Low-voltage ac-dc distribution network planning auxiliary decision-making method, system and device based on subjective and objective mixed weight and medium

Abstract

The present application relates to the technical field of low-voltage AC / DC distribution network planning, and discloses a low-voltage AC / DC distribution network planning auxiliary decision-making method, system, device and medium based on subjective and objective mixed weights, which comprises the following steps: constructing a multi-level evaluation index system; calculating the objective weights of each lower-level index by using an entropy weight method; constructing an upper and lower index judgment matrix and calculating the subjective weights by using an analytic hierarchy process; obtaining the lower-level comprehensive scores of each upper-level index based on the subjective weights and membership degree matrix of each lower-level index by using a fuzzy comprehensive evaluation method; synthesizing the subjective weights and lower-level comprehensive scores of each upper-level index to obtain the fuzzy comprehensive evaluation results of each planning scheme, and determining the optimal planning scheme according to the maximum membership degree principle.The present application realizes scientific coupling of subjective and objective weights, overcomes the limitations of existing evaluation methods which are focused on a single dimension, and can comprehensively reflect the adaptability of planning schemes in terms of economy, power supply capacity and power quality.

Description

Technical Field

[0001] This invention relates to the field of low-voltage AC / DC distribution network planning technology, and in particular to a method, system, device and medium for auxiliary decision-making in low-voltage AC / DC distribution network planning based on a mixture of subjective and objective weights. Background Technology

[0002] With the proposal of the goal of "carbon peaking and carbon neutrality" and the continuous advancement of new power systems, the penetration rate of new energy sources, represented by distributed photovoltaics, in low-voltage AC distribution networks continues to increase, bringing new challenges to the long-term planning of power distribution systems.

[0003] The randomness, volatility, and unpredictability of distributed photovoltaic (PV) output, coupled with the impact of asymmetrical loads in traditional distribution networks, lead to frequent power quality problems and the risk of multiple concurrent power quality issues, such as high voltage, low voltage, three-phase imbalance, and increased harmonic content. These problems not only affect the power quality on the user side but may also further cause overload of distribution equipment, increased line losses, and system instability. Furthermore, traditional distribution networks are constrained by a "closed-loop design, open-loop operation" model, resulting in relatively limited control methods, insufficient operational flexibility, and difficulties in long-term planning. For example, while traditional methods can suppress voltage exceedances by adjusting the tap changer of on-load tap changers or switching voltage regulators, existing control resources are insufficient to meet the rapid and variable voltage support demands given the high proportion of new energy sources and the large-scale integration of electric vehicles.

[0004] With the development of power electronics technology, low-voltage AC / DC hybrid distribution networks are gradually becoming an important development direction to replace traditional AC distribution networks due to their advantages in increasing distribution capacity, reducing network losses, and promoting the consumption of new energy sources. However, the structure of low-voltage AC / DC hybrid distribution networks is complex, involving multiple factors such as the coordinated operation of AC and DC systems, the optimized configuration of flexible interconnection equipment, and the access and management of new sources and loads. Their long-term planning effectiveness is influenced by a variety of indicators. Therefore, how to scientifically and comprehensively evaluate the adaptability of low-voltage AC / DC hybrid distribution networks has become an important issue in their long-term planning and design. However, current research on the evaluation of low-voltage AC / DC hybrid distribution networks mostly focuses on a single dimension, such as economy or power quality, lacking a systematic evaluation method that can comprehensively reflect its multi-level performance, including economy, power supply capacity, and power quality. Therefore, it is urgent to construct a low-voltage AC / DC distribution network planning auxiliary decision-making model that covers the three dimensions of economy, power supply capacity, and power quality with a mixture of subjective and objective weights, to provide a scientific basis for the planning and selection of low-voltage AC / DC hybrid distribution networks and promote the green, safe, and efficient operation of distribution networks. Summary of the Invention

[0005] In view of the aforementioned existing problems, the present invention provides a method, system, device and medium for auxiliary decision-making in low-voltage AC / DC distribution network planning based on a mixture of subjective and objective weights.

[0006] Therefore, the technical problem solved by this invention is that current research on the evaluation of low-voltage AC / DC hybrid distribution networks is mostly focused on a single dimension, such as economy or power quality, and lacks a systematic evaluation method with a mixture of subjective and objective weights that can comprehensively reflect its multi-level performance such as economy, power supply capacity and power quality.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a low-voltage AC / DC distribution network planning auxiliary decision-making method based on subjective and objective mixed weights, including: constructing a multi-level evaluation index system for low-voltage AC / DC distribution networks, including upper-level indicators and lower-level indicators belonging to each upper-level indicator; Based on the actual data of each planning scheme in the feasible set, the objective weight of each lower-level indicator is calculated using the entropy weight method. Using the Analytic Hierarchy Process (AHP) and combining the objective weights, a lower-level indicator judgment matrix is ​​constructed and the subjective weights of each lower-level indicator are calculated. At the same time, an upper-level indicator judgment matrix is ​​constructed and the subjective weights of each upper-level indicator are calculated. The fuzzy comprehensive evaluation method is adopted to obtain the lower-level comprehensive score corresponding to each upper-level indicator based on the subjective weight and membership matrix of each lower-level indicator. The subjective weights of each upper-level indicator are combined with the corresponding lower-level comprehensive scores to obtain the fuzzy comprehensive evaluation results of each planning scheme, and the optimal planning scheme is determined based on the principle of maximum membership.

[0008] As a preferred embodiment of the low-voltage AC / DC distribution network planning auxiliary decision-making method based on subjective and objective mixed weights as described in this invention, wherein: the calculation of the objective weights of each lower-level indicator using the entropy weight method includes constructing an evaluation matrix based on the actual values ​​of each planning scheme under each lower-level indicator; The evaluation matrix is ​​normalized. Calculate the information entropy value of each lower-level indicator based on the normalized data; The objective weight of each lower-level indicator is determined based on its information entropy value.

[0009] As a preferred embodiment of the low-voltage AC / DC distribution network planning auxiliary decision-making method based on subjective and objective mixed weights described in this invention, the construction of the lower-level indicator judgment matrix and the calculation of the subjective weights of each lower-level indicator include, referring to the objective weights of each lower-level indicator, determining the relative importance scale between any two lower-level indicators under the same upper-level indicator, and constructing the lower-level indicator judgment matrix. The maximum eigenvalue and corresponding eigenvector of the lower-level indicator judgment matrix are obtained, and the subjective weights of each lower-level indicator are obtained after normalization.

[0010] As a preferred embodiment of the low-voltage AC / DC distribution network planning auxiliary decision-making method based on subjective and objective mixed weights described in this invention, wherein: obtaining the lower-level comprehensive score corresponding to each upper-level indicator includes constructing an evaluation set and determining a membership function model; Based on the values ​​of each planning scheme under each lower-level indicator and the membership function model, construct the membership matrix corresponding to each lower-level indicator; The subjective weight vectors of the lower-level indicators under each upper-level indicator are combined with the corresponding membership matrix using fuzzy synthesis to obtain the lower-level comprehensive score corresponding to each upper-level indicator.

[0011] As a preferred embodiment of the low-voltage AC / DC distribution network planning auxiliary decision-making method based on subjective and objective mixed weights described in this invention, the fuzzy comprehensive evaluation results of each planning scheme include combining the lower-level comprehensive scores of each planning scheme under each upper-level indicator into a lower-level comprehensive score matrix of each upper-level indicator. The lower-level comprehensive scoring matrix is ​​synthesized with the upper-level indicator subjective weight vector through fuzzy operator operations to obtain the fuzzy comprehensive evaluation result vector of each planning scheme.

[0012] As a preferred embodiment of the low-voltage AC / DC distribution network planning auxiliary decision-making method based on subjective and objective mixed weights described in this invention, wherein: determining the optimal planning scheme according to the maximum membership principle includes sorting the planning schemes according to the numerical values ​​of each planning scheme in the fuzzy comprehensive evaluation result vector; The scheme with the largest value is determined as the optimal planning scheme for the low-voltage AC / DC distribution network.

[0013] As a preferred embodiment of the low-voltage AC / DC distribution network planning auxiliary decision-making method based on subjective and objective mixed weights described in this invention, the upper-level indicators of the multi-level evaluation index system include economic indicators, power supply capacity indicators, and power quality indicators. The lower-level indicators of the economic indicators include the load increase per unit investment, equipment investment cost, photovoltaic load increase per unit investment, DC distribution network investment ratio, average line loss rate of AC and DC distribution networks, and flexible equipment loss. The lower-level indicators of the power supply capacity include photovoltaic penetration rate, distributed power absorption capacity, maximum load rate of flexible equipment, and transmission efficiency of flexible equipment. The lower-level indicators of the power quality index include voltage deviation, three-phase current imbalance, and voltage sag.

[0014] This invention provides a low-voltage AC / DC distribution network planning auxiliary decision-making system based on a mixture of subjective and objective weights.

[0015] As a preferred embodiment of the low-voltage AC / DC distribution network planning auxiliary decision-making system based on subjective and objective weights as described in this invention, it includes: an indicator system construction module, an objective weight calculation module, a subjective weight calculation module, a fuzzy comprehensive evaluation module, and a comprehensive decision-making module; The indicator system construction module is used to construct a multi-level evaluation indicator system for low-voltage AC / DC distribution networks. The objective weight calculation module is used to calculate the objective weight of each lower-level indicator in the evaluation index system based on the actual data of each planning scheme in the feasible set and using the entropy weight method. The subjective weight calculation module is used to construct a judgment matrix by combining the analytic hierarchy process with objective weights, and to calculate the subjective weights of each lower-level indicator and each upper-level indicator respectively. The fuzzy comprehensive evaluation module is used to obtain the lower-level comprehensive score corresponding to each upper-level indicator by using the fuzzy comprehensive evaluation method based on the subjective weight and membership matrix of each lower-level indicator. The comprehensive decision-making module is used to synthesize the subjective weights of each upper-level indicator with the corresponding lower-level comprehensive score to obtain the fuzzy comprehensive evaluation results of each planning scheme, and determine the optimal planning scheme based on the principle of maximum membership.

[0016] The present invention provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of a low-voltage AC / DC distribution network planning auxiliary decision-making method based on a mixture of subjective and objective weights.

[0017] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of a low-voltage AC / DC distribution network planning auxiliary decision-making method based on a mixture of subjective and objective weights are implemented.

[0018] The beneficial effects of this invention are as follows: This invention constructs a multi-level evaluation index subjective and objective weight coupling model. By integrating the entropy weight method, the hierarchical analysis method and the fuzzy comprehensive evaluation method, it achieves the scientific coupling of subjective and objective weights, thereby improving the overall applicability and credibility of the evaluation system.

[0019] A decision-making auxiliary method for low-voltage AC / DC distribution network planning based on a hybrid subjective and objective weighting is proposed. By constructing a "feasibility set" and using a multi-level evaluation index subjective and objective weighting coupling model to score the schemes, a quantitative evaluation and scientific ranking of various low-voltage AC / DC distribution network planning schemes is realized. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of a low-voltage AC / DC distribution network planning auxiliary decision-making method based on a mixture of subjective and objective weights, provided as an embodiment of the present invention.

[0022] Figure 2 This invention provides a fuzzy comprehensive weight determination and evaluation process for a low-voltage AC / DC distribution network planning auxiliary decision-making method based on a mixture of subjective and objective weights, as an embodiment of the present invention.

[0023] Figure 3 This is a schematic diagram of a low-voltage AC / DC distribution network planning scheme 1, which is a low-voltage AC / DC distribution network planning auxiliary decision-making method based on subjective and objective weighting, provided in an embodiment of the present invention.

[0024] Figure 4 This is a schematic diagram of a low-voltage AC / DC distribution network planning scheme 2, which is a low-voltage AC / DC distribution network planning auxiliary decision-making method based on a mixture of subjective and objective weights, provided in an embodiment of the present invention.

[0025] Figure 5 This is a schematic diagram of a low-voltage AC / DC distribution network planning scheme 3, which is a low-voltage AC / DC distribution network planning auxiliary decision-making method based on a mixture of subjective and objective weights, provided in an embodiment of the present invention. Detailed Implementation

[0026] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0027] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a low-voltage AC / DC distribution network planning auxiliary decision-making method based on a mixture of subjective and objective weights, including: S1: Construct a multi-level evaluation index system for low-voltage AC / DC distribution networks, including upper-level indicators and lower-level indicators belonging to each upper-level indicator.

[0028] S2: Based on the actual data of each planning scheme in the feasible set, the objective weight of each lower-level indicator is calculated using the entropy weight method.

[0029] S3: Using the Analytic Hierarchy Process (AHP) and combining objective weights, construct a lower-level indicator judgment matrix and calculate the subjective weights of each lower-level indicator. At the same time, construct an upper-level indicator judgment matrix and calculate the subjective weights of each upper-level indicator.

[0030] S4: Using the fuzzy comprehensive evaluation method, based on the subjective weights and membership matrices of each lower-level indicator, the lower-level comprehensive score corresponding to each upper-level indicator is obtained.

[0031] S5: Combine the subjective weights of each upper-level indicator with the corresponding lower-level comprehensive score to obtain the fuzzy comprehensive evaluation results of each planning scheme, and determine the optimal planning scheme based on the principle of maximum membership.

[0032] It should be noted that, in this embodiment, firstly, based on the actual operating characteristics of the low-voltage AC / DC distribution network and its impact on the distribution network, a multi-level evaluation index system for the low-voltage AC / DC distribution network was constructed by selecting indicators that are difficult to directly observe in actual engineering but are relatively important. Secondly, a multi-level evaluation index subjective-objective weight coupling model was proposed, and the weights of each level of index were calculated by combining the analytic hierarchy process (AHP) and the entropy weight method. Fuzzy analysis was performed using the fuzzy comprehensive evaluation method to score the schemes in the feasible set. Finally, the fuzzy comprehensive evaluation results of the schemes in the "feasible set" were calculated using matrix multiplication, sorted according to the principle of maximum membership, and the final low-voltage AC / DC distribution network planning scheme with the highest adaptability was obtained.

[0033] Example 2, refer to Figure 2 As an embodiment of the present invention, based on the above embodiment, a low-voltage AC / DC distribution network planning auxiliary decision-making method based on subjective and objective weighting is provided.

[0034] Furthermore, in this embodiment of the application, step S1 constructs a multi-level evaluation index system for low-voltage AC / DC distribution networks, including upper-level indicators and lower-level indicators belonging to each upper-level indicator. Specific steps include: Considering the differences in power supply models for various AC / DC hybrid distribution networks and combining the assessment needs of low-voltage AC / DC hybrid distribution substations in my country, this paper proposes an adaptability assessment method for low-voltage AC / DC hybrid distribution networks. A first-class assessment index is established from three aspects: economy, power supply capacity, and power quality. Each category of assessment index includes multiple second-class indicators, collectively forming a comprehensive benefit assessment index system. The index system, based on considerations of economy and power quality, has been improved according to the operational characteristics of low-voltage AC / DC hybrid distribution networks and takes into account the impact of flexible interconnection equipment and new source-load grid connection technologies.

[0035] S101: Investment economics is mainly divided into the following four lower-level indicators.

[0036] (1) Investment in AC / DC power distribution to increase load The increased load from AC / DC power distribution investment represents the additional power supply capacity brought about by AC / DC power grid investment, as shown in the following formula: (2) Investment cost of AC / DC equipment The investment cost of AC / DC equipment includes the investment in purchasing new equipment, including AC-side equipment, DC-side equipment, and other necessary equipment in the interconnection system. The specific calculation method is as follows: In the formula: Let be the number of sets of the i-th type of equipment; Let be the unit price of the i-th type of equipment; This represents the total number of equipment types required for the construction of AC equipment, DC equipment, and other hybrid networks, where i is the variable index.

[0037] (3) Increase investment in AC / DC power distribution to include photovoltaic power. Investment in AC / DC power distribution can increase the capacity of distributed photovoltaic power in a region.

[0038] In the formula: This is to increase the planned photovoltaic capacity during the investment and construction phase.

[0039] (4) Investment proportion of DC power distribution The DC power distribution investment ratio refers to the proportion of the total cost of purchasing DC equipment out of the total equipment investment. This indicator reflects whether the new equipment investment is reasonable. The specific calculation method is as follows: S102: The operational economic indicators mainly reflect the problems encountered in the actual operation of low-voltage AC / DC distribution networks, and are divided into the following two lower-level indicators.

[0040] (1) AC / DC power distribution line loss rate The AC / DC power distribution line loss rate reflects the proportion of line losses in AC and DC systems. The specific calculation formula is as follows: In the formula: n is the number of main power lines in the power grid; Let be the power loss of the i-th line; Let be the active power of the power flow on the user side of the i-th line.

[0041] (2) Losses of flexible equipment Flexible equipment losses reflect the losses caused by power electronic components interconnecting AC / DC systems. Considering this indicator can better reflect the impact of newly added flexible equipment on the overall operational economy of low-voltage AC / DC distribution networks. This paper uses a generalized converter loss calculation model to calculate the losses of flexible transmission equipment. The specific calculation formula is as follows: In the formula: For the current flowing through the AC side of VSC, the direction from the AC side to VSC is set as positive. and These represent the active power and reactive power flowing through the VSC on the AC side, respectively. The rated capacity of the VSC; This is the rated voltage of the DC side of the VSC; VSC AC side rated voltage S103: In addition to economic factors, many objective factors and the positive impact of actual operation are difficult to reflect at the economic level. Therefore, the power supply capacity of the distribution network is set as the second upper-level indicator, and it is further divided into the following four lower-level indicators.

[0042] (1) Photovoltaic penetration rate Photovoltaic penetration rate is the ratio of installed photovoltaic capacity to planned load capacity of the distribution network. It can clearly reflect the proportion of photovoltaics in the distribution network and the power supply capacity of distributed generation. The specific calculation formula is as follows: In the formula: The photovoltaic power generation within the system during time period t; This represents the power required within the system.

[0043] (2) Distributed power absorption capacity The distributed photovoltaic (PV) grid integration capacity is based on the maximum transmission power that the lines in the distribution network can withstand, reflecting the maximum potential of the distribution network for PV grid integration. The specific calculation formula is as follows: In the formula: This represents the maximum transmission power of the lines within the system.

[0044] (3) Maximum load rate of flexible equipment The maximum load rate of flexible equipment is obtained by averaging the maximum load rate during operation of flexible equipment, which can reflect the power exchange demand on both AC and DC sides and its control capability on both AC and DC sides.

[0045] In the formula: Let be the rated transmission power of the i-th flexible device in the system; Let be the maximum actual transmission power of the i-th flexible device.

[0046] (4) Transmission efficiency of flexible equipment The transmission efficiency of flexible equipment is the ratio of the transmission power of flexible equipment to its rated maximum transmission power. It reflects whether the equipment selection is matched with the scale of the distribution network and the magnitude of the impact of flexible equipment on the distribution network.

[0047] In the formula: Let be the power of the i-th flexible device at time t; Let be the rated capacity of the i-th flexible device.

[0048] S104: Power quality, as one of the important reference indicators of the distribution network, can intuitively reflect the impact of flexible equipment on the user side, and can determine whether flexible equipment is conducive to the power supply reliability of the distribution network by comparison.

[0049] (1) Voltage deviation Voltage deviation is an important indicator for measuring power quality. It mainly reflects the relative deviation of the operating voltage from the nominal system voltage under normal operating conditions. The specific calculation formula is as follows: In the formula: , The actual voltage and rated voltage of the i-th node are respectively. (2) Three-phase current imbalance Three-phase load imbalance increases line power loss, reducing operational efficiency and negatively impacting power quality on the distribution network's user side. Since there are numerous methods for calculating three-phase imbalance, many of which are difficult to measure, this paper uses only one: calculating the index weight based on the three-phase current imbalance. The specific calculation formula is as follows: (3) Voltage sag The network voltage sag is calculated based on the maximum voltage sag of each node in the network, using the following formula: In the formula: for communication networks , Let RMS voltage sag and RMS rated voltage be the values ​​of the i-th node, respectively; for a DC network, , These are the voltage sag and rated voltage of the i-th node, respectively.

[0050] Furthermore, in this embodiment of the application, step S2 calculates the objective weights of each lower-level indicator based on the actual data of each planning scheme in the feasible set using the entropy weight method. The specific steps include: S201: Construct an evaluation matrix based on the actual values ​​of each planning scheme under each lower-level indicator; Constructing the evaluation matrix The actual values ​​of each indicator are entered into a matrix to form an evaluation matrix, where... This represents the actual value of the i-th indicator under the j-th scheme.

[0051] Voltage deviation is a negative lower-level indicator in power quality. Assuming three planning schemes, the values ​​obtained through simulation or actual measurement are: Scheme 1: 8.51%, Scheme 2: 8.23%, and Scheme 3: 4.58%. This row vector [8.51, 8.23, 4.58] represents the data for the "Voltage Deviation" indicator in the matrix. Similarly, the scheme data for all 13 lower-level indicators, such as the economic indicator "Increased Load per Unit Investment" (positive) and the power supply capacity indicator "Photovoltaic Penetration Rate" (positive), are filled in in this way to jointly construct the evaluation matrix.

[0052] S202: Normalize the evaluation matrix, calculate the information entropy value of each lower-level indicator based on the normalized data, and determine the objective weight of each lower-level indicator based on the information entropy value of each lower-level indicator. Since the elements in the rating matrix are actual values, the differences in the numerical dimensions and orders of magnitude between the various evaluation indicators make them difficult to calculate directly, thus requiring normalization. This paper uses a common normalization method in the entropy weight method: the range method. It should be noted that the range method differs in handling positive indicators (larger values ​​are better, such as increased electricity supply and photovoltaic absorption) and negative indicators (smaller values ​​are better, such as line loss rate and investment cost). Positive indicators: Negative indicators: In the formula: The data is after standardization; Let j be the index of the i-th evaluation object.

[0053] It should be noted that for the positive indicator "photovoltaic penetration rate," the original data for the three schemes are 110.45%, 118.38%, and 139.40%. The maximum value is 139.40%, and the minimum value is 110.45%. Therefore, the normalized value for Scheme 1 is (110.45-110.45) / (139.40-110.45) = 0, and the normalized value for Scheme 3 is (139.40-110.45) / (139.40-110.45) = 1. For the negative indicator "voltage deviation," the original data are 8.51%, 8.23%, and 4.58%. The maximum value is 8.51%, and the minimum value is 4.58%. Therefore, the normalized value of Scheme 1 is (8.51-8.51) / (8.51-4.58)=0, and the normalized value of Scheme 3 is (8.51-4.58) / (8.51-4.58)=1. Normalization maps all indicator data to the interval [0,1], and for positive indicators, 1 represents the optimal value; for negative indicators, 1 represents the optimal value (i.e., the original value is the minimum).

[0054] The proportion of the i-th option in this indicator: In the formula: This represents the total number of samples.

[0055] Calculate the entropy value of index j based on the calculated proportions: The weights are calculated based on the entropy values: Through the above normalization process, standard data for all lower-level indicators were obtained. The degree of dispersion of an indicator directly determines its entropy value and final weight.

[0056] For example, comparing the indicators "voltage deviation" and "flexible equipment transmission efficiency": the normalized values ​​of "voltage deviation" differ significantly across the three schemes, indicating that the data is scattered and highly discrete. According to information entropy theory, the calculated entropy value... It will be smaller. The value is relatively large, therefore this indicator will receive a larger objective weight. This indicates that, within this evaluation system, different planning schemes show significant differences in their effectiveness in improving voltage levels, and this indicator has a crucial objective impact on distinguishing the merits of different schemes. Conversely, if the normalized values ​​of a certain indicator (such as "three-phase current imbalance" in a specific scenario) are very close across different schemes, then its entropy value... Larger Smaller, resulting in a lower objective weight. The value is also relatively low. This indicates that the indicator provides limited objective information when distinguishing the merits of these options.

[0057] Furthermore, in this embodiment, step S3 utilizes the Analytic Hierarchy Process (AHP) to construct a lower-level indicator judgment matrix and calculate the subjective weights of each lower-level indicator, while simultaneously constructing an upper-level indicator judgment matrix and calculating the subjective weights of each upper-level indicator. The specific steps include: S301: Referencing the objective weights of each lower-level indicator, determine the relative importance scale between any two lower-level indicators under the same upper-level indicator, and construct the lower-level indicator judgment matrix; In the process of assigning values ​​to the judgment matrix, the rationality of its values ​​is related to the scientific nature of the subsequent evaluation. The judgment matrix of the indicators is determined by consulting relevant materials or expert experience.

[0058] Table 1. Meaning of Matrix Scale

[0059] Determine the relationships between the values ​​in the judgment matrix based on the table above. (Example provided.) This indicates the importance of indicator 2 compared to indicator m.

[0060] For the three upper-level indicators—economic efficiency, power supply capacity, and power quality—experts conduct pairwise comparisons based on project objectives and engineering experience. For example, in distribution network planning, "economic efficiency" might be considered significantly more important than "power supply capacity," thus assigning a scale of 5; "economic efficiency" might be slightly more important than "power quality," thus assigning a scale of 3; and "power supply capacity" might be somewhere between slightly less important and equally important compared to "power quality," thus assigning a scale of 1 / 3. Based on these judgments, an upper-level indicator judgment matrix can be constructed, and the subjective weight of each upper-level indicator can be calculated accordingly.

[0061] Let's take the six sub-indicators under the "economic efficiency" indicator as an example. When scoring, experts refer to the objective weight results calculated by the entropy weight method, and then make fine adjustments based on the actual engineering situation. For example, the entropy weight method results show that the objective weight of increasing photovoltaic capacity per unit investment is the highest (0.0941), while the objective weights of equipment investment cost, DC distribution network investment ratio, and flexible equipment loss are lower and close (approximately 0.060). When constructing the judgment matrix, experts may use this to determine whether increasing photovoltaic capacity per unit investment is "significantly important" or "strongly important" (scale 5 or 7) relative to equipment investment cost, DC distribution network investment ratio, and flexible equipment loss, while equipment investment cost, DC distribution network investment ratio, and flexible equipment loss may have a scale of 1 due to their close importance. This constructed judgment matrix respects the objective laws of the data while incorporating subjective strategic considerations.

[0062] S302: Solve for the maximum eigenvalue and corresponding eigenvector of the lower-level indicator judgment matrix, and obtain the subjective weight of each lower-level indicator after normalization.

[0063] Find the judgment matrix Maximum eigenvalue and its corresponding eigenvectors And calculate the weights of each indicator: Perform a consistency check: In the formula: the largest eigenvalue of the matrix is The order of the matrix is If CI=0, there is perfect consistency; if CI is close to 0, there is good consistency; and if CI is larger, the consistency is worse.

[0064] To measure the magnitude of CI, a random consistency index RI is introduced, and N pairwise comparison matrices are randomly constructed. ,but In practical applications, the value of RI can be obtained by looking up Table 2. Table 2 Reference Table of RI Values

[0065] Define the consistency ratio CR Generally, when the consistency ratio CR < 0.1, the consistency check is considered to have passed. If the consistency check fails, the process returns to the first step to readjust the values ​​of the judgment matrix, check for errors, or make appropriate modifications.

[0066] The above steps calculate the eigenvector corresponding to the largest eigenvalue of the indicator judgment matrix. If the consistency test is passed, the elements in the normalized eigenvector represent the weights of the corresponding indicators.

[0067] Furthermore, in this embodiment, step S4 employs a fuzzy comprehensive evaluation method to obtain the lower-level comprehensive score corresponding to each upper-level indicator based on the subjective weights and membership matrices of each lower-level indicator. The specific steps include: S401: Construct an evaluation set and determine the membership function model. Based on the values ​​of each planning scheme under each lower-level indicator and the membership function model, construct the membership matrix corresponding to each lower-level indicator. Based on the actual situation of the research subjects, z evaluation levels are determined to construct an evaluation set V. The values ​​of the elements in V represent the fuzzy comprehensive evaluation scores under different evaluation levels.

[0068] To determine the membership function model, this embodiment uses a trapezoidal model to quantitatively analyze the fuzzy evaluation of the applicability assessment of the AC / DC hybrid system. The formula for a single-level trapezoidal model is as follows: A membership matrix is ​​constructed based on the evaluation scores of each indicator, as shown in the following formula, where This represents the membership degree of the m-th indicator at the z-th evaluation level.

[0069] S402: Perform fuzzy synthesis operation on the subjective weight vector of the lower-level indicators under each upper-level indicator and the corresponding membership matrix to obtain the lower-level comprehensive score corresponding to each upper-level indicator. By combining the weights of each indicator determined by the entropy weight method with the membership matrix, a fuzzy comprehensive evaluation is performed based on the principle of maximum membership, and finally the applicability level B of the object is obtained according to the evaluation set.

[0070] Furthermore, in this embodiment, step S5 synthesizes the subjective weights of each upper-level indicator with the corresponding lower-level comprehensive score to obtain the fuzzy comprehensive evaluation results of each planning scheme, and determines the optimal planning scheme based on the principle of maximum membership. Specific steps include: The lower-level comprehensive scores of each planning scheme under each upper-level indicator are combined into a lower-level comprehensive score matrix for each upper-level indicator; The lower-level comprehensive scoring matrix is ​​synthesized with the upper-level indicator subjective weight vector through fuzzy operator operations to obtain the fuzzy comprehensive evaluation result vector of each planning scheme.

[0071] Sort the planning schemes according to the numerical values ​​of each scheme in the fuzzy comprehensive evaluation result vector; The scheme with the largest value is determined as the optimal planning scheme for the low-voltage AC / DC distribution network.

[0072] Example 3 is the third embodiment of the present invention, which differs from the previous two embodiments in that: This embodiment also provides a low-voltage AC / DC distribution network planning auxiliary decision-making system based on a mixture of subjective and objective weights, including: an indicator system construction module, an objective weight calculation module, a subjective weight calculation module, a fuzzy comprehensive evaluation module, and a comprehensive decision-making module; The indicator system construction module is used to construct a multi-level evaluation indicator system for low-voltage AC / DC distribution networks. The objective weight calculation module is used to calculate the objective weight of each lower-level indicator in the evaluation index system based on the actual data of each planning scheme in the feasible set and using the entropy weight method. The subjective weight calculation module is used to construct a judgment matrix by combining the analytic hierarchy process with objective weights, and to calculate the subjective weights of each lower-level indicator and each upper-level indicator respectively. The fuzzy comprehensive evaluation module is used to obtain the lower-level comprehensive score corresponding to each upper-level indicator by using the fuzzy comprehensive evaluation method based on the subjective weight and membership matrix of each lower-level indicator. The comprehensive decision-making module is used to synthesize the subjective weights of each upper-level indicator with the corresponding lower-level comprehensive score to obtain the fuzzy comprehensive evaluation results of each planning scheme, and determine the optimal planning scheme based on the principle of maximum membership.

[0073] This embodiment also provides an electronic device, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the low-voltage AC / DC distribution network planning auxiliary decision-making method based on subjective and objective mixed weights as proposed in the above embodiment.

[0074] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the low-voltage AC / DC distribution network planning auxiliary decision-making method based on a mixture of subjective and objective weights as proposed in the above embodiments.

[0075] The storage medium proposed in this embodiment and the method for implementing low-voltage AC / DC distribution network planning auxiliary decision-making based on subjective and objective weights proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0076] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0077] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0078] Example 4, refer to Figures 3-5 This is an embodiment of the present invention, used to verify a low-voltage AC / DC distribution network planning auxiliary decision-making method based on a mixture of subjective and objective weights.

[0079] A demonstration zone has seen a significant increase in distributed photovoltaic (PV) installations in recent years, but the power quality of the distribution network in the area is problematic. The proposed solution is to use flexible interconnection equipment to help absorb the PV power, reduce three-phase imbalance, and minimize voltage exceedances. For example... Figures 3-5 As shown, the main problems in Area-1 are frequent voltage exceedances and heavy load on the main transformer; Area-2 currently experiences large-scale voltage exceedances and light load on the main transformer. A feasible set of three schemes is being constructed for the AC / DC hybrid system planning of this demonstration area. These schemes represent different power supply modes and grid structure schemes under the same background. Please refer to the following for details. Figure 3 .

[0080] Scheme 1 in the "feasible set" interconnects the distribution networks of the two AC distribution areas at the mid-section node of the distribution area on the original basis. This scheme mainly alleviates the heavy load of the main transformer in Area-1 distribution area, solves the light load of the transformer in Area-2 distribution area, and can absorb more photovoltaic power.

[0081] Scheme 2 in the "feasible set" interconnects the two AC distribution networks at the end of the distribution area, based on the original scheme. This scheme can alleviate the power quality problem for users and balance the load between the two transformers to a certain extent. However, due to the long line, it will generate a high line loss rate in actual operation.

[0082] Scheme 3 in the "feasible set" first uses flexible equipment to interconnect the first and last nodes of Area-1 transformer area, and uses another pair of flexible equipment for interconnection between the two transformers. This scheme can not only solve the voltage over-limit problem of Area-1 to a large extent, but also balance the load rate of the two transformers. However, the two pairs of flexible equipment mean more initial investment and flexible equipment losses.

[0083] Based on the above distribution network structure and different power supply modes, and combined with data obtained from simulation and field investigation, the objective data for calculating the entropy weight method index are shown in Table 3 below: Table 3 Objective Data

[0084] (1) Calculation of objective weights using the entropy weight method Since the original data still contains units and has inconsistent dimensions and large differences in order of magnitude, it cannot be used directly. Therefore, the data is first processed by the range method standardization formula.

[0085] As can be seen from the above formula, during the standardization process using the range method, each indicator's data will inevitably produce one "1" and one "0". The presence of "0" in the standard matrix will prevent subsequent calculation of information entropy, thus preventing the acquisition of results. Therefore, special processing is required for "0". A common method is to replace "0" with a smaller value; in this paper, 0.01 is used to replace "0" in the standard matrix. Table 4 below shows the standardized data: Table 4. Range-based Standardization Matrix Based on the standardized matrix, the proportion of each indicator, the information entropy value of each indicator, and the weight results of each indicator under different schemes are calculated in turn and organized into the following table.

[0086] Table 5. Results of Calculating the Weights of Thirteen Indicators Using the Entropy Weight Method

[0087] The entropy weight method calculates objective indicators based on data collected through simulation and actual data collection from the schemes in the "feasible set." This method is now combined with the analytic hierarchy process (AHP) to increase its subjectivity, achieving a comprehensive consideration of both objective and subjective factors.

[0088] (2) Subjective weights in the analytic hierarchy process The Analytic Hierarchy Process (AHP) provides subjective perspectives for the evaluation system.

[0089] Based on expert scoring opinions and relevant literature, the judgment matrix of the upper-level indicator hierarchy model is determined according to the judgment matrix scaling meaning table as shown in Table 6 below: Table 6 Judgment Matrix of Upper-Level Indicator Hierarchy Model

[0090] The data in the table above is used to construct a judgment matrix for the hierarchical analysis model (hereinafter referred to as the judgment matrix). Based on the judgment matrix, the largest eigenvalue and its eigenvector are first calculated, and then the subjective weights of the upper-level indicators under the hierarchical analysis method are calculated and organized as follows.

[0091] Table 7 Subjective Weights of Upper-Level Indicators under the Analytic Hierarchy Process (AHP)

[0092] In the Analytic Hierarchy Process (AHP), the consistency test is used to ensure the reasonableness and validity of expert judgments. If the consistency test fails, the conclusions obtained are unacceptable, indicating that there are significant differences in the experts' judgments and a lack of consensus.

[0093] Only when the consistency ratio CR is less than 0.1 can the constructed matrix be considered equivalent to a consistent matrix, and only then can the properties of a consistent matrix be applied to the constructed matrix. This is to ensure that the conclusions obtained using the analytic hierarchy process (AHP) are basically reasonable. The consistency index CI of the upper-level indicator judgment matrix is ​​calculated; the RI is obtained from the table, and the consistency ratio CR of the upper-level indicator weights is found to be 0.0462 < 0.1, which satisfies the consistency check.

[0094] To combine objective and subjective weights, the weights calculated by both methods are typically processed mathematically, using methods such as least squares, composite normalization, Lagrange multiplier optimization, and weighted combination optimization. However, since the lower-level indicators selected in this paper require simulation or operational data for determination, expert confirmation is difficult, potentially increasing discrepancies in expert opinions. Furthermore, the lower-level indicators selected in this paper are relatively representative and important, having already undergone a layer of subjective screening. Therefore, performing analytic hierarchy process calculations again and obtaining the final weights through mathematical calculations will introduce significant objectivity errors.

[0095] Based on the above information and the lower-level indicator weights calculated in the previous section, an objective weight judgment matrix for the lower-level indicators is constructed. Experts use the entropy weight method to determine the level of the relationship between elements in the judgment matrix, resulting in the following three lower-level weight indicator judgment matrices.

[0096] Following the calculation process of the Analytic Hierarchy Process (AHP), the weights of the lower-level indicators, i.e., the consistency ratios, are calculated as follows: Table 8 Weights of Lower-Level Economic Indicators

[0097] Table 9 Weights of Lower-Level Indicators for Power Supply Capacity

[0098] Table 10 Weights of Lower-Level Power Quality Indicators

[0099] The table above shows that the consistency ratios of the three sets of lower-level indicator weights are 0.0015, 0.0116, and 0.0251, respectively, all less than 0.1, which meets the consistency test requirements. Furthermore, these three sets of weights are subjective weight calculations considering the entropy weight method, taking into account both subjective opinions and objective factors.

[0100] (3) Fuzzy comprehensive evaluation method The final score for each scheme is obtained by combining the fuzzy comprehensive evaluation method with the indicators calculated above. The foundation of fuzzy comprehensive evaluation analysis is fuzzy mathematics. Specifically, it involves treating all fuzzy objects to be considered and their corresponding fuzzy categories as fuzzy sets, setting appropriate membership functions, and using relevant calculation and transformation methods from fuzzy set theory to quantitatively analyze the characteristics of the fuzzy objects. First, a five-level evaluation system and scoring criteria were established: Excellent, Good, Average, Poor, and Poor. Points were assigned to each level from 5 to 1, from highest to lowest.

[0101] Data with the same indicators under the original schemes were set as "groups". Data within each group was standardized and linearly transformed according to the Min-Max principle, mapping the values ​​to the range [0,1]. Within the range [0,1], a five-level trapezoidal membership function was used to divide the data into five corresponding intervals according to the evaluation level from low to high. Level I corresponds to the highest score of 5 points, and Level V corresponds to the lowest score. When determining the level, it should be noted that the smaller the negative indicator value, the closer the level is to Level I, and the larger the positive indicator value, the closer the level is to Level I.

[0102] The membership levels of each indicator value in the "feasible set" in the five-level trapezoidal membership function are shown in Table 11 below: Table 11 Membership Levels

[0103] The membership levels obtained from the table above are used to score the schemes, and combined with the previously calculated weights, the final score for the low-voltage AC / DC hybrid adaptability is obtained.

[0104] The fuzzy judgment matrix and weight vector are operated using fuzzy operators to obtain the fuzzy evaluation result vector. Here, the fuzzy operator adopts the ordinary matrix method, which makes each factor in the fuzzy judgment matrix contribute. The weights of the lower-level indicators of the hierarchical analysis-entropy weight calculated in the previous section are further optimized using fuzzy evaluation to better reflect the overall picture of the evaluation object.

[0105] Taking the economic indicators of Scheme 1 as an example, the lower-level weights of the economic indicators using the analytic hierarchy process (AHP) and entropy weight method are: The membership level score matrix is ​​as follows: The matrix multiplication result is 2.4983 points. Similarly, the comprehensive scores for power supply capacity and power quality can be calculated as 1.5542 and 0.9999, respectively.

[0106] Similarly, the lower-level index score matrices of Scheme 2 and Scheme 3 obtained by fuzzy comprehensive evaluation method are as follows: Based on the above calculations, the comprehensive scores of each scheme obtained by the fuzzy comprehensive analysis method at the lower index level are obtained. Further fuzzy operator calculations are needed to obtain the comprehensive score of the adaptability evaluation system of the low-voltage AC / DC hybrid power distribution system.

[0107] The scores of the lower-level indicators are combined and multiplied by the upper-level weights on the right to obtain the comprehensive evaluation result vector of the suitability of the AC / DC hybrid distribution network system, as shown in the following formula: The final fuzzy comprehensive evaluation results are (1.8647, 2.2436, 4.4268). Based on the principle of maximum membership degree, the ranking is determined by comparing the values. It can be seen that under the low-voltage AC / DC hybrid system adaptability evaluation system used in this example, Scheme 3, which involves installing two pairs of flexible equipment within and between transformer substations, has the highest adaptability. Therefore, Scheme 3 should be selected for planning.

Claims

1. A low-voltage AC / DC distribution network planning auxiliary decision-making method based on a mixture of subjective and objective weights, characterized in that: include, Construct a multi-level evaluation index system for low-voltage AC / DC distribution networks, including upper-level indicators and lower-level indicators belonging to each upper-level indicator; Based on the actual data of each planning scheme in the feasible set, the objective weight of each lower-level indicator is calculated using the entropy weight method. Using the Analytic Hierarchy Process (AHP) and combining the objective weights, a lower-level indicator judgment matrix is ​​constructed and the subjective weights of each lower-level indicator are calculated. At the same time, an upper-level indicator judgment matrix is ​​constructed and the subjective weights of each upper-level indicator are calculated. The fuzzy comprehensive evaluation method is adopted to obtain the lower-level comprehensive score corresponding to each upper-level indicator based on the subjective weight and membership matrix of each lower-level indicator. The subjective weights of each upper-level indicator are combined with the corresponding lower-level comprehensive scores to obtain the fuzzy comprehensive evaluation results of each planning scheme, and the optimal planning scheme is determined based on the principle of maximum membership.

2. The low-voltage AC / DC distribution network planning auxiliary decision-making method based on subjective and objective hybrid weights as described in claim 1, characterized in that: The method of calculating the objective weights of each lower-level indicator using the entropy weight method includes constructing an evaluation matrix based on the actual values ​​of each planning scheme under each lower-level indicator. The evaluation matrix is ​​normalized. Calculate the information entropy value of each lower-level indicator based on the normalized data; The objective weight of each lower-level indicator is determined based on its information entropy value.

3. The low-voltage AC / DC distribution network planning auxiliary decision-making method based on subjective and objective hybrid weights as described in claim 2, characterized in that: The construction of the lower-level indicator judgment matrix and the calculation of the subjective weight of each lower-level indicator include, with reference to the objective weight of each lower-level indicator, determining the relative importance scale between any two lower-level indicators under the same upper-level indicator, and constructing the lower-level indicator judgment matrix. The maximum eigenvalue and corresponding eigenvector of the lower-level indicator judgment matrix are obtained, and the subjective weights of each lower-level indicator are obtained after normalization.

4. The low-voltage AC / DC distribution network planning auxiliary decision-making method based on subjective and objective hybrid weights as described in claim 3, characterized in that: The process of obtaining the lower-level comprehensive score corresponding to each upper-level indicator includes constructing an evaluation set and determining the membership function model. Based on the values ​​of each planning scheme under each lower-level indicator and the membership function model, construct the membership matrix corresponding to each lower-level indicator; The subjective weight vectors of the lower-level indicators under each upper-level indicator are combined with the corresponding membership matrix using fuzzy synthesis to obtain the lower-level comprehensive score corresponding to each upper-level indicator.

5. The low-voltage AC / DC distribution network planning auxiliary decision-making method based on a mixture of subjective and objective weights as described in claim 4, characterized in that: The fuzzy comprehensive evaluation results of each planning scheme are obtained by combining the lower-level comprehensive scores of each planning scheme under each upper-level indicator into a lower-level comprehensive score matrix of each upper-level indicator. The lower-level comprehensive scoring matrix is ​​synthesized with the upper-level indicator subjective weight vector through fuzzy operator operations to obtain the fuzzy comprehensive evaluation result vector of each planning scheme.

6. The low-voltage AC / DC distribution network planning auxiliary decision-making method based on subjective and objective hybrid weights as described in claim 5, characterized in that: The step of determining the optimal planning scheme based on the maximum membership principle includes sorting the planning schemes according to the numerical values ​​of each planning scheme in the fuzzy comprehensive evaluation result vector. The scheme with the largest value is determined as the optimal planning scheme for the low-voltage AC / DC distribution network.

7. The low-voltage AC / DC distribution network planning auxiliary decision-making method based on a mixture of subjective and objective weights as described in claim 6, characterized in that, The upper-level indicators of the multi-level evaluation indicator system include economic indicators, power supply capacity indicators, and power quality indicators; The lower-level indicators of the economic indicators include the load increase per unit investment, equipment investment cost, photovoltaic load increase per unit investment, DC distribution network investment ratio, average line loss rate of AC and DC distribution networks, and flexible equipment loss. The lower-level indicators of the power supply capacity include photovoltaic penetration rate, distributed power absorption capacity, maximum load rate of flexible equipment, and transmission efficiency of flexible equipment. The lower-level indicators of the power quality index include voltage deviation, three-phase current imbalance, and voltage sag.

8. A low-voltage AC / DC distribution network planning auxiliary decision-making system based on a mixture of subjective and objective weights, employing the method for low-voltage AC / DC distribution network planning auxiliary decision-making based on a mixture of subjective and objective weights as described in any one of claims 1 to 7, characterized in that, include: The system includes modules for constructing an indicator system, calculating objective weights, calculating subjective weights, performing fuzzy comprehensive evaluation, and making comprehensive decisions. The indicator system construction module is used to construct a multi-level evaluation indicator system for low-voltage AC / DC distribution networks. The objective weight calculation module is used to calculate the objective weight of each lower-level indicator in the evaluation index system based on the actual data of each planning scheme in the feasible set and using the entropy weight method. The subjective weight calculation module is used to construct a judgment matrix by combining the analytic hierarchy process with objective weights, and to calculate the subjective weights of each lower-level indicator and each upper-level indicator respectively. The fuzzy comprehensive evaluation module is used to obtain the lower-level comprehensive score corresponding to each upper-level indicator by using the fuzzy comprehensive evaluation method based on the subjective weight and membership matrix of each lower-level indicator. The comprehensive decision-making module is used to synthesize the subjective weights of each upper-level indicator with the corresponding lower-level comprehensive score to obtain the fuzzy comprehensive evaluation results of each planning scheme, and determine the optimal planning scheme based on the principle of maximum membership.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the low-voltage AC / DC distribution network planning auxiliary decision-making method based on subjective and objective mixed weights as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the low-voltage AC / DC distribution network planning auxiliary decision-making method based on subjective and objective mixed weights as described in any one of claims 1 to 7.

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