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Stochastic Unit Commitment with High-Penetration Offshore Wind Power Generation in Typhoon Scenarios

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Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 535-546页

作者： Yanqi Liu Dundun Liu Hongcai Zhang State Key Laboratory of Internet of Things for smart city and Department of Electrical and Computer Engineering University of MacaoMacao 999078China University of Macao Zhuhai UM Science and Technology Research institute ZhuhaiChina

To tackle the energy crisis and climate change,wind farms are being heavily invested in across the world.In China's coastal areas,there are abundant wind resources and numerous offshore wind farms are being constructed.The secure operation of these wind farms may suffer from typhoons,and researchers have studied power system operation and resilience enhancement in typhoon scenarios.However,the intricate movement of a typhoon makes it challenging to evaluate its spatial-temporal impacts.Most published papers only consider predefined typhoon trajectories neglecting uncertainties.To address this challenge,this study proposes a stochastic unit commitment model that incorporates high-penetration offshore wind power generation in typhoon scenarios.It adopts a data-driven method to describe the uncertainties of typhoon trajectories and considers the realistic anti-typhoon mode in offshore wind farms.A two-stage stochastic unit commitment model is designed to enhance power system resilience in typhoon scenarios.We formulate the model into a mixed-integer linear programming problem and then solve it based on the computationally-efficient progressive hedging algorithm(PHA).Finally,numerical experiments validate the effectiveness of the proposed method.

关键词： Unit commitment two-stage stochastic programming offshore wind farm typhoon

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A survey on the network models applied in the industrial network optimization

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Science China(Information Sciences) 2024年第2期67卷 85-103页

作者： Chao DONG Xiaoxiong XIONG Qiulin XUE Zhengzhen ZHANG Kai NIU Ping ZHANG Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and Telecommunications smart city College Beijing Union University

Network architecture design is critical for optimizing industrial networks. Network architectures can be classified into small-scale networks and large-scale networks based on scale. Graph theory is an efficient mathematical tool for network topology modeling. For small-scale networks, their structure often has regular topology. For large-scale ones, the current body of work mainly focuses on random characteristics of network nodes and edges. Recently, widely used models include random networks, small-world networks,and scale-free networks. In this study, starting from the scale of the network, network modeling methods based on graph theory as well as their industrial applications, are summarized and analyzed. Moreover, a novel network performance metric, called system entropy, is proposed. From the perspective of mathematical properties, an analysis of its non-negativity and concavity is performed. The advantage of system entropy is that it can cover the existing regular networks, random networks, small-world networks, and scale-free networks, and has strong generality. The simulation results reveal that this proposed metric can achieve the comparison of various industrial networks under different models.

关键词： industrial network small-scale network large-scale network graph theory system entropy

In situ generation of oxyanions-decorated cobalt(nickel)oxyhydroxide catalyst with high corrosion resistance for stable and efficient seawater oxidation

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Journal of Energy Chemistry 2024年第7期94卷 508-516页

作者： Fengting Luo Pei Yu Jueting Xiang Junjie Jiang Shijian Chen Chongqing Key Laboratory of Interface Physics in Energy Conversion College of PhysicsChongqing UniversityChongqing 401331China Center of Modern Physics Institute for Smart City of Chongqing University in LiyangLiyang 213300JiangsuChina

The development of efficient and robust anode materials for stable alkaline seawater electrolysis is severely limited by chlorine evolution reaction and chloride corrosion.Here,the sulfur-doped cobalt-nickel bimetallic phosphides(CoNiPS)are specifically designed as a pre-catalyst for navigating a surface reconstruction to fabricate the anions(PO^(3-)_(4)and SO^(2-)_(4))-decorated Co(Ni)OOH catalyst(R-CoNiPS)with exceptional durability and high activity for stable alkaline seawater oxidation(AsO).Various experiment techniques together with theoretical simulations both demonstrate that the in situ-generated PO^(3-)_(4)and SO^(2-)_(4)anions on catalyst surface can improve the oxygen evolution reaction(OER)activity,regulating and stabilizing the catalytic active species Co(Ni)OOH,as well as make a critical role in inhibiting the adsorp-tion of chloride ions and extending the service life of electrode.Therefore,this R-CoNipS electrode exhi-bits superb OER activity toward AsO and stands out among the non-precious AsO electrocatalysts reported recently,requiring low overpotentials of 420 and 440 mV to attain large current densities of 500 and 1000 mA cm^(-2)in an alkaline natural seawater electrolyte,respectively.Particularly,the catalyst displays a negligible chloride corrosion at room temperature during Aso operation(>200 h)at 500 mA cm^(-2).This work opens up a new viewpoint for designing high-activity and durable electrocata-lystsforseawaterelectrolysis.

关键词： Seawater electrolysis Anions Oxygen evolution reaction Chloride corrosion

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Improving the accuracy of precipitation estimates in a typical inland arid area of China using a dynamic Bayesian model averaging approach

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Journal of Arid Land 2024年第3期16卷 331-354页

作者： XU Wenjie DING Jianli BAO Qingling WANG Jinjie XU Kun College of Geography and Remote Sensing Sciences Xinjiang UniversityUrumqi 830017China Key Laboratory of smart city and Environment Modelling of Higher Education institute Xinjiang UniversityUrumqi 830017China

Xinjiang Uygur Autonomous Region is a typical inland arid area in China with a sparse and uneven distribution of meteorological stations,limited access to precipitation data,and significant water scarcity.Evaluating and integrating precipitation datasets from different sources to accurately characterize precipitation patterns has become a challenge to provide more accurate and alternative precipitation information for the region,which can even improve the performance of hydrological modelling.This study evaluated the applicability of widely used five satellite-based precipitation products(Climate Hazards Group InfraRed Precipitation with Station(CHIRPS),China Meteorological Forcing Dataset(CMFD),Climate Prediction Center morphing method(CMORPH),Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record(PERSIANN-CDR),and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis(TMPA))and a reanalysis precipitation dataset(ECMWF Reanalysis v5-Land Dataset(ERA5-Land))in Xinjiang using ground-based observational precipitation data from a limited number of meteorological stations.Based on this assessment,we proposed a framework that integrated different precipitation datasets with varying spatial resolutions using a dynamic Bayesian model averaging(DBMA)approach,the expectation-maximization method,and the ordinary Kriging interpolation method.The daily precipitation data merged using the DBMA approach exhibited distinct spatiotemporal variability,with an outstanding performance,as indicated by low root mean square error(RMSE=1.40 mm/d)and high Person's correlation coefficient(CC=0.67).Compared with the traditional simple model averaging(SMA)and individual product data,although the DBMA-fused precipitation data were slightly lower than the best precipitation product(CMFD),the overall performance of DBMA was more robust.The error analysis between DBMA-fused precipitation dataset and the more advanced Integrated Multi-satellite Retrievals for Global Pre

关键词： precipitation estimates satellite-based and reanalysis precipitation dynamic Bayesian model averaging streamflow simulation Ebinur Lake Basin Xinjiang

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Network Configuration Entity Extraction Method Based on Transformer with Multi-Head Attention Mechanism

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Computers, Materials & Continua 2024年第1期78卷 735-757页

作者： Yang Yang Zhenying Qu Zefan Yan Zhipeng Gao Ti Wang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China Product Development Department China Unicom Smart City Research InstituteBeijing100044China

Nowadays,ensuring thequality of networkserviceshas become increasingly vital.Experts are turning toknowledge graph technology,with a significant emphasis on entity extraction in the identification of device configurations.This research paper presents a novel entity extraction method that leverages a combination of active learning and attention mechanisms.Initially,an improved active learning approach is employed to select the most valuable unlabeled samples,which are subsequently submitted for expert labeling.This approach successfully addresses the problems of isolated points and sample redundancy within the network configuration sample set.Then the labeled samples are utilized to train the model for network configuration entity extraction.Furthermore,the multi-head self-attention of the transformer model is enhanced by introducing the Adaptive Weighting method based on the Laplace mixture distribution.This enhancement enables the transformer model to dynamically adapt its focus to words in various positions,displaying exceptional adaptability to abnormal data and further elevating the accuracy of the proposed model.Through comparisons with Random Sampling(RANDOM),Maximum Normalized Log-Probability(MNLP),Least Confidence(LC),Token Entrop(TE),and Entropy Query by Bagging(EQB),the proposed method,Entropy Query by Bagging and Maximum Influence Active Learning(EQBMIAL),achieves comparable performance with only 40% of the samples on both datasets,while other algorithms require 50% of the samples.Furthermore,the entity extraction algorithm with the Adaptive Weighted Multi-head Attention mechanism(AW-MHA)is compared with BILSTM-CRF,Mutil_Attention-Bilstm-Crf,Deep_Neural_Model_NER and BERT_Transformer,achieving precision rates of 75.98% and 98.32% on the two datasets,respectively.Statistical tests demonstrate the statistical significance and effectiveness of the proposed algorithms in this paper.

关键词： Entity extraction network configuration knowledge graph active learning transformer

Multi-population particle swarm optimization algorithm for automatic design of steel frames

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Frontiers of Structural and Civil Engineering 2024年第1期18卷 89-103页

作者： Wenchen SHAN Jiepeng LIU Yao DING Y.Frank CHEN Junwen ZHOU institute of smart city and Intelligent Transportation Southwest Jiaotong UniversityChengdu 610000China School of Civil Engineering Chongqing UniversityChongqing 400045China Department of Civil Engineering the Pennsylvania State UniversityMiddletownPA 17057USA

Steel structures are widely used;however,their traditional design method is a trial-and-error procedure which is neither efficient nor cost effective.Therefore,a multi-population particle swarm optimization(MPPSO)algorithm is developed to optimize the weight of steel frames according to standard design codes.Modifications are made to improve the algorithm performances including the constraint-based strategy,piecewise mean learning strategy and multi-population cooperative strategy.The proposed method is tested against the representative frame taken from American standards and against other steel frames matching Chinese design codes.The related parameter influences on optimization results are discussed.For the representative frame,MPPSO can achieve greater efficiency through reduction of the number of analyses by more than 65% and can obtain frame with the weight for at least 2.4%lighter.A similar trend can also be observed in cases subjected to Chinese design codes.In addition,a migration interval of 1 and the number of populations as 5 are recommended to obtain better MPPSO results.The purpose of the study is to propose a method with high efficiency and robustness that is not confined to structural scales and design codes.It aims to provide a reference for automatic structural optimization design problems even with dimensional complexity.The proposed method can be easily generalized to the optimization problem of other structural systems.

关键词： steel frame multi-population particle swarm optimization automatic structural optimization design

Monitoring shear deformation of sliding zone via fiber Bragg grating and particle image velocimetry

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第1期16卷 231-241页

作者： Deyang Wang Honghu Zhu Guyu Zhou Wenzhao Yu Baojun Wang Wanhuan Zhou School of Earth Sciences and Engineering Nanjing UniversityNanjing210023China institute of Earth Exploration and Sensing Nanjing UniversityNanjing210023China State Key Laboratory of Internet of Things for smart city University of MacaoMacaoChina

Monitoring shear deformation of sliding zones is of great significance for understanding the landslide evolution mechanism,in which fiber optic strain sensing has shown great potential.However,the cor-relation between strain measurements of quasi-distributed fiber Bragg grating(FBG)sensing arrays and shear displacements of surrounding soil remains elusive.In this study,a direct shear model test was conducted to simulate the shear deformation of sliding zones,in which the soil internal deformation was captured using FBG strain sensors and the soil surface deformation was measured by particle image velocimetry(PIV).The test results show that there were two main slip surfaces and two secondary ones,developing a spindle-shaped shear band in the soil.The formation of the shear band was successfully captured by FBG sensors.A sinusoidal model was proposed to describe the fiber optic cable deformation behavior.On this basis,the shear displacements and shear band widths were calculated by using strain measurements.This work provides important insight into the deduction of soil shear deformation using soil-embedded FBG strain sensors.

关键词： Landslide Shear band Fiber bragg grating(FBG) Particle image velocimetry(PIV) Sinusoidal model Strain‒displacement proportional coefficient

A Least-squares-based Iterative Method with Better Convergence for PF/OPF in Integrated Transmission and Distribution Networks

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CSEE Journal of Power and Energy Systems 2024年第3期10卷 953-962页

作者： Kunjie Tang Shufeng Dong Yonghua Song College of Electrical Engineering Zhejiang UniversityHangzhou 310027China State Key Laboratory of Internet of Things for smart city and Department of Electrical and Computer Engineering University of MacaoMacao SARChina IEEE

The limitations of the conventional master-slavesplitting(MSS)method,which is commonly applied to power flow and optimal power flow in integrated transmission and distribution(I-T&D)networks,are first analyzed.Considering that the MSS method suffers from a slow convergence rate or even divergence under some circumstances,a least-squares-based iterative(LSI)method is proposed.Compared with the MSS method,the LSI method modifies the iterative variables in each iteration by solving a least-squares problem with the information in previous iterations.A practical implementation and a parameter tuning strategy for the LSI method are discussed.Furthermore,a LSI-PF method is proposed to solve I-T&D power flow and a LSIheterogeneous decomposition(LSI-HGD)method is proposed to solve optimal power flow.Numerical experiments demonstrate that the proposed LSI-PF and LSI-HGD methods can achieve the same accuracy as the benchmark methods.Meanwhile,these LSI methods,with appropriate settings,significantly enhance the convergence and efficiency of conventional methods.Also,in some cases,where conventional methods diverge,these LSI methods can still converge.

关键词： Fixed-point theory integrated transmission and distribution networks least-squares-based iterative method master-slave-splitting method optimal power flow power flow

Constraint Learning-based Optimal Power Dispatch for Active Distribution Networks with Extremely Imbalanced Data

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 51-65页

作者： Yonghua Song Ge Chen Hongcai Zhang State Key Laboratory of Internet of Things for smart city and Department of Electrical and Computer Engineering University of MacaoMacao 999078China School of Electrical and Computer Engineering Purdue UniversityWest LafayetteIN 47907 USA

Transition towards carbon-neutral power systems has necessitated optimization of power dispatch in active distribution networks(ADNs)to facilitate integration of distributed renewable generation.Due to unavailability of network topology and line impedance in many distribution networks,physical model-based methods may not be applicable to their operations.To tackle this challenge,some studies have proposed constraint learning,which replicates physical models by training a neural network to evaluate feasibility of a decision(i.e.,whether a decision satisfies all critical constraints or not).To ensure accuracy of this trained neural network,training set should contain sufficient feasible and infeasible samples.However,since ADNs are mostly operated in a normal status,only very few historical samples are infeasible.Thus,the historical dataset is highly imbalanced,which poses a significant obstacle to neural network training.To address this issue,we propose an enhanced constraint learning method.First,it leverages constraint learning to train a neural network as surrogate of ADN's model.Then,it introduces Synthetic Minority Oversampling Technique to generate infeasible samples to mitigate imbalance of historical dataset.By incorporating historical and synthetic samples into the training set,we can significantly improve accuracy of neural network.Furthermore,we establish a trust region to constrain and thereafter enhance reliability of the solution.Simulations confirm the benefits of the proposed method in achieving desirable optimality and feasibility while maintaining low computational complexity.

关键词： Deep learning demand response distribution networks imbalanced data optimal power flow