燃煤机组耦合生物质直燃发电研究进展:非球形生物质大颗粒气固两相动力学模型

在双碳目标背景下,燃煤机组耦合生物质直燃发电是充分利用近零碳排放的生物质能源资源的可行和现实途径之一。目前,国内燃煤机组耦合生物质直燃发电的示范工程较少、技术成熟度还有待提升,存在燃料破碎和输运、燃烧组织、锅炉受热面腐蚀等问题。计算流体力学为能源与动力装置优化设计与运行提供了一种有效的研究方法。重点综述了生物质颗粒气固两相动力学模型的研究现状,总结了其存在的问题,并提出了研究建议。生物质颗粒较大,且呈不规则的非球形;在国内目前缺乏成熟技术方案和相关经验的情况下,准确模拟不规则、非球形生物质大颗粒的运动轨迹是准确模拟其燃烧过程的基础和关键,也是该领域的难点。但在稀相流条件下,目前还缺乏非球形生物质大颗粒气固两相动力学通用模型。建议加强生物质颗粒高效燃烧气固两相动力学模型的基础理论研究,通过颗粒分辨的直接数值模拟获得各种典型的非球形颗粒的曳力升力系数和力矩系数的新关联式,耦合非球形颗粒平移及旋转运动,构建适用于非球形生物质大颗粒的通用气固两相动力学模型,进一步开展试验验证后应用于工业界的多相流模拟中,为揭示生物质与煤粉直燃耦合过程中的颗粒输运和热转化特性提供支撑。

Battery impedance spectrum prediction from partial charging voltage curve by machine learning

Electrochemical impedance spectroscopy(EIS) is an effective technique for Lithium-ion battery state of health diagnosis, and the impedance spectrum prediction by battery charging curve is expected to enable battery impedance testing during vehicle operation. However, the mechanistic relationship between charging curves and impedance spectrum remains unclear, which hinders the development as well as optimization of EIS-based prediction techniques. In this paper, we predicted the impedance spectrum by the battery charging voltage curve and optimized the input based on electrochemical mechanistic analysis and machine learning. The internal electrochemical relationships between the charging curve,incremental capacity curve, and the impedance spectrum are explored, which improves the physical interpretability for this prediction and helps define the proper partial voltage range for the input for machine learning models. Different machine learning algorithms have been adopted for the verification of the proposed framework based on the sequence-to-sequence predictions. In addition, the predictions with different partial voltage ranges, at different state of charge, and with different training data ratio are evaluated to prove the proposed method have high generalization and robustness. The experimental results show that the proper partial voltage range has high accuracy and converges to the findings of the electrochemical analysis. The predicted errors for impedance spectrum are less than 1.9 mΩ with the proper partial voltage range selected by the corelative analysis of the electrochemical reactions inside the batteries. Even with the voltage range reduced to 3.65–3.75 V, the predictions are still reliable with most RMSEs less than 4 mO.

Overview of multi-stage charging strategies for Li-ion batteries

To reduce the carbon footprint in the transportation sector and improve overall vehicle efficiency,a large number of electric vehicles are being manufactured.This is due to the fact that environmental concerns and the depletion of fossil fuels have become significant global problems.Lithium-ion batteries(LIBs)have been distinguished themselves from alternative energy storage technologies for electric vehicles(EVs) due to superior qualities like high energy and power density,extended cycle life,and low maintenance cost to a competitive price.However,there are still certain challenges to be solved,like EV fast charging,longer lifetime,and reduced weight.For fast charging,the multi-stage constant current(MSCC) charging technique is an emerging solution to improve charging efficiency,reduce temperature rise during charging,increase charging/discharging capacities,shorten charging time,and extend the cycle life.However,there are large variations in the implementation of the number of stages,stage transition criterion,and C-rate selection for each stage.This paper provides a review of these problems by compiling information from the literature.An overview of the impact of different design parameters(number of stages,stage transition,and C-rate) that the MSCC charging techniques have had on the LIB performance and cycle life is described in detail and analyzed.The impact of design parameters on lifetime,charging efficiency,charging and discharging capacity,charging speed,and rising temperature during charging is presented,and this review provides guidelines for designing advanced fast charging strategies and determining future research gaps.

Lithium-ion battery degradation trajectory early prediction with synthetic dataset and deep learning

Knowing the long-term degradation trajectory of Lithium-ion(Li-ion) battery in its early usage stage is critical for the maintenance of the battery energy storage system(BESS) in reality. Previous battery health diagnosis methods focus on capacity and state of health(SOH) estimation which can receive only the short-term health status of the cell. This paper proposes a novel degradation trajectory prediction method with synthetic dataset and deep learning, which enables to grasp the characterization of the cell's health at a very early stage of Li-ion battery usage. A transferred convolutional neural network(CNN) is chosen to finalize the early prediction target, and the polynomial function based synthetic dataset generation strategy is designed to reduce the costly data collection procedure in real application. In this thread, the proposed method needs one full lifespan data to predict the overall degradation trajectories of other cells. With only the full lifespan cycling data from 4 cells and 100 cycling data from each cell in experimental validation, the proposed method shows a good prediction accuracy on a dataset with more than 100 commercial Li-ion batteries.

Understanding the mechanism of capacity increase during early cycling of commercial NMC/graphite lithium-ion batteries

A capacity increase is often observed in the early stage of Li-ion battery cycling.This study explores the phenomena involved in the capacity increase from the full cell,electrodes,and materials perspective through a combination of non-destructive diagnostic methods in a full cell and post-mortem analysis in a coin cell.The results show an increase of 1%initial capacity for the battery aged at 100%depth of discharge(DOD)and 45℃.Furthermore,large DODs or high temperatures accelerate the capacity increase.From the incremental capacity and differential voltage(IC-DV)analysis,we concluded that the increased capacity in a full cell originates from the graphite anode.Furthermore,graphite/Li coin cells show an increased capacity for larger DODs and a decreased capacity for lower DODs,thus in agreement with the full cell results.Post-mortem analysis results show that a larger DOD enlarges the graphite dspace and separates the graphite layer structure,facilitating the Li+diffusion,hence increasing the battery capacity.

Distributed Periodic Event-Triggered Optimal Control of DC Microgrids Based on Virtual Incremental Cost

This article presents a distributed periodic eventtriggered(PET)optimal control scheme to achieve generation cost minimization and average bus voltage regulation in DC microgrids.In order to accommodate the generation constraints of the distributed generators(DGs),a virtual incremental cost is firstly designed,based on which an optimality condition is derived to facilitate the control design.To meet the discrete-time(DT)nature of modern control systems,the optimal controller is directly developed in the DT domain.Afterward,to reduce the communication requirement among the controllers,a distributed event-triggered mechanism is introduced for the DT optimal controller.The event-triggered condition is detected periodically and therefore naturally avoids the Zeno phenomenon.The closed-loop system stability is proved by the Lyapunov synthesis for switched systems.The generation cost minimization and average bus voltage regulation are obtained at the equilibrium point.Finally,switch-level microgrid simulations validate the performance of the proposed optimal controller.

Robust Reactive Power Scheduling of Distribution Networks Based on Modified Bootstrap Technique

The uncertainties of the power load,wind power,and photovoltaic power lead to errors between point prediction values and real values,which challenges the safe operation of distribution networks.In this paper,a robust reactive power scheduling(RRPS)model based on a modified bootstrap technique is proposed to consider the uncertainties of power loads and renewable energy sources.Firstly,a deterministic reactive power scheduling(DRPS)model and an RRPS model are formulated.Secondly,a modified bootstrap technique is proposed to estimate prediction errors of power loads and renewable energy sources without artificially assuming the probability density function of prediction errors.To represent all possible scenarios,point prediction values and prediction errors are combined to construct two worst-case scenarios in the RRPS model.Finally,the RRPS model is solved to find a scheduling scheme,which ensures the security of distribution networks for all possible scenarios in theory.Simulation results show that the worst-case scenarios constructed by the modified bootstrap technique outperform popular baselines.Besides,the RRPS model based on the modified bootstrap technique balances economics and security well.

Review of Networked Microgrid Protection:Architectures,Challenges,Solutions,and Future Trends

Design and selection of advanced protection schemes have become essential for reliable and secure operation of networked microgrids.Various protection schemes that allow correct operation of microgrids have been proposed for individual systems in different topologies and connections.Nevertheless,protection schemes for networked microgrids are still in devel-opment,and further research is required to design and operate advanced protection in interconnected systems.Interconnection of these microgrids in different nodes with various intercon-nection technologies increases fault occurrence and complicates protection operation.This paper aims to point out challenges in developing protection for networked microgrids,potential solutions,and research areas that need to be addressed for their development.First,this article presents a systematic analysis of different microgrid clusters proposed since 2016,including several architectures of networked microgrids,operation modes,components,and utilization of renewable sources,which have not been widely explored in previous review papers.Second,the paper presents a discussion on protection systems currently available for microgrid clusters,current challenges,and solutions that have been proposed for these systems.Finally,it discusses the trend of protection schemes in networked microgrids and presents some conclusions related to implementation.IndexTerms—Adaptive eprotection,microgrid cluster,microgrid,multiple microgrid,networked microgrid,real-time simulation,smart grid.

Power Electronics:The Enabling Technology for Renewable Energy Integration

The markedly increased integration of renewable energy in the power grid is of significance in the transition to a sustainable energy future.The grid integration of renewables will be continuously enhanced in the future.According to the International Renewable Energy Agency(IRENA),renewable technology is the main pathway to reach zero carbon dioxide(CO_(2))emissions by 2060.Power electronics have played and will continue to play a significant role in this energy transition by providing efficient electrical energy conversion,distribution,transmission,and utilization.Consequently,the development of power electronics technologies,i.e.,new semiconductor devices,flexible converters,and advanced control schemes,is promoted extensively across the globe.Among various renewables,wind energy and photovoltaic(PV)are the most widely used,and accordingly these are explored in this paper to demonstrate the role of power electronics.The development of renewable energies and the demands of power electronics are reviewed first.Then,the power conversion and control technologies as well as grid codes for wind and PV systems are discussed.Future trends in terms of power semiconductors,reliability,advanced control,grid-forming operation,and security issues for largescale grid integration of renewables,and intelligent and full user engagement are presented at the end.

Scenario Generations for Renewable Energy Sources and Loads Based on Implicit Maximum Likelihood Estimations

Scenario generations for renewable energy sources and loads play an important role in the stable operation and risk assessment of integrated energy systems.This paper proposes a deep generative network based method to model time-series curves,e.g.,power generation curves and load curves,of renewable energy sources and loads based on implicit maximum likelihood estimations(IMLEs),which can generate realistic scenarios with similar patterns as real ones.After training the model,any number of new scenarios can be obtained by simply inputting Gaussian noises into the data generator of IMLEs.The proposed approach does not require any model assumptions or prior knowledge of the form in the likelihood function being made during the training process,which leads to stronger applicability than explicit density model based methods.The extensive experiments show that the IMLEs accurately capture the complex shapes,frequency-domain characteristics,probability distributions,and correlations of renewable energy sources and loads.Moreover,the proposed approach can be easily generalized to scenario generation tasks of various renewable energy sources and loads by fine-tuning parameters and structures.

Ultra-short-term Interval Prediction of Wind Power Based on Graph Neural Network and Improved Bootstrap Technique

Reliable and accurate ultra-short-term prediction of wind power is vital for the operation and optimization of power systems.However,the volatility and intermittence of wind power pose uncertainties to traditional point prediction,resulting in an increased risk of power system operation.To represent the uncertainty of wind power,this paper proposes a new method for ultra-short-term interval prediction of wind power based on a graph neural network(GNN)and an improved Bootstrap technique.Specifically,adjacent wind farms and local meteorological factors are modeled as the new form of a graph from the graph-theoretic perspective.Then,the graph convolutional network(GCN)and bi-directional long short-term memory(Bi-LSTM)are proposed to capture spatiotemporal features between nodes in the graph.To obtain highquality prediction intervals(PIs),an improved Bootstrap technique is designed to increase coverage percentage and narrow PIs effectively.Numerical simulations demonstrate that the proposed method can capture the spatiotemporal correlations from the graph,and the prediction results outperform popular baselines on two real-world datasets,which implies a high potential for practical applications in power systems.

Impact of Grid Topology on Pole-to-ground Fault Current in Bipolar DC Grids:Mechanism and Evaluation

The fault current level analysis is important for bipolar direct current(DC)grids,which determines the operation and protection requirements.The DC grid topology significantly impacts the current path and then the fault current level of the grid,which makes it possible to limit the fault current by optimizing the grid topology.However,the corresponding discussion in the literature is indigent.Aiming at this point,the impact of grid topology,i.e.,the connecting scheme of converters,on the pole-to-ground fault current in bipolar DC grids,is investigated in this paper,and the ground-return-based and metallic-return-based grounding schemes are considered,respectively.Firstly,the decoupled equivalent model in frequency domain for fault current analysis is obtained.Then,the impacts of converters with different distances to the fault point on the fault current can be analyzed according to the high-frequency impedance characteristics.Based on the analysis results,a simplified fault current index(SFCI)is proposed to realize the fast evaluation of impact of grid topology on the fault current level.The SFCI is then applied to evaluate the relative fault current level.Finally,the simulation results validate the model,the analysis method,and the SFCI,which can effectively evaluate the relative fault current level in a direct and fast manner.

Data-driven Reactive Power Optimization for Distribution Networks Using Capsule Networks

The construction of advanced metering infrastructure and the rapid evolution of artificial intelligence bring opportunities to quickly searching for the optimal dispatching strategy for reactive power optimization. This can be realized by mining existing prior knowledge and massive data without explicitly constructing physical models. Therefore, a novel datadriven approach is proposed for reactive power optimization of distribution networks using capsule networks(CapsNet). The convolutional layers with strong feature extraction ability are used to project the power loads to the feature space to realize the automatic extraction of key features. Furthermore, the complex relationship between input features and dispatching strategies is captured accurately by capsule layers. The back propagation algorithm is utilized to complete the training process of the CapsNet. Case studies show that the accuracy and robustness of the CapsNet are better than those of popular baselines(e.g.,convolutional neural network, multi-layer perceptron, and casebased reasoning). Besides, the computing time is much lower than that of traditional heuristic methods such as genetic algorithm, which can meet the real-time demand of reactive power optimization in distribution networks.

Controller Design-oriented Analysis of Grid-forming Converters for Stability Robustness Enhancement

Grid-forming converters can suffer from control interaction problems in grid connections that can result in small-signal instability.Their inner-loop voltage controller tends to interact with the outer-loop power controller,rendering the controller design more difficult.To conduct a design-oriented analysis,a control-loop decomposition approach for grid-forming converters is proposed.Combined with impedance-based stability analysis,the control-loop decomposition approach can reveal how different control loops affect the converter-grid interaction.This results in a robust controller design enabling grid-forming converters to operate within a wider range of grid short-circuit ratios.Finally,simulation and experimental results,which validate the approach,are presented.