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Multi-obje optimization Strategy for Distributed Energy Resources in Distribution networks Based on a Two-Stage Model

Multi-obje Optimization Strategy for Distributed Energy Reso...

引用

Robotics, Automation and Intelligent control (ICRAIC), International conference on

作者： Zhichao Wang Xianbo Sun Yiluo Ke College of Intelligent Systems Science and Engineering Hubei Minzu University Enshi China

With the rapid development of new energy, a large number of distributed generation (DG) are connected to the distribution network. As the number of DGs connected to the grid increases, it will exacerbate voltage fluctuations and instability in the power grid, which will directly lead to network losses and increased operating costs. To effectively reduce the operational costs for the power supply company and optimize the network’s losses, a two- stage model-based day-ahead optimization scheduling approach has been proposed. The first stage is the optimization scheduling stage of DG, which determines the unit combination of DG based on market electricity prices, DG operating costs, and the price of signing interruptible load (IL) contracts, as well as the purchase of electricity from the large power grid and IL reduction. The second stage focuses on reactive power optimization. In this stage, considering the reactive power characteristics of DG, the outputs of DG units and reactive power compensation devices are jointly optimized to ensure DG outputs remain within specified ranges while reducing distribution network losses. Simulation results conducted on a modified IEEE 33- node system demonstrate that the proposed two-stage day- ahead optimization scheduling model has reduced the network loss rate by 45%, further contributing to the reduction of operational costs for the power supply company.

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Distributed Drive Electric Bus Torque Allocation control Strategy Analysis with MOEA/D 3

Distributed Drive Electric Bus Torque Allocation Control Str...

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3rd International conference on Artificial Intelligence and Electromechanical Automation, AIEA 2022

作者： Su, Liang Jiang, Jin Chen, Chao Beijing Institute of Technology Beijing100081 China Xiamen King Long United Automotive Industry Co. Ltd. Xiamen361023 China

ISBN: (纸本)9781510657281

In this paper, the power performance, economical performance, handling stability and driving safety are regarded as major control targets of Distributed Drive Electric Bus (DDEB). According to vehicle driving information, the weight coefficients of multiple control targets are calculated by neural network. Furthermore, multi-objective optimization algorithm is carried out to determine the driving torque of each wheel with Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D). The slalom test results demonstrate that the torque allocation control strategy can satisfy the demand of longitudinal driving force and direct yaw moment as far as possible, and improve the handling stability of test bus effectively. © 2022 SPIE.

关键词： Torque

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Research on Power Flow optimization of New Energy AC-DC Hybrid Distribution network Based on NSGA-II Algorithm

Research on Power Flow Optimization of New Energy AC-DC Hybr...

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New Energy and Power Engineering (ICNEPE), International conference on

作者： Kun Jing Jian Wang Zifeng Li Ziwei Li School of Electrical Engineering Shenyang University of Technology Sheng yang China

In the present era, the integration of renewable energy into the power grid, along with the utilization of large-scale power electronic converters, has made power flow calculations more challenging. To ensure accurate power flow distribution and enhance the economic and reliable operation of the power system, this study focuses on optimizing the power flow in AC-DC hybrid distribution networks. Firstly, the power flow of an AC-DC hybrid distribution network is modeled. Then, the NSGA-II algorithm is employed to achieve multiple optimization objectives, including the coordinated control of active power network loss and voltage offset in the AC-DC hybrid distribution network. Finally, an example is presented to validate the proposed method.

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Flutter Suppression of Wind Turbine Blade Based on RBF Neural network Compensation Backstepping control 3

Flutter Suppression of Wind Turbine Blade Based on RBF Neura...

引用

3rd International conference on Modeling, Simulation, optimization and Algorithm, ICMSOA 2021

作者： Sun, Changle Wang, Zhiyuan Wang, Yingbo Sun, Haipeng Liu, Tingrui College of Mechanical and Electronic Engineering Shandong University of Science and Technology Qingdao266590 China

Aiming at the critical flutter problem of wind turbine blades, which is between classical flutter and stall flutter, a flutter suppression scheme based on radial basis function (RBF) neural network friction compensation backstepping is presented. The structure model is based on the typical 2D section of bending and twist model of spring-mass-damper, and the rotor variable exciter second-order model with friction disturbance is incorporated to control the rotor variable blade. A modified quasi - steady - state aerodynamic model was used for aerodynamics actuation. RBF compensation backstepping control scheme is a block-controlled backstepping controller designed based on the stability theorem of Lyapunov function, which approximates the frictional interference with nonlinear characteristics through RBF network, and cancels the friction existing in the actuator of variable rotor. Four wind speed environments were selected to analyze the response of blades under different wind speeds, and the flutter suppression effects under two wind speeds were selected to verify the ability of RBF network to approach the nonlinear function. The results show that the RBF backstepping control scheme can improve the robustness to suppress the critical flutter problem of wind turbine blades. © Published under licence by IOP Publishing Ltd.

关键词： Turbomachine blades

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Research and Practice of Algorithm Platform in Logistics Distribution network optimization

Research and Practice of Algorithm Platform in Logistics Dis...

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Data Analytics, Computing and Artificial Intelligence (ICDACAI), International conference on

作者： He Yu Changchun College of Electronic Technology Changchun China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

Logistics industry as the main distribution mode of online shopping, then the efficiency of logistics distribution will become a very important content for consumers, and will also become the optimization direction of logistics distribution network. Combined with many previous studies, this paper believes that the design of distribution route will become the main way to improve distribution efficiency, and studies the algorithm platform and its potential, and proposes to use an improved ant colony algorithm to design a more efficient distribution route, so as to realize the optimization of logistics distribution network. In addition, this paper also verified the method through a comparative experiment. The two sides of the comparison experiment were the distribution route based on the method in this paper as the experimental group and the conventional distribution route as the control group. In the comparison of delivery time, the average delivery time of the experimental group was 9.69h, and the average delivery time of the control group was 14.01h. The average delivery time of the experimental group based on the proposed method is nearly 5h less than that of the control group, which fully indicates that the proposed method can improve the efficiency of logistics distribution and achieve optimization.

关键词： Industries Data analysis Distribution networks Internet Electronic commerce Artificial intelligence optimization Logistics

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A voltage control method for isolated grid based on long short-term memory neural network optimized by improved particle swarm optimization algorithm

A voltage control method for isolated grid based on long sho...

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Intelligent Power and Systems (ICIPS), International conference on

作者： Xiangqiang He Mingshun Liu Qingxin Pu Chengxiang Li Dongxu Chang Power Dispatching Control Center of Guizhou Power Grid Co. Ltd Guizhou China State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou China

With the access of numerous distributed generations such as wind turbines, photovoltaic generators, and energy storage devices, the composition and operation mode of the power grid are becoming increasingly complex, and more uncertain factors affect the recovery of the isolated grid voltage. Aiming at the problem of optimal control of isolated grid voltage under uncertain factors, a voltage control method for isolated grid is proposed, based on long short-term memory neural network optimized by improved particle swarm optimization algorithm. Firstly, the relationship between the gain of the distributed power controller and the performance of the isolated grid controller is analyzed. Then, the particle swarm optimization algorithm is utilized to optimize the hyper-parameters such as the learning rate and neuron number of the long short-term memory neural network, so as to prevent the hyper-parameters from selecting training effect affecting network improperly. Finally, the optimized long short-term memory neural network is mapped to the relationship between the voltage deviation of the distributed generation and the controller gain, and a voltage optimization control model for the isolated grid is constructed. The simulation results show that the voltage of the distributed generation is stabilized at 1 after 0.92 s, which indicates that the proposed optimal control method improves the voltage level of the isolated grid, verifying the effectiveness of the method.

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Research on fault reconstruction algorithm of electric shipboard power system 3

Research on fault reconstruction algorithm of electric shipb...

引用

3rd International conference on Intelligent control, Measurement and Signal Processing and Intelligent Oil Field, ICMSP 2021

作者： Zhang, Xinyue Xiao, Jianmei Wang, Xihuai School of Logistics Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9781665437158

In order to better analyse and solve the problem of network fault reconstruction of China's naval power system, this paper establishes a network fault reconstruction model of ship power system with load loss and switching operation times as multi-objective optimization, and also proposes a new improved grey wolf optimization algorithm to solve it. The basic multi-objective grey wolf optimisation algorithm is improved by using Tent chaos initialisation, grey wolf individual discretization, adjustment of convergence factors. The algorithm can effectively enhance its convergence rate and speed after some improvements, and can overcome the problems of slow convergence speed, poor diversity of initialization population and easy to fall into local optimality in the traditional grey wolf optimisation algorithm. The example of network fault reconfiguration of shipboard power system shows that this method can obtain better system reconfiguration scheme, and has better optimal performance, which can better ensure the safe and stable operation of shipboard. © 2021 IEEE.

关键词： Multiobjective optimization

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Distributed Generation Planning for Improving Self-healing Ability of Distribution network

Distributed Generation Planning for Improving Self-healing A...

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Energy Engineering and Power Systems (EEPS), International conference on

作者： Haobin Li Huijuan Tan Yueting Lin Zhiwei Chen Ziheng Pan Bo Hu Electric Power Dispatching and Control Center Guangdong Power Grid Co. Ltd. Guangzhou China State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing China

ISBN: (数字)9798350375138

ISBN: (纸本)9798350375145

With a large number of Distributed Generation (DG) connected to the distribution network, the distribution network can be reconstructed and isolated island partition for self-healing. In this paper, the two-layer configuration DG operation-planning joint optimization model for improving self-healing ability of distribution network is established to achieve economy and self-healing ability of the system. IEEE 33-node system is used to verify the proposed model. The results show that the two-layer model can effectively coordinate the economy and self-healing ability of distribution network through interactive decision-making of DG allocation scheme.

关键词： optimization models Decision making Distribution networks Planning Distributed power generation Power systems Resource management optimization

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Energy Management in Decentralized network Microgrid Using Smart Contract Fostering a Seamless and Secure Energy Trading Ecosystem

Energy Management in Decentralized Network Microgrid Using S...

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Smart Technologies for Power, Energy and control (STPEC), IEEE International conference on

作者： Abdullah Umar Deepak Kumar Tirthadip Ghose Department of Electrical & Electronics Engineering Birla Institute of Technology Mesra Ranchi India

The need for effective and secure energy management systems has arisen as a result of increased demand for energy, the increasing use of renewable energy sources (RES), and the introduction of decentralized power generation. Here, we explore the urgent issues and exciting possibilities posed by traditional centralized energy networks, with a special focus on three crucial dimensions: achieving supply-demand equilibrium, seamlessly integrating renewable energy sources, and maintaining participant trust and transparency. This paper suggests leveraging blockchain technology (BT) to create a decentralized network grid energy management system to address these issues. Individual participants can directly exchange energy with one another within the Decentralized network Grid through peer-to-peer (P2P) trading. The network microgrid model’s mathematical optimization is designed to balance supply and demand, improve network microgrid stability, and optimize energy distribution. The aforementioned optimization uses information from grid constraints, usage patterns, and energy pricing to make wise decisions in real time. The concept aims to achieve energy efficiency, cost savings, and seamless integration of renewable energy sources by utilizing the capabilities of blockchain smart contract and proposed optimization algorithm.

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A Logical Architecture of Space Sky Integrated network Based on SDN controller and Low Earth Satellite Constellation network control Technology

A Logical Architecture of Space Sky Integrated Network Based...

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Communication Technology and Information Technology (ICCTIT), International conference on

作者： Jiawei Wu Yunjie Xiao Yunxia Su Information & Communication Company SMEPC Shanghai China

The integrated network logic architecture of space and space based on SDN controller and low orbit satellite constellation network control technology integrates software defined network (SDN) and low orbit satellite constellation network to achieve seamless communication and unified management on a global scale. This architecture leverages the centralized management and programming capabilities of SDN controllers, while also utilizing the global coverage and high bandwidth transmission characteristics of low orbit satellite constellation networks, to achieve close connectivity and collaborative work between space, air, and ground networks. Under this architecture, the SDN controller becomes the main node of various network devices, and achieves efficient management and optimization of the entire air space integrated network by guiding and coordinating network traffic routing, resource allocation, security policies, and other functions.

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