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

Motepalli Siva Rama Ganesh, Budithi Chandini, Veligatla Sri Pujitha et al.

Kratika Yadav, Mukesh Singh

Lars O. Schmidt, Houssin Wehbe, Sven Hartwig et al.

Lithium metal is a promising anode material for next-generation batteries due to its high specific capacity and low density. However, conventional mechanical processing methods are unsuitable due to lithium’s high reactivity and adhesion. Laser cutting offers a non-contact alternative, but photothermal effects can negatively impact the cutting quality and electrochemical performance. This study investigates the influence of pulse duration on the cutting-edge characteristics and electrochemical behavior of laser-cut 20 µm lithium metal on 10 µm copper foils using nanosecond and picosecond laser systems. It was demonstrated that shorter pulse durations significantly reduce the heat-affected zone (HAZ), resulting in improved cutting quality. Electrochemical tests in symmetric Li|Li cells revealed that laser-cut electrodes exhibit enhanced cycling stability compared with mechanically separated anodes, despite the presence of localized dead lithium “reservoirs”. While the overall pulse duration did not show a direct impact on ionic resistance, the characteristics of the cutting edge, particularly the extent of the HAZ, were found to influence the electrochemical performance.

Wenbo Li, Pengcheng Sha, Chao Li et al.

When sub-synchronous oscillations occur in the doubly fed induction generator-based wind farm connected to series-compensated transmission network, the transient behaviors are related to the operational conditions, including the number of online turbines, wind speed, level of series compensation, and control parameters. This paper proposes an adaptive damping device for sub-synchronous oscillations and its control strategy, which addresses suppression requirements under time-varying operational conditions. Unlike in conventional methods, this device is connected in series to transmission line. Suppression control is automatically activated when sub-synchronous oscillations are detected. Furthermore, the proposed strategy behaves as controlled voltage source that is only effective at sub-synchronous frequencies and does not affect normal operations. The effectiveness of the proposed strategy is validated through simulations of the Guyuan wind farm in PSCAD. Results demonstrate significant sub-synchronous oscillation suppression effects in different operational cases, especially those triggered by the interaction between the series-compensated capacitor and the converter controller.

Chen Ma, Siyi Liu, Xian Zhang et al.

Sandip S. Yeole, Prakash G. Burade

Yingli Wang, Meng Wen, Qiming Lu et al.

ZHOU Dan, YUAN Zhi, LI Ji et al.

ObjectivesThe existing hybrid energy storage system control strategy finds it difficult to maintain the state of charge (SOC) within a reasonable range while also meeting the advanced charging and discharging needs due to future wind power fluctuations. Therefore, a new advanced fuzzy control strategy for hybrid energy storage systems was proposed, which takes into account the smoothing of future wind power fluctuations.MethodsFirstly, the wind power needing to be smoothed by different types of energy storage devices was decomposed using the ensemble empirical mode decomposition (EEMD) method. Secondly, the power correction parameter was adjusted according to the SOC and power saturation level of the hybrid energy storage system to correct its output power. Thirdly, the wind power prediction algorithm was used to obtain the predicted value of wind power in the forward-looking cycle. The advance charging and discharging parameters were adjusted to correct the output power of the energy storage system based on the wind power fluctuations in the forward-looking cycle and the over-advance control theory. Finally, taking the actual data of a wind farm as an example, the validity of the proposed forward-looking fuzzy control strategy was validated through simulation.ResultsThe proposed control strategy can not only reduce the over-limit probability of wind power grid-connected fluctuation, significantly reduce the deviation between the total output power and the target power, but also maintain the SOC of the hybrid energy storage system within a reasonable range.ConclusionsThis strategy can provide a useful reference for research related to smoothing wind power fluctuations.

Mohammed Yousri Silaa, Aissa Bencherif, Oscar Barambones

This paper presents a novel approach to address the challenges associated with the trajectory tracking control of wheeled mobile robots (WMRs). The proposed control approach is based on an indirect adaptive control PID using a neural network and discrete extended Kalman filter (IAPIDNN-DEKF). The proposed IAPIDNN-DEKF scheme uses the NN to identify the system Jacobian, which is used for tuning the PID gains using the stochastic gradient descent algorithm (SGD). The DEKF is proposed for state estimation (localization), and the NN adaptation improves the tracking error performance. By augmenting the state vector, the NN captures higher-order dynamics, enabling more accurate estimations, which improves trajectory tracking. Simulation studies in which a WMR is used in different scenarios are conducted to evaluate the effectiveness of the IAPIDNN-DEKF control. In order to demonstrate the effectiveness of the IAPIDNN-DEKF control, its performance is compared with direct adaptive NN (DA-NN) control, backstepping control (BSC) and an adaptive PID. On lemniscate, IAPIDNN-DEKF achieves RMSE values of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.078769</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.12086</mn></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.1672</mn></mrow></semantics></math></inline-formula>. On sinusoidal trajectories, the method yields RMSE values of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.01233</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.015138</mn></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.088707</mn></mrow></semantics></math></inline-formula>, and on sinusoidal with perturbation, RMSE values are <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.021495</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.016504</mn></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.090142</mn></mrow></semantics></math></inline-formula> in <i>x</i>, <i>y</i> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>θ</mi></semantics></math></inline-formula>, respectively. These results demonstrate the superior performance of IAPIDNN-DEKF for achieving accurate control and state estimation. The proposed IAPIDNN-DEKF offers advantages in terms of accurate estimation, adaptability to dynamic environments and computational efficiency. This research contributes to the advancement of robust control techniques for WMRs and showcases the potential of IAPIDNN-DEKF to enhance trajectory tracking and state estimation capabilities in real-world applications.

Xiupeng Chen, Lu Wang, Yuning Jiang et al.

Abstract Peer‐to‐peer energy trading enhances distribution network resilience by reducing energy demand from central power plants and enabling distributed energy resources to support critical loads after extreme events. However, adequate reserves from main grids are still required to ensure real‐time energy balance in distribution networks due to the uncertainty in renewable generation. This paper introduces a novel two‐stage joint energy and reserve market for prosumers, wherein local flexible resources are fully utilized to manage renewable generation uncertainty. In contrast to cooperative optimization methods, the interactions between prosumers are modelled as a generalized Nash game, considering that prosumers are self‐interested and should follow distribution network constraints. Then, linear decision rules are employed to ensure a feasible market equilibrium and develop a privacy‐preserving algorithm to guide prosumers the market equilibrium with a proven convergence. Finally, the numerical study on a modified IEEE 33‐power system demonstrates that the designed market effectively manages renewable generation uncertainty, and that the algorithm converges to the market equilibrium.

Yang Li, Shuheng Tang, Jian Chen et al.

Microorganisms are one of the main driving forces of the cycle of carbon and other life elements in the underground environment. The natural environment is the comprehensive result of these microorganisms. In contrast, the study of coal reservoir microorganisms is mostly under laboratory conditions, which limits people's understanding of the symbiotic relationship between microorganisms, the interaction between microorganisms and the environment, and the distribution differences of microbial communities in the region. Similarly, the carbon cycle of the underground environment driven by essential microorganisms in coal reservoirs cannot be further studied. The geochemical process of underground methane generation and oxidation is critical in discussing the production and consumption of biomethane in the underground environment and the metabolic behavior of microorganisms. For this reason, we conducted biogeochemical tests and microbial sequencing on the water produced by coalbed methane wells in the south of the Qinshui Basin to analyze and improve the understanding of the distribution difference and metabolic behavior of microbial communities in coal reservoirs. The concentration of Cl − and HCO 3 − in the detention environment in the study area increases, while the concentration of SO 4 2− , NO 3 − , NO 2 − , and Fe 3+ decreases with the increase of coal seam depth, reflecting the distribution difference of hydrochemical environment and redox conditions of the underground reservoir in the study area. The results of microbial sequencing showed microbial methanogenesis in the study area, but it could also be consumed by microbial oxidation simultaneously. The microbial communities related to methane production and consumption had diversity distributions similar to geochemical parameters and geographical patterns. Methanogens and dissolved inorganic carbon isotopes confirmed the potential of in situ methane generation. Still, biomethane's enrichment and accumulation conditions and the impact of aerobic/anaerobic oxidation of methane need further study.

Zheyang Shen, Jeremias Knoblauch, Sam Power et al.

Deterministic mathematical models, such as those specified via differential equations, are a powerful tool to communicate scientific insight. However, such models are necessarily simplified descriptions of the real world. Generalised Bayesian methodologies have been proposed for inference with misspecified models, but these are typically associated with vanishing parameter uncertainty as more data are observed. In the context of a misspecified deterministic mathematical model, this has the undesirable consequence that posterior predictions become deterministic and certain, while being incorrect. Taking this observation as a starting point, we propose Prediction-Centric Uncertainty Quantification, where a mixture distribution based on the deterministic model confers improved uncertainty quantification in the predictive context. Computation of the mixing distribution is cast as a (regularised) gradient flow of the maximum mean discrepancy (MMD), enabling consistent numerical approximations to be obtained. Results are reported on both a toy model from population ecology and a real model of protein signalling in cell biology.

Ning Qi, Kaidi Huang, Zhiyuan Fan et al.

This paper studies the long-term energy management of a microgrid coordinating hybrid hydrogen-battery energy storage. We develop an approximate semi-empirical hydrogen storage model to accurately capture the power-dependent efficiency of hydrogen storage. We introduce a prediction-free two-stage coordinated optimization framework, which generates the annual state-of-charge (SoC) reference for hydrogen storage offline. During online operation, it updates the SoC reference online using kernel regression and makes operation decisions based on the proposed adaptive virtual-queue-based online convex optimization (OCO) algorithm. We innovatively incorporate penalty terms for long-term pattern tracking and expert-tracking for step size updates. We provide theoretical proof to show that the proposed OCO algorithm achieves a sublinear bound of dynamic regret without using prediction information. Numerical studies based on the Elia and North China datasets show that the proposed framework significantly outperforms the existing online optimization approaches by reducing the operational costs and loss of load by around 30% and 80%, respectively. These benefits can be further enhanced with optimized settings for the penalty coefficient and step size of OCO, as well as more historical references.

Shengdong Zu, Lijiang Sun

Tuo Xie, Meng Wu, Dingyu Zhang

Ye Li, Jiayu He, Qiang Zhang et al.

This paper presents the design of two predefined-time active fault-tolerant controllers for the trajectory tracking of autonomous underwater vehicles (AUVs) which can address actuator faults without causing actuator saturation. The first controller offers improved steady-state trajectory tracking precision, while the second ensures a nonsingular property. Firstly, a predefined-time sliding mode controller is formulated based on a predefined-time disturbance observer by integrating a novel predefined-time auxiliary system to prevent the control input from exceeding the actuator’s physical limitations. Subsequently, a non-singular backstepping controller is introduced to circumvent potential singularities in the sliding mode controller, guaranteeing that the trajectory tracking error is uniformly ultimately bounded (UUB) within the predefined time. Additionally, theoretical analysis and simulation results are presented to illustrate the advantages of the proposed method.

Hainan Gao, Yubing Gao, Jingchen Qi et al.

Severe deformation of surrounding rock and excess-gas are the main problems faced in mining of high-gas coal seam. This paper analyzes the deformation characteristics and mechanical model of surrounding rock in high-gas coal seam, and proposes the control technology of surrounding rock deformation and gas prevention and control. Based on this, the entry automatically formed by roof cutting (EAFRC) surrounding rock control technology and constant resistance large deformation anchor cable (CRLDA) support control technology in Shaqu coal mine are put forward. At the same time, the surrounding rock stress and gas migration law of the working face under traditional mining method and EAFRC mining were compared and analyzed. Through the field engineering test, the monitoring and analysis of surrounding rock deformation and gas concentration, the average surrounding rock deformation of roof cutting roadway is 310 mm, and the gas concentration of retained roadway by roof cutting is 0.31%. Through the research in this paper, the surrounding rock stability and gas control of the working face have been realized, and the non-pillar mining of EAFRC has ensured the safe mining of high gas working faces, which provides a reference for the mining of similar mines in non-pillar mining. At the same time, the technical system of EAFRC in non-pillar mining was established, which promoted the development and application of non-pillar mining.

Yang Cui, Yang Xu, Yang Li et al.

Multi-energy microgrid (MEMG) offers an effective approach to deal with energy demand diversification and new energy consumption on the consumer side. In MEMG, it is critical to deploy an energy management system (EMS) for efficient utilization of energy and reliable operation of the system. To help EMS formulate optimal dispatching schemes, a deep reinforcement learning (DRL)-based MEMG energy management scheme with renewable energy source (RES) uncertainty is proposed in this paper. To accurately describe the operating state of the MEMG, the off-design performance model of energy conversion devices is considered in scheduling. The nonlinear optimal dispatching model is expressed as a Markov decision process (MDP) and is then addressed by the twin delayed deep deterministic policy gradient (TD3) algorithm. In addition, to accurately describe the uncertainty of RES, the conditional-least squares generative adversarial networks (C-LSGANs) method based on RES forecast power is proposed to construct the scenarios set of RES power generation. The generated data of RES is used for scheduling to obtain caps and floors for the purchase of electricity and natural gas. Based on this, the superior energy supply sector can formulate solutions in advance to tackle the uncertainty of RES. Finally, the simulation analysis demonstrates the validity and superiority of the method.

Yang Li, Wenjie Ma, Fanjin Bu et al.

In order to coordinate energy interactions among various communities and energy conversions among multi-energy subsystems within the multi-community integrated energy system under uncertain conditions, and achieve overall optimization and scheduling of the comprehensive energy system, this paper proposes a comprehensive scheduling model that utilizes a multi-agent deep reinforcement learning algorithm to learn load characteristics of different communities and make decisions based on this knowledge. In this model, the scheduling problem of the integrated energy system is transformed into a Markov decision process and solved using a data-driven deep reinforcement learning algorithm, which avoids the need for modeling complex energy coupling relationships between multi-communities and multi-energy subsystems. The simulation results show that the proposed method effectively captures the load characteristics of different communities and utilizes their complementary features to coordinate reasonable energy interactions among them. This leads to a reduction in wind curtailment rate from 16.3% to 0% and lowers the overall operating cost by 5445.6 Yuan, demonstrating significant economic and environmental benefits.

Yanbo Chen, Chao Wu, Junjian Qi

Power flow (PF) is one of the most important calculations in power systems. The widely-used PF methods are the Newton-Raphson PF (NRPF) method and the fast-decoupled PF (FDPF) method. In smart grids, power generations and loads become intermittent and much more uncertain, and the topology also changes more frequently, which may result in significant state shifts and further make NRPF or FDPF difficult to converge. To address this problem, we propose a data-driven PF (DDPF) method based on historical/simulated data that includes an offline learning stage and an online computing stage. In the offline learning stage, a learning model is constructed based on the proposed exact linear regression equations, and then the proposed learning model is solved by the ridge regression (RR) method to suppress the effect of data collinearity. In online computing stage, the nonlinear iterative calculation is not needed. Simulation results demonstrate that the proposed DDPF method has no convergence problem and has much higher calculation efficiency than NRPF or FDPF while ensuring similar calculation accuracy.