize daily operating costs by optimally regulating controllable elements. For intra-day scheduling, a predictive control-based dynamic rolling optimization model is utilized, with the upper-level model handling slower thermal energy fluctuations and the lower-level model managing faster electrical energy fluctuations. Building on the day-ahead plan, different time intervals are used for fast and slow layers. The slow layer establishes a decision index for command cycle intervals, dynamically adjusting based on ultra-short-term forecasts and incremental balance corrections. Case studies demonstrate that this method effectively leverages energy network characteristics, optimizes scheduling intervals, reduces adjustment costs, and enhances system performance, achieving coordinated operation of the IES network and multi-energy equipment.

Design Of PV Plant to Improve a Voltage Profile Of 11 KV Grid in The South West of Libya

The use of photovoltaic (PV) system for electricity generation is growing up in the last decades. Thesouthern part of Libya suffers from voltage fluctuation and intermittence of electricity during thesummer time. The aim of this paper is firstly to study and analysis a distribution grid of small villagelocated in the southern west region of Libya. We analyzed the existed 11KV distribution overheadline supplied electricity of the village called Takarkibah using Power-World Simulator in order tointegrate it with PV plant. The results obtained show that injection of PV power about 70 % of thegrid power capacity gives good grid performance. After that, we use SAM simulation program todesign PV plant to improve the voltage profile in the distribution grid. Six designs (scenarios) aresimulated using different solar modules and inverters. The simulation results obtained from SAMillustrated the power capacity, energy yield, and losses of the designed PV plant.

Improving Electric Power Stability and Efficiency Using an Integrated Control System for Refrigerated Containers

In this study, a method is proposed to minimize electrical load fluctuations and improve the efficiency of engine generator operation by managing refrigerated ship containers through an integrated control system. The proposed system actively controls the electrical load by assigning operational priorities based on cargo temperature deviations to existing independently operated refrigerated containers, ensuring that they operate only within the available power of the engine generator. As a result, the average specific fuel oil consumption can be reduced. A 70 h simulation of the refrigerated containers, a power system, and an integrated control system demonstrated in MATLAB/Simulink 2021b that the magnitude of electrical load fluctuations decreases from 37.6% to 9.6% of the engine generator’s rated power compared with the conventional operation of refrigerated containers. In addition, a 1.88% fuel saving is realized.

Exact stark analytical function for Hα line based on the FFM model related with plasma parameters

Optical Emission Spectroscopy is a widely used technique for plasma diagnosis, with particular interest in hydrogen atomic emission due to its prevalence in plasmas. However, accurately determining plasma parameters like electron density, electron temperature, and gas temperature starting from the experimental profiles remains a challenge. This paper introduces a comprehensive model for Stark broadening of the Hα line in a wide range of plasma conditions, addressing the limitations of existing analytical expressions for line shapes. The proposed model encompasses the full splitting of the transition into fifteen Lorentzian profiles and electric micro-field fluctuations surrounding the emitting atoms due to collisions with charged particles. Starting from accurate spectral data obtained from realistic computer simulations, fitting parameters of the model, have been obtained by using an optimization method based on a genetic algorithm. The set of parameters of the model are reported for a wide range of plasma conditions. The behavior of these parameters is analyzed to understand their dependence in terms of the electron density and temperature and gas density of the plasma. The model parameters here obtained constitute a useful tool in plasma diagnosis to obtaining the values of the physical parameters of the plasma starting from the experimental profiles.

Electrical conductivity fluctuations as a tracer to determine time-dependent transport characteristics in hyporheic sediments

Assessing solute transport in riverbed sediments is important for quantifying the effective reactivity of hyporheic sediments and the magnitude of exchange flows between rivers and their river beds. A typical approach of estimating transport in riverbed sediments is by measuring natural tracers such as fluctuations of temperature or electrical conductivity (EC) and fitting models to them that assume time-independent travel time distributions, implying steady-state flow. Here, we use a transport parameterization that is based on the advection–dispersion equation (ADE) with coefficients that continuously vary in time. The ADE is solved numerically and its solution is fitted to measured EC time series using Bayesian parameter inference. A continuous function of model parameters is constructed by smoothly interpolating between point values with different temporal resolution, and Tikhonov regularization is used to avoid spurious parameter fluctuations. The approach is tested using EC time series synchronously measured in river water and hyporheic porewater of two urban rivers in Germany and one urban river in South Australia. For all datasets the goodness of fit was improved by introducing a time-dependent EC offset. Estimated porewater velocities were highly transient in three out of the four datasets with values increasing by up to a factor of six over the course of a day. Flux transients were likely related to both variations of hydraulic gradients along and spatial shifting of flow paths. Comparison to stationary, non-parametric deconvolution indicated that transient flow may induce multimodality in stationary travel time distributions. Given the high temporal dynamics of transport in the streambed sediments of the three investigated urban rivers, we envision that the presented model is also a valuable tool to improve the assessment of reactive transport in riverbed sediments.

Heavy ion beam probe for Wendelstein 7-X measurement capabilities as projected through its design.

A heavy ion beam probe (HIBP) diagnostic is being developed for studies of plasma equilibrium and turbulence in the optimized Wendelstein 7-X (W7-X) stellarator. Operation of W7-X has experimentally demonstrated that its optimized magnetic field results in improved neoclassical particle confinement and, as a result, turbulence is the predominant cause of energy transport. The HIBP will have the unique ability to provide experimental data needed to complement models of both neoclassical and turbulent transport. It will acquire direct measurements in the W7-X plasma interior of the electric potential (needed for understanding ambipolar particle flux) and fluctuations of electron density and potential (needed for understanding turbulence). The HIBP for W7-X will inject singly charged ion beams with energies of up to 2MeV and is designed to access the upper cross section of the W7-X plasma. We use trajectory simulations to illustrate the plasma coverage that the diagnostic can achieve in the reference magnetic configurations of W7-X. We calculate beam signal levels, discuss anticipated measurement sensitivity of broadband fluctuations of electron density and plasma potential, and show how they depend on plasma density. We also discuss the diagnostic sensitivity to equilibrium plasma potential.

Dynamics of the material ejection in a dipolar arc in continuous regime

Electrical and fast imaging measurements are performed on an experimental device designed to study the dynamics of electric arcs in the DC regime. The work presented here investigates the relationship between electrical fluctuations and material ejection from the electrode surface. Cross-analysis of the electrical and imaging data reveals a significant correlation between electrical fluctuations in the kHz range and particle emission from the electrode surface. Different types of ejection are presented and a study of the dynamics of the ejected particles is carried out through the analysis of a statistically significant number of trajectories.

DYNAMIC MODELLING OF THREE-PHASE MICROGRIDS INCLUDING DQ0 CURRENT CONTROLLED PHOTOVOLTAIC GENERATION UNDER TRANSIENT CONDITIONS

ABSTRACT: The photovoltaic generation can be interconnected to power systems or to insulated grids to satisfy the growing electrical demand as an alternative renewable energy source. The photovoltaic generator dynamics affects the control, protection, stability, and power quality of the electrical grid, this network dynamics requires to be examined and conveniently regulated and adjusted. The dynamic averaged model for the dq0 current controlled DC/AC inverter is applied to obtain the time-domain response to electromagnetic transients under special conditions such as electrical load fluctuations, unbalanced faults, voltage and current transients to verify e.g., the ride through capability or to adjust the protection and control devices, these transients can be present during the operation of electrical microgrids and power systems interconnected with photovoltaic generation. The system modelling includes the photovoltaic plant interconnected with the microgrid using the dq0 current controlled DC/AC inverter. The case studies are applied to analyse three-phase electrical microgrids, their time-domain response, and the obtained voltage and current waveforms can be used to verify the dynamic behaviour and stability of the electrical system interconnected with photovoltaic generation and similarly to verify the operation of the dq0 current controller for the inverter. The results can be used to control the microgrid dynamic behaviour. Keywords: current control, dq0 transform, inverter, microgrids, photovoltaic generation, power quality, protection, time-domain, transients, unbalanced faults, unbalanced voltage sags, voltage source converter

Battery Scheduling Optimization and Potential Revenue for Residential Storage Price Arbitrage

Residential energy storage systems offer significant potential for price arbitrage by capitalizing on fluctuations in electricity prices throughout the day. This study investigates the potential revenue from optimal battery scheduling for residential storage in different north-western European electricity price zones during 2023. Using Nord Pool day-ahead prices, we applied an optimization model to determine the revenue for two types of batteries: 5 kW/10 kWh and 10 kW/10 kWh. The analysis considered battery capacity, charging and discharging efficiency, and maximum charge/discharge rates. Our results show notable variations in potential revenue across different regions, with the Baltic states demonstrating the highest revenue potential. The findings indicate that while 10 kW batteries can generate higher total revenue, 5 kW batteries are more efficient in terms of revenue per cycle. These regional disparities underscore the need for targeted incentives and policies to enhance the economic viability of residential energy storage. The research results provide valuable insights into optimizing residential battery storage for price arbitrage, offering guidance for consumers, policymakers, and energy providers to maximize economic benefits in various electricity markets.

A cloud-oriented data-analysis framework to analyze peak demand dynamics in institutional building clusters

Peak loads in higher education institutional building clusters (IBCs) possess considerable economic repercussions on their overall operations. Thus, identifying electrically inefficient buildings presents a significant opportunity to curtail peak loads and promote energy efficiency in IBCs. Existing literature implements clustering algorithms to comprehend the electrical demand dynamics of buildings to analyze disparity in their load behavior. These techniques perform well in single building environment, however, fall short in comprehending the demand dynamics for building clusters, specifically during peak loads. This study introduces a cloud-oriented quantile-based data analysis framework, specifically designed for simultaneously evaluating demand profiles of multiple buildings within IBCs. Quantile-based metrics namely, Value-at-Risk, Conditional Value-at-Risk and Conditional Value-at-Risk standard deviation are implemented to comprehend the electrical fluctuations and quantify the electrical impact of each building during campus-wide peak demands. A cross-building comparison is established by linearly ranking buildings following two key criteria: (i) buildings with frequent demand fluctuations and (ii) buildings exerting a high electrical impact on overall IBC demand. Both criteria are equally weighted while ranking to identify the most inefficient buildings during peak loads. The framework is implemented in a Canadian university and resulted a substantial 50 MW demand reduction through recommissioning and retrofitting of inefficient buildings.

Thermal-flow-force-electrical coupling characteristics of microchannel cooling systems in high heat flux chips

To address the challenges faced by designers of multi-field coupled cooling systems for high heat flux chips, this paper proposes a “near-source” microchannel cooling strategy and establishes a thermal–flow-force electrically coupled model for chips. The effects of chip Joule heat and cooling water flow rate on the cooling performance with respect to multi-field coupling effects were studied. The impact of multi-field coupling effects was revealed, and a method for enhancing microchannel cooling performance in light of multi-field coupling effects is proposed. The results indicated that considering multi-field coupling effects in the microchannel cooling process leads to a deterioration in the thermal performance of chips, accompanied by a significant increase in electrical fluctuations and thermal deformation amplitude. Compared to chips upstream of the cooling water, chips downstream subjected to thermal cascade effects were more sensitive to multi-field coupling effects. Moreover, the temperature variance index, output current, and strain energy density of high heat flux chips were positively correlated with Joule heat but inversely proportional to the Reynolds number of the cooling water for cases in respect of multi-field coupling effects. Additionally, serpentine microchannels maximized the operational performance of high heat flux chips by reducing temperature of chip by up to 55.1%, decreasing strain energy density by 96.7%, and increasing input potential by 120%.

Drivers of Magnetic Field Amplification at Oblique Shocks: In Situ Observations

Collisionless shocks are ubiquitous structures throughout the Universe. Shock waves in space and astrophysical plasmas convert the energy of a fast-flowing plasma to other forms of energy, including thermal and magnetic energies. Plasma turbulence and high-amplitude electric and magnetic fluctuations are necessary for effective energy conversion and particle acceleration. We survey and characterize in situ observations of reflected ions and magnetic field amplification rates at quasiperpendicular shocks under a wide range of upstream conditions. We report magnetic amplification rates as high as 25 in our current data set. Reflected ions interacting with the incoming plasma create magnetic perturbations that cause magnetic amplification in upstream and downstream regions of quasiperpendicular shocks. Our observations show that, in general, magnetic amplification increases with the fraction of reflected ions, which itself increases with Mach number. Both parameters plateau once full reflection is reached. Magnetic amplification continuously increases with the inverse of the magnetization parameter of the upstream plasma. We find that the extended foot region upstream of shocks and nonlinear processes within that region are key factors for intense magnetic amplification. Our observations at nonrelativistic shocks provide the first experimental evidence that below a certain magnetization threshold, the magnetic amplification efficiency at quasiperpendicular shocks becomes comparable to that at the quasiparallel shocks.

Multi-Electric Aero Engine Control and Hardware-in-the-Loop Verification with Starter Generator Coordination

The starter generator, characterized by controllable starting torque and disturbance in generator load torque, poses challenges for the multi-electric aero engine control. The key to addressing this issue lies in multi-electric aero engine control with the collaboration of a starter generator. Firstly, a multi-electric aero engine model is established, comprising a full-state turbofan engine model to enhance low-speed simulation capability and an external characteristic model of a starter generator to improve real-time simulation capability. Subsequently, the control methods for a multi-electric aero engine with starter generator coordination are proposed in three processes, including the starting process, acceleration/deceleration process, and steady-state process. During the starting process, the acceleration is controlled by coordinating the torque of the starter generator and the fuel of the aero engine. During the acceleration/deceleration process, the fuel limit value is adjusted based on the electrical load of the starter generator. During the steady-state process, the fuel is compensated based on the q-axis current of the starting generator in response to load torque disturbance. Finally, hardware-in-the-loop simulation experiments are conducted for the control of a multi-electric aero engine. The results show that the coordination reduces the oscillation of the acceleration during the startup of a multi-electric aero engine, enhancing its ability to resist disturbances from electrical load fluctuations during power generation.

Modeling Microreactor Requirements for High-Performance Computing

The potential deployment of microreactors as a zero-emission source for critical applications within integrated energy systems such as microgrids has been gaining interest in recent years owing to the microreactors’ dispatchable nature, modular design, small site footprint, and carbon-free generation. A particularly high-value but challenging application with rapidly growing demand is in the deployment of high-performance computing (HPC) clusters within microgrids. In this work, a model of a HPC cluster in an energy-diverse microgrid is developed to determine the requirements of a technology-agnostic microreactor deployed for such a challenging application. The minute-resolution simulations revealed that the cluster’s electrical load fluctuation of up to 4.1 MW/min required a fast and responsive load-following capability. When the load-following capability of the microreactor was perturbed, the required microgrid storage capacity associated with having a 0.1 MW/min dispatchable microreactor decreased by two orders of magnitude as compared with load-following solely by energy storage devices, indicating that load-following capability in microreactors is of great value in such applications. The analysis methods described in this work can be extended to other microgrids, other HPC clusters, or other types of challenging applications, and can help microgrid planners in determining the storage size, output capacity, and ramping capabilities of the storage devices required for a given microgrid configuration.

Does electrical activity in fungi function as a language?

All cells generate electrical energy derived from the movements of ions across membranes. In animal neurons, action potentials play an essential role in the central nervous system. Plants utilize a variety of electrical signals to regulate a wide range of physiological processes, including wound responses, mimosa leaf movements, and cell turgor changes, such as those involved in stomatal movements. Although fungal hyphae exhibit electrical fluctuations, their regulatory role(s), if any, is still unknown. In his paper “Language of fungi derived from their electrical spiking activity”, Andrew Adamatzky, based on a quantitative analysis of voltage fluctuations in fungal mycelia, concludes that the patterns of electrical fluctuations he detects can be grouped into “words” analogous to those found in human languages. He goes on to speculate that this “fungal language” is used “to communicate and process information” between different parts of the mycelium. Here we argue on methodological grounds that the presumption of a fungal language is premature and unsupported by the evidence presented, that the voltage fluctuations he detects are likely to originate as nonbiological noise and experimental artifacts, and that the measured electrical patterns show no similarity to any properties of human language.

A Statistical Study of Quasi‐Electrostatic Magnetosonic Waves

AbstractQuasi‐electrostatic magnetosonic (QEMS) waves have been recently reported, referring to a distinct type of magnetosonic (MS) wave with only the electric fluctuation being detectable. Here a statistical study of QEMS waves is carried out with the Van Allen Probes data. 83% of 40,466 QEMS samples are observed with plasma density ne < 20 cm−3, and intense QEMS waves tend to appear in the lower density region. QEMS waves become strong in the case of more pronounced proton rings around 10 keV and larger suprathermal proton populations. High occurrence rates and large amplitudes of QEMS waves are confined near the dayside equator (|MLAT| < 3°). The wave frequencies are typically slightly below the nth harmonic of proton gyro‐frequency, and the wave intensity gradually decreases with an increasing harmonic number n. Our results further demonstrate that low plasma densities and abundant suprathermal protons are beneficial for intensifying QEMS waves and contribute to the establishment of QEMS wave model.

Determination of Tokamak Plasma Electrical Fluctuations using the Langmuir Probe [Withdrawn

Determination of Tokamak Plasma Electrical Fluctuations using the Langmuir Probe [Withdrawn