Power system energy transformation path determination method based on data driving

Abstract

The invention discloses a data-driven power system energy transformation path determination method, which comprises the following steps of: firstly, determining a dominant energy type through weighted average based on keyword frequency and Baidu search index of a WoS database, and constructing m energy transformation paths; then, energy transformation path evaluation indexes are obtained through a literature investigation method, and energy transformation path information is obtained through word frequency statistics of evaluation index keywords in the WoS core database and investigation of an EnergyPLAN model and an expert questionnaire; finally, the index word frequency is converted into pairwise preference comparison vectors, a BWM model based on data driving is constructed to solve the index weight, compared with a traditional BWM method, the subjectivity of decision making is reduced to a certain degree, an interval type-2 fuzzy set is introduced to quantify language terminology type evaluation information, then the interpretability of information quantification is enhanced, and the method has the advantages of being high in practicability and easy to popularize. An improved MULTIMOORA method is introduced to evaluate an energy transformation path, a Borda rule is utilized to replace an original dominant theory, and meanwhile, the influence of a sequence value and a utility value in an energy transformation path evaluation process is considered, so that the objectivity of an evaluation result is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system energy transformation, and more specifically, relates to a method for determining a power system energy transformation path based on data-driven. Background Art

[0002] The issue of global warming has attracted widespread attention from the international community. The 2015 Paris Agreement requires all countries to actively respond to climate change. However, in 2023, the United Nations conducted its first global inventory of the Paris Agreement, and the results showed that the trend of global warming is more serious than expected, and the overall situation is not optimistic. Greenhouse gas emissions from the combustion of fossil fuels are the main cause of global warming, of which about 40% come from the power sector. Decarbonization of the power system is a key measure to address climate change and ensure energy demand. Vigorously developing new energy technologies represented by wind and solar energy, building a new power system, selecting energy transformation paths, and promoting decarbonization in the power sector are the only way to achieve the goal of carbon neutrality.

[0003] The energy transformation path of the power system refers to a solution to achieve energy structure optimization and transformation by selecting a specific type of energy as the main energy source and supplementing it with other energy sources. Specifically, the energy transformation path of the power system is reflected in the proportion of installed capacity of different energy types, among which the installed capacity of the dominant energy accounts for the largest proportion, but it also includes a combination of other energy types to meet the overall needs and stability of the power system. The rapid development of clean energy technology has provided strong support for the construction of a variety of energy transformation paths, including photovoltaic energy transformation paths, natural gas energy transformation paths, and wind energy transformation paths. Overall, the selection of energy transformation paths for the power system mainly includes three major sections. The first is the establishment of clean energy types: the selection of energy types is crucial to the construction of energy transformation paths for the power system. How to scientifically and comprehensively select clean energy solutions suitable for the power system is challenging. At present, the research of Park et al. and Yang et al. mainly constructs local power system energy transformation paths based on local clean energy resource endowments, such as hydrogen energy, wind energy, and solar energy. Few studies consider the impact of public attention on the construction of energy transformation paths, and lack systematic analysis of the determination of clean energy types. The second is the acquisition of information on energy transformation paths: After the type of clean energy is determined, tools are needed to quantitatively analyze the technical, economic and environmental performance of different energy transformation paths. The current mainstream energy system analysis model software includes EnergyPLAN, LEAP, TIMES, Pypsa, etc. These software have been widely used in the transformation planning of energy systems. Among them, the EnergyPLAN model, compared with other model methods, can capture the details of system operation with a time resolution of hours, and consider the coupling relationship between different departments, and then simulate a high-proportion clean energy system, providing a scientific basis for the selection of energy transformation paths. Finally, the optimal energy transformation path is determined: the selection of the optimal path for energy transformation of the power system can be regarded as a multi-criteria evaluation problem. That is, based on the sustainable evaluation index system, the constructed energy transformation path is evaluated from multiple dimensions such as energy, economy, environment and society, providing decision support for the construction of the energy transformation path of the power system. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and propose a data-driven method for determining the energy transformation path of an electric power system, so as to reduce the subjectivity of path determination, enhance the interpretability of path information quantification, improve the objectivity of evaluation results, and select the current optimal energy transformation path for the electric power system.

[0005] To achieve the above-mentioned object of the invention, the present invention is based on a data-driven method for determining an energy transformation path of a power system, which is characterized by comprising the following steps:

[0006] (1) Establish clean energy types and build energy transformation paths

[0007] 1.1) WoS keyword collection: Use text mining to analyze high-impact journal papers included in the Web of Science (WoS) core database to obtain the keyword frequency of clean energy;

[0008] 1.2) Baidu Index Keyword Search: Use Baidu Index to obtain the search index of different clean energy;

[0009] 1.3) Energy transformation path construction: Based on the keyword frequency and search index, m clean energy types suitable for the power system energy transformation path are determined by weighted average, and they are used as the dominant energy types to construct energy transformation path a. i ,i=1,2,...,m, where energy transition path a i It is a combination of multiple energy sources, expressed as a i ={(e1,b i1 ),(e2,b i2 ),...,(e q ,b iq )},e k , k=1,2,...,q is the kth type of energy, q is the number of energy types, b ik For energy k In the energy transition path i Medium installed capacity;

[0010] For energy transition pathways i , if energy e k Energy transition pathway i The dominant energy type is energy e k Installed capacity b ik The highest proportion;

[0011] (2) Obtaining information on energy transition paths

[0012] 2.1) Constructing a sustainable evaluation index system: In constructing an energy transformation path i Afterwards, the EnergyPLAN model and expert questionnaire survey method were used to construct a sustainable evaluation index system covering four dimensions: "energy", "environment", "economy" and "society", among which the evaluation index was marked as c j ,j=1,2,…,n, the first g evaluation indicators are benefit indicators, the last ng evaluation indicators are cost indicators, and the energy transformation path a is obtained i In the evaluation index c j Evaluation information under p ij ;

[0013] 2.2) Statistical evaluation index keyword frequency statistics: By searching the evaluation index c in the WoS databasej Corresponding keywords, get keyword frequency N(c j );

[0014] (3) Determine the optimal energy transformation path

[0015] 3.1) Solving the weight of sustainable evaluation indicators: By converting the keyword frequency of the indicator into paired preference information, the best-worst-method (BWM) is used to solve the indicator weight. Let P BO ,P OW is the pairwise preference information vector of energy transition paths, where the jth element P BO (j), P OW (j) are:

[0016] P BO (j) = N(c B ) / N(c j )

[0017] P OW (j) = N(c j ) / N(c W )

[0018] Among them, N(c j ) represents the jth evaluation index c j The corresponding keyword frequency, c B is the highest evaluation index of keyword frequency, N(c B ) represents the evaluation index c B is the corresponding keyword frequency, c w is the evaluation index with the minimum keyword frequency, N(c w ) represents the evaluation index c w is the corresponding keyword frequency;

[0019] Let vector P BO and P OW The normalized vectors are Then its j-th elements are:

[0020]

[0021]

[0022] Where fix(·) is a function that rounds toward 0, arctan(·) is an inverse tangent function, and the parameter c,σ>0;

[0023] Based on the standardized pairwise preference information vector and Solve the following nonlinear model and obtain the evaluation index weight W = (w1, w2, ..., w n ) T :

[0024] minξ

[0025]

[0026]

[0027]

[0028] w j ≥0

[0029] 3.2) Quantitative language evaluation information: For social dimension evaluation index c j Language evaluation information p ij , the language term set {low (small, few), medium, high (large, many)} is used to represent the evaluation information, which is quantified with the help of interval type-2 fuzzy sets. The evaluation indicators under the other energy, environment, and economic dimensions are all numerical;

[0030] 3.3) Standardized evaluation information: Considering the dimensional differences of evaluation information under different evaluation indicators, the evaluation information P under the evaluation indicator is standardized. ij ) m×n ,i=1,2,…,m,j=1,2,...n for standardization:

[0031]

[0032] 3.4) Determine the utility values ​​of the three sub-methods of energy transformation path:

[0033] 3.4.1) Energy transformation path a under the calculation ratio method i The utility value

[0034]

[0035] 3.4.2) Calculate the energy transition path under the reference point method a i The utility value

[0036]

[0037] Where: r j is the jth evaluation index c j The following reference information:

[0038]

[0039] 3.4.3) Calculate the energy transition path a under full multiplication i The utility value of is:

[0040]

[0041] 3.5) Determine the comprehensive utility value of the energy transformation path:

[0042] First, the utility value and Standardize to get and Then, Borda's rule is introduced to integrate the utility value and ranking value of the energy transition path in the three sub-methods;

[0043]

[0044] in, Energy transition pathway i The comprehensive utility value of is the ranking value of m energy transformation paths according to utility value under the ratio method, is the ranking value of m energy transformation paths according to utility value under the reference point method, is the ranking value of m energy transformation paths according to utility value under full multiplication;

[0045] Based on comprehensive utility value Sort by and get energy transformation path a i The sort value of Among them, the first ranked is the power system energy transformation path with the best comprehensive benefits a opt .

[0046] The object of the invention is achieved in this way:

[0047] The present invention is based on a data-driven method for determining the energy transformation path of a power system. First, the dominant energy type is established through weighted average based on the keyword frequency of the WoS database and the Baidu search index, and m energy transformation paths are constructed; then, a sustainable evaluation index system is constructed, and the energy transformation path evaluation index is obtained by using the literature survey method. The evaluation index keyword frequency in the WoS core database is counted, and the energy transformation path information is obtained through the EnergyPLAN model and expert questionnaire survey; finally, the indicator frequency is converted into a pairwise preference comparison vector, and a data-driven BWM model is constructed to solve the indicator weight. Compared with the traditional BWM method, the subjectivity of decision-making is reduced to a certain extent. In the preprocessing of the energy transformation path information, interval type-2 fuzzy sets are introduced to quantify the linguistic terminology evaluation information, thereby enhancing the interpretability of information quantification. In the process of energy transformation path evaluation, an improved MULTIMOORA method is introduced to evaluate the energy transformation path, and the Borda rule is used to replace the original dominance theory. At the same time, the influence of ordinal value and utility value in the energy transformation path evaluation process is considered to improve the objectivity of the evaluation result.

[0048] The present invention has the following beneficial effects:

[0049] (1) From a data-driven perspective, the present invention extracts features of clean energy types from the Baidu Index and WoS database, providing strong support for the determination of clean energy types in the energy transformation path.

[0050] (2) The present invention constructs a sustainable evaluation index system from the dimensions of energy, environment, economy and society, and simulates the evaluation information of the energy transition path with the help of the EnergyPLAN model and expert questionnaire survey. Compared with other simulation models, the EnergyPLAN model can more accurately simulate the evaluation information of the energy transition path in the economic, environmental and technical dimensions. In addition, the present invention also expands the evaluation index system and adds indicators such as social acceptance and ecological impact.

[0051] (3) The present invention establishes an integrated multi-criteria decision-making model based on data-driven to select the optimal path for energy transformation of the power system. Interval type-2 fuzzy sets are introduced to quantify the fuzziness of language evaluation information in the power system. When solving the weights of sustainable evaluation indicators, the present invention collects indicator word frequencies through text analysis methods, and converts the indicator word frequencies into indicator pair preference vectors to reduce the subjectivity of the BWM method. Finally, the improved MULTIMOORA method is used to select the energy transformation path of the power system with the best comprehensive benefits. This integrated decision-making method solves the problem of sustainable evaluation in the power system to a certain extent, and provides strong support for the optimization of the energy transformation path of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flowchart of a specific implementation method of the method for determining the energy transformation path of the power system based on data-driven of the present invention;

[0053] Figure 2 It is a flow chart of examples of obtaining keywords of clean energy types;

[0054] Figure 3 This is the frequency distribution of keywords of clean energy types in the WoS database;

[0055] Figure 4 This is the distribution map of clean energy in Baidu search index;

[0056] Figure 5 It is the keyword frequency distribution diagram corresponding to the sustainable evaluation indicators;

[0057] Figure 6 is the benefit distribution of each energy transformation path under the energy dimension;

[0058] Figure 7 It is the comprehensive cost distribution of various energy transformation paths under the economic dimension;

[0059] Figure 8 It is the ranking distribution of energy paths when parameters α and β change. DETAILED DESCRIPTION

[0060] The specific implementation of the present invention is described below in conjunction with the accompanying drawings so that those skilled in the art can better understand the present invention. It should be noted that in the following description, when the detailed description of known functions and designs may dilute the main content of the present invention, these descriptions will be omitted here.

[0061] The overall idea of ​​the present invention is to construct a set of energy transformation paths A = {a1, a2, ..., a m}, in the evaluation index set C = {c1, c2, ..., c n}(The corresponding indicator weight vector is W=(w1,w2,…,w n ) T satisfy ), and the evaluation information of the indicators under the sequential numbering is P = (p ij ) m×n , where p ij is the i-th energy transition path a i In indicator c j Based on the evaluation information under the guidance of the data-driven integrated decision-making model, the present invention solves the optimal energy transformation path a opt .

[0062] Figure 1It is a flow chart of a specific implementation method of the data-driven method for determining the energy transformation path of the power system of the present invention.

[0063] In this embodiment, if Figure 1 As shown, the data-driven method for determining the energy transformation path of a power system of the present invention includes the following steps:

[0064] Step S1: Determine clean energy types and build energy transformation pathways

[0065] Step S1.1: WoS keyword collection: Use text mining to analyze high-impact journal articles included in the Web of Science (WoS) core database to obtain the keyword frequency of clean energy.

[0066] In this embodiment, if Figure 2 As shown in the figure, the search formula is input into the WoS database and the corresponding constraints are set for query and selection to obtain the keywords of clean energy types. Figure 3 shown.

[0067] Step S1.2: Baidu Index Keyword Search: Obtain the search index of different clean energy sources through Baidu Index. In this embodiment, the search index of different clean energy sources is as follows: Figure 4 shown.

[0068] Step S1.3: Energy transformation path construction: Based on the keyword frequency and search index, m clean energy types suitable for the power system energy transformation path are determined by weighted average, and they are used as the dominant energy types to construct energy transformation path a. i ,i=1,2,...,m, where energy transition path a i It is a combination of multiple energy sources, expressed as a i ={(e1,b i1 ),(e2,b i2 ),...,(e q ,b iq )},e k , k=1,2,...,q is the kth type of energy, q is the number of energy types, b ik For energy k In the energy transition path i Medium installed capacity. For energy transition path a i , if energy e k Energy transition pathway i The dominant energy type is energy e k Installed capacity b ik The highest proportion.

[0069] In this embodiment, weighted averaging (giving the same weight in this example) is performed to select m=4 clean energy types, such as photovoltaic, natural gas, wind power and geothermal biomass, as the dominant energy types for constructing the energy transformation path of the power system, and a1 is recorded as the photovoltaic energy transformation path, a2 is the natural gas energy transformation path, a3 is the wind energy transformation path, and a4 is the geothermal biomass energy transformation path. The comparison path is the coal power energy path, recorded as a5.

[0070] Step S2: Obtaining information on energy transition pathways

[0071] Step S2.1: Construct a sustainable evaluation indicator system: In constructing the energy transformation path i Afterwards, the EnergyPLAN model and expert questionnaire survey method were used to construct a sustainable evaluation index system covering four dimensions: "energy", "environment", "economy" and "society", among which the evaluation index was marked as c j ,j=1,2,...,n, the first g evaluation indicators are benefit indicators, the last ng evaluation indicators are cost indicators, and the energy transformation path a is obtained i In the evaluation index c j Evaluation information under p ij .

[0072] In this embodiment, 17 evaluation indicators are selected from the four dimensions of "energy", "environment", "economy" and "society". j , j = 1, 2, ..., 17, that is, n = 17. Among them, the income indicators are: c1: power supply, c2: primary energy supply, c3: clean energy power generation ratio, c4: clean energy consumption rate, c 15 : Public acceptance and c 16 : jobs; cost indicators are: c5: clean energy curtailment rate, c6: CO2 emissions, c7: carbon intensity, c8: total cost of the power system, c9: technology investment cost, c 10 : Energy cost, c 11 : Operation and maintenance cost, c 12 : Emission cost, c 13 : Cost per kWh, c 14 :CO2 emission reduction costs and c 17 : Ecological impact.

[0073] In this embodiment, for different energy transformation paths, the EnergyPLAN model is used to obtain the energy transformation paths in the "energy" dimension (c1, c2, c3, c4, c5), the "environment" dimension (c6, c7) and the "economy" dimension (c8, c9, c10, c11, c12, c13, c14, c15). 10 ,c 11 ,c 12 ,c 13,c 14 ) and the “social” dimension (c 15 ,c 16 ,c 17 )’s language evaluation information, and integrates it to get the evaluation information matrix P = (p ij ) m×n (i=1,2,…,5, j=1,2,…,17).

[0074] Step S2.2: Statistical evaluation index keyword frequency statistics: by searching the evaluation index c in the WoS database j Corresponding keywords, get keyword frequency N(c j The frequency distribution of keywords corresponding to sustainable evaluation indicators is as follows: Figure 5 As shown in the figure, an evaluation index corresponds to one or more keywords, and its keyword frequency N(c j ) is the sum of the word frequencies of all keywords.

[0075] Step S3: Determine the optimal energy transition path

[0076] Step S3.1: Solving the weight of sustainable evaluation indicators: By converting the keyword frequency of the indicator into paired preference information, the best-worst-method (BWM) is used to solve the indicator weight. BO ,P OW is the pairwise preference information vector of energy transition paths, where the jth element P BO (j), P OW (j) are:

[0077] P BO (j) = N(c B ) / N(c j )

[0078] P OW (j) = N(c j ) / N(c W )

[0079] Among them, N(c j ) represents the jth evaluation index c j The corresponding keyword frequency, c B is the highest evaluation index of keyword frequency, N(c B ) represents the evaluation index c B is the corresponding keyword frequency, c w is the evaluation index with the minimum keyword frequency, N(c w ) represents the evaluation index c w is the corresponding keyword frequency;

[0080] Let vector P BO and P OW The normalized vectors are Then its j-th elements are:

[0081]

[0082]

[0083] Where fix(·) is a function that rounds toward 0, arctan(·) is an inverse tangent function, and the parameter c,σ>0;

[0084] Based on the standardized pairwise preference information vector and Solve the following nonlinear model and obtain the evaluation index weight W = (w1, w2, ..., w n ) T :

[0085] minξ

[0086]

[0087]

[0088]

[0089] w j ≥0

[0090] Step S3.2: Quantifying language evaluation information: For social dimension evaluation index c j Language evaluation information p ij , the language term set {low (small, few), medium, high (large, many)} is used to represent the evaluation information, which is quantified with the help of interval type-2 fuzzy sets. The evaluation indicators under the other energy, environment, and economic dimensions are all numerical.

[0091] Step S3.3: Standardizing the evaluation information: Considering the dimensional differences of the evaluation information under different evaluation indicators, the evaluation information P under the evaluation indicator is standardized. ij ) m×n ,i=1,2,…,m,j=1,2,...n for standardization:

[0092]

[0093] Step S3.4: Determine the utility values ​​under the three sub-methods of the energy transition pathway:

[0094] Step S3.4.1: Calculate the energy transition pathway a under the ratio method i The utility value

[0095]

[0096] Step S3.4.2: Calculate the energy transition pathway a under the reference point method i The utility value

[0097]

[0098] Where: r j is the jth evaluation index c j The following reference information:

[0099]

[0100] Step S3.4.3: Calculate the energy transition path a under full multiplication i The utility value of is:

[0101]

[0102] Step S3.5: Determine the comprehensive utility value of the energy transition path:

[0103] 3.5) Determine the comprehensive utility value of the energy transformation path:

[0104] First, the utility value and Standardize to get and Then, Borda's rule is introduced to integrate the utility value and ranking value of the energy transition path in the three sub-methods;

[0105]

[0106] in, Energy transition pathway i The comprehensive utility value of is the ranking value of m energy transformation paths according to utility value under the ratio method, is the ranking value of m energy transformation paths according to utility value under the reference point method, is the ranking value of m energy transformation paths according to utility value under full multiplication.

[0107] Based on comprehensive utility value Sort by and get energy transformation path a i The sort value of Among them, the first ranked is the power system energy transformation path with the best comprehensive benefits a opt .

[0108] Experimental verification

[0109] In order to illustrate the technical effect of the present invention, a specific example is used to experimentally verify the present invention. i (i=1,2,…,5). Each energy transformation path consists of 10 energy sources such as river hydropower, wind power, photovoltaic power, etc. k (k=1,2,…,10), the difference in transformation paths is reflected in e k The installed capacity of different transition paths a1 is the largest, so it is called the photovoltaic energy transition path. i Energy under e k Installed capacity b ik The specific values ​​are shown in Table 1.

[0110]

[0111]

[0112] Table 1

[0113] In this experimental verification, the parameters set in Table 1 were input into the EnergyPLAN model for simulation to obtain the numerical evaluation information of the energy transformation path in the "energy" dimension, the "environment" dimension and the "economy" dimension, and the language evaluation information of the "social" dimension was obtained by using the expert questionnaire method. Then, the weights of sustainable evaluation indicators were obtained through the statistics of keyword frequency in the WoS database.

[0114] 1. Data preprocessing

[0115] First, the language information obtained through the expert questionnaire survey is quantified by introducing interval type-2 fuzzy sets. Then, the evaluation information is standardized. The results after data preprocessing are shown in Table 2.

[0116]

[0117]

[0118] Table 2

[0119] 2. Quantitative results

[0120] right Figure 5 The keyword frequency corresponding to the sustainable evaluation index in the index set is used to obtain the pairwise preference information P of the index set. BO and P OW Among them, the most concerned indicator c B Energy cost is the least concerned indicator. W is the "carbon intensity". BO (j),P OW(j)∈[1,32]. After normalization, we get and Through multiple experiments, it is found that when the parameters c = 5 and σ = 4, the preference information can be gathered in the range of 1 to 8. The processed pairwise comparison vector information is as follows:

[0121]

[0122] Based on the standardized pairwise preference information, this embodiment solves the nonlinear model to obtain the indicator weights. The corresponding indicator weights are w1 = 0.08, w2 = 0.03, w3 = 0.08, w4 = 0.04, w5 = 0.03, w6 = 0.04, w7 = 0.01, w8 = 0.14, w9 = 0.08, w 10 =0.14,w 11 =0.04,w 12 =0.05,w 13 =0.04,w 14 =0.02,w 15 =0.06,w 16 =0.04 and w 17 =0.08. Therefore, the priority of indicators is: energy cost Total cost of power system Power supply ~ Clean energy generation share ~ Technology investment cost ~ Ecological impact Public acceptance Emission costs Clean energy consumption rate ~ CO2 emissions ~ operation and maintenance costs ~ electricity cost ~ jobs Primary energy supply ~ clean energy abandonment rate CO2 emission reduction costs Carbon intensity. Figure 6 The distribution of energy evaluation values ​​of four clean energy transition paths and coal-fired power energy paths under the consideration of sustainable evaluation index weights is described. Among them, BAU on the horizontal axis represents the reference path, PVD represents photovoltaic energy transition path a1, NGD represents natural gas energy transition path a2, WID represents wind energy transition path a3, GBD represents geothermal biomass energy transition path a4, and TER represents coal-fired power energy path a5. Figure 7 The weighted cost distribution of each energy path under the economic dimension is described.

[0123] After obtaining the weights of the sustainable evaluation indicators, combined with the preprocessed evaluation information, this embodiment uses the improved MULTIMOORA method to solve the utility values ​​and ranking values ​​of the three sub-methods in turn. Table 3 shows the utility values ​​of each energy transformation path under the ratio method. and sort value Among them, photovoltaic energy transformation path a1 ranks highest, and coal-fired power energy path a5 ranks lowest.

[0124]

[0125] Table 3

[0126] Table 4 shows the utility values ​​of various energy transition paths under the reference point method. and sort value Among them, photovoltaic energy transformation path a1 still ranks the highest, while natural gas energy transformation path a2 ranks the lowest.

[0127]

[0128]

[0129] Table 4

[0130] Table 5 shows the utility values ​​of various energy transformation paths under full multiplication and sort value Among them, natural gas energy transformation path a2 ranks highest, and geothermal biomass energy transformation path a4 ranks lowest.

[0131]

[0132] Table 5

[0133] Finally, this example introduces the Borda rule and considers the utility values ​​and ranking results of the energy transformation paths under the ratio method, reference point method and full multiplication method. Table 6 shows the final utility values ​​of the energy transformation paths. and sort value It can be concluded that photovoltaic energy transformation path a1 ranks the highest, and the final utility value The utility value of geothermal biomass energy transformation path a4 and coal-fired power energy path a5 is lower. Natural gas energy transition path a2 Wind energy transition path a3 Coal-fired power energy path a5 Geothermal biomass energy transition path a4.

[0134]

[0135] Table 6

[0136] 3. Sensitivity analysis

[0137] In order to verify the robustness and effectiveness of the integrated multi-criteria decision-making model proposed in the present invention, this embodiment introduces the parameters 0<α, β<1 to the weights of three sub-methods, namely the randomized ratio method, the reference point method and the full multiplication method, to analyze the utility value and ranking of the energy transformation path, as shown in the following formula:

[0138]

[0139] This embodiment randomly simulates 1000 sets of α and β values ​​for analysis. Table 7 shows the probability distribution of energy path ranking under 1000 sets of parameters. Figure 8 The ranking distribution of energy paths when parameters α and β change. The chart results verify the robustness and effectiveness of the present invention.

[0140] First second third fourth fifth <![CDATA[a1]]> 0.549 0.438 0.000 0.000 0.013 <![CDATA[a2]]> 0.396 0.283 0.113 0.147 0.061 <![CDATA[a3]]> 0.051 0.026 0.544 0.089 0.290 <![CDATA[a4]]> 0.004 0.099 0.343 0.203 0.351 <![CDATA[a5]]> 0.000 0.154 0.000 0.561 0.285

[0141] Table 7

[0142] Although the above describes the illustrative specific embodiments of the present invention to facilitate those skilled in the art to understand the present invention, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations using the concept of the present invention are protected.

Claims

1. A method for determining an energy transformation path of a power system based on data-driven, characterized by comprising the following steps: (1) Establish clean energy types and build energy transformation paths 1.1) WoS keyword collection: Use text mining to analyze high-impact journal papers included in the Web of Science (WoS) core database to obtain the keyword frequency of clean energy; 1.2) Baidu Index Keyword Search: Use Baidu Index to obtain the search index of different clean energy; 1.3) Energy transformation path construction: Based on the keyword frequency and search index, m clean energy types suitable for the power system energy transformation path are determined by weighted average, and they are used as the dominant energy types to construct energy transformation path a. i ,i=1,2,...,m, where, Energy transition pathwaya i It is a combination of multiple energy sources, expressed as a i ={(e1,b i1 ),(e2,b i2 ),…,(e q ,b iq )},e k , k=1,2,...,q is the kth type of energy, q is the number of energy types, b ik For energy k In the energy transition path i Medium installed capacity; For energy transition pathways i , if energy e k Energy transition pathway i The dominant energy type is energy e k Installed capacity b ik The highest proportion; (2) Obtaining information on energy transition paths 2.1) Constructing a sustainable evaluation index system: In constructing an energy transformation path i Afterwards, the EnergyPLAN model and expert questionnaire survey method were used to construct a sustainable evaluation index system covering four dimensions: "energy", "environment", "economy" and "society", among which the evaluation index was marked as c j ,j=1,2,…,n, the first g evaluation indicators are benefit indicators, the last ng evaluation indicators are cost indicators, and the energy transformation path a is obtained i In the evaluation index c j Evaluation information under p ij ; 2.2) Statistical evaluation index keyword frequency statistics: By searching the evaluation index c in the WoS database j Corresponding keywords, get keyword frequency N(c j ); (3) Determine the optimal energy transformation path 3.1) Solving the weight of sustainable evaluation indicators: By converting the keyword frequency of the indicator into paired preference information, the best-worst-method (BWM) is used to solve the indicator weight. Let P BO ,P OW is the pairwise preference information vector of energy transition paths, where The jth element P BO (j), P OW (j) are: P BO (j)=N(c B / N(c j ) P OW (j)=N(c j ) / N(c W ) Among them, N(c j ) represents the jth evaluation index c j The corresponding keyword frequency, c B is the highest evaluation index of keyword frequency, N(c B ) represents the evaluation index c B is the corresponding keyword frequency, c w is the evaluation index with the minimum keyword frequency, N(c w ) represents the evaluation index c w is the corresponding keyword frequency; Let vector P BO and P OW The normalized vectors are Then its j-th elements are: Where fix(·) is a function that rounds toward 0, arctan(·) is an inverse tangent function, and the parameter c,σ>0; Based on the standardized pairwise preference information vector and Solve the following nonlinear model and obtain the evaluation index weight W = (w1, w2, ..., w n ) T : minξ w j ≥0 3.2) Quantitative language evaluation information: For social dimension evaluation index c j Language evaluation information p ij , the language term set {low (small, few), medium, high (large, many)} is used to represent the evaluation information, which is quantified with the help of interval type-2 fuzzy sets. The evaluation indicators under the other energy, environment, and economic dimensions are all numerical; 3.3) Standardized evaluation information: Considering the dimensional differences of evaluation information under different evaluation indicators, the evaluation information P under the evaluation indicator is standardized. ij ) m×n ,i=1,2,...,m,j=1,2,...n for standardization: 3.4) Determine the utility values ​​of the three sub-methods of energy transformation path: 3.4.1) Energy transformation path a under the calculation ratio method i The utility value 3.4.2) Calculate the energy transition path under the reference point method a i The utility value in: r j is the jth evaluation index c j The following reference information: 3.4.3) Calculate the energy transition path a under full multiplication i The utility value of is: 3.5) Determine the comprehensive utility value of the energy transformation path: First, the utility value and Standardize to get and Then, Borda's rule is introduced to integrate the utility value and ranking value of the energy transition path in the three sub-methods; in, Energy transition pathway i The comprehensive utility value of is the ranking value of m energy transformation paths according to utility value under the ratio method, is the ranking value of m energy transformation paths according to utility value under the reference point method, is the ranking value of m energy transformation paths according to utility value under full multiplication; Based on comprehensive utility value Sort by and get energy transformation path a i The sort value of Among them, the first ranked is the power system energy transformation path with the best comprehensive benefits. opt .

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