CN109636087B - A method and system for dynamically grouping demand response resources - Google Patents
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Abstract
本发明涉及一种需求响应资源动态分群方法及系统，所述方法包括根据各需求响应资源对应特征间的欧氏距离获取需求响应资源的初始聚类中心，进而利用所述需求响应资源初始聚类中心对需求响应资源进行分群，克服现有需求响应资源动态分群技术分群结果不精准的缺陷，获取精确、合理的需求响应资源分群结果，提高发电备用容量的计算可靠性，促进电网有序发展。
The present invention relates to a method and system for dynamic grouping of demand response resources. The method includes obtaining the initial clustering center of demand response resources according to the Euclidean distance between the corresponding characteristics of each demand response resource, and then using the demand response resources for initial clustering. The center groups demand response resources, overcomes the inaccurate defect of existing demand response resource dynamic grouping technology grouping results, obtains accurate and reasonable demand response resource grouping results, improves the calculation reliability of power generation reserve capacity, and promotes the orderly development of power grids.
Description
技术领域technical field
本发明涉及电网系统资源分类领域，具体涉及一种需求响应资源动态分群方法及系统。The invention relates to the field of power grid system resource classification, in particular to a dynamic grouping method and system for demand response resources.
背景技术Background technique
社会发展与科学技术的发展和资源的有效利用息息相关，其中，需求响应资源具有鲜明的特点，它作为电力能源的消耗者，不仅分散不均，数量也很庞大，因此实际应用中需要对其进行详细严格的分类，只有将需求响应资源合理分类，才能计算出足够的发电备用容量，以满足峰荷需求；Social development is closely related to the development of science and technology and the effective use of resources. Among them, demand response resources have distinctive characteristics. As consumers of electric energy, they are not only scattered unevenly, but also in large quantities. Detailed and strict classification, only by reasonably classifying the demand response resources can we calculate enough power generation reserve capacity to meet the peak load demand;
目前对需求响应资源的分类主要依据响应对象的类型将需求响应资源分为居民用户资源、商业用户资源和工业用户资源，这样的分类结果并不细致，且同一类资源中的单个资源的特性可能相差很大，进行需求响应分析时无法将其同时考虑，无法精确计算出足够的发电备用容量以满足峰荷需求，这样就会导致发电成本较高、市场电价波动大、系统安全可靠性较低和加重环境污染等问题。The current classification of demand response resources is mainly based on the types of response objects. Demand response resources are divided into residential user resources, commercial user resources and industrial user resources. Such classification results are not detailed, and the characteristics of individual resources in the same type of resources may be The difference is very large, and it cannot be considered at the same time when performing demand response analysis, and it is impossible to accurately calculate sufficient power generation reserve capacity to meet peak load demand, which will lead to high power generation costs, large fluctuations in market power prices, and low system safety and reliability and aggravate environmental pollution.
发明内容Contents of the invention
本发明提供一种需求响应资源动态分群方法及系统，其目的是先根据各需求响应资源对应特征间的欧氏距离获取需求响应资源的初始聚类中心，进而利用所述需求响应资源初始聚类中心对需求响应资源进行精确分群，对需求响应资源进行合理分类，以便计算出足够的发电备用容量，满足峰荷需求，克服现有技术中分类方法中分类结果同一类别资源对象差异性大和不利于资源相应分析的问题，节省电网系统运行成本。The present invention provides a dynamic grouping method and system for demand response resources, the purpose of which is to obtain the initial clustering center of demand response resources according to the Euclidean distance between the corresponding characteristics of each demand response resource, and then use the demand response resources for initial clustering The center accurately groups the demand response resources and reasonably classifies the demand response resources in order to calculate sufficient power generation reserve capacity to meet the peak load demand, and overcome the large differences in the classification results of the same category of resource objects in the classification method in the prior art and the unfavorable The problem of corresponding analysis of resources can save the operating cost of the power grid system.
本发明的目的是采用下述技术方案实现的：The object of the present invention is to adopt following technical scheme to realize:
一种需求响应资源动态分群方法，其改进之处在于，所述方法包括：A method for dynamically grouping demand response resources, the improvement of which is that the method includes:
根据各需求响应资源对应特征间的欧氏距离获取需求响应资源的初始聚类中心；According to the Euclidean distance between the corresponding features of each demand response resource, the initial clustering center of the demand response resource is obtained;
利用所述需求响应资源初始聚类中心对需求响应资源进行分群。The demand response resources are grouped by using the initial clustering center of the demand response resources.
优选地，所述需求响应资源对应特征，包括：需求响应资源的类型、需求响应资源的响应速度、需求响应资源的响应容量、需求响应资源的响应时长和需求响应资源的可调容量转化率。Preferably, the characteristics corresponding to demand response resources include: type of demand response resources, response speed of demand response resources, response capacity of demand response resources, response duration of demand response resources, and adjustable capacity conversion rate of demand response resources.
优选地，所述根据各需求响应资源对应特征间的欧氏距离获取需求响应资源的初始聚类中心，包括：Preferably, said obtaining the initial clustering center of the demand response resource according to the Euclidean distance between the corresponding features of each demand response resource includes:
步骤a.若各需求响应资源对应特征间的欧氏距离中两个需求响应资源对应特征间的欧氏距离最小，则所述两个需求响应资源对应特征的平均特征为初始聚类中心；Step a. If the Euclidean distance between the corresponding features of each demand response resource is the smallest among the Euclidean distances between the corresponding features of the two demand response resources, then the average feature of the corresponding features of the two demand response resources is the initial clustering center;
步骤b.去除与所述两个需求响应资源对应特征中任一需求响应资源对应特征间欧氏距离小于阀值α的需求响应资源对应特征，返回步骤a，直至全部需求响应资源对应特征均被去除，并输出全部初始聚类中心。Step b. Remove the corresponding features of demand response resources whose Euclidean distance between the corresponding features of the two demand response resources is smaller than the threshold α, and return to step a until all the corresponding features of demand response resources are Remove and output all initial cluster centers.
进一步地，按下式确定需求响应资源i对应特征与需求响应资源j对应特征间的欧氏距离dij：Further, the Euclidean distance d ij between the corresponding feature of demand response resource i and the corresponding feature of demand response resource j is determined according to the following formula:
式中，ir为需求响应资源i的第r个特征，jr为需求响应资源j的第r个特征，r∈[1,n],n为需求响应资源对应特征的数量。In the formula, i r is the rth feature of demand response resource i, j r is the rth feature of demand response resource j, r∈[1,n], n is the number of corresponding features of demand response resource.
优选地，所述利用所述需求响应资源初始聚类中心对需求响应资源进行分群，包括：Preferably, the clustering of demand response resources by using the initial clustering centers of demand response resources includes:
将所述需求响应资源初始聚类中心作为FCM聚类算法的初始聚类中心，对需求响应资源对应特征进行分群，获取需求响应资源的分群结果。The initial clustering center of the demand response resources is used as the initial clustering center of the FCM clustering algorithm, the corresponding characteristics of the demand response resources are grouped, and the clustering results of the demand response resources are obtained.
优选地，所述利用所述需求响应资源初始聚类中心对需求响应资源进行分群之后，包括：Preferably, after grouping the demand response resources by using the initial clustering center of the demand response resources, it includes:
根据所述需求响应资源的分群结果的分离程度和模糊程度对所述需求响应资源的分群结果进行评价；evaluating the grouping results of the demand response resources according to the degree of separation and fuzziness of the grouping results of the demand response resources;
其中，所述需求响应资源的分群结果的分离程度与所述需求响应资源的分群结果成正比，所述需求响应资源的分群结果的模糊程度与所述需求响应资源的分群结果成反比。Wherein, the degree of separation of the grouping results of the demand response resources is directly proportional to the grouping results of the demand response resources, and the fuzzy degree of the grouping results of the demand response resources is inversely proportional to the grouping results of the demand response resources.
进一步地，按下式确定所述需求响应资源的分群结果的分离程度KPC：Further, the separation degree K PC of the grouping results of the demand response resources is determined according to the following formula:
按下式确定所述需求响应资源的分群结果的模糊程度KCE：Determine the fuzzy degree K CE of the grouping results of the demand response resources according to the following formula:
式中，n为需求响应资源的数量，c为需求响应资源分群的数量，μij为第j个需求响应资源属于第i个群的隶属度值。In the formula, n is the number of demand response resources, c is the number of demand response resource groups, and μ ij is the membership value of the jth demand response resource belonging to the ith group.
一种需求响应资源动态分群系统，其改进之处在于，所述系统包括：A dynamic grouping system for demand response resources, the improvement of which is that the system includes:
获取模块，用于根据各需求响应资源对应特征间的欧氏距离获取需求响应资源的初始聚类中心；An acquisition module, configured to acquire the initial clustering center of the demand response resource according to the Euclidean distance between the corresponding features of each demand response resource;
分群模块，用于利用所述需求响应资源初始聚类中心对需求响应资源进行分群。A grouping module, configured to use the initial clustering center of the demand response resources to group the demand response resources.
优选地，所述需求响应资源对应特征，包括：需求响应资源的类型、需求响应资源的响应速度、需求响应资源的响应容量、需求响应资源的响应时长和需求响应资源的可调容量转化率。Preferably, the characteristics corresponding to demand response resources include: type of demand response resources, response speed of demand response resources, response capacity of demand response resources, response duration of demand response resources, and adjustable capacity conversion rate of demand response resources.
优选地，所述获取模块，用于：Preferably, the acquisition module is used for:
步骤a.若各需求响应资源对应特征间的欧氏距离中两个需求响应资源对应特征间的欧氏距离最小，则所述两个需求响应资源对应特征的平均特征为初始聚类中心；Step a. If the Euclidean distance between the corresponding features of each demand response resource is the smallest among the Euclidean distances between the corresponding features of the two demand response resources, then the average feature of the corresponding features of the two demand response resources is the initial clustering center;
步骤b.去除与所述两个需求响应资源对应特征中任一需求响应资源对应特征间欧氏距离小于阀值α的需求响应资源对应特征，返回步骤a，直至全部需求响应资源对应特征均被去除，并输出全部初始聚类中心。Step b. Remove the corresponding features of demand response resources whose Euclidean distance between the corresponding features of the two demand response resources is smaller than the threshold α, and return to step a until all the corresponding features of demand response resources are Remove and output all initial cluster centers.
进一步地，按下式确定需求响应资源i对应特征与需求响应资源j对应特征间的欧氏距离dij：Further, the Euclidean distance d ij between the corresponding feature of demand response resource i and the corresponding feature of demand response resource j is determined according to the following formula:
式中，ir为需求响应资源i的第r个特征，jr为需求响应资源j的第r个特征，r∈[1,n],n为需求响应资源对应特征的数量。In the formula, i r is the rth feature of demand response resource i, j r is the rth feature of demand response resource j, r∈[1,n], n is the number of corresponding features of demand response resource.
优选地，所述分群模块，用于：Preferably, the grouping module is used for:
将所述需求响应资源初始聚类中心作为FCM聚类算法的初始聚类中心，对需求响应资源对应特征进行分群，获取需求响应资源的分群结果。The initial clustering center of the demand response resources is used as the initial clustering center of the FCM clustering algorithm, the corresponding characteristics of the demand response resources are grouped, and the clustering results of the demand response resources are obtained.
优选地，所述系统还包括：Preferably, the system also includes:
评价模块，用于根据所述需求响应资源的分群结果的分离程度和模糊程度对所述需求响应资源的分群结果进行评价；An evaluation module, configured to evaluate the grouping results of the demand response resources according to the degree of separation and ambiguity of the grouping results of the demand response resources;
其中，所述需求响应资源的分群结果的分离程度与所述需求响应资源的分群结果成正比，所述需求响应资源的分群结果的模糊程度与所述需求响应资源的分群结果成反比。Wherein, the degree of separation of the grouping results of the demand response resources is directly proportional to the grouping results of the demand response resources, and the fuzzy degree of the grouping results of the demand response resources is inversely proportional to the grouping results of the demand response resources.
进一步地，按下式确定所述需求响应资源的分群结果的分离程度KPC：Further, the separation degree K PC of the grouping results of the demand response resources is determined according to the following formula:
按下式确定所述需求响应资源的分群结果的模糊程度KCE：Determine the fuzzy degree K CE of the grouping results of the demand response resources according to the following formula:
式中，n为需求响应资源的数量，c为需求响应资源分群的数量，μij为第j个需求响应资源属于第i个群的隶属度值。In the formula, n is the number of demand response resources, c is the number of demand response resource groups, and μ ij is the membership value of the jth demand response resource belonging to the ith group.
与最接近的现有技术相比，本发明还具有如下有益效果：Compared with the closest prior art, the present invention also has the following beneficial effects:
采用本发明的技术方案，根据各需求响应资源对应特征间的欧氏距离获取需求响应资源的初始聚类中心，基于需求响应资源对应的特征对需求响应资源进行分类，分类依据可靠，确保了分类结果的精确度；进一步利用所述需求响应资源初始聚类中心对需求响应资源进行分群，选取需求响应资源初始聚类中心，基于设定的初始聚类中心进行聚类算法实现需求响应资源的动态分群，避免了隶属度矩阵初始化步骤，并克服了聚类算法容易陷入局部收敛的问题，同时解决了现有分类技术导致的市场电价波动大、系统安全可靠性低、环境污染等问题，基于本发明的需求响应资源分群方法计算计算合理的发电备用容量，满足峰荷需求，有利于减少电网系统不必要的成本，促进电网系统的稳定运行。Adopting the technical scheme of the present invention, the initial clustering center of demand response resources is obtained according to the Euclidean distance between the corresponding features of each demand response resource, and the demand response resources are classified based on the characteristics corresponding to the demand response resources, and the classification basis is reliable, ensuring the classification The accuracy of the result; further use the initial clustering center of the demand response resource to group the demand response resources, select the initial clustering center of the demand response resource, and perform a clustering algorithm based on the set initial clustering center to realize the dynamics of the demand response resource Clustering avoids the initialization step of the membership matrix, and overcomes the problem that the clustering algorithm is easy to fall into local convergence. At the same time, it solves the problems of large market price fluctuations, low system security and reliability, and environmental pollution caused by the existing classification technology. Based on this The invented demand response resource grouping method calculates and calculates a reasonable power generation reserve capacity to meet the peak load demand, which is conducive to reducing unnecessary costs of the power grid system and promoting the stable operation of the power grid system.
附图说明Description of drawings
图1是本发明实施例需求响应资源动态分群方法的流程图；Fig. 1 is a flowchart of a method for dynamically grouping demand response resources according to an embodiment of the present invention;
图2是本发明实施例需求响应资源动态分群方法的流程明细图；Fig. 2 is a detailed flowchart of a method for dynamically grouping demand response resources according to an embodiment of the present invention;
图3是本发明实施例需求响应资源动态分群方法流程图。Fig. 3 is a flowchart of a method for dynamically grouping demand response resources according to an embodiment of the present invention.
具体实施方式Detailed ways
下面结合附图对本发明的具体实施方式作详细说明。The specific implementation manners of the present invention will be described in detail below in conjunction with the accompanying drawings.
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其它实施例，都属于本发明保护的范围。In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
本发明提供了一种需求响应资源动态分群方法及系统，下面进行说明。The present invention provides a method and system for dynamic grouping of demand response resources, which will be described below.
基于改进模糊C均值聚类算法将需求响应资源进行分类，使得具有相似特性的需求响应资源被归为同种类别，为解决因为需求响应资源分类不合理引起的发电成本较高、市场电价波动较大、系统安全可靠性较低、所造成的空气、水和土壤等环境污染打下了基础。Classify demand response resources based on the improved fuzzy C-means clustering algorithm, so that demand response resources with similar characteristics are classified into the same category. Large scale, low system safety and reliability, and the resulting environmental pollution of air, water and soil have laid the foundation.
在众多模糊聚类算法中，模糊C-均值fuzzy c-means algorithm(FCM)算法应用最广泛且较成功，它通过优化目标函数得到每个样本点对所有类中心的隶属度，从而决定样本点的类属以达到自动对样本数据进行分类的目的。模糊聚类分析作为无监督机器学习的主要技术之一，是用模糊理论对重要数据分析和建模的方法，建立了样本类属的不确定性描述，能比较客观地反映现实世界，它已经有效地应用在大规模数据分析、数据挖掘、矢量量化、图像分割、模式识别等领域，具有重要的理论与实际应用价值，随着应用的深入发展，模糊聚类算法的研究不断丰富。Among many fuzzy clustering algorithms, fuzzy c-means fuzzy c-means algorithm (FCM) algorithm is the most widely used and successful. It obtains the membership degree of each sample point to all cluster centers by optimizing the objective function, so as to determine the sample point category to achieve the purpose of automatically classifying sample data. As one of the main technologies of unsupervised machine learning, fuzzy clustering analysis is a method of analyzing and modeling important data with fuzzy theory, and establishes an uncertain description of sample categories, which can objectively reflect the real world. Effectively applied in large-scale data analysis, data mining, vector quantization, image segmentation, pattern recognition and other fields, it has important theoretical and practical application value. With the in-depth development of applications, the research on fuzzy clustering algorithms is constantly enriched.
图1示出了本发明实施例中需求响应资源动态分群方法的流程图，如图1所示，所述方法可以包括：Fig. 1 shows a flowchart of a method for dynamically grouping demand response resources in an embodiment of the present invention. As shown in Fig. 1, the method may include:
101.根据各需求响应资源对应特征间的欧氏距离获取需求响应资源的初始聚类中心；101. Obtain the initial clustering center of the demand response resource according to the Euclidean distance between the corresponding features of each demand response resource;
102.利用所述需求响应资源初始聚类中心对需求响应资源进行分群。102. Using the initial clustering center of the demand response resources to group the demand response resources.
图2示出了本发明实施例需求响应资源动态分群方法的流程明细图，如图2所示，需要先找出需求响应资源的聚类指标，即需求响应资源分类的影响因素，输入这些影响因素数据，所述需求响应资源对应特征，可以包括：需求响应资源的类型、需求响应资源的响应速度、需求响应资源的响应容量、需求响应资源的响应时长和需求响应资源的可调容量转化率；然后使用改进FCM算法对需求响应资源进行聚类评估，最后依据最佳聚类数下的隶属度矩阵确定需求响应资源的动态分群结果；Figure 2 shows a flow chart of the method for dynamically grouping demand response resources according to the embodiment of the present invention. Factor data, the corresponding characteristics of demand response resources, may include: the type of demand response resources, the response speed of demand response resources, the response capacity of demand response resources, the response time of demand response resources and the adjustable capacity conversion rate of demand response resources ; Then use the improved FCM algorithm to cluster and evaluate demand response resources, and finally determine the dynamic clustering results of demand response resources according to the membership matrix under the optimal cluster number;
其中，需求响应资源的聚类指标由其响应特性决定，从响应速度、响应容量、响应时长、可调容量转化率这4个方面衡量，划分为高、中、低三个等级；Among them, the clustering indicators of demand response resources are determined by their response characteristics, measured from the four aspects of response speed, response capacity, response time, and adjustable capacity conversion rate, and divided into three levels: high, medium, and low;
1)响应速度方面，考虑到发电侧调控速度一般在min级，高等需求响应资源的响应速度规定为5min以内；中等资源响应速度为5min-30min；低等资源响应速度大于30min。1) In terms of response speed, considering that the control speed of the power generation side is generally at the min level, the response speed of high-level demand response resources is stipulated within 5 minutes; the response speed of medium resources is 5-30 minutes; the response speed of low-level resources is greater than 30 minutes.
2)响应容量方面，个体响应容量大的用户为高等。高等资源的响应容量应大于3000kW，中等资源的响应容量介于1000-3000kW，低等资源的响应容量小于1000kW。2) In terms of response capacity, users with a large individual response capacity are considered to be higher. The response capacity of high-level resources should be greater than 3000kW, the response capacity of medium resources should be between 1000-3000kW, and the response capacity of low-level resources should be less than 1000kW.
3)响应时长方面，可持续超过3h为优，响应时长小于0.5h为普通型。3) In terms of response time, it is optimal if it lasts for more than 3 hours, and it is common if the response time is less than 0.5 hours.
4)可调容量转化率体现了需求响应资源调控难易程度，以及用户从潜力资源转换到可调资源的可能性，由资源分布是否集中、资源是否可直接调控、资源所属用户参与需求响应的意愿等共同决定。4) The conversion rate of adjustable capacity reflects the difficulty of regulating demand response resources and the possibility of users switching from potential resources to adjustable resources. willingness, etc. to decide jointly.
考虑到FCM算法对聚类中心敏感，若隶属度矩阵初始化不合理，算法易陷入局部最优解。本专利对需求响应资源的聚类分群分为2阶段执行，第1阶段按如下步骤进行聚类中心的选取，第2阶段根据第一阶段选取的初始聚类中心执行FCM聚类算法，从而避免了隶属度矩阵初始化步骤。Considering that the FCM algorithm is sensitive to the cluster center, if the initialization of the membership matrix is unreasonable, the algorithm will easily fall into a local optimal solution. This patent divides the clustering and grouping of demand response resources into two stages. In the first stage, the cluster center is selected according to the following steps. In the second stage, the FCM clustering algorithm is executed according to the initial cluster center selected in the first stage, so as to avoid The membership matrix initialization step is completed.
可以分别计算任意2种需求响应资源聚类指标间的欧式距离，生成距离矩阵D：The Euclidean distance between any two demand response resource clustering indicators can be calculated separately to generate a distance matrix D:
其中，M表示需求响应资源聚类指标的数量，drs表示第r种资源与第s种资源聚类指标间的欧式距离；Among them, M represents the number of demand response resource clustering indicators, and d rs represents the Euclidean distance between the r-th resource and the s-th resource clustering index;
将距离最近的2个聚类指标对应的需求响应资源归为一个群，取两个需求响应资源的中点作为第一个初始聚类中心。The demand response resources corresponding to the two closest clustering indicators are classified into a group, and the midpoint of the two demand response resources is taken as the first initial cluster center.
设定群间最小距离阀值α，通过距离矩阵D找出与第一个群中的2个聚类指标距离均大于α的聚类指标，并每次将D值最小的2个聚类指标对应的需求响应资源归为x群，取其中点作为第2个聚类中心。Set the minimum distance threshold α between groups, use the distance matrix D to find the clustering indicators whose distances from the two clustering indicators in the first group are both greater than α, and each time the two clustering indicators with the smallest D value The corresponding demand response resources are classified into the x group, and the midpoint is taken as the second cluster center.
具体地，所述根据各需求响应资源对应特征间的欧氏距离获取需求响应资源的初始聚类中心，可以包括：Specifically, the acquisition of the initial clustering center of the demand response resource according to the Euclidean distance between the corresponding features of each demand response resource may include:
步骤a.若各需求响应资源对应特征间的欧氏距离中两个需求响应资源对应特征间的欧氏距离最小，则所述两个需求响应资源对应特征的平均特征为初始聚类中心；Step a. If the Euclidean distance between the corresponding features of each demand response resource is the smallest among the Euclidean distances between the corresponding features of the two demand response resources, then the average feature of the corresponding features of the two demand response resources is the initial clustering center;
步骤b.去除与所述两个需求响应资源对应特征中任一需求响应资源对应特征间欧氏距离小于阀值α的需求响应资源对应特征，返回步骤a，直至全部需求响应资源对应特征均被去除，并输出全部初始聚类中心；Step b. Remove the corresponding features of demand response resources whose Euclidean distance between the corresponding features of the two demand response resources is smaller than the threshold α, and return to step a until all the corresponding features of demand response resources are Remove and output all initial cluster centers;
本发明选取初始聚类中心的原则是使得各个初始聚类中心间的距离大于距离阀值α，从而在多个可行区间进行初始聚类中心的选取，避免了初始聚类中心距离过近导致算法陷入局部收敛的缺点，因此距离阀值α应尽可能大一些，但是过大会导致选不出满足条件的c个初始聚类中心，此时需要减少α的值，采用这样的技术方案避免了隶属度矩阵的初始化，使得算法不会轻易陷入局部最优解。The principle of selecting initial clustering centers in the present invention is to make the distance between each initial clustering center greater than the distance threshold α, so that initial clustering centers can be selected in multiple feasible intervals, and the algorithm is prevented from being too close to the initial clustering centers. Falling into the shortcoming of local convergence, so the distance threshold α should be as large as possible, but if it is too large, c initial cluster centers that meet the conditions cannot be selected. At this time, the value of α needs to be reduced. This technical solution avoids the membership The initialization of the degree matrix makes the algorithm not easily fall into the local optimal solution.
其中，按下式确定需求响应资源i对应特征与需求响应资源j对应特征间的欧氏距离dij：Among them, the Euclidean distance d ij between the corresponding feature of demand response resource i and the corresponding feature of demand response resource j is determined according to the following formula:
式中，ir为需求响应资源i的第r个特征，jr为需求响应资源j的第r个特征，r∈[1,n],n为需求响应资源对应特征的数量。In the formula, i r is the rth feature of demand response resource i, j r is the rth feature of demand response resource j, r∈[1,n], n is the number of corresponding features of demand response resource.
所述利用所述需求响应资源初始聚类中心对需求响应资源进行分群，可以包括：The clustering of demand response resources by using the initial clustering center of demand response resources may include:
将所述需求响应资源初始聚类中心作为FCM聚类算法的初始聚类中心，对需求响应资源对应特征进行分群，获取需求响应资源的分群结果；Using the initial clustering center of the demand response resources as the initial clustering center of the FCM clustering algorithm, grouping the corresponding characteristics of the demand response resources, and obtaining the clustering results of the demand response resources;
所述FCM算法对需求响应资源进行聚类评估时，具体过程如下：When the FCM algorithm clusters and evaluates demand response resources, the specific process is as follows:
聚类算法是一个迭代寻优的过程，思想就是使得被聚到同一群的向量之间相似度较大，不同群的向量之间的相似度较小。在对需求响应资源聚类过程中，定义需求响应资源的使用值区间为[0,1]，FCM使得每种需求响应资源的使用值在其区间内的隶属度来确定其属于各个分群的程度，聚类结果所对应的隶属度矩阵就是一个模糊聚类矩阵。The clustering algorithm is an iterative optimization process, the idea is to make the similarity between the vectors clustered into the same group larger, and the similarity between the vectors of different groups smaller. In the process of clustering demand response resources, the use value interval of demand response resources is defined as [0,1], and FCM makes the membership degree of the use value of each demand response resource in its interval determine the degree to which it belongs to each group , the membership matrix corresponding to the clustering result is a fuzzy clustering matrix.
FCM聚类把n种需求响应资源对应的聚类指标xj(j＝1,2,…,n)分为c个分群，FCM clustering divides the clustering index x j (j=1,2,…,n) corresponding to n kinds of demand response resources into c groups,
并求出每个群中聚类指标的中心点vi(i＝1,2,…,n)，And calculate the central point v i (i=1,2,...,n) of the clustering index in each group,
使得目标函数值达到最小，目标函数Jm(c)如下：To minimize the value of the objective function, the objective function J m (c) is as follows:
式中：||vi-xj||2为第i个聚类中心点与第j种需求响应资源聚类指标间的欧式距离；m∈[1，∞)为模糊系数，一般取为2；uij表示第j种需求响应资源属于第i个聚类中心点对应的群的隶属度值，满足归一化规定：In the formula: ||v i -x j || 2 is the Euclidean distance between the i-th cluster center point and the j-th demand response resource clustering index; m∈[1,∞) is the fuzzy coefficient, generally taken as 2; u ij represents the membership degree value of the j-th demand response resource belonging to the group corresponding to the i-th cluster center point, which satisfies the normalization requirement:
为求使得式(2)达到最小值的必要条件，构造如下中间函数
式中：λj是式(3)中3个约束式的拉格朗日乘子。对所有输入参数求导，得到使式(2)达到最小值的必要条件为：In the formula: λ j is the Lagrangian multiplier of the three constraints in formula (3). Taking derivatives for all input parameters, the necessary conditions to obtain the minimum value of formula (2) are:
式中：k＝1,2,…,c；In the formula: k=1,2,...,c;
由此可见，FCM算法的输出包括2部分：第1部分为c个聚类中心点，每一个聚类中心点表示对应的群中需求响应资源聚类指标的平均特征；第2部分为一个c×n的模糊划分矩阵，表示每种需求响应资源属于各个群的隶属度，通常按照模糊集最大隶属原则确定每种需求响应资源归属的群；It can be seen that the output of the FCM algorithm includes two parts: the first part is c clustering center points, and each clustering center point represents the average feature of the demand response resource clustering index in the corresponding group; the second part is a c The fuzzy partition matrix of ×n indicates the membership degree of each demand response resource belonging to each group, and the group to which each demand response resource belongs is usually determined according to the principle of the maximum membership of fuzzy sets;
聚类指标值发生变化时，需要重新对需求响应资源聚类分群，而第j种需求响应资源属于第i个聚类中心点对应的群的隶属度值由式(3)获得，可减少需求响应资源分群所需时间，满足动态分群的实时性要求。When the value of the clustering index changes, it is necessary to re-cluster the demand response resources into clusters, and the membership degree value of the jth demand response resource belonging to the group corresponding to the i-th cluster center point is obtained by formula (3), which can reduce the demand The time required to respond to resource grouping meets the real-time requirements of dynamic grouping.
依据最佳聚类数下的隶属度矩阵确定需求响应资源的动态分群结果，因此，所述利用所述需求响应资源初始聚类中心对需求响应资源进行分群之后，可以包括：The dynamic grouping result of the demand response resources is determined according to the membership degree matrix under the optimal clustering number. Therefore, after the grouping of the demand response resources by using the initial clustering center of the demand response resources may include:
根据所述需求响应资源的分群结果的分离程度和模糊程度对所述需求响应资源的分群结果进行评价；evaluating the grouping results of the demand response resources according to the degree of separation and fuzziness of the grouping results of the demand response resources;
其中，所述需求响应资源的分群结果的分离程度与所述需求响应资源的分群结果成正比，所述需求响应资源的分群结果的模糊程度与所述需求响应资源的分群结果成反比。Wherein, the degree of separation of the grouping results of the demand response resources is directly proportional to the grouping results of the demand response resources, and the fuzzy degree of the grouping results of the demand response resources is inversely proportional to the grouping results of the demand response resources.
具体地，按下式确定所述需求响应资源的分群结果的分离程度KPC：Specifically, the separation degree K PC of the grouping results of the demand response resources is determined according to the following formula:
按下式确定所述需求响应资源的分群结果的模糊程度KCE：Determine the fuzzy degree K CE of the grouping results of the demand response resources according to the following formula:
式中，n为需求响应资源的数量，c为需求响应资源分群的数量，μij为第j个需求响应资源属于第i个群的隶属度值；KPC用于评价不同需求响应资源聚类分群间的分离程度，取值越大越好；KCE用于评价不同需求响应资源聚类分群间的模糊程度，取值越小越好。In the formula, n is the number of demand response resources, c is the number of demand response resource groups, μ ij is the membership degree value of the jth demand response resource belonging to the i-th group; KPC is used to evaluate different demand response resource clusters The degree of separation between clusters, the larger the value, the better; KCE is used to evaluate the degree of ambiguity between clusters of different demand response resources, and the smaller the value, the better.
依据最佳聚类数下的隶属度矩阵确定需求响应资源的动态分群结果如表1所示，具体为：According to the membership degree matrix under the optimal clustering number, the dynamic grouping results of demand response resources are shown in Table 1, specifically:
表1动态分群结果Table 1 Dynamic clustering results
表中，a1,…at+1；b1,…bt+1；e1,…et+1；f1,…ft+1分别表示响应速度、响应容量、响应时长、可调容量转化率数值区间中的分割点；t表示所有需求响应资源的种类数。In the table, a 1 ,…a t+1 ; b 1 ,…b t+1 ; e 1 ,…e t+1 ; f 1 ,…f t+1 represent the response speed, response capacity, response time, available The dividing point in the value interval of capacity adjustment conversion rate; t represents the number of types of all demand response resources.
图3示出了本发明实施例需求响应资源动态分群系统的结构示意图，如图3所示，所述系统可以包括：Fig. 3 shows a schematic structural diagram of a demand response resource dynamic grouping system according to an embodiment of the present invention. As shown in Fig. 3, the system may include:
获取模块，用于根据各需求响应资源对应特征间的欧氏距离获取需求响应资源的初始聚类中心；An acquisition module, configured to acquire the initial clustering center of the demand response resource according to the Euclidean distance between the corresponding features of each demand response resource;
分群模块，用于利用所述需求响应资源初始聚类中心对需求响应资源进行分群。A grouping module, configured to use the initial clustering center of the demand response resources to group the demand response resources.
其中，所述需求响应资源对应特征，可以包括：需求响应资源的类型、需求响应资源的响应速度、需求响应资源的响应容量、需求响应资源的响应时长和需求响应资源的可调容量转化率。Wherein, the corresponding characteristics of demand response resources may include: type of demand response resources, response speed of demand response resources, response capacity of demand response resources, response duration of demand response resources, and adjustable capacity conversion rate of demand response resources.
具体地，所述获取模块，用于：Specifically, the acquisition module is used to:
步骤a.若各需求响应资源对应特征间的欧氏距离中两个需求响应资源对应特征间的欧氏距离最小，则所述两个需求响应资源对应特征的平均特征为初始聚类中心；Step a. If the Euclidean distance between the corresponding features of each demand response resource is the smallest among the Euclidean distances between the corresponding features of the two demand response resources, then the average feature of the corresponding features of the two demand response resources is the initial clustering center;
步骤b.去除与所述两个需求响应资源对应特征中任一需求响应资源对应特征间欧氏距离小于阀值α的需求响应资源对应特征，返回步骤a，直至全部需求响应资源对应特征均被去除，并输出全部初始聚类中心。Step b. Remove the corresponding features of demand response resources whose Euclidean distance between the corresponding features of the two demand response resources is smaller than the threshold α, and return to step a until all the corresponding features of demand response resources are Remove and output all initial cluster centers.
按下式确定需求响应资源i对应特征与需求响应资源j对应特征间的欧氏距离dij：Determine the Euclidean distance d ij between the corresponding feature of demand response resource i and the corresponding feature of demand response resource j according to the following formula:
式中，ir为需求响应资源i的第r个特征，jr为需求响应资源j的第r个特征，r∈[1,n],n为需求响应资源对应特征的数量。In the formula, i r is the rth feature of demand response resource i, j r is the rth feature of demand response resource j, r∈[1,n], n is the number of corresponding features of demand response resource.
具体地，所述分群模块，用于：将所述需求响应资源初始聚类中心作为FCM聚类算法的初始聚类中心，对需求响应资源对应特征进行分群，获取需求响应资源的分群结果。Specifically, the grouping module is configured to: use the initial clustering center of the demand response resource as the initial clustering center of the FCM clustering algorithm, group the corresponding characteristics of the demand response resource, and obtain the clustering result of the demand response resource.
所述系统还可以包括：The system may also include:
评价模块，用于根据所述需求响应资源的分群结果的分离程度和模糊程度对所述需求响应资源的分群结果进行评价；An evaluation module, configured to evaluate the grouping results of the demand response resources according to the degree of separation and ambiguity of the grouping results of the demand response resources;
其中，所述需求响应资源的分群结果的分离程度与所述需求响应资源的分群结果成正比，所述需求响应资源的分群结果的模糊程度与所述需求响应资源的分群结果成反比。Wherein, the degree of separation of the grouping results of the demand response resources is directly proportional to the grouping results of the demand response resources, and the fuzzy degree of the grouping results of the demand response resources is inversely proportional to the grouping results of the demand response resources.
具体地，按下式确定所述需求响应资源的分群结果的分离程度KPC：Specifically, the separation degree K PC of the grouping results of the demand response resources is determined according to the following formula:
按下式确定所述需求响应资源的分群结果的模糊程度KCE：Determine the fuzzy degree K CE of the grouping results of the demand response resources according to the following formula:
式中，n为需求响应资源的数量，c为需求响应资源分群的数量，μij为第j个需求响应资源属于第i个群的隶属度值。In the formula, n is the number of demand response resources, c is the number of demand response resource groups, and μ ij is the membership value of the jth demand response resource belonging to the ith group.
本领域内的技术人员应明白，本申请的实施例可提供为方法、系统、或计算机程序产品。因此，本申请可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。Those skilled in the art should understand that the embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
本申请是参照根据本申请实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。The present application is described with reference to flowcharts and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each procedure and/or block in the flowchart and/or block diagram, and a combination of procedures and/or blocks in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions may be provided to a general purpose computer, special purpose computer, embedded processor, or processor of other programmable data processing equipment to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing equipment produce a An apparatus for realizing the functions specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions The device realizes the function specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。These computer program instructions can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby The instructions provide steps for implementing the functions specified in the flow chart or blocks of the flowchart and/or the block or blocks of the block diagrams.
最后应当说明的是：以上实施例仅用以说明本发明的技术方案而非对其限制，尽管参照上述实施例对本发明进行了详细的说明，所属领域的普通技术人员应当理解：依然可以对本发明的具体实施方式进行修改或者等同替换，而未脱离本发明精神和范围的任何修改或者等同替换，其均应涵盖在本发明的权利要求保护范围之内。Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the present invention can still be Any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention shall fall within the protection scope of the claims of the present invention.
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