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

Parvathi R.V.L.N.S., Gowthami K., Tejeswararao P. et al.

The main objective of this study is to develop a high-efficiency bidirectional DC-DC converter capable of ensuring reliable power exchange within Energy Storage Systems (ESS) and improving the lifespan of series-connected batteries through precise management of charging and discharging processes. The proposed work aims to achieve stable voltage–current regulation and effective battery protection under varying load and supply conditions. To accomplish these objectives, a Multiport Bidirectional Single Ended Primary Inductor Converter (SEPIC)–Luo Converter (MB-SLC) is designed, which operate efficiently in both boost and buck modes. In addition, a cascaded Proportional–Integral (PI) control architecture is implemented for regulating the voltage, the current, and the State of Charge (SOC) with high precision. The proposed converter and control performance are validated through MATLAB/Simulink simulations conducted under diverse operating scenarios. The results demonstrate smooth voltage and current profiles, rapid transient response, accurate SOC tracking, reduced ripple levels, and high conversion efficiency of 96% in step-up mode and 95.7% in step-down mode. These outcomes confirm the ability of the proposed system to maintain reliable power exchange while minimizing stress on the battery. The significance of the obtained results lies in their contribution to enhancing battery protection, extending operational lifespan, and providing a robust solution for renewable energy–integrated ESS. By combining advanced converter topology with cascaded PI control, the study offers a practical and scalable approach to improving energy storage reliability, efficiency, and sustainability.

István Bara, Gautham RAM Chandra Mouli, Pavol Bauer

Jane Chung, Xiaomeng Peng, Kyoung Yong Park et al.

CHEN Houhe, YANG Jinhui, ZHANG Rufeng, WU Chenghao, FU Linbo

[Objective] With the continuous increase of flexibility resources in distribution networks， they can now participate in flexibility markets to provide active and reactive power flexibility support for transmission networks （TN）. This study thoroughly explored flexibility resources in distribution networks and proposes a two-stage distributed energy-flexibility market-clearing method for transmission-distribution networks， considering photovoltaic storage systems to enhance grid operational flexibility. [Methods] First， a PV storage system model was constructed by integrating energy market mechanisms with active and reactive power flexibility market mechanisms and analyzing its potential for active and reactive power support. Second， a two-stage market-clearing model for transmission-distribution networks was developed with the objective of maximizing the overall economic efficiency. Third， to preserve the privacy of the TN and distribution network information during computation， the proposed model was solved using the Alternating Direction Method of Multipliers （ADMM）. Finally， the method was validated using a test system that couples an IEEE 30-bus transmission network with two 33-bus distribution networks. [Results] The results show that when distribution system operators （DSO） participate in flexibility market transactions， the procurement costs for active and reactive flexibility resources in the TN decrease by 7.93% and the flexibility supply-demand balance index improves from 4.021 to 5.736. [Conclusions] The proposed method enhances the economic efficiency of the system， effectively reduces the total cost of procuring active and reactive flexibilities， and supports operational stability. Additionally， active distribution networks （ADN） can leverage their abundant active and reactive flexibility resources to provide flexibility services to transmission system operators （TSO）， thereby increasing ADN revenue and enabling the optimal allocation of flexibility resources across the grid. This study demonstrates the feasibility of coordinated flexibility trading between transmission and distribution networks under high renewable energy penetration conditions.

Ya‐Lu Han, Hao Wang, Hui‐Fang Di et al.

ABSTRACT Lithium‐ion batteries (LIBs) suffer from float charge failure in the grid‐scale storage market. However, the lack of a unified descriptor for the diverse reasons behind float charge failure poses a challenge. Here, a quantitative analysis of active lithium loss is conducted across multiple temperatures into float charge of Li(Ni0.5Co0.2Mn0.3)O2–graphite batteries. It is proposed that the active lithium loss can be used as a descriptor to describe the reasons for float charge quantitatively. Approximately 6.88% and 0.96% of active lithium are lost due to solid electrolyte interphase thickening and lithium deposition, which are primary and secondary failure reasons, respectively. These findings are confirmed by X‐ray photoelectron spectroscopy depth profiling, scanning electron microscope, and accelerating rate calorimeter. Titration‐gas chromatography and nuclear magnetic resonance are utilized to quantitatively analyze active lithium loss. Additionally, electrolyte decomposition at high temperatures also contributes to active lithium loss, as determined by Auger electron spectrum and nondestructive ultrasound measurements. Notably, no failure is detected in the cathode due to the relatively low working voltage of the float charge. These findings suggest that inhibiting active lithium loss can be an efficient way of delaying failure during high‐temperature float charge processes in LIBs.

Sanaz Ghanbari

This paper deals with the unified framework of siting and sizing (UFSS) for real-time simultaneous placement and capacity determination of distributed generation (DG) and energy storage systems (ESS) in the power distribution grid. The presented method uses the decision tree-based optimum bus selection along with Random Forest algorithms for precise capacity estimation to improve computational efficiency, adaptability, and scalability comparing to the conventional heuristic methods. The proposed model is investigated on the IEEE 33-bus test system, where results indicate that it indeed adeptly addresses augmented reliability, operational efficiency, and economic sustainability. More specifically, the framework achieves a reduction of 36.88 % in active power losses, improves voltage profiles by 11.92 %, decreases unserved energy levels by 59.13 %, and saves at least 30.48 % the recovery time for the system, and reduces operational costs by 16.7 %, all leading to a much better, more resilient, and cost-effective power distribution grid. By mastering the intricacies of non-linear complexities and uncertainties, UFSS enables the integration of DG and ESS to optimize power distribution, cost efficiency, and system reliability. Results demonstrate that UFSS is a scalable, intelligent, and adaptive decision-making model that advances the development of autonomous, self-optimizing, and resilient smart grids, significantly enhancing the overall safety and efficiency of modern power grids.

Francesca Rossi, Sergi Costa Dilmé, Josep Arévalo-Soler et al.

Hybrid AC/DC transmission grids incorporate High-Voltage Direct-Current links, enabled by the presence of Interconnecting Power Converters (IPCs). The control role assigned to each IPC significantly influences grid dynamics. Traditionally, these converters operate with static control roles, but recent studies have proposed scheduling their roles based on day-ahead forecasts to enhance stability performance. However, in systems with high renewable energy penetration, forecast deviations can render scheduled control assignments suboptimal or even lead to instability. To address this challenge, this work proposes an online scheduling recalculation algorithm that dynamically adapts IPC control roles during system operation. The approach leverages a data-driven multi-criteria decision-making framework, integrating surrogate models of conventional small-signal stability analysis tools to enable a fast computation of system stability and stability performance indicators.

Alix Dupont, Jean-Baptiste Breal, Yezekael Hayel et al.

Cuiping Zhao, Yi Jin, Leihua Dong et al.

Simon Stock, Davood Babazadeh, Christian Becker et al.

While the uncertainty in generation and demand increases, accurately estimating the dynamic characteristics of power systems becomes crucial for employing the appropriate control actions to maintain their stability. In our previous work, we have shown that Bayesian Physics-informed Neural Networks (BPINNs) outperform conventional system identification methods in identifying the power system dynamic behavior under measurement noise. This paper takes the next natural step and addresses the more significant challenge, exploring how BPINN perform in estimating power system dynamics under increasing uncertainty from many Inverter-based Resources (IBRs) connected to the grid. These introduce a different type of uncertainty, compared to noisy measurements. The BPINN combines the advantages of Physics-informed Neural Networks (PINNs), such as inverse problem applicability, with Bayesian approaches for uncertainty quantification. We explore the BPINN performance on a wide range of systems, starting from a single machine infinite bus (SMIB) system and 3-bus system to extract important insights, to the 14-bus CIGRE distribution grid, and the large IEEE 118-bus system. We also investigate approaches that can accelerate the BPINN training, such as pretraining and transfer learning. Throughout this paper, we show that in presence of uncertainty, the BPINN achieves orders of magnitude lower errors than the widely popular method for system identification SINDy and significantly lower errors than PINN, while transfer learning helps reduce training time by up to 80 %.

Mostafa Sayahkarajy, Hartmut Witte

Bio-inspired robots with elongated anatomy, like eels, are studied to discover anguilliform swimming principles and improve the robots’ locomotion accordingly. Soft continuum robots replicate animal–environment physics better than noncompliant, rigid, multi-body eel robots. In this study, a slender soft robot was designed and tested in an actual swimming experiment in a still-water tank. The robot employs soft pneumatic muscles laterally connected to a flexible backbone and activated with a rhythmic input. The position of seven markers mounted on the robot’s backbone was recorded using QualiSys^® Tracking Manager (QTM) 1.6.0.1. The system was modeled as a fully coupled fluid–solid interaction (FSI) system using COMSOL Multiphysics^® 6.1. Further data postprocessing and analysis were conducted, proposing a new mode decomposition algorithm using simulation data. Experiments show the success of swimming with a velocity of 28 mm/s and at a frequency of 0.9 Hz. The mode analysis allowed the modeling and explanation of the fluctuation. Results disclose the presence of traveling waves related to anguilliform waves obtained by the superposition of two main modes. The similarities of the results with natural anguilliform locomotion are discussed. It is concluded that soft robot undulation is ruled by dynamic modes induced by robot–environment interaction.

Mandira S. Marambe, Bradley S. Duerstock, Juan P. Wachs

Individuals with disabilities and persons operating in inaccessible environments can greatly benefit from the aid of robotic manipulators in performing daily living activities and other remote tasks. Users relying on robotic manipulators to interact with their environment are restricted by the lack of sensory information available through traditional operator interfaces. These interfaces deprive users of somatosensory feedback that would typically be available through direct contact. Multimodal sensory feedback can bridge these perceptual gaps effectively. Given a set of object properties (e.g., temperature, weight) to be conveyed and sensory modalities (e.g., visual, haptic) available, it is necessary to determine which modality should be assigned to each property for an effective interface design. The goal of this study was to develop an effective multisensory interface for robot-assisted pouring tasks, which delivers nuanced sensory feedback while permitting the high visual demand necessary for precise teleoperation. To that end, an optimization approach was employed to generate a combination of feedback properties to modality assignments that maximizes effective feedback perception and minimizes cognitive load. A set of screening experiments tested twelve possible individual assignments to form this optimal combination. The resulting perceptual accuracy, load, and user preference measures were input into a cost function. Formulating and solving as a linear assignment problem, a minimum cost combination was generated. Results from experiments evaluating efficacy in practical use cases for pouring tasks indicate that the solution was significantly more effective than no feedback and had considerable advantage over an arbitrary design.

Ranjita Chowdhury, Bijoy K. Mukherjee, Puneet Mishra et al.

Lin Yufang, Chen Chang, Huang Huiyu et al.

Marco Giacomo Flammini, Giuseppe Prettico, Andreea Julea et al.

J. Frotey, E. Castex, E. Hittinger et al.

Qing‐Yuan Zhao, Guang‐Yuan Yin, Yi‐Feng Liu et al.

Abstract The pursuit of green and sustainable energy is a long‐term goal for modern society and people's life. Particularly under the context of carbon neutralization, decarbonization has become a consensus and propels the turning of research enthusiasm to explore new materials and chemistries for energy conversion and storage at a low expenditure. Zinc (Zn) enabled redox flow batteries (RFBs) are competitive candidates to fulfill the requirements of large‐scale energy storage at the power generation side and customer end. Considering the explosive growth, this review summarizes recent advances in material chemistry for zinc‐based RFBs, covering the cathodic redox pairs of metal ions, chalcogens, halogens, and organic molecules. After a brief introduction of common issues for Zn2+/Zn conversion reaction at the anode side, the focus is devoted to expounding challenges of redox species and possible problem‐solving strategies at the cathode side. Besides, the auxiliary components of separator and current collector are also discussed for achieving optimal RFBs' performance. At last, the conclusion and outlook of future endeavor for Zn‐based RFBs implementation are put forward.

Anna Szczucka, Katarzyna Lisek, Barbara Worek et al.

Artykuł przedstawia główne wnioski z I edycji badania sieci społecznych w obrębie Sieci Kompetencji ds. Energetyki Rozproszonej (SKER) oraz jej relacji z otoczeniem instytucjonalnym. Skupia się na relacjach pomiędzy członkami SKER i ich konsekwencjach dla komunikacji w organizacji, a także na najważniejszych aktorach poza jej strukturami, z którymi członkowie SKER aktywnie współpracują.

Giulia Gemme, Gian Marcello Andolina, Francesco Maria Dimitri Pellegrino et al.

We investigate a Dicke quantum battery in the dispersive regime, where the photons trapped in a resonant cavity are much more energetic with respect to the two-level systems embedded into it. Under such off-resonant conditions, even an empty cavity can lead to the charging of the quantum battery through a proper modulation of the matter–radiation coupling. This counterintuitive behaviour has its roots in the effective interaction between two-level systems mediated by virtual photons emerging from the fluctuations of the quantum electromagnetic field. In order to properly characterize it, we address relevant figures of merit such as the stored energy, the time required to reach the maximum charging, and the averaged charging power. Moreover, the possibility of efficiently extracting energy in various ranges of parameters is discussed. The scaling of stored energy and power as a function of the number <i>N</i> of two-level systems and for different values of the matter–radiation coupling is also discussed, showing, in the strong coupling regime, performances in line with what is reported for the Dicke quantum battery in the resonant regime.

Ana Pavlićević, Saša Mujović

Climate change at the global level has accelerated the energy transition around the world. With the aim of reducing CO2 emissions, the paradigm of using electric vehicles (EVs) has been globally accepted. The impact of EVs and their integration into the energy system is vital for accepting the increasing number of EVs. Considering the way the modern energy system functions, the role of EVs in the system may vary. A methodology for analyzing the impact of reactive power from public electric vehicle charging stations (EVCSs) on two main indicators of the distribution system is proposed as follows: globally, referring to active power losses, and locally, referring to transformer aging. This paper indicates that there is an optimal value of reactive power coming from EV chargers at EVCSs by which active energy losses and transformer aging are reduced. The proposed methodology is based on relevant models for calculating power flows and transformer aging and appropriately takes into consideration the stochastic nature of EV charging demand.