CN114759569B - Wind power consumption method for northeast region based on electric heating load fine adjustment - Google Patents

Abstract

The invention discloses a wind power consumption method based on electric heating load fine adjustment for northeast, which can carry out fine adjustment on the electric heating load according to temperature and time so as to more effectively consume the wind power load; the electric heating load can be continuously finely adjusted according to the wind power curve, so that the load curve can be more matched with the wind power output power curve on the one hand, and on the other hand, the adjustment amplitude is smaller and is similar to the original use habit of an electric heating user, so that the obtained adjustment result is more easily accepted; the invention has important function for more effectively utilizing wind power resources in northeast.

Description

A wind power consumption method based on electric heating load fine-tuning for Northeast China

Technical Field

The present invention discloses a wind power consumption method based on electric heating load fine-tuning for the Northeast region, which can fine-tune the electric heating power load according to temperature and time, thereby more effectively absorbing the wind power load, and belongs to the field of wind power system control technology.

Background Art

As a clean energy, wind power is very important for reducing carbon emissions and reducing the power supply system's dependence on resources such as coal. The actual load demand in the power system is usually different from the output of wind power, which leads to the phenomenon of wind abandonment and waste of wind power resources, and it is necessary to absorb wind power. In winter, since the load of electric heating accounts for a large proportion, it is very important to build a reasonable electric heating load demand and absorb wind power output as much as possible for power grid management and reducing carbon emissions.

To absorb wind power, the currently commonly used methods include: 1. Among many small loads, use genetic algorithms, clustering and other methods to find a load group close to the wind power output power to absorb as much wind power as possible; the biggest problem with this method is that for areas with similar industrial structures, the load curves of each unit are relatively similar, and it is difficult to find sufficiently diverse curves to combine and absorb wind power; in areas with diverse industrial structures, since the load forecast curves come from different units, multiple combinations will produce large error transmissions, and the calculated results may have a large deviation from the actual absorption results. 2. Introduce some energy storage equipment or adjust the enterprise's electricity consumption strategy. This model may not be able to respond at any time when wind power changes rapidly due to the influence of fixed strategies. In the winter in Northeast my country, the typical and stable load is electric heating equipment. A typical feature of this equipment when providing heat is that the time and duration of its load can be fine-tuned. There are also methods of using electric heating to improve the wind power absorption capacity in existing inventions or methods, but the existing methods are mostly based on large-scale adjustment of users' electricity consumption strategies, or specifying different peak and valley electricity prices in advance to guide users to adjust heating when electricity consumption is low; although these methods can solve certain problems, they also have the problem of not being able to change closely with wind power fluctuations. Although they can assist in peak shaving when the wind is strong, they will have side effects, especially when the wind output is occasionally low. In addition, large-scale load adjustments may be contrary to the actual use needs of users (such as: adjusting the heating time to outside the working hours of a unit), causing the adjustment strategy to be unable to be effectively implemented.

Therefore, a wind power consumption method needs to be proposed for the Northeast region, which can make fuller use of the adjustable power and duration of electric heating electricity to consume wind power, and build a load fine-tuning strategy that is more acceptable to heating users to improve the utilization rate of wind power.

Summary of the invention

The present invention provides a wind power consumption method based on electric heating load fine-tuning for the Northeast region. The method can fine-tune the power load of electric heating according to temperature and time, thereby more effectively absorbing the wind power load.

The present invention discloses a wind power consumption method based on electric heating load fine-tuning for the Northeast region, comprising the following steps:

S1. Input wind power output power array FengArray, input electric heating load array FuheArray, input maximum adjustable step value MaxStep, input temperature array QiwenArray; establish adjustment value array NumArray; obtain maximum temperature value HQiwen, obtain minimum temperature value LQiwen; obtain maximum electric heating load value HFuhe, obtain minimum electric heating load value LFuhe;

S101, input wind power output power array FengArray, FengArray is a floating point array of 96 elements, corresponding to wind power output power values at 96 time points with 0:00 as the starting point and 15 minutes as the interval in a day;

S102, inputting an electric heating load array FuheArray, which is a 96-element floating-point array corresponding to electric heating load values at 96 time points in a day starting at 0:00 and at intervals of 15 minutes;

S103, input the maximum adjustable step value MaxStep, MaxStep is an integer, and the default value is 5;

S104, inputting a temperature array QiwenArray, which is a floating-point array of 96 elements, corresponding to the temperature values of 96 time points in a day starting at 0:00 and at intervals of 15 minutes;

S105, creating an adjustment value array NumArray, NumArray is a floating point array of 96 elements, and all elements of the array are 0;

S106, obtaining the maximum temperature value HQiwen=the maximum value of QiwenArray; obtaining the minimum temperature value LQiwen=the minimum value of QiwenArray;

S107, obtaining a maximum electric heating load value HFuhe=the maximum value of FuheArray, and obtaining a minimum electric heating load value LFuhe=the minimum value of FuheArray;

S2, establish an adjustable amplitude operator OpKetao, the input of OpKetao is the adjustable position variable KetaoPos, and the output is the adjustable value KetiaoResult;

S201, establish an adjustable amplitude operator OpKetao, the input of OpKetao is the adjustable position variable KetaoPos, KetaoPos is an integer variable;

S202, adjustable amplitude temporary storage array KetaoTempArray = take out the KetaoPos-MaxStep element to the KetaoPos+MaxStep element of FuheArray to form an array of 2×MaxStep+1 elements;

S203, standard deviation of the first temporary variable KetaoTemp1=KetaoTempArray of adjustable amplitude;

S204, adjustable value KetiaoResult=tanh((FuheArray[KetaoPos]-LFuhe)/(HFuhe-LFuhe))×KetaoTemp1;

S205, KetiaoResult=KetiaoResult×((QiwenArray[KetaoPos]-LQiwen)/(HQiwen-LQiwen));

S206, outputting KetiaoResult as the result of the OpKetao operator;

S3, based on the OpKetao operator, establish the load single-step fine-tuning operator OpWeitiao, the input of OpWeitiao is the single-step fine-tuning position variable WeitiaoPos, and the output is the fine-tuning result array WeitiaoArray;

S301, establish a load single-step fine-tuning operator OpWeitiao, the input of OpWeitiao is a single-step fine-tuning position variable WeitiaoPos, and WeitiaoPos is an integer variable;

S302, establish a fine-tuning result array WeitiaoArray = an array of 2×MaxStep+1 elements, and the value of each element of the array is 0;

S303, establish a fine-tuning first temporary storage array WeitiaoTemp1 = an array of 2×MaxStep+1 elements, and the value of each element of the array is 0;

S303, load single-step fine-tuning operator iteration variable WeitiaoCounter=1;

S304, WeitiaoTemp1[WeitiaoCounter]=call OpKetao operator, wherein the value of KetaoPos of OpKetao operator is: KetaoPos=WeitiaoPos-MaxStep+WeitiaoCounter-1;

S305, WeitiaoCounter=WeitiaoCounter+1;

S306, if WeitiaoCounter>(2×MaxStep+1), go to S307, otherwise go to S304;

S307, fine-tune the temporary sum value WeitiaoSum = the sum of WeitiaoTemp1 - WeitiaoTemp1[MaxStep+1];

S308, single-step fine-tuning operator overall amplitude WeitiaoFudu=WeitiaoTemp1[MaxStep+1];

S309, real adjustment range RealWeitiao=WeitiaoFudu;

S310, if FuheArray[WeitiaoPos]>FengArray[WeitiaoPos] then RealWeitiao=(-RealWeitiao);

S311, WeitiaoCounter=1;

S312, modify the value of the WeitiaoArray element based on the following formula:

WeitiaoArray[WeitiaoCounter]=WeitiaoTemp1[WeitiaoCounter]/WeitiaoFudu×(-RealWeitiao);

S313, WeitiaoCounter=WeitiaoCounter+1;

S314, if WeitiaoCounter>(2×MaxStep+1), go to S315, otherwise go to S312;

S315, WeitiaoArray[MaxStep+1]=RealWeitiao;

S316, outputting WeitiaoArray as the result of OpWeitiao;

S4, based on the OpWeitiao operator, fine-tunes the electric heating load and outputs the adjusted result of wind power consumption;

S401, overall step counter OverAllCounter=MaxStep+1;

S402, overall step 1 temporary storage variable OverAllTemp1=OverAllCounter;

S403, overall step second temporary storage variable OverAllTemp2=96-OverAllCounter;

S404, the first adjustment array T1Array=is calculated using the OpWeitiao operator, and the WeitiaoPos of the operator is OverAllTemp1;

S405, the first adjustment array T1Array=is calculated using the OpWeitiao operator, and the WeitiaoPos of the operator is OverAllTemp2;

S406, local counter LocalCounter=1;

S407, calculation:

NumArray[LocalCounter+OverAllTemp1-MaxStep]=NumArray[LocalCounter+OverAllTemp1-MaxStep]+T1Array[LocalCounter];

S408, calculation:

NumArray[LocalCounter+OverAllTemp2-MaxStep]=NumArray[LocalCounter+OverAllTemp2-MaxStep]+T2Array[LocalCounter];

S409, LocalCounter=LocalCounter+1;

S410, if LocalCounter>(2×MaxStep+1), go to S411, otherwise go to S407;

S411, OverAllCounter=OverAllCounter+1;

S412, if OverAllCounter>(96-MaxStep-1), go to S413, otherwise go to S402;

S413, overall output counter OutputCounter=1;

S414, FuheArray[OutputCounter]=FuheArray[OutputCounter]+NumArray[OutputCounter];

S415, OutputCounter=OutputCounter+1;

S416, if OutputCounter>96, go to S417, otherwise go to S414;

S417, outputting FuheArray as the adjusted result.

The beneficial effects of the present invention are:

The power load of electric heating can be fine-tuned according to temperature and time, so as to more effectively absorb wind power load; the electric heating load can be continuously fine-tuned according to the wind power curve, which can make the load curve more matched with the wind power output power curve on the one hand, and on the other hand, since the adjustment range is small and similar to the original usage habits of electric heating users, the obtained adjustment result is easier to accept; the present invention plays a very important role in more effectively utilizing wind power resources in Northeast China.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is an input wind power output power array FengArray of Example 2;

FIG2 is an input electric heating load array FuheArray of Example 2;

FIG3 is an input temperature array QiwenArray of Example 2;

FIG. 4 is a result obtained by FuheArray output by the method of the present invention in Example 2.

DETAILED DESCRIPTION

The present invention is further described by way of the following examples, which do not limit the present invention in any way. Without departing from the technical solution of the present invention, any modification or change made to the present invention that is easily implemented by a person of ordinary skill in the art will fall within the scope of the claims of the present invention.

Example 1

The present invention provides a wind power consumption method based on electric heating load fine-tuning for the Northeast region, comprising the following steps:

S1. Input wind power output power array FengArray, input electric heating load array FuheArray, input maximum adjustable step value MaxStep, input temperature array QiwenArray; establish adjustment value array NumArray; obtain maximum temperature value HQiwen, obtain minimum temperature value LQiwen; obtain maximum electric heating load value HFuhe, obtain minimum electric heating load value LFuhe;

S101, input wind power output power array FengArray, FengArray is a floating point array of 96 elements, corresponding to wind power output power values at 96 time points with 0:00 as the starting point and 15 minutes as the interval in a day;

S102, inputting an electric heating load array FuheArray, which is a 96-element floating-point array corresponding to electric heating load values at 96 time points in a day starting at 0:00 and at intervals of 15 minutes;

S103, input the maximum adjustable step value MaxStep, MaxStep is an integer, and the default value is 5;

S104, inputting a temperature array QiwenArray, which is a floating-point array of 96 elements, corresponding to the temperature values of 96 time points in a day starting at 0:00 and at intervals of 15 minutes;

S105, creating an adjustment value array NumArray, NumArray is a floating point array of 96 elements, and all elements of the array are 0;

S106, obtaining the maximum temperature value HQiwen=the maximum value of QiwenArray; obtaining the minimum temperature value LQiwen=the minimum value of QiwenArray;

S107, obtaining a maximum electric heating load value HFuhe=the maximum value of FuheArray, and obtaining a minimum electric heating load value LFuhe=the minimum value of FuheArray;

S2, establish an adjustable amplitude operator OpKetao, the input of OpKetao is the adjustable position variable KetaoPos, and the output is the adjustable value KetiaoResult;

S201, establish an adjustable amplitude operator OpKetao, the input of OpKetao is the adjustable position variable KetaoPos, KetaoPos is an integer variable;

S202, adjustable amplitude temporary storage array KetaoTempArray = take out the KetaoPos-MaxStep element to the KetaoPos+MaxStep element of FuheArray to form an array of 2×MaxStep+1 elements;

S203, standard deviation of the first temporary variable KetaoTemp1=KetaoTempArray of adjustable amplitude;

S204, adjustable value KetiaoResult=tanh((FuheArray[KetaoPos]-LFuhe)/(HFuhe-LFuhe))×KetaoTemp1;

S205, KetiaoResult=KetiaoResult×((QiwenArray[KetaoPos]-LQiwen)/(HQiwen-LQiwen));

S205, outputting KetiaoResult as the result of the OpKetao operator;

S3, based on the OpKetao operator, establish the load single-step fine-tuning operator OpWeitiao, the input of OpWeitiao is the single-step fine-tuning position variable WeitiaoPos, and the output is the fine-tuning result array WeitiaoArray;

S301, establish a load single-step fine-tuning operator OpWeitiao, the input of OpWeitiao is a single-step fine-tuning position variable WeitiaoPos, and WeitiaoPos is an integer variable;

S302, establish a fine-tuning result array WeitiaoArray = an array of 2×MaxStep+1 elements, and the value of each element of the array is 0;

S303, establish a fine-tuning first temporary storage array WeitiaoTemp1 = an array of 2×MaxStep+1 elements, and the value of each element of the array is 0;

S303, load single-step fine-tuning operator iteration variable WeitiaoCounter=1;

S304, WeitiaoTemp1[WeitiaoCounter]=call OpKetao operator, wherein the value of KetaoPos of OpKetao operator is: KetaoPos=WeitiaoPos-MaxStep+WeitiaoCounter-1;

S305, WeitiaoCounter=WeitiaoCounter+1;

S306, if WeitiaoCounter>(2×MaxStep+1), go to S307, otherwise go to S304;

S307, fine-tune the temporary sum value WeitiaoSum = the sum of WeitiaoTemp1 - WeitiaoTemp1[MaxStep+1];

S308, single-step fine-tuning operator overall amplitude WeitiaoFudu=WeitiaoTemp1[MaxStep+1];

S309, real adjustment range RealWeitiao=WeitiaoFudu;

S310, if FuheArray[WeitiaoPos]>FengArray[WeitiaoPos] then RealWeitiao=(-RealWeitiao);

S311, WeitiaoCounter=1;

S312, modify the value of the WeitiaoArray element based on the following formula:

WeitiaoArray[WeitiaoCounter]=WeitiaoTemp1[WeitiaoCounter]/WeitiaoFudu×(-RealWeitiao);

S313, WeitiaoCounter=WeitiaoCounter+1;

S314, if WeitiaoCounter>(2×MaxStep+1), go to S315, otherwise go to S312;

S315, WeitiaoArray[MaxStep+1]=RealWeitiao;

S316, outputting WeitiaoArray as the result of OpWeitiao;

S4, based on the OpWeitiao operator, fine-tunes the electric heating load and outputs the adjusted result of wind power consumption;

S401, overall step counter OverAllCounter=MaxStep+1;

S402, overall step 1 temporary storage variable OverAllTemp1=OverAllCounter;

S403, overall step second temporary storage variable OverAllTemp2=96-OverAllCounter;

S404, the first adjustment array T1Array=is calculated using the OpWeitiao operator, and the WeitiaoPos of the operator is OverAllTemp1;

S405, the first adjustment array T1Array=is calculated using the OpWeitiao operator, and the WeitiaoPos of the operator is OverAllTemp2;

S406, local counter LocalCounter=1;

S407, calculation

NumArray[LocalCounter+OverAllTemp1-MaxStep]=NumArray[LocalCounter+OverAllTemp1-MaxStep]+T1Array[LocalCounter];

S408, calculation

NumArray[LocalCounter+OverAllTemp2-MaxStep]=NumArray[LocalCounter+OverAllTemp2-MaxStep]+T2Array[LocalCounter];

S409, LocalCounter=LocalCounter+1;

S410, if LocalCounter>(2×MaxStep+1), go to S411, otherwise go to S407;

S411, OverAllCounter=OverAllCounter+1;

S412, if OverAllCounter>(96-MaxStep-1), go to S413, otherwise go to S402;

S413, overall output counter OutputCounter=1;

S414, FuheArray[OutputCounter]=FuheArray[OutputCounter]+NumArray[OutputCounter];

S415, OutputCounter=OutputCounter+1;

S416, if OutputCounter>96, go to S417, otherwise go to S414;

S417, outputting FuheArray as the adjusted result.

Example 2

1) Take a wind turbine and an electric heating load in a certain area as an example; input the wind power output power array FengArray, the content of which is shown in Figure 1:

2) Input the electric heating load array FuheArray, the content of which is shown in Figure 2:

3) Enter the maximum adjustable step value MaxStep=5;

4) Input the temperature array QiwenArray. The content of the array is shown in Figure 3:

5) Establish the adjustment value array NumArray; obtain the maximum temperature value HQiwen=3, obtain the minimum temperature value LQiwen=-12.5; obtain the maximum electric heating load value HFuhe=130, obtain the minimum electric heating load value LFuhe=1;

The specific implementation process is as follows:

S1. Input wind power output power array FengArray, input electric heating load array FuheArray, input maximum adjustable step value MaxStep, input temperature array QiwenArray; establish adjustment value array NumArray; obtain maximum temperature value HQiwen, obtain minimum temperature value LQiwen; obtain maximum electric heating load value HFuhe, obtain minimum electric heating load value LFuhe;

S101, input wind power output power array FengArray, FengArray is a floating point array of 96 elements, corresponding to wind power output power values at 96 time points with 0:00 as the starting point and 15 minutes as the interval in a day;

S102, inputting an electric heating load array FuheArray, which is a 96-element floating-point array corresponding to electric heating load values at 96 time points in a day starting at 0:00 and at intervals of 15 minutes;

S103, input the maximum adjustable step value MaxStep, MaxStep is an integer, and the default value is 5;

S104, inputting a temperature array QiwenArray, which is a floating-point array of 96 elements, corresponding to the temperature values of 96 time points in a day starting at 0:00 and at intervals of 15 minutes;

S105, creating an adjustment value array NumArray, NumArray is a floating point array of 96 elements, and all elements of the array are 0;

S106, obtaining the maximum temperature value HQiwen=the maximum value of QiwenArray; obtaining the minimum temperature value LQiwen=the minimum value of QiwenArray;

S107, obtaining a maximum electric heating load value HFuhe=the maximum value of FuheArray, and obtaining a minimum electric heating load value LFuhe=the minimum value of FuheArray;

S2, establish an adjustable amplitude operator OpKetao, the input of OpKetao is the adjustable position variable KetaoPos, and the output is the adjustable value KetiaoResult;

S201, establishing an adjustable amplitude operator OpKetao, the input of OpKetao is an adjustable position variable KetaoPos, KetaoPos is an integer variable;

S202, the adjustable amplitude temporary storage array KetaoTempArray = takes out the KetaoPos-MaxStep element to the KetaoPos+MaxStep element of FuheArray to form an array of 2×MaxStep+1 elements;

S203, standard deviation of the first temporary variable KetaoTemp1=KetaoTempArray of adjustable amplitude;

S204, adjustable value KetiaoResult=tanh((FuheArray[KetaoPos]-LFuhe)/(HFuhe-LFuhe))×KetaoTemp1;

S205, KetiaoResult=KetiaoResult×((QiwenArray[KetaoPos]-LQiwen)/(HQiwen-LQiwen));

S205, outputting KetiaoResult as the result of the OpKetao operator;

S3, based on the OpKetao operator, establish the load single-step fine-tuning operator OpWeitiao, the input of OpWeitiao is the single-step fine-tuning position variable WeitiaoPos, and the output is the fine-tuning result array WeitiaoArray;

S301, establish a load single-step fine-tuning operator OpWeitiao, the input of OpWeitiao is a single-step fine-tuning position variable WeitiaoPos, and WeitiaoPos is an integer variable;

S302, establish a fine-tuning result array WeitiaoArray = an array of 2×MaxStep+1 elements, and the value of each element of the array is 0;

S303, establish a fine-tuning first temporary storage array WeitiaoTemp1 = an array of 2×MaxStep+1 elements, and the value of each element of the array is 0;

S303, load single-step fine-tuning operator iteration variable WeitiaoCounter=1;

S304, WeitiaoTemp1[WeitiaoCounter]=call OpKetao operator, wherein the value of KetaoPos of OpKetao operator is: KetaoPos=WeitiaoPos-MaxStep+WeitiaoCounter-1;

S305, WeitiaoCounter=WeitiaoCounter+1;

S306, if WeitiaoCounter>(2×MaxStep+1), go to S307, otherwise go to S304;

S307, fine-tune the temporary sum value WeitiaoSum = the sum of WeitiaoTemp1 - WeitiaoTemp1[MaxStep+1];

S308, single-step fine-tuning operator overall amplitude WeitiaoFudu=WeitiaoTemp1[MaxStep+1];

S309, real adjustment range RealWeitiao=WeitiaoFudu;

S310, if FuheArray[WeitiaoPos]>FengArray[WeitiaoPos] then RealWeitiao=(-RealWeitiao);

S311, WeitiaoCounter=1;

S312, modify the value of the WeitiaoArray element based on the following formula:

WeitiaoArray[WeitiaoCounter]=WeitiaoTemp1[WeitiaoCounter]/WeitiaoFudu×(-RealWeitiao);

S313, WeitiaoCounter=WeitiaoCounter+1;

S314, if WeitiaoCounter>(2×MaxStep+1), go to S315, otherwise go to S312;

S315, WeitiaoArray[MaxStep+1]=RealWeitiao;

S316, outputting WeitiaoArray as the result of OpWeitiao;

S4, based on the OpWeitiao operator, fine-tune the electric heating load and output the adjusted result;

S401, overall step counter OverAllCounter=MaxStep+1;

S402, overall step 1 temporary storage variable OverAllTemp1=OverAllCounter;

S403, overall step second temporary storage variable OverAllTemp2=96-OverAllCounter;

S404, the first adjustment array T1Array=is calculated using the OpWeitiao operator, and the WeitiaoPos of the operator is OverAllTemp1;

S405, the first adjustment array T1Array=is calculated using the OpWeitiao operator, and the WeitiaoPos of the operator is OverAllTemp2;

S406, local counter LocalCounter=1;

S407, calculation:

NumArray[LocalCounter+OverAllTemp1-MaxStep]=NumArray[LocalCounter+OverAllTemp1-MaxStep]+T1Array[LocalCounter];

S408, calculation:

NumArray[LocalCounter+OverAllTemp2-MaxStep]=NumArray[LocalCounter+OverAllTemp2-MaxStep]+T2Array[LocalCounter];

S409, LocalCounter=LocalCounter+1;

S410, if LocalCounter>(2×MaxStep+1), go to S411, otherwise go to S407;

S411, OverAllCounter=OverAllCounter+1;

S412, if OverAllCounter>(96-MaxStep-1), go to S413, otherwise go to S402;

S413, overall output counter OutputCounter=1;

S414, FuheArray[OutputCounter]=FuheArray[OutputCounter]+NumArray[OutputCounter];

S415, OutputCounter=OutputCounter+1;

S416, if OutputCounter>96, go to S417, otherwise go to S414;

S417, outputting FuheArray as the adjusted result.

After the above calculations, the result obtained by the FuheArray output by the method of the present invention is shown in FIG4:

Conclusion: The load of the power supply is adjusted to make it more matching the output of wind power.

Example 3

The output of a wind farm and 30 heating loads in Northeast China in the winter of 2019 was used as the test object for wind power consumption. Before wind power consumption, the wind power utilization rate was 30.2%. The method of the present invention was compared with the traditional method: Methods used Wind power utilization rate Cause Analysis Directly provide load adjustment strategy 31.3% only increased by 1.1% Although some strategies can absorb wind power to a large extent, they are far from the original heating habits and are difficult for most units to implement. Specify peak and valley electricity prices 30.8% only increased by 0.6% Some units are not sensitive to prices, and some units have strict heating requirements, so it is also difficult to implement. Method of the present invention 39.7%, an increase of 9.5%, which significantly improved the utilization rate of wind power. Since it is a fine-tuning based on the original load, the obtained solution is more easily accepted by users, which greatly improves the degree of acceptance of the solution.

Conclusion: The present invention can fine-tune the electricity load of electric heating according to temperature and time, and continuously fine-tune the electric heating load using the wind power curve, so that the load curve better matches the wind power output power curve, thereby more effectively absorbing the wind power load.

Claims (1)

1. A wind power consumption method based on electric heating load fine adjustment for northeast areas comprises the following steps:

S1, inputting a wind power output power array FENGARRAY, inputting an electric heating load array FuheArray, inputting a maximum adjustable pace value MaxStep, inputting an air temperature array QIWENARRAY, and establishing an adjustment value array NumArray; acquiring an air temperature maximum value HQiwen and an air temperature minimum value LQiwen; obtaining an electric heating load maximum value HFuhe and an electric heating load minimum value LFuhe;

S101, an input wind power output power array FENGARRAY, FENGARRAY is a floating point array of 96 elements, and corresponds to wind power output power values of 96 time points in total with 0 time 0 as a starting point and 15 minutes as an interval in one day;

S102, an input electric heating load array FuheArray, fuheArray is a floating point array of 96 elements, and corresponds to electric heating load values of 96 time points in total with 0 time 0 as a starting point and 15 minutes as an interval in one day;

S103, inputting a maximum adjustable pace value MaxStep, maxStep which is an integer number, wherein the default value is 5;

S104, an input air temperature array QIWENARRAY, QIWENARRAY is a floating point array of 96 elements, and corresponds to air temperature values of 96 time points in total with 0 time 0 as a starting point and 15 minutes as an interval in one day;

S105, establishing an adjustment value array NumArray, numArray which is a floating point type array with 96 elements, wherein all elements of the array are 0;

s106, obtaining the maximum value of the maximum air temperature value HQiwen = QIWENARRAY, and obtaining the minimum value of the minimum air temperature value LQiwen = QIWENARRAY;

s107, obtaining the highest value of the electric heating load highest value HFuhe = FuheArray, and obtaining the lowest value of the electric heating load lowest value LFuhe = FuheArray;

s2, establishing the input of the adjustable amplitude operator OpKetao, opKetao as an adjustable position variable KetaoPos and outputting as an adjustable value KetiaoResult;

S201, establishing an input of an adjustable amplitude operator OpKetao, opKetao as an adjustable position variable KetaoPos, ketaoPos as an integer variable;

S202, the adjustable amplitude temporary storage array KetaoTempArray = KetaoPos-MaxStep elements to KetaoPos + MaxStep elements of FuheArray are taken out to form an array of 2× MaxStep +1 elements;

s203, the standard deviation of the adjustable amplitude first temporary variable KetaoTemp1 = KetaoTempArray;

s204, adjustable value KetiaoResult =tanh ((FuheArray [ KetaoPos ] -LFuhe)/(HFuhe-LFuhe)) × KetaoTemp1;

S205，KetiaoResult=KetiaoResult×((QiwenArray[KetaoPos]-LQiwen)/(HQiwen-LQiwen))；

s206, outputting KetiaoResult as a result of the OpKetao operator;

S3, establishing that the input of the load single-step fine tuning operator OpWeitiao, opWeitiao is a single-step fine tuning position variable WeitiaoPos based on OpKetao operators, and outputting the input as a fine tuning result array WeitiaoArray;

S301, establishing that the input of a load single-step fine tuning operator OpWeitiao, opWeitiao is a single-part fine tuning position variable WeitiaoPos, weitiaoPos as an integer variable;

s302, establishing a fine tuning result array WeitiaoArray = an array of 2× MaxStep +1 elements, wherein the value of each element of the array is 0;

S303, establishing an array of fine-tuning first temporary storage arrays WeitiaoTemp & lt 1 & gt = a 2 x MaxStep & lt 1 & gt element, wherein the value of each element of the array is 0;

s303, load single step fine tuning operator iteration variable WeitiaoCounter =1;

S304, weitiaoTemp1[ WeitiaoCounter ] = call OpKetao operator, wherein the value of KetaoPos of OpKetao operator is: ketaoPos = WeitiaoPos-MaxStep + WeitiaoCounter-1;

S305，WeitiaoCounter=WeitiaoCounter+1;

s306, if WeitiaoCounter > (2× MaxStep +1), go to S307, otherwise go to S304;

S307, fine tuning the sum value WeitiaoSum = WeitiaoTemp1 of the sum-WeitiaoTemp [ MaxStep +1];

S308, single step fine tuning operator overall amplitude WeitiaoFudu = WeitiaoTemp [ MaxStep +1];

S309, true adjustment amplitude RealWeitiao = WeitiaoFudu;

s310, realWeitiao = (-RealWeitiao) if FuheArray [ WeitiaoPos ] > FENGARRAY [ WeitiaoPos ];

S311，WeitiaoCounter=1；

S312, modifying WeitiaoArray the value of the element based on the following formula:

WeitiaoArray[WeitiaoCounter]=WeitiaoTemp1[WeitiaoCounter]/WeitiaoFudu×(-RealWeitiao)；

S313，WeitiaoCounter=WeitiaoCounter+1;

s314, if WeitiaoCounter > (2× MaxStep +1), go to S315, otherwise go to S312;

S315，WeitiaoArray[MaxStep+1]=RealWeitiao;

s316, outputting WeitiaoArray as a result of OpWeitiao;

S4, fine adjustment is carried out on the electric heating load based on OpWeitiao operators, and a result after wind power absorption adjustment is output;

s401, overall step counter OverAllCounter = MaxStep +1;

S402, the first temporary variable OverAllTemp1 = OverAllCounter;

S403, the second temporary variable OverAllTemp2 =96-OverAllCounter;

s404, the first adjustment Array T1Array = is calculated using OpWeitiao operators, weitiaoPos of which is OverAllTemp;

S405, the first adjustment Array T1 array=is calculated using OpWeitiao operator, weitiaoPos of which is OverAllTemp;

S406, local counter localcounter=1;

S407, calculating

NumArray[LocalCounter+OverAllTemp1-MaxStep]=NumArray[LocalCounter+OverAllTemp1-MaxStep]+T1Array[LocalCounter];

S408, calculating

NumArray[LocalCounter+OverAllTemp2-MaxStep]=NumArray[LocalCounter+OverAllTemp2-MaxStep]+T2Array[LocalCounter];

S409，LocalCounter=LocalCounter+1；

S410, if LocalCoulter > (2× MaxStep +1) then go to S411, otherwise go to S407;

S411，OverAllCounter=OverAllCounter+1；

S412, if OverAllCounter > (96-MaxStep-1) then go to S413, otherwise go to S402;

s413, total output counter OutputCounter =1;

S414，FuheArray[OutputCounter]=FuheArray[OutputCounter]+NumArray[OutputCounter];

S415，OutputCounter=OutputCounter+1；

S416, go to S417 if OutputCounter >96, otherwise go to S414;

s417, fuheArray is output as the adjusted result.