CN109816398A - A kind of method, apparatus and medium for screening Power Generation collusion behavior - Google Patents
A kind of method, apparatus and medium for screening Power Generation collusion behavior Download PDFInfo
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Abstract
The invention discloses a kind of method, apparatus and medium for screening Power Generation collusion behavior, and method includes: the offer curve for obtaining each Power Generation；Offer curve reflects the relationship of the quotation capacity and electricity price of unit；Offer curve carries out segment quantization and mark is changed；Wherein, the quotation capacity of unit is on the basis of unit active volume, and electricity price is on the basis of the market clearing price upper limit；According to the difference integral formula of setting, the characterization vector of the offer curve after calculating segment quantization and mark change, i.e., the quotation sequence of each Power Generation；The degree of association of the quotation sequence of each Power Generation between any two is calculated, and degree of association matrix is constructed according to the degree of association；Clustering is carried out to the degree of association matrix, filters out the Power Generation group that the degree of association is greater than or equal to degree of association threshold value；Collection and the Power Generation group correlative factor, and collusion analysis, output analysis result are carried out to the Power Generation group according to correlative factor.The present invention can accurately screen Power Generation collusion behavior in power spot market transaction.
Description
Technical field
The present invention relates to big data digging technology field more particularly to a kind of methods for screening Power Generation collusion behavior, dress
It sets and medium.
Background technique
With the propulsion of electricity market reform, power spot market construction is gradually brought into schedule.Currently, has south
It is explored as the construction that first electric power pilot opens power spot market in 8 areas such as (being started to walk using Guangdong).
Under the environment of spot market, most possibly there is Power Generation collusion in Day-ahead electricity market, market member passes through collusion
Behavior improves the market influence to the significantly more efficient exposure for reaping staggering profits and avoiding single main body high quoting.Institute
With, the central task of various countries' Electric Power Market Regulation is to prevent market member abuse of market power, is rigged the market, damage other markets at
The interests of member, the validity for competition of maintaining market.Currently, external power spot market is stable, be unable to do without from multi-angle into
The contribution of quotations field force detection, but since the spot market that China will carry out not fully is identical to external electricity market.
So market forces detect on the basis of foreign successful experience, it will also be according to the practical spot market operation rule in China
Formulate efficient market power surveillance and control measure.China is in spot market operation initial stage, trade variety is few, trading rules are not multiple
In the case where miscellaneous, collusion behavior can bring very big influence to the normal operation of electricity market, so needing monitoring appropriate true
Protect the mutual effective competition of market member.
However, in the research and practice to the prior art, it was found by the inventors of the present invention that failing to mention in the prior art
The technological means accurately screened for Power Generation collusion behavior in trading to power spot market.
Summary of the invention
Technical problem to be solved by the present invention lies in, provide it is a kind of screen Power Generation collusion behavior method, apparatus and
Medium can accurately screen Power Generation collusion behavior in power spot market transaction.
In order to solve the above-mentioned technical problem, the invention proposes a kind of methods for screening Power Generation collusion behavior, comprising:
Obtain the offer curve of each Power Generation；The relationship of the quotation capacity and electricity price of the offer curve reflection unit；
Segment quantization is carried out to the offer curve and mark is changed；Wherein, the quotation capacity of the unit is available with unit
On the basis of capacity, the electricity price is on the basis of the market clearing price upper limit；
According to the difference integral formula of setting, calculates segment quantization and mark the characterization vector of the offer curve after changing, i.e.,
The quotation sequence of each Power Generation；
The degree of association of the quotation sequence of each Power Generation between any two is calculated, and degree of association square is constructed according to the degree of association
Battle array；
Clustering is carried out to the degree of association matrix, filters out the Power Generation that the degree of association is greater than or equal to degree of association threshold value
Group；
Collection and the Power Generation group correlative factor, and collusion analysis is carried out to the Power Generation group according to the correlative factor,
Output analysis result.
Further, segment quantization is carried out to the offer curve and mark is changed, specific:
The capacity of the offer curve is uniformly divided into n sections, uses QaIt indicates,
Wherein, AC is unit active volume, and n is the segments of quotation capacity；
The per unit value P of each section of electricity priceaIt indicates, the per unit value P of system annual rate for incorporation into the power networkasIt indicates,
Wherein, PsFor power grid annual rate for incorporation into the power network, unit is member/MWh；P is each section of electricity price of Bidding, and unit is
Member/MWh；PulFor the ceiling price of electricity market, unit is member/MWh.
Further, the offer curve according to the difference integral formula of the setting, after calculating segment quantization and mark change
Characterization vector, i.e., the quotation sequence of each Power Generation specifically includes the characterization vector for calculating the Bidding curve of a certain period
With the characterization vector of the Bidding curve of one day all period；Wherein,
The difference integral formula are as follows:
Wherein, k represents kth time period, and i represents i-th section after offer curve segment quantization；
Calculate the characterization vector of the Bidding curve of a certain period:
Offer curve after segment quantization and mark are changed is converted into discrete 1 × n dimension group, uses Px,kIndicate original offer
Relationship between curve and average rate for incorporation into the power network, x are x-th of Power Generation,
Px,k=[Px,k,1,Px,k,2,...,Px,k,n-1,Px,k,n]；
With vector PkCharacterize the offer curve of certain unit of kth time period；
Calculate the characterization vector of the Bidding curve of one day all period:
With per half an hour for a period, whole day is divided into 48 periods, carries out difference integral to 48 periods of certain day
Processing, then convert 1 × 48n of array for the P value of above-mentioned 48 periods, and use PxIndicate that offer curve deviates average electricity in one day
The variation of valence,
Px=[P1,P2,...,Pn,...,P2n,...,P48n], x=1,2 ..., 48n.
Further, the calculation of relationship degree formula are as follows:
Wherein, SabFor the quotation sequence P of a-th of Power Generationx,aWith the quotation sequence P of b-th of Power Generationx,bCovariance；
Wherein, x is the element sum that one day offer curve characterizes vector, and x=48n, n are the segmentation of offer curve capacity
Number.
Further, the correlative factor includes electricity market operating mechanism, the market concentration, demand elasticity, transmission of electricity resistance
Plug, relation between supply and demand and advance and retreat barrier.
Further, the degree of association is 0.95.
The invention also provides a kind of devices for screening Power Generation collusion behavior, comprising:
Basic data obtains module, for obtaining the offer curve of each Power Generation；The report of the offer curve reflection unit
The relationship of valence capacity and electricity price；
Segment quantization and mark change module, for changing to offer curve progress segment quantization and mark；Wherein, described
The quotation capacity of unit is on the basis of unit active volume, and the electricity price is on the basis of the market clearing price upper limit；
Vector calculation module is characterized, for the difference integral formula according to setting, after calculating segment quantization and mark change
The characterization vector of offer curve, i.e., the quotation sequence of each Power Generation；
Degree of association matrix constructs module, for calculating the degree of association of the quotation sequence of each Power Generation between any two, and according to
The degree of association constructs degree of association matrix；
Cluster Analysis module filters out the degree of association and is greater than or equal to for carrying out clustering to the degree of association matrix
The Power Generation group of degree of association threshold value；
Output module, for collection and the Power Generation group correlative factor, and according to the correlative factor to the Power Generation group
Carry out collusion analysis, output analysis result.
Further, the segment quantization and mark are changed module and are changed to offer curve progress segment quantization and mark,
It is specific:
The capacity of the offer curve is uniformly divided into n sections, uses QaIt indicates,
Wherein, AC is unit active volume, and n is the segments of quotation capacity；
The per unit value P of each section of electricity priceaIt indicates, the per unit value P of system annual rate for incorporation into the power networkasIt indicates,
Wherein, PsFor power grid annual rate for incorporation into the power network, unit is member/MWh；P is each section of electricity price of Bidding, and unit is
Member/MWh；PulFor the ceiling price of electricity market, unit is member/MWh.
Further, the characterization vector calculation module calculates segment quantization and mark according to the difference integral formula of setting
The characterization vector of offer curve after changing, i.e., the quotation sequence of each Power Generation specifically include the unit report for calculating a certain period
The characterization vector of the Bidding curve of characterization one day all period of vector sum of valence curve；Wherein,
The difference integral formula are as follows:
Wherein, k represents kth time period, and i represents i-th section after offer curve segment quantization；
Calculate the characterization vector of the Bidding curve of a certain period:
Offer curve after segment quantization and mark are changed is converted into discrete 1 × n dimension group, uses Px,kIndicate original offer
Relationship between curve and average rate for incorporation into the power network, x are x-th of Power Generation,
Px,k=[Px,k,1,Px,k,2,...,Px,k,n-1,Px,k,n]；
With vector PkCharacterize the offer curve of certain unit of kth time period；
Calculate the characterization vector of the Bidding curve of one day all period:
With per half an hour for a period, whole day is divided into 48 periods, carries out difference integral to 48 periods of certain day
Processing, then convert 1 × 48n of array for the P value of above-mentioned 48 periods, and use PxIndicate that offer curve deviates average electricity in one day
The variation of valence,
Px=[P1,P2,...,Pn,...,P2n,...,P48n], x=1,2 ..., 48n.
The invention also provides a kind of computer readable storage medium, the computer readable storage medium includes storage
Computer program, wherein equipment where controlling the computer readable storage medium in computer program operation executes
The method as described above for screening Power Generation collusion behavior.
The present invention can accurately screen Power Generation collusion behavior in power spot market transaction.
Detailed description of the invention
Fig. 1 is a kind of flow diagram of method for screening Power Generation collusion behavior provided by the invention；
Fig. 2 is the offer curve segment quantization signal in a kind of method for screening Power Generation collusion behavior provided by the invention
Figure；
Fig. 3 is a kind of structural schematic diagram of device for screening Power Generation collusion behavior provided by the invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
First aspect.
Please refer to Fig. 1-2.A kind of method for screening Power Generation collusion behavior provided in this embodiment can be by relevant clothes
Be engaged in device execute, and hereafter using server as executing subject for be illustrated.
The method for screening Power Generation collusion behavior, at least includes the following steps S1~S6.
S1, the offer curve for obtaining each Power Generation.The relationship of the quotation capacity and electricity price of the offer curve reflection unit.
In specific embodiment, needs to obtain as the basic data for screening Power Generation collusion behavior, specifically include
The offer curve (the quotation capacity of unit, electricity price) of Power Generation, unit active volume, the ceiling price of electricity market, power grid year
(or upper one year) average rate for incorporation into the power network, each section of electricity price of Bidding.
S2, segment quantization and mark change are carried out to the offer curve.Wherein, the quotation capacity of the unit can with unit
On the basis of capacity, the electricity price is on the basis of the market clearing price upper limit.
Specifically, the capacity of the offer curve is uniformly divided into n sections, Q is usedaIt indicates:
Wherein, AC is unit active volume, and n is the segments of quotation capacity.
The per unit value P of each section of electricity priceaIt indicates, the per unit value P of system annual rate for incorporation into the power networkasIt indicates:
Wherein, PsFor power grid annual rate for incorporation into the power network, unit is member/MWh；P is each section of electricity price of Bidding, and unit is
Member/MWh；PulFor the ceiling price of electricity market, unit is member/MWh.
It should be noted that the offer curve of each Power Generation is carried out to mark change, the quotation capacity of unit is available with unit
On the basis of capacity, electricity price is on the basis of the market clearing price upper limit.Changed by establishing mark, between the quotation of different capabilities unit
There is comparability.
Wherein, the schematic diagram of offer curve segment quantization is as shown in Figure 2.
In specific embodiment,
S3, the difference integral formula according to setting, the characterization vector of the offer curve after calculating segment quantization and mark change,
The quotation sequence of i.e. each Power Generation.
The characterization vector of offer curve includes that characterization vector sum one day for calculating the Bidding curve of a certain period owns
The characterization vector of the Bidding curve of period.
The difference integral formula are as follows:
Wherein, k represents kth time period, and i represents i-th section after offer curve segment quantization.
Calculate the characterization vector of the Bidding curve of a certain period:
Offer curve after segment quantization and mark are changed is converted into discrete 1 × n dimension group, uses Px,kIndicate original offer
Relationship between curve and average rate for incorporation into the power network, x are x-th of Power Generation:
Px,k=[Px,k,1,Px,k,2,...,Px,k,n-1,Px,k,n]；
With vector PkCharacterize the offer curve of certain unit of kth time period.
It should be noted that this is aobvious as shown in Fig. 2, ladder-like offer curve is available to be converted into discrete 1 × n dimension group
The dimension n (segments) of the array is so increased, to more efficiently reflect between original offer curve and average rate for incorporation into the power network
Relationship.
Calculate the characterization vector of the Bidding curve of one day all period:
With per half an hour for a period, whole day is divided into 48 periods, carries out difference integral to 48 periods of certain day
Processing, then convert 1 × 48n of array for the P value of above-mentioned 48 periods, and use PxIndicate that offer curve deviates average electricity in one day
The variation of valence:
Px=[P1,P2,...,Pn,...,P2n,...,P48n], x=1,2 ..., 48n.
It should be noted that in practical application, PxIt can be very good offer curve in reflection one day and deviate average electricity price
Variation can also be used to the variation for indicating rate for incorporation into the power network offer curve in one day.
S4, the degree of association of the quotation sequence of each Power Generation between any two is calculated, and the degree of association is constructed according to the degree of association
Matrix.
Wherein, the calculation of relationship degree formula are as follows:
Wherein, SabFor the quotation sequence P of a-th of Power Generationx,aWith the quotation sequence P of b-th of Power Generationx,bCovariance.
Wherein, x is the element sum that one day offer curve characterizes vector, and x=48n, n are the segmentation of offer curve capacity
Number.
S5, clustering is carried out to the degree of association matrix, filters out the hair that the degree of association is greater than or equal to degree of association threshold value
Electric business group.
It is understood that a possibility that market forces are exercised by alliance between the high each Power Generation of the quotation sequence degree of correlation compared with
Greatly.Therefore, the degree of association can be set as 0.95, but is not limited to 0.95.
S6, collection and the Power Generation group correlative factor, and collusion point is carried out to the Power Generation group according to the correlative factor
Analysis, output analysis result.
Wherein, correlative factor includes at least electricity market operating mechanism, the market concentration, demand elasticity, Congestion, confession
Need relationship and advance and retreat barrier.
In specific embodiment, clustering is carried out according to the degree of association matrix between Power Generation, it is considered that close
Connection degree correlation between 0.95 or more Power Generation is high, and clustering finds out these Power Generation groups, it is believed that these power generations
There are the possibility of alliance for quotient's group.
After carrying out quantitative analysis, it is also necessary to carry out the correlative factor of collusion to Power Generation and motivation is analyzed, wrap
It includes to influence factors such as the market concentration, demand elasticity, Congestion, relation between supply and demand, electricity market operating mechanism, advance and retreat barriers
Analysis, and analysis Power Generation between whether there is joint investment some or certain several power plant the case where, if there are interests
Contact and cooperation agreement, if it does, the basis that this kind of Power Generation just has bigger collusion to rig the market, finally determines whether to deposit
In collusion.
Wherein, whether the analysis result of output should be comprising there is collusion behavior of the power plant in spot market, which
There is collusion behavior in power plant.
The present embodiment by obtaining a large amount of basic data, the sample size including Power Generation, required related data and
Correlative factor, from whole and many-sided examination that Power Generations a large amount of in power spot market are carried out with collusion behavior, in certain journey
Also it is avoided that part Power Generation is screened in omission on degree.And electricity can accurately be screened by the degree of association matrix of clustering Power Generation
Power Generation collusion behavior in the transaction of power spot market, it is ensured that a wide range of standard screened Power Generation collusion behavior and improve analysis result
True property.
Second aspect.
Please refer to Fig. 3.The present embodiment also proposed a kind of device for screening Power Generation collusion behavior, comprising:
Basic data obtains module 21, for obtaining the offer curve of each Power Generation.The offer curve reflection unit
The relationship of quotation capacity and electricity price.
In specific embodiment, needs to obtain as the basic data for screening Power Generation collusion behavior, specifically include
The offer curve (the quotation capacity of unit, electricity price) of Power Generation, unit active volume, the ceiling price of electricity market, power grid year
(or upper one year) average rate for incorporation into the power network, each section of electricity price of Bidding.
Segment quantization and mark change module 22, for changing to offer curve progress segment quantization and mark.Wherein, institute
The quotation capacity of unit is stated on the basis of unit active volume, the electricity price is on the basis of the market clearing price upper limit.
Specifically, the capacity of the offer curve is uniformly divided into n sections, Q is usedaIt indicates:
Wherein, AC is unit active volume, and n is the segments of quotation capacity.
The per unit value P of each section of electricity priceaIt indicates, the per unit value P of system annual rate for incorporation into the power networkasIt indicates:
Wherein, PsFor power grid annual rate for incorporation into the power network, unit is member/MWh；P is each section of electricity price of Bidding, and unit is
Member/MWh；PulFor the ceiling price of electricity market, unit is member/MWh.
It should be noted that the offer curve of each Power Generation is carried out to mark change, the quotation capacity of unit is available with unit
On the basis of capacity, electricity price is on the basis of the market clearing price upper limit.Changed by establishing mark, between the quotation of different capabilities unit
There is comparability.
Wherein, the schematic diagram of offer curve segment quantization is as shown in Figure 2.
In specific embodiment,
Vector calculation module 23 is characterized, for the difference integral formula according to setting, after calculating segment quantization and mark change
Offer curve characterization vector, i.e., the quotation sequence of each Power Generation.
The characterization vector of offer curve includes that characterization vector sum one day for calculating the Bidding curve of a certain period owns
The characterization vector of the Bidding curve of period.
The difference integral formula are as follows:
Wherein, k represents kth time period, and i represents i-th section after offer curve segment quantization.
Calculate the characterization vector of the Bidding curve of a certain period:
Offer curve after segment quantization and mark are changed is converted into discrete 1 × n dimension group, uses Px,kIndicate original offer
Relationship between curve and average rate for incorporation into the power network, x are x-th of Power Generation:
Px,k=[Px,k,1,Px,k,2,...,Px,k,n-1,Px,k,n]；
With vector PkCharacterize the offer curve of certain unit of kth time period.
It should be noted that this is aobvious as shown in Fig. 2, ladder-like offer curve is available to be converted into discrete 1 × n dimension group
The dimension n (segments) of the array is so increased, to more efficiently reflect between original offer curve and average rate for incorporation into the power network
Relationship.
Calculate the characterization vector of the Bidding curve of one day all period:
With per half an hour for a period, whole day is divided into 48 periods, carries out difference integral to 48 periods of certain day
Processing, then convert 1 × 48n of array for the P value of above-mentioned 48 periods, and use PxIndicate that offer curve deviates average electricity in one day
The variation of valence:
Px=[P1,P2,...,Pn,...,P2n,...,P48n], x=1,2 ..., 48n.
It should be noted that in practical application, PxIt can be very good offer curve in reflection one day and deviate average electricity price
Variation can also be used to the variation for indicating rate for incorporation into the power network offer curve in one day.
Degree of association matrix constructs module 24, for calculating the degree of association of the quotation sequence of each Power Generation between any two, and root
Degree of association matrix is constructed according to the degree of association.
Wherein, the calculation of relationship degree formula are as follows:
Wherein, SabFor the quotation sequence P of a-th of Power Generationx,aWith the quotation sequence P of b-th of Power Generationx,bCovariance.
Wherein, x is the element sum that one day offer curve characterizes vector, and x=48n, n are the segmentation of offer curve capacity
Number.
Cluster Analysis module 25 filters out the degree of association and is greater than or waits for carrying out clustering to the degree of association matrix
In the Power Generation group of degree of association threshold value.
It is understood that a possibility that market forces are exercised by alliance between the high each Power Generation of the quotation sequence degree of correlation compared with
Greatly.Therefore, the degree of association can be set as 0.95, but is not limited to 0.95.
Output module 26, for collection and the Power Generation group correlative factor, and according to the correlative factor to the Power Generation
Group carries out collusion analysis, output analysis result.
Wherein, correlative factor includes at least electricity market operating mechanism, the market concentration, demand elasticity, Congestion, confession
Need relationship and advance and retreat barrier.
In specific embodiment, clustering is carried out according to the degree of association matrix between Power Generation, it is considered that close
Connection degree correlation between 0.95 or more Power Generation is high, and clustering finds out these Power Generation groups, it is believed that these power generations
There are the possibility of alliance for quotient's group.
After carrying out quantitative analysis, it is also necessary to carry out the correlative factor of collusion to Power Generation and motivation is analyzed, wrap
It includes to influence factors such as the market concentration, demand elasticity, Congestion, relation between supply and demand, electricity market operating mechanism, advance and retreat barriers
Analysis, and analysis Power Generation between whether there is joint investment some or certain several power plant the case where, if there are interests
Contact and cooperation agreement, if it does, the basis that this kind of Power Generation just has bigger collusion to rig the market, finally determines whether to deposit
In collusion.
Wherein, whether the analysis result of output should be comprising there is collusion behavior of the power plant in spot market, which
There is collusion behavior in power plant.
The present embodiment by obtaining a large amount of basic data, the sample size including Power Generation, required related data and
Correlative factor, from whole and many-sided examination that Power Generations a large amount of in power spot market are carried out with collusion behavior, in certain journey
Also it is avoided that part Power Generation is screened in omission on degree.And electricity can accurately be screened by the degree of association matrix of clustering Power Generation
Power Generation collusion behavior in the transaction of power spot market, it is ensured that a wide range of standard screened Power Generation collusion behavior and improve analysis result
True property.
The present embodiment also proposed a kind of computer readable storage medium, and the computer readable storage medium includes storage
Computer program, wherein the computer program operation when control the computer readable storage medium where equipment hold
The row method as described above for screening Power Generation collusion behavior.
The above is a preferred embodiment of the present invention, it is noted that for those skilled in the art
For, without departing from the principle of the present invention, several improvement and deformations can also be made, these improvement and deformations are also considered as
Protection scope of the present invention.
Those of ordinary skill in the art will appreciate that realizing all or part of the process in above-described embodiment method, being can be with
Relevant hardware is instructed to complete by computer program, the program can be stored in a computer-readable storage medium
In, the program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein, the storage medium can be magnetic
Dish, CD, read-only memory (Read-Only Memory, ROM) or random access memory (Random Access
Memory, RAM) etc..
Claims (10)
1. a kind of method for screening Power Generation collusion behavior characterized by comprising
Obtain the offer curve of each Power Generation；The relationship of the quotation capacity and electricity price of the offer curve reflection unit；
Segment quantization is carried out to the offer curve and mark is changed；Wherein, the quotation capacity of the unit is with unit active volume
On the basis of, the electricity price is on the basis of the market clearing price upper limit；
According to the difference integral formula of setting, the characterization vector of the offer curve after calculating segment quantization and mark change, i.e., each hair
The quotation sequence of electric business；
The degree of association of the quotation sequence of each Power Generation between any two is calculated, and degree of association matrix is constructed according to the degree of association；
Clustering is carried out to the degree of association matrix, filters out the Power Generation group that the degree of association is greater than or equal to degree of association threshold value；
Collection and the Power Generation group correlative factor, and collusion analysis, output are carried out to the Power Generation group according to the correlative factor
Analyze result.
2. it is according to claim 1 screen Power Generation collusion behavior method, which is characterized in that the offer curve into
Row segment quantization and mark are changed, specific:
The capacity of the offer curve is uniformly divided into n sections, uses QaIt indicates,
Wherein, AC is unit active volume, and n is the segments of quotation capacity；
The per unit value P of each section of electricity priceaIt indicates, the per unit value P of system annual rate for incorporation into the power networkasIt indicates,
Wherein, PsFor power grid annual rate for incorporation into the power network, unit is member/MWh；P be Bidding each section of electricity price, unit be member/
MWh；PulFor the ceiling price of electricity market, unit is member/MWh.
3. the method according to claim 1 for screening Power Generation collusion behavior, which is characterized in that the difference according to setting
It is worth integral formula, the characterization vector of the offer curve after calculating segment quantization and mark change, i.e., the quotation sequence of each Power Generation, tool
Body includes the characterization for calculating the Bidding curve of characterization one day all period of vector sum of Bidding curve of a certain period
Vector；Wherein,
The difference integral formula are as follows:
Wherein, k represents kth time period, and i represents i-th section after offer curve segment quantization；
Calculate the characterization vector of the Bidding curve of a certain period:
Offer curve after segment quantization and mark are changed is converted into discrete 1 × n dimension group, uses Px,kIndicate original offer curve
With the relationship between average rate for incorporation into the power network, x is x-th of Power Generation,
Px,k=[Px,k,1,Px,k,2,...,Px,k,n-1,Px,k,n]；
With vector PkCharacterize the offer curve of certain unit of kth time period；
Calculate the characterization vector of the Bidding curve of one day all period:
With per half an hour for a period, whole day is divided into 48 periods, carries out difference Integral Processing to 48 periods of certain day,
1 × 48n of array then is converted by the P value of above-mentioned 48 periods, and uses PxIndicate that offer curve deviates average electricity price in one day
Variation,
Px=[P1,P2,...,Pn,...,P2n,...,P48n], x=1,2 ..., 48n.
4. the method according to claim 1 for screening Power Generation collusion behavior, which is characterized in that the calculation of relationship degree is public
Formula are as follows:
Wherein, SabFor the quotation sequence P of a-th of Power Generationx,aWith the quotation sequence P of b-th of Power Generationx,bCovariance；
Wherein, x is the element sum that one day offer curve characterizes vector, and x=48n, n are the segments of offer curve capacity.
5. the method according to claim 1 for screening Power Generation collusion behavior, which is characterized in that the correlative factor includes
Electricity market operating mechanism, the market concentration, demand elasticity, Congestion, relation between supply and demand and advance and retreat barrier.
6. the method according to claim 1 for screening Power Generation collusion behavior, which is characterized in that the degree of association is
0.95。
7. a kind of device for screening Power Generation collusion behavior characterized by comprising
Basic data obtains module, for obtaining the offer curve of each Power Generation；The quotation of the offer curve reflection unit is held
The relationship of amount and electricity price；
Segment quantization and mark change module, for changing to offer curve progress segment quantization and mark；Wherein, the unit
Quotation capacity on the basis of unit active volume, the electricity price is on the basis of the market clearing price upper limit；
Vector calculation module is characterized, the quotation for the difference integral formula according to setting, after calculating segment quantization and mark change
The characterization vector of curve, i.e., the quotation sequence of each Power Generation；
Degree of association matrix constructs module, for calculating the degree of association of the quotation sequence of each Power Generation between any two, and according to described
The degree of association constructs degree of association matrix；
Cluster Analysis module filters out the degree of association and is greater than or equal to association for carrying out clustering to the degree of association matrix
Spend the Power Generation group of threshold value；
Output module carries out the Power Generation group for collection and the Power Generation group correlative factor, and according to the correlative factor
Collusion analysis, output analysis result.
8. the device according to claim 7 for screening Power Generation collusion behavior, which is characterized in that the segment quantization and mark
Change module and segment quantization and mark change are carried out to the offer curve, specific:
The capacity of the offer curve is uniformly divided into n sections, uses QaIt indicates,
Wherein, AC is unit active volume, and n is the segments of quotation capacity；
The per unit value P of each section of electricity priceaIt indicates, the per unit value P of system annual rate for incorporation into the power networkasIt indicates,
Wherein, PsFor power grid annual rate for incorporation into the power network, unit is member/MWh；P be Bidding each section of electricity price, unit be member/
MWh；PulFor the ceiling price of electricity market, unit is member/MWh.
9. the device according to claim 7 for screening Power Generation collusion behavior, which is characterized in that the characterization vector calculates
Difference integral formula of the module according to setting, the characterization vector of the offer curve after calculating segment quantization and mark change, i.e., each hair
The quotation sequence of electric business specifically includes the machine of the characterization one day all period of vector sum for the Bidding curve for calculating a certain period
The characterization vector of group offer curve；Wherein,
The difference integral formula are as follows:
Wherein, k represents kth time period, and i represents i-th section after offer curve segment quantization；
Calculate the characterization vector of the Bidding curve of a certain period:
Offer curve after segment quantization and mark are changed is converted into discrete 1 × n dimension group, uses Px,kIndicate original offer curve
With the relationship between average rate for incorporation into the power network, x is x-th of Power Generation,
Px,k=[Px,k,1,Px,k,2,...,Px,k,n-1,Px,k,n]；
With vector PkCharacterize the offer curve of certain unit of kth time period；
Calculate the characterization vector of the Bidding curve of one day all period:
With per half an hour for a period, whole day is divided into 48 periods, carries out difference Integral Processing to 48 periods of certain day,
1 × 48n of array then is converted by the P value of above-mentioned 48 periods, and uses PxIndicate that offer curve deviates average electricity price in one day
Variation,
Px=[P1,P2,...,Pn,...,P2n,...,P48n], x=1,2 ..., 48n.
10. a kind of computer readable storage medium, which is characterized in that the computer readable storage medium includes the calculating of storage
Machine program, wherein equipment where controlling the computer readable storage medium in computer program operation is executed as weighed
Benefit require 1 to 6 described in screen Power Generation collusion behavior method.
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| CN112348274A (en) * | 2020-11-17 | 2021-02-09 | 国家电网有限公司 | Peak shaving-considered power spot market clearing method, system, equipment and medium |
| CN112348274B (en) * | 2020-11-17 | 2023-11-24 | 国家电网有限公司 | Electric power spot market clearing method, system, equipment and medium considering peak shaving |
| CN113011472B (en) * | 2021-02-26 | 2023-09-01 | 广东电网有限责任公司电力调度控制中心 | Multi-section electric power quotation curve similarity judging method and device |
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| CN113344589B (en) * | 2021-05-12 | 2022-10-21 | 兰州理工大学 | Intelligent identification method for collusion behavior of power generation enterprise based on VAEGMM model |
| CN113344589A (en) * | 2021-05-12 | 2021-09-03 | 兰州理工大学 | Intelligent identification method for collusion behavior of power generation enterprise based on VAEGMM model |
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