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

Bhagyashri Govindrao Sherkhane, Sudarshan L. Chavan

Zhaoxuan Zhu, Sihan Xiong, Jing Li et al.

In the original publication [...]

Sahithi Maddipatla, Huzaifa Rauf, Michael Osterman et al.

Electrochemical processes within a lithium-ion battery cause electrode expansion and gas generation, thus resulting in battery swelling and, in severe cases, reliability and safety issues. This paper presents the mechanisms responsible for swelling, including thermal expansion, lithium intercalation, electrode interphase layer growth, lithium plating, and gas generation, while highlighting their dependence on material properties, design considerations, C-rate, temperature, state of charge (SoC), and voltage. The paper then discusses how swelling correlates with capacity fade, impedance rise, and thermal runaway, and demonstrates the potential of using swelling as a diagnostic and prognostic metric for battery health. Swelling models that connect microscopic mechanisms to macroscopic deformation are then presented. Finally, the paper presents strategies to mitigate swelling, including materials engineering, surface coatings, electrolyte formulation, and mechanical design modifications.

Ростислав Ярославович Скрипник, Владислав Віталійович Гриценко

У статті розглянуто актуальні питання забезпечення надійності та стійкості синхронної роботи енергосистеми в умовах стрімкого зростання частки генерації відновлюваних джерел енергії. Зокрема, проаналізовано, як збільшення обсягів генерації від відновлюваних джерел енергії призводить до зниження інерційності енергосистеми, що ускладнює підтримання частоти та балансу потужності в реальному часі. Така ситуація створює нові виклики для операторів систем передачі та розподілу, які змушені адаптувати традиційні підходи до управління режимами роботи мережі. Розглянуто сучасні виклики, пов’язані з інтеграцією відновлюваних джерел енергії в енергосистему, зокрема сонячної та вітрової генерації, які характеризуються високою варіативністю та низькою передбачуваністю. Визначено необхідність впровадження допоміжних системних послуг, таких як регулювання частоти, підтримка напруги та резерви потужності, для забезпечення стабільної роботи мережі в умовах зростаючої децентралізації генерації. Особливу увагу приділено аналізу алгоритмів керування інверторами, які можуть реалізовувати функції віртуальних синхронних машин, імітуючи поведінку традиційних синхронних генераторів. Такий підхід дозволяє забезпечити інерційний відгук, демпфування коливань частоти та синхронізацію з мережею без використання обертових мас. Розглянуто варіанти застосування систем накопичення електроенергії як ключового елементу для компенсації нестабільності відновлюваних джерел енергії. Формалізовано загальний підхід до моделювання та оптимізації використання систем накопичення електроенергії в електричних мережах, з використанням для цього розрахунків за формулами, які враховують характеристики інерції синхронного генератора, регуляторів, систем збудження та моделей ротора. Проведено оцінку ефективності цих моделей для визначення оптимальних режимів роботи з урахуванням змін навантаження, аварійних ситуацій та коливань генерації. Розглянуто можливості спеціалізованого програмного забезпечення на платформі C# (.NET), щодо інтегрування з DIgSILENT PowerFactory для проведення розрахунків статичної та динамічної стійкості, а також моделювання електричних мереж напругою 35–750 кВ.

Aisling Power, Cara-Lena Nies, Stefan Schulz

Controlling the crystal phase and lattice mismatch of semiconductors offers a powerful route to engineer electronic and optical properties of heterostructures. As a consequence, semiconductors in the wurtzite phase are increasingly sought after, superseding the thermodynamically favored cubic zinc blende phase. Empirical atomistic modeling, required for large scale simulations of heterostructures and their properties, relies heavily on valence force field (VFF) methods to find the equilibrium atomic positions in an alloy. For zinc blende crystals, VFF models are well-established. In the case of wurtzite, such parameters are frequently adopted without rigorous analysis, despite subtle but consequential differences from the zinc blende structure. Such an approach can compromise accuracy in describing material properties, since the structural differences between zinc blende and wurtzite directly influence electronic and optical characteristics. Based on the analytical VFF model by Tanner et al., and using structural similarities between wurtzite and [111]-oriented zinc blende, we construct a wurtzite VFF without introducing additional parameters. Our framework relies on analytic expressions and minimization routines to project zinc blende models onto wurtzite systems. Beyond elastic tensors, we train the model to reproduce bond length asymmetries and band gaps by using output of the VFF model in density functional theory calculations. Applied to wurtzite III-N compounds and BN, the model accurately reproduces targeted observables but also properties it has not been trained on, including the internal parameter u. We further validate the model on highly mismatched alloys such as (B,Ga)N and (B,In,Ga)N, exhibiting good agreement between VFF and density functional theory results when using identical supercells in these calculations.

Thibaud Cambronne, Samuel Bobick, Wente Zeng et al.

Demand charge, a utility fee based on an electricity customer's peak power consumption, often constitutes a significant portion of costs for commercial electric vehicle (EV) charging station operators. This paper explores control methods to reduce peak power consumption at workplace EV charging stations in a joint price and power optimization framework. We optimize a menu of price options to incentivize users to select controllable charging service. Using this framework, we propose a model predictive control approach to reduce both demand charge and overall operator costs. Through a Monte Carlo simulation, we find that our algorithm outperforms a state-of-the-art benchmark optimization strategy and can significantly reduce station operator costs.

Taiyu Gu, Ye Tian, Jiarui Yuan et al.

Samrat Acharya, Robert Mieth, Charalambos Konstantinou et al.

Chen Ma, Siyi Liu, Xian Zhang et al.

Georgia Pierrou, Claudia Valero-De la Flor, Gabriela Hug

Junting Bao, Yuan Mao, Youbing Zhang et al.

This paper reviews recent advancements in predicting the temperature of lithium-ion batteries in electric vehicles. As environmental and energy concerns grow, the development of new energy vehicles, particularly electric vehicles, has become a significant trend. Lithium-ion batteries, as the core component of electric vehicles, have their performance and safety significantly impacted by temperature. This paper begins by introducing the fundamental components and operating principles of lithium-ion batteries, followed by an analysis of how temperature affects battery performance and safety. Next, the methods for measuring and predicting battery temperature are categorized and discussed, including model-based methods, data-driven methods, and hybrid approaches that combine both. Finally, the paper summarizes the application of temperature prediction in a BMS and provides an outlook on future research directions.

Renan Lima Baima, Iván Abellán Álvarez, Ivan Pavić et al.

In response to the European Commission's aim of cutting carbon emissions by 2050, there is a growing need for cutting-edge solutions to promote low-carbon energy consumption in public infrastructures. This paper introduces a Proof of Concept (PoC) that integrates the transparency and immutability of blockchain and the Internet of Things (IoT) to enhance energy efficiency in tangible government-held public assets, focusing on curbing carbon emissions. Our system design utilizes a forecasting and optimization framework, inscribing the scheduled operations of heat pumps on a public sector blockchain. Registering usage metrics on the blockchain facilitates the verification of energy conservation, allows transparency in public energy consumption, and augments public awareness of energy usage patterns. The system fine-tunes the operations of electric heat pumps, prioritizing their use during low-carbon emission periods in power systems occurring during high renewable energy generations. Adaptive temperature configuration and schedules enable energy management in public venues, but blockchains' processing power and latency may represent bottlenecks setting scalability limits. However, the proof-of-concept weakness and other barriers are surpassed by the public sector blockchain advantages, leading to future research and tech innovations to fully exploit the synergies of blockchain and IoT in harnessing sustainable, low-carbon energy in the public domain.

Kevin Chan, Pedram Asef, Alexandre Benoit

Ahmad Bashaireh, Duaa Obeidat, Abdullah A. Almehizia et al.

Fernando Marques da Silva

Given the 516 Arouca pedestrian suspension bridge’s design and characteristics, the owner, a municipality, required a set of tests in order to evaluate its aerodynamic characteristics and dynamic response, aiming at both structural safety and user comfort. Wind tunnel tests were performed over a sectional scaled model to obtain the static aerodynamic coefficients and dynamic response. The tests were carried out on different bridge configurations—a deck with people and a deck with an arch for secondary cables (connecting each suspension point to the catenary on the opposite side of the deck)—for the static coefficients. For the dynamic response, only the deck alone was tested. A major challenge had to be overcome, as the main displacement mode belonged to a swing movement, to assemble a wind tunnel setting, requiring a suspension system allowing wind displacements. A persistent trend of small amplitude displacements was identified, influencing user comfort and contributing to the installation of the secondary cables, but no aerodynamic instabilities were identified.

Xiaofeng YANG, Yihang FANG, Pengzhen ZHAO et al.

In order to achieve the goal of “double carbon”, the development of wind power generation technology is essential. At the same time, with the increasing complexity of power grid, the real-time detection and accurate evaluation of the state of wind turbines and other power equipment are becoming increasingly important. In recent years, the development of big data technology and the improvement of power equipment data monitoring technology makes possible the application of big data technology in power equipment state recognition. Compared with the conventional methods, the above-mentioned methods are independent of accurate empirical thresholds or quantitative models, and have better adaptability to the rapid increase and variability of data. Thus, this paper applies the unsupervised (K-means) and supervised (BPNN) machine learning methods to state recognition of wind turbines, while exploring the variation of accuracy and computational efficiency after the application of dimensionality reduction methods. The results show that both machine learning methods are effective in state recognition of wind turbines, while the dimensionality reduction method can effectively improve the computational efficiency with limited accuracy loss.

Anaely Saunders Vázquez, Jyrki Luukkanen, Jari Roy Lee Kaivo-oja et al.

Sustainability Window (SuWi) analysis is a novel tool for analyzing the different dimensions of sustainability. This research defines the minimum economic development that meets social sustainability requirements and the maximum economic development so as not to exceed environmental limits. In addition, the method provides quantitative measures to define whether the development of real GDP is within the limits of sustainability. Cuba has an exciting profile among developing countries since its development has been related to sustainability in various fields. Unfortunately, the United States blockade against Cuba has severely limited development possibilities in all spheres of life. However, Cuba has developed successful sustainability policies to achieve compliance with the SDGs. SuWi's results are visualized in the Sustainability Donut, illustrating critical areas of development where policy intervention may be needed to achieve sustainability. In the Cuban case, the visualization of the donut's economy is built to analyze both strong and weak sustainability.

Mengshan Chen, Reza Abazari, Soheila Sanati et al.

Abstract Water‐splitting reactions such as the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER) typically require expensive noble metal‐based electrocatalysts. This has motivated researchers to develop novel, cost‐effective electrocatalytic systems. In this study, a new multicomponent nanocomposite was assembled by combining functionalized multiwalled carbon nanotubes, a Cu‐based metal–organic framework (MOF) (HKUST‐1 or HK), and a sulfidized NiMn‐layered double hydroxide (NiMn‐S). The resulting nanocomposite, abbreviated as MW/HK/NiMn‐S, features a unique architecture, high porosity, numerous electroactive Cu/Ni/Mn sites, fast charge transfer, excellent structural stability, and conductivity. At a current density of 10 mA cm−2, this dual‐function electrocatalyst shows remarkable performance, with ultralow overpotential values of 163 mV (OER) or 73 mV (HER), as well as low Tafel slopes (57 and 75 mV dec−1, respectively). Additionally, its high turnover frequency values (4.43 s−1 for OER; 3.96 s−1 for HER) are significantly superior to those of standard noble metal‐based Pt/C and IrO2 systems. The synergistic effect of the nanocomposite's different components is responsible for its enhanced electrocatalytic performance. A density functional theory study revealed that the multi‐interface and multicomponent heterostructure contribute to increased electrical conductivity and decreased energy barrier, resulting in superior electrocatalytic HER/OER activity. This study presents a novel vision for designing advanced electrocatalysts with superior performance in water splitting. Various composites have been utilized in water‐splitting applications. This study investigates the use of the MW/HK/NiMn‐S electrocatalyst for water splitting for the first time to indicate the synergistic effect between carbon‐based materials along with layered double hydroxide compounds and porous compounds of MOF. The unique features of each component in this composite can be an interesting topic in the field of water splitting.

Yongkang Ding, Xinjiang Chen, Jianxiao Wang

The joint optimization of power systems, mobile energy storage systems (MESSs), and renewable energy involves complex constraints and numerous decision variables, and it is difficult to achieve optimization quickly through the use of commercial solvers, such as Gurobi and Cplex. To address this challenge, we present an effective joint optimization approach for MESSs and power grids that consider various renewable energy sources, including wind power (WP), photovoltaic (PV) power, and hydropower. The integration of MESSs could alleviate congestion, minimize renewable energy waste, fulfill unexpected energy demands, and lower the operational costs for power networks. To model the entire system, a mixed-integer programming (MIP) model was proposed that considered both the MESSs and the power grid, with the goal of minimizing costs. Furthermore, this research proposed a highly efficient deep reinforcement learning (DRL)-based method to optimize route selection and charging/discharging operations. The efficacy of the proposed method was demonstrated through many numerical simulations.

Cheng ZHONG, Di ZHAI, Yang LU et al.

To address the problem of reduced communication quality in narrow underground power pipe gallery, where wireless sensor network coverage is affected by irregular shapes, obstacles, and electromagnetic interference, a power monitoring coverage sensing model is constructed based on the minimum access rate constraint, and an improved gray wolf coverage optimization algorithm is proposed by combining neuron mapping and differential evolution. Firstly, a uniform initial population is generated by neuron chaos mapping. Then, the nonlinear convergence factor is used to balance the global and local search ability. And finally, a differential evolution algorithm is introduced to mutate the gray wolf individuals. A comparative simulation analysis is made of various coverage optimization methods, and the results show that the proposed algorithm has robust search capabilities and it can significantly improve the network coverage performance in the narrow underground power pipe galleries, while effectively satisfying the communication needs of the monitored nodes.