Combined hybrid energy storage system and transmission grid model for peak shaving based on time series operation simulation

This study proposes a combined hybrid energy storage system(HESS) and transmission grid(TG) model, and a corresponding time series operation simulation(TSOS) model is established to relieve the peak-shaving pressure of power systems under the integration of renewable energy. First, a linear model for the optimal operation of the HESS is established, which considers the different power-efficiency characteristics of the pumped storage system, electrochemical storage system, and a new type of liquid compressed air energy storage. Second, a TSOS simulation model for peak shaving is built to maximize the power entering the grid from the wind farms and HESS. Based on the proposed model, this study considers the transmission capacity of a TG. By adding the power-flow constraints of the TG, a TSOS-based HESS and TG combination model for peak shaving is established. Finally, the improved IEEE-39 and IEEE-118 bus systems were considered as examples to verify the effectiveness and feasibility of the proposed model.

Analysis of factors influencing carbon emissions in the Yangtze River Delta region and projections of carbon peak scenarios

Based on the supply-side perspective,the improved STIRPAT model is applied to reveal the mechanisms of supply-side factors such as human,capital,technology,industrial synergy,institutions and economic growth on carbon emissions in the Yangtze River Delta(YRD)through path analysis,and to forecast carbon emissions in the YRD from the baseline scenario,factor regulation scenario and integrated scenario to reach the peak.The results show that:(1)Jiangsu's high carbon emission pattern is the main reason for the YRD hindering the synergistic regulation of carbon emissions.(2)Human factors,institutional factors and economic growth factors can all contribute to carbon emissions in the YRD region,while technological and industrial factors can generally suppress carbon emissions in the YRD region.(3)Under the capital regulation scenario,the YRD region has the highest level of carbon emission synergy,with Jiangsu reaching its peak five years earlier.Under the balanced regulation scenario,the YRD region as a whole,Jiangsu,Zhejiang and Anhui reach the peak as scheduled.

Spatiotemporal variations of ecosystem services and driving factors in the Tianchi Bogda Peak Natural Reserve of Xinjiang,China

Nature reserves play a significant role in providing ecosystem services and are key sites for biodiversity conservation.The Tianchi Bogda Peak Natural Reserve(TBPNR),located in Xinjiang Uygur Autonomous Region,China,is an important ecological barrier area in the temperate arid zone.The evaluation of its important ecosystem services is of great significance to improve the management level and ecological protection efficiency of the reserve.In the present study,we assessed the spatiotemporal variations of four ecosystem services(including net primary productivity(NPP),water yield,soil conservation,and habitat quality)in the TBPNR from 2000 to 2020 based on the environmental and social data using the Integrated Valuation of Ecosystem Services and Trade-offs(InVEST)model.In addition,the coldspot and hotspot areas of ecosystem services were identified by hotspot analysis,and the trade-off and synergistic relationships between ecosystem services were analyzed using factor analysis in a geographic detector.During the study period,NPP and soil conservation values in the reserve increased by 48.20%and 25.56%,respectively;conversely,water yield decreased by 16.56%,and there was no significant change in habitat quality.Spatially,both NPP and habitat quality values were higher in the northern part and lower in the southern part,whereas water yield showed an opposite trend.Correlation analysis revealed that NPP showed a synergistic relationship with habitat quality and soil conservation,and exhibited a trade-off relationship with water yield.Water yield and habitat quality also had a trade-off relationship.NPP and habitat quality were affected by annual average temperature and Normalized Difference Vegetation Index(NDVI),respectively,while water yield and soil conservation were more affected by digital elevation model(DEM).Therefore,attention should be paid to the spatial distribution and dynamics of trade-off and synergistic relationships between ecosystem services in future ecological management.The findings of the present study provide a reference that could facilitate the sustainable utilization of ecosystem services in the typical fragile areas of Northwest China.

Multi-model comparison of CO2 emissions peaking in China:Lessons from CEMF01 study

The paper summarizes results of the China Energy Modeling Forum's(CEMF)first study.Carbon emissions peaking scenarios,consistent with China's Paris commitment,have been simulated with seven national and industry-level energy models and compared.The CO2 emission trends in the considered scenarios peak from 2015 to 2030 at the level of 9e11 Gt.Sector-level analysis suggests that total emissions pathways before 2030 will be determined mainly by dynamics of emissions in the electric power industry and transportation sector.Both sectors will experience significant increase in demand,but have low-carbon alternative options for development.Based on a side-by-side comparison of modeling input and results,conclusions have been drawn regarding the sources of emissions projections differences,which include data,views on economic perspectives,or models'structure and theoretical framework.Some suggestions have been made regarding energy models'development priorities for further research.

Collaborative optimization model of renewable energy development considering peak shaving costs of various flexibility resources

China has set carbon emission goals for 2030 and 2060.Renewable energy sources,primarily wind and photovoltaic power,are being considered as the future of power generation.The major limitation to the development of new energies is the limited flexibility of regulations on power system resources,resulting in insufficient consumption capacity.Thus,the flexible resource costs for peak shaving as well as the reasonable coordinated development and operation optimization of regional renewable energy need to be considered.In this study,a renewable energy development layout configuration analysis method was established by considering the composite cost of a power system,comprehensively analyzing the potential of various flexibility regulation resources for the power system and its composite peak shaving cost,and combining renewable energy output characteristics,load forecasting,grid development,and other factors.For the optimization of various flexible resource utilization methods,a peak shaving cost estimation method from the perspective of the entire power system was established by combining the on-grid electricity prices and operating costs of different power sources.A collaborative optimization model of power system operation that aims at the lowest peak shaving cost and satisfies the constraints of operation,safety,and environmental protection was proposed.Finally,a certain area of Gansu Province was used as an example to perform detailed analysis and calculation,which demonstrated that the model has an optimal effect.This model can provide an analysis method for regional renewable energy development layout configurations and system optimization operations.

Development of Upstream Data-Input Models to Estimate Downstream Peak Flow in Two Mediterranean River Basins of Chile

Accurate flood prediction is an important tool for risk management and hydraulic works design on a watershed scale. The objective of this study was to calibrate and validate 24 linear and non-linear regression models, using only upstream data to estimate real-time downstream flooding. Four critical downstream estimation points in the Mataquito and Maule river basins located in central Chile were selected to estimate peak flows using data from one, two, or three upstream stations. More than one thousand paper-based storm hydrographs were manually analyzed for rainfall events that occurred between 1999 and 2006, in order to determine the best models for predicting downstream peak flow. The Peak Flow Index (IQP) (defined as the quotient between upstream and downstream data) and the Transit Times (TT) between upstream and downstream points were also obtained and analyzed for each river basin. The Coefficients of Determination (R2), the Standard Error of the Estimate (SEE), and the Bland-Altman test (ACBA) were used to calibrate and validate the best selected model at each basin. Despite the high variability observed in peak flow data, the developed models were able to accurately estimate downstream peak flows using only upstream flow data.

Modeling the blast load induced by a close-in explosion considering cylindrical charge parameters

Structural damage is significantly influenced by the various parameters of a close-in explosion.To establish a close-in blast loading model for cylindrical charges according to these parameters,a series of field experiments and a systematic numerical analysis were conducted.A high-fidelity finite element model developed using AUTODYN was first validated using blast data collected from field tests conducted in this and previous studies.A quantitative analysis was then performed to determine the influence of the charge shape,aspect ratio(length to diameter),orientation,and detonation configuration on the characteristics and distributions of the blast loading(incident peak overpressure and impulse)according to scaled distance.The results revealed that the secondary peak overpressure generated by a cylindrical charge was mainly distributed along the axial direction and was smaller than the overpressure generated by an equivalent spherical charge.The effects of charge shape on the blast loading at 45°and 67.5°in the axial plane could be neglected at scaled distances greater than 2 m/kg^(1/3);the effect of aspect ratios greater than 2 on the peak overpressure in the 90°(radial)direction could be neglected at all scaled distances;and double-end detonation increased the radial blast loading by up to 60%compared to singleend detonation.Finally,an empirical cylindrical charge blast loading model was developed considering the influences of charge aspect ratio,orientation,and detonation configuration.The results obtained in this study can serve as a reference for the design of blast tests using cylindrical charges and aid engineers in the design of blast-resistant structures.

A Comparative Study on Asperity Peak Modeling Methods

The peak identification scheme based method(three-point definition)and the spectral moments based method(spectral moment approach)are both widely used for asperity peak modeling in tribology.To discover the differences between the two methods,a great number of rough surface profile samples with various statistical distributions are first randomly generated using FFT.Then the distribution parameters of asperity peaks are calculated for the generated samples with both methods.The obtained results are compared and verified by experiment.The variation rules of the differences between the two methods with statistical characteristics of rough surfaces are investigated.To explain for the discovered differences,the assumptions by spectral moment approach that the joint distribution of surface height,slope and curvature is normal and that the height distribution of asperities is Gaussian,are examined.The results show that it is unreasonable to assume a joint normal distribution without inspecting the correlation pattern of[z],[z′]and[z′′],and that the height distribution of asperities is not exactly Gaussian before correlation length of rough surface increases to a certain extent,20 for instance.

Application of a Bayesian Network Complex System Model Examining the Importance of Customer-Industry Engagement to Peak Electricity Demand Reduction

This paper explores the importance of customer-industry engagement (CIE) to peak energy demand by means of a newly developed Bayesian Network (BN) complex systems model entitled the Residential Electricity Peak Demand Model (REPDM). The REPDM is based on a multi-disciplinary perspective designed to solve the complex problem of residential peak energy demand. The model provides a way to conceptualise and understand the factors that shift and reduce consumer demand in peak times. To gain insight into the importance of customer-industry engagement in affecting residential peak demand, this research investigates intervention impacts and major influences through testing five scenarios using different levels of customer-industry engagement activities. Scenario testing of the model outlines the dependencies between the customer-industry engagement interventions and the probabilities that are estimated to govern the dependencies that influence peak demand. The output from the model shows that there can be a strong interaction between the level of CIE activities and interventions. The influence of CIE activity can increase public and householder support for peak reduction and the model shows how the economic, technical and social interventions can achieve greater peak demand reductions when well-designed with appropriate levels of CIE activities.

Evaluation Model Construction of Peak Sweet Processing Suitability of Different Varieties of Chestnut

[Objective]The aims was to construct peak sweet processing suitability evaluation model and determine the suitable varieties of peak sweet processing in chestnut.[Method]"Zaofeng","Yan Long"etc.15 chestnut varieties in Yan Shan area were taken as research objects and investigated the sensory,physicochemical nutrition and processing indexes.The correlation analysis,principal component analysis and cluster analysis were adopted to simplify and calculate the evaluation index,and set up the mathematical model.[Result]Obvious differences in different varieties of raw materials and products of each index and some indicators existed significant correlation relationship;principal component analysis determined the five principal components:hardness,b value,moisture content,total sugar,browning degree and edible rate or the core indicators of quality evaluation in peak sweet chestnut,and the establishment of products comprehensive value scoring model:Y=0.033 509 hardness+0.033 509b value+0.185 173 moisture content+0.208 983 total sugar+0.108 499 browning degree+0.430 327 ratio of feed,peak sweet chestnut quality and raw material associated model:Y=-1.109+0.015 ratio of good fruit-0.018 kernel hardness+0.008 starch.[Conclusion]Peak sweet chestnut processing suitability evaluation model can provide the basis for chestnut processing and the suitable processing of the chestnut breeding;"Yanguang","Yan Long","Yankui",are very suitable,and"Zibo","Yan Ping"are more appropriate for peak sweet processing.

A method to predict the peak shear strength of rock joints based on machine learning

In geotechnical and tunneling engineering,accurately determining the mechanical properties of jointed rock holds great significance for project safety assessments.Peak shear strength(PSS),being the paramount mechanical property of joints,has been a focal point in the research field.There are limitations in the current peak shear strength(PSS)prediction models for jointed rock:(i)the models do not comprehensively consider various influencing factors,and a PSS prediction model covering seven factors has not been established,including the sampling interval of the joints,the surface roughness of the joints,the normal stress,the basic friction angle,the uniaxial tensile strength,the uniaxial compressive strength,and the joint size for coupled joints;(ii)the datasets used to train the models are relatively limited;and(iii)there is a controversy regarding whether compressive or tensile strength should be used as the strength term among the influencing factors.To overcome these limitations,we developed four machine learning models covering these seven influencing factors,three relying on Support Vector Regression(SVR)with different kernel functions(linear,polynomial,and Radial Basis Function(RBF))and one using deep learning(DL).Based on these seven influencing factors,we compiled a dataset comprising the outcomes of 493 published direct shear tests for the training and validation of these four models.We compared the prediction performance of these four machine learning models with Tang’s and Tatone’s models.The prediction errors of Tang’s and Tatone’s models are 21.8%and 17.7%,respectively,while SVR_linear is at 16.6%,SVR_poly is at 14.0%,and SVR_RBF is at 12.1%.DL outperforms the two existing models with only an 8.5%error.Additionally,we performed shear tests on granite joints to validate the predictive capability of the DL-based model.With the DL approach,the results suggest that uniaxial tensile strength is recommended as the material strength term in the PSS model for more reliable outcomes.

Evaluating Functions of Reservoirs'Storage Capacities and Locations on Daily Peak Attenuation for Ganjiang River Basin Using Xinanjiang Model

Flooding is the most prevalent and costly natural disaster in the world and building reservoirs is one of the major structural measures for flood control and management. In this paper, a framework was proposed to evaluate functions of reservoirs′ locations and magnitudes on daily peak flow attenuation for a large basin of China, namely Ganjiang River Basin. In this study, the Xinanjiang model was adopted to simulate inflows of the reservoirs and flood hydrographs of all sub-catchments of the basin, and simple reservoir operation rules were established for calculating outflows of the reservoirs. Four reservoirs scenarios were established to analyze reservoirs′ locations on daily peak flow attenuation. The results showed that: 1) reservoirs attenuated the peak discharges for all simulated floods, when the flood storage capacities increase as new reservoirs were built, the peak discharge attenuation by reservoirs showed an increasing tendency both in absolute and relative measures; 2) reservoirs attenuated more peak discharge relatively for small floods than for large ones; 3) reservoirs reduced the peak discharge more efficiently for the floods with single peak or multi peaks with main peak occurred first; and 4) effect of upstream reservoirs on peak attenuation decreased from upper reaches to lower reaches; upstream and midstream reservoirs played important roles in decreasing peak discharge both at middle and lower reaches, and downstream reservoirs had less effect on large peak discharge attenuation at outlet of the basin. The proposed framework of evaluating functions of multiple reservoirs′ storage capacities and locations on peak attenuation is valuable for flood control planning and management at basin scale.

Adaptive Wind Gust and Associated Gust-factor Model for the Gust-producing Weather over the Northern South China Sea

Wind gusts are common environmental hazards that can damage buildings,bridges,aircraft,and cruise ships and interrupt electric power distribution,air traffic,waterway transport and port operations.Accurately predicting peak wind gusts in numerical models is essential for saving lives and preventing economic losses.This study investigates the climatology of peak wind gusts and their associated gust factors(GFs)using observations in the coastal and open ocean of the northern South China Sea(NSCS),where severe gust-producing weather occurs throughout the year.The stratified climatology demonstrates that the peak wind gust and GF vary with seasons and particularly with weather types.Based on the inversely proportional relationship between the GF and mean wind speed(MWS),a variety of GF models are constructed through least squares regression analysis.Peak gust speed(PGS)forecasts are obtained through the GF models by multiplying the GFs by observed wind speeds rather than forecasted wind speeds.The errors are thus entirely due to the representation of the GF models.The GF models are improved with weather-adaptive GFs,as evaluated by the stratified MWS.Nevertheless,these weather-adaptive GF models show negative bias for predicting stronger PGSs due to insufficient data representation of the extreme wind gusts.The evaluation of the above models provides insight into maximizing the performance of GF models.This study further proposes a stratified process for forecasting peak wind gusts for routine operations.

有界流场中潜艇水压场特性的预报方法和重要特征

潜艇水压场特性是信息化海战场的重要信息源。基于势流理论研究潜艇水压场特性的工程简化预报方法,构建基于兰金体线型的解析模型和基于回转体线型的数值模型,预估多种线型的潜艇水压场特性,并在验证性研究基础上,揭示潜艇线型、潜深等因素对潜艇水压场负压峰值、负压持续时间等重要特征的影响规律。研究结果表明:随着潜深增大,潜艇水压场负压区形状由单峰值的V形逐渐变为U形,甚至双峰值的W形,线型、潜深等因素对重要特征的影响逐渐增强;同一潜深下,随着航速增大,负压持续时间均呈现先突增、后缓降规律,且无论艇体胖瘦,其水压场重要特征在1倍艇长的横距外均已衰减,即该横距外水雷已难以捕捉其目标特性。

集成蓄热装置的火电机组调峰特性分析

【目的】随着新能源的大规模发展,新能源出力不确定性和波动性问题展现出来,而为了弥补新能源出力缺点,火电机组承担起了调峰作用。为了提升火电机组的调峰能力,对其调峰特性进行了研究。【方法】首先,以某350 MW供热机组作为分析对象,应用仿真软件搭建热力系统模型,并验证该模型的精确性。其次,以蓄热系统为辅助系统,研究机组在满足供热需求情况下的机组调峰能力,并分析蓄热等储能单元对机组调峰能力的影响。最后,采用启发式粒子群算法对蓄热水罐运行策略进行优化,得到随热负荷变化的储热罐最优运行模式。【结果】通过蓄热水罐与火电机组耦合的方法有效提升了机组的调峰和供热能力,并提出可以根据实际热负荷数据确定最大化收益的运行模式。【结论】该方法对机组的运行策略具有指导意义。

中国经济增长与煤炭消费脱钩及影响因素分析

结合中国各省(区、市)2005~2019年的煤炭消费数据及社会经济发展数据,借助Tapio脱钩模型分析了各省(区、市)煤炭消费与经济增长之间的脱钩情况及时空演变.结果表明:各省(区、市)煤炭消费脱钩状态总体呈现出从增长负脱钩、增长联结向弱脱钩、强脱钩转变的较好趋势,大部分地区处于弱脱钩状态.从空间分布来看,东部地区煤炭消费与经济增长脱钩情况明显优于中部地区与西部地区,强脱钩省(区、市)呈现出由北京市、上海市和四川省这3个省(区、市)向外扩张的趋势,而宁夏与新疆的脱钩状态尚不理想,在增长联结与增长负脱钩之间波动.在此基础上,通过拓展的STIRPAT模型和空间杜宾模型分析了影响各省(区、市)脱钩情况的主要因素,并发现能源结构、能源强度、R&D强度、人口密度、对外开放水平、新造林面积、产业结构因素对煤炭消费脱钩指数具有显著影响.未来应加强减煤工作的顶层设计、技术投入与研发和调整能源结构来促进煤炭消费脱钩.

混凝土内部受压力学性能试验与本构分析

为了精准测量受压混凝土内部应力-应变关系,通过在丙烯酸树脂杆上布置电阻式应变片,测量得到混凝土试块内部应变,研发大量程微型土压力盒实现混凝土内部应力测量。将相关测试装置预埋在混凝土试块内部,完成混凝土试块单轴压缩试验,获得精准的混凝土内部应力-应变关系数据。结果表明,混凝土内部本构模型相对于传统本构模型具有良好的延性,且峰值强度远高于传统本构模型。结合国内外几种具有代表性的混凝土本构方程对试验进行了有限元模拟,针对应力-应变曲线、峰值强度及峰值应变进行了对比分析。分析结果表明:实测的混凝土内部应力-应变曲线合理准确,相关测量方法具有良好的精度和可行性;基于我国规范模型的有限元模拟结果与试验结果吻合最好,可进一步推广使用。

基于核相关滤波和卡尔曼滤波预测的混合跟踪方法

针对核相关滤波(KCF)跟踪算法在遮挡场景中出现跟踪性能降低甚至跟踪失败的问题,提出了一种核相关滤波和卡尔曼滤波(KF)预测相结合的模型自适应抗遮挡图像目标跟踪算法KCF-KF。首先,考虑到传统KCF目标跟踪算法中缺少遮挡评估的问题,通过引入响应图的峰值旁瓣比来对图像目标的遮挡情况进行判断,并将遮挡类型划分为部分遮挡和严重遮挡。其次,根据遮挡程度采取不同的模型更新策略,当目标无遮挡或者部分遮挡时,替代传统KCF跟踪算法中采用固定学习率更新模型的方法,通过自适应地调整模型学习率来更新目标外观模型,避免跟踪漂移;当目标被严重遮挡时,停止KCF模型更新。最后,应用严重遮挡之前的运动信息构建卡尔曼滤波器状态空间和位置输出模型,设计卡尔曼滤波算法预测运动目标轨迹来估计遮挡情景下的目标位置,从而解决在遮挡场景中目标跟踪失败的问题。采用OTB-2013标准数据集进行大量实验,结果表明:所提的混合跟踪算法KCF-KF的距离精度为0.796,重叠成功率为0.692。与其他传统跟踪算法相比,该混合算法的跟踪精度和跟踪成功率均优于其他算法,并且在遇到目标遮挡挑战时具有更好的跟踪性能,有效地解决了跟踪过程中的遮挡干扰问题。

区域碳排放达峰预测模型构建与实现路径研究

我国于2020年明确提出了碳排放峰值目标,但学术界在区域碳排放达峰预测模型构建和实现路径方面的研究还相对缺乏。为探索和构建更为有效、准确的区域碳排放达峰预测模型并提出可行的实现路径,首先,分析和选取影响二氧化碳排放量的重要因素与指标,建立起STIRPAT拓展模型;其次,依托排放系数法及多尺度排放清单模型(MEIC)来计算历史数据,运用岭回归算法得出STIRPAT拓展模型中各个指标的弹性系数;最后,结合情景分析法,对区域碳排放量进行不同发展情景的预测。其中,预测模型采用天津市统计年鉴(2000—2021年)的数据,预测天津市未来的碳排放量、碳排放峰值和碳达峰时间,进而深入探索区域实现碳达峰的最优路径。研究结果以期为区域碳排放达峰的预测理论拓展和实践,以及绿色减排的可持续发展决策提供辅助,为实现区域碳达峰碳中和目标提供科学依据和路径参考。