CN114819530A - A demand-side flexible resource adjustment potential forecasting method and system - Google Patents
A demand-side flexible resource adjustment potential forecasting method and system Download PDFInfo
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Abstract
本发明提供了一种需求侧灵活资源可调潜力预测方法和系统,包括:对选定区域多个用户的负荷数据依次从时间尺度和行业尺度进行聚类,得到各季节下各行业用户的典型日负荷曲线;基于各季节下各行业用户的典型日负荷曲线,采用差分自回归移动平均模型预测未来时刻各行业用户的负荷值;根据未来时刻各行业用户的负荷值,计算需求侧灵活资源可调潜力;本发明采用差分自回归移动平均模型预测未来时刻各行业用户的负荷值,可有效处理输入数据可能出现的不平稳性,有效减少数据不平稳性对预测结果的影响,同时基于未来时刻各行业用户的负荷值,计算需求侧灵活资源可调潜力,可不需建立复杂的设备负荷模型,有效简化计算可调潜力的模型。
The invention provides a method and system for predicting the adjustable potential of flexible resources on the demand side, including: clustering the load data of multiple users in a selected area in turn from the time scale and the industry scale, to obtain typical users of each industry in each season. Daily load curve: Based on the typical daily load curve of users in various industries in each season, the differential autoregressive moving average model is used to predict the load value of users in various industries in the future; The present invention uses the differential autoregressive moving average model to predict the load value of users in various industries in the future, which can effectively deal with the possible instability of the input data, effectively reduce the impact of the data instability on the prediction results, and based on the future moment. The load value of users in various industries can be used to calculate the adjustable potential of flexible resources on the demand side. It is not necessary to establish a complex equipment load model, which effectively simplifies the model for calculating the adjustable potential.
Description
技术领域technical field
本发明属于可调节负荷潜力计算方法技术领域,具体涉及一种需求侧灵活资源可调潜力预测方法和系统。The invention belongs to the technical field of an adjustable load potential calculation method, and in particular relates to a demand-side flexible resource adjustable potential prediction method and system.
背景技术Background technique
随着社会的发展和产业结构的调整,电力系统正面临新的挑战,表现为峰谷差持续增长、新能源发电比例逐渐增高带来的清洁能源消纳等问题。需求侧资源由于其灵活性和可调节性,有利于解决电力系统所面临的这一系列问题,电力企业对其参与电网调度互动逐渐引起重视。因此,研究和预测可调节负荷的调控潜力,对电力系统优化运行和控制具有重要作用,对电力需求侧管理、负荷调度也有十分重要的意义。With the development of society and the adjustment of industrial structure, the power system is facing new challenges, such as the continuous growth of peak-to-valley difference and the consumption of clean energy brought about by the gradual increase in the proportion of new energy power generation. Due to its flexibility and adjustability, demand-side resources are conducive to solving a series of problems faced by the power system. Therefore, researching and predicting the regulation potential of adjustable loads plays an important role in the optimal operation and control of power systems, as well as in power demand side management and load dispatching.
近年来,随着新型电力电子技术和控制手段的发展,负荷特性发生了根本性变化。一方面,用户能够针对市场价格或激励机制改变原有电力消费模式,主动参与电网运行控制;另一方面,电动汽车、储能设备等具备与电网双向互动的能力,具备削峰填谷的功能,为电网调控提供了新的手段。负荷参与电网互动调度方面的研究成为关注焦点。需求侧的可调负荷可作为削减高峰负荷、平衡电力供应缺口的重要手段。针对负荷特性的研究方法主要有统计综合法、总体辨识法、故障仿真法等,为研究可调潜力提供数据支撑。目前,对于可调潜力的预测方法主要是基于设备模型的,这种方法需要建立大量的设备模型。设备类型、数量、运行特性等设备模型所需要的参数种类繁多且不宜获得,这成为预测可调潜力时的一大难题。In recent years, with the development of new power electronic technology and control methods, the load characteristics have undergone fundamental changes. On the one hand, users can change the original power consumption mode according to market prices or incentive mechanisms, and actively participate in the control of power grid operation; on the other hand, electric vehicles, energy storage equipment, etc. have the ability to interact with the power grid in two directions, and have the function of peak shaving and valley filling. , which provides a new means for power grid regulation. The research on load participation in grid interactive dispatch has become the focus of attention. The adjustable load on the demand side can be used as an important means to reduce the peak load and balance the power supply gap. The research methods for load characteristics mainly include statistical synthesis method, overall identification method, fault simulation method, etc., which provide data support for the study of adjustable potential. At present, the prediction methods for tunable potential are mainly based on equipment models, which require the establishment of a large number of equipment models. The parameters required by the equipment model, such as equipment type, quantity, and operating characteristics, are various and inappropriate to obtain, which becomes a major problem in predicting the tunable potential.
发明内容SUMMARY OF THE INVENTION
为克服上述现有技术的不足,本发明提出一种需求侧灵活资源可调潜力预测方法,包括:In order to overcome the above-mentioned deficiencies of the prior art, the present invention proposes a demand-side flexible resource adjustment potential prediction method, including:
对选定区域多个用户的负荷数据依次从时间尺度和行业尺度进行聚类,得到各季节下各行业用户的典型日负荷曲线;The load data of multiple users in the selected area are clustered in turn from the time scale and the industry scale, and the typical daily load curve of users in each industry in each season is obtained;
基于各季节下各行业用户的典型日负荷曲线,采用差分自回归移动平均模型预测未来时刻各行业用户的负荷值;Based on the typical daily load curve of users in various industries in each season, the differential autoregressive moving average model is used to predict the load value of users in various industries in the future;
根据未来时刻各行业用户的负荷值,计算需求侧灵活资源可调潜力。According to the load value of users in various industries in the future, the adjustable potential of flexible resources on the demand side is calculated.
优选的,所述对选定区域多个用户的负荷数据依次从时间尺度和行业尺度进行聚类,得到各季节下各行业用户的典型日负荷曲线,包括:Preferably, the load data of multiple users in the selected area are clustered sequentially from the time scale and the industry scale to obtain typical daily load curves of users in each industry in each season, including:
对选定区域多个用户的负荷数据,利用Canopy-Kmeans聚类算法从时间尺度进行聚类,得到各季节下各用户的典型日负荷曲线;For the load data of multiple users in the selected area, the Canopy-Kmeans clustering algorithm is used to cluster from the time scale, and the typical daily load curve of each user in each season is obtained;
对各用户各季节的典型日负荷曲线,利用Canopy-Kmeans聚类算法从行业尺度进行聚类,得到各季节下各行业用户的典型日负荷曲线。For the typical daily load curve of each user in each season, the Canopy-Kmeans clustering algorithm is used to cluster from the industry scale, and the typical daily load curve of each industry user in each season is obtained.
优选的,所述对选定区域多个用户的负荷数据,利用Canopy-Kmeans聚类算法从时间尺度进行聚类,得到各季节下各用户的典型日负荷曲线,包括:Preferably, the load data of multiple users in the selected area is clustered from the time scale using the Canopy-Kmeans clustering algorithm to obtain the typical daily load curve of each user in each season, including:
基于选定区域多个用户的负荷数据,计算各用户的负荷率、峰谷差和平均负荷;Based on the load data of multiple users in the selected area, calculate the load rate, peak-to-valley difference and average load of each user;
基于各用户的负荷率、峰谷差和平均负荷,采用Canopy聚类算法从多个用户的负荷数据中找到多个时间聚类中心点;Based on the load rate, peak-to-valley difference and average load of each user, the Canopy clustering algorithm is used to find multiple time clustering center points from the load data of multiple users;
基于所述时间聚类中心点和各用户的负荷率、峰谷差和平均负荷,利用Kmeans聚类算法从时间尺度对多个用户的负荷数据进行聚类,得到各季节下各用户的典型日负荷曲线。Based on the time clustering center point and the load rate, peak-to-valley difference and average load of each user, the Kmeans clustering algorithm is used to cluster the load data of multiple users from the time scale, and the typical day of each user in each season is obtained. load curve.
优选的,所述对各用户各季节的典型日负荷曲线,利用Canopy-Kmeans聚类算法从行业尺度进行聚类,得到各季节下各行业用户的典型日负荷曲线,包括:Preferably, the typical daily load curve of each user in each season is clustered from the industry scale using the Canopy-Kmeans clustering algorithm to obtain the typical daily load curve of each industry user in each season, including:
基于各用户的负荷率、峰谷差和平均负荷,采用Canopy聚类算法从各用户各季节的典型日负荷曲线中找到多个行业聚类中心点;Based on the load rate, peak-valley difference and average load of each user, the Canopy clustering algorithm is used to find multiple industry cluster center points from the typical daily load curve of each user in each season;
基于所述行业聚类中心点和各用户的负荷率、峰谷差和平均负荷,利用Kmeans聚类算法从行业尺度对各用户各季节的典型日负荷曲线进行聚类,得到各季节下各行业用户的典型日负荷曲线。Based on the industry clustering center point and the load rate, peak-to-valley difference and average load of each user, the Kmeans clustering algorithm is used to cluster the typical daily load curve of each user in each season from the industry scale, and obtain each industry in each season. Typical daily load profile of the user.
优选的,所述基于各季节下各行业用户的典型日负荷曲线,采用差分自回归移动平均模型预测未来时刻各行业用户的负荷值,包括:Preferably, based on the typical daily load curves of users in various industries in each season, a differential autoregressive moving average model is used to predict the load values of users in various industries in the future, including:
用单位根检验方法分别对各季节下各行业用户的典型日负荷曲线进行平稳性检验:若检验结果为平稳,则将典型日负荷曲线的差分阶数设置为预设值,否则对典型日负荷曲线进行差分运算,得到差分阶数;The unit root test method is used to test the stationarity of the typical daily load curves of users in each industry in each season: if the test results are stable, the difference order of the typical daily load curve is set to the preset value, otherwise the typical daily load The difference operation is performed on the curve to obtain the difference order;
分别计算各季节下各行业用户的典型日负荷曲线的自相关系数和偏自相关系数,并根据所述自相关系数和偏自相关系数确定自回归模型阶数和移动平均模型阶数;Calculate the autocorrelation coefficient and partial autocorrelation coefficient of the typical daily load curve of users in each industry in each season, and determine the autoregressive model order and the moving average model order according to the autocorrelation coefficient and the partial autocorrelation coefficient;
分别基于各季节下各行业用户的典型日负荷曲线的差分阶数、自回归模型阶数和移动平均模型阶数,构建差分自回归移动平均模型;Based on the difference order, autoregressive model order and moving average model order of the typical daily load curves of users in various industries in each season, the difference autoregressive moving average model is constructed;
分别采用对应的差分自回归移动平均模型预测未来时刻各行业用户的负荷值。The corresponding differential autoregressive moving average models are used to predict the load value of users in various industries in the future.
优选的,所述根据未来时刻各行业用户的负荷值,计算需求侧灵活资源可调潜力,包括:Preferably, according to the load value of users in various industries in the future, calculating the adjustable potential of flexible resources on the demand side, including:
针对各行业的用户,统计未来时刻前指定时长范围内,所述行业用户在同一历史时刻的负荷值分布,并根据负荷值的分布计算负荷的均值和方差;For users in various industries, count the load value distribution of users in the industry at the same historical moment within the specified time range before the future time, and calculate the mean and variance of the load according to the distribution of load values;
基于所述负荷的均值和方差,取满足三西格玛原则中的最大值和最小值作为对应行业用户在未来时刻的最大基线负荷和最小基线负荷;Based on the mean value and variance of the load, the maximum and minimum values satisfying the Three Sigma principle are taken as the maximum baseline load and the minimum baseline load of the corresponding industry user in the future;
基于未来时刻各行业用户的负荷值、最大基线负荷和最小基线负荷,计算需求侧灵活资源可调潜力。Based on the load value, maximum baseline load and minimum baseline load of users in various industries in the future, the adjustable potential of flexible resources on the demand side is calculated.
优选的,所述基于未来时刻各行业用户的负荷值、最大基线负荷和最小基线负荷,计算需求侧灵活资源可调潜力,包括:Preferably, based on the load value, the maximum baseline load and the minimum baseline load of users in various industries in the future, the calculation of the demand-side flexible resource adjustment potential includes:
基于未来时刻各行业用户的负荷值和最大基线负荷,计算需求侧负荷上调潜力,并基于未来时刻各行业用户的负荷值和最小基线负荷,计算需求侧负荷下调潜力;Based on the load value and the maximum baseline load of users in various industries in the future, calculate the potential of demand-side load increase, and based on the load value and minimum baseline load of users in various industries in the future, calculate the potential of demand-side load reduction;
以所述需求侧负荷上调潜力和需求侧负荷下调潜力作为需求侧灵活资源可调潜力。The demand-side load increasing potential and the demand-side load decreasing potential are used as the demand-side flexible resource adjustment potential.
优选的,所述需求侧负荷上调潜力的计算式如下:Preferably, the calculation formula of the demand-side load upward adjustment potential is as follows:
η1,j=Mj×(L1,j-HM-N,j)η 1,j =M j ×(L 1,j -H MN,j )
式中,η1,j为行业j的需求侧负荷上调潜力,Mj为行业j的用户数量,L1,j为行业j的最大基线负荷,HM-N,j为未来时刻行业j用户的负荷值;In the formula, η 1,j is the demand-side load increase potential of industry j, M j is the number of users in industry j, L 1,j is the maximum baseline load of industry j, and H MN,j is the load of users in industry j in the future value;
所述需求侧负荷下调潜力的计算式如下:The calculation formula of the demand-side load reduction potential is as follows:
η2,j=Mj×(HM-N,j-L2,j)η 2,j =M j ×(H MN,j -L 2,j )
式中,η2,j为行业j的需求侧负荷下调潜力,L2,j为行业j的最小基线负荷。where η 2,j is the demand-side load reduction potential of industry j, and L 2,j is the minimum baseline load of industry j.
基于同一发明构思,本发明还提供了一种需求侧灵活资源可调潜力预测系统,包括:负荷聚类模块、负荷预测模块和可调潜力模块;Based on the same inventive concept, the present invention also provides a demand-side flexible resource adjustable potential prediction system, including: a load clustering module, a load prediction module and an adjustable potential module;
所述负荷聚类模块,用于对选定区域多个用户的负荷数据依次从时间尺度和行业尺度进行聚类,得到各季节下各行业用户的典型日负荷曲线;The load clustering module is used for clustering the load data of multiple users in the selected area in turn from the time scale and the industry scale to obtain the typical daily load curves of users in each industry in each season;
所述负荷预测模块,用于基于各季节下各行业用户的典型日负荷曲线,采用差分自回归移动平均模型预测未来时刻各行业用户的负荷值;The load prediction module is used to predict the load value of users in various industries in the future by using a differential autoregressive moving average model based on the typical daily load curves of users in various industries in each season;
所述可调潜力模块,用于根据未来时刻各行业用户的负荷值,计算需求侧灵活资源可调潜力。The adjustable potential module is used to calculate the adjustable potential of demand-side flexible resources according to the load values of users in various industries in the future.
优选的,所述负荷聚类模块具体用于:Preferably, the load clustering module is specifically used for:
对选定区域多个用户的负荷数据,利用Canopy-Kmeans聚类算法从时间尺度进行聚类,得到各季节下各用户的典型日负荷曲线;For the load data of multiple users in the selected area, the Canopy-Kmeans clustering algorithm is used to cluster from the time scale, and the typical daily load curve of each user in each season is obtained;
对各用户各季节的典型日负荷曲线,利用Canopy-Kmeans聚类算法从行业尺度进行聚类,得到各季节下各行业用户的典型日负荷曲线。For the typical daily load curve of each user in each season, the Canopy-Kmeans clustering algorithm is used to cluster from the industry scale, and the typical daily load curve of each industry user in each season is obtained.
优选的,所述负荷预测模块,具体用于:Preferably, the load prediction module is specifically used for:
用单位根检验方法分别对各季节下各行业用户的典型日负荷曲线进行平稳性检验:若检验结果为平稳,则将典型日负荷曲线的差分阶数设置为预设值,否则对典型日负荷曲线进行差分运算,得到差分阶数;The unit root test method is used to test the stationarity of the typical daily load curves of users in each industry in each season: if the test results are stable, the difference order of the typical daily load curve is set to the preset value, otherwise the typical daily load The difference operation is performed on the curve to obtain the difference order;
分别计算各季节下各行业用户的典型日负荷曲线的自相关系数和偏自相关系数,并根据所述自相关系数和偏自相关系数确定自回归模型阶数和移动平均模型阶数;Calculate the autocorrelation coefficient and partial autocorrelation coefficient of the typical daily load curve of users in each industry in each season, and determine the autoregressive model order and the moving average model order according to the autocorrelation coefficient and the partial autocorrelation coefficient;
分别基于各季节下各行业用户的典型日负荷曲线的差分阶数、自回归模型阶数和移动平均模型阶数,构建差分自回归移动平均模型;Based on the difference order, autoregressive model order and moving average model order of the typical daily load curves of users in various industries in each season, the difference autoregressive moving average model is constructed;
分别采用对应的差分自回归移动平均模型预测未来时刻各行业用户的负荷值。The corresponding differential autoregressive moving average models are used to predict the load value of users in various industries in the future.
优选的,所述可调潜力模块具体用于:Preferably, the adjustable potential module is specifically used for:
针对各行业的用户,统计未来时刻前指定时长范围内,所述行业用户在同一历史时刻的负荷值分布,并根据负荷值的分布计算负荷的均值和方差;For users in various industries, count the load value distribution of users in the industry at the same historical moment within the specified time range before the future time, and calculate the mean and variance of the load according to the distribution of load values;
基于所述负荷的均值和方差,取满足三西格玛原则中的最大值和最小值作为对应行业用户在未来时刻的最大基线负荷和最小基线负荷;Based on the mean value and variance of the load, the maximum and minimum values satisfying the Three Sigma principle are taken as the maximum baseline load and the minimum baseline load of the corresponding industry user in the future;
基于未来时刻各行业用户的负荷值、最大基线负荷和最小基线负荷,计算需求侧灵活资源可调潜力。Based on the load value, maximum baseline load and minimum baseline load of users in various industries in the future, the adjustable potential of flexible resources on the demand side is calculated.
本发明还提供一种计算机设备,包括:一个或多个处理器;The present invention also provides a computer device, comprising: one or more processors;
存储器,用于存储一个或多个程序;memory for storing one or more programs;
当所述一个或多个程序被所述一个或多个处理器执行时,实现如前所述的需求侧灵活资源可调潜力预测方法。When the one or more programs are executed by the one or more processors, the aforementioned method for predicting the adjustable potential of demand-side flexible resources is implemented.
本发明还提供一种计算机可读存储介质,其上存有计算机程序,所述计算机程序被执行时,实现如前所述的需求侧灵活资源可调潜力预测方法。The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed, the aforementioned method for predicting the adjustable potential of flexible resources on the demand side is implemented.
与最接近的现有技术相比,本发明具有的有益效果如下:Compared with the closest prior art, the present invention has the following beneficial effects:
本发明提供了一种需求侧灵活资源可调潜力预测方法和系统,包括:对选定区域多个用户的负荷数据依次从时间尺度和行业尺度进行聚类,得到各季节下各行业用户的典型日负荷曲线;基于各季节下各行业用户的典型日负荷曲线,采用差分自回归移动平均模型预测未来时刻各行业用户的负荷值;根据未来时刻各行业用户的负荷值,计算需求侧灵活资源可调潜力;本发明采用差分自回归移动平均模型预测未来时刻各行业用户的负荷值,可有效处理输入数据可能出现的不平稳性,有效减少数据不平稳性对预测结果的影响,同时基于未来时刻各行业用户的负荷值,计算需求侧灵活资源可调潜力,可不需建立复杂的设备负荷模型,有效简化计算可调潜力的模型。The invention provides a method and system for predicting the adjustable potential of flexible resources on the demand side, including: clustering the load data of multiple users in a selected area in turn from the time scale and the industry scale, to obtain the typical users of each industry in each season. Daily load curve: Based on the typical daily load curve of users in various industries in each season, the differential autoregressive moving average model is used to predict the load value of users in various industries in the future; The present invention uses the differential autoregressive moving average model to predict the load value of users in various industries in the future, which can effectively deal with the possible instability of the input data, effectively reduce the impact of the data instability on the prediction results, and based on the future moment. The load value of users in various industries can be used to calculate the adjustable potential of flexible resources on the demand side. It is not necessary to establish a complex equipment load model, which effectively simplifies the model for calculating the adjustable potential.
本发明利用Canopy-Kmeans聚类算法对大量数据进行了时间和行业尺度上的分类,为分季节分行业分析用户的可调潜力提供了分类的依据,且根据得到的典型日负荷曲线作为该类负荷的计算依据,简化了计算过程。The invention uses the Canopy-Kmeans clustering algorithm to classify a large amount of data on the time and industry scales, which provides a classification basis for analyzing the user's adjustable potential by season and industry, and the typical daily load curve obtained is used as the classification basis. The calculation basis of the load simplifies the calculation process.
附图说明Description of drawings
图1为本发明提供的一种需求侧灵活资源可调潜力预测方法流程示意图;1 is a schematic flowchart of a method for predicting the adjustable potential of demand-side flexible resources provided by the present invention;
图2为本发明实施例中的Canopy-Kmeans聚类算法的流程示意图;2 is a schematic flowchart of the Canopy-Kmeans clustering algorithm in the embodiment of the present invention;
图3为本发明实施例中的ARIMA预测模型算法的流程示意图;3 is a schematic flowchart of an ARIMA prediction model algorithm in an embodiment of the present invention;
图4(a)为本发明实施例中两次聚类后春秋用户典型负荷示意图;Figure 4(a) is a schematic diagram of a typical load of Spring and Autumn users after two clustering in an embodiment of the present invention;
图4(b)为本发明实施例中两次聚类后夏用户典型负荷示意图;Figure 4(b) is a schematic diagram of a typical load of summer users after two clusterings in an embodiment of the present invention;
图4(c)为本发明实施例中两次聚类后冬用户典型负荷示意图;Fig. 4(c) is a schematic diagram of typical load of winter users after two clustering in the embodiment of the present invention;
图5(a)为本发明实施例中为春秋各类用户的典型日负荷曲线示意图;Figure 5(a) is a schematic diagram of typical daily load curves of various types of users in Spring and Autumn in the embodiment of the present invention;
图5(b)为本发明实施例中为夏季各类用户的典型日负荷曲线示意图;Figure 5(b) is a schematic diagram of typical daily load curves of various types of users in summer in the embodiment of the present invention;
图5(c)为本发明实施例中为冬季各类用户的典型日负荷曲线示意图;Figure 5(c) is a schematic diagram of typical daily load curves of various users in winter in the embodiment of the present invention;
图6(a)为本发明实施例中ARIMA算法预测的春秋用户1负荷;Fig. 6 (a) is the spring and
图6(b)为本发明实施例中ARIMA算法预测的春秋用户2负荷;Fig. 6(b) is the spring and
图6(c)为本发明实施例中ARIMA算法预测的春秋用户3负荷;Fig. 6 (c) is the spring and
图7(a)为本发明实施例中ARIMA算法预测的夏季用户1负荷;Fig. 7(a) is the
图7(b)为本发明实施例中ARIMA算法预测的夏季用户2负荷;Fig. 7(b) is the summer load of
图8(a)为本发明实施例中ARIMA算法预测的冬季用户1负荷;Fig. 8(a) is the winter load of
图8(b)为本发明实施例中ARIMA算法预测的冬季用户2负荷;Fig. 8(b) is the winter load of
图9(a)为本发明实施例中春秋用户1负荷上调潜力、下调潜力示意图;Figure 9(a) is a schematic diagram of the load up-regulation potential and down-regulation potential of
图9(b)为本发明实施例中春秋用户2负荷上调潜力、下调潜力示意图;Fig. 9(b) is a schematic diagram of the load up-regulation potential and down-regulation potential of Spring and
图9(c)为本发明实施例中春秋用户3负荷上调潜力、下调潜力示意图;Figure 9(c) is a schematic diagram of the load up-regulation potential and down-regulation potential of Spring and
图10(a)为本发明实施例中夏用户1负荷上调潜力、下调潜力示意图;Figure 10(a) is a schematic diagram of the load up-regulation potential and down-regulation potential of
图10(b)为本发明实施例中夏用户2负荷上调潜力、下调潜力示意图;Figure 10(b) is a schematic diagram of the load up-regulation potential and down-regulation potential of
图11(a)为本发明实施例中冬用户1负荷上调潜力、下调潜力示意图;Fig. 11(a) is a schematic diagram of the load up-regulation potential and down-regulation potential of
图11(b)为本发明实施例中冬用户2负荷上调潜力、下调潜力示意图;Figure 11(b) is a schematic diagram of the load up-regulation potential and down-regulation potential of
图12为本发明提供的一种需求侧灵活资源可调潜力预测系统结构示意图。FIG. 12 is a schematic structural diagram of a demand-side flexible resource adjustment potential prediction system provided by 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做进一步的详细说明。The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.
实施例1:Example 1:
本发明提供的一种需求侧灵活资源可调潜力预测方法流程示意图如图1所示,包括:A schematic flowchart of a method for predicting the adjustable potential of demand-side flexible resources provided by the present invention is shown in FIG. 1 , including:
步骤1:对选定区域多个用户的负荷数据依次从时间尺度和行业尺度进行聚类,得到各季节下各行业用户的典型日负荷曲线;Step 1: Cluster the load data of multiple users in the selected area in turn from the time scale and the industry scale to obtain the typical daily load curves of users in each industry in each season;
步骤2:基于各季节下各行业用户的典型日负荷曲线,采用差分自回归移动平均模型预测未来时刻各行业用户的负荷值;Step 2: Based on the typical daily load curves of users in various industries in each season, the differential autoregressive moving average model is used to predict the load value of users in various industries in the future;
步骤3:根据未来时刻各行业用户的负荷值,计算需求侧灵活资源可调潜力。Step 3: Calculate the adjustable potential of flexible resources on the demand side according to the load values of users in various industries in the future.
本发明所采用的需求侧灵活资源可调潜力预测方法可用于需求侧灵活资源短期可调潜力预测。The method for predicting the adjustable potential of demand-side flexible resources adopted in the present invention can be used for short-term adjustable potential prediction of demand-side flexible resources.
具体的,步骤1包括:Specifically,
步骤1.1,利用Canopy聚类算法寻找KN个聚类中心点,包括:Step 1.1, use the Canopy clustering algorithm to find K N cluster center points, including:
步骤1.1.1,选取N个用户负荷数据,分别针对同一用户的历史1年的负荷数据分别进行随机排列,分别设初始数据集S1,S2,…,SN。由于负荷率、峰谷差、平均负荷这几个指标可以有效反映用户负荷的可调性、可调能力,因此根据这三个指标从初始聚类样本集中选择一个中心点PN,其中,Step 1.1.1, select N user load data, randomly arrange the historical load data of the same user for one year respectively, and set up initial data sets S 1 , S 2 , . . . , S N respectively. Since the load rate, peak-to-valley difference, and average load can effectively reflect the adjustability and adjustability of user load, a central point P N is selected from the initial clustering sample set according to these three indicators, among which,
负荷率:Load factor:
其中,为日负荷曲线的平均值,Gm为日负荷曲线的最大值。in, is the average value of the daily load curve, and G m is the maximum value of the daily load curve.
峰谷差:Peak-to-valley difference:
Δ=Gm-Gn Δ=G m -G n
其中,Gn为日负荷曲线的最小值。Among them, G n is the minimum value of the daily load curve.
步骤1.1.2,选取距离中心点最近的距离为距离阈值T2-N(即图2中的T2),距离中心点最远的距离为距离阈值T1-N(即图2中的T1),且T1-N>T2-N;Step 1.1.2, select the distance closest to the center point as the distance threshold T2 -N (ie T2 in Figure 2), and the farthest distance from the center point is the distance threshold T1 -N (ie T1 in Figure 2) , and T 1-N >T 2-N ;
步骤1.1.3,将PN作为第一个簇的聚类中心点,并将点PN从初始聚类样本集SN中移除;Step 1.1.3, take PN as the cluster center point of the first cluster, and remove the point PN from the initial clustering sample set SN ;
步骤1.1.4,从余下的数据样本集合SN中随机选取一个点QN,计算QN到所有已知聚类中心点的距离,考察其中最小距离DN:如果T2-N≤DN≤T1-N,则用一个弱标记记录QN,表示点QN属于该簇,将QN加入其中;如果DN≤T2-N,则用一个强标记记录点QN,表示点QN属于该簇,将QN从数据样本集合SN中删除;如果DN>T1-N,则QN形成一个新聚类,将QN从数据样本集合SN中删除;Step 1.1.4, randomly select a point Q N from the remaining data sample set S N , calculate the distance from Q N to all known cluster center points, and examine the minimum distance D N : if T 2-N ≤ D N ≤T 1-N , record Q N with a weak mark, indicating that point Q N belongs to the cluster, and add Q N to it; if D N ≤T 2-N , record point Q N with a strong mark, indicating point Q N belongs to this cluster, and Q N is deleted from the data sample set S N ; if D N >T 1-N , then Q N forms a new cluster, and Q N is deleted from the data sample set S N ;
步骤1.1.5,重复步骤1.4直到集合SN中的元素个数为零,最终得到KN个聚类中心点。Step 1.1.5, repeat step 1.4 until the number of elements in the set S N is zero, and finally obtain K N cluster center points.
步骤1.2,利用Kmeans聚类算法将原始数据集(即用户的负荷数据)进行分类,包括:Step 1.2, use the Kmeans clustering algorithm to classify the original data set (that is, the user's load data), including:
步骤1.2.1,根据步骤1.1,SN个数据集共生成KN个聚类中心,分别为k1N,k2N,……,kkN,并将负荷率、峰谷差、平均负荷作为聚类的评价指标;Step 1.2.1, according to step 1.1, the S N data sets generate a total of K N cluster centers, which are respectively k 1N , k 2N , ..., k kN , and the load rate, peak-to-valley difference, and average load are used as the cluster centers. class evaluation index;
步骤1.2.2,计算每个用户典型日负荷与聚类中心点的聚类评价指标间的相似度;Step 1.2.2, calculate the similarity between the typical daily load of each user and the cluster evaluation index of the cluster center point;
步骤1.2.3,将用户加入与中心点相似度最高的簇,更新聚类中心点;Step 1.2.3, add the user to the cluster with the highest similarity with the center point, and update the cluster center point;
步骤1.2.4,迭代步骤1.2.2~步骤1.2.3,直到迭代步数为500停止;Step 1.2.4, iterate steps 1.2.2 to 1.2.3 until the number of iteration steps is 500;
步骤1.2.5,得到不同季节下的典型用户日负荷曲线。Step 1.2.5, obtain the typical daily load curve of users in different seasons.
步骤1.1-步骤1.2的Canopy-Kmeans聚类算法的流程如图2所示。The flowchart of the Canopy-Kmeans clustering algorithm of step 1.1-step 1.2 is shown in Figure 2.
步骤1.3,对步骤1.2得到的聚类结果按行业尺度进行二次聚类,聚类方法相同,得到不同季节下不同行业用户的典型日负荷曲线,将聚类中心用户的历史数据作为差分自回归移动平均模型(即ARIMA预测模型)算法的输入数据。Step 1.3: Perform secondary clustering on the clustering results obtained in step 1.2 according to the industry scale. The clustering method is the same to obtain the typical daily load curves of users in different industries in different seasons. The historical data of users in the cluster center are used as differential autoregression. Input data for the moving average model (aka ARIMA forecast model) algorithm.
具体地,步骤1.3中,将步骤1.2得到的N个用户的KN个典型日负荷曲线按步骤1.1~步骤1.2的聚类方法进行二次聚类,得到KM个行业的KN个典型用户。对不同季节下典型用户的日负荷曲线进行分析,按照聚出的典型用户的负荷数据作为ARIMA预测模型输入数据。Specifically, in step 1.3, perform secondary clustering on the K N typical daily load curves of N users obtained in step 1.2 according to the clustering method in steps 1.1 to 1.2, to obtain K N typical users in K M industries . The daily load curve of typical users in different seasons is analyzed, and the collected load data of typical users is used as the input data of the ARIMA prediction model.
步骤1和步骤2之间,还可对聚类结果的数据进行数据预处理。Between
具体的,对步骤1聚类的得到的不同类别的输入数据进行处理,其中缺省值用其所在特征的均值代替,越限值和奇异值用其所在特征该前后两个时刻的均值代替。Specifically, the input data of different categories obtained by the clustering in
步骤2具体包括:
步骤2.1,构建原始序列:Step 2.1, build the original sequence:
Xt=α1Xt-1+α2Xt-2+…αpXt-p+εt+β1εt-1+β2εt-2+…βqεt-q X t =α 1 X t-1 +α 2 X t-2 +…α p X tp +ε t +β 1 ε t-1 +β 2 ε t-2 +…β q ε tq
式中,p、q分别为自回归与滑动平均的阶数;α1,α2,…,αp为自回归系数;β1,β2,…,βq为滑动平均系数,X为利用原始典型用户负荷数据构建序列中的稳态部分,ε为原始序列中的白噪声序列。X和ε的下标分别表示时间。In the formula, p and q are the order of autoregression and moving average respectively; α 1 ,α 2 ,…,α p are autoregressive coefficients; β 1 ,β 2 ,…,β q are moving average coefficients, X is the use of The steady-state part of the original typical user load data construction sequence, ε is the white noise sequence in the original sequence. The subscripts of X and ε represent time, respectively.
步骤2.2,由于进行ARIMA算法时,时间序列需保证时平稳的,所以在此步骤利用ADF检验(即单位根检验)方法进行平稳性检验;In step 2.2, since the time series needs to be guaranteed to be stationary when the ARIMA algorithm is performed, the ADF test (ie, unit root test) method is used to test the stationarity in this step;
步骤2.3,若步骤2.2中检验后原始序列是非平稳的,此步骤则针对该非平稳时间序列进行差分运算,得到差分阶数d,使得该序列平稳;若步骤4.2中检验后原始序列是平稳的,则差分阶数设为预设值,通常将预设值设为0;Step 2.3, if the original sequence is non-stationary after the test in step 2.2, this step is to perform a difference operation on the non-stationary time series to obtain the difference order d, so that the sequence is stationary; if the original sequence is stationary after the test in step 4.2 , the difference order is set to the default value, usually the default value is set to 0;
步骤2.4,求自相关系数ACF和偏自相关系数PACF,其中自相关系数ACF计算公式:Step 2.4, find the autocorrelation coefficient ACF and the partial autocorrelation coefficient PACF, where the calculation formula of the autocorrelation coefficient ACF is:
偏自相关系数PACF计算公式:Partial autocorrelation coefficient PACF calculation formula:
其中,为协方差,EXt为期望值,为方差。in, is the covariance, EX t is the expected value, is the variance.
自相关系数ACF用以确定日负荷中的t时刻与过去一周内该时刻负荷的相关性。偏相关系数用以确定气温变化等因素影响下日负荷中的t时刻与过去一周内该时刻负荷的相关性。The autocorrelation coefficient ACF is used to determine the correlation between the daily load at time t and the load at this time in the past week. The partial correlation coefficient is used to determine the correlation between the time t in the daily load and the load at this time in the past week under the influence of factors such as temperature changes.
步骤2.5,根据上述自相关系数和偏自相关系数确定模型参数,即AR自回归模型阶数p和MA移动平均模型阶数q,其中,p阶AR自回归模型:Step 2.5: Determine the model parameters according to the above autocorrelation coefficient and partial autocorrelation coefficient, that is, the AR autoregressive model order p and the MA moving average model order q, where the p-order AR autoregressive model:
Xt=α1Xt-1+α2Xt-2+…αpXt-p+ut X t =α 1 X t-1 +α 2 X t-2 +...α p X tp +u t
q阶MA移动平均模型:q-order MA moving average model:
Xt=εt+β1εt-1+β2εt-2+…βqεt-q X t =ε t +β 1 ε t-1 +β 2 ε t-2 +…β q ε tq
其中,εt,εt-1,…,εt-q为t,t-1,…,t-q时刻的白噪声序列。Among them, ε t , ε t-1 ,…,ε tq are white noise sequences at time t, t-1,…, tq.
步骤2.6,将自回归模型(AR)、移动平均模型(MA)和差分法结合,的到差分自回归移动平均模型ARIMA(p、d、q);Step 2.6, combine the autoregressive model (AR), the moving average model (MA) and the difference method to obtain the difference autoregressive moving average model ARIMA (p, d, q);
步骤2.7,对该模型进行验证优化,将利用该模型得到的预测值与预测时段的实际值进行对比分析,验证该方法的准确度,可利用此方法预测未知时段的负荷。把ARIMA算法得到的预测值HM-N作为计算可调潜力时的实际负荷值。Step 2.7, verify and optimize the model, compare and analyze the predicted value obtained by using the model and the actual value of the predicted period to verify the accuracy of the method, and can use this method to predict the load in an unknown period. The predicted value H MN obtained by the ARIMA algorithm is used as the actual load value when calculating the adjustable potential.
ARIMA预测模型算法的流程示意图如图3所示,图3中还包括了步骤1和步骤2间的输入数据处理过程。A schematic flowchart of the ARIMA prediction model algorithm is shown in Figure 3, which also includes the input data processing process between
步骤3具体包括:
步骤3.1,根据步骤1得到的不同季节下各行业聚类结果,不同季节下各行业基线负荷的计算方法相同,即统计预测日前指定时长范围内(例如一周)t时刻负荷的分布,计算它们的均值Δf和方差取满足(即三西格玛)原则中的最大值L1和最小值L2作为t时刻的最大基线负荷和最小基线负荷;Step 3.1, according to the clustering results of various industries in different seasons obtained in
步骤3.2,根据步骤2.7得到的预测值HM-N和步骤3.1得到的最大基线负荷L1和最小基线负荷L2,建立可调潜力计算模型:Step 3.2, according to the predicted value H MN obtained in step 2.7 and the maximum baseline load L 1 and the minimum baseline load L 2 obtained in step 3.1, establish a calculation model of adjustable potential:
单日负荷上调潜力:Single-day load escalation potential:
η2,j=Mj×(HM-N,j-L2,j)η 2,j =M j ×(H MN,j -L 2,j )
单日负荷下调潜力:Single-day load reduction potential:
η2=Mj×(HM-N-L2)η 2 =M j ×(H MN -L 2 )
其中,式中,η1,j为行业j的需求侧负荷上调潜力,Mj为行业j的用户数量,L1,j为行业j的最大基线负荷,HM-N,j为未来时刻行业j用户的负荷值;η2,j为行业j的需求侧负荷下调潜力,L2,j为行业j的最小基线负荷。where, η 1,j is the demand-side load increase potential of industry j, M j is the number of users in industry j, L 1,j is the maximum baseline load of industry j, and H MN,j is the user of industry j in the future The load value of ; η 2,j is the demand-side load reduction potential of industry j, and L 2,j is the minimum baseline load of industry j.
步骤3.3,以需求侧负荷上调潜力和需求侧负荷下调潜力作为需求侧灵活资源可调潜力。Step 3.3, take the demand-side load increase potential and demand-side load decrease potential as the demand-side flexible resource adjustment potential.
实施例2:Example 2:
下面给出一个需求侧灵活资源可调潜力预测方法的具体算例,根据某地38个用户负荷样本数据,对该区域不同季节不同行业用户的可调负荷进行预测;其中,该区域有38个用户,采样点为每天24个。A specific calculation example of the method for predicting the adjustable potential of demand-side flexible resources is given below. According to the sample data of 38 user loads in a certain place, the adjustable load of users in different seasons and industries in the region is predicted; among them, there are 38 users in this region. Users, the sampling point is 24 per day.
图4(a)~(c)为两次聚类后的结果,由图可知,根据季节特性,可知时间尺度下可以分为春秋、夏、冬三个季节,按照行业特性可分为工业用户、商业用户、居民用户。图5(a)~(c)为各类用户不同季节下的典型日负荷曲线。根据该地区的行业特性分析得到典型用户1负荷和典型用户2负荷分别为商业用户和工业用户,都有两个高峰期,冬夏两季负荷较大,典型用户3负荷为居民用户负荷,曲线较为平稳。Figures 4(a)-(c) are the results of two clusterings. It can be seen from the figure that according to the seasonal characteristics, it can be known that the time scale can be divided into three seasons: spring and autumn, summer and winter, and according to the industry characteristics, it can be divided into industrial users , commercial users, and residential users. Figures 5(a)-(c) are typical daily load curves of various users in different seasons. According to the analysis of the industry characteristics of the region, the
图6(a)~图8(b)为ARIMA算法预测结果,得到不同季节下3个行业的预测曲线,根据该地区行业特性分析得到用户1为商业用户,用户2为工业用户,用户3为居民用户。由曲线可看出与实际值误差不大,证明预测值是可采用的。Figures 6(a) to 8(b) are the prediction results of the ARIMA algorithm, and the prediction curves of three industries in different seasons are obtained. According to the analysis of the industry characteristics in the region, it is obtained that
图9(a)~图11(b)为工商民三个行业典型用户的负荷上调潜力、下调潜力示意图。图9(a)~图11(b)中,分别有用户预测负荷、最小基线负荷和最大基线负荷三条曲线,最大基线负荷与用户预测负荷间即为负荷上调潜力,用户预测负荷与最小基线负荷间即为负荷下调潜力。根据该地区行业特性分析得到用户1为商业用户,用户2为工业用户,用户3为居民用户。乘以该行业用户数量即可得到该行业的日可调潜力。根据此方法可预测某地区不同季节不同行业的可调潜力,为电力需求侧管理、负荷调度提供技术支持。Figures 9(a) to 11(b) are schematic diagrams of the load up-regulation potential and down-regulation potential of typical users in the three industries of industrial, commercial and civil. In Figures 9(a) to 11(b), there are three curves of user predicted load, minimum baseline load and maximum baseline load, respectively. The difference between the maximum baseline load and the user's predicted load is the load increase potential, and the user's predicted load and the minimum baseline load The time is the load reduction potential. According to the analysis of industry characteristics in this area, it is obtained that
实施例3:Example 3:
基于同一发明构思,本发明还提供了一种需求侧灵活资源可调潜力预测系统,该系统结构如图12所示,包括:负荷聚类模块、负荷预测模块和可调潜力模块;Based on the same inventive concept, the present invention also provides a demand-side flexible resource adjustment potential prediction system, the system structure is shown in Figure 12, including: a load clustering module, a load prediction module and an adjustable potential module;
其中,负荷聚类模块,用于对选定区域多个用户的负荷数据依次从时间尺度和行业尺度进行聚类,得到各季节下各行业用户的典型日负荷曲线;Among them, the load clustering module is used to cluster the load data of multiple users in the selected area in turn from the time scale and the industry scale, and obtain the typical daily load curve of users in each industry in each season;
负荷预测模块,用于基于各季节下各行业用户的典型日负荷曲线,采用差分自回归移动平均模型预测未来时刻各行业用户的负荷值;The load forecasting module is used to predict the load value of users in various industries in the future by using the differential autoregressive moving average model based on the typical daily load curves of users in various industries in each season;
可调潜力模块,用于根据未来时刻各行业用户的负荷值,计算需求侧灵活资源可调潜力。The adjustable potential module is used to calculate the adjustable potential of flexible resources on the demand side according to the load value of users in various industries in the future.
其中,负荷聚类模块具体用于:Among them, the load clustering module is specifically used for:
对选定区域多个用户的负荷数据,利用Canopy-Kmeans聚类算法从时间尺度进行聚类,得到各季节下各用户的典型日负荷曲线;For the load data of multiple users in the selected area, the Canopy-Kmeans clustering algorithm is used to cluster from the time scale, and the typical daily load curve of each user in each season is obtained;
对各用户各季节的典型日负荷曲线,利用Canopy-Kmeans聚类算法从行业尺度进行聚类,得到各季节下各行业用户的典型日负荷曲线。For the typical daily load curve of each user in each season, the Canopy-Kmeans clustering algorithm is used to cluster from the industry scale, and the typical daily load curve of each industry user in each season is obtained.
其中,对选定区域多个用户的负荷数据,利用Canopy-Kmeans聚类算法从时间尺度进行聚类,得到各季节下各用户的典型日负荷曲线,包括:Among them, for the load data of multiple users in the selected area, the Canopy-Kmeans clustering algorithm is used to cluster from the time scale, and the typical daily load curve of each user in each season is obtained, including:
基于选定区域多个用户的负荷数据,计算各用户的负荷率、峰谷差和平均负荷;Based on the load data of multiple users in the selected area, calculate the load rate, peak-to-valley difference and average load of each user;
基于各用户的负荷率、峰谷差和平均负荷,采用Canopy聚类算法从多个用户的负荷数据中找到多个时间聚类中心点;Based on the load rate, peak-to-valley difference and average load of each user, the Canopy clustering algorithm is used to find multiple time clustering center points from the load data of multiple users;
基于时间聚类中心点和各用户的负荷率、峰谷差和平均负荷,利用Kmeans聚类算法从时间尺度对多个用户的负荷数据进行聚类,得到各季节下各用户的典型日负荷曲线。Based on the time clustering center point and the load rate, peak-to-valley difference and average load of each user, the Kmeans clustering algorithm is used to cluster the load data of multiple users from the time scale, and the typical daily load curve of each user in each season is obtained. .
其中,对各用户各季节的典型日负荷曲线,利用Canopy-Kmeans聚类算法从行业尺度进行聚类,得到各季节下各行业用户的典型日负荷曲线,包括:Among them, for the typical daily load curve of each user in each season, the Canopy-Kmeans clustering algorithm is used to cluster from the industry scale, and the typical daily load curve of each industry user in each season is obtained, including:
基于各用户的负荷率、峰谷差和平均负荷,采用Canopy聚类算法从各用户各季节的典型日负荷曲线中找到多个行业聚类中心点;Based on the load rate, peak-valley difference and average load of each user, the Canopy clustering algorithm is used to find multiple industry cluster center points from the typical daily load curve of each user in each season;
基于行业聚类中心点和各用户的负荷率、峰谷差和平均负荷,利用Kmeans聚类算法从行业尺度对各用户各季节的典型日负荷曲线进行聚类,得到各季节下各行业用户的典型日负荷曲线。Based on the industry clustering center point and the load rate, peak-to-valley difference and average load of each user, the Kmeans clustering algorithm is used to cluster the typical daily load curve of each user in each season from the industry scale, and the average daily load curve of each user in each season is obtained. Typical daily load curve.
其中,负荷预测模块,具体用于:Among them, the load forecasting module is specifically used for:
用单位根检验方法分别对各季节下各行业用户的典型日负荷曲线进行平稳性检验:若检验结果为平稳,则将典型日负荷曲线的差分阶数设置为预设值,否则对典型日负荷曲线进行差分运算,得到差分阶数;The unit root test method is used to test the stationarity of the typical daily load curves of users in each industry in each season: if the test results are stable, the difference order of the typical daily load curve is set to the preset value, otherwise the typical daily load The difference operation is performed on the curve to obtain the difference order;
分别计算各季节下各行业用户的典型日负荷曲线的自相关系数和偏自相关系数,并根据自相关系数和偏自相关系数确定自回归模型阶数和移动平均模型阶数;Calculate the autocorrelation coefficient and partial autocorrelation coefficient of the typical daily load curve of users in various industries in each season, and determine the autoregressive model order and moving average model order according to the autocorrelation coefficient and the partial autocorrelation coefficient;
分别基于各季节下各行业用户的典型日负荷曲线的差分阶数、自回归模型阶数和移动平均模型阶数,构建差分自回归移动平均模型;Based on the difference order, autoregressive model order and moving average model order of the typical daily load curves of users in various industries in each season, the difference autoregressive moving average model is constructed;
分别采用对应的差分自回归移动平均模型预测未来时刻各行业用户的负荷值。The corresponding differential autoregressive moving average models are used to predict the load value of users in various industries in the future.
其中,可调潜力模块具体用于:Among them, the adjustable potential module is specifically used for:
针对各行业的用户,统计未来时刻前指定时长范围内,行业用户在同一历史时刻的负荷值分布,并根据负荷值的分布计算负荷的均值和方差;For users in various industries, count the load value distribution of industry users at the same historical moment within the specified time range before the future time, and calculate the mean and variance of the load according to the distribution of load values;
基于负荷的均值和方差,取满足三西格玛原则中的最大值和最小值作为对应行业用户在未来时刻的最大基线负荷和最小基线负荷;Based on the mean and variance of the load, take the maximum and minimum values that satisfy the Three Sigma principle as the maximum baseline load and minimum baseline load of the corresponding industry users in the future;
基于未来时刻各行业用户的负荷值、最大基线负荷和最小基线负荷,计算需求侧灵活资源可调潜力。Based on the load value, maximum baseline load and minimum baseline load of users in various industries in the future, the adjustable potential of flexible resources on the demand side is calculated.
其中,基于未来时刻各行业用户的负荷值、最大基线负荷和最小基线负荷,计算需求侧灵活资源可调潜力,包括:Among them, based on the load value, maximum baseline load and minimum baseline load of users in various industries in the future, the adjustable potential of flexible resources on the demand side is calculated, including:
基于未来时刻各行业用户的负荷值和最大基线负荷,计算需求侧负荷上调潜力,并基于未来时刻各行业用户的负荷值和最小基线负荷,计算需求侧负荷下调潜力;Based on the load value and the maximum baseline load of users in various industries in the future, calculate the potential of demand-side load increase, and based on the load value and minimum baseline load of users in various industries in the future, calculate the potential of demand-side load reduction;
以需求侧负荷上调潜力和需求侧负荷下调潜力作为需求侧灵活资源可调潜力。The demand-side load-up potential and demand-side load-down potential are taken as the demand-side flexible resource adjustment potential.
其中,需求侧负荷上调潜力的计算式如下:Among them, the calculation formula of the demand-side load increase potential is as follows:
η1,j=Mj×(L1,j-HM-N,j)η 1,j =M j ×(L 1,j -H MN,j )
式中,η1,j为行业j的需求侧负荷上调潜力,Mj为行业j的用户数量,L1,j为行业j的最大基线负荷,HM-N,j为未来时刻行业j用户的负荷值;In the formula, η 1,j is the demand-side load increase potential of industry j, M j is the number of users in industry j, L 1,j is the maximum baseline load of industry j, and H MN,j is the load of users in industry j in the future value;
需求侧负荷下调潜力的计算式如下:The calculation formula of the demand-side load reduction potential is as follows:
η2,j=Mj×(HM-N,j-L2,j)η 2,j =M j ×(H MN,j -L 2,j )
式中,η2,j为行业j的需求侧负荷下调潜力,L2,j为行业j的最小基线负荷。where η 2,j is the demand-side load reduction potential of industry j, and L 2,j is the minimum baseline load of industry j.
实施例4:Example 4:
基于同一种发明构思,本发明还提供了一种计算机设备,该计算机设备包括处理器以及存储器,所述存储器用于存储计算机程序,所述计算机程序包括程序指令,所述处理器用于执行所述计算机存储介质存储的程序指令。处理器可能是中央处理单元(CentralProcessing Unit,CPU),还可以是其他通用处理器、数字信号处理器(Digital SignalProcessor、DSP)、专用集成电路(Application SpecificIntegrated Circuit,ASIC)、现成可编程门阵列(Field-Programmable GateArray,FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等,其是终端的计算核心以及控制核心,其适于实现一条或一条以上指令,具体适于加载并执行计算机存储介质内一条或一条以上指令从而实现相应方法流程或相应功能,以实现上述实施例中一种需求侧灵活资源可调潜力预测方法的步骤。Based on the same inventive concept, the present invention also provides a computer device, the computer device includes a processor and a memory, the memory is used for storing a computer program, the computer program includes program instructions, and the processor is used for executing the Program instructions stored by the computer storage medium. The processor may be a central processing unit (Central Processing Unit, CPU), or other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate array ( Field-Programmable GateArray, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, specifically suitable for loading And execute one or more instructions in the computer storage medium to realize the corresponding method process or corresponding function, so as to realize the steps of the method for predicting the adjustable potential of demand-side flexible resources in the above embodiment.
实施例5:Example 5:
基于同一种发明构思,本发明还提供了一种存储介质,具体为计算机可读存储介质(Memory),所述计算机可读存储介质是计算机设备中的记忆设备,用于存放程序和数据。可以理解的是,此处的计算机可读存储介质既可以包括计算机设备中的内置存储介质,当然也可以包括计算机设备所支持的扩展存储介质。计算机可读存储介质提供存储空间,该存储空间存储了终端的操作系统。并且,在该存储空间中还存放了适于被处理器加载并执行的一条或一条以上的指令,这些指令可以是一个或一个以上的计算机程序(包括程序代码)。需要说明的是,此处的计算机可读存储介质可以是高速RAM存储器,也可以是非不稳定的存储器(non-volatile memory),例如至少一个磁盘存储器。可由处理器加载并执行计算机可读存储介质中存放的一条或一条以上指令,以实现上述实施例中一种需求侧灵活资源可调潜力预测方法的步骤。Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that, the computer-readable storage medium here may include both a built-in storage medium in a computer device, and certainly also an extended storage medium supported by the computer device. The computer-readable storage medium provides storage space in which the operating system of the terminal is stored. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions may be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one disk memory. One or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the steps of the method for predicting the adjustable potential of demand-side flexible resources in the above embodiment.
本领域内的技术人员应明白,本发明的实施例可提供为方法、系统、或计算机程序产品。因此,本发明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,本发明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。As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention 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.
本发明是参照根据本发明实施例的方法、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 invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block in the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce Means for implementing the functions specified in a flow or flow of a flowchart and/or a block or blocks of a 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 function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions The apparatus implements the functions specified in the flow or flow of the flowcharts and/or the block or blocks of the block diagrams.
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 on a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in the flow or blocks of the flowcharts and/or the block or blocks of the block diagrams.
最后应当说明的是:以上实施例仅用于说明本发明的技术方案而非对其保护范围的限制,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:本领域技术人员阅读本发明后依然可对申请的具体实施方式进行种种变更、修改或者等同替换,但这些变更、修改或者等同替换,均在申请待批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 the scope of its protection. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand: Those skilled in the art can still make various changes, modifications or equivalent replacements to the specific embodiments of the application after reading the present disclosure, but these changes, modifications or equivalent replacements are all within the protection scope of the pending claims of the application.
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