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

Ankeshwarapu Sunil

Shaohua Han, Hongji Zhu, Jinian Pang et al.

The widespread adoption of electric vehicles (EVs) has turned charging demand into a substantial load on the power grid. To satisfy the rapidly growing demand of EVs, the construction of charging infrastructure has received sustained attention in recent years. As charging stations become more widespread, how to attract EV users in a competitive charging market while optimizing the internal charging process is the key to determine the charging station’s operational efficiency. This paper tackles this issue by presenting the following contributions. Firstly, a simulation method based on prospect theory is proposed to simulate EV users’ preferences in selecting charging stations. The selection behavior of EV users is simulated by establishing coupling relationship among the transportation network, power grid, and charging network as well as the model of users’ preference. Secondly, a two-stage joint stochastic optimization model for a charging station is developed, which considers both charging pricing and energy control. At the first stage, a Stackelberg game is employed to determine the day-ahead optimal charging price in a competitive market. At the second stage, real-time stochastic charging control is applied to maximize the operational profit of the charging station considering renewable energy integration. Finally, a scenario-based Alternating Direction Method of Multipliers (ADMM) approach is introduced in the first stage for optimal pricing learning, while a simulation-based Rollout method is applied in the second stage to update the real-time energy control strategy based on the latest pricing. Numerical results demonstrate that the proposed method can achieve as large as 33% profit improvement by comparing with the competitive charging stations considering 1000 EV integration.

Jae-Sung Lim, Haesong Cho, Do-Hoon Kwon et al.

Diagnosing system faults is essential for ensuring the safety and reliability of Medium-Voltage Direct Current (MVDC) systems. In this regard, this study proposes a highly accurate Artificial Intelligence (AI)-based fault diagnosis scheme for MVDC systems. The proposed scheme pre-processes the measured voltage and current data using a Discrete Wavelet Transform (DWT), considering a 60 × 100 2D window size. Subsequently, a bi-directional long short-term memory (Bi-LSTM) network is employed to diagnose and classify fault types and locations accurately. A stack method is applied in the data post-processing stage to achieve 100 % fault diagnosis accuracy. The effectiveness of the proposed fault diagnosis scheme was verified by comparing its accuracy in 4-terminal MVDC system with that of existing schemes that employ other AI algorithms, such as CNN and LSTM. The proposed fault diagnosis scheme shows improved accuracy by 1.6 %, 3.8 %, and 2.9 %, 2.4 %, respectively, compared to existing schemes such as Bi-LSTM without stack method, LSTM, and CNN, GRU. Moreover, the scalability of the fault diagnosis scheme was verified by training and testing the scheme on a 5-terminal system and 4-terminal system, respectively. To a limited extent, the results demonstrate that the proposed fault diagnosis scheme improves accuracy even when the training and testing systems differ.

Emil Mihai Pavelescu, Saroj Kumar Patra, Cosmin Romaniţan et al.

Intermediate band solar cells (IBSCs) have the potential to overcome the efficiency limit of single-bandgap solar cells. Dilute nitride III–V alloys, with splitting of the conduction band due to band anticrossing, can be used as the intermediate-band material for solar cell applications. In this work, we report on the introduction of engineered GaInNAs alloy, with low (dilute) In and N contents, as IBSC material and the comparison of the performances of GaInNAs-based and the corresponding In-free GaNAs-based IBSCs grown on GaAs (100) substrate. Introduction of a small amount of In (3%) in a GaN _0.011 As _0.989 -based IBSC was found to noticeably increase the short-circuit current, I _sc , at the expense of a small decrease in the open-circuit voltage, V _oc . When annealed at 750 °C for 90 s, significant enhancements in I _sc and V _oc are seen, especially in the In-containing solar cell. The observed In-related enhancement in cell parameters after annealing is related to In-promoted bandgap tailoring and efficient curing of carrier traps during annealing. This occurred without change in the macroscopic composition of the GaInNAs alloy, most likely by In-N bond formation upon annealing, a phenomenon which does not exist in the GaNAs alloy.

Guorui Xu, Yin Wang, Zhiqiang Li et al.

The fluid and temperature distributions of the large air-cooled Synchronous Condenser (SC) are very complex, thereby the interaction of the stator and rotor airflows is often neglected in the previous study of the temperature field. In order to calculate the precise temperature rise of the SC under different operating conditions, this paper studies the effect of the temperature variation at the rotor airflow outlet on the stator temperature distribution. The loss, fluid and temperature distributions of a 300-MVar air-cooled SC are calculated based on the electromagnetic, fluid and heat transfer models. The temperature variation at the rotor airflow outlet along with the operating condition is revealed, and its effect on the stator temperature rise is analyzed. Further, it is studied that the variation laws of the stator and rotor maximum temperatures along with the air volume allocation, and the optimal air volume allocation is determined. The results can provide the reference for the accurate temperature calculation and the optimization design of the cooling systems for the large air-cooled SCs.

Junjie Hou, Changjian Zhang, Yanfang Fan et al.

The flexible DC transmission system for renewable energy has become a key solution for the large-scale integration of renewable energy into the power grid. This system typically consists of a two-terminal weak-feed AC system, which relies entirely on power electronic equipment. In the event of a transmission line fault, the fault ride-through (FRT) control strategy implemented by the converters at both ends causes changes in the phase angle of their equivalent impedance, thereby reducing the sensitivity of the directional relay. First, this paper theoretically derives the phase characteristics of the positive-sequence equivalent impedance (PSEI) at both ends of the two-terminal weak feed AC system under FRT control, and performs an adaptability analysis of the positive-sequence fault-component-based directional relay (PFDR) in the two-terminal weak feed AC system. Second, at the control strategy level, a positive-sequence impedance reconstruction (PSIR) control strategy based on the FRT framework is proposed. This strategy not only enhances the performance of PFDR but also meets the FRT requirements of the two-terminal weak feed AC system. Third, at the protection principle level, an adaptive-function-based PFDR is proposed, which enhances the fault characteristics at the protection principle boundary, thereby improving the adaptability and sensitivity of PFDR under complex fault conditions. Finally, an improved PFDR is proposed for fault direction detection in two-terminal weak feed AC system. The proposed PFDR combines the PSIR control strategy with an adaptive-function-based tuning principle. This approach enhances the performance of fault detection while maintaining the system’s stability. The simulation results demonstrate that the proposed protection scheme can reliably identify the fault direction in the two-terminal weak feed AC system, even under fault conditions with a 300 Ω fault impedance and 30 dB noise interference.© 2017 Elsevier Inc. All rights reserved.

Junyeol Maeng, Jisun Ham, Gayoung Park et al.

Recently, the rapid integration of renewable energy sources has been reducing power system inertia, which threatens frequency stability. To address this issue, E-STATCOMs with supercapacitor-based energy storage have emerged as a persuasive solution, providing synthetic inertia in weak grids. This paper proposes a novel control methodology to minimize the size of supercapacitors for E-STATCOMs. Firstly, the embedded droop characteristics of virtual synchronous generator (VSG) control are quantitatively analyzed and eliminated, allowing the supercapacitor to store only the necessary energy. Additionally, the proposed supercapacitor energy management strategy tightly couples the stored energy with the VSG control. This enables the automatic management of supercapacitor energy while providing adequate inertial response. A small-signal stability analysis is also performed to ensure the stability of the system when the proposed control strategy is applied. Experimental results verify the effectiveness of the proposed method under various scenarios.

Alfonso J Carrillo, María Balaguer, Cecilia Solís et al.

Nanoparticle exsolution is a powerful technique for functionalizing redox oxides in energy applications, particularly at high temperatures. It shows promise for solid oxide fuel cells and electrolyzers. However, exsolution of other chemistries like metal oxides is not well studied, and the mechanism is poorly understood. This work explores oxide exsolution in PrBa _1− _x Co _2 O _6− _δ ( x = 0, 0.05, 0.1, 0.15) double perovskites, practiced electrodes in proton ceramic fuel cells and electrolyzers. Oxide exsolution in PrBa _1− _x Co _2 O _6− _δ aimed at boosting the electrocatalytic activity and was evaluated by varying intrinsic materials-related properties, viz. A-site deficiency and external parameters (temperature, under fixed time, and p O _2 = 10 ^−5 atm conditions). The materials were analyzed with conventional characterization tools and synchrotron-based small-angle x-ray scattering. Unlike metal-nanoparticle exsolution, increasing the A-site deficiency did not enhance the extent of oxide-nanoparticle exsolution, whereas larger nanoparticles were obtained by increasing the exsolution temperature. Combined Raman spectroscopy and electron microscopy analysis revealed that BaCoO _3 , Co _3 O _4 , and amorphous BaCO _3 nanoparticles were formed on the surface of the double perovskites after the reductive treatments. The present results demonstrate the complexity of oxide-nanoparticle exsolution in comparison with metal-nanoparticle exsolution. Further materials screening and mechanistic studies are needed to enhance our understanding of this method for functionalizing proton ceramic electrochemical cells (PCEC) electrodes.

Ziyue Duan, Yongqing Meng, Shuhao Yan et al.

The fractional frequency transmission systems occurs different oscillations due to the interactions between wind farm, submarine cable, and M3C components under the large signal disturbances. However, current Lyapunov based stability analysis for high-order systems typically employs methods such as T–S fuzzy theory to construct Lyapunov functions. Unlike the participation factors in small signal stability analysis, it leads to an inability to quantitatively analyze the impact of interactions between different frequency components in system with multi-frequency coupling characteristics. This paper firstly proposes the Lyapunov function for the fractional frequency PMSG offshore wind power transmission system (FOWS), which considers the dynamics of the mentioned components. Additionally, the stability regions are compared for unveiling the pattern of the components interaction, when analyzing the dynamic characteristics of different blocks from the generalized system matrix individually with the overall analysis results. Furthermore, the generalized participation factor is defined to quantitatively assess large signal interaction stability. Subsequently it is also analyzed how control and circuit parameters within individual components affect system stability, revealing instability mechanisms under various disturbance conditions. And the comprehensive stability enhancement strategies are proposed that consider interactions involving multiple frequencies and components. Finally, MATLAB model is established to ensure the effectiveness.

Rouzbeh Haghighi, Ali Hassan, Van-Hai Bui et al.

The rapid rise in electric vehicle (EV) adoption presents significant challenges in managing the vast number of retired EV batteries. Research indicates that second-life batteries (SLBs) from EVs typically retain considerable residual capacity, offering extended utility. These batteries can be effectively repurposed for use in EV charging stations (EVCS), providing a cost-effective alternative to new batteries and reducing overall planning costs. Integrating battery energy storage systems (BESS) with SLBs into EVCS is a promising strategy to alleviate system overload. However, efficient operation of EVCS with integrated BESS is hindered by uncertainties such as fluctuating EV arrival and departure times and variable power prices from the grid. This paper presents a deep reinforcement learning-based (DRL) planning framework for EV charging stations with BESS, leveraging SLBs. We employ the advanced soft actor-critic (SAC) approach, training the model on a year's worth of data to account for seasonal variations, including weekdays and holidays. A tailored reward function enables effective offline training, allowing real-time optimization of EVCS operations under uncertainty.

Kai Kang, Feng Liu, Yifan Su et al.

Power systems with high renewable energy penetration are highly influenced by weather conditions, often facing significant challenges such as persistent power shortages and severe power fluctuations over long time scales. This paper addresses the critical need for effective characterization of extreme scenarios under these situations. First, novel risk indices are proposed to quantify the severity of continuous power shortages and substantial power fluctuations over long-term operations. These indices are independent of specific scheduling strategies and incorporate the system's resource regulation capabilities. By employing a filtering-based approach, the proposed indices focus on retaining key characteristics of continuous power shortages and fluctuation events, enabling the identification of extreme scenarios on long time scales. Secondly, an extreme scenario generation method is developed using Gaussian mixture models and sequential Monte Carlo simulation. Especially, this method periodically evaluates the severity of generated scenarios based on the defined risk indices, retaining extreme scenarios while discarding less critical ones. Finally, case studies based on real-world data demonstrate the efficacy of the proposed method. The results confirm that integrating the identified extreme scenarios significantly enhances the system's ability to ensure long-term security and reliability under high renewable energy penetration.

Fabien Bernier, Jun Cao, Maxime Cordy et al.

Efficiently solving Optimal Power Flow (OPF) problems in power systems is crucial for operational planning and grid management. There is a growing need for scalable algorithms capable of handling the increasing variability, constraints, and uncertainties in modern power networks while providing accurate and fast solutions. To address this, machine learning techniques, particularly Graph Neural Networks (GNNs) have emerged as promising approaches. This letter introduces PowerGraph-LLM, the first framework explicitly designed for solving OPF problems using Large Language Models (LLMs). The proposed approach combines graph and tabular representations of power grids to effectively query LLMs, capturing the complex relationships and constraints in power systems. A new implementation of in-context learning and fine-tuning protocols for LLMs is introduced, tailored specifically for the OPF problem. PowerGraph-LLM demonstrates reliable performances using off-the-shelf LLM. Our study reveals the impact of LLM architecture, size, and fine-tuning and demonstrates our framework's ability to handle realistic grid components and constraints.

Jinping Zhao, Ali Arefi, Alberto Borghetti et al.

There is a general concern that the increasing penetration of electric vehicles (EVs) will result in higher aging failure probability of equipment and reduced network reliability. The electricity costs may also increase, due to the exacerbation of peak load led by uncontrolled EV charging. This paper proposes a linear optimization model for the assessment of the benefits of EV smart charging on both network reliability improvement and electricity cost reduction. The objective of the proposed model is the cost minimization, including the loss of load, repair costs due to aging failures, and EV charging expenses. The proposed model incorporates a piecewise linear model representation for the failure probability distributions and utilizes a machine learning approach to represent the EV charging load. Considering two different test systems (a 5-bus network and the IEEE 33-bus network), this paper compares aging failure probabilities, service unavailability, expected energy not supplied, and total costs in various scenarios with and without the implementation of EV smart charging.

Stanislav Shabala, Patrick Yates-Jones, Larissa Jerrim et al.

Odd Radio Circles (ORCs) are a class of low surface brightness, circular objects approximately one arcminute in diameter. ORCs were recently discovered in the Australian Square Kilometre Array Pathfinder (ASKAP) data, and subsequently confirmed with follow-up observations on other instruments, yet their origins remain uncertain. In this paper, we suggest that ORCs could be remnant lobes of powerful radio galaxies, re-energised by the passage of a shock. Using relativistic hydrodynamic simulations with synchrotron emission calculated in post-processing, we show that buoyant evolution of remnant radio lobes is alone too slow to produce the observed ORC morphology. However, the passage of a shock can produce both filled and edge-brightnened ORC-like morphologies for a wide variety of shock and observing orientations. Circular ORCs are predicted to have host galaxies near the geometric centre of the radio emission, consistent with observations of these objects. Significantly offset hosts are possible for elliptical ORCs, potentially causing challenges for accurate host galaxy identification. Observed ORC number counts are broadly consistent with a paradigm in which moderately powerful radio galaxies are their progenitors.

William Yourey

Historically, lithium cobalt oxide and graphite have been the positive and negative electrode active materials of choice for commercial lithium-ion cells. It has only been over the past ~15 years in which alternate positive electrode materials have been used. As new positive and negative active materials, such as NMC811 and silicon-based electrodes, are being developed, it is crucial to evaluate the potential of these materials at a stack or cell level to fully understand the possible increases in energy density which can be achieved. Comparisons were made between electrode stack volumetric energy densities for designs containing either LCO or NMC811 positive electrode and silicon-graphite negative electrodes, where the weight percentages of silicon were evaluated between zero and ninety percent. Positive electrode areal loadings were evaluated between 2.00 and 5.00 mAh cm⁻². NMC811 at 200 mAh g⁻¹ has the ability to increase stack energy density between 11% and 20% over LCO depending on percentage silicon and areal loading. At a stack level, the percentage of silicon added results in large increases in energy density but delivers a diminishing return, with the greatest increase observed as the percentage of silicon is increased from zero percent to approximately 25–30%.

Xinyi Kong, Jianwen Zhang, Jianqiao Zhou et al.

The bipolar low-voltage DC (LVDC) distribution system has become a prospective solution to better integration of renewables and improvement of system efficiency and reliability. However, it also faces the challenge of power and voltage imbalance between two poles. To solve this problem, an interface converter with bipolar asymmetrical operating capabilities is applied in this paper. The steady-state models of the bipolar LVDC distribution system equipped with this interface converter in the grid connected mode and off-grid mode are analyzed. A control scheme based on DC offset injection at the secondary side of the interface converter is proposed, enabling the bipolar LVDC distribution system to realize the unbalanced power transfer between two poles in the grid-connected mode and maintain the inherent pole voltage balance in the off-grid mode. Furthermore, this paper also proposes a primary-side DC offset injection control scheme according to the analysis of the magnetic circuit model, which can eliminate the DC bias flux caused by the secondary-side DC offset. Thereby, the potential core magnetic saturation and overcurrent issues can be prevented, ensuring the safety of the interface converter and distribution system. Detailed simulations based on the proposed control scheme are conducted to validate the function of power and voltage balance under the operation conditions of different DC loads.

Xiaoling Chen, Cory Miller, Mithun Goutham et al.

In this paper, a real time implementable load coordination strategy is developed for the optimization of electric demands in a smart home. The strategy minimizes the electricity cost to the home owner, while limiting the disruptions associated with the deferring of flexible power loads. A multi-objective nonlinear mixed integer programming is formulated as a sequential model predictive control, which is then solved using genetic algorithm. The load shifting benefits obtained by deploying an advanced coordination strategy are compared against a baseline controller for various home characteristics, such as location, size and equipment. The simulation study shows that the deployment of the smart home energy management strategy achieves approximately 5% reduction in grid cost compared to a baseline strategy. This is achieved by deferring approximately 50\% of the flexible loads, which is possible due to the use of the stationary energy storage.

Cheng Zhong, Yicheng Ding, Husai Wang et al.

With the rapid growth of wind power penetration, wind farms (WFs) are required to implement frequency regulation that active power control to track a given power reference. Due to the wake interaction of the wind turbines (WTs), there is more than one solution to distributing power reference among the operating WTs, which can be exploited as an optimization problem for the second goal, such as fatigue load alleviation. In this paper, a closed-loop model predictive controller is developed that minimizes the wind farm tracking errors, the dynamical fatigue load, and and the load equalization. The controller is evaluated in a mediumfidelity model. A 64 WTs simulation case study is used to demonstrate the control performance for different penalty factor settings. The results indicated the WF can alleviate dynamical fatigue load and have no significant impact on power tracking. However, the uneven load distribution in the wind turbine system poses challenges for maintenance. By adding a trade-off between the load equalization and dynamical fatigue load, the load differences between WTs are significantly reduced, while the dynamical fatigue load slightly increases when selecting a proper penalty factor.

Hongjie Jia, Huiyuan Wang, Yan Cao et al.

Abstract In modern society, system integration that enables multiple subsystems to function as one is emerging in various fields like industry, commerce, and infrastructure. Although it has been proved that integration value could be tapped to the maximum with controllable cost by optimising the integration schemes in certain fields, there is still a lack of a general method for modelling and analysing the process of system integration. To address this need, this paper proposes an analysis framework of system integration. The concepts of integration object, integration strategy, integration time, integration cost and integration value are introduced to describe the integration process. Further, three optimisation models of the local optimisation (OPT1), phase optimisation (OPT2) and integration optimisation (OPT3) are constructed. The proposed framework can also supervise and compare the performance of intermediate processes of different integration schemes. Two case studies in the commerce and energy fields are analysed to illustrate the function of the proposed framework.

Pascal A. Schirmer, Iosif Mporas

Non-Intrusive Load Monitoring aims to extract the energy consumption of individual electrical appliances through disaggregation of the total power consumption as measured by a single smart meter in a household. Although when data from the same household are used to train a disaggregation model the device disaggregation accuracy is quite high (80% - 95%), depending on the number of devices, the use of pre-trained disaggregation models in new households in most cases results in a significant reduction of disaggregation accuracy. In this article we propose a transferability approach for Non-Intrusive Load Monitoring using fractional calculus and normalized Karhunen Loeve Expansion based spectrograms followed by a Convolutional Neural Network in order to generate device characteristic features that do not change significantly across different households. The performance of the proposed methodology was evaluated using two publicly available datasets, namely REDD and REFIT. The proposed transferability approach improves the Mean Absolute Error by 13.1% when compared to other transfer learning approaches for energy disaggregation.