检索结果-内蒙古大学图书馆

An Artificial Intelligence Approach for Solving stochastic Transportation Problems

Computers, Materials & Continua 2022年第1期70卷 817-829页

作者： Prachi Agrawal Khalid Alnowibet Talari Ganesh Adel F.Alrasheedi Hijaz Ahmad Ali Wagdy Mohamed Department of Mathematics and Scientific Computing National Institute of Technology HamirpurHimachal Pradesh177005India Statistics and Operations Research Department College of ScienceKing Saud UniversityRiyadh11451Kingdom of Saudi Arabia Section of Mathematics International Telematic University UninettunoRoma00186Italy Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt

Recent years witness a great deal of interest in artificial intelligence(AI)tools in the area of *** has developed a large number of tools to solve themost difficult search-and-optimization problems in computer science and operations ***,metaheuristic-based algorithms are a sub-field of *** study presents the use of themetaheuristic algorithm,that is,water cycle algorithm(WCA),in the transportation problem.A stochastic transportation problem is considered in which the parameters supply and demand are considered as random variables that follow the Weibull *** the parameters are stochastic,the corresponding constraints are *** are converted into deterministic constraints using the stochastic programming *** this study,we propose evolutionary algorithms to handle the difficulties of the complex high-dimensional optimization *** is influenced by the water cycle process of how streams and rivers flow toward the sea(optimal solution).WCA is applied to the stochastic transportation problem,and obtained results are compared with that of the new metaheuristic optimization algorithm,namely the neural network algorithm which is inspired by the biological nervous *** is concluded that WCA presents better results when compared with the neural network algorithm.

关键词： Artificial intelligence metaheuristic algorithm stochastic programming transportation problem water cycle algorithm weibull distribution

来源：评论

学校读者我要写书评

暂无评论

A novel decentralized control strategy for microgrids, combining renewable energy resources 2

A novel decentralized control strategy for microgrids, combi...

引用

2nd IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2024

作者： Yu, Zhe Wang, Zhenqi Kou, Jinchi State Grid Benxi Electric Power Supply Company Benxi117000 China

ISBN: (纸本)9798350368208

The expanding integration of Renewable Vitality Assets (RERs) into conveyance systems comes about in Organized Renewable Vitality Assets (NRERs). The agreeable Peer-to-Peer (P2P) control design completely leverages NRERs' vigor and versatility. This work presents a multi-agent framework for P2P control of the NRERs-based Web of Things (IoT). The regulative framework is completely disseminated and comprises of two control layers that are worked by the specialist of each RER. For essential control, each RER-agent employments a hang control to share control locally. For auxiliary control, a dispersed dissemination procedure is recommended that permits RERs to share self-assertive control. A disseminated real-time sharing control procedure is given as an imperfect arrangement to the compelled stochastic programming issue that does not require any framework measurements. The Web Convention (IP)/Transmission Control Convention (TCP) is utilized at the neighborhood communication level, whereas the Message Lining Telemetry Transport (MQTT) convention is utilized universally. The adequacy of the suggested framework is approved by numerical recreation utilizing the altered IEEE 9 hub test feeder. A few scenarios are developed based on framework stack sorts, with private structures and little commercial outlets. The recreation comes about affirmed the possibility and viability of the proposed method for the MGs' differing working circumstances. © 2024 IEEE.

关键词： stochastic programming

来源：评论

学校读者我要写书评

暂无评论

Delay-Optimal Scheduling for Massive Wireless Access: A Mean-Field Optimization Approach

Delay-Optimal Scheduling for Massive Wireless Access: A Mean...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Changkun Chen, Wei Tsinghua University Department of Electronic Engineering Beijing100084 China China

ISBN: (纸本)9798350351255

Massive access has attracted considerable recent attention due to its potential in 6G's usage scenarios including massive communications and ubiquitous connectivity. To assure its quality-of-service (QoS), both channel and queue state information have to be exploited to efficiently schedule multiple devices to attain low latency or strike the optimal delay-power tradeoff. However, such a cross-layer scheduling will suffer from the high complexity when the number of users grows huge, thereby causing the curse of dimensionality. In this paper, we present joint channel- and queue-aware scheduling that minimizes the queueing delay of both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) in low complexity, by leveraging a mean-field approach originating from statistical mechanics. In particular, we conceive a unified mean-field approximation framework that substantially simplifies both the objective function and constraints of the constrained Markov decision process (CMDP) for optimizing the queueing delay in cross-layer scheduling of multiple users. The mean-field approximation allows us to find an asymptoticly optimal solution to the original CMDP or stochastic programming with very high dimension by solving a variational optimization with much lower dimension instead, thereby substantially reducing the computational complexity. Numerical results demonstrate the effectiveness of our mean-field optimization approach for massive access. © 2024 IEEE.

关键词： stochastic programming

来源：评论

学校读者我要写书评

暂无评论

Towards a sustainable power grid: stochastic hierarchical planning for high renewable integration

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第1期302卷 381-391页

作者： Atakan, Semih Gangammanavar, Harsha Sen, Suvrajeet Univ Southern Calif Daniel J Epstein Dept Ind & Syst Engn CA Los Angeles CA 90007 USA Southern Methodist Univ Dept Operat Res & Engn Management Dallas TX 75275 USA

Driven by ambitious renewable portfolio standards, large-scale inclusion of variable energy resources (such as wind and solar) are expected to introduce unprecedented levels of uncertainty into power systems operations. The current practice of operations planning with deterministic optimization models may be ill-suited for a future with abundant uncertainty. To overcome the potential reliability and economic challenges, we present a stochastic hierarchical planning (SHP) framework for power systems coordinated by a centralized planner. This framework captures operations at day-ahead, short-term and hour-ahead timescales, along with the interactions between the stochastic processes and decisions. In contrast to earlier studies where stochastic optimization of individual problems (e.g., unit commitment, economic dispatch) have been studied, this paper studies an integrated framework of planning under uncertainty , where stochastic optimization models are stitched together in a hierarchical setting, which parallels the deterministic hierarchical planning approach that is widely adopted in the power industry. Our experiments, based on the NREL-118 dataset, reveal that under high renewable integration, significant operational improvements can be expected by transitioning to the SHP paradigm. In particular, the computational results show that significant improvements can be achieved in several metrics, including system reliability, environmental sustainability, and system economics, solely by making a strategic choice to adopt the new SHP paradigm. (c) 2022 Elsevier B.V. All rights reserved.

关键词： OR in energy Power systems operations planning stochastic programming Hierarchical planning

来源：评论

学校读者我要写书评

暂无评论

A branch-and-cut algorithm for the irregular strip packing problem with uncertain demands

引用

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2022年第6期29卷 3486-3513页

作者： de Souza Queiroz, Layane Rodrigues Andretta, Marina Univ Sao Paulo Inst Ciencias Matemat & Comp BR-13566590 Sao Carlos SP Brazil

This work presents a tailored branch-and-cut algorithm for the two-dimensional irregular strip packing problem with uncertain demand for the items to be cut. A two-stage stochastic programming model is developed, considering a discrete and finite set of scenarios. The strip is discretized over a mesh of points in the model and includes constraints to ensure items are non-overlapping based on the concepts of inner-fit raster and no-fit raster. The algorithm considers lower and upper bounds from a heuristic based on the variable neighborhood search. This heuristic is also used during optimization to obtain new solutions and help to prune unsatisfactory nodes. The numerical results indicate the effectiveness of the proposed algorithm when observing other exact algorithms on the same problem without uncertainty. The algorithm can also provide optimal solutions for instances with uncertainty having more than 60 scenarios within some hours of execution. Besides, the conclusions show it is preferable to handle uncertainty to achieve minimum cost decisions.

关键词： stochastic programming irregular strip packing problem uncertain demands variable neighborhood search

来源：评论

学校读者我要写书评

暂无评论

Farm management optimization under uncertainty with impacts on water quality and economic risk

引用

IISE TRANSACTIONS 2022年第12期54卷 1143-1160页

作者： Emirhuseyinoglu, Gorkem Ryan, Sarah M. Iowa State Univ Dept Ind & Mfg Syst Engn Ames IA 50011 USA

Farm management decisions under uncertainty are important, not only for farmers trying to maximize their net income, but also for policy makers responsible for incentives and regulations to achieve environmental goals. We focus on corn production as a significant contributor to the economy of the US Midwest. Nitrogen is one of the key nutrients needed to increase production efficiency, but its leaching and loss as nitrate through subsurface flow and agricultural drainage systems poses a threat to water quality. We build a novel two-stage stochastic mixed-integer program to find the annual farm management decisions that maximize the expected farm profit. A decomposition-based solution strategy is suggested to reduce the computational complexity resulting from the predominance of binary variables and complicated constraints. Case study results indicate that farmers may compensate for the additional risks associated with nutrient reduction strategies by increasing the planned nitrogen application rate. Significant financial incentives would be required for farmers to achieve substantial reductions in nitrate loss by fertilizer management alone. The complicated interactions between fertilizer management and crop insurance decisions observed in the numerical study suggest that crop insurance programs can affect water quality by influencing the adoption of environmentally beneficial practices.

关键词： Farm management optimization stochastic programming nitrate loss crop insurance fertilizer application

来源：评论

学校读者我要写书评

暂无评论

Mixed-integer stochastic evaluation of battery energy storage system integration strategies in distribution systems

引用

IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第4期16卷 641-655页

作者： Sepulveda Rangel, Camilo Alberto Canha, Luciane Neves Sperandio, Mauricio Miranda, Vladimiro Univ Fed Santa Maria Dept Elect Engineer Rodovia BR 287 1000 Santa Maria RS Brazil Univ Porto FEUP Fac Engn R Dr Roberto Frias Porto Portugal INESC TEC Inst Syst & Comp Engn Technol & Sci R Dr Roberto Frias Porto Portugal

This paper presents a new approach to the problem of defining an investment policy in battery energy storage systems in active distribution networks, taking into account a diversity of uncertainties. The proposed methodology allows the selection of type, capacity, and location of battery energy storage systems in distribution networks with distributed generation and electric vehicle charging stations. A mixed-integer stochastic programming problem is cunningly approached with a metaheuristic, where fitness calculation with stochastic scenarios is performed by introducing an approximation to the operation costs in the form of a polynomial neural network, generated according to the Group Method of Data Handling-GMDH method, with strong computing speeding-up. The quality of this approximation for heavy Monte Carlo simulations is assessed in a first case study using a 33-bus distribution test system. The optimization planning model is then validated in the same test system using real data collected from solar and wind sources, demand, prices, and charging stations. Four types of batteries are compared considering degradation impact. The results demonstrate the practicality and advantages of this process.

关键词： battery powered vehicles GMDH method group method of data handling method polynomial neural network electric vehicle charging stations distribution systems integer programming mixed-integer stochastic evaluation stochastic programming electric vehicle charging optimization planning model Monte Carlo simulations battery energy storage system battery storage plants distributed power generation neural nets distributed generation Monte Carlo methods polynomials power distribution planning power generation planning battery energy storage systems mixed-integer stochastic programming problem active distribution networks power engineering computing metaheuristic approached

来源：评论

学校读者我要写书评

暂无评论

K-adaptability in stochastic optimization

引用

MATHEMATICAL programming 2022年第1-2期196卷 567-595页

作者： Malaguti, Enrico Monaci, Michele Pruente, Jonas Univ Bologna DEI Guglielmo Marconi Viale Risorgimento 2 I-40136 Bologna Italy TU Dortmund Fak Math Vogelpothsweg 87 D-44227 Dortmund Germany

We consider stochastic problems in which both the objective function and the feasible set are affected by uncertainty. We address these problems using a K-adaptability approach, in which K solutions for a given problem are computed before the uncertainty dissolves and afterwards the best of them can be chosen for the realized scenario. We analyze the complexity of the resulting problem from a theoretical viewpoint, showing that, even in case the deterministic problem can be solved in polynomial time, deciding if a feasible solution exists is NP-hard for discrete probability distributions. Besides that, we prove that an approximation factor for the underlying problem can be carried over to our problem. Finally, we present exact approaches including a branch-and-price algorithm. An extensive computational analysis compares the performances of the proposed algorithms on a large set of randomly generated instances.

关键词： stochastic programming K-adaptability Exact algorithms Branch-and-price Computational experiments

来源：评论

学校读者我要写书评

暂无评论

Electricity allocation strategy for on-site hydrogen refueling stations in the forward and spot markets

引用

IET RENEWABLE POWER GENERATION 2022年第12期16卷 2532-2546页

作者： Wu, Sirui Lin, Jin Li, Jiarong Song, Yonghua Tsinghua Univ Dept Elect Engn State Key Lab Control & Simulat Power Syst & Gene Beijing 100087 Peoples R China Tsinghua Sichuan Energy Internet Res Inst Chengdu 610213 Peoples R China Univ Macau Dept Elect & Comp Engn Macau Peoples R China

Green hydrogen in fuel cell vehicles (FCVs) is a promising alternative in the transportation sector. However, high hydrogen costs hinder progress. An on-site hydrogen refueling station (OHRS) can avoid hydrogen transportation and participate in the market to reduce costs. In the forward market, it can lock in inexpensive electricity in advance;meanwhile, lower prices can be attained in the spot market. However, due to the operational constraints of OHRS, allocating electricity between the forward and spot markets is a multi-time scale optimization problem. This paper establishes electricity transaction models and a state equation of the OHRS. A two-stage stochastic optimization model is then proposed to address the allocation problem. Actual market data are used in numerical studies. The results show that storage tanks and electrolysers capacity are critical to arbitrage. Moreover, participating in both the forward and spot markets can usually achieve the lowest levelized cost of hydrogen(LCOH).

关键词： OHRS spot market forward spot markets power markets multitime scale optimization problem power generation economics optimisation stochastic programming fuel cell vehicles pricing inexpensive electricity on-site hydrogen electricity transaction models forward market electricity allocation strategy hydrogen transportation transportation sector green hydrogen allocation problem high hydrogen costs actual market data

来源：评论

学校读者我要写书评

暂无评论

Optimal Bilevel Operation-Planning Framework of Distributed Generation Hosting Capacity Considering Rival DISCO and EV Aggregator

引用

IEEE SYSTEMS JOURNAL 2022年第3期16卷 5023-5034页

作者： Najafi, Arsalan Pourakbari-Kasmaei, Mahdi Contreras, Javier Lehtonen, Matti Leonowicz, Zbigniew Wroclaw Univ Sci & Technol Fac Elect Engn Dept Elect Engn Fundamentals PL-50370 Wroclaw Poland Islamic Azad Univ Sepidan Branch Sepidan 73617 Iran Aalto Univ Dept Elect Engn & Automat Espoo 02150 Finland Univ Castilla La Mancha ETS Ingn Ind Ciudad Real 13071 Spain

The aggressive goal of having a 100% renewable energy system requires preparing an appropriate infrastructure for deploying as much distributed generation (DG) as possible into the electricity network. This article aims at proposing a new framework to maximize the hosting capacity (HC) of DGs in a distribution network. This operation-planning framework provides a suitable and interactive environment for the electricity market, distribution company (DISCO), electric vehicle (EV) aggregator, and rival DISCOs to increase the HC. The problem is modeled via a bilevel conditional-value-at-risk-constrained stochastic programming approach. The DISCO, in the upper level (UL), tries to maximize the HC and minimize the operating costs while interacting with a passive electricity market and an EV aggregator. The aggregator, in the lower level, aims to maximize their profit by interacting with the primary DISCO in the UL and the rival DISCO, to satisfy the EV owners. The Karush-Kuhn-Tucker conditions are used to recast the model into an equivalent single level. The proposed framework is tested on a 33-node distribution network. Results show how an interactive-based framework can contribute to maximizing the HC of DGs.

关键词： Aggregator electric vehicle (EV) locational marginal price (LMP) maximum hosting capacity (HC) stochastic programming

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：