Settlement data verification method and system for electricity transaction market

Abstract

The invention relates to the technical field of electric power settlement, solves the technical problem of low accuracy of data caused by large time span during settlement of an electric power transaction market in the prior art, and particularly relates to a settlement data verification method of the electric power transaction market. The method comprises the following steps: S1, calculating a pulse difference value delta a of a to-be-detected ammeter according to standard parameters of a standard ammeter, S2, calculating an error rate alpha of the to-be-detected ammeter according to the pulse difference value delta a, and judging whether the to-be-detected ammeter is a qualified ammeter or not, if so, determining the to-be-detected ammeter as a qualified ammeter and entering S3, and if not, determining the to-be-detected ammeter as an unqualified ammeter, and entering S3. According to the method, the long time period is equally divided into the small time periods, so that the accuracy of the electric quantity data is improved, the power generation end and the user end can know the condition of large fluctuation of the electric quantity, and more serious consequences caused by the fact that hidden dangers accompanied by the large fluctuation of the electric quantity are not checked for a long time are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of power settlement, and in particular to a method and system for verifying settlement data in a power trading market. Background Art

[0002] The electricity trading market generally settles in two ways: one is to settle according to the system marginal electricity price, and the other is to settle according to the actual declared price of the unit. In the process of settling the actual declared price of the unit, the electricity is generally settled based on the data in the system. The deviation electricity calculated at the time of settlement is generally the total or average electricity within the month. This settlement method has a large time span and low accuracy. During the statistical process, it is impossible to verify the electricity consumption in a smaller time period. Once the electricity consumption in a certain time period fluctuates greatly, this large fluctuation may be accompanied by certain risks. However, the settlement end and the user cannot know the electricity consumption and potential risks, which makes it easy for hidden dangers to occur during the electricity use process. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides a method and system for verifying settlement data in an electricity trading market, which solves the technical problem of low accuracy of data due to a large time span during settlement in the electricity trading market, and achieves the purpose of dividing a long time period into small time periods and performing electricity measurement and verification to improve accuracy.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a settlement data verification method for an electricity trading market, the method comprising the following steps:

[0005] S1. Calculate the pulse difference Δa of the meter to be tested according to the standard parameters of the standard meter;

[0006] S2. Calculate the error rate α of the electric meter to be tested according to the pulse difference Δa, and determine whether the electric meter to be tested is a qualified electric meter;

[0007] If yes, the meter to be tested is a qualified meter, and the process goes to step S3;

[0008] If not, the meter to be tested is an unqualified meter, and an unqualified log is generated and sent to the detection end;

[0009] S3, based on the power market data, obtaining the power generation data set of the power generation end, removing the data with little correlation with the decision attribute from the power generation data set and obtaining the settlement data set;

[0010] S4. Calculate the monthly network loss value ΔW during the power generation process based on the settlement data set, and calculate the monthly actual power generation W at the user end based on the monthly network loss value ΔW S ;

[0011] S5. Based on the actual monthly power generation WS Calculate the calibration coefficient And obtain the calibration analysis result based on the calibration coefficient g;

[0012] S6. According to the power consumption W D Calculate the deviation power W e ;

[0013] S7, based on the actual monthly power generation W S , calibration coefficient g and deviation power W e Generate user electricity usage reports.

[0014] Preferably, in step S1, the specific implementation steps are as follows:

[0015] S11. Obtain standard parameters of standard electric meter, including pulse constant C0, external current transformer ratio K A and external voltage transformer ratio K U ;

[0016] S12. Obtain the high-frequency pulse number N and active pulse constant C of the meter to be tested L , and calculate the qualitative pulse value m0 of the meter to be tested according to the standard parameters. The calculation formula is as follows:

[0017]

[0018] Among them, m0 represents the qualitative pulse value, C0 represents the pulse constant of the standard meter, and C L Indicates the active pulse constant of the meter to be tested;

[0019] S13, obtain the actual pulse value m of the meter to be tested s , and calculate the pulse difference Δa of the meter to be tested based on the qualitative pulse value m0. The calculation formula is as follows:

[0020] Δa=m0-m s

[0021] Where Δa represents the pulse difference, m s Indicates the actual pulse value of the meter under test.

[0022] Preferably, in step S2, the specific implementation steps are as follows:

[0023] S21, according to the pulse difference Δa and the actual pulse value m s Calculate the error rate α of the meter to be tested, the calculation formula is as follows:

[0024]

[0025] Among them, α represents the error rate of the meter to be tested;

[0026] S22, using an iterative method based on historical data to obtain a threshold value δ0;

[0027] S23, judging whether the electric meter to be tested is qualified according to the threshold value δ0;

[0028] If α≤δ0, the meter to be tested is a qualified meter and is marked with a qualified number;

[0029] If α>δ0, the meter to be tested is an unqualified meter, and the result is sent to the detection end;

[0030] S24. Repeat steps S12-S23 until all the electric meters to be tested are tested.

[0031] Preferably, in step S3, the specific implementation steps are as follows:

[0032] S31, set time granularity t p , based on the electricity market data, obtain each time granularity t p The power generation data set B of the internal power generation end, B = {B1, B2, ... B a}, where B a Indicates the ath power generation data;

[0033] S32, obtaining the power generation W of the power generation end in a single day u , calculate the power generation W u The average The calculation formula is as follows:

[0034]

[0035] in, Indicates the power generation W u The average value, W u represents the power generation of the power generation end in a single day, and n represents the power generation of the power generation end W u the number of

[0036] S33, calculating power generation data B in power generation data set B a The average The calculation formula is as follows:

[0037]

[0038] Where m represents the power generation data B a the number of

[0039] S34, based on the average and average Calculate the correlation coefficient β, the calculation formula is as follows:

[0040]

[0041] Among them, β represents the correlation coefficient, B a represents the power generation data of the ath power generation terminal, W u represents the power generation of the uth power generation terminal, k represents B a and W u The number of groups;

[0042] S35, judging power generation data B a and power generation W u The size of the correlation between

[0043] If |β-1|≤0.5 or |β+1|≤0.5, the power generation data B of the power generation end is retained. a , power generation data B at the power generation end a Including the resistance value R of the power generation line, each time granularity t p Active power P u , reactive power P n And line voltage U q ;

[0044] If -0.5<β-0<0.5, the power generation data B at the power generation end is eliminated. a ;

[0045] S36, based on power generation data B a Generate a settlement dataset.

[0046] Preferably, in step S4, the specific implementation steps are as follows:

[0047] S41. Calculate the time granularity t within a single month p The number N is calculated as follows:

[0048]

[0049] Among them, N represents the time granularity t within each month. p The number of p Indicates the time granularity;

[0050] S42. Calculate monthly active power P based on quantity N U and reactive power P N , the calculation formula is as follows:

[0051] P U =N×P u

[0052] P N =N×P n

[0053] Among them, P U Represents monthly active power, P N Indicates the reactive power per month;

[0054] S43, the monthly active power P U , reactive power P N And line voltage U q Generate monthly power generation data set;

[0055] S44. Calculate the monthly grid loss value ΔW during the power generation process based on the monthly power generation data set. The calculation formula is as follows:

[0056]

[0057] Among them, ΔW represents the monthly network loss value during the power generation process, R represents the resistance value of the power generation line, and N represents the time granularity t p the number of

[0058] S45. Obtain the total power generation W of the user end in a single month based on the power market data Z , calculate the actual monthly power generation W at the user end based on the monthly network loss value ΔW S , the calculation formula is as follows:

[0059] W S =W Z -ΔW

[0060] Among them, W S Indicates the actual monthly power generation, W Z It represents the total power generation at the user end in a single month, and ΔW represents the monthly network loss value during the power generation process.

[0061] Preferably, the specific implementation steps in step S5 are as follows:

[0062] S51. Obtain the time granularity t of the qualified electric meter used by the user end according to the qualified number. p Power consumption in W D ;

[0063] S52, based on the actual monthly power generation W of the user end S Calculation time granularity t p The amount of electricity generated by the particles in W x , the calculation formula is as follows:

[0064]

[0065] Among them, W x Indicates the time granularity t p The amount of electricity generated by the particles inside;

[0066] S53, according to the power consumption W D and particle power generation W x Calculate the calibration coefficient g;

[0067] S54, determine the verification threshold μ by the maximum likelihood method based on the power market data, and judge the particle power generation W x and power consumption W D Whether the calibration is accurate;

[0068] If g ≥ μ, the particle power generation W x and power consumption W D The deviation is large, and the verification analysis result B is sent to the settlement end;

[0069] If g<μ, the particle power generation W x and power consumption W D The verification is accurate, forming a verification analysis result A and entering step S6.

[0070] Preferably, the calculation formula of the calibration coefficient g is as follows:

[0071]

[0072] Among them, g represents the calibration coefficient, N represents the power consumption W D and particle power generation W x The number of groups, W x Represents the power generation of the pellet, W D Indicates the time granularity t p The power consumption inside.

[0073] Preferably, in step S6, the specific implementation steps are as follows:

[0074] S61. Obtain the user's contracted monthly electricity consumption W from the electricity market data h ;

[0075] S62, according to the time granularity t p Power consumption in W D Calculate monthly power consumption W y , the calculation formula is as follows:

[0076] W y =N×W D

[0077] Among them, W y represents monthly electricity consumption, N represents the time granularity t p The number of D Indicates each time granularity t p The electricity consumption within

[0078] S63, based on monthly power consumption W y and the contract monthly electricity consumption W h Calculate the deviation power W e , the calculation formula is as follows:

[0079] We =W h -W y

[0080] Among them, W e Indicates the deviation power, W h Indicates the monthly electricity consumption in the contract, W y Indicates monthly electricity consumption.

[0081] The technical solution also provides a system for the settlement data verification method of the above-mentioned power trading market, the system comprising: a pulse detection module, which obtains the qualitative pulse value m0 of the meter to be tested based on the standard meter, and calculates the pulse difference Δa according to the qualitative pulse value m0;

[0082] The electric meter detection module calculates the error rate α of the electric meter to be tested according to the pulse difference Δa, and determines whether the electric meter to be tested is a qualified electric meter;

[0083] If so, you can use and enter the data filtering module;

[0084] If not, it cannot be used and the result is sent to the detection end;

[0085] The data screening module obtains the power generation data set of the power generation end based on the power market data, screens the power generation data set to remove the data with little correlation with the decision attribute and obtains the settlement data set;

[0086] The power calculation module calculates the monthly network loss value ΔW during the power generation process based on the settlement data set, and calculates the actual monthly power generation W at the user end based on the network loss value ΔW S ;

[0087] Verification module, obtains the power consumption W of the qualified electric meter at the user end D , and according to the actual monthly power generation W S Calculate the calibration coefficient g, and obtain the calibration analysis result based on the calibration coefficient g;

[0088] Deviation power module, obtains the contract monthly power consumption W from the power market data h , according to the power consumption W D Calculate the deviation power W e ;

[0089] User report module, based on the actual monthly power generation W S , calibration coefficient g and deviation power W e Generate user electricity usage reports.

[0090] By means of the above technical solution, the present invention provides a settlement data verification method and system for the power trading market, which has at least the following beneficial effects:

[0091] 1. The present invention ensures that the accuracy of the electric meter meets the requirements of electricity settlement by performing prior detection on the electric meter, so that the subsequent verification process can accurately reduce errors and avoid the situation where large data deviations occur due to inaccurate electric meter during verification. The prior detection of the electric meter greatly improves the accuracy of settlement, and the accuracy of settlement further improves the user experience.

[0092] 2. The present invention divides a long time period into small time periods, so that the power data and the electric meter in the power measurement can measure the power consumption in a smaller time period, and calibrate the power consumption in the smaller time period to obtain the calibration coefficient of each smaller time period. This can not only improve the accuracy of the power data calibration, but also enable the power generation end and the user end to know the specific time when the power consumption fluctuates greatly, and make reasonable responses to this situation, so as to avoid the hidden dangers caused by the large fluctuations and cause more serious consequences if they are not checked for a long time.

[0093] 3. When settling accounts, the present invention comprehensively calculates the reactive power and active power in the power generation process of the power generation end to obtain the network loss value of the transmission network, which not only improves the accuracy of electricity settlement, but also enables the user end to reduce unnecessary expenses caused by network losses during settlement. This calculation method is more reasonable for users and improves user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0095] Figure 1 It is a flow chart of the settlement data verification method of the power trading market of the present invention;

[0096] Figure 2 It is a structural block diagram of the settlement data verification system of the power trading market of the present invention. DETAILED DESCRIPTION

[0097] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments, so that the implementation process of how the present application uses technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly.

[0098] Due to the technical problem of low accuracy of data in power trading market settlement due to the long time span, please refer to Figure 1 This embodiment provides a settlement data verification method for an electric power trading market, which improves accuracy by dividing a long time period into small time periods and performing electric quantity measurement and verification to solve technical problems. The method includes the following steps:

[0099] S1. Calculate the pulse difference Δa of the meter to be tested according to the standard parameters of the standard meter; the standard meter is a meter used to measure electric energy, which is usually designed and works in a controlled laboratory environment to obtain the highest accuracy and stability. The parameters of the standard meter are used as standard parameters. In step S1, the specific implementation steps are as follows:

[0100] S11. Obtain standard parameters of standard electric meter, including pulse constant C0, external current transformer ratio K A and external voltage transformer ratio K U ; The pulse constant C0 refers to the amount of electric energy output by the energy meter each time it outputs a pulse, and the external current transformer ratio K A and external voltage transformer ratio K U Refers to the ratio between the converted currents or voltages on both sides of the current and voltage transformer. In practice, one side of the current and voltage transformer is connected to the high-voltage line and the other side is connected to the low-voltage line.

[0101] S12. Obtain the high-frequency pulse number N and active pulse constant C of the meter to be tested L , and calculate the qualitative pulse value m0 of the meter to be tested according to the standard parameters. The calculation formula is as follows:

[0102]

[0103] Among them, m0 represents the qualitative pulse value, C0 represents the pulse constant of the standard meter, and C L Indicates the active pulse constant of the meter to be tested; the high-frequency pulse number N refers to the high-frequency pulse transmission process of "power on, power off, power on" repeated in a short period of time, and the active pulse constant C L It is an important parameter of the electric energy meter. It indicates the number of pulses that the electric energy meter should generate when consuming unit electric energy, that is, 1kWh. The above data can be directly obtained from the electric energy meter nameplate or electricity market data.

[0104] S13, obtain the actual pulse value m of the meter to be tested s , and calculate the pulse difference Δa of the meter to be tested based on the qualitative pulse value m0. The calculation formula is as follows:

[0105] Δa=m0-m s

[0106] Where Δa represents the pulse difference, m s Indicates the actual pulse value of the meter to be tested. The actual pulse value m s It can be directly obtained using the oscilloscope method.

[0107] S2. Calculate the error rate α of the electric meter to be tested according to the pulse difference Δa, and determine whether the electric meter to be tested is a qualified electric meter;

[0108] If yes, the meter to be tested is a qualified meter, and the process goes to step S3;

[0109] If not, the meter to be tested is an unqualified meter, and an unqualified log is generated and sent to the detection end; in order to ensure that the error between the meter used by the user and the standard meter is within a small range, so that the electric energy measurement data at the user end is more accurate and reliable, it is necessary to calculate the error rate α of the meter to be tested and compare it with the threshold δ0 to select a qualified meter. In step S2, the specific implementation steps are as follows:

[0110] S21, according to the pulse difference Δa and the actual pulse value m s Calculate the error rate α of the meter to be tested, the calculation formula is as follows:

[0111]

[0112] Among them, α represents the error rate of the meter to be tested;

[0113] S22. The threshold value δ0 is obtained by adopting an iterative method based on historical data. The historical data is the past electricity-related data in the electricity market data, which is stored in the electricity trading market database. The electricity market data is the database of the electricity trading market. The iterative method is a process of continuously recursively deriving new values ​​from old values ​​of variables. It is often used to obtain thresholds in data. Only the iterative method is needed to obtain the threshold value δ0 without additional calculation, which will not be elaborated here.

[0114] S23, judging whether the electric meter to be tested is qualified according to the threshold value δ0;

[0115] If α≤δ0, the meter to be tested is a qualified meter and is marked with a qualified number;

[0116] If α>δ0, the meter to be tested is an unqualified meter, and the result is sent to the detection end. The qualified number is used to quickly obtain the user's qualified meter in the subsequent steps. The unqualified meter requires further maintenance by the staff, and then the result needs to be sent to the detection end to notify the staff. The detection end is the end where the staff is located and can provide a visual working interface for the staff.

[0117] S24. Repeat steps S12-S23 until all the electric meters to be tested are tested.

[0118] S3, based on the power market data, the power generation data set of the power generation end is obtained, and the data with little correlation with the decision attribute is removed from the power generation data set to obtain the settlement data set. There are many data in the power generation data of the power generation end that have little correlation with the settled electric energy or electric power, so it is necessary to remove these data with little correlation to improve the data processing speed. In step S3, the specific implementation steps are as follows:

[0119] S31, set time granularity tp , based on the electricity market data, obtain each time granularity t p The power generation data set B of the internal power generation end, B = {B1, B2, ... B a}, where B a Indicates the ath power generation data, time granularity t p For a short period of time, in order to accurately detect the use of electric energy, the long period of time is divided into several smaller periods of time, which can improve the accuracy in the calculation and verification of subsequent steps.

[0120] S32, obtaining the power generation W of the power generation end in a single day u , calculate the power generation W u The average The calculation formula is as follows:

[0121]

[0122] in, Indicates the power generation W u The average value, W u represents the power generation of the power generation end in a single day, and n represents the power generation of the power generation end W u The number of W is generally used to obtain the power generation W of a single day in half a year. u , the number n is exactly the same as the number of days.

[0123] S33, calculating power generation data B in power generation data set B a The average The calculation formula is as follows:

[0124]

[0125] Where m represents the power generation data B a The number of, because not eliminated, power generation data B a The quantity is m.

[0126] S34, based on the average and average Calculate the correlation coefficient β, the calculation formula is as follows:

[0127]

[0128] Among them, β represents the correlation coefficient, B a represents the power generation data of the ath power generation terminal, W u represents the power generation of the uth power generation terminal, and k represents the power generation data B a and power generation W u In order to make the calculation accurate, the power generation data B a and power generation W uThe number of is generally consistent, so the k value in the calculation is the power generation data B a and power generation W u The number of groups.

[0129] S35, judging power generation data B a and power generation W u The size of the correlation between

[0130] If |β-1|≤0.5 or |β+1|≤0.5, the power generation data B of the power generation end is retained. a , power generation data B at the power generation end a Including the resistance value R of the power generation line, each time granularity t p Active power P u , reactive power P n and line voltage U q ;

[0131] If -0.5<β-0<0.5, the power generation data B at the power generation end is eliminated. a ;

[0132] S36, based on power generation data B a Generate a settlement dataset.

[0133] S4. Calculate the monthly network loss value ΔW during the power generation process based on the settlement data set, and calculate the monthly actual power generation W at the user end based on the monthly network loss value ΔW S In order to improve the accuracy of the settlement process, the network loss value ΔW is subtracted from the power generation data. Therefore, it is necessary to calculate the size of the network loss in the power generation, so that the power consumption data at the user end is more accurate and reliable. In step S4, the specific implementation steps are as follows:

[0134] S41. Calculate the time granularity t within a single month p The number N is calculated as follows:

[0135]

[0136] Among them, N represents the time granularity t within each month. p The number of p Indicates the time granularity. If a month is calculated as 30 days, the total number of hours is 720. In order to calculate the monthly electricity consumption, it is necessary to know the time granularity t contained in each month. p The number of

[0137] S42. Calculate monthly active power P based on quantity N U and reactive power P N , the calculation formula is as follows:

[0138] P U =N×P u

[0139] P N =N×P n

[0140] Among them, P U Represents monthly active power, P N Indicates the reactive power per month;

[0141] S43, the monthly active power P U , reactive power P N And line voltage U q Generate monthly power generation data set;

[0142] S44. Calculate the monthly grid loss value ΔW during the power generation process based on the monthly power generation data set. The calculation formula is as follows:

[0143]

[0144] Among them, ΔW represents the monthly network loss value during the power generation process, R represents the resistance value of the power generation line, and N represents the time granularity t p The network loss value ΔW refers to the energy loss caused by the total loss on the power grid line and the loss of the hub transformer in the power system. This loss is inevitable, so the network loss value needs to be calculated and subtracted when settling electricity.

[0145] S45. Obtain the total power generation W of the user end in a single month based on the power market data Z , calculate the actual monthly power generation W at the user end based on the monthly network loss value ΔW S , the calculation formula is as follows:

[0146] W S =W Z -ΔW

[0147] Among them, W S Indicates the actual monthly power generation, W Z It represents the total power generation at the user end in a single month, and ΔW represents the monthly network loss value during the power generation process.

[0148] S5. According to the actual monthly power generation W S Calculate the calibration coefficient g, and obtain the calibration analysis result based on the calibration coefficient g. In order to improve the accuracy of the calibration during the calibration process, it is necessary to use the actual monthly power generation W S Calculation time granularity t p The amount of electricity generated by the particles in W x , so that the verification result is more accurate, the specific implementation steps in step S5 are as follows:

[0149] S51. Obtain the time granularity t of the qualified electric meter used by the user end according to the qualified number. pPower consumption in W D ;

[0150] S52, based on the actual monthly power generation W of the user end S Calculation time granularity t p The amount of electricity generated by the particles in W x , the calculation formula is as follows:

[0151]

[0152] Among them, W x Indicates the time granularity t p The amount of power generated by the particles in the x is the time granularity t p The power generation inside.

[0153] S53, according to the power consumption W D and particle power generation W x Calculate the calibration coefficient g, the calculation formula of the calibration coefficient g is as follows:

[0154]

[0155] Among them, g represents the calibration coefficient, N represents the power consumption W D and particle power generation W x The number of groups, W x Represents the power generation of the pellet, W D Indicates the time granularity t p The power consumption inside.

[0156] S54, determine the verification threshold μ by the maximum likelihood method based on the power market data, and judge the particle power generation W x and power consumption W D Whether the calibration is accurate;

[0157] If g ≥ μ, the particle power generation W x and power consumption W D If the deviation is large, a verification analysis result B is generated and sent to the settlement end;

[0158] If g<μ, the particle power generation W x and power consumption W D The verification is accurate, and a verification analysis result A is generated and the process goes to step S6. The verification threshold is determined using the maximum likelihood method, and the verification threshold μ is obtained. The maximum likelihood method is a commonly used threshold determination method and will not be described in detail here.

[0159] S6. According to the power consumption W D Calculate the deviation power W eWhen settling the electricity consumption of the user, since the user may purchase an annual electricity package or a monthly electricity package, that is, the monthly electricity consumption of the contract, and since the electricity consumption of the user is mostly different from the contract electricity consumption, it is necessary to calculate the deviation electricity so that the user can understand his own electricity consumption during settlement. In step S6, the specific implementation steps are as follows:

[0160] S61. Obtain the user's contracted monthly electricity consumption W from the electricity market data h ;

[0161] S62, according to the time granularity t p Power consumption in W D Calculate monthly power consumption W y , the calculation formula is as follows:

[0162] W y =N×W D

[0163] Among them, W y represents monthly electricity consumption, N represents the time granularity t p The number of D Indicates each time granularity t p The electricity consumption within

[0164] S63, based on monthly power consumption W y and the contract monthly electricity consumption W h Calculate the deviation power W e , the calculation formula is as follows:

[0165] W e =W h -W y

[0166] Among them, W e Indicates the deviation power, W h Indicates the monthly electricity consumption in the contract, W y Indicates monthly electricity consumption.

[0167] S7, based on the actual monthly power generation W S , calibration coefficient g and deviation power W e Generate user electricity consumption reports so that users can clearly see their own electricity consumption and each time granularity t p The calibration coefficient g and the deviation power W e , so that the user can judge the safety of their own electricity consumption and the accuracy of settlement according to the electricity consumption situation, and generate a user electricity consumption report so that users can see it at a glance. The electricity consumption report uses a line chart or a bar chart to reflect the granularity of each time t p The electricity usage inside.

[0168] See also Figure 2 , which is a structural block diagram of the settlement data verification system of the power trading market provided in this embodiment, and the settlement data verification system includes: a pulse detection module, an electric meter detection module, a data screening module, an electric quantity calculation module, a verification module, a deviation electric quantity module, and a user reporting module.

[0169] The pulse detection module obtains the qualitative pulse value m0 of the meter to be tested based on the standard meter, and calculates the pulse difference Δa according to the qualitative pulse value m0. The meter detection module is used to calculate the error rate α of the meter to be tested according to the pulse difference Δa, and judge whether the meter to be tested is a qualified meter. If so, it can be used and enter the data screening module. If not, it cannot be used and the result is sent to the detection end. The data screening module obtains the power generation data set of the power generation end based on the power market data, screens the power generation data set to eliminate the data with little correlation with the decision attribute and obtains the settlement data set. The power calculation module calculates the monthly network loss value ΔW in the power generation process based on the settlement data set, and calculates the actual monthly power generation W of the user end according to the network loss value ΔW. S , verification module, used to obtain the power consumption W of the qualified electric meter at the user end D , and according to the actual monthly power generation W S Calculate the calibration coefficient g, and obtain the calibration analysis result based on the calibration coefficient g. The deviation power module is used to obtain the contract monthly power consumption W from the power market data. h , according to the power consumption W D Calculate the deviation power W e , user report module, used to calculate the actual monthly power generation W S , calibration coefficient β and deviation power W e Generate user electricity usage reports.

[0170] Those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, so the present application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application can be in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0171] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the above embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0172] The above implementation methods have been described in detail. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A settlement data verification method for an electricity trading market, characterized in that: The method comprises the following steps: S1. Calculate the pulse difference Δa of the meter to be tested according to the standard parameters of the standard meter; S2. Calculate the error rate α of the electric meter to be tested according to the pulse difference Δa, and determine whether the electric meter to be tested is a qualified electric meter; If yes, the meter to be tested is a qualified meter, and the process goes to step S3; If not, the meter to be tested is an unqualified meter, and an unqualified log is generated and sent to the detection end; S3, based on the power market data, obtaining the power generation data set of the power generation end, removing the data with little correlation with the decision attribute from the power generation data set and obtaining the settlement data set; S4. Calculate the monthly network loss value ΔW during the power generation process based on the settlement data set, and calculate the monthly actual power generation W at the user end based on the monthly network loss value ΔW S ; S5. Based on the actual monthly power generation W S Calculate the calibration coefficient g, and obtain the calibration analysis result based on the calibration coefficient g; S6. According to the power consumption W D Calculate the deviation power W e ; S7, based on the actual monthly power generation W S , calibration coefficient g and deviation power W e Generate user electricity usage reports.

2. The settlement data verification method according to claim 1, characterized in that: In step S1, the specific implementation steps are as follows: S11. Obtain standard parameters of standard electric meter, including pulse constant C0, external current transformer ratio K A and external voltage transformer ratio K U ; S12. Obtain the high-frequency pulse number N and active pulse constant C of the meter to be tested L , and calculate the qualitative pulse value m0 of the meter to be tested according to the standard parameters. The calculation formula is as follows: Among them, m0 represents the qualitative pulse value, C0 represents the pulse constant of the standard meter, and C L Indicates the active pulse constant of the meter to be tested; S13, obtain the actual pulse value m of the meter to be tested s , and calculate the pulse difference Δa of the meter to be tested based on the qualitative pulse value m0. The calculation formula is as follows: Δa=m0-m s Among them, Δa represents the pulse difference, m s Indicates the actual pulse value of the meter under test.

3. The settlement data verification method according to claim 1, characterized in that: In step S2, the specific implementation steps are as follows: S21, according to the pulse difference Δa and the actual pulse value m s Calculate the error rate α of the meter to be tested, the calculation formula is as follows: Among them, α represents the error rate of the meter to be tested; S22, using an iterative method based on historical data to obtain a threshold value δ0; S23, judging whether the electric meter to be tested is qualified according to the threshold value δ0; If α≤δ0, the meter to be tested is a qualified meter and is marked with a qualified number; If α>δ0, the meter to be tested is an unqualified meter, and the result is sent to the detection end; S24. Repeat steps S12-S23 until all the electric meters to be tested are tested.

4. The settlement data verification method according to claim 1, characterized in that: In step S3, the specific implementation steps are as follows: S31, set time granularity t p , based on the electricity market data, obtain each time granularity t p The power generation data set B of the internal power generation end, B = {B1, B2, ... B a }, where B a Indicates the ath power generation data; S32, obtaining the power generation W of the power generation end in a single day u , calculate the power generation W u The average The calculation formula is as follows: in, Indicates the power generation W u The average value, W u represents the power generation of the power generation end in a single day, and n represents the power generation of the power generation end W u the number of S33, calculating power generation data B in power generation data set B a The average The calculation formula is as follows: Where m represents the power generation data B a the number of S34, based on the average and average Calculate the correlation coefficient β, the calculation formula is as follows: Among them, β represents the correlation coefficient, B a represents the power generation data of the ath power generation terminal, W u represents the power generation of the uth power generation terminal, k represents B a and W u The number of groups; S35, judging power generation data B a and power generation W u The size of the correlation between If |β-1|≤0.5 or |β+1|≤0.5, the power generation data B of the power generation end is retained. a , power generation data B at the power generation end a Including the resistance value R of the power generation line, each time granularity t p Active power P u , reactive power P n and line voltage U q ; If -0.5<β-0<0.5, the power generation data B of the power generation end is eliminated a ; S36, based on power generation data B a Generate a settlement dataset.

5. The settlement data verification method according to claim 1, characterized in that: In step S4, the specific implementation steps are as follows: S41. Calculate the time granularity t within a single month p The number N is calculated as follows: Among them, N represents the time granularity t within each month. p The number of p Indicates the time granularity; S42. Calculate monthly active power P based on quantity N U and reactive power P N , the calculation formula is as follows: P U =N×P u P N =N×P n Among them, P U Represents monthly active power, P N Indicates the reactive power per month; S43, the monthly active power P U , reactive power P N and line voltage U q Generate monthly power generation data set; S44. Calculate the monthly grid loss value ΔW during the power generation process based on the monthly power generation data set. The calculation formula is as follows: Among them, ΔW represents the monthly network loss value during the power generation process, R represents the resistance value of the power generation line, and N represents the time granularity t p the number of S45. Obtain the total power generation W of the user end in a single month based on the power market data Z , calculate the actual monthly power generation W at the user end based on the monthly network loss value ΔW S , the calculation formula is as follows: IN S =In z --ΔW Among them, W S Indicates the actual monthly power generation, W Z It represents the total power generation at the user end in a single month, and ΔW represents the monthly network loss value during the power generation process.

6. The settlement data verification method according to claim 1, characterized in that: The specific implementation steps in step S5 are as follows: S51, obtaining the time granularity t of the qualified electric meter used by the user end according to the qualified number p Power consumption in W D ; S52, based on the actual monthly power generation W of the user end S Calculation time granularity t p The amount of electricity generated by the particles in W x , the calculation formula is as follows: Among them, W x Indicates the time granularity t p The amount of electricity generated by the particles inside; S53, according to the power consumption W D and particle power generation W x Calculate the calibration coefficient g; S54, determine the verification threshold μ by the maximum likelihood method based on the power market data, and judge the particle power generation W x and power consumption W D Whether the calibration is accurate; If g ≥ μ, the particle power generation W x and power consumption W D If the deviation is large, a verification analysis result B is generated and sent to the settlement end; If g<μ, the particle power generation W x and power consumption W D If the verification is accurate, a verification analysis result A is generated and the process proceeds to step S6.

7. The settlement data verification method according to claim 6, characterized in that: The calculation formula of the calibration coefficient g is as follows: Among them, g represents the calibration coefficient, N represents the power consumption W D and particle power generation W x The number of groups, W x Represents the power generation of the pellet, W D Indicates the time granularity t p The power consumption inside.

8. The settlement data verification method according to claim 1, characterized in that: In step S6, the specific implementation steps are as follows: S61. Obtain the user's contracted monthly electricity consumption W from the electricity market data h ; S62, according to the time granularity t p Power consumption in W D Calculate monthly power consumption W y , the calculation formula is as follows: IN y =N×W D Among them, W y represents monthly electricity consumption, N represents the time granularity t p The number of D Indicates each time granularity t p The electricity consumption within S63, based on monthly power consumption W y and the contract monthly electricity consumption W h Calculate the deviation power W e , the calculation formula is as follows: IN e =In h -IN y Among them, W e Indicates the deviation power, W h Indicates the monthly electricity consumption in the contract, W y Indicates monthly electricity consumption.

9. A system for implementing the settlement data verification method of the power trading market as described in any one of claims 1 to 8, characterized in that: The system includes: The pulse detection module obtains the qualitative pulse value m0 of the meter to be tested based on the standard meter, and calculates the pulse difference Δa according to the qualitative pulse value m0; The electric meter detection module calculates the error rate α of the electric meter to be tested according to the pulse difference Δa, and determines whether the electric meter to be tested is a qualified electric meter; If so, you can use and enter the data filtering module; If not, it cannot be used and the result is sent to the detection end; The data screening module obtains the power generation data set of the power generation end based on the power market data, screens the power generation data set to remove the data with little correlation with the decision attribute and obtains the settlement data set; The power calculation module calculates the monthly network loss value ΔW during the power generation process based on the settlement data set, and calculates the actual monthly power generation W at the user end based on the network loss value ΔW S ; Verification module, obtains the power consumption W of the qualified electric meter at the user end D , and according to the actual monthly power generation W S Calculate the calibration coefficient g, and obtain the calibration analysis result based on the calibration coefficient g; Deviation power module, obtains the contract monthly power consumption W from the power market data h , according to the power consumption W D Calculate the deviation power W e ; User report module, based on the actual monthly power generation W S , calibration coefficient g and deviation power W e Generate user electricity usage reports.

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