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相关概念视频

Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

95
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.
95
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

172
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...
172
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

173
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:
173
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

105
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
105
Multimachine Stability01:25

Multimachine Stability

141
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
141
Power System Distribution01:25

Power System Distribution

226
Power system distribution involves delivering electrical energy from power plants to consumers through a network of transmission and distribution systems. The process begins at power plants, where energy from coal, gas, nuclear, water, and wind is converted into electrical energy. These plants use three-phase generators, typically rated between 50 to 1300 MVA, with terminal voltages ranging from a few kV to 20 kV, depending on the size and age of the units.
The transmission system is designed...
226

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相关实验视频

Updated: Jun 6, 2025

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

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改进了基于PICEA-g的多目标优化调度方法,用于配送网络的大型电动汽车.

Meiyi Huo1,2, Songling Pang3,4, Hailong Zhao1,2

  • 1Electric Power Research Institute of Hainan Power Grid Co., Ltd., Haikou, 570311, China.

Scientific reports
|November 23, 2024
PubMed
概括

大规模的电动汽车 (EV) 充电影响电网稳定性. 本研究提出了一种最佳的调度方法,使用改进的PICEA-g算法来有效管理电动汽车负载,平衡电网需求和用户偏好.

关键词:
分布网络的分销网络.灵活的负载是灵活的.改善了PICEA-g的使用情况.大型电动汽车的大型电动汽车优化调度的时间表.

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Last Updated: Jun 6, 2025

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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
  • 优化算法 优化算法

背景情况:

  • 大规模的电动汽车 (EV) 整合给配电网的安全性和经济运营带来了挑战.
  • 管理电动汽车充电作为灵活负载对于电网稳定性至关重要.

研究的目的:

  • 为大规模电动汽车电网接入开发一个最佳的调度方法.
  • 解决电动汽车充电对电网负载波动,用户成本和环境因素的影响.
  • 为了提高用户在旅行时间和电池充电状态方面的灵活性.

主要方法:

  • 开发了一个大规模的响应调度模型,将电动汽车视为灵活的负载.
  • 建立了一个考虑电网负载,用户成本,环境,行程时间和充电状态的多目标优化模型.
  • 利用一种改进的偏好启发的共同进化算法,使用目标向量 (PICEA-g) 进行优化.

主要成果:

  • 改进的PICEA-g算法在EV大小超过50个单位的其他算法相比,表现出更高的性能.
  • 实现了区域负载的有效管理.
  • 降低了微电网管理成本和环境污染控制费用.

结论:

  • 建议的最佳调度方法和PICEA-g算法有效地管理大规模的电动汽车集成.
  • 该战略平衡了电网运营要求与用户需求,包括旅行时间和充电状态.
  • 经营成本和环境影响的显著降低.