CN112308427A - New energy consumption restriction factor evaluation method and system based on combined empowerment-grey correlation - Google Patents
New energy consumption restriction factor evaluation method and system based on combined empowerment-grey correlation Download PDFInfo
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Abstract
The invention discloses a new energy consumption restriction factor evaluation method and system based on combined empowerment-grey correlation, wherein a new energy consumption restriction factor comprehensive evaluation index system is established in a layered mode; carrying out quantitative evaluation on the secondary indexes by adopting a combined weighting method to obtain combined weight; the combined weighting method can fully combine subjective and objective weights, analyzes the internal relation between index data while referring to expert knowledge and experience, and is beneficial to more scientific and accurate selection of new energy consumption evaluation index weights.
Description
Technical Field
The invention belongs to the technical field of electric power system evaluation, and particularly relates to a new energy consumption restriction factor evaluation method and system based on combined empowerment-grey correlation.
Background
Under the influence of unstable natural conditions such as wind speed and illumination intensity, wind power and photovoltaic power generation have obvious uncertainty, difficult controllability and space-time distribution characteristics, and large-scale access of the wind power and photovoltaic power generation also brings a series of problems to be solved urgently for a power system. The new energy consumption is a system project and is closely related to various factors such as power consumption demand, grid structure, peak regulation capacity, system stability and the like.
After the large-scale new energy is connected to the grid, the fundamental reason for restricting the consumption capability of the new energy is that the system regulating capability is insufficient, and the main factors restricting the large-scale new energy grid-connected consumption capability are deeply analyzed from the three aspects of source, network and load. System regulation capacity: the method is mainly related to the self-regulation performance of the unit (the power supply structure in China is mainly thermal power, and flexible units such as gas, water and electricity with deep peak regulation capability, pumped storage and the like are relatively few, and the whole regulation performance of a power system is poor), and the starting mode of the system, and because of the uncertainty of the output of new energy, the peak regulation condition of a power grid can be seriously influenced by the fluctuation of the generated power, so that the consumption level of the system on the new energy is restricted; system spare capacity: in order to ensure the safety and stability of the system, a certain reserve is reserved when the power system runs, the higher the rotation reserve rate is, the larger the required starting capacity is, the more insufficient the down-regulation reserve of the system is, the possibility of electricity abandonment is increased when new energy is greatly generated in the load valley period, and the new energy consumption capacity is reduced; ③ new energy permeability: the larger the installed capacity of the new energy is, the higher the permeability is, the larger the peak load capacity demand on the power system is, the more difficult the consumption is, and the installed scale of the new energy is reasonably planned; electric network transmission capacity: wind power installations in China are mainly distributed in the three-north region, photovoltaic installations are mainly distributed in the western region, and the new energy consumption space on site is limited, so that the transmission capacity of a power grid needs to be improved, and the trans-provincial and trans-regional transmission of wind power and photovoltaic power generation is realized; system load characteristics: the load characteristic has certain randomness, the larger the peak-valley difference is, the more difficult the system peak regulation pressure is, and the peak-valley difference can be reduced by means of electricity price response, introduction of interruptible load and the like.
The quantitative evaluation of the consumption restriction factors is to measure the importance and the action degree of each index on the consumption of the new energy, the size is given in a quantitative form, the larger the weight is, the larger the action of the index is, and the smaller the action is otherwise. Whether the index is endowed with the right plays a decisive role in evaluation or decision results, and the same index data is endowed with different weight values, so that the evaluation results are different greatly and even opposite results are obtained.
The existing research for calculating the weight is mainly developed aiming at the subjective weighting method and the objective weighting method. The subjective weighting method is relatively early and mature in research, an original weight comparison matrix is obtained by judging according to subjective preference and related experience and by combining reality by experts, processing is performed by using a related mathematical theory to obtain a weight value, and the commonly used subjective weighting methods mainly include a feature vector method, a specialist survey (Delphi) method, a least squares sum method, a binomial coefficient method, an Analytic Hierarchy Process (AHP) and the like. The method mainly comprises the steps of calculating the weight by mining the internal relation among index data, not considering subjective judgment and tendency of people, generally combining strong mathematical theory basis, and the commonly used methods mainly comprise an entropy method, a principal component analysis method, a multi-target planning method, a mean square error method, a dispersion method and the like.
Disclosure of Invention
The purpose of the invention is as follows: in order to solve the problem of unreasonable index empowerment in the existing evaluation method of consumption restriction factors, the invention provides a new energy consumption restriction factor evaluation method and system based on combined empowerment-gray correlation.
The technical scheme adopted by the invention is as follows: a new energy consumption restriction factor evaluation method based on combined empowerment-gray correlation comprises the following steps:
step 1: establishing a new energy consumption restriction factor comprehensive evaluation index system in a layered manner, wherein the evaluation index system comprises a primary index and a secondary index corresponding to the primary index;
step 2: carrying out quantitative evaluation on the secondary indexes by adopting a combined weighting method to obtain the combined weight of each secondary index;
and step 3: and (3) based on the combined weight of the secondary indexes obtained in the step (2), evaluating the evaluation index system established in the step (1) by adopting a grey correlation method to obtain the influence degree of the primary indexes.
Further, the step 1 specifically includes:
the power supply index, the net rack index, the load index and the peak regulation index are used as first-level indexes, and second-level indexes corresponding to the power supply index comprise: the output fluctuation rate and the output synchronization rate; the second-level indexes corresponding to the grid indexes include: the method comprises the following steps of (1) line heavy load rate, transformer substation main transformer heavy load rate, line N-1 passing rate and voltage qualification rate; the secondary indexes corresponding to the load indexes include: a load rate; the secondary indexes corresponding to the peak shaving indexes comprise: peak-to-valley difference rate;
and performing convergence treatment on the secondary index values by acquiring the secondary index values under different operation schemes, and establishing a new energy consumption restriction factor comprehensive evaluation index system.
Further, the performing of the convergence process on the secondary index value specifically includes:
classifying the secondary indexes according to the monotonicity of the secondary indexes on the action of the secondary indexes on a decision target, wherein the benefit indexes comprise an N-1 passing rate and a voltage qualified rate, and the cost indexes comprise an output fluctuation rate, an output synchronization rate, a line heavy load rate, a transformer substation main transformer heavy load rate, a load rate and a peak-valley difference rate;
carrying out forward processing on all secondary indexes, wherein all cost indexes are changed into 1 and the original numerical value is subtracted, and all benefit index numerical values are unchanged;
and (4) performing range transformation on all secondary indexes subjected to forward processing to obtain a new energy consumption restriction factor comprehensive evaluation index system.
Further, the combined weighting method is a combined weighting method combining an analytic hierarchy process and an entropy value method.
Further, the step 2 specifically includes:
evaluating the values of the secondary indexes subjected to the convergence treatment by adopting an analytic hierarchy process to obtain a weight vector of each secondary index
Evaluating the two-level index value after the convergence treatment by adopting an entropy method to obtain a weight vector of each two-level index
By linear weighted combination method, the weight vector is
And weight vector
Combining to obtain a combined weight vector:
in the formula, delta and 1-delta are preference coefficients of the main and objective weights of each secondary index respectively.
Further, the step 3 specifically includes:
calculating the correlation coefficient epsilon according to the following formulaij：
In the formula ujIs a reference value of the secondary index, dijIs the actual value of the second-level index, rho is the resolution coefficient,
is an absolute maximum value，
Is the absolute minimum;
based on the correlation coefficient of each index, combining the combined weight W ═ omega of the secondary index1,ω2,...,ωm]Calculating the grey correlation degree g of the corresponding first-level index according to the following formulai：
In the formula, ωjThe combined weight of the jth secondary index;
and obtaining the influence degree of the first-level index based on the grey correlation degree of each first-level index, wherein the higher the grey correlation degree is, the greater the influence degree of the first-level index is.
The invention also discloses a new energy consumption restriction factor evaluation system based on the combined empowerment-gray association, which comprises the following steps:
the evaluation index system establishing module is used for establishing a new energy consumption restriction factor comprehensive evaluation index system in a layering way, and the evaluation index system comprises a primary index and a secondary index corresponding to the primary index;
the combined weighting calculation module is used for carrying out quantitative evaluation on the secondary indexes by adopting a combined weighting method to obtain the combined weight of each secondary index;
and the grey correlation degree calculation module is used for evaluating the established evaluation index system by adopting a grey correlation degree method based on the combined weight of the secondary indexes to obtain the influence degree of the primary indexes.
Further, the evaluation index system establishing module comprises the following sub-modules:
the classification module is used for classifying the secondary indexes according to the monotonicity of the secondary indexes on the action of the secondary indexes on a decision target, wherein the benefit indexes comprise an N-1 passing rate and a voltage qualified rate, and the cost indexes comprise an output fluctuation rate, an output synchronization rate, a line heavy load rate, a transformer substation main transformer heavy load rate, a load rate and a peak-valley difference rate;
the forward processing module is used for performing forward processing on all secondary indexes, wherein the forward processing comprises the steps of changing all cost indexes into 1 and subtracting the original numerical value, and keeping all benefit index numerical values unchanged;
and the range transformation module is used for carrying out range transformation on all secondary indexes subjected to forward processing to obtain a new energy consumption restriction factor comprehensive evaluation index system.
Further, the combined entitlement calculation module comprises the following sub-modules:
a hierarchical analysis evaluation module for evaluating the two-level index value after the convergence treatment by adopting a hierarchical analysis method to obtain a weight vector of each two-level index
An entropy evaluation module for evaluating the two-stage index values after the convergence processing by using an entropy method to obtain weight vectors of the two-stage indexes
A combination weight calculation module for adopting linear weighting combination method to make weight vector be
And weight vector
And combining to obtain a combined weight vector.
Further, the gray correlation calculation module includes the following sub-modules:
a correlation coefficient calculating module for calculating a correlation coefficient epsilon according to the following formulaij：
In the formula ujIs a second-level fingerReference value, dijIs the actual value of the second-level index, rho is the resolution coefficient,
is the maximum value of the absolute value of the,
is the absolute minimum;
a gray relevance operator module for combining the combined weight W of the secondary index into [ omega ] based on the relevance coefficient of each index1,ω2,...,ωm]Calculating the grey correlation degree g of the corresponding first-level index according to the following formulai：
In the formula, ωjIs the combined weight of the jth secondary index.
Has the advantages that: compared with the prior art, the invention has the following advantages:
1. the invention adopts the combination empowerment-grey correlation to carry out quantitative evaluation on the absorption restriction factors, wherein, the combination empowerment method based on the analytic hierarchy process and the entropy method is used for the quantitative evaluation of the secondary indexes, the combination empowerment method fully combines the subjective and objective weights, the evaluation result is more scientific and accurate, the grey correlation method is used for the quantitative evaluation of the primary indexes, because the primary indexes are too general in range and can not be quantitatively evaluated, the quantitative evaluation is carried out on the secondary indexes firstly, and then the sequencing of the primary indexes is obtained, so that the evaluation result is more accurate, and a solid foundation is laid for the follow-up research on how to improve the absorption capacity of new energy;
2. the multi-level comprehensive evaluation index system for the new energy consumption restriction factors is constructed, on the basis of actual operation experience of a power grid and related theoretical research results, main factors restricting new energy consumption are comprehensively considered, and index division of different levels is determined.
Drawings
FIG. 1 is a flow chart of a new energy consumption constraint factor evaluation method based on combined empowerment-gray correlation;
FIG. 2 is a schematic diagram of a comprehensive evaluation index system for new energy consumption restriction factors.
Detailed Description
The invention is further illustrated below with reference to the figures and examples.
As shown in fig. 1, a new energy consumption restriction factor evaluation method based on combined empowerment-gray association specifically includes the following steps:
step 1: and establishing a comprehensive evaluation index system of the new energy consumption restriction factors in a layered manner, wherein the evaluation indexes comprise 4 types of first-level indexes and 8 second-level indexes. This step 1 will now be further explained:
according to the actual operation experience of the power grid and the research results of related theories, main factors for restricting the new energy consumption capacity are grid transmission capacity, new energy permeability, power grid regulation capacity, load characteristics and the like. And constructing a comprehensive evaluation index system of new energy consumption restriction factors from four dimensions of a power supply, a network frame, a load and a peak regulation by applying the idea of establishing an index system by hierarchical classification, wherein the power supply, the network frame, the load and the peak regulation are primary indexes. And selecting 8 indexes of output fluctuation rate, output synchronization rate, line heavy load rate, main transformer heavy load rate, N-1 passing rate, voltage qualification rate, load rate and peak-valley difference rate as secondary indexes by combining the influence of large-scale new energy grid connection and the actual engineering situation, wherein the 8 indexes correspond to the primary indexes.
The 4 types of primary indexes and 8 secondary indexes indicated above will now be described with reference to the evaluation index system block diagram shown in fig. 2. 1. Power supply index: aiming at the time-space characteristics of the new energy output, two indexes of output fluctuation rate and output synchronization rate are provided, wherein the output fluctuation rate reflects the output change rule on a time scale, and the output synchronization rate reflects the output characteristics on a space scale.
(1) Output fluctuation rate:
the index of the fluctuation rate of the output is the single step change amount delta P ═ P [ (i +1) · delta T]-P (i · Δ T) and single step rate of change k ═ Δ P/PNDefinition on the basis, selecting a typical daily classification of new energy outputThe statistical output of n time intervals is calculated by the following formula:
in the formula: Δ T is a reference time interval; i represents the number of reference time intervals corresponding to the time; p (i · Δ T) represents an actual output at a time immediately before the new energy power generation; p [ (i + 1). DELTA.T]Representing the actual output at the moment after the new energy is generated; pNRepresenting the rated power of the new energy power generation.
(2) Simultaneous output rate:
the output simultaneous rate refers to the probability that the power generation of each wind field and the photovoltaic power station simultaneously reaches the highest, and is the ratio of the highest daily generated output to the total capacity of the new energy grid-connected unit on the same day. The formula is as follows:
in the formula, P∑Is the total output of the distributed power supply; sigma PNThe sum of installed (grid-tied) capacity for all distributed power supplies.
The synchronous rate reflects the probability that the power generation of each distributed power supply reaches the highest simultaneously, and the higher the synchronous rate is, the more the situation of limiting the power generation output of the new energy is, generally below 70%, and the method is mainly applied to the aspects of space correlation analysis under a certain time scale, power balance in power grid planning, power supply access system research and the like.
2. The network frame indexes are as follows: the method mainly reflects the power supply capacity of the power grid, and is an important index for judging whether the power grid structure is reasonable and whether the transmission capacity of transmission and distribution is matched. The large-scale access of wind power can cause the problems of too low node voltage, overload circuit and the like in the grid structure.
(1) Line overload rate:
the heavy-load line is generally a power transmission bottleneck, the transmission power of a power grid is limited by the position of the heavy-load line, meanwhile, the voltage of a nearby node is greatly influenced, and the heavy-load line is one of the most main evaluation indexes of the abundance of the power grid. Lines reaching 80% or more of rated transmission capacity of the transmission line are specified as heavy-load lines.
In the formula, m is the number of heavy load lines, and n is the number of all buses.
(2) The heavy load rate of a main transformer of a transformer substation is as follows:
the transformer is an important device for connecting lines of various voltage classes, and the load rate of the transformer can reflect the load condition (load density, distribution characteristics and the like) of the relevant area.
In the formula: pTi、STiRespectively the maximum load and the rated capacity of the transformer i.
The transformer with the load factor in a certain normal range belongs to a normally-operated transformer, and for the transformers with different voltage grades, the judgment standards of the transformers are different, and the judgment standards are as follows:
220kV and above transformer substation: the load rate is more than or equal to 70 percent, and the method is called heavy load;
a 110kV transformer substation: the load rate is more than or equal to 80 percent, and the method is called heavy load.
Heavy load factor C of main transformer of transformer substation4The calculation formula of (2) is the same as the line overloading rate.
(3) Line N-1 pass rate:
the grid-connected mode is inspected through the line N-1 passing rate index, and the more single radiation lines accessed by new energy, the greater the verification contribution rate of the line N-1. The index is calculated by the formula
In the formula: n is a radical ofDGThe number of lines with improved line contact rate is shown when new energy is accessed, N represents the lines which pass the N-1 verification in the power grid, and NallRepresenting an electric networkAll lines in operation.
(4) Voltage qualification rate:
after the wind power generation is put into operation, the voltage level after the corresponding reactive compensation is added to the access point. By not over-limiting the number of nodes N1And the total number of selected nodes N0The ratio of (A) to (B) is used as an index value:
the voltage fluctuation range is less than or equal to +/-5 percent, and the voltage out-of-limit is determined when the voltage fluctuation range is exceeded.
3. Load index: the load condition can reflect the distribution condition of the load in the whole power grid, and is an extremely important aspect of the safe operation analysis of the power grid and an aspect of economic operation consideration.
The load rate is selected as a load index, and the load rate is the ratio of the average load to the maximum load of the power grid. From the perspective of safe operation of the power grid, the closer the load rate ratio is to 1, the closer the peak-to-valley value of the power grid load is.
In the formula: pPThe average load of the whole network within a certain time; pMAXThe maximum load of the whole network in a certain time.
4. Peak regulation index: under the condition of large amount of new energy grid connection, the power system has to have enough peak regulation capacity to stabilize the output fluctuation of the new energy unit and ensure the reliable operation of the power grid and the new energy power plant.
The peak-valley difference rate is used as a peak regulation index, the peak-valley difference refers to the difference between the maximum load and the minimum load of the power system in a period of time, and the peak-valley difference rate is the ratio of the peak-valley difference to the maximum load. A typical day is selected for research, and the calculation formula is as follows:
in the formula: pmaxFor the maximum load of the whole network within a certain time, PminThe minimum load of the whole network in a certain time. The outstanding problem of the peak-to-valley difference rate is that the peak load of the power grid is insufficient, so that the difference between the low-valley load and the high-peak load of the power grid is large, and the peak load adjusting capacity of the power grid can be reflected through the index.
Step 2: obtaining secondary index values under different schemes to obtain data samples and data samples CijThe method is a matrix formed by j secondary index original data under i schemes, and convergence processing is carried out on data samples.
Because of the difference between the index attributes, the evaluation indexes are classified according to the monotonicity of the action of the evaluation indexes on the decision target, and the three types are total: a benefit type (the larger the better the type), a cost type (the smaller the better the type), and a fixed type (the closer to a fixed value the better the type). Different indexes have different units and different dimensions, so that convergence processing needs to be performed on basic index data. Vector normalization, linear scaling and range scaling are commonly used.
In the comprehensive evaluation index system established in the step 1, the N-1 pass rate and the voltage qualification rate are benefit type indexes, and the rest are cost type indexes. Considering the specific situation of the index system established in the step 1, all indexes are processed in the forward direction, namely the benefit index value is unchanged, the cost index value is changed by subtracting the original value from 1, and then range conversion is carried out. Now, taking the output fluctuation rate (cost index) and the N-1 pass rate index (benefit index) as examples, the range transformation is described, and the transformation formula refers to formula (9):
in the formula, C1(i) And C5(i) Is the index value of the output fluctuation rate and the N-1 passing rate under the ith scheme, maxC and minC are the maximum value and the minimum value of the index under the i schemes,
and
is the output fluctuation ratio and the N-1 passing ratio of the ith scheme after the convergence treatment.
And step 3: and carrying out quantitative evaluation on the secondary indexes by adopting a combined weighting method combining an analytic hierarchy process and an entropy value method to obtain combined weight.
After the index system is established, the weighting problem of each level of index is considered. Because the range of the first-level indexes is too general and cannot be quantitatively evaluated, the second-level indexes are quantitatively evaluated first, and then the sequence of the first-level indexes is obtained.
And (3) evaluating the data samples subjected to the convergence processing in the step (2) by adopting a combined weighting method combining an Analytic Hierarchy Process (AHP) and an entropy method, wherein the combined weighting method can fully combine subjective and objective weights, analyzes the internal relation between index data while referring to expert knowledge and experience, and is beneficial to more scientific and accurate selection of new energy consumption evaluation index weights.
The above-mentioned analytic hierarchy process is a decision-making process for performing qualitative and quantitative analysis on the basis of decomposing elements related to decision-making into levels of targets, criteria, schemes, etc. The method has the advantages of systematic, flexible and simple structure, and can be generally carried out according to the following four steps:
firstly, establishing a hierarchical structure model;
constructing all judgment matrixes in each layer;
the judgment matrix a ═ (a) is defined with reference to the numbers 1 to 9 and their inverses as scalesij)nxn。
Thirdly, hierarchical single sequencing and consistency inspection:
and (3) calculating a consistency index:
wherein λ ismaxThe maximum eigenvalue of the decision matrix.
The index number n is 8, and the average random identity index RI is 1.41.
And (3) calculating a consistency ratio:
when CR is less than or equal to 0.1, the consistency of the judgment matrix is considered to be acceptable; when CR is more than or equal to 0.1, the judgment matrix needs to be properly adjusted to ensure that the judgment matrix has satisfactory consistency.
Fourthly, total sequencing of layers and consistency inspection:
and calculating the weight by adopting a geometric mean method (a root method), and finally obtaining the sequencing weight of each factor so as to select the scheme.
A calculation step: multiplying the elements of the first step by rows to obtain a new vector; opening each component of the new vector by the power of n; normalizing the obtained vector to obtain the weight vector.
The above-mentioned entropy method is a method of objectively setting the index weight. The concept of entropy is originally derived from the second law of thermodynamics and is used for describing the discrete degree of certain data, and the larger the entropy is, the larger the discrete degree is; the smaller the entropy, the smaller the degree of dispersion. When the index is judged by using the entropy method, the judgment is mainly determined according to the information quantity value provided by the entropy of the index in the whole evaluation system. The method can enable the evaluation result to be more consistent with the actual situation, and effectively avoid artificial interference factors. The calculation steps are as follows:
calculating the jth scheme index value f to be evaluated under the kth indexkjSpecific gravity p ofkj：
Computing the information entropy e of the k index in the systemk：
In the formula, a constant k (k > 0) and the number of samples (number of schemes) m in the system are related. In a system where the information is completely unordered, his degree of order is 0, but its entropy is the largest, e-1; however, if m samples are in a completely disordered distribution, p is knownjk1/m, obtainable from formula (14):
then k is 1/lnm, and 0 ≦ ek≤1。
Calculating coefficient g of difference under k index in systemk：
gk＝1-ek (16)
Entropy of information ekIs used to measure the utility value of the k-th index value in the system, so if it is out of order, ekAt the time e ═ 1kThe utility value of the information of (2) to the entire comprehensive evaluation system is 0. gkThe larger the index, the more important the index.
Fourthly, defining weight wk：
The weight of each index is calculated by using an information entropy method, the essence of the method is that the weight is calculated by using an information value coefficient of the index, when the value coefficient is higher, the importance of the evaluation is higher, and then the weight of the kth index in the system is as follows:
a combined weighting method combining an Analytic Hierarchy Process (AHP) and an entropy method will now be described.
Suppose that the weight vector obtained by adopting the Analytic Hierarchy Process (AHP) calculation theory is
Weight vector obtained by entropy method
Generally, a linear weighted combination method is used to determine the weight of the evaluation index, and then a combined weight vector obtained by combining the two methods is:
in the formula, δ and 1- δ are preference coefficients of the subjective and objective weights of each evaluation index. The coefficient of the subjective weight is taken to be 0.8, and the coefficient of the objective weight is taken to be 0.2. The combined weighting method can fully combine subjective and objective weights, and analyze the internal relation between index data while referring to expert knowledge and experience, thereby being beneficial to more scientific and accurate selection of new energy consumption evaluation index weights.
And 4, step 4: and evaluating the evaluation index system by adopting a grey correlation method according to the weight of the secondary index to obtain the influence degree sequence of the primary index.
And (3) according to the secondary index weight obtained in the step (3), combining the calculated value of each specific index (the distance difference between the existing data and the reference sample is obtained by comparing the existing data with the reference sample, namely the correlation coefficient), analyzing the evaluation index system by adopting a gray correlation method (the gray correlation is the index weight and the correlation coefficient), and judging the influence degree of the evaluation index system from four dimensions of a power supply, a grid frame, a load and a peak regulation, wherein the influence degree of the index is larger when the correlation degree is higher.
The grey correlation method mentioned above is an application of grey system theory in multi-attribute decision making, measures the degree of correlation between factors according to the similarity or dissimilarity degree of development situations between factors, realizes quantitative analysis of the dynamic development process of the system, and is essentially a geometric process. Specifically, the degree of association between the actual sequence curve and the target sequence curve is determined by judging the degree of similarity of the geometric shapes of the actual sequence curve and the target sequence curve, if the target sequence is an ideal optimal sequence, the degree of association represents the degree of closeness between the actual scheme and the optimal scheme, namely the greater the degree of association, the closer the scheme is to the optimal scheme, and vice versa. The basic steps of grey correlation analysis are as follows:
determining a decision matrix R:
the decision matrix R ═ { U; d, wherein U is a reference number sequence of the decision matrix and is formed by the optimal value of each index, and U is { U ═ U }1,u2,...,un}，unThe optimal value representing the nth index may be a recognized optimal value or a calculated optimal value; d represents a normalized index set, and D ═ D1,d2,...,dn),dnExpressing the nth index to form a comparison array set of a decision matrix, wherein a specific expression of the decision matrix R is as follows:
calculating a correlation coefficient:
coefficient of correlation epsilonijIs a relative difference value between the ith comparison series curve and the reference series curve at the jth point, i.e. the jth index, and the expression of the correlation coefficient is as follows:
in the formula ujIs a reference value of the secondary index; dijThe actual value of the second-level index is obtained; ρ is a resolution coefficient, and is generally taken as 0.5;
is the absolute maximum;
is the absolute minimum.
Determining the grey correlation degree of the scheme:
based on the correlation coefficient of each index, the index weight W is [ omega ]1,ω2,...,ωm]So as to obtain the grey correlation degree g between the whole scheme and the optimal scheme seti：
In the formula, when ω isjFor the weight of the jth index, the schemes are sorted by comparing the gray relevance degrees of the schemes, obviously, the scheme with the high gray relevance degree is closer to the optimal scheme, and vice versa, namely, the indexes are sorted by comparing the gray relevance degrees of the first-level indexes, and the influence degree sorting of the first-level indexes is obtained.
The method considers the operation characteristics of a large-scale new energy grid-connected system, analyzes the properties of different consumption restriction factors and the relation among the different restriction factors, establishes a perfect comprehensive evaluation index system, and more comprehensively reflects all aspects of characteristics of the consumption restriction factors. Meanwhile, a combined weighting method combining an analytic hierarchy process and an entropy weight method is adopted, subjective and objective weights are fully combined, expert knowledge and experience are used for reference, internal relation among index data is analyzed, and more scientific and accurate selection of new energy consumption evaluation index weights is facilitated. Now, the evaluation method of the present invention is further explained by using a power grid in a certain area as an example sample and based on 6 groups of load flows in peak operation modes of 2021-2023 in summer and winter.
The data samples were:
according to engineering practice and expert opinions, the judgment matrix of the analytic hierarchy process is as follows:
and (5) judging that the consistency of the matrix is qualified when the consistency ratio CR is 0.032509 < 0.1.
And calculating to obtain the combined weight of the secondary indexes by adopting a combined weighting method:
w＝(0.0495,0.0783,0.0933,0.1041,0.1738,0.0432,0.3007,0.1572)T
and (3) sorting the weights of the secondary indexes, wherein the importance sorting of the indexes is as follows: the load factor is greater than N-1 passing rate, peak-valley difference rate, main transformer heavy load rate, line heavy load rate, output simultaneous rate, output fluctuation rate and voltage qualification rate.
The grey correlation degree is adopted to analyze the index system, and the net rack which is the most main factor for restricting the new energy consumption of the area in 2021-2023 is obtained, and accounts for 43 percent. The next is load, peak shaving and power, 29%, 15% and 13% respectively.
The sequencing of the first-level and second-level indexes shows that safety constraints such as a main transformer of a power grid in a new energy access region, the tidal current transmission capability of a line channel and the like are the most main factors for restricting the consumption of new energy, and the main channel (such as a river channel) for sending the new energy out is further enhanced; the system load scale is the second major factor influencing the new energy accepting capacity, and the larger the load level is, the more the new energy is locally absorbed, and the larger the accessible capacity of the new energy in the whole network is; the new energy anti-peak-shaving characteristic weakens the system peak-shaving capacity, after a large amount of new energy is accessed, the peak-valley difference of the power grid is increased, the peak-shaving contradiction is more prominent, and the power grid peak-shaving becomes the third major factor restricting the new energy access; the grid-connected characteristic of the new energy is another factor influencing the access of the new energy, and the larger the fluctuation and the synchronization rate of the new energy are, the smaller the capacity of the power grid for receiving the new energy is.
The invention also provides a new energy consumption restriction factor evaluation system based on the combined empowerment-grey correlation, which comprises the following steps:
the evaluation index system establishing module is used for establishing a new energy consumption restriction factor comprehensive evaluation index system in a layering way, and the evaluation index system comprises a primary index and a secondary index corresponding to the primary index;
the combined weighting calculation module is used for carrying out quantitative evaluation on the secondary indexes by adopting a combined weighting method to obtain the combined weight of each secondary index;
and the grey correlation degree calculation module is used for evaluating the established evaluation index system by adopting a grey correlation degree method based on the combined weight of the secondary indexes to obtain the influence degree of the primary indexes.
The evaluation index system establishing module comprises the following sub-modules:
the classification module is used for classifying the secondary indexes according to the monotonicity of the secondary indexes on the action of the secondary indexes on a decision target, wherein the benefit indexes comprise an N-1 passing rate and a voltage qualified rate, and the cost indexes comprise an output fluctuation rate, an output synchronization rate, a line heavy load rate, a transformer substation main transformer heavy load rate, a load rate and a peak-valley difference rate;
the forward processing module is used for performing forward processing on all secondary indexes, wherein the forward processing comprises the steps of changing all cost indexes into 1 and subtracting the original numerical value, and keeping all benefit index numerical values unchanged;
and the range transformation module is used for carrying out range transformation on all secondary indexes subjected to forward processing to obtain a new energy consumption restriction factor comprehensive evaluation index system.
The combined empowerment calculation module comprises the following sub-modules:
a hierarchical analysis evaluation module for evaluating the two-level index value after the convergence treatment by adopting a hierarchical analysis method to obtain a weight vector of each two-level index
An entropy evaluation module for evaluating the two-stage index values after the convergence processing by using an entropy method to obtain weight vectors of the two-stage indexes
A combination weight calculation module for adopting linear weighting combination method to make weight vector be
And weight vector
And combining to obtain a combined weight vector.
The grey correlation calculation module of the invention comprises the following sub-modules:
a correlation coefficient calculating module for calculating a correlation coefficient epsilon according to the following formulaij：
In the formula ujIs a reference value of the secondary index, dijIs the actual value of the second-level index, rho is the resolution coefficient,
is the maximum value of the absolute value of the,
is the absolute minimum;
a gray relevance operator module for combining the combined weight W of the secondary index into [ omega ] based on the relevance coefficient of each index1,ω2,...,ωm]Calculating the grey correlation degree g of the corresponding first-level index according to the following formulai：
In the formula, ωjIs the combined weight of the jth secondary index.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A new energy consumption restriction factor evaluation method based on combined empowerment-gray correlation is characterized by comprising the following steps: the method comprises the following steps:
step 1: establishing a new energy consumption restriction factor comprehensive evaluation index system in a layered manner, wherein the evaluation index system comprises a primary index and a secondary index corresponding to the primary index;
step 2: carrying out quantitative evaluation on the secondary indexes by adopting a combined weighting method to obtain the combined weight of each secondary index;
and step 3: and (3) based on the combined weight of the secondary indexes obtained in the step (2), evaluating the evaluation index system established in the step (1) by adopting a grey correlation method to obtain the influence degree of the primary indexes.
2. The method for evaluating new energy consumption constraint factors based on combined empowerment-gray association according to claim 1, wherein the method comprises the following steps: the step 1 specifically comprises:
the power supply index, the net rack index, the load index and the peak regulation index are used as first-level indexes, and second-level indexes corresponding to the power supply index comprise: the output fluctuation rate and the output synchronization rate; the second-level indexes corresponding to the grid indexes include: the method comprises the following steps of (1) line heavy load rate, transformer substation main transformer heavy load rate, line N-1 passing rate and voltage qualification rate; the secondary indexes corresponding to the load indexes include: a load rate; the secondary indexes corresponding to the peak shaving indexes comprise: peak-to-valley difference rate;
and performing convergence treatment on the secondary index values by acquiring the secondary index values under different operation schemes, and establishing a new energy consumption restriction factor comprehensive evaluation index system.
3. The method for evaluating new energy consumption constraint factors based on combined empowerment-gray association according to claim 2, wherein: the convergence treatment of the secondary index values specifically comprises the following steps:
classifying the secondary indexes according to the monotonicity of the secondary indexes on the action of the secondary indexes on a decision target, wherein the benefit indexes comprise an N-1 passing rate and a voltage qualified rate, and the cost indexes comprise an output fluctuation rate, an output synchronization rate, a line heavy load rate, a transformer substation main transformer heavy load rate, a load rate and a peak-valley difference rate;
carrying out forward processing on all secondary indexes, wherein all cost indexes are changed into 1 and the original numerical value is subtracted, and all benefit index numerical values are unchanged;
and (4) performing range transformation on all secondary indexes subjected to forward processing to obtain a new energy consumption restriction factor comprehensive evaluation index system.
4. The method for evaluating new energy consumption constraint factors based on combined empowerment-gray association according to claim 1, wherein the method comprises the following steps: the combined weighting method is a combined weighting method combining an analytic hierarchy process and an entropy value process.
5. The method for evaluating new energy consumption constraint factors based on combined empowerment-gray association according to claim 3, wherein: the step 2 specifically comprises:
evaluating the values of the secondary indexes subjected to the convergence treatment by adopting an analytic hierarchy process to obtain a weight vector of each secondary index
Evaluating the two-level index value after the convergence treatment by adopting an entropy method to obtain a weight vector of each two-level index
By linear weighted combination method, the weight vector is
And weight vector
Combining to obtain a combined weight vector:
in the formula, delta and 1-delta are preference coefficients of the main and objective weights of each secondary index respectively.
6. The method for evaluating new energy consumption constraint factors based on combined empowerment-gray association according to claim 1, wherein the method comprises the following steps: the step 3 specifically includes:
calculating the correlation coefficient epsilon according to the following formulaij：
In the formula ujIs a reference value of the secondary index, dijIs the actual value of the second-level index, rho is the resolution coefficient,
is the maximum value of the absolute value of the,
is the absolute minimum;
based on the correlation coefficient of each index, combining the combined weight W ═ omega of the secondary index1,ω2,...,ωm]Calculating the grey correlation degree g of the corresponding first-level index according to the following formulai：
In the formula, ωjThe combined weight of the jth secondary index;
and obtaining the influence degree of the first-level index based on the grey correlation degree of each first-level index, wherein the higher the grey correlation degree is, the greater the influence degree of the first-level index is.
7. A new energy consumption restriction factor evaluation system based on combined empowerment-gray association is characterized in that: the method comprises the following steps:
the evaluation index system establishing module is used for establishing a new energy consumption restriction factor comprehensive evaluation index system in a layering way, and the evaluation index system comprises a primary index and a secondary index corresponding to the primary index;
the combined weighting calculation module is used for carrying out quantitative evaluation on the secondary indexes by adopting a combined weighting method to obtain the combined weight of each secondary index;
and the grey correlation degree calculation module is used for evaluating the established evaluation index system by adopting a grey correlation degree method based on the combined weight of the secondary indexes to obtain the influence degree of the primary indexes.
8. The system according to claim 7, wherein the system comprises: the evaluation index system establishing module comprises the following sub-modules:
the classification module is used for classifying the secondary indexes according to the monotonicity of the secondary indexes on the action of the secondary indexes on a decision target, wherein the benefit indexes comprise an N-1 passing rate and a voltage qualified rate, and the cost indexes comprise an output fluctuation rate, an output synchronization rate, a line heavy load rate, a transformer substation main transformer heavy load rate, a load rate and a peak-valley difference rate;
the forward processing module is used for performing forward processing on all secondary indexes, wherein the forward processing comprises the steps of changing all cost indexes into 1 and subtracting the original numerical value, and keeping all benefit index numerical values unchanged;
and the range transformation module is used for carrying out range transformation on all secondary indexes subjected to forward processing to obtain a new energy consumption restriction factor comprehensive evaluation index system.
9. The system according to claim 7, wherein the system comprises: the combined empowerment calculation module comprises the following sub-modules:
a hierarchical analysis evaluation module for evaluating the two-level index value after the convergence treatment by adopting a hierarchical analysis method to obtain a weight vector of each two-level index
An entropy evaluation module for evaluating the two-stage index values after the convergence processing by using an entropy method to obtain weight vectors of the two-stage indexes
A combination weight calculation module for adopting linear weighting combination method to make weight vector be
And weight vector
And combining to obtain a combined weight vector.
10. The system for evaluating new energy consumption constraint factors based on combined empowerment-gray association according to claim 7, wherein: the gray relevance calculating module comprises the following sub-modules:
a correlation coefficient calculating module for calculating a correlation coefficient epsilon according to the following formulaij：
In the formula ujIs a reference value of the secondary index, dijIs of two stagesThe index actual value, p, is the resolution coefficient,
is the absolute minimum;
a gray relevance operator module for combining the combined weight W of the secondary index into [ omega ] based on the relevance coefficient of each index1,ω2,...,ωm]Calculating the grey correlation degree g of the corresponding first-level index according to the following formulai：
In the formula, ωjIs the combined weight of the jth secondary index.
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