WO2013174146A1

WO2013174146A1 - Optimized evaluation method for wind power output power under multi-constraint condition for reducing transmission loss

Info

Publication number: WO2013174146A1
Application number: PCT/CN2013/000579
Authority: WO
Inventors: 王维洲; 智勇; 拜润卿; 郑晶晶; 梁琛; 马呈霞; 刘巍; 周喜超; 吕斌; 高磊
Original assignee: 国家电网公司; 甘肃省电力公司; 甘肃省电力公司电力科学研究院
Priority date: 2012-05-23
Filing date: 2013-05-15
Publication date: 2013-11-28
Also published as: CA2864241A1; CN102684223B; CA2864241C; CN102684223A

Abstract

An optimized evaluation method for wind power output power under multi-constraint condition for reducing transmission loss comprises: determining a set U of factors influencing transmission loss of a wind power system, calculating a characteristic of influence imposed by each factor on the transmission loss, and forming a hierarchical model comprising a solution layer and a factor layer; grouping U into a set U_N of non-changeable factors and a set U_C of changeable factors according to an operation manner of a wind power system, and an operation scheduling personnel determining a priority matrix W of U_C; obtaining multiple feasible solutions through primary screening according to U_N and a changing characteristic of the transmission loss of the wind power system along with each factor; establishing a subjection degree matrix R of the factor layer and a subjection degree matrix S of the solution layer according to W and multiple feasible solutions; and calculating a priority N of each feasible solution in the multiple feasible solutions according to a preset remark set and S, and selecting a feasible solution with the greatest priority as an optimal solution. The method can overcome the defects that multiple factors cannot be combined for quantitative evaluation and the transmission loss is high in the prior art.

Description

Wind power transmission power optimization evaluation method under multi-constraint conditions aiming at reducing network loss

The invention relates to the field of wind power grid-connecting technology, and in particular relates to a wind power sending power optimization evaluation method under the multi-constraint condition aiming at reducing the network loss.

Background technique

With the rapid development of wind power technology and the importance of national policy on renewable energy generation, China's wind power construction has entered a period of rapid development; large-scale wind farm group access to power systems has a direct impact on system network losses. Therefore, it is of great significance to establish a wind power delivery power optimization evaluation model under multi-constraint conditions with the goal of reducing network loss.

However, the impact of large-scale wind power access to power systems on network losses is constrained by many factors, such as external power system injection power, total wind power output, wind power output of multiple wind farms at different access points, and system load. Level, and system voltage levels and other factors.

At present, the research on the impact of large-scale wind power on power system network loss is mostly based on a single power system index constraint. However, under the actual power system operating conditions, it is necessary to comprehensively consider the above five factors. The optimization scheme of network loss is required, so it is necessary to establish a multi-constrained wind power delivery power optimization evaluation model aiming at reducing network loss.

In the process of implementing the present invention, the inventors have found that at least there are at least a plurality of factors that cannot be combined for quantitative evaluation and high network loss.

Summary of the invention

The object of the present invention is to provide an optimization evaluation method for wind power transmission power under multi-constraint conditions aiming at reducing network loss, in order to achieve the advantages of being able to comprehensively evaluate multiple factors and reduce network loss.

In order to achieve the above object, the technical solution adopted by the present invention is: a wind power transmission power optimization evaluation method under the multi-constraint condition aiming at reducing the network loss, including:

a. Based on the selected wind power system, according to the current domestic and international research on the impact of large-scale wind power on the power system, determine the set of factors affecting the network loss of the wind power system (^ ⁷¹ ' ^ ^{2 5} ''' , ^ ⁷ ) The power flow calculation software calculates the influence characteristics of each factor of the factor set C7 on the network loss based on the single variable method, and forms a hierarchical model including at least the solution layer and the factor layer; The factor set t includes at least an external power system injection power, a total wind power output, a respective wind power output of a plurality of different access points, a system voltage level, and a system load level; b. In step a, the current actual operation mode of the wind power system is selected. According to the operation mode, the factor set U is divided into an immutable factor set ^U N and a variable factor set ^Uc , and the scheduling operator determines the priority of the variable factor set ^Uc . matrix

c. According to the change characteristics of the factors of the selected network of the wind power system in step a, and the invariable factor set obtained in step b, preliminary screening is performed to obtain a plurality of feasible solutions; d, determining a comment set; According to the priority matrix of the variable factor set ^ ⁷ ^ obtained in step b, and the plurality of feasible schemes obtained in step c, respectively establish a factor layer membership degree matrix and a scheme layer membership degree matrix ^ _; e, a comment set according to the step d And the solution layer membership degree matrix, calculating the priority of each feasible solution among the plurality of feasible solutions obtained in step c; and, based on the priority of the obtained plurality of feasible solutions, selecting the feasible solution with the maximum priority N as the optimal Program. Further, in step b, the priority matrix ^ of the variable factor set ^c is -

W = {W _{l 9} W ₂ ^ ; W _k ) _{k = m} - _{b ;} where ^ is the number of factors in the factor set c, which is the set of immutable factors in the current actual operating mode

The number of unchangeable factors in ^N.

Further, in step c, the operation initial screening, comprises: the steps resulting b immutable factor set ^ ^7, the determination section possible options; based on the obtained section viable option grid wind power system further selected according to step a. The variation characteristic of each factor in the loss factor set determines a "preferred scheme" in the partial feasible scheme; based on the "preferred scheme", the optimal scheme in the current actual operation mode cannot be determined. Further, in step d, the factor layer membership matrix ^ is - R = {R _l , R _2i -, R _p ) ^T _;

The membership level of the solution layer is ¹ ^:

Further, in step d, the operation of establishing the membership matrix of the factor layer specifically includes:

The variable factor is _Uc = (^, u ₂ .., u _k ,, touch

Based on the selected feasible solution

The value of the prime corresponds to the membership function, and the membership of the five rating levels is:

According to the membership function Σ ^z=1 , the above membership degree is normalized to obtain the factor layer membership degree matrix ^ and the scheme layer membership degree matrix.

{R _X , R ₂ , '-, R ₅ y .

Further, in step d, the comment set is a set of comments made at various levels of the commented item, 1, 2,

Expressed as a review set ···, )

Representing high-low reviews at all levels;

For each comment in the comment set, you can give the corresponding score, you can get the score corresponding to the comment set.

£ is: ^ ^ , ^,..., further, in step e, the comment set obtained according to step d, and the membership level membership matrix

The operation of calculating the priority of each of the plurality of feasible solutions obtained in the step c includes:

Ν二ES. In the embodiments of the present invention, the wind power transmission power optimization evaluation method under the multi-constraint condition aiming at reducing the network loss includes: determining a factor set t affecting the network loss of the wind power system, calculating the influence characteristics of each factor on the network loss, forming the inclusion The hierarchical model of the scheme layer and the factor layer; according to the operation mode of the wind power system, it will be divided into an immutable factor set and a variable factor set ^G, a priority matrix determined by the dispatching operator ^; according to the network loss of the wind power system The change characteristics of factors and w, preliminary screening to obtain a number of feasible solutions; according to a number of feasible solutions, the establishment of the factor layer membership degree matrix R and the program layer membership degree matrix. According to the preset comment set and S, calculate multiple feasible solutions The priority N of each feasible scheme is selected as the optimal scheme with the highest priority; by considering the influence of multiple factors on the network loss of the power system (ie, wind power system) with large-scale wind power access In the actual power system operation mode, provide a quantitative calculation method, calculate the priority of the feasible solution, Provide guidance on running the power system; which can not be overcome integrated a number of factors prior art high-quantitative assessment of the net loss defects, in order to achieve a comprehensive variety of factors can be quantified Evaluate and reduce the benefits of network loss.

Other features and advantages of the invention will be set forth in the description which follows, and The objectives and other advantages of the invention will be realized and attained by the <RTI

The technical solution of the present invention will be further described in detail below through the accompanying drawings and embodiments.

DRAWINGS

The drawings are intended to provide a further understanding of the invention, and are intended to be a part of the description of the invention. In the drawing:

1 is a schematic flow chart of a method for optimizing wind power delivery power under multi-constraint conditions with reduced mesh loss according to the present invention;

2 is a logic block diagram of a method for optimizing wind power delivery power under multi-constraint conditions with reduced mesh loss in accordance with the present invention;

Figure 3 is a schematic diagram of the layout of a regional power system with a large-scale wind power base;

Figure 4 is a schematic diagram of the structure of the hierarchical model.

detailed description

The preferred embodiments of the present invention are described in conjunction with the accompanying drawings.

According to an embodiment of the present invention, as shown in FIG. 1 to FIG. 4, an optimization evaluation method for wind power sending power under multi-constraint conditions aiming at reducing network loss is provided, which is used for solving the analysis method of the influence of existing wind power sending power on network loss. There are problems.

As shown in FIG. 1 , in this embodiment, a method for optimizing the wind power delivery power optimization under the multi-constraint condition aiming at reducing the network loss includes:

Step 100: Based on the selected wind power system, according to the current domestic and international research on the impact of large-scale wind power on the power system, determine the factor set that affects the network loss of the wind power system ^ ⁷ : ^^, ² , '"' ^ ⁷ ), Using the power flow calculation software, the influence characteristics of each factor on the network loss are calculated based on the single variable method, and a hierarchical model including at least the scheme layer and the factor layer is formed (see Figure 4).

The above factor set ί/ includes at least any of the external power system injection power, the total wind power output, the respective wind power output of the wind farm group of the plurality of different access points, the system voltage level, and the system load level; Step 101: Obtaining the current actual operation mode of the selected wind power system in step 100, according to the operation mode, The factor set is divided into an immutable factor set and a variable factor set ^, and the scheduling operator determines the priority matrix of the variable factor set e; in step 101, the priority matrix of the variable factor set ^ is: ⁼ (^ , ^ ² '"', ^) , k = mb _t where, is the number of factor concentration factors (generally, w takes 5), which is the number of non-variable factors in the immutable factor set w in the current actual operating mode Step 102: Perform preliminary screening according to the network loss of the selected wind power system in step 100 according to the variation characteristics of the factors in the factor concentration and the invariable factor set obtained in step 101, and obtain a plurality of feasible solutions. In step 102, The operation of performing the preliminary screening specifically includes: determining a part of the feasible solution according to the invariable factor set obtained in step 101; further determining the factors in the network loss factor factor U of the selected wind power system according to the obtained partial feasible solution according to the step a The change characteristic, determining "a preferred solution" in the partial feasible solution; based on the W preferred solutions, the optimal solution in the current actual operation mode cannot be determined; Step 103: determining the comment set And, according to the priority matrix of the variable factor set obtained in step 101, and the plurality of feasible solutions obtained in step 102, respectively establishing a factor layer membership matrix and a scheme layer membership matrix; in step 103, the factor layer membership matrix is -

The membership level membership matrix is:

In step 103, the operation of the factor layer membership matrix is established, specifically including: the variable factor based on the selected feasible solution is

By the value of the 'factors corresponding to the membership function, the membership degrees of the five comment levels are:

According to the membership function ^i=l , the above membership degree is normalized to obtain the factor layer membership degree matrix ^R and the scheme layer membership degree matrix s.

',RJ„

Here, the membership function is a functional relationship that maps the value of the factor layer to the comment set. The factor layer belongs to the degree of each comment in the comment set, and can be described by a semi-trapezoidal and triangular membership function;

In the collection of the vocabulary in step 103, it is possible to represent a collection of comments made at various levels of the commented thing.

For the comment set ^p , which represents the high-low level of reviews, ie the rating level (eg best, better, better, general and poor, etc.);

V;

Each of the reviews ¹ can give a corresponding score, then the score set corresponding to the comment set can be obtained as -

E = (e _l , e ₂ , -', e _p )

„ _ V― ίν, . v"Vr )

For example, when - ³ , the set of comments ^{15 2} , ³ , ⁴ , ⁵对应 corresponding to the comment set ^ can be ^{9,7,5,3,1};

Step 104: Calculate the priority of each feasible solution in the plurality of feasible solutions obtained in step 102 according to the comment set obtained in step 103, and the solution layer membership matrix ¹ ; and, based on the priority N of the obtained plurality of feasible solutions, select The most feasible solution is the optimal solution. In step 104, according to the comment set obtained in step 103 and the solution layer membership matrix, the operation of the priority N of each feasible solution in the plurality of feasible solutions obtained in step 102 is calculated. These include: N = .

According to the method shown in the above steps 100-104, referring to Fig. 2, the regional power system shown in Fig. 3 is used as the verification model, and the analysis is as follows:

Step 100: Based on the selected power system with large-scale wind power as shown in FIG. 3, according to the current domestic and international research on the impact of large-scale wind power on the power system, determine the factor set that affects the network loss of the wind power system, including the provincial direction. B province's injection power, total wind power output, wind farm group 1 and wind farm group 2 output ratio, system voltage level and system voltage level five factors. Using the power flow calculation software, the influence characteristics of each factor in the factor concentration on the network loss are calculated based on the single variable method, and the hierarchical model including the scheme layer and the factor layer is formed as shown in FIG. 4 .

Step 101: A power system with large-scale wind power as shown in FIG. 3 is used. The operation mode is that the system load is constant, which is an immutable factor, and the composition of the immutable factor set A province to B province, the total power output, the wind farm group 1 and the wind farm group 2 output ratio and the system voltage level constitute a variable factor set. ^e . In step loi, the priority matrix ^ of the variable factor set is: = (3, 4, 2, iy\ Step 102: According to the network loss of the selected wind power system in step 100, the factors in the factor set ί/ The characteristics, and the set of immutable factors obtained in step 101, are initially screened to obtain η feasible solutions:

Step 103: Determine the comment set = ( ^V 1, ^V 2, ^V 3, ^V 4, ^V 5 ), and the score set corresponding to the comment set may be ^ ^ ⁷ , ⁵ , ³ , ¹ ) ; The priority matrix of the variable factor set ^ and the three feasible solutions obtained in step 102 respectively establish a factor layer membership degree matrix ^ _;

Option One:

0 0 1 1

1 0.5 0 0

0 0.5 0 0

0 0 0 0

0 0 0 0 -

T

After normalization, the membership factor-level membership matrix is obtained: = (0.3, 0.5, 0.2, 0, 0) Similarly, the membership factor matrix of the two-factor layer is obtained: R = (0.8, 0.2, 0, 0, 0f). The scheme three-factor layer membership degree matrix: = (0.5,0.5,0,0,0f. From this, the scheme layer membership degree matrix S is obtained.

Step 104: Calculate the priority of each feasible solution in the plurality of feasible solutions obtained in step 102 according to the comment set £ obtained in step 103 and the solution layer membership degree matrix;

0.3 0.8 0.5

0.5 0.2 0.5

N = ES = (9 7 5 3 1) 0.2 0 0 = (6.6 8.6 8.0)

0 0 0

After analysis and calculation, the priority results of each scheme are obtained, as shown in Table 1.

Table 1: Feasibility plan priority results

The above example analysis shows that: the wind power transmission power optimization evaluation method under the multi-constraint condition aiming at reducing the network loss in the above embodiment, through the characteristic analysis of the factors affecting the power system network loss of the large-scale wind power, combined The actual power system operation mode is given, and the feasible scheme is given. The hierarchical model is established, and the fuzzy membership function is used to quantitatively evaluate the priority of each scheme, which provides network loss optimization guidance for the grid operation of large-scale wind power transmission; Influencing factors, quantitatively assess the problem of wind power power transmission scheme under multi-constraint conditions with the goal of reducing network loss under the actual operation mode.

In an actual power system operation mode, because it is limited by multiple constraints, it is necessary to find a network loss optimal solution in a feasible solution; and the wind power transmission under the multi-constraint condition aiming at reducing the network loss in the above embodiment The power optimization evaluation method comprehensively considers the influence of multiple factors on the network loss of a power system with large-scale wind power access; provides a quantitative calculation method, and calculates the priority of the feasible solution for the actual operation. The power system provides guidance.

In summary, the wind power transmission power optimization evaluation method under the multi-constraint condition aiming at reducing the network loss in the above embodiment of the present invention includes: determining a factor set affecting the system network loss - (^^''''''^ ⁷ ) Calculate the influence characteristics of each factor on the network loss, form a hierarchical model with the scheme layer and the factor layer; obtain the current actual operation mode of the power system, and divide the factor set into an immutable factor set and a variable factor set according to the operation mode. ^ ⁷ . And the scheduling operator determines the priority matrix of the variable factor ^ ⁷ ^ ^ According to the variation characteristics of the system network loss with the influencing factors and the set of invariable factors, a number of feasible solutions are initially selected; the set of comments is determined, and the variable factors are determined. The priority matrix ^ ^ and the feasible scheme establishment factor layer membership degree matrix and the scheme layer membership degree matrix calculate the priority of each scheme from the comment set and the scheme layer membership degree matrix; select the scheme with the highest priority as the optimal scheme; In a power system with large-scale wind power access, the influence of multiple factors on the network loss of the system; in the actual power system operation mode, provides a quantitative calculation method, and calculates the priority of the feasible solution for the actual operation. The power system provides guidance.

It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, The technical solutions described in the foregoing embodiments may be modified, or some of the technical features may be equivalently replaced. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and scope of the present invention are intended to be included within the scope of the present invention.

u

Claims

Claim

1 . A method for optimizing wind power delivery power under multi-constraint conditions aiming at reducing network loss, characterized in that it comprises:

a. Based on the selected wind power system, according to the current domestic and international research on the impact of large-scale wind power on the power system, determine the set of factors affecting the network loss of the wind power system (^ ^ ⁷² ''' . ' ), using the power flow calculation software, Based on a single variable method, the influence characteristics of each factor on the network loss are calculated separately, and a hierarchical model including at least a scheme layer and a factor layer is formed; the factor set ί includes at least an external power system injection power and a total wind power output. , the wind power output of the plurality of different access points, the wind power output, the system voltage level, and any one of the system load levels; b, obtaining the current actual operation mode of the selected wind power system in step a, according to the operation mode , the factor set (7 is divided into the immutable factor set ^ ^/ w and the variable factor set:, the scheduling operator determines the priority matrix of the variable factor set;

c. According to the network loss of the selected wind power system in step a, the variation characteristics of each factor in the factor set ί/ and the immutable factor set obtained in step b, preliminary screening is performed, and a plurality of feasible solutions are obtained; d. determining a comment set; And, according to the priority matrix ^^ of the variable factor set ^^ in step b, and the plurality of feasible solutions obtained in step c, respectively establishing a factor layer membership degree matrix ^ and a scheme layer membership degree matrix; e, obtaining a comment according to step d Set, and the program layer membership degree matrix, calculate the priority of each feasible solution among the plurality of feasible solutions obtained in step c and, based on the priority of the obtained plurality of feasible solutions, ^, select the feasible solution with the maximum priority N as the optimal Program.

2. The method for optimizing wind power delivery power optimization under multi-constraint conditions with reduced network loss according to claim 1, wherein in step b, the priority matrix ^ of the variable factor set is:

Among them, the number of factor concentration factors is the set of immutable factors in the current actual operation mode. The number of unchangeable factors.

The method for optimizing the wind power delivery power under the multi-constraint condition for reducing the network loss according to claim 1, wherein in the step c, performing the preliminary screening operation comprises: variable factor set determination section viable option; viable option based on the resulting part, further variation characteristic set according to various factors ^U loss factor with wind power system selected in step a, preferred embodiment W is determined in the portion of the possible options; Based on the W preferred schemes, the optimal scheme in the current actual operating mode cannot be determined.

4. The method for optimizing wind power delivery power under multi-constraint conditions with reduced mesh loss according to claim 1, wherein in d, the factor layer membership matrix is -

The scheme layer membership matrix ¹³ is:

The method for optimizing the wind power delivery power under the multi-constraint condition with the target of reducing the network loss according to claim 1 or 4, wherein in step d, the operation of establishing the membership matrix of the factor layer is specifically Including: Based on the variable factor of the selected feasible scheme is ^^ = ( , ₂ '"', , the value of the factor corresponds to the membership function,

The membership of the five rating levels is

R =

According to the membership letter

Number, normalize the above membership degree, and obtain the factor layer membership degree matrix R and the scheme layer membership degree matrix:

6. The method for optimizing wind power delivery power optimization under multi-constraint conditions with reduced network loss according to claim 1, wherein in step d, the comment set is a level made for a commented object. A collection of comments, expressed as a comment set = ( , ^,..., ) (/two l,2,..., ) represents a high-low level of comment; each comment on the comment set

Then you can get the score set E corresponding to the comment set _: ^{£ =} ^1, ^β 2,···Ά).

The method for optimizing the wind power delivery power optimization under the multi-constraint condition for reducing the network loss according to claim 1 or 6, wherein in step e, the comment set and the plan layer obtained according to step d are The membership matrix, the operation of calculating the priority ^ of each feasible solution among the plurality of feasible solutions obtained in step c, specifically: ^{N = ES} .