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相关概念视频

Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

593
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.
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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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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...
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Power System Distribution01:25

Power System Distribution

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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...
1.0K
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

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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:
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Continuous Charge Distributions01:17

Continuous Charge Distributions

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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...
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Distributed Loads01:19

Distributed Loads

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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...
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相关实验视频

Updated: Jan 18, 2026

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

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多代理 DDPG 基于 IIoT 智能电网的多设备充电安排.

Haiyong Zeng1,2, Yuanyan Huang1, Kaijie Zhan1

  • 1Guangxi Key Laboratory of Braininspired Computing and Intelligent Chips, School of Electronic and Information Engineering/School of Integrated Circuits, Guangxi Normal University, Guilin 541001, China.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种新的多代理深度决定性政策梯度 (MADDPG) 算法,用于电动汽车 (EV) 充电安排. 新方法显著降低了电动汽车充电成本,并提高了智能电网的稳定性.

关键词:
成本优化管理 管理成本优化管理深度强化学习的学习.电动汽车充电时间表工业物联网的工业物联网.智能电网是一个智能电网.智能传感器智能传感器

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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Last Updated: Jan 18, 2026

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科学领域:

  • 电气工程 电气工程
  • 人工智能的人工智能
  • 智能电网技术 智能电网技术

背景情况:

  • 电动汽车 (EV) 和工业物联网 (IIoT) 智能电网的广泛采用需要协调的充电策略.
  • 传统算法在多设备环境中面临着可扩展性挑战,在连续控制场景中面临着局限性.

研究的目的:

  • 为电动汽车提出使用多代理深度决定性政策梯度 (MADDPG) 的动态充电调度算法.
  • 优化连续动作空间中的多个电动汽车的充电和放电策略,降低成本和平衡电网负载.

主要方法:

  • 开发了一个基于MADDPG的算法,集成实时电价,电池状态和传感器数据.
  • 员工协调多代理学习,以动态优化电动汽车的充电和放电.
  • 利用车辆到电网 (V2G) 技术,以适应价格波动和用户需求.

主要成果:

  • 与基线方法相比,在30天的评估期内,收费成本减少了41.12%.
  • 证明有效适应电价波动和用户需求变化.
  • 通过优化充电时间分配,展示了增强的电网稳定性.

结论:

  • 拟议的MADDPG算法为IIoT智能电网中的动态电动汽车充电安排提供了一个可扩展和有效的解决方案.
  • 使用V2G技术协调的多代理学习显著提高了经济效率和电网稳定性.
  • 这种方法解决了传统方法在管理复杂,连续控制场景方面的局限性.