In the era of massive data,the study of distributed data is a significant topic.Model averaging can be effectively applied to distributed data by combining information from all machines.For linear models,the model ave...In the era of massive data,the study of distributed data is a significant topic.Model averaging can be effectively applied to distributed data by combining information from all machines.For linear models,the model averaging approach has been developed in the context of distributed data.However,further investigation is needed for more complex models.In this paper,the authors propose a distributed optimal model averaging approach based on multivariate additive models,which approximates unknown functions using B-splines allowing each machine to have a different smoothing degree.To utilize the information from the covariance matrix of dependent errors in multivariate multiple regressions,the authors use the Mahalanobis distance to construct a Mallows-type weight choice criterion.The criterion can be computed by transmitting information between the local machines and the center machine in two steps.The authors demonstrate the asymptotic optimality of the proposed model averaging estimator when the covariates are subject to uncertainty,and obtain the convergence rate of the weight vector to the theoretically optimal weights.The results remain novel even for additive models with a single response variable.The numerical examples show that the proposed method yields good performance.展开更多
A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is proposed.An ambiguity set considering the inherent uncer...A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is proposed.An ambiguity set considering the inherent uncertainties of renewable energy sources(RESs)is constructed without requiring the full distribution knowledge of the uncertainties.The power balance chance constraint is reformulated within the framework of the distributionally robust optimization(DRO)approach.With the exchange of information and energy flow,each microgrid can achieve its local supply-demand balance.Furthermore,the closed-loop stability and recursive feasibility of the proposed algorithm are proved.The comparative results with other DSMPC methods show that a trade-off between robustness and economy can be achieved.展开更多
With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation ...With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and load.Accounting for these issues,this paper proposes a multi-timescale coordinated optimization dispatch method for distribution networks.First,the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts,based on which,the requirements for flexibility dispatch and capacity constraints of the grid were calculated and analyzed.Subsequently,a multi-timescale optimization framework was constructed,incorporating the generation and load forecast uncertainties.This framework included optimization models for dayahead scheduling,intra-day optimization,and real-time adjustments,aiming to meet flexibility needs across different timescales and improve the economic efficiency of the grid.Furthermore,an improved soft actor-critic algorithm was introduced to enhance the uncertainty exploration capability.Utilizing a centralized training and decentralized execution framework,a multi-agent SAC network model was developed to improve the decision-making efficiency of the agents.Finally,the effectiveness and superiority of the proposed method were validated using a modified IEEE-33 bus test system.展开更多
With the continuous growth of power demand and the diversification of power consumption structure,the loss of distribution network has gradually become the focus of attention.Given the problems of single loss reductio...With the continuous growth of power demand and the diversification of power consumption structure,the loss of distribution network has gradually become the focus of attention.Given the problems of single loss reduction measure,lack of economy,and practicality in existing research,this paper proposes an optimization method of distribution network loss reduction based on tabu search algorithm and optimizes the combination and parameter configuration of loss reduction measure.The optimization model is developed with the goal of maximizing comprehensive benefits,incorporating both economic and environmental factors,and accounting for investment costs,including the loss of power reduction.Additionally,the model ensures that constraint conditions such as power flow equations,voltage deviations,and line transmission capacities are satisfied.The solution is obtained through a tabu search algorithm,which is well-suited for solving nonlinear problems with multiple constraints.Combined with the example of 10kV25 node construction,the simulation results show that the method can significantly reduce the network loss on the basis of ensuring the economy and environmental protection of the system,which provides a theoretical basis for distribution network planning.展开更多
Optimal sizing and allocation of distributed generators(DGs)have become essential computational challenges in improving the performance,efficiency,and reliability of electrical distribution networks.Despite extensive ...Optimal sizing and allocation of distributed generators(DGs)have become essential computational challenges in improving the performance,efficiency,and reliability of electrical distribution networks.Despite extensive research,existing approaches often face algorithmic limitations such as slow convergence,premature stagnation in local minima,or suboptimal accuracy in determining optimal DG placement and capacity.This study presents a comprehensive scientometric and systematic review of global research focused on computer-based modelling and algorithmic optimization for renewable DG sizing and placement.It integrates both quantitative and qualitative analyses of the scholarly landscape,mapping influential research domains,co-authorship structures,the articles’citation networks,keyword clusters,and international collaboration patterns.Moreover,the study classifies and evaluates the most prominent objective functions,key computational models and optimization algorithms,DG technologies,and strategic approaches employed in the field.The findings reveal that advanced algorithmic frameworks substantially enhance network stability,minimize real power losses,and improve voltage profiles under various operational constraints.This review serves as a foundational resource for researchers and practitioners,highlighting emerging algorithmic trends,modelling innovations,and data-driven methodologies that can guide future development of intelligent,optimization-based DG integration strategies in smart distribution systems.展开更多
In order to simulate electrical characteristics of a lithium-ion battery used in electric vehicles in a good manner,a three-layer battery model is established.The charge of the lithium-ion battery is assumed to distri...In order to simulate electrical characteristics of a lithium-ion battery used in electric vehicles in a good manner,a three-layer battery model is established.The charge of the lithium-ion battery is assumed to distribute among the three layers and their interaction is used to depict hysteresis and relaxation effect observed in the lithium-ion battery.The model parameters are calibrated and optimized through a numerically nonlinear least squares algorithm in Simulink Parameter Estimation Toolbox for an experimental data set sampled in a hybrid pulse test of the battery.Evaluation results showed that the established model is able to provide an acceptable accuracy in estimating the State of Charge of the lithium-ion battery in an open-loop fashion for a sufficiently long time and to describe the battery voltage behavior more accurately than a commonly used battery model.The battery modeling accuracy can thereby satisfy the requirement for practical electric vehicle applications.展开更多
To solve the coupling relationship between the strip automatic gauge control and the looper control in traditional control strategy of tandem hot rolling,a distributed model predictive control(DMPC)strategy for the ta...To solve the coupling relationship between the strip automatic gauge control and the looper control in traditional control strategy of tandem hot rolling,a distributed model predictive control(DMPC)strategy for the tandem hot rolling was explored,and a series of simulation experiments were carried out.Firstly,based on the state space analysis method,the multivariable dynamic transition process of hot strip rolling was studied,and the state space model of a gauge-looper integrated system in tandem hot rolling was established.Secondly,DMPC strategy based on neighborhood optimization was proposed,which fully considered the coupling relationship in this integrated system.Finally,a series of experiments simulating disturbances and emergency situations were completed with actual rolling data.The experimental results showed that the proposed DMPC control strategy had better performance compared with the traditional proportional-integral control and centralized model predictive control,which is applicable for the gauge-looper integrated system.展开更多
In this paper, we study the interconnect buffer and wiresizing optimization problem under a distributed RLC model to optimize not just area and delay, but also crosstalk for RLC circuit with non-monotone signal respon...In this paper, we study the interconnect buffer and wiresizing optimization problem under a distributed RLC model to optimize not just area and delay, but also crosstalk for RLC circuit with non-monotone signal response. We present a new multiobjective genetic algorithm（MOGA） which uses a single objective sorting（SOS） method for constructing the non-dominated set to solve this multi-objective interconnect optimization problem. The MOGA/SOS optimal algorithm provides a smooth trade-off among signal delay, wave form, and routing area. Furthermore, we use a new method to calculate the lower bound of crosstalk. Extensive experimental results show that our algorithm is scalable with problem size. Furthermore, compared to the solution based on an Elmore delay model, our solution reduces the total routing area by up to 30%, the delay to the critical sinks by up to 25%, while further improving crosstalk up to 25.73% on average.展开更多
The paper proposes a new swarm intelligence-based distributed Model Predictive Control(MPC)approach for coordination control of multiple Unmanned Aerial Vehicles(UAVs).First,a distributed MPC framework is designed and...The paper proposes a new swarm intelligence-based distributed Model Predictive Control(MPC)approach for coordination control of multiple Unmanned Aerial Vehicles(UAVs).First,a distributed MPC framework is designed and each member only shares the information with neighbors.The Chaotic Grey Wolf Optimization(CGWO)method is developed on the basis of chaotic initialization and chaotic search to solve the local Finite Horizon Optimal Control Problem(FHOCP).Then,the distributed cost function is designed and integrated into each FHOCP to achieve multi-UAV formation control and trajectory tracking with no-fly zone constraint.Further,an event-triggered strategy is proposed to reduce the computational burden for the distributed MPC approach,which considers the predicted state errors and the convergence of cost function.Simulation results show that the CGWO-based distributed MPC approach is more computationally efficient to achieve multi-UAV coordination control than traditional method.展开更多
As a carrier of rural population,rural residential area carries important functions such as rural production and life. This paper studied the characteristics of rural residential area and optimization model,to lay cer...As a carrier of rural population,rural residential area carries important functions such as rural production and life. This paper studied the characteristics of rural residential area and optimization model,to lay certain foundation for integrating urban and rural development and increasing economic income. With the aid of Arc GIS 10. 1 and Fragstats 3. 4 software,based on the theory of landscape ecology,it analyzed the distribution characteristics of residential area in Wangcun River Basin of Anze County,Linrfen City,Shanxi Province. Besides,according to distribution characteristics,it analyzed three optimization models: priority development model,retention limit development model,and migration development model.展开更多
The literature on multi-attribute optimization for renewable energy source(RES)placement in deregulated power markets is extensive and diverse in methodology.This study focuses on the most relevant publications direct...The literature on multi-attribute optimization for renewable energy source(RES)placement in deregulated power markets is extensive and diverse in methodology.This study focuses on the most relevant publications directly addressing the research problem at hand.Similarly,while the body of work on optimal location and sizing of renewable energy generators(REGs)in balanced distribution systems is substantial,only the most pertinent sources are cited,aligning closely with the study’s objective function.A comprehensive literature review reveals several key research areas:RES integration,RES-related optimization techniques,strategic placement of wind and solar generation,and RES promotion in deregulated powermarkets,particularly within transmission systems.Furthermore,the optimal location and sizing of REGs in both balanced and unbalanced distribution systems have been extensively studied.RESs demonstrate significant potential for standalone applications in remote areas lacking conventional transmission and distribution infrastructure.Also presents a thorough review of current modeling and optimization approaches for RES-based distribution system location and sizing.Additionally,it examines the optimal positioning,sizing,and performance of hybrid and standalone renewable energy systems.This paper provides a comprehensive review of current modeling and optimization approaches for the location and sizing of Renewable Energy Sources(RESs)in distribution systems,focusing on both balanced and unbalanced networks.展开更多
Crude oil scheduling optimization is an effective method to enhance the economic benefits of oil refining.But uncertainties,including uncertain demands of crude distillation units(CDUs),might make the production plans...Crude oil scheduling optimization is an effective method to enhance the economic benefits of oil refining.But uncertainties,including uncertain demands of crude distillation units(CDUs),might make the production plans made by the traditional deterministic optimization models infeasible.A data-driven Wasserstein distributionally robust chance-constrained(WDRCC)optimization approach is proposed in this paper to deal with demand uncertainty in crude oil scheduling.First,a new deterministic crude oil scheduling optimization model is developed as the basis of this approach.The Wasserstein distance is then used to build ambiguity sets from historical data to describe the possible realizations of probability distributions of uncertain demands.A cross-validation method is advanced to choose suitable radii for these ambiguity sets.The deterministic model is reformulated as a WDRCC optimization model for crude oil scheduling to guarantee the demand constraints hold with a desired high probability even in the worst situation in ambiguity sets.The proposed WDRCC model is transferred into an equivalent conditional value-at-risk representation and further derived as a mixed-integer nonlinear programming counterpart.Industrial case studies from a real-world refinery are conducted to show the effectiveness of the proposed method.Out-of-sample tests demonstrate that the solution of the WDRCC model is more robust than those of the deterministic model and the chance-constrained model.展开更多
With the growing integration of renewable energy sources(RESs)and smart interconnected devices,conventional distribution networks have turned to active distribution networks(ADNs)with complex system model and power fl...With the growing integration of renewable energy sources(RESs)and smart interconnected devices,conventional distribution networks have turned to active distribution networks(ADNs)with complex system model and power flow dynamics.The rapid fluctuation of RES power may easily result in frequent voltage violation issues.Taking the flexible RES reactive power as control variables,this paper proposes a two-layer control scheme with Koopman wide neural network(WNN)based model predictive control(MPC)method for optimal voltage regulation and network loss reduction.Based on Koopman operator theory,a data-driven WNN method is presented to fit a high-dimensional linear model of power flow.With the model,voltage and network loss sensitivities are computed analytically,and utilized for ADN partition and control model formulation.In the lower level,a dual-mode adaptive switching MPC strategy is put forward for optimal voltage control and network loss optimization in each individual partition to decide the RES reactive power.The upper level is to calculate the adjustment coefficients of the RES reactive power given in the low level by taking the coupling effects of different partitions into account,and then the final reactive power dispatches of RESs are obtained to realize optimal control of voltage and network loss.Simulation results on two ADNs demonstrate that the proposed strategy can reliably maintain the voltage at each node within the secure range,reduce network power losses,and enhance the overall system security and economic efficiency.展开更多
A systematic investigation is made on the problems which are related to the optimal control of the municipal water distribution network.A mathematical model of forecasting the water short term demand is proposed using...A systematic investigation is made on the problems which are related to the optimal control of the municipal water distribution network.A mathematical model of forecasting the water short term demand is proposed using the time series trigonometric function analysis method;the service discharge based macroscopic model of network performance is established using the network structuring method;a relatively satisfactory mathematical model for the optimal control of water distribution network is put forward in view of security and economy,and solved by the constrained mixed discrete variable complex arithmetic.The model is applied in many examples and the results are satisfactory.展开更多
Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the ...Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the configuration of photovoltaic(3.8 MW),wind power(2.5 MW),energy storage(2.2 MWh),and SVC(1.2 Mvar)through interaction between upper and lower layers,and modifies lines 2–3,8–9,etc.to improve transmission capacity and voltage stability.The author uses normal distribution and Monte Carlo method to model load uncertainty,and combines Weibull distribution to describe wind speed characteristics.Compared to the traditional three-layer model(TLM),Benders decomposition-based two-layer model(BLBD)has a 58.1%reduction in convergence time(5.36 vs.12.78 h),a 51.1%reduction in iteration times(23 vs.47 times),a 8.07%reduction in total cost(12.436 vs.13.528 million yuan),and a 9.62%reduction in carbon emissions(12,456 vs.13,782 t).After optimization,the peak valley difference decreased from4.1 to 2.9MW,the renewable energy consumption rate reached 93.4%,and the energy storage efficiency was 87.6%.Themodel has been validated in the IEEE 33 node system,demonstrating its superiority in terms of economy,low-carbon,and reliability.展开更多
This paper studies the dynamic optimization problem for multi-agent systems in the presence of external disturbances. Different from the existing distributed optimization results, we formulate an optimization problem ...This paper studies the dynamic optimization problem for multi-agent systems in the presence of external disturbances. Different from the existing distributed optimization results, we formulate an optimization problem of continuous-time mufti-agent systems with time-varying disturbance generated by an exosystem. Based on internal model and Lyapunov-based method, a distributed design is proposed to achieve the optimization. Finally, design. an example is given to illustrate the proposed optimization design.展开更多
The optimal operation of water distribution networks under local pipe failures, such as water main breaks, was proposed. Based on a hydraulic analysis and a simulation of water distribution networks, a macroscopic mod...The optimal operation of water distribution networks under local pipe failures, such as water main breaks, was proposed. Based on a hydraulic analysis and a simulation of water distribution networks, a macroscopic model for a network under a local pipe failure was established by the statistical regression. After the operation objectives under a local pipe failure were determined, the optimal operation model was developed and solved by the genetic algorithm. The program was developed and examined by a city distribution network. The optimal operation alternative shows that the electricity cost is saved approximately 11%, the income of the water corporation is increased approximately 5%, and the pressure in the water distribution network is distributed evenly to ensure the network safe operation. Therefore, the proposed method for optimal operation under local pipe failure is feasible and cost-effective.展开更多
This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously a...This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.展开更多
A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation （DISOPE） was proposed for nonlinear cascade systems under network environment. Under the d...A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation （DISOPE） was proposed for nonlinear cascade systems under network environment. Under the distributed control structure, online optimization of the cascade system was composed of several cascaded agents that can cooperate and exchange information via network communication. By iterating on modified distributed linear optimal control problems on the basis of estimating parameters at every iteration the correct optimal control action of the nonlinear model predictive control problem of the cascade system could be obtained, assuming that the algorithm was convergent. This approach avoids solving the complex nonlinear optimization problem and significantly reduces the computational burden. The simulation results of the fossil fuel power unit are illustrated to verify the effectiveness and practicability of the proposed algorithm.展开更多
A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorith...A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorithm of system identification, which can gradually forget past information. The grey series part of the model uses an equal dimension new information model (EDNIM) and it applies 3 points smoothing method to preprocess the original data and modify remnant difference by GM(1,1). Through the optimization of the coefficient of the model, we are able to minimize the error variance of predictive data. A case study shows that the proposed method achieved high calculation precision and speed and it can be used to obtain the predictive value in real time state estimation of power distribution networks.展开更多
基金supported by Youth Academic Innocation Team Construction project of Capital University of Economics and Business under Grant No.QNTD202303supported by the Beijing Outstanding Young Scientist Program under Grant No.JWZQ20240101027the National Natural Science Foundation of China under Grant Nos.12031016,12531012 and 12426308。
文摘In the era of massive data,the study of distributed data is a significant topic.Model averaging can be effectively applied to distributed data by combining information from all machines.For linear models,the model averaging approach has been developed in the context of distributed data.However,further investigation is needed for more complex models.In this paper,the authors propose a distributed optimal model averaging approach based on multivariate additive models,which approximates unknown functions using B-splines allowing each machine to have a different smoothing degree.To utilize the information from the covariance matrix of dependent errors in multivariate multiple regressions,the authors use the Mahalanobis distance to construct a Mallows-type weight choice criterion.The criterion can be computed by transmitting information between the local machines and the center machine in two steps.The authors demonstrate the asymptotic optimality of the proposed model averaging estimator when the covariates are subject to uncertainty,and obtain the convergence rate of the weight vector to the theoretically optimal weights.The results remain novel even for additive models with a single response variable.The numerical examples show that the proposed method yields good performance.
基金Supported by the National Natural Science Foundation of China(No.U24B20156)the National Defense Basic Scientific Research Program of China(No.JCKY2021204B051)the National Laboratory of Space Intelligent Control of China(Nos.HTKJ2023KL502005 and HTKJ2024KL502007)。
文摘A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is proposed.An ambiguity set considering the inherent uncertainties of renewable energy sources(RESs)is constructed without requiring the full distribution knowledge of the uncertainties.The power balance chance constraint is reformulated within the framework of the distributionally robust optimization(DRO)approach.With the exchange of information and energy flow,each microgrid can achieve its local supply-demand balance.Furthermore,the closed-loop stability and recursive feasibility of the proposed algorithm are proved.The comparative results with other DSMPC methods show that a trade-off between robustness and economy can be achieved.
基金funded by Jilin Province Science and Technology Development Plan Project,grant number 20220203163SF.
文摘With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and load.Accounting for these issues,this paper proposes a multi-timescale coordinated optimization dispatch method for distribution networks.First,the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts,based on which,the requirements for flexibility dispatch and capacity constraints of the grid were calculated and analyzed.Subsequently,a multi-timescale optimization framework was constructed,incorporating the generation and load forecast uncertainties.This framework included optimization models for dayahead scheduling,intra-day optimization,and real-time adjustments,aiming to meet flexibility needs across different timescales and improve the economic efficiency of the grid.Furthermore,an improved soft actor-critic algorithm was introduced to enhance the uncertainty exploration capability.Utilizing a centralized training and decentralized execution framework,a multi-agent SAC network model was developed to improve the decision-making efficiency of the agents.Finally,the effectiveness and superiority of the proposed method were validated using a modified IEEE-33 bus test system.
文摘With the continuous growth of power demand and the diversification of power consumption structure,the loss of distribution network has gradually become the focus of attention.Given the problems of single loss reduction measure,lack of economy,and practicality in existing research,this paper proposes an optimization method of distribution network loss reduction based on tabu search algorithm and optimizes the combination and parameter configuration of loss reduction measure.The optimization model is developed with the goal of maximizing comprehensive benefits,incorporating both economic and environmental factors,and accounting for investment costs,including the loss of power reduction.Additionally,the model ensures that constraint conditions such as power flow equations,voltage deviations,and line transmission capacities are satisfied.The solution is obtained through a tabu search algorithm,which is well-suited for solving nonlinear problems with multiple constraints.Combined with the example of 10kV25 node construction,the simulation results show that the method can significantly reduce the network loss on the basis of ensuring the economy and environmental protection of the system,which provides a theoretical basis for distribution network planning.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number:IMSIU-DDRSP2503)。
文摘Optimal sizing and allocation of distributed generators(DGs)have become essential computational challenges in improving the performance,efficiency,and reliability of electrical distribution networks.Despite extensive research,existing approaches often face algorithmic limitations such as slow convergence,premature stagnation in local minima,or suboptimal accuracy in determining optimal DG placement and capacity.This study presents a comprehensive scientometric and systematic review of global research focused on computer-based modelling and algorithmic optimization for renewable DG sizing and placement.It integrates both quantitative and qualitative analyses of the scholarly landscape,mapping influential research domains,co-authorship structures,the articles’citation networks,keyword clusters,and international collaboration patterns.Moreover,the study classifies and evaluates the most prominent objective functions,key computational models and optimization algorithms,DG technologies,and strategic approaches employed in the field.The findings reveal that advanced algorithmic frameworks substantially enhance network stability,minimize real power losses,and improve voltage profiles under various operational constraints.This review serves as a foundational resource for researchers and practitioners,highlighting emerging algorithmic trends,modelling innovations,and data-driven methodologies that can guide future development of intelligent,optimization-based DG integration strategies in smart distribution systems.
基金Sponsored by the National Natural Science Foundation of China (Grant No.50905015)the National High Technology Research and Development Program of China (Grant No.2003AA501800)
文摘In order to simulate electrical characteristics of a lithium-ion battery used in electric vehicles in a good manner,a three-layer battery model is established.The charge of the lithium-ion battery is assumed to distribute among the three layers and their interaction is used to depict hysteresis and relaxation effect observed in the lithium-ion battery.The model parameters are calibrated and optimized through a numerically nonlinear least squares algorithm in Simulink Parameter Estimation Toolbox for an experimental data set sampled in a hybrid pulse test of the battery.Evaluation results showed that the established model is able to provide an acceptable accuracy in estimating the State of Charge of the lithium-ion battery in an open-loop fashion for a sufficiently long time and to describe the battery voltage behavior more accurately than a commonly used battery model.The battery modeling accuracy can thereby satisfy the requirement for practical electric vehicle applications.
基金This work was supported by the National Key R&D Program of China(Grant Nos.2018YFB1308700)the National Natural Science Foundation of China(Grant Nos.U21A20117 and 52074085+1 种基金the Fundamental Research Funds for the Central Univer-sities(Grant No.N2004010)the Liaoning Revitalization Talents651 Program(XLYC1907065).
文摘To solve the coupling relationship between the strip automatic gauge control and the looper control in traditional control strategy of tandem hot rolling,a distributed model predictive control(DMPC)strategy for the tandem hot rolling was explored,and a series of simulation experiments were carried out.Firstly,based on the state space analysis method,the multivariable dynamic transition process of hot strip rolling was studied,and the state space model of a gauge-looper integrated system in tandem hot rolling was established.Secondly,DMPC strategy based on neighborhood optimization was proposed,which fully considered the coupling relationship in this integrated system.Finally,a series of experiments simulating disturbances and emergency situations were completed with actual rolling data.The experimental results showed that the proposed DMPC control strategy had better performance compared with the traditional proportional-integral control and centralized model predictive control,which is applicable for the gauge-looper integrated system.
基金Supported by the National Natural Science Foundation of China (90307017)
文摘In this paper, we study the interconnect buffer and wiresizing optimization problem under a distributed RLC model to optimize not just area and delay, but also crosstalk for RLC circuit with non-monotone signal response. We present a new multiobjective genetic algorithm（MOGA） which uses a single objective sorting（SOS） method for constructing the non-dominated set to solve this multi-objective interconnect optimization problem. The MOGA/SOS optimal algorithm provides a smooth trade-off among signal delay, wave form, and routing area. Furthermore, we use a new method to calculate the lower bound of crosstalk. Extensive experimental results show that our algorithm is scalable with problem size. Furthermore, compared to the solution based on an Elmore delay model, our solution reduces the total routing area by up to 30%, the delay to the critical sinks by up to 25%, while further improving crosstalk up to 25.73% on average.
基金co-supported by the National Natural Science Foundation of China(Nos.61803009,61903084)Fundamental Research Funds for the Central Universities of China(No.YWF-20-BJ-J-542)Aeronautical Science Foundation of China(No.20175851032)。
文摘The paper proposes a new swarm intelligence-based distributed Model Predictive Control(MPC)approach for coordination control of multiple Unmanned Aerial Vehicles(UAVs).First,a distributed MPC framework is designed and each member only shares the information with neighbors.The Chaotic Grey Wolf Optimization(CGWO)method is developed on the basis of chaotic initialization and chaotic search to solve the local Finite Horizon Optimal Control Problem(FHOCP).Then,the distributed cost function is designed and integrated into each FHOCP to achieve multi-UAV formation control and trajectory tracking with no-fly zone constraint.Further,an event-triggered strategy is proposed to reduce the computational burden for the distributed MPC approach,which considers the predicted state errors and the convergence of cost function.Simulation results show that the CGWO-based distributed MPC approach is more computationally efficient to achieve multi-UAV coordination control than traditional method.
文摘As a carrier of rural population,rural residential area carries important functions such as rural production and life. This paper studied the characteristics of rural residential area and optimization model,to lay certain foundation for integrating urban and rural development and increasing economic income. With the aid of Arc GIS 10. 1 and Fragstats 3. 4 software,based on the theory of landscape ecology,it analyzed the distribution characteristics of residential area in Wangcun River Basin of Anze County,Linrfen City,Shanxi Province. Besides,according to distribution characteristics,it analyzed three optimization models: priority development model,retention limit development model,and migration development model.
文摘The literature on multi-attribute optimization for renewable energy source(RES)placement in deregulated power markets is extensive and diverse in methodology.This study focuses on the most relevant publications directly addressing the research problem at hand.Similarly,while the body of work on optimal location and sizing of renewable energy generators(REGs)in balanced distribution systems is substantial,only the most pertinent sources are cited,aligning closely with the study’s objective function.A comprehensive literature review reveals several key research areas:RES integration,RES-related optimization techniques,strategic placement of wind and solar generation,and RES promotion in deregulated powermarkets,particularly within transmission systems.Furthermore,the optimal location and sizing of REGs in both balanced and unbalanced distribution systems have been extensively studied.RESs demonstrate significant potential for standalone applications in remote areas lacking conventional transmission and distribution infrastructure.Also presents a thorough review of current modeling and optimization approaches for RES-based distribution system location and sizing.Additionally,it examines the optimal positioning,sizing,and performance of hybrid and standalone renewable energy systems.This paper provides a comprehensive review of current modeling and optimization approaches for the location and sizing of Renewable Energy Sources(RESs)in distribution systems,focusing on both balanced and unbalanced networks.
基金the supports from National Natural Science Foundation of China(61988101,62073142,22178103)National Natural Science Fund for Distinguished Young Scholars(61925305)International(Regional)Cooperation and Exchange Project(61720106008)。
文摘Crude oil scheduling optimization is an effective method to enhance the economic benefits of oil refining.But uncertainties,including uncertain demands of crude distillation units(CDUs),might make the production plans made by the traditional deterministic optimization models infeasible.A data-driven Wasserstein distributionally robust chance-constrained(WDRCC)optimization approach is proposed in this paper to deal with demand uncertainty in crude oil scheduling.First,a new deterministic crude oil scheduling optimization model is developed as the basis of this approach.The Wasserstein distance is then used to build ambiguity sets from historical data to describe the possible realizations of probability distributions of uncertain demands.A cross-validation method is advanced to choose suitable radii for these ambiguity sets.The deterministic model is reformulated as a WDRCC optimization model for crude oil scheduling to guarantee the demand constraints hold with a desired high probability even in the worst situation in ambiguity sets.The proposed WDRCC model is transferred into an equivalent conditional value-at-risk representation and further derived as a mixed-integer nonlinear programming counterpart.Industrial case studies from a real-world refinery are conducted to show the effectiveness of the proposed method.Out-of-sample tests demonstrate that the solution of the WDRCC model is more robust than those of the deterministic model and the chance-constrained model.
基金supported by the Science and Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.(J2024162).
文摘With the growing integration of renewable energy sources(RESs)and smart interconnected devices,conventional distribution networks have turned to active distribution networks(ADNs)with complex system model and power flow dynamics.The rapid fluctuation of RES power may easily result in frequent voltage violation issues.Taking the flexible RES reactive power as control variables,this paper proposes a two-layer control scheme with Koopman wide neural network(WNN)based model predictive control(MPC)method for optimal voltage regulation and network loss reduction.Based on Koopman operator theory,a data-driven WNN method is presented to fit a high-dimensional linear model of power flow.With the model,voltage and network loss sensitivities are computed analytically,and utilized for ADN partition and control model formulation.In the lower level,a dual-mode adaptive switching MPC strategy is put forward for optimal voltage control and network loss optimization in each individual partition to decide the RES reactive power.The upper level is to calculate the adjustment coefficients of the RES reactive power given in the low level by taking the coupling effects of different partitions into account,and then the final reactive power dispatches of RESs are obtained to realize optimal control of voltage and network loss.Simulation results on two ADNs demonstrate that the proposed strategy can reliably maintain the voltage at each node within the secure range,reduce network power losses,and enhance the overall system security and economic efficiency.
基金Foundation for University Key Teacher by the Min-istry of Education
文摘A systematic investigation is made on the problems which are related to the optimal control of the municipal water distribution network.A mathematical model of forecasting the water short term demand is proposed using the time series trigonometric function analysis method;the service discharge based macroscopic model of network performance is established using the network structuring method;a relatively satisfactory mathematical model for the optimal control of water distribution network is put forward in view of security and economy,and solved by the constrained mixed discrete variable complex arithmetic.The model is applied in many examples and the results are satisfactory.
文摘Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the configuration of photovoltaic(3.8 MW),wind power(2.5 MW),energy storage(2.2 MWh),and SVC(1.2 Mvar)through interaction between upper and lower layers,and modifies lines 2–3,8–9,etc.to improve transmission capacity and voltage stability.The author uses normal distribution and Monte Carlo method to model load uncertainty,and combines Weibull distribution to describe wind speed characteristics.Compared to the traditional three-layer model(TLM),Benders decomposition-based two-layer model(BLBD)has a 58.1%reduction in convergence time(5.36 vs.12.78 h),a 51.1%reduction in iteration times(23 vs.47 times),a 8.07%reduction in total cost(12.436 vs.13.528 million yuan),and a 9.62%reduction in carbon emissions(12,456 vs.13,782 t).After optimization,the peak valley difference decreased from4.1 to 2.9MW,the renewable energy consumption rate reached 93.4%,and the energy storage efficiency was 87.6%.Themodel has been validated in the IEEE 33 node system,demonstrating its superiority in terms of economy,low-carbon,and reliability.
基金This work was supported by the National Natural Science Foundation of China （Nos. 611 74071, 61333001 ）.
文摘This paper studies the dynamic optimization problem for multi-agent systems in the presence of external disturbances. Different from the existing distributed optimization results, we formulate an optimization problem of continuous-time mufti-agent systems with time-varying disturbance generated by an exosystem. Based on internal model and Lyapunov-based method, a distributed design is proposed to achieve the optimization. Finally, design. an example is given to illustrate the proposed optimization design.
基金Project(50278062) supported by the National Natural Science Foundation of ChinaProject(003611611)supported by the Natural Science Foundation of Tianjin, China
文摘The optimal operation of water distribution networks under local pipe failures, such as water main breaks, was proposed. Based on a hydraulic analysis and a simulation of water distribution networks, a macroscopic model for a network under a local pipe failure was established by the statistical regression. After the operation objectives under a local pipe failure were determined, the optimal operation model was developed and solved by the genetic algorithm. The program was developed and examined by a city distribution network. The optimal operation alternative shows that the electricity cost is saved approximately 11%, the income of the water corporation is increased approximately 5%, and the pressure in the water distribution network is distributed evenly to ensure the network safe operation. Therefore, the proposed method for optimal operation under local pipe failure is feasible and cost-effective.
文摘This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.
基金This work was supportedbytheNationalNaturalScienceFoundationofChina(No.60474051),theProgramforNewCenturyExcellentTalentsinUniversityofChina(NCET),andtheSpecializedResearchFundfortheDoctoralProgramofHigherEducationofChina(No.20020248028).
文摘A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation （DISOPE） was proposed for nonlinear cascade systems under network environment. Under the distributed control structure, online optimization of the cascade system was composed of several cascaded agents that can cooperate and exchange information via network communication. By iterating on modified distributed linear optimal control problems on the basis of estimating parameters at every iteration the correct optimal control action of the nonlinear model predictive control problem of the cascade system could be obtained, assuming that the algorithm was convergent. This approach avoids solving the complex nonlinear optimization problem and significantly reduces the computational burden. The simulation results of the fossil fuel power unit are illustrated to verify the effectiveness and practicability of the proposed algorithm.
文摘A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorithm of system identification, which can gradually forget past information. The grey series part of the model uses an equal dimension new information model (EDNIM) and it applies 3 points smoothing method to preprocess the original data and modify remnant difference by GM(1,1). Through the optimization of the coefficient of the model, we are able to minimize the error variance of predictive data. A case study shows that the proposed method achieved high calculation precision and speed and it can be used to obtain the predictive value in real time state estimation of power distribution networks.
