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相关概念视频

Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

184
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
184
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

741
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
741
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

296
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
296
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

348
Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk​, phase angle δk​, real power Pk​, and reactive power Qk​. Two of these four variables are inputs, while the...
348
Distributed Loads01:19

Distributed Loads

624
Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
624
Continuous Charge Distributions01:17

Continuous Charge Distributions

7.3K
Imagine a bucket of water. It contains many molecules, of the order of 1026 molecules. Thus, although it contains discrete elements (molecules) at the microscopic level, macroscopically, it can be considered continuous. Small volume elements of water, infinitesimal compared to the bulk of the bucket's volume, still contain many molecules. Under this framework, quantized matter is approximated as continuous for practical purposes.
The electric charge can also be subjected to an analogical...
7.3K

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相关实验视频

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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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电动汽车充电站分配基于负载配置预测和Dijkstra的算法,以实现最佳路径规划.

Sahbi Boubaker1, Sameer Al-Dahidi2, Souad Kamel3

  • 1Department of Computer and Network Engineering, College of Computer Science and Engineering, University of Jeddah, 21959, Jeddah, Saudi Arabia. sboubaker@uj.edu.sa.

Scientific reports
|July 3, 2025
PubMed
概括

电动汽车 (EV) 充电是优化使用一个新的框架,预测需求和计划路线. 无人机提供实时数据,减少电动汽车等待时间和前往充电站 (CS) 的距离.

关键词:
充电站分配的充电站分配狄克斯特拉的算法是什么?电动汽车 电动汽车是什么负载概况预测的预测非线性自回归与外源输入.最佳路径规划的最佳路径规划

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科学领域:

  • 电气工程 电气工程
  • 计算机科学 计算机科学
  • 运输系统 运输系统

背景情况:

  • 广泛采用电动汽车 (EV) 给充电站 (CS) 带来了挑战,因为充电站的可用性有限和需求变化.
  • 有效地将电动汽车分配给最佳的CS,对于管理充电基础设施至关重要.

研究的目的:

  • 为优化电动汽车充电站分配提出一个综合框架.
  • 根据实时和预测数据,加强决策,引导电动汽车到最合适的CS.

主要方法:

  • 使用一个非线性自动回归与外源输入 (NARX) 模型来预测CSs.的未来负载配置文件.
  • 应用了Dijkstra的最短路径计算算法来确定从EV到CS的最佳路线.
  • 集成的无人机辅助边缘计算,用于实时交换关于插槽可用性和当地条件的数据.

主要成果:

  • 在CS真实数据预测中,NARX模型实现了90%的相关系数.
  • 迪克斯特拉的算法有效地优化了EV路由到最近的充电站.
  • 模拟显示,电动汽车分配效率显著提高,等待时间减少,旅行距离缩短.

结论:

  • 拟议的框架有效地解决了电动汽车充电站分配的挑战.
  • 无人机辅助边缘计算和预测建模改善了充电基础设施管理.
  • 建议对无人机部署的监管和后勤方面的进一步研究.