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Boundary Decision-Based Multi-Objective Robust Optimization for Microgrid Dispatching 认领 引用
1
作者 Junjian Wu Jingliao Sun +2 位作者 Yejun Xiang Zhenyu Zhou Zhengchai Shi 《Energy Engineering》 EI 2026年第7期404-423,共20页
The inherent unpredictability of renewable energy generation poses significant challenges to the reliable and economic dispatch of grid-connected microgrids.In response,this paper proposes a novel robust optimization ... The inherent unpredictability of renewable energy generation poses significant challenges to the reliable and economic dispatch of grid-connected microgrids.In response,this paper proposes a novel robust optimization strategy grounded in uncertain boundary decision-making and enhanced through innovations in the multi-objective cross-entropy method.An uncertainty budget-aware environmental economic dispatch model is first established,integrating photovoltaic and wind power generation.By employing mathematical sophistication-particularly Lagrangian transformation-the proposed method effectively resolves embedded uncertainties,transforming the original model into a deterministic multi-objective optimization framework robust against renewable energy volatility.Furthermore,by incorporating the dynamic operational demands of microgrids,this paper culminates in a robust optimization approach that is both fundamentally based on and adaptively responsive to uncertainty boundaries.To address the critical challenges of convergence and diversity in multi-objective optimization,crossover operators and an adaptive parameter update mechanism are introduced,significantly refining the conventional multi-objective cross-entropy algorithm.Case studies demonstrate the rationality and effectiveness of the proposed dispatch strategy and corroborate the superior performance and applicability of the enhanced algorithm. 展开更多
关键词 Microgrid environmental economic dispatch uncertain boundary robust optimization multi-objective cross entropy algorithm
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An approach to characterizing the power system security region by integrating distributionally robust optimization and Transformer-based deep learning 认领 引用
2
作者 Yuekai CHEN Zhejing BAO Miao YU 《ENGINEERING Information Technology & Electronic Engineering》 SCIE EI CSCD 2026年第5期49-62,共14页
Renewable generation and load uncertainty pose significant challenges to power system security,necessitating efficient approaches to characterizing high-dimensional security regions.To overcome the curse of dimensiona... Renewable generation and load uncertainty pose significant challenges to power system security,necessitating efficient approaches to characterizing high-dimensional security regions.To overcome the curse of dimensionality,uncertainty neglect,and undue conservatism in existing methods,this paper proposes an approach integrating distributionally robust optimization(DRO)and deep learning for security region characterization.First,to properly account for uncertainty while avoiding excessive conservatism,a DRO-based active search strategy is developed to identify critical boundary points,where diffusion-generated renewable scenarios and load-deviation samples constructed around typical demand profiles are jointly used to build a robust probabilistic ambiguity set.Subsequently,a Transformer-based model learns from these boundary points to reconstruct the full high-dimensional security region.The model’s self-attention mechanism captures the global nonlinear dependencies among dimensions,enabling a precise and efficient boundary fit.Simulations on IEEE test systems confirm that the approach accurately characterizes high-dimensional security regions at a low computational cost,yielding a security region with strong robustness to renewable-load uncertainty.This work offers a new paradigm for security assessment and decision support in power systems under high uncertainty. 展开更多
关键词 Security region Distributionally robust optimization Deep learning Transformer model Data-driven
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Distributionally robust optimization-based scheduling for a hydrogen-coupled integrated energy system considering carbon trading and demand response 认领 引用 被引量：2
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作者 Zhichun Yang Lin Cheng +2 位作者 Huaidong Min Yang Lei Yanfeng Yang 《Global Energy Interconnection》 EI CSCD 2025年第2期175-187,共13页
Addressing climate change and facilitating the large-scale integration of renewable energy sources(RESs)have driven the development of hydrogen-coupled integrated energy systems(HIES),which enhance energy sustainabili... Addressing climate change and facilitating the large-scale integration of renewable energy sources(RESs)have driven the development of hydrogen-coupled integrated energy systems(HIES),which enhance energy sustainability through coordinated electricity,thermal,natural gas,and hydrogen utilization.This study proposes a two-stage distributionally robust optimization(DRO)-based scheduling method to improve the economic efficiency and reduce carbon emissions of HIES.The framework incorporates a ladder-type carbon trading mechanism to regulate emissions and implements a demand response(DR)program to adjustflexible multi-energy loads,thereby prioritizing RES consumption.Uncertainties from RES generation and load demand are addressed through an ambiguity set,enabling robust decision-making.The column-and-constraint generation(C&CG)algorithm efficiently solves the two-stage DRO model.Case studies demonstrate that the proposed method reduces operational costs by 3.56%,increases photovoltaic consumption rates by 5.44%,and significantly lowers carbon emissions compared to conventional approaches.Furthermore,the DRO framework achieves a superior balance between conservativeness and robustness over conventional stochastic and robust optimization methods,highlighting its potential to advance cost-effective,low-carbon energy systems while ensuring grid stability under uncertainty. 展开更多
关键词 Hydrogen-coupled integrated energy system(HIES) Low-carbon operation Distributionally robust optimization(DRO) Carbon trading Demand response(DR) Economy
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Optimal scheduling method for multi-regional integrated energy system based on dynamic robust optimization algorithm and bi-level Stackelberg model 认领 引用 被引量：1
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作者 Bo Zhou Erchao Li Wenjing Liang 《Global Energy Interconnection》 EI CSCD 2025年第3期510-521,共12页
In this study,we construct a bi-level optimization model based on the Stackelberg game and propose a robust optimization algorithm for solving the bi-level model,assuming an actual situation with several participants ... In this study,we construct a bi-level optimization model based on the Stackelberg game and propose a robust optimization algorithm for solving the bi-level model,assuming an actual situation with several participants in energy trading.Firstly,the energy trading process is analyzed between each subject based on the establishment of the operation framework of multi-agent participation in energy trading.Secondly,the optimal operation model of each energy trading agent is established to develop a bi-level game model including each energy participant.Finally,a combination algorithm of improved robust optimization over time(ROOT)and CPLEX is proposed to solve the established game model.The experimental results indicate that under different fitness thresholds,the robust optimization results of the proposed algorithm are increased by 56.91%and 68.54%,respectively.The established bi-level game model effectively balances the benefits of different energy trading entities.The proposed algorithm proposed can increase the income of each participant in the game by an average of 8.59%. 展开更多
关键词 Robust optimization over time Integrated energy system Dynamic problem Stackelberg game
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Distributed stochastic model predictive control for energy dispatch with distributionally robust optimization 认领 引用 被引量：1
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作者 Mengting LIN Bin LI C.C.ECATI 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2025年第2期323-340,共18页
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
关键词 distributed stochastic model predictive control(DSMPC) distributionally robust optimization(DRO) islanded multi-microgrid energy dispatch strategy
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Data-Driven Two-Stage Robust Optimization Allocation and Loading for Salt Lake Chemical Enterprise Products Under Demand Uncertainty 认领 引用
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作者 Yiyin Tang Yalin Wang +4 位作者 Chenliang Liu Qingkai Sui Yishun Liu Keke Huang Weihua Gui 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第5期989-1003,共15页
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
关键词 Data-driven modeling demand uncertainty product resource allocation and loading salt lake chemical enterprise twostage robust optimization
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Multi-stage robust optimization for a class of UAV trajectory planning problems with uncertain nonlinear dynamics 认领 引用
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作者 Zixin FENG Wenchao XUE +1 位作者 Ran ZHANG Huifeng LI 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2025年第11期228-234,共7页
Trajectory planning under uncertain dynamics is critical for safety-critical systems like Unmanned Aerial Vehicles(UAVs),where uncertainties in aerodynamic force and control surface failure can lead to mission failure... Trajectory planning under uncertain dynamics is critical for safety-critical systems like Unmanned Aerial Vehicles(UAVs),where uncertainties in aerodynamic force and control surface failure can lead to mission failure.This paper proposes a Multi-stage Robust Optimization(MRO)framework to address nonlinear trajectory planning with bounded but unknown parameters.By integrating first-order sensitivity analysis and sequential optimization,the proposed method ensures robustness against worst-case parameter deviations while maintaining high terminal accuracy.Unlike existing approaches,this paper explicitly quantifies uncertainty propagation through sensitivity bounds and divides long-term planning into sub-stages to reduce cumulative errors.Simulations on a UAV model with uncertainties in aerodynamic coefficients,wind fields and coefficients of control inputs demonstrate that MRO achieves high terminal state accuracy and strong robustness. 展开更多
关键词 Robust optimization UAV trajectory planning Optimal control Uncertain parameters Sensitivity analysis
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Robust Optimization Control for Cyber-Physical Systems Subject to Jamming Attack:A Nested Game Approach 认领 引用
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作者 Min Shi Yuan Yuan 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第6期1286-1288,共3页
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
关键词 decreasing network performancegame theory cyber physical systems signal game model robust optimization game theory industrial fields jamming attack urban water cycleinternet
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A Distributionally Robust Optimization Method for Passenger Flow Control Strategy and Train Scheduling on an Urban Rail Transit Line 认领 引用 被引量：10
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作者 Yahan Lu Lixing Yang +4 位作者 Kai Yang Ziyou Gao Housheng Zhou Fanting Meng Jianguo Qi 《Engineering》 SCIE EI CAS CSCD 2022年第5期202-220,共19页
Regular coronavirus disease 2019(COVID-19)epidemic prevention and control have raised new require-ments that necessitate operation-strategy innovation in urban rail transit.To alleviate increasingly seri-ous congestio... Regular coronavirus disease 2019(COVID-19)epidemic prevention and control have raised new require-ments that necessitate operation-strategy innovation in urban rail transit.To alleviate increasingly seri-ous congestion and further reduce the risk of cross-infection,a novel two-stage distributionally robust optimization(DRO)model is explicitly constructed,in which the probability distribution of stochastic scenarios is only partially known in advance.In the proposed model,the mean-conditional value-at-risk(CVaR)criterion is employed to obtain a tradeoff between the expected number of waiting passen-gers and the risk of congestion on an urban rail transit line.The relationship between the proposed DRO model and the traditional two-stage stochastic programming(SP)model is also depicted.Furthermore,to overcome the obstacle of model solvability resulting from imprecise probability distributions,a discrepancy-based ambiguity set is used to transform the robust counterpart into its computationally tractable form.A hybrid algorithm that combines a local search algorithm with a mixed-integer linear programming(MILP)solver is developed to improve the computational efficiency of large-scale instances.Finally,a series of numerical examples with real-world operation data are executed to validate the pro-posed approaches. 展开更多
关键词 Passenger flow control Train scheduling Distributionally robust optimization Stochastic and dynamic passenger demand Ambiguity set
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Comparison between 4D robust optimization methods for carbon-ion treatment planning 认领 引用 被引量：3
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作者 Wen-Yu Wang Yuan-Yuan Ma +4 位作者 Hui Zhang Xin-Yang Zhang Jing-Fen Yang Xin-Guo Liu Qiang Li 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2023年第9期94-105,共12页
Intensity-modulated particle therapy(IMPT)with carbon ions is comparatively susceptible to various uncertainties caused by breathing motion,including range,setup,and target positioning uncertainties.To determine relat... Intensity-modulated particle therapy(IMPT)with carbon ions is comparatively susceptible to various uncertainties caused by breathing motion,including range,setup,and target positioning uncertainties.To determine relative biological effectiveness-weighted dose(RWD)distributions that are resilient to these uncertainties,the reference phase-based four-dimensional(4D)robust optimization(RP-4DRO)and each phase-based 4D robust optimization(EP-4DRO)method in carbon-ion IMPT treatment planning were evaluated and compared.Based on RWD distributions,4DRO methods were compared with 4D conventional optimization using planning target volume(PTV)margins(PTV-based optimization)to assess the effectiveness of the robust optimization methods.Carbon-ion IMPT treatment planning was conducted in a cohort of five lung cancer patients.The results indicated that the EP-4DRO method provided better robustness(P=0.080)and improved plan quality(P=0.225)for the clinical target volume(CTV)in the individual respiratory phase when compared with the PTV-based optimization.Compared with the PTV-based optimization,the RP-4DRO method ensured the robustness(P=0.022)of the dose distributions in the reference breathing phase,albeit with a slight sacrifice of the target coverage(P=0.450).Both 4DRO methods successfully maintained the doses delivered to the organs at risk(OARs)below tolerable levels,which were lower than the doses in the PTV-based optimization(P<0.05).Furthermore,the RP-4DRO method exhibited significantly superior performance when compared with the EP-4DRO method in enhancing overall OAR sparing in either the individual respiratory phase or reference respiratory phase(P<0.05).In general,both 4DRO methods outperformed the PTV-based optimization in terms of OAR sparing and robustness. 展开更多
关键词 Intensity-modulated particle therapy Carbon-ion radiotherapy Uncertainties Four-dimensional robust optimization Lung cancer Relative biological effectiveness-weighted dose Robustness Treatment planning system
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Distributionally robust optimization based chance-constrained energy management for hybrid energy powered cellular networks 认领 引用 被引量：1
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作者 Pengfei Du Hongjiang Lei +2 位作者 Imran Shafique Ansari Jianbo Du Xiaoli Chu 《Digital Communications and Networks》 SCIE CSCD 2023年第3期797-808,共12页
Energy harvesting has been recognized as a promising technique with which to effectively reduce carbon emis-sions and electricity expenses of base stations.However,renewable energy is inherently stochastic and inter-m... Energy harvesting has been recognized as a promising technique with which to effectively reduce carbon emis-sions and electricity expenses of base stations.However,renewable energy is inherently stochastic and inter-mittent,imposing formidable challenges on reliably satisfying users'time-varying wireless traffic demands.In addition,the probability distribution of the renewable energy or users’wireless traffic demand is not always fully known in practice.In this paper,we minimize the total energy cost of a hybrid-energy-powered cellular network by jointly optimizing the energy sharing among base stations,the battery charging and discharging rates,and the energy purchased from the grid under the constraint of a limited battery size at each base station.In solving the formulated non-convex chance-constrained stochastic optimization problem,a new ambiguity set is built to characterize the uncertainties in the renewable energy and wireless traffic demands according to interval sets of the mean and covariance.Using this ambiguity set,the original optimization problem is transformed into a more tractable second-order cone programming problem by exploiting the distributionally robust optimization approach.Furthermore,a low-complexity distributionally robust chance-constrained energy management algo-rithm,which requires only interval sets of the mean and covariance of stochastic parameters,is proposed.The results of extensive simulation are presented to demonstrate that the proposed algorithm outperforms existing methods in terms of the computational complexity,energy cost,and reliability. 展开更多
关键词 Cellular networks Energy harvesting Energy management Chance-constrained Distributionally robust optimization
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Robust optimization design on impeller of mixed-flow pump 认领 引用 被引量：1
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作者 ZHAO Binjuan LIAO Wenyan +3 位作者 XIE Yuntong HAN Luyao FU Yanxia HUANG Zhongfu 《排灌机械工程学报》 CSCD 北大核心 2021年第7期671-677,共7页
To increase the robustness of the optimization solutions of the mixed-flow pump,the impeller was firstly indirectly parameterized based on the 2D blade design theory.Secondly,the robustness of the optimization solutio... To increase the robustness of the optimization solutions of the mixed-flow pump,the impeller was firstly indirectly parameterized based on the 2D blade design theory.Secondly,the robustness of the optimization solution was mathematically defined,and then calculated by Monte Carlo sampling method.Thirdly,the optimization on the mixed-flow pump′s impeller was decomposed into the optimal and robust sub-optimization problems,to maximize the pump head and efficiency and minimize the fluctuation degree of them under varying working conditions at the same time.Fourthly,using response surface model,a surrogate model was established between the optimization objectives and control variables of the shape of the impeller.Finally,based on a multi-objective genetic optimization algorithm,a two-loop iterative optimization process was designed to find the optimal solution with good robustness.Comparing the original and optimized pump,it is found that the internal flow field of the optimized pump has been improved under various operating conditions,the hydraulic performance has been improved consequently,and the range of high efficient zone has also been widened.Besides,with the changing of working conditions,the change trend of the hydraulic performance of the optimized pump becomes gentler,the flow field distribution is more uniform,and the influence degree of the varia-tion of working conditions decreases,and the operating stability of the pump is improved.It is concluded that the robust optimization method proposed in this paper is a reasonable way to optimize the mixed-flow pump,and provides references for optimization problems of other fluid machinery. 展开更多
关键词 mixed-flow pump multi-objective genetic optimization robust optimization response surface method 2D blade design theory
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Six Sigma Robust Optimization of Fatigue Life for the Passenger Car Battery Hanging Device 认领 引用 被引量：2
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作者 李永华 胡明广 王剑 《Journal of Donghua University(English Edition)》 CAS 2016年第2期223-226,共4页
In engineering practice, influencing factors including material properties,external load,dimension error and so on,are uncertain to structural fatigue life,and those uncertain factors make the structure fatigue life h... In engineering practice, influencing factors including material properties,external load,dimension error and so on,are uncertain to structural fatigue life,and those uncertain factors make the structure fatigue life have a wide dispersion. Aiming at this problem,the parametric model is built in this paper,and it is used to obtain the structural first principal stress in the module of probabilistic design system( PDS). Parameters of P-S-N are added to the parametric model,and then,the fatigue life of chosen points has been calculated automatically under the reliability of 0. 99. The fatigue life response surface model is obtained by simulation of the sampling points using Monte Carlo method. The six sigma robust optimization mathematical model of fatigue life is established with the combination of six sigma robust optimization and fatigue life response surface model. Take a railway passenger car battery hanging device as an example, and the mathematical model of optimization is established. The minimum mean and mean squared of structural fatigue life are obtained under the requirements of design fatigue life. The results show that the material has been saved by the new method,and the robustness of the fatigue life has been improved. 展开更多
关键词 robustness response surface methodology fatigue life six sigma robust optimization
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Integrated fire/flight control of armed helicopters based on C-BFGS and distributionally robust optimization 认领 引用 被引量：1
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作者 ZHOU Zeyu WANG Yuhui WU Qingxian 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第6期1604-1620,共17页
To meet the requirements of modern air combat,an integrated fire/flight control(IFFC)system is designed to achieve automatic precision tracking and aiming for armed helicopters and release the pilot from heavy target ... To meet the requirements of modern air combat,an integrated fire/flight control(IFFC)system is designed to achieve automatic precision tracking and aiming for armed helicopters and release the pilot from heavy target burden.Considering the complex dynamic characteristics and the couplings of armed helicopters,an improved automatic attack system is con-structed to integrate the fire control system with the flight con-trol system into a unit.To obtain the optimal command signals,the algorithm is investigated to solve nonconvex optimization problems by the contracting Broyden Fletcher Goldfarb Shanno(C-BFGS)algorithm combined with the trust region method.To address the uncertainties in the automatic attack system,the memory nominal distribution and Wasserstein distance are introduced to accurately characterize the uncertainties,and the dual solvable problem is analyzed by using the duality the-ory,conjugate function,and dual norm.Simulation results verify the practicality and validity of the proposed method in solving the IFFC problem on the premise of satisfactory aiming accu-racy. 展开更多
关键词 integrated fire/flight control(IFFC) armed helicopter improved contracting Broyden Fletcher Goldfarb Shanno(C-BFGS)algorithm memory nominal distribution Wasserstein dis-tance distributionally robust optimization
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A Metamodeling Method Based on Support Vector Regression for Robust Optimization 认领 引用 被引量：5
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作者 XIANG Guoqi HUANG Dagui 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS 2010年第2期242-251,共10页
Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationally expensiv... Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationally expensive simulation models. Existing metamodels main focus on polynomial regression（PR）, neural networks（NN） and Kriging models, these metamodels are not well suited for large-scale robust optimization problems with small size training sets and high nonlinearity. To address the problem, a reduced approximation model technique based on support vector regression（SVR） is introduced in order to improve the accuracy of metamodels. A robust optimization method based on SVR is presented for problems that involve high dimension and nonlinear. First appropriate design parameter samples are selected by experimental design theories, then the response samples are obtained from the simulations such as finite element analysis, the SVR metamodel is constructed and treated as the mean and the variance of the objective performance functions. Combining other constraints, the robust optimization model is formed which can be solved by genetic algorithm （GA）. The applicability of the method developed is demonstrated using a case of two-bar structure system study. The performances of SVR were compared with those of PR, Kriging and back-propagation neural networks（BPNN）, the comparison results show that the prediction accuracy of the SVR metamodel was higher than those of other metamodels under uncertainty. The robust optimization solutions are near to the real result, and the proposed method is found to be accurate and efficient for robust optimization. This reaserch provides an efficient method for robust optimization problems with complex structure. 展开更多
关键词 support vector regression metamodeling robust optimization genetic algorithm
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Influence of the Hook Position on the Vertical Vibrations of an Automobile Exhaust System:Application of the Robust Optimization Design 认领 引用
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作者 Jianqiang Xiong 《Fluid Dynamics & Materials Processing》 EI 2021年第3期555-567,共13页
A robust optimization design method is proposed to investigate the influence of the hook position on the vertical vibration(bending)of an automobile exhaust system.A block diagram for the robustness analysis of the ex... A robust optimization design method is proposed to investigate the influence of the hook position on the vertical vibration(bending)of an automobile exhaust system.A block diagram for the robustness analysis of the exhaust system is initially constructed from the major affecting factors.Secondly,the second-order inertia force is set as the vibration excitation source of the exhaust system and the displacement of four hooks of the exhaust system is selected as the variable factor.Then tests are carried out to investigate the resulting vertical bending considering four influencing factors and three levels of analysis.Finally,a variance analysis of the vertical bending is performed.The present study provides a set of guidelines to control the key factors affecting the vibration of vehicle exhaust systems while proposing an effective method to reduce vehicle vibration and improve noise analysis。 展开更多
关键词 Robust optimization design exhaust system modal analysis excitation source
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A robust optimization model for demand response management with source-grid-load collaboration to consume wind-power 认领 引用 被引量：6
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作者 Xiangfeng Zhou Chunyuan Cai +3 位作者 Yongjian Li Jiekang Wu Yaoguo Zhan Yehua Sun 《Global Energy Interconnection》 EI CSCD 2023年第6期738-750,共13页
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
关键词 Renewable power system Optimal dispatching Wind-power consumption Source-grid-load collaboration Load demand response Two-stage robust optimization model
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Multi-parameter Sensitivity Analysis and Application Research in the Robust Optimization Design for Complex Nonlinear System 认领 引用 被引量：5
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作者 MA Tao ZHANG Weigang +1 位作者 ZHANG Yang TANG Ting 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2015年第1期55-62,共8页
The current research of complex nonlinear system robust optimization mainly focuses on the features of design parameters, such as probability density functions, boundary conditions, etc. After parameters study, high-d... The current research of complex nonlinear system robust optimization mainly focuses on the features of design parameters, such as probability density functions, boundary conditions, etc. After parameters study, high-dimensional curve or robust control design is used to find an accurate robust solution. However, there may exist complex interaction between parameters and practical engineering system. With the increase of the number of parameters, it is getting hard to determine high-dimensional curves and robust control methods, thus it＇s difficult to get the robust design solutions. In this paper, a method of global sensitivity analysis based on divided variables in groups is proposed. By making relevant variables in one group and keeping each other independent among sets of variables, global sensitivity analysis is conducted in grouped variables and the importance of parameters is evaluated by calculating the contribution value of each parameter to the total variance of system response. By ranking the importance of input parameters, relatively important parameters are chosen to conduct robust design analysis of the system. By applying this method to the robust optimization design of a real complex nonlinear system-a vehicle occupant restraint system with multi-parameter, good solution is gained and the response variance of the objective function is reduced to 0.01, which indicates that the robustness of the occupant restraint system is improved in a great degree and the method is effective and valuable for the robust design of complex nonlinear system. This research proposes a new method which can be used to obtain solutions for complex nonlinear system robust design. 展开更多
关键词 complex nonlinear system global sensitivity analysis robust optimization design grouped variables
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Two-Stage Robust Optimization Under Decision Dependent Uncertainty 认领 引用 被引量：2
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作者 Yunfan Zhang Feng Liu +3 位作者 Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2022年第7期1295-1306,共12页
In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty set.In many applications,however,uncertainties are affected by decisions,making the c... In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty set.In many applications,however,uncertainties are affected by decisions,making the current RO framework inapplicable.This paper investigates a class of two-stage RO problems that involve decision-dependent uncertainties.We introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision coupling.The computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical proof.Four motivating application examples that feature the decision-dependent uncertainties are provided.Finally,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem. 展开更多
关键词 Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)
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Two-stage robust optimization of power cost minimization problem in gunbarrel natural gas networks by approximate dynamic programming 认领 引用 被引量：3
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作者 Yi-Ze Meng Ruo-Ran Chen Tian-Hu Deng 《Petroleum Science》 SCIE CAS CSCD 2022年第5期2497-2517,共21页
In short-term operation of natural gas network,the impact of demand uncertainty is not negligible.To address this issue we propose a two-stage robust model for power cost minimization problem in gunbarrel natural gas ... In short-term operation of natural gas network,the impact of demand uncertainty is not negligible.To address this issue we propose a two-stage robust model for power cost minimization problem in gunbarrel natural gas networks.The demands between pipelines and compressor stations are uncertain with a budget parameter,since it is unlikely that all the uncertain demands reach the maximal deviation simultaneously.During solving the two-stage robust model we encounter a bilevel problem which is challenging to solve.We formulate it as a multi-dimensional dynamic programming problem and propose approximate dynamic programming methods to accelerate the calculation.Numerical results based on real network in China show that we obtain a speed gain of 7 times faster in average without compromising optimality compared with original dynamic programming algorithm.Numerical results also verify the advantage of robust model compared with deterministic model when facing uncertainties.These findings offer short-term operation methods for gunbarrel natural gas network management to handle with uncertainties. 展开更多
关键词 Natural gas Gunbarrel gas pipeline networks Robust optimization Approximate dynamic programming
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