检索结果-内蒙古大学图书馆

robust multi-objective optimization for energy production scheduling in microgrids

ENGINEERING optimization 2019年第2期51卷 332-351页

作者： Wang, Luhao Li, Qiqiang Zhang, Bingying Ding, Ran Sun, Mingshun Univ Jinan Sch Elect Engn Jinan Shandong Peoples R China Shandong Univ Sch Control Sci & Engn Jinan Shandong Peoples R China Arizona State Univ Elect & Comp Engn Tucson AZ USA

In order to achieve better economic and environmental benefits of microgrids (MGs) under multiple uncertainties in renewable energy resources and loads, a novel energy production scheduling method is proposed based on robust multi-objective optimization with minimax criterion. Firstly, a mixed integer minimax multi-objective formulation is developed to capture uncertainties as well as minimize economic and environmental objectives. Secondly, the primal problem is decomposed into a bi-level optimization problem, which attempts to seek robust scheduling scheme set under the worst-case realization of uncertainties in a multi-objective framework. Finally, a hierarchical meta-heuristic solution strategy, including multi-objective cross entropy algorithm and delta+ indicator, is designed to solve the reconstructed problem. Numerical results demonstrate that the proposed scheduling method can effectively attenuate the disturbance of uncertainties as well as reduce energy costs and emissions, as compared with single-objective robust optimization and multi-objective optimization scheduling approaches. This study could offer useful insights which help decision-makers balance robustness and comprehensive benefits in the operation of MGs.

关键词： Microgrid robust multi-objective optimization scheduling uncertainty

来源：评论

学校读者我要写书评

暂无评论

robust multi-objective optimization for EE-SE Tradeoff in D2D Communications Underlaying Heterogeneous Networks

引用

IEEE TRANSACTIONS ON COMMUNICATIONS 2018年第10期66卷 4936-4949页

作者： Hao, Yuanyuan Ni, Qiang Li, Hai Hou, Shujuan Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Univ Lancaster Sch Comp & Commun Lancaster LA1 4WA England

In this paper, we concentrate on the robust multi-objective optimization (MOO) for the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in device-to-device (D2D) communications underlaying heterogeneous networks (HetNets). Different from traditional resource optimization, we focus on finding robust Pareto optimal solutions for spectrum allocation and power coordination in D2D communications underlaying HetNets with the consideration of interference channel uncertainties. The problem is formulated as an uncertain MOO problem to maximize EE and SE of cellular users (CUs) simultaneously while guaranteeing the minimum rate requirements of both CUs and D2D pairs. With the aid of epsilon-constraint method and strict robustness, we propose a general framework to transform the uncertain MOO problem into a deterministic single-objective optimization problem. As exponential computational complexity is required to solve this highly non-convex problem, the power coordination and the spectrum allocation problems are solved separately, and an effective two-stage iterative algorithm is developed. Finally, simulation results validate that our proposed robust scheme converges fast and significantly outperforms the non-robust scheme in terms of the effective EE-SE tradeoff and the quality of service satisfying probability of D2D pairs.

关键词： D2D communications energy efficiency HetNets power coordination robust multi-objective optimization spectral efficiency spectrum allocation

来源：评论

学校读者我要写书评

暂无评论

robust multi-objective optimization Applied to Engineering Systems Design

引用

LATIN AMERICAN JOURNAL OF SOLIDS AND STRUCTURES 2016年第9期13卷 1802-1822页

作者： Moreira, Fernando Ricardo Lobato, Fran Sergio Cavalini, Aldemir Ap., Jr. Steffen, Valder, Jr. Univ Fed Goias Dept Math BR 364Km 1953800Campus Cidade Univ BR-75801615 Jatai Go Brazil Univ Fed Uberlandia Sch Chem Engn Av Joao Naves de Avila 2121Campus Santa Monica BR-38408144 Uberlandia MG Brazil Univ Fed Uberlandia Sch Mech Engn LMEst Struct Mech Lab Av Joao Naves de Avila 2121Campus Santa Monica BR-38408144 Uberlandia MG Brazil

In engineering systems design, theoretical deterministic solutions can be hardly applied directly to real-world scenarios. Basically, this is due to manufacturing limitations and environmental conditions under which the real system will operate. Therefore, a small variation in the design variables vector can result in a meaningful change on the theoretical optimal design as represented by the minimization of the corresponding vector of objective functions. In this context, it is important to develop methodologies that are able to produce solutions (even suboptimal) that are less sensitive to perturbations in the design variable vector and, consequently, leading to a robust optimal design. In this contribution, first the proposed approach is tested on various mathematical functions. Then, the methodology is applied to the design of two representative engineering systems through multi-objective optimization using the Firefly Colony Algorithm in association with the Effective Mean Concept is presented. The results obtained demonstrate that the design strategy conveyed represents an interesting alternative approach to obtain robust design for a number of engineering applications.

关键词： robust multi-objective optimization Pareto's Curve Effective Mean Firefly Colony Algorithm

来源：评论

学校读者我要写书评

暂无评论

robust multi-objective optimization for sustainable design of distributed energy supply systems

引用

COMPUTERS & CHEMICAL ENGINEERING 2017年 102卷 26-39页

作者： Majewski, Dinah Elena Wirtz, Marco Lampe, Matthias Bardow, Andre Rhein Westfal TH Aachen Chair Tech Thermodynam D-52056 Aachen Germany

Sustainable design of distributed energy supply systems involves multiple aims. Therefore, multi objective optimization is the appropriate concept for sustainable design. However, input parameters are in general uncertain. If uncertainties are disregarded in the optimization, solutions usually become infeasible in practice. To incorporate uncertain parameters, we apply the concept of minmax robust multi objective optimization for designing sustainable energy supply systems. We propose a mixed-integer linear problem formulation. The proposed formulation allows to identify robust sustainable designs easily guaranteeing security of energy supply. Energy systems are shown to typically exhibit objective-wise uncertainties. Thus, a Pareto front can still be derived. In a real-world case study, robust designs are identified with a good trade-off between economic and ecologic criteria. The robust designs perform remarkably well in the nominal scenario. The presented problem formulation transfers the important theoretical concept of minmax robust multi-objective optimization into engineering practice for the design of sustainable energy systems. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Sustainable energy supply systems robust optimization multi-objective optimization robust multi-objective optimization Mixed-integer linear programming (MILP) Energy system design

来源：评论

学校读者我要写书评

暂无评论

robust multi-objective optimization for islanded data center microgrid operations

引用

APPLIED ENERGY 2023年第PartB期330卷

作者： Lian, Yicheng Li, Yuanzheng Zhao, Yong Yu, Chaofan Zhao, Tianyang Wu, Lei Huazhong Univ Sci & Technol Key Lab Image Informat Proc & Intelligent Control Minist China Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol China EU Inst Clean & Renewable Energy Wuhan 430074 Peoples R China Univ Bath Dept Elect & Elect Engn Bath BA2 England Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ 07030 USA

Electricity cost has become a critical concern of data center operations with the rapid increasing of information processing demand. Data center microgrid (DCMG) is a promising way to reduce electric energy consumption from traditional fossil fuel generators and the billing cost, by effectively utilizing local renewable energy, e.g., wind power. However, uncertainties of wind power generation and real-time workload of data center would have significant impacts on the operational efficiency of DCMG, especially when it is in the island mode. For this reason, a novel affinely adjustable policy based robust multi-objective optimization model under flexible uncertainty set is proposed in this paper, which simultaneously optimizes wind power curtailment, the operation cost, and the over-plus level of computation resource, while considering uncertainties of both the wind power and real-time workload. Through numerical simulation studies, the validity of robust multi -objective optimization model for the island operation of DCMG is verified. Besides, the effectiveness of the proposed methods, i.e., the novel affinely adjustable policy and the flexible uncertainty set, in handling uncertainties are evaluated. Compared to the conventional robust multi-objective optimization model, the proposed approach reduces the operating costs of about 10% in average while maintaining similar reliability in numerical simulations. Moreover, the complex quantitative relationship among these multiple objectives is further investigated. Simulation results indicate the minimization of wind power curtailment and over-plus level of computation resource increases about 25% of the operation cost. These quantitative relationships can well support the decision making of DCMG operation management.

关键词： Data center microgrid Flexible uncertainty set Affinely adjustable policy robust multi-objective optimization

来源：评论

学校读者我要写书评

暂无评论

robust multi-objective optimization for Gas Turbine Operation Based on Kriging Surrogate Model 40

Robust Multi-Objective Optimization for Gas Turbine Operatio...

引用

40th Chinese Control Conference (CCC)

作者： Xia, Hao Jia, Peilin Ma, Liang Dalian Univ Technol Sch Control Sci & Control Engn Dalian 116024 Peoples R China China State Shipbldg Corp Ltd 703 Res Inst Harbin 150078 Peoples R China

ISBN: (纸本)9789881563804

This paper presents a systematic method for the optimal settings of gas turbine operation. After building the high-fidelity model of the gas turbine, the fuel flow, variable inlet guide-vane(VIGV) of the high and low pressure compressors, ambient air pressure and temperature are selected as the decision variables, while the pollutant emissions and output power of whole system are chosen as response variables to be optimized. In order to guarantee the accurate response prediction, the performance of three different types of surrogate models, namely polynomial response surface (PRS), Kriging and radial basis function (RBF) models are built with sampling points generated by Latin hypercube design (LHD) method. With Kriging surrogate model best fitting the data, it is chosen as the model for performance prediction during the optimization process. It is also shown that the NSGA-II algorithm is suitable for the multi-objective optimization of gas turbine. The robustness of the Pareto solutions are checked under the varying ambient pressure and temperature. By considering the solution robustness, and the nature of gas turbine performance change under perturbation, the optimal operating conditions are identified.

关键词： Gas turbine Kriging model robust multi-objective optimization

来源：评论

学校读者我要写书评

暂无评论

robust multi-objective optimization based on NSGA-II for UCAV weapon delivery

Robust multi-objective optimization based on NSGA-II for UCA...

引用

4th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Gu, Xueqiang Zhang, Yu Wang, Nan Chen, Jing Natl Univ Def Technol Coll Mechatron Engn & Automat Changsha Hunan Peoples R China

ISBN: (纸本)9780769547213

The problem of generating robust optimal air-toground weapon delivery planning (WDP) for UCAVs is studied. In order to deal with the uncertainties in combat operation, such as disturbing in battlefield, model imprecise, operating errors and so on, a strategy based on robust multi-objective optimization (RMO) approach is proposed. In this paper, several constraints include flight capability constraint, weapon constraint and battlefield constraint, are considered. And some robust optimal cost functions are built, using Monte Carlo method simulating criteria of weapon delivery, and then the weapon delivery planning problem is transformed into a robust multi-objective optimization problem. In order to improve the convergence performance, a combining robust multi-objective optimization algorithm based on an improved NSGA-II and Monte Carlo simulation is designed, and then a tactical basic flight maneuver (BFM) is presented to generate weapon delivery trajectory. The proposed approach is demonstrated on a typical air-to-ground attack mission scenario. The simulated results show that the proposed approach is capable of generating the robust and optimal weapon delivery trajectories efficiently.

关键词： weapon delivery robust multi-objective optimization non-dominated sorting genetic algorithm basic flight maneuver

来源：评论

学校读者我要写书评

暂无评论

Novel performance metrics for robust multi-objective optimization algorithms

引用

SWARM AND EVOLUTIONARY COMPUTATION 2015年 21卷 1-23页

作者： Mirjalili, Seyedali Lewis, Andrew Griffith Univ Sch Informat & Commun Technol Brisbane Qld 4111 Australia Queensland Inst Business & Technol Brisbane Qld 4122 Australia

Performance metrics are essential for quantifying the performance of optimization algorithms in the field of evolutionary multi-objective optimization. Such metrics allow researchers to compare different algorithms quantitatively. In the field of robust multi-objective optimization, however, there is currently no performance metric despite its significant importance. This motivates our proposal of three novel specific metrics for measuring the convergence, coverage, and success rate of robust Pareto optimal solutions obtained by robust multi-objective algorithms. The proposed metrics are employed to quantitatively evaluate and compare robust multi-objective Particle Swarm optimization (RMOPSO) and robust Non-dominated Sorting Genetic Algorithm (RNSGA-II) on seven selected benchmark problems. The results show that the proposed metrics are effective in quantifying the performance of robust multi-objective algorithms in terms of convergence, coverage, and the ratio of the robust/non-robust Pareto optimal solutions obtained. (C) 2014 Elsevier B.V. All rights reserved.

关键词： robust multi-objective optimization multi-objective optimization Performance metric Convergence metric Coverage metric Success ratio

来源：评论

学校读者我要写书评

暂无评论

robust multi-objective optimization for Gas Turbine Operation Based on Kriging Surrogate Model

Robust Multi-Objective Optimization for Gas Turbine Operatio...

引用

第40届中国控制会议

作者： Hao Xia Peilin Jia Liang Ma School of Control Science and Control Engineering Dalian University of Technology No.703 Research Institute of China State Shipbuilding Corporation Limited

This paper presents a systematic method for the optimal settings of gas turbine operation. After building the highfidelity model of the gas turbine, the fuel flow, variable inlet guide-vane（VIGV） of the high and low pressure compressors,ambient air pressure and temperature are selected as the decision variables, while the pollutant emissions and output power of whole system are chosen as response variables to be optimized. In order to guarantee the accurate response prediction,the performance of three different types of surrogate models, namely polynomial response surface（PRS）, Kriging and radial basis function（RBF） models are built with sampling points generated by Latin hypercube design（LHD） method. With Kriging surrogate model best fitting the data, it is chosen as the model for performance prediction during the optimization process. It is also shown that the NSGA-II algorithm is suitable for the multi-objective optimization of gas turbine. The robustness of the Pareto solutions are checked under the varying ambient pressure and temperature. By considering the solution robustness, and the nature of gas turbine performance change under perturbation, the optimal operating conditions are identified.

关键词： Gas turbine Kriging model robust multi-objective optimization

来源：评论

学校读者我要写书评

暂无评论

Implementation of robust multi-objective optimization in the Build Orientation Problem 21st

Implementation of Robust Multi-objective Optimization in the...

引用

21st International Conference on Computational Science and Its Applications (ICCSA)

作者： Matos, Marina A. Rocha, Ana Maria A. C. Costa, Lino A. Pereira, Ana, I Univ Minho ALGORITMI Ctr P-4710057 Braga Portugal Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI P-5300253 Braganca Portugal

ISBN: (纸本)9783030869762;9783030869755

Additive manufacturing (AM) is an emerging technology to create 3D objects layer-by-layer directly from a 3D CAD model. The build orientation is a critical issue in AM and its optimization will significantly reduce the building costs and improve object accuracy. This paper aims to optimize the build orientation problem of a 3D CAD model using a robust multi-objective approach, taking into account the staircase effect and the support area characteristics. Thus, themain objective is to obtain a robust Pareto optimal front, composed of solutions that are not quite sensitive to perturbations in the variables. In this manner, a set of robust solutions is presented as alternatives and the decision-maker can identify the compromise solutions and choose according to his/her preferences.

关键词： Additive manufacturing 3D Printing robust multi-objective optimization Build orientation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：