In the context of the“dual carbon”goals,to address issues such as high energy consumption,high costs,and low power quality in the rapid development of electrified railways,this study focused on the China Railways Hi...In the context of the“dual carbon”goals,to address issues such as high energy consumption,high costs,and low power quality in the rapid development of electrified railways,this study focused on the China Railways High-Speed 5 Electric Multiple Unit and proposed a mathematical model and capacity optimization method for an onboard energy storage system using lithium batteries and supercapacitors as storage media.Firstly,considering the electrical characteristics,weight,and volume of the storage media,a mathematical model of the energy storage system was established.Secondly,to tackle problems related to energy consumption and power quality,an energy management strategy was proposed that comprehensively considers peak shaving and valley filling and power quality by controlling the charge/discharge thresholds of the storage system.Thecapacity optimization adopted a bilevel programming model,with the series/parallel number of storage modules as variables,considering constraints imposed by the Direct Current to Direct Current converter,train load,and space.An improved Particle Swarm Optimization algorithm and linear programming solver were used to solve specific cases.The results show that the proposed onboard energy storage system can effectively achieve energy savings,reduce consumption,and improve power qualitywhile meeting the load and space limitations of the train.展开更多
This study introduces a novel approach to addressing the challenges of high-dimensional variables and strong nonlinearity in reservoir production and layer configuration optimization.For the first time,relational mach...This study introduces a novel approach to addressing the challenges of high-dimensional variables and strong nonlinearity in reservoir production and layer configuration optimization.For the first time,relational machine learning models are applied in reservoir development optimization.Traditional regression-based models often struggle in complex scenarios,but the proposed relational and regression-based composite differential evolution(RRCODE)method combines a Gaussian naive Bayes relational model with a radial basis function network regression model.This integration effectively captures complex relationships in the optimization process,improving both accuracy and convergence speed.Experimental tests on a multi-layer multi-channel reservoir model,the Egg reservoir model,and a real-field reservoir model(the S reservoir)demonstrate that RRCODE significantly reduces water injection and production volumes while increasing economic returns and cumulative oil recovery.Moreover,the surrogate models employed in RRCODE exhibit lightweight characteristics with low computational overhead.These results highlight RRCODE's superior performance in the integrated optimization of reservoir production and layer configurations,offering more efficient and economically viable solutions for oilfield development.展开更多
HOMER(Hybrid OptimizationModel for Electric Renewables)is an effective simulation and optimization platform for hybrid renewable energy.By inputting specific users’energy resource data(such as wind speed,solar radiat...HOMER(Hybrid OptimizationModel for Electric Renewables)is an effective simulation and optimization platform for hybrid renewable energy.By inputting specific users’energy resource data(such as wind speed,solar radiation,etc.)and load data,and by determining the types and models of components selected by the user,HOMER calculates and simulates the operational status of each component at every time step.Ultimately,it computes the energy balance of the system within specified constraints to simulate the overall system operation.This approach enables the reasonable determination of system component capacities,the evaluation of system feasibility,and the calculation of costs over the entire lifecycle of the system.In response to the challenges of matching capacities and high construction costs in wind-solar-storage multi-energy complementary power generation systems,This paper addresses issues such as difficulty in matching component capacities,high construction costs,and low system reliability in multi-energy complementary power generation systems.Using the HOMER hybrid renewable energy simulation and optimization platform,we constructed various hybrid energy systems for a specific region and considered multiple power supply modes.Thesoftware was used to solve for the optimal capacities and costs of each system.Four scenarios were analyzed:grid-only,grid-connected(purchase-sale)wind-solar-storage system,grid-connected(sale)wind-solar-storage system,and off-grid wind-solar-storage system.The results were compared and analyzed.HOMER can assess systemfeasibility and calculate the cost over its entire lifecycle.By inputting 8760 h of wind and solar resource data and load data for a specific region,and considering multiple system structures and power supply modes,the configuration results were evaluated using indicators such as cost and renewable energy utilization ratio.The simulation results indicate that the Net Present Cost(NPC)values across four different scenarios range from 1,877,292 CNY to 3,222,724 CNY,demonstrating significant cost differences.Among these scenarios,the grid-connected(purchase-sell)wind-solarstorage system exhibited the lowest NPC and the highest renewable energy utilization rate.Compared to a system relying solely on the grid,the NPC was reduced by 305,695 CNY,and the renewable energy utilization rate reached 74.7%.展开更多
To integrate different renewable energy resources effectively in a microgrid, a configuration optimization model of a multi-energy distributed generation（DG） system and its auxiliary equipment is proposed. The model...To integrate different renewable energy resources effectively in a microgrid, a configuration optimization model of a multi-energy distributed generation（DG） system and its auxiliary equipment is proposed. The model mainly consists of two parts, the determination of initial configuration schemes according to user preference and the selection of the optimal scheme. The comprehensive evaluation index（CEI）, which is acquired through the analytic hierarchy process（AHP） weight calculation method, is adopted as the evaluation criterion to rank the initial schemes. The optimal scheme is obtained according to the ranking results. The proposed model takes the diversity of different equipment parameters and investment cost into consideration and can give relatively suitable and economical suggestions for system configuration.Additionally, unlike Homer Pro, the proposed model considers the complementation of different renewable energy resources, and thus the rationality of the multi-energy DG system is improved compared with the single evaluation criterion method which only considers the total cost.展开更多
Optimizing convolutional neural networks(CNNs)for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy.This study proposes a unified...Optimizing convolutional neural networks(CNNs)for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy.This study proposes a unified and flexible optimization framework that leverages metaheuristic algorithms to automatically optimize CNN configurations for IoT attack detection.Unlike conventional single-objective approaches,the proposed method formulates a global multi-objective fitness function that integrates accuracy,precision,recall,and model size(speed/model complexity penalty)with adjustable weights.This design enables both single-objective and weightedsum multi-objective optimization,allowing adaptive selection of optimal CNN configurations for diverse deployment requirements.Two representativemetaheuristic algorithms,GeneticAlgorithm(GA)and Particle Swarm Optimization(PSO),are employed to optimize CNNhyperparameters and structure.At each generation/iteration,the best configuration is selected as themost balanced solution across optimization objectives,i.e.,the one achieving themaximum value of the global objective function.Experimental validation on two benchmark datasets,Edge-IIoT and CIC-IoT2023,demonstrates that the proposed GA-and PSO-based models significantly enhance detection accuracy(94.8%–98.3%)and generalization compared with manually tuned CNN configurations,while maintaining compact architectures.The results confirm that the multi-objective framework effectively balances predictive performance and computational efficiency.This work establishes a generalizable and adaptive optimization strategy for deep learning-based IoT attack detection and provides a foundation for future hybrid metaheuristic extensions in broader IoT security applications.展开更多
Wind farm layout optimization is a critical challenge in renewable energy development,especially in regions with complex terrain.Micro-siting of wind turbines has a significant impact on the overall efficiency and eco...Wind farm layout optimization is a critical challenge in renewable energy development,especially in regions with complex terrain.Micro-siting of wind turbines has a significant impact on the overall efficiency and economic viability of wind farm,where the wake effect,wind speed,types of wind turbines,etc.,have an impact on the output power of the wind farm.To solve the optimization problem of wind farm layout under complex terrain conditions,this paper proposes wind turbine layout optimization using different types of wind turbines,the aim is to reduce the influence of the wake effect and maximize economic benefits.The linear wake model is used for wake flow calculation over complex terrain.Minimizing the unit energy cost is taken as the objective function,considering that the objective function is affected by cost and output power,which influence each other.The cost function includes construction cost,installation cost,maintenance cost,etc.Therefore,a bi-level constrained optimization model is established,in which the upper-level objective function is to minimize the unit energy cost,and the lower-level objective function is to maximize the output power.Then,a hybrid evolutionary algorithm is designed according to the characteristics of the decision variables.The improved genetic algorithm and differential evolution are used to optimize the upper-level and lower-level objective functions,respectively,these evolutionary operations search for the optimal solution as much as possible.Finally,taking the roughness of different terrain,wind farms of different scales and different types of wind turbines as research scenarios,the optimal deployment is solved by using the algorithm in this paper,and four algorithms are compared to verify the effectiveness of the proposed algorithm.展开更多
Given the power system balancing challenges induced by high-penetration renewable energy integration,this study systematically reviews international balancing mechanism practices and conducts an in-depth deconstructio...Given the power system balancing challenges induced by high-penetration renewable energy integration,this study systematically reviews international balancing mechanism practices and conducts an in-depth deconstruction of Germany’s balancing group mechanism(BGM).Building on this foundation,this research pioneers the integration of virtual power plants(VPPs)with the BGM in the Chinese context to overcome the limitations of traditional single-entity regulation models in flexibility provision and economic efficiency.A balancing responsibility framework centered on VPPs is innovatively proposed and a regional multi-entity collaboration and bi-level responsibility transfer architecture is constructed.This architecture enables cross-layer coordinated optimization of regional system costs and VPP revenues.The upper layer minimizes regional operational costs,whereas the lower layer enhances the operational revenues of VPPs through dynamic gaming between deviation regulation service income and penalty costs.Compared with traditional centralized regulation models,the proposed method reduces system operational costs by 29.1%in typical regional cases and increases VPP revenues by 24.9%.These results validate its dual optimization of system economics and participant incentives through market mechanisms,providing a replicable theoretical paradigm and practical pathway for designing balancing mechanisms in new power systems.展开更多
Satellite constellation configuration design is a complicated and time-consuming simulation optimization problem. In this paper, a new method called the rapid method for satellite constellation performance calculation...Satellite constellation configuration design is a complicated and time-consuming simulation optimization problem. In this paper, a new method called the rapid method for satellite constellation performance calculation is developed by the Hermite interpolation technique to reduce the computing complication and time. The constellation configuration optimization model is established on the basis of the rapid performance calculation. To reduce the search space and enhance the optimization efficiency, this paper presents a new constellation optimization strategy based on the ordinal optimization （00） theory and expands the algorithm realization for constellation optimization including precise and crude models, ordered performance curves, selection rules and selected subsets. Two experiments about navigation constellation and space based surveillance system （SBSS） are carried out and the analysis of simulation results indicates that the ordinal optimization for satellite constellation configuration design is effective.展开更多
Traditional demand response(DR)programs for energy-intensive industries(EIIs)primarily rely on electricity price signals and often overlook carbon emission factors,limiting their effectiveness in supporting lowcarbon ...Traditional demand response(DR)programs for energy-intensive industries(EIIs)primarily rely on electricity price signals and often overlook carbon emission factors,limiting their effectiveness in supporting lowcarbon transitions.To address this challenge,this paper proposes an electricity–carbon integratedDR strategy based on a bi-level collaborative optimization framework that coordinates the interaction between the grid and EIIs.At the upper level,the grid operatorminimizes generation and curtailment costs by optimizing unit commitment while determining real-time electricity prices and dynamic carbon emission factors.At the lower level,EIIs respond to these dual signals by minimizing their combined electricity and carbon trading costs,considering their participation in medium-and long-term electricity markets,day-ahead spot markets,and carbon emissions trading schemes.The model accounts for direct and indirect carbon emissions,distributed photovoltaic(PV)generation,and battery energy storage systems.This interaction is structured as a Stackelberg game,where the grid acts as the leader and EIIs as followers,enabling dynamic feedback between pricing signals and load response.Simulation studies on an improved IEEE 30-bus system,with a cement plant as a representative user form EIIs,show that the proposed strategy reduces user-side carbon emissions by 7.95% and grid-side generation cost by 4.66%,though the user’s energy cost increases by 7.80% due to carbon trading.Theresults confirmthat the joint guidance of electricity and carbon prices effectively reshapes user load profiles,encourages peak shaving,and improves PV utilization.This coordinated approach not only achieves emission reduction and cost efficiency but also offers a theoretical and practical foundation for integrating carbon pricing into demand-side energy management in future low-carbon power systems.展开更多
Using both numerical and experimental methods, we studied the effect of coil configuration of pulsed magneto-oscillation（PMO） on distribution of electromagnetic field, flow field and solidification structure with th...Using both numerical and experimental methods, we studied the effect of coil configuration of pulsed magneto-oscillation（PMO） on distribution of electromagnetic field, flow field and solidification structure with the same pulse currentparameters in A1 ingots. We designed and constructed three types of coils： surface pulsed magneto-oscillation, hot-toppulsed magneto-oscillation （HPMO） and combined pulsed magneto-oscillation （CPMO）. PMO treatment refined thesolidification structure in all the ingots. The configuration of the PMO, however, introduced differences in magnetic fieldintensity, electromagnetic force, Joule heat, flow field, equiaxed grain zone, grain size and growth direction of columnargrains. The largest equiaxed grain zone was found in CPMO treated ingot, and the smallest grain size was found in bothHPM0 and CPMO treated ingots. Numerical simulation indicated that difference in electromagnetic field and flow fieldresulted in differences in solidification structure. HPMO is more advantageous over others for large ingot production.展开更多
Before the dispatch of the carrier-based aircraft,a series of pre-flight preparation operations need to be completed on the flight deck.Flight deck fixed aviation support resource station configuration has an importan...Before the dispatch of the carrier-based aircraft,a series of pre-flight preparation operations need to be completed on the flight deck.Flight deck fixed aviation support resource station configuration has an important impact on operation efficiency and sortie rate.However,the resource station configuration is determined during the aircraft carrier design phase and is rarely modified as required,which may not be suitable for some pre-flight preparation missions.In order to solve the above defects,the joint optimization of flight deck resource station configuration and aircraft carrier pre-flight preparation scheduling is studied in this paper,which is formulated as a two-tier optimization decision-making framework.An improved variable neighborhood search algorithm with four original neighborhood structures is presented.Dispatch mission experiment and algorithm performance comparison experiment are carried out in the computational experiment section.The correlation between the pre-flight preparation time(makespan)and flight deck cabin occupancy percentage is given,and advantages of the proposed algorithm in solving the mathematical model are verified.展开更多
Upper stage solid rocket motors （SRMS） for launch vehicles require a highly efficient propulsion system. Grain design proves to be vital in terms of minimizing inert mass by adopting a high volumetric efficiency wit...Upper stage solid rocket motors （SRMS） for launch vehicles require a highly efficient propulsion system. Grain design proves to be vital in terms of minimizing inert mass by adopting a high volumetric efficiency with minimum possible sliver. In this arti- cle, a methodology has been presented for designing three-dimensional （3D） grain configuration of radial slot for upper stage solid rocket motors. The design process involves parametric modeling of the geometry in computer aided design （CAD） software through dynamic variables that define the complex configuration. Grain bum back is achieved by making new surfaces at each web increment and calculating geometrical properties at each step. Geometrical calculations are based on volume and change-in-volume calculations. Equilibrium pressure method is used to calculate the internal ballistics. Genetic algorithm （GA） has been used as the optimizer because of its robustness and efficient capacity to explore the design space for global optimum solution and eliminate the requirement of an initial guess. Average thrust maximization under design constraints is the objective function.展开更多
An extended crowding genetic algorithm (ECGA) is introduced for solvingoptimal pump configuration problem, which was presented by T. Westerlund in 1994. This problem hasbeen found to be non-convex, and the objective f...An extended crowding genetic algorithm (ECGA) is introduced for solvingoptimal pump configuration problem, which was presented by T. Westerlund in 1994. This problem hasbeen found to be non-convex, and the objective function contained several local optima and globaloptimality could not be ensured by all the traditional MINLP optimization method. The concepts ofspecies conserving and composite encoding are introduced to crowding genetic algorithm (CGA) formaintain the diversity of population more effectively and coping with the continuous and/or discretevariables in MINLP problem. The solution of three-levels pump configuration got from DICOPT++software (OA algorithm) is also given. By comparing with the solutions obtained from DICOPT++, ECPmethod, and MIN-MIN method, the ECGA algorithm proved to be very effective in finding the globaloptimal solution of multi-levels pump configuration via using the problem-specific information.展开更多
In order to improve the in-flight characteristics of the atomizing droplets during high velocity wire arc spraying (HVAS), some changes have been operated on the original design of the HVAS gun configuration. A compar...In order to improve the in-flight characteristics of the atomizing droplets during high velocity wire arc spraying (HVAS), some changes have been operated on the original design of the HVAS gun configuration. A comparative study was carried out to investigate the microstructure and properties of the coatings produced by the original design spraying gun and the modified one, using 3Cr13 wires of 3 mm in diameter. The characteristics of their jets were examined during spraying. The results indicate that, the included angle between the two wires and the distance from the nozzle to the meeting point of the two vires may have a strong influence on the characteristics of the in-flight droplets and then the coatings. The jet divergence is found to be lower than that of the original one (about 12° against 25°). By modified gun, the adhesion strength, the microhardness and porosity of the coating deposited by modified gun are increased by 39% and 9% respectively. And the porosity of the coatings is decreased by 57%.展开更多
The radar radiation source signals hold extremely high reconnaissance value.Accurately positioning these signals constitutes one of the key technologies in safeguarding the security of the electromagnetic space.The po...The radar radiation source signals hold extremely high reconnaissance value.Accurately positioning these signals constitutes one of the key technologies in safeguarding the security of the electromagnetic space.The positioning error in multi-station scenarios is influenced not only by the accuracy of positioning parameter estimation but also by the geometric configuration of the positioning platform.This paper focuses on the direction of arrival(DOA),frequency difference of arrival(FDOA),and time difference of arrival(TDOA)methods,analyzing the optimal configuration,optimal detection area,and optimal position dilution of precision in both elevation-known and elevation-unknown scenarios.Specifically,the paper constructs a signal receiving model,establishes the corresponding positioning equations,and performs dimensional normalization on these equations to derive measurement values in meters.Through differential processing,the position dilution of precision is obtained,which is then used as the optimization function to determine the optimal configuration,optimal detection area,and optimal position dilution of precision.Simulation results validate the accuracy of the proposed formulas.展开更多
Industrial robots are increasingly being used in machining tasks because of their high flexibility and intelligence.However,the low structural stiffness of a robot significantly affects its positional accuracy and the...Industrial robots are increasingly being used in machining tasks because of their high flexibility and intelligence.However,the low structural stiffness of a robot significantly affects its positional accuracy and the machining quality of its operation equipment.Studying robot stiffness characteristics and optimization methods is an effective method of improving the stiffness performance of a robot.Accordingly,aiming at the poor accuracy of stiffness modeling caused by approximating the stiffness of each joint as a constant,a variable stiffness identification method is proposed based on space gridding.Subsequently,a task-oriented axial stiffness evaluation index is proposed to quantitatively assess the stiffness performance in the machining direction.In addition,by analyzing the redundant kinematic characteristics of the robot machining system,a configuration optimization method is further developed to maximize the index.For numerous points or trajectory-processing tasks,a configuration smoothing strategy is proposed to rapidly acquire optimized configurations.Finally,experiments on a KR500 robot were conducted to verify the feasibility and validity of the proposed stiffness identification and configuration optimization methods.展开更多
Embryonic Array(EA) with different configuration methods will directly affect its reliability and hardware consumption. At present, EA configuration design is lack of quantitative analysis method. In order to reasonab...Embryonic Array(EA) with different configuration methods will directly affect its reliability and hardware consumption. At present, EA configuration design is lack of quantitative analysis method. In order to reasonably optimize EA configuration design, an EA configuration optimization design method is proposed, which is based on the constraints of EA hardware consumption and reliability. Through the analysis of EA working process and composition, quantitative analysis of EA reliability and hardware consumption are completed. Based on the constraints of EA hardware consumption and reliability, the mathematical model of EA configuration optimization design is established, which transfers EA configuration optimization design into an integer nonlinear programming model problem. According to the difference of the fitness value of individual waiting for mutation in population, adaptive mutation operator and crossover operator are selected, and a novel Modified Adaptive Differential Evolution(MADE) algorithm is proposed,which is used to solve EA configuration optimization design problem. Simulation experiments and analysis indicate that the MADE is able to effectively improve the speed, accuracy and stability of algorithm. Moreover, the proposed EA configuration optimization design method can select the most reasonable EA configuration design, and play an important guiding role in EA optimization design.展开更多
The production process plan design and configurations of reconfigurable machine tool （RMT） interact with each other. Reasonable process plans with suitable configurations of RMT help to improve product quality and r...The production process plan design and configurations of reconfigurable machine tool （RMT） interact with each other. Reasonable process plans with suitable configurations of RMT help to improve product quality and reduce production cost. Therefore, a cooperative strategy is needed to concurrently solve the above issue. In this paper, the cooperative optimization model for RMT configurations and production process plan is presented. Its objectives take into account both impacts of process and configuration. Moreover, a novel genetic algorithm is also developed to provide optimal or near-optimal solutions： firstly, its chromosome is redesigned which is composed of three parts, operations, process plan and configurations of RMTs, respectively; secondly, its new selection, crossover and mutation operators are also developed to deal with the process constraints from operation processes （OP） graph, otherwise these operators could generate illegal solutions violating the limits; eventually the optimal configurations for RMT under optimal process plan design can be obtained. At last, a manufacturing line case is applied which is composed of three RMTs. It is shown from the case that the optimal process plan and configurations of RMT are concurrently obtained, and the production cost decreases 6.28% and nonmonetary performance increases 22%. The proposed method can figure out both RMT configurations and production process, improve production capacity, functions and equipment utilization for RMT.展开更多
A thruster configuration and optimization method for GEO satellite is proposed in this paper.All thrusters are installed on the back panel of satellite,which is not only compatible with the design of satellite subdivi...A thruster configuration and optimization method for GEO satellite is proposed in this paper.All thrusters are installed on the back panel of satellite,which is not only compatible with the design of satellite subdivision structure,but also able to provide a large installation room for the payloads and components carried by the satellite.Chemical thruster and electric thruster were selected because they have complementary advantages and can effectively reduce propellant consumption.The working mode and tasks of the thruster were analyzed in detail for thruster configuration,which was optimized in order to minimize propellant consumption.Lastly,a GEO satellite thruster was configured and optimized using this method,with results showing that the method is feasible and effective,and consequently has value for engineering applications.展开更多
基金funded by the National Natural Science Foundation of China(52167013)the Key Program of Natural Science Foundation of Gansu Province(24JRRA225)Natural Science Foundation of Gansu Province(23JRRA891).
文摘In the context of the“dual carbon”goals,to address issues such as high energy consumption,high costs,and low power quality in the rapid development of electrified railways,this study focused on the China Railways High-Speed 5 Electric Multiple Unit and proposed a mathematical model and capacity optimization method for an onboard energy storage system using lithium batteries and supercapacitors as storage media.Firstly,considering the electrical characteristics,weight,and volume of the storage media,a mathematical model of the energy storage system was established.Secondly,to tackle problems related to energy consumption and power quality,an energy management strategy was proposed that comprehensively considers peak shaving and valley filling and power quality by controlling the charge/discharge thresholds of the storage system.Thecapacity optimization adopted a bilevel programming model,with the series/parallel number of storage modules as variables,considering constraints imposed by the Direct Current to Direct Current converter,train load,and space.An improved Particle Swarm Optimization algorithm and linear programming solver were used to solve specific cases.The results show that the proposed onboard energy storage system can effectively achieve energy savings,reduce consumption,and improve power qualitywhile meeting the load and space limitations of the train.
基金supported by the National Natural Science Foundation of China under Grant 52325402,52274057,and 52074340the National Key R&D Program of China under Grant 2023YFB4104200+2 种基金the Major Scientific and Technological Projects of CNOOC under Grant CCL2022RCPS0397RSN111 Project under Grant B08028China Scholarship Council under Grant 202306450108.
文摘This study introduces a novel approach to addressing the challenges of high-dimensional variables and strong nonlinearity in reservoir production and layer configuration optimization.For the first time,relational machine learning models are applied in reservoir development optimization.Traditional regression-based models often struggle in complex scenarios,but the proposed relational and regression-based composite differential evolution(RRCODE)method combines a Gaussian naive Bayes relational model with a radial basis function network regression model.This integration effectively captures complex relationships in the optimization process,improving both accuracy and convergence speed.Experimental tests on a multi-layer multi-channel reservoir model,the Egg reservoir model,and a real-field reservoir model(the S reservoir)demonstrate that RRCODE significantly reduces water injection and production volumes while increasing economic returns and cumulative oil recovery.Moreover,the surrogate models employed in RRCODE exhibit lightweight characteristics with low computational overhead.These results highlight RRCODE's superior performance in the integrated optimization of reservoir production and layer configurations,offering more efficient and economically viable solutions for oilfield development.
基金supported in part by the Inner Mongolia Autonomous Region Science and Technology Project Fund(2021GG0336)Inner Mongolia Natural Science Fund(2023ZD20).
文摘HOMER(Hybrid OptimizationModel for Electric Renewables)is an effective simulation and optimization platform for hybrid renewable energy.By inputting specific users’energy resource data(such as wind speed,solar radiation,etc.)and load data,and by determining the types and models of components selected by the user,HOMER calculates and simulates the operational status of each component at every time step.Ultimately,it computes the energy balance of the system within specified constraints to simulate the overall system operation.This approach enables the reasonable determination of system component capacities,the evaluation of system feasibility,and the calculation of costs over the entire lifecycle of the system.In response to the challenges of matching capacities and high construction costs in wind-solar-storage multi-energy complementary power generation systems,This paper addresses issues such as difficulty in matching component capacities,high construction costs,and low system reliability in multi-energy complementary power generation systems.Using the HOMER hybrid renewable energy simulation and optimization platform,we constructed various hybrid energy systems for a specific region and considered multiple power supply modes.Thesoftware was used to solve for the optimal capacities and costs of each system.Four scenarios were analyzed:grid-only,grid-connected(purchase-sale)wind-solar-storage system,grid-connected(sale)wind-solar-storage system,and off-grid wind-solar-storage system.The results were compared and analyzed.HOMER can assess systemfeasibility and calculate the cost over its entire lifecycle.By inputting 8760 h of wind and solar resource data and load data for a specific region,and considering multiple system structures and power supply modes,the configuration results were evaluated using indicators such as cost and renewable energy utilization ratio.The simulation results indicate that the Net Present Cost(NPC)values across four different scenarios range from 1,877,292 CNY to 3,222,724 CNY,demonstrating significant cost differences.Among these scenarios,the grid-connected(purchase-sell)wind-solarstorage system exhibited the lowest NPC and the highest renewable energy utilization rate.Compared to a system relying solely on the grid,the NPC was reduced by 305,695 CNY,and the renewable energy utilization rate reached 74.7%.
基金The National Natural Science Foundation of China(No.51377021)the Science and Technology Project of State Grid Corporation of China(No.SGTJDK00DWJS1600014)
文摘To integrate different renewable energy resources effectively in a microgrid, a configuration optimization model of a multi-energy distributed generation（DG） system and its auxiliary equipment is proposed. The model mainly consists of two parts, the determination of initial configuration schemes according to user preference and the selection of the optimal scheme. The comprehensive evaluation index（CEI）, which is acquired through the analytic hierarchy process（AHP） weight calculation method, is adopted as the evaluation criterion to rank the initial schemes. The optimal scheme is obtained according to the ranking results. The proposed model takes the diversity of different equipment parameters and investment cost into consideration and can give relatively suitable and economical suggestions for system configuration.Additionally, unlike Homer Pro, the proposed model considers the complementation of different renewable energy resources, and thus the rationality of the multi-energy DG system is improved compared with the single evaluation criterion method which only considers the total cost.
文摘Optimizing convolutional neural networks(CNNs)for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy.This study proposes a unified and flexible optimization framework that leverages metaheuristic algorithms to automatically optimize CNN configurations for IoT attack detection.Unlike conventional single-objective approaches,the proposed method formulates a global multi-objective fitness function that integrates accuracy,precision,recall,and model size(speed/model complexity penalty)with adjustable weights.This design enables both single-objective and weightedsum multi-objective optimization,allowing adaptive selection of optimal CNN configurations for diverse deployment requirements.Two representativemetaheuristic algorithms,GeneticAlgorithm(GA)and Particle Swarm Optimization(PSO),are employed to optimize CNNhyperparameters and structure.At each generation/iteration,the best configuration is selected as themost balanced solution across optimization objectives,i.e.,the one achieving themaximum value of the global objective function.Experimental validation on two benchmark datasets,Edge-IIoT and CIC-IoT2023,demonstrates that the proposed GA-and PSO-based models significantly enhance detection accuracy(94.8%–98.3%)and generalization compared with manually tuned CNN configurations,while maintaining compact architectures.The results confirm that the multi-objective framework effectively balances predictive performance and computational efficiency.This work establishes a generalizable and adaptive optimization strategy for deep learning-based IoT attack detection and provides a foundation for future hybrid metaheuristic extensions in broader IoT security applications.
基金supported by the National Natural Science Foundation of China[Grant No.12461035]Qinghai University Students Innovative Training Program Project[2024-QX-57].
文摘Wind farm layout optimization is a critical challenge in renewable energy development,especially in regions with complex terrain.Micro-siting of wind turbines has a significant impact on the overall efficiency and economic viability of wind farm,where the wake effect,wind speed,types of wind turbines,etc.,have an impact on the output power of the wind farm.To solve the optimization problem of wind farm layout under complex terrain conditions,this paper proposes wind turbine layout optimization using different types of wind turbines,the aim is to reduce the influence of the wake effect and maximize economic benefits.The linear wake model is used for wake flow calculation over complex terrain.Minimizing the unit energy cost is taken as the objective function,considering that the objective function is affected by cost and output power,which influence each other.The cost function includes construction cost,installation cost,maintenance cost,etc.Therefore,a bi-level constrained optimization model is established,in which the upper-level objective function is to minimize the unit energy cost,and the lower-level objective function is to maximize the output power.Then,a hybrid evolutionary algorithm is designed according to the characteristics of the decision variables.The improved genetic algorithm and differential evolution are used to optimize the upper-level and lower-level objective functions,respectively,these evolutionary operations search for the optimal solution as much as possible.Finally,taking the roughness of different terrain,wind farms of different scales and different types of wind turbines as research scenarios,the optimal deployment is solved by using the algorithm in this paper,and four algorithms are compared to verify the effectiveness of the proposed algorithm.
基金supported by the National Natural Science Foundation of China(no.72471087)Beijing Nova Program(no.20250484853)+1 种基金Beijing Natural Science Foundation(no.9242015)National Social Science Foundation of China(no.24&ZD111).
文摘Given the power system balancing challenges induced by high-penetration renewable energy integration,this study systematically reviews international balancing mechanism practices and conducts an in-depth deconstruction of Germany’s balancing group mechanism(BGM).Building on this foundation,this research pioneers the integration of virtual power plants(VPPs)with the BGM in the Chinese context to overcome the limitations of traditional single-entity regulation models in flexibility provision and economic efficiency.A balancing responsibility framework centered on VPPs is innovatively proposed and a regional multi-entity collaboration and bi-level responsibility transfer architecture is constructed.This architecture enables cross-layer coordinated optimization of regional system costs and VPP revenues.The upper layer minimizes regional operational costs,whereas the lower layer enhances the operational revenues of VPPs through dynamic gaming between deviation regulation service income and penalty costs.Compared with traditional centralized regulation models,the proposed method reduces system operational costs by 29.1%in typical regional cases and increases VPP revenues by 24.9%.These results validate its dual optimization of system economics and participant incentives through market mechanisms,providing a replicable theoretical paradigm and practical pathway for designing balancing mechanisms in new power systems.
文摘Satellite constellation configuration design is a complicated and time-consuming simulation optimization problem. In this paper, a new method called the rapid method for satellite constellation performance calculation is developed by the Hermite interpolation technique to reduce the computing complication and time. The constellation configuration optimization model is established on the basis of the rapid performance calculation. To reduce the search space and enhance the optimization efficiency, this paper presents a new constellation optimization strategy based on the ordinal optimization （00） theory and expands the algorithm realization for constellation optimization including precise and crude models, ordered performance curves, selection rules and selected subsets. Two experiments about navigation constellation and space based surveillance system （SBSS） are carried out and the analysis of simulation results indicates that the ordinal optimization for satellite constellation configuration design is effective.
基金supported by the Science and Technology Project of Yunnan Power Grid Co.,Ltd.under Grant No.YNKJXM20222410.
文摘Traditional demand response(DR)programs for energy-intensive industries(EIIs)primarily rely on electricity price signals and often overlook carbon emission factors,limiting their effectiveness in supporting lowcarbon transitions.To address this challenge,this paper proposes an electricity–carbon integratedDR strategy based on a bi-level collaborative optimization framework that coordinates the interaction between the grid and EIIs.At the upper level,the grid operatorminimizes generation and curtailment costs by optimizing unit commitment while determining real-time electricity prices and dynamic carbon emission factors.At the lower level,EIIs respond to these dual signals by minimizing their combined electricity and carbon trading costs,considering their participation in medium-and long-term electricity markets,day-ahead spot markets,and carbon emissions trading schemes.The model accounts for direct and indirect carbon emissions,distributed photovoltaic(PV)generation,and battery energy storage systems.This interaction is structured as a Stackelberg game,where the grid acts as the leader and EIIs as followers,enabling dynamic feedback between pricing signals and load response.Simulation studies on an improved IEEE 30-bus system,with a cement plant as a representative user form EIIs,show that the proposed strategy reduces user-side carbon emissions by 7.95% and grid-side generation cost by 4.66%,though the user’s energy cost increases by 7.80% due to carbon trading.Theresults confirmthat the joint guidance of electricity and carbon prices effectively reshapes user load profiles,encourages peak shaving,and improves PV utilization.This coordinated approach not only achieves emission reduction and cost efficiency but also offers a theoretical and practical foundation for integrating carbon pricing into demand-side energy management in future low-carbon power systems.
基金financially supported by the National Natural Science Foundation of China(No.51704210)the National Key Research and Development Program of China(No.2017YFB0701802)the Natural Science Foundation of Hebei Province(No.E2017105016)
文摘Using both numerical and experimental methods, we studied the effect of coil configuration of pulsed magneto-oscillation（PMO） on distribution of electromagnetic field, flow field and solidification structure with the same pulse currentparameters in A1 ingots. We designed and constructed three types of coils： surface pulsed magneto-oscillation, hot-toppulsed magneto-oscillation （HPMO） and combined pulsed magneto-oscillation （CPMO）. PMO treatment refined thesolidification structure in all the ingots. The configuration of the PMO, however, introduced differences in magnetic fieldintensity, electromagnetic force, Joule heat, flow field, equiaxed grain zone, grain size and growth direction of columnargrains. The largest equiaxed grain zone was found in CPMO treated ingot, and the smallest grain size was found in bothHPM0 and CPMO treated ingots. Numerical simulation indicated that difference in electromagnetic field and flow fieldresulted in differences in solidification structure. HPMO is more advantageous over others for large ingot production.
文摘Before the dispatch of the carrier-based aircraft,a series of pre-flight preparation operations need to be completed on the flight deck.Flight deck fixed aviation support resource station configuration has an important impact on operation efficiency and sortie rate.However,the resource station configuration is determined during the aircraft carrier design phase and is rarely modified as required,which may not be suitable for some pre-flight preparation missions.In order to solve the above defects,the joint optimization of flight deck resource station configuration and aircraft carrier pre-flight preparation scheduling is studied in this paper,which is formulated as a two-tier optimization decision-making framework.An improved variable neighborhood search algorithm with four original neighborhood structures is presented.Dispatch mission experiment and algorithm performance comparison experiment are carried out in the computational experiment section.The correlation between the pre-flight preparation time(makespan)and flight deck cabin occupancy percentage is given,and advantages of the proposed algorithm in solving the mathematical model are verified.
文摘Upper stage solid rocket motors （SRMS） for launch vehicles require a highly efficient propulsion system. Grain design proves to be vital in terms of minimizing inert mass by adopting a high volumetric efficiency with minimum possible sliver. In this arti- cle, a methodology has been presented for designing three-dimensional （3D） grain configuration of radial slot for upper stage solid rocket motors. The design process involves parametric modeling of the geometry in computer aided design （CAD） software through dynamic variables that define the complex configuration. Grain bum back is achieved by making new surfaces at each web increment and calculating geometrical properties at each step. Geometrical calculations are based on volume and change-in-volume calculations. Equilibrium pressure method is used to calculate the internal ballistics. Genetic algorithm （GA） has been used as the optimizer because of its robustness and efficient capacity to explore the design space for global optimum solution and eliminate the requirement of an initial guess. Average thrust maximization under design constraints is the objective function.
基金This project is supported by Provincial Science Foundation of Hebei (No.01213553).
文摘An extended crowding genetic algorithm (ECGA) is introduced for solvingoptimal pump configuration problem, which was presented by T. Westerlund in 1994. This problem hasbeen found to be non-convex, and the objective function contained several local optima and globaloptimality could not be ensured by all the traditional MINLP optimization method. The concepts ofspecies conserving and composite encoding are introduced to crowding genetic algorithm (CGA) formaintain the diversity of population more effectively and coping with the continuous and/or discretevariables in MINLP problem. The solution of three-levels pump configuration got from DICOPT++software (OA algorithm) is also given. By comparing with the solutions obtained from DICOPT++, ECPmethod, and MIN-MIN method, the ECGA algorithm proved to be very effective in finding the globaloptimal solution of multi-levels pump configuration via using the problem-specific information.
基金Project (50235030) supported by the National Natural Science Foundation of China
文摘In order to improve the in-flight characteristics of the atomizing droplets during high velocity wire arc spraying (HVAS), some changes have been operated on the original design of the HVAS gun configuration. A comparative study was carried out to investigate the microstructure and properties of the coatings produced by the original design spraying gun and the modified one, using 3Cr13 wires of 3 mm in diameter. The characteristics of their jets were examined during spraying. The results indicate that, the included angle between the two wires and the distance from the nozzle to the meeting point of the two vires may have a strong influence on the characteristics of the in-flight droplets and then the coatings. The jet divergence is found to be lower than that of the original one (about 12° against 25°). By modified gun, the adhesion strength, the microhardness and porosity of the coating deposited by modified gun are increased by 39% and 9% respectively. And the porosity of the coatings is decreased by 57%.
基金supported by the National Natural Science Foundation of China(Nos.62027801,62301035).
文摘The radar radiation source signals hold extremely high reconnaissance value.Accurately positioning these signals constitutes one of the key technologies in safeguarding the security of the electromagnetic space.The positioning error in multi-station scenarios is influenced not only by the accuracy of positioning parameter estimation but also by the geometric configuration of the positioning platform.This paper focuses on the direction of arrival(DOA),frequency difference of arrival(FDOA),and time difference of arrival(TDOA)methods,analyzing the optimal configuration,optimal detection area,and optimal position dilution of precision in both elevation-known and elevation-unknown scenarios.Specifically,the paper constructs a signal receiving model,establishes the corresponding positioning equations,and performs dimensional normalization on these equations to derive measurement values in meters.Through differential processing,the position dilution of precision is obtained,which is then used as the optimization function to determine the optimal configuration,optimal detection area,and optimal position dilution of precision.Simulation results validate the accuracy of the proposed formulas.
基金National Natural Science Foundation of China(Grant No.51875287)National Defense Basic Scientific Research Program of China(Grant No.JCKY2018605C002)Jiangsu Provincial Natural Science Foundation of China(Grant No.BK20190417).
文摘Industrial robots are increasingly being used in machining tasks because of their high flexibility and intelligence.However,the low structural stiffness of a robot significantly affects its positional accuracy and the machining quality of its operation equipment.Studying robot stiffness characteristics and optimization methods is an effective method of improving the stiffness performance of a robot.Accordingly,aiming at the poor accuracy of stiffness modeling caused by approximating the stiffness of each joint as a constant,a variable stiffness identification method is proposed based on space gridding.Subsequently,a task-oriented axial stiffness evaluation index is proposed to quantitatively assess the stiffness performance in the machining direction.In addition,by analyzing the redundant kinematic characteristics of the robot machining system,a configuration optimization method is further developed to maximize the index.For numerous points or trajectory-processing tasks,a configuration smoothing strategy is proposed to rapidly acquire optimized configurations.Finally,experiments on a KR500 robot were conducted to verify the feasibility and validity of the proposed stiffness identification and configuration optimization methods.
基金supported by the National Natural Science Foundation of China(Nos.61372039 and 61601495)
文摘Embryonic Array(EA) with different configuration methods will directly affect its reliability and hardware consumption. At present, EA configuration design is lack of quantitative analysis method. In order to reasonably optimize EA configuration design, an EA configuration optimization design method is proposed, which is based on the constraints of EA hardware consumption and reliability. Through the analysis of EA working process and composition, quantitative analysis of EA reliability and hardware consumption are completed. Based on the constraints of EA hardware consumption and reliability, the mathematical model of EA configuration optimization design is established, which transfers EA configuration optimization design into an integer nonlinear programming model problem. According to the difference of the fitness value of individual waiting for mutation in population, adaptive mutation operator and crossover operator are selected, and a novel Modified Adaptive Differential Evolution(MADE) algorithm is proposed,which is used to solve EA configuration optimization design problem. Simulation experiments and analysis indicate that the MADE is able to effectively improve the speed, accuracy and stability of algorithm. Moreover, the proposed EA configuration optimization design method can select the most reasonable EA configuration design, and play an important guiding role in EA optimization design.
基金supported by National Natural Science Foundation of China (Grant Nos. 51005169, 50875187, 50975209)Shanghai Municipal Natural Science Foundation of China (Grant No. 10ZR1432300)+1 种基金International Science & Technology Cooperation Program of China (Grant No. 2012DFG72210)Zhejiang Provincial Key International Science & Technology Cooperation Program of China (Grant No. 2011C14025)
文摘The production process plan design and configurations of reconfigurable machine tool （RMT） interact with each other. Reasonable process plans with suitable configurations of RMT help to improve product quality and reduce production cost. Therefore, a cooperative strategy is needed to concurrently solve the above issue. In this paper, the cooperative optimization model for RMT configurations and production process plan is presented. Its objectives take into account both impacts of process and configuration. Moreover, a novel genetic algorithm is also developed to provide optimal or near-optimal solutions： firstly, its chromosome is redesigned which is composed of three parts, operations, process plan and configurations of RMTs, respectively; secondly, its new selection, crossover and mutation operators are also developed to deal with the process constraints from operation processes （OP） graph, otherwise these operators could generate illegal solutions violating the limits; eventually the optimal configurations for RMT under optimal process plan design can be obtained. At last, a manufacturing line case is applied which is composed of three RMTs. It is shown from the case that the optimal process plan and configurations of RMT are concurrently obtained, and the production cost decreases 6.28% and nonmonetary performance increases 22%. The proposed method can figure out both RMT configurations and production process, improve production capacity, functions and equipment utilization for RMT.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61333008)
文摘A thruster configuration and optimization method for GEO satellite is proposed in this paper.All thrusters are installed on the back panel of satellite,which is not only compatible with the design of satellite subdivision structure,but also able to provide a large installation room for the payloads and components carried by the satellite.Chemical thruster and electric thruster were selected because they have complementary advantages and can effectively reduce propellant consumption.The working mode and tasks of the thruster were analyzed in detail for thruster configuration,which was optimized in order to minimize propellant consumption.Lastly,a GEO satellite thruster was configured and optimized using this method,with results showing that the method is feasible and effective,and consequently has value for engineering applications.
