摘要翻译：
随着插电式混合动力汽车在配电系统中的日益普及，迫切需要更准确、更快速地解决概率配电潮流问题。本文提出了一种基于有限样本点确定概率分布潮流结果的概率密度函数的新算法。在概率分布潮流问题中，对插电式混合动力汽车在充电站的概率充电行为及其与居民高峰负荷的重叠进行了修正。该算法比蒙特卡罗模拟速度快，同时保持了足够的精度。将其应用于两个维度不同的测试系统的概率分布潮流求解，并与现有的概率求解方法进行了比较。仿真结果表明了该算法计算不确定输出概率密度函数的准确性和有效性。
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英文标题：
《Probabilistic Distribution Power Flow Based on Finite Smoothing of Data
  Samples Considering Plug-in Hybrid Electric Vehicles》
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作者：
Mohammadhadi Rouhani and Mohammad Mohammadi
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最新提交年份：
2017
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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：
  The ever increasing penetration of plug-in hybrid electric vehicles in distribution systems has triggered the need for a more accurate and at the same time fast solution to probabilistic distribution power flow problem. In this paper a novel algorithm is introduced based on finite sample points to determine probabilistic density function of probabilistic distribution power flow results. A modified probabilistic charging behavior of plug-in hybrid electric vehicles at charging stations and their overlap with residential peak load is evaluated in probabilistic distribution power flow problem. The proposed algorithm is faster than Monte Carlo Simulation and at the same time keeps adequate accuracy. It is applied to solve probabilistic distribution power flow for two dimensionally different test systems and is compared with recent probabilistic solutions. Simulation results show the accuracy and efficiency of the proposed algorithm to calculate probability density function of uncertain outputs.
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PDF链接：
https://arxiv.org/pdf/1710.10775