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[电气工程与系统科学] 电化学存储调度与一次频率控制 系统级验证 [推广有奖]

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能者818 在职认证  发表于 2022-4-3 13:15:00 来自手机 |AI写论文

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摘要翻译:
已知可再生能源发电和需求预测中的不确定性水平会影响以给定的可靠性水平运行电网所需的备用数量。本文量化了随机需求和可再生能源就地调度对系统备用和可靠性的影响。考虑IEEE39节点系统的模型进行分析,包括常规发电、风力发电、需求和低频减载机构的详细动态模型。分析比较了基本情况,即可再生能源发电和需求功率是随机的,电力储备由常规发电提供,而传统随机资源的运行由于控制了局部灵活性而按照预先制定的调度计划进行调度。仿真再现了发电机停运后电网的动态行为。选择在频率减载机制下触发的偶发事件,从而为两个案例研究展示了系统运行可靠性的不同水平。仿真结果表明,传统的随机发电调度方法对于期望能量未被服务的情况有较好的分配效果,提高了系统的可靠性。
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英文标题:
《Dispatch and Primary Frequency Control with Electrochemical Storage: a
  System-wise Verification》
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作者:
Yihui Zuo, Fabrizio Sossan, Mokhtar Bozorg and Mario Paolone
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最新提交年份:
2018
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分类信息:

一级分类:Electrical Engineering and Systems Science        电气工程与系统科学
二级分类:Signal Processing        信号处理
分类描述:Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用,包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、(生物)医学、图像、视频和多模态自然和人为信号,包括通信信号和数据。感兴趣的主题包括:统计信号处理、谱估计和系统辨识;滤波器设计;自适应滤波/随机学习;(压缩)采样、传感和变换域方法,包括快速算法;用于机器学习的信号处理和用于信号处理应用的机器学习;网络与图形信号处理;信号处理中的凸和非凸优化方法;雷达、声纳和传感器阵列波束形成和测向;通信信号处理;低功耗、多核、片上系统信号处理;信息物理系统的传感、通信、分析和优化,如电网和物联网。
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英文摘要:
  Uncertainty levels in forecasting of renewable generation and demand are known to affect the amount of reserve required to operate the power grid with a given level of reliability. In this paper, we quantify the effects on the system reserve and reliability, due to the local dispatch of stochastic demand and renewable generation. The analysis is performed considering the model of the IEEE 39-bus system, with detailed dynamic models of conventional generation, wind generation, demand and an under-frequency load shedding mechanism. The analysis compares to cases: the base case, where renewable generation and demand power are stochastic and the power reserve is provided by conventional generation, against the case where the operation of traditionally stochastic resources is dispatched according to pre-established dispatch plans thanks to controlling local flexibility. Simulations reproduce the post-contingency dynamic behavior of the grid due to outages of generators. The contingencies are selected to trigger under frequency load shedding mechanisms, hence to demonstrate the different levels of system operation reliability for the two case studies. Simulation results show that dispatching traditionally stochastic generation scores better regarding to expected energy not served, producing an increase of the system reliability.
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PDF链接:
https://arxiv.org/pdf/1806.05825
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关键词:电化学 Applications Conventional Optimization Verification 电网 机制 备用 analysis 模型

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