CN110942250A - Power capacity retention detection method, device and equipment - Google Patents
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Abstract
The application discloses a method, a device and equipment for detecting power capacity retention. Firstly, acquiring electric energy declaration data and frequency modulation auxiliary service declaration data; judging whether the electric energy declaration data and the frequency modulation auxiliary service declaration data are abnormal or not; if the electric energy declaration data is abnormal or the frequency modulation auxiliary declaration data is abnormal, determining a first electricity purchasing cost based on a declared capacity decision model, and determining a second electricity purchasing cost based on a non-declared capacity decision model; and if the difference value between the first electricity purchasing cost and the second electricity purchasing cost is larger than or equal to a preset threshold value, judging that the power capacity is reserved. Because the electric energy and the frequency modulation auxiliary service have a high coupling relation, comprehensive analysis is carried out on the basis of the electric energy and the frequency modulation auxiliary service, and the method is beneficial to timely and effectively detecting whether a generator has a capacity retention problem.
Description
Technical Field
The present application relates to the field of power technologies, and in particular, to a method, an apparatus, and a device for detecting power capacity retention.
Background
With the reform of the power industry, the power industry gradually shifts from the traditional monopolized operation to competitive market operation, so that the optimal configuration of resources is realized through competition, and the production efficiency of the power industry is improved.
Under the power marketization environment, governments or regulatory agencies no longer guarantee the recovery of power generation investment and reasonable return on investment, and correspondingly, power generators no longer assume responsibility for guaranteeing adequate power supply. Therefore, in order to obtain a higher profit, a generator may take a capacity retention action, for example, intentionally schedule maintenance or shut down a generator set during a period of high power demand and insufficient supply, so that the power supply is insufficient to increase the electricity price.
Since the capacity retention behavior of the power generator can cause a plurality of serious hazards such as power supply shortage, power price increase and the like, and endanger the stability of the power market, how to judge whether the capacity retention occurs becomes a problem to be solved urgently, and the problem can be solved only by timely and effectively discovering the capacity retention problem.
Disclosure of Invention
The embodiment of the application provides a method, a device and equipment for detecting power capacity retention, which are used for timely and effectively detecting whether a generator has a capacity retention problem.
In a first aspect, a power capacity retention detection method provided in an embodiment of the present application includes:
acquiring electric energy declaration data and frequency modulation auxiliary service declaration data;
judging whether the electric energy declaration data and the frequency modulation auxiliary service declaration data are abnormal or not;
if the electric energy declaration data is abnormal or the frequency modulation auxiliary declaration data is abnormal, determining a first electricity purchasing cost based on a declared capacity decision model, and determining a second electricity purchasing cost based on a non-declared capacity decision model;
and if the difference value between the first electricity purchasing cost and the second electricity purchasing cost is larger than or equal to a first preset threshold value, judging that the power capacity is reserved.
In the method, considering that the electric energy and the frequency modulation auxiliary service have a high coupling relation, when detecting whether a capacity retention problem exists, comprehensively analyzing the electric energy and the frequency modulation auxiliary service data; in addition, not only is the abnormity reported, but also the judgment on whether the electricity purchasing cost is abnormal is added, so that the problem of capacity retention is timely and accurately judged.
In a second aspect, an embodiment of the present application provides a power capacity retention detection apparatus, including:
the acquisition module is used for acquiring electric energy declaration data and frequency modulation auxiliary service declaration data;
the first judgment module is used for judging whether the electric energy declaration data and the frequency modulation auxiliary service declaration data are abnormal or not;
the determining module is used for determining a first electricity purchasing cost based on a declared capacity decision model and a second electricity purchasing cost based on a non-declared capacity decision model when the electric energy declaration data is abnormal or the frequency modulation auxiliary declaration data is abnormal;
and the second judging module is used for judging that the power capacity is reserved when the difference value of the first electricity purchasing cost and the second electricity purchasing cost is greater than or equal to a preset threshold value.
In a third aspect, an embodiment of the present application provides a power capacity retention detection apparatus, including: a processor, a memory for storing a program, and a communication interface through which the processor invokes the program stored in the memory to perform the method according to the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing computer instructions, which, when executed on a computer, cause the computer to perform the power capacity retention detection method according to the first aspect.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of a power capacity retention detection method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a power capacity retention detection apparatus according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a power capacity retention detection device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail below. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without making any creative effort, shall fall within the protection scope of the present application.
Capacity retention of electricity (hoarding curiosity) is a common means for generators to use market force, and has high hazard, such as causing power supply shortage, electricity price rise and the like. The power capacity retention behavior of the power generator not only has higher harmfulness, but also has stronger concealment, namely, the behavior is not easy to be perceived by the outside. Therefore, how to detect whether a power generator has a capacity retention behavior in time becomes a problem to be solved urgently.
Traditional test capacity retention mainly includes two kinds of behavioral and outcome tests. The behavior detection method mainly judges whether capacity retention exists or not through abnormal quotation behaviors of the power generation enterprises, and judges whether malicious capacity retention exists in declarations of the power generation enterprises or not through analyzing market concentration. The consequence inspection rule is to judge whether the capacity retention exists or not by analyzing the expected income of the power generation enterprises from the viewpoint of the income of the power generation enterprises.
However, the behavior detection method can only analyze the abnormal declaration behavior of the market member, but cannot reveal whether the abnormal declaration affects other members, and when the power generation enterprise equipment is abnormal, the declaration behavior of the power generation enterprise equipment must change, so that frequent alarms are often given by adopting the detection method, and the pertinence is not strong. The consequence inspection method has high dependence on accurate approval of the cost of a power generation enterprise, and if the cost approval is unscientific, the verification result is easy to be inaccurate.
In order to timely and effectively detect whether a generator has a capacity retention problem, the embodiment of the application provides a method, a device and equipment for detecting the power capacity retention.
Considering that, with the continuous and deep innovation of the electric power market in China, an auxiliary service market represented by frequency modulation is rapidly developed outside the electric energy market, and a market bidding system with multi-variety transaction cost combined clearing is formed. Because the electric energy and the frequency modulation auxiliary service have a high coupling relation, the market clearing results mutually influence and mutually restrict. Therefore, in the embodiment of the application, comprehensive analysis can be performed according to the electric energy data and the frequency modulation auxiliary service data declared by the generator, so as to judge whether capacity retention exists.
Referring to fig. 1, a schematic flow chart of a power capacity retention detection method provided in an embodiment of the present application, as shown in the figure, the method may include the following steps:
For example, the declared power generation amount, the declared electricity price, the declared amount of the frequency modulation auxiliary service, the declared quotation and the like of each generator set of each generator can be acquired.
And 102, judging whether the electric energy declaration data and the frequency modulation auxiliary service declaration data are abnormal or not.
In consideration of the high coupling relationship between the electric energy and the frequency modulation auxiliary service, in the embodiment of the present application, whether the abnormality exists in both the electric energy declaration data and the frequency modulation auxiliary service declaration data is determined.
Optionally, when judging whether the electric energy declaration data is abnormal, whether the declaration of the generator is abnormal can be judged by analyzing the market concentration. Specifically, the ratio of the electric energy declaration amount of the first N power generators to the total electric energy declaration amount can be calculated, and if the ratio of the electric energy declaration amounts of the N power generators is greater than or equal to a preset threshold, it is determined that the electric energy declaration data is abnormal.
For example, the ratio of the electric energy declaration amount of the generator whose electric energy declaration amount is ranked in the top N to the electric energy declaration amount can be determined by a Herfindahl-herschman Index (HHI). Taking the ratio of the electric energy declared by the first 3 generators to the total declared amount as an example, the ratio can be determined by the formula (1).
Wherein gp belongs to Top3 represents the generator with the reported electric energy quantity in the front 3, Ngp represents the number of generator sets of gp of the generator, NG represents the total number of reported generator sets,representing the maximum power generation declared by the generator set gu.
If HHIEPThe exponent being greater than or equal to a limit of the exponent of the electric energy HHI, i.e. HHIEP≥HHIEP,maxThe market power of the generator is considered to be significant, and there is a possibility that the power capacity is retained.
Optionally, when judging whether the frequency modulation auxiliary service declaration data is abnormal, whether the declaration of the power generator is abnormal can be judged by analyzing the market concentration. Specifically, the occupation ratio of the fm auxiliary service declaration amount of the first N generators in the total fm auxiliary service declaration amount may be calculated, and if the occupation ratio of the fm auxiliary service declaration amount of the N generators is greater than or equal to a preset threshold, it is determined that the fm auxiliary service declaration data is abnormal.
For example, the fraction of the reporting volume of the first N generators in the total number of fm auxiliary service reports can be determined by the Herfindahl-herschman Index (HHI). Taking the ratio of the reported amount of the frequency modulation auxiliary service of the first 3 generators to the total reported amount as an example, the determination can be performed by formula (2).
Wherein the content of the first and second substances,representing the fm auxiliary service exposure of the genset gu.
If HHIFMExponent equal to or greater than frequency modulation HHI exponent limit, i.e. HHIFN≥HHIFM,maxThe market power of the generator is considered to be significant, and there is a possibility that the power capacity is retained.
And 103, if the electric energy declaration data is abnormal or the frequency modulation auxiliary service declaration data is abnormal, determining a first electricity purchasing cost based on the declared capacity decision model, and determining a second electricity purchasing cost based on the non-declared capacity decision model.
Optionally, the declared capacity decision model and the non-declared capacity decision model respectively include one or a combination of the following constraints: transmission capacity constraint, power balance constraint, frequency modulation demand constraint, unit power generation capacity constraint, and unit climbing capacity constraint.
The declared capacity decision model further comprises scalar constraints in electric energy and scalar constraints in frequency modulation besides the constraints.
In some embodiments, the non-declared capacity decision model may be as shown in equations (3) to (10).
And (3) transmission capacity constraint:
and (3) power balance constraint:
and (3) restricting the frequency modulation requirement:
and (3) constraint of generating capacity of the unit:
unit climbing capacity constraint:
wherein the content of the first and second substances,andrespectively representing the electric energy price and the frequency modulation auxiliary service price of the t time period reported by the generator set g,andrespectively represent hairThe generated energy and the reported frequency modulation auxiliary service amount in the t period reported by the generator set g, NG represents the number of the generator sets, NT represents the number of the time periods,andrespectively representing the lower limit value and the upper limit value of the operating section os, GTDFb,os、GTDFb(g),osRepresenting the distribution factor of the credit transfer between node b and the operating profile os, NB representing the number of nodes,representing the predicted value of the load of node b in the t-th period, PFM,minIndicating a minimum fm auxiliary service requirement,andrespectively represents the minimum power generation amount and the maximum power generation amount of the generator set g,andrespectively representing the minimum climbing capacity and the maximum climbing capacity of the generator set g.
Based on the decision model without declared capacity shown in the above formulas (3) to (8), the total electricity purchasing cost of the electricity energy and the frequency modulation auxiliary service in the market without intermediate scalar constraint can be calculated and is marked as FeeN,DecI.e., the second electricity purchase cost.
And (3) a declared capacity decision model is formed by further adding electric energy medium scalar constraint and frequency modulation medium scalar constraint on the basis of the non-declared capacity decision model shown in the formula (3) to the formula (8). Specifically, the scalar constraint in the electrical energy can be as shown in equation (9).
Wherein the content of the first and second substances,and the generated energy reported by the generator set g is represented.
Scalar constraints in frequency modulation can be shown as equation (10).
Wherein the content of the first and second substances,and the frequency modulation auxiliary service supply quantity reported by the generator set g is represented.
Based on the declared capacity decision model shown in the above formulas (3) to (10), the total electricity purchasing cost of the electricity energy and the frequency modulation auxiliary service in the market under the condition of medium and medium price constraint can be calculated and is marked as FeeDecI.e. the first electricity purchase cost mentioned above.
And 104, if the difference value of the first electricity purchasing cost and the second electricity purchasing cost is larger than or equal to a preset threshold value, judging that the power capacity is reserved.
If the capacity retention behavior of the power generator for obtaining the excess profit does not exist, the first electricity purchasing cost determined based on the declared capacity decision model is generally closer to the second electricity purchasing cost determined based on the non-declared capacity decision model. If the difference between the first electricity purchase cost and the second electricity purchase cost is large, i.e. Pro ═ feDec-FeeN,Dec≥PromaxThen, the generator can be considered to reduce the declared power generation amount intentionally to obtain more profits.
In the embodiment, considering that the electric energy and the frequency modulation auxiliary service have a high coupling relation, when detecting whether the capacity retention problem exists, comprehensively analyzing the data of the electric energy and the frequency modulation auxiliary service; in addition, not only is the abnormity reported, but also the judgment on whether the electricity purchasing cost is abnormal is added, so that the problem of capacity retention is timely and accurately judged.
Based on the same technical concept, the embodiment of the application also provides a power capacity retention detection device, which is used for realizing the embodiment of the method. Referring to fig. 2, for a schematic structural diagram of the power capacity retention detection apparatus, as shown in the figure, the apparatus may include an obtaining module 201, a first determining module 202, a determining module 203, and a second determining module 204.
Specifically, the obtaining module 201 is configured to obtain the electric energy declaration data and the frequency modulation auxiliary service declaration data.
The first determining module 202 is configured to determine whether the power reporting data and the fm auxiliary service reporting data are abnormal.
The determining module 203 is configured to determine a first electricity purchasing cost based on the declared capacity decision model and determine a second electricity purchasing cost based on the non-declared capacity decision model when the power declaration data is abnormal or the frequency modulation auxiliary declaration data is abnormal.
A second determining module 204, configured to determine that there is power capacity remaining when a difference between the first power purchase cost and the second power purchase cost is greater than or equal to a preset threshold.
Optionally, when determining whether the power declaration data is abnormal, the first determining module 202 is specifically configured to:
determining the ratio of the electric energy declaration quantity of the first N generators in the electric energy declaration total quantity;
and if the ratio is larger than or equal to a second preset threshold value, judging that the electric energy declaration data is abnormal.
Optionally, when determining the proportion of the electric energy declaration amount of the first N power generators in the total electric energy declaration amount, the first determining module 202 may determine the proportion of the electric energy declaration amount of the first N power generators in the total electric energy declaration amount through the HHI index.
Optionally, when determining whether the frequency modulation auxiliary service declaration data is abnormal, the first determining module 202 is specifically configured to:
determining the ratio of the frequency modulation auxiliary service reporting quantity of the first N generators in the frequency modulation auxiliary service reporting quantity in the total frequency modulation auxiliary service reporting quantity;
and if the ratio is larger than or equal to a third preset threshold value, judging that the frequency modulation auxiliary service declaration data is abnormal.
Optionally, when determining the proportion of the fm auxiliary service reporting volume of the first N generators in the total fm auxiliary service reporting volume, the first determining module 202 may determine the proportion of the fm auxiliary service reporting volume of the first N generators in the total fm auxiliary service reporting volume through the HHI index.
Optionally, the declared capacity decision model and the non-declared capacity decision model respectively include one or a combination of the following constraints: the method comprises the following steps of (1) transmission capacity constraint, power balance constraint, frequency modulation demand constraint, unit power generation capacity constraint and unit climbing capacity constraint;
the declared capacity decision model further comprises: scalar constraints in power and scalar constraints in frequency modulation.
Optionally, the declared capacity decision model is shown as formula (3) to formula (10).
Optionally, the non-declared capacity decision model is shown in formula (3) to formula (8).
Based on the same technical concept, the embodiment of the application also provides power capacity retention detection equipment which is used for realizing the method embodiment. Referring to fig. 3, the apparatus includes: a processor 301, a memory 302 and a communication interface 303, wherein the memory 302 is used for storing programs, the processor 301 calls the programs stored in the memory 302, and the following steps are executed through the communication interface 303:
acquiring electric energy declaration data and frequency modulation auxiliary service declaration data;
judging whether the electric energy declaration data and the frequency modulation auxiliary service declaration data are abnormal or not;
if the electric energy declaration data is abnormal or the frequency modulation auxiliary declaration data is abnormal, determining a first electricity purchasing cost based on a declared capacity decision model, and determining a second electricity purchasing cost based on a non-declared capacity decision model;
and if the difference value between the first electricity purchasing cost and the second electricity purchasing cost is larger than or equal to a first preset threshold value, judging that the power capacity is reserved.
Optionally, the processor 301 is specifically configured to:
determining the ratio of the electric energy declaration quantity of the first N generators in the electric energy declaration total quantity;
and if the ratio is larger than or equal to a second preset threshold value, judging that the electric energy declaration data is abnormal.
Optionally, the processor 301 is specifically configured to:
and determining the proportion of the electric energy declaration quantity of the first N generators of the electric energy declaration quantity in the total electric energy declaration quantity through the Herfendaer-Herhmann HHI index.
Optionally, the processor 301 is specifically configured to:
determining the ratio of the frequency modulation auxiliary service reporting quantity of the first N generators in the frequency modulation auxiliary service reporting quantity in the total frequency modulation auxiliary service reporting quantity;
and if the ratio is larger than or equal to a third preset threshold value, judging that the frequency modulation auxiliary service declaration data is abnormal.
Optionally, the processor 301 is specifically configured to:
and determining the ratio of the frequency modulation auxiliary service declaration quantity of the first N generators in the frequency modulation auxiliary service declaration total quantity through the HHI index.
Optionally, the declared capacity decision model and the non-declared capacity decision model respectively include one or a combination of the following constraints: the method comprises the following steps of (1) transmission capacity constraint, power balance constraint, frequency modulation demand constraint, unit power generation capacity constraint and unit climbing capacity constraint;
the declared capacity decision model further comprises: scalar constraints in power and scalar constraints in frequency modulation.
Optionally, the declared capacity decision model is shown as formula (3) to formula (10).
Optionally, the non-declared capacity decision model is shown in formula (3) to formula (8).
Based on the same technical concept, embodiments of the present application provide a computer-readable storage medium storing computer instructions that, when executed on a computer, cause the computer to execute the aforementioned power capacity retention detection method.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (10)
1. A method for detecting power capacity retention, comprising:
acquiring electric energy declaration data and frequency modulation auxiliary service declaration data;
judging whether the electric energy declaration data and the frequency modulation auxiliary service declaration data are abnormal or not;
if the electric energy declaration data is abnormal or the frequency modulation auxiliary declaration data is abnormal, determining a first electricity purchasing cost based on a declared capacity decision model, and determining a second electricity purchasing cost based on a non-declared capacity decision model;
and if the difference value between the first electricity purchasing cost and the second electricity purchasing cost is larger than or equal to a first preset threshold value, judging that the power capacity is reserved.
2. The method of claim 1, wherein said determining whether said power declaration data is abnormal comprises:
determining the ratio of the electric energy declaration quantity of the first N generators in the electric energy declaration total quantity;
and if the ratio is larger than or equal to a second preset threshold value, judging that the electric energy declaration data is abnormal.
3. The method of claim 2, wherein determining a percentage of the total amount of power declared to the top N generators of the amount of power declared comprises:
and determining the proportion of the electric energy declaration quantity of the first N generators of the electric energy declaration quantity in the total electric energy declaration quantity through the Herfendaer-Herhmann HHI index.
4. The method of claim 1, wherein said determining whether said fm auxiliary service declaration data is abnormal comprises:
determining the ratio of the frequency modulation auxiliary service reporting quantity of the first N generators in the frequency modulation auxiliary service reporting quantity in the total frequency modulation auxiliary service reporting quantity;
and if the ratio is larger than or equal to a third preset threshold value, judging that the frequency modulation auxiliary service declaration data is abnormal.
5. The method of claim 4, wherein determining a fraction of FM auxiliary service claim quantities of generators with a top N number of FM auxiliary service claim quantities in a total FM auxiliary service claim quantity comprises:
and determining the ratio of the frequency modulation auxiliary service declaration quantity of the first N generators in the frequency modulation auxiliary service declaration total quantity through the HHI index.
6. The method of claim 1, wherein the declared capacity decision model and the non-declared capacity decision model each include one or a combination of the following constraints: the method comprises the following steps of (1) transmission capacity constraint, power balance constraint, frequency modulation demand constraint, unit power generation capacity constraint and unit climbing capacity constraint;
the declared capacity decision model further comprises: scalar constraints in power and scalar constraints in frequency modulation.
7. The method of claim 6, wherein the declared capacity decision model is as follows:
wherein the content of the first and second substances,andrespectively representing the electric energy price and the frequency modulation auxiliary service price of the t time period reported by the generator set g,andrespectively representing the generating capacity and the frequency modulation auxiliary service supply of the generator set g in the period t, wherein NG represents the number of the generator sets, NT represents the number of the periods,andrespectively representing the lower limit value and the upper limit value of the operating section os, GTDFb,os、GTDFb(g),osRepresenting the distribution factor of the credit transfer between node b and the operating profile os, NB representing the number of nodes,representing the predicted value of the load of node b in the t-th period, PFM,minIndicating a minimum fm auxiliary service requirement,andrespectively represents the minimum power generation amount and the maximum power generation amount of the generator set g,andrespectively representing the minimum climbing capacity and the maximum climbing capacity of the generator set g,the power generation amount reported by the generator set g is shown,to representAnd the supply amount of the frequency modulation auxiliary service reported by the generator set g.
8. The method of claim 6, wherein the non-declared capacity decision model is represented by the following equation:
wherein the content of the first and second substances,andrespectively representing the electric energy price and the frequency modulation auxiliary service price of the t time period reported by the generator set g,andare respectively provided withRepresenting the generated energy and the frequency modulation auxiliary service supply of the generator set g in the period t, NG representing the number of the generator sets, NT representing the number of the periods,andrespectively representing the lower limit value and the upper limit value of the operating section os, GTDFb,os、GTDFb(g),osRepresenting the distribution factor of the credit transfer between node b and the operating profile os, NB representing the number of nodes,representing the predicted value of the load of node b in the t-th period, PFM,minIndicating a minimum fm auxiliary service requirement,andrespectively represents the minimum power generation amount and the maximum power generation amount of the generator set g,andrespectively representing the minimum climbing capacity and the maximum climbing capacity of the generator set g.
9. A power capacity retention detection device, comprising:
the acquisition module is used for acquiring electric energy declaration data and frequency modulation auxiliary service declaration data;
the first judgment module is used for judging whether the electric energy declaration data and the frequency modulation auxiliary service declaration data are abnormal or not;
the determining module is used for determining a first electricity purchasing cost based on a declared capacity decision model and a second electricity purchasing cost based on a non-declared capacity decision model when the electric energy declaration data is abnormal or the frequency modulation auxiliary declaration data is abnormal;
and the second judging module is used for judging that the power capacity is reserved when the difference value of the first electricity purchasing cost and the second electricity purchasing cost is greater than or equal to a preset threshold value.
10. A power capacity retention detection apparatus, characterized by comprising: a processor, a memory for storing a program, and a communication interface through which the processor calls the program stored in the memory to perform the method of any of claims 1 to 8.
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