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

Aidin Shaghaghi, Mohammad Taghitahooneh, Sajad Qezelbigloo et al.

Hongchun Shu, Baisong Liu, Weijie Lou et al.

As global temperatures rise, the frequency of lightning increases, making lightning one of the principal threats to power-system security. Field incidents indicate that, within the automatic reclosing time window, multiple-stroke lightning can exhibit behavior resembling a permanent fault, which leads misoperation and reclosure failure and, in many cases, severe outages. Analysis in this paper indicates that, within this window, transient impulses on the microsecond scale from successive strokes superimpose on the secondary arc current, driving dynamic arc re-ignition and in extreme cases, sustained arcing on the transmission line. This mechanism distorts the steady-state electrical quantities used by conventional criteria and accounts for the observed misoperations and failures to operate. To address this problem, this paper proposes an identification method for quasi-single phase permanent fault (Q-SPF) based on multi-level confidence rules. The scheme adopts a cascaded logic that comprises arc sustainment identification, fault occurrence identification, and lightning fault identification, together with corresponding confidence assessments, to accurately distinguish Q-SPF events induced by multiple strokes. Robustness is verified on an RTDS model under variations in sensor deployment, lightning striking location, conventional short-circuit faults, positive-polarity lightning, and double-circuit same-tower configurations.© 2017 Elsevier Inc. All rights reserved.

Qian Xiao, Zheng Ran, Wu Jiayi et al.

Bobok I.I., Kobozieva A.A.

In the context of the rapid development of information technologies, their implementation in the process of functioning of critical infrastructure, in particular energy, of any state is extreme-ly relevant. At the same time, the continuity and quality of functioning of any automated system in the digital space critically depends on ensuring the integrity of the information used. The ef-fectiveness of digital content integrity expertise methods is determined by their theoretical foun-dations. Existing theoretical approaches do not allow obtaining a final solution to this problem. The aim of this work is to develop a general approach to the analysis of the state of information systems, based on the theory of matrices, for its use in the examination of the integrity of digital content. The objective was achieved by investigating the perturbation properties of singular val-ues and singular vectors of the image (or video frame) matrix as a result of perturbation for orig-inal and non-original contents. The most important results of the work are: substantiation for non-original content of the destruction of the monotony of the trend of the function of depend-ence of the disturbance of the singular number on its number, which takes place for original content; substantiation of the fundamental possibility of estimating the magnitude of the perturb-ing effect. The significance of the obtained results lies in their subsequent use for the develop-ment of universal methods for examining the integrity of digital images, video, in particular ste-ganoanalytical methods, which make it possible not only to identify the fact of integrity viola-tion, but also to assess the magnitude of the perturbing effect, which is extremely important in steganoanalysis, where this value characterizes the throughput of a hidden communication channel.

Ziyue Song, Tianjie Feng, Donald W. Kirk et al.

This study investigated the electrochemical properties of supercapacitors by pre-modifying thick birch biochar monoliths with FeCl₃ or C₆H₅FeO₇ solutions prior to wood pyrolysis. The pre-modification introduced iron species to the surface, promoting the specific surface area, charge-stored species, and surface functionalities, which enhanced the gravimetric capacitance. X-ray diffraction confirmed the successful loading of Fe₃O₄ and Fe. SEM implied the wider distribution of iron-rich particulates and porous carbon via self-pyrolysis on the biochar surface modified with 1.0 M C₆H₅FeO₇. Contact angle measurements demonstrated the enhanced wettability of the biochar surfaces following pre-modification, with the C₆H₅FeO₇-modified samples exhibiting superior wettability compared to the other groups. The gravimetric capacitance of the supercapacitor was dramatically promoted and reached 210 F/g and 219 F/g, respectively, when modified with 1.0M C₆H₅FeO₇ and 1.0 M FeCl₃ at a 5 mA/g current density. Compared to the birch biochar modified with 1.0 M FeCl₃, the 1.0 M C₆H₅FeO₇ had a higher current response peak and capacitive behavior in the CV analysis, demonstrated better ion diffusion capacity, and had lower charge-transfer resistance in the EIS results. But, a slight irreversible process on the electrode of the 1.0 M C₆H₅FeO₇ group led to a lower level of the supercapacitor capacitance retention. The results using ferric solution pre-impregnation show how iron species doping can improve capacitance behavior, providing a feasible scheme for the modification of thick biochar monolith.

Yujun Wu, Shaobing Tang, Wenbo Shi et al.

ABSTRACT Development of high‐efficiency bifunctional oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) electrocatalysts is vital for the widespread application of zinc–air batteries (ZABs). However, it still remains a great challenge to avoid the inhomogeneous distribution and aggregation of metal single‐atomic active centers in the construction of bifunctional electrocatalysts with atomically dispersed multimetallic sites because of the common calcination method. Herein, we report a novel catalyst with phthalocyanine‐assembled Fe‐Co‐Ni single‐atomic triple sites dispersed on sulfur‐doped graphene using a simple ultrasonic procedure without calcination, and X‐ray absorption fine structure (XAFS), aberration‐corrected scanning transmission electron microscopy (AC‐STEM), and other detailed characterizations are performed to demonstrate the successful synthesis. The novel catalyst shows extraordinary bifunctional ORR/OER activities with a fairly low potential difference (ΔE = 0.621 V) between the OER overpotential (Ej10 = 315 mV at 10 mA cm−2) and the ORR half‐wave potential (Ehalf‐wave = 0.924 V). Moreover, the above catalyst shows excellent ZAB performance, with an outstanding specific capacity (786 mAh g−1), noteworthy maximum power density (139 mW cm−2), and extraordinary rechargeability (discharged and charged at 5 mA cm−2 for more than 1000 h). Theoretical calculations reveal the vital importance of the preferable synergetic coupling effect between adjacent active sites in the Fe‐Co‐Ni trimetallic single‐atomic sites during the ORR/OER processes. This study provides a new avenue for the investigation of bifunctional electrocatalysts with atomically dispersed trimetallic sites, which is intended for enhancing the ORR/OER performance in ZABs.

Paweł Ocłoń, Marek Czamara, Franciszek Ścisłowicz et al.

Artykuł przedstawia system RESHeat, który łączy odnawialne źródła energii (OZE) służące do produkcji energii elektrycznej, ciepła i chłodu dla budynków wielorodzinnych, z naciskiem na zrównoważone rozwiązania energetyczne. System, opracowany w ramach programu Horyzont 2020, integruje technologie takie jak panele fotowoltaiczne (PV), moduły fotowoltaiczno-termiczne (PV-T), pompy ciepła oraz sezonowe magazynowanie energii cieplnej (STES). Jego głównym zadaniem jest zmniejszenie emisji gazów cieplarnianych oraz zapewnienie efektywności energetycznej budynków. W artykule omówiono szczegóły instalacji demonstracyjnej w Krakowie, gdzie system osiągnął wysoką wydajność, dostarczając do 70% energii potrzebnej do ogrzewania i chłodzenia budynku. System RESHeat ma duży potencjał komercjalizacji i skalowania, a także jest obiecującym rozwiązaniem dla ogrzewania i produkcji energii elektrycznej w budynkach wielorodzinnych.

Matteo Cederle, Andrea Mazzucco, Andrea Demartini et al.

Electric vehicles (EV) charging stations are one of the critical infrastructures needed to support the transition to renewable-energy-based mobility, but ensuring their reliability and efficiency requires effective anomaly detection to identify irregularities in charging behavior. However, in such a productive scenario, it is also crucial to determine the underlying cause behind the detected anomalies. To achieve this goal, this study investigates unsupervised anomaly detection techniques for EV charging infrastructure, integrating eXplainable Artificial Intelligence techniques to enhance interpretability and uncover root causes of anomalies. Using real-world sensors and charging session data, this work applies Isolation Forest to detect anomalies and employs the Depth-based Isolation Forest Feature Importance (DIFFI) method to identify the most important features contributing to such anomalies. The efficacy of the proposed approach is evaluated in a real industrial case.

Ramón Piedra-de-la-Cuadra, Francisco Ortega

This work consists of a procedure to optimally select, among a group of candidate sites where gas stations were already located, a sufficient number of charging points in order to guarantee that an electric vehicle can make its journey without a problem of energy autonomy and that each selected charging station has another one that serves as useful support in case of failure (reinforced coverage service). For this purpose, we propose a bilevel model that, in a former level, minimizes the number of refuelling points necessary to guarantee a reinforced service coverage for all users who transit from their origin to destination and, as a second level, maximize the volume of demand that can be satisfied subject to budgetary restrictions. With the first of the objectives we are addressing the typical requirement of the administration, which consists of guaranteeing the viability of the solutions, and the second of the objectives is a criterion typically used by the private sector initiative, compatible with the profit maximization.

Zhen Zhang, Wenjun Xian, Weijun Tan et al.

The optimal dispatching of renewable energy power stations is particularly crucial in scenarios where the stations face energy rationing due to the large proportion of renewable energy integrated into the power system. In order to achieve safe, economical, and fair scheduling of renewable energy power stations, this paper proposes a two-stage scheduling framework. Specifically, in the initial stage, the maximum consumption space of renewable energy for the system can be optimized by optimizing the formulated safe-economic dispatch model. In the second stage, the fair allocation mechanism of renewable energy power stations is proposed based on the game-fairness empowerment approach. In order to obtain a comprehensive evaluation of renewable energy power stations, an evaluation index system is constructed considering equipment performance, output characteristics, reliability, flexibility, and economy. Subsequently, the cooperative game weighting method is proposed to rank the performance of renewable energy power stations as the basis for fair dispatching. Simulation results show that the proposed scheduling strategy can effectively ensure the priority of renewable energy power stations based on their comprehensive ranking, and improve the safety, economy, and fairness of power station participation in scheduling.

Ahmed Abd Elaziz Elsayed, Hany E. Z. Farag

Xiaofei You, Xiangjun Liu, Chaoxian Wu

Vicentiu-Iulian Savu, Chris Brace, Georg Engel et al.

Equivalent circuit models represent one of the most efficient virtual representations of battery systems, with numerous applications supporting the design of electric vehicles, such as powertrain evaluation, power electronics development, and model-based state estimation. Due to their popularity, their parameter extraction and model parametrization procedures present high interest within the research community, with novel approaches at an elementary level still being identified. This article introduces and compares in detail two novel parameter extraction methods based on the distinct application of least squares linear regression in relation to the autoregressive exogenous as well as the state-space equations of the double polarization equivalent circuit model in an iterative optimization-type manner. Following their application using experimental data obtained from an NCA Sony VTC6 cell, the results are benchmarked against a method employing differential evolution. The results indicate the least squares linear regression applied to the state-space format of the model as the best overall solution, providing excellent accuracy similar to the results of differential evolution, but averaging only 1.32% of the computational cost. In contrast, the same linear solver applied to the autoregressive exogenous format proves complementary characteristics by being the fastest process but presenting a penalty over the accuracy of the results.

Matthias J. Bosch, Markus Nitzlader, Matthias Bachmann et al.

A high proportion of the CO₂ emissions worldwide are caused by the construction sector or are associated with buildings. Every part of the industry needs to reduce its share of emissions, so the building sector must also do its part. One possible solution for achieving this reduction in the field of load-bearing structures is the use of adaptive structures. This research focuses on adaptive slab structures, which require specific actuators to be integrated into the system. Conventional actuators are not suitable due to the prevailing requirements, namely installation space and performance. For this investigation, the actuator is divided into different functional components. A rough description of the requirements for one component, namely the energy converter, is given. Different concepts are developed, tested, and compared with numerical results. Due to the requirements, the concepts are limited to hydraulics. The authors then present a comparison of different simulation strategies for the energy converter. Overall, this paper provides a new contribution to the design of energy converter concepts for integrated hydraulic actuators in slabs, along with experimental verification of the working principle of the energy converters to meet the requirements. A simplified numerical model is proposed to estimate the behavior of the energy converter during the early design phase.

Yuanpeng Liu, Jiawei Zhang, Bowen Zhang et al.

We establish an analytical criterion for stable electrodeposition by combining electrochemistry and mechanics. In situ experiments combined with molecular simulations are performed to validate the robustness of the theoretical predictions. Our analysis shows that stable electrodeposition is intimately linked with the interfacial defect size, fracture toughness, and molar volume of solid electrolytes. We find an exponential scaling law between the critical current density and the interfacial defect size, and the inherent softening in strength of grain boundaries lowers the critical current density in polycrystalline solid electrolytes. Modeling and analyses provide roadmaps to design solid electrolytes with metallic anodes for achieving stable electrodeposition.

Qionghua Liao, Guilong Li, Jiajie Yu et al.

With the proliferation of electric vehicles (EVs), the transportation network and power grid become increasingly interdependent and coupled via charging stations. The concomitant growth in charging demand has posed challenges for both networks, highlighting the importance of charging coordination. Existing literature largely overlooks the interactions between power grid security and traffic efficiency. In view of this, we study the en-route charging station (CS) recommendation problem for EVs in dynamically coupled transportation-power systems. The system-level objective is to maximize the overall traffic efficiency while ensuring the safety of the power grid. This problem is for the first time formulated as a constrained Markov decision process (CMDP), and an online prediction-assisted safe reinforcement learning (OP-SRL) method is proposed to learn the optimal and secure policy by extending the PPO method. To be specific, we mainly address two challenges. First, the constrained optimization problem is converted into an equivalent unconstrained optimization problem by applying the Lagrangian method. Second, to account for the uncertain long-time delay between performing CS recommendation and commencing charging, we put forward an online sequence-to-sequence (Seq2Seq) predictor for state augmentation to guide the agent in making forward-thinking decisions. Finally, we conduct comprehensive experimental studies based on the Nguyen-Dupuis network and a large-scale real-world road network, coupled with IEEE 33-bus and IEEE 69-bus distribution systems, respectively. Results demonstrate that the proposed method outperforms baselines in terms of road network efficiency, power grid safety, and EV user satisfaction. The case study on the real-world network also illustrates the applicability in the practical context.

Jean-Michel Clairand, Mario González-Rodríguez, Rajesh Kumar et al.

Mostafa M. Mousa, Saber M. Saleh, Mohamed Mahmoud Samy et al.

Hirotada Gamo, Atsushi Nagai, Atsunori Matsuda

All-solid-state batteries (ASSBs) are promising to be next-generation battery that provides high energy density and intrinsic safety. Research in the field of ASSBs has so far focused on the development of highly conductive solid electrolytes (SEs). The commercialization of ASSBs requires well-established large-scale manufacturing for sulfide SEs with high ionic conductivity. However, the synthesis for sulfide SEs remains at the laboratory scale with limited scalability owing to their air sensitivity. The liquid-phase synthesis would be an economically viable manufacturing technology for sulfide SEs. Herein, we review a chemical perspective in liquid-phase synthesis that offers high scalability, low cost, and high reaction kinetics. This review provides a guideline for desirable solvent selection based on the solubility and polarity characterized by the donor number and dielectric permittivity of solvents. Additionally, we offer a deeper understanding of the recent works on scalable liquid-phase synthesis using solubilizers and reactant agents. We present an outlook on a universal liquid-phase synthesis of sulfide SEs toward the commercialization of sulfide-based ASSBs.

Yang Li, Wenjie Ma, Yuanzheng Li et al.

Optimally scheduling multi-energy flow is an effective method to utilize renewable energy sources (RES) and improve the stability and economy of integrated energy systems (IES). However, the stable demand-supply of IES faces challenges from uncertainties that arise from RES and loads, as well as the increasing impact of cyber-attacks with advanced information and communication technologies adoption. To address these challenges, this paper proposes an innovative model-free resilience scheduling method based on state-adversarial deep reinforcement learning (DRL) for integrated demand response (IDR)-enabled IES. The proposed method designs an IDR program to explore the interaction ability of electricity-gas-heat flexible loads. Additionally, the state-adversarial Markov decision process (SA-MDP) model characterizes the energy scheduling problem of IES under cyber-attack, incorporating cyber-attacks as adversaries directly into the scheduling process. The state-adversarial soft actor-critic (SA-SAC) algorithm is proposed to mitigate the impact of cyber-attacks on the scheduling strategy, integrating adversarial training into the learning process to against cyber-attacks. Simulation results demonstrate that our method is capable of adequately addressing the uncertainties resulting from RES and loads, mitigating the impact of cyber-attacks on the scheduling strategy, and ensuring a stable demand supply for various energy sources. Moreover, the proposed method demonstrates resilience against cyber-attacks. Compared to the original soft actor-critic (SAC) algorithm, it achieves a 10% improvement in economic performance under cyber-attack scenarios.