技术领域technical field
本发明涉及可再生能源电力并网可靠性研究，尤其涉及一种光伏出力建模方法以及发电系统可靠性评估方法。The invention relates to research on the reliability of grid-connected renewable energy power, in particular to a photovoltaic output modeling method and a power generation system reliability evaluation method.
背景技术Background technique
随着经济的飞速发展，电力需求不断增加，可再生能源的大规模应用满足了社会对环境保护、节能减排和可持续性发展的要求。经过合理的规划之后，它们可以在推迟输配电网络的建设或升级、减少网络传输损耗以及增强系统运行安全性与可靠性、改善系统电能质量等方面产生有益影响。其中，光伏发电近年来发展迅速，准确评估含光伏发电的系统可靠性变得十分重要，有研究预测，到2020 年我国光伏发电装机容量将达到2 000MWp，计划年增长15％以上。With the rapid development of the economy, the demand for electricity continues to increase, and the large-scale application of renewable energy meets the society's requirements for environmental protection, energy conservation and emission reduction, and sustainable development. After reasonable planning, they can have beneficial effects in delaying the construction or upgrading of power transmission and distribution networks, reducing network transmission loss, enhancing system operation safety and reliability, and improving system power quality. Among them, photovoltaic power generation has developed rapidly in recent years, and it is very important to accurately evaluate the reliability of photovoltaic power generation systems. Some studies predict that by 2020, my country's photovoltaic power generation installed capacity will reach 2000MWp, with a planned annual growth rate of more than 15%.
在含光伏发电系统的可靠性研究中，光伏出力的建模是当下研究的热点。目前，国内外对光伏出力建模的研究可以分为两类：一类是以太阳能资源为基础的物理建模。该类模型中，基于分析辐照强度，温度和光伏阵列角等因素，通过光电转化过程，最终得到光伏系统的输出功率。其建模过程需要的数据量大，计算较复杂；另一类是以光伏历史出力为基础的统计建模，直接通过对光伏电站历史输出功率的统计分析，利用蒙特卡罗抽样技术模拟出光伏电站输出功率，该方法大大简化了光电转换模型。文献一《基于马尔可夫链的光伏发电系统输出功率短期预测方法》(电网技术，2011年，第35卷，第1期，第152页至157页)采用了基于马尔可夫法的聚类技术对光伏发电出力进行了统计建模，文献二《基于神经网络的光伏阵列发电预测模型的设计》(电工技术学报，2009年，第24卷，第9期，第153页至158页)使用了神经网络的聚类技术，以上方法相对较繁琐。文献三《A time-dependentapproach to evaluate capacity value of wind and solar PV generation》(IEEETransactions on Sustainable Energy，2016年，第7卷，第1期，第129页至138页)中考虑到光伏出力和负荷具有的日周期特性和相关性，采用一种聚类的技术，分析了光伏发电系统的置信容量。然而文献三采用的可靠性指标只能反映年时间尺度的平均水平，不能精确刻画一年中各时段光伏对发电系统的影响，其可靠性指标过于粗糙，不能反映光伏出力季节特性。In the reliability research of photovoltaic power generation systems, the modeling of photovoltaic output is a hot research topic at present. At present, research on photovoltaic output modeling at home and abroad can be divided into two categories: one is physical modeling based on solar energy resources. In this type of model, based on the analysis of factors such as radiation intensity, temperature and photovoltaic array angle, the output power of the photovoltaic system is finally obtained through the photoelectric conversion process. The modeling process requires a large amount of data and the calculation is more complicated; the other is statistical modeling based on the historical output of photovoltaic power plants, directly through the statistical analysis of the historical output power of photovoltaic power plants, and using Monte Carlo sampling technology to simulate the photovoltaic The output power of the power station, this method greatly simplifies the photoelectric conversion model. Document 1 "Short-term Prediction Method of Photovoltaic Power Generation System Output Power Based on Markov Chain" (Power Grid Technology, 2011, Volume 35, Issue 1, Pages 152-157) uses clustering based on Markov method Statistical modeling of photovoltaic power generation is carried out by the technology, and the literature 2 "Design of photovoltaic array power generation prediction model based on neural network" (Journal of Electrotechnical Society, 2009, Vol. 24, No. 9, pp. 153-158) uses The above methods are relatively cumbersome. Document 3 "A time-dependent approach to evaluate capacity value of wind and solar PV generation" (IEEE Transactions on Sustainable Energy, 2016, Volume 7, Issue 1, Pages 129-138) considers that photovoltaic output and load have Using a clustering technique, the confidence capacity of the photovoltaic power generation system is analyzed. However, the reliability index used in Document 3 can only reflect the average level of the annual time scale, and cannot accurately describe the impact of photovoltaics on the power generation system at various times of the year. Its reliability index is too rough to reflect the seasonal characteristics of photovoltaic output.
在对含光伏电站的系统可靠性研究中，通常针对的是一整年的光伏及负荷数据，计算的结果也只能表征光伏发电对系统全年充裕度的综合贡献，该可靠性评估的应用范围较窄，对实际生产指导作用有限。然而对于不同地区，其光伏出力特性必定会不相同。仅仅从全年的时间尺度，很难对光伏发电对系统可靠性的影响做精细化的研究。所以有必要研究不同季节以及日内不同时段光伏发电对系统可靠性的影响，以达到在更短的时间维度上，更加精细的考虑光伏资源特性以及负荷特性对系统可靠性的影响。而最常用的分段方法是按照季节的划分方式，即将年时间尺度划分为4个时间段，分别对应：春夏秋冬。然而该方法是一种模糊的、人为设定的分段，没有现实的依据，不能针对某地区的实际光伏出力特性进行具有针对性的最优分段。In the research on the system reliability of photovoltaic power plants, the photovoltaic and load data of the whole year are usually aimed at, and the calculated results can only represent the comprehensive contribution of photovoltaic power generation to the system's annual adequacy. The application of this reliability assessment The scope is narrow, and its role in guiding actual production is limited. However, for different regions, the characteristics of photovoltaic output will be different. It is difficult to do detailed research on the impact of photovoltaic power generation on system reliability only from the time scale of the whole year. Therefore, it is necessary to study the impact of photovoltaic power generation on system reliability in different seasons and at different times of the day, in order to achieve a more detailed consideration of the influence of photovoltaic resource characteristics and load characteristics on system reliability in a shorter time dimension. The most commonly used segmentation method is according to the division method of seasons, that is, the annual time scale is divided into four time periods, corresponding to: spring, summer, autumn and winter. However, this method is a kind of fuzzy and artificially set segmentation, which has no realistic basis, and cannot perform targeted optimal segmentation for the actual photovoltaic output characteristics of a certain area.
发明内容Contents of the invention
本发明目的在于克服上述现有技术之不足，提供了一种光伏出力建模方法以及发电系统可靠性评估方法，具体有以下技术方案实现：The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art, and provide a photovoltaic output modeling method and a power generation system reliability evaluation method, which are specifically realized by the following technical solutions:
所述光伏出力建模方法，包括：The photovoltaic output modeling method includes:
根据光伏日有效出力时间长短采用Fisher最优分段算法对样本的年时序出According to the length of photovoltaic daily effective output time, the Fisher optimal segmentation algorithm is used to output the annual time series of samples.
力进行最优分段，形成各时段的数据集；Optimal segmentation of forces to form data sets for each time period;
构建模糊c均值聚类算法模型，并利用所述模糊c均值聚类算法模型对所述各时段数据集逐一进行聚类分析，进行聚类分析时首先构建目标函数，再对目标函数迭代寻优直至数据集与相应聚类中心的欧式距离最小。Construct a fuzzy c-means clustering algorithm model, and use the fuzzy c-means clustering algorithm model to perform cluster analysis on the data sets of each time period one by one. When performing cluster analysis, first construct an objective function, and then iteratively optimize the objective function Until the Euclidean distance between the data set and the corresponding cluster center is the smallest.
所述光伏出力建模方法的进一步设计在于，所述Fisher最优分段算法模型的构建包括：The further design of the photovoltaic output modeling method is that the construction of the Fisher optimal segmentation algorithm model includes:
设定{X1,X2,…,Xn}是n个有序样本，每个样本为m维向量，对该样本进行划分，记录样本的某一段为：Set {X1 ,X2 ,…,Xn } to be n ordered samples, each sample is an m-dimensional vector, divide the sample, and record a section of the sample as:
G(i,j)＝{Xi,Xi+1,…,Xj} (1)G(i,j)={Xi ,Xi+1 ,…,Xj } (1)
采用离差平方和作为该段直径的描述，即：The sum of squared deviations is used as the description of the diameter of this segment, namely:
式中，D(i,j)为该有序样本的从第i个样本到第j个样本的离差平方和；为该段有序样本的均值；In the formula, D(i, j) is the sum of squared deviations from the i-th sample to the j-th sample of the ordered sample; is the mean value of the ordered samples in this segment;
将该有序样本分为k段，每段的样本下标记为Bj＝{ij,ij+1,…,ij+1-1}， j∈{1,2,…,k}，其中的分位点满足：1＝i1＜i2＜…＜ik＜n＝ik+1，得到总的目标函数，如式(4)：Divide the ordered sample into k segments, and the samples of each segment are marked as Bj ={ij ,ij +1,…,ij+1 -1}, j∈{1,2,…,k} , where the quantile points satisfy: 1=i1 <i2 <...<ik <n=ik+1 , and the overall objective function is obtained, as shown in formula (4):
式中，L为各段的离差平方总和，L值越小，分段越合理。In the formula, L is the sum of the squared deviations of each segment, and the smaller the value of L, the more reasonable the segment.
所述光伏出力建模方法的进一步设计在于，所述模糊c均值聚类算法模型的目标函数如式(5)：The further design of the photovoltaic output modeling method is that the objective function of the fuzzy c-means clustering algorithm model is as formula (5):
式中，U为隶属度矩阵；C为聚类中心矩阵；m为加权倍数；dij为第i个聚类中心与第j个数据点间的欧几里得距离。In the formula, U is the membership matrix; C is the cluster center matrix; m is the weighting multiple; dij is the Euclidean distance between the i-th cluster center and the j-th data point.
3、根据权利要求1所述的光伏出力建模方法，其特征在于所述模糊c均值聚类算法为迭代寻优过程，通过式(7)、式(8)对聚类中心阵和隶属度阵进行更新：3. The photovoltaic output modeling method according to claim 1, characterized in that the fuzzy c-means clustering algorithm is an iterative optimization process, and the clustering center matrix and membership degree are determined by formula (7) and formula (8). The array is updated:
若前后两次迭代过程，目标函数的改变量小于某个阈值，则聚类过程结束：If the change of the objective function is less than a certain threshold during the two iterations before and after, the clustering process ends:
||J(U(z+1)，C(z+1))-J(U(z)，C(z))||＜9 (9)||J(U(z+1) ，C(z+1) )-J(U(z) ，C(z) )||＜9 (9)
式中：J(U(z),C(z))为第z次迭代的目标函数值，阈值ε一般取10-4。In the formula: J(U(z) ,C(z) ) is the objective function value of the zth iteration, and the threshold ε is generally taken as 10-4 .
根据所述光伏出力建模方法提供了一种发电系统可靠性评估方法，基于最优分段和多维聚类算法，步骤如下：According to the photovoltaic output modeling method, a power generation system reliability evaluation method is provided, based on the optimal segmentation and multidimensional clustering algorithm, the steps are as follows:
步骤1)对光伏出力序列进行重组，构成以一天24小时排列的数据方式，构成矩阵形式为365行*24列的光伏出力阵Ppv；Step 1) Reorganize the photovoltaic output sequence to form a data format arranged 24 hours a day, forming a photovoltaic output array Ppv with a matrix form of 365 rows*24 columns;
步骤2)统计得到光伏有效出力时间序列Tpv，根据光伏每天有效出力时间长短的原则，使用Fisher最优分段算法，对年模拟周期的光伏历史出力数据进行时序分段，得到最优分段点；Step 2) Statistically obtain the time series Tpv of photovoltaic effective output, and use the Fisher optimal segmentation algorithm to segment the historical photovoltaic output data of the annual simulation cycle in time series according to the principle of the effective output time of photovoltaics per day to obtain the optimal segmentation point;
步骤3)在每个时间段，分别对光伏功率和负荷需求数据，通过FCM聚类法确定各时段光伏功率的聚类中心和模糊隶属度矩阵，各时段负荷的聚类中心和模糊隶属度矩阵；Step 3) In each time period, for the photovoltaic power and load demand data, the cluster center and fuzzy membership matrix of photovoltaic power in each period, the cluster center and fuzzy membership degree matrix of load in each period are determined by FCM clustering method ;
步骤4)对每个时间段的光伏功率、负荷需求和常规机组的运行状态进行模拟，统计系统的可靠性指标。Step 4) Simulate the photovoltaic power, load demand and operating status of conventional units in each time period, and calculate the reliability index of the system.
所述发电系统可靠性评估方法的进一步设计在于，所述步骤4)通过蒙特卡罗状态抽样方法，规定抽样次数为105次。A further design of the power generation system reliability assessment method is that, in the step 4), the Monte Carlo state sampling method is used, and the number of sampling is specified to be 105 times.
所述发电系统可靠性评估方法的进一步设计在于，步骤4)中采用电量不足期望LOEE作为可靠性指标，LOEE的表达式如式(10)：The further design of the power generation system reliability evaluation method is that in step 4), the expected LOEE of insufficient power is used as the reliability index, and the expression of LOEE is as formula (10):
式中：s为抽样模拟中的迭代次数；t为时刻点，h；N为设定的迭代总次数；Tday为该时间段的天数；g为全部常规机组的数量；Ds,t和Ps,t分别为第s次迭代中，在时刻t的负荷需求量和光伏功率。In the formula: s is the number of iterations in the sampling simulation;t is the time point, h; N is the total number of iterations set; Tday is the number of days in this time period; g is the number of all conventional units; Ps,t are the load demand and photovoltaic power at time t in the sth iteration, respectively.
本发明的有益效果：Beneficial effects of the present invention:
1、光伏出力建模方法，其特征在于包括：1. Photovoltaic output modeling method, characterized by including:
根据光伏日有效出力时间长短采用Fisher最优分段算法对样本的年时序出力进行最优分段，形成各时段的数据集；According to the effective daily output time of photovoltaics, the Fisher optimal segmentation algorithm is used to optimally segment the annual time-series output of the sample to form a data set for each period;
构建模糊c均值聚类算法模型，并利用所述模糊c均值聚类算法模型对所述各时段数据集逐一进行聚类分析，直至数据集与相应聚类中心的欧式距离最小。Constructing a fuzzy c-means clustering algorithm model, and using the fuzzy c-means clustering algorithm model to perform cluster analysis on the data sets of each time period one by one until the Euclidean distance between the data set and the corresponding cluster center is the smallest.
具体实施方式detailed description
下面对本发明方案进行详细说明。The scheme of the present invention will be described in detail below.
本实施例的光伏出力建模方法，包括两个步骤分别为：1)根据光伏日有效出力时间长短采用Fisher最优分段算法对样本的年时序出力进行最优分段，形成各时段的数据集。2)构建模糊c均值聚类算法模型，并利用所述模糊c均值聚类算法模型对所述各时段数据集逐一进行聚类分析，直至数据集与相应聚类中心的欧式距离最小。Fisher最优分段算法模型：The photovoltaic output modeling method of this embodiment includes two steps: 1) According to the length of effective photovoltaic daily output time, the Fisher optimal segmentation algorithm is used to optimally segment the annual time-series output of the sample to form the data of each period set. 2) Constructing a fuzzy c-means clustering algorithm model, and using the fuzzy c-means clustering algorithm model to perform cluster analysis on the data sets of each time period one by one until the Euclidean distance between the data set and the corresponding cluster center is the smallest. Fisher optimal segmentation algorithm model:
假设{X1,X2,…,Xn}是n个有序样本，每个样本为m维向量。对该样本进行划分，记录样本的某一段为：Suppose {X1 ,X2 ,…,Xn } are n ordered samples, and each sample is an m-dimensional vector. Divide the sample and record a section of the sample as:
G(i,j)＝{Xi,Xi+1,…,Xj} (1)G(i,j)={Xi ,Xi+1 ,…,Xj } (1)
采用离差平方和作为该段直径的描述，即：The sum of squared deviations is used as the description of the diameter of this segment, namely:
式中：D(i，j)为该有序样本的从第i个样本到第j个样本的离差平方和；为该段有序样本的均值。In the formula: D(i, j) is the sum of squared deviations from the i-th sample to the j-th sample of the ordered sample; is the mean value of the ordered samples in this segment.
在得到了每段的离差平方和时，若将该有序样本分为k段，每段的样本下标记为Bj＝{ij,ij+1,…,ij+1-1}，j∈{1,2,…,k}，其中的分位点满足： 1＝i1＜i2＜…＜ik＜n＝ik+1。从而得到总的目标函数，如公式(4)所示。When the sum of squared deviations of each segment is obtained, if the ordered sample is divided into k segments, the samples of each segment are marked as Bj ＝{ij ,ij +1,…,ij+1 -1 }, j∈{1,2,...,k}, where the quantile points satisfy: 1=i1 <i2 <...<ik <n=ik+1 . Thus the overall objective function is obtained, as shown in formula (4).
式中：L为各段的离差平方总和，L值越小，分段越合理。In the formula: L is the sum of the squared deviations of each segment, the smaller the value of L, the more reasonable the segment.
有研究表明，特定地区的光照辐照度往往与该地区的日照时间呈现线性的关系。即某一时间段内，一天的日照时间长段大致的反映了辐照度强弱。而对于光伏发电系统，光伏出力又直接受到辐照度影响，所以日照时间的长短间接体现出光伏出力的强弱。基于此，本发明公开一种新的分段方式，即根据一天中光伏有效出力时间长短的原则，对光伏年出力进行有序分段。Studies have shown that the light irradiance in a specific area often has a linear relationship with the sunshine time in the area. That is to say, within a certain period of time, the long period of sunshine in a day roughly reflects the intensity of irradiance. For photovoltaic power generation systems, photovoltaic output is directly affected by irradiance, so the length of sunshine time indirectly reflects the strength of photovoltaic output. Based on this, the present invention discloses a new segmentation method, that is, according to the principle of the length of effective photovoltaic output time in a day, the annual output of photovoltaics is segmented in an orderly manner.
模糊c均值聚类算法模型：Fuzzy c-means clustering algorithm model:
该算法的目标是使得数据集与相应聚类中心的欧式距离最小，目标函数如下：The goal of the algorithm is to minimize the Euclidean distance between the data set and the corresponding cluster center. The objective function is as follows:
式中：U为隶属度矩阵；C为聚类中心矩阵；m为加权倍数；dij为第i个聚类中心与第j个数据点间的欧几里得距离。In the formula: U is the membership matrix; C is the cluster center matrix; m is the weighting multiple; dij is the Euclidean distance between the i-th cluster center and the j-th data point.
该算法是一个迭代寻优过程，可通过下面两个式子对聚类中心阵和隶属度阵进行更新。The algorithm is an iterative optimization process, and the cluster center matrix and membership matrix can be updated by the following two formulas.
若前后两次迭代过程，目标函数的改变量小于某个阈值，则聚类过程结束：If the change of the objective function is less than a certain threshold during the two iterations before and after, the clustering process ends:
||J(U(z+1)，C(z+1))-J(U(z)，C(z))||＜ε (9)||J(U(z+1) ，C(z+1) )-J(U(z) ，C(z) )||＜ε (9)
式中：J(U(z),C(z))为第z次迭代的目标函数值，阈值ε一般取10-4。In the formula: J(U(z) ,C(z) ) is the objective function value of the zth iteration, and the threshold ε is generally taken as 10-4 .
本发明采用电量不足期望(loss of energy expection,LOEE)作为可靠性指标，该指标不但较好的反映了系统缺电量的大小，更能反映在各个时间段内的系统可靠性。The present invention uses the loss of energy expectation (LOEE) as the reliability index, which not only better reflects the magnitude of the system power shortage, but also reflects the system reliability in various time periods.
式中：s为抽样模拟中的迭代次数；t为时刻点，h；N为设定的迭代总次数； Tday为该时间段的天数；g为全部常规机组的数量；Ds,t和Ps,t分别为第s次迭代中，在时刻t的负荷需求量和光伏功率。In the formula: s is the number of iterations in the sampling simulation; t is the time point, h; N is the total number of iterations set; Tday is the number of days in this time period; g is the number of all conventional units; Ds,t and Ps,t are the load demand and photovoltaic power at time t in the sth iteration, respectively.
本实施例根据上述光伏出力序列建模的方法，提供一种基于最优分段和多维聚类的可靠性建模方法。该方法首先对光伏出力进行时序分段处理；然后对每段进行聚类分析，抽样模拟得到系统的可靠性指标值。具体的计算步骤如下：This embodiment provides a reliability modeling method based on optimal segmentation and multi-dimensional clustering according to the above method for modeling photovoltaic output sequences. In this method, the photovoltaic output is first processed in time series and segmented; then cluster analysis is performed on each segment, and the reliability index value of the system is obtained through sampling simulation. The specific calculation steps are as follows:
步骤1)对光伏出力序列进行重组，构成以一天24小时排列的数据方式，构成矩阵形式为365行*24列的光伏出力阵Ppv。Step 1) Reorganize the photovoltaic output sequence to form a data format arranged 24 hours a day, and form a photovoltaic output array Ppv with a matrix form of 365 rows*24 columns.
步骤2)统计得到光伏有效出力时间序列Tpv，根据光伏每天有效出力时间长短的原则，使用Fisher最优分段算法，对年模拟周期的光伏历史出力数据进行时序分段，得到最优分段点。Step 2) Statistically obtain the time series Tpv of photovoltaic effective output. According to the principle of the length of effective photovoltaic output time per day, use Fisher's optimal segmentation algorithm to segment the historical photovoltaic output data of the annual simulation cycle in time series to obtain the optimal segmentation point.
步骤3)在每个时间段，分别对光伏功率和负荷需求数据，利用FCM聚类法确定各时间段的聚类数，聚类中心和模糊隶属度阵。需要说明的是，负荷需求的聚类分析与光伏功率分析方法相同，这里不再赘述。Step 3) In each time period, for the photovoltaic power and load demand data, use the FCM clustering method to determine the number of clusters, cluster centers and fuzzy membership degree arrays in each time period. It should be noted that the cluster analysis of load demand is the same as the photovoltaic power analysis method, and will not be repeated here.
步骤4)综合以上3步得到的：各个时段光伏功率的聚类中心和模糊隶属度矩阵，各个时段负荷的聚类中心和模糊隶属度矩阵，RTS测试系统的常规机组的容量和故障率。利用蒙特卡罗状态抽样技术，规定抽样次数为105次，对每个时间段的光伏功率、负荷需求和常规机组的运行状态进行模拟，统计系统的可靠性指标。Step 4) The above three steps are combined to obtain: the cluster center and fuzzy membership matrix of photovoltaic power in each period, the cluster center and fuzzy membership matrix of load in each period, and the capacity and failure rate of conventional units of the RTS test system. Using the Monte Carlo state sampling technique, the number of samples is specified to be 105 times, and the photovoltaic power, load demand, and operating state of conventional units are simulated in each time period, and the reliability indicators of the system are counted.
与现有技术相比，本发明突出的优点包括：首先，在含光伏发电系统可靠性评估中考虑了光伏出力的昼夜日周期性和季节时序周期性，利用Fisher最优分段方法对光伏出力序列逐段精细建模，更能体现该地区光伏功率的局部时序特点，具有较强的实用性和针对性；其次，采用FCM多维聚类算法，对各时段光伏功率和负荷需求进行聚类分析，该方法简单且高效的模拟了随机变量具有的随机性和时序性的特点；最后，结合蒙特卡罗状态抽样方法对含光伏发电系统进行可靠性分析，所提方法可以有效提升系统可靠性评估精度，量化分析接入光伏电站后，系统在不同时间段可靠性的变化情况，对光伏电站的并网容量规划具有重要指导意义。Compared with the prior art, the outstanding advantages of the present invention include: First, in the reliability assessment of photovoltaic power generation systems, the diurnal and seasonal periodicity of photovoltaic output is considered, and the Fisher optimal segmentation method is used to analyze the photovoltaic output The sequence is modeled segment by segment, which can better reflect the local timing characteristics of photovoltaic power in the region, and has strong practicability and pertinence; secondly, the FCM multi-dimensional clustering algorithm is used to perform cluster analysis on photovoltaic power and load demand in each period , the method simply and efficiently simulates the randomness and timing characteristics of random variables; finally, combined with the Monte Carlo state sampling method to analyze the reliability of photovoltaic power generation systems, the proposed method can effectively improve system reliability assessment Accuracy, quantitative analysis of the reliability changes of the system in different time periods after connecting to the photovoltaic power station, has important guiding significance for the grid-connected capacity planning of the photovoltaic power station.
以上对本发明提供的光伏出力建模方法以及发电系统可靠性评估方法进行了详细介绍，以便于理解本发明和其核心思想。对于本领域的一般技术人员，在具体实施时，可根据本发明的核心思想进行多种修改和演绎。综上所述，本说明书不应视为对本发明的限制。The photovoltaic output modeling method and the power generation system reliability evaluation method provided by the present invention are described above in detail, so as to facilitate the understanding of the present invention and its core ideas. For those skilled in the art, various modifications and deductions can be made according to the core idea of the present invention during specific implementation. In summary, this specification should not be considered as limiting the present invention.
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710761852.6ACN107679687A (en) | 2017-08-29 | 2017-08-29 | A kind of photovoltaic output modeling method and Generation System Reliability appraisal procedure |
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710761852.6ACN107679687A (en) | 2017-08-29 | 2017-08-29 | A kind of photovoltaic output modeling method and Generation System Reliability appraisal procedure |
| Publication Number | Publication Date |
|---|---|
| CN107679687Atrue CN107679687A (en) | 2018-02-09 |
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710761852.6APendingCN107679687A (en) | 2017-08-29 | 2017-08-29 | A kind of photovoltaic output modeling method and Generation System Reliability appraisal procedure |
| Country | Link |
|---|---|
| CN (1) | CN107679687A (en) |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108376262A (en)* | 2018-02-23 | 2018-08-07 | 新疆大学 | A kind of analysis model construction method of wind power output typical characteristics |
| CN109494792A (en)* | 2018-11-21 | 2019-03-19 | 国网青海省电力公司 | Method and device for determining light abandoning electric quantity of photovoltaic power station |
| CN111090926A (en)* | 2019-11-14 | 2020-05-01 | 远景智能国际私人投资有限公司 | Photovoltaic power curve modeling method and device, computer equipment and storage medium |
| CN111293681A (en)* | 2020-01-22 | 2020-06-16 | 重庆大学 | A Quantitative Evaluation Method for Output Volatility of PV Stations Based on RankBoost |
| CN112381409A (en)* | 2020-11-16 | 2021-02-19 | 江西电力交易中心有限公司 | Dynamic adjustment method for peak time period |
| CN113659631A (en)* | 2021-08-09 | 2021-11-16 | 大连理工大学 | Wind-solar power station group output description method considering time-varying characteristics |
| CN114398493A (en)* | 2021-12-29 | 2022-04-26 | 中国人民解放军92728部队 | Unmanned aerial vehicle type spectrum construction method based on fuzzy clustering and cost-effectiveness value |
| CN114896871A (en)* | 2022-04-22 | 2022-08-12 | 国网江苏省电力有限公司电力科学研究院 | Photovoltaic output decomposition method based on minimum mutual information |
| CN118249741A (en)* | 2024-05-28 | 2024-06-25 | 南京欧陆电气股份有限公司 | Intelligent monitoring control method and system for photovoltaic light-overlapping controller |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105931134A (en)* | 2016-04-25 | 2016-09-07 | 华北电力大学 | Method for evaluating reliability of system with grid-connected photovoltaic power station |
| CN106096753A (en)* | 2016-05-30 | 2016-11-09 | 北京中电普华信息技术有限公司 | A kind of photovoltaic power generation output forecasting method and apparatus |
| CN106779175A (en)* | 2016-11-28 | 2017-05-31 | 西安交通大学 | It is a kind of to consider that non-linear photovoltaic at times is exerted oneself Forecasting Methodology |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105931134A (en)* | 2016-04-25 | 2016-09-07 | 华北电力大学 | Method for evaluating reliability of system with grid-connected photovoltaic power station |
| CN106096753A (en)* | 2016-05-30 | 2016-11-09 | 北京中电普华信息技术有限公司 | A kind of photovoltaic power generation output forecasting method and apparatus |
| CN106779175A (en)* | 2016-11-28 | 2017-05-31 | 西安交通大学 | It is a kind of to consider that non-linear photovoltaic at times is exerted oneself Forecasting Methodology |
| Title |
|---|
| 杨苏等: "含光伏电站发电系统可靠性评估方法研究", 《电力需求侧管理》* |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108376262B (en)* | 2018-02-23 | 2021-08-10 | 新疆大学 | Analytical model construction method for typical characteristics of wind power output |
| CN108376262A (en)* | 2018-02-23 | 2018-08-07 | 新疆大学 | A kind of analysis model construction method of wind power output typical characteristics |
| CN109494792A (en)* | 2018-11-21 | 2019-03-19 | 国网青海省电力公司 | Method and device for determining light abandoning electric quantity of photovoltaic power station |
| CN109494792B (en)* | 2018-11-21 | 2022-05-13 | 国网青海省电力公司 | Determination method and device of photovoltaic power station abandoned photovoltaic power |
| CN111090926A (en)* | 2019-11-14 | 2020-05-01 | 远景智能国际私人投资有限公司 | Photovoltaic power curve modeling method and device, computer equipment and storage medium |
| WO2021096432A1 (en)* | 2019-11-14 | 2021-05-20 | Envision Digital International Pte. Ltd. | Method and apparatus for modeling photovoltaic power curve, and computer device and storage medium thereof |
| CN111090926B (en)* | 2019-11-14 | 2023-09-12 | 远景智能国际私人投资有限公司 | Photovoltaic power curve modeling method and device, computer equipment and storage medium |
| CN111293681B (en)* | 2020-01-22 | 2023-04-25 | 重庆大学 | Photovoltaic field station output fluctuation quantitative evaluation method based on RankBoost |
| CN111293681A (en)* | 2020-01-22 | 2020-06-16 | 重庆大学 | A Quantitative Evaluation Method for Output Volatility of PV Stations Based on RankBoost |
| CN112381409A (en)* | 2020-11-16 | 2021-02-19 | 江西电力交易中心有限公司 | Dynamic adjustment method for peak time period |
| CN113659631A (en)* | 2021-08-09 | 2021-11-16 | 大连理工大学 | Wind-solar power station group output description method considering time-varying characteristics |
| CN113659631B (en)* | 2021-08-09 | 2022-07-26 | 大连理工大学 | Wind-solar power station group output description method considering time-varying characteristics |
| CN114398493A (en)* | 2021-12-29 | 2022-04-26 | 中国人民解放军92728部队 | Unmanned aerial vehicle type spectrum construction method based on fuzzy clustering and cost-effectiveness value |
| CN114398493B (en)* | 2021-12-29 | 2022-12-16 | 中国人民解放军92728部队 | Unmanned aerial vehicle type spectrum construction method based on fuzzy clustering and cost-effectiveness value |
| CN114896871A (en)* | 2022-04-22 | 2022-08-12 | 国网江苏省电力有限公司电力科学研究院 | Photovoltaic output decomposition method based on minimum mutual information |
| CN114896871B (en)* | 2022-04-22 | 2023-12-22 | 国网江苏省电力有限公司电力科学研究院 | Photovoltaic output decomposition method based on minimum mutual information |
| CN118249741A (en)* | 2024-05-28 | 2024-06-25 | 南京欧陆电气股份有限公司 | Intelligent monitoring control method and system for photovoltaic light-overlapping controller |
| CN118249741B (en)* | 2024-05-28 | 2024-08-27 | 南京欧陆电气股份有限公司 | Intelligent monitoring control method and system for photovoltaic light-overlapping controller |
| Publication | Publication Date | Title |
|---|---|---|
| CN107679687A (en) | A kind of photovoltaic output modeling method and Generation System Reliability appraisal procedure | |
| CN106529814B (en) | A distributed photovoltaic ultra-short-term forecast method based on Adaboost clustering and Markov chain | |
| CN104200275B (en) | Classification and control method of power consumption mode based on user behavior characteristics | |
| CN115293415A (en) | Multi-wind-farm short-term power prediction method considering time evolution and space correlation | |
| CN112100911B (en) | A Solar Radiation Prediction Method Based on Deep BILSTM | |
| CN110942205B (en) | A Short-Term Photovoltaic Power Forecasting Method Based on HIMVO-SVM | |
| CN107612016B (en) | Planning method of distributed power supply in power distribution network based on maximum voltage correlation entropy | |
| CN110909911B (en) | Aggregation method of multidimensional time series data considering space-time correlation | |
| CN104200032B (en) | Based on period horizontal time shafts clustering method in broad sense load modeling | |
| CN103218674A (en) | Method for predicating output power of photovoltaic power generation system based on BP (Back Propagation) neural network model | |
| CN105069521A (en) | Photovoltaic power plant output power prediction method based on weighted FCM clustering algorithm | |
| CN106650784A (en) | Feature clustering comparison-based power prediction method and device for photovoltaic power station | |
| CN109086928A (en) | Photovoltaic plant realtime power prediction technique based on SAGA-FCM-LSSVM model | |
| CN111859283B (en) | Scene generation method considering multi-energy charge time sequence and correlation | |
| CN103729685A (en) | Method for photovoltaic power station group region power prediction on basis of improved RBF neural network | |
| CN107194495A (en) | A kind of longitudinal Forecasting Methodology of photovoltaic power excavated based on historical data | |
| CN108053149A (en) | A kind of photovoltaic electric station grid connection methods of risk assessment for considering meteorologic factor | |
| CN105160423A (en) | Photovoltaic power generation prediction method based on Markov residual error correction | |
| CN104021427A (en) | Method for predicting daily generating capacity of grid-connected photovoltaic power station based on factor analysis | |
| CN112149264A (en) | An Active Distribution Network Planning Method Based on Improved Harris Eagle Optimization Algorithm | |
| CN105303250A (en) | Wind power combination prediction method based on optimal weight coefficient | |
| CN110852492A (en) | An ultra-short-term forecasting method for photovoltaic power based on Mahalanobis distance | |
| CN103530473A (en) | Random production analog method of electric system with large-scale photovoltaic power station | |
| CN105931134A (en) | Method for evaluating reliability of system with grid-connected photovoltaic power station | |
| CN114091317A (en) | Power prediction method of photovoltaic power station based on NWP irradiance correction and error prediction |
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | Application publication date:20180209 | |
| RJ01 | Rejection of invention patent application after publication |