CN114819530A - Demand side flexible resource adjustable potential prediction method and system - Google Patents
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Abstract
The invention provides a demand side flexible resource adjustable potential prediction method and a demand side flexible resource adjustable potential prediction system, which comprise the following steps: clustering the load data of a plurality of users in the selected area from the time scale and the industry scale in sequence to obtain a typical daily load curve of each industry user in each season; based on typical daily load curves of users in various industries in various seasons, predicting load values of the users in various industries at a future moment by adopting a differential autoregressive moving average model; calculating the flexible resource adjustable potential of a demand side according to the load values of users in various industries at a future moment; the method adopts the differential autoregressive moving average model to predict the load value of each industry user at the future time, can effectively process the possible instability of input data, effectively reduce the influence of the instability of the data on the prediction result, and simultaneously calculate the flexible resource adjustable potential at the demand side based on the load value of each industry user at the future time, so that the complex equipment load model does not need to be established, and the model for calculating the adjustable potential is effectively simplified.
Description
Technical Field
The invention belongs to the technical field of adjustable load potential calculation methods, and particularly relates to a demand side flexible resource adjustable potential prediction method and system.
Background
With the development of the society and the adjustment of industrial structures, a power system faces new challenges, which are expressed by problems of clean energy consumption and the like caused by the continuous increase of peak-valley difference and the gradual increase of the new energy power generation ratio. Due to the flexibility and the adjustability of demand side resources, the demand side resources are beneficial to solving a series of problems faced by a power system, and power enterprises pay attention to participation in power grid dispatching interaction. Therefore, research and prediction of the regulation potential of the adjustable load have important effects on the optimized operation and control of the power system, and have very important significance on power demand side management and load scheduling.
In recent years, with the development of new power electronic technology and control means, load characteristics have been fundamentally changed. On one hand, the user can change the original power consumption mode aiming at the market price or an incentive mechanism and actively participate in the operation control of the power grid; on the other hand, the electric automobile, the energy storage equipment and the like have the capability of bidirectional interaction with the power grid, have the functions of peak clipping and valley filling, and provide a new means for power grid regulation and control. The research on the aspect of load participation in power grid interactive scheduling becomes a focus of attention. The adjustable load on the demand side can be an important means to cut down peak loads and balance the power supply gap. The research method aiming at the load characteristics mainly comprises a statistical synthesis method, a total identification method, a fault simulation method and the like, and provides data support for researching adjustable potential. At present, the prediction method for the tunable potential is mainly based on an equipment model, and the method needs to establish a large number of equipment models. The parameters required by the equipment models, such as the type, the number, the operating characteristics and the like of the equipment, are various and are not easy to obtain, and the parameters become a big problem in predicting the adjustable potential.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a demand side flexible resource adjustable potential prediction method, which comprises the following steps:
clustering the load data of a plurality of users in the selected area from the time scale and the industry scale in sequence to obtain a typical daily load curve of each industry user in each season;
based on typical daily load curves of users in various industries in various seasons, predicting load values of the users in various industries at a future moment by adopting a differential autoregressive moving average model;
and calculating the flexible resource adjustable potential of the demand side according to the load values of users in various industries at a future moment.
Preferably, the clustering the load data of multiple users in the selected area sequentially from the time scale and the industry scale to obtain a typical daily load curve of each industry user in each season includes:
clustering load data of a plurality of users in the selected area from a time scale by using a Canopy-Kmeans clustering algorithm to obtain a typical daily load curve of each user in each season;
and clustering the typical daily load curve of each user in each season by using a Canopy-Kmeans clustering algorithm from the industry scale to obtain the typical daily load curve of each industry user in each season.
Preferably, the clustering the load data of a plurality of users in the selected area from the time scale by using a Canopy-Kmeans clustering algorithm to obtain a typical daily load curve of each user in each season includes:
calculating the load rate, the peak-valley difference and the average load of each user based on the load data of a plurality of users in the selected area;
based on the load rate, the peak-valley difference and the average load of each user, adopting a Canopy clustering algorithm to find a plurality of time clustering center points from the load data of a plurality of users;
and based on the time clustering center point and the load rate, the peak-valley difference and the average load of each user, clustering the load data of a plurality of users from a time scale by using a Kmeans clustering algorithm to obtain a typical daily load curve of each user in each season.
Preferably, the clustering the typical daily load curve of each user in each season by using a Canopy-Kmeans clustering algorithm from the industry scale to obtain the typical daily load curve of each industry user in each season includes:
based on the load rate, the peak-valley difference and the average load of each user, a Canopy clustering algorithm is adopted to find a plurality of industry clustering center points from typical daily load curves of each season of each user;
and based on the industry clustering center point and the load rate, the peak-valley difference and the average load of each user, clustering the typical daily load curve of each user in each season by using a Kmeans clustering algorithm from the industry scale to obtain the typical daily load curve of each industry user in each season.
Preferably, the predicting the load value of each industry user at a future time by using a differential autoregressive moving average model based on the typical daily load curve of each industry user in each season includes:
and (3) carrying out stability test on the typical daily load curves of users in various industries in various seasons by using a unit root test method: if the test result is stable, setting the difference order of the typical daily load curve as a preset value, otherwise, carrying out difference operation on the typical daily load curve to obtain the difference order;
respectively calculating autocorrelation coefficients and partial autocorrelation coefficients of typical daily load curves of users of various industries in various seasons, and determining an autoregressive model order and a moving average model order according to the autocorrelation coefficients and the partial autocorrelation coefficients;
constructing a differential autoregressive moving average model based on the differential order, the autoregressive model order and the moving average model order of the typical daily load curve of each industry user in each season;
and respectively adopting corresponding difference autoregressive moving average models to predict the load value of each industry user at the future moment.
Preferably, the calculating the flexible resource adjustable potential of the demand side according to the load values of users in various industries at a future moment includes:
counting load value distribution of the industry users at the same historical moment in a specified duration range before the future moment aiming at the users of each industry, and calculating the mean value and the variance of the load according to the load value distribution;
based on the mean value and the variance of the load, taking the maximum value and the minimum value which meet the three-sigma principle as the maximum baseline load and the minimum baseline load of the corresponding industry user at the future moment;
and calculating the flexible resource adjustable potential of the demand side based on the load value, the maximum baseline load and the minimum baseline load of each industry user at the future moment.
Preferably, the calculating the flexible resource adjustable potential at the demand side based on the load value, the maximum baseline load and the minimum baseline load of each industry user at the future time includes:
calculating the load up-regulation potential of the demand side based on the load values and the maximum baseline loads of all industry users at the future time, and calculating the load down-regulation potential of the demand side based on the load values and the minimum baseline loads of all industry users at the future time;
and taking the demand side load up-regulation potential and the demand side load down-regulation potential as demand side flexible resource adjustable potential.
Preferably, the calculation formula of the demand side load up-regulation potential is as follows:
η 1,j ＝M j ×(L 1,j -H M-N,j )
in the formula eta 1,j Load up potential for demand side of industry j, M j Number of users of industry j, L 1,j Is the maximum baseline load, H, of industry j M-N,j The load value of the industry j user at the future moment;
the calculation formula of the demand side load down-regulation potential is as follows:
η 2,j ＝M j ×(H M-N,j -L 2,j )
in the formula eta 2,j For demand side load turndown potential, L, of industry j 2,j The minimum baseline load for industry j.
Based on the same invention concept, the invention also provides a demand side flexible resource adjustable potential prediction system, which comprises the following steps: the system comprises a load clustering module, a load forecasting module and an adjustable potential module;
the load clustering module is used for sequentially clustering the load data of a plurality of users in a selected area from a time scale and an industry scale to obtain a typical daily load curve of each industry user in each season;
the load prediction module is used for predicting the load value of each industry user at a future moment by adopting a differential autoregressive moving average model based on the typical daily load curve of each industry user in each season;
and the adjustable potential module is used for calculating the flexible resource adjustable potential of the demand side according to the load values of users in various industries at a future moment.
Preferably, the load clustering module is specifically configured to:
clustering load data of a plurality of users in the selected area from a time scale by using a Canopy-Kmeans clustering algorithm to obtain a typical daily load curve of each user in each season;
and clustering the typical daily load curve of each user in each season by using a Canopy-Kmeans clustering algorithm from the industry scale to obtain the typical daily load curve of each industry user in each season.
Preferably, the load prediction module is specifically configured to:
and (3) carrying out stability test on the typical daily load curves of users in various industries in various seasons by using a unit root test method: if the test result is stable, setting the difference order of the typical daily load curve as a preset value, otherwise, carrying out difference operation on the typical daily load curve to obtain the difference order;
respectively calculating autocorrelation coefficients and partial autocorrelation coefficients of typical daily load curves of users of various industries in various seasons, and determining an autoregressive model order and a moving average model order according to the autocorrelation coefficients and the partial autocorrelation coefficients;
constructing a differential autoregressive moving average model based on the differential order, the autoregressive model order and the moving average model order of the typical daily load curve of each industry user in each season;
and respectively adopting the corresponding difference autoregressive moving average model to predict the load value of each industry user at the future time.
Preferably, the adjustable potential module is specifically configured to:
counting load value distribution of the industry users at the same historical moment in a specified duration range before the future moment aiming at the users of each industry, and calculating the mean value and the variance of the load according to the load value distribution;
based on the mean value and the variance of the load, taking the maximum value and the minimum value which meet the three-sigma principle as the maximum baseline load and the minimum baseline load of the corresponding industry user at the future moment;
and calculating the flexible resource adjustable potential of the demand side based on the load value, the maximum baseline load and the minimum baseline load of each industry user at the future moment.
The present invention also provides a computer apparatus comprising: one or more processors;
a memory for storing one or more programs;
when the one or more programs are executed by the one or more processors, the demand-side flexible resource tunable potential prediction method as described above is implemented.
The present invention also provides a computer readable storage medium having stored thereon a computer program which, when executed, implements a demand-side flexible resource tunable potential prediction method as described above.
Compared with the closest prior art, the invention has the following beneficial effects:
the invention provides a demand side flexible resource adjustable potential prediction method and a demand side flexible resource adjustable potential prediction system, which comprise the following steps: clustering the load data of a plurality of users in the selected area from the time scale and the industry scale in sequence to obtain a typical daily load curve of each industry user in each season; based on typical daily load curves of users in various industries in various seasons, predicting load values of the users in various industries at a future moment by adopting a differential autoregressive moving average model; calculating the flexible resource adjustable potential of a demand side according to the load values of users in various industries at a future moment; the method adopts the differential autoregressive moving average model to predict the load value of each industry user at the future time, can effectively process the possible instability of input data, effectively reduce the influence of the instability of the data on the prediction result, and simultaneously calculate the flexible resource adjustable potential at the demand side based on the load value of each industry user at the future time, so that the complex equipment load model does not need to be established, and the model for calculating the adjustable potential is effectively simplified.
According to the method, a large amount of data is classified on time and industry scales by using a Canopy-Kmeans clustering algorithm, a classification basis is provided for analyzing the adjustable potential of users by seasons and industries, and the obtained typical daily load curve is used as a calculation basis of the load, so that the calculation process is simplified.
Drawings
FIG. 1 is a schematic flow chart of a demand-side flexible resource adjustable potential prediction method provided by the present invention;
FIG. 2 is a schematic flow chart of a Canopy-Kmeans clustering algorithm according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of an ARIMA predictive model algorithm in an embodiment of the present invention;
FIG. 4(a) is a typical load diagram of spring and autumn users after twice clustering in the embodiment of the present invention;
FIG. 4(b) is a schematic diagram of typical loads of users in summer after twice clustering in the embodiment of the present invention;
FIG. 4(c) is a schematic diagram of typical loads of winter users after twice clustering in the embodiment of the present invention;
FIG. 5(a) is a diagram illustrating a typical daily load curve for users of spring and autumn according to an embodiment of the present invention;
FIG. 5(b) is a diagram illustrating typical daily load curves for various summer users in an embodiment of the present invention;
FIG. 5(c) is a diagram illustrating typical daily load curves for various types of users during winter in an embodiment of the present invention;
fig. 6(a) shows the load of the ARIMA algorithm predicted by the user 1 in spring and autumn in the embodiment of the present invention;
fig. 6(b) shows the load of the ARIMA algorithm predicted by the user 2 in spring and autumn in the embodiment of the present invention;
fig. 6(c) shows the load of the ARIMA algorithm predicted by the user 3 in spring and autumn in the embodiment of the present invention;
FIG. 7(a) is the summer user 1 load predicted by the ARIMA algorithm in an embodiment of the present invention;
FIG. 7(b) is the summer user 2 load predicted by the ARIMA algorithm in an embodiment of the present invention;
fig. 8(a) shows the load of user 1 in winter predicted by ARIMA algorithm in an embodiment of the present invention;
fig. 8(b) shows the load of user 2 in winter predicted by ARIMA algorithm in an embodiment of the present invention;
fig. 9(a) is a schematic diagram of load up-regulation potential and down-regulation potential of a spring and autumn user 1 according to an embodiment of the present invention;
fig. 9(b) is a schematic diagram of the load up-regulation potential and the load down-regulation potential of the spring and autumn user 2 according to the embodiment of the present invention;
fig. 9(c) is a schematic diagram of load up-regulation potential and down-regulation potential of a spring and autumn user 3 according to an embodiment of the present invention;
FIG. 10(a) is a schematic diagram of the load up-regulation potential and the load down-regulation potential of summer user 1 according to an embodiment of the present invention;
FIG. 10(b) is a schematic diagram of the load up-regulation potential and the load down-regulation potential of summer user 2 according to the embodiment of the present invention;
fig. 11(a) is a schematic diagram of the load up-regulation potential and the load down-regulation potential of the winter user 1 in the embodiment of the present invention;
fig. 11(b) is a schematic diagram of the load up-regulation potential and the load down-regulation potential of the winter user 2 in the embodiment of the present invention;
fig. 12 is a schematic structural diagram of a demand-side flexible resource adjustable potential prediction system provided by the present invention.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
Example 1:
the schematic flow chart of the demand-side flexible resource adjustable potential prediction method provided by the invention is shown in fig. 1, and the method comprises the following steps:
step 1: clustering the load data of a plurality of users in the selected area from the time scale and the industry scale in sequence to obtain a typical daily load curve of each industry user in each season;
step 2: based on typical daily load curves of users in various industries in various seasons, predicting load values of the users in various industries at a future moment by adopting a differential autoregressive moving average model;
and step 3: and calculating the flexible resource adjustable potential of the demand side according to the load values of users in various industries at a future moment.
The demand side flexible resource adjustable potential prediction method can be used for demand side flexible resource short-term adjustable potential prediction.
Specifically, the step 1 comprises:
step 1.1, searching K by using Canopy clustering algorithm N A cluster center point comprising:
step 1.1.1, selecting N user load data, respectively randomly arranging the load data of the same user in history of 1 year, and respectively setting an initial data set S 1 ,S 2 ,…,S N . Because the load rate, the peak-valley difference and the average load can effectively reflect the adjustability and the adjustability of the load of the user, a central point P is selected from the initial clustering sample set according to the three indexes N Wherein
load factor:
wherein,
average of daily load curves, G m The maximum value of the daily load curve.
Peak-to-valley difference:
Δ＝G m -G n
wherein G is n The minimum value of the daily load curve.
Step 1.1.2, selecting the distance closest to the central point as a distance threshold T 2-N (i.e., T2 in FIG. 2), the distance farthest from the center point is the distance threshold T 1-N (i.e., T1 in FIG. 2), and T 1-N ＞T 2-N ；
Step 1.1.3, adding P N As the cluster center point of the first cluster, and the point P N From the initial cluster sample set S N Removing;
step 1.1.4, from the remaining set of data samples S N In randomly selecting a point Q N Calculating Q N Distances to all known cluster center points, wherein the minimum distance D is examined N : if T is 2-N ≤D N ≤T 1-N Record Q with a weak mark N Represents a point Q N Belongs to the cluster, and Q N Adding into the mixture; if D is N ≤T 2-N Recording the point Q with a strong mark N Represents a point Q N Belongs to the cluster, and Q N From a set of data samples S N Deleting; if D is N ＞T 1-N Then Q is N Forming a new cluster, and combining Q N From a set of data samples S N Deleting;
step 1.1.5, repeat step 1.4 until set S N The number of the elements in the K is zero, and finally the K is obtained N And (4) clustering the central points.
Step 1.2, classifying the original data set (namely the load data of the user) by using a Kmeans clustering algorithm, comprising the following steps:
step 1.2.1, according to step 1.1, S N Individual data set co-formation K N Each cluster center is k 1N ，k 2N ，……，k kN And the load rate, the peak-valley difference and the average load are used as the evaluation indexes of the clustering;
step 1.2.2, calculating the similarity between the typical daily load of each user and the cluster evaluation index of the cluster center point;
step 1.2.3, adding the user into the cluster with the highest similarity to the central point, and updating the clustering central point;
step 1.2.4, iterating step 1.2.2-step 1.2.3, and stopping until the number of iteration steps is 500;
and 1.2.5, obtaining typical user daily load curves in different seasons.
The flow of the Canopy-Kmeans clustering algorithm from step 1.1 to step 1.2 is shown in FIG. 2.
And step 1.3, performing secondary clustering on the clustering result obtained in the step 1.2 according to an industry scale, obtaining typical daily load curves of users in different industries in different seasons by using the same clustering method, and taking historical data of a user in a clustering center as input data of a differential autoregressive moving average model (namely an ARIMA prediction model) algorithm.
Specifically, in step 1.3, the K of the N users obtained in step 1.2 is used N Carrying out secondary clustering on the typical daily load curve according to the clustering method of the step 1.1-the step 1.2 to obtain K M K of individual trade N A typical user. And analyzing daily load curves of typical users in different seasons, and using the gathered load data of the typical users as ARIMA prediction model input data.
And between the step 1 and the step 2, data preprocessing can be carried out on the data of the clustering result.
Specifically, the input data of different categories obtained by clustering in the step 1 are processed, wherein the default value is replaced by the mean value of the feature where the default value is located, and the threshold value and the singular value are replaced by the mean values of the feature where the default value is located at the previous moment and the next moment.
The step 2 specifically comprises the following steps:
step 2.1, constructing an original sequence:
X t ＝α 1 X t-1 +α 2 X t-2 +…α p X t-p +ε t +β 1 ε t-1 +β 2 ε t-2 +…β q ε t-q
in the formula, p and q are the orders of autoregressive and moving average respectively; alpha is alpha 1 ,α 2 ,…,α p Is an autoregressive coefficient; beta is a 1 ,β 2 ,…,β q For the moving average coefficient, X is the steady-state portion in the constructed sequence using the original typical user load data, and epsilon is the white noise sequence in the original sequence. The subscripts for X and ε denote time, respectively.
2.2, because the time sequence needs to be ensured to be stable when the ARIMA algorithm is carried out, the stability test is carried out by using an ADF (automatic document feeder) test (namely unit root test) method in the step;
step 2.3, if the original sequence is non-stationary after the check in step 2.2, performing differential operation on the non-stationary time sequence to obtain a differential order d, so that the sequence is stationary; if the original sequence is stable after the check in step 4.2, the difference order is set as a preset value, and the preset value is usually set as 0;
step 2.4, solving an autocorrelation coefficient ACF and a partial autocorrelation coefficient PACF, wherein the autocorrelation coefficient ACF is calculated by the formula:
the partial autocorrelation coefficient PACF is calculated by the formula:
wherein,
is covariance, EX t In order to be the desired value,
is the variance.
The autocorrelation coefficient ACF is used to determine the correlation between the time t of the daily load and the load at that time in the past week. The partial correlation coefficient is used for determining the correlation between the time t of the next day load and the load at the time in the past week under the influence of factors such as temperature change.
Step 2.5, determining model parameters, namely an AR autoregressive model order p and an MA moving average model order q, according to the autocorrelation coefficient and the partial autocorrelation coefficient, wherein the order p is an AR autoregressive model:
X t ＝α 1 X t-1 +α 2 X t-2 +…α p X t-p +u t
order q MA moving average model:
X t ＝ε t +β 1 ε t-1 +β 2 ε t-2 +…β q ε t-q
wherein epsilon t ,ε t-1 ,…,ε t-q White noise sequence at time t, t-1, …, t-q.
Step 2.6, combining the autoregressive model (AR), the moving average Model (MA) and a difference method to obtain a difference autoregressive moving average model ARIMA (p, d, q);
and 2.7, verifying and optimizing the model, comparing and analyzing a predicted value obtained by using the model and an actual value of a predicted time period, verifying the accuracy of the method, and predicting the load of an unknown time period by using the method. The predicted value H obtained by the ARIMA algorithm M-N As the actual load value in calculating the tunable potential.
The flow diagram of the ARIMA predictive model algorithm is shown in fig. 3, and fig. 3 also includes an input data processing procedure between step 1 and step 2.
The step 3 specifically comprises the following steps:
step 3.1, according to the clustering results of the industries in different seasons obtained in the step 1, the calculation methods of the baseline loads of the industries in different seasons are the same, namely, the distribution of the loads at t moment in a specified time length range (such as one week) in the day is counted and predicted, and the mean value delta of the loads is calculated f Sum variance
Get satisfied with
Maximum value L in the (i.e. three sigma) principle 1 And a minimum value L 2 As the maximum baseline load and minimum baseline load at time t;
step 3.2, obtaining the predicted value H according to the step 2.7 M-N And maximum baseline load L obtained in step 3.1 1 And minimum baseline load L 2 Establishing an adjustable potential calculation model:
single day load up-regulation potential:
η 2,j ＝M j ×(H M-N,j -L 2,j )
single day load down-regulation potential:
η 2 ＝M j ×(H M-N -L 2 )
wherein, in the formula, eta 1,j Load up potential for demand side of industry j, M j Number of users of industry j, L 1,j Is the maximum baseline load, H, of industry j M-N,j The load value of the industry j user at the future moment; eta 2,j For demand side load turndown potential, L, of industry j 2,j The minimum baseline load for industry j.
And 3.3, taking the demand side load up-regulation potential and the demand side load down-regulation potential as demand side flexible resource adjustable potentials.
Example 2:
the specific calculation example of the demand side flexible resource adjustable potential prediction method is given below, and according to load sample data of 38 users in a certain place, the adjustable loads of users in different industries in different seasons of the area are predicted; there are 38 users in the area, and the sampling point is 24 per day.
Fig. 4(a) to (c) show results after twice clustering, and it can be seen from the graphs that the time scale can be divided into three seasons of spring and autumn, summer and winter according to the seasonal characteristics, and can be divided into industrial users, commercial users and residential users according to the industrial characteristics. Fig. 5(a) - (c) are typical daily load curves of various users in different seasons. According to the analysis of the industrial characteristics of the region, the typical user 1 load and the typical user 2 load are respectively a commercial user and an industrial user, both have two peak periods, the load is large in winter and summer, the typical user 3 load is the load of a resident user, and the curve is stable.
Fig. 6(a) to 8(b) show prediction results of ARIMA algorithm, and obtain prediction curves of 3 industries in different seasons, and according to the analysis of the regional industry characteristics, obtain that the user 1 is a commercial user, the user 2 is an industrial user, and the user 3 is a residential user. The curve shows that the error from the actual value is not large, and the predicted value is proved to be applicable.
Fig. 9(a) to fig. 11(b) are schematic diagrams of load up-regulation potential and down-regulation potential of typical users in three industries, namely industry, commerce and people. In fig. 9(a) to 11(b), there are three curves of the user predicted load, the minimum baseline load, and the maximum baseline load, respectively, where the distance between the maximum baseline load and the user predicted load is the load up-regulation potential, and the distance between the user predicted load and the minimum baseline load is the load down-regulation potential. And analyzing according to the regional industry characteristics to obtain that the user 1 is a commercial user, the user 2 is an industrial user, and the user 3 is a residential user. Multiplying the number of the users in the industry to obtain the daily adjustable potential of the industry. According to the method, the adjustable potential of different industries in different seasons in a certain area can be predicted, and technical support is provided for power demand side management and load scheduling.
Example 3:
based on the same inventive concept, the present invention further provides a demand-side flexible resource adjustable potential prediction system, the system structure is shown in fig. 12, and the system comprises: the system comprises a load clustering module, a load forecasting module and an adjustable potential module;
the load clustering module is used for sequentially clustering the load data of a plurality of users in a selected area from a time scale and an industry scale to obtain a typical daily load curve of each industry user in each season;
the load prediction module is used for predicting the load value of each industry user at a future moment by adopting a differential autoregressive moving average model based on the typical daily load curve of each industry user in each season;
and the adjustable potential module is used for calculating the flexible resource adjustable potential of the demand side according to the load values of users in various industries at a future moment.
Wherein, the load clustering module is specifically configured to:
clustering load data of a plurality of users in the selected area from a time scale by using a Canopy-Kmeans clustering algorithm to obtain a typical daily load curve of each user in each season;
and clustering the typical daily load curve of each user in each season by using a Canopy-Kmeans clustering algorithm from the industry scale to obtain the typical daily load curve of each industry user in each season.
The method for clustering the load data of a plurality of users in the selected area from the time scale by using a Canopy-Kmeans clustering algorithm to obtain a typical daily load curve of each user in each season comprises the following steps:
calculating the load rate, the peak-valley difference and the average load of each user based on the load data of a plurality of users in the selected area;
based on the load rate, the peak-valley difference and the average load of each user, adopting a Canopy clustering algorithm to find a plurality of time clustering center points from the load data of a plurality of users;
based on the time clustering center point and the load rate, the peak-valley difference and the average load of each user, clustering the load data of a plurality of users from the time scale by using a Kmeans clustering algorithm to obtain a typical daily load curve of each user in each season.
The typical daily load curve of each user in each season is clustered from an industry scale by using a Canopy-Kmeans clustering algorithm to obtain the typical daily load curve of each industry user in each season, and the clustering method comprises the following steps:
based on the load rate, the peak-valley difference and the average load of each user, a Canopy clustering algorithm is adopted to find a plurality of industry clustering center points from typical daily load curves of each season of each user;
and based on the load rate, the peak-valley difference and the average load of the industry clustering center point and each user, clustering the typical daily load curve of each season of each user by using a Kmeans clustering algorithm from the industry scale to obtain the typical daily load curve of each industry user in each season.
The load prediction module is specifically configured to:
and (3) carrying out stability test on the typical daily load curves of users in various industries in various seasons by using a unit root test method: if the test result is stable, setting the difference order of the typical daily load curve as a preset value, otherwise, carrying out difference operation on the typical daily load curve to obtain the difference order;
respectively calculating autocorrelation coefficients and partial autocorrelation coefficients of typical daily load curves of users of various industries in various seasons, and determining an autoregressive model order and a moving average model order according to the autocorrelation coefficients and the partial autocorrelation coefficients;
constructing a differential autoregressive moving average model based on the differential order, the autoregressive model order and the moving average model order of the typical daily load curve of each industry user in each season;
and respectively adopting the corresponding difference autoregressive moving average model to predict the load value of each industry user at the future time.
Wherein, adjustable potentiality module is specifically used for:
counting load value distribution of the industry users at the same historical moment in a specified duration range before the future moment aiming at the users of each industry, and calculating the mean value and the variance of the load according to the load value distribution;
based on the mean value and the variance of the load, taking the maximum value and the minimum value which meet the three-sigma principle as the maximum baseline load and the minimum baseline load of the corresponding industry user at the future moment;
and calculating the flexible resource adjustable potential of the demand side based on the load value, the maximum baseline load and the minimum baseline load of each industry user at the future moment.
The method comprises the following steps of calculating the adjustable potential of flexible resources on a demand side based on the load value, the maximum baseline load and the minimum baseline load of each industry user at a future moment, and comprises the following steps:
calculating the load up-regulation potential of the demand side based on the load values and the maximum baseline loads of all industry users at the future time, and calculating the load down-regulation potential of the demand side based on the load values and the minimum baseline loads of all industry users at the future time;
and taking the demand side load up-regulation potential and the demand side load down-regulation potential as the demand side flexible resource adjustable potential.
The calculation formula of the load up-regulation potential on the demand side is as follows:
η 1,j ＝M j ×(L 1,j -H M-N,j )
in the formula eta 1,j For demand side load up-regulation potential, M, of industry j j Number of users of industry j, L 1,j Is the maximum baseline load, H, of industry j M-N,j The load value of the industry j user at the future time;
the calculation of the demand side load turndown potential is as follows:
η 2,j ＝M j ×(H M-N,j -L 2,j )
in the formula eta 2,j For demand side load turndown potential, L, of industry j 2,j The minimum baseline load for industry j.
Example 4:
based on the same inventive concept, the present invention also provides a computer apparatus comprising a processor and a memory, the memory being configured to store a computer program comprising program instructions, the processor being configured to execute the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc., which is a computing core and a control core of the terminal, and is specifically adapted to implement one or more instructions, and specifically adapted to load and execute one or more instructions in a computer storage medium to implement a corresponding method flow or a corresponding function, so as to implement the steps of the demand-side flexible resource adjustable potential prediction method in the foregoing embodiments.
Example 5:
based on the same inventive concept, the present invention further provides a storage medium, in particular, a computer-readable storage medium (Memory), which is a Memory device in a computer device and is used for storing programs and data. It is understood that the computer readable storage medium herein can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also, one or more instructions, which may be one or more computer programs (including program code), are stored in the memory space and are adapted to be loaded and executed by the processor. It should be noted that the computer-readable storage medium may be a high-speed RAM memory, or may be 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 may be loaded and executed by the processor to implement the steps of the demand-side flexible resource tunable potential prediction method in the above embodiments.
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, and the like) having computer-usable program code embodied therein.
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 of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting the protection scope thereof, and although the present invention is described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that after reading the present invention, they can make various changes, modifications or equivalents to the specific embodiments of the application, but these changes, modifications or equivalents are all within the protection scope of the claims of the application.
Claims (14)
1. A demand side flexible resource adjustable potential prediction method is characterized by comprising the following steps:
clustering the load data of a plurality of users in the selected area from the time scale and the industry scale in sequence to obtain a typical daily load curve of each industry user in each season;
based on typical daily load curves of users in various industries in various seasons, predicting load values of the users in various industries at a future moment by adopting a differential autoregressive moving average model;
and calculating the flexible resource adjustable potential of the demand side according to the load values of users in various industries at a future moment.
2. The method of claim 1, wherein the clustering load data of a plurality of users in a selected area sequentially from a time scale and an industry scale to obtain a typical daily load curve for each industry user in each season comprises:
clustering load data of a plurality of users in the selected area from a time scale by using a Canopy-Kmeans clustering algorithm to obtain a typical daily load curve of each user in each season;
and clustering the typical daily load curve of each user in each season by using a Canopy-Kmeans clustering algorithm from the industry scale to obtain the typical daily load curve of each industry user in each season.
3. The method of claim 2, wherein the clustering load data of a plurality of users in the selected area from the time scale using a Canopy-Kmeans clustering algorithm to obtain a typical daily load curve for each user in each season comprises:
calculating the load rate, the peak-valley difference and the average load of each user based on the load data of a plurality of users in the selected area;
based on the load rate, the peak-valley difference and the average load of each user, adopting a Canopy clustering algorithm to find a plurality of time clustering center points from the load data of a plurality of users;
and based on the time clustering center point and the load rate, the peak-valley difference and the average load of each user, clustering the load data of a plurality of users from a time scale by using a Kmeans clustering algorithm to obtain a typical daily load curve of each user in each season.
4. The method of claim 3, wherein the clustering the typical daily load curve of each user in each season from the industry scale by using a Canopy-Kmeans clustering algorithm to obtain the typical daily load curve of each industry user in each season comprises:
based on the load rate, the peak-valley difference and the average load of each user, a Canopy clustering algorithm is adopted to find a plurality of industry clustering center points from typical daily load curves of each season of each user;
and based on the industry clustering center point and the load rate, the peak-valley difference and the average load of each user, clustering the typical daily load curve of each user in each season by using a Kmeans clustering algorithm from the industry scale to obtain the typical daily load curve of each industry user in each season.
5. The method of claim 1, wherein the predicting the load value of each industry user at a future time by using a differential autoregressive moving average model based on a typical daily load curve of each industry user in each season comprises:
and (3) carrying out stability test on the typical daily load curves of users in various industries in various seasons by using a unit root test method: if the test result is stable, setting the difference order of the typical daily load curve as a preset value, otherwise, carrying out difference operation on the typical daily load curve to obtain the difference order;
respectively calculating autocorrelation coefficients and partial autocorrelation coefficients of typical daily load curves of users of various industries in various seasons, and determining an autoregressive model order and a moving average model order according to the autocorrelation coefficients and the partial autocorrelation coefficients;
constructing a differential autoregressive moving average model based on the differential order, the autoregressive model order and the moving average model order of the typical daily load curve of each industry user in each season;
and respectively adopting the corresponding difference autoregressive moving average model to predict the load value of each industry user at the future time.
6. The method of claim 1, wherein calculating the demand-side flexible resource tunable potential according to the load values of the industry users at the future time comprises:
counting load value distribution of the industry users at the same historical moment in a specified duration range before the future moment aiming at the users of each industry, and calculating the mean value and the variance of the load according to the load value distribution;
based on the mean value and the variance of the load, taking the maximum value and the minimum value which meet the three-sigma principle as the maximum baseline load and the minimum baseline load of the corresponding industry user at the future moment;
and calculating the flexible resource adjustable potential of the demand side based on the load value, the maximum baseline load and the minimum baseline load of each industry user at the future moment.
7. The method of claim 6, wherein calculating the demand-side flexible resource tunable potential based on the load values, the maximum baseline loads, and the minimum baseline loads of the industry users at the future time comprises:
calculating the load up-regulation potential of the demand side based on the load values and the maximum baseline loads of all industry users at the future time, and calculating the load down-regulation potential of the demand side based on the load values and the minimum baseline loads of all industry users at the future time;
and taking the demand side load up-regulation potential and the demand side load down-regulation potential as demand side flexible resource adjustable potential.
8. The method of claim 7, wherein the demand side load up-regulation potential is calculated as follows:
η 1,j ＝M j ×(L 1,j -H M-N,j )
in the formula eta 1,j For demand side load up-regulation potential, M, of industry j j Number of users of industry j, L 1,j Is the maximum baseline load, H, of industry j M-N,j The load value of the industry j user at the future moment;
the calculation formula of the demand side load down-regulation potential is as follows:
η 2,j ＝M j ×(H M-N,j -L 2,j )
in the formula eta 2,j For demand side load turndown potential, L, of industry j 2,j The minimum baseline load for industry j.
9. A demand-side flexible resource tunable potential prediction system, comprising: the system comprises a load clustering module, a load forecasting module and an adjustable potential module;
the load clustering module is used for sequentially clustering the load data of a plurality of users in a selected area from a time scale and an industry scale to obtain a typical daily load curve of each industry user in each season;
the load prediction module is used for predicting the load value of each industry user at a future moment by adopting a differential autoregressive moving average model based on the typical daily load curve of each industry user in each season;
and the adjustable potential module is used for calculating the flexible resource adjustable potential of the demand side according to the load values of users in various industries at a future moment.
10. The system of claim 9, wherein the load clustering module is specifically configured to:
clustering load data of a plurality of users in the selected area from a time scale by using a Canopy-Kmeans clustering algorithm to obtain a typical daily load curve of each user in each season;
and clustering the typical daily load curve of each user in each season by using a Canopy-Kmeans clustering algorithm from the industry scale to obtain the typical daily load curve of each industry user in each season.
11. The system of claim 9, wherein the load prediction module is specifically configured to:
and (3) carrying out stability test on the typical daily load curves of users in various industries in various seasons by using a unit root test method: if the test result is stable, setting the difference order of the typical daily load curve as a preset value, otherwise, carrying out difference operation on the typical daily load curve to obtain the difference order;
respectively calculating autocorrelation coefficients and partial autocorrelation coefficients of typical daily load curves of users of various industries in various seasons, and determining an autoregressive model order and a moving average model order according to the autocorrelation coefficients and the partial autocorrelation coefficients;
constructing a differential autoregressive moving average model based on the differential order, the autoregressive model order and the moving average model order of the typical daily load curve of each industry user in each season;
and respectively adopting corresponding difference autoregressive moving average models to predict the load value of each industry user at the future moment.
12. The system of claim 9, wherein the adjustable potential module is specifically configured to:
counting load value distribution of the industry users at the same historical moment in a specified duration range before the future moment aiming at the users of each industry, and calculating the mean value and the variance of the load according to the load value distribution;
based on the mean value and the variance of the load, taking the maximum value and the minimum value which meet the three-sigma principle as the maximum baseline load and the minimum baseline load of the corresponding industry user at the future moment;
and calculating the flexible resource adjustable potential of the demand side based on the load value, the maximum baseline load and the minimum baseline load of each industry user at the future moment.
13. A computer device, comprising: one or more processors;
a memory for storing one or more programs;
the one or more programs, when executed by the one or more processors, implement the demand-side flexible resource tunable potential prediction method of any one of claims 1 to 8.
14. A computer-readable storage medium having stored thereon a computer program which, when executed, implements the demand-side flexible resource tunable potential prediction method of any one of claims 1 to 8.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115833190A (en) * | 2023-02-20 | 2023-03-21 | 广东电网有限责任公司中山供电局 | Distributed resource edge autonomous control method and system |
| CN116383598A (en) * | 2023-06-07 | 2023-07-04 | 中网联合(北京)能源服务有限公司 | Power consumer energy stability analysis method based on autoregressive algorithm |
| CN116579590A (en) * | 2023-07-13 | 2023-08-11 | 北京圆声能源科技有限公司 | Demand response evaluation method and system in virtual power plant |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115833190A (en) * | 2023-02-20 | 2023-03-21 | 广东电网有限责任公司中山供电局 | Distributed resource edge autonomous control method and system |
| CN116383598A (en) * | 2023-06-07 | 2023-07-04 | 中网联合(北京)能源服务有限公司 | Power consumer energy stability analysis method based on autoregressive algorithm |
| CN116383598B (en) * | 2023-06-07 | 2023-09-05 | 中网联合(北京)能源服务有限公司 | Power consumer energy stability analysis method based on autoregressive algorithm |
| CN116579590A (en) * | 2023-07-13 | 2023-08-11 | 北京圆声能源科技有限公司 | Demand response evaluation method and system in virtual power plant |
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