检索结果-内蒙古大学图书馆

Multi-Objective optimization of Liquid-Cooled Power Unit With Multiple Pumps in Parallel Under Variable Operating Conditions

引用

IEEE ACCESS 2024年 12卷 168825-168836页

作者： Hu, Hao Liu, Yang Ma, Xiqiang Li, Wenju Yue, Lingyun Yang, Fang Henan Univ Sci & Technol Sch Mech & Elect Engn Luoyang 471000 Henan Peoples R China Xinxiang Aviat Ind Grp Co LTD Xinxiang 453000 Henan Peoples R China Henan Univ Sci & Technol Key Lab Mech Design & Transmiss Syst Luoyang 471000 Henan Peoples R China

Advanced airborne equipment liquid cooling system presents the development trend of multiple pumps in parallel, centralised heat dissipation and high efficiency circulation, but this type of system has the characteristics of many variable parameters and strong non-linearity, which makes the system operation and control become extraordinarily complex, posing a serious challenge to the system performance. Although some progress has been made in the current optimization of liquid cooling system performance, there are still gaps such as insufficient consideration of the multi-objective optimization problem and limited optimization effect. To fill this research gap, this study takes minimum specific energy consumption, minimum flow deviation and minimum impeller load as the optimization objectives, establishment of multi-objective optimization model for liquid-cooled power unit, the optimal solution set of the Pareto model is solved using the multi-objective grey wolf optimization algorithm (MOGWO), and the optimal solution set is substituted for the optimal solution, and the evaluation indexes are designed in combination with the actual working conditions to obtain the optimal schedule. By example analysis, the optimized system under variable operating conditions, the impeller load and specific energy consumption decreased by 16.88% and 3.64% respectively, while the rise in flow deviation was controlled within 0.76%. The newly established optimisation model effectively mitigates potential safety hazards during system operation. Finally, by combining all the indicators, three scheduling options are provided under variable operating conditions, focusing on reliability and economy, overall balance and high-precision flow, respectively, in order to adapt to the diversified requirements of different operating environments.

关键词： Impellers Pumps optimization Energy consumption Reliability optimization models Valves Liquids Systems operation Load modeling Impeller load liquid-cooled power unit multi-objective optimization Pareto optimal solution set specific energy consumption

来源：评论

学校读者我要写书评

暂无评论

Adaptive and Low-Cost Traffic Engineering: A Traffic Matrix Clustering Perspective

引用

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2025年第2期43卷 510-523页

作者： Liu, Yi Geng, Nan Xu, Mingwei Yang, Yuan Dong, Enhuan Liu, Chenyi Gan, Qiaoyin Li, Qing Wu, Jianping Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Tsinghua Univ Inst Network Sci & Cyberspace Beijing 100084 Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China

Traffic engineering (TE) has attracted extensive attention over the years. Operators expect to design a TE scheme that accommodates traffic dynamics well and achieves good TE performance with little overhead. Some approaches like oblivious routing compute an optimal static routing based on a large traffic matrix (TM) range, which usually leads to much performance loss. Many approaches compute routing solutions based on one or a few representative TMs obtained from observed historical TMs. However, they may suffer from performance degradation for unexpected TMs and usually induce much overhead of system operating. In this paper, we propose ALTE, an adaptive and low-cost TE scheme based on TM classification. We develop a novel clustering algorithm to properly group a set of historical TMs into several clusters and compute a candidate routing solution for each TM cluster. A machine learning classifier is trained to infer the proper candidate routing solution online based on the features extracted from some easily measured statistics. We implement a system prototype of ALTE and do extensive simulations and experiments using both real and synthetic traffic traces. The results show that ALTE achieves near-optimal performance for dynamic traffic and introduces little overhead of routing updates.

关键词： Routing Real-time systems Feature extraction Computational modeling Clustering algorithms Quality of service optimization models Classification algorithms Transforms Topology Traffic engineering traffic matrix classification machine learning

来源：评论

学校读者我要写书评

暂无评论

How to Operate Electricity Markets Using a Trilevel optimization Framework

引用

IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2025年 72卷 888-906页

作者： Yang, Zhiyuan Li, Zifan Wang, Shuaian Zhen, Lu Shanghai Univ Sch Management Shanghai 200444 Peoples R China Hong Kong Polytech Univ Fac Business Hong Kong Peoples R China

In the context of significant changes in the global energy landscape and the rapid transformation of the power industry, the complexity of electricity markets is growing. There is an urgent need for innovative management strategies and optimization solutions. Against this background, we investigate the optimization problems in electricity markets. We focus on the interactions among distribution network operators, microgrid operators, and prosumers and develop a trilevel optimization framework. This framework models the complex processes of electricity price setting, risk management, and prosumer participation to maximize profitability and operational efficiency. To solve this complex model efficiently, we propose a customized exact algorithm. It is based on the Karush-Kuhn-Tucker conditions and the L-shaped algorithm. Extensive computational experiments prove the effectiveness of the algorithm. We conduct sensitivity analyses on the electricity market operations, and the results provide valuable management insights for electricity markets. Our findings indicate that microgrid operators should prioritize flexible energy storage capacities and adaptable pricing mechanisms to effectively manage peak demand, especially during periods with high renewable generation.

关键词： Microgrids Electricity supply industry Electricity Energy storage optimization Renewable energy sources Pricing optimization models Computational modeling Complexity theory Energy technology exact optimization microgrid three-layer electricity market trilevel optimization model

来源：评论

学校读者我要写书评

暂无评论

Dual Cyber-Physical Network-Based optimization of Cloudlet Formation for the Internet of Vehicles

引用

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第6期26卷 7486-7495页

作者： Meng, Yun Liu, Fan Liu, Xinyi Wang, Wei Dai, Liang Changan Univ Sch Elect & Control Engn Xian 710064 Peoples R China Changan Univ Sch Informat Engn Xian 710064 Peoples R China

The vehicular cloudlet (VC), capable of leveraging synergies to support emerging cooperative services and task offloading among neighboring vehicles, will be adopted in the Internet of Vehicles (IoV). Compared with traditional clustering methods based on link connectivity, VCs require higher transmission capacity and stability. Three major challenges must be addressed when optimally establishing the VC structure. First, the transmission capacity is affected by inherent stochastic characteristics, including channel fading and interference. Second, the mobility of vehicles introduces instability. Third, a comprehensive optimization model is required to jointly improve the stability and transmission capacity of the established VC. Therefore, this paper proposes a dual cyber-physical network (DCP) model to represent the dynamic physical network and the coupled transmission network. Generalized analytical expressions for channel quality are derived using moment-generating functions based on a Nakagami-m small-scale fading model to handle stochastic characteristics. Furthermore, a DCP association density optimization model is proposed that considers the stability of the physical topology and the transmission capacity of the channel. Symmetric non-negative matrix factorization is used to solve the optimization problem with low complexity. Simulation results confirm that our proposed method achieves higher transmission capacity and stability compared existing link connectivity-based clustering methods.

关键词： Interference Stochastic processes Stability criteria optimization models Symmetric matrices optimization Information processing Vehicle dynamics Indexes Clustering methods Vehicular cloudlet (VC) dual cyber-physical network (DCP) association density

来源：评论

学校读者我要写书评

暂无评论

Multi-Microgrid System optimization Addressing the Uncertainties in Generation and Load Demand

引用

IEEE ACCESS 2025年 13卷 62165-62178页

作者： Abu-El-Haija, Lena E'Layan, Basel Refai, Yousef Alsafadi, Mohamed Kenan Abdel-Rahman, Mohammad J. Abu-El-Haija, Ahmad I. German Jordanian Univ Ind Engn Dept Amman 11181 Jordan Housing Bank Amman 11118 Jordan BI Consulting Riyadh 13217 Saudi Arabia Potenza Chem Ind Amman 11511 Jordan Princess Sumaya Univ Technol Data Sci Dept Amman 11941 Jordan Jordan Univ Sci & Technol Elect Engn Dept Irbid 22110 Jordan

A complete graph network imposes high costs for power routing in a multi-microgrid system when considering a separate connection between every two microgrids. Clustering highly interacting microgrids into subsets yields a more cost-effective substitute for the complete graph assumption. In this paper, we develop a mathematical optimization framework to capture the interactions between multiple microgrids in a multi-microgrid energy management system. The framework aims to achieve optimal profit while meeting the load demand for the system. Each microgrid is subject to various sources of energy generation that entail uncertainty. Moreover, this paper proposes a model that could cluster these microgrids through optimization, in which the microgrids can transact energy within their respective clusters. Our model has yielded savings that could prove to be beneficial in the long run. Also, this project provides a stepping stone into dynamic pricing, which could be implemented to manipulate the consumers' behavior in terms of energy consumption. This body of work has proven to come up with economical, environmentally, and more reliable energy transactions.

关键词： Load modeling Energy management Uncertainty optimization Pricing Costs Microgrids Routing optimization models Generators Multi-microgrid renewable energy sources uncertainty stochastic optimization simulation

来源：评论

学校读者我要写书评

暂无评论

Tree-based machine learning for predicting Neochloris oleoabundans biomass growth and biological nutrient removal from tertiary municipal wastewater

引用

CHEMICAL ENGINEERING RESEARCH & DESIGN 2024年 210卷 614-624页

作者： Razzak, Shaikh Abdur Alam, Md Shafiul Hossain, S. M. Zakir Rahman, Syed Masiur King Fahd Univ Petr & Minerals Dept Chem Engn Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Refining & Adv Chem Dhahran Saudi Arabia Univ Asia Pacific Dept Elect & Elect Engn Dhaka 1205 Bangladesh Univ Bahrain Dept Chem Engn Zallaq Bahrain King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Environm & Marine Studie Dhahran Saudi Arabia

Recently, computational models have been increasingly recognized as valuable tools for addressing key challenges in the operational performance of biological wastewater treatment facilities. In this study, tree-based machine learning approaches, such as decision tree regressor (DTR) and extra tree regressor (ETR), were developed to predict microalgae (Neochloris oleoabundans) biomass growth, culture pH, and nutrient removal efficacy (total nitrogen, TN and total phosphorus, TP) for the first time. The experimental data was obtained through a central composite design (CCD) matrix, and Bayesian optimization was applied to fine-tune the models' hyperparameters. Model performance was evaluated using indicators such as the coefficient of determination (R-2), mean absolute error (MAE), and mean-squared error (MSE). The results showed comparable performance between the DTR and ETR models. For TN removal during testing, the R-2 values for DTR and ETR were 0.9262 and 0.9789, respectively, with DTR (MSE: 0.00895, MAE: 0.0615) and ETR (MSE: 0.00255, MAE: 0.0352) demonstrating reliable predictions. Overall, the ETR model outperformed DTR in predicting responses. The models' generalization capabilities were also assessed by introducing variations in environmental factors.

关键词： Biological nutrient removal (BNR) Microalgae Wastewater treatment Machine learning optimization models

来源：评论

学校读者我要写书评

暂无评论

An Interactive Preference Disaggregation Method for Renewable Energy Project Sorting With Multiple Experts

引用

IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2024年 71卷 15569-15584页

作者： Zhang, Zhiying Liao, Huchang Zhang, Chonghui Zeng, Xiao-Jun Sichuan Normal Univ Sch Engn Chengdu 610101 Peoples R China Sichuan Univ Business Sch Chengdu 610064 Peoples R China Zhejiang Gongshang Univ Coll Stat & Math Hangzhou 310018 Peoples R China Univ Manchester Dept Comp Sci Manchester M13 9PL England

To mitigate power supply and demand imbalances and achieve carbon reduction goals, provinces in China have recently been promoting the development of renewable energy (RE) projects. Multiple criteria analysis is commonly employed for RE project assessment as energy considerations from multiple perspectives are required. To support governments' reserve work on RE projects and facilitate their smooth development, this study introduces an interactive preference disaggregation approach for RE project sorting. The sorting model accounts for the influence of different experts. To resolve contradictory preferences among experts, mathematical programming models that can ensure the reliability of the decision-making results are constructed for conflicting analysis and adjustment strategy recommendations. The proposed approach achieves a certain form of "cognitive convergence" through iterative processes and finally derives sorting results aligning with the perceptions of all experts. In addition, to promote the high-quality development of RE projects, an optimization model is given to generate improvement strategies for inferior projects based on the sorting results. An illustrative example of solar photovoltaic project sorting is provided to validate the applicability of the proposed approach.

关键词： Sorting Government Decision making Renewable energy sources optimization models Investment Stakeholders Reliability Mathematical programming MCDM Conflicting analysis multiple criteria analysis preference disaggregation project evaluation renewable energy project

来源：评论

学校读者我要写书评

暂无评论

Stochastic EV Charging Dispatch in Unbalanced Three-Phase Networks Based on Interpretable Fuzzy Representation of User Preferences

引用

IEEE TRANSACTIONS ON POWER SYSTEMS 2025年第2期40卷 1623-1635页

作者： Li, Xiangyu Luo, Fengji Zhang, Cuo Dong, Zhao Yang Univ Sydney Sch Civil Engn Sydney NSW 2006 Australia Univ Sydney Sch Elect & Comp Engn Sydney NSW 2006 Australia City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China

Electric Vehicle (EV) charging dispatch can effectivelycoordinate EV charging demand to mitigate power unbalances and bus voltage violations in distribution networks. However, the existing EV charging dispatch models neglect users' fuzzy and uncertain preferences regarding the features of different charging stations and mainly operate in three-phase balanced distribution networks. Hence, a novel EV charging dispatch scheme is proposed to achieve EV charging management in an unbalanced three-phase network with the consideration of users' preferences. This scheme deploys linguistic term sets to capture imprecise preferences in an easily understandable way for ordinary users. A preference-constrained stochastic optimization model is then proposed for day-ahead EV charging dispatch, achieving an optimal three-phase EV charging schedule to enhance PV utilization while considering user preferences. Additionally, a real-time stage EV charging dispatch model is proposed to adapt to changes in charging requests and user preferences during operation. Case studies indicate that the proposed method enables EVs to support the operation of an unbalanced three-phase network and meet users' charging demands and fuzzy preferences.

关键词： Electric vehicle charging Linguistics Distribution networks Load modeling Voltage control Schedules optimization models Electric vehicle charging dispatch unbalanced distribution network photovoltaic fuzzy decision-making

来源：评论

学校读者我要写书评

暂无评论

Adjustment of Energy-Saving Train Operations Based on Synergies of All Trains in the Same Power Supply Area

引用

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第4期26卷 4606-4620页

作者： Jiao, Xiangmeng Zhou, Yonghua Fujita, Hamido Beijing Jiaotong Univ Sch Automat & Intelligence Beijing 100044 Peoples R China Princess Nourah Bint Abdul Rahman Univ Coll Sci Riyadh 11564 Saudi Arabia Univ Hradec Kralove Fac Sci Hradec Kralove 50003 Czech Republic Univ Teknol Malaysia Malaysia Japan Int Inst Technol Kuala Lumpur 54100 Malaysia Iwate Prefectural Univ Reg Res Ctr Takizawa 0200693 Japan

As a major use of electricity, the energy efficiency of urban railways is of great concern. To reduce the operational energy consumption of a metro line, this paper proposes a two-stage optimization model for energy-efficient train operations based on three aspects: train operation strategies at inter-stations, running time allocation within an operating cycle, and utilization of regenerative braking energy of multiple collaborative trains in the same power supply area. The first stage is based on a multi-objective optimization algorithm, which optimizes the position-speed curves at each inter-station to obtain a series of energy-saving running curves and the corresponding Pareto fronts. In the second stage, a single-objective optimization algorithm and parallel computing method are used to solve the integrated optimization model for train operations with the minimum net energy consumption as the objective function, to reduce the net energy consumption in an operating cycle. Finally, numerical experiments are carried out with data on the Beijing Yizhuang line. The results show that the proposed bi-level programming model and solution methods have a good energy-saving effect.

关键词： optimization Energy efficiency Energy consumption optimization models Resource management Trajectory Heuristic algorithms Parallel processing Numerical models Symbols Multi-train coordination energy-saving operation regenerative energy utilization parallel computing comprehensive optimization

来源：评论

学校读者我要写书评

暂无评论

Optimal FNN-Based Energy Management System With High Real-Time Performance and Good Interpretability for Battery in Grid-Connected Microgrid

引用

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年

作者： Liu, Bin Wang, Dan Huang, Jiawei Mao, Chengxiong Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Technol Wuhan 430074 Peoples R China

In this article, a novel energy management system (EMS) is presented for a grid-connected microgrid using the fuzzy neural network (FNN), aiming to minimize the mismatch between renewable power generation and load power demand in the microgrid by controlling the battery charge/discharge power in real time, so as to effectively promote the local consumption of renewable energy. An on-line FNN controller is used to rapidly generate energy management instructions in response to the random variations of the net load power in real time, where the parameters are updated through off-line training periodically. The simulation results show that: 1) the presented FNN-based EMS can get better optimization results compared to each benchmark EMS, achieving an average decrease of 18.0217% on the optimization function value in all tested cases when regarding the best performing one among benchmark EMSs as the comparison object;2) the presented FNN-based EMS has high real-time performance on level of seconds;and 3) the presented FNN-based EMS has good interpretability that all the used parameters in the FNN have interpretable meanings. The experimental results on the testbed match well with the corresponding simulation results, demonstrating the effectiveness and practicability of the presented FNN-based EMS for practical applications.

关键词： Energy management Microgrids Real-time systems Fuzzy neural networks Artificial neural networks Fuzzy control Renewable energy sources Costs optimization models Wind speed Fuzzy neural network (FNN) high real-time performance interpretability microgrid optimal energy management

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：