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

Madeline Karod, Jillian L Goldfarb

Hydrothermal carbonization (HTC) thermochemically upcycles agro-industrial waste into a two-phase hydrochar (HC) consisting of primary char (PC) and amorphous secondary char (SC). SC limits the use of HC; its furan groups are phytotoxic, and the SC is thermally reactive, making it unsuitable for many direct combustion applications. Apple pomace (AP) was carbonized at 175, 200, and 250 °C, and SC was extracted using an organic solvent. The SC showed particularly high concentrations of 5-hydroxymethylfurfural (5-HMF). While such concentrations may inhibit germination if HC is used as a soil amendment, 5-HMF is an important biorefinery compound and is recoverable via solvent extraction. The resulting extracted hydrochar (called PC) has a higher surface area, fixed carbon content, and increased bioavailability of key nutrients than the as-carbonized HC. The PC is also more thermally stable than HC, particularly at 250 °C carbonization, where the SC contains considerable oxygenated compounds. HTC at 250 °C likely breaks down more lignin in the AP, resulting in more phenols in the SC, which are more reactive. At lower temperatures, we observe more phenols (comprising up to 96% of SC for HTC at 200 °C), which suggests that it may be possible to engineer SC to yield lucrative biorefinery intermediates from high-sugar biomasses such as AP. While the extraction of HC offers the potential to valorize SC as a biofuel, it also yields a more stable PC for use as a soil amendment and solid fuel.

Mohmmad Hossein Atazadegan, Jaber Moosanezhad, Mustafa Habeeb Chyad et al.

WEI Wei, WANG Yudong, JIN Xiaolong

[Objective] The large-scale integration of distributed renewable energy generation （REG） has significantly enhanced the flexible regulation capabilities of distribution systems. However， the inherent randomness and volatility of REG output characteristics present serious challenges to the security and stability of distribution system operations. [Methods] To effectively improve the adaptability of day-ahead dispatch plans to uncertainties， this study proposes a distributionally robust day-ahead dispatch optimization method for active distribution networks （ADN） based on an improved conditional generative adversarial network （CGAN）. First， an improved CGAN model designed by three-dimensional convolution （Conv3D） is proposed to address the problem of generating day-ahead scenarios for wind turbines （WT） and photovoltaic （PV） outputs considering spatio-temporal correlation， which effectively reduces the conservatism of the generated scenario set. Second， based on the generated day-ahead scenario samples of the WT and PV outputs， a Wasserstein ambiguity set construction method based on kernel density estimation （KDE） is proposed， which realizes full utilization of the sample distribution information. On this basis， a two-stage distributionally robust day-ahead dispatch optimization （DRO） model for ADN is established， considering multiple grid-side resource coordination. The original model is reconstructed into a mixed-integer linear programming problem to obtain a solution based on the affine strategy and strong duality theory. [Results] The findings demonstrate that although the day-ahead dispatch plan cost of the proposed method increases by 1.87% and 0.21% compared with the deterministic optimization （DO） and stochastic optimization （SO） methods， the integrated operation cost decreases by 5.38% and 0.46% under the worst-case scenario， respectively. [Conclusions] The analysis revealed that the proposed DRO model exhibits better adaptability to REG uncertainty and can effectively decrease the operational adjustment cost of the day-ahead dispatch plan while maintaining robustness， especially under the worst-case scenario.

Rafał Wiśniowski

Akademia Górniczo-Hutnicza w Krakowie jest wiodącą krajową uczelnią techniczną, której już sama nazwa wskazuje na związki z energetyką. Wieloletnia tradycja badań w tym kierunku, wykwalifikowana kadra naukowa oraz nowoczesna infrastruktura badawcza – to wszystko czyni z AGH istotny podmiot w zachodzącym właśnie w Polsce i Europie procesie transformacji energetycznej (TE). Badania nad nową energetyką prowadzone są niemal w każdej jednostce Akademii. Poszczególni naukowcy i zespoły badawcze mogą pochwalić się obiecującymi, a nawet imponującymi osiągnięciami na arenie krajowej i międzynarodowej. Działania te nierzadko pozostają jednak rozproszone w uczelnianej strukturze, co utrudnia wzajemne interakcje, tak potrzebne we współczesnej nauce. Naprzeciw tym trudnościom wychodzi inicjatywa redakcji czasopisma „Energetyka Rozproszona”. Celem publikacji, którą oddajemy w Państwa ręce, jest zaprezentowanie potencjału Akademii Górniczo- Hutniczej w obszarze transformacji energetycznej, a w szczególności w dziedzinie badań nad energetyką rozproszoną i surowcami energetycznymi. Opracowanie numeru specjalnego „ER” poprzedziła inwentaryzacja niezależnych działań prowadzonych właściwie w każdej jednostce naszej uczelni.

Curd Schade, Parinaz Aliasghari, Ruud Egging-Bratseth et al.

The electricity landscape is constantly evolving, with intermittent and distributed electricity supply causing increased variability and uncertainty. The growth in electric vehicles, and electrification on the demand side, further intensifies this issue. Managing the increasing volatility and uncertainty is of critical importance to secure and minimize costs for the energy supply. Smart neighborhoods offer a promising solution to locally manage the supply and demand of energy, which can ultimately lead to cost savings while addressing intermittency features. This study assesses the impact of different electric vehicle charging strategies on smart grid energy costs, specifically accounting for battery degradation due to cycle depths, state of charge, and uncertainties in charging demand and electricity prices. Employing a comprehensive evaluation framework, the research assesses the impacts of different charging strategies on operational costs and battery degradation. Multi-stage stochastic programming is applied to account for uncertainties in electricity prices and electric vehicle charging demand. The findings demonstrate that smart charging can significantly reduce expected energy costs, achieving a 10% cost decrease and reducing battery degradation by up to 30%. We observe that the additional cost reductions from allowing Vehicle-to-Grid supply compared to smart charging are small. Using the additional flexibility aggravates degradation, which reduces the total cost benefits. This means that most benefits are obtainable just by optimized the timing of the charging itself.

Shouqi Jiang, Demao Lu, Guoqing Li et al.

To enhance frequency support and fault ride-through capabilities of MMC-HVDC system, an MMC multi-time scale coordinated control strategy considering the regulation dead zone is proposed, which achieves wide-area coordinated complementarity of multiple types of regulatory resources while ensuring system safety. For frequency support requirements at sending-end system, an adaptive control strategy considering a frequency regulation dead zone is proposed for sending-end MMC, and the dead zone value is designed based on the maximum power regulation margin of sending-end system, which reduces disturbance impact while maintaining frequency stability. For frequency support and fault ride-through requirements at receiving-end system, the coupling of frequency, DC voltage, and the inserted number of submodules in MMC is established, a control strategy considering the modulation ratio margin is proposed to improve the inertia support capability of MMC-HVDC. Then, an adaptive control strategy for frequency support and DC overvoltage suppression of sending-end MMC, along with a control mode selection method, is designed, and the DC voltage dead zone value is designed based on the maximum power regulation margin of receiving-end system, which effectively enhances the MMC-HVDC system’s frequency support and fault ride-through capabilities. Finally, the validity of proposed control strategy is verified through real-time digital simulation.

Panpan Hu, Chun Yin Li, Chi Chung Lee

Accurate State of Charge (SOC) estimation is critical for optimizing the performance and longevity of lithium-ion batteries (LIBs), which are widely used in applications ranging from electric vehicles to renewable energy storage. Traditional SOC estimation methods, such as Coulomb counting and open-circuit voltage measurement, suffer from cumulative errors and slow response times. This paper proposes a novel machine learning-based approach for SOC estimation by integrating Electrochemical Impedance Spectroscopy (EIS) with the SHapley Additive exPlanations (SHAP) method, Atom Search Optimization (ASO), and Light Gradient Boosting Machine (LightGBM). This study focuses on large-capacity lithium iron phosphate (LFP) batteries (3.2 V, 104 Ah), addressing a gap in existing research. EIS data collected at various SOC levels and temperatures were processed using SHAP for feature extraction (FE), and the ASO–LightGBM model was employed for SOC prediction. Experimental results demonstrate that the proposed SHAP–ASO–LightGBM method significantly improves estimation accuracy, achieving an RMSE of 3.3%, MAE of 1.86%, and R² of 0.99, outperforming traditional methods like LSTM and DNN. The findings highlight the potential of EIS and machine learning (ML) for robust SOC estimation in large-capacity LIBs.

Wentao Huang, Xinyue Chang, Yixun Xue et al.

As the installed capacity of wind and solar power keeps rising and electric vehicle charging loads are integrated on a large scale, the uncertainty on both the source and load sides of virtual power plants has notably grown. This makes it arduous to guarantee stable and optimal dispatching. A multiple-time-scale energy management strategy for virtual power plants is proposed to mitigate these uncertainties. On the generation side, a hybrid neural network method for wind-solar output prediction with dynamic weights is proposed, which can greatly eliminate the errors caused by the increase of prediction time scale. On the load side, a data-model driven electric vehicle charging load prediction method is proposed, which combines neural network prediction with road network models. The proposed method improves the accuracy of the basic parameters of the road network model. In addition, improvements are made on the basis of the traditional Multi server Markovian Arrival and Exponential Service Time Queueing Model to accurately describe the queueing and charging behaviours of electric vehicles at charging stations. Case studies validate that the proposed energy management method achieves higher accuracy than the method that directly predicts the electric vehicle charging load using neural networks and the traditional rolling optimization method without dynamic weights, leading to reduced overall costs for virtual power plants. Notably, the proposed method reduces the dispatch cost by 14.83%.© 2017 Elsevier Inc. All rights reserved.

Mohammed Altoumaimi, V. V. Loboda

The present study provides the consideration of a mode III interface crack in one-dimentional (1D) piezoelectric quasicrystal under antiplane phonon and phason loading and inplane electric field. Due to complex function approach all required electromechanical parameters are presented through vector-functions analytic in the whole complex plane except the crack region. The cases of electrically impermeable (insulated) and electrically limited permeable conditions on the crack faces are considered. In the first case a vector Hilbert problem in the complex plane is formulated and solved exactly and in the second one the quadratic equation with respect to the electric flux through the crack region is obtained additionally. Its solution permits to find phonon and phason stresses, displacement jumps (sliding) and also electric characteristics along the material interface. Analytical formulas are also obtained for the corresponding stress intensity factors related to each field. The numerical computations for three selected variants of the loading conditions was conducted and the resulting field distributions are visualised on the crack continuation beyond the crack and also inside of the crack region.

Saba Sanami, Hesam Mosalli, Yu Yang et al.

As the number of electric vehicles (EVs) continues to grow, the demand for charging stations is also increasing, leading to challenges such as long wait times and insufficient infrastructure. High-precision forecasting of EV charging demand is crucial for efficient station management, to address some of these challenges. This paper presents an approach to predict the charging demand at 15-minute intervals for the day ahead using a multivariate long short-term memory (LSTM) network with an attention mechanism. Additionally, the model leverages explainable AI techniques to evaluate the influence of various factors on the predictions, including weather conditions, day of the week, month, and any holiday. SHapley Additive exPlanations (SHAP) are used to quantify the contribution of each feature to the final forecast, providing deeper insights into how these factors affect prediction accuracy. As a result, the framework offers enhanced decision-making for infrastructure planning. The efficacy of the proposed method is demonstrated by simulations using the test data collected from the EV charging stations at California State University, Long Beach.

Kaili Zhao, Jue Wang, Liuchao Qiu

Panagiotis Mouratidis

Jingjing Huang, Chao Zhu, Qiu Liang et al.

Rebeca Peláez, Germán Aneiros, Juan M. Vilar

The aim of this paper is to compute one-day-ahead prediction regions for daily curves of electricity demand and price. Three model-based procedures to construct general prediction regions are proposed, all of them using bootstrap algorithms. The first proposed method considers any norm for functional data to measure the distance between curves, the second one is designed to take different variabilities along the curve into account, and the third one takes advantage of the notion of depth of a functional data. The regression model with functional response on which our proposed prediction regions are based is rather general: it allows to include both endogenous and exogenous functional variables, as well as exogenous scalar variables; in addition, the effect of such variables on the response one is modelled in a parametric, nonparametric or semi-parametric way. A comparative study is carried out to analyse the performance of these prediction regions for the electricity market of mainland Spain, in year 2012. This work extends and complements the methods and results in Aneiros et al. (2016) (focused on curve prediction) and Vilar et al. (2018) (focused on prediction intervals), which use the same database as here.

Shoulong Ma, Qi Zong

The efficient extraction of gas from low-permeability coal seams is an urgent problem in coal mine safety production. The traditional gas extraction technology generally suffers from problems that limited penetration enhancement or extraction effect, low construction efficiency, large workload, etc. Thus, it is especially urgent and important to explore the new technology applicable to efficient underground gas extraction. In this paper, based on the principle of hydraulic fracturing to increase permeability, we innovatively propose a technique to enhance the effect of hydraulic fracturing to increase permeability and further improve the efficiency of gas extraction using the gas desorption activity of native microorganisms in coal seams. Herein, the composition of the primary microbial community of a coal seam in Xinji No.2 mine was analyzed by bacterial and archaeal 16SrDNA amplicon sequencing, the community structure of the main functional microorganisms was clarified, the optimal combination of functional microorganisms for organic matter degradation in coal seam under anaerobic culture conditions was obtained. Besides the Biolog microplate technology was used to screen the nutrients of the excitation carbon source to stimulate the rapid decomposition of coal organic matter by microorganisms and to define the optimal ratio of the excitation carbon source to microorganisms. Finally, the effect of this technology on the application of coal seam fracturing and gas extraction was tested through field industrial tests, revealing that the extraction effect of this technology was more significant than that of the common coal seam perforation extraction technology. The results of this paper provide a new technical idea for gas extraction from low permeability coal seams, which is an important reference value for subsequent similar studies.

Dan LI, Shiyao QIN, Shaolin LI et al.

The high-accuracy simulation model is the basis for transient stability analysis of large-scale wind power integration. However, the control strategies and parameters of doubly-fed wind turbines are technical secrets that are difficult to obtain, and the accuracy of model simulation is difficult to guarantee. In order to address the fault transient modeling problems of doubly-fed wind turbines, a measured data-based modeling and parameter identification method of doubly-fed wind turbines is proposed. Firstly, based on the DFIG model and control structure of the Power System Integrated Stability Program (PSASP), a low voltage ride through (LVRT) control mathematical model is established to analyze the fault transient process, and the LVRT transient control core parameters are clarified. Secondly, based on part of the field measured LVRT data of doubly-fed wind turbines, the fault transient parameters are identified with the chaotic particle swarm optimization algorithm. Finally, the accuracy of the identification parameters are analyzed and verified based on the remaining measured data. The simulation results have verified the effectiveness and accuracy of the proposed parameter identification method. The proposed method has strong generalization ability and high accuracy of identification results, and is of great engineering application value.

Lizi Luo, Wei Gu, Suyang Zhou et al.

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

Yuting Tian, Xiaozhu Li, Shaowei Huang et al.

Rudranarayan Pradhan, Mohd Azeem, Premalata Jena