Hasil untuk "Production of electric energy or power. Powerplants. Central stations"

Ananthu Shibu Nair, Xiao-Yu Wu, Prodip K. Das et al.

Lithium-ion batteries (LIBs) are pivotal in electric vehicles (EVs), grid storage, and portable electronics, but their high energy density introduces safety risks, particularly thermal runaway (TR). TR can lead to fires, explosions, and hazardous emissions, posing severe health and environmental threats. Experimental investigation of TR commonly relies on abuse testing methods, among which mechanical abuse via nail penetration (NP) and thermal abuse (TA) are widely used to simulate crash-induced and heat-driven failure scenarios, respectively. This review provides a comprehensive and comparative synthesis of NP and TA testing methodologies, examining how variations in test configuration, cell parameters (capacity, state of charge, and chemistry), and environmental conditions influence TR behavior and emission characteristics. Particular emphasis is placed on comparing reported emission profiles from NP- and TA-triggered TR events, including CO₂, CO, HF, hydrocarbons, and solvent vapors, and identifying the methodological origins of discrepancies across studies. By systematically linking emission variability to gas collection methods, analytical techniques, and data normalization approaches, this review highlights key limitations in current testing standards related to emission characterization. Finally, recommendations are offered for harmonizing abuse testing protocols and improving experimental design to enhance reproducibility, enabling meaningful cross-study comparison, and supporting safer deployment of LIBs in high-risk applications such as EVs and grid-scale energy storage.

Juan Pablo Bertucci, Theo Hofman, Mauro Salazar

Electrification of marine transport is a promising solution to reduce sector greenhouse gas emissions and operational costs. However, the large upfront cost of electric vessels and the required charging infrastructure can be a barrier to the development of this technology. Optimization algorithms that jointly design the charging infrastructure and the operation of electric vessels can help to reduce these costs and make these projects viable. In this paper, we present a mixed-integer linear programming optimization framework that jointly schedules ferry operations, charging infrastructure and ship battery size. We analyze our algorithms with the case of the China Zorrilla, the largest electric ferry in the world, which will operate between Buenos Aires and Colonia del Sacramento in 2025. We find that the joint system and operations design can reduce the total costs by 7.8\% compared to a scenario with fixed power limits and no port energy management system.

Rafael S.F. Ferraz, Renato S.F. Ferraz, Augusto C. Rueda-Medina et al.

Yan Li, Mingjun Ke, Jing Deng

Junda Lu, Xiangwu Yan, Jiaoxin Jia et al.

In a traditional distribution network, the weak grid structure and high penetration of renewable energy sources restrict the carrying capacity. Flexible interconnection devices (FIDs) and energy storage systems (ESSs) offer a valuable solution to these challenges by coordinating and optimizing them regarding time and space. This paper proposes a coordinated bi-level planning method for the rotary power flow controller (RPFC) and ESS. The goal is to improve both economic efficiency and the comprehensive carrying capacity of the distribution network. First, the economic advantages of RPFC over traditional FIDs are analyzed. Then, mathematical models for the RPFC and ESS are developed. A bi-level planning framework is established. The upper level minimizes total costs by optimizing the siting and sizing of RPFC and ESS. The lower level is the coordinated operation optimization level, where a comprehensive carrying capacity index system for flexible interconnection is constructed, considering system stability, economy, and flexibility. A hybrid algorithm is introduced to solve the model efficiently. It combines the improved gravitation field algorithm (IGFA) with second-order cone programming (SOCP). The algorithm uses a tent chaos map for initialization and adopts an elite retention strategy to enhance convergence. The simulation is based on a three-feeder distribution network using MATLAB and the GUROBI solver. Compared to Scheme 1, the total cost is reduced by 22.6 %, and the carrying capacity index is improved by 22.3 %, demonstrating enhanced economic performance and carrying capacity through coordinated planning of RPFC and ESS.

Eberton Rodrigues de Oliveira Neto, Fábio Júnior Damasceno Fernandes, Tuany Younis Abdul Fatah et al.

Predicting fracture intensity is essential for optimising reservoir production and mitigating drilling risks in the Brazilian pre-salt layer. However, previous studies rely excessively on conceptual models and typically do not integrate multiple types of data to perform such task. Moreover, to date, no feasibility-like studies have assessed the reasonableness of such approaches. We propose a data-driven approach that utilises upscaled well logs (Young's modulus, Poisson's ratio, and silica content) alongside seismic attributes (curvature, distance to fault) to predict fracture intensity. The distance to fault is measured using the fault probability volume estimated by a pre-trained convolutional neural network (CNN). We evaluate the effectiveness of this data-driven approach employing two tree-ensemble models, eXtreme Gradient Boosting (XGBoost) and Random Forest, to estimate the volumetric fracture intensity (P32) in the wells. Regression and residual analyses indicate that XGBoost outperforms Random Forest. Results from feature importance methods, such as permutation importance and Shapley Additive explanations (SHAP), highlight curvature as the most important feature, followed by distance to fault, Young's modulus (or P-Impedance), silica content, and Poisson's ratio. The approach has been validated with rock sampling information and two blind tests. Consequently, we believe this workflow can be applied to other wells in nearby fields. The study offers a valuable tool for quantitatively estimating fracture intensity in pre-salt reservoirs. Future research may use this study as a reference for estimating fracture intensity within a seismic volume. The predicted fracture intensity estimates can enhance the reliability of reservoir porosity models and serve as a geohazard indicator to mitigate drilling risks.

Shan-Ju Yeh, Yi-Chuan Wang, Wei-Chien Fang et al.

Impairment of upper limb function is common after a stroke and is closely linked to decreased functional independence in activities of daily living. Robot-assisted training has been used in clinical settings to improve hand function in stroke patients; however, many existing devices are costly and require specialized training to operate. This study aimed to propose a novel powered hand exoskeleton (EO) and verify its effectiveness on upper extremity function in people with chronic stroke. Thirty participants were randomly assigned to either the experimental group or the control group. Each participant underwent 30 min interventions twice a week for 8 weeks. The experimental group received 15 min of conventional therapy followed by 15 min of training with the powered hand EO, while the control group received 30 min of conventional therapy. The primary outcome measures included the Fugl-Meyer Assessment for upper extremity function (FMA-UE), the Box and Block Test (BBT), and handgrip dynamometer. Assessments were conducted at baseline and then at 4-week intervals throughout the 8-week period. Results showed that, after the 8-week intervention, the average changes in FMA-UE scores for the experimental group were significantly greater than those for the control group (<i>p</i> < 0.01). A clear upward trend in both FMA-UE and BBT scores was observed in the EO group. Statistical analysis revealed significant improvements in the overall, proximal, and distal components of the FMA-UE scores (all <i>p</i> < 0.01) and in BBT scores (both <i>p</i> < 0.05) in the EO group compared to the control group at 4 and 8 weeks, respectively. However, no significant differences in grip strength were observed between the groups at either time point. Our findings suggest that the proposed powered hand EO is both feasible and safe for training the impaired hand in stroke survivors. Given the characteristics of the device, it has potential for use in hand rehabilitation aimed at regaining upper extremity function.

Bo Sun, Mou Huang, Lingfeng Wei et al.

Lijun Fan, Changshi Liu, Bo Dai et al.

Siyang Xu, Jiebei Zhu, Bingsen Li et al.

With large-scale grid integration of renewable energy sources (RES), power grid operations gradually exhibit the new characteristics of high-order uncertainty, leading to significant challenges for system operational security. Traditional model-driven generation dispatch methods require large computational resources, whereas the widely concerned Reinforcement Learning (RL)-based methods lead to issues such as slow training speed due to the high complexity and dimension of processed grid state information. For this reason, this paper proposes a novel Grid Expert Strategy Imitation Learning (GESIL)-based real-time (5 min intervals in this paper) dispatch method. Firstly, a grid model is established based on the graph theory. Secondly, a pure rule-based grid expert strategy (GES) considering detailed power grid operations is proposed. Then, the GES is combined with the established model to obtain a GESIL agent using imitation learning by offline–online training, which can produce specific grid dispatch decisions for real-time. By designing a graph theory-based grid model, a model-driven purely rule-based GES, and embedding a penalty factor-based loss function into IL offline–online training, GESIL ultimately achieves high training speed, high solution speed, and strong generalization capability. A modified IEEE 118-node system is employed to compare the proposed GESIL to traditional dispatch method and RL method. Results show that GESIL has significantly improved computational efficiency by approximately 17 times and training speed by 14.5 times. GESIL can more stably and efficiently compute real-time dispatch decisions of grid operations, enhancing the optimization effect in terms of transmission overloading mitigation, transmission loading optimization, and power balancing control.

Tomohiro Nozawa, Renke Liu, Hideyuki Sawada

In this paper, we propose a novel tactile display that can present vibration patterns and thermal stimuli simultaneously. The vibration actuator employs a shape memory alloy (SMA) wire to generate micro-vibration with a frequency control of up to 300 Hz. The micro-vibration is conducted to a tactile pin for amplifying the vibration, to be sufficiently recognized by a user. A thermal stimulation unit, on the other hand, consists of four Peltier elements with heatsinks for heat radiation. Four vibration actuators and a thermal unit are arranged in a flat plane with a size of 20 mm × 20 mm, on which a user places the tip of an index finger to feel the presented vibratory stimuli under different temperature conditions. We conducted an experiment by employing nine subjects to evaluate the performance of the proposed tactile display and also to investigate the effects of temperature on recognizing tactile sensation. The results demonstrated that the proposed device was feasible for the quantitative diagnosis of tactile sensation. In addition, we verified that the sensitivity of tactile sensation decreased with colder stimuli.

Wenting Liu, Xianzhong Sun, Xinyu Yan et al.

Capacitors exhibit exceptional power density, a vast operational temperature range, remarkable reliability, lightweight construction, and high efficiency, making them extensively utilized in the realm of energy storage. There exist two primary categories of energy storage capacitors: dielectric capacitors and supercapacitors. Dielectric capacitors encompass film capacitors, ceramic dielectric capacitors, and electrolytic capacitors, whereas supercapacitors can be further categorized into double-layer capacitors, pseudocapacitors, and hybrid capacitors. These capacitors exhibit diverse operational principles and performance characteristics, subsequently dictating their specific application scenarios. To make informed decisions in selecting capacitors for practical applications, a comprehensive knowledge of their structure and operational principles is imperative. Consequently, this review delved into the structure, working principles, and unique characteristics of the aforementioned capacitors, aiming to clarify the distinctions between dielectric capacitors, supercapacitors, and lithium-ion capacitors.

WU Renbo, HUANG Yijun

ObjectivesAs the proportion of renewable energy in power grids increases year by year, the volatility and uncertainty of the grid are significantly heightened, posing challenges to the safe operation of distribution networks. To address the issue of distributed network reconfiguration in high-proportion renewable energy grids, this paper proposed an online rolling optimization framework.MethodsThe framework utilized a distributed consensus protocol to obtain network topology and node operation information. It can enable automatic reconfiguration in the event of N-1 and N-2 line failures, allowing the distribution network to automatically restore normal operation without the need for additional external triggering signals, thus ensuring economic operation of the grid. Additionally, a rolling optimization method was employed to handle grid fluctuations caused by the high proportion of renewable energy, and generative adversarial network (GAN) technology was used to generate new data, which combined with historical data. It can help to achieve high-precision forecasting of grid operation data.ResultsThe proposed method can achieve automatic economic optimization and self-healing in normal, single-point failure, and two-point failure scenarios.ConclusionsThis method provides an effective solution for ensuring the safe operation of distributed networks in high-proportion renewable energy grids.

Sassi Rekik, Souheil El Alimi

Renewable energy systems have emerged as a viable option to mitigate the environmental impacts of traditional fossil fuels. However, the intermittent nature of these renewables, such as solar and wind, makes it challenging to ensure a stable energy supply using only one type. Therefore, combining more than a single technology offers significant advantages in addressing the limitations associated with each individual system. Nevertheless, developing these systems requires substantial financial investments, making it crucial to identify the most suitable locations prior to installing them. In this article, the prime objective was to propose a preliminary evaluation of land suitability for constructing solar and wind hybrid facilities (PV–wind, PV–CSP, and CS–wind) in Tataouine, southern Tunisia. To this end, a GIS-based MCDA methodology was developed based on an extensive literature review and experts’ feedback while considering climate, topography, accessibility, and environmental factors. The results obtained revealed that the optimal area for a CSP–PV hybrid system is about 793 km 2 , indicating that this combination has the highest potential in terms of available resources and compatibility. On the other hand, well-suited locations for hosting CSP–wind and PV–wind systems covered areas of 412 and 333 km 2 , respectively. Such specific locations are capable of generating an annual technical potential of 316.169, 91.252, and 62.970 TWh for CSP–PV, CSP–wind, and PV–wind, respectively. Interestingly, comprising almost all of the most appropriate sites, Remada and Dhiba stand as the ideal locations for accommodating such hybrid systems. Considering this outcome, Tataouine can position itself as a model for renewable energy adoption in Tunisia. Therefore, it is imperative for policymakers, investors, and local communities to collaborate and embrace these hybrid systems to capitalize on this immense potential and pave the way for a greener and more prosperous future.

Wang Chenggen, Tang Weijia, Li Qun et al.

Zhiming Shi, Zhiyong Liu, Xiaoxi Chen et al.

Jing Huang, Xiuli Wang, Chengcheng Shao et al.

Zhiwei Wang, Dexin Li, Ziyue Yang et al.

Dominic Power, Stefan Mijin, Kevin Verhaegh et al.

Plasma-impurity reaction rates are a crucial part of modelling tokamak scrape-off layer (SOL) plasmas. To avoid calculating the full set of rates for the large number of important processes involved, a set of effective rates are typically derived which assume Maxwellian electrons. However, non-local parallel electron transport may result in non-Maxwellian electrons, particularly close to divertor targets. Here, the validity of using Maxwellian-averaged rates in this context is investigated by computing the full set of rate equations for a fixed plasma background from kinetic and fluid SOL simulations. We consider the effect of the electron distribution as well as the impact of the electron transport model on plasma profiles. Results are presented for lithium, beryllium, carbon, nitrogen, neon and argon. It is found that electron distributions with enhanced high-energy tails can result in significant modifications to the ionisation balance and radiative power loss rates from excitation, on the order of 50-75% for the latter. Fluid electron models with Spitzer-Härm or flux-limited Spitzer-Härm thermal conductivity, combined with Maxwellian electrons for rate calculations, can increase or decrease this error, depending on the impurity species and plasma conditions. Based on these results, we also discuss some approaches to experimentally observing non-local electron transport in SOL plasmas.