Optical storage DC flexible power distribution system energy efficiency evaluation method based on G1-anti-entropy weight method

Abstract

The invention discloses a G1-anti-entropy weight method-based energy efficiency evaluation method for an optical storage direct-flexible power distribution system. The method comprises the following steps of S1, calculating each loss index; s2, determining a subjective weight by using a G1 group method; s3, determining an objective weight by using an anti-entropy weight method; and S4, integrating the weights and calculating an energy efficiency score. The method is a comprehensive evaluation method fusing a G1 group method and an anti-entropy weight method. A G1 group method and an anti-entropy weight method are combined, a combined weight is generated through linear weighting, and expert knowledge and data characteristics are balanced. A weighted summation model is used for outputting an energy efficiency total score, static loss characteristics of the system are reflected, dynamic changes of indexes are captured, and a scientific basis is provided for energy efficiency optimization of the optical storage direct-flexible system. According to the method, the blank of lack of an energy efficiency evaluation system in the prior art is filled, and the comprehensiveness and engineering applicability of an evaluation result are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution systems, and in particular to an energy efficiency evaluation method for a photovoltaic-storage-direct-flexible distribution system based on the G1-entropy weight method. Background Art

[0002] The existing photovoltaic-storage-direct-flexible distribution system is a key technology for a new power system to support the "dual carbon" goal. It takes photovoltaic power generation, energy storage buffering, DC distribution, and flexible regulation as the core, reduces the multi-stage AC / DC conversion losses (such as inverters, rectifiers) in traditional AC distribution, adapts to the high proportion of new energy access and the growth demand of DC loads, and improves energy efficiency and grid flexibility. However, the equipment coupling of this system is complex (photovoltaic, energy storage, DCDC converters, etc.), and the operation scenarios are dynamically variable (such as energy storage charge and discharge strategies, flexible load response). The energy efficiency evaluation methods of traditional AC distribution are difficult to apply due to problems such as differences in DC line loss models and difficulties in quantifying the multi-source collaborative efficiency, resulting in a lack of scientific basis for system design and optimization. For example, the use of a photovoltaic-storage-direct-flexible system in industrial parks can reduce the conversion loss by 10%-15%, but due to the lack of evaluation criteria such as unified loss rate thresholds and dynamic weight allocation methods, the actual energy efficiency improvement effect is difficult to accurately evaluate. Therefore, establishing an energy efficiency evaluation system suitable for its technical characteristics has become the key to promoting large-scale application in this field.

[0003] Therefore, there is still a need to improve the existing technology. Summary of the Invention

[0004] The purpose of the present invention is to provide an energy efficiency evaluation method for a photovoltaic-storage-direct-flexible distribution system based on the G1-entropy weight method, aiming to solve the technical problems that the energy efficiency evaluation methods of existing AC distribution systems are difficult to apply due to differences in DC line loss models and difficulties in quantifying the multi-source collaborative efficiency, resulting in a lack of scientific basis for system design and optimization.

[0005] To achieve the above purpose, the technical solution of the present invention is: an energy efficiency evaluation method for a photovoltaic-storage-direct-flexible distribution system based on the G1-entropy weight method, which includes the following steps:

[0006] S1: Calculate each loss index;

[0007] S2: Use the G1 group method to determine the subjective weight;

[0008] S3: Use the entropy weight method to determine the objective weight;

[0009] S4: Combine the weights and calculate the energy efficiency score.

[0010] The described energy efficiency evaluation method for a photovoltaic energy storage DC-AC flexible power distribution system based on the G1-entropy weight method. Among them, in step S1, in the energy efficiency evaluation of the photovoltaic energy storage DC-AC flexible power distribution system, the losses of the photovoltaic energy storage DC-AC flexible power distribution system mainly come from the energy losses in the power transmission and conversion links; specifically, they are divided into two categories: line transmission losses and power conversion equipment losses. On the one hand, the line losses caused by line resistance will significantly affect the system efficiency with the current fluctuation. On the other hand, power electronic devices such as transformers and DCDC converters will generate fixed losses and variable losses during the voltage level conversion process. To establish a scientific and reasonable energy efficiency evaluation system, it is necessary to first quantitatively calculate the key loss indicators, including the line loss rate based on power flow analysis, the transformer loss rate meeting the national standard requirements, and the DCDC converter loss rate reflecting the performance of power electronic devices.

[0011] The described energy efficiency evaluation method for a photovoltaic energy storage DC-AC flexible power distribution system based on the G1-entropy weight method. Among them, the specific calculation of each loss indicator in step S1 includes the following steps:

[0012] S11: Calculation of line transmission loss indicators;

[0013] The line loss rate of the low-voltage distribution network is an important indicator to measure the energy efficiency of the system; according to the power flow calculation results, the active power loss values of each line can be obtained, and the formula for calculating the line loss rate is as follows:

[0014]

[0015] When the line loss rate α > 10%, it is determined that the system is unqualified, and at this time S1 = 0; if α ≤ 10%, the formula for calculating the line loss index is:

[0016]

[0017] S12: Calculation of power conversion equipment loss indicators;

[0018] Transformer: According to "Power Transformers - Part 1: General" (GB 1094.1-2013), the total loss rate β of the distribution transformer should be less than 10%; so the scoring S2 of the transformer loss index is shown in the following formula:

[0019]

[0020] The efficiency of the DCDC converter in the low-voltage DC distribution network is crucial, directly affecting the overall energy efficiency and operating cost of the system. The target efficiency should not be less than 95%, that is, the loss rate γ should be less than 5%; so the scoring S3 of the DCDC converter loss index is shown in the following formula:

[0021]

[0022] The described energy efficiency evaluation method for a photovoltaic-storage-direct-current flexible power distribution system based on the G1-entropy weight method. Among them, in step S2, the G1 group method is a subjective weighting method based on expert experience. Its core is to derive the weights of each index by establishing a strict order relationship chain of evaluation indexes and the importance ratio between adjacent indexes. The G1 group method realizes the calculation of subjective weights through the following three stages, specifically as follows:

[0023] S21: Determine the order relationship between attributes;

[0024] Suppose there are n attributes, namely S = {S1, S2,..., S n}. The decision maker sorts the attributes from high to low according to his own judgment according to the degree of importance, and obtains the attribute order relationship

[0025]

[0026] where the symbol > means "more important than". After sorting, is the most important index, is the least important index;

[0027] S22: Quantify the importance ratio of adjacent indexes;

[0028] For adjacent indexes and define the relative importance ratio r j :

[0029]

[0030] In the formula, is the subjective weight of index ; r j takes values following the semantic scaling rules in Table 1:

[0031]

[0032] S23: Calculate the weights of each attribute;

[0033] Calculate the subjective weights of each index through the recurrence formula:

[0034] Subjective weight of the least important index:

[0035]

[0036] Recursively calculate the subjective weights of adjacent indexes in reverse order:

[0037]

[0038] Normalization verification:

[0039] The described energy efficiency evaluation method for a photovoltaic-storage-direct-soft power distribution system based on the G1-entropy weight method. Among them, the entropy weight method in step S3 is an improved objective weighting method based on the information entropy theory, mainly used for multi-index decision-making analysis; the entropy weight method reversely utilizes the information entropy principle to positively transform the degree of dispersion of index data into weight values: the greater the difference in index data, that is, the smaller the entropy value, the higher its anti-entropy weight; this method innovatively establishes a positive correlation between the degree of index variation and weight allocation, and is particularly suitable for evaluation scenarios that need to focus on reflecting the volatility characteristics of indicators. Its calculation process is realized through steps such as standardizing the data matrix, calculating the anti-entropy value, and normalization processing, showing unique advantages in fields such as financial risk assessment and environmental monitoring, and can effectively capture the dynamic change characteristics of the index system, providing more sensitive quantitative support for complex decision-making problems.

[0040] The described energy efficiency evaluation method for a photovoltaic-storage-direct-soft power distribution system based on the G1-entropy weight method. Among them, the objective weight calculation steps based on the entropy weight method in step S3 are as follows:

[0041] S31: Construct an evaluation index matrix;

[0042] Suppose there are m evaluation objects and n evaluation indicators, and the original data forms an m×n-order matrix X:

[0043]

[0044] S32: Normalization processing;

[0045] To eliminate the dimension difference, the range method is used to normalize the indicators; for benefit-type indicators, that is, the larger the value, the better, the formula is:

[0046]

[0047] In the formula, x ij is the jth evaluation indicator of the ith evaluation object, and x i ′ j is the normalized evaluation indicator, and x j is the jth type of evaluation indicator, that is, the jth column of the original evaluation matrix.

[0048] After normalization, the index evaluation matrix X' is obtained, and the normalized element x′ ij ∈[0,1].

[0049] S33: Calculate the anti-entropy;

[0050] For the normalized data, define the proportion p ij of the ith evaluation object under the jth index as:

[0051]

[0052] Anti - entropy h j The calculation formula is as follows:

[0053]

[0054] The larger the anti - entropy value, the higher the degree of dispersion of this index, and a greater weight should be assigned in the evaluation;

[0055] S34: Determine the objective weight;

[0056] The objective weight of the j - th index Is obtained by normalizing the anti - entropy value:

[0057]

[0058] Among them, the denominator is the sum of the anti - entropy values of all indexes to ensure

[0059] For the energy efficiency evaluation method of the photovoltaic - energy storage - DC - AC flexible distribution system based on the G1 - anti - entropy weight method, where the specific operation of the step S4 is as follows: The linear weighted method is used to fuse the subjective and objective weights, and the formula is as follows:

[0060]

[0061] In the formula: ω j Is the weight of the j - th index, Is the subjective weight determined by the G1 group method; Is the objective weight determined by the anti - entropy weight method; λ ∈ [0, 1] is the preference coefficient of the subjective and objective weights; Based on the combined weight and the scores of each index, the system energy efficiency evaluation value S is calculated by weighted summation:

[0062]

[0063] In the formula: S is the total score of the energy efficiency evaluation; S j Is the score of the j - th index; ω j Is the weight of the j - th index. Beneficial effects: The present invention is a comprehensive evaluation method that combines the G1 group method and the anti - entropy weight method. By innovatively combining the G1 group method and the anti - entropy weight method and combining the technical characteristics of the photovoltaic - energy storage - DC - AC flexible distribution system, a multi - dimensional and dynamic energy efficiency evaluation system is constructed. The specific analysis of the technical progress and implementation effects of each step is as follows:

[0064] I. Loss Index Calculation: Breaking through the limitation of traditional AC power distribution systems that only focus on total losses, a sub-item calculation model for line loss rate (α), transformer loss rate (β), and DCDC converter loss rate (γ) based on power flow analysis is established. By introducing national standard constraints and the efficiency threshold of power electronic devices (such as DCDC converter ≥ 95%), a scientific grading and scoring rule is established to achieve the quantification and standardization of loss indicators. Aiming at the operation characteristics of the optical storage direct-soft system with multi-source coupling and flexible regulation, the active power loss of the line is obtained in real time through power flow calculation, solving the defect that the traditional static model cannot reflect the influence of current fluctuations on line loss, and providing data support for dynamic energy efficiency evaluation.

[0065] II. G1-Anti-Entropy Weight Fusion Method: The G1 group method establishes a strict order relationship of indicators through expert experience, avoiding the complexity of judgment matrix consistency test in the traditional Analytic Hierarchy Process (AHP), and simplifying the weight calculation process. The present invention uses a specific semantic scale to quantify the importance difference between adjacent indicators, significantly improving the objectivity and interpretability of weight assignment compared with the fuzzy evaluation method. Compared with the traditional entropy weight method, the anti-entropy weight method is more sensitive to indicator fluctuations: when the data difference of an indicator increases (such as the DCDC converter loss rate fluctuates due to load mutation), its weight automatically increases, effectively capturing dynamic characteristics.

[0066] III. Synergistic Optimization of Combined Weight and Comprehensive Evaluation: Combining the G1 group method and the anti-entropy weight method, breaking through the limitation of single weight assignment, and dynamically balancing expert experience (λ adjustable) and data characteristics through a linear weighted model to achieve the scenario adaptability of the evaluation system. For example, when the system is in the design stage (lack of data), set λ = 0.7 to emphasize expert knowledge; when in the operation stage (abundant data), set λ = 0.3 to strengthen objective analysis.

[0067] This method fills the gap in the existing energy efficiency evaluation system and significantly improves the comprehensiveness and engineering applicability of the evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 is the flow chart of the present invention.

[0069] Figure 2 is the flow chart of the G1 group method in the present invention.

[0070] Figure 3 is the flow chart of the anti-entropy weight method in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0071] To make the objectives, technical solutions and advantages of the present invention clearer and more definite, the following further describes the present invention in detail with reference to the accompanying drawings and by way of examples.

[0072] As Figure 1As shown in the figure, the present invention discloses an energy efficiency evaluation method for a photovoltaic-storage-direct-current-soft distribution system based on the G1-entropy weight method, which includes the following steps:

[0073] S1: Calculate each loss index;

[0074] S2: Use the G1 group method to determine the subjective weight;

[0075] S3: Use the anti-entropy weight method to determine the objective weight;

[0076] S4: Combine the weights and calculate the energy efficiency score.

[0077] The present invention solves the technical problem of the lack of a scientific and comprehensive evaluation system in the energy efficiency evaluation of a photovoltaic-storage-direct-current-soft distribution system. By integrating the G1 group method and the anti-entropy weight method, it solves the problem that the existing energy efficiency evaluation methods for photovoltaic-storage-direct-current-soft distribution systems mostly rely on single subjective weighting (such as expert experience) or objective weighting (such as data statistics), which are prone to subjective deviation or neglecting the volatility of key indicators. Using specific semantic scales to quantify the importance differences between adjacent indicators, compared with the fuzzy evaluation method, it significantly improves the objectivity and interpretability of weight allocation. Compared with the traditional entropy weight method, the anti-entropy weight method is more sensitive to indicator fluctuations: when the data difference of a certain indicator increases (such as the loss rate of the DCDC converter fluctuates due to sudden changes in the load), its weight automatically increases, effectively capturing dynamic characteristics. It solves the defect that the traditional static model cannot reflect the impact of current fluctuations on line loss, and provides data support for dynamic energy efficiency evaluation. It fills the gap in the existing energy efficiency evaluation system and significantly improves the comprehensiveness and engineering applicability of the evaluation results.

[0078] Specifically, in the step S1, in the energy efficiency evaluation of a photovoltaic-storage-direct-current-soft distribution system, the losses of the photovoltaic-storage-direct-current-soft distribution system mainly come from the energy losses in the power transmission and conversion links; specifically, they are divided into two categories: line transmission losses and power conversion equipment losses: on the one hand, the line losses caused by line resistance will significantly affect the system efficiency with the current fluctuations; on the other hand, power electronic devices such as transformers and DCDC converters will generate fixed losses and variable losses during the voltage level conversion process: to establish a scientific and reasonable energy efficiency evaluation system, it is necessary to first quantitatively calculate the key loss indicators, including the line loss rate based on power flow analysis, the transformer loss rate that meets the national standard requirements, and the DCDC converter loss rate that reflects the performance of power electronic devices.

[0079] Specifically, the calculation of each loss index in the step S1 specifically includes the following steps:

[0080] S11: Calculate the line transmission loss index;

[0081] The line loss rate of the low-voltage distribution network is an important indicator to measure the system energy efficiency; according to the power flow calculation results, the active power loss values of each line can be obtained, and the formula for calculating the line loss rate is as follows:

[0082]

[0083] When the line loss rate α > 10%, it is determined that the system is unqualified, and at this time S1 = 0; if α ≤ 10%, the calculation formula for the line loss index is as follows:

[0084]

[0085] S12: Calculation of the loss index of the power conversion equipment;

[0086] Transformer: According to "Power Transformers - Part 1: General" (GB 1094.1 - 2013), the total loss rate β of the distribution transformer should be less than 10%; therefore, the scoring S2 of the transformer loss index is shown in the following formula:

[0087]

[0088] The efficiency of the DCDC converter in the low - voltage DC distribution network is crucial, directly affecting the overall energy efficiency and operating cost of the system. The target efficiency should be not less than 95%, that is, the loss rate γ should be less than 5%; therefore, the scoring S3 of the DCDC converter loss index is shown in the following formula:

[0089]

[0090] As Figure 2 shown, the G1 group method in step S2 is a subjective weighting method based on expert experience. Its core is to derive the weights of each index by establishing a strict order relationship chain of evaluation indexes and the importance ratio between adjacent indexes; the G1 group method realizes the calculation of subjective weights through the following three stages, specifically as follows:

[0091] S21: Determine the order relationship between attributes;

[0092] S22: Quantify the importance ratio of adjacent indexes;

[0093] S23: Calculate the weights of each attribute.

[0094] The specific steps in step S2 are as follows: (1) Determine the order relationship between attributes

[0095] Suppose there are n attributes, which are respectively S = {S1, S2,..., S n}. The decision - maker sorts the attributes from high to low according to their own judgment according to the importance degree, and obtains the attribute order relationship

[0096]

[0097] where the symbol > means "more important than". After sorting, is the most important index, Is the least important indicator.

[0098] (2) Quantify the importance ratio of adjacent indicators

[0099] For adjacent indicators And Define the relative importance ratio r j :

[0100]

[0101] Wherein, Is the subjective weight of indicator ; r j The value follows the semantic scaling rule in Table 1:

[0102]

[0103] (3) Calculate the weights of each attribute

[0104] Calculate the subjective weights of each indicator through a recurrence formula:

[0105] Subjective weight of the least important indicator:

[0106]

[0107] Recursively calculate the subjective weights of adjacent indicators in reverse order:

[0108]

[0109] Normalization verification:

[0110] The anti-entropy weight method in step S3 is an improved objective weighting method based on the information entropy theory, mainly used for multi-index decision-making analysis; the anti-entropy weight method reversely uses the information entropy principle to positively transform the dispersion degree of index data into weight values: the greater the difference in index data, that is, the smaller the entropy value, the higher the anti-entropy weight; this method innovatively establishes a positive correlation between the index variation degree and weight distribution, and is particularly suitable for evaluation scenarios that need to focus on reflecting the volatility characteristics of indicators. Its calculation process is realized through steps such as standardizing the data matrix, calculating the anti-entropy value, and normalization processing, showing unique advantages in fields such as financial risk assessment and environmental monitoring, and can effectively capture the dynamic change characteristics of the index system, providing more sensitive quantitative support for complex decision-making problems.

[0111] As Figure 3 Shown, the objective weight calculation steps based on the anti-entropy weight method in step S3 are as follows:

[0112] S31: Construct an evaluation index matrix;

[0113] There are m evaluation objects and n evaluation indicators, and the original data forms an m×n matrix X:

[0114]

[0115] S32: Normalization processing;

[0116] To eliminate the difference in dimension, the range method is used to normalize the indicators; for benefit-type indicators, that is, the larger the value, the better, and the formula is:

[0117]

[0118] In the formula, x ij is the jth evaluation indicator of the ith evaluation object, x′ ij is the normalized evaluation indicator, and x j is the jth type of evaluation indicator, that is, the jth column of the original evaluation matrix.

[0119] After normalization, the indicator matrix X' is obtained, and the normalized element x i ′ j ∈[0,1].

[0120] S33: Calculate the anti-entropy;

[0121] For the normalized data, define the proportion p ij of the ith evaluation object under the jth indicator as:

[0122]

[0123] The calculation formula of the anti-entropy h j is:

[0124]

[0125] The larger the anti-entropy value, the higher the degree of dispersion of the indicator, and a greater weight should be assigned in the evaluation;

[0126] S34: Determine the objective weight;

[0127] The objective weight of the jth indicator is obtained by normalizing the anti-entropy value:

[0128]

[0129] Among them, the denominator is the sum of the anti-entropy values of all indicators to ensure

[0130] Specifically, the specific operation of the step S4 is as follows: The linear weighted method is used to fuse the subjective and objective weights, and the formula is as follows:

[0131]

[0132] where: ω j is the weight of the j-th index, is the subjective weight determined by the G1 group method; is the objective weight determined by the anti-entropy weight method; λ ∈ [0, 1] is the preference coefficient of the subjective and objective weights; based on the combined weight and the scores of each index, the system energy efficiency evaluation value S is calculated by weighted summation:

[0133]

[0134] where: S is the total score of the energy efficiency evaluation; S j is the score of the j-th index; ω j is the weight of the j-th index. The present invention is a comprehensive evaluation method that combines the G1 group method and the anti-entropy weight method: accurately calculates the line loss rate, transformer loss rate, and DCDC converter loss rate through power flow analysis, sets grading scoring rules, and establishes a quantifiable energy efficiency evaluation benchmark. Combining the G1 group method (determining the index order relationship and importance ratio based on expert experience) with the anti-entropy weight method (allocating weights based on data dispersion), generates a combined weight through linear weighting, balancing expert knowledge and data characteristics. Using the weighted summation model to output the total energy efficiency score, which not only reflects the static loss characteristics of the system but also captures the dynamic changes of the indicators, providing a scientific basis for the energy efficiency optimization of the photovoltaic energy storage DC-AC flexible system. This method fills the gap in the existing energy efficiency evaluation system and significantly improves the comprehensiveness and engineering applicability of the evaluation results.

[0135] The above is only the preferred embodiment of the present invention. Of course, the scope of the rights of the present invention cannot be limited by this. It should be pointed out that for those skilled in the art of this technology, without creative labor, the modification or equivalent replacement of the technical solution of the present invention does not depart from the protection scope of the technical solution of the present invention.

Claims

1. An energy efficiency evaluation method for a photovoltaic-storage-direct-current flexible power distribution system based on the G1-entropy weight method, characterized in that It includes the following steps: S1: Calculate each loss index; S2: Use the G1 group method to determine the subjective weight; S3: Use the anti-entropy weight method to determine the objective weight; S4: Synthesize the weights and calculate the energy efficiency score.

2. The energy efficiency evaluation method of the photovoltaic-storage-direct-current flexible power distribution system based on the G1-entropy weight method according to claim 2, characterized in that, In the step S1, in the energy efficiency evaluation of the photovoltaic-storage-direct-current-soft power distribution system, the losses of the photovoltaic-storage-direct-current-soft power distribution system mainly come from the energy losses in the power transmission and conversion links; specifically, they are divided into two categories: line transmission losses and power conversion equipment losses: on the one hand, the line losses caused by the line resistance will significantly affect the system efficiency with the current fluctuation; on the other hand, power electronic devices such as transformers and DCDC converters will generate fixed losses and variable losses during the voltage level conversion: in order to establish a scientific and reasonable energy efficiency evaluation system, it is necessary to first quantitatively calculate the key loss indexes, including the line loss rate based on power flow analysis, the transformer loss rate meeting the national standard requirements, and the DCDC converter loss rate reflecting the performance of power electronic devices.

3. The energy efficiency evaluation method of the photovoltaic-storage-direct-current flexible power distribution system based on the G1-entropy weight method according to claim 2, characterized in that, The specific steps for calculating each loss index in the step S1 include the following steps: S11: Calculate the line transmission loss index; The line loss rate of the low-voltage distribution network is an important indicator to measure the energy efficiency of the system; according to the power flow calculation results, the active power loss value P of each line can be obtained loss , let P input be the total input power of the line, and the formula for calculating the line loss rate is as follows: When the line loss rate α > 10%, it is determined that the system is unqualified, and at this time S1 = 0; if α ≤ 10%, the line loss index calculation formula is: S12: Calculate the power conversion equipment loss index; Transformer: Since the total distribution transformer loss rate β should be less than 10%, the transformer loss index score S2 is as follows: The efficiency of the DCDC converter in the low-voltage DC distribution network is crucial, directly affecting the overall energy efficiency and operating cost of the system. The target efficiency should be not less than 95%, that is, the loss rate γ should be less than 5%; so the DCDC converter loss index score S3 is as follows:

4. The energy efficiency evaluation method of the photovoltaic-storage-direct-soft power distribution system based on the G1-entropy weight method according to claim 1, wherein The G1 group method in the step S2 is a subjective weighting method based on expert experience. Its core is to derive the weights of each index by establishing a strict order relationship chain of evaluation indexes and the importance ratio between adjacent indexes; the G1 group method realizes the calculation of subjective weights through the following three stages, specifically as follows: S21: Determine the order relationship between attributes; Suppose there are n attributes, namely S = {S1, S2,..., S n}; The decision maker sorts the attributes from high to low according to their own judgment to obtain the attribute order relationship: Among them, the symbol > means "more important than". After sorting, is the most important indicator, is the least important indicator; S22: Quantify the importance ratio of adjacent indexes; For adjacent indicators and define the relative importance ratio r j : In the formula, is the subjective weight of the index ; r j takes values according to the semantic scaling rule in Table 1: S23: Calculate the weights of each attribute; Calculate the subjective weights of each index through the recurrence formula: The subjective weight of the least important index: Recursively calculate the subjective weights of adjacent indexes in reverse order: Normalization verification:

5. The energy efficiency evaluation method of the photovoltaic-storage-direct-current flexible power distribution system based on the G1-entropy weight method according to claim 4, wherein, The anti-entropy weight method in the step S3 is an improved objective weighting method based on the information entropy theory, mainly used for multi-index decision analysis; The anti-entropy weight method reversely uses the information entropy principle to positively transform the dispersion degree of index data into weight values: the greater the difference in index data, that is, the smaller the entropy value, the higher its anti-entropy weight; this method innovatively establishes a positive correlation between the index variation degree and weight distribution, and is particularly suitable for evaluation scenarios that need to focus on reflecting the volatility characteristics of indexes; Its calculation process is realized through steps such as standardizing the data matrix, calculating the anti-entropy value, and normalization processing. It shows unique advantages in fields such as financial risk assessment and environmental monitoring, can effectively capture the dynamic change characteristics of the index system, and provide more sensitive quantitative support for complex decision-making problems.

6. The energy efficiency evaluation method of the photovoltaic-storage-direct-current flexible power distribution system based on the G1-entropy weight method according to claim 1, characterized in that The objective weight calculation steps based on the anti-entropy weight method in the step S3 are as follows: S31: Construct an evaluation index matrix; There are m evaluation objects and n evaluation indicators, and the original data forms an m×n matrix X: S32: Normalization processing; To eliminate the dimension difference, the range method is used to normalize the indicators; for benefit-type indicators, that is, the larger the value, the better, and the formula is: where x ij is the j-th evaluation index of the i-th evaluation object, and x' ij is the normalized evaluation index, and x j is the j-th type of evaluation index, that is, the j-th column of the original evaluation matrix. After normalization, the index evaluation matrix X' is obtained, and the element x′ after normalization ij ∈[0, 1]. S33: Calculate the anti-entropy; For the normalized data, define the proportion p of the i-th evaluation object under the j-th index ij as follows: Anti-entropy h j The calculation formula is as follows: The larger the anti-entropy value, the higher the dispersion degree of the indicator, and a greater weight should be given in the evaluation; S34: Determine the objective weight; The objective weight of the j-th index Obtained by anti-entropy value normalization: where the denominator is the sum of the anti-entropy values of all indicators, ensuring 7. The energy efficiency evaluation method of the photovoltaic-storage-direct-current flexible power distribution system based on the G1-entropy weight method according to claim 6, wherein The specific operation of the step S4 is as follows: The linear weighting method is used to fuse the subjective and objective weights, and the formula is as follows: where: ω j is the weight of the j-th index, is the subjective weight determined by the G1 group method; is the objective weight determined by the anti-entropy weight method; λ∈[0,1] is the preference coefficient of the subjective and objective weights; based on the combined weight and the scores of each indicator, the system energy efficiency evaluation value S is calculated by weighted summation: Where: S is the total score of energy efficiency evaluation; S j is the score of the j-th indicator.

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