CN111222787A - Receiving-end power grid large-scale energy storage business mode decision method and system - Google Patents

Receiving-end power grid large-scale energy storage business mode decision method and system Download PDF

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CN111222787A
CN111222787A CN202010010068.3A CN202010010068A CN111222787A CN 111222787 A CN111222787 A CN 111222787A CN 202010010068 A CN202010010068 A CN 202010010068A CN 111222787 A CN111222787 A CN 111222787A
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叶小宁
李琼慧
王彩霞
雷雪姣
时智勇
袁伟
李梓仟
何永胜
黄碧斌
胡静
冯凯辉
洪博文
闫湖
李琥
刘国静
高骞
谢国辉
李娜娜
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Economic and Technological Research Institute of State Grid Jiangsu Electric Power Co Ltd
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Economic and Technological Research Institute of State Grid Jiangsu Electric Power Co Ltd
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Abstract

The invention discloses a receiving-end power grid large-scale energy storage business mode decision method and a system, wherein the method comprises the following steps: step 1, selecting a business mode of receiving-end power grid large-scale energy storage, determining a receiving-end power grid large-scale energy storage application scene under the business mode, and selecting large-scale energy storage evaluation indexes related to the business mode and the application scene; step 2, calculating the comprehensive benefits of the large-scale energy storage of the receiving-end power grid in the business mode according to the large-scale energy storage evaluation indexes; and 3, repeating the step 1 and the step 2 to obtain the comprehensive benefits of all the business modes and determine the optimal business mode. The method can accurately quantify the benefits brought by the energy storage to the reliability of the power grid, has simple and convenient calculation and analysis process and higher efficiency, accurately quantifies the benefits of the energy storage project under different business modes, and finally provides scientific decision reference basis for all participants.

Description

Receiving-end power grid large-scale energy storage business mode decision method and system
Technical Field
The invention relates to the technical field of large-scale energy storage evaluation, in particular to a receiving-end power grid large-scale energy storage business mode decision method and system.
Background
Clean and low-carbon transformation of energy is an inevitable trend of global energy development. With the rapid development of new energy such as offshore wind power, distributed photovoltaic and the like, the scale of power grid new energy access is continuously enlarged, the power grid new energy access is interleaved and coupled with the influence generated by external electricity in a high-proportion area, and the pressure of the aspects of safe and stable operation, new energy consumption and the like of a power grid is increased day by day. Research results of organizations related to continents show that if the proportion of installed power (or electric quantity) of new energy power generation such as wind power generation, photovoltaic power generation and the like to the total installed power (or generated power) of the power generation in China exceeds a certain limit value (different systems and limit values are different), large-capacity energy storage facilities need to be configured to further improve the capacity of a power grid for receiving new energy.
The energy storage is a key technology for constructing a new generation of electric power system, and can effectively support the construction of a novel electric power system with the characteristics of wide interconnection, intelligent interaction, flexibility, safety, controllability and open sharing. In recent years, the energy storage industry in China has a good development situation, the main electrochemical energy storage performance is continuously improved, and the cost is continuously reduced. Under the situation, a power system gradually evolves from a source network charge system to a source network charge storage system, the theoretical system and practice foundation of traditional power grid planning face challenges, and how to apply a new energy storage technology to relieve the power grid development problem becomes a problem which needs to be researched and solved urgently.
However, the energy storage industry in China still faces a series of challenges of unclear business model and unsound standard system at present, most of the benefit evaluation analysis of energy storage application is due to economic consideration, and a mechanism for comprehensive benefit evaluation analysis of environment, society and the like is lacked; although the application in partial electric power auxiliary service market and user side has the possibility of preliminary profit, the market space is narrow, the market mechanism is not formed, the large-scale energy storage application is blocked, and a typical business mode and profit point facing the power grid side large-scale energy storage application are urgently needed to be explored, so that the market and the policy mechanism are sound.
The existing energy storage project benefit evaluation method is mostly aimed at a single energy storage project. The energy storage participation power system has different application scenes, and the existing evaluation method does not consider the actual application scene and has larger limitation; on the other hand, the existing evaluation scheme does not provide an effective evaluation method for the benefit of the large-scale energy storage project.
Disclosure of Invention
The embodiment of the invention provides a receiving-end power grid large-scale energy storage business mode decision method and system, which are used for solving the problems in the prior art.
The embodiment of the invention provides a receiving-end power grid large-scale energy storage business mode decision method, which comprises the following steps:
step 1, selecting a business mode of receiving-end power grid large-scale energy storage, determining a receiving-end power grid large-scale energy storage application scene under the business mode, and selecting large-scale energy storage evaluation indexes related to the business mode and the application scene;
step 2, calculating the comprehensive benefits of the large-scale energy storage of the receiving-end power grid in the business mode according to the large-scale energy storage evaluation indexes;
and 3, repeating the step 1 and the step 2 to obtain the comprehensive benefits of all the business models and determine the optimal business model.
The embodiment of the invention also provides a receiving-end power grid large-scale energy storage business mode decision system, which comprises:
the acquisition module is used for selecting a business mode of receiving-end power grid large-scale energy storage, determining a receiving-end power grid large-scale energy storage application scene in the business mode, and selecting large-scale energy storage evaluation indexes related to the business mode and the application scene;
the calculation module is used for calculating the comprehensive benefits of the large-scale energy storage of the receiving-end power grid in the business mode according to the large-scale energy storage evaluation indexes;
and the determining module is used for repeatedly calling the obtaining module and the calculating module until the comprehensive benefits of all the business modes are obtained, and determining the optimal business mode.
The embodiment of the invention also provides a receiving-end power grid large-scale energy storage business mode decision device, which comprises: the system comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein when the computer program is executed by the processor, the steps of the large-scale energy storage business mode decision method for the receiving-end power grid are realized.
The embodiment of the invention also provides a computer-readable storage medium, wherein an implementation program for information transmission is stored on the computer-readable storage medium, and the implementation program is executed by a processor to implement the steps of the large-scale energy storage business mode decision method for the receiving-end power grid.
By adopting the embodiment of the invention, the benefits brought to the reliability of the power grid by the stored energy can be accurately quantized, the calculation and analysis process is simple and convenient, the efficiency is higher, the benefits of the energy storage project can be accurately quantized under different business modes, and finally, scientific decision reference basis is provided for each participant.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a flowchart of a receiving-end grid large-scale energy storage business model decision method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a large-scale energy storage application scenario of a receiving-end power grid according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an architecture of a large-scale energy storage application AGC of a receiving-end power grid according to an embodiment of the present invention;
fig. 4 is a detailed flowchart of a receiving-end grid large-scale energy storage business model decision method according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a large-scale energy storage business model decision system of a receiving-end power grid according to an embodiment of the invention;
fig. 6 is a schematic diagram of a receiving-end grid large-scale energy storage business model decision device according to a second embodiment of the apparatus of the present invention.
Detailed Description
The embodiment of the invention provides a receiving-end power grid large-scale energy storage business model decision method and a receiving-end power grid large-scale energy storage business model decision system, which are generally suitable for benefit evaluation of a single energy storage project and a power grid side large-scale energy storage project; moreover, the simulation result of whether the energy is stored in the system is analyzed and calculated, so that the benefit brought to the reliability of the power grid by the energy storage is accurately quantized; the calculation and analysis process is simple and convenient, and the efficiency is higher. On the basis, the scheme combs the existing business model, accurately quantifies the benefit of the energy storage project in different business models, and finally provides scientific decision reference basis for each participant.
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Method embodiment
According to an embodiment of the present invention, a receiving-end power grid large-scale energy storage business mode decision method is provided, fig. 1 is a flowchart of the receiving-end power grid large-scale energy storage business mode decision method according to the embodiment of the present invention, and as shown in fig. 1, the receiving-end power grid large-scale energy storage business mode decision method according to the embodiment of the present invention specifically includes:
step 101, selecting a business mode of receiving-end power grid large-scale energy storage, determining a receiving-end power grid large-scale energy storage application scene under the business mode, and selecting large-scale energy storage evaluation indexes related to the business mode and the application scene;
the application scenes and the application values of different types of energy storage in the receiving-end power grid are analyzed, and the energy storage is mainly considered as typical receiving-end power grid application scenes for improving power supply reliability, power grid operation flexibility, new energy consumption, peak shaving, delaying power grid upgrading and transformation and the like and the application values of millisecond-level, minute-level, hour-level, year-level and other different time scales. The typical application scenario of the energy storage project is shown in table 1 below:
table 1 energy storage project typical application scenario
Figure BDA0002356820010000051
The application of the large-scale energy storage in the receiving-end power grid can mainly play the following roles: the operation efficiency and the benefit of a receiving-end power grid are improved; ensuring safe and stable operation of a receiving-end power grid; and the operation and response flexibility of the receiving-end power grid is improved. Therefore, according to the actual situation and the demand of the receiving-end power grid, the large-scale energy storage application scenario for the receiving-end power grid is as shown in fig. 2 and fig. 3.
The energy efficiency characteristics of various types of energy storage are analyzed by carrying out technical and economic evaluation on the application of the battery energy storage project in the power grid side, the energy efficiency influence factors of the energy storage project are comprehensively cleared by combining typical application scenes of various types of energy storage, and the benefits of the energy storage project are calculated from four aspects of energy efficiency evaluation, economic benefit evaluation, social benefits and environmental benefits.
1) The performance evaluation indexes are mainly used for evaluating the application of the battery energy storage system in the receiving-end power grid side from the aspect of self performance, and the selection of different types of battery energy storage systems can also affect other evaluation indexes to a certain extent.
The technical evaluation indexes in the embodiment of the invention are mainly considered as follows: monomer voltage, energy density, power density, self-discharge rate, cycle life, charge-discharge efficiency and safety.
2) The economic evaluation index is based on the economic benefit of the energy storage project, and the cost and the benefit brought to the receiving-end power grid by the development of the energy storage system project are analyzed from the aspects of cost and benefit. The cost mainly comprises: initial investment costs and operation and maintenance costs. Initial investment costs for energy storage systems include: site construction cost, battery capacity cost, battery power cost, other equipment cost. The economic benefits mainly include participation in the electric power market to obtain peak-valley price difference, reduction of new energy grid-connected electricity abandonment quantity, delay of power grid investment, recovery of energy storage projects and other benefits.
3) The social benefit is subjected to index selection from two angles of a power grid side and a user side, and the comprehensive voltage qualification rate change rate, the responsibility frequency unqualified frequency change rate, the power failure improvement degree and the frequency modulation efficient multiple are selected on the benefit index of the power grid side by referring to a first-class power grid index system of Jiangsu province; selecting a customer satisfaction increasing rate on the user side benefit; and calculating the social benefit of applying the large-scale energy storage to the receiving-end power grid by comparing the improvement degrees of indexes before and after the energy storage is added into the receiving-end power grid.
4) The environmental benefit refers to the fact that the stored energy is added into a receiving-end power grid, the output of new energy can be smoothed, and good conditions are provided for the consumption of the new energy, so that carbon emission caused by traditional power generation can be reduced, the environmental benefit of the stored energy applied to the receiving-end power grid is evaluated from two angles of the new energy and the carbon emission, and the change rate of the consumption of the new energy, energy conservation and emission reduction benefit indexes are selected.
TABLE 2 energy storage project comprehensive benefit evaluation index system
Figure BDA0002356820010000061
Figure BDA0002356820010000071
Grid-side typical business model: the currently operated power grid side energy storage project is mainly a demonstration project and is used for improving the safety stability level of a power grid and participating in peak shaving frequency modulation. The business model of the current project in exploration mainly comprises 3. Firstly, the operating lease mode, the energy storage project is invested and constructed by a third party, and the power grid enterprise leases and operates. For example, in the 100MW energy storage project of the power grid in Jiangsu, manufacturers such as a permit and the like invest and build, and power companies in Jiangsu lease and operate. And secondly, under the contract energy management and electricity purchasing and selling mode, the energy storage device manufacturer invests and constructs, contracts with the company and divides the income according to the agreed proportion. For example, the Henan power grid 100 MW-level energy storage demonstration project is operated by a Henan comprehensive energy service company, which is built by the investment of a high-level cluster. And thirdly, the energy storage device manufacturer participates in the auxiliary service market as an independent trading subject, and the energy storage manufacturer and the power grid enterprise directly settle accounts to obtain auxiliary service benefits such as energy storage peak shaving, frequency modulation and the like. If the mid-energy intelligent energy technology (Shanghai) company Limited plans to store energy on the 120MW/480MWh power grid side of the investment construction of Gansu province, the investment recovery of the energy storage projects adopts a marketization mechanism.
According to the difference of business modes of energy storage projects participating in the power grid side, the cost needing to be considered is different, if a third party invests and constructs the energy storage projects and the power grid enterprises rent operation, only the lease cost and the operation and maintenance cost need to be considered for the power grid enterprises, and the energy storage project recovery income cannot be included in the receiving-end power grid income; if a contract energy management and electricity purchasing mode is adopted, for the power grid side, the calculated income needs to be divided according to the income of an actual contract, and if an energy storage device manufacturer is adopted as an independent trading subject to participate in an auxiliary service market, the power grid side cannot obtain the direct benefit brought by peak-shaving frequency modulation, and the cost does not need to be borne.
102, calculating the comprehensive benefit of the large-scale energy storage of the receiving-end power grid in a commercial mode according to the large-scale energy storage evaluation index;
in the embodiment of the present invention, the flow shown in fig. 4 may be referred to for calculating the comprehensive benefit of the large-scale energy storage of the receiving-end power grid in the business model.
And 103, repeating the step 101 and the step 102 to obtain the comprehensive benefits of all the business models and determine the best business model.
In the above processing, in step 102, during the evaluation of the comprehensive benefits of the energy storage project, based on a least square method, determining an index weight by using a combined weight method of an analytic hierarchy process and an entropy weight method, and objectively and subjectively combining the index weight and the index weight, then calculating the overall benefits of the project in different business modes by using a fuzzy comprehensive evaluation model, and combining the two methods, thereby establishing a fuzzy comprehensive evaluation model introducing subjective and objective weight combinations, specifically including the following processing:
step 1021, determining subjective weighting of each large-scale energy storage evaluation index by adopting an analytic hierarchy process;
step 1, classifying the influence factors according to attributes, establishing a hierarchical model from top to bottom, and arranging according to the sequence of a target layer, a criterion layer and a scheme layer, wherein the target layer is the final purpose of analysis, the criterion layer is a plurality of factors influencing a target, and the scheme layer is various schemes and measures adopted;
that is, step 1 needs to establish a hierarchical structure to hierarchy the evaluation targets, specifically, classifying the influence factors according to attributes, establishing a hierarchical relationship from top to bottom, and arranging according to the order of the target layer, the criterion layer, and the scheme layer. The target layer usually has only one factor, the ultimate purpose of the analysis; the criterion layer is a plurality of factors influencing the target; the scheme layer is various schemes and measures adopted. When the weight coefficient is determined by using a hierarchical method, firstly, a complex problem in a system is decomposed into a plurality of parts, and then the parts are combined together again through the association and membership among the parts to establish a multi-level evaluation model.
Step 2, comparing any two indexes A in the criterion layer by a 1-9 scale methodiAnd AjThe importance and the value of the standard layer are assigned, and a judgment matrix of the standard layer is constructed;
that is, after the hierarchical model is determined, the membership between the upper and lower layers is determined. The following is a model of a three-layer index system. Let O be the target layer in the model, A be the criterion layer in the model, and B be the index layer in the model. The target layer has a dominant relationship with the indexes A1, A2.. An of the criterion layer, and in order to determine the influence degree of each index in the layer A on the target O, a pairwise comparison method is adopted, namely, any two indexes Ai and Aj in the layer A1, the layer A2.. An are compared by a 1-9 scale method, wherein the significance of the indexes is shown in the following table 5-2.
TABLE 3 meanings on scale
Figure BDA0002356820010000091
Step 3, calculating the maximum eigenvalue lambda of the judgment matrix of the criterion layermaxThen according to equation 1, obtain λmaxAnd (3) carrying out normalization calculation on W according to the corresponding characteristic vector W to obtain a weight vector AW of the importance of the elements in the criterion layer to the target layer:
AW＝λmaxw formula 1;
and (3) carrying out consistency check on the judgment matrix of the criterion layer according to a formula 2:
CR ═ CI/RI formula 2;
wherein CI ═ λmax-n)/(n-1), CI being the deviation from consistency index; RI is an average random consistency index, and n is the number of evaluation indexes;
that is, in step 3, the sub-level index weight needs to be determined and checked for consistency
In the construction of the evaluation system model, a feature root method is adopted to calculate the vector of the index weight. Firstly, solving the maximum eigenvalue lambda of the judgment matrix AmaxThen according to equation 1, obtain λmaxAnd (4) carrying out normalization calculation on W according to the corresponding characteristic vector W, wherein the result is the weight vector of the importance of the elements in the layer A to the layer O.
When the weight coefficient is determined by the analytic hierarchy process, special conditions sometimes occur, such as that A is extremely important than B, B is extremely important than C, but C is important than A, which is obtained by calculation. The above situation is a serious violation of the conventional method, so in order to increase the decision significance, we need to perform consistency check on the judgment matrix. The random consistency ratio CR is a criterion used in the analytic hierarchy process to check whether the matrix meets the consistency requirement. In formula 2, RI is an average random consistency index, and a table can be looked up to obtain a corresponding value. The RI values for the different values are given in Table 4.
When the CR value is less than 0.1, the inconsistency degree of the matrix a is determined to be within an allowable range, and when the CR value is greater than 0.1, the values of the elements in the determination matrix are required to be correspondingly adjusted, so that the elements can be reused after meeting the requirement of consistency. And only the vector corresponding to the maximum characteristic root value obtained from the judgment matrix meeting the consistency requirement is the weight vector of the index system after standardization.
Table 4 decision matrix RI value taking table
Figure BDA0002356820010000101
And 4, determining the weight of all factors of each layer to the total target layer by layer according to the sequence from the high layer to the low layer until the weight of the bottommost element to the topmost element is calculated, and carrying out consistency check.
Specifically, after the above steps are completed, the weights of all the factors of each layer to the total target are determined, and the determination is performed layer by layer according to the sequence from the high layer to the low layer until the weight of the bottommost element to the topmost element is obtained, and the consistency is checked.
Step 1022, determining objective weighting of each large-scale energy storage evaluation index by using an entropy weight method; the method specifically comprises the following steps:
step 1, setting n evaluation indexes to decide and evaluate m candidate schemes, wherein the evaluation indexes i of the candidate schemes k are estimated values xikIdeal value of evaluation index i
Figure BDA0002356820010000102
Figure BDA0002356820010000103
The value varies depending on the characteristics of the evaluation index, and for the profitability index,
Figure BDA0002356820010000104
the larger the better; for the loss index (inverse index),
Figure BDA0002356820010000105
the smaller the size, the better (the positive index may be obtained).
Step 2, calculating x according to formula 3ikFor the
Figure BDA0002356820010000111
Proximity D ofik：
Figure BDA0002356820010000112
Step 3, D is carried out according to formula 4ikNormalization treatment:
Figure BDA0002356820010000113
wherein d is not less than 0ik≤1，
Figure BDA0002356820010000114
Step 4, calculating the entropy E of the m candidate schemes evaluated by the n evaluation indexes according to the formula 5:
Figure BDA0002356820010000115
step 5, if the relative importance of the evaluation index is irrelevant to the scheme to be selected, calculating the entropy E according to a formula 6:
Figure BDA0002356820010000116
wherein the content of the first and second substances,
Figure BDA0002356820010000117
thus, the uncertainty of the relative importance of the evaluation index i to the candidate decision evaluation can be determined by the following conditional entropy.
Step 6, determining the conditional entropy E of the evaluation index i according to the formula 7i：
Figure BDA0002356820010000118
Wherein, according to the extreme value of the entropy,
Figure BDA0002356820010000119
k is 1 to m, i.e., di1≈di2≈...dikThe closer to the same, the larger the conditional entropy, and the larger the uncertainty of the evaluation index to the evaluation decision of the scheme to be selected; when in use
Figure BDA00023568200100001110
Are equal, i.e. di1≈di2≈...dikWhen d is greater thani≈mdik，
Figure BDA0002356820010000121
While
Figure BDA0002356820010000122
Figure BDA0002356820010000123
Then the conditional entropy is maximum Emax＝lnm；
Step 7, by EmaxTo pair
Figure BDA0002356820010000124
Carrying out normalization processing to obtain an entropy value representing the evaluation decision importance of the evaluation index i:
Figure BDA0002356820010000125
step 8, from e (d)i) Determining an evaluation weight theta of an evaluation index iiComprises the following steps:
Figure BDA0002356820010000126
wherein the content of the first and second substances,
Figure BDA0002356820010000127
and thetaiSatisfies the following conditions: theta is not less than 0i≤1，
Figure BDA0002356820010000128
(this definition is also a requirement for normalization);
evaluation weight value thetaiIs dependent onIn the inherent information of the candidate scheme, the experience judgment of a decision maker cannot be ignored for multi-target decision, and therefore, another weight W is introducedi，WiThe subjective judgment ability of the decision maker is shown, and the subjective judgment ability are combined into a practical weight lambda according to a formula 10i：
Figure BDA0002356820010000129
Wherein λ isiTheta i is more than or equal to 0 and less than or equal to 1,
Figure BDA00023568200100001210
step 9, calculating the weight of each evaluation index by adopting a variation coefficient method, and determining the reliability of the entropy weight if the weights obtained by the two weight determination methods are consistent; that is, in order to prevent the occurrence of one-sidedness in entropy weight, the weight of each evaluation index is calculated by a coefficient of variation method, and the former is verified by the latter. If the two weight determination methods result in similar weights, the entropy weights can be proved to have strong confidence.
Step 10, weighting lambda with entropyiAs weights, entropy evaluation was performed, and S was calculated according to equation 11kThe value:
Figure BDA00023568200100001211
1023, determining the comprehensive weight of each large-scale energy storage evaluation index by a combined weight method based on subjective weighting and objective weighting by adopting a least square method;
the subjective weighting method reflects the value quantity (economic significance or technical significance) of the index, the objective weighting method reflects the information quantity (variation and correlation) of the index, the subjective weighting method and the objective weighting method have the characteristics, and the comprehensive evaluation should reflect the unification of the subjective weighting method and the objective weighting method. An optimization model for determining the index weight is established by taking a least square method as a tool, so that the empowerment of the index achieves the purposes of subjective and objective unification and unification of value quantity and information quantity.
The method specifically comprises the following steps:
step 1, let each index weight given by analytic hierarchy process be U ═ U1,u2,...,um]TThe weight of each index given by the entropy weight method is V ═ V1,v2,...,vm]TThe optimized combination weight of each index is W ═ W1,w2,...,wm]T；
Step 2, the normalized data matrix with n evaluation indexes and m candidate schemes is Z ═ Z (Zij)n×mThe evaluation value of the ith candidate scheme is
Figure BDA0002356820010000131
i＝1,2,....,m；
Step 3, for all indexes of all evaluation objects, the deviation of the evaluation value under subjective and objective weighting should be as small as possible, and the minimum square method optimization combination evaluation model is established for the purpose, namely the minimum square method optimization combination evaluation model shown in formula 12 is established;
Figure BDA0002356820010000132
wherein the content of the first and second substances,
Figure BDA0002356820010000133
j＝1,2,....,m；
step 4, solving a least square method optimization combination evaluation model to obtain a weight W:
making a Langcange function:
Figure BDA0002356820010000134
order to
Figure BDA0002356820010000135
Wherein j is 1, 2.. times, m,
Figure BDA0002356820010000136
then expressed as:
Figure BDA0002356820010000137
a matrix equation 14 is obtained:
Figure BDA0002356820010000141
e＝[1,1,...,1]T
W＝[w1,w2,...,wm]T
Figure BDA0002356820010000142
wherein A is an m multiplied by m diagonal matrix, and e, W and B are m multiplied by 1 vectors;
solving the matrix equation yields:
Figure BDA0002356820010000143
and 1024, calculating the comprehensive benefits of the large-scale energy storage in the commercial mode by adopting a fuzzy comprehensive evaluation model based on the comprehensive weight. The fuzzy evaluation model is used for converting qualitative problems in an evaluation index system into quantitative problems for evaluation by adopting the theory of fuzzy mathematics and is used for representing the uncertainty of objective objects. The method specifically comprises the following steps:
step 1, determining a factor set U ═ U of an evaluation object1+U2+...+Un}; whether the selection of the evaluation index is proper or not directly influences the accuracy of the comprehensive evaluation. Therefore, the selected evaluation indexes have representation and difference, and the indexes have hierarchy without overlapping or causal relationship.
Step 2, determining a comment set V ═ V of an evaluation object1+V2+...+Vm}; the comment set is a set of all the possible results of the evaluation made by the evaluator on the evaluation object. For example, the set of comments to assess risk may be { low, medium, high }, and the set of comments for quality may be { poor, normal, better }.
Step 3, determining the weight vector A of the evaluation factor as (a)1,a2,...,an) (ii) a The weight vector represents the importance degree of each factor to the judgment result, and different weights can sometimes lead to different conclusions. The method of determining the weight includes a Delphi method (expert survey method), a feature value method, a weighted average method, a frequency distribution determination weight method, an analytic hierarchy process, and the like.
Step 4, evaluating one by one from a single factor, determining affiliation functions of evaluation objects to an evaluation set, and constructing a fuzzy relation matrix R;
and evaluating one by one from a single factor to determine the membership degree of the evaluation object to the evaluation set. This step needs to select a proper membership function to construct a good fuzzy relation evaluation matrix.
Step 5, selecting a proper operator to synthesize the fuzzy relation matrix R and the comprehensive weight, and calculating to obtain a comprehensive judgment vector; and 5, carrying out multi-index comprehensive evaluation. The step realizes the evaluation of the comprehensive influence of a plurality of indexes, and a proper operator is required to be selected to synthesize the fuzzy relation matrix and the weight. The fuzzy synthesis operators commonly used are four: m (V-shaped),
Figure BDA0002356820010000151
) Type, M (· to,
Figure BDA0002356820010000152
) And (4) molding. Wherein b isjRepresenting the degree of membership, a, to the jth comment taking into account all factorsiRepresents the weight occupied by the ith factor, rijIndicates the evaluation factor UiFor rank fuzzy subset VjDegree of membership.
M (Λ, v) type:
Figure BDA0002356820010000153
m (, v) type:
Figure BDA0002356820010000154
M(∧,
Figure BDA0002356820010000155
) Type (2):
Figure BDA0002356820010000156
M(·，
Figure BDA0002356820010000157
) Type (2):
Figure BDA0002356820010000158
the comparative analysis of the characteristics of the four operators is shown in Table 5.
TABLE 5 comparison table of four operator characteristics
Figure BDA0002356820010000159
Step 6, adopting a maximum membership method to comprehensively judge the maximum value max (b) in the vectorj) Corresponding comment VjAs a combined benefit of the final large-scale energy storage in the commercial mode, wherein bjRepresenting the degree of membership to the jth comment when all factors are considered together.
The evaluation index system of the technical scheme of the embodiment of the invention has stronger comprehensiveness, is comprehensive and is generally suitable for decision-making auxiliary evaluation of various energy storage projects, and in addition, the evaluation result is determined by adopting the membership function, so that the evaluation index system is more intuitive, clear and scientific.
Apparatus embodiment one
According to an embodiment of the present invention, a receiving-end grid large-scale energy storage business mode decision system is provided, fig. 5 is a schematic diagram of the receiving-end grid large-scale energy storage business mode decision system according to the embodiment of the present invention, and the receiving-end grid large-scale energy storage business mode decision system shown in fig. 5 specifically includes:
the acquisition module 50 is used for selecting a business mode of receiving-end power grid large-scale energy storage, determining a receiving-end power grid large-scale energy storage application scene in the business mode, and selecting large-scale energy storage evaluation indexes related to the business mode and the application scene;
the calculating module 52 is used for calculating the comprehensive benefit of the large-scale energy storage of the receiving-end power grid in the commercial mode according to the large-scale energy storage evaluation index;
and the determining module 54 is used for repeatedly calling the obtaining module and the calculating module until the comprehensive benefits of all the business models are obtained, and determining the optimal business model.
The calculating module 52 specifically includes:
the subjective weighting module is used for determining subjective weighting of each large-scale energy storage evaluation index by adopting an analytic hierarchy process; the subjective weighting module is specifically configured to:
classifying the influence factors according to attributes, establishing a hierarchical model from top to bottom, and arranging according to the sequence of a target layer, a criterion layer and a scheme layer, wherein the target layer is the final purpose of analysis, the criterion layer is a plurality of factors influencing a target, and the scheme layer is various schemes and measures adopted;
comparing any two indexes A in the criterion layer by a 1-9 scale methodiAnd AjThe importance of the standard layer is assigned, and a judgment matrix of the standard layer is constructed;
calculating maximum eigenvalue lambda of judgment matrix of criterion layermaxThen according to equation 1, obtain λmaxAnd (3) carrying out normalization calculation on the corresponding characteristic vector W to obtain a weight vector AW of the importance of the element in the criterion layer to the target layer:
AW＝λmaxw formula 1;
and (3) carrying out consistency check on the judgment matrix of the criterion layer according to a formula 2:
CR ═ CI/RI formula 2;
wherein CI ═ λmax-n)/(n-1), CI being the deviation from consistency index; RI is an average random consistency index, and n is the number of evaluation indexes;
and determining the weight of all factors of each layer to the total target layer by layer according to the sequence from the high layer to the low layer until the weight of the bottommost element to the topmost element is calculated, and carrying out consistency check.
The objective weighting module is used for determining objective weighting of each large-scale energy storage evaluation index by adopting an entropy weight method;
the objective weighting module is specifically configured to:
setting n evaluation indexes to decide and evaluate m candidate schemes, wherein the estimated value x of the evaluation index i of the candidate scheme k isikIdeal value of evaluation index i
Figure BDA0002356820010000171
Calculating x according to equation 3ikFor the
Figure BDA0002356820010000172
Proximity D ofik：
Figure BDA0002356820010000173
D according to equation 4ikNormalization treatment:
Figure BDA0002356820010000174
wherein d is not less than 0ik≤1，
Figure BDA0002356820010000175
And (3) calculating the entropy E of the m candidate schemes evaluated by using n evaluation indexes according to a formula 5:
Figure BDA0002356820010000176
if the relative importance of the evaluation index is not related to the candidate scheme, the entropy E is calculated according to the formula 6:
Figure BDA0002356820010000181
wherein the content of the first and second substances,
Figure BDA0002356820010000182
determining conditional entropy E of evaluation index i according to formula 7i：
Figure BDA0002356820010000183
Wherein, according to the extreme value of the entropy,
Figure BDA0002356820010000184
k is 1 to m, i.e., di1≈di2≈...dikThe closer to the same, the larger the conditional entropy, and the larger the uncertainty of the evaluation index to the evaluation decision of the scheme to be selected; when in use
Figure BDA0002356820010000185
Are equal, i.e. di1≈di2≈...dikWhen d is greater thani≈mdik，
Figure BDA0002356820010000186
While
Figure BDA0002356820010000187
Figure BDA0002356820010000188
Then the conditional entropy is maximum Emax＝lnm；
By EmaxTo pair
Figure BDA0002356820010000189
Carrying out normalization processing to obtain an entropy value representing the importance of the evaluation decision of the evaluation index i:
Figure BDA00023568200100001810
from e (d)i) Determining an evaluation weight theta of an evaluation index iiComprises the following steps:
Figure BDA00023568200100001811
wherein the content of the first and second substances,
Figure BDA00023568200100001812
and thetaiSatisfies the following conditions: theta is not less than 0i≤1，
Figure BDA00023568200100001813
Introducing another weight Wi，WiThe subjective judgment ability of the decision maker is shown, and the subjective judgment ability are combined into a practical weight lambda according to a formula 10i：
Figure BDA00023568200100001814
Wherein λ isiTheta i is more than or equal to 0 and less than or equal to 1,
Figure BDA0002356820010000191
calculating the weight of each evaluation index by adopting a coefficient of variation method, and determining the reliability of the entropy weight if the weights obtained by the two weight determination methods are consistent;
with entropy weight λiAs weights, entropy evaluation was performed, and S was calculated according to equation 11kThe value:
Figure BDA0002356820010000192
the combined weight module is used for determining the comprehensive weight of each large-scale energy storage evaluation index by adopting a least square method and a combined weight method based on subjective weighting and objective weighting;
the combination weight module is specifically configured to:
let each index weight given by analytic hierarchy process be U ═ U1,u2,...,um]TThe weight of each index given by the entropy weight method is V ═ V1,v2,...,vm]TThe optimized combination weight of each index is W ═ W1,w2,...,wm]T；
Setting a normalized data matrix with n evaluation indexes and m candidate schemes as Z ═ Z (Z)ij)n×mThe evaluation value of the ith candidate scheme is
Figure BDA0002356820010000193
i＝1,2,....,m；
Establishing a least square method optimized combination evaluation model shown in formula 12;
Figure BDA0002356820010000194
wherein the content of the first and second substances,
Figure BDA0002356820010000195
j＝1,2,....,m；
solving a least square method optimization combination evaluation model to obtain a weight W:
making a Langcange function:
Figure BDA0002356820010000196
order to
Figure BDA0002356820010000197
Wherein j is 1, 2.. times, m,
Figure BDA0002356820010000198
then expressed as:
Figure BDA0002356820010000201
a matrix equation 14 is obtained:
Figure BDA0002356820010000202
e＝[1,1,...,1]T
W＝[w1,w2,...,wm]T
Figure BDA0002356820010000203
wherein A is an m multiplied by m diagonal matrix, and e, W and B are m multiplied by 1 vectors;
solving the matrix equation yields:
Figure BDA0002356820010000204
and the comprehensive benefit calculation module is used for calculating the comprehensive benefit of the large-scale energy storage in the business mode by adopting a fuzzy comprehensive evaluation model based on the comprehensive weight.
The comprehensive benefit calculation module is specifically used for:
determining a factor set U-U of an evaluation object1+U2+...+Un}；
Comment set V ═ { V) for specifying evaluation target1+V2+...+Vm}；
Determining a weight vector a ═ a of the evaluation factors1,a2,...,an)；
Evaluating one by one from a single factor, determining a membership function of an evaluation object to an evaluation set, and constructing a fuzzy relation matrix R;
selecting a proper operator to synthesize the fuzzy relation matrix R and the comprehensive weight, and calculating to obtain a comprehensive evaluation vector;
adopting a maximum membership degree method to comprehensively judge the maximum value max (b) in the vectorj) Corresponding comment VjAs a combined benefit of the final large-scale energy storage in the commercial mode, wherein bjRepresenting the degree of membership to the jth comment when all factors are considered together.
The embodiment of the apparatus of the present invention is an embodiment corresponding to the embodiment of the method described above, and can be understood by referring to the embodiment of the method described above, which is not described herein again.
Device embodiment II
The embodiment of the invention provides a receiving-end power grid large-scale energy storage business mode decision device, as shown in fig. 6, which comprises: a memory 60, a processor 62 and a computer program stored on the memory 60 and executable on the processor 62, the computer program realizing the following method steps when executed by the processor 62:
step 1, selecting a business mode of receiving-end power grid large-scale energy storage, determining a receiving-end power grid large-scale energy storage application scene under the business mode, and selecting large-scale energy storage evaluation indexes related to the business mode and the application scene;
step 2, calculating the comprehensive benefits of the large-scale energy storage of the receiving-end power grid in the business mode according to the large-scale energy storage evaluation indexes;
and 3, repeating the step 1 and the step 2 to obtain the comprehensive benefits of all the business models and determine the optimal business model.
The detailed processing of each step can be understood by referring to the method embodiment, and is not described herein again.
Device embodiment III
The embodiment of the invention provides a computer readable storage medium, wherein an implementation program for information transmission is stored on the computer readable storage medium, and when being executed by a processor 62, the implementation program realizes the following method steps:
step 1, selecting a business mode of receiving-end power grid large-scale energy storage, determining a receiving-end power grid large-scale energy storage application scene under the business mode, and selecting large-scale energy storage evaluation indexes related to the business mode and the application scene;
step 2, calculating the comprehensive benefits of the large-scale energy storage of the receiving-end power grid in the business mode according to the large-scale energy storage evaluation indexes;
and 3, repeating the step 1 and the step 2 to obtain the comprehensive benefits of all the business models and determine the optimal business model.
The detailed processing of each step can be understood by referring to the method embodiment, and is not described herein again.
In conclusion, by means of the technical scheme of the embodiment of the invention, the benefits brought by the energy storage to the reliability of the power grid can be accurately quantified, the calculation and analysis process is simple and convenient, the efficiency is higher, the benefits of the energy storage project are accurately quantified under different business modes, and finally, a scientific decision reference basis is provided for each participant.
The computer-readable storage medium of the embodiment includes, but is not limited to: ROM, RAM, magnetic or optical disks, and the like.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented in a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented in program code that is executable by a computing device, such that they may be stored in a memory device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (14)

1. A receiving-end power grid large-scale energy storage business mode decision method is characterized by comprising the following steps:
step 1, selecting a receiving-end power grid large-scale energy storage business mode, determining a receiving-end power grid large-scale energy storage application scene in the business mode, and selecting large-scale energy storage evaluation indexes related to the business mode and the application scene;
step 2, calculating the comprehensive benefit of the large-scale energy storage of the receiving-end power grid in the business mode according to the large-scale energy storage evaluation index;
and 3, repeating the step 1 and the step 2 to obtain the comprehensive benefits of all the business modes and determine the optimal business mode.
2. The method according to claim 1, wherein the step of calculating the comprehensive benefits of the large-scale energy storage of the receiving-end power grid in the business model according to the large-scale energy storage evaluation index specifically comprises the following steps:
determining subjective weighting of each large-scale energy storage evaluation index by adopting an analytic hierarchy process;
determining objective weighting of each large-scale energy storage evaluation index by adopting an entropy weight method;
determining the comprehensive weight of each large-scale energy storage evaluation index by adopting a least square method and a combined weight method based on subjective weighting and objective weighting;
and based on the comprehensive weight, calculating the comprehensive benefit of the large-scale energy storage in the business mode by adopting a fuzzy comprehensive evaluation model.
3. The method of claim 2, wherein determining subjective weighting of each scale energy storage assessment index using an analytic hierarchy process comprises:
step 1, classifying the influence factors according to attributes, establishing a hierarchical model from top to bottom, and arranging according to the sequence of a target layer, a criterion layer and a scheme layer, wherein the target layer is the final purpose of analysis, the criterion layer is a plurality of factors influencing a target, and the scheme layer is various schemes and measures adopted;
step 2, comparing any two indexes A in the criterion layer by a 1-9 scale methodiAnd AjThe importance of the standard layer is assigned, and a judgment matrix of the standard layer is constructed;
step 3, calculating the maximum eigenvalue lambda of the judgment matrix of the criterion layermaxThen according to equation 1, obtain λmaxAnd (3) carrying out normalization calculation on W according to the corresponding characteristic vector W to obtain a weight vector AW of the importance of the elements in the criterion layer to the target layer:
AW＝λmaxw formula 1;
and (3) carrying out consistency check on the judgment matrix of the criterion layer according to a formula 2:
CR ═ CI/RI formula 2;
wherein CI ═ λmax-n)/(n-1), CI being the deviation from consistency index; RI is an average random consistency index, and n is the number of evaluation indexes;
and 4, determining the weight of all factors of each layer to the total target layer by layer according to the sequence from the high layer to the low layer until the weight of the element at the bottom layer to the element at the highest layer is calculated, and carrying out consistency check.
4. The method of claim 2, wherein determining objective weightings using the entropy weight method specifically comprises:
step 1, setting n evaluation indexes to decide and evaluate m candidate schemes, wherein the evaluation indexes i of the candidate schemes k have estimation values xikIdeal value of evaluation index i
Figure FDA0002356818000000021
Step 2, calculating x according to formula 3ikFor the
Figure FDA0002356818000000022
Proximity D ofik：
Figure FDA0002356818000000023
Step 3, D is carried out according to formula 4ikNormalization treatment:
Figure FDA0002356818000000024
wherein d is not less than 0ik≤1，
Figure FDA0002356818000000025
Step 4, calculating the entropy E of the m candidate schemes evaluated by the n evaluation indexes according to the formula 5:
Figure FDA0002356818000000026
step 5, if the relative importance of the evaluation index is irrelevant to the scheme to be selected, calculating the entropy E according to a formula 6:
Figure FDA0002356818000000031
wherein the content of the first and second substances,
Figure FDA0002356818000000032
step 6, determining the conditional entropy E of the evaluation index i according to the formula 7i：
Figure FDA0002356818000000033
Wherein, according to the extreme value of the entropy,
Figure FDA0002356818000000034
k is 1 to m, i.e., di1≈di2≈…dikThe closer to the same, the larger the conditional entropy, and the larger the uncertainty of the evaluation index to the evaluation decision of the scheme to be selected; when in use
Figure FDA0002356818000000035
Are equal, i.e. di1≈di2≈…dikWhen d is greater thani≈mdik，
Figure FDA0002356818000000036
While
Figure FDA0002356818000000037
Figure FDA0002356818000000038
Then the conditional entropy is maximum Emax＝lnm；
Step 7, get throughPer EmaxTo pair
Figure FDA0002356818000000039
Carrying out normalization processing to obtain an entropy value representing the evaluation decision importance of the evaluation index i:
Figure FDA00023568180000000310
step 8, from e (d)i) Determining an evaluation weight theta of an evaluation index iiComprises the following steps:
Figure FDA00023568180000000311
wherein the content of the first and second substances,
Figure FDA00023568180000000312
and thetaiSatisfies the following conditions: theta is not less than 0i≤1，
Figure FDA00023568180000000313
Introducing another weight Wi，WiThe subjective judgment ability of the decision maker is shown, and the subjective judgment ability are combined into a practical weight lambda according to a formula 10i：
Figure FDA00023568180000000314
Wherein λ isiTheta i is more than or equal to 0 and less than or equal to 1,
Figure FDA0002356818000000041
step 9, calculating the weight of each evaluation index by adopting a variation coefficient method, and determining the reliability of the entropy weight if the weights obtained by the two weight determination methods are consistent;
step 10, weighting lambda with entropyiAs weights, entropy evaluation was performed, and S was calculated according to equation 11kThe value:
Figure FDA0002356818000000042
5. the method of claim 2, wherein determining the composite weight of each scale energy storage evaluation index by a combined weight method based on subjective weighting and objective weighting using a least squares method specifically comprises:
step 1, let each index weight given by analytic hierarchy process be U ═ U1,u2,...,um]TThe weight of each index given by the entropy weight method is V ═ V1,v2,...,vm]TThe optimized combination weight of each index is W ═ W1,w2,...,wm]T；
Step 2, the normalized data matrix with n evaluation indexes and m candidate schemes is Z ═ Z (Zij)n×mThe evaluation value of the ith candidate scheme is
Figure FDA0002356818000000043
Step 3, establishing a least square method optimization combination evaluation model shown in formula 12;
Figure FDA0002356818000000044
wherein the content of the first and second substances,
Figure FDA0002356818000000045
step 4, solving a least square method optimization combination evaluation model to obtain a weight W:
making a Langcange function:
Figure FDA0002356818000000046
order to
Figure FDA0002356818000000047
Wherein j is 1,2, …, m,
Figure FDA0002356818000000048
then expressed as:
Figure FDA0002356818000000051
a matrix equation 14 is obtained:
Figure FDA0002356818000000052
e＝[1,1,...,1]T
W＝[w1,w2,...,wm]T
Figure FDA0002356818000000053
wherein A is an m multiplied by m diagonal matrix, and e, W and B are m multiplied by 1 vectors;
solving the matrix equation yields:
Figure FDA0002356818000000054
6. the method of claim 2, wherein calculating the comprehensive benefit of the large-scale energy storage in the business model by using a fuzzy comprehensive evaluation model based on the comprehensive weight specifically comprises:
step 1, determining a factor set U ═ U of an evaluation object1+U2+...+Un}；
Step 2, determining a comment set V ═ V of an evaluation object1+V2+...+Vm}；
Step 3, determining the weight vector A of the evaluation factor as (a)1,a2,...,an)；
Step 4, evaluating one by one from a single factor, determining a membership function of an evaluation object to an evaluation set, and constructing a fuzzy relation matrix R;
step 5, selecting a proper operator to synthesize the fuzzy relation matrix R and the comprehensive weight, and calculating to obtain a comprehensive judgment vector;
step 6, adopting a maximum membership method to comprehensively judge the maximum value max (b) in the vectorj) Corresponding comment VjA combined benefit in the business model as a final large-scale energy storage, wherein bjRepresenting the degree of membership to the jth comment when all factors are considered together.
7. A receiving end power grid large-scale energy storage business model decision making system is characterized in that,
the acquisition module is used for selecting a receiving-end power grid large-scale energy storage business mode, determining a receiving-end power grid large-scale energy storage application scene in the business mode, and selecting large-scale energy storage evaluation indexes related to the business mode and the application scene;
the calculation module is used for calculating the comprehensive benefit of the large-scale energy storage of the receiving-end power grid in the business mode according to the large-scale energy storage evaluation index;
and the determining module is used for repeatedly calling the obtaining module and the calculating module until the comprehensive benefits of all the business modes are obtained, and determining the optimal business mode.
8. The system of claim 7, wherein the computing module specifically comprises:
the subjective weighting module is used for determining subjective weighting of each large-scale energy storage evaluation index by adopting an analytic hierarchy process;
the objective weighting module is used for determining objective weighting of each large-scale energy storage evaluation index by adopting an entropy weight method;
the combination weight module is used for determining the comprehensive weight of each large-scale energy storage evaluation index by adopting a least square method and a combination weight method based on subjective weighting and objective weighting;
and the comprehensive benefit calculation module is used for calculating the comprehensive benefit of the large-scale energy storage in the business mode by adopting a fuzzy comprehensive evaluation model based on the comprehensive weight.
9. The system of claim 8, wherein the subjective weighting module is specifically configured to:
classifying the influence factors according to attributes, establishing a hierarchical model from top to bottom, and arranging according to the sequence of a target layer, a criterion layer and a scheme layer, wherein the target layer is the final purpose of analysis, the criterion layer is a plurality of factors influencing a target, and the scheme layer is various schemes and measures adopted;
comparing any two indexes A in the criterion layer by a 1-9 scale methodiAnd AjThe importance of the standard layer is assigned, and a judgment matrix of the standard layer is constructed;
calculating maximum eigenvalue lambda of judgment matrix of criterion layermaxThen according to equation 1, obtain λmaxAnd (3) carrying out normalization calculation on W according to the corresponding characteristic vector W to obtain a weight vector AW of the importance of the elements in the criterion layer to the target layer:
AW＝λmaxw formula 1;
and (3) carrying out consistency check on the judgment matrix of the criterion layer according to a formula 2:
CR ═ CI/RI formula 2;
wherein CI ═ λmax-n)/(n-1), CI being the deviation from consistency index; RI is an average random consistency index, and n is the number of evaluation indexes;
and determining the weights of all factors of each layer to the total target layer by layer according to the sequence from the high layer to the low layer until the weight of the element at the bottom layer to the element at the top layer is calculated, and carrying out consistency check.
10. The system of claim 8, wherein the objective weighting module is specifically configured to:
setting n evaluation indexes to decide and evaluate m candidate schemes, wherein the evaluation index i of the candidate scheme k has an estimated value xikIdeal value of evaluation index i
Figure FDA0002356818000000071
Calculating x according to equation 3ikFor the
Figure FDA0002356818000000072
Proximity D ofik：
Figure FDA0002356818000000073
D according to equation 4ikNormalization treatment:
Figure FDA0002356818000000074
wherein d is not less than 0ik≤1，
Figure FDA0002356818000000075
And (3) calculating the entropy E of the m candidate schemes evaluated by using n evaluation indexes according to a formula 5:
Figure FDA0002356818000000076
if the relative importance of the evaluation index is not related to the candidate scheme, the entropy E is calculated according to the formula 6:
Figure FDA0002356818000000077
wherein the content of the first and second substances,
Figure FDA0002356818000000078
determining conditional entropy E of evaluation index i according to formula 7i：
Figure FDA0002356818000000079
Wherein extreme according to entropy，
Figure FDA00023568180000000710
k is 1 to m, i.e., di1≈di2≈…dikThe closer to the same, the larger the conditional entropy, and the larger the uncertainty of the evaluation index to the evaluation decision of the scheme to be selected; when in use
Figure FDA0002356818000000081
Are equal, i.e. di1≈di2≈…dikWhen d is greater thani≈mdik，
Figure FDA0002356818000000082
While
Figure FDA0002356818000000083
Figure FDA0002356818000000084
Then the conditional entropy is maximum Emax＝lnm；
By EmaxTo pair
Figure FDA0002356818000000085
Carrying out normalization processing to obtain an entropy value representing the evaluation decision importance of the evaluation index i:
Figure FDA0002356818000000086
from e (d)i) Determining an evaluation weight theta of an evaluation index iiComprises the following steps:
Figure FDA0002356818000000087
wherein the content of the first and second substances,
Figure FDA0002356818000000088
and thetaiSatisfies the following conditions: theta is not less than 0i≤1，
Figure FDA0002356818000000089
Introducing another weight Wi，WiThe subjective judgment ability of the decision maker is shown, and the subjective judgment ability are combined into a practical weight lambda according to a formula 10i：
Figure FDA00023568180000000810
Wherein λ isiTheta i is more than or equal to 0 and less than or equal to 1,
Figure FDA00023568180000000811
calculating the weight of each evaluation index by adopting a coefficient of variation method, and determining the reliability of the entropy weight if the weights obtained by the two weight determination methods are consistent;
with entropy weight λiAs weights, entropy evaluation was performed, and S was calculated according to equation 11kThe value:
Figure FDA00023568180000000812
11. the system of claim 8, wherein the combining weights module is specifically configured to:
let each index weight given by analytic hierarchy process be U ═ U1,u2,...,um]TThe weight of each index given by the entropy weight method is V ═ V1,v2,...,vm]TThe optimized combination weight of each index is W ═ W1,w2,...,wm]T；
Setting a normalized data matrix with n evaluation indexes and m candidate schemes as Z ═ Z (Z)ij)n×mThe evaluation value of the ith candidate scheme is
Figure FDA0002356818000000091
Establishing a least square method optimized combination evaluation model shown in formula 12;
Figure FDA0002356818000000092
wherein the content of the first and second substances,
Figure FDA0002356818000000093
solving a least square method optimization combination evaluation model to obtain a weight W:
making a Langcange function:
Figure FDA0002356818000000094
order to
Figure FDA0002356818000000095
Wherein j is 1,2, …, m,
Figure FDA0002356818000000096
then expressed as:
Figure FDA0002356818000000097
a matrix equation 14 is obtained:
Figure FDA0002356818000000098
e＝[1,1,...,1]T
W＝[w1,w2,...,wm]T
Figure FDA0002356818000000099
wherein A is an m multiplied by m diagonal matrix, and e, W and B are m multiplied by 1 vectors;
solving the matrix equation to obtain：
Figure FDA00023568180000000910
12. The system of claim 8, wherein the composite benefit calculation module is specifically configured to:
determining a factor set U-U of an evaluation object1+U2+...+Un}；
Comment set V ═ { V) for specifying evaluation target1+V2+...+Vm}；
Determining a weight vector a ═ a of the evaluation factors1,a2,...,an)；
Evaluating one by one from a single factor, determining a membership function of an evaluation object to an evaluation set, and constructing a fuzzy relation matrix R;
selecting a proper operator to synthesize the fuzzy relation matrix R and the comprehensive weight, and calculating to obtain a comprehensive judgment vector;
adopting a maximum membership degree method to comprehensively judge the maximum value max (b) in the vectorj) Corresponding comment VjA combined benefit in the business model as a final large-scale energy storage, wherein bjRepresenting the degree of membership to the jth comment when all factors are considered together.
13. A receiving end power grid large-scale energy storage business mode decision-making device is characterized by comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program when executed by the processor implementing the steps of the receiving grid large-scale energy storage business model decision method according to any one of claims 1 to 6.
14. A computer-readable storage medium, wherein the computer-readable storage medium stores thereon an implementation program for information transfer, and the program, when executed by a processor, implements the steps of the receiving-end grid scale energy storage business model decision method according to any one of claims 1 to 6.
CN202010010068.3A 2020-01-06 2020-01-06 Receiving-end power grid large-scale energy storage business mode decision method and system Pending CN111222787A (en)

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CN112785060A (en) * 2021-01-25 2021-05-11 国网山西省电力公司经济技术研究院 Lean operation and maintenance level optimization method for power distribution network
CN114638550A (en) * 2022-05-12 2022-06-17 国网江西省电力有限公司电力科学研究院 Index screening method and system for energy storage power station configuration scheme
CN115146939A (en) * 2022-06-24 2022-10-04 国网江苏省电力有限公司经济技术研究院 Power grid engineering project comprehensive technical level pre-evaluation method
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112132424A (en) * 2020-09-07 2020-12-25 国网河北省电力有限公司经济技术研究院 Large-scale energy storage multi-attribute decision type selection method
CN112132424B (en) * 2020-09-07 2023-12-05 国网河北省电力有限公司经济技术研究院 Large-scale energy storage multi-attribute decision type selection method
CN112785060A (en) * 2021-01-25 2021-05-11 国网山西省电力公司经济技术研究院 Lean operation and maintenance level optimization method for power distribution network
CN114638550A (en) * 2022-05-12 2022-06-17 国网江西省电力有限公司电力科学研究院 Index screening method and system for energy storage power station configuration scheme
CN115146939A (en) * 2022-06-24 2022-10-04 国网江苏省电力有限公司经济技术研究院 Power grid engineering project comprehensive technical level pre-evaluation method
CN116485211A (en) * 2023-06-16 2023-07-25 中国石油大学(华东) Multi-criterion decision method for evaluating comprehensive performance of battery stack
CN116485211B (en) * 2023-06-16 2023-09-05 中国石油大学(华东) Multi-criterion decision method for evaluating comprehensive performance of battery stack

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