The increasing integration of renewable energy sources has caused spatially and temporally uneven inertia distribution in power systems. This paper proposes a regional inertia estimation and virtual inertia configuration method using dynamic mode decomposition (DMD). A regional equivalent inertia model is established based on inertia estimation theory for renewable-penetrated power systems. The power system is partitioned via spectral clustering, and frequency measurement nodes are selected according to Pearson correlation coefficients of regional nodes. Regional inertia is then estimated through the DMD method. Critical regional inertia requirements are calculated based on frequency security constraints to configure virtual inertia for renewable energy and energy storage systems. An improved IEEE 10-machine 39-node model with wind-storage systems is implemented in PSCAD for validation. Simulation results demonstrate that the proposed method achieves regional inertia estimation with errors below 5%. The online virtual inertia configuration strategy for wind turbines and energy storage systems effectively prevents frequency violations and enhances frequency security and stability.
Giacomo Madella, Federico Tesser, Lluis Alonso
et al.
Large-scale computing clusters have been the basis of scientific progress for several decades and have now become a commodity fuelling the AI revolution. Dark Silicon, energy efficiency, power consumption, and hot spots are no longer looming threats of an Information and Communication Technologies (ICT) niche but are today the limiting factor of the capability of the entire human society and a contributor to global carbon emissions. However, from the end user, system administrators, and system integrator perspective, handling and optimising the system for these constraints is not straightforward due to the elevated degree of fragmentation in the software tools and interfaces which handles the power management in high-performance computing (HPC) clusters. In this paper, we present the REGALE Library. It is the result of a collaborative effort in the EU EuroHPC JU REGALE project, which aims to effectively materialize the HPC PowerStack initiative, providing a single layer of communication among different power management tools, libraries, and software. The proposed framework is based on the data distribution service (DDS) and real-time publish–subscribe (RTPS) protocols and FastDDS as their implementation. This enables the various actors in the ecosystem to communicate and exchange messages without any further modification inside their implementation. In this paper, we present the blueprint, functionality tests, and performance and scalability evaluation of the DDS implementation currently used in the REGALE Library in the HPC context.
Due to their advantages, such as abundant raw material reserves, excellent thermal stability, and superior low-temperature performance, sodium-ion batteries (SIBs) exhibit significant potential for future applications in energy storage and electric vehicles. Therefore, in this study, a commercial pouch-type SIB with sodium iron sulfate cathode material was investigated. Firstly, a second-order RC equivalent circuit model was established through parameter identification using multi-rate hybrid pulse power characterization (M-HPPC) tests at various temperatures. Then, both the specific heat capacity and entropy coefficient of the sodium-ion battery were measured through experiments. Building upon this, an electro-thermal coupling model was developed by incorporating a lumped-parameter thermal model that accounts for the heat generation of the tabs. Finally, the prediction performance of this model was validated through discharge tests under different temperature conditions. The results demonstrate that the proposed electro-thermal coupling model can achieve the simultaneous prediction of both temperature and voltage, providing valuable references for the future development of thermal management systems for SIBs.
Production of electric energy or power. Powerplants. Central stations, Industrial electrochemistry
ObjectivesAs an essential sustainable energy technology, renewable energy-powered water electrolysis for hydrogen production has attracted widespread attention due to its advantages in environmental protection and low carbon emissions. However, conventional water electrolysis technologies for hydrogen production face challenges in terms of efficiency and cost, the rapid development of artificial intelligence (AI) provides an effective way to solve the difficult problems of hydrogen production technology through electrolysis of water. To address this, this study aims to explore the key applications and development prospects of AI for optimizing the efficiency and economic performance of water electrolysis systems for hydrogen production.MethodsCommon AI tools such as MATLAB, Python, and SimuNPS are employed for algorithm development, deep learning model training, and multi-physics simulation in water electrolysis systems for hydrogen production. By integrating AI technologies, applications such as output prediction, system capacity optimization and scheduling, and fault diagnosis are implemented to improve system performance and stability. A comparative analysis of performance of different AI models in various real-world scenarios is conducted to explore their specific roles and implementation methods in enhancing system performance and controllability.ConclusionsAI technology offers new avenues for enhancing the efficiency and intelligent scheduling of renewable energy-powered water electrolysis hydrogen production systems. Future research should focus on the application of AI in output forecasting, scheduling optimization, and fault diagnosis, promoting deep integration between AI and system operation. Moreover, innovative applications of AI in intelligent monitoring, automatic control, and multi-source coordination should be explored to provide strong support for the development of efficient, stable, and low-carbon hydrogen energy systems.
Applications of electric power, Production of electric energy or power. Powerplants. Central stations
Zhengqiang Xiong, Chang Han, Xiaoliang Wang
et al.
Coverage path planning solves the problem of moving an effector over all points within a specific region with effective routes. Most existing studies focus on geometric constraints, often overlooking robot-specific features, like the available energy, weight, maximum speed, sensor resolution, etc. This paper proposes a coverage path planning algorithm for Unmanned Aerial Vehicles (UAVs) that minimizes energy consumption while satisfying a set of other requirements, such as coverage and observation resolution. To deal with these issues, we propose a novel energy-optimal coverage path planning framework for monitoring tasks. Firstly, the 3D terrain’s spatial characteristics are digitized through a combination of parametric modeling and meshing techniques. To accurately estimate actual energy expenditure along a segmented trajectory, a power estimation module is introduced, which integrates dynamic feasibility constraints into the energy computation. Utilizing a Digital Surface Model (DSM), a global energy consumption map is generated by constructing a weighted directed graph over the terrain. Subsequently, an energy-optimal coverage path is derived by applying a Genetic Algorithm (GA) to traverse this map. Extensive simulation results validate the superiority of the proposed approach compared to existing methods.
This study introduces a robust trajectory-tracking control strategy in a two-degrees-of-freedom (2-DOF) helicopter system, combining passivity theory and integral sliding mode control (ISMC) strengths. The proposed integral passivity-based sliding mode control (IPBSMC) integrates passivity-based energy shaping, which inherently accounts for the system's natural dynamics, with integral sliding mode control to enhance tracking performance and reduce control effort. The controller's effectiveness is validated through simulations and real-time experiments with band-limited white noise disturbances using the Quanser AERO 2 platform interfaced with MATLAB/Simulink. Results indicate good trajectory tracking, with steady-state errors below ±0.2 rad under non-vanishing Gaussian disturbances. The experimental implementation further validates the proposed method's practical applicability and highlights its potential for real-world deployment in coupled systems requiring high accuracy and robustness under varied operating conditions.
Applications of electric power, Electric apparatus and materials. Electric circuits. Electric networks
The introduction of Smart Electric Vehicles (SEVs) represents an increasingly disruption on automotive area, once integrates advanced computer and communication technologies to highly electrical cars, which come with high performances, environment friendly and user friendly characteristics . But the increasing complexity of SEVs prompted by greater dependence on interconnected systems, autonomous capabilities and electrification, presents new challenges in cybersecurity as well as functional safety. The safety and reliability of such vehicles is paramount, as unsafe or unreliable operation in either case represents an unacceptable risk in terms of the performance of the vehicle and safety of the passenger. This paper investigates the integrated development of cybersecurity and functional safety for SEVs, emphasizing the requirement for the parallel development of these domains as components that are not treated separately. In SEVs, cybersecurity is quite crucial in order to prevent the threats of hacking, data breaches and unauthorized access to vehicle systems. Functional safety ensures that important vehicle functions (braking, steering, battery control, etc.) keep working even if some part fails. This convergence of functional safety issues with cybersecurity issues is becoming more crucial, since a security incident can result in a failure of catastrophic consequences for a functional safety system and, conversely. This paper reports the current state of cybersecurity and functional safety standards for SEVs, highlighting challenges that include the weaknesses of communication networks, the potential security threats of over-the-air updates, and the demand for real-time responsive systems for failure.
Angel Pan Du, Miguel Arana-Catania, Enric Grustan Gutiérrez
Artificial Intelligence algorithms are introduced in this work as a tool to predict the performance of new chemical compounds as alternative propellants for electric propulsion, focusing on predicting their ionisation characteristics and fragmentation patterns. The chemical properties and structure of the compounds are encoded using a chemical fingerprint, and the training datasets are extracted from the NIST WebBook. The AI-predicted ionisation energy and minimum appearance energy have a mean relative error of 6.87% and 7.99%, respectively, and a predicted ion mass with a 23.89% relative error. In the cases of full mass spectra due to electron ionisation, the predictions have a cosine similarity of 0.6395 and align with the top 10 most similar mass spectra in 78% of instances within a 30 Da range.
Abstract This paper presents design and dynamic performance investigation of a full‐scale, modular topology (arranged on the sides of a regular hexagon), medium voltage direct current (MVDC) permanent magnet (PM) biased presaturated fault current limiter (PMFCL). This PMFCL represents a cost‐effective design with enhanced longevity, reliability, scalability, and controllability. The scalability of this modular design can be extended by adding or removing CI (letters: CI) core units for different power system applications in a voltage range from to or more. The rated steady‐state (DC) and fault currents of and , respectively. The detection free and self‐triggering performance of this PMFCL is designed and simulated through a 3D coupled model of electric‐circuit magnetic‐field of COMSOL Multiphysics. Accurate representation of PM behaviour, especially in the second quadrant of its hysteresis loop of Jiles‐Atherton method gives realistic performance of the PMFCL. Comprehensive finite element simulations are carried out to study the effect of design parameters on the dynamic performance of PMFCL. Good agreement is found between COMSOL simulation results of DC‐biased PFCL and experimental results of a developed small‐scale prototype. Results reveal that the MVDC PMFCL shows significant improvement and satisfactory performance, in terms of fault current clipping ratio, fault current slope, and power losses, as compared to the conventional MVDC DC‐biased presaturated CI iron‐core fault current limiter (PFCL).
Energy management strategies (EMSs) are a core technology in hybrid electric vehicles (HEVs) and have a significant impact on their fuel economy. Optimal solutions for EMSs in the literature usually focus on improving fuel efficiency by operating the engine within a high efficiency range, without considering the drivability, which is affected by noise–vibration–harshness (NVH) constraints at low vehicle speeds. In this paper, a dual-mode combustion engine was implemented in a plug-in series hybrid electric vehiclethat could operate efficiently either at low loads in homogeneous charge compression ignition (HCCI) mode or at high loads in spark ignition (SI) mode. An equivalent consumption minimization strategy (ECMS) combined with a dual-loop particle swarm optimization (PSO) algorithm was designed to solve the optimal control problem. A MATLAB/Simulink simulation was performed using a well-calibrated model of the target HEV to validate the proposed method, and the results showed that it can achieve a reduction in fuel consumption of around 1.3% to 9.9%, depending on the driving cycle. In addition, the operating power of the battery can be significantly reduced, which benefits the health of the battery. Furthermore, the proposed ECMS-PSO is computationally efficient, which guarantees fast offline optimization and enables real-time applications.
Mohammad Qasem, Stoyan Stoyanov, Sadam Ratrout
et al.
Lithium-ion batteries play a crucial role in present-day energy storage systems, necessitating the development of precise prediction models to improve their performance and ensure safety. The present study introduces a comprehensive methodology that encompasses the calibration, validation, and application of two separate Li-ion battery electrochemical models: the equivalent circuit model and the electrochemistry-based model. The calibration and validation of these models are based on experimental data conducted under various operating conditions, including charge/discharge rates, calendaring temperature, and Hybrid Pulse Power Characterization (HPPC) tests. After the successful validation process, these models are used to generate synthetic data tailored to real-world applications, particularly electric vertical takeoff and landing vehicles (eVTOL). The primary objective is to assess the precision of battery performance prediction, wherein the synthetic data is thoroughly compared with real experimental data. The results demonstrate the effectiveness of the proposed approach in developing a model that reduces dependence on labor-intensive testing and associated equipment costs, reduces time for experimentation, and accelerates controller development for batteries. This work highlights the importance of precise predictive models in lithium-ion batteries, facilitating the effective investigation of practical applications and advancements in energy storage technology.
Anthropogenetic environmental deterioration and climate change caused by energy production and consumption pose a significant threat to the future of humanity. Renewable, environmentally friendly, and cost-effective energy sources are becoming increasingly important for addressing future energy demands. Mechanical power is the most common type of external energy that can be converted into useful electric power. Because of its strong electromechanical coupling ability, the piezoelectric mechanism is a far more successful technique for converting mechanics energy to electrical energy when compared to electrostatic, electromagnetic, and triboelectric transduction systems. Currently, the scientific community has maintained a strong interest in piezoelectric micro-power generators because of their great potential for powering a sensor unit in the distributed network nodes. A national network usually has a large mass of sensor units distributed in each city, and a self-powered sensor network is eagerly required. This paper presents a comprehensive review of the development of piezoelectric micro-power generators. The fundamentals of piezoelectric energy conversion, including operational modes and working mechanisms, are introduced. Current research progress in piezoelectric materials including zinc oxide, ceramics, single crystals, organics, composite, bio-inspired and foam materials are reviewed. Piezoelectric energy harvesting at the nano- and microscales, and its applications in a variety of fields such as wind, liquid flow, body movement, implantable and sensing devices are discussed. Finally, the future development of multi-field coupled, hybrid piezoelectric micropower generators and their potential applications are discussed.
The correlation between net baryon number and electric charge, $χ_{11}^{\rm BQ}$, can serve as a magnetometer of QCD. This is demonstrated by lattice QCD computations using the highly improved staggered quarks with physical pion mass of $M_π=135~$MeV on $N_τ=8$ and 12 lattices. We find that $χ_{11}^{\rm BQ}$ along the transition line starts to increase rapidly with magnetic field strength $eB\gtrsim 2M_π^2$ and by a factor 2 at $eB\simeq 8M_π^2$. Furthermore, the ratio of electric charge chemical potential to baryon chemical potential, $μ_{\rm Q}/μ_{\rm B}$, shows significant dependence on the magnetic field strength and varies from the ratio of electric charge to baryon number in the colliding nuclei in heavy ion collisions. These results can provide baselines for effective theory and model studies, and both $χ_{11}^{\rm BQ}$ and $μ_{\rm Q}/μ_{\rm B}$ could be useful probes for the detection of magnetic fields in relativistic heavy ion collision experiments as compared with corresponding results from the hadron resonance gas model.
Traveling wave theory is deployed today to improve the monitoring of transmission lines in electrical power grids. Most traveling wave methods require prior knowledge of the wave propagation of the transmission line, which is a major source of error as the value changes during the operation of the line. To improve the localization of events on transmission lines, we propose a new online localization method that simultaneously determines the frequency-dependent wave propagation characteristic from the traveling wave measurements of the event. Compared to conventional methods, this is achieved with one additional traveling wave measurement, but the method can still be applied in different measurement setups. We have derived the method based on the complex continuous wavelet transform. The accuracy of the method is evaluated in a simulation with a frequency-dependent transmission line model of the IEEE 39-bus system. The method was developed independently of the type of event and evaluated in test setups considering different lengths of the monitored line, line types and event locations. The localization accuracy is compared with existing online methods and analyzed with regard to the characterization capabilities. The method has a high relative localization accuracy in the range of 0.1\,\% under different test conditions.
This PhD thesis thoroughly examines the utilization of deep learning techniques as a means to advance the algorithms employed in the monitoring and optimization of electric power systems. The first major contribution of this thesis involves the application of graph neural networks to enhance power system state estimation. The second key aspect of this thesis focuses on utilizing reinforcement learning for dynamic distribution network reconfiguration. The effectiveness of the proposed methods is affirmed through extensive experimentation and simulations.
In order to mitigate the power density shortage of current energy storage systems (ESSs) in pure electric vehicles (PEVs or EVs), a hybrid ESS (HESS), which consists of a battery and a supercapacitor, is considered in this research. Due to the use of the two ESSs, an energy management should be carried out for the HESS. An optimal energy management strategy is proposed based on the Pontryagin's minimum principle in this research, which instantaneously distributes the required propulsion power to the two ESSs during the vehicle's propulsion and also instantaneously allocates the regenerative braking energy to the two ESSs during the vehicle's braking. The objective of the proposed energy management strategy is to minimize the electricity usage of the EV and meanwhile to maximize the battery lifetime. A simulation study is conducted for the proposed energy management strategy and also for a rule-based energy management strategy. The simulation results show that the proposed strategy saves electricity compared to the rule-based strategy and the single ESS case for the three typical driving cycles studied in this research. Meantime, the proposed strategy has the effect of prolonging the battery lifetime compared to the other two cases.
KHAWLA DIB, YOUCEF ZENNIR, HICHEM BOUNEZOUR
et al.
the main object of this paper is to evaluate safety barriers intervening against overpressure implemented on a crude oil heater using layers of protection analysis approach suggested in IEC 61508 (International Electrotechnical Commission) Standard for the determination of safety requirements are illustrated. Accident scenarios are pre-identified using Hazard an Operability approach, Fault tree approach is required for an effective risk assessment process. In order to better appreciate accident scenarios, PHAST (Process Hazard Analysis Software Tool) is utilized to simulate them.
Applications of electric power, Electric apparatus and materials. Electric circuits. Electric networks