CN113379440B - Similar day-based electric power spot market electric quantity declaration information optimization method and system - Google Patents
Similar day-based electric power spot market electric quantity declaration information optimization method and system Download PDFInfo
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Abstract
The invention discloses a method and a system for optimizing electric power spot market electric quantity declaration information based on similar days, which comprises the following steps: s1, obtaining historical weather information, and classifying spot market trading days according to the weather information; s2, acquiring 96-point electricity price information of the electric power spot market in real time before the transaction history; s3, acquiring power quantity declaration information of 96 points in the history of the power spot market transaction for a long time and in the day ahead and actual power quantity information of a user; and S4, calculating an electric quantity declaration optimized value of 96 points in the medium-long term according to the information obtained in the S2 and the S3, and calculating an electric quantity declaration optimized value of 96 points in the day ahead. The invention can assist the power selling company to complete the electric quantity declaration of the electric power spot market transaction, obtain relatively stable income for the power selling company and reduce the transaction risk.
Description
Technical Field
The invention relates to a method and a system for optimizing electricity price information, in particular to a method and a system for optimizing electricity price information of an electricity spot market based on similar days.
Background
The spot market generally refers to a real-time market for real-time physical delivery of goods, and the time range of the electric power spot market generally includes the time from one day before the day of real-time delivery of the system to the real-time operation in consideration of the feature that the instantaneous supply and demand needs of electric power goods delivery keep balance. The electric power spot market generally adopts a unified clearing mode, market members voluntarily participate in declaration, and the formed transaction plan is subjected to physical delivery and settlement, and generally comprises a day-ahead market, a day-in-the-day market and a real-time market part.
The spot trial run has been carried out in multiple regions, but the electric power spot trial run is in a starting stage, market participating bodies do not have deep knowledge of spot transaction rules and the like, in order to keep normal and stable operation of the electric power spot market, relevant regulations on the spot rules are made in partial regions, for example, the price report of a power generation side is regulated, the price report of a user side is not quoted, meanwhile, the time-sharing checking electric quantity of the real-time market of the user side exceeds an allowable deviation range, the price difference income of the time-sharing price of the real-time market outside the allowable deviation range of the user and the day-ahead market is deducted according to day for the profit space of partial electric quantity of which the medium-long term contract electric quantity of the user side is lower than 95% of the daily total electric quantity of electricity, and the price difference between the medium-long term contract maximum price in the day of the full-province electric power market and the daily average price of the spot real-time market is deducted according to day; and for the condition that the medium-and-long-term trading contract electricity quantity and the actual electricity consumption quantity of the user side per hour exceed the allowable deviation range, recovering the exceeding electricity quantity according to the highest price difference and the lowest price difference of the current market user side unified settlement point.
According to investigation and research, when the electricity price spot market is traded, the electricity quantity declaration information cannot be unified, the medium-long term 96-point electricity quantity declaration and the day-ahead 96-point electricity quantity declaration are not equal to the actual electricity quantity of the user side, and the electricity price spot market cannot normally and stably run. Based on the method, the prospect of the electric power spot market is considered, and an electric power spot market auxiliary transaction method based on similar days is developed to provide support for spot market transaction for an electricity selling company.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a method and a system for optimizing electric power spot market electric quantity declaration information based on similar days so as to solve the technical problems.
The technical scheme is as follows: the invention relates to a similar day-based electric power spot market auxiliary transaction method, which comprises the following steps:
s1, obtaining historical weather information, classifying spot market trading days according to the weather information, and obtaining an electric power spot market electricity price information group based on similar days;
s2, acquiring 96-point real-time electricity price information before the transaction history of the electric power spot market from the information group;
s3, acquiring power quantity declaration information and user actual power quantity information of 96 points in the long-term and day-ahead power spot market transaction history;
and S4, calculating an electric quantity reporting optimized value of 96 points in the medium and long periods according to the information obtained in the S2 and the S3, and calculating an electric quantity reporting optimized value of 96 points in the day ahead.
The invention relates to a similar day-based electric power spot market electric quantity declaration information optimization system, which comprises:
the data acquisition and storage unit is used for acquiring historical weather information, acquiring 96-point power price information before the current power spot market transaction history, acquiring 96-point power declaration information before the current power spot market transaction history and long-term power declaration information in the current power spot market transaction history and user actual power information, and storing the information;
the data processing unit is used for classifying spot market trading days according to historical weather information to obtain an electric power spot market electricity price information group based on similar days, calculating medium and long term 96-point electric quantity declaration optimized values and calculating 96-point electric quantity declaration optimized values before the day;
and the communication unit is used for connecting external equipment, writing data required by the data acquisition and storage unit and transmitting a calculation result.
Has the beneficial effects that: compared with the prior art, the invention has the following remarkable advantages:
the method realizes the selection of similar days by combining historical weather information and date types, reasonably calculates the medium-long term 96-point electric quantity declaration optimized value and the day-ahead 96-point electric quantity declaration optimized value by combining historical day-ahead and real-time 96-point electric price information, historical medium-long term and day-ahead 96-point electric quantity declaration information and user actual electric quantity information provided by an electric power trading platform on the basis of the similar days, assists an electric power selling company to finish the electric quantity declaration of electric power spot market trading, and reduces trading risks.
Drawings
FIG. 1 is a schematic flow diagram of the present invention.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
The present embodiment includes the following steps:
step S1: in step S101, the historical weather information acquired by the electric power spot market transaction includes a date, a date type, a temperature curve, a highest temperature and a lowest temperature, and in this step, the historical weather information is acquired from a weather website of the central weather bureau; in step S102, the information such as the highest temperature and the lowest temperature of the historical day is preprocessed in combination with the historical weather information, and a standard format file including fields such as date, highest temperature, lowest temperature, average temperature and the like is generated by sorting; in step S103, dividing the electric power spot market transaction history date into working days, holidays and legal holidays according to types, and generating a standard format file containing fields such as date, day of the week and date type; in step S104, a unified analysis is performed based on the information organized in steps S102 and S103, and if the information such as the highest temperature, the lowest temperature, and the average temperature on the same type of date on the current day is the same or similar, such date is defined as a similar date.
Step S2: in step S201, the historical day-ahead and real-time 96-point electricity price information of the electricity spot market transaction is preprocessed by combining the similar day information, and the historical day-ahead and real-time 96-point electricity price information of the electricity spot market transaction in this step is obtained from the electricity transaction platform; in step S202, the average value of the electricity prices at each time point of 96 real-time points before the day of the history of similar days is calculated and obtained { P } Day ahead 1 ，P Day 2 before ,...P Day-ahead 96 }、 {P Real time 1 ，P Real time 2 ,...P Real time 96 }; in step S203, the maximum value P of the electricity price average value at each time point of 96 points before the day of the plurality of days of the similar day history is calculated and obtained based on the information arranged in step S202 Day-ahead max Minimum value P Day-ahead min Total average value P Day-ahead avg (ii) a In step S204, the price difference { P) between the 96-point real-time electricity price mean value and the 96-point day-ahead electricity price mean value of the multiple days of the similar day history is calculated and obtained according to the information arranged in step S202 Valence difference 1 ，P Valence difference 2 ,...P Price difference 96 }; in step S205, the maximum value P of the price difference between the 96-point real-time electricity price average value and the 96-point day-ahead electricity price average value of the multiple days of the similar day history is calculated and obtained based on the information sorted in step S204 Price difference max Minimum value P Price difference min 。
And step 3: in step S301, the information of the electric quantity declared at 96 points in the long term and in the day before in the history of the electric power spot market transaction and the information of the actual electric quantity of the user are preprocessed by combining the similar day information, and the information of the electric quantity declared at 96 points in the long term and in the day before in the history and the information of the actual electric quantity of the user in the step are all obtained from the electric power transaction platform; in step S302, the average value { m } of the reported electric quantity of 96 long-term points in the history is calculated and obtained according to the information sorted in step S301 1 ,m 2 ...m 96 }; in step S303, the user history actual electric quantity average value { p } is calculated and obtained from the information sorted in step S301 1 ,p 2 ...p 96 }。
And 4, step 4: in step S401, according to the linear equation y = ax + b, andcombining the upper limit lambda of the allowable proportion of the medium-long term curve deviation Upper limit of medium-long term curve Lower limit lambda of allowable proportion of deviation of medium and long-term curves Lower limit of middle and long term curve I.e. according to the formula lambda Upper limit of medium-long term curve ＝a Upper limit of medium-long term curve *P Day-ahead max +λ Lower limit of middle and long term curve And 1=a Lower limit of middle and long term curve *P Day-ahead avg +λ Lower limit of medium-long term curve Calculating to obtain a Upper limit of medium-long term curve ＝(λ Upper limit of medium-long term curve -λ Upper limit of medium-long term curve )/P Day-ahead m 、 a Lower limit of middle and long term curve ＝(1-λ Lower limit of middle and long term curve )/P Day-ahead avg And b Upper limit of medium-long term curve ＝b Lower limit of medium-long term curve ＝λ Lower limit of middle and long term curve In this step, the method combines the 'Shanxi Power saving market rule compilation V7.0', lambda Upper limit of medium-long term curve ＝2，λ Lower limit of middle and long term curve =0.5; in step S402, the average value of the electricity prices at each time point and the total average value P at 96 points in the day are judged Day-ahead avg In combination with the linear equation y = ax + b, if the point { P } is reached Day ahead 1 ，P Day 2 before ,...P Day-ahead 96 The average value of electricity prices is larger than the total average value, then the formula y = a is used Upper limit of medium-long term curve x+b Upper limit of medium-long term curve If the time point electricity price mean value is smaller than the total mean value, the formula y = a is used Lower limit of middle and long term curve x+b Lower limit of medium-long term curve Multiplying power { y) at each time point is obtained 1 ,y 2 ...y 96 }; in step S403, the 96-point time scaling factor { y } is adjusted based on the step S402 arrangement information 1 ,y 2 ...y 96 Reporting electric quantity mean value (m) at 96 points in history of steps S302 1 ,m 2 ...m 96 Multiplying to obtain medium and long term day 96-point reported electric quantity basic value { n } 1 ,n 2 ...n 96 }; in step S404, the average value of the electric quantity { m } is reported from 96 long-term points in the history of step S302 1 ,m 2 ...m 96 The sum of the electric quantity and the basic value of the reported electric quantity (n) at 96 points of the long-term day in the step S403 1 ,n 2 ...n 96 The sum of the four points is compared to obtain the medium-long term day 96-point optimized multiplying power k1, namely
In step S405, the medium-and-long-term day 96-point optimization multiplying power k1 in step S404 and the medium-and-long-term day 96-point declared electric quantity base value { n } in step S403 are compared 1 ,n 2 ...n 96 Multiplying to obtain a final medium-long term 96-point electric quantity declaration optimized value { z } 1 ,z 2 ...z 96 }。
And 5: in step S501, according to the linear equation f = cx + d, the user-side real-time market time-sharing excess profit margin deviation ratio is considered, where d =1 is set, and the upper limit λ of the user-side real-time market time-sharing excess profit margin allowable deviation ratio is combined Upper limit of excess And the lower limit of the margin excess allowable deviation ratio lambda Lower limit of excess I.e. according to the formula lambda Upper limit of excess ＝c Upper limit of excess *P Price difference max +1 and λ Lower limit of excess ＝c Lower limit of excess *P Price difference min +1, calculating to obtain c Upper limit of excess ＝(λ Upper limit of excess -1)/P Price difference ma 、c Lower limit of excess ＝(λ Lower limit of excess -1)/P Price difference min In this step, the power saving market rule assembly V7.0 of Shanxi is combined Upper limit of excess ＝1.5，λ Lower limit of excess =0.5; in step S502, the relationship between the historical multi-day 96-point real-time and 96-point day-ahead electricity price mean price difference and 0 is determined, and in combination with the linear equation f = cx + d in step S501, if the time price difference is greater than 0, the equation f = c is used Upper limit of excess x +1, if the time-valence difference is less than 0, using the formula f = c Lower limit of excess x +1, and calculating the multiplying power { f) at each time point 1 ,f 2 ...f 96 }; in step S503, the magnification { f } of each time point in step S502 is set 1 ,f 2 ...f 96 And S303, the average value of the historical actual electric quantity of the user in the step p 1 ,p 2 ...p 96 Multiplying, and obtaining the basic value (q) of the reported electric quantity at 96 points before the day 1 ,q 2 ...q 96 }; in step S504, the user history actual power mean value { p } in step S303 is compared 1 ,p 2 ...p 96 The sum of the electric quantity and the reported electric quantity basic value (q) 96 points before the day of the step S503 1 ,q 2 ...q 96 Comparing the sum to obtain 96 point optimization multiplying power k2 before the day, namely
In step S505, the day-ahead 96-point optimization multiplying power k2 in step S504 and the day-ahead 96-point reported electric quantity basic value { q } in step S503 are combined 1 ,q 2 ...q 96 Multiplying to obtain the final day-ahead 96-point electric quantity reporting optimized value { o } 1 ,o 2 ...o 96 }。
The embodiment further includes a power spot market power declaration information optimization system based on similar days, which specifically includes:
the data acquisition and storage unit is used for acquiring historical weather information, acquiring 96-point power price information before the current power spot market transaction history, acquiring 96-point power declaration information before the current power spot market transaction history and long-term power declaration information in the current power spot market transaction history and user actual power information, and storing the information;
the data processing unit is used for classifying spot market trading days according to historical weather information to obtain an electric power spot market electricity price information group based on similar days, calculating medium and long term 96-point electric quantity declaration optimized values and calculating 96-point electric quantity declaration optimized values before the day;
and the communication unit is used for connecting external equipment, writing data required by the data acquisition and storage unit and transmitting a calculation result.
Claims (6)
1. A similar day-based electric power spot market electric quantity declaration information optimization method is characterized by comprising the following steps:
s1, obtaining historical weather information, classifying spot market trading days according to the weather information, and obtaining an electric power spot market electricity price information group based on similar days;
s2, acquiring 96-point real-time electricity price information before the transaction history of the electric power spot market from the information group;
s3, acquiring power quantity declaration information of 96 points in the history of the power spot market transaction for a long time and in the day ahead and actual power quantity information of a user;
s4, calculating an electric quantity declaration optimized value of 96 points in the medium-long term according to the information obtained in the S2 and the S3, and calculating an electric quantity declaration optimized value of 96 points in the day ahead;
the calculation of the medium-long term 96-point electric quantity declaration optimized value comprises the following steps:
s401, according to a linear equation y = ax + b and combining a middle-long term curve deviation allowable proportion upper limit lambda Upper limit of medium-long term curve Middle and long term curve deviation allowable proportion lower limit lambda Lower limit of middle and long term curve Namely the formula:
λ upper limit of medium-long term curve ＝a Upper limit of medium-long term curve *P Day-ahead max +λ Lower limit of medium-long term curve
1＝a Lower limit of medium-long term curve *P Day-ahead avg +λ Lower limit of middle and long term curve
Calculating to obtain:
a upper limit of medium-long term curve ＝(λ Upper limit of medium-long term curve -λ Lower limit of middle and long term curve )/P Day-ahead max
a Lower limit of middle and long term curve ＝(1-λ Lower limit of middle and long term curve )/P Day-ahead avg
b Upper limit of medium-long term curve ＝b Lower limit of middle and long term curve ＝λ Lower limit of medium-long term curve ；
S402, judging the average value of the electricity prices at each time point of 96 points in the day and the total average value P Day-ahead avg And substituting the linear equation y = ax + b;
if { P Day ahead 1 ，P Day 2 before ,...P Day-ahead 96 The mean value of electricity prices is larger than P Day-ahead avg Substituting the data into the formula
y＝a Upper limit of medium-long term curve x+b Upper limit of medium-long term curve ，
If the average value of electricity price is less than P Day-ahead avg Substituting the data into the formula
y＝a Lower limit of middle and long term curve x+b Lower limit of middle and long term curve ，
Obtaining multiplying power { y) of each time point 1 ,y 2 ...y 96 }；
S403, multiplying power { y) at each time point of 96 points 1 ,y 2 ...y 96 Reporting electric quantity mean value (m) at 96 points in history of steps S302 1 ,m 2 ...m 96 Multiplying to obtain medium and long term day 96-point reported electric quantity basic value { n } 1 ,n 2 ...n 96 }；
S404, reporting the average value of the electric quantity { m } of 96 long-term points in the history of the step S302 1 ,m 2 ...m 96 The sum of the electric quantity and the basic value of the reported electric quantity (n) at 96 points of the long-term day in the step S403 1 ,n 2 ...n 96 The sum of the four points is compared to obtain the medium-long term day 96-point optimized multiplying power k1, namely
S405, optimizing multiplying power k1 at 96 points of long-term day in the step S404 and reporting the basic value of electric quantity { n at 96 points of long-term day in the step S403 1 ,n 2 ...n 96 Multiplying to obtain final medium-long term 96-point electric quantity reporting optimized value { z } 1 ,z 2 ...z 96 }；
The calculation of the electric quantity declaration optimized value of 96 points in the day comprises the following steps:
s501, according to a linear equation f = cx + d, considering a time-sharing excess profit margin deviation ratio of the real-time market of the user side, wherein d =1 is set, and an upper limit lambda of the time-sharing excess profit margin allowable deviation ratio of the real-time market of the user side is combined Upper limit of excess And the lower limit of the margin excess allowable deviation ratio lambda Lower limit of excess I.e. according to the formula
λ Upper limit of excess ＝c Upper limit of excess *P Price difference max +1
λ Lower limit of excess ＝c Lower limit of excess *P Price difference min +1
Calculating to obtain:
c upper limit of excess ＝(λ Upper limit of excess -1)/P Price difference max
c Lower limit of excess ＝(λ Lower limit of excess -1)/P Price difference min ；
S502, judging historical multi-day 96-point real-time electricity price mean value and 96-point day-ahead electricity price mean valueCombining the magnitude relation between the valence difference and 0, and substituting the data into the formula f = c if the time valence difference is greater than 0 by combining the linear equation f = cx + d in the step S501 Upper limit of excess x +1, if the time-valence difference is less than 0, substituting the data into the formula f = c Lower limit of excess x +1, and obtaining multiplying power { f at each time point 1 ,f 2 ...f 96 }；
S503, multiplying power { f) at each time point in the step S502 1 ,f 2 ...f 96 And step S303, the average value of historical actual electric quantity of the user in step S p 1 ,p 2 ...p 96 Multiplying to obtain basic value q of reported electric quantity at 96 points in the day 1 ,q 2 ...q 96 }；
S504, averaging the actual historical electric quantity { p } of the user in the step S303 1 ,p 2 ...p 96 The sum of the sum and the reported electric quantity basic value (q) 96 points before the step S503 1 ,q 2 ...q 96 Comparing the sum to obtain 96 point optimization multiplying power k2 before the day, namely
S505, optimizing multiplying power k2 at 96 points in the day before step S504 and reporting the electric quantity basic value { q at 96 points in the day before step S503 1 ,q 2 ...q 96 Multiplying to obtain a final day-ahead 96-point electric quantity declaration optimized value { o } 1 ,o 2 ...o 96 }。
2. The similar day-based power spot market electricity quantity declaration information optimization method according to claim 1, wherein the step S1 specifically includes the steps of:
s101, historical weather information required to be acquired by electric power spot market transaction comprises date, a daily temperature change curve, a highest temperature and a lowest temperature;
s102, preprocessing the highest temperature information and the lowest temperature information of the historical days by combining the historical weather information, and sorting to generate a standard format file containing fields of date, highest temperature and lowest temperature;
s103, dividing the historical transaction date of the power spot market into a working day, a rest day and a legal holiday according to types, and arranging to generate a standard format file containing date, day of week and date type fields;
and S104, performing unified analysis according to the information arranged in the steps S102 and S103, if the highest temperature information and the lowest temperature information of the same type of date on the same day are the same, defining the date as a similar date, and if the temperature information cannot be met, defining the same type of date of the last 30 days as the similar date.
3. The similar day-based power spot market electricity quantity declaration information optimization method according to claim 1, wherein the step S2 specifically includes the steps of:
s201, sorting the historical day-ahead and real-time 96-point electricity price information of the electric power spot market transaction to generate a standard format file according to the similar day information;
s202, calculating and obtaining the electricity price mean value { P } of each time point of 96 real-time points in the day before the multiple days of the similar day history according to the information arranged in the step S201 Day ahead 1 ，P Day 2 before ,...P Day-ahead 96 }、{P Real time 1 ，P Real time 2 ,...P Real time 96 }；
S203, calculating and obtaining the maximum value P of the electricity price mean value of each time point of 96 points in the day before the days of the history of similar days according to the calculation result of the step S202 Day-ahead max Minimum value P Day-ahead min Total average value P Day-ahead avg ；
S204, calculating the price difference { P) between the 96-point real-time electricity price mean value of multiple days of the similar day history and the 96-point day-ahead electricity price mean value according to the calculation result in the step S202 Valence difference 1 ，P Valence difference 2 ,...P Price difference 96 }；
S205, calculating and obtaining the maximum value P of the price difference between the 96-point real-time electricity price mean value of the multiple days of the history of the similar days and the 96-point day-ahead electricity price mean value according to the calculation result of the step S204 Price difference max Minimum value P Price difference min 。
4. The similar day-based power spot market electricity quantity declaration information optimization method according to claim 1, wherein the step S3 specifically includes the steps of:
s301, calculating and declaring an electric quantity mean value { m } according to the electric quantity information of 96 long-term points in the electric power spot market transaction history 1 ,m 2 ...m 96 }；
S302, calculating and solving a user historical actual electric quantity mean value { p }according to user actual electric quantity information 1 ,p 2 ...p 96 }。
5. A similar day-based electric power spot market electric quantity declaration information optimization system is characterized by comprising:
the data acquisition and storage unit is used for acquiring historical weather information, acquiring 96-point power price information before the current power spot market transaction history, acquiring 96-point power declaration information before the current power spot market transaction history and long-term power declaration information in the current power spot market transaction history and user actual power information, and storing the information;
the data processing unit is used for classifying spot market trading days according to historical weather information to obtain an electric power spot market electricity price information group based on similar days, calculating medium and long term 96-point electric quantity declaration optimized values and calculating 96-point electric quantity declaration optimized values before the day;
the communication unit is used for connecting external equipment, writing data required by the data acquisition and storage unit and transmitting a calculation result;
the data processing unit calculation process comprises the steps of:
s201, sorting the historical day-ahead and real-time 96-point electricity price information of the electric power spot market transaction to generate a standard format file according to the similar day information;
s202, calculating and obtaining the electricity price mean value { P } of each time point of 96 real-time points in the day before the multiple days of the similar day history according to the information arranged in the step S201 Day before 1 ，P Day 2 before ,...P Day-ahead 96 }、{P Real time 1 ，P Real time 2 ,...P Real time 96 }；
S203, calculating each time of 96 points before the day for obtaining multiple days of similar day history according to the calculation result of the step S202Maximum value P of point electricity price mean value Day-ahead max Minimum value P Day-ahead min Total average value P Day-ahead avg ；
S204, calculating the price difference { P) between the 96-point real-time electricity price mean value of multiple days of the similar day history and the 96-point day-ahead electricity price mean value according to the calculation result in the step S202 Valence difference 1 ，P Valence difference 2 ,...P Price difference 96 }；
S205, calculating and obtaining the maximum value P of the price difference between the 96-point real-time electricity price mean value of the multiple days of the similar day history and the 96-point day-ahead electricity price mean value according to the calculation result of the step S204 Price difference max Minimum value P Price difference min ；
S301, calculating and declaring the mean value of electric quantity { m } according to the electric quantity information of 96 long-term points in the electric power spot market transaction history 1 ,m 2 ...m 96 }；
S302, calculating and solving a user historical actual electric quantity mean value { p }according to user actual electric quantity information 1 ,p 2 ...p 96 }
S401, according to a linear equation y = ax + b and combining a middle-long term curve deviation allowable proportion upper limit lambda Upper limit of medium-long term curve Middle and long term curve deviation allowable proportion lower limit lambda Lower limit of middle and long term curve Namely the formula:
λ upper limit of medium-long term curve ＝a Upper limit of medium-long term curve *P Day-ahead max +λ Lower limit of middle and long term curve
1＝a Lower limit of medium-long term curve *P Day-ahead avg +λ Lower limit of middle and long term curve
Calculating to obtain:
a upper limit of medium-long term curve ＝(λ Upper limit of medium-long term curve -λ Lower limit of middle and long term curve )/P Day-ahead max
a Lower limit of middle and long term curve ＝(1-λ Lower limit of middle and long term curve )/P Day-ahead avg
b Upper limit of medium-long term curve ＝b Lower limit of middle and long term curve ＝λ Lower limit of middle and long term curve ；
S402, judging the average value and the total average value of the electricity price at each time point of 96 points in the dayP Day-ahead avg And substituting the linear equation y = ax + b;
if { P Day ahead 1 ，P Day 2 before ,...P Day-ahead 96 The electricity price mean value of is larger than P Day-ahead avg The data is substituted into the formula
y＝a Upper limit of medium-long term curve x+b Upper limit of medium-long term curve ，
If the average value of electricity price is less than P Day-ahead avg Substituting the data into the formula
y＝a Lower limit of middle and long term curve x+b Lower limit of middle and long term curve ，
Obtaining multiplying power { y) of each time point 1 ,y 2 ...y 96 }；
S403, multiplying power { y) at each time point of 96 points 1 ,y 2 ...y 96 Reporting electric quantity mean value (m) at 96 points in history of steps S302 1 ,m 2 ...m 96 Multiplying to obtain medium and long term day 96-point reported electric quantity basic value { n } 1 ,n 2 ...n 96 }；
S404, reporting the average value of the electric quantity { m } of 96 long-term points in the history of the step S302 1 ,m 2 ...m 96 The sum of the electric quantity and the basic value of the reported electric quantity (n) at 96 points of the long-term day in the step S403 1 ,n 2 ...n 96 The sum of the four points is compared to obtain the medium-long term day 96-point optimized multiplying power k1, namely
S405, optimizing multiplying power k1 at 96 points on a long-term day in the step S404 and reporting electric quantity basic value { n } at 96 points on the long-term day in the step S403 1 ,n 2 ...n 96 Multiplying to obtain a final medium-long term 96-point electric quantity declaration optimized value { z } 1 ,z 2 ...z 96 }；
The method for calculating the electric quantity declaration optimized value of 96 points in the day comprises the following steps:
s501, according to a linear equation f = cx + d, considering the time-sharing excess profit deviation proportion of the real-time market at the user side, wherein d =1 is set, and the time-sharing excess profit deviation proportion is combined with the real-time market at the user sideUpper limit lambda of margin excess allowable deviation ratio Upper limit of excess And lower limit of allowable deviation ratio λ of excess profit Lower limit of excess I.e. according to the formula
λ Upper limit of excess ＝c Upper limit of excess *P Price difference max +1
λ Lower limit of excess ＝c Lower limit of excess *P Price difference min +1
Calculating to obtain:
c upper limit of excess ＝(λ Upper limit of excess -1)/P Price difference max
c Lower limit of excess ＝(λ Lower limit of excess -1)/P Price difference min ；
S502, judging the size relation between the difference value of the historical multi-day 96-point real-time electricity price average value and the 96-point day-ahead electricity price average value and 0, combining the linear equation f = cx + d in the step S501, and substituting data into the formula f = c if the time price difference is larger than 0 Upper limit of excess x +1, if the time-valence difference is less than 0, substituting the data into the formula f = c Lower limit of excess x +1, and obtaining multiplying power { f at each time point 1 ,f 2 ...f 96 }；
S503, multiplying power { f) at each time point in the step S502 1 ,f 2 ...f 96 And S303, the average value of the historical actual electric quantity of the user in the step p 1 ,p 2 ...p 96 Multiplying to obtain basic value q of reported electric quantity at 96 points in the day 1 ,q 2 ...q 96 }；
S504, averaging the actual historical electric quantity { p } of the user in the step S303 1 ,p 2 ...p 96 The sum of the electric quantity and the reported electric quantity basic value (q) 96 points before the day of the step S503 1 ,q 2 ...q 96 Comparing the sum to obtain 96 point optimization multiplying power k2 before the day, namely
S505, optimizing multiplying power k2 at 96 points in the day before step S504 and reporting the electric quantity basic value { q at 96 points in the day before step S503 1 ,q 2 ...q 96 Multiply to get the mostElectric quantity declaration optimized value { o ] 96 points before final day 1 ,o 2 ...o 96 }。
6. The similar day-based power spot market electricity quantity declaration information optimization system of claim 5, wherein the process of classifying spot market trading days by the data processing unit comprises the steps of:
(1) Preprocessing the highest temperature and lowest temperature information of the historical day by combining historical weather information, and arranging to generate a standard format file containing fields of date, highest temperature and lowest temperature;
(2) Dividing the transaction historical date of the electric power spot market into a working day, a rest day and a legal holiday according to types, and arranging and generating a standard format file containing fields of date, day of week and date type;
(3) And (3) performing unified analysis according to the information collated in the steps (1) and (2), if the highest temperature information and the lowest temperature information of the same type of date are the same, defining the date as a similar date, and if the temperature information cannot be met, defining the same type of date of the last 30 days as the similar date.
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