Disclosure of Invention
Aiming at the problems of complex data required by a virtual power plant model, insufficient adaptability of the existing evaluation model and large calculation amount in the model evaluation process in the prior art, the invention provides a virtual power plant demand response potential evaluation method, a system, a terminal and a storage medium, so as to solve the technical problems.
In a first aspect, the present invention provides a method for evaluating demand response potential of a virtual power plant, comprising:
acquiring historical electricity load data in a preset time period;
Performing dimension reduction calculation on the historical electricity load data according to a preselected load characteristic index to obtain dimension reduction data;
performing cluster analysis based on the dimension reduction data to obtain N groups of dimension reduction load data corresponding to the maximum cluster number N, carrying out dimension increase on the N groups of dimension reduction load data, and obtaining a corresponding cluster center curve based on the N groups of dimension increase load data, wherein the cluster center is a typical daily load curve;
and carrying out demand response potential analysis on the N groups of load curves after dimension increase by using an elastic coefficient method to respectively obtain response potential values of peak time periods and valley time periods, and subtracting the response potential values of the peak time periods or adding the response potential values of the valley time periods from the typical daily load curve to obtain an adjusted response potential value curve.
Further, obtaining historical electricity load data within a preset time period includes:
historical electricity load data of continuous hours of a preset day n are obtained.
Further, performing dimension reduction calculation on the historical electricity load data according to a pre-selected load characteristic index to obtain dimension reduction data, including:
calculating daily average load
=
Wherein P isA load value for an i-th time period;
Calculating daily load rate
=
Wherein,The maximum load value of the day;
Peak Gu Chalv was calculated
=
Wherein,Is the minimum load value of the current day;
Calculating peak load rate
=
Wherein,Is the sum of the load of the peak periods,Hours for peak period;
Calculating the load rate of the flat period
=
Wherein,Is the sum of the loads of the flat period,Hours for flat period;
Calculating the load rate in the valley period
=
Wherein,Is the sum of the loads of the valley period,Is the number of hours of the valley period.
Further, performing cluster analysis based on the dimension reduction data to obtain N groups of dimension reduction load data corresponding to the maximum cluster number N, performing dimension increase on the N groups of dimension reduction load data, and obtaining a corresponding cluster center curve based on the N groups of dimension increase load data, wherein the cluster center is a typical daily load curve, and the method comprises the following steps:
Importing the dimension reduction data into matlab for cluster analysis to obtain a maximum cluster number N and N groups of dimension reduction load data corresponding to the maximum cluster number N;
carrying out dimension lifting on each group of dimension-reduced load data to obtain load data after each group of dimension lifting;
And obtaining a cluster center curve in the load data after each group of dimension increase, wherein the cluster center is a typical daily load curve.
Further, carrying out demand response potential analysis on the load curves after N groups of dimension increase by using an elasticity coefficient method to respectively obtain response potential values of peak time periods and valley time periods, wherein the method comprises the following steps:
Acquisition of the firstDay 3Load amount of electricity consumption in each periodCalculating to obtain the firstAverage value of electricity load of each period
;
Based onCalculate the firstCoefficient of elasticity of time period
;
Based onAndCalculate the firstLoad factor of each period
;
Based onAndCalculating response potential values within peak periodsResponse potential values in the off-peak period
Wherein,AndRespectively representing a peak period and a valley period,For each ofThe maximum value of the period load rate,From day 0:00 to 24:00Each time period is 1h in length, and n is the total number of days of user load data.
Further, the method further comprises the step of preprocessing the historical electricity load data before performing dimension reduction calculation on the historical electricity load data according to the pre-selected load characteristic index to obtain dimension reduction data, wherein the step of deleting abnormal values in the historical electricity load data is included.
In a second aspect, the present invention provides a virtual power plant demand response potential assessment system comprising:
The data acquisition module is used for acquiring historical electricity load data in a preset time period;
the dimension reduction calculation module is used for carrying out dimension reduction calculation on the historical electricity load data according to the pre-selected load characteristic index to obtain dimension reduction data;
the cluster analysis module is used for carrying out cluster analysis based on the dimension reduction data to obtain N groups of dimension reduction load data corresponding to the maximum cluster number N, carrying out dimension increase on the N groups of dimension reduction load data, and obtaining a corresponding cluster center curve based on the N groups of dimension increase load data, wherein the cluster center is a typical daily load curve;
The demand response potential analysis module is used for carrying out demand response potential analysis on the N groups of load curves after dimension rising by using an elastic coefficient method to respectively obtain response potential values of peak periods and valley periods, and subtracting the response potential values of the peak periods or adding the response potential values of the valley periods from the typical daily load curve to obtain an adjusted response potential value curve.
Further, the cluster analysis module includes:
The cluster analysis unit is used for importing the dimension reduction data into matlab to perform cluster analysis to obtain a maximum cluster number N and N groups of dimension reduction load data corresponding to the maximum cluster number N;
the dimension increasing unit comprises dimension increasing steps of carrying out dimension increasing on each group of dimension-reducing load data to obtain load data after each group of dimension increasing;
And the typical daily load curve generation unit is used for acquiring a cluster center curve in the load data after each group of dimension increase, wherein the cluster center is the typical daily load curve.
In a third aspect, a terminal is provided, including:
a processor, a memory, wherein,
The memory is used for storing a computer program,
The processor is configured to call and run the computer program from the memory, so that the terminal performs the method of the terminal as described above.
In a fourth aspect, there is provided a computer storage medium having instructions stored therein which, when run on a computer, cause the computer to perform the method of the above aspects.
The virtual power plant demand response potential evaluation method, the system, the terminal and the storage medium have the beneficial effects that firstly, through simplifying data acquisition, dependence on complex data (such as electricity price information, power policies and the like) is reduced, the demand response potential evaluation is carried out only by means of historical electricity load data of a user, the data acquisition difficulty and the system calculation cost are greatly reduced, and the evaluation operability is improved. And secondly, the K-means clustering method is adopted to replace the traditional K-means clustering, so that the problem of deviation of using the mean value as a clustering center in the K-means method is avoided. By using the actual point closest to the cluster center as the cluster center, the clustering result is more in line with the actual electricity consumption condition of the user, and the evaluation accuracy is improved. In addition, the invention can accurately evaluate the demand response potential of the user in different time periods through the cluster analysis and the elasticity coefficient method, particularly on the load adjustment of peak periods and valley periods, helps the power system to realize more accurate peak clipping and valley filling, and improves the optimal configuration capability of power resources. In general, the method not only improves the accuracy and efficiency of demand response potential evaluation, but also provides powerful technical support for intelligent scheduling of novel power systems such as virtual power plants.
Detailed Description
In order to make the technical solution of the present invention better understood by those skilled in the art, the technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
The virtual power plant demand response potential evaluation method provided by the embodiment of the invention is executed by the computer equipment, and accordingly, the virtual power plant demand response potential evaluation system is operated in the computer equipment.
FIG. 1 is a schematic flow chart of a method of one embodiment of the invention. Wherein, the execution subject of fig. 1 may be a virtual power plant demand response potential evaluation system. The order of the steps in the flow chart may be changed and some may be omitted according to different needs.
In order to facilitate understanding of the present invention, the method for evaluating the demand response potential of a virtual power plant provided by the present invention is further described below with reference to the process of evaluating the demand response potential of a virtual power plant in the embodiment.
Specifically, as shown in fig. 1, the virtual power plant demand response potential evaluation method includes:
S1, acquiring historical electricity load data in a preset time period.
Historical electricity load data of continuous hours of a preset day n are obtained.
Specifically, electricity load data of a user in 115 days of each small period (24 periods in total) of 0:00-24:00 per day in 2017 for 4-8 months is collected, and an original daily load curve graph (the abscissa is a time period, the ordinate is an electricity load amount, and the data is 115 x 24 dimensions) of the user is drawn, wherein the curve graph has the effect that the most original electricity load data approximately change along with time before the steps of data preprocessing, clustering and the like are performed on the user in 2017 for 4-8 months can be seen more clearly.
In one embodiment, the method further comprises preprocessing the historical electrical load data before performing dimension reduction calculation on the historical electrical load data according to the pre-selected load characteristic index to obtain dimension reduction data, wherein the preprocessing comprises deleting abnormal values in the historical electrical load data.
Specifically, the abnormal value existing in the S1 original data is removed, the power consumption day of the user is changed from 115 days to 111 days, and 4 days of abnormal data (111×24 dimensions) are removed in total.
S2, performing dimension reduction calculation on the historical electricity load data according to the pre-selected load characteristic index to obtain dimension reduction data.
Calculating daily average load
=
Wherein P isA load value for an i-th time period;
Calculating daily load rate
=
Wherein,The maximum load value of the day;
Peak Gu Chalv was calculated
=
Wherein,Is the minimum load value of the current day;
Calculating peak load rate
=
Wherein,Is the sum of the load of the peak periods,Hours for peak period;
Calculating the load rate of the flat period
=
Wherein,Is the sum of the loads of the flat period,Hours for flat period;
Calculating the load rate in the valley period
=
Wherein,Is the sum of the loads of the valley period,Is the number of hours of the valley period.
Specifically, the preselected load characteristic indexes may be daily average load, daily load rate, peak Gu Chalv, peak load rate, flat load rate and valley load rate, and the indexes are used to better extract the electricity consumption information and characteristics of the user in one day. The calculation of each index is related to the load of each time period of the day, for example, the daily average load is obtained by averaging the sum of the loads of each time period of 24 time periods of the day, and the peak Gu Chalv is the difference between the maximum load and the minimum load of the day divided by the maximum load, and the purpose of the step is to replace 24-dimensional data of the original data per day by 6-dimensional indexes so as to achieve the purpose of dimension reduction.
The original power load of each hour of 0:00-24:00 of each day in the step S1 is replaced by the six indexes, so that the original data is reduced from 24 dimensions to 6 dimensions. The step is used for achieving information dimension reduction through index extraction, and 24-dimensional single load data is replaced by 6-dimensional index data, so that the data contains more electricity utilization information and characteristics, and the subsequent clustering is more accurate and effective (111 multiplied by 6).
S3, carrying out cluster analysis based on the dimension reduction data to obtain N groups of dimension reduction load curves corresponding to the maximum cluster number N, carrying out dimension increase on the N groups of dimension reduction load curves, and obtaining corresponding cluster center curves based on the N groups of dimension increase load curves, wherein the cluster center is a typical daily load curve.
Importing the dimension reduction data into matlab for cluster analysis to obtain a maximum cluster number N and N groups of dimension reduction load data corresponding to the maximum cluster number N;
carrying out dimension lifting on each group of dimension-reduced load data to obtain load data after each group of dimension lifting;
And obtaining a cluster center curve in the load data after each group of dimension increase, wherein the cluster center is a typical daily load curve.
Specifically, before the clustering operation is performed by using the software matlab, a contour coefficient test is performed to determine the optimal classification number. The process of the profile coefficient is to compare the effect of different clustering situations of the original data, for example, when the data are classified into 1 class, the profile coefficient is a, and when the data are classified into 2 classes, the profile coefficient is b.
The result of the contour coefficient test is shown in fig. 2. It can be seen that the maximum classification number is set to 10, and the profile coefficient approaches to 1 most when the classification number is 3, so that the data is gathered into 3 classes.
Specifically, a clustering operation may be performed using software matlab. In a specific operation, the 6-dimensional data (111×6-dimensional) obtained in step S2 is required to be input, so that clustering can be performed according to index data including abundant effective information, such as daily average load and daily load rate.
For example, the specific code may be used as:
In the first step, data is input, one sample per row, and one feature per column.
And secondly, inputting the maximum value of the clustering quantity of the data into the group to be 10.
And thirdly, setting the iteration number to be 500.
And fourthly, executing a clustering algorithm.
And fifthly, outputting the maximum clustering number, and clustering the clustering center and the clusters of which each row of data is divided.
When the clustering analysis is performed, the clustering is performed based on the 6-dimensional data index after dimension reduction, the output classification data is still index data, and a 24-dimensional load curve graph with time as an abscissa is drawn, so that each piece of data needs to be restored to 24-dimensional data in a one-to-one correspondence mode. The effect of dimension reduction is only to perform clustering more scientifically and accurately. At this time, the original daily load curve graph is divided into 3 classes after dimension reduction, profile coefficient inspection and clustering, wherein the first class is 71 days (71×24 dimensions), the second class is 25 days (25×24 dimensions), and the third class is 15 days (15×24 dimensions).
The most representative curve of each of the three groups of categories, i.e., the cluster center of the group of categories is the typical daily load curve of the group of categories. Since the principle of k-media clustering is to take the actual point closest to the cluster center as the cluster center, the cluster center of each category is the actual data closest to the cluster center of the group generated by codes.
Since the output cluster center is also index data, the index data needs to be restored to 24-dimensional data in a one-to-one correspondence. Since there are a total of 3 classes, there are 3 typical daily load curves, each of which is 1 x 24 dimensions (time period on the abscissa and load on the ordinate). The function of drawing the graph is to classify the original power consumption information with disorder of 111 days, and extract the most representative 3 power consumption rules of the user in 111 days.
S4, carrying out demand response potential analysis on the N groups of load curves after dimension rising by using an elasticity coefficient method to respectively obtain response potential values of peak time periods and valley time periods, and subtracting the response potential values of the peak time periods or adding the response potential values of the valley time periods from the typical daily load curve to obtain an adjusted response potential value curve.
Acquisition of the firstDay 3Load amount of electricity consumption in each periodCalculating to obtain the firstAverage value of electricity load of each period
;
Based onCalculate the firstCoefficient of elasticity of time period
;
Based onAndCalculate the firstLoad factor of each period
;
Based onAndCalculating response potential values within peak periodsResponse potential values in the off-peak period
Wherein,AndRespectively representing a peak period and a valley period,For each ofThe maximum value of the period load rate,From day 0:00 to 24:00Each time period is 1h in length, and n is the total number of days of user load data.
In particular, when calculating the demand response potential of the first class,At 71, when the demand response potential of the second class is calculated,25, When calculating the demand response potential of the third class,15.
Specifically, a demand response potential curve (time period on the abscissa and load amount on the ordinate) may be drawn when the demand response potential analysis is performed. Each demand response potential curve has 2 demand response potential curves, one of which is a typical daily load curve in S3 for comparison, and the other is an ideal daily load curve which is obtained by adding or subtracting the obtained response potential value on the basis of the typical daily load curve. It is clear and intuitive that the demand response potential of the user is maximum in which period. Analysis concludes that the enterprise exhibits higher response potentials at 9:00-12:00 and 19:00-22:00, and lower response potentials at 0:00-9:00. Enterprises can concentrate on relevant demand response policies in the time period so as to achieve better response effect and realize peak clipping and valley filling power resource adjustment balance.
In some embodiments, the virtual power plant demand response potential evaluation system may include a plurality of functional modules comprised of computer program segments. The computer program of each program segment in the virtual power plant demand response potential evaluation system may be stored in a memory of a computer device and executed by at least one processor to perform (see fig. 1 for details) the functions of virtual power plant demand response potential evaluation.
In this embodiment, the virtual power plant demand response potential evaluation system may be divided into a plurality of functional modules according to the functions performed by the virtual power plant demand response potential evaluation system, as shown in fig. 3. Functional modules of the system 200 may include a data acquisition module 210, a dimension reduction calculation module 220, a cluster analysis building module 230, and a demand response potential analysis module 240. The module referred to in the present invention refers to a series of computer program segments capable of being executed by at least one processor and of performing a fixed function, stored in a memory. In the present embodiment, the functions of the respective modules will be described in detail in the following embodiments.
The data acquisition module is used for acquiring historical electricity load data in a preset time period;
the dimension reduction calculation module is used for carrying out dimension reduction calculation on the historical electricity load data according to the pre-selected load characteristic index to obtain dimension reduction data;
the cluster analysis module is used for carrying out cluster analysis based on the dimension reduction data to obtain N groups of dimension reduction load data corresponding to the maximum cluster number N, carrying out dimension increase on the N groups of dimension reduction load data, and obtaining a corresponding cluster center curve based on the N groups of dimension increase load data, wherein the cluster center is a typical daily load curve;
The demand response potential analysis module is used for carrying out demand response potential analysis on the N groups of load curves after dimension rising by using an elastic coefficient method to respectively obtain response potential values of peak periods and valley periods, and subtracting the response potential values of the peak periods or adding the response potential values of the valley periods from the typical daily load curve to obtain an adjusted response potential value curve.
Optionally, as an embodiment of the present invention, the cluster analysis module includes:
The cluster analysis unit is used for importing the dimension reduction data into matlab to perform cluster analysis to obtain a maximum cluster number N and N groups of dimension reduction load data corresponding to the maximum cluster number N;
the dimension increasing unit comprises dimension increasing steps of carrying out dimension increasing on each group of dimension-reducing load data to obtain load data after each group of dimension increasing;
And the typical daily load curve generation unit is used for acquiring a cluster center curve in the load data after each group of dimension increase, wherein the cluster center is the typical daily load curve.
Fig. 4 is a schematic structural diagram of a terminal 300 according to an embodiment of the present invention, where the terminal 300 may be used to execute the virtual power plant demand response potential evaluation method according to the embodiment of the present invention.
The terminal 300 may include a processor 310, a memory 320, and a communication unit 330. The components may communicate via one or more buses, and it will be appreciated by those skilled in the art that the configuration of the server as shown in the drawings is not limiting of the invention, as it may be a bus-like structure, a star-like structure, or include more or fewer components than shown, or may be a combination of certain components or a different arrangement of components.
The memory 320 may be used to store instructions for execution by the processor 310, and the memory 320 may be implemented by any type of volatile or non-volatile memory terminal or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk. The execution of the instructions in memory 320, when executed by processor 310, enables terminal 300 to perform some or all of the steps in the method embodiments described below.
The processor 310 is a control center of the storage terminal, connects various parts of the entire electronic terminal using various interfaces and lines, and performs various functions of the electronic terminal and/or processes data by running or executing software programs and/or modules stored in the memory 320, and invoking data stored in the memory. The processor may be comprised of an integrated circuit (INTEGRATED CIRCUIT, simply referred to as an IC), for example, a single packaged IC, or may be comprised of multiple packaged ICs connected to one another for the same function or for different functions. For example, the processor 310 may include only a central processing unit (Central Processing Unit, CPU for short). In the embodiment of the invention, the CPU can be a single operation core or can comprise multiple operation cores.
And a communication unit 330 for establishing a communication channel so that the storage terminal can communicate with other terminals. Receiving user data sent by other terminals or sending the user data to other terminals.
The present invention also provides a computer storage medium in which a program may be stored, which program may include some or all of the steps in the embodiments provided by the present invention when executed. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random-access memory (random access memory RAM), or the like.
Therefore, the system can combine the advantages of the inertial measurement unit and the laser radar point cloud data to position the vehicle by fusing the data of a plurality of sensors. IMU can provide high frequency motion information and lidar provides accurate spatial data of the environment. Through combining the two, the positioning accuracy of the vehicle in a complex environment can be effectively improved under the condition that GNSS signals are weak or disturbed.
According to the virtual power plant demand response potential evaluation method, system, terminal and storage medium, firstly, through simplifying data acquisition, dependence on complex data (such as electricity price information, power policies and the like) is reduced, demand response potential evaluation is carried out only by means of historical electricity load data of a user, data acquisition difficulty and system calculation cost are greatly reduced, and evaluation operability is improved. And secondly, the K-means clustering method is adopted to replace the traditional K-means clustering, so that the problem of deviation of using the mean value as a clustering center in the K-means method is avoided. By using the actual point closest to the cluster center as the cluster center, the clustering result is more in line with the actual electricity consumption condition of the user, and the evaluation accuracy is improved. In addition, the invention can accurately evaluate the demand response potential of the user in different time periods through the cluster analysis and the elasticity coefficient method, particularly on the load adjustment of peak periods and valley periods, helps the power system to realize more accurate peak clipping and valley filling, and improves the optimal configuration capability of power resources. In general, the method not only improves the accuracy and efficiency of demand response potential evaluation, but also provides powerful technical support for intelligent scheduling of novel power systems such as virtual power plants. The technical effects achieved by this embodiment may be referred to above, and will not be described herein.
It will be apparent to those skilled in the art that the techniques of embodiments of the present invention may be implemented in software plus a necessary general purpose hardware platform. Based on such understanding, the technical solution in the embodiments of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium such as a U-disc, a mobile hard disc, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, etc. various media capable of storing program codes, including several instructions for causing a computer terminal (which may be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the method described in the embodiments of the present invention.
The same or similar parts between the various embodiments in this specification are referred to each other. In particular, for the terminal embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and reference should be made to the description in the method embodiment for relevant points.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods may be implemented in other ways. For example, the system embodiments described above are merely illustrative, e.g., the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple modules or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with respect to each other may be through some interface, indirect coupling or communication connection of systems or modules, electrical, mechanical, or other form.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module.
Although the present invention has been described in detail by way of preferred embodiments with reference to the accompanying drawings, the present invention is not limited thereto. Various equivalent modifications and substitutions may be made in the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and it is intended that all such modifications and substitutions be within the scope of the present invention/be within the scope of the present invention as defined by the appended claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.