Power resource determination method and device, equipment, storage medium and program product

Abstract

The invention relates to a power resource determination method and device, equipment, a storage medium and a program product. The method comprises the following steps: calculating a load change interval according to each power load value in a statistical period; performing sub-interval division on the load change interval to obtain corresponding sub-load change intervals; determining a price difference corresponding to each piece of load change data in the sub-load change interval, and constructing a price difference set of the sub-load change interval based on the obtained price difference; the price difference is a difference value between a day-ahead price and a real-time price, and the price differences in the price difference set meet normal distribution; calculating a price difference distribution probability based on the price difference set and a mean value and a variance of the price difference set; and determining the day-ahead declaration amount of the electric power resource based on the price difference distribution probability. By adopting the method, the declaration accuracy of the electricity selling company on the electricity resources can be effectively improved, and the transaction risk is reduced.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a method, device, equipment, storage medium and program product for determining electric power resources. Background Art

[0002] In recent years, the rapid changes in the electricity supply and demand situation and the fluctuations in the price of energy required for power generation have increased the pressure on power supply. Therefore, based on the power balance situation and the willingness of market entities to declare, the demand for power purchase and sales is matched, and the resources of the entire network are coordinated to achieve optimized mutual assistance, which plays a positive role in ensuring power supply for the entire network.

[0003] In the current electricity market, the usual declaration strategy is to understand the supply situation on the power generation side, make a subjective judgment based on the day-ahead price and the real-time price, and then determine the declaration amount for declaration. However, this declaration method is likely to lead to transaction risks for power sales companies. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, equipment, storage medium and program product for determining power resources in response to the above-mentioned technical problems, which can effectively improve the accuracy of power sales companies' declaration of power resources and reduce transaction risks.

[0005] In a first aspect, the present application provides a method for determining electric power resources, the method comprising:

[0006] Calculate the load variation interval according to each power load value within the statistical period;

[0007] Dividing the load change interval into sub-intervals to obtain corresponding sub-load change intervals;

[0008] Determine the price difference corresponding to each load change data in the sub-load change interval, and construct a price difference set for the sub-load change interval based on the obtained price difference; the price difference is the difference between the day-ahead price and the real-time price, and the price difference in the price difference set satisfies a normal distribution;

[0009] Calculating a price difference distribution probability based on the price difference set, the mean and the variance of the price difference set;

[0010] The day-ahead declared quantity of electric power resources is determined based on the price difference distribution probability.

[0011] In one embodiment, the load change interval includes a load change sequence, the load change data includes a load change value or a load change rate, and the method further includes:

[0012] Obtaining power data within a statistical period; calculating the load change interval based on each power load value within the statistical period includes: determining a load change value or a load change rate based on the power load value at each time in the power data; and sorting each load change value or the load change rate to obtain the load change sequence.

[0013] In one embodiment, determining the price difference corresponding to each load change data in the sub-load change interval, and constructing the price difference set of the sub-load change interval based on the obtained price difference includes:

[0014] In the electric power data, the electric power load value corresponding to each load change data within the sub-load change interval is selected; the day-ahead price and the real-time price corresponding to each of the electric power load values ​​are obtained; the price difference is determined based on the day-ahead price and the real-time price corresponding to each of the load values; and a price difference set of the sub-load change interval is constructed based on the price difference corresponding to each of the load change data.

[0015] In one embodiment, dividing the load change interval into sub-intervals to obtain corresponding sub-load change intervals includes:

[0016] The load change interval is divided into sub-intervals in a uniform manner to obtain n sub-load change intervals; or, each time the sub-interval division is performed, the i-th sub-load change interval is divided in the load change interval according to the i-th division value, until n sub-load change intervals are obtained; wherein, when i=1, the i-th division value is obtained based on the maximum load change data, the minimum load change data and the number of sub-intervals in the load change interval; when i>1, the i-th division value is obtained based on the maximum load change data, the dynamic load change data and the number of sub-intervals, and the dynamic load change data is determined based on the i-1-th sub-load change interval.

[0017] In one embodiment, the number of the sub-load change intervals is n, and the number of the price difference sets is n; and the calculating the price difference distribution probability based on the price difference sets, the mean and the variance of the price difference sets includes:

[0018] Determine the mean and variance of the n price difference sets; determine the price difference distribution probability of the n sub-load change intervals based on the n price difference sets and the mean and variance of the n price difference sets;

[0019] The determining of the day-ahead declared quantity of electric power resources based on the price difference distribution probability comprises:

[0020] The day-ahead declared quantity of electric power resources is determined based on the price difference distribution probability of the n sub-load change intervals.

[0021] In one embodiment, determining the day-ahead declared amount of electric power resources based on the price difference distribution probability of the n sub-load change intervals includes:

[0022] Obtain the published target power load values ​​for different time periods; determine the target load change data based on the target power load values ​​for different time periods; select the target sub-load change interval among the n sub-load change intervals based on the target load change data; determine the day-ahead declared quantity of power resources based on the price difference distribution probability corresponding to the target sub-load change interval.

[0023] In a second aspect, the present application further provides a device for determining electric power resources, the device comprising:

[0024] The first calculation module is used to calculate the load change interval according to each power load value within the statistical period;

[0025] A division module, used for dividing the load change interval into sub-intervals to obtain corresponding sub-load change intervals;

[0026] A construction module, used to determine the price difference corresponding to each load change data in the sub-load change interval, and construct a price difference set of the sub-load change interval based on the obtained price difference; the price difference is the difference between the day-ahead price and the real-time price, and the price difference in the price difference set satisfies a normal distribution;

[0027] A second calculation module, configured to calculate a price difference distribution probability based on the price difference set, the mean and the variance of the price difference set;

[0028] A determination module is used to determine the day-ahead declared quantity of electric power resources based on the price difference distribution probability.

[0029] In one embodiment, the load change interval includes a load change sequence, and the device further includes:

[0030] An acquisition module is used to acquire power data within the statistical period;

[0031] The first calculation module is used to determine the load change value or the load change rate according to the power load value at each time in the power data; and to sort the load change values ​​or the load change rates to obtain the load change sequence.

[0032] In one of the embodiments, the construction module is used to select the power load value corresponding to each load change data within the sub-load change interval in the power data; obtain the day-ahead price and real-time price corresponding to each of the power load values; determine the price difference based on the day-ahead price and real-time price corresponding to each of the load values; and construct a price difference set for the sub-load change interval based on the price difference corresponding to each of the load change data.

[0033] In one of the embodiments, the division module is used to divide the load change interval into sub-intervals in a uniform manner to obtain n sub-load change intervals; or, each time the sub-interval division is performed, the i-th sub-load change interval is divided in the load change interval according to the i-th division value until n sub-load change intervals are obtained; wherein, when i=1, the i-th division value is obtained based on the maximum load change data, the minimum load change data and the number of sub-intervals in the load change interval; when i>1, the i-th division value is obtained based on the maximum load change data, the dynamic load change data and the number of sub-intervals, and the dynamic load change data is determined based on the i-1-th sub-load change interval.

[0034] In one of the embodiments, the number of the sub-load change intervals is n, and the number of the price difference sets is n;

[0035] The second calculation module is used to determine the mean and variance of the n price difference sets; based on the n price difference sets, the mean and variance of the n price difference sets, determine the price difference distribution probability of the n sub-load change intervals;

[0036] The determination module is used to determine the day-ahead declared quantity of electric power resources based on the price difference distribution probability of the n sub-load change intervals.

[0037] In one of the embodiments, the determination module is used to obtain the published target power load values ​​for different time periods; determine the target load change data based on the target power load values ​​for different time periods; select the target sub-load change interval among the n sub-load change intervals based on the target load change data; and determine the day-ahead declared quantity of power resources based on the price difference distribution probability corresponding to the target sub-load change interval.

[0038] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method for determining electric power resources when executing the computer program.

[0039] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method for determining electric power resources are implemented.

[0040] In a fifth aspect, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the method for determining power resources are implemented.

[0041] The above-mentioned method, device, equipment, storage medium and program product for determining electric power resources calculate the load change interval according to each electric power load value within the statistical period, divide the load change interval into sub-intervals, calculate the price difference corresponding to each load change data in the sub-load change interval obtained by the division, and obtain a price difference set of the sub-load change interval. The price difference is the difference between the day-ahead price and the real-time price. Since the change in electric power load can well reflect the operating status of the power grid and has a close relationship with the electricity price, the electric power load situation is associated with the day-ahead and real-time price difference. The price difference distribution probability is determined based on the price difference set, the mean and variance of the price difference set, and the day-ahead declaration amount of electric power resources is determined based on the price difference distribution probability. This can effectively improve the accuracy of the declaration of electric power resources by power sales companies, avoid the probability of transaction losses caused by subjective judgment, and effectively reduce transaction risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] FIG1 is a diagram of an application environment of a method for determining electric power resources in one embodiment;

[0043] FIG2 is a schematic flow chart of a method for determining electric power resources in one embodiment;

[0044] FIG3 is a schematic flow chart of a method for determining electric power resources in another embodiment;

[0045] FIG4 is a structural block diagram of a device for determining electric power resources in one embodiment;

[0046] FIG5 is a structural block diagram of a device for determining electric power resources in another embodiment;

[0047] FIG. 6 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0048] In order to facilitate the understanding of the present application, the present application will be described more comprehensively below in conjunction with the specific embodiments. The preferred embodiments of the present application are provided in the specific embodiments. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used herein in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0050] Unless otherwise specified or there is a contradiction, the terms and phrases used in this application have the following meanings:

[0051] In this application, "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features.

[0052] In the description of the present application, “plurality” means at least two, for example, two, three, etc., unless otherwise clearly and specifically defined.

[0053] The method for determining power resources provided in the embodiment of the present application can be applied in the application environment shown in FIG1 . The terminal 102 communicates with the server 104 via a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers.

[0054] Among them, the terminal 102 can be a smart phone, a tablet computer, a laptop computer, a desktop computer, an IoT device and a portable wearable device. The IoT device can be a smart speaker, a smart TV, a smart air conditioner and a smart car device. The server 104 can be an independent physical server or a service node in a blockchain system. A peer-to-peer network is formed between the service nodes in the blockchain system. The peer-to-peer protocol is an application layer protocol running on the TCP (Transmission Control Protocol) protocol. In addition, the server 104 can also be a server cluster composed of multiple physical servers, which can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0055] The terminal 102 and the server 104 may be connected via Bluetooth, USB (Universal Serial Bus) or a network or other communication connection methods, which are not limited in this application.

[0056] In one embodiment, as shown in FIG. 2 , a method for determining power resources is provided. The method may be executed by the server or the terminal in FIG. 1 , or by the server and the terminal in collaboration. The method is described by taking the method executed by the server in FIG. 1 as an example, and includes the following steps:

[0057] S202: Calculate the load variation interval according to each power load value within the statistical period.

[0058] The statistical period may be a preset statistical period, such as the most recent year, month, or a period of other lengths.

[0059] The power load value may be the total power drawn from the power system at a certain moment or in a shorter period (such as one hour), for example, the total power drawn from the power system every hour in the past year.

[0060] The load change interval is an ordered set consisting of multiple load change data (such as load change values ​​or load change rates), which can also be called a load change sequence. The load change value can be the difference between the power load values ​​of different time periods, and the different time periods can be adjacent time periods, such as the difference between the power load value of time period i and the power load value of time period i+1; the load change rate can be the ratio of the load difference between the power load values ​​of different time periods to the time difference, which is used to describe the speed of the power load change over time. For example, the load change rate = (M(t6) - M(t4)) / (t6- t4), where M(t6) and M(t4) refer to the power load values ​​at time t6 and t4.

[0061] In one embodiment, the load change interval includes a load change sequence, and the load change data in the load change interval includes a load change value or a load change rate. The method also includes: the server obtains power data within the statistical period; then, according to the power load value at each time in the power data, the load change value or the load change rate is determined; and each load change value or load change rate is sorted to obtain a load change sequence.

[0062] The above-mentioned times may refer to adjacent times or times at interval a. The adjacent times may refer to two adjacent moments or two time periods, such as two adjacent hours.

[0063] For example, obtain the power data of a province in the past year, and then calculate the load change value based on the power load value of every two consecutive hours in the power data, and then sort these load change values ​​from small to large to obtain the load change sequence. The calculation formula of the load change value is as follows:

[0064]

[0065] Among them, j in the above calculation formula represents time or time period (hereinafter, j is used as an example to represent time period), and They represent the power load value of period j and the power load value of period j+1 respectively. It represents the difference between the power load value in the j+1 period and the power load value in the j period.

[0066] S204, dividing the load change interval into sub-intervals to obtain corresponding sub-load change intervals.

[0067] The sub-load change interval may be a sub-interval obtained by dividing the load change interval into multiple segments, and the number of the sub-load change intervals may be n, where n is a positive integer greater than or equal to 2. It should be noted that different sub-load change intervals may have intersections, such as the same load change value.

[0068] In one embodiment, the server may divide the load variation interval into sub-intervals in a uniform manner to obtain n sub-load variation intervals.

[0069] In the process of dividing the sub-intervals, the server may first determine the interval length according to the maximum power load value and the minimum power load value, determine the window length and sliding step of the sliding window according to the interval length and the number of sub-intervals (i.e., the number of sub-intervals to be divided), and then move the sliding window according to the sliding step in the load change interval, thereby obtaining n sub-load change intervals. The sliding step may be equal to the window length or less than the window length.

[0070] In another embodiment, the server may divide the load change interval into an i-th sub-load change interval according to the i-th division value each time the sub-interval division is performed, until n sub-load change intervals are obtained; wherein, when i=1, the i-th division value is obtained based on the maximum load change data, the minimum load change data and the number of sub-intervals in the load change interval; when i>1, the i-th division value is obtained based on the maximum load change data, the dynamic load change data and the number of sub-intervals, and the dynamic load change data is determined based on the i-1-th sub-load change interval.

[0071] The i-th sub-load change interval may be the i-th sub-load change interval. i may be a positive integer greater than or equal to 1 and less than or equal to n. When performing sub-interval division, the value of i may start from 1, so that the first sub-load change interval may be divided, and then i may be incremented, such as incremented by 1.

[0072] The maximum load change data may be a maximum load change value or a maximum load change rate in the load change interval; and the minimum load change data may be a minimum load change value or a minimum load change rate in the load change interval.

[0073] The dynamic load change data may refer to the minimum load change data after dynamic adjustment. For example, if the dynamic load change data is a dynamic load change value, the dynamic load change value may refer to the minimum load change value after dynamic adjustment, that is, after each sub-interval division, the minimum load change value is dynamically adjusted.

[0074] The i-th partition value may be the i-th interval partition value used for sub-interval partitioning, and the calculation formula of the i-th partition value is as follows:

[0075]

[0076] It should be pointed out that after each sub-interval division, A dynamic adjustment is performed to ensure that the subinterval obtained when the next subinterval is divided does not have an intersection with the previous subinterval, or the number of elements in the intersection is less than or equal to K (K can be an integer less than or equal to 2), so the i-th division value obtained each time the subinterval is divided is different.

[0077] For example, when dividing the first sub-load interval, the first division value is determined according to the maximum load change value, the minimum load change value and the number of sub-intervals in the load change interval, and the first sub-load interval is divided from the load change interval using the first division value. When dividing the second sub-load interval, the second division value is determined according to the maximum load change value, the dynamic load change value and the number of sub-intervals, and the dynamic load change value is determined according to the first sub-load interval, such as taking the maximum load change value in the first sub-load interval or the maximum load change value + a preset value (such as +1) as the current dynamic load change value, and then using the second division value to divide the second sub-load interval from the load change interval. Similarly, other sub-load intervals are divided according to the division method of the second sub-load interval.

[0078] S206, determining the price difference corresponding to each load change value within the sub-load change interval, and constructing a price difference set for the sub-load change interval based on the obtained price difference.

[0079] The price difference is the difference between the day-ahead price and the real-time price, and specifically can be the difference between the day-ahead price and the real-time price corresponding to the load change value within the sub-load change interval.

[0080] For each sub-load change interval, there is a corresponding price difference set. For example, assuming that there are k price differences in the price difference set, represents the sub-load change interval, then the price difference set of different sub-load change intervals is as follows:

[0081]

[0082]

[0083] in, , , …, Represent different time periods. express The day-ahead price for the time period, express The current price of the session, Respectively , , …, The price difference between time periods.

[0084] According to the central limit theorem, when the sampling number k of price differences is relatively large, no matter what the distribution of the population is, the distribution of the price difference set is always approximately normal, so the price differences in the price difference set satisfy the normal distribution.

[0085] In one embodiment, the server selects the power load value corresponding to each load change data within the sub-load change interval from the power data; obtains the day-ahead price and real-time price corresponding to each power load value; determines the price difference based on the day-ahead price and real-time price corresponding to each load value; and constructs a price difference set for the sub-load change interval based on the price difference corresponding to each load change data.

[0086] For example, in the power data, the server selects the power load values ​​corresponding to each load change value or load change rate in the sub-load change interval. If the load change value is a, the power load values ​​of two adjacent times with the load change value a are selected; the day-ahead price and real-time price corresponding to the power load values ​​of each adjacent time that meet the above conditions are obtained; the price difference is determined based on the day-ahead price and real-time price corresponding to each load value; and a price difference set of the sub-load change interval is constructed based on the price difference corresponding to each load change data.

[0087] S208, calculating the price difference distribution probability based on the price difference set, the mean and the variance of the price difference set.

[0088] For each sub-load variation interval, there is a corresponding set of price difference distribution probabilities, and the number of price difference distribution probabilities may be consistent with the number of price differences in the price difference set. The price difference distribution probability may include a distribution probability of a price difference greater than a preset value and a distribution probability of a price difference less than or equal to a preset value.

[0089] In one embodiment, the number of sub-load change intervals is n, and the number of price difference sets is n; the server may first determine the mean and variance of the n price difference sets; based on the n price difference sets, the mean and variance of the n price difference sets, determine the price difference distribution probability of the n sub-load change intervals.

[0090] For example, the server can calculate the mean of the price difference set corresponding to each sub-load change interval and variance Therefore, it can be considered that the price difference set of each sub-load change interval follows the mean , the variance is The normal distribution of the sub-load change interval is calculated using the normal distribution function, the price difference in the price difference set, the mean and the variance. The price difference in the price difference set is used as the variable of the normal distribution function.

[0091] S210, determining the day-ahead declared quantity of electric power resources based on the price difference distribution probability.

[0092] Among them, the day-ahead reported quantity may refer to the day-ahead quantity of reported electric power resources, such as the day-ahead electricity quantity reported.

[0093] In one embodiment, when the number of sub-load change intervals (ie, sub-interval quantity) is n, the server determines the day-ahead declared quantity of electric power resources based on the price difference distribution probability of the n sub-load change intervals.

[0094] In one embodiment, the server can also obtain the published target power load values ​​for different time periods; determine the target load change data based on the target power load values ​​for different time periods; select the target sub-load change interval among n sub-load change intervals based on the target load change data; and determine the day-ahead declared amount of power resources based on the price difference distribution probability corresponding to the target sub-load change interval.

[0095] For example, the electricity market publishes the target electricity load value for each hour of the day, determines the target load change value based on the target electricity load value for each adjacent hour, selects the subload change interval into which the target load change value falls among the n subload change intervals, and then determines the day-ahead declared quantity of electricity resources based on the price difference distribution probability corresponding to the target subload change interval.

[0096] As an example, when the price difference distribution probability is that the price difference is less than or equal to 0, the day-ahead declared quantity of electricity resources can be increased; when the price difference distribution probability is that the price difference is greater than 0, the day-ahead declared quantity of electricity resources can be decreased.

[0097] In the above embodiment, the load change interval is calculated according to each power load value in the statistical period, the load change interval is divided into sub-intervals, and the price difference corresponding to each load change data in the divided sub-load change interval is calculated to obtain a price difference set of the sub-load change interval. The price difference is the difference between the day-ahead price and the real-time price. Since the change in power load can well reflect the operating status of the power grid and is closely related to the electricity price, the power load situation is associated with the day-ahead and real-time price difference. The price difference distribution probability is determined based on the price difference set, the mean and variance of the price difference set, and the day-ahead declaration amount of power resources is determined based on the price difference distribution probability. This can effectively improve the accuracy of power sales companies' declaration of power resources, avoid the probability of transaction losses due to subjective judgment, and effectively reduce transaction risks.

[0098] As an example, taking the electricity spot market of the corresponding province as an example, the load change value is obtained by calculating the change of the hourly electricity load in the spot province, and the price difference between the day-ahead and the real-time corresponding load change value is calculated. The price difference set corresponding to the load change interval is established through the above data of multiple days, and the data distribution is fitted to find the distribution probability of the spot price difference of the corresponding load change interval, as follows:

[0099] S302, collecting statistics on electricity data for the most recent year.

[0100] The electricity data includes the electricity load value for each hour of each day, the day-ahead electricity price, the real-time electricity price and other data.

[0101] S304, calculating the load change value of the power load value, such as using a load difference algorithm for two consecutive hours to calculate the load change value, the load difference algorithm is as follows:

[0102]

[0103] By sorting these load change values, we can get the load change interval within the statistical period, and calculate the interval length of the load change interval within the statistical period:

[0104]

[0105] The load change interval is divided into N segments (N can be 20), and N sub-load change intervals can be obtained. It should be pointed out that the sub-load change intervals are divided in the load change interval in turn according to the following division method.

[0106]

[0107] in, For the smallest , the first endpoint value of the sub-load change interval is , and the other endpoint value is .

[0108] After each segment, It will be adjusted dynamically, and after the adjustment, it will continue to be segmented according to the above formula.

[0109] S306, calculating the price difference and establishing a price difference set for the sub-load change interval.

[0110] First, calculate the price difference between the day-ahead price and the real-time price. The price difference is calculated as follows:

[0111]

[0112] in, for The day-ahead price for the time period, for Real-time prices for the session.

[0113] Secondly, based on the data in the statistical period calculated above, a price difference set for different sub-load change intervals is established, as shown in the following expression:

[0114]

[0115] S308, estimating the price difference distribution of each sub-load variation interval.

[0116] After obtaining the price difference set corresponding to each sub-load change interval, the price difference distribution of each sub-load change interval can be obtained. According to the central limit theorem, when the number of price difference samples k is relatively large, no matter what the overall distribution is, the distribution of the price difference set is always approximately normal, so the price difference in the price difference set satisfies the normal distribution.

[0117] For example, find the mean of the price difference set corresponding to the sub-load change interval and variance Therefore, it can be considered that the price difference set of each sub-load change interval follows the mean , the variance is The normal distribution of .

[0118] S310, calculating the probability distribution of the price difference being greater than 0.

[0119] Through the distribution of price differences corresponding to each sub-load change interval, the price difference of each sub-load change interval is obtained. The probability is greater than 0; then, repeat the above steps, roll over and update once a month.

[0120] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0121] Based on the same inventive concept, the embodiment of the present application also provides a device for determining electric resources for implementing the method for determining electric resources involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more devices for determining electric resources provided below can refer to the limitations of the method for determining electric resources above, and will not be repeated here.

[0122] In one embodiment, as shown in FIG. 4 , a device for determining power resources is provided, comprising: a first calculation module 402 , a division module 404 , a construction module 406 , a second calculation module 408 and a determination module 410 , wherein:

[0123] The first calculation module 402 is used to calculate the load change interval according to each power load value in the statistical period;

[0124] A division module 404 is used to divide the load change interval into sub-intervals to obtain corresponding sub-load change intervals;

[0125] A construction module 406 is used to determine the price difference corresponding to each load change data in the sub-load change interval, and construct a price difference set of the sub-load change interval based on the obtained price difference; the price difference is the difference between the day-ahead price and the real-time price, and the price difference in the price difference set satisfies a normal distribution;

[0126] A second calculation module 408, for calculating the price difference distribution probability based on the price difference set, the mean and the variance of the price difference set;

[0127] The determination module 410 is used to determine the day-ahead declared quantity of electric power resources based on the price difference distribution probability.

[0128] In one embodiment, the load change interval includes a load change sequence, as shown in FIG5 , and the device further includes:

[0129] The acquisition module 412 is used to acquire power data within the statistical period;

[0130] The first calculation module 402 is used to determine a load change value or a load change rate according to the power load value at each time in the power data; and to sort each load change value or load change rate to obtain a load change sequence.

[0131] In one embodiment, construction module 406 is used to select the power load value corresponding to each load change data within the sub-load change interval in the power data; obtain the day-ahead price and real-time price corresponding to each power load value; determine the price difference based on the day-ahead price and real-time price corresponding to each load value; and construct a price difference set for the sub-load change interval based on the price difference corresponding to each load change data.

[0132] In one embodiment, the division module 404 is used to divide the load change interval into sub-intervals in a uniform manner to obtain n sub-load change intervals; or, each time the sub-interval division is performed, the i-th sub-load change interval is divided in the load change interval according to the i-th division value until n sub-load change intervals are obtained; wherein, when i=1, the i-th division value is obtained based on the maximum load change data, the minimum load change data and the number of sub-intervals in the load change interval; when i>1, the i-th division value is obtained based on the maximum load change data, the dynamic load change data and the number of sub-intervals, and the dynamic load change data is determined based on the i-1-th sub-load change interval.

[0133] In one embodiment, the number of sub-load change intervals is n, and the number of price difference sets is n;

[0134] The second calculation module 408 is used to determine the mean and variance of the n price difference sets; based on the n price difference sets, the mean and variance of the n price difference sets, determine the price difference distribution probability of the n sub-load change intervals;

[0135] The determination module 410 is used to determine the day-ahead declared quantity of electric power resources based on the price difference distribution probability of n sub-load change intervals.

[0136] In one embodiment, a determination module 410 is used to obtain target power load values ​​for different time periods; determine target load change data based on the target power load values ​​for different time periods; select a target sub-load change interval among n sub-load change intervals based on the target load change data; and determine the day-ahead declared quantity of power resources based on the price difference distribution probability corresponding to the target sub-load change interval.

[0137] In the above embodiment, the load change interval is calculated according to each power load value in the statistical period, the load change interval is divided into sub-intervals, and the price difference corresponding to each load change data in the divided sub-load change interval is calculated to obtain a price difference set of the sub-load change interval. The price difference is the difference between the day-ahead price and the real-time price. Since the change in power load can well reflect the operating status of the power grid and is closely related to the electricity price, the power load situation is associated with the day-ahead and real-time price difference. The price difference distribution probability is determined based on the price difference set, the mean and variance of the price difference set, and the day-ahead declaration amount of power resources is determined based on the price difference distribution probability. This can effectively improve the accuracy of power sales companies' declaration of power resources, avoid the probability of transaction losses due to subjective judgment, and effectively reduce transaction risks.

[0138] Each module in the above-mentioned power resource determination device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0139] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG6. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store power data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for determining power resources is implemented.

[0140] In one embodiment, a computer device is provided, which may also be a terminal, and includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, the memory, and the input / output interface are connected via a system bus, and the communication interface, the display unit, and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for determining power resources is implemented. The display unit of the computer device is used to form a visually visible image, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse, etc.

[0141] Those skilled in the art will understand that the structure shown in FIG. 6 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0142] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps of the above-mentioned method for determining power resources when executing the computer program.

[0143] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for determining power resources are implemented.

[0144] In one embodiment, a computer program product is provided, comprising a computer program, which implements the steps of the above-mentioned method for determining power resources when executed by a processor.

[0145] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0146] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0147] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for determining electric power resources, characterized in that: The method comprises: Calculate the load variation interval according to each power load value within the statistical period; Dividing the load change interval into sub-intervals to obtain corresponding sub-load change intervals; Determine the price difference corresponding to each load change data in the sub-load change interval, and construct a price difference set for the sub-load change interval based on the obtained price difference; the price difference is the difference between the day-ahead price and the real-time price, and the price difference in the price difference set satisfies a normal distribution; Calculating a price difference distribution probability based on the price difference set, the mean and the variance of the price difference set; The day-ahead declared quantity of electric power resources is determined based on the price difference distribution probability.

2. The method according to claim 1, characterized in that The load change interval includes a load change sequence, the load change data includes a load change value or a load change rate, and the method further includes: Obtain electricity data within the statistical period; The calculation of the load change interval according to each power load value in the statistical period includes: Determining a load change value or a load change rate according to the power load value at each time in the power data; The load change values ​​or the load change rates are sorted to obtain the load change sequence.

3. The method according to claim 1, characterized in that The determining of the price difference corresponding to each load change data within the sub-load change interval, and constructing the price difference set of the sub-load change interval based on the obtained price difference includes: In the power data, the power load value corresponding to each load change data in the sub-load change interval is selected; Obtaining the day-ahead price and the real-time price corresponding to each of the power load values; Determining a price difference based on the day-ahead price and the real-time price corresponding to each of the load values; Based on the price differences corresponding to the load change data, a price difference set of the sub-load change interval is constructed.

4. The method according to claim 1, characterized in that: The sub-dividing the load change interval into sub-intervals to obtain corresponding sub-load change intervals includes: Divide the load change interval into sub-intervals in a uniform manner to obtain n sub-load change intervals; or, Each time the sub-interval division is performed, the i-th sub-load change interval is divided out in the load change interval according to the i-th division value until n sub-load change intervals are obtained; wherein, when i=1, the i-th division value is obtained based on the maximum load change data, the minimum load change data and the number of sub-intervals in the load change interval; when i>1, the i-th division value is obtained based on the maximum load change data, the dynamic load change data and the number of sub-intervals, and the dynamic load change data is determined based on the i-1-th sub-load change interval.

5. The method according to any one of claims 1 to 4, characterized in that: The number of the sub-load change intervals is n, and the number of the price difference sets is n; and the calculating of the price difference distribution probability based on the price difference sets, the mean and the variance of the price difference sets includes: Determining the mean and variance of n sets of said price differences; Determine the price difference distribution probability of the n sub-load change intervals based on the n price difference sets, the means and variances of the n price difference sets; The determining of the day-ahead declared quantity of electric power resources based on the price difference distribution probability comprises: The day-ahead declared quantity of electric power resources is determined based on the price difference distribution probability of the n sub-load change intervals.

6. The method according to claim 5, characterized in that The determining of the day-ahead declared quantity of electric power resources based on the price difference distribution probability of the n sub-load change intervals comprises: Obtain the published target power load values ​​for different time periods; Determining target load change data based on the target power load values ​​in different time periods; Selecting a target sub-load change interval among the n sub-load change intervals based on the target load change data; The day-ahead declared quantity of electric power resources is determined based on the price difference distribution probability corresponding to the target sub-load change interval.

7. A device for determining electric power resources, characterized in that: The device comprises: The first calculation module is used to calculate the load change interval according to each power load value within the statistical period; A division module, used for dividing the load change interval into sub-intervals to obtain corresponding sub-load change intervals; A construction module, used to determine the price difference corresponding to each load change data in the sub-load change interval, and construct a price difference set of the sub-load change interval based on the obtained price difference; the price difference is the difference between the day-ahead price and the real-time price, and the price difference in the price difference set satisfies a normal distribution; A second calculation module, configured to calculate a price difference distribution probability based on the price difference set, the mean and the variance of the price difference set; A determination module is used to determine the day-ahead declared quantity of electric power resources based on the price difference distribution probability.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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