Because of descriptive nonlinearity and computational inefficiency,topology optimization with fatigue life under aperiodic loads has developed slowly.A fatigue constraint topology optimization method based on bidirect...Because of descriptive nonlinearity and computational inefficiency,topology optimization with fatigue life under aperiodic loads has developed slowly.A fatigue constraint topology optimization method based on bidirectional evolutionary structural optimization(BESO)under an aperiodic load is proposed in this paper.In viewof the severe nonlinearity of fatigue damagewith respect to design variables,effective stress cycles are extracted through transient dynamic analysis.Based on the Miner cumulative damage theory and life requirements,a fatigue constraint is first quantified and then transformed into a stress problem.Then,a normalized termination criterion is proposed by approximatemaximum stress measured by global stress using a P-normaggregation function.Finally,optimization examples show that the proposed algorithm can not only meet the requirements of fatigue life but also obtain a reasonable configuration.展开更多
Modern power grids face the challenge of increasing renewable energy penetration that is stochastic in nature and calls for accurate demand predictions to provide the optimized power supply.Hence,increasing the self-c...Modern power grids face the challenge of increasing renewable energy penetration that is stochastic in nature and calls for accurate demand predictions to provide the optimized power supply.Hence,increasing the self-consumption of renewable energy through demand response in households,local communities,and micro-grids is essential and calls for high demand prediction performance at lower levels of demand aggregations to achieve optimal performance.Although many of the recent studies have investigated both macro and micro scale short-term load forecasting(STLF),a comprehensive investigation on the effects of electrical demand aggregation size on STLF is minimal,especially with large sample sizes,where it is essential for optimal sizing of residential micro-grids,demand response markets,and virtual power plants.Hence,this study comprehensively investigates STLF of five aggregation levels(3,10,30,100,and 479)based on a dataset of 479 residential dwellings in Osaka,Japan,with a sample size of(159,47,15,4,and 1)per level,respectively,and investigates the underlying challenges in lower aggregation forecasting.Five deep learning(DL)methods are utilized for STLF and fine-tuned with extensive methodological sensitivity analysis and a variation of early stopping,where a detailed comparative analysis is developed.The test results reveal that a MAPE of(2.47-3.31%)close to country levels can be achieved on the highest aggregation,and below 10%can be sustained at 30 aggregated dwellings.Furthermore,the deep neural network(DNN)achieved the highest performance,followed by the Bi-directional Gated recurrent unit with fully connected layers(Bi-GRU-FCL),which had close to 15%faster training time and 40%fewer learnable parameters.展开更多
The dynamic characteristics of compacted loess are of great significance to the seismic construction of the Loess Plateau area in Northwest China,where earthquakes frequently occur.To study the change in the dynamic m...The dynamic characteristics of compacted loess are of great significance to the seismic construction of the Loess Plateau area in Northwest China,where earthquakes frequently occur.To study the change in the dynamic modulus of the foundation soil under the combined action of vertical and horizontal earthquakes,a hollow cy-lindrical torsion shear instrument capable of vibrating in four directions was used to perform two-way coupling of compression and torsion of Xi'an compacted loess under different dry density and deviator stress ratios.The results show that increasing the dry density can improve the initial dynamic compression modulus and initial dynamic shear modulus of compacted loess.With an increase in the deviator stress ratio,the initial dynamic compression modulus increases,to a certain extent,but the initial dynamic shear modulus decreases slightly.The dynamic modulus gradually decreases with the development of dynamic strain and tends to be stable,and the dynamic modulus that reaches the same strain increases with an increasing dry density.At the initial stage of dynamic loading,the attenuation of the dynamic shear modulus with the strain development is faster than that of the dynamic compression modulus.Compared with previous research results,it is determined that the dynamic modulus of loess under bidirectional dynamic loading is lower and the attenuation rate is faster than that under single-direction dynamic loading.The deviator stress ratio has a more obvious effect on the dynamic compression modulus.The increase in the deviator stress ratio can increase the dynamic compression modulus,to a certain extent.However,the deviator stress ratio has almost no effect on the dynamic shear modulus,and can therefore be ignored.展开更多
This paper presents an advanced methodology for optimizing a UK network load demand with various uncertainties which are related to individual driving behaviours. Without the optimized regulation for traditional power...This paper presents an advanced methodology for optimizing a UK network load demand with various uncertainties which are related to individual driving behaviours. Without the optimized regulation for traditional power system demand, EVs （electric vehicles） would have an adverse impact on the stability of power systems. This becomes more significant for large-scale EVs plugging into the power grid. Traditional optimized methodologies are effective only for EV charging. The proposed techniques improve the system flexibility and stability through an advanced optimization model and flexible bidirectional charging/discharging control. Three scenarios with different charging and discharging power levels and various penetration levels of EVs are discussed in detail in this paper. Simulation results demonstrate that bidirectional EV power flow control has vast potentials to improve the load demand profile, with increased proportion of EVs, and charging/discharging power levels.展开更多
基金Chinese National Natural Science Foundation(No.51890881)Science and Technology Project of Hebei Education Department(Nos.ZD2020156,QN2018228).
文摘Because of descriptive nonlinearity and computational inefficiency,topology optimization with fatigue life under aperiodic loads has developed slowly.A fatigue constraint topology optimization method based on bidirectional evolutionary structural optimization(BESO)under an aperiodic load is proposed in this paper.In viewof the severe nonlinearity of fatigue damagewith respect to design variables,effective stress cycles are extracted through transient dynamic analysis.Based on the Miner cumulative damage theory and life requirements,a fatigue constraint is first quantified and then transformed into a stress problem.Then,a normalized termination criterion is proposed by approximatemaximum stress measured by global stress using a P-normaggregation function.Finally,optimization examples show that the proposed algorithm can not only meet the requirements of fatigue life but also obtain a reasonable configuration.
文摘Modern power grids face the challenge of increasing renewable energy penetration that is stochastic in nature and calls for accurate demand predictions to provide the optimized power supply.Hence,increasing the self-consumption of renewable energy through demand response in households,local communities,and micro-grids is essential and calls for high demand prediction performance at lower levels of demand aggregations to achieve optimal performance.Although many of the recent studies have investigated both macro and micro scale short-term load forecasting(STLF),a comprehensive investigation on the effects of electrical demand aggregation size on STLF is minimal,especially with large sample sizes,where it is essential for optimal sizing of residential micro-grids,demand response markets,and virtual power plants.Hence,this study comprehensively investigates STLF of five aggregation levels(3,10,30,100,and 479)based on a dataset of 479 residential dwellings in Osaka,Japan,with a sample size of(159,47,15,4,and 1)per level,respectively,and investigates the underlying challenges in lower aggregation forecasting.Five deep learning(DL)methods are utilized for STLF and fine-tuned with extensive methodological sensitivity analysis and a variation of early stopping,where a detailed comparative analysis is developed.The test results reveal that a MAPE of(2.47-3.31%)close to country levels can be achieved on the highest aggregation,and below 10%can be sustained at 30 aggregated dwellings.Furthermore,the deep neural network(DNN)achieved the highest performance,followed by the Bi-directional Gated recurrent unit with fully connected layers(Bi-GRU-FCL),which had close to 15%faster training time and 40%fewer learnable parameters.
基金the National Natural Science Foundation of China(No.41272320,52108342)the Key Scientific Research Projects of Higher Education Institutions in Henan Province,China(No.21A560009).
文摘The dynamic characteristics of compacted loess are of great significance to the seismic construction of the Loess Plateau area in Northwest China,where earthquakes frequently occur.To study the change in the dynamic modulus of the foundation soil under the combined action of vertical and horizontal earthquakes,a hollow cy-lindrical torsion shear instrument capable of vibrating in four directions was used to perform two-way coupling of compression and torsion of Xi'an compacted loess under different dry density and deviator stress ratios.The results show that increasing the dry density can improve the initial dynamic compression modulus and initial dynamic shear modulus of compacted loess.With an increase in the deviator stress ratio,the initial dynamic compression modulus increases,to a certain extent,but the initial dynamic shear modulus decreases slightly.The dynamic modulus gradually decreases with the development of dynamic strain and tends to be stable,and the dynamic modulus that reaches the same strain increases with an increasing dry density.At the initial stage of dynamic loading,the attenuation of the dynamic shear modulus with the strain development is faster than that of the dynamic compression modulus.Compared with previous research results,it is determined that the dynamic modulus of loess under bidirectional dynamic loading is lower and the attenuation rate is faster than that under single-direction dynamic loading.The deviator stress ratio has a more obvious effect on the dynamic compression modulus.The increase in the deviator stress ratio can increase the dynamic compression modulus,to a certain extent.However,the deviator stress ratio has almost no effect on the dynamic shear modulus,and can therefore be ignored.
文摘This paper presents an advanced methodology for optimizing a UK network load demand with various uncertainties which are related to individual driving behaviours. Without the optimized regulation for traditional power system demand, EVs （electric vehicles） would have an adverse impact on the stability of power systems. This becomes more significant for large-scale EVs plugging into the power grid. Traditional optimized methodologies are effective only for EV charging. The proposed techniques improve the system flexibility and stability through an advanced optimization model and flexible bidirectional charging/discharging control. Three scenarios with different charging and discharging power levels and various penetration levels of EVs are discussed in detail in this paper. Simulation results demonstrate that bidirectional EV power flow control has vast potentials to improve the load demand profile, with increased proportion of EVs, and charging/discharging power levels.
