CN109742799B - D-U space mixed multi-attribute wind power plant installed capacity interval decision method - Google Patents

D-U space mixed multi-attribute wind power plant installed capacity interval decision method Download PDF

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CN109742799B
CN109742799B CN201910067610.6A CN201910067610A CN109742799B CN 109742799 B CN109742799 B CN 109742799B CN 201910067610 A CN201910067610 A CN 201910067610A CN 109742799 B CN109742799 B CN 109742799B
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江岳文
陈晓榕
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Abstract

The invention relates to a D-U space mixed multi-attribute-based wind power plant installed capacity interval decision method, which is characterized in that a wind power plant to-be-decided interval attribute matrix is established by using wind power plant power generation social benefits, wind abandon rate, power grid line transmission safety margin and power shortage risk as attribute indexes, mixed multi-attribute evaluation based on set pair analysis D-U space is introduced, and the optimal installed capacity interval of the wind power plant is determined.

Description

D-U space mixed multi-attribute wind power plant installed capacity interval decision method
Technical Field
The invention relates to the technical field of power systems, in particular to a D-U space hybrid multi-attribute wind power plant installed capacity interval decision method.
Background
With the attention of various countries in the world on environmental protection and the requirement of sustainable energy development, the center of gravity of energy development turns to renewable energy, and wind power is the main utilization mode of the renewable energy, so that the rapid and large-scale development of the wind power is out of gear. The problems of frequent occurrence of the wind abandoning phenomenon, uncertainty of power grid operation, gliding of power quality and the like are accompanied. Therefore, a great deal of research is conducted to evaluate the capacity of the power grid for receiving wind power from the aspects of safety, economy, resource utilization and the like. Due to the change of the operation mode of the power grid, the expansion of the grid planning and the change of the dispatching mode, the wind power acceptance capacity of the power grid is continuously changed. The change directly influences the estimation of the installed capacity of the wind power plant, and is a premise for determining the installed capacity of the wind power plant. In addition, wind resources are another crucial factor affecting installed capacity. The uncertainty of the wind power admission level and the strong random wind resource enable the installed capacity optimization of the wind power plant to become a complex and uncertain optimization problem. Therefore, several installed capacity intervals to be optimized may appear in the process of installed capacity evaluation for the decision-maker to select. How to determine which installed capacity interval is optimal is a problem that the present invention needs to solve. At present, research on how to optimize the installed capacity interval of the wind power plant is rare.
Disclosure of Invention
In view of the above, the invention aims to provide a D-U space hybrid multi-attribute-based wind farm installed capacity interval decision method, which is characterized in that a set pair analysis-based mixed multi-attribute evaluation is introduced by taking wind farm power generation social benefits, wind abandon rate, grid line transmission safety margin and power shortage risk as indexes, and an optimal installed capacity interval of a wind farm is determined.
In order to achieve the purpose, the invention adopts the following technical scheme:
a wind power plant installed capacity interval decision method based on D-U space mixed multi-attribute comprises the following steps:
step S1, acquiring an installed capacity interval of a wind power plant to be optimized, and operation parameters and unit parameters of the wind power plant to be accessed to a power grid;
step S2, setting a multi-attribute decision index:
step S3, establishing an interval number attribute matrix of the installed capacity interval of the wind power plant to be optimized;
step S4, mapping the obtained interval number attribute matrix to a D-U space;
step S5, calculating comprehensive certainty measure and comprehensive uncertainty measure of each installed capacity interval scheme to be decided in consideration of different weights of each attribute in the decision making process;
step S6: and calculating a joint coefficient of each interval scheme of the wind power plant to be decided according to the obtained comprehensive certainty measure and the comprehensive uncertainty measure of each installed capacity interval scheme to be decided, and sequencing the performance of each interval scheme according to the magnitude of the joint coefficient.
Further, the multi-attribute decision index includes:
(1) power generation social benefit B of wind farm:
B＝Bs+Be-Cc-Cm
wherein: bsGenerating capacity gain for the wind power plant; b iseEnvironmental benefits for wind farms; ccInvestment cost for wind power plants; cmThe operating and maintenance costs of the wind farm.
(2) Wind curtailment rate D of wind farm:
Figure BDA0001956235410000031
wherein:
Figure BDA0001956235410000032
and
Figure BDA0001956235410000033
indicating the amount of electricity generated and the actual amount of electricity generated.
(3) Risk of power shortage in wind farm R:
Figure BDA0001956235410000034
wherein: t is tfThe total duration that the downward change rate of the wind power output in a random simulation period T is greater than the system rotation standby reaction rate is adopted.
(4) Power grid line transmission safety margin M
The line transmission safety margin M of the power system is defined as the minimum of all line safety margins.
M＝min{Ml,t}(t＝1,2,…,Tt；l＝1,2,...,L)。
Further, the step S3 is specifically:
step S31, remember P ═ P1，P2，…，PmThe decision-making method comprises the steps that a set containing m schemes to be decided, namely m machine capacity planning intervals to be installed; a ═ A1，A2，…AnIs a set of n attributes; scheme PiFor attribute AjIs given as
Figure BDA0001956235410000035
Figure BDA0001956235410000036
Respectively representing the lower limit and the upper limit of the attribute value of the attribute j of the interval scheme i to be decided; establishing an attribute matrix C of the scheme set P to the attribute set Am×n。
Step S32 for attribute matrix Cm×nAnd (3) carrying out normalization:
when attribute j is a cost-type attribute:
Figure BDA0001956235410000041
when attribute j is a benefit type attribute:
Figure BDA0001956235410000042
step S33, calculating social benefits B, a wind abandon rate D, a line transmission safety margin M and a power shortage risk R corresponding to the installed capacity interval of the wind power plant by using a random simulation method, and obtaining an attribute matrix through standardized processing:
Figure BDA0001956235410000043
wherein, the '1' represents the attribute parameter of the installed capacity which is taken as the value of the interval '1'; "2" represents the attribute parameter of the installed capacity at the value of the interval "2"; m represents the attribute parameter of the installed capacity which is taken as a value in the interval m; "-" represents the lower limit of the index interval; "+" indicates the upper limit of the indicator interval;
Figure BDA0001956235410000044
indicating the number of intervals, and so on.
Further, the step S4 is specifically: for number of intervals
Figure BDA0001956235410000045
It is converted into a coefficient a by the following formulaij+biji, wherein:
Figure BDA0001956235410000046
Figure BDA0001956235410000047
in the formula, aij、bijRespectively, the identity and the diversity of the solution i represented by the joint coefficient with respect to the attribute j.
Further, the step S5 is specifically: each is calculated by the following formulaComprehensive certainty measure S of individual to-be-decided installed capacity interval schemei,DAnd synthesize the uncertainty measure Si,U:
Figure BDA0001956235410000051
Figure BDA0001956235410000052
Wherein, ω isjIs the weight of the index j to the measure.
Further, the step S6 is specifically: the decision making by using the joint coefficients must make clear the size between two joint coefficients, for any two joint coefficients (X)1＝S1,D+S1,Ui；X2＝S2,D+S2,Ui) The rules of comparison are:
1) if S1,D＝S2,DWhen S is1,U＝S2,UWhen it is called X1Is equal to X2；
2) If S1,D＝S2,DWhen S is1,U＞S2,UWhen it is called X1To be greater than X2；
3) When S is1,D＞S2,DWhen it is called X1Greater than X2(ii) a If there is also S1,D+S1,U＞S2,D+S2,UThen is called X1Is significantly greater than X2；
If X isfIs significantly greater than Xi(i ≠ f) 1,2,.. m, and i ≠ f), then the f-th decision scheme is best.
Compared with the prior art, the invention has the following beneficial effects:
according to the method, the power generation social benefits, the wind abandoning rate, the transmission safety margin of the power grid line and the power shortage risk indexes of the wind power plant are considered, the mixed multi-attribute evaluation to-be-determined optimal installed capacity interval of the wind power plant based on the set pair analysis D-U space is introduced, and the economical efficiency and the safety of wind power plant access are considered.
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Detailed Description
The present invention is further illustrated by the following examples.
The invention provides a D-U space mixed multi-attribute-based wind power plant installed capacity interval decision method, which comprises the following steps
Step S1: acquiring a mounted capacity interval of a wind power plant to be optimized, and acquiring operation parameters and unit parameters of a power grid to be accessed by the wind power plant;
step S2: and selecting a multi-attribute decision index, and taking the wind power plant power generation social benefit B, the wind abandoning rate D, the power grid line transmission safety margin M and the power shortage risk R as attribute indexes.
(1) Social benefit of one year of power generation in wind power plant B:
B＝Bs+Be-Cc-Cm
wherein: b issGenerating capacity earnings for the wind power plant; b iseEnvironmental benefits for wind farms; ccInvestment cost for wind power plants; cmThe operating and maintaining cost of the wind power plant is reduced.
(2) Wind abandon rate D of wind power plant for one year:
Figure BDA0001956235410000061
wherein:
Figure BDA0001956235410000062
and
Figure BDA0001956235410000063
indicating the amount of electricity generated and the actual amount of electricity generated.
(3) The power shortage risk R of a wind farm for one year is defined as follows:
Figure BDA0001956235410000064
wherein: t is tfThe downward change rate of the wind power output in a random simulation period T (one year) is greater than the rotation rate of the systemThe total length of the standby reaction rate.
(4) Power grid line transmission safety margin M
In an electric power system, the safety margin of a line is the active power transfer capacity remaining on the line.
The line transmission safety margin M of the power system is defined as the minimum of the safety margins of all lines in one year.
M＝min{Ml,t}(t＝1,2,…,Tt；l＝1,2,...,L)
Step S3: establishing an interval number attribute matrix of an interval of installed capacity of a wind power plant to be optimized
(1) And (5) interval attribute matrixes. Let P be { P ═ P1，P2，…，PmThe decision-making method comprises the steps that a set containing m schemes to be decided, namely m machine capacity planning intervals to be installed; a ═ A1，A2，…AnIs a set of n attributes; scheme PiFor attribute AjIs given as
Figure BDA0001956235410000071
(
Figure BDA0001956235410000072
Respectively representing the lower limit and the upper limit of the interval scheme i to be decided on about the attribute value of the attribute j). Establishing an attribute matrix C of the scheme set P to the attribute set Am×n。
(2) And normalizing the interval attribute matrix. To eliminate the effect of different dimensions on the result, attribute matrix C is subjected tom×nAnd (5) carrying out normalization.
When attribute j is a cost-type attribute:
Figure BDA0001956235410000073
when attribute j is a benefit type attribute:
Figure BDA0001956235410000074
(3) and establishing an attribute matrix of the interval to be decided of the wind power plant. Calculating social benefit B, wind abandon rate D, line transmission safety margin M and power shortage risk R corresponding to the installed capacity interval of the wind power plant by using a random simulation method, and obtaining an attribute matrix through standardized processing:
Figure BDA0001956235410000081
wherein, the '1' represents the attribute parameter of the installed capacity which is taken as the value of the interval '1'; "2" represents the attribute parameter of the installed capacity at the value of the interval "2"; m represents the attribute parameter of the installed capacity which is taken as a value in the interval m; "-" represents the lower limit of the index interval; "+" indicates the upper limit of the index interval;
Figure BDA0001956235410000082
indicating the number of intervals, and so on.
Step S4: mapping the interval number to a D-U space;
number of intervals
Figure BDA0001956235410000083
The uncertainty of (2) is that although the value is definite from the interval level, it is uncertain in the specific value. For the number of intervals, it is converted into a joint coefficient a by the following two equationsij+biji, wherein:
Figure BDA0001956235410000084
Figure BDA0001956235410000085
in the formula, aij、bijRespectively, the identity and the difference of the scheme i represented by the joint coefficient with respect to the attribute j.
Step S5: considering the different weights of the attributes in the decision making process, the decision to be made is calculated by the following two formulasComprehensive certainty measure S of installed capacity interval schemei,DAnd synthesize the uncertainty measure Si,U。
Figure BDA0001956235410000086
Figure BDA0001956235410000087
Wherein, ω isjIs the weight of the index j to the measure.
Step S6: calculating joint coefficient X of each wind power plant interval scheme to be decidedi＝Si,D+Si,Ui(XiA joint coefficient for the ith decision scheme) and by a joint coefficient XiThe size of (A) ranks the performance of each interval scheme, XiThe maximum is the final wind power plant installed capacity decision interval.
The decision making by using the joint coefficients must make clear the size between two joint coefficients, for any two joint coefficients (X)1＝S1,D+S1,Ui；X2＝S2,D+S2,Ui) The rules of comparison are:
1) if S1,D＝S2,DWhen S is1,U＝S2,UWhen it is called X1Is equal to X2；
2) If S1,D＝S2,DWhen S is1,U＞S2,UWhen it is called X1To be greater than X2；
3) When S is1,D＞S2,DWhen it is called X1Greater than X2(ii) a If there is S1,D+S1,U＞S2,D+S2,UThen call X1Is significantly greater than X2。
If X isfIs significantly greater than Xi(i ≠ f) 1,2,.. m, and i ≠ f), then the f-th decision scheme is best.
Example 1:
taking an IEEE30 node system as an example, the service life of the wind turbine generator is 20 years; the operating parameters of the schedulable conventional generator set in the system are shown in table 1; the return rate (depreciation rate) of the capital used by the project is 10 percent; the power selling price of the wind power plant is 0.5 mg/(kW & h), the environmental benefit is reduced to 0.0926 mg/(kW & h), all confidence levels are 0.9, and the optimization result is shown in Table 2. Installed capacity interval [99,154] ] MW (interval 1) and [85,99] MW (interval 2) for wind farms.
TABLE 1 IEEE30 node system dispatchable conventional crew parameters
Figure BDA0001956235410000091
Figure BDA0001956235410000101
Calculating social benefits, a wind abandoning rate, a line transmission safety margin and a power shortage risk corresponding to the installed capacity of the wind power plant by using a random simulation method to obtain an attribute matrix:
Figure BDA0001956235410000102
because the wind abandoning rate and the power shortage risk are cost-type attributes, the social benefit of wind power plant power generation and the line transmission safety margin are benefit-type attributes, and after the standardization processing, the attribute matrix is as follows:
Figure BDA0001956235410000103
the importance of each decision attribute is considered, and a weight is given, such as ω ═ 0.6, 0.2, 0.1, 0.1. X corresponding to the installed capacity interval 1 of the wind power plant1X corresponding to installed capacity interval 2 of wind farm ═ 0.469+0.125i2＝0.94+0.552i。
It can be seen that the comprehensive evaluation result of the installed capacity interval 2 is significantly greater than that of the installed capacity in the interval 1, so the option 2 is selected as the optimal installed capacity interval.
The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.

Claims (1)

1. A wind power plant installed capacity interval decision method based on D-U space mixed multi-attribute is characterized by comprising the following steps:
step S1, acquiring an installed capacity interval of a wind power plant to be optimized, and operation parameters and unit parameters of the wind power plant to be accessed to a power grid;
step S2, setting a multi-attribute decision index:
step S3, establishing an interval number attribute matrix of the installed capacity interval of the wind power plant to be optimized;
step S4, mapping the obtained interval number attribute matrix to a D-U space;
step S5, calculating comprehensive certainty measure and comprehensive uncertainty measure of each installed capacity interval scheme to be decided in consideration of different weights of each attribute in the decision making process;
step S6: calculating a joint coefficient of each interval scheme of the wind power plant to be decided according to the obtained comprehensive certainty measure and comprehensive uncertainty measure of each installed capacity interval scheme to be decided, and sequencing the performance of each interval scheme according to the magnitude of the joint coefficient;
the multi-attribute decision index comprises:
(1) power generation social benefit B of wind farm:
B＝Bs+Be-Cc-Cm
wherein: b issGenerating capacity gain for the wind power plant; b iseEnvironmental benefits for wind farms; ccInvestment cost for wind power plants; cmOperating and maintaining costs for the wind farm;
(2) wind curtailment rate D of wind farm:
Figure FDA0003620506140000021
wherein:
Figure FDA0003620506140000022
and
Figure FDA0003620506140000023
representing the electricity generation amount and the actual electricity generation amount;
(3) risk of power shortage in wind farm R:
Figure FDA0003620506140000024
wherein: t is tfThe downward change rate of the wind power output in a random simulation period T is greater than the total duration of the system rotation standby reaction rate;
(4) power grid line transmission safety margin M
The line transmission safety margin M of the power system is defined as the minimum value of all line safety margins
M＝min{Ml,t}，t＝1,2,…,Tt；l＝1,2,...,L；
The step S3 specifically includes:
step S31, remember P ═ P1，P2，…，PmThe decision-making method comprises the steps that a set containing m schemes to be decided, namely m machine capacity planning intervals to be installed; a ═ A1，A2，…AnIs a set of n attributes; scheme PiFor attribute AjIs given as
Figure FDA0003620506140000025
Figure FDA0003620506140000026
Respectively representing the lower limit and the upper limit of the attribute value of the attribute j of the interval scheme i to be decided; establishing an attribute matrix C of the scheme set P to the attribute set Am×n；
Step S32, for attribute matrix Cm×nAnd (3) carrying out normalization:
when attribute j is a cost-type attribute:
Figure FDA0003620506140000027
when attribute j is a benefit type attribute:
Figure FDA0003620506140000031
step S33, calculating power generation social benefits B, wind abandon rate D, line transmission safety margin M and power shortage risk R corresponding to the installed capacity interval of the wind power plant by using a random simulation method, and obtaining an attribute matrix through standardized processing:
Figure FDA0003620506140000032
wherein, the '1' represents the attribute parameter of the installed capacity which is taken as the value of the interval '1'; "2" represents the attribute parameter of the installed capacity at the value of the interval "2"; m represents the attribute parameter of the installed capacity which is taken as a value in the interval m; "-" represents the index section lower limit; "+" indicates the upper limit of the indicator interval;
Figure FDA0003620506140000033
representing the number of intervals, and so on;
the step S4 specifically includes: for number of intervals
Figure FDA0003620506140000034
It is converted into a coefficient a by the following two formulasij+biji, wherein:
Figure FDA0003620506140000035
Figure FDA0003620506140000036
in the formula, aij、bijRespectively representing the identity and the difference of the scheme i represented by the joint coefficient with respect to the attribute j;
the step S5 specifically includes: calculating comprehensive certainty measure S of each installed capacity interval scheme to be decided according to the following formulai,DAnd the comprehensive uncertainty measure Si,U:
Figure FDA0003620506140000037
Figure FDA0003620506140000041
Wherein, ω isjIs the weight of index j to the measure;
the step S6 specifically includes: the decision making by using the joint coefficients must make clear the size between two joint coefficients, and for any two joint coefficients X1＝S1,D+S1,Ui；X2＝S2,D+S2,Ui, the rules of comparison are:
1) if S1,D＝S2,DWhen S is1,U＝S2,UWhen it is called X1Is equal to X2；
2) If S1,D＝S2,DWhen S is1,U＞S2,UWhen it is called X1To be greater than X2；
3) When S is1,D＞S2,DWhen it is called X1Greater than X2(ii) a If there is S1,D+S1,U＞S2,D+S2,UThen call X1Is significantly greater than X2；
If X isfIs significantly greater than XiThen the f-th decision scheme is the best.
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