发电系统短期可靠性评估方法

本文在计及发电机组可靠性短期行为的基础上，研究了发电系统短期可靠性的评估方法，并讨论了短期可靠性指标方法在电源运行调度计划中的应用。     

基于序贯蒙特卡罗法的复杂配电网可靠性分析

光伏发电具有随机性和间歇性的特点,使得配电网的可靠性存在下行风险。针对含光伏电源的配电网系统,传统的可靠性评估方法往往忽略光伏系统对可靠性的影响。利用序贯蒙特卡罗法对配网进行可靠性分析,考虑太阳光辐照度、光伏输出功率、时序故障率等因素的影响,建立基于时间序列的光伏发电系统可靠性模型;同时构建了考虑负荷功率随机性的中、短期负荷（月、小时）可靠性模型;最后引入可靠性灵敏度指标,评估了不同节点接入光伏发电系统后的可靠性。通过对含光伏发电系统配电网算例系统的可靠性进行评估,结果证明了算法的有效性,该方法为光伏接入位置的选择、容量的确定和配网可靠性的提高提供了重要的理论依据。     

计及天气预测的电力系统运行可靠性短期评估

多变的天气环境和电气量等运行条件会对电力系统短期运行的可靠性产生重要的影响.研究了计及天气预测的电力系统运行可靠性短期评估.建立了基于支持向量机的天气预测模型,并将其与元件短期可靠性模型相结合,以反映气温、风速、天气状况、元件服役时间、负荷水平等运行条件的影响;拓展了短期可靠性指标,并开发了相应的评估算法;通过短期可靠性评估实现了系统薄弱环节定位.对修正的IEEE RTS-79系统的算例分析表明所提模型和算法是合理有效的.该方案可应用到短期运行规划和在线调度领域,为规划和运行人员的科学决策提供必要的决策依据和技术支持.     

盲数理论在发电系统可靠性评估中的应用

针对电力市场条件下发电系统可靠性评估中存在的多种不确定性信息,提出了以盲数理论为基础的包括评估理论、评估指标、评估方法在内的发电系统可靠性柔性评估数学模型。该模型利用盲数表示各节点信息,有效地处理了原始参数不确定性对可靠性评估的影响。应用可靠性指标的灵敏度公式有效地找到制约系统可靠性的薄弱环节,能够对所研究的发电系统进行可靠性成本效益评估。通过对IEEE-24节点可靠性测试系统进行计算,证明了所提方法的正确性和有效性。     

孤立发电系统的可靠性区间评估

考虑发电系统原始参数具有不确定性,结合概率论和区间运算概念,提出了一种孤立发电系统的可靠性区间评估方法.它不但可以处理可靠性原始参数的不确定性对发电系统可靠性指标的影响,还可以求出参数在一定范围内变化时的系统可靠性指标区间值,更能体现出系统可靠性的真实性.最后采用一个三机的算例对所提方法的正确性进行了验证,并对采用点值与采用区间数得到的发电系统可靠性评估结果进行了对比分析,进一步说明了该方法的有效性.     

光伏发电接入配电网可靠性研究现状与展望

对含有光伏发电系统的配电网进行可靠性评估,有利于配电系统的安全可靠运行。概括了光伏发电接入配电网带来的影响,从光伏出力随机模型、光伏出力和负荷相关性、光伏发电系统结构等方面阐述了光伏可靠性模型,总结了光伏发电孤岛运行模型,并对光伏发电接入配电网的供电可靠性评估中解析法和模拟法的应用进行了分析,指出可靠性评估方法的改进、光储混合发电系统以及光伏发电接入交直流配电网可靠性是今后光伏发电接入配电网可靠性研究的重点。     

电力系统短期可靠性评估综述

短期可靠性评估是监测、量度、预测电力系统短期运行风险的有效工具.电力市场化改革、大规模风电接入与极端天气变化严重影响了电力系统短期可靠性水平.针对短期可靠性评估元件模型、评估框架与指标体系三个方面内容进行了论述.综述了输电线路、发电机以及净负荷的短期可靠性模型,并在输电线路模型中重点考虑了天气状况影响、可靠性的模糊表征、计及暂态稳定性的影响等.总结了三类可靠性评估特征与短期可靠性分析过程,考虑了短期可靠性安全性分析、连锁反应事故防范以及特殊保护方案制定.从系统状态、影响程度等多个角度对短期可靠性指标体系进行了归纳分析.进一步指出短期可靠性评估未来的研究重点及方向.     

配电系统短期可靠性评估方案

现有配电系统可靠性评估大多基于长期历史数据的平均可靠性,缺少对配电系统短期可靠性快速评估的模型及方法。针对配电网具体元件特点及短期内元件状态,提出一种配电系统短期可靠性评估方案,分别由基于失负荷的配电系统短期可靠性指标,基于改进条件相依的元件短期停运率模型以及基于前推故障扩散算法的配电系统短期可靠性评估分析方法。通过仿真分析,说明所定义的短期可靠性指标的实用性以及评估算法的有效性,为配电系统的短期现场运行提供理论基础与调度指导。     

含光伏发电配电网可靠性评估

针对越来越多光伏发电接入配电网使传统配电网可靠性评估解析算法难以适用的问题,计及光伏发电出力间歇性、随机性的等特点,根据光伏发电功率特性曲线获得其输出功率,推导配电网负荷获得光伏供电的概率计算公式。在此基础上,采用改进供电概率算法对含光伏发电配电网进行可靠性评估。以IEEE典型配电网系统为例,对配电网供电可靠性进行了计算分析,结果证明了该方法能全面、定量地评估含光伏发电配电网的供电可靠性。     

我国发电系统可靠性指标研究

在对我国发电系统的可靠性进行全面评估的基础上,提出了发电系统最优可靠性指标的定义和计算方法,归纳出我国综合最优发电系统可靠性指标曲线,并推荐了可靠性指标。     

A hybrid artificial neural network—differential dynamic programming approach for short-term hydro scheduling

In this paper, a hybrid artificial neural network-differential dynamic programming (ANN-DDP) method for the scheduling of short-term hydro generation is developed. The purpose of short-term hydro scheduling is to find the optimal amounts of generated powers for the hydro units in the system for the next N (N= 24 in this work) hours in the future. In the proposed method, the DDP procedures are performed offline on historical load data. The results are compiled and valuable information is obtained by using ANN algorithms. The DDP algorithm is then performed online according to the obtained information to give the hydro generation schedule for the forecasted load. Two types of ANN algorithm, the supervised learning neural network by Rumelhart et al. and the unsupervised learning neural network by Kohonen, are employed and compared in this paper. The effectiveness of the proposed approach is demonstrated by the short-term hydro scheduling of Taiwan power system which consists of ten hydro plants. It is concluded from the results that the proposed approach can significantly reduce the execution time of the conventional differential dynamic programming algorithm which is required to reach proper hydro generation schedules.     

基于布谷鸟-Elman算法的光伏发电预测

提高光伏发电功率预测的精度对于保证电网的安全稳定运行、提高光伏资源的开发和利用率具有重要的意义。文中提出了一种基于天气相似度以及改进布谷鸟算法优化Elman神经网络的光伏发电短期功率预测模型。首先在选取相似日上,提出一种基于距离和角度趋势的相似度计算方法,选出与待预测日相似度更高的相似日。其次,利用改进后的布谷鸟算法对Elman神经网络的权值和阈值进行优化并构建光伏发电短期功率预测模型。最后将文中提出的光伏发电预测模型与传统Elman神经网络模型的预测结果及实际输出值进行比较,结果表明改进布谷鸟算法优化Elman神经网络的光伏发电短期功率预测模型预测精度更高。     

基于分时长短期记忆神经网络的光伏发电超短期功率预测

准确预测光伏发电功率对电力系统运行调度至关重要。提出一种基于Spearman相关系数和分时长短期记忆网络的光伏发电功率预测方法。首先利用Spearman相关系数分析每个时刻下影响光伏发电功率的因素,选取相关度高的影响因素作为长短期记忆网络模型的输入变量;然后,对每个时刻建立一个基于长短期记忆网络的预测模型,实现分时光伏发电功率的预测。最后,利用实际光伏发电站的历史发电功率和数值天气预报数据进行案例分析。结果表明,所提方法比单一长短期记忆网络预测模型具有更高的预测精度。     

ECONOMICAL GENERATION RESERVE IN ISOLATED POWER SYSTEMS

ABSTRACT

This paper presents a new technique to determine the economical generation reserve for isolated power systems. The reserve is classified as short-term and long-term. For planning short-term power reserve to determine the required load shedding for avoiding blackout for severe emergency conditions, the dynamic behavior of the system after generating unit outages is performed, and the resulting frequency decay is analyzed. Therefore, the economical short-term reserve is determined to minimize the summation costs for reserve provision, plus the equivalent cost of the required load shedding.

For planning of long-term reserve, the utility investments and other decisions factors that affect electric service reliability should be explicitly evaluated, on the basis of their cost and benefit implications. A cost benefit approach, that quantifies the power reserve benefit in terms of the reduction in unserved energy cost, enables the evaluation of generation capacity additions.

To determine the economic short-term and long-term generation reserve, these approaches have been applied to areal regional utility     

基于自适应Kmeans和LSTM的短期光伏发电预测

光伏发电功率预测是可持续电力系统设计,能源转换管理和智能电网建设领域的重要主题。精准的光伏发电功率预测是电网日常调度管理与安全稳定运行的关键。本文提出了一种基于自适应Kmeans和长短期记忆(LSTM)的短期光伏发电功率预测模型。根据短期光伏发电特性,选取了预测模型的初始训练集。采用自适应Kmeans对初始训练集以及预测日的光伏发电功率进行聚类。在各类别的初始训练集数据上分别训练LSTM,结合训练完成的LSTM进行发电功率的预测。最后,考虑三种典型天气类型,采用所提方法进行仿真分析。结果表明,与其他三种方法相比,本文提出的方法的精度有了明显提升,误差更小。     

Probabilistic load flow techniques applied to power systemexpansion planning

An application of the probabilistic load flow (PLF) techniques to the expansion planning of power systems is presented. A brief review of the PLF formulation and solution is included to identify differences between this technique and other available tools such as composite generation and transmission reliability evaluation algorithms. The potential of the PLF technique is demonstrated by a case study using the Brazilian North/Northeastern system. The network expansion planning of an area of this system is studied using a conventional load flow program and the results are compared with those obtained from a PLF program that models generation capacity unavailabilities and peak load uncertainties. The significant differences demonstrate the benefits of the PLF technique. Special emphasis is given to short-term and long-term modeling using analyses of real system load data     

Short-term wind power prediction based on extreme learning machine with error correction

Introduction: Large-scale integration of wind generation brings great challenges to the secure operation of the power systems due to the intermittence nature of wind. The fluctuation of the wind generation has a great impact on the unit commitment. Thus accurate wind power forecasting plays a key role in dealing with the challenges of power system operation under uncertainties in an economical and technical way. Methods: In this paper, a combined approach based on Extreme Learning Machine (ELM) and an error correction model is proposed to predict wind power in the short-term time scale. Firstly an ELM is utilized to forecast the short-term wind power. Then the ultra-short-term wind power forecasting is acquired based on processing the short-term forecasting error by persistence method. Results: For short-term forecasting, the Extreme Learning Machine (ELM) doesn’t perform well. The overall NRMSE (Normalized Root Mean Square Error) of forecasting results for 66 days is 21.09 %. For the ultra-short term forecasting after error correction, most of forecasting errors lie in the interval of [?10 MW, 10 MW]. The error distribution is concentrated and almost unbiased. The overall NRMSE is 5.76 %. Conclusion: The ultra-short- erm wind power forecasting accuracy is further improved by using error correction in terms of normalized root mean squared error NRMSE).     

基于全系统可靠性评估方法的海上油田群电网可靠性评估

海上油田群电力系统可靠性的优劣显著影响石油生产。考虑到该类电力系统是一个包含发电、输电、变电、配电系统的独立电力系统,故障时切负荷措施也不同于陆上电力系统,因此传统可靠性评估的模型和算法不能直接使用。文中基于全系统可靠性评估理论提出一种海上油田群电力系统可靠性评估方法,并建立了全系统可靠性指标体系。在状态采样时,根据全系统可靠性评估理论将全系统分为发输电系统采样和配电系统采样,并建立综合评估方法。在发输电系统状态评估中,考虑到海上油田群电网及其负荷的特点,建立基于优先脱扣的切负荷模型。在配电系统状态评估中,考虑了级联故障的影响。最后以某油田群电力系统为实际算例,得出全系统指标并分析4类故障的影响,验证了所提方法的正确性和有效性。     

基于天气融合和LSTM网络的分布式光伏短期功率预测方法

分布式光伏发电功率高精度预测对配电网安全稳定运行有重要意义。针对分布式光伏发电设备的功率预测问题,基于天气信息和深度学习方法提出了一种分布式光伏短期功率预测方法。首先将天气进行分类融合,实现训练集的全面覆盖;然后基于长短期记忆网络（long short-term memory,LSTM）深度学习方法构建分布式光伏短期功率预测模型;最后实现分布式光伏功率预测。