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

Javier Martínez-Gómez

Electric mobility has emerged as a pivotal component of global decarbonization and sustainable transport strategies [...]

Ismail A. Soliman, Vladimir Tulsky, Hossam A. Abd el-Ghany

Mukminov I., Pysarevskyi I., Volgusheva N. et al.

The main objective of this study is to determine the energy efficiency of a sensible heat storage system employing a dense crushed stone bed in a vertical heat exchange channel, as well as a latent heat storage system using a phase change material based on paraffin T3, intended for greenhouse applications. To achieve this objective, several tasks were performed, including analytical and experimental investigation of heat transfer processes in thermal energy storage system elements using a greenhouse model, analysis of the temporal variation of temperature profiles and solar radiation intensity, and a comparative evaluation of the energy efficiency of phase change heat storage materials, represented by modified paraffin, and capacitive heat storage systems, represented by crushed stone. The most significant results demonstrate that the derived analytical relationships for calculating working medium temperatures adequately describe the physical process of heat accumulation when experimental heat transfer coefficients are considered. Heat exchange between the dense crushed stone bed and the water flow occurs with high intensity, with an average heat transfer coefficient of α = 80 W/(m²·K). The emissivity of the surface of paraffin-filled heat storage tubes was determined to be εp = 0.65. The significance of the results lies in defining conditions for efficient application of thermal energy storage systems in greenhouse practice. For thermal stabilization of the internal greenhouse volume, the use of modified paraffin T3 is recommended, as its heat storage efficiency is 7.5–9.3 times higher than that of a dense crushed stone bed.

Ismail A. Soliman, Vladimir Tulsky, Hossam A. Abd el-Ghany et al.

Xiaotang Xia, Tingyang Li

G. Carpinelli, A.R. Di Fazio, S. Perna et al.

In order to exploit the flexibility provided by distributed energy resources (DERs), a multi-objective optimization (MOO) approach is proposed to minimize the bus voltage deviations, the network losses, and the current security index. Effective linear power flow equations are included into both the objective functions and the inequality constraints of the MOO model, thus yielding benefits in terms of reduced model dimension and computational complexity. The weighted sum (WS) method with the a-priori assignment of weights is used to transform the MOO into a single-objective optimization (SOO) that directly provides the final solution on the Pareto front. Six surrogate weight methods (SWMs) are utilized to support the decision-maker in the weight assignment. A validation procedure, based on Monte Carlo simulation, is introduced to determine on a case-by-case basis the best SWM for the short-term dispatch of the DERs. The MOO is tested on a real low voltage smart grid with photovoltaic systems, battery storages, and controllable loads. The obtained results demonstrate the high accuracy and low computational effort of the proposed method, indicate the most accurate SWM in the specific application, and show the effectiveness of the proposal with respect to other MOO approaches.

M Yalçınöz, L Fallarino, M de Lasen-Tejada et al.

Sodium (Na) metal is a highly attractive anode material for next-generation batteries due to its natural abundance and low cost, but its practical use is limited by the poor reversibility of the Na plating/stripping process and instability during cycling. Herein, nanoscale metallic films (Ti, Ni, Ge and In) with different thicknesses are grown by magnetron sputtering to coat commercial stainless steel 316L current collectors, and the substrate-dependent Na plating/stripping characteristics are thoroughly explored. The results reveal that sodiophilic interphases can be achieved by in situ formation of M–Na (M = Ge, In) alloys. A controlled protocol is developed to electrochemically form ultrathin Ge–Na or In–Na alloy buffer layers in the first cycle prior to Na plating, serving as pillars for a stable subsequent Na metal deposition and boosting the buildup of a highly efficient thin Na metal anode. Among the tested materials, 50 nm thick In coatings exhibit the most stable long-term plating/stripping process. These findings demonstrate a simple and effective interfacial engineering strategy to enhance the performance of Na metal anodes, providing a pathway toward safer and long-lasting sodium metal batteries.

Djordje Stanković, Andjela Draganić, Irena Orović et al.

This paper proposes a novel approach for detecting and classifying partial discharge transients in power cable signals recorded from substation environments, based on analysis in the Hermite transform domain. Partial discharges are short-duration, fast-changing phenomena commonly observed within power cables used in substations and communication systems. Accurate partial discharge detection plays a critical role in predictive maintenance of power transmission and distribution networks, as early identification of such events can prevent costly insulation failures. While traditional partial discharge detection techniques rely on energy thresholds, dictionary projections, Fourier transform or time–frequency representations such as spectrograms, recent methods increasingly emphasize sparse representations and entropy-based analysis to improve sensitivity and robustness. In line with this direction, the research introduces an approach based on the Hermite transform for transient events detection, due to the inherent similarity between Hermite functions and the shapes of partial discharge-related transients. To enhance sparsity and representation quality, a scaled and optimized Hermite transform is employed. Concentration measures such as the ℓ1-norm and Rényi entropy, calculated in the Hermite domain, are used to classify transient signals. The proposed method is experimentally validated using real-world signal sets containing three distinct classes of transients recorded from substation equipment. Results demonstrate that the combination of the scaled Hermite transform, concentration measures, and statistical parameters enables effective classification when integrated with the Expectation-Maximization algorithm. Furthermore, the procedure can be automated, and techniques such as the Parzen window method can be employed to further improve classification performance.

Yanis Hamoudi, Maher G.M. Abdolrasol, Hocine Amimeur et al.

Wind energy systems are often located in remote areas or offshore, making maintenance and repair both expensive and logistically challenging. Fault-tolerant systems can assist in minimizing the frequency and urgency of maintenance, ultimately reducing operational costs. This study aims to design a simple and efficient Open-Phase Fault Tolerant (OPFT) control for a wind power system (WPS) based on an asymmetric six-phase induction generator using Finite-Set Predictive Power Control (FS-PPC). The suggested technique involves three key steps. Firstly, the current harmonics in the ( x, y ) plane are analyzed to discover faults. Then, open-phase localization is achieved using the Support Vector Machine (SVM) with hyperparameter Bayesian Optimization (BO). Finally, the phase that forms 90 degrees with the faulty phase is opened to restore the system’s stability. Importantly, this approach does not require reconfiguring the control algorithm while preserving the system’s effective performance. Simulation results demonstrate the effectiveness of the OPFT-SVM-PPC control strategy in preserving control over the machine while ensuring high energy quality for the grid with a THD of 2.71%. By implementing this fault tolerance control, the system can operate reliably and deliver high-quality power, even in the presence of open-phase faults.

Shaokang Ren, Lei Ren, Biancheng Wei et al.

Metal structures with special shapes at the length scales of electromagnetic waves, particularly visible light (∼10–7 m), hold great promise in the development of next-generation electronic/optical devices. However, downscaling the metal structure features to the sub-10 nm scale remains a challenge due to the resolution limitations inherent in conventional top-down microfabrication techniques. In recent years, DNA nanotechnology has garnered significant attention due to its capability to construct nanostructures with programmable shapes at the nanometer scale, which can serve as templates for the fabrication of metal nanostructures. Here, we review the development of DNA-templated metal nanostructures with unique shapes, focusing on their electronic and optical properties and applications. We discuss the advantages and limitations of these strategies and provide an outlook for this research area.

Hyeon Hye Kim, Kay-Hyeok An, Byung-Joo Kim

The increasing concentration of carbon dioxide (CO₂) in the atmosphere necessitates the development of efficient carbon capture and storage (CCS) technologies. Among these, adsorption-based methods using porous carbon (PC) have attracted considerable attention due to their low energy requirements and cost-effectiveness. Biomass waste-derived porous carbon is particularly attractive as a sustainable alternative, offering environmental benefits and high-value applications with low costs. In this study, coffee grounds (CGs) were selected as a precursor due to their abundance and cost-effectiveness compared with other biomass wastes. To improve the pore characteristics of CG-derived carbon (CCG), boric acid treatment was applied during carbonization followed by steam activation to prepare boron-doped CG-derived porous carbon (B-PCG). The N₂/77K adsorption–desorption isotherms revealed a significant increase in the specific surface area and total pore volume of B-PCG from 1590 m²/g and 0.71 cm³/g to 2060 m²/g and 1.01 cm³/g, respectively, compared with PCG. Furthermore, high pressure CO₂ adsorption analysis at 298 K up to 50 bar showed an approximately 50% improvement in CO₂ adsorption capacity for B-PCG compared with PCG. These results suggest that boron doping is an effective strategy to optimize the pore structure and adsorption performance of biomass-derived porous carbon materials for CCS application.

ZHANG Wenxuan, SU Jia, DU Xinhui, ZHANG Zhishuo, WANG Qianchun, JIANG Haipeng

[Objective] To completely exploit the coupling flexibility of the electric-hydrogen-gas-storage-demand response， a data-driven two-stage distributed robust collaborative planning model for integrated energy systems is proposed. [Methods] To address the problems of model inaccuracy and low solving efficiency of existing equipment modeling methods， a refined modeling method for an integrated energy system was proposed， which considered a refined model of distributed power supply， energy coupling equipment， hybrid energy storage， and demand response mechanism. A demand response incentive mechanism considering baseline uncertainty was developed. [Results] The MATLAB simulation results showed that the baseline load prediction model based on Gaussian process regression can calculate the baseline load more accurately and rapidly while simultaneously considering the response uncertainty. In addition， the equipment refinement model proposed in this study effectively reduced the comprehensive planning cost of the system， in which the operation， planning， carbon trading， and demand response costs were reduced by 2.55%， 10.78%， 1.08%， and 2.55%， respectively. Simultaneously， through the collaborative optimization of carbon trading and demand response mechanisms， the system could reduce the power purchased by the upper power grid and use flexible loads and distributed power sources to achieve a low-carbon and stable operation of the integrated energy system. The example showed that compared with the SO and RO methods， the proposed DRO planning method had more advantages in terms of the balance of economy and robustness and verified its applicability in integrated energy system planning. [Conclusions] The integrated energy system planning model based on demand response can significantly reduce the annual comprehensive cost of the system， improve the utilization rate of renewable energy， and reduce carbon emissions， providing ideas for subsequent research on the planning of the electric-hydrogen-gas integrated energy systems.

Zesen Li, Xiaoyan Hu, Jing Shi et al.

Jinggang Wang, Zhe Liu, Weijun Teng et al.

Azreen Junaida Abd Aziz, Nurul Akidah Baharuddin, Rasyikah Md. Khalid et al.

In 2022, Malaysia was ranked 28th worldwide in terms of its energy oil consumption. Energy consumption in Malaysia has been predominantly reliant on natural gas and coal in both the past and present. Oil and gas in Malaysia are predicted to be depleted in 14 years due to the high energy consumption, especially from petroleum sources. Thus, the Malaysian government aims to expand renewable energy (RE) in the country's energy mix as an alternative source of energy. As of 2022, Malaysia has generated roughly 2% of its electricity from various renewable sources, which is still far from the initial target of reaching 20% RE penetration by 2030. However, since 2017, RE has started to contribute to energy mix generation. Several policies, including an act, have been implemented in Malaysia to achieve the target in RE, but many challenges and difficulties have hindered the progress. Thus, the present study explored the current status and challenges for RE in Malaysia and discussed the effectiveness of the available energy policies and programs. The outcomes are potentially valuable to Malaysian policymakers, industries and researchers to improve their current practices for achieving the initial national RE target by 2030 as well as to move forward towards net zero emissions by 2050. This study provides a crucial roadmap for Malaysia to achieve its RE objectives and contribute significantly to the international transition towards a more environmentally friendly and sustainable future.

Chengshuai HUANG, Jian LIANG, Bo LI et al.

In order to achieve low-carbon and high-efficiency operation of natural gas stations driven by hydrogen, a novel integrated energy system is proposed in this paper. The steam cycle is used to recover the waste heat generated by gas turbines. The electrical energy is used to drive the solid oxide electrolysis hydrogen production system to produce hydrogen, and then the mixture of methane and hydrogen is used as the fuel of gas turbine, and the compressed air energy storage technology is used to convert renewable energy into stable electrical energy output. The calculation results indicate that under design conditions, the energy efficiency, exergy efficiency and levelized cost of energy are 85.66%, 41.37% and 294.70 Yuan·(MW·h)–1, respectively. Parameter sensitivity analysis shows that the operating parameters of gas turbine pressure ratio, gas turbine hydrogen blending ratio, steam cycle low-pressure boiler pressure, steam cycle extraction coefficient, compressed air energy storage technology energy release power have significant impact on system thermodynamic performance, while the operating parameters of gas turbine pressure ratio, gas turbine hydrogen blending ratio, and steam cycle extraction coefficient have significant impact on system economic performance.The multi-objective optimization results indicate that the optimal exergy efficiency and standardized unit energy cost of the system are 42.31% and 284.33 Yuan·(MW·h)–1, respectively.

Hongtian Song, Yong Xiao, Shanshan Hu et al.

Abstract Super low frequency electric field measurements are crucial in analysing electromagnetic compatibility, assessing equipment status, and other related fields. Rydberg atom‐based super low frequency electric field measurements are performed by observing the Stark shift in the spectrum of the Rydberg state. In a specific range of field strength (E < Eavoid, where Eavoid is the threshold to avoid crossing electric fields), the Rydberg atomic spectrum experiences a quadratic frequency shift in relation to the field strength, with the coefficient being determined by the atomic polarisability α. The authors establish a dynamic equation for the interaction between the external electric field and the atomic system, and present the Stark structure diagram of the Caesium Rydberg atom. The mathematical formulae for α and Eavoid in different Rydberg states are also obtained: α = A × (n*)6 + B × (n*)7 and Eavoid = C/(n*)5 + D/(n*)7, where A(B) = 2.2503 × 10−9(7.49,948 × 10−11) and C(D) = 1.68,868 × 108(2.45,991 × 109). The error of α and Eavoid compared with the experimental values does not exceed 8% and is even lower in the low Rydberg states. Accurately calculating the values of α and Eavoid is crucial in incorporating the Rydberg atom quantum coherence effect into super low frequency electric field measurements in new power systems.

Yufei WANG, Tong DU, Weiguo BIAN et al.

Multi-user power load forecasting refers to the power load forecasting of multiple users or regions based on historical loads data，which can make the grid companies understand the power demands of different users or regions，so as to better carry out the planning and scheduling optimization of the power system. However, different users have complex and diverse power consumption behaviors, so it is difficult to use traditional methods to universally model different power users' loads and achieve accurate prediction. Therefore, a new multi-user short-term load prediction model based on DTW K-medoids and VMD-multi-branch neural network is established. Firstly, in order to improve the clustering performance of traditional clustering methods, the DTW K-medoids method is used to cluster users' load data, and the distance between loads data is calculated using the dynamic time warping (DTW) instead of the traditional Euclidean distance measurement method in K-medoids to improve the clustering effects of multiple users' load. Secondly, in order to fully characterize the long short-term time series-dependent characteristics of load history data, a parallel load forecasting method based on VMD-multi-branch neutral network model is established for multi-user short-term load forecasting. Finally, the 365-day load data of 20 users in a region is used for clustering, training and experiment, and the results show that the MAE and RMSE indexes of the proposed model significantly decrease compared with that of the comparative models, indicating that the proposed method can effectively characterize the power consumption behaviors of multiple users and improve the prediction efficiency and accuracy of multi-user loads.

Fatemeh Asgharzadeh, Vahid Sohrabi Tabar, Saeid Ghassemzadeh

Ali Hosseini, Ali Moradi Amani, Kianoosh Keshavarzain et al.