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相关概念视频

Reinforcement Schedules01:24

Reinforcement Schedules

144
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
144
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

107
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.
107
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

621
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
621

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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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海洋可再生能源的多时间尺度最佳调度策略,基于深度强化学习算法.

Ren Xu1, Fei Lin1, Wenyi Shao1

  • 1School of Information and Automation, Qilu University of Technology, Jinan 250353, China.

Entropy (Basel, Switzerland)
|April 26, 2024
PubMed
概括

本研究介绍了波海和黄海的可再生能源调度策略,将波浪,风能和太阳能集成在一起. 该方法通过管理能源输出波动和降低成本来提高电网稳定性.

科学领域:

  • 海洋可再生能源海洋可再生能源
  • 能源系统工程 能源系统工程
  • 在电力系统中的人工智能

背景情况:

  • 波海和黄海提供了重要的海洋能源潜力,但风能和太阳能发电由于季节性和日内变化而面临挑战.
  • 整合多种可再生能源对电网稳定性至关重要,但调度补充能源发电却是一个巨大的挑战.

研究的目的:

  • 为波浪,海上光伏和风力发电的互补发送提出一个多时间尺度的滚动优化调度策略.
  • 解决博海和黄海地区可再生能源的季节性和日内不确定性的问题.
  • 通过智能能源管理来降低系统成本并确保电网稳定性.

主要方法:

  • 利用了来自山东半岛地区的实际气象数据.
  • 使用CNN-LSTM神经网络进行前一天 (24小时) 和一天内 (1小时) 的发电和负载需求预测.
  • 应用DDPG算法用于基于预测数据的滚动优化调度.

主要成果:

  • 拟议的战略通过对波浪,风力和光伏发电进行互补的调度,有效地满足负载需求.
  • 已证明能降低能源变化对电网负面影响.
  • 实现了系统成本降低16.1% (冬季) 和22% (夏季) 与仅仅前一天调度相比.
关键词:
深度强化学习的学习.能源预报 能源预报能源调度计划 能源调度计划值的值是一个值.多种能源的互补性.

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结论:

  • 集成的波浪,风力和光伏发电战略,再加上滚动优化,提高了电网稳定性和经济效率.
  • 开发的调度方法有效地利用了博海和黄海地区的气候特征,以实现可靠的可再生能源整合.
  • 这项研究为在复杂的海上环境中管理间歇性可再生能源提供了一个强大的框架.