CN111784114A - Client-side distributed energy storage system operation performance evaluation method and system - Google Patents
Client-side distributed energy storage system operation performance evaluation method and system Download PDFInfo
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Abstract
A client-side distributed energy storage system operation performance evaluation method comprises the following steps: acquiring running state information of a client side distributed energy storage system; bringing the running state information into a pre-constructed comprehensive evaluation system model of the energy storage system to calculate to obtain the membership degree of each index; determining the operation performance level of the client-side distributed energy storage system based on the membership degree of each index according to a fuzzy comprehensive evaluation method, and evaluating the operation performance of the client-side distributed energy storage system based on the operation performance level; the comprehensive evaluation system model of the energy storage system comprises: the system comprises a target layer and a layering criterion layer constructed based on the relationship among the influence factors of a client-side distributed energy storage system; the method and the device accurately evaluate the operation performance of the client-side distributed energy storage system.
Description
Technical Field
The invention relates to the field of performance evaluation, in particular to a method and a system for evaluating the running performance of a client-side distributed energy storage system.
Background
The client-side distributed energy storage system is used for storing surplus electric energy generated by renewable energy sources of users or reducing electric charge by helping load curve peak shifting of the users. The operation performance of the client-side distributed energy storage system is evaluated, the operation condition of the energy storage system can be measured, the operation strategy and the maintenance method of the energy storage system can be adjusted according to the evaluation result, and the operation defects of the distributed energy storage system can be timely discovered and corrected. However, the prior art cannot accurately evaluate the operation performance of the client-side distributed energy storage system, and further cannot timely find and correct the operation defects of the distributed energy storage system.
Disclosure of Invention
In order to overcome the defect that the operation performance of a client-side distributed energy storage system cannot be accurately evaluated in the prior art, the invention provides a method for evaluating the operation performance of the client-side distributed energy storage system, which comprises the following steps:
acquiring running state information of a client side distributed energy storage system;
bringing the running state information into a pre-constructed comprehensive evaluation system model of the energy storage system to calculate to obtain the membership degree of each index;
determining the operation performance level of the client-side distributed energy storage system based on the membership degree of each index according to a fuzzy comprehensive evaluation method, and evaluating the operation performance of the client-side distributed energy storage system based on the operation performance level;
the comprehensive evaluation system model of the energy storage system comprises: the system comprises a target layer and a layering criterion layer constructed based on the relationship among the influence factors of the client-side distributed energy storage system.
Preferably, the comprehensive evaluation system model of the energy storage system comprises: economic, technical and social benefits;
the secondary indicators of economic benefit include: net present value, internal rate of return, and return on investment period;
secondary indicators of the technical performance include: average discharge rate per day, electric energy loss rate, comprehensive efficiency, cycle life percentage, energy density percentage, average time to failure, average availability factor, failure rate and load fluctuation improvement rate;
the secondary indicators of social benefit include: energy saving benefit and environmental protection benefit.
Preferably, the bringing the running state information into a pre-constructed energy storage system comprehensive evaluation system model for calculation to obtain the membership degree of each index includes:
based on the indexes of the same layer belonging to each index of the previous layer, determining the value of the relative importance degree of the comparative indexes by adopting a pairwise comparison method and a 1-9 comparison scale, and constructing a pairwise comparison array;
obtaining a judgment matrix by taking the reciprocal of the pair comparison matrix;
calculating the maximum characteristic root of each judgment matrix, and taking the characteristic vector corresponding to the maximum characteristic root as a weight coefficient;
determining a weight matrix of each layer based on each weight coefficient, and calculating the combined weight of each index of the same layer based on the weight matrix;
and determining the membership degree of the secondary indexes of the technical performance by adopting a trapezoidal membership function based on the combined weight of each index, and determining the membership degree of the secondary indexes of the economic benefit and the social benefit by adopting an expert scoring mode.
Preferably, the determining the membership degree of the secondary index of the technical performance by using a trapezoidal membership function based on the combined weight of each index includes:
based on the combined weight of each index, determining the membership degree of a secondary index with better technical performance by adopting a half-raised trapezoidal function if the index value is larger;
solving the membership degree of a secondary index with better technical performance when the index value is smaller by adopting a halving trapezoidal function;
and solving the membership degree of the secondary index with better technical performance as the index value approaches to a certain interval by adopting an intermediate trapezoidal function.
Preferably, the raised half-trapezoid function is represented by the following formula:
wherein μ (x): degree of membership; x, a1、a2Weights respectively representing different factors;
the decreasing half trapezoidal function is shown as follows:
the intermediate trapezoidal function is shown as follows:
in the formula, a3、a4Representing the weight of the respectively different factors.
Preferably, determining the membership degree of the secondary indexes of the economic benefit and the social benefit by adopting an expert scoring mode comprises the following steps:
giving a comment based on the combined weight of the secondary indexes of the economic benefit and the social benefit;
obtaining corresponding evaluation levels according to the comments, counting the voting frequency of each evaluation level, and taking the frequency as the membership degree of a secondary index of economic benefit and social benefit;
wherein the evaluation grades comprise excellent, good, medium, qualified and poor.
Preferably, the method further comprises the following steps: and carrying out consistency check on the weight coefficients.
Preferably, the checking the consistency of the weight coefficients comprises:
normalizing the eigenvector corresponding to the maximum characteristic root of the judgment matrix;
and calculating the deviation value of the judgment matrix by using a consistency ratio formula based on the feature vector after the normalization processing, judging whether the deviation value is in an allowable range, and if so, passing the consistency test of the judgment matrix, otherwise, failing to pass the consistency test.
Preferably, the determining the operation performance level of the client-side distributed energy storage system based on the membership degree of each index according to a fuzzy comprehensive evaluation method includes:
respectively constructing a first-level index fuzzy judgment matrix of the economic benefit and the social benefit based on the membership degree of the secondary indexes of the economic benefit and the social benefit by adopting a hierarchical analysis method;
normalizing the first-level index fuzzy judgment matrix to obtain a comprehensive judgment vector;
taking the grade corresponding to the vector with the maximum membership degree as the evaluation result of the economic benefit and the social benefit of the client-side distributed energy storage system;
constructing a fuzzy relation matrix based on the membership degree of the secondary indexes of the technical performance;
calculating the weight of the secondary indexes of the technical performance, and constructing a fuzzy weight vector by using the weights of all the secondary indexes of the technical performance;
and synthesizing the fuzzy weight vector and the fuzzy relation matrix to obtain a fuzzy comprehensive evaluation result vector of the technical performance index.
A client-side distributed energy storage system operation performance evaluation system comprises:
the information acquisition module is used for acquiring the running state information of the client side distributed energy storage system;
the calculation module is used for substituting the running state information into a pre-constructed comprehensive evaluation system model of the energy storage system to calculate to obtain the membership degree of each index;
and the grade evaluation module is used for determining the operation performance grade of the client-side distributed energy storage system based on the membership degree of each index according to a fuzzy comprehensive evaluation method.
Compared with the prior art, the invention has the beneficial effects that:
a client-side distributed energy storage system operation performance evaluation method comprises the following steps: acquiring running state information of a client side distributed energy storage system; bringing the running state information into a pre-constructed comprehensive evaluation system model of the energy storage system to calculate to obtain the membership degree of each index; determining the operation performance level of the client-side distributed energy storage system based on the membership degree of each index according to a fuzzy comprehensive evaluation method; the comprehensive evaluation system model of the energy storage system comprises: the system comprises a target layer and a layering criterion layer constructed based on the relationship among the influence factors of a client-side distributed energy storage system; the invention is based on an energy storage system comprehensive evaluation system model constructed by a layering criterion layer, determines the score value of each index according to a fuzzy comprehensive evaluation method, and accurately evaluates the operation performance of the client-side distributed energy storage system.
Drawings
Fig. 1 is a flowchart of a method for evaluating operation performance of a client-side distributed energy storage system according to the present invention;
FIG. 2 is a diagram illustrating a comprehensive evaluation index of application benefits of a client-side distributed energy storage system;
fig. 3 is a schematic diagram of a system for evaluating the operation performance of a client-side distributed energy storage system according to the present invention.
Detailed Description
The invention discloses a method and a system for evaluating the running performance of a client-side distributed energy storage system, wherein the device realizes the accurate evaluation of the running performance of the client-side distributed energy storage system:
example 1: a method for evaluating operation performance of a client-side distributed energy storage system is shown in fig. 1: the method comprises the following steps:
step 1: acquiring running state information of a client side distributed energy storage system;
step 2: bringing the running state information into a pre-constructed comprehensive evaluation system model of the energy storage system to calculate to obtain the membership degree of each index;
and step 3: determining the operation performance level of the client-side distributed energy storage system based on the membership degree of each index according to a fuzzy comprehensive evaluation method, and evaluating the operation performance of the client-side distributed energy storage system based on the operation performance level;
the comprehensive evaluation system model of the energy storage system comprises: the system comprises a target layer and a layering criterion layer constructed based on the relationship among the influence factors of the client-side distributed energy storage system.
The specific embodiment of the invention is as follows:
comprehensive application benefit evaluation method based on hierarchical analysis and fuzzy evaluation method
Through the comparative analysis of the comprehensive evaluation method, the result of the fuzzy evaluation method contains a large amount of information, the model is easy to calculate and popularize, but information related to an evaluation target is easy to lose in the modeling calculation process, and the scientificity of the final evaluation result is influenced. The analytic hierarchy process model is relatively simple and easy to construct, the evaluation result is subjected to consistency test, the result reliability is relatively high, and the analytic hierarchy process model can be applied to qualitative evaluation and quantitative evaluation and is wide in application range. Therefore, the characteristic that the client-side distribution is the energy storage system is fully analyzed, the two methods are effectively combined, and the comprehensive evaluation method combining the analytic hierarchy process and the fuzzy evaluation is adopted to model and evaluate the application benefits of the client-side distributed energy storage system demonstration project. A hierarchical model is constructed by using an analytic hierarchy process, and the scoring value of each index is determined according to a fuzzy comprehensive evaluation method.
Step 1: the method for acquiring the running state information of the client side distributed energy storage system specifically comprises the following steps:
the method comprises the steps of obtaining application benefits of a client-side distributed energy storage system, selecting 3 primary indexes of economic benefits, technical performance levels and social benefits, and obtaining net present value, internal profitability, investment recovery period, daily average discharge rate and electric energy loss rate.
Step 2: and substituting the running state information into a pre-constructed energy storage system comprehensive evaluation system model for calculation to obtain the membership degree of each index, wherein the method specifically comprises the following steps:
aiming at the application benefits of the client-side distributed energy storage system, 3 primary indexes of economic benefit, technical performance level and social benefit, and 14 secondary indexes of net present value, internal profitability, investment recovery period, daily average discharge rate, electric energy loss rate and the like are selected to establish an energy storage system comprehensive evaluation system, as shown in fig. 2.
Firstly, a target is determined, wherein P is the application benefit of the client-side distributed energy storage system, and then, an evaluation factor set is constructed, which is specifically shown in the following table.
Table 1 comprehensive evaluation index hierarchy table for application benefit of client-side distributed energy storage system
If the above hierarchical structure is expressed by a mathematical expression, U ═ U1, U2, U3}
In the formula: u1 ═ U11, U12, U13, U2 ═ U21, U22, U3 ═ U31, U32, U33, U34, U35, U36, U37, U38, U39.
(1) Structural judgment matrix
For the multilayer comprehensive indexes of the comprehensive benefit evaluation of the client-side distributed energy storage system, the weights of all factors are difficult to give under the condition of no large amount of data and experience. The analytic hierarchy process is used, and pairwise comparison is adopted to compare all factors in the same group pairwise. Starting from level 2 of the hierarchical model, for the same level of factors that depend on (or affect) each factor in the previous level, a pair-wise comparison method and a comparison scale of 1-9 are used to determine the value of the relative importance of the comparison factors, and a 'pair-wise comparison matrix' is constructed to the uppermost level. The values of 1-9 are shown in the following table.
Tables 21-9 Scale Table
If aijThe ratio of the importance of the two factors is expressed, namely the relative importance of the ith factor to the jth factor; the reciprocal of which represents the corresponding opposite case, namely aij＝1/ajiThen, a decision matrix is determined therefrom:
A＝(aij)n×n(4-29)
where n is the order of the matrix.
(2) Consistency check
And calculating the maximum characteristic root of each judgment matrix to obtain a characteristic vector corresponding to the maximum characteristic root, wherein the characteristic vector is the solved weight coefficient. However, when the order of the matrix is judged to be large, it is often difficult to construct a matrix satisfying the consistency. The judgment matrix deviation consistency condition is within an acceptable range, so that the consistency check of the judgment matrix is required. And when the consistency check is passed, the weight coefficient distribution is reasonable.
Corresponding to the maximum characteristic root lambda of the judgment matrix AmaxNormalized, the feature vector of (a) is denoted as W ═ W1,w2,…,wn) And the element of W is the corresponding weight for that level. The formula for checking the consistency of the matrix A is as follows
In the formula: w is aiThe weight of the i-th factor of the feature vector W;
λmaxjudging the maximum characteristic root of the matrix;
CI is an index of consistency test, and when CI is 0, the CI has complete consistency;
CI is close to 0, and the consistency is satisfactory;
the larger the CI, the more severe the inconsistency.
To measure the CI size of different orders, a random consistency index RI was introduced, the values of which are shown in tables 4-3.
TABLE 3 random consistency index RI
| Order of the scale | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| RI | 0 | 0 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 | 1.49 |
And judging whether the A meets the requirement or not by using a consistency ratio formula. The consistency ratio is formulated as
Generally, when CR < 0.1, the degree of inconsistency of A is considered to be within the allowable range, and satisfactory consistency is obtained by the consistency test. The normalized feature vector W can be used as the weight vector of the index, otherwise, the judgment matrix A needs to be reconstructed.
(3) The weight matrix of each level can be determined by the method. Recording the weight matrix of the third index layer to the second index layer as W3 kWhere k is 1, 2.., 10, representing 10 secondary indices. Recording the weight matrix of the first index layer to the second index layer as W2 kWhere k is 1,2,3, representing 3 primary indices. And the weight matrix of the first-level index layer to the target layer is marked as W.
If the weights of the m factors in the previous layer are known as a1, a2, …, am, respectively, the combined weight of each factor in the current layer should be:
the calculation is carried out layer by layer from top to bottom until all the factors of the bottom layer are weighted.
(4) Normalization processing of data
Different units make difficult the interpretation between the variables of the coefficients, since different variables have different units and degrees of variation. In order to compare the relationships between the variables, the corresponding index data needs to be standardized. And converting all index values into standard values Zij between 0 and 1 according to different characteristics of the index data.
Determination of degree of membership
(1) Determination of comment set
The text uses five-level comments, which are: the membership degree relationship of excellent, good, medium, qualified and poor is shown in tables 4-4.
TABLE 4 evaluation of quantitative grading standards
(2) Determination of membership of quantitative evaluation index
For quantitative indices, trapezoidal membership functions are used herein to determine their degree of membership. The trapezoid membership functions comprise a rising half trapezoid function, a falling half trapezoid function and a middle trapezoid function. The half-rising trapezoid function is suitable for indexes with better index values, and the distribution is as follows:
the decreasing half trapezoid function is suitable for the index with the smaller index value, and the distribution is
The intermediate trapezoidal function is suitable for the index with better index value approaching to a certain interval, and the distribution is
(3) Determination of membership of qualitative evaluation index
For qualitative indexes, an expert scoring mode is adopted to determine the membership degree of the indexes. Firstly, corresponding evaluation scores are obtained according to the comments (for example, the scores are 1, 0.8, 0.6, 0.4 and 0.2 respectively corresponding to excellence, goodness, middle, qualification and difference); then, the expert grasps the relevant information through comprehensive and actual understanding of the evaluation object, gives the comment to the qualitative index expression, and finally calculates the frequency of the vote number of the expert at each comment level as a single-factor fuzzy evaluation vector.
And step 3: determining the operation performance level of the client-side distributed energy storage system based on the membership degree of each index according to a fuzzy comprehensive evaluation method, and evaluating the operation performance of the client-side distributed energy storage system based on the operation performance level, wherein the method specifically comprises the following steps:
(1) economic benefit index
And evaluating the economic benefit grade by a qualitative method. The calculation of net present value, internal rate of return and return on investment is done according to the demonstration project. Please the expert to evaluate the three 2-level indexes of the demonstration project according to five evaluation levels of excellent economy, good quality, medium quality, qualified quality and poor quality. And obtaining a fuzzy judgment matrix B of the secondary indexes. And then obtaining a 1-level index economic benefit fuzzy judgment matrix according to the weight coefficient matrix A obtained by the analytic hierarchy process, normalizing to obtain a comprehensive judgment vector, wherein five elements of the vector respectively represent judgment values of five grades, namely excellent, good, medium, qualified and poor. And (4) selecting a maximum membership rule to determine the final evaluation result of the demonstration item, namely selecting the grade corresponding to the highest evaluation value as the evaluation result.
(2) Index of social benefit
And the social benefit index adopts an evaluation method the same as the economic benefit index, after the energy-saving benefit and the environmental protection benefit of the demonstration project are calculated, experts are requested to judge according to four evaluation levels, then a fuzzy judgment matrix and a comprehensive judgment vector of the social benefit index are obtained, and an evaluation result is determined according to the maximum value of four elements of the vector.
(3) Technical performance index
1) Average rate of discharge
TABLE 4-5 mean discharge Rate Scoring standards
2) Rate of electric energy loss
Meter 6 electric energy loss rate scoring standard
3) Combined efficiency
TABLE 7 Total efficiency score criteria
4) Percent current cycle life
TABLE 8 percent Current cycle Life score criteria
5) Current percent energy density
TABLE 9 percent Current energy Density score criteria
6) Mean time between failures
And determining the grading standard of the average fault-free time of the distributed energy storage equipment according to the evaluation period T.
TABLE 4-10 mean time to failure scoring criteria
7) Mean available coefficient
TABLE 11 average available coefficient score criteria
8) Failure rate
TABLE 12 failure Rate Scoring criteria
9) Rate of improvement of load fluctuation
TABLE 13 load fluctuation improvement Rate Scoring criteria
It is worth noting that the scoring standard is not invariable, and the scoring standard value can be corrected according to accumulated experience of operation of a client-side distributed energy storage system in the future so as to obtain the most objective and scientific scoring standard.
Let V be { V ═ V1，v2，…，vnAnd (n is 5 in the model) is an evaluation grade set, which represents a set of evaluation results that an evaluator may make on an evaluation object for each evaluation index. To evaluation factor set UkEach factor U inklJudging according to n evaluation levels in the V set, quantizing the evaluated object one by one from each factor, determining the membership of the evaluated object to each evaluation level fuzzy subset from a single factor, and obtaining a fuzzy relation matrix Rk＝(rij)m×n。
Wherein r isijIndicates the ith factor pair evaluation grade vjDegree of membership. r isi＝(ri1,ri2,…,rin) The fuzzy vector is called a single-factor evaluation matrix and is used for depicting the performance of an evaluated object (a primary index) on a factor i (a secondary index).
Using a suitable fuzzy synthesis operator, the fuzzy weight vector W is formedkAnd fuzzy relation matrix RkSynthesizing to obtain fuzzy comprehensive evaluation result vector B of each primary indexk. Because the factors are more and the weight obtained by each factor is smaller, in order to prevent the model from being invalid due to information loss, the fuzzy synthesis operator takes a weighted average operator, and the calculation formula is as follows:
wherein, bjIs a fuzzy comprehensive evaluation result vector BkThe element (b) represents the membership degree of the evaluation object belonging to the jth grade;
in the formula, wiIs a fuzzy weight vector WkElement (ii) represents the weight of the i-th evaluation index; r isijAnd (4) representing the degree of membership of the ith evaluation index to the jth grade.
If ∑ bjNot equal to 1, B needs to be pairedkCarrying out normalization operation:
in the formula, BkA fuzzy comprehensive evaluation result vector as a primary index, bjIs a fuzzy comprehensive evaluation result vector BkThe element (b) in (d) represents the degree of membership of the evaluation object to the jth level.
(4) Determination of comprehensive evaluation value
The final fuzzy comprehensive evaluation result vector calculation mode is as follows:
wherein, B0Representing a final fuzzy comprehensive evaluation result vector before normalization operation; wiRepresenting a primary index weight;
is a first-level index fuzzy evaluation result vector set.
Then, for B0The normalization operation is performed as shown in the following formula:
in the formula, B is the final fuzzy comprehensive evaluation result vector.
To reduce bias and preserve information, the membership grade is determined here using a weighted average method:
in the formula, V is the final evaluation value, and the evaluation level of the evaluation object can be determined by tables 4 to 13.
Example 2
The invention based on the same inventive concept also provides a client-side distributed energy storage system operation performance evaluation system, as shown in fig. 3:
the information acquisition module is used for acquiring the running state information of the client side distributed energy storage system;
the calculation module is used for substituting the running state information into a pre-constructed comprehensive evaluation system model of the energy storage system to calculate to obtain the membership degree of each index;
and the grade evaluation module is used for determining the operation performance grade of the client-side distributed energy storage system based on the membership degree of each index according to a fuzzy comprehensive evaluation method.
The comprehensive evaluation system model of the energy storage system comprises: economic, technical and social benefits;
the secondary indicators of economic benefit include: net present value, internal rate of return, and return on investment period;
secondary indicators of the technical performance include: average discharge rate per day, electric energy loss rate, comprehensive efficiency, cycle life percentage, energy density percentage, average time to failure, average availability factor, failure rate and load fluctuation improvement rate;
the secondary indicators of social benefit include: energy saving benefit and environmental protection benefit.
The calculation module comprises:
the construction submodule determines the value of the relative importance degree of the comparison indexes by adopting a pair comparison method and a comparison scale of 1-9 on the basis of the indexes of the same layer belonging to each index of the previous layer, constructs a pair comparison array, and obtains the reciprocal of the pair comparison array to obtain a judgment matrix;
the weight coefficient calculation submodule is used for calculating the maximum characteristic root of each judgment matrix and taking the characteristic vector corresponding to the maximum characteristic root as a weight coefficient;
the combined weight calculation submodule determines a weight matrix of each layer based on each weight coefficient and calculates the combined weight of each index of the same layer based on the weight matrix;
and the membership degree operator module is used for determining the membership degree of the secondary indexes of the technical performance by adopting a trapezoidal membership function based on the combined weight of each index, and determining the membership degree of the secondary indexes of the economic benefit and the social benefit by adopting an expert scoring mode.
The membership operator module includes: a technical performance index calculation unit and an economic and social benefit index calculation unit;
the technical performance index calculation unit: based on the combined weight of each index, determining the membership degree of a secondary index with better technical performance by adopting a half-raised trapezoidal function if the index value is larger; solving the membership degree of a secondary index with better technical performance when the index value is smaller by adopting a halving trapezoidal function; solving the membership degree of a secondary index with better technical performance as the index value approaches to a certain interval by adopting an intermediate trapezoidal function;
the rising half trapezoid function is shown as follows:
wherein μ (x): degree of membership; x, a1、a2Weights respectively representing different factors;
the decreasing half trapezoidal function is shown as follows:
the intermediate trapezoidal function is shown as follows:
in the formula, a3、a4Representing the weight of the respectively different factors.
The economic and social benefit index calculation unit: giving a comment based on the combined weight of the secondary indexes of the economic benefit and the social benefit; obtaining corresponding evaluation levels according to the comments, counting the voting frequency of each evaluation level, and taking the frequency as the membership degree of a secondary index of economic benefit and social benefit;
wherein the evaluation grades comprise excellent, good, medium, qualified and poor.
Further comprising: a consistency checking module: for consistency checking of the weight coefficients.
The consistency check module comprises:
the normalization submodule is used for normalizing the eigenvector corresponding to the maximum characteristic root of the judgment matrix;
the deviation value operator module is used for calculating the deviation value of the judgment matrix by using a consistency ratio formula based on the feature vector after the normalization processing;
and the judgment submodule is used for judging whether the deviation value is in an allowable range, and the judgment matrix passes the consistency check when the deviation value is in the allowable range, otherwise, the judgment matrix does not pass the consistency check.
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 present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.
Claims (10)
1. A method for evaluating the operation performance of a client-side distributed energy storage system is characterized by comprising the following steps:
acquiring running state information of a client side distributed energy storage system;
bringing the running state information into a pre-constructed comprehensive evaluation system model of the energy storage system to calculate to obtain the membership degree of each index;
determining the operation performance level of the client-side distributed energy storage system based on the membership degree of each index according to a fuzzy comprehensive evaluation method, and evaluating the operation performance of the client-side distributed energy storage system based on the operation performance level;
the comprehensive evaluation system model of the energy storage system comprises: the system comprises a target layer and a layering criterion layer constructed based on the relationship among the influence factors of the client-side distributed energy storage system.
2. The operation performance evaluation method according to claim 1, wherein the energy storage system comprehensive evaluation system model includes: economic, technical and social benefits;
the secondary indicators of economic benefit include: net present value, internal rate of return, and return on investment period;
secondary indicators of the technical performance include: average discharge rate per day, electric energy loss rate, comprehensive efficiency, cycle life percentage, energy density percentage, average time to failure, average availability factor, failure rate and load fluctuation improvement rate;
the secondary indicators of social benefit include: energy saving benefit and environmental protection benefit.
3. The operation performance evaluation method according to claim 2, wherein the step of bringing the operation state information into a pre-constructed energy storage system comprehensive evaluation system model to calculate to obtain the membership degree of each index comprises the steps of:
based on the indexes of the same layer belonging to each index of the previous layer, determining the value of the relative importance degree of the comparative indexes by adopting a pairwise comparison method and a 1-9 comparison scale, and constructing a pairwise comparison array;
obtaining a judgment matrix by taking the reciprocal of the pair comparison matrix;
calculating the maximum characteristic root of each judgment matrix, and taking the characteristic vector corresponding to the maximum characteristic root as a weight coefficient;
determining a weight matrix of each layer based on each weight coefficient, and calculating the combined weight of each index of the same layer based on the weight matrix;
and determining the membership degree of the secondary indexes of the technical performance by adopting a trapezoidal membership function based on the combined weight of each index, and determining the membership degree of the secondary indexes of the economic benefit and the social benefit by adopting an expert scoring mode.
4. The method of claim 3, wherein determining the degree of membership of the secondary indicators of technical performance using a trapezoidal membership function based on the combined weight of each indicator comprises:
based on the combined weight of each index, determining the membership degree of a secondary index with better technical performance by adopting a half-raised trapezoidal function if the index value is larger;
solving the membership degree of a secondary index with better technical performance when the index value is smaller by adopting a halving trapezoidal function;
and solving the membership degree of the secondary index with better technical performance as the index value approaches to a certain interval by adopting an intermediate trapezoidal function.
5. The running performance evaluation method according to claim 4, wherein the raised-half trapezoidal function is represented by the following formula:
wherein μ (x): degree of membership; x, a1、a2Weights respectively representing different factors;
the decreasing half trapezoidal function is shown as follows:
the intermediate trapezoidal function is shown as follows:
in the formula, a3、a4Representing the weight of the respectively different factors.
6. The method of claim 3, wherein determining the degree of membership of the secondary indicators of economic and social benefits using an expert scoring method comprises:
giving a comment based on the combined weight of the secondary indexes of the economic benefit and the social benefit;
obtaining corresponding evaluation levels according to the comments, counting the voting frequency of each evaluation level, and taking the frequency as the membership degree of a secondary index of economic benefit and social benefit;
wherein the evaluation grades comprise excellent, good, medium, qualified and poor.
7. The operation performance evaluation method according to claim 3, further comprising: and carrying out consistency check on the weight coefficients.
8. The operation performance evaluation method according to claim 7, wherein the checking for consistency of the weight coefficients comprises:
normalizing the eigenvector corresponding to the maximum characteristic root of the judgment matrix;
and calculating the deviation value of the judgment matrix by using a consistency ratio formula based on the feature vector after the normalization processing, judging whether the deviation value is in an allowable range, and if so, passing the consistency test of the judgment matrix, otherwise, failing to pass the consistency test.
9. The method for evaluating the operation performance according to claim 6, wherein the determining the operation performance level of the client-side distributed energy storage system based on the membership degree of each index according to a fuzzy comprehensive evaluation method comprises:
respectively constructing a first-level index fuzzy judgment matrix of the economic benefit and the social benefit based on the membership degree of the secondary indexes of the economic benefit and the social benefit by adopting a hierarchical analysis method;
normalizing the first-level index fuzzy judgment matrix to obtain a comprehensive judgment vector;
taking the grade corresponding to the vector with the maximum membership degree as the evaluation result of the economic benefit and the social benefit of the client-side distributed energy storage system;
constructing a fuzzy relation matrix based on the membership degree of the secondary indexes of the technical performance;
calculating the weight of the secondary indexes of the technical performance, and constructing a fuzzy weight vector by using the weights of all the secondary indexes of the technical performance;
and synthesizing the fuzzy weight vector and the fuzzy relation matrix to obtain a fuzzy comprehensive evaluation result vector of the technical performance index.
10. A client-side distributed energy storage system operation performance evaluation system, comprising:
the information acquisition module is used for acquiring the running state information of the client side distributed energy storage system;
the calculation module is used for substituting the running state information into a pre-constructed comprehensive evaluation system model of the energy storage system to calculate to obtain the membership degree of each index;
and the grade evaluation module is used for determining the operation performance grade of the client-side distributed energy storage system based on the membership degree of each index according to a fuzzy comprehensive evaluation method.
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