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
Most enterprises rely on railway transportation to deliver their products to customers,particularly in the salt lake chemical industry.Notably,allocating products to freight spaces and their assembly on transport vehi...Most enterprises rely on railway transportation to deliver their products to customers,particularly in the salt lake chemical industry.Notably,allocating products to freight spaces and their assembly on transport vehicles are critical pre-transportation processes.However,due to demand fluctuations from changing product orders and unforeseen railway scheduling delays,manually adjusted allocation and loading may lead to excessive loading and unloading distances and times,ultimately increasing transportation costs for enterprises.To address these issues,this paper proposes a data-driven two-stage robust optimization(TSRO)framework embedding with the gated stacked temporal autoencoder clustering based on the attention mechanism(GSTAC-AM),which aims to overcome demand uncertainty and enhance the efficiency of freight allocation and loading.Specifically,GSTAC-AM is developed to help predict the deviation level of demand uncertainty and mitigate the impact of potential outliers.Then,a robust counterpart model is formulated to ensure computational tractability.In addition,a multi-stage hybrid heuristic algorithm is designed to handle the large scale and complexity inherent in the freight space allocation and loading processes.Finally,the effectiveness and applicability of the proposed framework are validated through a real case study conducted in a large salt lake chemical enterprise.展开更多
Dear Editor,With the advances in computing and communication technologies,the cyber-physical system(CPS),has been used in lots of industrial fields,such as the urban water cycle,internet of things,and human-cyber syst...Dear Editor,With the advances in computing and communication technologies,the cyber-physical system(CPS),has been used in lots of industrial fields,such as the urban water cycle,internet of things,and human-cyber systems[1],[2],which has to face up to malicious cyber-attacks towards cyber communication of control commands.Specifically,jamming attack is regarded as one of the most common attacks of decreasing network performance.Game theory is widely regarded as a method of accurately describing the interaction between jamming attacker and legitimate user[3].In the cyber layer,the signal game model has been utilized to describe the transmission between the attacker and defender[4].However,most previous game theoretical researches are not feasible to meet the demands of industrial CPSs mainly due to the shared communication network nature.Specifically,it leads to incomplete information for players of game owing to various network-induced phenomena and employed communication protocols.In the physical layer,the secure control[5]and estimation[6]under attack detection have been studied for CPSs.However,these methods not only rely heavily on signals injection detection,but also have no access to smart attackers who launch covert attacks so that data receivers cannot observe the attack behaviour[7].Accordingly,the motivation arising here is to tackle the nested game problem for CPSs subject to jamming attack.展开更多
The design optimization taking into account the impact of uncertainties favors improving the robustness of the design.A Surrogate-Assisted Gradient-Based(SAGB)method for the robust aerodynamic design optimization of t...The design optimization taking into account the impact of uncertainties favors improving the robustness of the design.A Surrogate-Assisted Gradient-Based(SAGB)method for the robust aerodynamic design optimization of turbomachinery blades considering large-scale uncertainty is introduced,verified and validated in the study.The gradient-based method is employed due to its high optimization efficiency and any one surrogate model with sufficient response accuracy can be employed to quantify the nonlinear performance changes.The gradients of objective performance function to the design parameters are calculated first for all the training samples,from which the gradients of cost function can be fast determined.To reveal the high efficiency and high accuracy of SAGB on gradient calculation,the number of flow computations needed is evaluated and compared with three other methods.Through the aerodynamic design optimization of a transonic turbine cascade minimizing total pressure loss at the outlet,the SAGB-based gradients of the base and optimized blades are compared with those obtained by the Monte Carlo-assisted finite difference method.Moreover,the results of both the robust and deterministic aerodynamic design optimizations are presented and compared to demonstrate the practicability of SAGB on improving the aerodynamic robustness of turbomachinery blades.展开更多
To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitme...To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.展开更多
The influence of processing parameters on the precision of parts fabricated by fused deposition modeling （FDM） technology is studied based on a series of performed experiments. Processing parameters of FDM in terms ...The influence of processing parameters on the precision of parts fabricated by fused deposition modeling （FDM） technology is studied based on a series of performed experiments. Processing parameters of FDM in terms of wire-width compensation, extrusion velocity, filing velocity, and layer thickness are chosen as the control fac- tors. Robust design analysis and multi-index fuzzy comprehensive assessment method are used to obtain the opti- mal parameters. Results show that the influencing degrees of these four factors on the precision of as-processed parts are different. The optimizations of individual parameters and their combined effects are of the same impor- tance for a high precision manufacturing.展开更多
Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainti...Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainties of new energies and various types of loads in the IES.Accordingly,a robust optimal dispatching method for the IES based on a robust economic model predictive control(REMPC)strategy considering source-load power interval prediction is proposed.First,an operation model of the IES is established,and an interval prediction model based on the bidirectional long short-term memory network optimized by beetle antenna search and bootstrap is formulated and applied to predict the photovoltaic power and the cooling,heating,and electrical loads.Then,an optimal dispatching scheme based on REMPC is devised for the IES.The source-load interval prediction results are used to improve the robustness of the REPMC and reduce the influence of source-load uncertainties on dispatching.An actual IES case is selected to conduct simulations;the results show that compared with other prediction techniques,the proposed method has higher prediction interval coverage probability and prediction interval normalized averaged width.Moreover,the operational cost of the IES is decreased by the REMPC strategy.With the devised dispatching scheme,the ability of the IES to handle the dispatching risk caused by prediction errors is enhanced.Improved dispatching robustness and operational economy are also achieved.展开更多
Natural Laminar Flow(NLF)technology is very effective for reducing the skin friction drag of aircraft engine nacelle,but the aerodynamic performance of NLF nacelle is highly sensitive to uncertain working conditions.T...Natural Laminar Flow(NLF)technology is very effective for reducing the skin friction drag of aircraft engine nacelle,but the aerodynamic performance of NLF nacelle is highly sensitive to uncertain working conditions.Therefore,it’s imperative to incorporate uncertainties into the design of NLF nacelle.In this study,for a robust optimization of NLF nacelle and for improving its efficiency,an adaptive-surrogate-based robust optimization strategy is established,which is an iterative optimization process where the surrogate model is updated to obtain the real Pareto front of multi-objective optimization problem.A case study is carried out to validate its feasibility and effectiveness.The results show that the optimization increases the favorable pressure gradient region and the volume ratio of the nacelle by increasing its lip radius and reducing its maximum diameter.And the aerodynamic robustness of the NLF nacelle is mainly determined by the lip radius,maximum diameter of nacelle and location of the maximum diameter.Compared to the initial nacelle,the optimized nacelle maintains a wide range of low drag and high laminar flow ratio in the disturbance space,which extends the average laminar flow region to 21.6%and facilitates a decrease of 1.98 counts in the average drag coefficient.展开更多
This paper presents a novel optimization technique for an efficient multi-fidelity model building approach to reduce computational costs for handling aerodynamic shape optimization based on high-fidelity simulation mo...This paper presents a novel optimization technique for an efficient multi-fidelity model building approach to reduce computational costs for handling aerodynamic shape optimization based on high-fidelity simulation models. The wing aerodynamic shape optimization problem is solved by dividing optimization into three steps—modeling 3D（high-fidelity） and 2D（lowfidelity） models, building global meta-models from prominent instead of all variables, and determining robust optimizing shape associated with tuning local meta-models. The adaptive robust design optimization aims to modify the shape optimization process. The sufficient infilling strategy—known as adaptive uniform infilling strategy—determines search space dimensions based on the last optimization results or initial point. Following this, 3D model simulations are used to tune local meta-models. Finally, the global optimization gradient-based method—Adaptive Filter Sequential Quadratic Programing（AFSQP） is utilized to search the neighborhood for a probable optimum point. The effectiveness of the proposed method is investigated by applying it, along with conventional optimization approach-based meta-models, to a Blended Wing Body（BWB） Unmanned Aerial Vehicle（UAV）. The drag coefficient is defined as the objective function, which is subjected to minimum lift coefficient bounds and stability constraints. The simulation results indicate improvement in meta-model accuracy and reduction in computational time of the method introduced in this paper.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
Because of the tire nonlinearity and vehicle＇s parameters＇uncertainties,robust control methods based on the worst cases,such as H_∞,μsynthesis,have been widely used in active front steering control,however,in orde...Because of the tire nonlinearity and vehicle＇s parameters＇uncertainties,robust control methods based on the worst cases,such as H_∞,μsynthesis,have been widely used in active front steering control,however,in order to guarantee the stability of active front steering system（AFS）controller,the robust control is at the cost of performance so that the robust controller is a little conservative and has low performance for AFS control.In this paper,a generalized internal model robust control（GIMC）that can overcome the contradiction between performance and stability is used in the AFS control.In GIMC,the Youla parameterization is used in an improved way.And GIMC controller includes two sections：a high performance controller designed for the nominal vehicle model and a robust controller compensating the vehicle parameters＇uncertainties and some external disturbances.Simulations of double lane change（DLC）maneuver and that of braking on split-μroad are conducted to compare the performance and stability of the GIMC control,the nominal performance PID controller and the H_∞controller.Simulation results show that the high nominal performance PID controller will be unstable under some extreme situations because of large vehicle＇s parameters variations,H_∞controller is conservative so that the performance is a little low,and only the GIMC controller overcomes the contradiction between performance and robustness,which can both ensure the stability of the AFS controller and guarantee the high performance of the AFS controller.Therefore,the GIMC method proposed for AFS can overcome some disadvantages of control methods used by current AFS system,that is,can solve the instability of PID or LQP control methods and the low performance of the standard H_∞controller.展开更多
Although industrial processes often perform perfectly under design conditions, they may deviate from the optimal operating point owing to parameters drift, environmental disturbances, etc. Thus, it is necessary to dev...Although industrial processes often perform perfectly under design conditions, they may deviate from the optimal operating point owing to parameters drift, environmental disturbances, etc. Thus, it is necessary to develop efficacious strategies or procedure to assess the process performance online. In this paper, we explore the issue of operating optimality assessment for complex industrial processes based on performance-similarity considering nonlinearities and outliers simultaneously, and a general enforced online performance assessment framework is proposed. In the offline part, a new and modified total robust kernel projection to latent structures algorithm,T-KPRM, is proposed and used to evaluate the complex nonlinear industrial process, which can effectively extract the optimal-index-related process variation information from process data and establish assessment models for each performance grades overcoming the effects of outlier. In the online part, the online assessment results can be obtained by calculating the similarity between the online data from a sliding window and each of the performance grades. Furthermore, in order to improve the accuracy of online assessment, we propose an online assessment strategy taking account of the effects of noise and process uncertainties. The Euclidean distance between the sliding data window and the optimal evaluation level is employed to measure the contribution rates of variables, which indicate the possible reason for the non-optimal operating performance. The proposed framework is tested on a real industrial case: dense medium coal preparation process, and the results shows the efficiency of the proposed method comparing to the existing method.展开更多
For the purpose of dealing with uncertainty factors in engineering optimization problems,this paper presents a new non-probabilistic robust optimal design method based on maximum variation estimation.The method analyz...For the purpose of dealing with uncertainty factors in engineering optimization problems,this paper presents a new non-probabilistic robust optimal design method based on maximum variation estimation.The method analyzes the effect of uncertain factors to objective and constraints functions,and then the maximal variations to a solution are calculated.In order to guarantee robust feasibility the maximal variations of constraints are added to original constraints as penalty term;the maximal variation of objective function is taken as a robust index to a solution;linear physical programming is used to adjust the values of quality characteristic and quality variation,and then a bi-level mathematical robust optimal model is constructed.The method does not require presumed probability distribution of uncertain factors or continuous and differentiable of objective and constraints functions.To demonstrate the proposed method,the design of the two-bar structure acted by concentrated load is presented.In the example the robustness of the normal stress,feasibility of the total volume and the buckling stress are studied.The robust optimal design results show that in the condition of maintaining feasibility robustness,the proposed approach can obtain a robust solution which the designer is satisfied with the value of objective function and its variation.展开更多
A new method of robust damper design is presented for elastic-plastic multi-degree-of-freedom(MDOF)building structures under multi-level ground motions(GMs).This method realizes a design that is effective for various ...A new method of robust damper design is presented for elastic-plastic multi-degree-of-freedom(MDOF)building structures under multi-level ground motions(GMs).This method realizes a design that is effective for various levels of GMs.The robustness of a design is measured by an incremental dynamic analysis(IDA)curve and an ideal drift response curve(IDRC).The IDRC is a plot of the optimized maximum deformation under a constraint on the total damper quantity vs.the design level of the GMs.The total damper quantity corresponds to the total cost of the added dampers.First,a problem of generation of IDRCs is stated.Then,its solution algorithm,which consists of the sensitivity-based algorithm(SBA)and a local search method,is proposed.In the application of the SBA,the passive added dampers are removed sequentially under the specified-level GMs.On the other hand,the proposed local search method can search the optimal solutions for a constant total damper quantity under GMs’increased levels.In this way,combining these two algorithms enables the comprehensive search of the optimal solutions for various conditions of the status of the GMs and the total damper quantity.The influence of selecting the type of added dampers(oil,hysteretic,and so on)and the selection of the input GMs on the IDRCs are investigated.Finally,a robust optimal design problem is formulated,and a simple local search-based algorithm is proposed.A simple index using the IDRC and the IDA curve of the model is used as the objective function.It is demonstrated that the proposed algorithm works well in spite of its simplicity.展开更多
A distributionally robust model predictive control(DRMPC)scheme is proposed based on neural network(NN)modeling to achieve the trajectory tracking control of robot manipulators with state and control torque constraint...A distributionally robust model predictive control(DRMPC)scheme is proposed based on neural network(NN)modeling to achieve the trajectory tracking control of robot manipulators with state and control torque constraints.First,an NN is used to fit the motion data of robot manipulators for data-driven dynamic modeling,converting it into a linear prediction model through gradients.Then,by statistically analyzing the stochastic characteristics of the NN modeling errors,a distributionally robust model predictive controller is designed based on the chance constraints,and the optimization problem is transformed into a tractable quadratic programming(QP)problem under the distributionally robust optimization(DRO)framework.The recursive feasibility and convergence of the proposed algorithm are proven.Finally,the effectiveness of the proposed algorithm is verified through numerical simulation.展开更多
This paper has investigated how the optimization methods can be used to deal with plant uncertainty in linear feedback control design. Firstly, we define a weighted sensitivity error function based on robust redesign...This paper has investigated how the optimization methods can be used to deal with plant uncertainty in linear feedback control design. Firstly, we define a weighted sensitivity error function based on robust redesign. Then, by modifying the nominal controller to minimize the variance of the actual system performanee from the desired performance over the whole frequency range, we obtain an optimal robust design method for a class of stochastic model errors. Moreover, the result can be used to give a good prediction to the achievable average tracking performance and control energy for practical system designs. The validity of obtained results can be illustrated by the simulation research.展开更多
To adapt to the uncertainty of new energy,increase new energy consumption,and reduce carbon emissions,a high-voltage distribution network energy storage planning model based on robustness-oriented planning and distrib...To adapt to the uncertainty of new energy,increase new energy consumption,and reduce carbon emissions,a high-voltage distribution network energy storage planning model based on robustness-oriented planning and distributed new energy consumption is proposed.Firstly,the spatio-temporal correlation of large-scale wind-photovoltaic energy is modeled based on the Vine Copula model,and the spatial correlation of the generated wind-photovoltaic power generation is corrected to get the spatio-temporal correlation of wind-photovoltaic power generation scenarios.Finally,considering the subsequent development of new energy on demand for high-voltage distribution network peaking margin and the economy of the system peaking,we propose the optimization model of high-voltage distribution network energy storage plant siting and capacity setting for source-storage cooperative peaking.The simulation results show that the proposed energy storage plant planning method can effectively alleviate the branch circuit blockage,promote new energy consumption,reduce the burden of the main grid peak shifting,and leave sufficient peak shifting margin for the subsequent development of a new energy distribution network while ensuring the economy.展开更多
Rapid development of power-to-gas technology provides a potential solution for virtual power plants(VPP)to achieve near-zero carbon emissions.In this paper,a bi-level hybrid stochastic/robust optimization model is pro...Rapid development of power-to-gas technology provides a potential solution for virtual power plants(VPP)to achieve near-zero carbon emissions.In this paper,a bi-level hybrid stochastic/robust optimization model is proposed for low-carbon VPP day-ahead dispatch considering uncertainties from renewable generation and market prices.First,Karush-Kuhn-Tucker optimality conditions are employed to convert the bi-level model to a single level one.Next,the single level problem is decomposed into a master problem in the base case and several subproblems in extreme cases,which can then be solved by using the column-and-constraint generation algorithm iteratively.Numerical results indicate the proposed approach can effectively satisfy system operation constraints including the carbon emission limit,enhance computational efficiency and algorithm robustness compared with the stochastic method,and improve VPP revenue compared with the robust method.展开更多
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supported in part by the National Natural Science Foundation of China(NSFC)(92267205)the Natural Science Foundation of Hunan Province(2025JJ10007,2025JJ60423)the Open Research Project of the State Key Laboratory of Industrial Control Technology,China(ICT2024 B66).
文摘Most enterprises rely on railway transportation to deliver their products to customers,particularly in the salt lake chemical industry.Notably,allocating products to freight spaces and their assembly on transport vehicles are critical pre-transportation processes.However,due to demand fluctuations from changing product orders and unforeseen railway scheduling delays,manually adjusted allocation and loading may lead to excessive loading and unloading distances and times,ultimately increasing transportation costs for enterprises.To address these issues,this paper proposes a data-driven two-stage robust optimization(TSRO)framework embedding with the gated stacked temporal autoencoder clustering based on the attention mechanism(GSTAC-AM),which aims to overcome demand uncertainty and enhance the efficiency of freight allocation and loading.Specifically,GSTAC-AM is developed to help predict the deviation level of demand uncertainty and mitigate the impact of potential outliers.Then,a robust counterpart model is formulated to ensure computational tractability.In addition,a multi-stage hybrid heuristic algorithm is designed to handle the large scale and complexity inherent in the freight space allocation and loading processes.Finally,the effectiveness and applicability of the proposed framework are validated through a real case study conducted in a large salt lake chemical enterprise.
基金supported by the National Natural Science Foundation of China(62173136)the Natural Science Foundation of Hunan Province(2020JJ2013,2021JJ50047).
文摘Dear Editor,With the advances in computing and communication technologies,the cyber-physical system(CPS),has been used in lots of industrial fields,such as the urban water cycle,internet of things,and human-cyber systems[1],[2],which has to face up to malicious cyber-attacks towards cyber communication of control commands.Specifically,jamming attack is regarded as one of the most common attacks of decreasing network performance.Game theory is widely regarded as a method of accurately describing the interaction between jamming attacker and legitimate user[3].In the cyber layer,the signal game model has been utilized to describe the transmission between the attacker and defender[4].However,most previous game theoretical researches are not feasible to meet the demands of industrial CPSs mainly due to the shared communication network nature.Specifically,it leads to incomplete information for players of game owing to various network-induced phenomena and employed communication protocols.In the physical layer,the secure control[5]and estimation[6]under attack detection have been studied for CPSs.However,these methods not only rely heavily on signals injection detection,but also have no access to smart attackers who launch covert attacks so that data receivers cannot observe the attack behaviour[7].Accordingly,the motivation arising here is to tackle the nested game problem for CPSs subject to jamming attack.
基金National Natural Science Foundation of China(Nos.51676003,51976183)National Science and Technology Major Project of China(No.J2019II-0012-0032)。
文摘The design optimization taking into account the impact of uncertainties favors improving the robustness of the design.A Surrogate-Assisted Gradient-Based(SAGB)method for the robust aerodynamic design optimization of turbomachinery blades considering large-scale uncertainty is introduced,verified and validated in the study.The gradient-based method is employed due to its high optimization efficiency and any one surrogate model with sufficient response accuracy can be employed to quantify the nonlinear performance changes.The gradients of objective performance function to the design parameters are calculated first for all the training samples,from which the gradients of cost function can be fast determined.To reveal the high efficiency and high accuracy of SAGB on gradient calculation,the number of flow computations needed is evaluated and compared with three other methods.Through the aerodynamic design optimization of a transonic turbine cascade minimizing total pressure loss at the outlet,the SAGB-based gradients of the base and optimized blades are compared with those obtained by the Monte Carlo-assisted finite difference method.Moreover,the results of both the robust and deterministic aerodynamic design optimizations are presented and compared to demonstrate the practicability of SAGB on improving the aerodynamic robustness of turbomachinery blades.
基金supported by the Special Research Project on Power Planning of the Guangdong Power Grid Co.,Ltd.
文摘To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.
基金Supported by the Science and Technology Support Key Project of 12th Five-Year of China(2011BAD20B00-4)~~
文摘The influence of processing parameters on the precision of parts fabricated by fused deposition modeling （FDM） technology is studied based on a series of performed experiments. Processing parameters of FDM in terms of wire-width compensation, extrusion velocity, filing velocity, and layer thickness are chosen as the control fac- tors. Robust design analysis and multi-index fuzzy comprehensive assessment method are used to obtain the opti- mal parameters. Results show that the influencing degrees of these four factors on the precision of as-processed parts are different. The optimizations of individual parameters and their combined effects are of the same impor- tance for a high precision manufacturing.
基金supported by the National Key Research and Development Project of China(2018YFE0122200).
文摘Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainties of new energies and various types of loads in the IES.Accordingly,a robust optimal dispatching method for the IES based on a robust economic model predictive control(REMPC)strategy considering source-load power interval prediction is proposed.First,an operation model of the IES is established,and an interval prediction model based on the bidirectional long short-term memory network optimized by beetle antenna search and bootstrap is formulated and applied to predict the photovoltaic power and the cooling,heating,and electrical loads.Then,an optimal dispatching scheme based on REMPC is devised for the IES.The source-load interval prediction results are used to improve the robustness of the REPMC and reduce the influence of source-load uncertainties on dispatching.An actual IES case is selected to conduct simulations;the results show that compared with other prediction techniques,the proposed method has higher prediction interval coverage probability and prediction interval normalized averaged width.Moreover,the operational cost of the IES is decreased by the REMPC strategy.With the devised dispatching scheme,the ability of the IES to handle the dispatching risk caused by prediction errors is enhanced.Improved dispatching robustness and operational economy are also achieved.
基金financially supported by the Commercial Aircraft Corporation of China Ltd.
文摘Natural Laminar Flow(NLF)technology is very effective for reducing the skin friction drag of aircraft engine nacelle,but the aerodynamic performance of NLF nacelle is highly sensitive to uncertain working conditions.Therefore,it’s imperative to incorporate uncertainties into the design of NLF nacelle.In this study,for a robust optimization of NLF nacelle and for improving its efficiency,an adaptive-surrogate-based robust optimization strategy is established,which is an iterative optimization process where the surrogate model is updated to obtain the real Pareto front of multi-objective optimization problem.A case study is carried out to validate its feasibility and effectiveness.The results show that the optimization increases the favorable pressure gradient region and the volume ratio of the nacelle by increasing its lip radius and reducing its maximum diameter.And the aerodynamic robustness of the NLF nacelle is mainly determined by the lip radius,maximum diameter of nacelle and location of the maximum diameter.Compared to the initial nacelle,the optimized nacelle maintains a wide range of low drag and high laminar flow ratio in the disturbance space,which extends the average laminar flow region to 21.6%and facilitates a decrease of 1.98 counts in the average drag coefficient.
文摘This paper presents a novel optimization technique for an efficient multi-fidelity model building approach to reduce computational costs for handling aerodynamic shape optimization based on high-fidelity simulation models. The wing aerodynamic shape optimization problem is solved by dividing optimization into three steps—modeling 3D（high-fidelity） and 2D（lowfidelity） models, building global meta-models from prominent instead of all variables, and determining robust optimizing shape associated with tuning local meta-models. The adaptive robust design optimization aims to modify the shape optimization process. The sufficient infilling strategy—known as adaptive uniform infilling strategy—determines search space dimensions based on the last optimization results or initial point. Following this, 3D model simulations are used to tune local meta-models. Finally, the global optimization gradient-based method—Adaptive Filter Sequential Quadratic Programing（AFSQP） is utilized to search the neighborhood for a probable optimum point. The effectiveness of the proposed method is investigated by applying it, along with conventional optimization approach-based meta-models, to a Blended Wing Body（BWB） Unmanned Aerial Vehicle（UAV）. The drag coefficient is defined as the objective function, which is subjected to minimum lift coefficient bounds and stability constraints. The simulation results indicate improvement in meta-model accuracy and reduction in computational time of the method introduced in this paper.
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 National Natural Science Foundation of China(Grant Nos.11072106,51375009)
文摘Because of the tire nonlinearity and vehicle＇s parameters＇uncertainties,robust control methods based on the worst cases,such as H_∞,μsynthesis,have been widely used in active front steering control,however,in order to guarantee the stability of active front steering system（AFS）controller,the robust control is at the cost of performance so that the robust controller is a little conservative and has low performance for AFS control.In this paper,a generalized internal model robust control（GIMC）that can overcome the contradiction between performance and stability is used in the AFS control.In GIMC,the Youla parameterization is used in an improved way.And GIMC controller includes two sections：a high performance controller designed for the nominal vehicle model and a robust controller compensating the vehicle parameters＇uncertainties and some external disturbances.Simulations of double lane change（DLC）maneuver and that of braking on split-μroad are conducted to compare the performance and stability of the GIMC control,the nominal performance PID controller and the H_∞controller.Simulation results show that the high nominal performance PID controller will be unstable under some extreme situations because of large vehicle＇s parameters variations,H_∞controller is conservative so that the performance is a little low,and only the GIMC controller overcomes the contradiction between performance and robustness,which can both ensure the stability of the AFS controller and guarantee the high performance of the AFS controller.Therefore,the GIMC method proposed for AFS can overcome some disadvantages of control methods used by current AFS system,that is,can solve the instability of PID or LQP control methods and the low performance of the standard H_∞controller.
基金Supported by the National Natural Science Foundation of China(61503384,61603393)Natural Science Foundation of Jiangsu(BK20150199,BK20160275)+1 种基金the Foundation Research Funds for the Central Universities(2015QNA65)the Postdoctoral Foundation of Jiangsu Province(1501081B)
文摘Although industrial processes often perform perfectly under design conditions, they may deviate from the optimal operating point owing to parameters drift, environmental disturbances, etc. Thus, it is necessary to develop efficacious strategies or procedure to assess the process performance online. In this paper, we explore the issue of operating optimality assessment for complex industrial processes based on performance-similarity considering nonlinearities and outliers simultaneously, and a general enforced online performance assessment framework is proposed. In the offline part, a new and modified total robust kernel projection to latent structures algorithm,T-KPRM, is proposed and used to evaluate the complex nonlinear industrial process, which can effectively extract the optimal-index-related process variation information from process data and establish assessment models for each performance grades overcoming the effects of outlier. In the online part, the online assessment results can be obtained by calculating the similarity between the online data from a sliding window and each of the performance grades. Furthermore, in order to improve the accuracy of online assessment, we propose an online assessment strategy taking account of the effects of noise and process uncertainties. The Euclidean distance between the sliding data window and the optimal evaluation level is employed to measure the contribution rates of variables, which indicate the possible reason for the non-optimal operating performance. The proposed framework is tested on a real industrial case: dense medium coal preparation process, and the results shows the efficiency of the proposed method comparing to the existing method.
基金supported by Program for New Century Excellent Talents in University,Ministry of Education of China(Grant No.NCET-05-0285)
文摘For the purpose of dealing with uncertainty factors in engineering optimization problems,this paper presents a new non-probabilistic robust optimal design method based on maximum variation estimation.The method analyzes the effect of uncertain factors to objective and constraints functions,and then the maximal variations to a solution are calculated.In order to guarantee robust feasibility the maximal variations of constraints are added to original constraints as penalty term;the maximal variation of objective function is taken as a robust index to a solution;linear physical programming is used to adjust the values of quality characteristic and quality variation,and then a bi-level mathematical robust optimal model is constructed.The method does not require presumed probability distribution of uncertain factors or continuous and differentiable of objective and constraints functions.To demonstrate the proposed method,the design of the two-bar structure acted by concentrated load is presented.In the example the robustness of the normal stress,feasibility of the total volume and the buckling stress are studied.The robust optimal design results show that in the condition of maintaining feasibility robustness,the proposed approach can obtain a robust solution which the designer is satisfied with the value of objective function and its variation.
基金Part of the present work is supported by the Grant-in-Aid for Scientific Research(KAKENHI)of the Japan Society for the Promotion of Science(Nos.18H01584,JP20J20811)This support is greatly appreciated.
文摘A new method of robust damper design is presented for elastic-plastic multi-degree-of-freedom(MDOF)building structures under multi-level ground motions(GMs).This method realizes a design that is effective for various levels of GMs.The robustness of a design is measured by an incremental dynamic analysis(IDA)curve and an ideal drift response curve(IDRC).The IDRC is a plot of the optimized maximum deformation under a constraint on the total damper quantity vs.the design level of the GMs.The total damper quantity corresponds to the total cost of the added dampers.First,a problem of generation of IDRCs is stated.Then,its solution algorithm,which consists of the sensitivity-based algorithm(SBA)and a local search method,is proposed.In the application of the SBA,the passive added dampers are removed sequentially under the specified-level GMs.On the other hand,the proposed local search method can search the optimal solutions for a constant total damper quantity under GMs’increased levels.In this way,combining these two algorithms enables the comprehensive search of the optimal solutions for various conditions of the status of the GMs and the total damper quantity.The influence of selecting the type of added dampers(oil,hysteretic,and so on)and the selection of the input GMs on the IDRCs are investigated.Finally,a robust optimal design problem is formulated,and a simple local search-based algorithm is proposed.A simple index using the IDRC and the IDA curve of the model is used as the objective function.It is demonstrated that the proposed algorithm works well in spite of its simplicity.
基金Project supported by the National Natural Science Foundation of China(Nos.62273245 and 62173033)the Sichuan Science and Technology Program of China(No.2024NSFSC1486)the Opening Project of Robotic Satellite Key Laboratory of Sichuan Province of China。
文摘A distributionally robust model predictive control(DRMPC)scheme is proposed based on neural network(NN)modeling to achieve the trajectory tracking control of robot manipulators with state and control torque constraints.First,an NN is used to fit the motion data of robot manipulators for data-driven dynamic modeling,converting it into a linear prediction model through gradients.Then,by statistically analyzing the stochastic characteristics of the NN modeling errors,a distributionally robust model predictive controller is designed based on the chance constraints,and the optimization problem is transformed into a tractable quadratic programming(QP)problem under the distributionally robust optimization(DRO)framework.The recursive feasibility and convergence of the proposed algorithm are proven.Finally,the effectiveness of the proposed algorithm is verified through numerical simulation.
文摘This paper has investigated how the optimization methods can be used to deal with plant uncertainty in linear feedback control design. Firstly, we define a weighted sensitivity error function based on robust redesign. Then, by modifying the nominal controller to minimize the variance of the actual system performanee from the desired performance over the whole frequency range, we obtain an optimal robust design method for a class of stochastic model errors. Moreover, the result can be used to give a good prediction to the achievable average tracking performance and control energy for practical system designs. The validity of obtained results can be illustrated by the simulation research.
基金supported by State Grid Anhui Electric Power Co.,Ltd.Research Program(B3120923000C).
文摘To adapt to the uncertainty of new energy,increase new energy consumption,and reduce carbon emissions,a high-voltage distribution network energy storage planning model based on robustness-oriented planning and distributed new energy consumption is proposed.Firstly,the spatio-temporal correlation of large-scale wind-photovoltaic energy is modeled based on the Vine Copula model,and the spatial correlation of the generated wind-photovoltaic power generation is corrected to get the spatio-temporal correlation of wind-photovoltaic power generation scenarios.Finally,considering the subsequent development of new energy on demand for high-voltage distribution network peaking margin and the economy of the system peaking,we propose the optimization model of high-voltage distribution network energy storage plant siting and capacity setting for source-storage cooperative peaking.The simulation results show that the proposed energy storage plant planning method can effectively alleviate the branch circuit blockage,promote new energy consumption,reduce the burden of the main grid peak shifting,and leave sufficient peak shifting margin for the subsequent development of a new energy distribution network while ensuring the economy.
基金supported by the Shenzhen Science and Technology Program(JCYJ20210324130811031)Tsinghua Shenzhen International Graduate School Interdisciplinary Research and Innovation Fund(JC2021004).
文摘Rapid development of power-to-gas technology provides a potential solution for virtual power plants(VPP)to achieve near-zero carbon emissions.In this paper,a bi-level hybrid stochastic/robust optimization model is proposed for low-carbon VPP day-ahead dispatch considering uncertainties from renewable generation and market prices.First,Karush-Kuhn-Tucker optimality conditions are employed to convert the bi-level model to a single level one.Next,the single level problem is decomposed into a master problem in the base case and several subproblems in extreme cases,which can then be solved by using the column-and-constraint generation algorithm iteratively.Numerical results indicate the proposed approach can effectively satisfy system operation constraints including the carbon emission limit,enhance computational efficiency and algorithm robustness compared with the stochastic method,and improve VPP revenue compared with the robust method.
