CN112288496A - Load classification calculation method and tracking analysis method for power industry - Google Patents
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Abstract
The invention discloses a load classification calculation method in the power industry, which comprises the following steps of carrying out industry division on power loads by taking the electricity price as a division principle; carrying out industry division on the power load by taking the industry as a division principle; acquiring load characteristic data of each load according to the division result; carrying out data processing on the acquired load characteristic data; and carrying out load classification calculation on the data to obtain a final load classification calculation result in the power industry. The invention also discloses a tracking analysis method comprising the power industry load classification calculation method. The load classification calculation method and the tracking analysis method for the power industry provided by the invention realize the load classification calculation method and the tracking analysis for the power industry through load classification, data processing and centralized classification calculation, and have the advantages of high reliability, good accuracy and wide application range.
Description
Technical Field
The invention belongs to the field of electrical automation, and particularly relates to a load classification calculation method and a tracking analysis method for the power industry.
Background
With the development of economic technology and the improvement of living standard of people, electric energy becomes the most important secondary energy in production and life of people, and brings endless convenience to production and life of people. Therefore, stable and reliable operation of the power system becomes one of the most important tasks of the power system.
Grid load prediction is one of the important operations of a power system. At present, the power load analysis and prediction work is mostly based on monthly, quarterly and annual electric quantity data, and the short-term and real-time characteristics of the power load data are not fully utilized on the data frequency. Meanwhile, the power energy supply system is mostly faced with the power supply situation of 'lack of power and no electricity quantity', so that the power load analysis and prediction based on monthly, quarterly and annual electricity quantity data is difficult to meet the requirements of accurately and timely prejudging the real-time variation of the power load and the short-term operation situation at the present stage.
Disclosure of Invention
One of the purposes of the invention is to provide a load classification calculation method for the power industry, which has high reliability, good accuracy and wide application range.
The invention also aims to provide a tracking analysis method comprising the power industry load classification calculation method.
The invention provides a load classification calculation method for the power industry, which comprises the following steps:
s1, carrying out industry division on the power load by taking the electricity price as a division principle;
s2, carrying out industry division on the power load by taking the industry as a division principle;
s3, acquiring load characteristic data of each load according to the division results of the step S1 and the step S2;
s4, carrying out data processing on the load characteristic data acquired in the step S3;
and S5, carrying out load classification calculation on the processed data obtained in the step S4, so as to obtain a final load classification calculation result in the power industry.
The step S1 is to divide the power load into industry categories, specifically, a large industry category, a general industry and business category, a resident category, a wholesale category, and an agricultural category, with the power price as a division principle, and with the power supply unit as a dimension, the year, month, and day as a statistical period, and the power price as a division principle.
The step S2, which uses the industry as a division principle, performs industry division on the power load, specifically, divides the power load into a first industry load, a second industry load and a third industry load according to the classification standard of the national economy industry of 2017 edition by using a power supply unit as a dimension and using years, months and days as a statistical period:
first industrial load: including agricultural loads, forestry loads, animal husbandry loads, and fishery loads;
second industrial load: including industrial and construction loads;
third industrial load: including loads of remaining industries other than the first industrial load and the second industrial load.
The industrial loads include mining industrial loads, manufacturing industrial loads, tap water loads, electrical loads, steam loads, and gas loads.
The step S3 of acquiring load characteristic data of each load specifically acquires the following load characteristic data:
daily electricity load characteristic data:
daily maximum load: maximum in 96 point load during a day;
daily minimum load: minimum in 96 point load during a day;
average daily load: the average value of the 96-point load is adopted;
average daily load rate: the ratio of daily average load to daily maximum load;
daily minimum load rate: a ratio of daily minimum load to daily maximum load;
difference between daily peak and valley: the difference between the daily maximum load and the daily minimum load;
daily peak-to-valley difference rate: the ratio of the daily peak-to-valley difference to the daily maximum load;
monthly electrical load characteristic data
The maximum load per month: maximum value of maximum load for each day within one month;
minimum load per month: minimum value of minimum load for each day within one month;
average daily load per month: average of the average load per day over a month;
average daily load rate per month: average daily load rate over one month;
minimum daily load rate per month: minimum value of the daily minimum load rate within one month;
maximum peak-to-valley difference of the month: maximum peak-to-valley difference per day within one month;
maximum daily peak-to-valley rate of the month: maximum value of peak-to-valley difference rate of each day in one month;
monthly unbalance coefficient: the ratio of the average daily electric quantity to the maximum daily electric quantity within one month; or the ratio of the average daily load per month to the maximum load per month;
annual electrical load characteristic data
Annual maximum load: maximum value of 96 points of electrical load all year round;
annual minimum load: minimum value of 96 points of electrical load all year round;
average daily load per year: average value of average load of each day in one year;
average annual daily load rate: average value of daily load rate in one year;
minimum annual daily load rate: minimum value of the daily minimum load rate in one year;
maximum annual peak-to-valley difference: maximum value of peak-to-valley difference in each day of the year;
maximum annual daily peak-to-valley difference rate: maximum value of peak-to-valley difference rate in each day of the year;
quarterly imbalance coefficient: the ratio of the average of the sums of the maximum loads of the months of the year to the maximum load of the year;
annual load rate: the ratio of the average daily annual load to the maximum annual load.
In step S4, the data processing on the load characteristic data acquired in step S3 specifically includes the following rules:
rule one is as follows: diagnosis and correction for null and zero data points:
and (3) diagnosis:
matching power failure information, and judging whether a user has power failure: if the power failure exists, the null data point is 0 and is determined as normal data; if no power failure exists, determining that the data belongs to abnormal data;
and (3) correction:
if the number of the continuous abnormal data is less than the set X1, performing interpolation and completion by adopting an interpolation method; if the continuous abnormal data points are larger than or equal to the set X1 points, a curve smoothing method is adopted for completing; x1 is a positive integer;
rule two: diagnosis and correction of continuous constant and load data:
and (3) diagnosis:
judging the continuous constant value and the load data as abnormal data;
and (3) correction:
if the number of the continuous abnormal data is less than the set X2, performing interpolation and completion by adopting an interpolation method; if the continuous abnormal data points are larger than or equal to the set X2 points, a curve smoothing method is adopted for completing; x2 is a positive integer;
rule three: diagnosis and correction of step or sudden change values:
there are two aspects of step or jump values: one is that a plurality of abnormal values appear in a load curve of a certain day; the other is that the change of the whole load level exceeds a set threshold value;
and (3) diagnosis:
for the case of several outliers in the load curve of a day: considering the load type of the user: if the user load type is a burr type load or an impact type load, correction is not needed; if the user load is a conventional load, correcting the abnormal value;
for the case where the change in the overall load level exceeds a set threshold: observing the electricity utilization behaviors of the user for a plurality of consecutive days, and if the change shows continuity, determining the change as normal data; if the change is suddenly appeared, checking the daily settlement/monthly settlement electric quantity data of the user, and correcting an abnormal value of which the checking deviation is greater than a set value;
and (3) correction:
for the case of several outliers in the load curve of a day: averaging the data of the previous point and the data of the next point of the step value or the mutation value to obtain the load data of the point;
for the case where the change in the overall load level exceeds a set threshold: and carrying out smooth correction processing by adopting load data of several days.
In step S5, the load classification calculation is performed on the processed data obtained in step S4, specifically, the following steps are adopted for calculation:
a.96 point load calculation:
according to the private public transformation power data, the power price industry classification of the private public transformation, the national industry classification of the private public transformation and the meter multiplying power, the 96-point load data of each private public transformation and each public transformation is obtained by adopting the following calculation formula: y ═ P × T; wherein Y is 96-point load data of each private and public transformer; p is the specific public variable power data; t is the meter multiplying power;
the load of 96 points of users under the public transformer area is specifically that different proportional values are set for each user under the transformer area according to the power utilization information of the users under the public transformer area, and the load of 96 points of a single user Y1 is calculated by adopting the following formula; where Y1 is the 96 point load for a single user; x is a proportional value set for a user; y is 96-point load data of each private and public transformer;
B. summarizing industry 96 point loads:
96-point load of electricity price subdivision industry:
according to the electricity price industry to which the user belongs, 96-point loads of a large industrial load D1, a general industrial and commercial load D2, a residential load D3, an agricultural load D4 and a wholesale load D5 are summarized layer by layer according to the classification of the electricity price industry;
the national economy industry-wide classification 96-point load:
according to the national economy industry to which the user belongs, 96-point load of H granularity of the minimum industry of the national economy industry is obtained in a gathering mode; and then, correlating the upper and lower level relations of the national economy industry, accumulating the 96-point load values of the minimum granularity industry to obtain the 96-point load of the upper level industry, and repeating the steps until the 96-point load information of the first industry, the second industry, the third industry and the residential industry in the whole social industry is calculated.
The invention also provides a tracking analysis method comprising the power industry load classification calculation method, which further comprises the following steps:
s6, carrying out hierarchical progressive analysis on the load classification calculation result obtained in the step S5 from the space dimension so as to obtain the reason of the power load change;
s7, carrying out fine analysis on the load classification calculation result obtained in the step S5 from the time dimension, and thus carrying out tracking and early warning on the power load;
and S8, carrying out deep analysis on the load classification calculation result obtained in the step S5 in the industry classification, so as to accurately position the power load.
The load classification calculation method and the tracking analysis method for the power industry provided by the invention realize the load classification calculation method and the tracking analysis for the power industry through load classification, data processing and centralized classification calculation, and have the advantages of high reliability, good accuracy and wide application range.
Drawings
FIG. 1 is a schematic method flow diagram of the classification calculation method of the present invention.
FIG. 2 is a schematic flow chart of a tracking analysis method according to the present invention.
Detailed Description
Fig. 1 is a schematic flow chart of the classification calculation method of the present invention: the invention provides a load classification calculation method for the power industry, which comprises the following steps:
s1, carrying out industry division (electricity price industry division) on the power load by taking the electricity price as a division principle; the method specifically comprises the steps of dividing the power load into a large industrial category, a general industrial and commercial category, a resident category, a wholesale category and an agricultural category by taking a power supply unit as a dimension, taking a year, a month and a day as a statistical period and taking the electricity price as a dividing principle;
s2, carrying out industry division on the power load (national economy industry) by taking the industry as a division principle; specifically, the method comprises the steps of taking a power supply unit as a dimension, taking years, months and days as a statistical period, and dividing a power load into a first industrial load, a second industrial load and a third industrial load according to a 2017 edition national economy industry category division standard:
first industrial load: including agricultural loads, forestry loads, animal husbandry loads, and fishery loads;
second industrial load: including industrial and construction loads; wherein the industrial loads include mining industrial loads, manufacturing industrial loads, tap water loads, electrical loads, steam loads, and gas loads;
third industrial load: including loads of remaining industries other than the first industrial load and the second industrial load
S3, acquiring load characteristic data of each load according to the division results of the step S1 and the step S2; specifically, the following load characteristic data are obtained:
daily electricity load characteristic data:
daily maximum load: maximum in 96 point load during a day;
daily minimum load: minimum in 96 point load during a day;
average daily load: average of 96 point loads;
average daily load rate: also known as daily load rate, the ratio of daily average load to daily maximum load;
daily minimum load rate: a ratio of daily minimum load to daily maximum load;
difference between daily peak and valley: the difference between the daily maximum load and the daily minimum load;
daily peak-to-valley difference rate: the ratio of the daily peak-to-valley difference to the daily maximum load;
monthly electrical load characteristic data
The maximum load per month: the maximum value of the maximum load of each day in one month, namely the maximum value of each point load in one month;
minimum load per month: the minimum value of the minimum load of each day in one month, namely the minimum value of each point load in one month;
average daily load per month: average value of average load of each day in a month, namely monthly electricity consumption divided by hours in the month;
average daily load rate per month: average daily load rate over one month;
minimum daily load rate per month: minimum value of the daily minimum load rate within one month;
maximum peak-to-valley difference of the month: maximum peak-to-valley difference per day within one month;
maximum daily peak-to-valley rate of the month: maximum value of peak-to-valley difference rate of each day in one month;
monthly unbalance coefficient: also called monthly load rate, the ratio of the average daily electric quantity to the maximum daily electric quantity in a month; the ratio of the average daily load per month to the maximum load per month may also be used;
annual electrical load characteristic data
Annual maximum load: maximum value of 96 points of electrical load all year round;
annual minimum load: minimum value of 96 points of electrical load all year round;
average daily load per year: average value of average load of each day in a year, namely annual power consumption divided by current year hours;
average annual daily load rate: average value of daily load rate in one year;
minimum annual daily load rate: minimum value of the daily minimum load rate in one year;
maximum annual peak-to-valley difference: maximum value of peak-to-valley difference in each day of the year;
maximum annual daily peak-to-valley difference rate: maximum value of peak-to-valley difference rate in each day of the year;
quarterly imbalance coefficient: the ratio of the average of the sum of the maximum loads of the months of the year to the annual maximum load is also called the seasonal load rate;
annual load rate: the ratio of the average annual daily load to the annual maximum load;
s4, carrying out data processing on the load characteristic data acquired in the step S3; specifically, the following rules are adopted for data processing:
rule one is as follows: diagnosis and correction for null and zero data points:
and (3) diagnosis:
matching power failure information, and judging whether a user has power failure: if the power failure exists, the null data point is 0 and is determined as normal data; if no power failure exists, determining that the data belongs to abnormal data;
and (3) correction:
if the number of the continuous abnormal data is less than the set X1, performing interpolation and completion by adopting an interpolation method; if the continuous abnormal data points are larger than or equal to the set X1 points, a curve smoothing method is adopted for completing; x1 is a positive integer;
rule two: diagnosis and correction of continuous constant and load data:
and (3) diagnosis:
judging the continuous constant value and the load data as abnormal data;
and (3) correction:
if the number of the continuous abnormal data is less than the set X2, performing interpolation and completion by adopting an interpolation method; if the continuous abnormal data points are larger than or equal to the set X2 points, a curve smoothing method is adopted for completing; x2 is a positive integer;
rule three: diagnosis and correction of step or sudden change values:
there are two aspects of step or jump values: one is that a plurality of abnormal values appear in a load curve of a certain day; the other is that the change of the whole load level exceeds a set threshold value;
and (3) diagnosis:
for the case of several outliers in the load curve of a day: considering the load type of the user: if the user load type is a burr type load or an impact type load, correction is not needed; if the user load is a conventional load, correcting the abnormal value;
for the case where the change in the overall load level exceeds a set threshold: observing the electricity utilization behaviors of the user for a plurality of consecutive days, and if the change shows continuity, determining the change as normal data; if the change is suddenly appeared, checking the daily settlement/monthly settlement electric quantity data of the user, and correcting an abnormal value of which the checking deviation is greater than a set value;
and (3) correction:
for the case of several outliers in the load curve of a day: averaging the data of the previous point and the data of the next point of the step value or the mutation value to obtain the load data of the point;
for the case where the change in the overall load level exceeds a set threshold: carrying out smooth correction processing by adopting load data of a plurality of days;
s5, carrying out load classification calculation on the processed data obtained in the step S4 so as to obtain a final load classification calculation result in the power industry; specifically, the following steps are adopted for calculation:
a.96 point load calculation:
according to the private public transformation power data, the power price industry classification of the private public transformation, the national industry classification of the private public transformation and the meter multiplying power, the 96-point load data of each private public transformation and each public transformation is obtained by adopting the following calculation formula: y ═ P × T; wherein Y is 96-point load data of each private and public transformer; p is the specific public variable power data; t is the meter multiplying power;
the load of 96 points of users under the public transformer area is specifically that different proportional values are set for each user under the transformer area according to the power utilization information of the users under the public transformer area, and the load of 96 points of a single user Y1 is calculated by adopting the following formula; where Y1 is the 96 point load for a single user; x is a proportional value set for a user; y is 96-point load data of each private and public transformer;
B. summarizing industry 96 point loads:
96-point load of electricity price subdivision industry:
according to the electricity price industry to which the user belongs, 96-point loads of a large industrial load D1, a general industrial and commercial load D2, a residential load D3, an agricultural load D4 and a wholesale load D5 are summarized layer by layer according to the classification of the electricity price industry;
d1 is the sum of 96 loads of the users with the large industrial electricity price;
d2 is the sum of 96 loads of users belonging to the general commercial electricity price;
d3 is the sum of 96 loads of users of the electricity prices of the belonged residents;
d4 is the sum of 96 loads of the users belonging to the agricultural electricity price;
d5 is the sum of the loads of 96 points of the user who belongs to the wholesale electricity price;
the national economy industry-wide classification 96-point load:
according to the national economy industry to which the user belongs, 96-point load of H granularity of the minimum industry of the national economy industry is obtained in a gathering mode (H is 96-point load accumulation of the minimum granularity industry to which the user belongs); and then, correlating the upper and lower level relations of the national economy industry, accumulating the 96-point load values of the minimum granularity industry to obtain the 96-point load of the upper level industry, and repeating the steps until the 96-point load information of the first industry, the second industry, the third industry and the residential industry in the whole social industry is calculated.
Fig. 2 is a schematic flow chart of the tracking analysis method of the present invention: the invention provides a tracking analysis method comprising the power industry load classification calculation method, which comprises the following steps:
s1, carrying out industry division (electricity price industry division) on the power load by taking the electricity price as a division principle; the method specifically comprises the steps of dividing the power load into a large industrial category, a general industrial and commercial category, a resident category, a wholesale category and an agricultural category by taking a power supply unit as a dimension, taking a year, a month and a day as a statistical period and taking the electricity price as a dividing principle;
s2, carrying out industry division on the power load (national economy industry) by taking the industry as a division principle; specifically, the method comprises the steps of taking a power supply unit as a dimension, taking years, months and days as a statistical period, and dividing a power load into a first industrial load, a second industrial load and a third industrial load according to a 2017 edition national economy industry category division standard:
first industrial load: including agricultural loads, forestry loads, animal husbandry loads, and fishery loads;
second industrial load: including industrial and construction loads; wherein the industrial loads include mining industrial loads, manufacturing industrial loads, tap water loads, electrical loads, steam loads, and gas loads;
third industrial load: including loads of remaining industries other than the first industrial load and the second industrial load
S3, acquiring load characteristic data of each load according to the division results of the step S1 and the step S2; specifically, the following load characteristic data are obtained:
daily electricity load characteristic data:
daily maximum load: maximum in 96 point load during a day;
daily minimum load: minimum in 96 point load during a day;
average daily load: average of 96 point loads;
average daily load rate: also known as daily load rate, the ratio of daily average load to daily maximum load;
daily minimum load rate: a ratio of daily minimum load to daily maximum load;
difference between daily peak and valley: the difference between the daily maximum load and the daily minimum load;
daily peak-to-valley difference rate: the ratio of the daily peak-to-valley difference to the daily maximum load;
monthly electrical load characteristic data
The maximum load per month: the maximum value of the maximum load of each day in one month, namely the maximum value of each point load in one month;
minimum load per month: the minimum value of the minimum load of each day in one month, namely the minimum value of each point load in one month;
average daily load per month: average value of average load of each day in a month, namely monthly electricity consumption divided by hours in the month;
average daily load rate per month: average daily load rate over one month;
minimum daily load rate per month: minimum value of the daily minimum load rate within one month;
maximum peak-to-valley difference of the month: maximum peak-to-valley difference per day within one month;
maximum daily peak-to-valley rate of the month: maximum value of peak-to-valley difference rate of each day in one month;
monthly unbalance coefficient: also called monthly load rate, the ratio of the average daily electric quantity to the maximum daily electric quantity in a month; the ratio of the average daily load per month to the maximum load per month may also be used;
annual electrical load characteristic data
Annual maximum load: maximum value of 96 points of electrical load all year round;
annual minimum load: minimum value of 96 points of electrical load all year round;
average daily load per year: average value of average load of each day in one year;
average annual daily load rate: average value of daily load rate in one year;
minimum annual daily load rate: minimum value of the daily minimum load rate in one year;
maximum annual peak-to-valley difference: maximum value of peak-to-valley difference in each day of the year;
maximum annual daily peak-to-valley difference rate: maximum value of peak-to-valley difference rate in each day of the year;
quarterly imbalance coefficient: the ratio of the average of the sum of the maximum loads of the months of the year to the annual maximum load is also called the seasonal load rate;
annual load rate: the ratio of the average annual daily load to the annual maximum load;
s4, carrying out data processing on the load characteristic data acquired in the step S3; specifically, the following rules are adopted for data processing:
rule one is as follows: diagnosis and correction for null and zero data points:
and (3) diagnosis:
matching power failure information, and judging whether a user has power failure: if the power failure exists, the null data point is 0 and is determined as normal data; if no power failure exists, determining that the data belongs to abnormal data;
and (3) correction:
if the number of the continuous abnormal data is less than the set X1, performing interpolation and completion by adopting an interpolation method; if the continuous abnormal data points are larger than or equal to the set X1 points, a curve smoothing method is adopted for completing; x1 is a positive integer;
rule two: diagnosis and correction of continuous constant and load data:
and (3) diagnosis:
judging the continuous constant value and the load data as abnormal data;
and (3) correction:
if the number of the continuous abnormal data is less than the set X2, performing interpolation and completion by adopting an interpolation method; if the continuous abnormal data points are larger than or equal to the set X2 points, a curve smoothing method is adopted for completing; x2 is a positive integer;
rule three: diagnosis and correction of step or sudden change values:
there are two aspects of step or jump values: one is that a plurality of abnormal values appear in a load curve of a certain day; the other is that the change of the whole load level exceeds a set threshold value;
and (3) diagnosis:
for the case of several outliers in the load curve of a day: considering the load type of the user: if the user load type is a burr type load or an impact type load, correction is not needed; if the user load is a conventional load, correcting the abnormal value;
for the case where the change in the overall load level exceeds a set threshold: observing the electricity utilization behaviors of the user for a plurality of consecutive days, and if the change shows continuity, determining the change as normal data; if the change is suddenly appeared, checking the daily settlement/monthly settlement electric quantity data of the user, and correcting an abnormal value of which the checking deviation is greater than a set value;
and (3) correction:
for the case of several outliers in the load curve of a day: averaging the data of the previous point and the data of the next point of the step value or the mutation value to obtain the load data of the point;
for the case where the change in the overall load level exceeds a set threshold: carrying out smooth correction processing by adopting load data of a plurality of days;
s5, carrying out load classification calculation on the processed data obtained in the step S4 so as to obtain a final load classification calculation result in the power industry; specifically, the following steps are adopted for calculation:
a.96 point load calculation:
according to the private public transformation power data, the power price industry classification of the private public transformation, the national industry classification of the private public transformation and the meter multiplying power, the 96-point load data of each private public transformation and each public transformation is obtained by adopting the following calculation formula: y ═ P × T; wherein Y is 96-point load data of each private and public transformer; p is the specific public variable power data; t is the meter multiplying power;
the load of 96 points of users under the public transformer area is specifically that different proportional values are set for each user under the transformer area according to the power utilization information of the users under the public transformer area, and the load of 96 points of a single user Y1 is calculated by adopting the following formula; where Y1 is the 96 point load for a single user; x is a proportional value set for a user; y is 96-point load data of each private and public transformer;
B. summarizing industry 96 point loads:
96-point load of electricity price subdivision industry:
according to the electricity price industry to which the user belongs, 96-point loads of a large industrial load D1, a general industrial and commercial load D2, a residential load D3, an agricultural load D4 and a wholesale load D5 are summarized layer by layer according to the classification of the electricity price industry;
d1 is the sum of 96 loads of the users with the large industrial electricity price;
d2 is the sum of 96 loads of users belonging to the general commercial electricity price;
d3 is the sum of 96 loads of users of the electricity prices of the belonged residents;
d4 is the sum of 96 loads of the users belonging to the agricultural electricity price;
d5 is the sum of the loads of 96 points of the user who belongs to the wholesale electricity price;
the national economy industry-wide classification 96-point load:
according to the national economy industry to which the user belongs, 96-point load of H granularity of the minimum industry of the national economy industry is obtained in a gathering mode (H is 96-point load accumulation of the minimum granularity industry to which the user belongs); then, correlating the upper and lower level relations of the national economy industry, accumulating the 96-point load values of the minimum granularity industry to obtain the 96-point load of the upper level industry, and repeating the steps until the 96-point load information of the first industry, the second industry, the third industry and the residential industry in the whole social industry is calculated;
s6, carrying out hierarchical progressive analysis on the load classification calculation result obtained in the step S5 from the space dimension so as to obtain the reason of the power load change; establishing a step drilling type analysis mechanism to form a province-city-county hierarchical progressive analysis mode and quickly positioning market change causes; (ii) a
S7, carrying out fine analysis on the load classification calculation result obtained in the step S5 from the time dimension, and thus carrying out tracking and early warning on the power load; establishing a gradual refined analysis mode of 'year-season-month-week-day', and tracking and early warning market fluctuation in time;
s8, carrying out deep analysis on the load classification calculation result obtained in the step S5 in the aspect of industry classification, and thus carrying out accurate positioning on the power load; an industry decomposition analysis mechanism is established, a 'whole network-industry-user' deep dialysis analysis mode is established, and key objects are accurately positioned.
Claims (8)
1. A load classification calculation method for the power industry comprises the following steps:
s1, carrying out industry division on the power load by taking the electricity price as a division principle;
s2, carrying out industry division on the power load by taking the industry as a division principle;
s3, acquiring load characteristic data of each load according to the division results of the step S1 and the step S2;
s4, carrying out data processing on the load characteristic data acquired in the step S3;
and S5, carrying out load classification calculation on the processed data obtained in the step S4, so as to obtain a final load classification calculation result in the power industry.
2. The electric power industry load classification calculation method according to claim 1, wherein in step S1, the electric power prices are used as a classification rule to classify the electric power loads into industry classification, specifically, into large industry classification, general industry and business classification, residential classification, wholesale classification and agricultural classification, with the power supply unit as a dimension, with the year, month and day as a statistical period, and with the electric power prices as a classification rule.
3. The method for classifying and calculating the load in the power industry according to claim 1, wherein in the step S2, the power load is classified into industry according to the classification standard of the national economic industry of 2017 edition, with the industry as a classification principle, specifically, with a power supply unit as a dimension, with years, months and days as a statistical period, and with the industry as a classification rule, the power load is classified into a first industry load, a second industry load and a third industry load:
first industrial load: including agricultural loads, forestry loads, animal husbandry loads, and fishery loads;
second industrial load: including industrial and construction loads;
third industrial load: including loads of remaining industries other than the first industrial load and the second industrial load.
4. The electric power industry load classification calculation method according to claim 3, wherein the industrial loads include mining industrial loads, manufacturing industrial loads, tap water loads, electric power loads, steam loads, and gas loads.
5. The electric power industry load classification calculation method according to any one of claims 1 to 4, wherein the step S3 is to obtain load characteristic data of each load, specifically to obtain the following load characteristic data:
daily electricity load characteristic data:
daily maximum load: maximum in 96 point load during a day;
daily minimum load: minimum in 96 point load during a day;
average daily load: the average value of the 96-point load is adopted;
average daily load rate: the ratio of daily average load to daily maximum load;
daily minimum load rate: a ratio of daily minimum load to daily maximum load;
difference between daily peak and valley: the difference between the daily maximum load and the daily minimum load;
daily peak-to-valley difference rate: the ratio of the daily peak-to-valley difference to the daily maximum load;
monthly electrical load characteristic data
The maximum load per month: maximum value of maximum load for each day within one month;
minimum load per month: minimum value of minimum load for each day within one month;
average daily load per month: average of the average load per day over a month;
average daily load rate per month: average daily load rate over one month;
minimum daily load rate per month: minimum value of the daily minimum load rate within one month;
maximum peak-to-valley difference of the month: maximum peak-to-valley difference per day within one month;
maximum daily peak-to-valley rate of the month: maximum value of peak-to-valley difference rate of each day in one month;
monthly unbalance coefficient: the ratio of the average daily electric quantity to the maximum daily electric quantity within one month; or the ratio of the average daily load per month to the maximum load per month;
annual electrical load characteristic data
Annual maximum load: maximum value of 96 points of electrical load all year round;
annual minimum load: minimum value of 96 points of electrical load all year round;
average daily load per year: average value of average load of each day in one year;
average annual daily load rate: average value of daily load rate in one year;
minimum annual daily load rate: minimum value of the daily minimum load rate in one year;
maximum annual peak-to-valley difference: maximum value of peak-to-valley difference in each day of the year;
maximum annual daily peak-to-valley difference rate: maximum value of peak-to-valley difference rate in each day of the year;
quarterly imbalance coefficient: the ratio of the average of the sums of the maximum loads of the months of the year to the maximum load of the year;
annual load rate: the ratio of the average daily annual load to the maximum annual load.
6. The electric power industry load classification calculation method according to claim 5, wherein the step S4 is to perform data processing on the load characteristic data obtained in the step S3, specifically to perform data processing by adopting the following rules:
rule one is as follows: diagnosis and correction for null and zero data points:
and (3) diagnosis:
matching power failure information, and judging whether a user has power failure: if the power failure exists, the null data point is 0 and is determined as normal data; if no power failure exists, determining that the data belongs to abnormal data;
and (3) correction:
if the number of the continuous abnormal data is less than the set X1, performing interpolation and completion by adopting an interpolation method; if the continuous abnormal data points are larger than or equal to the set X1 points, a curve smoothing method is adopted for completing; x1 is a positive integer;
rule two: diagnosis and correction of continuous constant and load data:
and (3) diagnosis:
judging the continuous constant value and the load data as abnormal data;
and (3) correction:
if the number of the continuous abnormal data is less than the set X2, performing interpolation and completion by adopting an interpolation method; if the continuous abnormal data points are larger than or equal to the set X2 points, a curve smoothing method is adopted for completing; x2 is a positive integer;
rule three: diagnosis and correction of step or sudden change values:
there are two aspects of step or jump values: one is that a plurality of abnormal values appear in a load curve of a certain day; the other is that the change of the whole load level exceeds a set threshold value;
and (3) diagnosis:
for the case of several outliers in the load curve of a day: considering the load type of the user: if the user load type is a burr type load or an impact type load, correction is not needed; if the user load is a conventional load, correcting the abnormal value;
for the case where the change in the overall load level exceeds a set threshold: observing the electricity utilization behaviors of the user for a plurality of consecutive days, and if the change shows continuity, determining the change as normal data; if the change is suddenly appeared, checking the daily settlement/monthly settlement electric quantity data of the user, and correcting an abnormal value of which the checking deviation is greater than a set value;
and (3) correction:
for the case of several outliers in the load curve of a day: averaging the data of the previous point and the data of the next point of the step value or the mutation value to obtain the load data of the point;
for the case where the change in the overall load level exceeds a set threshold: and carrying out smooth correction processing by adopting load data of several days.
7. The electric power industry load classification calculation method according to claim 6, wherein the step S5 is to perform load classification calculation on the processed data obtained in the step S4, specifically, the following steps are adopted for calculation:
a.96 point load calculation:
according to the private public transformation power data, the power price industry classification of the private public transformation, the national industry classification of the private public transformation and the meter multiplying power, the 96-point load data of each private public transformation and each public transformation is obtained by adopting the following calculation formula: y ═ P × T; wherein Y is 96-point load data of each private and public transformer; p is the specific public variable power data; t is the meter multiplying power;
the load of 96 points of users under the public transformer area is specifically that different proportional values are set for each user under the transformer area according to the power utilization information of the users under the public transformer area, and the load of 96 points of a single user Y1 is calculated by adopting the following formula; where Y1 is the 96 point load for a single user; x is a proportional value set for a user; y is 96-point load data of each private and public transformer;
B. summarizing industry 96 point loads:
96-point load of electricity price subdivision industry:
according to the electricity price industry to which the user belongs, 96-point loads of a large industrial load D1, a general industrial and commercial load D2, a residential load D3, an agricultural load D4 and a wholesale load D5 are summarized layer by layer according to the classification of the electricity price industry;
d1 is the sum of 96 loads of the users with the large industrial electricity price;
d2 is the sum of 96 loads of users belonging to the general commercial electricity price;
d3 is the sum of 96 loads of users of the electricity prices of the belonged residents;
d4 is the sum of 96 loads of the users belonging to the agricultural electricity price;
d5 is the sum of the loads of 96 points of the user who belongs to the wholesale electricity price;
the national economy industry-wide classification 96-point load:
according to the national economy industry to which the user belongs, 96-point load of H granularity of the minimum industry of the national economy industry is obtained in a gathering mode; h is 96-point load accumulation of the minimum granularity industry to which the user belongs; and then, correlating the upper and lower level relations of the national economy industry, accumulating the 96-point load values of the minimum granularity industry to obtain the 96-point load of the upper level industry, and repeating the steps until the 96-point load information of the first industry, the second industry, the third industry and the residential industry in the whole social industry is calculated.
8. A tracking analysis method comprising the power industry load classification calculation method according to any one of claims 1 to 7, characterized by further comprising the steps of:
s6, carrying out hierarchical progressive analysis on the load classification calculation result obtained in the step S5 from the space dimension so as to obtain the reason of the power load change;
s7, carrying out fine analysis on the load classification calculation result obtained in the step S5 from the time dimension, and thus carrying out tracking and early warning on the power load;
and S8, carrying out deep analysis on the load classification calculation result obtained in the step S5 in the industry classification, so as to accurately position the power load.
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