CN111523083A - Method and device for determining power load declaration data - Google Patents
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Abstract
The invention is suitable for the technical field of energy, and provides a method and a device for determining a power load, wherein the method comprises the following steps: determining predicted price difference probability distribution information of a declaration point and load prediction data of the declaration point, wherein the predicted price difference probability distribution information at least comprises price rising probability data, price falling probability data, price rising data and price falling data; determining actual expected income data according to the predicted price difference probability distribution information; and determining the power load declaration data of the declaration point according to the relationship between the price rising probability data and the price falling probability data of the declaration point, the actual expected income data and the load prediction data. The method for determining the power load provided by the invention enables an electricity selling company to more comprehensively and accurately predict the load declaration value, so that a more excellent declaration strategy can be specified, and a greater benefit can be obtained in the price change of the market in the future.
Description
Technical Field
The invention belongs to the technical field of energy, and particularly relates to a method and a device for determining power load declaration data.
Background
In the spot-purchase market before the power day, in order to balance the deviation between contract trading and actual load, people carry out electric energy trading 24 hours in the next day in advance of real-time operation, namely on the day before each execution day, an electricity selling company needs to predict the hourly electricity utilization load of a contracted user in the execution day and predict the hourly electricity price in the execution day so as to make a declaration strategy according to the prediction result, thereby trading arbitrage can be carried out in the price change of the market before the day.
However, in the current load reporting, the load reporting is performed only according to the current predicted load electric quantity value, and the greater benefit can not be obtained in the execution day after the reporting.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method, an apparatus, a terminal device and a computer-readable storage medium for determining power load declaration data, so as to solve the technical problem that load declaration only according to the current predicted load electric quantity value in the prior art cannot obtain a greater benefit in the execution day after declaration.
In a first aspect of the embodiments of the present invention, a method for determining power load declaration data is provided, where the method includes:
determining predicted price difference probability distribution information of a declaration point and load prediction data of the declaration point, wherein the predicted price difference probability distribution information at least comprises price rising probability data, price falling probability data, price rising data and price falling data;
determining actual expected income data according to the predicted price difference probability distribution information;
and determining the power load declaration data of the declaration point according to the relationship between the price rising probability data and the price falling probability data of the declaration point, the actual expected income data and the load prediction data.
Preferably, the determining the power load declaration data of the declaration point according to the relationship between the price rising probability data and the price falling probability data of the declaration point, the actual expected income data, and the load prediction data includes:
judging whether the price rising probability data is larger than the price falling probability data or not;
if the price rising probability data is larger than the price falling probability data, judging whether the actual expected income data is larger than first preset income threshold data;
if the actual expected income data is larger than the first preset income threshold value data, processing the actual expected income according to a preset floating proportion model, and determining first actual floating proportion data;
and determining power load declaration data of the declaration point according to the first actual floating proportion data and the load prediction data.
Preferably, after the step of determining whether the actual expected income data is greater than a first preset income threshold value data if the price rising probability data is greater than the price falling probability data, the method further includes:
and if the actual expected income data is less than or equal to the first preset threshold data, determining the load prediction data as the power load declaration data of the declaration point.
Preferably, the actual expected income is processed according to a preset floating proportion model, and first actual floating proportion data is determined, including;
processing actual expected income according to a preset floating proportion model, and determining first floating proportion data;
judging whether the first floating proportion data is larger than or equal to first preset proportion threshold data or not;
if the first floating proportion data is larger than or equal to the first preset proportion threshold value data, determining the first preset proportion threshold value data as first actual floating proportion data;
and if the first floating proportion data is smaller than the first preset proportion threshold value data, determining the first floating proportion data as first actual floating proportion data.
Preferably, after the step of determining whether the price rising probability data is greater than the price falling probability data, the method further includes:
if the price rising probability data is smaller than the price falling probability data, judging whether the actual expected income data is larger than second preset income threshold data;
if the actual expected income data is smaller than the second preset income threshold value data, processing the actual expected income according to a preset floating proportion model, and determining second actual floating proportion data;
and determining power load declaration data of the declaration point according to the second actual floating proportion data and the load prediction data.
Preferably, after the step of determining whether the actual expected revenue data is greater than a second preset revenue threshold data if the price increase probability data is smaller than the price decrease probability data, the method further includes:
and if the actual expected income data is greater than or equal to the second preset threshold data, determining the load prediction data as the power load declaration data of the declaration point.
Preferably, the processing the actual expected profit according to the preset floating proportion model to determine the second actual floating proportion data includes:
processing the actual expected income according to a preset floating proportion model, and determining second floating proportion data;
judging whether the second floating proportion data is larger than or equal to second preset proportion threshold data or not;
if the second floating proportion data is larger than or equal to the second preset proportion threshold value data, determining the second preset proportion threshold value data as second actual floating proportion data;
and if the second floating proportion data is smaller than the second preset proportion threshold value data, determining the second floating proportion data as second actual floating proportion data.
In a second aspect of the embodiments of the present invention, there is provided an apparatus for determining power load declaration data, including:
the system comprises a first determining module, a second determining module and a third determining module, wherein the first determining module is used for determining predicted price difference probability distribution information of reporting points and load prediction data of the reporting points, and the predicted price difference probability distribution information at least comprises price rising probability data, price falling probability data, price rising data and price falling data;
the second determining module is used for determining actual expected income data according to the predicted price difference probability distribution information;
and the third determining module is used for determining the power load declaration data of the declaration point according to the relationship between the price rising probability data and the price falling probability data of the declaration point, the actual expected income data and the load prediction data.
In a third aspect of the embodiments of the present invention, there is provided a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method for determining the power load declaration data when executing the computer program.
In a fourth aspect of the embodiments of the present invention, there is provided a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method for determining power load declaration data.
The method for determining the power load declaration data provided by the embodiment of the invention has the beneficial effects that at least: the embodiment of the invention firstly determines the forecast price difference probability distribution information of the declaration point and the load forecast data of the declaration point, wherein the forecast price difference probability distribution information at least comprises price rising probability data, price falling probability data, price rising data and price falling data, secondly determines the actual expected income data according to the forecast price difference probability distribution information, and finally determines the power load declaration data of the declaration point according to the relation between the price rising probability data and the price falling probability data of the declaration point, the actual expected income data and the load forecast data, thereby formulating a declaration strategy according to the forecast result and carrying out trade arbitrage in the price change of the day-ahead market.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flow chart of an implementation of a method for determining power load declaration data according to an embodiment of the present invention;
fig. 2 is a schematic flow chart illustrating an implementation of another method for determining power load declaration data according to an embodiment of the present invention;
fig. 3 is a flowchart of an implementation of determining power declaration compliance data in the method for determining power load declaration data according to the embodiment of the present invention;
fig. 4 is a flowchart of another implementation of determining power reporting compliance data in the method for determining power load reporting data according to the embodiment of the present invention;
fig. 5 is a flowchart of an implementation of determining first actual floating proportion data in the method for determining power load declaration data according to the embodiment of the present invention;
FIG. 6 is a flow chart of an implementation of determining second actual floating scale data in a method for determining power load declaration data according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of an apparatus for determining power load declaration data, provided by an embodiment of the present invention;
fig. 8 is a schematic diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention, are within the scope of the invention. Unless otherwise specified, the technical means used in the examples are conventional means well known to those skilled in the art.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
The embodiment of the invention provides a prediction method of power load declaration data, so as to realize more accurate prediction of the power load declaration data. As shown in fig. 1, the method for predicting the power load declaration data in the present embodiment includes the following steps:
s101: and determining the forecast price difference probability distribution information of the declaration points and the load forecast data of the declaration points.
It is understood that the predicted price difference probability distribution information includes the predicted price difference on the current day of execution and the probability corresponding to the predicted price difference. The price difference is the difference between the electricity price of a certain time period on the day of the predicted execution day and the actual price of the corresponding time period on the day before the execution day. In the electric current market, the electric power price on the current day of the execution day is in a proportional relation with the actual electric power load value on the current day of the execution day, namely when the actual electric power load value on the current day of the execution day is increased, the electric power price on the current day of the execution day is considered to be increased along with the increase of the electric power price, otherwise, the electric power price on the current day of the execution day is considered to be decreased, and when the electric power load value on the current day of the execution day is the same as the actual electric power load value on the current day of the execution day, the electric power price on the current day of. The actual power load value on the day of the execution day is related to the influencing factors (such as temperature, humidity, weather, whether it is a weekday or holiday, etc.) on the day of the execution day. In the embodiment of the invention, the predicted price difference probability distribution information at least comprises price rising probability data, price falling probability data and corresponding price rising data and price falling data.
In this step, load prediction data corresponding to each data point on the execution day may be predicted from the influence information and the historical load data using a prediction model. The impact information may include historical weather, such as maximum temperature data, minimum temperature data, average temperature data, weather conditions, etc., and date factors, such as whether the day of execution is a weekday or a holiday, etc. The prediction model can comprise a related regression algorithm, and the load corresponding to each data point on the execution day can be predicted by using the regression algorithm through adopting weather factors and date factors, so that a data source is provided for subsequent calculation, and the result is more accurate.
S102: and determining actual expected income data according to the predicted price difference probability distribution information.
In the embodiment of the present invention, the actual expected profit data calculation formula is:
actual expected income data is price rising data and price falling data
Actual expected revenue data may be understood as revenue expectations of the reporting points, providing a source of data for subsequent calculations.
S103: and determining the power load declaration data of the declaration point according to the relationship between the price rising probability data and the price falling probability data of the declaration point, the actual expected income data and the load prediction data.
In the embodiment of the invention, the declaration points are divided into two types for calculation according to the relationship between price rising probability data and price falling probability data of the declaration points, and the types of the declaration points comprise: the price rising probability data is larger than the price falling probability data, namely the point where the probability is likely to be profitable, and more reports can be made on the reporting day so as to obtain profit on the execution day; points where the price rise probability data is less than the price fall probability data, and points where the probable rate will be lost, may be declared less on the declaration day, so as to reduce the loss on the execution day.
According to the technical scheme, the beneficial effects of the embodiment at least comprise:
the embodiment of the invention firstly determines the forecast price difference probability distribution information of the declaration point and the load forecast data of the declaration point, wherein the forecast price difference probability distribution information at least comprises price rising probability data, price falling probability data, price rising data and price falling data, secondly determines the actual expected income data according to the forecast price difference probability distribution information, and finally determines the power load declaration data of the declaration point according to the relation between the price rising probability data and the price falling probability data of the declaration point, the actual expected income data and the load forecast data, thereby formulating a declaration strategy according to the forecast result and carrying out trade arbitrage in the price change of the day-ahead market.
According to the embodiment of the invention, the reporting points are classified according to the relationship between the price rising probability and the price falling probability, and then the load prediction data corresponding to the reporting points are determined by adopting different methods, so that the obtained power load data is more comprehensive and accurate.
Fig. 1 shows only a basic embodiment of the method of the present invention, and based on this, certain optimization and expansion can be performed, and other preferred embodiments of the method can also be obtained. Fig. 2 shows another embodiment of the method for predicting the power load declaration data according to the present invention. The present embodiment is described with reference to specific scenarios on the basis of the foregoing embodiments. The method specifically comprises the following steps:
step S201: and determining the forecast price difference probability distribution information of the declaration points and the load forecast data of the declaration points.
Step S202: and determining actual expected income data according to the predicted price difference probability distribution information.
Step S203: judging whether the price rising probability data is larger than the price falling probability data or not;
if the price rising probability data is larger than the price falling probability data, the following step S204 is carried out;
if the price rising probability data is smaller than the price falling probability data, the process proceeds to step S205 described below.
If the price rising probability data is equal to the price falling probability data, in order to avoid pursuing uncertain gains to risk unnecessary risks, the conforming prediction data is directly determined as the power load declaration data, and at the moment, zero gains and zero risks are corresponded.
Step S204: and determining the power load data of the declaration point according to the relation between the actual expected income data and a first preset income data threshold value.
Referring to fig. 3, in the present embodiment, the determining the power load data of the declaration point in step S204 includes the following steps:
step S301: judging whether the actual expected income data is larger than first preset income threshold data or not;
if the actual expected income data is larger than the first preset income threshold value data, entering a next step S302;
if the actual expected revenue data is less than or equal to the first preset threshold data, the method proceeds to the next step S304.
In this step, the first preset threshold data may be determined as 0, when the actual expected income data is greater than 0, the declaration point may be considered as a point with a certain probability of profitability, and when the actual expected income data is less than 0, the declaration point may be considered as a point with a certain probability of loss.
Step S302: and processing the actual expected income according to the preset floating proportion model, and determining first actual floating proportion data.
Referring to fig. 5, the step S302 of this embodiment may include the following steps:
step S501: and processing the actual expected income according to a preset floating proportion model, and determining first floating proportion data.
In this step, since the declaration point is a point that has a certain probability of profitability, and there are multiple declarations, it is necessary to calculate how many power load values that need to be declared more specifically, that is, it is necessary to determine the ratio of the multiple declarations, that is, the first floating ratio data.
In this step, the preset floating proportion model is: and the floating proportion data is actual expected income data/expected price rising income data and preset risk proportion data, wherein the expected price rising income is the arithmetic mean value of the actual expected income data when the statistical history price rising probability data is greater than the price falling probability data, and the expected risk proportion data represents the amplitude of the multi-declaration data. Since the declaration has a certain risk during the declaration process, at this time, a risk bias value needs to be set, and assuming that the risk bias value is any integer from 1 to 10 (which can be adjusted), it can be considered that the risk to be borne is minimum when the risk bias value is 1, the risk to be borne is gradually increased with the increase of the risk bias value, and the risk to be borne is maximum when the risk bias value is 10. When the risk preference value is small, the power load declaration value can be increased in a small range, and when the risk preference value is increased, the power load declaration value can be increased greatly, namely when the risk preference value is small, the obtained floating proportion data is small, and when the risk preference value is large, the obtained floating proportion data is large.
In this step, the preset risk ratio data is calculated according to the preset risk preference value and the preset maximum risk ratio data. The maximum risk ratio data may be determined from historical data. For example, the preset maximum risk ratio data is 50%, and the risk preference value is divided into ten levels of 1 to 10, so that the maximum risk ratio data can be divided into 10 sections, each of which is 5%, i.e., the preset risk ratio data is increased by 5% for each increase of 1 risk preference value.
In the step, the expected price-rise income data and the maximum risk proportion data which are determined according to the risk preference value and the historical data can be used for more comprehensively and accurately calculating the first floating proportion data, so that the subsequently determined power load declaration data is more accurate.
Step S502: and determining first actual floating proportion data according to the relation between the first floating proportion data and first preset proportion threshold value data.
If the first floating proportion data is larger than or equal to the first preset proportion threshold value data, determining the first preset proportion threshold value data as first actual floating proportion data;
and if the first floating proportion data is smaller than the first preset proportion threshold value data, determining the first floating proportion data as first actual floating proportion data.
In this step, the first preset proportion threshold data is preset maximum risk proportion data. And when the first floating proportion data is larger than the preset maximum risk proportion data, determining the preset maximum risk proportion data as the first floating proportion data. In the spot trading market, since there is a market trading rule that the surplus portion is not available for profit calculation when the floating proportion exceeds 100%, the first floating proportion data is set to 100% when the floating proportion exceeds 100%.
Since in actual practice the expected price increase benefit may be negative due to a small number of historical statistical samples, or a small probability of a large drop at that time (an intolerable risk that is hard to predict that could affect the average) often occurs, the first floating proportion data is set to 0%.
After step S302 is executed, step S303 is executed.
Step S303: and determining power load declaration data of the declaration point according to the first actual floating proportion data and the load prediction data.
In this step, the calculation formula of the power load declaration data is as follows: the power load reporting data is load prediction data (1+ first actual floating proportion).
Step S304: and determining the load prediction data as the power load declaration data of the declaration point.
In this step, when the actual expected profit data is negative, the reporting point does not perform a profit operation, that is, the load prediction data is the power load reporting data of the reporting point.
Step S205: and determining the power load data of the declaration point according to the relation between the actual expected income data and a second preset income data threshold value.
Referring to fig. 4, in the present embodiment, the determining the power load data of the declaration point in step S205 includes the following steps:
step S401: judging whether the actual expected income data is larger than second preset income threshold data or not;
if the actual expected income data is smaller than the second preset income threshold data, entering a next step S402;
if the actual expected revenue data is greater than or equal to the second preset threshold data, the process proceeds to step S404.
Step S402: and processing the actual expected income according to the preset floating proportion model, and determining second actual floating proportion data.
Referring to fig. 6, the step S402 of this embodiment may include the following steps:
step S601: and processing the actual expected income according to the preset floating proportion model, and determining second floating proportion data.
In this step, since the declaration point is a point where there is a certain probability that the power load will fall and be damaged, and the declaration point can be declared less, it is necessary to calculate how many power load values that need to be declared less specifically, that is, it is necessary to determine the ratio of less declaration, that is, the second floating ratio data.
In this step, the preset floating proportion model is: and the floating proportion data is actual expected income data/expected price drop income data and preset risk proportion data, wherein the expected price rise income is the arithmetic mean value of the actual expected income data under the condition that the statistical history and the time price drop probability data are greater than the price rise probability data, and the expected risk proportion data represents the amplitude of calculating the multi-declaration data. In the synchronization step S501, since the declaration has a certain risk during the declaration process, at this time, a risk bias value needs to be set, and assuming that the risk bias value is any integer from 1 to 10 (which can be adjusted), it can be considered that, when the risk bias value is 1, the risk to be borne is the smallest, as the risk bias value increases, the risk to be borne is gradually increased, and when the risk bias value is the maximum value 10, the risk to be borne is the largest. When the risk preference value is small, the electric power load declaration value can be reduced in a small range, and when the risk preference value is increased, the electric power load declaration value can be reduced greatly, namely when the risk preference value is small, the obtained floating proportion data are small, and when the risk preference value is large, the obtained floating proportion data are large.
In this step, the preset risk ratio data is calculated according to the preset risk preference value and the preset maximum risk ratio data. The maximum risk ratio data may be determined from historical data. For example, the preset maximum risk ratio data is 50%, and the risk preference value is divided into ten levels of 1 to 10, so that the maximum risk ratio data can be divided into 10 sections, each of which is 5%, i.e., the preset risk ratio data is increased by 5% for each increase of 1 risk preference value.
In the step, the expected price-fall income data and the maximum risk proportion data which are determined according to the risk preference value and the historical data can be used for calculating the second floating proportion data more comprehensively and accurately, so that the subsequent determination of the power load declaration data is more accurate.
Step S602: and determining second actual floating proportion data according to the relation between the second floating proportion data and second preset proportion threshold value data.
If the second floating proportion data is larger than or equal to the second preset proportion threshold value data, determining the second preset proportion threshold value data as second actual floating proportion data;
and if the second floating proportion data is smaller than the second preset proportion threshold value data, determining the first floating proportion data as second actual floating proportion data.
In this step, the second preset proportion threshold data is a negative value of the preset maximum risk proportion data, that is, the historical average price-dropping risk proportion data. And when the second floating proportion data is larger than the negative value of the preset maximum risk proportion data, determining the negative value of the preset maximum risk proportion data as the second floating proportion data. In the spot trading market, since there is a market trading rule, when the second floating proportion data is less than-100%, the power load declaration value is a negative value at this time, and therefore, when the second floating proportion data is less than-100%, the second floating proportion data is set to-100%.
Since in practice the expected price drop deficit condition may be negative due to a small number of historical statistical samples, or a small probability of a large rise in the time (a difficult to predict risk of intolerability that could affect the average) that is often present, the first floating proportion data is set to 0%.
After step S402 is executed, step S403 is executed.
Step S403: and determining power load declaration data of the declaration point according to the second actual floating proportion data and the load prediction data.
In this step, the calculation formula of the power load declaration data is as follows: the power load reporting data is load prediction data (1+ second actual floating proportion).
Step S404: and determining the load prediction data as the power load declaration data of the declaration point.
In this step, when the actual expected profit data is positive, the reporting point does not perform the operation of less reporting, that is, the load prediction data is the power load reporting data of the reporting point.
In this embodiment, the declaration points are divided into two types for processing according to the relationship between price rising probability data and price falling probability data, and then the types of the declaration points are further divided according to the calculated relationship between actual expected income data and preset income threshold data, so that the electric power load declaration data can be obtained more comprehensively and accurately, more benefits can be obtained in actual production operation, and the loss of enterprises is reduced.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
It is a further object of an embodiment of the present invention to provide a device for determining power load declaration data, and fig. 7 is a schematic diagram of the device for determining power load declaration data, and for convenience of explanation, only the portions related to the embodiment of the present application are shown.
Fig. 7 shows an embodiment of the power load declaration data determining apparatus according to the present invention. The apparatus of this embodiment is a physical apparatus for performing the methods described in fig. 1-6. The technical solution is essentially the same as that in the above embodiment, and the corresponding description in the above embodiment is also applicable to this embodiment.
The device in this embodiment includes:
the first determination module 701: determining predicted price difference probability distribution information of a declaration point and load prediction data of the declaration point, wherein the predicted price difference probability distribution information at least comprises price rising probability data, price falling probability data, price rising data and price falling data;
the second determination module 702: determining actual expected income data according to the predicted price difference probability distribution information;
the third determining module 703: and determining the power load declaration data of the declaration point according to the relationship between the price rising probability data and the price falling probability data of the declaration point, the actual expected income data and the load prediction data.
Fig. 8 is a schematic diagram of a terminal device according to an embodiment of the present invention. As shown in fig. 8, the terminal device 8 of this embodiment includes: a processor 80, a memory 81, and a computer program 82 stored in the memory 81 and operable on the processor 80, wherein the processor 80 executes the computer program 82 to implement the steps in the above-mentioned embodiments of the method for predicting the evaporator maintenance time, such as the steps S101 to S103 shown in fig. 1. Alternatively, the processor 80, when executing the computer program 82, implements the functions of each module/unit in each device embodiment described above, for example, the functions of the modules 701 to 703 shown in fig. 7.
Illustratively, the computer program 82 may be partitioned into one or more modules/units that are stored in the memory 81 and executed by the processor 80 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 82 in the terminal device 11.
The terminal device 8 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, the processor 80 and the memory 81. Those skilled in the art will appreciate that fig. 8 is merely an example of a terminal device 8 and does not constitute a limitation of terminal device 8 and may include more or fewer components than shown, or some components may be combined, or different components, e.g., the terminal device may also include input-output devices, network access devices, buses, etc.
The Processor 80 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 81 may be an internal storage unit of the terminal device 8, such as a hard disk or a memory of the terminal device 8. The memory 81 may also be an external storage device of the terminal device 8, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) and the like provided on the terminal device 8. Further, the memory 81 may also include both an internal storage unit and an external storage device of the terminal device 8. The memory 81 is used for storing the computer program and other programs and data required by the terminal device. The memory 81 may also be used to temporarily store data that has been output or is to be output.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
Specifically, the present application further provides a computer-readable storage medium, which may be a computer-readable storage medium contained in the memory in the foregoing embodiments; or it may be a separate computer-readable storage medium not incorporated into the terminal device. The computer readable storage medium stores one or more computer programs:
a computer-readable storage medium comprising a computer program stored thereon which, when executed by a processor, performs the steps of the method for determining the power load declaration data.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A method for determining power load declaration data, comprising:
determining predicted price difference probability distribution information of a declaration point and load prediction data of the declaration point, wherein the predicted price difference probability distribution information at least comprises price rising probability data, price falling probability data, price rising data and price falling data;
determining actual expected income data according to the predicted price difference probability distribution information;
and determining the power load declaration data of the declaration point according to the relationship between the price rising probability data and the price falling probability data of the declaration point, the actual expected income data and the load prediction data.
2. The method for determining electric power load declaration data of claim 1, wherein the determining of the electric power load declaration data of the declaration point based on the relationship between the price increase probability data and the price decrease probability data of the declaration point, the actual expected profit data, and the load prediction data includes:
judging whether the price rising probability data is larger than the price falling probability data or not;
if the price rising probability data is larger than the price falling probability data, judging whether the actual expected income data is larger than first preset income threshold data;
if the actual expected income data is larger than the first preset income threshold value data, processing the actual expected income according to a preset floating proportion model, and determining first actual floating proportion data;
and determining power load declaration data of the declaration point according to the first actual floating proportion data and the load prediction data.
3. The method for determining power load declaration data of claim 2, wherein after the step of determining whether the actual expected revenue data is greater than a first predetermined revenue threshold data if the price increase probability data is greater than the price decrease probability data, the method further comprises:
and if the actual expected income data is less than or equal to the first preset threshold data, determining the load prediction data as the power load declaration data of the declaration point.
4. A method for determining electrical load declaration data as claimed in claim 2 wherein said processing of actual expected revenue based on a predetermined floating scale model determines first actual floating scale data comprising;
processing actual expected income according to a preset floating proportion model, and determining first floating proportion data;
judging whether the first floating proportion data is larger than or equal to first preset proportion threshold data or not;
if the first floating proportion data is larger than or equal to the first preset proportion threshold value data, determining the first preset proportion threshold value data as first actual floating proportion data;
and if the first floating proportion data is smaller than the first preset proportion threshold value data, determining the first floating proportion data as first actual floating proportion data.
5. The method for determining power load declaration data of claim 2 wherein, after the step of determining whether the price increase probability data is greater than the price decrease probability data, the method further comprises:
if the price rising probability data is smaller than the price falling probability data, judging whether the actual expected income data is larger than second preset income threshold data;
if the actual expected income data is smaller than the second preset income threshold value data, processing the actual expected income according to a preset floating proportion model, and determining second actual floating proportion data;
and determining power load declaration data of the declaration point according to the second actual floating proportion data and the load prediction data.
6. The method for determining power load declaration data of claim 5 wherein, after the step of determining whether the actual expected revenue data is greater than a second predetermined revenue threshold data if the price increase probability data is less than the price decrease probability data, further comprising:
and if the actual expected income data is greater than or equal to the second preset threshold data, determining the load prediction data as the power load declaration data of the declaration point.
7. The method for determining power load declaration data of claim 5 wherein said processing the actual expected revenue based on the predetermined floating scale model to determine the second actual floating scale data includes:
processing the actual expected income according to a preset floating proportion model, and determining second floating proportion data;
judging whether the second floating proportion data is larger than or equal to second preset proportion threshold data or not;
if the second floating proportion data is larger than or equal to the second preset proportion threshold value data, determining the second preset proportion threshold value data as second actual floating proportion data;
and if the second floating proportion data is smaller than the second preset proportion threshold value data, determining the second floating proportion data as second actual floating proportion data.
8. An apparatus for determining power load declaration data, comprising:
the system comprises a first determining module, a second determining module and a third determining module, wherein the first determining module is used for determining predicted price difference probability distribution information of reporting points and load prediction data of the reporting points, and the predicted price difference probability distribution information at least comprises price rising probability data, price falling probability data, price rising data and price falling data;
the second determining module is used for determining actual expected income data according to the predicted price difference probability distribution information;
and the third determining module is used for determining the power load declaration data of the declaration point according to the relationship between the price rising probability data and the price falling probability data of the declaration point, the actual expected income data and the load prediction data.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114219188A (en) * | 2022-02-23 | 2022-03-22 | 国网浙江电动汽车服务有限公司 | Charging pile aggregated load active power setting method, device, equipment and medium |
| CN114418626A (en) * | 2021-12-31 | 2022-04-29 | 新奥数能科技有限公司 | Method, device, equipment and storage medium for estimating electric power resource declaration amount |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114418626A (en) * | 2021-12-31 | 2022-04-29 | 新奥数能科技有限公司 | Method, device, equipment and storage medium for estimating electric power resource declaration amount |
| CN114219188A (en) * | 2022-02-23 | 2022-03-22 | 国网浙江电动汽车服务有限公司 | Charging pile aggregated load active power setting method, device, equipment and medium |
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Application publication date: 20200811 |