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Review on Optimization of Forecasting and Coordination Strategies for Electric Vehicle Charging

Journal of Modern Power systems and Clean Energy 2023年第2期11卷 389-400页

作者： Zixuan Jia Jianing Li Xiao-Ping Zhang Ray Zhang Department of Electronic Electrical and Systems EngineeringSchool of EngineeringUniversity of BirminghamBirminghamUnited Kingdom

The rapid development of electric vehicles(EVs)has benefited from the fact that more and more countries or regions have begun to attach importance to clean energy and environmental *** paper focuses on the optimization of EV charging,which cannot be ignored in the rapid development of *** increase in the penetration of EVs will generate new electrical loads during the charging process,which will bring new challenges to local power ***,the uncoordinated charging of EVs may increase the peak-to-valley difference in the load,aggravate harmonic distortions,and affect auxiliary *** stabilize the operations of power grids,many studies have been carried out to optimize EV *** paper reviews these studies from two aspects:EV charging forecasting and coordinated EV charging *** analyses are carried out to identify the advantages and disadvantages of different methods or *** the end of this paper,recommendations are given to address the challenges of EV charging and associated charging strategies.

关键词： Electric vehicle(EV) forecasting aggregator coordination strategy smart charging

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On-Policy and Off-Policy Value Iteration Algorithms for Stochastic Zero-Sum Dynamic Games

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Journal of systems Science & Complexity 2025年第1期38卷 421-435页

作者： GUO Liangyuan WANG Bing-Chang ZHANG Ji-Feng School of Control Science and Engineering Shandong UniversityJinan 250063China School of Automation and Electrical Engineering Zhongyuan University of TechnologyZhengzhou 450007China Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China

This paper considers the value iteration algorithms of stochastic zero-sum linear quadratic games with unkown ***-policy and off-policy learning algorithms are developed to solve the stochastic zero-sum games,where the system dynamics is not *** analyzing the value function iterations,the convergence of the model-based algorithm is *** equivalence of several types of value iteration algorithms is *** effectiveness of model-free algorithms is demonstrated by a numerical example.

关键词： Approximate dynamic programming on-policy off-policy stochastic zero-sum games valueiteration

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Exact Box-Constrained Economic Operating Region for Power Grids Considering Renewable Energy Sources

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Journal of Modern Power systems and Clean Energy 2024年第2期12卷 514-523页

作者： Huating Xu Bin Feng Chutong Wang Chuangxin Guo Jian Qiu Mingyang Sun College of Electrical Engineering Zhejiang UniversityHangzhou 310027China Department of Control Science and Engineering Zhejiang UniversityHangzhou 310027China

The growing integration of renewable energy sources manifests as an effective strategy for reducing carbon emissions. This paper strives to efficiently approximate the set of optimal scheduling plans(OSPs) to enhance the performance of the steady-state adaptive cruise method(SACM) of power grid, improving the ability of dealing with operational uncertainties. Initially, we provide a mathematical definition of the exact boxconstrained economic operating region(EBC-EOR) for the power grid and its dispatchable components. Following this, we introduce an EBC-EOR formulation algorithm and the corresponding bi-level optimization models designed to explore the economic operating boundaries. In addition, we propose an enhanced big-M method to expedite the computation of the EBCEOR. Finally, the effectiveness of the EBC-EOR formulation, its economic attributes, correlation with the scheduling plan underpinned by model predictive control, and the significant improvement in computational efficiency(over twelvefold) are verified through case studies conducted on two test systems..

关键词： Exact box-constrained economic operating region(EBC-EOR) big-M method intelligent scheduling steady-state adaptive cruise uncertainty renewable energy source

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Storing Freshwater Versus Storing Electricity in Power systems with High Freshwater Electric Demand

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Journal of Modern Power systems and Clean Energy 2024年第2期12卷 323-333页

作者： Mubarak J.Al-Mubarak Antonio J.Conejo Department of Electrical and Computer Engineering The Ohio State UniversityColumbusOHUSA Department of Electrical Engineering Kuwait UniversityP.O.Box 5969Safat 13060Kuwait Department of Integrated Systems Engineering and the Department of Electrical and Computer Engineering The Ohio State UniversityColumbusOH 43210USA IEEE

We consider a power system whose electric demand pertaining to freshwater production is high(high freshwater electric demand),as in the Middle East,and investigate the tradeoff of storing freshwater in tanks versus storing electricity in batteries at the day-ahead operation *** storing freshwater and storing electricity increase the actual electric demand at valley hours and decrease it at peak hours,which is generally beneficial in term of cost and ***,to what extent?We analyze this question considering three power systems with different generation-mix configurations,i.e.,a thermal-dominated mix,a renewable-dominated one,and a fully renewable *** generation-mix configurations are inspired by how power systems may evolve in different countries in the Middle *** production uncertainty is compactly modeled using chance *** draw conclusions on how both storage facilities(freshwater and electricity)complement each other to render an optimal operation of the power system.

关键词： Power system operation freshwater production and transportation coordination day-ahead scheduling

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Matching Synchronous Machine control for Improving Active Support of Grid-forming PV systems with Enhanced DC Voltage Dynamics

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Journal of Modern Power systems and Clean Energy 2025年第1期13卷 179-189页

作者： Zizhen Guo Wenchuan Wu State Key Laboratory of Power Systems Department of Electrical EngineeringTsinghua UniversityBeijing 100084China

With photovoltaic(PV)sources becoming more prevalent in the energy generation mix,transitioning grid-connected PV systems from grid-following(GFL)mode to gridforming(GFM)mode becomes essential for offering self-synchronization and active support *** numerous GFM methods have been proposed,the potential of DC voltage control malfunction during the provision of the primary and inertia support in a GFM PV system remains insufficiently *** fill the gap,some main GFM methods have been integrated into PV systems featuring detailed DC source *** conduct a comparative analysis of their performance in active support and DC voltage *** GFM methods such as virtual synchronous machine(VSM)face a significant risk of DC voltage failure in situations like alterations in solar radiation,leading to PV system tripping and jeopardizing local system *** the case of DC GFM methods such as matching control(MC),the active support falls short due to the absence of an accurate and dispatchable droop *** address the issue,a matching synchronous machine(MSM)control method is developed to provide dispatchable active support and enhance the DC voltage dynamics by integrating the MC and VSM control *** active support capability of the PV systems with the proposed method is quantified analytically and verified by numerical simulations and field tests.

关键词： Active support DC voltage dynamics grid-forming(GFM) matching control photovoltaic(PV) virtual synchronous machine

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Economically optimized heat exchanger design: a synergistic approach using differential evolution and equilibrium optimizer within an evolutionary algorithm framework

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Neural Computing and Applications 2024年第24期36卷 14999-15026页

作者： Moharam, Amal Haikal, Amira Y. Elhosseini, Mostafa Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

This study introduces the CP-EODE algorithm, a novel hybrid of the Equilibrium Optimizer (EO), and the Differential Evolution (DE) algorithm. It addresses EO’s tendency toward premature convergence by enhancing its exploration capabilities. The motivation for this research stems from the need for more efficient and economically viable designs in engineering, particularly in the optimization of shell-and-tube heat exchangers (STHEs) and photovoltaic module parameters. The CP-EODE algorithm leverages DE's exploration strengths to enhance EO’s performance adopting a cooperative parallel approach that divides the population into EO and DE sub-populations for a more effective search space exploration. The validation of CP-EODE’s effectiveness commenced with its application to 29 benchmark functions, where a Friedman test yielded an Fr value of 22.19, significantly surpassing the critical value of 7.81, and a Wilcoxon signed rank test confirmed statistical improvements with a z-score beyond − 1.96. Four quality metrics were discussed to provide a detailed assessment of CP-EODE’s performance highlighting its robust exploration and exploitation capabilities. In practical applications, CP-EODE demonstrated significant advancements in STHE design achieving up to 19.706% reduction in heat exchanger surface area compared to traditional and other algorithmic designs, showcasing its potential to enhance engineering design efficiency and cost-effectiveness. A critical examination of the algorithm's convergence rate revealed its swift optimization capability, with rapid attainment of optimal solutions in various test cases, further establishing CP-EODE’s advantage in handling complex optimization challenges. Through rigorous statistical analysis and performance metrics, CP-EODE emerges as a superior solution for complex optimization challenges, underscoring the value of hybrid algorithmic strategies in engineering optimizations. © The Author(s), under exclusive licence to Springer-

关键词： Cost effectiveness

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Resilence control of DC Microgrid Integrated with Multi-Nanogrids Considering Connected-Islanded States

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CSEE Journal of Power and Energy systems 2024年第2期10卷 786-796页

作者： Hossien Faraji Reza Hemmati Pierluigi Siano Department of Electrical Engineering Amirkabir University of TechnologyTehranIran Department of Electrical Engineering Kermanshah University of TechnologyKermanshahIran Department of Management&Innovation Systems University of Salernoand also with the Department of Electrical and Electronic Engineering ScienceUniversity of JohannesburgJohannesburg 2006South Africa

In this paper, a DC microgrid (DCMG) integrated with a set of nano-grids (NG) is studied. DCMG exchanges predetermined active and reactive power with the upstream network. DCMG and NGs are coordinately controlled and managed in such a way the exchanged P-Q power with external grid are kept on scheduled level following all events and operating conditions. The proposed control system, in addition to the ability of mutual support between DCMG and NGs, makes NGs support each other in critical situations. On the other hand, in all operating conditions, DCMG not only feeds three-phase loads with time-varying active and reactive power on the grid side but also injects constant active power into the grid. During events, NGs support each other, NGs support DCMG, and DCMG supports NGs. Such control strategies are realized by the proposed control method to increase resilience of the system. For these purposes, all resources and loads in DCMG and NGs are equipped with individual controllers. Then, a central control unit analyzes, monitors, and regularizes performance of individual controllers in DCMG and NGs. Nonlinear simulations show the proposed model can effectively control DCMG and NGs under normal and critical conditions.

关键词： control system DC microgrid nanogrid resilience

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ELCD-NSC2: a novel early lung cancer detection and non-small cell classification framework

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Neural Computing and Applications 2024年第24期36卷 15149-15164页

作者： Helaly, Hadeer A. Badawy, Mahmoud El-Gendy, Eman M. Haikal, Amira Y. Electrical Engineering Department Faculty of Engineering Damietta University Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Computer Science and Informatics Taibah University Medina Saudi Arabia

The survival rate of lung cancer relies significantly on how far the disease has spread when it is detected, how it reacts to the treatment, the patient’s overall health, and other factors. Therefore, the earlier the lung cancer diagnosis, the higher the survival rate. For radiologists, recognizing malignant lung nodules from computed tomography (CT) scans is a challenging and time-consuming process. As a result, computer-aided diagnosis (CAD) systems have been suggested to alleviate these burdens. Deep-learning approaches have demonstrated remarkable results in recent years, surpassing traditional methods in different fields. Researchers are currently experimenting with several deep-learning strategies to increase the effectiveness of CAD systems in lung cancer detection with CT. This work proposes a deep-learning framework for detecting and diagnosing lung cancer. The proposed framework used recent deep-learning techniques in all its layers. The autoencoder technique structure is tuned and used in the preprocessing stage to denoise and reconstruct the medical lung cancer dataset. Besides, it depends on the transfer learning pre-trained models to make multi-classification among different lung cancer cases such as benign, adenocarcinoma, and squamous cell carcinoma. The proposed model provides high performance while recognizing and differentiating between two types of datasets, including biopsy and CT scans. The Cancer Imaging Archive and Kaggle datasets are utilized to train and test the proposed model. The empirical results show that the proposed framework performs well according to various performance metrics. According to accuracy, precision, recall, F1-score, and AUC metrics, it achieves 99.60, 99.61, 99.62, 99.70, and 99.75%, respectively. Also, it depicts 0.0028, 0.0026, and 0.0507 in mean absolute error, mean squared error, and root mean square error metrics. Furthermore, it helps physicians effectively diagnose lung cancer in its early stages and allows spe

关键词： Image classification

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electrical tree degradation of MgO/epoxy resin composites at different voltage frequencies

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High Voltage 2024年第3期9卷 581-590页

作者： Yunxiao Zhang Wenxin Lin Yuanxiang Zhou Weiwei Xing Jiayu Cheng Chenyuan Teng College of Electrical Engineering and Automation Fuzhou UniversityFujian Key Laboratory of New Energy Generation and Power ConversionFuzhouChina State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical EngineeringTsinghua UniversityBeijingChina

electrical tree degradation is one of the main causes of insulation failure in high-frequency *** tree degradation is studied on pure epoxy resin(EP)and MgO/EP composites at frequencies ranging from 50 Hz to 130 *** results show that the tree initiation voltage of EP decreases,while the growth rate and the expansion coefficient increase with ***,the bubble phenomenon at high frequencies in EP composites is *** with trap distribution character-istics within the material,the intrinsic mechanism of epoxy composites to inhibit the growth of the electrical tree at different frequencies is *** can be concluded that more deep traps and blocking effect are introduced by doping nano-MgO into EP bulks,which can improve the electrical tree resistance performance of EP composites in a wide frequency range.

关键词： composites tree electrical

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Real-time drone detection framework based on advanced texture feature extraction and pattern recognition model using GUI

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Neural Computing and Applications 2025年第5期37卷 3435-3454页

作者： Hussen, Noha Salem, Mofreh Eldesouky, Ali I. Sakr, Noha Elghamrawy, Sally Computers and Control Systems Engineering Department Mansoura University Mansoura Egypt Computer Engineering Department Misr Higher Institute for Engineering and Technology Mansoura Egypt

The increasing prevalence of drones has raised significant concerns regarding their potential for misuse in activities such as smuggling, terrorism, and unauthorized access to restricted airspace. Consequently, the development of robust and efficient drone detection systems has become paramount. Traditional detection methods such as radar and acoustic sensors have limitations in detecting small drones and can be costly to implement. This can lead to lower detection rates and increased security breaches and safety concerns. However, prevailing methodologies often falter in achieving real-time detection with requisite precision and efficacy, particularly within complex operational environments. Motivated by the imperative to surmount these challenges, our research endeavors to introduce a pioneering real-time drone detection framework. Our study encapsulates several seminal advancements. Firstly, we proffer a groundbreaking framework that synergizes advanced integration technique based on texture feature extraction and pattern recognition techniques for real-time drone detection to increase accuracy to detect drones in different conditions such as bad weather and low resolution. Secondly, we introduce an intuitive graphical user interface, enhancing the usability and accessibility of the system in real-time scenario. Lastly, through exhaustive evaluation and comparative analysis, we substantiate the superior performance of our framework in terms of accuracy, precision, and real-time detection capabilities compared to conventional DDS methodologies. Its stability and effectiveness render it a compelling solution for security-focused entities, notably those within air force and military systems. Our experimental results reveal a commendable accuracy rate of 97%, affirming the reliability and precision 98% and recall parameter 98% of our framework in accurately identifying and detecting drones, thus surpassing recent models in the field. © The Author(s), under exclusive

关键词： Graphical user interfaces

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