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Tie line power allocation model based on day ahead scheduling

Tie line power allocation model based on day ahead schedulin...

引用

Energy, Power and Grid (ICEPG), International Conference on

作者： Xiangzhong Xie Rui Yang Guoliang Zhang Yixing Chen Yinguo Yang Yun Yang Xuchen Tang Guangdong Power Grid Co. Ltd. Power Dispatch Control Center Wuhan Hubei China Beiiing Tsintergy Technology Co..Ltd Guangzhou Guangdong China Guangdong Power Grid Co. Ltd. Power Dispatch Control Center Maoming Guangdong China Guangdong Power Grid Co. Ltd. Power Dispatch Control Center Xiaogan Hubei China Guangdong Power Grid Co. Ltd. Power Dispatch Control Center Guangzhou Guangdong China Guangdong Power Grid Co. Ltd. Power Dispatch Control Center Changchun Jilin China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

In the electricity market, the interconnection line serves as a key link connecting different power grid regions, and the optimization of its planned allocation is an important link to ensure the safe, economical, and efficient operation of the power system. For the tie line plan allocation problem, this paper proposes a tie line power allocation model based on the day ahead scheduling plan. through algorithm optimization and reference to historical values, the tie line power allocation is carried out on the basis of meeting the constraints related to clearing in the province, which to a certain extent reduces the problem that the formal link of planning causes the related operating constraints to exceed the limit. the model can also adapt to the changes in grid operating conditions, and dynamically adjust the tie line power allocation. the effectiveness and applicability of the model proposed in this paper are verified through numerical example analysis.

关键词： Adaptation models Heuristic algorithms Market research Power grids Dispatching Numerical models planning Resource management Security Optimization

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Research on shortest path planning for unmanned aerial vehicles based on genetic algorithm

Research on shortest path planning for unmanned aerial vehic...

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IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Bohan Chen Jinshan Huang School of Mechanical and Electrical Engineering Guangzhou University Guangzhou China School of Electronics and Communications Engineering Guangzhou University Guangzhou China

ISBN: (数字)9798350389418

ISBN: (纸本)9798350389425

A genetic algorithm based on roulette selection method and integer crossover operator is proposed for the UAV path planning problem. First, this paper introduces the application background and historical origin of the path planning problem. Subsequently, this paper elaborates the mathematical model of the UAV path planning problem and illustrates the UAV path planning problem with graphical examples. the algorithm introduces a roulette selection operator and an integer crossover operator to ensure that the algorithm explores more solution space during the search process, thus increasing the chance of finding a globally optimal solution. the variation operator adopts the strategy of local perturbation and retaining excellent individuals, which makes it possible for the algorithm to break through the local extremes towards a globally better evolution. In the simulation experiments, the path planning problems of 30 cities, 51 cities and 76 cities are solved respectively, and their trajectory graphs and iteration curves are obtained. the experimental results show that the algorithm has good performance in solving the UAV path planning problem.

关键词： Perturbation methods Urban areas Autonomous aerial vehicles Path planning Mathematical models Space exploration Trajectory Safety Genetic algorithms Testing

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Research on Multi-source Heterogeneous Data Preprocessing Based on Improved Crow Search Algorithm

Research on Multi-source Heterogeneous Data Preprocessing Ba...

引用

Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Shuai Yuan Xiaolu Pei Shulin Guo Yu Long Ke Li Beijing CLP Feihua Communication Co. Ltd Beijing China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

the integrity of multi-source heterogeneous data in vehicle-grid interaction is crucial to the security of the vehicle-grid system. Aiming at the problem that the missing of multi-source heterogeneous data can easily lead to inaccurate analysis results, which in turn affects the charging scheduling of electric vehicles and the load management of the power grid, this paper proposes a multi-source heterogeneous data filling algorithm based on the improved crow search algorithm. First, based on the characteristics of multi-source heterogeneous data in vehicle-grid interaction, a data filling model is constructed to clarify the correspondence between parameters and data filling models. Secondly, in view of the problem of insufficient search accuracy of the crow search algorithm, an optimal solution solidification mechanism and an adaptive step size strategy are proposed to improve the algorithm. then, the improved crow search algorithm is used to find the optimal solution for the data filling model. Finally, the filling model optimized by the algorithm is used to construct a heterogeneous data set. Taking accuracy as the performance index, the proposed algorithm is compared with the unimproved crow search algorithm and whale optimization algorithm, and the results show that the data accuracy of the proposed algorithm is increased by more than 20%, which effectively improves the data integrity.

关键词： Adaptation models Accuracy Stability criteria Search problems Filling Data models Whale optimization algorithms scheduling Security Load modeling

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Site planning for Mobile Communication Networks Based on Neighborhood Finding and Depth Search algorithms

Site Planning for Mobile Communication Networks Based on Nei...

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IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Xiaoke Zhu Jia Hao Ruipeng Guo Runhan Xiao College of Intelligent Equipment Shandong University of Science and Technology Tai'an China College of Hydraulic Science and Engineering Taiyuan University of Technology Taiyuan China College of Science Qiqihar University Qiqihar China College of Engineering Shantou University Shantou China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

Mobile communication network site planning is one of the important links in the construction of mobile communication system, dense base station construction will increase the cost of station building exponentially, how to balance the cost and coverage, accurate positioning of the weak coverage area has become a research hotspot of mobile communication network site planning. Focusing on this problem, this paper firstly establishes a mathematical model of mobile communication network site planning with 90% coverage rate as a constraint, and minimizes the station construction cost as an objective, and designs the nearest neighbor point finding method to obtain the base station planning scheme; Based on this, DFS clustering partitioning is performed on the remaining coverage points, and the weak coverage area is determined; Taking the actual mobile communication network data of a certain region as an example, the experimental results show that the method effectively reduces the coverage rate in the case of 90%. the experimental results show that this method effectively reduces the cost of station construction in the case of 90% coverage, and accurately locates the weak coverage area, which can effectively assist in mobile communication network site planning.

关键词： Base stations Solid modeling Costs Clustering algorithms Mobile communication Search problems Mathematical models planning Time complexity Optimization

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Research on Multi-Objective Coordinated scheduling Optimization of Microgrids Considering Uncertainty

Research on Multi-Objective Coordinated Scheduling Optimizat...

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International Conference on Power and Energy Technology (ICPET)

作者： Huihui Xu Jing Peng Yunfei Tian Yuyang Zhao Yi Chai Jing Li Ziyun Cheng Rong Chen Economic And Technological Research Institute of State Grid Gansu Electric Power Company State Grid Gansu Electric Power Company Gansu China State Grid Pingliang Electric Power Supply Company State Grid Gansu Electric Power Company Gansu China Chengdu Xingtong Dianyan Electric Power Technology Co. Ltd Sichuan China

ISBN: (数字)9798350375855

ISBN: (纸本)9798350375862

To effectively enhance the renewable energy optimal allocation capabilities of microgrids and reduce the impact of new energy uncertainties, this paper proposes a multi-objective coordinated scheduling optimization model for microgrids that considers the uncertainty of new energy sources. Firstly, by integrating the Graph Convolutional Network (Graph Convolutional Network, GUN) with the Gated Recurrent Unit (gated recurrent unit, GRU), a spatiotemporal graph convolutional neural network is established, which leverages the advantages of graph convolution in extracting spatial features and the strengths of gated recurrent units in capturing temporal features, thereby deeply exploring the hidden spatiotemporal dependencies among photovoltaic stations. Secondly, a two-stage rolling scheduling optimization model for microgrids is developed, followed by the application of the Column-and-Constraint Generation (C&CG) algorithm, which decomposes the two-stage optimization model into master and sub-problems for iterative interaction and solution. Finally, the effectiveness and economic benefits of the proposed methods are validated through case study analysis.

关键词： Photovoltaic systems Economics Uncertainty Graph convolutional networks Optimization models Microgrids Feature extraction scheduling Iterative algorithms Spatiotemporal phenomena

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An Optimistic planning Approach for the Aircraft Landing Problem 6th

An Optimistic Planning Approach for the Aircraft Landing Pro...

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6th ENRI International workshop on Air Traffic Management and communication, navigation and surveillance, ATM/CNS EIWAC2019

作者： Ikli, S. Mancel, C. Mongeau, M. Olive, X. Rachelson, E. ENAC Université de Toulouse Toulouse France ONERA DTIS Université de Toulouse Toulouse France ISAE-SUPAERO Université de Toulouse Toulouse France

ISBN: (纸本)9789813346680

the Aircraft Landing Problem consists in sequencing aircraft on the available runways and scheduling their landing times taking into consideration several operational constraints, in order to increase the runway capacity and/or to reduce delays. In this work we propose a new Mixed Integer Programming (MIP) model for sequencing and scheduling aircraft landings on a single or multiple independent runways incorporating safety constraints by means of separation between aircraft at runways threshold. Due to the NP-hardness of the problem, solving directly the MIP model for large realistic instances yields redhibitory computation times. therefore, we introduce a novel heuristic search methodology based on Optimistic planning that significantly improve the FCFS (First-Come First-Served) solution, and provides good-quality solutions in reasonable computational time. the solution approach is then tested on medium and large realistic instances generated from real-world traffic on Paris-Orly airport to show the benefit of our approach. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Heuristic algorithms

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algorithms for Distributing Goals in a Group of Robots

Algorithms for Distributing Goals in a Group of Robots

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International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Aung Zayar Olga Mikhailovna Gerget Nu Nu War thein Toe Aung Tomsk State University Tomsk Russian Federation V.A. Trapeznikov Institute of Management Problems of the Russian Academy of Sciences Moscow Russian Federation University of Co-operative and Management of Sagaing (UCMS) Sagaing Myanmar

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

this article proposes a method for creating a multi-agent adaptive work planning system for a group of robots for joint missions. the goal of the current and future development is to create comprehensive scientific and technical solutions for coordinating the planning and management of the actions of a group of robots. Within the framework of this direction, the article considers the algorithm for distributing goals between the agents of the group. the algorithm is aimed at solving the problem of optimizing the distribution of tasks in order to achieve a common goal with minimal cost and maximum benefit. the problem of collective management of groups of robots in the sequential or parallel solution of subtasks (sub-goals) generated by decomposition of common tasks. Moreover, optimal or similar solutions for these tasks are generated as global tasks for the entire group of robots. the article describes in detail the algorithm and an example of the distribution of tasks in a group of robots.

关键词： Costs Automation Adaptive systems Robot kinematics Control systems Mathematical models Energy efficiency planning Computational complexity Optimization

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Multi-UAV Search Route planning Based on the Improved Genetic Algorithm

Multi-UAV Search Route Planning Based on the Improved Geneti...

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Internet of things, Automation and Artificial Intelligence (IoTAAI), International Conference on

作者： Xinyu Sheng Sichuan University Chengdu China

ISBN: (数字)9798350386974

ISBN: (纸本)9798350386981

In response to the need for target search for multiple UAVs in a specified area, this paper proposes an improved adaptive genetic optimisation algorithm, which incorporates an enhanced greedy search strategy on the basis of the original genetic algorithm in order to improve the search efficiency and target matching accuracy. Based on the pre-acquired information, this study subdivided the search area into multiple grid-like structures, and based on this, combined with the the state update cycle of UAVs and their performance limitations, a mathematical model of cooperative search is constructed. the binary coding form of 0-1 is utilised to associate the UAV's heading control sequence with the probability of the search, and for the continuous detection carried out by the airborne radar in a specific region, this study proposes an improved strategy that incorporates greedy operators and employs greedy mutation with a view to enhancing the accuracy of the search results, and in order to enhance the algorithm's performance of the local search at a later stage, a mutation strategy that can be dynamically adjusted according to the probability of the search is introduced In order to enhance the performance of the algorithm in the later local search, a mutation strategy selection threshold that can be dynamically adjusted according to the change of the search probability is introduced. the simulation experimental data illustrate that the optimised adaptive genetic algorithm has a significant improvement in the overall efficacy, the search efficiency and the robustness.

关键词： Accuracy Heuristic algorithms Search problems Genetics Robustness Mathematical models planning Internet of things Optimization Genetic algorithms

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Proactive Adaptation of Rail Infrastructure to Accelerated Climate Change

Proactive Adaptation of Rail Infrastructure to Accelerated C...

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International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： A.K. Enaleev V.V. Tsyganov V.A. Trapeznikov Institute of Control Sciences of RAS Moscow Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

Accelerating climate change creates deep uncertainty and new challenges for the strategic management of rail infrastructure. A paradigm for managing this infrastructure under conditions of such uncertainty is proposed, based on a proactive approach to solving these problems. Under this paradigm, a strategic vision for the future is created, short-term actions are regulated, and a regulatory framework is created to allocate investments to adapt the rail infrastructure to these changes. A model and an algorithm have been developed to predict and plan the volume of such investments with incomplete statistical data. A model and an algorithm have been developed for the distribution of these investments. the developed models and algorithms were used to manage weather and climate risks of the transport infrastructure of JSC Russian Railways in a changing climate for the period until 2050.

关键词： Climate change Rail transportation Uncertainty Strategic planning Management Statistics Public infrastructure Public transportation Transportation

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Comparison of Artificial Neural Network Technique-Based Failure Predictions of Mechanical Components

Comparison of Artificial Neural Network Technique-Based Fail...

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International Conference on System Reliability and Science (ICSRS)

作者： Basheer Shaheen István Németh Ádám Kocsis Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest Hungary

Failure prediction is considered one of the most critical methods for prognostic health management and maintenance planning and scheduling of manufacturing systems. this article presents a comparative analysis and shows the main characteristics of different artificial neural network approaches used to predict failures in mechanical components. this comparative analysis is based on the use of different network architectures, training algorithms, and prediction results. Also, a brief comparison is conducted between a newly developed artificial neural network-based failure prediction algorithm and the previously developed models confirming that the new algorithm outperforms the others in terms of its ability to deal with complex manufacturing systems, the prediction accuracy, and the early predictions required to improve the maintenance plans and schedules.

关键词： Training Degradation Schedules Job shop scheduling Stochastic processes Artificial neural networks Predictive models

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