CN117709703A - Photovoltaic power station comprehensive evaluation method based on game theory and remorse theory - Google Patents
Photovoltaic power station comprehensive evaluation method based on game theory and remorse theory Download PDFInfo
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Abstract
The invention discloses a comprehensive photovoltaic power station evaluation method based on a game theory and a remorse theory, which solves the problem that the performance of a single independent photovoltaic power station cannot be quantitatively evaluated, and the influence of seasonal variation on the performance of the power station is ignored during evaluation, realizes the comprehensive evaluation of the performance of the photovoltaic power station, carries out combination optimization weighting on the subjective weighting of a G1 method and the objective weighting of an anti-entropy weighting method through the game theory, calculates the comprehensive score of the performance of the photovoltaic power station by adopting the remorse theory method, helps a decision maker avoid acquisition risks caused by seasonal factors, can capture the performance variation of the photovoltaic power station at different seasonal time points, and brings the performance variation into the overall performance evaluation, thereby improving the accuracy and reliability of the performance evaluation of the photovoltaic power station and helping a decision maker avoid risks.
Description
Technical Field
The invention belongs to the technical field of comprehensive performance evaluation, and particularly relates to a photovoltaic power station comprehensive evaluation method based on a game theory and a remorse theory.
Background
More and more enterprises and investors also enter the field of photovoltaic power stations, and the investment value of the photovoltaic power stations is more and more concerned. Meanwhile, government support and encouragement for new energy has also driven rapid development of photovoltaic power plant transactions. The performance of a photovoltaic power plant is an important factor in the trading of the plant, directly related to the quality and trading value of the plant. During the transaction, the buyer and seller need to carefully evaluate the performance of the photovoltaic power plant to make appropriate transaction decisions.
Many scientific research institutions and enterprises in China conduct research on performance evaluation of photovoltaic power stations, and related theoretical methods are continuously developed, such as an AHP method and fuzzy comprehensive evaluation method combined method, a TOPSIS sorting method, a minimum information identification principle, an entropy weight method, a rank sum ratio method, a gray correlation degree analysis method and the like.
The comprehensive energy efficiency evaluation method of the photovoltaic power station system is disclosed in CN112926895A, and is an energy efficiency evaluation method of the photovoltaic power station system by combining an AHP method and a fuzzy comprehensive evaluation method, the comprehensive energy efficiency of a plurality of photovoltaic power station systems can be evaluated, a perfect comprehensive energy efficiency evaluation index system is established, however, most of existing researches are that the performance of a plurality of photovoltaic power stations is relatively compared, the performance of a single independent photovoltaic power station cannot be quantitatively evaluated, and the influence of seasonal variation on the performance of the power station is ignored. During outdoor operation, seasonal changes of the light resource can directly affect the power generation capacity of the photovoltaic power station, the output current and voltage of the photovoltaic power station can also be affected by the seasonal changes, and seasonal extreme weather can also affect the operation safety of the power station. Therefore, seasonal factors at different time points should be considered when evaluating the performance of the photovoltaic power station, otherwise, accuracy and reliability of an evaluation result are difficult to ensure, and risks are brought to transactions.
Disclosure of Invention
The invention aims to realize comprehensive evaluation of the performance of a photovoltaic power station, and provides a comprehensive evaluation method of the photovoltaic power station based on a game theory and a remorse theory, wherein the G1 method subjective weighting and the anti-entropy weighting method objective weighting are combined and optimized to be weighted through the game theory, and the remorse theory method is adopted to calculate comprehensive scores of the performance of the photovoltaic power station, so that a decision maker is helped to avoid acquisition risks caused by seasonal factors, and meanwhile, in order to solve the problem that the performance of a single independent photovoltaic power station cannot be quantitatively evaluated and influence of seasonal variation on the performance of the power station is ignored, the invention provides the following technical scheme: a photovoltaic power station comprehensive evaluation method based on game theory and remorse theory comprises the following steps:
s1, establishing a comprehensive evaluation index system of a photovoltaic power station; the index system comprises 4 first-level indexes and 18 second-level indexes;
s2, calculating subjective weights of each first-level index and each secondary index by adopting a G1 theory according to the established comprehensive evaluation index system of the photovoltaic power station;
s3, calculating objective weights of each first-level index and each secondary index by adopting an inverse entropy weight method according to the established comprehensive evaluation index system of the photovoltaic power station;
s4, fusing the subjective weight of the evaluation index calculated in the step S3 with the objective weight of the evaluation index calculated in the step S2 by adopting a game theory method, and calculating the comprehensive weight;
s5, integrating seasonal factors by adopting a remorse theory method, and calculating by combining the comprehensive weights to obtain the comprehensive evaluation score of the photovoltaic power station. The comprehensive evaluation index system of the photovoltaic power station is obtained by establishing a comprehensive evaluation index system of the photovoltaic power station, which is obtained by acquiring data from the photovoltaic power station reliability, the grid-connected safety of the photovoltaic power station, 4 first-level indexes of the photovoltaic power station economical efficiency, the photovoltaic power station environmental protection and the photovoltaic power station reliability, 16 secondary indexes contained in the photovoltaic power station environmental protection, the subjective weights of all the first-level indexes and the secondary indexes are calculated based on the index system and recorded, the objective weights are calculated by an inverse entropy weight method, the calculated subjective weights and objective weights are fused to calculate the comprehensive weights, the comprehensive evaluation score of the photovoltaic power station is calculated to obtain the comprehensive evaluation score of the photovoltaic power station, and the quantitative evaluation of the performance of a single independent photovoltaic power station is realized by establishing the comprehensive evaluation index system of the performance of the photovoltaic power station, which covers the safety, the economical efficiency and the environmental protection of the photovoltaic power station, the performance change of the photovoltaic power station under different seasons can be captured and incorporated into the overall performance evaluation power station, so that the accuracy of the performance evaluation of the photovoltaic power station is improved.
Preferably, the specific process of step S2 includes:
s201, determining an importance sequence of the same-level index;
s202, quantifying the importance ratio between adjacent indexes;
s203, calculating subjective weights among all peer evaluation indexes. The subjective weight is used for reflecting the duty ratio of the overall index occupied by 4 first-stage indexes of the photovoltaic power station.
Preferably, the specific process of step S3 includes:
s301, carrying out dimensionless treatment on the original data;
s302, calculating an evaluation index inverse entropy value;
s303, calculating index weights. The index weight is used for reflecting the duty ratio of the integral index occupied by 18 secondary indexes of the photovoltaic power station.
Preferably, the calculation process of the comprehensive weight in step S4 includes:
if the weights of n evaluation indexes are determined by selecting the V different weight calculation methods, the weight vector determined by the t weight calculation method is expressed as:
F t ＝(f t1 ，f t2 ,…,f tn ) t＝(1,2,…,V) ；
then, the comprehensive weight vector F obtained by linearly combining the V weight vectors is calculated as follows:
wherein a is t Represents the t-th linear combination coefficient; then according to the theory of game theory, the comprehensive weight vector satisfies:
wherein v= (1, 2, …, V),represents a 2-norm; and then deriving according to a differential principle, and obtaining the conditions of optimizing the first derivative:
and calculating a linear combination coefficient equation according to the obtained result, wherein the linear combination coefficient equation is as follows:
solving the equation set, and obtaining (a 1 ,a 2 ,…,a V ) Carry-over formulaAnd obtaining the comprehensive weight vector F.
Preferably, the specific process of step S5 includes:
s501, acquiring historical operation data of various secondary indexes at different time points according to an established comprehensive evaluation index system of the performance of the photovoltaic power station, and constructing an original matrix;
s502, constructing an ideal point matrix, establishing a utility value matrix and establishing a perception utility matrix;
s503, constructing a perception utility function matrix according to the utility value matrix and the remorse-happiness function matrix;
s504, calculating a first-level evaluation index score;
s505, calculating the comprehensive score of the performance of the photovoltaic power station, and calculating the performance grade of the photovoltaic power station.
Preferably, the specific calculation process of step S201 includes:
n evaluation indexes in the same stage are set to evaluate the evaluation object, n is more than or equal to 2, and the importance ranking among the n evaluation indexes is determined according to the following steps: selecting the most important evaluation index from n indexes by expert, and marking as X 1 The method comprises the steps of carrying out a first treatment on the surface of the Selecting the most important evaluation index from the remaining n-1 indexes, and marking the evaluation index as X 2 The method comprises the steps of carrying out a first treatment on the surface of the After n-1 selections, until the last evaluation index was X n Obtain the importance sequence (X 1 ,X 2 ,…,X n )；
The specific calculation process of step S202 includes:
pair sequence (X) 1 ,X 2 ,…,X n ) Medium adjacent evaluation index X n-1 And X n The importance degree is digitally quantized, and the quantization calculation method comprises the following steps:
wherein r is j Representing adjacent evaluation index X n-1 And X n The importance ratio between r j The expert based on the index X n-1 And X n The relevance condition between the two is determined; omega j-1 And omega j Indicating index X n-1 And X n Weights of (2);
the method for calculating the subjective weight among all the peer evaluation indexes comprises the following steps:
wherein r is k The importance ratio of two adjacent evaluation indexes of the same level is represented, wherein k=2, 3, … and n; omega n For the evaluation index X n Is included in the set of parameters.
Preferably, the calculation process of the dimensionless number processing includes:
the method comprises the steps of providing m evaluation objects and n evaluation indexes, wherein each evaluation object and each evaluation index form an original data matrix Z, Z ij The calculation process of Z, which represents the value of the ith evaluation object to the jth index, is as follows:
for a photovoltaic power generation system, the dimensionless formula of the evaluation index with larger numerical value is as follows:
the dimensionless formula of the evaluation index with smaller value is as follows:
calculating the value of each index after dimensionless treatment as Y= (Y) ij ) m×n ；
The evaluation index inverse entropy calculation process comprises the following steps: the calculation formula of the inverse entropy value of the j-th evaluation index is as follows:
wherein p is ij Index value specific gravity, e, of the j-th index representing the i-th evaluation target j Representing the inverse entropy value of the j-th evaluation index; the calculation process of the index weight comprises the following steps: the weight calculation formula of the j-th evaluation index is as follows:
wherein mu j An objective weight value representing the j-th evaluation index.
Preferably, the construction process of the original matrix includes:
setting and selecting m time points and establishing n evaluation indexes, wherein an original matrix Q formed by the collected historical operation data of the n evaluation indexes at the m time points is as follows:
wherein q ij A value representing a j-th evaluation index at the i-th time point;
the construction process of the ideal point matrix comprises the following steps: building an ideal point matrix P according to the original matrix Q:
P＝(p j ) n ＝(p 1 p 2 ...p n )
wherein p is j An ideal value representing the j-th index; for the index of higher value, p j Taking the maximum value of the j index at all time points; for the index of smaller and better value, p j Taking the minimum value of the j index at all time points;
the utility value matrix is constructed by the following steps: when the index utility value of each time point is calculated, a power function is selected as a utility function, and a calculation formula is as follows:
h ij ＝(q ij ) α
wherein h is ij Represents q ij Is a function utility value of (2); alpha is a parameter of the utility function, 0<α<1, showing the tendency of a decision maker to avoid risks, wherein the smaller the alpha value is, the more the decision maker tends to avoid risks; the utility value matrix H is as follows:
preferably, the construction process of the perception utility matrix comprises the following steps: by constructing a regret-happiness function matrix R m×n To calculate the remorse value at each time point, the remorse-happiness function R ij Expression ofThe formula is:
wherein b ij The utility of index j and ideal point at representative time point i is poor; beta > 0 is the remorse avoidance coefficient, and the larger the numerical value is, the more obvious the decision maker has to the remorse risk avoidance degree; the construction process of the perception utility value matrix D comprises the following steps: constructing a perception utility function matrix D from a utility matrix H and a regret-happiness function matrix R:
D＝(d ij ) m×n ＝H+R
wherein d ij ＝h ij +R ij ；
The first-level evaluation index score is calculated in the following way:
wherein K is the number of first-level evaluation indexes, G k Representing the score of the kth first-level evaluation index; c is the number of secondary evaluation indexes contained in the kth primary evaluation index; omega kj The weight of the jth secondary evaluation index under the representative kth primary evaluation index.
Preferably, the specific calculation mode of the comprehensive performance score of the photovoltaic power station is as follows:
wherein omega k A weight representing a kth first-level evaluation index;
the photovoltaic power station performance grade classification mode is as follows:
g is more than or equal to 8 and less than or equal to 10, and is an excellent grade; g is 6-8, which is a good grade; g is more than or equal to 4 and less than or equal to 6 is a general grade; g <4 > is 2 or less; g <2 is 0.ltoreq.G <2 is a very poor grade. The comprehensive scores of the performance of the photovoltaic power station are calculated through a weight analysis method of the 4 primary indexes and the 18 secondary indexes, the calculated comprehensive scores and an evaluation table are subjected to table lookup to obtain the performance grade of the photovoltaic power station, the higher the comprehensive score is, the better the performance of the photovoltaic power station is, and the lower the comprehensive score is, the worse the performance of the photovoltaic power station is.
The beneficial effects of the invention are as follows: 1. the comprehensive performance evaluation index system for covering the safety, economy and environmental protection of the photovoltaic power station is established, and the quantitative evaluation of the performance of the single independent photovoltaic power station is realized. 2. The game theory method is introduced to realize the fusion processing of subjective weight and objective weight, so that the index weight calculation scientificity is improved. 3. By constructing the comprehensive performance score model based on the regret theory method, the invention can capture the performance change of the photovoltaic power station at different season time points and bring the performance change into the overall performance evaluation, thereby improving the accuracy and reliability of the performance evaluation of the photovoltaic power station. 4. The grid-connected period of the photovoltaic power station is used as a basis, the value of the key parameter in the comprehensive performance score model is refined, and a decision maker is further helped to avoid risks.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a comprehensive evaluation index system for the performance of the photovoltaic power station established by the method of the invention.
Detailed Description
The following describes a specific embodiment of the technical scheme of the present invention by way of examples and with reference to the accompanying drawings.
Example 1:
referring to fig. 1, an embodiment of the invention provides a photovoltaic power station comprehensive evaluation method based on game theory and improved remorse theory, which comprises the following steps:
s1, establishing a comprehensive evaluation index system of a photovoltaic power station; the index system comprises 4 first-level indexes and 18 second-level indexes;
s2, calculating subjective weights of each first-level index and each secondary index by adopting a G1 theory according to the established comprehensive evaluation index system of the photovoltaic power station;
s3, calculating objective weights of each first-level index and each secondary index by adopting an inverse entropy weight method according to the established comprehensive evaluation index system of the photovoltaic power station;
s4, fusing the subjective weight of the evaluation index calculated in the step S3 with the objective weight of the evaluation index calculated in the step S2 by adopting a game theory method, and calculating the comprehensive weight;
s5, integrating seasonal factors by adopting a remorse theory method, and calculating by combining the comprehensive weights to obtain the comprehensive evaluation score of the photovoltaic power station. The comprehensive evaluation index system of the photovoltaic power station is obtained by establishing a comprehensive evaluation index system of the photovoltaic power station, which is obtained by acquiring data from the photovoltaic power station reliability, the grid-connected safety of the photovoltaic power station, 4 first-level indexes of the photovoltaic power station economical efficiency, the photovoltaic power station environmental protection and the photovoltaic power station reliability, 16 secondary indexes contained in the photovoltaic power station environmental protection, the subjective weights of all the first-level indexes and the secondary indexes are calculated based on the index system and recorded, the objective weights are calculated by an inverse entropy weight method, the calculated subjective weights and objective weights are fused to calculate the comprehensive weights, the comprehensive evaluation score of the photovoltaic power station is calculated to obtain the comprehensive evaluation score of the photovoltaic power station, and the quantitative evaluation of the performance of a single independent photovoltaic power station is realized by establishing the comprehensive evaluation index system of the performance of the photovoltaic power station, which covers the safety, the economical efficiency and the environmental protection of the photovoltaic power station, the performance change of the photovoltaic power station under different seasons can be captured and incorporated into the overall performance evaluation power station, so that the accuracy of the performance evaluation of the photovoltaic power station is improved.
In step S2, the priority of importance of the peer indexes needs to be determined, as shown in fig. 2, in the comprehensive evaluation index system of the photovoltaic power station, there are 4 first-level indexes and 18 second-level indexes, where the 4 first-level indexes include: photovoltaic power plant reliability, photovoltaic power plant grid-connected safety, photovoltaic power plant economy and photovoltaic power plant environmental protection, wherein photovoltaic power plant reliability has 6 secondary indexes, respectively: component breakage rate, component infrared defect rate, component efficiency attenuation standard reaching rate, power station weather station light resource data accuracy, main electrical equipment failure rate and power station system energy efficiency value;
the grid-connected safety of the photovoltaic power station comprises 4 secondary indexes which are respectively: off-grid time, high-voltage ride through running time, grid-connected point voltage deviation rate and dynamic reactive response standard rate;
the photovoltaic power plant economy includes 4 secondary indexes, respectively: net cash flow, net present value, dynamic reporting period, internal rate of return;
the photovoltaic power plant feature of environmental protection includes 4 secondary indexes, is respectively: greenhouse gas emission rate, unit installed smoke emission reduction, unit installed SO2 emission reduction, and unit installed NOX emission reduction.
The method comprises the following steps that n evaluation indexes in the same stage are assumed to evaluate an evaluation object, n is more than or equal to 2, and importance ranking among the n evaluation indexes is determined according to the following steps: selecting the most important evaluation index from n indexes by expert, and marking as X 1 The method comprises the steps of carrying out a first treatment on the surface of the Selecting the most important evaluation index from the remaining n-1 indexes, and marking the evaluation index as X 2 The method comprises the steps of carrying out a first treatment on the surface of the After n-1 selections, until the last evaluation index was X n Obtain the importance sequence (X 1 ,X 2 ,…,X n )。
Then quantifying the importance ratio between adjacent indexes, and comparing the sequence (X 1 ,X 2 ,…,X n ) Medium adjacent evaluation index X n-1 And X n The importance degree is digitally quantized, and the quantization calculation method comprises the following steps:
wherein r is j Representing adjacent evaluation index X n-1 And X n The importance ratio between r j The expert based on the index X n-1 And X n The relevance condition between the two is determined; omega j-1 And omega j Indicating index X n-1 And X n Is a weight of (2).
And then calculating subjective weights among all the peer evaluation indexes, wherein the calculation method comprises the following steps:
wherein r is k Represents the importance ratio of two adjacent evaluation indexes of the same level,k＝2,3,…,n；ω n for the evaluation index X n And obtaining the subjective weight.
The calculation method of the index weight is as follows:
firstly, carrying out dimensionless treatment on the original data: assuming that m evaluation objects and n evaluation indexes are provided, each evaluation object and each evaluation index form an original data matrix Z, Z ij A numerical value indicating the j-th index of the i-th evaluation object;
for a photovoltaic power generation system, the dimensionless formula of the evaluation index with larger numerical value is as follows:
for a photovoltaic power generation system, the dimensionless formula of the evaluation index with smaller numerical value and better numerical value is as follows
The value of each index after dimensionless is Y= (Y) ij ) m×n ；
Then calculating the inverse entropy value of the evaluation index, wherein the calculation formula of the inverse entropy value of the j-th evaluation index is as follows:
wherein p is ij Index value specific gravity, e, of the j-th index representing the i-th evaluation target j The inverse entropy value representing the j-th evaluation index is then calculated to obtain an objective weight, and the weight calculation formula of the j-th evaluation index is as follows:
wherein mu j Represents the j-th evaluationObjective weight value of the price index.
The specific method for obtaining the comprehensive weight by fusing and calculating the subjective weight and the objective weight comprises the following steps:
if the weights of n evaluation indexes are determined by selecting the V different weight calculation methods, the weight vector determined by the t weight calculation method is expressed as:
F t ＝(f t1 ，f t2 ，…,f tn )，t＝(1,2,…,V)；
then, the comprehensive weight vector F obtained by linearly combining the V weight vectors is calculated as follows:
wherein a is t Represents the t-th linear combination coefficient; then according to the theory of game theory, the comprehensive weight vector satisfies:
wherein v= (1, 2, …, V),represents a 2-norm; and then deriving according to a differential principle, and obtaining the conditions of optimizing the first derivative:
and calculating a linear combination coefficient equation according to the obtained result, wherein the linear combination coefficient equation is as follows:
solving the equation set, and obtaining (a 1 ，a 2 ，…，a v ) Carry-over formulaAnd obtaining the comprehensive weight vector F.
After the comprehensive weight is obtained, a regret theory method is adopted, and the comprehensive evaluation score of the photovoltaic power station is calculated by combining the comprehensive weight, wherein the calculation process is as follows:
firstly, according to an established comprehensive performance evaluation index system of a photovoltaic power station, collecting historical operation data of various secondary indexes at different time points, and constructing an original matrix Q;
assuming that m time points are selected and n evaluation indexes are established, an original matrix Q formed by the collected historical operation data of the n evaluation indexes at the m time points is as follows:
wherein q ij A value representing a j-th evaluation index at the i-th time point;
then building an ideal point matrix: building an ideal point matrix P according to the original matrix Q:
P＝(p j ) n ＝(p 1 p 2 ...p n )
wherein p is j An ideal value representing the j-th index; for the index of higher value, p j Taking the maximum value of the j index at all time points; for the index of smaller and better value, p j Taking the minimum value of the j index at all time points;
and then establishing a utility value matrix: when the index utility value of each time point is calculated, a power function is selected as a utility function, and a calculation formula is as follows:
h ij ＝(q ij ) α
wherein h is ij Represents q ij Is a function utility value of (2); alpha is a parameter of the utility function, 0<α<1, showing the tendency of a decision maker to avoid risks, wherein the smaller the alpha value is, the more the decision maker tends to avoid risks; utility value matrix H:
and then establishing a perception utility matrix: by constructingRegret-happiness function matrix R m×n To calculate the remorse value at each time point, the remorse-happiness function R ij The expression formula is:
wherein b ij The utility of index j and ideal point at representative time point i is poor; beta > 0 is the remorse avoidance coefficient, and the larger the numerical value is, the more obvious the decision maker has to the remorse risk avoidance degree;
then constructing a perception utility function matrix D from the utility matrix H and the regret-happiness function matrix R:
D＝(d ij ) m×n ＝H+R
wherein d ij ＝h ij +R ij ；
Calculating a first-level evaluation index score by the following calculation method:
wherein K is the number of first-level evaluation indexes, G k Representing the score of the kth first-level evaluation index; c is the number of secondary evaluation indexes contained in the kth primary evaluation index; omega kj The weight of the jth secondary evaluation index under the represented kth primary evaluation index;
and finally, calculating the comprehensive score of the performance of the photovoltaic power station, and determining the performance grade of the photovoltaic power station, wherein the calculation method comprises the following steps:
wherein omega k Representing the weight of the kth first-level evaluation index.
Example 2:
the following embodiment further illustrates the invention by the application of the invention to the photovoltaic power station, and the invention provides a comprehensive evaluation method for the performance of the photovoltaic power station based on the game theory and the remorse theory, which comprises the following steps:
step one, building a comprehensive evaluation index system of a photovoltaic power generation system from four aspects of reliability of a photovoltaic power station, grid-connected safety of the photovoltaic power station, economical efficiency of the photovoltaic power station and environmental protection of the photovoltaic power station, wherein the index system comprises 4 primary indexes and 18 secondary indexes;
the 4 first-level indexes comprise: reliability of photovoltaic power station, grid-connected safety of photovoltaic power station, economical efficiency of photovoltaic power station and environmental protection of photovoltaic power station
The reliability of the photovoltaic power station has 6 secondary indexes, which are respectively: component breakage rate, component infrared defect rate, component efficiency attenuation standard reaching rate, power station weather station light resource data accuracy, main electrical equipment failure rate and power station system energy efficiency value;
the grid-connected safety of the photovoltaic power station comprises 4 secondary indexes which are respectively: off-grid time, high-voltage ride through running time, grid-connected point voltage deviation rate and dynamic reactive response standard rate;
the photovoltaic power plant economy includes 4 secondary indexes, respectively: net cash flow, net present value, dynamic reporting period, internal rate of return;
the photovoltaic power plant feature of environmental protection includes 4 secondary indexes, is respectively: emission rate of greenhouse gases, emission reduction of unit installed smoke dust and SO of unit installed smoke dust 2 Emission reduction, unit loading NO X And reducing the discharge capacity.
Step two, the subjective weights of the first-level evaluation index and the secondary evaluation index are respectively calculated by using the G1 theory, and the specific method is as follows:
(1) Determining the importance sequence of the peer index: the method comprises the following steps that n evaluation indexes in the same stage are assumed to evaluate an evaluation object, n is more than or equal to 2, and importance ranking among the n evaluation indexes is determined according to the following steps: selecting the most important evaluation index from n indexes by expert, and marking as X 1 The method comprises the steps of carrying out a first treatment on the surface of the Selecting the most important evaluation index from the remaining n-1 indexes, and marking the evaluation index as X 2 The method comprises the steps of carrying out a first treatment on the surface of the After n-1 selections, until the last evaluation index was X n Obtain the importance sequence (X 1 ,X 2 ,…,X n ). In this embodiment, n is 4 when the subjective weight calculation of the first-level evaluation index is performedN is 6 when the subjective weight calculation of the secondary evaluation index of the reliability of the photovoltaic power station is carried out, and is 4 when the subjective weight calculation of the secondary evaluation index of the grid-connected safety, economy and environmental protection of the photovoltaic power station is carried out;
(2) Quantifying the importance ratio between adjacent indexes: pair sequence (X) 1 ，X 2 ，…，X n ) Medium adjacent evaluation index X n-1 And X n The importance degree is digitally quantized, and the quantization calculation method comprises the following steps:
wherein r is j Representing adjacent evaluation index X n-1 And X n The importance ratio between r j The expert based on the index X n-1 And X n The conditions of the correlations between the two are determined by referring to table 1; omega j-1 And omega j Indicating index X n-1 And X n Is a weight of (2).
(3) The subjective weight among all the peer evaluation indexes is calculated by the following steps:
/>
wherein r is k The importance ratio of two adjacent evaluation indexes of the same level is represented, wherein k=2, 3, … and n; omega n For the evaluation index X n Is shown in Table 1 below:
| r j | correlation situation | r j | Correlation situation |
| 1 | Equally important | 1.2 | Slightly more important |
| 1.4 | Significantly more important | 1.6 | Is very important |
| 1.8 | Is of great importance | 1.1，1.3，1.5，1.7，1.9 | Between the above cases |
Step three, calculating objective weights of all indexes among the same-level indexes by using an inverse entropy weight method, wherein the specific method comprises the following steps:
(1) Dimensionless treatment of the original data: assuming that m evaluation objects and n evaluation indexes are provided, each evaluation object and each evaluation index form an original data matrix Z, Z ij A numerical value indicating the j-th index of the i-th evaluation object;
for a photovoltaic power generation system, the dimensionless formula of the evaluation index with larger numerical value is as follows:
for a photovoltaic power generation system, the dimensionless formula of the evaluation index with smaller numerical value and better numerical value is as follows
The value of each index after dimensionless is Y= (Y) ij ) m×n 。
(2) Calculating an evaluation index inverse entropy value: the calculation formula of the inverse entropy value of the j-th evaluation index is as follows:
wherein p is ij Index value specific gravity, e, of the j-th index representing the i-th evaluation target j Representing the inverse entropy value of the j-th evaluation index.
(3) Calculating index weight: the weight calculation formula of the j-th evaluation index is as follows:
wherein mu j An objective weight value representing the j-th evaluation index.
And step four, carrying out fusion calculation on the subjective weight and the objective weight by using a game theory method to obtain the comprehensive weight of each index, wherein the specific method comprises the following steps:
assuming that the weights of n evaluation indexes are determined by selecting V different weight calculation methods, the weight vector determined by the t-th weight calculation method is expressed as
F t ＝(f t1 ，f t2 ，...，f tn )，t＝(1，2，…，V)
Thus, the comprehensive weight vector F obtained by linearly combining the V weight vectors is:
wherein a is t Represents the t-th linear combination coefficient;
according to the theory of game theory, the comprehensive weight vector needs to satisfy:
wherein v=(1，2，…，V)，|||| 2 Represents a 2-norm;
the conditions for obtaining the optimized first derivative are as follows:
further, the linear combination coefficient equation is obtained as follows:
solving the equation set, and obtaining (a 1 ，a 2 ，…，a v ) Carry-over formulaAnd obtaining the comprehensive weight vector F.
Further, in embodiment 2 of the present invention, V has a value of 2, so that the linear combination coefficient equation is obtained as follows:
solving the equation set, and applying (a) 1 ，a 2 ) Carry-inThe integrated weight vector F can be obtained.
Fifth, the regret theory method is adopted to integrate seasonal factors, and comprehensive evaluation scores of the photovoltaic power station are calculated by combining comprehensive weights, and the specific method is as follows:
(1) According to the established comprehensive evaluation index system of the photovoltaic power station performance, collecting historical operation data of various secondary indexes at different time points, and constructing an original matrix Q;
assuming that m time points are selected and n evaluation indexes are established, an original matrix Q formed by the collected historical operation data of the n evaluation indexes at the m time points is as follows:
wherein q ij A value representing a j-th evaluation index at the i-th time point;
in the embodiment 2 of the invention, the value of m is 4, and the 4 time points are respectively one quarter end, two quarter end, three quarter end and four quarter end.
(2) Building an ideal point matrix: building an ideal point matrix P according to the original matrix Q:
P＝(p j ) n ＝(p 1 p 2 ...p n )
wherein p is j An ideal value representing the j-th index; for the index of higher value, p j Taking the maximum value of the j index at all time points; for the index of smaller and better value, p j Taking the minimum value of the j index at all time points;
(3) Establishing a utility value matrix: when the index utility value of each time point is calculated, a power function is selected as a utility function, and a calculation formula is as follows:
h ij ＝(q ij ) α
wherein h is ij Represents q ij Is a function utility value of (2); alpha is a parameter of the utility function, 0<α<1, showing the tendency of a decision maker to avoid risks, wherein the smaller the alpha value is, the more the decision maker tends to avoid risks; utility value matrix H:
in the embodiment 2 of the invention, for the photovoltaic power station with the grid connection time of more than 10 years, 0< alpha <0.6; for photovoltaic power stations with grid connection time greater than 5 years and less than 10 years, 0< alpha <0.8; for photovoltaic power stations with grid connection time less than 5 years, 0< alpha <1; the reliability of the safe operation of the photovoltaic power station can be gradually reduced when the grid-connected time is longer under the influence of the aging of various devices, and a decision maker can be further guided to avoid risks through the refinement treatment of the value of the utility parameter alpha.
(4) Establishing a perception utility matrix: by constructing a regret-happiness function matrix R m×n To calculate the remorse value at each time point, the remorse-happiness function R ij The expression formula is:
wherein b ij The utility of index j and ideal point at representative time point i is poor; beta > 0 is the remorse avoidance coefficient, and the larger the numerical value is, the more obvious the decision maker has to the remorse risk avoidance degree; in embodiment 2 of the invention, for photovoltaic power stations with grid connection time of more than 10 years, 0<β<1, a step of; for photovoltaic power stations with grid connection time of more than 5 years and less than 10 years, beta is more than 0 and less than 0.7; for the photovoltaic power station with the grid connection time less than 5 years, beta is more than 0 and less than 0.5; (5) Constructing a perception utility function matrix D from a utility matrix H and a regret-happiness function matrix R:
D＝(d ij ) m×n ＝H+R
wherein d ij ＝h ij +R ij ；
(6) Calculating a first-level evaluation index score by the following calculation method:
wherein K is the number of first-level evaluation indexes, G k Representing the score of the kth first-level evaluation index; c is the number of secondary evaluation indexes contained in the kth primary evaluation index; omega ki The weight of the jth secondary evaluation index under the represented kth primary evaluation index;
(7) The comprehensive score of the performance of the photovoltaic power station is calculated, the performance grade of the photovoltaic power station is determined, and the calculation method comprises the following steps:
wherein omega k A weight representing a kth first-level evaluation index;
the relationship between the photovoltaic power plant performance grade and the G value was determined according to table 2, and the following table is table 2:
| performance grade | Excellent and excellent properties | Good quality | In general | Poor quality | Very poor |
| Comprehensive score | 8≤G≤10 | 6≤G<8 | 4≤G<6 | 2≤G<4 | 0≤G<2 |
The foregoing examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention.
Claims (10)
1. The comprehensive photovoltaic power station evaluation method based on the game theory and the remorse theory is characterized by comprising the following steps of:
s1, establishing a comprehensive evaluation index system of a photovoltaic power generation system; the index system comprises 4 first-level indexes and 18 second-level indexes;
s2, calculating subjective weights of each first-level index and each secondary index by adopting a G1 theory according to the established comprehensive evaluation index system of the photovoltaic power station;
s3, calculating objective weights of each first-level index and each secondary index by adopting an inverse entropy weight method according to the established comprehensive evaluation index system of the photovoltaic power station;
s4, fusing the subjective weight of the evaluation index calculated in the step S3 with the objective weight of the evaluation index calculated in the step S2 by adopting a game theory method, and calculating the comprehensive weight;
s5, adopting a remorse theory method to integrate seasonal factors, constructing an original matrix, and calculating by combining the comprehensive weights to obtain the comprehensive evaluation score of the photovoltaic power station.
2. The comprehensive photovoltaic power station evaluation method based on game theory and remorse theory according to claim 1, wherein the specific process of the step S2 comprises the following steps:
s201, determining an importance sequence of the same-level index;
s202, quantifying the importance ratio between adjacent indexes;
s203, calculating subjective weights among all peer evaluation indexes.
3. The comprehensive photovoltaic power station evaluation method based on the game theory and the remorse theory according to claim 1, wherein the specific process of the step S3 comprises the following steps:
s301, carrying out dimensionless treatment on the original data;
s302, calculating an evaluation index inverse entropy value;
s303, calculating objective weights.
4. The photovoltaic power station comprehensive evaluation method based on game theory and remorse theory according to claim 1, wherein the calculation process of the comprehensive weight in the step S4 comprises the following steps:
if the weights of n evaluation indexes are determined by selecting the V different weight calculation methods, the weight vector determined by the t weight calculation method is expressed as:
F t ＝(f t1 ，f t2 ，...，f tn )，t＝(1,2,…,V)；
then, the comprehensive weight vector F obtained by linearly combining the V weight vectors is calculated as follows:
wherein a is t Represents the t-th linear combination coefficient; then according to the theory of game theory, the comprehensive weight vector satisfies:
wherein v= (1, 2, …, V),represents a 2-norm; and then deriving according to a differential principle, and obtaining the conditions of optimizing the first derivative:
and calculating a linear combination coefficient equation according to the obtained result, wherein the linear combination coefficient equation is as follows:
solving the equation set, and obtaining (a 1 ,a 2 ,…,a V ) Carry-over formulaAnd obtaining the comprehensive weight vector F.
5. The comprehensive photovoltaic power station evaluation method based on game theory and remorse theory according to claim 1, wherein the specific process of the step S5 comprises the following steps:
s501, acquiring historical operation data of various secondary indexes at different time points according to an established comprehensive evaluation index system of the performance of the photovoltaic power station, and constructing an original matrix;
s502, constructing an ideal point matrix, establishing a utility value matrix and establishing a perception utility matrix;
s503, constructing a perception utility function matrix according to the utility value matrix and the remorse-happiness function matrix;
s504, calculating a first-level evaluation index score;
s505, calculating the comprehensive score of the performance of the photovoltaic power station, and calculating the performance grade of the photovoltaic power station.
6. The photovoltaic power station comprehensive evaluation method based on game theory and remorse theory according to claim 2, wherein the specific calculation process of step S201 includes:
n evaluation indexes in the same stage are set to evaluate the evaluation object, n is more than or equal to 2, and the importance ranking among the n evaluation indexes is determined according to the following steps: selecting the most important evaluation index from n indexes by expert, and marking as X 1 The method comprises the steps of carrying out a first treatment on the surface of the Selecting the most important evaluation index from the remaining n-1 indexes, and marking the evaluation index as X 2 The method comprises the steps of carrying out a first treatment on the surface of the After n-1 selections, until the last evaluation index was X n Obtain the importance sequence (X 1 ,X 2 ,…,X n )；
The specific calculation process of step S202 includes:
pair sequence (X) 1 ,X 2 ,…,X n ) Medium adjacent evaluation index X n-1 And X n The importance degree is digitally quantized, and the quantization calculation method comprises the following steps:
wherein r is j Representing adjacent evaluation index X n-1 And X n The importance ratio between r j The expert based on the index X n-1 And X n The relevance condition between the two is determined; omega j-1 And omega j Indicating index X n-1 And X n Weights of (2);
the method for calculating the subjective weight among all the peer evaluation indexes comprises the following steps:
wherein r is k The importance ratio of two adjacent evaluation indexes of the same level is represented, wherein k=2, 3, … and n; omega n For the evaluation index X n Is included in the set of parameters.
7. The comprehensive evaluation method of the photovoltaic power station based on the game theory and the remorse theory according to claim 1 or 3, wherein,
the calculation process of the dimensionless number processing comprises the following steps:
the method comprises the steps of providing m evaluation objects and n evaluation indexes, wherein each evaluation object and each evaluation index form an original data matrix Z, Z ij The calculation process of Z, which represents the value of the ith evaluation object to the jth index, is as follows:
for a photovoltaic power generation system, the dimensionless formula of the evaluation index with larger numerical value is as follows:
the dimensionless formula of the evaluation index with smaller value is as follows:
calculating the value of each index after dimensionless treatment as Y= (Y) ij ) m×n ；
The evaluation index inverse entropy calculation process comprises the following steps: the calculation formula of the inverse entropy value of the j-th evaluation index is as follows:
wherein p is ij Index value specific gravity, e, of the j-th index representing the i-th evaluation target j Representing the inverse entropy value of the j-th evaluation index; the calculation process of the index weight comprises the following steps: the weight calculation formula of the j-th evaluation index is as follows:
wherein mu j An objective weight value representing the j-th evaluation index.
8. The comprehensive evaluation method of the photovoltaic power station based on the game theory and the remorse theory according to claim 1 or 5, wherein the comprehensive evaluation method is characterized in that,
the construction process of the original matrix comprises the following steps:
setting and selecting m time points and establishing n evaluation indexes, wherein an original matrix Q formed by the collected historical operation data of the n evaluation indexes at the m time points is as follows:
wherein q ij A value representing a j-th evaluation index at the i-th time point;
the construction process of the ideal point matrix comprises the following steps: building an ideal point matrix P according to the original matrix Q:
P＝(p j ) n ＝(p 1 p 2 ... p n )
wherein p is j An ideal value representing the j-th index; for the index of higher value, p j Taking the maximum value of the j index at all time points; for the index of smaller and better value, p j Taking the minimum value of the j index at all time points;
the utility value matrix is constructed by the following steps: when the index utility value of each time point is calculated, a power function is selected as a utility function, and a calculation formula is as follows:
h ij ＝(q ij ) α
wherein h is ij Represents q ij Is a function utility value of (2); alpha is a parameter of the utility function, 0<a<1, showing the tendency of a decision maker to avoid risks, wherein the smaller the alpha value is, the more the decision maker tends to avoid risks; the utility value matrix H is as follows:
9. the photovoltaic power station comprehensive evaluation method based on game theory and remorse theory according to claim 5, wherein the construction process of the perception utility matrix is as follows: by constructing a regret-happiness function matrix R m×n To calculate the remorse value at each time point, the remorse-happiness function R ij The expression formula is:
wherein b ij The utility of index j and ideal point at representative time point i is poor; beta > 0 is the remorse avoidance coefficient, and the larger the numerical value is, the more obvious the decision maker has to the remorse risk avoidance degree;
the construction process of the perception utility value matrix D comprises the following steps: constructing a perception utility function matrix D from a utility matrix H and a regret-happiness function matrix R:
D＝(d ij ) m×n ＝H+R
wherein d ij ＝h ij +R ij ；
The first-level evaluation index score is calculated in the following way:
wherein K is the number of first-level evaluation indexes, G k Representing the score of the kth first-level evaluation index; c is the number of secondary evaluation indexes contained in the kth primary evaluation index; omega kj The weight of the jth secondary evaluation index under the representative kth primary evaluation index.
10. The photovoltaic power station comprehensive evaluation method based on the game theory and the remorse theory according to claim 1 or 5, wherein the specific calculation mode of the photovoltaic power station performance comprehensive score is as follows:
wherein omega k A weight representing a kth first-level evaluation index;
the photovoltaic power station performance grade classification mode is as follows:
g is more than or equal to 8 and less than or equal to 10, and is an excellent grade; g is 6-8, which is a good grade; g is more than or equal to 4 and less than or equal to 6 is a general grade; g <4 > is 2 or less; g <2 is 0.ltoreq.G <2 is a very poor grade.
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