User theoretical response potential evaluation method and device, terminal and storage medium

Abstract

The application provides a user theoretical response potential evaluation method and device, a terminal and a storage medium. The method comprises the following steps: obtaining daily load data of a target user in a historical time period corresponding to an implementation time period of demand response; the daily load data comprises load sampling values at each sampling time point of each day; based on each load sampling value in the daily load data of each day in the historical time period, a load elasticity coefficient of each target sampling time point corresponding to a peak / valley time period of demand response is calculated; according to the load elasticity coefficient of each target sampling time point corresponding to the peak / valley time period and the load sampling value at the corresponding target sampling time point in the daily load data of each day in the historical time period, the theoretical response potential of the target user in the peak / valley time period is calculated. The theoretical response potential of the target user in the peak / valley time period calculated by the application is more accurate, thereby providing more accurate and effective guidance for power grid dispatching planning, arrangement of standby capacity and other businesses.

Description

Technical Field

[0001] This invention relates to the field of power dispatching technology, and in particular to a method, apparatus, terminal and storage medium for evaluating user theoretical response potential. Background Technology

[0002] With the large-scale integration of distributed power sources into the grid and the rapid development of loads such as electric vehicles and energy storage, the balance between power supply and demand is facing increasingly severe challenges. Demand Response (DR), as an important means of resolving the contradiction between power supply and demand, can promote the transformation of power users from passive recipients of power to active participants in the grid. It can fully leverage the market's ability to optimize resource allocation, enabling renewable energy and controllable loads to actively participate in grid regulation and control within a certain range, achieving mutual benefit and win-win results for all parties involved. It can also solve operational problems caused by the randomness and volatility of distributed power sources and loads, and achieve the goals of peak shaving and valley filling and improving load characteristics of the grid.

[0003] When formulating demand response plans and strategies, prior assessment of the demand response potential of users or industries can effectively improve the accuracy and effectiveness of demand response capacity allocation plans, enabling more load to be reduced or shifted, thereby achieving peak shaving and valley filling. Demand response potential can be categorized into theoretical response potential, technical response potential, economic response potential, and available response potential. The assessment of theoretical response potential plays a crucial role in screening potential demand response users and enhancing their participation in demand response. Therefore, accurately assessing the theoretical response potential of a particular user or industry is a pressing issue that needs to be addressed. Summary of the Invention

[0004] This invention provides a method, apparatus, terminal, and storage medium for evaluating a user's theoretical response potential, in order to solve the problem of how to accurately evaluate the theoretical response potential of a user or an industry.

[0005] In a first aspect, embodiments of the present invention provide a method for evaluating a user's theoretical response potential, comprising:

[0006] Obtain daily load data for the target user within a historical time period; the historical time period is the time period corresponding to the implementation period of the demand response; the daily load data includes load sampling values ​​at each sampling time of each day;

[0007] Based on the load sample values ​​in the daily load data of each day within the historical time period, calculate the load elasticity coefficient for each target sampling time corresponding to the peak / valley period of demand response;

[0008] Based on the load elasticity coefficients of each target sampling time corresponding to the peak / valley period and the load sampling values ​​of the daily load data of each day in the historical period at the corresponding target sampling time, the theoretical response potential of the target user during the peak / valley period is calculated.

[0009] In one possible implementation, the calculation of the load resilience coefficient for each target sampling time corresponding to the peak / valley periods of demand response, based on the load sample values ​​from the daily load data of each day within the historical time period, includes:

[0010] Based on the load sampling values ​​in the daily load data of each day within the historical time period, determine the target sampling time corresponding to the peak / valley period of demand response in the target load sampling value in the daily load data of each day within the historical time period.

[0011] For each target sampling time corresponding to the peak / valley period, the normalized load value corresponding to each target load sampling value at that target sampling time is determined based on each target load sampling value at that target sampling time and the maximum value among all target load sampling values.

[0012] Calculate the average value of each target load sample at the target sampling time, and determine the normalized average value at the target sampling time based on the average value and the maximum value;

[0013] Based on the normalized load values ​​and the normalized average value, calculate the load resilience coefficient for the target sampling time corresponding to the peak / valley period of the demand response.

[0014] In one possible implementation, calculating the load resilience coefficient for the target sampling time corresponding to the peak / valley periods of the demand response based on each of the normalized load values ​​and the normalized average value includes:

[0015] according to Calculate the load elasticity coefficient at the target sampling time corresponding to the peak / valley period of the demand response;

[0016] Where E(t) is the load resilience coefficient at the target sampling time t corresponding to the peak / valley period of the demand response, and N is the number of days included in the implementation period of the demand response. This represents the normalized load value corresponding to the target load sample value on the r-th day within the historical time period at the target sampling time t. This is the normalized average value at sampling time t for the target.

[0017] In one possible implementation, the theoretical response potential of the target user during peak hours is calculated based on the load elasticity coefficients at each target sampling time corresponding to the peak period and the load sampling values ​​at the corresponding target sampling times from the daily load data of each day within the historical time period. This includes:

[0018] according to Calculate the theoretical response potential of the target user during peak hours;

[0019] in, T represents the theoretical response potential of target user i during peak hours. F Let P be the set of target sampling times corresponding to the peak period, E(t) be the load elasticity coefficient of the target sampling time t corresponding to the peak period of demand response, and P be the load elasticity coefficient of the target sampling time t corresponding to the peak period of demand response. ave (t) represents the average value of each load sample at the target sampling time t in the daily load data of each day within the historical time period.

[0020] In one possible implementation, the theoretical response potential of the target user during the off-peak period is calculated based on the load elasticity coefficient of each target sampling time corresponding to the off-peak period and the load sampling value at the corresponding target sampling time from the daily load data of each day within the historical time period, including:

[0021] according to Calculate the theoretical response potential of the target user during off-peak hours;

[0022] in, T represents the theoretical response potential of target user i during the off-peak period. G Let E(t) be the set of target sampling times corresponding to the valley period, and let E(t) be the load elasticity coefficient of the target sampling time t corresponding to the valley period of the demand response. P represents the maximum value of each load sample at the target sampling time t from the daily load data for each day within the historical time period. ave (t) represents the average value of each load sample at the target sampling time t in the daily load data of each day within the historical time period.

[0023] In one possible implementation, after obtaining the daily load data of the target user for each day within a historical time period, the following is also included:

[0024] The amount of missing data in the daily load data of the target user within the historical time period is calculated.

[0025] If the amount of missing data is greater than the first preset threshold, then the target user is discarded.

[0026] If the amount of missing data is not greater than the first preset threshold, then the amount of abnormal data that is too large or too small and the amount of repeated data that is continuously repeated are counted in the daily load data of the target user in the historical time period.

[0027] If the sum of the missing data, the abnormal data, and the duplicate data exceeds the second preset threshold, then the target user is discarded.

[0028] If the sum of the missing data, the abnormal data, and the duplicate data is not greater than the second preset threshold, then the missing data, abnormal data, and duplicate data in the daily load data of the target user in the historical time period are repaired to obtain the repaired daily load data of the target user in the historical time period.

[0029] The calculation of the load elasticity coefficient for each target sampling time corresponding to the peak / valley periods of demand response, based on the daily load data of each day within the historical time period, includes:

[0030] Based on the load sampling values ​​in the daily load data of each day within the historical time period, the load elasticity coefficient of each target sampling time corresponding to the peak / valley period of demand response is calculated.

[0031] In one possible implementation, after calculating the theoretical response potential of the target user during peak / valley periods based on the load elasticity coefficients at each target sampling time corresponding to the peak / valley periods and the load sampling values ​​at the corresponding target sampling times from the daily load data of each day within the historical time period, the method further includes:

[0032] The theoretical response potential of all users in the target user's industry during peak / valley periods is calculated using the same method as the method used to calculate the target user's theoretical response potential during peak / valley periods.

[0033] Based on the theoretical response potential of all users in the target user's industry during peak / valley periods, calculate the theoretical response potential assessment of the target user's industry during peak / valley periods.

[0034] Secondly, embodiments of the present invention provide a user theoretical response potential assessment device, comprising:

[0035] The acquisition module is used to acquire the daily load data of the target user for each day within a historical time period; the historical time period is the time period corresponding to the implementation time period of the demand response; the daily load data includes the load sampling values ​​at each sampling time of each day;

[0036] The processing module is used to calculate the load elasticity coefficient for each target sampling time corresponding to the peak / valley period of demand response, based on the load sampling values ​​in the daily load data of each day within the historical time period.

[0037] The evaluation module is used to calculate the theoretical response potential of the target user during peak / valley periods based on the load elasticity coefficient of each target sampling time corresponding to the peak / valley period and the load sampling value at the corresponding target sampling time in the daily load data of each day within the historical time period.

[0038] Thirdly, embodiments of the present invention provide a terminal, including a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to perform the steps of the method as described in the first aspect or any possible implementation thereof.

[0039] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method as described in the first aspect or any possible implementation thereof.

[0040] This invention provides a method, apparatus, terminal, and storage medium for assessing a user's theoretical response potential. After determining the implementation time period for demand response, the method first acquires the daily load data (including load sampling values ​​at each sampling time each day) of the target user within the historical time period corresponding to the implementation time period. Then, based on the load sampling values ​​from the daily load data of each day within the historical time period, it calculates the load elasticity coefficient for each target sampling time corresponding to the peak / valley period of demand response. Finally, based on the load elasticity coefficients for each target sampling time corresponding to the peak / valley period and the load sampling values ​​at the corresponding target sampling times from the daily load data of each day within the historical time period, it calculates the theoretical response potential of the target user during the peak / valley period. Since the load elasticity coefficients calculated based on the load sampling values ​​from the daily load data of each day within the historical time period can identify the flexibility and controllability of the target user's electricity load changes over time, the theoretical response potential of the target user during the peak / valley period calculated based on the load elasticity coefficients for each target sampling time corresponding to the peak / valley period is more accurate and can provide more accurate and effective guidance for grid dispatching in planning and allocating reserve capacity. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1This is a flowchart illustrating the implementation of the user theoretical response potential assessment method provided in this embodiment of the invention.

[0043] Figure 2 This is a flowchart illustrating the implementation of the bad data identification and repair method provided in this embodiment of the invention.

[0044] Figure 3 This is a flowchart illustrating the implementation of calculating the load elasticity coefficient according to an embodiment of the present invention;

[0045] Figure 4 This is a schematic diagram of the user theoretical response potential assessment device provided in an embodiment of the present invention;

[0046] Figure 5 This is a schematic diagram of the terminal provided in an embodiment of the present invention. Detailed Implementation

[0047] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0048] To make the objectives, technical solutions, and advantages of the present invention clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0049] See Figure 1 The flowchart illustrating the implementation of the user theoretical response potential assessment method provided in this embodiment of the invention is described in detail below:

[0050] In step 101, the daily load data of the target user for each day within the historical time period is obtained.

[0051] The historical time period is the time period corresponding to the implementation period of the demand response; the daily load data includes the load sampling values ​​at each sampling time of each day.

[0052] Demand-side response (DSR) is typically implemented during periods of peak load and potential power shortages. Therefore, the implementation timeframe for DSR can be determined based on the peak load day for a given region. For example, the month containing the peak load day in a region can be used as the real-time timeframe for DSR, or the timeframe corresponding to ±10 days of the peak load day in a region can be used as the real-time timeframe for DSR.

[0053] After determining the implementation timeframe for demand response, daily load data for the target users within the corresponding historical timeframe can be obtained. For example, based on the load data for region A in 2021, if August 15, 2021, is determined to be the peak load day for region A, then August can be designated as the real-time timeframe for subsequent demand response. Based on this, daily load data for the target users in August 2021 can be obtained. The daily load data within the historical timeframe can be obtained by sampling at a preset sampling frequency. The daily load data can be stored in the form of a load dataset or a load data curve. For example, daily load data for the target users within the historical timeframe can be collected and stored at a frequency of once every 15 minutes.

[0054] Optionally, after obtaining the daily load data of the target user for each day within a historical time period, it may also include:

[0055] The amount of missing data in the daily load data of the target user within a historical time period is calculated.

[0056] If the amount of missing data exceeds the first preset threshold, the target user will be discarded.

[0057] If the amount of missing data is not greater than the first preset threshold, then the amount of abnormal data that is too large or too small and the amount of repeated data that are continuously repeated are counted in the daily load data of the target user in the historical time period.

[0058] If the sum of missing data, abnormal data, and duplicate data exceeds the second preset threshold, the target user will be discarded.

[0059] If the sum of missing data, abnormal data, and duplicate data does not exceed the second preset threshold, then the missing data, abnormal data, and duplicate data in the daily load data of the target user within the historical time period are repaired to obtain the repaired daily load data of the target user within the historical time period.

[0060] Accordingly, based on the load sample values ​​from the daily load data for each day within the historical time period, the load elasticity coefficient for each target sampling time corresponding to the peak / valley periods of demand response is calculated, which may include:

[0061] Based on the load sampling values ​​in the daily repair load data of each day within the historical time period, the load elasticity coefficient of each target sampling time corresponding to the peak / valley period of demand response is calculated.

[0062] During data acquisition, transmission, and storage, a large amount of various types of substandard data may be intentionally or unintentionally introduced due to network attacks, equipment failures, poor communication, and other reasons. The presence of substandard data may lead to deviations in the estimation of subsequent theoretical response potential, thereby affecting the implementation of demand response. Therefore, it is necessary to identify and correct this substandard data.

[0063] The daily load data obtained in this embodiment for the historical time period mainly contains the following three types of poor data: (1) missing data; (2) abnormally large or small data; and (3) continuously repeated data. Therefore, in order to improve data quality and ensure the accuracy of subsequent estimation results of theoretical response potential, this embodiment proposes the following... Figure 2 The method for identifying and repairing defective data is shown. This method mainly includes the following three parts:

[0064] (1) Identification of missing data. To address the issue of missing data, this embodiment introduces the concept of user data missing rate, defined as follows:

[0065]

[0066] Considering that a user's active power data is unlikely to be zero in reality, data points with 0 or null values ​​(NaN) in the daily load data (i.e., the user's original load data) within the historical time period can be considered as missing data points. Based on this, a user data missing rate threshold (e.g., 10%) can be determined first. Then, based on the number of daily load data points for the target user within the historical time period (i.e., the total number of user data points), the user data missing rate threshold is transformed into a preset threshold (i.e., the first preset threshold) a0. Finally, the amount of missing data A in the daily load data of the target user within the historical time period is determined. n The user data missing rate is determined by whether the number of missing user data points reaches a preset threshold a0. Users whose missing data rate reaches the threshold are discarded. For example, users with a missing data rate greater than 10% are discarded, and their theoretical response potential is no longer analyzed. Alternatively, the daily load data for each user within the historical time period is re-acquired. Users with a missing data rate less than or equal to 10% are then further analyzed.

[0067] (2) Identification of Continuously Repeating Data. When detecting continuously repeating data, a threshold for the number of consecutively repeating load sample values ​​can be used to determine whether the load data is continuously repeating, based on the actual situation. For example, if a user's daily load data for a certain day contains more than 5 consecutively repeating load sample values, then these consecutively repeating load sample values ​​are considered as one instance of repeating data, thus obtaining the number of continuously repeating data C. n .

[0068] (3) Identification of abnormally large and small data. For example, the 6σ criterion can be used to identify abnormally large or small load sample values. That is, when the difference between a certain load sample value of a target user and the average load value of all load sample values ​​of the target user is greater than 6 times the standard deviation of the target user's load, the load sample value is determined to be abnormally large or small load data, thereby obtaining the amount of abnormally large and small data B. n .

[0069] (4) Further screening of users and repair of defective data. To further screen users whose retained data missing rate is less than or equal to a user data missing rate threshold, such as 10%, the concept of user data defect rate can be introduced, defined as follows:

[0070]

[0071] Based on this, for users whose data missing rate is less than or equal to a user data missing rate threshold (e.g., 10%), users whose data defect rate is 10% or higher are further discarded. For the users who are ultimately retained, linear interpolation can be used to fill in missing values ​​and repair consecutive duplicate data, excessively large data, and excessively small data.

[0072] The user data missing rate threshold and the user data defect rate threshold can be set to be the same, so that the first preset threshold and the second preset threshold are identical. Alternatively, the user data defect rate threshold can be set to be greater than or less than the user data missing rate threshold, depending on actual needs.

[0073] In step 102, based on the load sampling values ​​in the daily load data of each day within the historical time period, the load elasticity coefficient for each target sampling time corresponding to the peak / valley period of demand response is calculated.

[0074] In this embodiment, the mean squared error of each load sample value at each sampling time can be used to measure the load elasticity at each sampling time. Since the mean squared error reflects the dispersion between individuals within an array, for daily load data of each day within a historical time period, if the mean squared error of each load sample value at a certain sampling time is large, it indicates that the similarity of the user's historical electricity load is not high, the user has more time periods to choose from for electricity use, the load regularity is not strong or has a certain degree of flexibility, and therefore the load adjustability is relatively large, and the user's load elasticity at that sampling time is strong. Conversely, if the mean squared error of each load sample value at a certain sampling time is small, it indicates that the similarity of the user's historical electricity load is high, the user has fewer time periods to choose from for electricity use, the load regularity is strong or has low flexibility, and therefore the load adjustability is relatively small, and the user's load elasticity at that sampling time is weak.

[0075] Since demand response addresses the power supply and demand imbalance by encouraging electricity users to participate in peak shaving and valley filling, it is possible to further identify peak periods where there may be power shortages and valley periods where there may be power surpluses. Based on the load elasticity coefficients of the peak and valley periods, the theoretical response potential of users can be assessed.

[0076] This involves statistically determining the first point in time when the annual maximum load occurs and the second point in time when the annual minimum load occurs in a given region. The peak demand period is obtained by subtracting a first preset time from the first point in time, and the valley demand period is obtained by subtracting a second preset time from the second point in time. For example, the peak demand period can be obtained by subtracting 2 hours from the first point in time, and the valley demand period by subtracting 4 hours from the second point in time. It should be noted that, under normal circumstances, the centrally dispatched load will experience two load peaks, one in the morning and one in the afternoon, so two peak periods can be selected throughout the day using the above method. In addition, the four hours with the lowest centrally dispatched load at night can also be selected as the valley period.

[0077] Optional, see Figure 3 Based on the daily load data of each day within a historical time period, the load elasticity coefficient for each target sampling time corresponding to the peak / valley periods of demand response is calculated, which may include:

[0078] In step 301, based on the load sampling values ​​in the daily load data of each day within the historical time period, the target load sampling value in the daily load data of each day within the historical time period is determined for each target sampling time corresponding to the peak / valley period of demand response.

[0079] In this embodiment, after determining the peak and valley periods of demand response, the sampling times corresponding to the peak and valley periods are first selected from the load sampling values ​​of each sampling time in the daily load data of each day in the historical time period as target sampling times, and the load sampling values ​​of each target sampling time in each day are selected as target load sampling values.

[0080] In step 302, for each target sampling time corresponding to the peak / valley period, the normalized load value corresponding to each target load sampling value at the target sampling time is determined based on each target load sampling value at the target sampling time and the maximum value among the target load sampling values.

[0081] In step 303, the average value of each target load sample value at the target sampling time is calculated, and the normalized average value at the target sampling time is determined based on the average value and the maximum value.

[0082] To make the subsequent load resilience coefficients more representative and facilitate the quantification of the user's theoretical response potential, the parameters required for calculating the mean square error can be normalized before calculating the mean square error of each target load sample value at each target sampling time of the demand response. For a specific target sampling time t, the normalized load values ​​corresponding to each target load sample value at that target sampling time t can be calculated as follows:

[0083]

[0084] in, P is the normalized load value corresponding to the target load sample value on the r-th day within the historical time period at the target sampling time t. r (t) represents the target load sample value on the r-th day within the historical time period at the target sampling time t. This represents the maximum value of each target load sample at the target sampling time t.

[0085] The average value P of each target load sample at the target sampling time t can be calculated. ave (t) is as follows:

[0086]

[0087] Where N represents the number of days included in the implementation period of the demand response.

[0088] The normalized average value of the target sampling time t can be calculated. as follows:

[0089]

[0090] In step 304, the load elasticity coefficient corresponding to the target sampling time for the peak / valley period of the demand response is calculated based on each normalized load value and the normalized average value.

[0091] Optionally, based on each normalized load value and the normalized average value, the load resilience coefficient corresponding to the target sampling time for the peak / valley period of the demand response can be calculated, which may include:

[0092] according to Calculate the load elasticity coefficient at the target sampling time corresponding to the peak / valley period of the demand response.

[0093] Where E(t) is the load resilience coefficient at the target sampling time t corresponding to the peak / valley period of the demand response, and N is the number of days included in the implementation period of the demand response. This represents the normalized load value corresponding to the target load sample value on the r-th day within the historical time period at the target sampling time t. This is the normalized average value at sampling time t for the target.

[0094] Based on the formula provided in this embodiment for calculating the load elasticity coefficient at the target sampling time corresponding to the peak / valley periods of demand response, the load elasticity coefficient for each target sampling time corresponding to the peak and valley periods can be calculated. This allows us to determine during which periods the user's electricity load exhibits load elasticity, effectively measuring the degree to which the user's electricity load can be adjusted and migrated. Furthermore, by combining this with the daily load data from historical periods and the load rates during peak and valley periods, the magnitude of the theoretical response potential can be assessed.

[0095] In step 103, the theoretical response potential of the target user during peak / valley periods is calculated based on the load elasticity coefficient of each target sampling time corresponding to the peak / valley period and the load sampling value of each day in the historical time period at the corresponding target sampling time in the daily load data.

[0096] Optionally, based on the load elasticity coefficients at each target sampling time corresponding to the peak period and the load sampling values ​​at the corresponding target sampling time from the daily load data of each day within the historical time period, the theoretical response potential of the target user during the peak period can be calculated, which may include:

[0097] according to Calculate the theoretical response potential of the target users during peak hours.

[0098] in, T represents the theoretical response potential of target user i during peak hours. F Let P be the set of target sampling times corresponding to the peak period, E(t) be the load elasticity coefficient of the target sampling time t corresponding to the peak period of demand response, and P be the load elasticity coefficient of the target sampling time t corresponding to the peak period of demand response. ave(t) represents the average value of each load sample at the target sampling time t in the daily load data for each day within the historical time period.

[0099] Optionally, based on the load elasticity coefficients at each target sampling time corresponding to the off-peak period and the load sampling values ​​at the corresponding target sampling time from the daily load data of each day within the historical time period, the theoretical response potential of the target user during the off-peak period can be calculated, which may include:

[0100] according to Calculate the theoretical response potential of the target user during off-peak hours.

[0101] in, T represents the theoretical response potential of target user i during the off-peak period. G Let E(t) be the set of target sampling times corresponding to the valley period, and let E(t) be the load elasticity coefficient of the target sampling time t corresponding to the valley period of the demand response. P represents the maximum value of each load sample at the target sampling time t from the daily load data for each day within the historical time period. ave (t) represents the average value of each load sample at the target sampling time t in the daily load data for each day within the historical time period.

[0102] In this embodiment, after calculating the load elasticity coefficient for each target sampling time corresponding to peak and valley periods, if the load elasticity coefficient for a certain target sampling time is large, it indicates that the user's electricity load during that sampling time period is time-elastic, and the user can choose to transfer peak-period electricity consumption to valley or flat periods according to the electricity price policy at that time. If the load elasticity coefficient for a certain target sampling time is small, it indicates that the user's electricity load during that sampling time period is not time-elastic or has low elasticity, and the user's electricity consumption during that period is relatively regular or fixed, making it impossible to transfer electricity consumption according to time-of-use pricing or the amount of electricity that can be transferred is very small. Furthermore, the theoretical response potential is also considered to be affected by the current load's electricity consumption and load rate during each period. The more peak-period electricity a user consumes and the higher the load rate, the larger the peak-period load capacity that can be transferred, and the higher the average electricity cost, the stronger the user's willingness to be guided by electricity price incentives. The less valley-period electricity a user consumes and the lower the load rate, the larger the additional load capacity that can be carried during valley periods.

[0103] Therefore, based on the load elasticity coefficients of each target sampling time corresponding to the peak / valley period and the load sampling values ​​of each day in the historical period at the corresponding target sampling time, the theoretical response potential of the target user during the peak / valley period is calculated.

[0104] in, In addition to determining the target load by the maximum value of each load sample at the target sampling time t, the typical daily load data of the target user can also be determined based on the daily load data of the target user for each day within a historical time period, and the load value at the target sampling time t in the typical daily load data can be used as the target load data. Based on this, through Calculate the remaining response potential of the time period corresponding to each target sampling time during the valley period. Based on this, the theoretical response potential of the target user during the peak period is determined by the load of the time period corresponding to each target sampling time during the peak period and the load elasticity coefficient of that time period. The larger the load and the higher the load elasticity, the greater the load response potential during the peak period. Similarly, the larger the remaining response potential and the higher the load elasticity coefficient during the valley period, the greater the load response potential during the valley period.

[0105] Optionally, after calculating the theoretical response potential of the target user during peak / valley periods based on the load elasticity coefficients at each target sampling time corresponding to the peak / valley periods and the load sampling values ​​at the corresponding target sampling times from the daily load data of each day within the historical time period, the calculation may further include:

[0106] Based on the method for calculating the theoretical response potential of the target user during peak / valley periods, calculate the theoretical response potential of all users in the target user's industry during peak / valley periods.

[0107] Based on the theoretical response potential of all users in the target user's industry during peak / valley periods, calculate the theoretical response potential assessment of the target user's industry during peak / valley periods.

[0108] For example, the theoretical response potential of all users in the target user's industry during peak hours and during off-peak hours can be averaged to obtain the theoretical response potential C of the target user's industry during peak hours. F The theoretical response potential C during the trough period G .

[0109]

[0110]

[0111] In the formula, C F C represents the theoretical response potential of the target user's industry during peak hours. G I represents the theoretical response potential of the target user's industry during the off-peak period, and I represents the total number of users in the target user's industry.

[0112] In this embodiment of the invention, after determining the implementation period of demand response, the daily load data of the target user for each day within the historical period corresponding to the implementation period (the daily load data includes load sampling values ​​at each sampling time each day) is first obtained. Then, based on the load sampling values ​​in the daily load data of each day within the historical period, the load elasticity coefficient for each target sampling time corresponding to the peak / valley period of demand response is calculated. Finally, based on the load elasticity coefficient for each target sampling time corresponding to the peak / valley period and the load sampling values ​​at the corresponding target sampling times in the daily load data of each day within the historical period, the theoretical response potential of the target user during the peak / valley period is calculated. Since the load elasticity coefficient calculated based on the load sampling values ​​in the daily load data of each day within the historical period can identify the flexibility and controllability of the target user's electricity load changes over time, and effectively measure the degree to which the user's electricity load can be adjusted and migrated, the theoretical response potential of the target user during the peak / valley period calculated based on the load elasticity coefficient for each target sampling time corresponding to the peak / valley period is more accurate and can provide more accurate and effective guidance for power grid dispatching to formulate plans and arrange reserve capacity.

[0113] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0114] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0115] Figure 4 A schematic diagram of the user theoretical response potential assessment device provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below:

[0116] like Figure 4 As shown, the user theoretical response potential assessment device 4 includes: an acquisition module 41, a processing module 42, and an assessment module 43.

[0117] The acquisition module 41 is used to acquire the daily load data of the target user for each day within a historical time period; the historical time period is the time period corresponding to the implementation time period of the demand response; the daily load data includes the load sampling values ​​at each sampling time of each day;

[0118] Processing module 42 is used to calculate the load elasticity coefficient of each target sampling time corresponding to the peak / valley period of demand response based on the load sampling values ​​in the daily load data of each day within the historical time period.

[0119] The evaluation module 43 is used to calculate the theoretical response potential of the target user during peak / valley periods based on the load elasticity coefficient of each target sampling time corresponding to the peak / valley period and the load sampling value of the daily load data of each day in the historical time period at the corresponding target sampling time.

[0120] In this embodiment of the invention, after determining the implementation period of demand response, the daily load data of the target user for each day within the historical period corresponding to the implementation period (the daily load data includes load sampling values ​​at each sampling time each day) is first obtained. Then, based on the load sampling values ​​in the daily load data of each day within the historical period, the load elasticity coefficient for each target sampling time corresponding to the peak / valley period of demand response is calculated. Finally, based on the load elasticity coefficients for each target sampling time corresponding to the peak / valley period and the load sampling values ​​at the corresponding target sampling times in the daily load data of each day within the historical period, the theoretical response potential of the target user during the peak / valley period is calculated. Since the load elasticity coefficients calculated based on the load sampling values ​​in the daily load data of each day within the historical period can identify the flexibility and controllability of the target user's electricity load changes over time, the theoretical response potential of the target user during the peak / valley period calculated based on the load elasticity coefficients for each target sampling time corresponding to the peak / valley period is more accurate and can provide more accurate and effective guidance for power grid dispatching in planning and arranging reserve capacity.

[0121] In one possible implementation, the processing module 42 can be used to determine the target sampling time corresponding to the peak / valley period of demand response based on the target load sampling value in the daily load data of each day in the historical time period, according to the load sampling value in the daily load data of each day in the historical time period.

[0122] For each target sampling time corresponding to the peak / valley period, the normalized load value corresponding to each target load sampling value at that target sampling time is determined based on each target load sampling value at that target sampling time and the maximum value among all target load sampling values.

[0123] Calculate the average value of each target load sample at the target sampling time, and determine the normalized average value at the target sampling time based on the average value and the maximum value;

[0124] Based on the normalized load values ​​and the normalized average value, calculate the load resilience coefficient for the target sampling time corresponding to the peak / valley period of the demand response.

[0125] In one possible implementation, the processing module 42 can be used to... Calculate the load elasticity coefficient at the target sampling time corresponding to the peak / valley period of the demand response;

[0126] Where E(t) is the load resilience coefficient at the target sampling time t corresponding to the peak / valley period of the demand response, and N is the number of days included in the implementation period of the demand response. This represents the normalized load value corresponding to the target load sample value on the r-th day within the historical time period at the target sampling time t. This is the normalized average value at sampling time t for the target.

[0127] In one possible implementation, the evaluation module 43 can be used to evaluate based on Calculate the theoretical response potential of the target user during peak hours;

[0128] iT F

[0129] in, T represents the theoretical response potential of target user i during peak hours. F Let P be the set of target sampling times corresponding to the peak period, E(t) be the load elasticity coefficient of the target sampling time t corresponding to the peak period of demand response, and P be the load elasticity coefficient of the target sampling time t corresponding to the peak period of demand response. ave (t) represents the average value of each load sample at the target sampling time t in the daily load data of each day within the historical time period.

[0130] In one possible implementation, the evaluation module 43 can be used to evaluate based on Calculate the theoretical response potential of the target user during off-peak hours;

[0131] in, T represents the theoretical response potential of target user i during the off-peak period. G Let E(t) be the set of target sampling times corresponding to the valley period, and let E(t) be the load elasticity coefficient of the target sampling time t corresponding to the valley period of the demand response. P represents the maximum value of each load sample at the target sampling time t from the daily load data for each day within the historical time period. ave (t) represents the average value of each load sample at the target sampling time t in the daily load data of each day within the historical time period.

[0132] In one possible implementation, the acquisition module 41 can also be used to count the amount of missing data in the daily load data of the target user within a historical time period.

[0133] If the amount of missing data is greater than the first preset threshold, then the target user is discarded.

[0134] If the amount of missing data is not greater than the first preset threshold, then the amount of abnormal data that is too large or too small and the amount of repeated data that is continuously repeated are counted in the daily load data of the target user in the historical time period.

[0135] If the sum of the missing data, the abnormal data, and the duplicate data exceeds the second preset threshold, then the target user is discarded.

[0136] If the sum of the missing data, the abnormal data, and the duplicate data is not greater than the second preset threshold, then the missing data, abnormal data, and duplicate data in the daily load data of the target user in the historical time period are repaired to obtain the repaired daily load data of the target user in the historical time period.

[0137] Processing module 42 can be used to calculate the load elasticity coefficient of each target sampling time corresponding to the peak / valley period of demand response based on the load sampling values ​​in the daily load data of each day within the historical time period.

[0138] In one possible implementation, the evaluation module 43 can also be used to calculate the theoretical response potential of all users in the industry to which the target user is located during peak / valley periods, in accordance with the method for calculating the theoretical response potential of the target user during peak / valley periods.

[0139] Based on the theoretical response potential of all users in the target user's industry during peak / valley periods, calculate the theoretical response potential assessment of the target user's industry during peak / valley periods.

[0140] Figure 5 This is a schematic diagram of a terminal provided in an embodiment of the present invention. Figure 5 As shown, the terminal 5 in this embodiment includes: a processor 50, a memory 51, and a computer program 52 stored in the memory 51 and executable on the processor 50. When the processor 50 executes the computer program 52, it implements the steps described in the various user theoretical response potential assessment method embodiments above, for example... Figure 1 Steps 101 to 103 shown, or Figure 3 Steps 301 to 304 are shown. Alternatively, when processor 50 executes computer program 52, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 4 The functions of modules / units 41 to 43 shown.

[0141] For example, computer program 52 can be divided into one or more modules / units, one or more of which are stored in memory 51 and executed by processor 50 to complete the present invention. One or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 52 in terminal 5. For example, computer program 52 can be divided into... Figure 4 Modules / units 41 to 43 are shown.

[0142] Terminal 5 can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. Terminal 5 may include, but is not limited to, a processor 50 and a memory 51. Those skilled in the art will understand that... Figure 5 This is merely an example of terminal 5 and does not constitute a limitation on terminal 5. It may include more or fewer components than shown, or combine certain components, or different components. For example, the terminal may also include input / output devices, network access devices, buses, etc.

[0143] The processor 50 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0144] The memory 51 can be an internal storage unit of the terminal 5, such as the hard disk or RAM of the terminal 5. The memory 51 can also be an external storage device of the terminal 5, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the terminal 5. Furthermore, the memory 51 can include both internal and external storage units of the terminal 5. The memory 51 is used to store computer programs and other programs and data required by the terminal. The memory 51 can also be used to temporarily store data that has been output or will be output.

[0145] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0146] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0147] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0148] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0149] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0150] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0151] If integrated modules / units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various user theoretical response potential assessment method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in a computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0152] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for evaluating the theoretical response potential of users, characterized in that, include: Obtain daily load data for the target user within a historical time period; the historical time period is the time period corresponding to the implementation period of the demand response; the daily load data includes load sampling values ​​at each sampling time of each day; Based on the load sample values ​​in the daily load data of each day within the historical time period, calculate the load elasticity coefficient for each target sampling time corresponding to the peak / valley period of demand response; Based on the load elasticity coefficients of each target sampling time corresponding to the peak / valley period and the load sampling values ​​of the daily load data of each day in the historical period at the corresponding target sampling time, the theoretical response potential of the target user during the peak / valley period is calculated. The calculation of the load elasticity coefficient for each target sampling time corresponding to the peak / valley periods of demand response, based on the daily load data of each day within the historical time period, includes: Based on the load sampling values ​​in the daily load data of each day within the historical time period, determine the target sampling time corresponding to the peak / valley period of demand response in the target load sampling value in the daily load data of each day within the historical time period. For each target sampling time corresponding to the peak / valley period, the normalized load value corresponding to each target load sampling value at that target sampling time is determined based on each target load sampling value at that target sampling time and the maximum value among all target load sampling values. Calculate the average value of each target load sample at the target sampling time, and determine the normalized average value at the target sampling time based on the average value and the maximum value; Based on each of the normalized load values ​​and the normalized average value, calculate the load elasticity coefficient at the target sampling time corresponding to the peak / valley period of the demand response; Based on the load elasticity coefficients at each target sampling time corresponding to the peak period and the load sampling values ​​at the corresponding target sampling times from the daily load data of each day within the historical time period, the theoretical response potential of the target user during the peak period is calculated, including: according to Calculate the theoretical response potential of the target user during peak hours; in, For target users Theoretical response potential during peak hours, This is the set of sampling times for each target corresponding to the peak period. The target sampling time corresponding to the peak period of demand response. The load elasticity coefficient, The daily load data for each day within the historical time period at the target sampling time. The average value of each load sample; Based on the load elasticity coefficients at each target sampling time corresponding to the off-peak period and the load sampling values ​​at the corresponding target sampling times from the daily load data of each day within the historical time period, the theoretical response potential of the target user during the off-peak period is calculated, including: according to Calculate the theoretical response potential of the target user during the off-peak period; in, For target users The theoretical response potential during the trough period, This is the set of all target sampling times corresponding to the valley period. The target sampling time corresponding to the valley period of demand response. The load elasticity coefficient, The daily load data for each day within the historical time period at the target sampling time. The maximum value of each load sample value, The daily load data for each day within the historical time period at the target sampling time. The average value of each load sample.

2. The user theoretical response potential assessment method according to claim 1, characterized in that, The step of calculating the load resilience coefficient for the target sampling time corresponding to the peak / valley periods of demand response based on each of the normalized load values ​​and the normalized average value includes: according to Calculate the load elasticity coefficient at the target sampling time corresponding to the peak / valley period of the demand response; in, The target sampling time corresponding to the peak / valley period of demand response. The load elasticity coefficient, The number of days included in the implementation period of the demand response. For the sampling time of the target Within the historical time period, the first The normalized load value corresponding to the target load sample value for the day For the sampling time of the target The normalized mean.

3. The user theoretical response potential assessment method according to claim 1, characterized in that, After obtaining the daily load data of the target user within a historical time period, the following is also included: The amount of missing data in the daily load data of the target user within the historical time period is calculated. If the amount of missing data is greater than the first preset threshold, then the target user is discarded. If the amount of missing data is not greater than the first preset threshold, then the amount of abnormal data that is too large or too small and the amount of repeated data that is continuously repeated are counted in the daily load data of the target user in the historical time period. If the sum of the missing data, the abnormal data, and the duplicate data exceeds the second preset threshold, then the target user is discarded. If the sum of the missing data, the abnormal data, and the duplicate data is not greater than the second preset threshold, then the missing data, abnormal data, and duplicate data in the daily load data of the target user in the historical time period are repaired to obtain the repaired daily load data of the target user in the historical time period. The calculation of the load elasticity coefficient for each target sampling time corresponding to the peak / valley periods of demand response, based on the daily load data of each day within the historical time period, includes: Based on the load sampling values ​​in the daily repair load data of each day within the historical time period, the load elasticity coefficient of each target sampling time corresponding to the peak / valley period of demand response is calculated.

4. The user theoretical response potential assessment method according to claim 1, characterized in that, After calculating the theoretical response potential of the target user during peak / valley periods based on the load elasticity coefficients at each target sampling time corresponding to the peak / valley periods and the load sampling values ​​at the corresponding target sampling times from the daily load data of each day within the historical time period, the method further includes: The theoretical response potential of all users in the target user's industry during peak / valley periods is calculated using the same method as the method used to calculate the target user's theoretical response potential during peak / valley periods. Based on the theoretical response potential of all users in the target user's industry during peak / valley periods, calculate the theoretical response potential assessment of the target user's industry during peak / valley periods.

5. A device for evaluating the theoretical response potential of users, characterized in that, include: The acquisition module is used to acquire the daily load data of the target user for each day within a historical time period; the historical time period is the time period corresponding to the implementation time period of the demand response; the daily load data includes the load sampling values ​​at each sampling time of each day; The processing module is used to calculate the load elasticity coefficient for each target sampling time corresponding to the peak / valley period of demand response, based on the load sampling values ​​in the daily load data of each day within the historical time period. The evaluation module is used to calculate the theoretical response potential of the target user during peak / valley periods based on the load elasticity coefficient of each target sampling time corresponding to the peak / valley period and the load sampling value of the daily load data of each day in the historical time period at the corresponding target sampling time. The processing module is specifically used for: Based on the load sampling values ​​in the daily load data of each day within the historical time period, determine the target sampling time corresponding to the peak / valley period of demand response in the target load sampling value in the daily load data of each day within the historical time period. For each target sampling time corresponding to the peak / valley period, the normalized load value corresponding to each target load sampling value at that target sampling time is determined based on each target load sampling value at that target sampling time and the maximum value among all target load sampling values. Calculate the average value of each target load sample at the target sampling time, and determine the normalized average value at the target sampling time based on the average value and the maximum value; Based on each of the normalized load values ​​and the normalized average value, calculate the load elasticity coefficient at the target sampling time corresponding to the peak / valley period of the demand response; The evaluation module is specifically used for: according to Calculate the theoretical response potential of the target user during peak hours; in, For target users Theoretical response potential during peak hours, This is the set of sampling times for each target corresponding to the peak period. The target sampling time corresponding to the peak period of demand response. The load elasticity coefficient, The daily load data for each day within the historical time period at the target sampling time. The average value of each load sample; according to Calculate the theoretical response potential of the target user during the off-peak period; in, For target users The theoretical response potential during the trough period, This is the set of all target sampling times corresponding to the valley period. The target sampling time corresponding to the valley period of demand response. The load elasticity coefficient, The daily load data for each day within the historical time period at the target sampling time. The maximum value of each load sample value, The daily load data for each day within the historical time period at the target sampling time. The average value of each load sample.

6. A terminal, characterized in that, It includes a memory and a processor, the memory being used to store a computer program, and the processor being used to call and run the computer program stored in the memory to perform the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 4 above.

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