CN110942250B - Power capacity retention detection method, device and equipment - Google Patents

Power capacity retention detection method, device and equipment Download PDF

Info

Publication number
CN110942250B
CN110942250B CN201911178332.8A CN201911178332A CN110942250B CN 110942250 B CN110942250 B CN 110942250B CN 201911178332 A CN201911178332 A CN 201911178332A CN 110942250 B CN110942250 B CN 110942250B
Authority
CN
China
Prior art keywords
frequency modulation
auxiliary service
electric energy
capacity
declaration
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201911178332.8A
Other languages
Chinese (zh)
Other versions
CN110942250A (en
Inventor
梁阳豆
卓毅鑫
吴茵
李秋文
林洁
莫东
李一铭
梁振成
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangxi Power Grid Co Ltd
Original Assignee
Guangxi Power Grid Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangxi Power Grid Co Ltd filed Critical Guangxi Power Grid Co Ltd
Priority to CN201911178332.8A priority Critical patent/CN110942250B/en
Publication of CN110942250A publication Critical patent/CN110942250A/en
Application granted granted Critical
Publication of CN110942250B publication Critical patent/CN110942250B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/067—Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S50/00—Market activities related to the operation of systems integrating technologies related to power network operation or related to communication or information technologies
    • Y04S50/14—Marketing, i.e. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards

Abstract

The application discloses a method, a device and equipment for detecting power capacity retention. In the method, firstly, electric energy declaration data and frequency modulation auxiliary service declaration data are obtained; 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

Power capacity retention detection method, device and equipment
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 marketized operation, so that the optimal configuration of resources is realized through competition, and the production efficiency of the power industry is improved.
In the power marketization environment, governments or regulatory agencies no longer guarantee recovery of power generation investment and reasonable return on investment, and correspondingly, power generators no longer assume responsibility for ensuring 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, the high coupling relation between the electric energy and the frequency modulation auxiliary service is considered, and when the capacity retention problem is detected, the data of the electric energy and the frequency modulation auxiliary service are comprehensively analyzed; in addition, the abnormity is declared, and the judgment of whether the electricity purchasing cost is abnormal is added, so that the problem of capacity retention is judged timely and accurately.
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 reporting data and the frequency modulation auxiliary service reporting 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.
Drawings
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 embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art 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 both behavioral and outcome assays. 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 behaviors of market members, but cannot reveal whether the abnormal declaration has an influence on other members, and when the power generation enterprise equipment is abnormal, the declaration behaviors of the power generation enterprise equipment are bound to change, so that frequent alarms are often given by adopting the verification method, and the pertinence is not strong. The consequence inspection method has higher dependence on accurate approval of the cost of power generation enterprises, and if the cost approval is unscientific, the result of verification 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.
In consideration of the fact that with the continuous deepening of the reform 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 the combination of multi-variety transaction cost 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 reported by the power generator, so as to judge whether the 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:
step 101, acquiring electric energy declaration data and frequency modulation auxiliary service declaration data.
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).
Figure BDA0002290585380000041
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,
Figure BDA0002290585380000042
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 services of the first 3 generators to the total reported amount as an example, the ratio can be determined by the formula (2).
Figure BDA0002290585380000051
Wherein the content of the first and second substances,
Figure BDA0002290585380000052
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: the method comprises the following steps of 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).
Figure BDA0002290585380000061
And (3) transmission capacity constraint:
Figure BDA0002290585380000062
and (3) power balance constraint:
Figure BDA0002290585380000063
and (3) restricting the frequency modulation requirement:
Figure BDA0002290585380000064
and (3) constraint of generating capacity of the unit:
Figure BDA0002290585380000065
unit climbing capacity constraint:
Figure BDA0002290585380000066
wherein the content of the first and second substances,
Figure BDA0002290585380000067
and
Figure BDA0002290585380000068
respectively representing the electric energy price and the frequency modulation auxiliary service price of the t time period reported by the generator set g,
Figure BDA0002290585380000069
and
Figure BDA00022905853800000610
respectively representing the generated energy and the reported quantity of the frequency modulation auxiliary service in the t period reported by the generator set g, wherein NG represents the number of the generator sets, NT represents the number of the time periods,
Figure BDA00022905853800000611
and
Figure BDA00022905853800000612
respectively 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,
Figure BDA00022905853800000613
representing the predicted value of the load of node b in the t-th period, PFM,minIndicating a minimum fm auxiliary service requirement,
Figure BDA00022905853800000614
and
Figure BDA00022905853800000615
respectively represents the minimum power generation amount and the maximum power generation amount of the generator set g,
Figure BDA00022905853800000616
and
Figure BDA00022905853800000617
respectively 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, scalar constraints in the electrical energy can be shown in equation (9).
Figure BDA0002290585380000071
Wherein the content of the first and second substances,
Figure BDA0002290585380000072
and the generated energy reported by the generator set g is represented.
Scalar constraints in frequency modulation can be shown as equation (10).
Figure BDA0002290585380000073
Wherein the content of the first and second substances,
Figure BDA0002290585380000074
and the frequency modulation auxiliary service supply 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 step 104, 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.
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 occupation ratio of the electric energy declared quantities of the first N power generators in the electric energy declared quantity line in the total electric energy declared quantity, the first determining module 202 may determine the occupation ratio of the electric energy declared quantities of the first N power generators in the electric energy declared quantity line in the total electric energy declared quantity 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 occupation ratio is greater 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 constraint in electric energy and scalar constraint 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 as 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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 (9)

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 reporting data and the frequency modulation auxiliary service reporting data are abnormal or not;
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 a declared capacity decision model, and determining a second electricity purchasing cost based on a non-declared capacity decision model;
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;
the declared capacity decision model is as follows:
Figure FDA0003612338950000011
Figure FDA0003612338950000012
Figure FDA0003612338950000013
Figure FDA0003612338950000014
Figure FDA0003612338950000015
Figure FDA0003612338950000016
Figure FDA0003612338950000017
Figure FDA0003612338950000018
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003612338950000021
and
Figure FDA0003612338950000022
respectively representing the electric energy price and the frequency modulation auxiliary service price of the t time period reported by the generator set g,
Figure FDA0003612338950000023
and
Figure FDA0003612338950000024
respectively 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,
Figure FDA0003612338950000025
and
Figure FDA0003612338950000026
respectively representing the lower limit value and the upper limit value of the operating section os, GTDFb，os、GTDFb(g)，osTo representThe credit transfer distribution factor between node b and the operating profile os, NB represents the number of nodes,
Figure FDA0003612338950000027
representing the predicted value of the load of node b in the t-th period, PFM，minIndicating a minimum fm auxiliary service requirement,
Figure FDA0003612338950000028
and
Figure FDA0003612338950000029
respectively represents the minimum power generation amount and the maximum power generation amount of the generator set g,
Figure FDA00036123389500000210
and
Figure FDA00036123389500000211
respectively representing the minimum climbing capacity and the maximum climbing capacity of the generator set g,
Figure FDA00036123389500000212
the power generation amount reported by the generator set g is shown,
Figure FDA00036123389500000213
and the frequency modulation auxiliary service supply quantity reported by the generator set g is represented.
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 the percentage of the total quantity of electric energy declared to be the electric energy declared quantity of the top N generators 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 occupation ratio of the reported quantities of the frequency modulation auxiliary services of the first N generators in the reported quantity of the frequency modulation auxiliary services;
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 top N of the 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 non-declared capacity decision model is represented by the following equation:
Figure FDA0003612338950000031
Figure FDA0003612338950000032
Figure FDA0003612338950000041
Figure FDA0003612338950000042
Figure FDA0003612338950000043
Figure FDA0003612338950000044
wherein the content of the first and second substances,
Figure FDA0003612338950000045
and
Figure FDA0003612338950000046
respectively representing the electric energy price and the frequency modulation auxiliary service price of the t time period reported by the generator set g,
Figure FDA0003612338950000047
and
Figure FDA0003612338950000048
respectively represents the generated energy and the frequency modulation auxiliary service supply in the t time period reported by the generator set g, NG represents the number of the generator sets, NT represents the number of the time period,
Figure FDA0003612338950000049
and
Figure FDA00036123389500000410
respectively 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,
Figure FDA00036123389500000411
representing the predicted value of the load of node b in the t-th period, PFM，minIndicating a minimum fm auxiliary service requirement,
Figure FDA00036123389500000412
and
Figure FDA00036123389500000413
respectively represents the minimum power generation amount and the maximum power generation amount of the generator set g,
Figure FDA00036123389500000414
and
Figure FDA00036123389500000415
respectively representing the minimum climbing capacity and the maximum climbing capacity of the generator set g.
8. 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 reported capacity decision model and a second electricity purchasing cost based on a non-reported capacity decision model when the electric energy reporting data are abnormal or the frequency modulation auxiliary service reporting data are abnormal;
the second judgment 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 larger than or equal to a preset threshold value;
the declared capacity decision model is as follows:
Figure FDA0003612338950000051
Figure FDA0003612338950000052
Figure FDA0003612338950000053
Figure FDA0003612338950000054
Figure FDA0003612338950000055
Figure FDA0003612338950000056
Figure FDA0003612338950000057
Figure FDA0003612338950000058
wherein the content of the first and second substances,
Figure FDA0003612338950000059
and
Figure FDA00036123389500000510
respectively represents the electric energy price and the frequency modulation auxiliary service price of the t time period reported by the generator set g,
Figure FDA00036123389500000511
and
Figure FDA00036123389500000512
respectively represents the generated energy and the frequency modulation auxiliary service supply in the t time period reported by the generator set g, NG represents the number of the generator sets, NT represents the number of the time period,
Figure FDA00036123389500000513
and
Figure FDA00036123389500000514
respectively representing the lower limit value and the upper limit value of the operating section os, GTDFb，os、GTDFb(g)，osIndicating the distribution factor of the credit transfer between the node b and the operating profile os, NB indicating the number of nodes,
Figure FDA00036123389500000515
representing the predicted value of the load of node b in the t-th period, PFM，minIndicating a minimum fm auxiliary service requirement,
Figure FDA00036123389500000516
and
Figure FDA00036123389500000517
respectively represents the minimum power generation amount and the maximum power generation amount of the generator set g,
Figure FDA0003612338950000061
and
Figure FDA0003612338950000062
respectively representing the minimum climbing capacity and the maximum climbing capacity of the generator set g,
Figure FDA0003612338950000063
the power generation amount reported by the generator set g is shown,
Figure FDA0003612338950000064
and the frequency modulation auxiliary service supply reported by the generator set g is represented.
9. 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 7.
CN201911178332.8A 2019-11-27 2019-11-27 Power capacity retention detection method, device and equipment Active CN110942250B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911178332.8A CN110942250B (en) 2019-11-27 2019-11-27 Power capacity retention detection method, device and equipment

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911178332.8A CN110942250B (en) 2019-11-27 2019-11-27 Power capacity retention detection method, device and equipment

Publications (2)

Publication Number Publication Date
CN110942250A CN110942250A (en) 2020-03-31
CN110942250B true CN110942250B (en) 2022-07-12

Family

ID=69909043

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911178332.8A Active CN110942250B (en) 2019-11-27 2019-11-27 Power capacity retention detection method, device and equipment

Country Status (1)

Country Link
CN (1) CN110942250B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111476493B (en) * 2020-04-10 2022-07-29 中国南方电网有限责任公司 Method and device for detecting economic persistence behavior of unit and computer equipment
CN112184309A (en) * 2020-05-11 2021-01-05 国网宁夏电力有限公司 Capacity retention determination method and device for power spot market

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107482689A (en) * 2017-08-07 2017-12-15 国电南瑞科技股份有限公司 A kind of lean frequency modulation unserved capacity optimal method
CN108022178A (en) * 2017-11-16 2018-05-11 清华大学 The electricity that market supply and demand is adjusted based on remaining supply index goes out clearing method and device
CN109598399A (en) * 2018-05-11 2019-04-09 国家电网公司 A kind of moving electric power transaction risk appraisal procedure based on step analysis
CN110378534A (en) * 2019-07-22 2019-10-25 中国南方电网有限责任公司 Frequency modulation under electricity market combines clearing method, device and equipment out with electric energy

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10169726B2 (en) * 2014-02-06 2019-01-01 Siemens Industry, Inc. Systems, methods and apparatus for improved operation of electricity markets

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107482689A (en) * 2017-08-07 2017-12-15 国电南瑞科技股份有限公司 A kind of lean frequency modulation unserved capacity optimal method
CN108022178A (en) * 2017-11-16 2018-05-11 清华大学 The electricity that market supply and demand is adjusted based on remaining supply index goes out clearing method and device
CN109598399A (en) * 2018-05-11 2019-04-09 国家电网公司 A kind of moving electric power transaction risk appraisal procedure based on step analysis
CN110378534A (en) * 2019-07-22 2019-10-25 中国南方电网有限责任公司 Frequency modulation under electricity market combines clearing method, device and equipment out with electric energy

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
于壮状 等.基于电能-备用联合优化的容量持留监测方法.《电气安全》.2018,第37卷(第11期),文章第2节，图1. *
基于电能-备用联合优化的容量持留监测方法;于壮状 等;《电气安全》;20180605;第37卷(第11期);文章第2节，图1 *

Also Published As

Publication number Publication date
CN110942250A (en) 2020-03-31

Similar Documents

Publication Publication Date Title
US9194899B2 (en) Utility network and revenue assurance
CN110942250B (en) Power capacity retention detection method, device and equipment
Felderer et al. Integrating manual and automatic risk assessment for risk-based testing
Jiang et al. Does energy efficiency increase at the expense of output performance: Evidence from manufacturing firms in Jiangsu province, China
CN105588995A (en) Line-loss anomaly detection method for electric power measurement automation system
CN111008193A (en) Data cleaning and quality evaluation method and system
CN105005575A (en) Quick developing interface method for enterprise intelligent prediction
CN111080133A (en) Photovoltaic power station financing risk assessment method, system, equipment and storage medium
CN114548650A (en) Power grid asset life cycle cost analysis method and system
CN109215816A (en) Steam generator heat-transfer pipe integrity assessment method, system and terminal device
CN108121691A (en) Manage the method and system of project of transmitting and converting electricity bill of quantities
CN107784578B (en) Bank foreign exchange data supervision method and device
Panwar et al. New method to find the maximum number of faults by analyzing reliability and reusability in component-based software
Yue et al. An enhanced probabilistic contingency analysis for development of risk-informed transmission planning
Johansson et al. Tracking degradation in software product lines through measurement of design rule violations
CN112434967A (en) Electric power market online monitoring method and system for market force control
SpA et al. Regulation of continuity of supply in the electricity sector and cost of energy not supplied
CN113805564A (en) Equipment fault early warning and diagnosis system based on CEP
Antinyan Monitoring evolution of code complexity and magnitude of changes
CN111798102A (en) Data supervision method, system, computer equipment and readable storage medium
Curley Power plant performance indices in new market environment: IEEE standard 762 working group activities and GADS database
CN110727910A (en) Method and system for realizing risk prediction and business linkage of reading and checking
de Oliveira et al. A Study on the Anatomy of Losses in Electric Energy Distribution: Un estudio sobre la anatomía de las pérdidas en la distribución de energía eléctrica
CN111598385B (en) Method and system for determining electricity consumption behavior based on fuzzy analytic hierarchy process and comprehensive evaluation
Lata et al. An approach to optimize the cost of software quality assurance analysis

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant