检索结果-内蒙古大学图书馆

Bi-level stochastic optimization model of smart distribution grids considering the transmission network to reduce vulnerability

引用

ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2025年第2期16卷 851-884页

作者： Ghanaei, Niki Moghaddam, Mahmoud Samiei Alibeaki, Esmaeil Salehi, Nasrin Davarzani, Reza Islamic Azad Univ Dept Elect Engn Shahrood Branch Shahrood Iran Islamic Azad Univ Dept Elect Engn Damghan Branch Damghan Iran Islamic Azad Univ Dept Basic Sci Shahrood Branch Shahrood Iran

This paper presents a novel optimization framework for integrating transmission and distribution networks. The proposed framework consists of a bi-level optimization model, which comprises an upper-level problem and a lower-level problem. The upper-level problem addresses unit commitment (UC) in the transmission network, aiming to minimize operating costs and load shedding. It is formulated as a mixed-integer linear programming model (MILP). Meanwhile, the lower-level problem focuses on the optimal operation of the distribution network, considering both renewable and non-renewable resources, as well as electric vehicle charging stations (EVCS). This problem is formulated as a linear model with the objectives of reducing power purchase costs from the transmission network and maximizing the utilization of renewable energy and EVCS charging power. The literature review highlights three main research gaps. Firstly, it is crucial to develop a convex model that ensures globally optimal solutions. Secondly, considering the problem-solving time, it is important to devise a method that can achieve globally optimal solutions in the shortest possible time, accounting for the dimensions, number of variables, and parameters involved in coordinating the transmission and distribution networks. Lastly, there is a need to develop a modeling approach that allows the integration of any number of distribution networks with the transmission network. To address these research gaps, we propose a solution methodology that involves rewriting the optimization problems using reformulation and decomposition methods. This approach enables the modeling of globally optimal solutions while ensuring faster solution times and the ability to incorporate numerous distribution networks into the transmission network. We validate the proposed model and methodology using various networks, and the results demonstrate the efficiency of our approach in coordinating the operation of transmission and smart dis

关键词： Coordination of transmission and distribution network Bi-level stochastic optimization Renewable energy mixed integer linear programming

来源：评论

学校读者我要写书评

暂无评论

Regional Constellation Reconfiguration Problem: integer linear programming Formulation and Lagrangian Heuristic Method

引用

JOURNAL OF SPACECRAFT AND ROCKETS 2023年第6期60卷 1828-1845页

作者： Lee, Hang Woon Ho, Koki West Virginia Univ Dept Mech & Aerosp Engn Morgantown WV 26506 USA Georgia Inst Technol Daniel Guggenheim Sch Aerosp Engn Atlanta GA 30332 USA

A group of satellites, with either homogeneous or heterogeneous orbital characteristics and/or hardware specifications, can undertake a reconfiguration process due to variations in operations pertaining to Earth observation missions. This paper investigates the problem of optimizing a satellite constellation reconfiguration process against two competing mission objectives: 1) the maximization of the total coverage reward, and 2) the minimization of the total cost of the transfer. The decision variables for the reconfiguration process include the design of the new configuration and the assignment of satellites from one configuration to another. We present a novel biobjective integer linear programming formulation that combines constellation design and transfer problems. The formulation lends itself to the use of generic mixed-integer linear programming (MILP) methods such as the branch-and-bound algorithm for the computation of provably optimal solutions;however, these approaches become computationally prohibitive even for moderately sized instances. In response to this challenge, this paper proposes a Lagrangian relaxation-based heuristic method that leverages the assignment problem structure embedded in the problem. The results from the computational experiments attest to the near-optimality of the Lagrangian heuristic solutions and a significant improvement in the computational runtime as compared to a commercial MILP solver.

关键词： Satellite Constellations Earth Observation mixed integer linear programming Orbital Maneuvers Computing and Informatics Reconfiguration Lagrangian Relaxation

来源：评论

学校读者我要写书评

暂无评论

Virtual Target Selection for a Multiple-Pursuer-Multiple-Evader Scenario

引用

JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2025年第5期22卷 391-400页

作者： Weintraub, Isaac E. Von Moll, Alexander Casbeer, David W. Manyam, Satyanarayana G. Air Force Res Lab Control Sci Ctr Wright Patterson AFB OH 45433 USA InfoSciTex Corp 4027 Col Glenn Hwy Dayton OH 45431 USA

This paper considers an N-pursuer-M-evader scenario involving L virtual targets. The virtual targets serve as an intermediary target for the pursuers, allowing the pursuers to delay their final assignment to the evaders. However, upon reaching the virtual target, the pursuers must decide which evader to capture. It is assumed that there are more pursuers than evaders and that the pursuers are faster than the evaders. The objective is two-part: first, assign each pursuer to a virtual target and evader such that the pursuer team's energy is minimized, and, second, choose the virtual targets' locations for this minimization problem. The approach taken is to consider the Apollonius geometry between each pursuer's virtual target location and each evader. Using the constructed Apollonius circles, the pursuer's travel distance and maneuver at a virtual target are obtained. These metrics serve as a gauge for the total energy required to capture a particular evader and are used to solve the joint virtual target selection and pursuer-evader assignment problem. This paper provides a mathematical definition of this problem, the solution approach taken, and an example. Following the example, a Monte Carlo analysis is performed, demonstrating the efficacy of the algorithm and its suitability for real-time applications.

关键词： mixed integer linear programming Optimal Control Theory Optimization Algorithm Weapon Target Assignment Monte Carlo Analysis Guidance, Navigation, and Control Systems Missile Defense

来源：评论

学校读者我要写书评

暂无评论

Compact Models to Solve the Precedence-Constrained Minimum-Cost Arborescence Problem with Waiting Times

引用

ALGORITHMS 2024年第1期17卷 12页

作者： Dell'Amico, Mauro Jamal, Jafar Montemanni, Roberto Univ Modena & Reggio Emilia Dept Sci & Methods Engn Via Amendola 2 I-42122 Reggio Emilia RE Italy Univ Modena & Reggio Emilia Interdept Ctr En&Tech Capannone 19 TecnopoloPiazza Europa 1 I-42122 Reggio Emilia RE Italy

The minimum-cost arborescence problem is a well-studied problem. Polynomial-time algorithms for solving it exist. Recently, a new variation of the problem called the Precedence-Constrained Minimum-Cost Arborescence Problem with Waiting Times was presented and proven to be NP-hard. In this work, we propose new polynomial-size models for the problem that are considerably smaller in size compared to those previously proposed. We experimentally evaluate and compare each new model in terms of computation time and quality of the solutions. Several improvements to the best-known upper and lower bounds of optimal solution costs emerge from the study.

关键词： arborescences precedence constraints mixed integer linear programming constraint programming

来源：评论

学校读者我要写书评

暂无评论

Stochastic optimal scheduling of wind power and pumped-storage hydropower complementary systems with multiple uncertainties

引用

JOURNAL OF ENERGY STORAGE 2024年 78卷

作者： Lu, Na Su, Chengguo Guo, Chaoyu Wang, Peilin Wu, Huijun Sui, Quan Zhengzhou Univ Coll Water Conservancy & Transportat Zhengzhou 450001 Peoples R China Zhengzhou Univ Yellow River Lab Zhengzhou 450001 Peoples R China Design & Res Co Ltd China Water Resources Bei Fang Invest Tianjin 300222 Peoples R China China Southern Power Grid Co LTD Power Dispatching & Control Ctr Guangzhou Peoples R China Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China

The joint operation of wind farms (WFs) and pumped-storage hydropower plants (PSHPs) is an effective way to smooth out the random fluctuations of wind power and improve its consumption rate. In this paper, a novel method to describe the multiple uncertainties of wind power outputs is proposed. A WFs-PSHP joint optimal scheduling model based on chance-constrained programming is then developed. The optimization model aims to maximize the total generation profit of the WFs-PSHP complementary system, and comprehensively considers the hydraulic constraints, unit operation constraints and system power balance constraint of the PSHP. To improve the model solving efficiency, this study converts the previous model with high-dimensional, nonconvex, nonlinear and stochastic characteristics to the deterministic mixed integer linear programming (MILP) one. The optimization results demonstrate that the PSHP not only compensates for the fluctuations in wind power output, but also increases the total generation profit of this complementary system to 966.48 x 104 CNY, which is 17.56 % higher than that of independent operation.

关键词： Chance -constrained programming WFs-PSHP complementary systems Multiple uncertainties of wind power output mixed integer linear programming

来源：评论

学校读者我要写书评

暂无评论

Automatic MILP solver configuration by learning problem similarities

引用

ANNALS OF OPERATIONS RESEARCH 2024年第1-2期339卷 909-936页

作者： Hosny, Abdelrahman Reda, Sherief Brown Univ Dept Comp Sci Providence RI 02912 USA Brown Univ Sch Engn Providence RI 02912 USA

A large number of real-world optimization problems can be formulated as mixed integer linear Programs (MILP). MILP solvers expose numerous configuration parameters to control their internal algorithms. Solutions, and their associated costs or runtimes, are significantly affected by the choice of the configuration parameters, even when problem instances have the same number of decision variables and constraints. On one hand, using the default solver configuration leads to suboptimal solutions. On the other hand, searching and evaluating a large number of configurations for every problem instance is time-consuming and, in some cases, infeasible. In this study, we aim to predict configuration parameters for unseen problem instances that yield lower-cost solutions without the time overhead of searching-and-evaluating configurations at the solving time. Toward that goal, we first investigate the cost correlation of MILP problem instances that come from the same distribution when solved using different configurations. We show that instances that have similar costs using one solver configuration also have similar costs using another solver configuration in the same runtime environment. After that, we present a methodology based on Deep Metric Learning to learn MILP similarities that correlate with their final solutions' costs. At inference time, given a new problem instance, it is first projected into the learned metric space using the trained model, and configuration parameters are instantly predicted using previously-explored configurations from the nearest neighbor instance in the learned embedding space. Empirical results on real-world problem benchmarks show that our method predicts configuration parameters that improve solutions' costs by up to 38% compared to existing approaches.

关键词： mixed integer linear programming Algorithm configuration Metric learning Deep learning

来源：评论

学校读者我要写书评

暂无评论

A novel reduced local search algorithm for optimization of digital filter coefficients

引用

RESULTS IN ENGINEERING 2024年 23卷

作者： Gawande, Gopal Katare, Pravin Chan, Choon Kit Pande, Milind Gohokar, Vijay Marathwada Mitra Mandals Coll Engn Fac Elect & Telecommun Engn Pune 411052 India Marathwada Mitra Mandals Coll Engn Fac Mech Engn Pune 411052 India INTI Int Univ Persiaran Perdana BBN Nilai 71800 Negeri Sembilan Malaysia INTI Int Univ Fac Engn & Quant Surveying Persiaran Perdana BBN Nilai 71800 Negeri Sembilan Malaysia MIT World Peace Univ Fac Elect & Elect Engn Pune 411038 India Marathwada Mitra Mandals Coll Engn Fac Elect Engn Pune 411052 India

Digital Filtering is an important operation in DSP. Most of the DSP applications demand low power implementations. In CMOS digital circuits, switching from logic 0 to logic 1 and vice-versa decides the dynamic power consumption. This paper proposes and develops a novel algorithm based on mixed integer linear programming to minimize the number of ones (filter cost) in filter coefficients, which in turn minimizes the switching factor of CMOS circuits and the number of additional adders needed for hardware implementation. The search space for finding the optimal value of the coefficient is reduced thereby increasing the speed of execution of the algorithm. The average reduction of 18.02 % in the number of transitions is obtained with the proposed algorithm. The suggested technique results in an 18.72 % decrease in the typical price of digital filters. Also the proposed algorithm works faster than the previously proposed algorithm used for finding the optimized coefficients.

关键词： Industrial and innovation FIR filter coefficients mixed integer linear programming Upper and lower bounds

来源：评论

学校读者我要写书评

暂无评论

A mathematical programming approach for a wildfire suppression problem

引用

OPERATIONAL RESEARCH 2025年第1期25卷 1-27页

作者： Granda, Bibiana Vitoriano, Begona Figueira, Jose Rui Interdisciplinary Math Inst IMI HUMLOG Res Grp Plaza Ciencias 3 Madrid 28040 Spain Univ Lisbon CEGIST Inst Super Tecn P-1049001 Lisbon Portugal

Wildfires are natural recurrent events, that may be devastating if not addressed correctly. In these situations, where quick and accurate decisions are needed, Operational Research can be helpful for providing fast and robust solutions. This paper focuses on the response actions taken during the suppression stage of a wildfire. A mixed integer linear programming model is proposed to obtain a wildfire suppression strategy, including the wildfire behaviour changes induced by the solution. The selected wildfire suppression strategy is modelled in detail, pointing out which locations to control and their timing, based on available paths between them, avoiding engagement in dangerous situations. A computational study is carried out to determine the most suitable solver to provide exact solutions of the model. Also, a two-stage version of the model is proposed to deal with the multicriteria nature of the problem. A case study is also included to validate the model's applicability, which is solved using the two proposed versions of the model and an iterative approach to compare their performance.

关键词： Wildfire management Wildfire suppression Optimization mixed integer linear programming

来源：评论

学校读者我要写书评

暂无评论

Rail freight transportation service network design with mixed blocking policies: Formulation and solution methods

引用

JOURNAL OF RAIL TRANSPORT PLANNING & MANAGEMENT 2025年 33卷

作者： Xin, Xiaoyu Wu, Xu Wang, Jiaxi Beijing Jiaotong Univ Sch Traff & Transportat Beijing Peoples R China Zhejiang Lab Res Ctr High Efficiency Comp Syst Hangzhou Zhejiang Peoples R China

In the current China railway system, the freight transportation service network is demand category oriented and is designed by using a hierarchical approach. This simplifies the design procedure, but reduces the flexibility and optimality of the plan. To make full use of the limited railway capacity and to improve the service flexibility and quality, this paper addresses the freight service network design problem with simultaneous consideration of mixed blocking policies and mixed service classes for heterogeneous demands. A mixed integer linear programming model is developed for the problem to maximize the operator's total transportation profit. Furthermore, a two-stage decomposition framework is proposed to solve the optimization model, where both exact and heuristic algorithms are employed. Extensive computational experiments on artificial and real-world instances indicate that the proposed solution approaches can generate high-quality solutions within reasonable time frame. Moreover, the mixed blocking policies could attract more freight demands and tend to result in higher profits, compared to the single blocking policy.

关键词： Rail freight transportation Network design Blocking policy mixed integer linear programming Lagrangian relaxation

来源：评论

学校读者我要写书评

暂无评论

Ensemble Provably Robust Learn-to-Optimize Approach for Security-Constrained Unit Commitment

引用

IEEE TRANSACTIONS ON POWER SYSTEMS 2023年第6期38卷 5073-5087页

作者： Sang, Linwei Xu, Yinliang Sun, Hongbin Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen 518055 Peoples R China Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing 100084 Peoples R China

Security-constrained unit commitment (SCUC) is the basis for power systems and markets operation, which is solved periodically via mixed-integer programming (MIP) with limited input data changes to historical solved instances. This paper proposes an ensemble provably robust learn-to-optimize approach (EPR-L2O) for transforming the MIP-based SCUC into the tractable convex problem with predictable solving time by identifying the integer part of MIP, denoted by unit status integer solution. It formulates the SCUC model for unit status integer solution generation, prunes the generated integer solutions, clusters the units based on their operation status, and proposes the provably robust learning approach to training the unit status integer solution identifiers, which are provably robust against the input adversarial perturbation. After learning, the integer solutions from different identifiers are assembled to identify the integer variables of SCUC. Case study verifies that: 1) EPR-L2O achieves 6.91, 19.71, and 22.14 times acceleration in the 30-bus, 118-bus, and 300-bus systems than the Branch-and-Bound;2) compared to cross-entropy-based learning, EPR-L2O reduces the MIP gap by 0.031%, 0.21%, and 0.0084% in the 30-bus, 118-bus, and 300-bus systems, indicating its operation economy and robustness.

关键词： Predictive models Optimization Ensemble learning mixed integer linear programming Security-constrained unit commitment ensemble learning provably robust learning learn-to-optimize

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：