The increasing penetration of second-life battery energy storage systems(SLBESS)in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain deg...The increasing penetration of second-life battery energy storage systems(SLBESS)in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain degradation patterns of repurposed batteries.This paper presents a novel model-free adaptive voltage controlembedded dung beetle-inspired heuristic optimization algorithmfor optimal SLBESS capacity configuration and power dispatch.To simultaneously address the computational complexity and ensure system stability,this paper develops a comprehensive bilevel optimization framework.At the upper level,a dung beetle optimization algorithmdetermines the optimal SLBESS capacity configuration byminimizing total lifecycle costswhile incorporating the charging/discharging power trajectories derived from the model-free adaptive voltage control strategy.At the lower level,a health-priority power dispatch optimization model intelligently allocates power demands among heterogeneous battery groups based on their real-time operational states,state-of-health variations,and degradation constraints.The proposed model-free approach circumvents the need for complex battery charging/discharging power controlmodels and extensive historical data requirements whilemaintaining system stability through adaptive controlmechanisms.A novel cycle life degradation model is developed to quantify the relationship between remaining useful life,depth of discharge,and operational patterns.The integrated framework enables simultaneous strategic planning and operational control,ensuring both economic efficiency and extended battery lifespan.The effectiveness of the proposed method is validated through comprehensive case studies on hybrid energy storage systems,demonstrating superior computational efficiency,robust performance across different network configurations,and significant improvements in battery utilization compared to conventional approaches.展开更多
New energy vehicles have better clean and environmental protection characteristics than traditional fuel vehicles.The new energy engine cooling technology is critical in the design of new energy vehicles.This paper us...New energy vehicles have better clean and environmental protection characteristics than traditional fuel vehicles.The new energy engine cooling technology is critical in the design of new energy vehicles.This paper used oneand three-way joint simulation methods to simulate the refrigeration system of new energy vehicles.Firstly,a k-εturbulent flow model for the cooling pump flow field is established based on the principle of computational fluid dynamics.Then,the CFD commercial fluid analysis software FLUENT is used to simulate the flow field of the cooling pump under different inlet flow conditions.This paper proposes an optimization scheme for new energy vehicle engines’“boiling”phenomenon under high temperatures and long-time climbing conditions.The simulation results show that changing the radiator’s structure and adjusting the thermostat’s parameters can solve the problem of a“boiling pot.”The optimized new energy vehicle engine can maintain a better operating temperature range.The algorithm model can reference each cryogenic system component hardware selection and control strategy in the new energy vehicle’s engine.展开更多
Currently,experimental research on variable stiffness design mainly focuses on laminates.To ensure adaptability in practical application,it is imperative to conduct a systematic study on stiffened variable stiffness s...Currently,experimental research on variable stiffness design mainly focuses on laminates.To ensure adaptability in practical application,it is imperative to conduct a systematic study on stiffened variable stiffness structures,including design,manufacture,experiment,and simulation.Based on the minimum curvature radius and process schemes,two types of T-stiffened panels were designed and manufactured.Uniaxial compression tests have been carried out and the results indicate that the buckling load of variable stiffness specimens is increased by 26.0%,while the failure load is decreased by 19.6%.The influence mechanism of variable stiffness design on the buckling and failure behavior of T-stiffened panels was explicated by numerical analysis.The primary reason for the reduced strength is the significantly increased load bearing ratio of stiffeners.As experimental investigations of stiffened variable stiffness structures are very rare,this study can be considered a reference for future work.展开更多
To ensure the safety and reliability of the distribution network and adapt to the uncertain development of renewable energy sources and loads,a two-stage distributionally robust optimization model is proposed for the ...To ensure the safety and reliability of the distribution network and adapt to the uncertain development of renewable energy sources and loads,a two-stage distributionally robust optimization model is proposed for the active distribution network(ADN)optimization problem considering the uncertainties of the source and load in this paper.By establishing an ambiguity set to capture the uncertainties of the photovoltaic(PV)power,wind power and load,the piecewise-linear function and auxiliary parameters are introduced to help characterize the probability distribution of uncertain variables.The optimization goal of the model is to minimize the total expected cost under the worst-case distribution in the ambiguity set.The first-stage expected cost is obtained based on the predicted value of the uncertainty variable.The second-stage expected cost is based on the actual value of the uncertainty variable to solve the first-stage decision.The generalized linear decision rule approximates the two-stage optimization model,and the affine function is introduced to provide a closer approximation to the second-stage optimization model.Finally,the improved IEEE 33-node and IEEE 118-node systems are simulated and analyzed with deterministic methods,stochastic programming,and robust optimization methods to verify the feasibility and superiority of the proposed model and algorithm.展开更多
基金Financial support was provided by the State Grid Sichuan Electric Power Company Science and Technology Project“Key Research on Development Path Planning and Key Operation Technologies of New Rural Electrification Construction”under Grant No.52199623000G.
文摘The increasing penetration of second-life battery energy storage systems(SLBESS)in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain degradation patterns of repurposed batteries.This paper presents a novel model-free adaptive voltage controlembedded dung beetle-inspired heuristic optimization algorithmfor optimal SLBESS capacity configuration and power dispatch.To simultaneously address the computational complexity and ensure system stability,this paper develops a comprehensive bilevel optimization framework.At the upper level,a dung beetle optimization algorithmdetermines the optimal SLBESS capacity configuration byminimizing total lifecycle costswhile incorporating the charging/discharging power trajectories derived from the model-free adaptive voltage control strategy.At the lower level,a health-priority power dispatch optimization model intelligently allocates power demands among heterogeneous battery groups based on their real-time operational states,state-of-health variations,and degradation constraints.The proposed model-free approach circumvents the need for complex battery charging/discharging power controlmodels and extensive historical data requirements whilemaintaining system stability through adaptive controlmechanisms.A novel cycle life degradation model is developed to quantify the relationship between remaining useful life,depth of discharge,and operational patterns.The integrated framework enables simultaneous strategic planning and operational control,ensuring both economic efficiency and extended battery lifespan.The effectiveness of the proposed method is validated through comprehensive case studies on hybrid energy storage systems,demonstrating superior computational efficiency,robust performance across different network configurations,and significant improvements in battery utilization compared to conventional approaches.
文摘New energy vehicles have better clean and environmental protection characteristics than traditional fuel vehicles.The new energy engine cooling technology is critical in the design of new energy vehicles.This paper used oneand three-way joint simulation methods to simulate the refrigeration system of new energy vehicles.Firstly,a k-εturbulent flow model for the cooling pump flow field is established based on the principle of computational fluid dynamics.Then,the CFD commercial fluid analysis software FLUENT is used to simulate the flow field of the cooling pump under different inlet flow conditions.This paper proposes an optimization scheme for new energy vehicle engines’“boiling”phenomenon under high temperatures and long-time climbing conditions.The simulation results show that changing the radiator’s structure and adjusting the thermostat’s parameters can solve the problem of a“boiling pot.”The optimized new energy vehicle engine can maintain a better operating temperature range.The algorithm model can reference each cryogenic system component hardware selection and control strategy in the new energy vehicle’s engine.
基金Supported by the National Natural Science Foundation of China(No.11902124).
文摘Currently,experimental research on variable stiffness design mainly focuses on laminates.To ensure adaptability in practical application,it is imperative to conduct a systematic study on stiffened variable stiffness structures,including design,manufacture,experiment,and simulation.Based on the minimum curvature radius and process schemes,two types of T-stiffened panels were designed and manufactured.Uniaxial compression tests have been carried out and the results indicate that the buckling load of variable stiffness specimens is increased by 26.0%,while the failure load is decreased by 19.6%.The influence mechanism of variable stiffness design on the buckling and failure behavior of T-stiffened panels was explicated by numerical analysis.The primary reason for the reduced strength is the significantly increased load bearing ratio of stiffeners.As experimental investigations of stiffened variable stiffness structures are very rare,this study can be considered a reference for future work.
基金supported by Natural Science Foundation of Beijing Municipality(No.3161002)National Key R&D Program(No.2017YFB0903300).
文摘To ensure the safety and reliability of the distribution network and adapt to the uncertain development of renewable energy sources and loads,a two-stage distributionally robust optimization model is proposed for the active distribution network(ADN)optimization problem considering the uncertainties of the source and load in this paper.By establishing an ambiguity set to capture the uncertainties of the photovoltaic(PV)power,wind power and load,the piecewise-linear function and auxiliary parameters are introduced to help characterize the probability distribution of uncertain variables.The optimization goal of the model is to minimize the total expected cost under the worst-case distribution in the ambiguity set.The first-stage expected cost is obtained based on the predicted value of the uncertainty variable.The second-stage expected cost is based on the actual value of the uncertainty variable to solve the first-stage decision.The generalized linear decision rule approximates the two-stage optimization model,and the affine function is introduced to provide a closer approximation to the second-stage optimization model.Finally,the improved IEEE 33-node and IEEE 118-node systems are simulated and analyzed with deterministic methods,stochastic programming,and robust optimization methods to verify the feasibility and superiority of the proposed model and algorithm.
