Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longe...Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longer period.A multi-objective genetic algorithm(MOGA)and state of charge(SOC)region division for the batteries are introduced to solve the objective function and configuration of the system capacity,respectively.MATLAB/Simulink was used for simulation test.The optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system,with a combination of a 300 Ah lithium battery,a 200 Ah lead-acid battery,and a water storage tank,the proposed strategy reduces the system construction cost by approximately 18,000 yuan.Additionally,the cycle count of the electrochemical energy storage systemincreases from4515 to 4660,while the depth of discharge decreases from 55.37%to 53.65%,achieving shallow charging and discharging,thereby extending battery life and reducing grid voltage fluctuations significantly.The proposed strategy is a guide for stabilizing the grid connection of wind and solar power generation,capability allocation,and energy management of energy conservation systems.展开更多
Large-scale electric vehicles(EVs) connected to the micro grid would cause many problems. In this paper, with the consideration of vehicle to grid(V2 G), two charging and discharging load modes of EVs were constructed...Large-scale electric vehicles(EVs) connected to the micro grid would cause many problems. In this paper, with the consideration of vehicle to grid(V2 G), two charging and discharging load modes of EVs were constructed. One was the disorderly charging and discharging mode based on travel habits, and the other was the orderly charging and discharging mode based on time-of-use(TOU) price;Monte Carlo method was used to verify the case. The scheme of the capacity optimization of photovoltaic charging station under two different charging and discharging modes with V2 G was proposed. The mathematical models of the objective function with the maximization of energy efficiency, the minimization of the investment and the operation cost of the charging system were established. The range of decision variables, constraints of the requirements of the power balance and the strategy of energy exchange were given. NSGA-Ⅱ and NSGA-SA algorithm were used to verify the cases, respectively. In both algorithms, by comparing with the simulation results of the two different modes, it shows that the orderly charging and discharging mode with V2 G is obviously better than the disorderly charging and discharging mode in the aspects of alleviating the pressure of power grid, reducing system investment and improving energy efficiency.展开更多
To solve the problem of residual wind power in offshore wind farms,a hydrogen production system with a reasonable capacity was configured to enhance the local load of wind farms and promote the local consumption of re...To solve the problem of residual wind power in offshore wind farms,a hydrogen production system with a reasonable capacity was configured to enhance the local load of wind farms and promote the local consumption of residual wind power.By studying the mathematical model of wind power output and calculating surplus wind power,as well as considering the hydrogen production/storage characteristics of the electrolyzer and hydrogen storage tank,an innovative capacity optimization allocation model was established.The objective of the model was to achieve the lowest total net present value over the entire life cycle.The model took into account the cost-benefit breakdown of equipment end-of-life cost,replacement cost,residual value gain,wind abandonment penalty,hydrogen transportation,and environmental value.The MATLAB-based platform invoked the CPLEX commercial solver to solve the model.Combined with the analysis of the annual average wind speed data from an offshore wind farm in Guangdong Province,the optimal capacity configuration results and the actual operation of the hydrogen production system were obtained.Under the calculation scenario,this hydrogen production system could consume 3,800 MWh of residual electricity from offshore wind power each year.It could achieve complete consumption of residual electricity from wind power without incurring the penalty cost of wind power.Additionally,it could produce 66,500 kg of green hydrogen from wind power,resulting in hydrogen sales revenue of 3.63 million RMB.It would also reduce pollutant emissions from coal-based hydrogen production by 1.5 tons and realize an environmental value of 4.83 million RMB.The annual net operating income exceeded 6 million RMB and the whole life cycle NPV income exceeded 50 million RMB.These results verified the feasibility and rationality of the established capacity optimization allocation model.The model could help advance power system planning and operation research and assist offshore wind farm operators in improving economic and environmental benefits.展开更多
The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of...The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of energy systems.To enhance the consumption capacity of green power,the green power system consumption optimization scheduling model(GPS-COSM)is proposed,which comprehensively integrates green power system,electric boiler,combined heat and power unit,thermal energy storage,and electrical energy storage.The optimization objectives are to minimize operating cost,minimize carbon emission,and maximize the consumption of wind and solar curtailment.The multi-objective particle swarm optimization algorithm is employed to solve the model,and a fuzzy membership function is introduced to evaluate the satisfaction level of the Pareto optimal solution set,thereby selecting the optimal compromise solution to achieve a dynamic balance among economic efficiency,environmental friendliness,and energy utilization efficiency.Three typical operating modes are designed for comparative analysis.The results demonstrate that the mode involving the coordinated operation of electric boiler,thermal energy storage,and electrical energy storage performs the best in terms of economic efficiency,environmental friendliness,and renewable energy utilization efficiency,achieving the wind and solar curtailment consumption rate of 99.58%.The application of electric boiler significantly enhances the direct accommodation capacity of the green power system.Thermal energy storage optimizes intertemporal regulation,while electrical energy storage strengthens the system’s dynamic regulation capability.The coordinated optimization of multiple devices significantly reduces reliance on fossil fuels.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
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
Because of the randomness of wind power and photovoltaic(PV)output of new energy bases,the problem of peak regulation capability and voltage stability of ultra-high voltage direct current(UHVDC)transmission lines,we p...Because of the randomness of wind power and photovoltaic(PV)output of new energy bases,the problem of peak regulation capability and voltage stability of ultra-high voltage direct current(UHVDC)transmission lines,we proposed an optimum allocation method of installed capacity of the solar-thermal power station based on chance constrained programming in this work.Firstly,we established the uncertainty model of wind power and PV based on the chance constrained planning theory.Then we used the K-medoids clusteringmethod to cluster the scenarios considering the actual operation scenarios throughout the year.Secondly,we established the optimal configuration model based on the objective function of the strongest transient voltage stability and the lowest overall cost of operation.Finally,by quantitative analysis of actual wind power and photovoltaic new energy base,this work verified the feasibility of the proposed method.As a result of the simulations,we found that using the optimal configuration method of solar-thermal power stations could ensure an accurate allocation of installed capacity.When the installed capacity of the solar-thermal power station is 1×106 kW,the transient voltage recovery index(TVRI)is 0.359,which has a strong voltage support capacity for the system.Based on the results of this work,the optimal configuration of the installed capacity of the solar-thermal power plant can improve peak shaving performance,transient voltage support capability,and new energy consumption while satisfying the Direct Current(DC)outgoing transmission premise.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
As the number of electric vehicles(EVs)continues to grow and the demand for charging infrastructure is also increasing,how to improve the charging infrastructure has become a bottleneck restricting the development of ...As the number of electric vehicles(EVs)continues to grow and the demand for charging infrastructure is also increasing,how to improve the charging infrastructure has become a bottleneck restricting the development of EVs.In other words,reasonably planning the location and capacity of charging stations is important for development of the EV industry and the safe and stable operation of the power system.Considering the construction and maintenance of the charging station,the distribution network loss of the charging station,and the economic loss on the user side of the EV,this paper takes the node and capacity of charging station planning as control variables and the minimum cost of system comprehensive planning as objective function,and thus proposes a location and capacity planning model for the EV charging station.Based on the problems of low efficiency and insufficient global optimization ability of the current algorithm,the simulated annealing immune particle swarm optimization algorithm(SA-IPSO)is adopted in this paper.The simulated annealing algorithm is used in the global update of the particle swarm optimization(PSO),and the immune mechanism is introduced to participate in the iterative update of the particles,so as to improve the speed and efficiency of PSO.Voronoi diagram is used to divide service area of the charging station,and a joint solution process of Voronoi diagram and SA-IPSO is proposed.By example analysis,the results show that the optimal solution corresponding to the optimisation method proposed in this paper has a low overall cost,while the average charging waiting time is only 1.8 min and the charging pile utilisation rate is 75.5%.The simulation comparison verifies that the improved algorithm improves the operational efficiency by 18.1%and basically does not fall into local convergence.展开更多
With the rapid development of urban road traffic and the increasing number of vehicles,how to alleviate traffic congestion is one of the hot issues that need to be urgently addressed in building smart cities.Therefore...With the rapid development of urban road traffic and the increasing number of vehicles,how to alleviate traffic congestion is one of the hot issues that need to be urgently addressed in building smart cities.Therefore,in this paper,a nonlinear multi-objective optimization model of urban intersection signal timing based on a Genetic Algorithm was constructed.Specifically,a typical urban intersection was selected as the research object,and drivers’acceleration habits were taken into account.What’s more,the shortest average delay time,the least average number of stops,and the maximum capacity of the intersection were regarded as the optimization objectives.The optimization results show that compared with the Webster method when the vehicle speed is 60 km/h and the acceleration is 2.5 m/s^(2),the signal intersection timing scheme based on the proposed Genetic Algorithm multi-objective optimization reduces the intersection signal cycle time by 14.6%,the average vehicle delay time by 12.9%,the capacity by 16.2%,and the average number of vehicles stop by 0.4%.To verify the simulation results,the authors imported the optimized timing scheme into the constructed Simulation of the Urban Mobility model.The experimental results show that the authors optimized timing scheme is superior to Webster’s in terms of vehicle average loss time reduction,carbon monoxide emission,particulate matter emission,and vehicle fuel consumption.The research in this paper provides a basis for Genetic algorithms in traffic signal control.展开更多
Steel catenary riser represents the pioneering riser technology implemented in China’s deep-sea oil and gas opera-tions.Given the complex mechanical conditions of the riser,extensive research has been conducted on it...Steel catenary riser represents the pioneering riser technology implemented in China’s deep-sea oil and gas opera-tions.Given the complex mechanical conditions of the riser,extensive research has been conducted on its dynamic analysis and structural design.This study investigates a deep-sea oil and gas field by developing a coupled model of a semi-submersible platform and steel catenary riser to analyze it mechanical behavior under extreme marine condi-tions.Through multi-objective optimization methodology,the study compares and analyzes suspension point tension and touchdown point stress under various conditions by modifying the suspension position,suspension angle,and catenary length.The optimal configuration parameters were determined:a suspension angle of 12°,suspension position in the southwest direction of the column,and a catenary length of approximately 2000 m.These findings elucidate the impact of configuration parameters on riser dynamic response and establish reasonable parameter layout ranges for adverse sea conditions,offering valuable optimization strategies for steel catenary riser deployment in domestic deep-sea oil and gas fields.展开更多
Automation advancements prompts the extensive integration of collaborative robot(cobot)across a range of industries.Compared to the commonly used design approach of increasing the payload-to-weight ratio of cobot to e...Automation advancements prompts the extensive integration of collaborative robot(cobot)across a range of industries.Compared to the commonly used design approach of increasing the payload-to-weight ratio of cobot to enhance load capacity,equal attention should be paid to the dynamic response characteristics of cobot during the design process to make the cobot more flexible.In this paper,a new method for designing the drive train parameters of cobot is proposed.Firstly,based on the analysis of factors influencing the load capacity and dynamic response characteristics,design criteria for both aspects are established for cobot with all optimization design criteria normalized within the design domain.Secondly,with the cobot in the horizontal pose,the motor design scheme is discretized and it takes the joint motor diameter and gearbox speed ratio as optimization design variables.Finally,all the discrete values of the optimization objectives are obtained through the enumeration method and the Pareto front is used to select the optimal solution through multi-objective optimization.Base on the cobot design method proposed in this paper,a six-axis cobot is designed and compared with the commercial cobot.The result shows that the load capacity of the designed cobot in this paper reaches 8.4 kg,surpassing the 5 kg load capacity commercial cobot which is used as a benchmark.The minimum resonance frequency of the joints is 42.70 Hz.展开更多
This paper presents a solution for energy storage system capacity configuration and renewable energy integration in smart grids using a multi-disciplinary optimization method.The solution involves a hybrid prediction ...This paper presents a solution for energy storage system capacity configuration and renewable energy integration in smart grids using a multi-disciplinary optimization method.The solution involves a hybrid prediction framework based on an improved grey regression neural network(IGRNN),which combines grey prediction,an improved BP neural network,and multiple linear regression with a dynamic weight allocation mechanism to enhance prediction accuracy.Additionally,an improved cuckoo search(ICS)algorithm is designed to empower the neural network model,incorporating a gamma distribution disturbance factor and adaptive inertia weight to balance global exploration and local exploitation,achieving a 40%faster convergence rate.A multi-objective snake optimization algorithm is also developed to optimize economic cost,grid stability,and energy utilization efficiency using energy storage capacity as the decision variable.The experimental results,based on a 937-day load dataset from a chemical park in Jiangsu Province,show that the IGRNN model has better prediction accuracy than traditional models,with an RMSE of 11.1361,an MAE of 8.264,and an R^(2) of 96.90%.The optimized energy storage system stabilizes the daily load curve at 800 kW,reduces the peak-valley difference by 62%,and decreases grid regulation pressure by 58.3%.This research provides theoretical and practical support for energy storage planning in high renewable energy proportion grids.Future work will focus on integrating weather data and dynamic optimization strategies under policy constraints to improve system applicability in real-world scenarios.展开更多
A problem of upgrading to the Next Generation Wireless Network (NGWN) is backward compatibility with pre-existing networks, the cost and operational benefit of gradually enhancing networks, by replacing, upgrading and...A problem of upgrading to the Next Generation Wireless Network (NGWN) is backward compatibility with pre-existing networks, the cost and operational benefit of gradually enhancing networks, by replacing, upgrading and installing new wireless network infrastructure elements that can accommodate both voice and data demand. In this paper, we propose a new genetic algorithm has double population to solve Multi-Objectives Optimal of Upgrading Infrastructure (MOOUI) problem in NGWN. We modeling network topology for MOOUI problem has two levels in which mobile users are sources and both base stations and base station controllers are concentrators. Our objective function is the sources to concentrators connectivity cost as well as the cost of the installation, connection, replacement, and capacity upgrade of infrastructure equipment. We generate two populations satisfy constraints and combine them to build solutions and evaluate the performance of my algorithm with data randomly generated. Numerical results show that our algorithm is a promising approach to solve this problem.展开更多
Environmental impact assessments and subsequent regulation measures of urban development plans are critical to human progress toward sustainability,since these plans set the scale and structure targets of future socio...Environmental impact assessments and subsequent regulation measures of urban development plans are critical to human progress toward sustainability,since these plans set the scale and structure targets of future socioeconomic development.A three-step methodology for assessing and optimizing an urban development plan focusing on its impacts on the water system was developed.The methodology first predicted the pressure on the water system caused by implementation of the plan under distinct scenarios then compared the pressure with the carrying capacity threshold to verity the system status;finally,a multi-objected the pressure with the carrying capacity threshold to verity the system status;finally,a multi-objective optimization method was used to propose regulation solutions.The methodology enabled evaluation of the water system carrying state,taking socioeconomic development uncertainties into account,and multiple sets of improvement measures under different decisionmaker preferences were generated.The methodology was applied in the case of Zhoushan city in South-east China.The assessment results showed that overloading problems occurred in 11 out of the 13 zones in Zhoushan,with the potential pressure varying from 1.1 to 18.3 times the carrying capacity.As a basic regulation measure,and environmental efficiency upgrade could relieve the overloading in 4 zones and reduce 9%-63%of the pressure.The optimization of industrial development showed that the pressure could be controlled under the carrying capacity threshold if the planned scale was reduced by 24%and the industrial structure was transformed.Various regulation schemes including a more suitable scale and structure with necessary efficiency standards are provided for decisionmakers that can help the case city approach a more sustainable development pattern.展开更多
Parameter optimization integrating operation parameters and structure parameters for the purpose of high permeate flux,high productivity and low exergy consumption of direct contact membrane distillation (DCMD) proces...Parameter optimization integrating operation parameters and structure parameters for the purpose of high permeate flux,high productivity and low exergy consumption of direct contact membrane distillation (DCMD) process was conducted based on Taguchi experimental design. L16(45) orthogonal experiments were carried out with feed inlet temperature,permeate stream inlet temperature,flow rate,module packing density and length-diameter ratio as optimization parameters and with permeate flux,water productivity per unit volume of module and water production per unit exergy loss separately as optimization objectives. By using range analysis method,the dominance degree of the various influencing factors for the three objectives was analyzed and the optimum condition was obtained for the three objectives separately. Furthermore,the multi-objectives optimization was performed based on a weight grade method. The combined optimum conditions are feed inlet temperature 75℃,packing density 30% ,length-diameter ratio 10,permeate stream inlet temperature 30 ℃ and flow rate 25 L/h,which is in order of their dominance degree,and the validity of the optimization scheme was confirmed.展开更多
Among CSP technologies,the linear Fresnel reflector(LFR)can provide reliable carbon-neutral electricity for large-scale applications.In this study,the performance of a large solar LFR power plant under varying climati...Among CSP technologies,the linear Fresnel reflector(LFR)can provide reliable carbon-neutral electricity for large-scale applications.In this study,the performance of a large solar LFR power plant under varying climatic conditions and the dependency of the performance on major plant design specifications,such as solar multiple and full-load thermal storage hours,were examined.Next,artificial neural network(ANN)surrogate models were introduced to predict the annual capacity factor of 100 MWe power plants operating with LFR technology.Single-hidden-layer ANN models with different numbers of neurons in the hidden layer were used and the Levenberg–Marquardt training algorithm was adopted.To overcome overfitting,validation and Bayesian Regularization approaches were compared.As training and testing data,36 geographical sites with various combinations of design parameters were used.Through multi-objective optimization techniques,including the Multi-Objective Particle-Swarm Optimizer and Multi-Objective Grey Wolf Optimizer coupled with ANN surrogate modeling,this study navigates the trade-offs to identify Pareto-optimal solutions for large-scale LFR-based CSP integration based on the energy and cost criteria.The study also identified Site 4(S4)as a promising candidate for optimal balance between the capacity factor(51.05%)and specific cost(5246.71$/kW),showcasing the practical implications of the research for sustainable and efficient CSP plant implementation.展开更多
Supercritical CO_(2)Brayton cycle has high efficiency,compactness,and excellent power generation potential.In the design of the cycle,some parameters,such as recuperator pinch point temperature difference(ΔTrec,pp),t...Supercritical CO_(2)Brayton cycle has high efficiency,compactness,and excellent power generation potential.In the design of the cycle,some parameters,such as recuperator pinch point temperature difference(ΔTrec,pp),turbine inlet temperature(Ttur,in),and maximum cycle pressure(pmax),are often preset without optimization.Furthermore,different preferences on efficiency and cost tradeoff can significantly affect the optimal design of the cycle,and the influence of different parameters on the design condition and the optimum cycle configuration becomes unclear as the preference changes.In this study,different preferences on efficiency and cost tradeoff are considered,and the effects of cycle configuration and optimization parameter addition on the tradeoff are investigated.In addition,four configurations under different preferences on tradeoff are recommended.Results show that the design condition parametersΔT_(rec,pp) decrease and T_(tur,in) and pmax increase as the preference of thermal efficiency(W_(th))increases.Different optimized parameters affect the results of the design point and cycle performance.In addition,the simple recuperative cycle and reheating cycle are recommended when low cycle initial cost dominates(W_(th)<0.598),and the recompression cycle and intercooling cycle are recommended when high cycle thermal efficiency dominates(W_(th)>0.701).The decision maker can select appropriate configuration according to specific preferences.展开更多
基金supported by a Horizontal Project on the Development of a Hybrid Energy Storage Simulation Model for Wind Power Based on an RT-LAB Simulation System(PH2023000190)the Inner Mongolia Natural Science Foundation Project and the Optimization of Exergy Efficiency of a Hybrid Energy Storage System with Crossover Control for Wind Power(2023JQ04).
文摘Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longer period.A multi-objective genetic algorithm(MOGA)and state of charge(SOC)region division for the batteries are introduced to solve the objective function and configuration of the system capacity,respectively.MATLAB/Simulink was used for simulation test.The optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system,with a combination of a 300 Ah lithium battery,a 200 Ah lead-acid battery,and a water storage tank,the proposed strategy reduces the system construction cost by approximately 18,000 yuan.Additionally,the cycle count of the electrochemical energy storage systemincreases from4515 to 4660,while the depth of discharge decreases from 55.37%to 53.65%,achieving shallow charging and discharging,thereby extending battery life and reducing grid voltage fluctuations significantly.The proposed strategy is a guide for stabilizing the grid connection of wind and solar power generation,capability allocation,and energy management of energy conservation systems.
基金Project(3502Z20179026)supported by Xiamen Science and Technology Project,China。
文摘Large-scale electric vehicles(EVs) connected to the micro grid would cause many problems. In this paper, with the consideration of vehicle to grid(V2 G), two charging and discharging load modes of EVs were constructed. One was the disorderly charging and discharging mode based on travel habits, and the other was the orderly charging and discharging mode based on time-of-use(TOU) price;Monte Carlo method was used to verify the case. The scheme of the capacity optimization of photovoltaic charging station under two different charging and discharging modes with V2 G was proposed. The mathematical models of the objective function with the maximization of energy efficiency, the minimization of the investment and the operation cost of the charging system were established. The range of decision variables, constraints of the requirements of the power balance and the strategy of energy exchange were given. NSGA-Ⅱ and NSGA-SA algorithm were used to verify the cases, respectively. In both algorithms, by comparing with the simulation results of the two different modes, it shows that the orderly charging and discharging mode with V2 G is obviously better than the disorderly charging and discharging mode in the aspects of alleviating the pressure of power grid, reducing system investment and improving energy efficiency.
基金supported by Manage Innovation Project of China Southern Power Grid Co.,Ltd.(No.GZHKJXM20210232).
文摘To solve the problem of residual wind power in offshore wind farms,a hydrogen production system with a reasonable capacity was configured to enhance the local load of wind farms and promote the local consumption of residual wind power.By studying the mathematical model of wind power output and calculating surplus wind power,as well as considering the hydrogen production/storage characteristics of the electrolyzer and hydrogen storage tank,an innovative capacity optimization allocation model was established.The objective of the model was to achieve the lowest total net present value over the entire life cycle.The model took into account the cost-benefit breakdown of equipment end-of-life cost,replacement cost,residual value gain,wind abandonment penalty,hydrogen transportation,and environmental value.The MATLAB-based platform invoked the CPLEX commercial solver to solve the model.Combined with the analysis of the annual average wind speed data from an offshore wind farm in Guangdong Province,the optimal capacity configuration results and the actual operation of the hydrogen production system were obtained.Under the calculation scenario,this hydrogen production system could consume 3,800 MWh of residual electricity from offshore wind power each year.It could achieve complete consumption of residual electricity from wind power without incurring the penalty cost of wind power.Additionally,it could produce 66,500 kg of green hydrogen from wind power,resulting in hydrogen sales revenue of 3.63 million RMB.It would also reduce pollutant emissions from coal-based hydrogen production by 1.5 tons and realize an environmental value of 4.83 million RMB.The annual net operating income exceeded 6 million RMB and the whole life cycle NPV income exceeded 50 million RMB.These results verified the feasibility and rationality of the established capacity optimization allocation model.The model could help advance power system planning and operation research and assist offshore wind farm operators in improving economic and environmental benefits.
基金funded by the National Key Research and Development Program of China(2024YFE0106800)Natural Science Foundation of Shandong Province(ZR2021ME199).
文摘The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of energy systems.To enhance the consumption capacity of green power,the green power system consumption optimization scheduling model(GPS-COSM)is proposed,which comprehensively integrates green power system,electric boiler,combined heat and power unit,thermal energy storage,and electrical energy storage.The optimization objectives are to minimize operating cost,minimize carbon emission,and maximize the consumption of wind and solar curtailment.The multi-objective particle swarm optimization algorithm is employed to solve the model,and a fuzzy membership function is introduced to evaluate the satisfaction level of the Pareto optimal solution set,thereby selecting the optimal compromise solution to achieve a dynamic balance among economic efficiency,environmental friendliness,and energy utilization efficiency.Three typical operating modes are designed for comparative analysis.The results demonstrate that the mode involving the coordinated operation of electric boiler,thermal energy storage,and electrical energy storage performs the best in terms of economic efficiency,environmental friendliness,and renewable energy utilization efficiency,achieving the wind and solar curtailment consumption rate of 99.58%.The application of electric boiler significantly enhances the direct accommodation capacity of the green power system.Thermal energy storage optimizes intertemporal regulation,while electrical energy storage strengthens the system’s dynamic regulation capability.The coordinated optimization of multiple devices significantly reduces reliance on fossil fuels.
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.
基金funded by Major Science and Technology Projects in Gansu Province(19ZD2GA003).
文摘Because of the randomness of wind power and photovoltaic(PV)output of new energy bases,the problem of peak regulation capability and voltage stability of ultra-high voltage direct current(UHVDC)transmission lines,we proposed an optimum allocation method of installed capacity of the solar-thermal power station based on chance constrained programming in this work.Firstly,we established the uncertainty model of wind power and PV based on the chance constrained planning theory.Then we used the K-medoids clusteringmethod to cluster the scenarios considering the actual operation scenarios throughout the year.Secondly,we established the optimal configuration model based on the objective function of the strongest transient voltage stability and the lowest overall cost of operation.Finally,by quantitative analysis of actual wind power and photovoltaic new energy base,this work verified the feasibility of the proposed method.As a result of the simulations,we found that using the optimal configuration method of solar-thermal power stations could ensure an accurate allocation of installed capacity.When the installed capacity of the solar-thermal power station is 1×106 kW,the transient voltage recovery index(TVRI)is 0.359,which has a strong voltage support capacity for the system.Based on the results of this work,the optimal configuration of the installed capacity of the solar-thermal power plant can improve peak shaving performance,transient voltage support capability,and new energy consumption while satisfying the Direct Current(DC)outgoing transmission premise.
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.
基金Key R&D Program of Tianjin,China(No.20YFYSGX00060).
文摘As the number of electric vehicles(EVs)continues to grow and the demand for charging infrastructure is also increasing,how to improve the charging infrastructure has become a bottleneck restricting the development of EVs.In other words,reasonably planning the location and capacity of charging stations is important for development of the EV industry and the safe and stable operation of the power system.Considering the construction and maintenance of the charging station,the distribution network loss of the charging station,and the economic loss on the user side of the EV,this paper takes the node and capacity of charging station planning as control variables and the minimum cost of system comprehensive planning as objective function,and thus proposes a location and capacity planning model for the EV charging station.Based on the problems of low efficiency and insufficient global optimization ability of the current algorithm,the simulated annealing immune particle swarm optimization algorithm(SA-IPSO)is adopted in this paper.The simulated annealing algorithm is used in the global update of the particle swarm optimization(PSO),and the immune mechanism is introduced to participate in the iterative update of the particles,so as to improve the speed and efficiency of PSO.Voronoi diagram is used to divide service area of the charging station,and a joint solution process of Voronoi diagram and SA-IPSO is proposed.By example analysis,the results show that the optimal solution corresponding to the optimisation method proposed in this paper has a low overall cost,while the average charging waiting time is only 1.8 min and the charging pile utilisation rate is 75.5%.The simulation comparison verifies that the improved algorithm improves the operational efficiency by 18.1%and basically does not fall into local convergence.
基金supported by the joint NNSF&FDCT Project Number (0066/2019/AFJ)joint MOST&FDCT Project Number (0058/2019/AMJ),City University of Macao,Macao,China.
文摘With the rapid development of urban road traffic and the increasing number of vehicles,how to alleviate traffic congestion is one of the hot issues that need to be urgently addressed in building smart cities.Therefore,in this paper,a nonlinear multi-objective optimization model of urban intersection signal timing based on a Genetic Algorithm was constructed.Specifically,a typical urban intersection was selected as the research object,and drivers’acceleration habits were taken into account.What’s more,the shortest average delay time,the least average number of stops,and the maximum capacity of the intersection were regarded as the optimization objectives.The optimization results show that compared with the Webster method when the vehicle speed is 60 km/h and the acceleration is 2.5 m/s^(2),the signal intersection timing scheme based on the proposed Genetic Algorithm multi-objective optimization reduces the intersection signal cycle time by 14.6%,the average vehicle delay time by 12.9%,the capacity by 16.2%,and the average number of vehicles stop by 0.4%.To verify the simulation results,the authors imported the optimized timing scheme into the constructed Simulation of the Urban Mobility model.The experimental results show that the authors optimized timing scheme is superior to Webster’s in terms of vehicle average loss time reduction,carbon monoxide emission,particulate matter emission,and vehicle fuel consumption.The research in this paper provides a basis for Genetic algorithms in traffic signal control.
基金financially supported by the National Key Research and Development Program of China(Grant No.2022YFC2806100)the National Natural Science Foundation of China(Grant Nos.U22B20126 and 52374020)+1 种基金Science Foundation of China University of Petroleum,Beijing(Grant No.2462025QNXZ009)Beijing Nova Program(Grant No.20250484913).
文摘Steel catenary riser represents the pioneering riser technology implemented in China’s deep-sea oil and gas opera-tions.Given the complex mechanical conditions of the riser,extensive research has been conducted on its dynamic analysis and structural design.This study investigates a deep-sea oil and gas field by developing a coupled model of a semi-submersible platform and steel catenary riser to analyze it mechanical behavior under extreme marine condi-tions.Through multi-objective optimization methodology,the study compares and analyzes suspension point tension and touchdown point stress under various conditions by modifying the suspension position,suspension angle,and catenary length.The optimal configuration parameters were determined:a suspension angle of 12°,suspension position in the southwest direction of the column,and a catenary length of approximately 2000 m.These findings elucidate the impact of configuration parameters on riser dynamic response and establish reasonable parameter layout ranges for adverse sea conditions,offering valuable optimization strategies for steel catenary riser deployment in domestic deep-sea oil and gas fields.
基金Supported by National Key Research and Development Program of China (Grant Nos.2022YFB4703000,2019YFB1309900)。
文摘Automation advancements prompts the extensive integration of collaborative robot(cobot)across a range of industries.Compared to the commonly used design approach of increasing the payload-to-weight ratio of cobot to enhance load capacity,equal attention should be paid to the dynamic response characteristics of cobot during the design process to make the cobot more flexible.In this paper,a new method for designing the drive train parameters of cobot is proposed.Firstly,based on the analysis of factors influencing the load capacity and dynamic response characteristics,design criteria for both aspects are established for cobot with all optimization design criteria normalized within the design domain.Secondly,with the cobot in the horizontal pose,the motor design scheme is discretized and it takes the joint motor diameter and gearbox speed ratio as optimization design variables.Finally,all the discrete values of the optimization objectives are obtained through the enumeration method and the Pareto front is used to select the optimal solution through multi-objective optimization.Base on the cobot design method proposed in this paper,a six-axis cobot is designed and compared with the commercial cobot.The result shows that the load capacity of the designed cobot in this paper reaches 8.4 kg,surpassing the 5 kg load capacity commercial cobot which is used as a benchmark.The minimum resonance frequency of the joints is 42.70 Hz.
基金funded by Huaian Hongeng Group Co.,Ltd.Relying on theproject“Researchon Key Technologies of Integrated Photovoltaic and Energy Storage Electric Vehicle Charging Stations”(Project Number:SGTYHT/23-JS-001).
文摘This paper presents a solution for energy storage system capacity configuration and renewable energy integration in smart grids using a multi-disciplinary optimization method.The solution involves a hybrid prediction framework based on an improved grey regression neural network(IGRNN),which combines grey prediction,an improved BP neural network,and multiple linear regression with a dynamic weight allocation mechanism to enhance prediction accuracy.Additionally,an improved cuckoo search(ICS)algorithm is designed to empower the neural network model,incorporating a gamma distribution disturbance factor and adaptive inertia weight to balance global exploration and local exploitation,achieving a 40%faster convergence rate.A multi-objective snake optimization algorithm is also developed to optimize economic cost,grid stability,and energy utilization efficiency using energy storage capacity as the decision variable.The experimental results,based on a 937-day load dataset from a chemical park in Jiangsu Province,show that the IGRNN model has better prediction accuracy than traditional models,with an RMSE of 11.1361,an MAE of 8.264,and an R^(2) of 96.90%.The optimized energy storage system stabilizes the daily load curve at 800 kW,reduces the peak-valley difference by 62%,and decreases grid regulation pressure by 58.3%.This research provides theoretical and practical support for energy storage planning in high renewable energy proportion grids.Future work will focus on integrating weather data and dynamic optimization strategies under policy constraints to improve system applicability in real-world scenarios.
文摘A problem of upgrading to the Next Generation Wireless Network (NGWN) is backward compatibility with pre-existing networks, the cost and operational benefit of gradually enhancing networks, by replacing, upgrading and installing new wireless network infrastructure elements that can accommodate both voice and data demand. In this paper, we propose a new genetic algorithm has double population to solve Multi-Objectives Optimal of Upgrading Infrastructure (MOOUI) problem in NGWN. We modeling network topology for MOOUI problem has two levels in which mobile users are sources and both base stations and base station controllers are concentrators. Our objective function is the sources to concentrators connectivity cost as well as the cost of the installation, connection, replacement, and capacity upgrade of infrastructure equipment. We generate two populations satisfy constraints and combine them to build solutions and evaluate the performance of my algorithm with data randomly generated. Numerical results show that our algorithm is a promising approach to solve this problem.
基金the National Natural Science Foundation of China(Grant No.51578311).
文摘Environmental impact assessments and subsequent regulation measures of urban development plans are critical to human progress toward sustainability,since these plans set the scale and structure targets of future socioeconomic development.A three-step methodology for assessing and optimizing an urban development plan focusing on its impacts on the water system was developed.The methodology first predicted the pressure on the water system caused by implementation of the plan under distinct scenarios then compared the pressure with the carrying capacity threshold to verity the system status;finally,a multi-objected the pressure with the carrying capacity threshold to verity the system status;finally,a multi-objective optimization method was used to propose regulation solutions.The methodology enabled evaluation of the water system carrying state,taking socioeconomic development uncertainties into account,and multiple sets of improvement measures under different decisionmaker preferences were generated.The methodology was applied in the case of Zhoushan city in South-east China.The assessment results showed that overloading problems occurred in 11 out of the 13 zones in Zhoushan,with the potential pressure varying from 1.1 to 18.3 times the carrying capacity.As a basic regulation measure,and environmental efficiency upgrade could relieve the overloading in 4 zones and reduce 9%-63%of the pressure.The optimization of industrial development showed that the pressure could be controlled under the carrying capacity threshold if the planned scale was reduced by 24%and the industrial structure was transformed.Various regulation schemes including a more suitable scale and structure with necessary efficiency standards are provided for decisionmakers that can help the case city approach a more sustainable development pattern.
文摘Parameter optimization integrating operation parameters and structure parameters for the purpose of high permeate flux,high productivity and low exergy consumption of direct contact membrane distillation (DCMD) process was conducted based on Taguchi experimental design. L16(45) orthogonal experiments were carried out with feed inlet temperature,permeate stream inlet temperature,flow rate,module packing density and length-diameter ratio as optimization parameters and with permeate flux,water productivity per unit volume of module and water production per unit exergy loss separately as optimization objectives. By using range analysis method,the dominance degree of the various influencing factors for the three objectives was analyzed and the optimum condition was obtained for the three objectives separately. Furthermore,the multi-objectives optimization was performed based on a weight grade method. The combined optimum conditions are feed inlet temperature 75℃,packing density 30% ,length-diameter ratio 10,permeate stream inlet temperature 30 ℃ and flow rate 25 L/h,which is in order of their dominance degree,and the validity of the optimization scheme was confirmed.
文摘Among CSP technologies,the linear Fresnel reflector(LFR)can provide reliable carbon-neutral electricity for large-scale applications.In this study,the performance of a large solar LFR power plant under varying climatic conditions and the dependency of the performance on major plant design specifications,such as solar multiple and full-load thermal storage hours,were examined.Next,artificial neural network(ANN)surrogate models were introduced to predict the annual capacity factor of 100 MWe power plants operating with LFR technology.Single-hidden-layer ANN models with different numbers of neurons in the hidden layer were used and the Levenberg–Marquardt training algorithm was adopted.To overcome overfitting,validation and Bayesian Regularization approaches were compared.As training and testing data,36 geographical sites with various combinations of design parameters were used.Through multi-objective optimization techniques,including the Multi-Objective Particle-Swarm Optimizer and Multi-Objective Grey Wolf Optimizer coupled with ANN surrogate modeling,this study navigates the trade-offs to identify Pareto-optimal solutions for large-scale LFR-based CSP integration based on the energy and cost criteria.The study also identified Site 4(S4)as a promising candidate for optimal balance between the capacity factor(51.05%)and specific cost(5246.71$/kW),showcasing the practical implications of the research for sustainable and efficient CSP plant implementation.
基金supported by the Beijing Natural Science Foundation(Grant No.3202014).
文摘Supercritical CO_(2)Brayton cycle has high efficiency,compactness,and excellent power generation potential.In the design of the cycle,some parameters,such as recuperator pinch point temperature difference(ΔTrec,pp),turbine inlet temperature(Ttur,in),and maximum cycle pressure(pmax),are often preset without optimization.Furthermore,different preferences on efficiency and cost tradeoff can significantly affect the optimal design of the cycle,and the influence of different parameters on the design condition and the optimum cycle configuration becomes unclear as the preference changes.In this study,different preferences on efficiency and cost tradeoff are considered,and the effects of cycle configuration and optimization parameter addition on the tradeoff are investigated.In addition,four configurations under different preferences on tradeoff are recommended.Results show that the design condition parametersΔT_(rec,pp) decrease and T_(tur,in) and pmax increase as the preference of thermal efficiency(W_(th))increases.Different optimized parameters affect the results of the design point and cycle performance.In addition,the simple recuperative cycle and reheating cycle are recommended when low cycle initial cost dominates(W_(th)<0.598),and the recompression cycle and intercooling cycle are recommended when high cycle thermal efficiency dominates(W_(th)>0.701).The decision maker can select appropriate configuration according to specific preferences.
