CN102663518A - Annual maximum load prediction method based on temperature reduction model - Google Patents
Annual maximum load prediction method based on temperature reduction model Download PDFInfo
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Abstract
The invention discloses an annual maximum load prediction method based on a temperature reduction model used in the power grid control field. The method comprises the following steps of: a historical data processing step comprising obtaining an annual maximum load of each year in the history and a maximum temperature on a maximum load day, carrying out accumulative temperature correction on the maximum temperature on the maximum load day of each year in the history, calculating an annual basic load of each year in the history, and calculating a temperature sensitive coefficient at each temperature; a load reduction step comprising determining a maximum reference temperature, solving an adjustment coefficient, and calculating an annual reduction maximum load of each year in the history; a load extrapolation prediction and result adjustment step comprising establishing a regression model, carrying out extrapolation prediction on an annual reduction maximum load of a target year by using the annual reduction maximum load, predicting an annual maximum temperature of the target year, determining a prediction interval of the annual maximum temperature of the target year, and determining an annual maximum load prediction interval according to the prediction interval of the annual maximum temperature of the target year.
Description
Technical field
The present invention relates to a kind of be used for the power grid control field based on the temperature biggest yearly load prediction method of master mould also.
Background technology
In the electrical network, temperature factor is one of influence factor of short-term load forecasting emphasis consideration, and its influence is not generally considered in traditional medium-term and long-term load prediction.Yet; Along with the raising of expanding economy and resident living level, in recent years, in the most large-and-medium size cities of China; Especially season clearly demarcated southern provinces and cities, resident's type load and tertiary industry load shared proportion in social total load all is the trend that rises year by year.Characteristics when the year of analysis urban distribution network, peak load took place are not difficult to find, the appearance of above-mentioned load and weather conditions, and especially there is substantial connection in temperature, shows as the sensitivity characteristic to temperature.Particularly in seasonal variations evident characteristic urban area, this characteristics are more outstanding.Temperature has become a sensible factor of can not ignore that influences the urban distribution network load variations.
In the medium-term and long-term load prediction that with the year is unit; General main with historical each annual peak load be the basis carry out target year and each level year year peak load prediction; And year peak load is subject to the influence of annual maximum temperature; Often there is bigger responsive to temperature component in the data of historical each annual peak load, or perhaps the temperature random component.Therefore, need the incidence relation between primary study temperature and year peak load, and in prediction, reject the influence of responsive to temperature component, could effectively improve the accuracy of the biggest yearly load prediction in target year.Be meant the time that to carry out biggest yearly load prediction target year.
Summary of the invention
The objective of the invention is in order to overcome the deficiency of prior art; Provide a kind of based on the temperature biggest yearly load prediction method of master mould also; It can make prediction accurately and effectively to the year peak load in target year, for the regulation and control of electric power in electrical network provide foundation, ensures the security of operation of electrical network.
A kind of technical scheme that realizes above-mentioned purpose is: based on the temperature biggest yearly load prediction method of master mould also, the year peak load in target year is predicted said biggest yearly load prediction method comprises the following steps:
S1 historical data treatment step comprises:
S11 asks for the year peak load and peak day maximum temperature operation in historical each year; S12 carries out the accumulated temperature correction to the year peak day maximum temperature in historical each year, asks for the year peak day correction maximum temperature operation in historical each year; S13 asks for the year basic load operation in historical each year; S14 asks for temperature-sensitivity coefficient operation at each temperature;
The S2 reduction step of loading comprises:
S21 confirms the highest reference temperature operation; S22 finds the solution adjustment coefficient operation; S23 asks for the year reduction peak load operation in historical each year;
S3 load outside forecast and set-up procedure as a result comprise:
S31 sets up regression model, with the year reduction peak load operation in year reduction peak load outside forecast target year; The annual maximum temperature in S32 target of prediction year is confirmed the annual maximum temperature forecast interval operation in target year; S33 confirms the interval operation of biggest yearly load prediction in target year according to the annual maximum temperature forecast interval predicted value in target year.
Further, in the said S12 operation, the solution formula of said year peak day correction maximum temperature is:
Wherein, T '
0Be year peak day correction maximum temperature, T
0Be year peak day maximum temperature, T
iBe maximum temperature before year peak day i days; α is the cumulative effect coefficient;
N is higher than 28 ℃ fate continuously for a day maximum temperature, and p is the statistics fate.
Further again, the method for said S13 operation by: use HP wave filter day peak load of historical each annual whole year is carried out HP filtering, obtain the trend component and the periodic component of historical each peak load annual day; Ask for historical each June to September in year day peak load trend component, ask for the mean value of said trend component, with this year basic load as historical each year.
Further; The method of said S14 operation is: the year peak day correction maximum temperature that reads historical each year; And the arithmetic mean and the root-mean-square value of the year peak day correction maximum temperature in each year of computation history, with the mean value of said arithmetic mean and said root-mean-square value as the highest said reference temperature.
Also want further; The method of said S22 operation is: read temperature-sensitivity coefficient at each temperature; And according to the said temperature-sensitivity coefficient under the root temperature; Set up optimization restricted problem equation, confirm adjustment COEFFICIENT K 2 respectively in 28 ℃ of adjustment COEFFICIENT K 1 in the highest said reference temperature and the highest said reference temperature to 40 ℃.
Also further again, in the said S23 operation, the formula of asking for said year reduction peak load is:
Wherein, P
RefBe year reduction peak load, T
RefThe highest reference temperature, P
0Be year peak load, T '
0Be year peak day correction maximum temperature.
Also will be further, in the said S31 operation, according to the year reduction peak load in historical each year, adopt the method for function regression, the year reduction peak load in outside forecast target year.
Want further, in 33 operations, the interval formula that is adopted of biggest yearly load prediction that calculates target year is again:
Wherein, P
Ref1Be the year reduction peak load in target year, T
RefThe highest reference temperature, P
Max1Be the biggest yearly load prediction in target the year interval upper limit or lower limit, T
Max1The upper limit or lower limit for the annual maximum temperature forecast interval in target year.
Adopted of the present invention based on the temperature technical scheme of the biggest yearly load prediction method of master mould also, promptly historical data treatment step, load reduction step and load outside forecast and as a result set-up procedure obtain the biggest yearly load prediction value in target year.Its technique effect is: can overcome with historical each annual peak load is the defective that biggest yearly load prediction was had that target year and each level year are carried out in the basis; The influence of responsive to temperature component in the data of historical each annual peak load; Improve target year year peak load accuracy; For the regulation and control of electric power in electrical network provide foundation, ensure the security of operation of electrical network.
Description of drawings
Fig. 1 is of the present invention based on the temperature process flow diagram of the biggest yearly load prediction method of master mould also.
Fig. 2 is of the present invention based on the temperature temperature reduction modular concept figure of the biggest yearly load prediction method of master mould also.
Embodiment
See also Fig. 1 and Fig. 2,, pass through embodiment particularly below, and combine accompanying drawing at length to explain in order to understand technical scheme of the present invention better:
Based on the temperature biggest yearly load prediction method of master mould also, the year peak load in target year is predicted said biggest yearly load prediction method comprises the following steps:
S1 historical data treatment step comprises:
S11 asks for the year peak load and peak day maximum temperature operation in historical each year; S12 carries out the accumulated temperature correction to the year peak day maximum temperature in historical each year, asks for the year peak day correction maximum temperature operation in historical each year; S13 asks for the year basic load operation in historical each year; S14 asks for temperature-sensitivity coefficient operation at each temperature;
The S2 reduction step of loading comprises:
S21 confirms the highest reference temperature operation; S22 finds the solution adjustment coefficient operation; S23 asks for the year reduction peak load operation in historical each year;
S3 load outside forecast and set-up procedure as a result comprise:
S31 sets up regression model, with the year reduction peak load operation in year reduction peak load outside forecast target year; The annual maximum temperature in S32 target of prediction year is confirmed the annual maximum temperature forecast interval operation in target year; S33 confirms the interval operation of biggest yearly load prediction in target year according to the annual maximum temperature forecast interval in target year.
In the S1 step; The purpose of said S12 operation is: revise the influence of accumulated temperature effect to peak load; Said accumulated temperature effect be in a few days peak load to change with the day be that unit lags behind the phenomenon that day maximum temperature changes, the Changing Pattern to the load influence of accumulated temperature effect may be summarized as follows:
Promptly had only when the same day, maximum temperature was between 28 ℃～38 ℃, the accumulated temperature effect is only obviously for effects of load, and in the time of between 33 ℃～34 ℃, the accumulated temperature effect is the most obvious for effects of load.
Only when the day maximum temperature was higher than 28 ℃ in continuous 0-3 days, the influence of accumulated temperature effect was only significantly.
Based on above analysis, can adopt in following two formula one to the same day maximum temperature carry out the accumulated temperature correction:
In the formula: T '
0For revising maximum temperature, T
0Actual maximum temperature, T
iBe the actual maximum temperature before i days;
For
Actual maximum temperature before it, α is the cumulative effect coefficient;
N is higher than 28 ℃ fate continuously for a day maximum temperature, and p promptly adds up fate for the fate of statistics accumulated temperature effect.
About the value of cumulative effect alpha, need get different values according to the different temperatures interval in the 28-40 ℃ of interval.And the concrete value of each temperature range α need carry out curve fitting to historical data in theory, and the alpha of cumulative effect described in the present embodiment can be chosen according to table 1:
Table 1 cumulative effect alpha is in different temperatures T value
| Temperature range T/ ℃ | The cumulative effect alpha |
| ＞38 | 0 |
| (37，38] | 0.10 |
| (36，37] | 0.30 |
| (35，36] | 0.45 |
| (34，35] | 0.65 |
| (33，34] | 0.50 |
| (31，33] | 0.35 |
| (30，31] | 0.20 |
| (28，30] | 0.10 |
Traditionally, the method that the S13 operation is taked is: choose the maximal value of historical each peak load annual every other day two months Mays of April, October Dan Yue day peak load maximal value, they are averaged, as historical each annual year basic load.But adopted more scientific methods among the present invention.This method is to read the year peak day correction maximum temperature in historical each year; And the arithmetic mean and the root-mean-square value of the year peak day correction maximum temperature in each year of computation history, with the mean value of said arithmetic mean and said root-mean-square value as the highest said reference temperature.Wherein the HP filtering method that adopted of HP wave filter is a kind of filtering method that Hodrick and Prescott propose.
In said 14 operations; Said temperature-sensitivity coefficient k is meant that unit temperature at each temperature changes the ratio of caused day peak load increment and said basic load: the method for solving of the said temperature-sensitivity coefficient in a certain year is in history: with the day maximum temperature in a certain year in history is independent variable; Day peak load with this year is a dependent variable; Carry out the cubic function match; Find the solution the slope of said cubic function under each day maximum temperature,, be the said temperature-sensitivity coefficient in this year again with the said basic load of said slope divided by this year.In the present embodiment, described temperature-sensitivity coefficient k is the mean value of nearly 5 years each annual temperature-sensitivity coefficient, and the temperature span of said temperature-sensitivity coefficient k correspondence is between 28 ℃～40 ℃.
Table 2, the tabulation of temperature-sensitivity coefficient k value
Also master mould such as Fig. 2 are said,
Expression year correction maximum temperature is with respect to the variable quantity of benchmark maximum temperature;
Expression is with respect to the year peak load under the said benchmark maximum temperature, i.e. the variable quantity of year reduction peak load;
The axle with
The T at axle intersection point place
RefExpression benchmark maximum temperature is called for short the highest reference temperature; K1, K2 represent: year the highest correction temperature is when being below or above said the highest reference temperature; Year is revised the every decline of maximum temperature or rises 1 ℃; The variable quantity of said year peak load accounts for year ratio of reduction peak load, i.e. the adjustment coefficient of annual maximum temperature when being higher or lower than said the highest reference temperature.Set up following relational expression:
In the following formula, P
MaxBe actual year peak load of year; P
RefFor the highest reference temperature is reduced peak load next year, corresponding to the year peak load value under the highest said reference temperature; T
MaxExpression year correction maximum temperature is as T '
0Be lower than T
RefThe time, K is for the adjustment coefficient, as T '
0Be lower than T
RefThe time, K=K1; As T '
0Be higher than T
RefThe time, K=K2.
Therefore, the said year formula of reduction peak load of finding the solution is:
In the S2 step; The method of S21 operation is: the correction maximum temperature that reads peak load same day in year in historical each year; And the root-mean-square value of the year peak day correction maximum temperature in the arithmetic mean of the year peak day correction maximum temperature in each year of computation history and historical each year, with the mean value of said arithmetic mean and said root-mean-square value as the highest said reference temperature.
Chosen nearly 5 years year peak day correction maximum temperature in the present embodiment; Be respectively: 36.72 ℃, 37.57 ℃, 37.63 ℃, 38.17 ℃ and 39.50 ℃; The arithmetic mean of year peak day correction maximum temperature is 37.9169 ℃, and the root-mean-square value of year peak day correction maximum temperature is 37.9280 ℃.Get both 37.92 ℃ of mean values, as the highest said reference temperature.
In the S22 operation, asking for said load adjustment COEFFICIENT K is to be the basis with historical each annual year peak load, year peak day correction maximum temperature, year reduction peak load and basic load value, as shown in table 3.
Table 3 historical each annual year reduction peak load and basic load value list
This is said year reduction peak load and the optimization problem of said basic load correlativity maximum; The upper and lower bound that limits the restriction range of K1 is respectively 28 ℃ of maximal value and minimum value to the said temperature-sensitivity coefficient k between the highest said reference temperature; The upper and lower bound that limits the restriction range of K2 is respectively maximal value and the minimum value of the said temperature-sensitivity coefficient k between the highest said reference temperature to 40 ℃; If negative value appears in said temperature-sensitivity coefficient k, then replace with 0.001.And find the solution the equation of constraint of optimization problem with these two qualificationss.
According to table 2, in the present embodiment, the restriction range of K1 is [0.0062,0.0524], and the restriction range of K2 is [0.001,0.0062], solves K1=0.0396, K2=0.0062.At this moment, a year reduction peak load and year coefficient R=0.9494 of two sequences of basic load, correlativity is very high.
According to the S23 operation, according to the formula of asking for of year reduction peak load, the year peak load that obtains nearly five year is as shown in table 4.
The year peak load in each year of table 4 history is tabulated with a year reduction peak load
In the S31 operation of S3 step, be foundation, adopt the year peak load in the method target of prediction year of function regression with the said year peak load in historical each year and the peak load of reducing in said year.Choose match relative coefficient R
2Value is the most near 5 function models of 1, the year reduction peak load predicted value in outside forecast target year respectively, and according to said relative coefficient R
2Value and the average proportions of F test value F are set weights, carry out weighted mean, obtain the predicting the outcome of year reduction peak load in the year in target year thus.Then revise between highest temperature zone according to the year in target year again and adjust predicting the outcome.As shown in table 5.The year reduction peak load predicted value in target of prediction year is 3255.300MW.
The year reduction peak load predicted value tabulation in table 5 target year
Through predicting the outcome of obtaining of table 5 is on the basis of said the highest reference temperature, to obtain, and need predict the outcome according to maximum temperature and revise.
In the S32 operation, the predicted value of the annual maximum temperature in the target year that obtains from meteorological department is 40 ℃, and the upper limit of the annual maximum temperature forecast interval in target year or lower limit are respectively 39.5 ℃-40.5 ℃.
So, in the S33 operation, calculate the interval upper and lower bound of biggest yearly load prediction in target year.
Be limited on the biggest yearly load prediction interval in target year:
=year reduction peak load predicted value * [1+0.0062 * (39.5-37.92)]
＝3255.300×(1+0.0062×1.58)＝3287.189MW。
The upper limit that the biggest yearly load prediction in target year is interval:
=target year reduction peak load result * [1+0.0062 * (39.5-37.92)]
＝3255.300×(1+0.0062×2.58)＝3307.372MW。
Those of ordinary skill in the art will be appreciated that; Above embodiment is used for explaining the present invention; And be not to be used as qualification of the present invention; As long as in connotation scope of the present invention, all will drop in claims scope of the present invention variation, the modification of the above embodiment.
Claims (7)
1. based on the temperature biggest yearly load prediction method of master mould also, the year peak load in target year is predicted it is characterized in that: said biggest yearly load prediction method comprises the following steps:
S1 historical data treatment step comprises:
S11 asks for the year peak load and peak day maximum temperature operation in historical each year; S12 carries out the accumulated temperature correction to the year peak day maximum temperature in historical each year, asks for the year peak day correction maximum temperature operation in historical each year; S13 asks for the year basic load operation in historical each year; S14 asks for temperature-sensitivity coefficient operation at each temperature;
The S2 reduction step of loading comprises:
S21 confirms the highest reference temperature operation; S22 finds the solution adjustment coefficient operation; S23 asks for the year reduction peak load operation in historical each year;
S3 load outside forecast and set-up procedure as a result comprise:
S31 sets up regression model, with the year reduction peak load operation in year reduction peak load outside forecast target year; The annual maximum temperature in S32 target of prediction year is confirmed the annual maximum temperature forecast interval operation in target year; S33 confirms the interval operation of biggest yearly load prediction in target year according to the annual maximum temperature forecast interval in target year.
2. biggest yearly load prediction method according to claim 1 is characterized in that: in the said S12 operation, the solution formula of said year peak day correction maximum temperature is:
Wherein, T '
0Be year peak day correction maximum temperature, T
0Be year peak day maximum temperature, T
iBe maximum temperature before year peak day i days; α is the cumulative effect coefficient; P=min (n, 3), n is higher than 28 ℃ fate continuously for a day maximum temperature, and p is the statistics fate.
3. biggest yearly load prediction method according to claim 2; It is characterized in that: the method for said S13 operation is: use the HP wave filter that day peak load of historical each annual whole year is carried out HP filtering, obtain the trend component and the periodic component of historical each peak load annual day; Ask for historical each June to September in year day peak load trend component, ask for the mean value of said trend component, with this year basic load as historical each year.
4. biggest yearly load prediction method according to claim 3; It is characterized in that: the method for said S14 operation is: the year peak day correction maximum temperature that reads historical each year; And the arithmetic mean and the root-mean-square value of the year peak day correction maximum temperature in each year of computation history, with the mean value of said arithmetic mean and said root-mean-square value as the highest said reference temperature.
5. biggest yearly load prediction method according to claim 4; It is characterized in that: the method for said S22 operation is: read temperature-sensitivity coefficient at each temperature; And according to the said temperature-sensitivity coefficient under the root temperature; Set up optimization restricted problem equation, confirm adjustment COEFFICIENT K 2 respectively in 28 ℃ of adjustment COEFFICIENT K 1 in the highest said reference temperature and the highest said reference temperature to 40 ℃.
6. biggest yearly load prediction method according to claim 5 is characterized in that: in the said S23 operation, the formula of asking for said year reduction peak load is:
Wherein, P
RefBe year reduction peak load, T
RefThe highest reference temperature, P
0Be year peak load, T '
0Be year peak day correction maximum temperature.
7. biggest yearly load prediction method according to claim 6 is characterized in that: in the said S31 operation, according to the year reduction peak load in historical each year, adopt the method for function regression, the year reduction peak load in outside forecast target year.
8 biggest yearly load prediction methods according to claim 7 is characterized in that, in the S33 operation, the interval formula that is adopted of biggest yearly load prediction that calculates target year is:
P
max1＝P
ref1(1+K(T
max1-T
ref)；
Wherein, P
Ref1Be the year reduction peak load in target year, T
RefThe highest reference temperature, P
Max1Be the biggest yearly load prediction in target the year interval upper limit or lower limit, T
Max1The upper limit or lower limit for the annual maximum temperature forecast interval in target year.
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| CN107909186A (en) * | 2017-10-13 | 2018-04-13 | 深圳供电局有限公司 | A kind of target yearly peak load method |
| JPWO2017126069A1 (en) * | 2016-01-21 | 2018-09-13 | 富士通株式会社 | Power demand value calculation system, power demand value calculation method, and power demand value calculation program |
| CN117895659A (en) * | 2024-03-14 | 2024-04-16 | 山东理工大学 | Automatic scheduling method and system for smart power grid |
| CN117895659B (en) * | 2024-03-14 | 2024-05-31 | 山东理工大学 | Automatic scheduling method and system for smart power grid |
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2012
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| JPWO2017126069A1 (en) * | 2016-01-21 | 2018-09-13 | 富士通株式会社 | Power demand value calculation system, power demand value calculation method, and power demand value calculation program |
| EP3407450A4 (en) * | 2016-01-21 | 2019-01-16 | Fujitsu Limited | Power demand value calculation system, power demand value calculation method, and power demand value calculation program |
| US10989743B2 (en) | 2016-01-21 | 2021-04-27 | Fujitsu Limited | Power-demand-value calculating system, power-demand-value calculating method, and recording medium recording power-demand-value calculating program |
| CN107909186A (en) * | 2017-10-13 | 2018-04-13 | 深圳供电局有限公司 | A kind of target yearly peak load method |
| CN117895659A (en) * | 2024-03-14 | 2024-04-16 | 山东理工大学 | Automatic scheduling method and system for smart power grid |
| CN117895659B (en) * | 2024-03-14 | 2024-05-31 | 山东理工大学 | Automatic scheduling method and system for smart power grid |
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