Distribution network distributed photovoltaic optimal configuration method and terminal

Abstract

The invention discloses a power distribution network distributed photovoltaic optimization configuration method and a terminal, and the method comprises the steps: determining a combination weight of a first group of indexes including distributed photovoltaic configuration capacity, total cost and multiple safety margins through employing a combination weighting method based on analytic hierarchy process and gray correlation analysis; based on the combined weight, an objective function is established with the maximum distributed photovoltaic configuration capacity, the minimum total cost and the maximum multi-safety margin, corresponding constraint conditions are established, all adjustable measures are traversed, and for the traversed adjustable measures, the optimal distribution of the distributed photovoltaic system is realized. Implementing adjustable measures on the basis of the obtained power distribution network frame parameters, typical daily load data and distributed photovoltaic power generation efficiency data, solving the distributed photovoltaic optimal configuration model to obtain distributed photovoltaic configuration results, screening out an optimal distributed photovoltaic configuration result from all the distributed photovoltaic configuration results, and providing the optimal distributed photovoltaic configuration result for the distributed photovoltaic power generation efficiency. Therefore, safe and stable operation of the power distribution network is effectively ensured, and the method has reference significance for ordered development of the distributed photovoltaic industry.

Description

Technical Field

[0001] The present invention relates to the field of distributed photovoltaic configuration, and in particular to a distributed photovoltaic optimization configuration method and terminal for a distribution network. Background Art

[0002] With the continuous advancement of the "dual carbon" goals and the whole-county photovoltaic development strategy, the intensive access of distributed photovoltaics has increased the penetration rate of distribution networks year by year. The access of a large number of distributed photovoltaics not only changes the flow direction of the distribution network line power flow, but also its output has intermittent and fluctuating characteristics, which brings risks such as node voltage limit and line current overload to the distribution network. Some experts and scholars have made useful explorations on the multi-objective optimization configuration method of distributed photovoltaics, but when constructing the objective function, most of its safety indicators only consider a single safety margin, such as overvoltage margin or overcurrent margin, which cannot guarantee the safe and stable operation of the distribution network; and there are shortcomings in the indicator weighting work of the existing distribution network multi-objective function, that is, it is highly subjective, and experts often judge and score based on the operation status of the distribution network and relevant experience, which cannot provide reasonable guidance for the optimal configuration of distributed photovoltaics. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a distributed photovoltaic optimization configuration method and terminal for a distribution network, which can effectively ensure the safe and stable operation of the distribution network.

[0004] In order to solve the above technical problems, a technical solution adopted by the present invention is:

[0005] A method for optimizing the configuration of distributed photovoltaic power generation in a power distribution network, comprising the steps of:

[0006] Determine the combined weights of the first group of indicators using a combined weighting method based on hierarchical analysis and grey correlation analysis, wherein the first group of indicators includes distributed photovoltaic configuration capacity, total cost and multiple safety margins;

[0007] Based on the combined weight of the first group of indicators, an objective function is established with the distributed photovoltaic configuration capacity being maximized, the total cost being minimized, and the multiple safety margins being maximized, and constraints corresponding to the objective function are established;

[0008] Generate a distributed photovoltaic optimization configuration model according to the objective function and the constraint conditions;

[0009] Obtaining distribution network parameters, typical daily load data and distributed photovoltaic power generation efficiency data, and traversing all adjustable measures, wherein the adjustable measures are determined from inverter power angle adjustment and demand response;

[0010] For the traversed adjustable measures, based on the distribution network grid parameters, the typical daily load data and the distributed photovoltaic power generation efficiency data, the traversed adjustable measures are implemented, and the distributed photovoltaic optimization configuration model is solved to obtain the distributed photovoltaic configuration results corresponding to the traversed adjustable measures;

[0011] The optimal distributed photovoltaic configuration result is selected from all distributed photovoltaic configuration results.

[0012] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0013] A distributed photovoltaic optimization configuration terminal for a power distribution network includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0014] Determine the combined weights of the first group of indicators using a combined weighting method based on hierarchical analysis and grey correlation analysis, wherein the first group of indicators includes distributed photovoltaic configuration capacity, total cost and multiple safety margins;

[0015] Based on the combined weight of the first group of indicators, an objective function is established with the distributed photovoltaic configuration capacity being maximized, the total cost being minimized, and the multiple safety margins being maximized, and constraints corresponding to the objective function are established;

[0016] Generate a distributed photovoltaic optimization configuration model according to the objective function and the constraint conditions;

[0017] Obtaining distribution network parameters, typical daily load data and distributed photovoltaic power generation efficiency data, and traversing all adjustable measures, wherein the adjustable measures are determined from inverter power angle adjustment and demand response;

[0018] For the traversed adjustable measures, based on the distribution network grid parameters, the typical daily load data and the distributed photovoltaic power generation efficiency data, the traversed adjustable measures are implemented, and the distributed photovoltaic optimization configuration model is solved to obtain the distributed photovoltaic configuration results corresponding to the traversed adjustable measures;

[0019] The optimal distributed photovoltaic configuration result is selected from all distributed photovoltaic configuration results.

[0020] The beneficial effects of the present invention are as follows: a combined weighting method based on hierarchical analysis and grey correlation analysis is used to determine the combined weights of a first group of indicators including distributed photovoltaic configuration capacity, total cost and multiple safety margins; an objective function is established based on the combined weights of the first group of indicators with the maximum distributed photovoltaic configuration capacity, the minimum total cost and the maximum multiple safety margins, and corresponding constraints are established; all adjustable measures are traversed, and the adjustable measures are determined from inverter power angle adjustment and demand response; for the traversed adjustable measures, the traversed adjustable measures are implemented based on the acquired distribution network grid parameters, typical daily load data and distributed photovoltaic power generation efficiency data, and a distributed photovoltaic optimization configuration model is solved. , and obtain the distributed photovoltaic configuration results corresponding to the traversed adjustable measures, and select the optimal distributed photovoltaic configuration results from all distributed photovoltaic configuration results. The distributed photovoltaic optimization configuration model takes into account multiple safety margins and source-load coordination, which is more comprehensive, and the combined weighting method based on hierarchical analysis and grey correlation analysis is adopted for indicator weighting to avoid strong subjective color. First, the distributed photovoltaic configuration results solved by implementing different adjustable measures are obtained, and then the optimal distributed photovoltaic configuration results are selected from them. The distributed photovoltaic can be reasonably and comprehensively optimized, thereby effectively ensuring the safe and stable operation of the distribution network, which has reference significance for the orderly development of the distributed photovoltaic industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flowchart of the steps of a method for optimizing configuration of distributed photovoltaic power generation in a power distribution network according to an embodiment of the present invention;

[0022] Figure 2 A schematic diagram of the structure of a distributed photovoltaic optimization configuration terminal for a distribution network according to an embodiment of the present invention;

[0023] Figure 3 (a) is a power flow analysis diagram of a passive distribution network before distributed photovoltaic access in a distributed photovoltaic optimization configuration method for a distribution network according to an embodiment of the present invention;

[0024] Figure 3 (b) is a power flow analysis diagram of a passive distribution network after distributed photovoltaic access in a distribution network distributed photovoltaic optimization configuration method according to an embodiment of the present invention;

[0025] Figure 4 It is a flow chart of selecting the optimal distributed photovoltaic configuration result in the method for optimizing the configuration of distributed photovoltaic in the distribution network according to an embodiment of the present invention;

[0026] Figure 5 It is an IEEE33 node topology diagram in the distributed photovoltaic optimization configuration method of the distribution network according to an embodiment of the present invention;

[0027] Figure 6A typical day distributed photovoltaic power generation efficiency diagram in the distribution network distributed photovoltaic optimization configuration method according to an embodiment of the present invention;

[0028] Figure 7 (a) is a schematic diagram of residential load data in the method for optimizing the configuration of distributed photovoltaic power generation in a distribution network according to an embodiment of the present invention;

[0029] Figure 7 (b) is a schematic diagram of commercial load data in the method for optimizing the configuration of distributed photovoltaic power generation in a distribution network according to an embodiment of the present invention;

[0030] Figure 7 (c) is a schematic diagram of industrial load data in the distributed photovoltaic optimization configuration method of the distribution network according to an embodiment of the present invention;

[0031] Figure 8 It is a result diagram of the distributed photovoltaic optimization configuration scheme of scenario 4 in the distribution network distributed photovoltaic optimization configuration method according to an embodiment of the present invention;

[0032] Fig. 9 A distribution network node voltage mean value diagram considering different adjustable measures in the distribution network distributed photovoltaic optimization configuration method according to an embodiment of the present invention;

[0033] Fig.10 This is a power angle mean value diagram of distributed photovoltaic inverters considering different adjustable measures in the distributed photovoltaic optimization configuration method of the distribution network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0035] Please refer to Figure 1 , a method for optimizing the configuration of distributed photovoltaic power generation in a distribution network, comprising the steps of:

[0036] Determine the combined weights of the first group of indicators using a combined weighting method based on hierarchical analysis and grey correlation analysis, wherein the first group of indicators includes distributed photovoltaic configuration capacity, total cost and multiple safety margins;

[0037] Based on the combined weight of the first group of indicators, an objective function is established with the distributed photovoltaic configuration capacity being maximized, the total cost being minimized, and the multiple safety margins being maximized, and constraints corresponding to the objective function are established;

[0038] Generate a distributed photovoltaic optimization configuration model according to the objective function and the constraint conditions;

[0039] Obtaining distribution network parameters, typical daily load data and distributed photovoltaic power generation efficiency data, and traversing all adjustable measures, wherein the adjustable measures are determined from inverter power angle adjustment and demand response;

[0040] For the traversed adjustable measures, based on the distribution network grid parameters, the typical daily load data and the distributed photovoltaic power generation efficiency data, the traversed adjustable measures are implemented, and the distributed photovoltaic optimization configuration model is solved to obtain the distributed photovoltaic configuration results corresponding to the traversed adjustable measures;

[0041] The optimal distributed photovoltaic configuration result is selected from all distributed photovoltaic configuration results.

[0042] From the above description, it can be seen that the beneficial effects of the present invention are: using a combined weighting method based on hierarchical analysis and grey correlation analysis to determine the combined weights of a first group of indicators including distributed photovoltaic configuration capacity, total cost and multiple safety margins, establishing an objective function based on the combined weights of the first group of indicators with the maximum distributed photovoltaic configuration capacity, the minimum total cost and the maximum multiple safety margins, and establishing corresponding constraints, traversing all adjustable measures, the adjustable measures are determined from the inverter power angle adjustment and demand response, for the traversed adjustable measures, the traversed adjustable measures are implemented based on the acquired distribution network grid parameters, typical daily load data and distributed photovoltaic power generation efficiency data, and the distributed photovoltaic optimization is solved. Configuration model is established to obtain the distributed photovoltaic configuration results corresponding to the traversed adjustable measures, and the optimal distributed photovoltaic configuration results are screened out from all distributed photovoltaic configuration results. The distributed photovoltaic optimization configuration model takes into account multiple safety margins and source-load coordination, which is more comprehensive, and the combined weighting method based on hierarchical analysis and grey correlation analysis is adopted for indicator weighting to avoid strong subjective color. The distributed photovoltaic configuration results solved by different adjustable measures are first obtained, and then the optimal distributed photovoltaic configuration results are screened out. The distributed photovoltaic can be reasonably and comprehensively optimized, thereby effectively ensuring the safe and stable operation of the distribution network, which has reference significance for the orderly development of the distributed photovoltaic industry.

[0043] Furthermore, the method of determining the combined weights of the first group of indicators using a combined weighting method based on hierarchical analysis and grey relational analysis includes:

[0044] Use the 1-9 scale method to make quantitative comparisons between the indicators in the first group and construct a judgment matrix;

[0045] Obtaining the eigenvector of the judgment matrix according to the square root method;

[0046] Calculating a consistency index of the judgment matrix, and performing a consistency test on the judgment matrix based on the consistency index to obtain a test result;

[0047] If the test result is passed, the weighting result of each indicator in the first group of indicators is obtained to obtain the indicator weight matrix of the expert group;

[0048] Determine a reference sequence of weights according to the indicator weight matrix of the expert group;

[0049] Calculating the relative distance between the indicator weight matrix of the expert group and the reference sequence;

[0050] The combined weight of the first group of indicators is calculated according to the relative distance.

[0051] From the above description, it can be seen that choosing a reasonable weighting method is crucial to solving multi-indicator and multi-objective optimization problems. Traditional single subjective weighting or objective weighting methods often have certain limitations and cannot ensure the consistency of subjectivity and objectivity when obtaining weights. The combined weighting method based on hierarchical analysis and grey correlation analysis relies on the experience judgment and priority information of multiple experts to ensure the subjectivity of weights. By optimizing the closeness of the correlation between indicators and system characteristics, the numerical calculation process of weights is made objective and accurate, thereby realizing the complementary advantages of subjective and objective weighting methods, making the combined weights of the first group of indicators more reasonable and reliable.

[0052] Furthermore, while determining the combined weights of the first group of indicators using a combined weighting method based on hierarchical analysis and grey relational analysis, it also includes:

[0053] Determine the combined weights of the second group of indicators using a combined weighting method based on hierarchical analysis and grey relational analysis, wherein the second group of indicators includes overvoltage margin, overcurrent margin and reverse power supply margin;

[0054] The objective function established based on the combined weight of the first group of indicators with the maximum distributed photovoltaic configuration capacity, the minimum total cost and the maximum multiple safety margins includes:

[0055] Determining the distributed photovoltaic configuration capacity;

[0056] The total cost is determined based on the investment cost, operation and maintenance cost, interaction cost between the distribution network and the upper power grid, and demand response cost generated in the distributed photovoltaic configuration;

[0057] Determining the overvoltage margin, the overcurrent margin, and the reverse power margin, and determining the multiple safety margins based on the combined weight of the second group of indicators, the overvoltage margin, the overcurrent margin, and the reverse power margin;

[0058] An objective function is established based on the combined weights of the first group of indicators with the distributed photovoltaic configuration capacity being maximized, the total cost being minimized, and the multiple safety margins being maximized.

[0059] From the above description, it can be seen that multiple safety margins are determined based on the combined weights of the second group of indicators, overvoltage margin, overcurrent margin and reverse power supply margin, which ensures that the distribution network can operate safely and stably after the distributed photovoltaic optimization configuration. Based on the combined weights of the first group of indicators, the objective function is established with the maximum distributed photovoltaic configuration capacity, the minimum total cost and the maximum multiple safety margins to ensure the economy and safety and reliability of the distributed photovoltaic configuration results.

[0060] Further, the determining the overvoltage margin, the overcurrent margin and the reverse power margin, and determining the multiple safety margins based on the combined weight of the second group of indicators, the overvoltage margin, the overcurrent margin and the reverse power margin includes:

[0061]

[0062] In the formula, D represents the multiple safety margin, λ 1 represents the combined weight of the overvoltage margin, U out Indicates the overvoltage margin, U out,min Indicates the minimum value of the overvoltage margin, U out,max Indicates the maximum value of the overvoltage margin, λ 2 represents the combined weight of the overcurrent margin, I out Indicates the overcurrent margin, I out,min Indicates the minimum value of the overcurrent margin, I out,max Indicates the maximum value of the overcurrent margin, λ 3 represents the combined weight of the reverse power margin, P back,out Represents the reverse power margin, P back,out,min Indicates the minimum value of the reverse power margin, P back,out,max Indicates the maximum value of the reverse power margin, U max Indicates the upper limit of the node voltage, represents the voltage value of node i at time t, I max Indicates the upper limit of the line current. represents the current value flowing through line j at time t, P back,max Indicates the upper limit of the reverse power. It represents the active power transmitted by the distribution network to the upper grid at time t.

[0063] From the above description, it can be seen that the multiple safety margins take into account a variety of safety margins and effectively ensure the safety and stability of the distribution network.

[0064] Furthermore, the objective function established based on the combined weight of the first group of indicators with the maximum capacity of the distributed photovoltaic configuration, the minimum total cost and the maximum multiple safety margins includes:

[0065]

[0066] In the formula, F represents the objective function, τ 1 represents the combined weight of distributed photovoltaic configuration capacity, S represents the total capacity of distributed photovoltaic configuration, S min represents the minimum total configuration capacity of distributed photovoltaics, S max represents the maximum total configuration capacity of distributed photovoltaics, τ 2 represents the combined weight of the total cost, C represents the total cost, C min represents the minimum value of the total cost, C max represents the maximum value of the total cost, τ 3 represents the combined weight of multiple safety margins, D represents multiple safety margins, D min Indicates the minimum value of multiple safety margins, D max Indicates the maximum value of the multiple safety margins.

[0067] From the above description, it can be seen that the established objective function can comprehensively consider the economy and safety of distributed photovoltaic optimization configuration, thereby effectively improving the optimization configuration effect of distributed photovoltaic.

[0068] Furthermore, the establishing of constraint conditions corresponding to the objective function includes:

[0069] Establish distributed photovoltaic configuration capacity constraints, distributed photovoltaic output constraints, distributed photovoltaic inverter power angle constraints, node voltage constraints, line current constraints, power flow balance constraints, distribution network power purchase and sales constraints and demand response constraints corresponding to the objective function.

[0070] From the above description, it can be seen that the distributed photovoltaic configuration capacity constraints, distributed photovoltaic output constraints, distributed photovoltaic inverter power angle constraints, node voltage constraints, line current constraints, power flow balance constraints, distribution network power purchase and sales constraints and demand response constraints corresponding to the objective function are established, and the safety constraints such as node voltage safety, line current carrying capacity, and reverse power transmission limit are comprehensively considered to ensure the safety of distributed photovoltaic optimization configuration in the distribution network.

[0071] Furthermore, the screening out the best distributed photovoltaic configuration result from all distributed photovoltaic configuration results includes:

[0072] The approximate ideal solution sorting method is used to screen out the optimal distributed photovoltaic configuration result from all distributed photovoltaic configuration results.

[0073] From the above description, it can be seen that the approximate ideal solution sorting method can make full use of the information of the original data, accurately reflect the gap between the distributed photovoltaic configuration schemes, and make the screening of the optimal distributed photovoltaic configuration results more objective and accurate.

[0074] Furthermore, before solving the distributed photovoltaic optimization configuration model to obtain the distributed photovoltaic configuration result corresponding to the traversed adjustable measures, the method further includes:

[0075] Linearizing the node voltage constraint, the line current constraint, and the power flow balance constraint in the distributed photovoltaic optimization configuration model to obtain a processed distributed photovoltaic optimization configuration model;

[0076] The step of solving the distributed photovoltaic optimization configuration model to obtain a distributed photovoltaic configuration result corresponding to the traversed adjustable measures includes:

[0077] Solve the processed distributed photovoltaic optimization configuration model to obtain a distributed photovoltaic configuration result corresponding to the traversed adjustable measures.

[0078] From the above description, it can be seen that since the node voltage constraints, line current constraints and power flow balance constraints in the model are nonlinear constraints, in order to facilitate the solution of the distributed photovoltaic optimization configuration model, the above constraints are linearized, which is conducive to a more efficient solution of the distributed photovoltaic optimization configuration model.

[0079] Further, all the adjustable measures include a first adjustable measure, a second adjustable measure, a third adjustable measure and a fourth adjustable measure;

[0080] Also includes:

[0081] determining a first adjustable measure without considering the inverter power angle adjustment and the demand response;

[0082] determining a second adjustable measure including adjusting a power angle of the inverter;

[0083] determining a third adjustable measure including said demand response;

[0084] A fourth adjustable measure including the inverter power angle adjustment and the demand response is determined.

[0085] From the above description, it can be seen that determining different adjustable measures, including inverter power angle adjustment and the fourth adjustable measure of demand response, can further analyze the synergy of the adjustable measures on both the source and load sides on the distributed photovoltaic optimization configuration scheme, thereby achieving a more effective distributed photovoltaic optimization configuration.

[0086] Please refer to Figure 2 Another embodiment of the present invention provides a distribution network distributed photovoltaic optimization configuration terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, each step in the above-mentioned distribution network distributed photovoltaic optimization configuration method is implemented.

[0087] The above-mentioned distributed photovoltaic optimization configuration method and terminal of the distribution network of the present invention can be applied to the optimization configuration scenario of distributed photovoltaics, which is described below through specific implementation methods:

[0088] Please refer to Figure 1 , Figure 3-Figure 10 , Embodiment 1 of the present invention is:

[0089] A method for optimizing the configuration of distributed photovoltaic power generation in a power distribution network, comprising the steps of:

[0090] Under the general environment of "connecting as much as possible" for distributed photovoltaics, the large-scale development of distributed photovoltaics and their large-scale access to the distribution network have caused the distribution network to change from "passive" to "active", affecting the voltage distribution and line flow of the distribution network. The dense access of distributed photovoltaics may cause the distributed photovoltaic output of some nodes to be greater than the load demand. These nodes will send power back to the distribution network, resulting in reverse flow. When the total output of distributed photovoltaics connected to the distribution network is greater than the total load demand, the distribution network will send power back to the upper grid through the balancing node. The flow direction of the distribution network before and after the access of distributed photovoltaics is as follows: Figure 3 shown.

[0091] Assuming that the distribution network has n nodes, after the distributed photovoltaic is connected, the voltage U at node m is m for:

[0092]

[0093] Where U 0 represents the voltage at the balance node, i.e., the connection point between the distribution network and the upper power grid; r represents the resistance of the line per unit length; l i represents the length of the line between node i and node i-1, P L,j represents the load active power of node j, P DG,j represents the distributed photovoltaic active output of node j, x represents the reactance of the line per unit length, Q L,j represents the load reactive power of node j, Q DG,j represents the distributed photovoltaic reactive power output of node j, U i-1 represents the voltage at node i-1.

[0094] It can be seen from the above formula that the node voltage will increase with the increase of distributed photovoltaic output, and the longer the line length from the balancing node, the more obvious the voltage rise. When the capacity of the distributed photovoltaic configuration connected to the distribution network is too large, it may cause the node voltage to exceed the limit and the line current to overload, which is not conducive to the safe and stable operation of the distribution network; at the same time, if the power of the distribution network to the upper power grid through the balancing node is not restricted, the power of the transformer may exceed the allowable value, thereby endangering the safety of the upper power grid. Therefore, when optimizing the configuration of distributed photovoltaics in the distribution network, it is necessary to comprehensively consider safety constraints such as node voltage safety, line current carrying capacity, and reverse power not exceeding the limit.

[0095] S1. Determine the combined weights of the first group of indicators using a combined weighting method based on hierarchical analysis and grey correlation analysis. The first group of indicators includes distributed photovoltaic configuration capacity, total cost and multiple safety margins, specifically including S11-S17:

[0096] S11. Use the 1-9 scale method to make quantitative comparisons between the indicators in the first group of indicators and construct a judgment matrix, which is as follows:

[0097]

[0098] In the formula, A represents the judgment matrix, A=(a ij ) n×n , a ij Indicates index a i and indicator a j The relative importance of a ij =1 / a ji , n represents the order of the judgment matrix.

[0099] S12. Obtain the eigenvector of the judgment matrix according to the square root method. According to the relevant matrix theory, the eigenvector value of the matrix is ​​the weight value of each indicator in the matrix, specifically:

[0100]

[0101] W=[w 1 ′,w 2 ′,…,w n ′];

[0102] Where M i represents the product of the elements in the i-th row of the judgment matrix, A ij represents the judgment matrix, w i represents the nth root of the product of the elements in the i-th row of the judgment matrix, w i ′ represents the normalized value of the nth root of the product of the elements in the i-th row of the judgment matrix, and W represents the eigenvector matrix of the judgment matrix.

[0103] S13, calculating the consistency index of the judgment matrix, and performing a consistency test on the judgment matrix based on the consistency index to obtain a test result to ensure the scientific nature of the judgment matrix.

[0104] The calculation of the consistency index of the judgment matrix is ​​specifically as follows:

[0105]

[0106] In the formula, C R represents the consistency index of the judgment matrix, λ max represents the maximum eigenvalue of the judgment matrix, R i It represents the random consistency index of the judgment matrix, which is related to the order n.

[0107] The consistency test of the judgment matrix based on the consistency index to obtain the test result includes:

[0108] If the consistency index is less than the preset value, it is considered that the consistency of the judgment matrix meets the acceptable range, and the test result is determined to be passed. If the consistency index is equal to or greater than the preset value, it is considered that the consistency of the judgment matrix does not meet the acceptable range, and the test result is determined to be failed. In an optional implementation, the preset value is 0.1.

[0109] S14. If the test result is passed, the weighting result of each indicator in the first group of indicators is obtained to obtain the indicator weight matrix of the expert group.

[0110] Specifically, if the test result is passed, the weighting results of each indicator in the first group of indicators determined by multiple experts are obtained to obtain the indicator weight matrix of the expert group, which is specifically:

[0111]

[0112] In the formula, B represents the indicator weight matrix of the expert group, b ij represents the weight coefficient of the j-th expert on the i-th indicator, and m represents the number of experts.

[0113] S15. Determine a reference sequence of weights according to the indicator weight matrix of the expert group, specifically:

[0114] D=(d 1 ,d 2 ,...,d i ,...,d n ) T ;

[0115] In the formula, D represents the reference sequence of weights, d iIt represents the maximum value of the element in the i-th row in the indicator weight matrix of the expert group. That is, the maximum value of the weight of each indicator in the indicator weight matrix of the expert group is the reference sequence of the weight.

[0116] S16. Calculate the relative distance between the indicator weight matrix of the expert group and the reference sequence, specifically:

[0117]

[0118] Where, L i Represents the relative distance between the indicator weight matrix of the expert group corresponding to the i-th indicator and the reference sequence. The importance of each indicator weight can be reflected by the relative distance between the indicator weight matrix of the expert group corresponding to each indicator and the reference sequence. The smaller the relative distance, the more important the indicator.

[0119] S17, calculating the combined weight of the first group of indicators according to the relative distance, specifically:

[0120]

[0121] In the formula, δ i represents the combined weight of the i-th indicator, δ i ' represents the normalized combined weight of the i-th indicator.

[0122] While executing S1, it also includes:

[0123] The combined weights of the second group of indicators are determined using a combined weighting method based on hierarchical analysis and grey relational analysis. The second group of indicators includes overvoltage margin, overcurrent margin and reverse power supply margin.

[0124] The specific process of determining the combined weights of the second group of indicators is similar to the specific process of determining the combined weights of the first group of indicators, as described in S11-S17, and will not be repeated here.

[0125] The combined weighting method based on hierarchical analysis and grey correlation analysis of the present invention ensures the subjectivity of weights by relying on the experience judgment of multiple experts and priority information. By optimizing the closeness of the correlation between indicators and system characteristics, the numerical calculation process of weights is made objective and accurate, thereby realizing the complementary advantages of subjective weighting and objective weighting methods.

[0126] S2. Based on the combined weight of the first group of indicators, an objective function is established with the maximum capacity of the distributed photovoltaic configuration, the minimum total cost, and the maximum multiple safety margins, and constraints corresponding to the objective function are established, specifically including S21-S25:

[0127] S21. Determine the distributed photovoltaic configuration capacity, specifically:

[0128]

[0129] In the formula, S represents the total capacity of distributed photovoltaic configuration, N represents the number of nodes in the distribution network, and S pv,i Represents the configuration capacity of distributed photovoltaic at node i.

[0130] S22. Determine the total cost based on the investment cost, operation and maintenance cost, interaction cost between the distribution network and the upper power grid, and demand response cost generated in the distributed photovoltaic configuration, specifically:

[0131] C=C inv +C ope +C grid +C IDR ;

[0132]

[0133] In the formula, C represents the total cost, C inv represents the investment cost in distributed photovoltaic configuration, C ope represents the operation and maintenance cost, C grid represents the interaction cost between the distribution network and the upper power grid, C IDR represents the demand response cost, r represents the project discount rate of distributed photovoltaic configuration, y represents the operating life of distributed photovoltaic, c inv represents the cost of distributed photovoltaic unit configuration capacity, T represents the optimization time scale, which is 96 hours, represents the power generation of distributed photovoltaic at node i at time t, c ope represents the operation and maintenance cost coefficient of distributed photovoltaics, Indicates the unit price of electricity purchased by the distribution network. The electricity purchase price adopts the time-of-use electricity price. represents the amount of electricity purchased by the distribution network at time t, c sell Indicates the unit price of electricity sold by the distribution network, represents the power sales of the distribution network at time t, where the power sales of the distribution network to the upper grid is the reverse power supply, c IDR represents the compensation cost coefficient of the demand response load adjustment, represents the increase in demand response load, Represents the reduction in demand response load.

[0134] S23, determining the overvoltage margin, the overcurrent margin and the reverse power margin, and determining the multiple safety margins based on the combined weight of the second group of indicators, the overvoltage margin, the overcurrent margin and the reverse power margin, to ensure that the distribution network can operate safely and stably after the distributed photovoltaic optimization configuration, including:

[0135]

[0136] In the formula, D represents the multiple safety margin, λ 1 represents the combined weight of the overvoltage margin, U out Indicates the overvoltage margin, U out,min Indicates the minimum value of the overvoltage margin, U out,max Indicates the maximum value of the overvoltage margin, λ 2 represents the combined weight of the overcurrent margin, I out Indicates the overcurrent margin, I out,min Indicates the minimum value of the overcurrent margin, I out,max Indicates the maximum value of the overcurrent margin, λ 3 represents the combined weight of the reverse power margin, P back,out Represents the reverse power margin, P back,out,min Indicates the minimum value of the reverse power margin, P back,out,max Indicates the maximum value of the reverse power margin, U max Indicates the upper limit of the node voltage, represents the voltage value of node i at time t, I max Indicates the upper limit of the line current. represents the current value flowing through line j at time t, P back,max Indicates the upper limit of the reverse power. It represents the active power transmitted by the distribution network to the upper grid at time t.

[0137] S24, establishing an objective function based on the combined weight of the first group of indicators with the distributed photovoltaic configuration capacity being maximized, the total cost being minimized, and the multiple safety margins being maximized, including:

[0138]

[0139] In the formula, F represents the objective function, τ 1 represents the combined weight of distributed photovoltaic configuration capacity, S min represents the minimum total configuration capacity of distributed photovoltaics, S max represents the maximum total configuration capacity of distributed photovoltaics, τ 2 represents the combined weight of the total cost, C min represents the minimum value of the total cost, C max represents the maximum value of the total cost, τ 3 Denotes the combined weight of multiple safety margins, D min Indicates the minimum value of multiple safety margins, D max Indicates the maximum value of the multiple safety margins.

[0140] S25. Establish distributed photovoltaic configuration capacity constraints, distributed photovoltaic output constraints, distributed photovoltaic inverter power angle constraints, node voltage constraints, line current constraints, power flow balance constraints, distribution network power purchase and sales constraints and demand response constraints corresponding to the objective function.

[0141] The distributed photovoltaic configuration capacity constraint, that is, the distributed photovoltaic configured at each node should meet the capacity threshold constraint, specifically:

[0142] S pv,min ≤S pv,i ≤S pv,max ;

[0143] In the formula, S pv,min represents the lower limit of distributed photovoltaic configuration capacity, S pv,max Indicates the upper limit of distributed photovoltaic configuration capacity.

[0144] The output of distributed photovoltaic power includes active power and reactive power. The output constraints of distributed photovoltaic power are as follows:

[0145]

[0146] In the formula, represents the power generation efficiency of distributed photovoltaics, represents the reactive power output of distributed photovoltaic at node i at time t, Represents the inverter power angle of the distributed photovoltaic at node i at time t.

[0147] When adjusting the power angle of the distributed photovoltaic inverter, the adjustable range constraint should be met. The distributed photovoltaic inverter power angle constraint is specifically:

[0148]

[0149] In the formula, θ pv,max Represents the upper limit of the power angle of the distributed photovoltaic inverter, θ pv,min Indicates the lower limit of the power angle of the distributed photovoltaic inverter.

[0150] To ensure the safe and stable operation of the distribution network, the node voltage should be kept within a safe range and should not exceed the limit. The node voltage constraints are specifically:

[0151]

[0152] Where U max Indicates the upper limit of the node voltage, U min Indicates the lower limit of the node voltage.

[0153] The current flowing through the line cannot exceed the maximum current carrying capacity of the line. The line current constraint is specifically:

[0154]

[0155] In the formula, I max Indicates the maximum current carrying capacity of the line.

[0156] The power flow balance constraints include active and reactive power balance constraints, node voltage balance constraints and second-order cone constraints.

[0157] The active and reactive power balance constraints are specifically:

[0158]

[0159] In the formula, represents the active load of node i at time t, Ω up,i represents the set of lines starting from node i, represents the active transmission power of line j at time t, Ω down,i represents the set of lines with node i as the end node, r j represents the resistance of line j, represents the reactive load of node i at time t, represents the reactive transmission power of line j at time t, x j represents the reactance of line j.

[0160] Since for the balance node:

[0161]

[0162] In the formula, It represents the reactive power that the distribution network sends back to the upper grid at time t;

[0163] The node voltage balance constraint is specifically:

[0164]

[0165] In the formula, represents the voltage of the end node i2 of line j at time t, Represents the voltage of the starting node i1 of line j at time t.

[0166] The second-order cone constraint is specifically:

[0167]

[0168] Due to the constraints of transformer capacity and reverse load rate, there is a maximum limit on the power purchase and sale of distribution network, and power purchase and sale cannot be carried out at the same time. The power purchase and sale constraints of distribution network are specifically as follows:

[0169]

[0170] In the formula, A binary indicator variable representing the power purchased by the distribution network at time t, A binary indicator variable representing the electricity sold by the distribution network at time t, and cannot be 1 at the same time to ensure that the purchase and sale of electricity by the distribution network cannot be carried out at the same time. buy,max Indicates the maximum value of the power purchased by the distribution network, P back,max It represents the maximum value of the power sold by the distribution network, where the power sold is the reverse power transmitted, which is subject to the reverse load rate constraint of the transformer.

[0171] The adjustment of demand response load includes load increase and load reduction, both of which should meet a certain adjustment range and should be balanced in each typical day. The demand response constraints are specifically:

[0172]

[0173] In the formula, A binary indicator variable representing the increase in demand response load at time t, A binary indicator variable representing the demand response load reduction at time t, and cannot be 1 at the same time to ensure that the increase and decrease of demand response load cannot be carried out at the same time. lu,max represents the maximum value of the demand response load increase, P ld,max Represents the maximum value of demand response load reduction.

[0174] S3. Generate a distributed photovoltaic optimization configuration model according to the objective function and the constraint conditions.

[0175] The distributed photovoltaic optimization configuration model takes into account multiple safety margins, and comprehensively considers the coordinated coordination of the power angle adjustment of the distributed photovoltaic inverter on the source side and the demand response on the load side in the model to improve the optimization configuration effect of distributed photovoltaics.

[0176] S4, obtaining distribution network parameters, typical daily load data and distributed photovoltaic power generation efficiency data, and traversing all adjustable measures, the adjustable measures are determined from inverter power angle adjustment and demand response, and all adjustable measures include a first adjustable measure, a second adjustable measure, a third adjustable measure and a fourth adjustable measure, such as Figure 4 As shown;

[0177] For the traversed adjustable measures, the traversed adjustable measures are implemented based on the distribution network grid parameters, the typical daily load data and the distributed photovoltaic power generation efficiency data, and the distributed photovoltaic optimization configuration model is solved to obtain the distributed photovoltaic configuration results corresponding to the traversed adjustable measures, such as Figure 4 shown.

[0178] The distributed photovoltaic optimization configuration model considering multiple safety margins has nonlinear constraints and cannot be solved directly using the CPLEX solver. The model needs to be linearized. and Alternative and Eliminate, therefore, before solving the distributed photovoltaic optimization configuration model to obtain the distributed photovoltaic configuration result corresponding to the traversed adjustable measures, it also includes:

[0179] The node voltage constraint, the line current constraint and the power flow balance constraint in the distributed photovoltaic optimization configuration model are linearized to obtain a processed distributed photovoltaic optimization configuration model.

[0180] Specifically, the node voltage constraints after linearization are:

[0181]

[0182] The line current constraint after linearization is:

[0183]

[0184] The power flow balance constraints after linearization are as follows:

[0185]

[0186]

[0187] The step of solving the distributed photovoltaic optimization configuration model to obtain a distributed photovoltaic configuration result corresponding to the traversed adjustable measures includes:

[0188] Solve the processed distributed photovoltaic optimization configuration model to obtain a distributed photovoltaic configuration result corresponding to the traversed adjustable measures.

[0189] Specifically, programming is performed in Python, combining the distribution network parameters, the typical daily load data, the distributed photovoltaic power generation efficiency data and the traversed adjustable measures, and calling the CPLEX solver to solve the processed distributed photovoltaic optimization configuration model to obtain the distributed photovoltaic configuration results corresponding to the traversed adjustable measures.

[0190] In an optional implementation manner, before S4, the method further includes:

[0191] determining a first adjustable measure without considering the inverter power angle adjustment and the demand response;

[0192] determining a second adjustable measure including adjusting a power angle of the inverter;

[0193] determining a third adjustable measure including said demand response;

[0194] A fourth adjustable measure including the inverter power angle adjustment and the demand response is determined.

[0195] S5. Filter out the optimal distributed photovoltaic configuration result from all distributed photovoltaic configuration results.

[0196] In an optional implementation, the optimal distributed photovoltaic configuration result is selected from all distributed photovoltaic configuration results using the approximate ideal solution sorting method, such as Figure 4 shown.

[0197] Specifically, a positive ideal solution and a negative ideal solution are determined according to the indicator type; the distance and closeness between each distributed photovoltaic configuration result and the positive ideal solution and the negative ideal solution are calculated, and all distributed photovoltaic configuration results are sorted in descending order according to the closeness to obtain all sorted distributed photovoltaic configuration results; and the first distributed photovoltaic configuration result is selected from all sorted distributed photovoltaic configuration results as the optimal distributed photovoltaic configuration result.

[0198] The example uses an IEEE 33-node system, and the system topology is shown in the figure below. Figure 5 As shown in Figure 1, there are 33 nodes and 32 lines, of which node 1 is a balancing node. The system base capacity is 10MVA, the base voltage is 12.66kV, the node voltage safety range is [0.95, 1.05]pu, and the maximum long-term current allowed by the line is 800A. The node load types include residential load, commercial load and industrial load. The load types corresponding to each node are shown in Table 1.

[0199] Table 1 Node load types

[0200] Load Type node Residential load 4 / 7 / 10 / 13 / 16 / 19 / 22 / 25 / 28 Commercial load 2 / 5 / 8 / 11 / 14 / 17 / 20 / 23 / 26 / 29 / 32 Industrial load 3 / 6 / 9 / 12 / 15 / 18 / 21 / 24 / 27 / 30 / 33

[0201] The unit capacity investment cost of distributed photovoltaic is 8000 yuan / kW, and the unit power generation operation and maintenance cost is 0.15 yuan / kWh; the operation period is 15 years, and the project discount rate is 6%. Except for the balancing node, all other nodes can be equipped with distributed photovoltaics, and the capacity configuration range is [0, 800] kW. The compensation cost of the unit load adjustment of demand response is 0.25 yuan / kWh, and the unit price of electricity sold by the distribution network to the upper power grid is 0.5 yuan / kWh. The time-of-use electricity price for purchasing electricity is shown in Table 2.

[0202] Table 2 Time-of-use electricity prices

[0203] Time Electricity price / (yuan / kWh) 01:00—07:00、23:00—24:00 0.31 08:00—11:00、15:00—18:00 0.45 12:00—14:00、19:00—22:00 0.82

[0204] The distributed photovoltaic power generation efficiency and load data on a typical day are as follows: Figure 6 and Figure 7 As shown, the power reference values ​​of residential load, commercial load and industrial load are 100kW, 80kW and 80kW respectively.

[0205] Set up the following four distributed photovoltaic optimization configuration scenarios:

[0206] Scenario 1: Distributed photovoltaic optimization configuration scheme considering only overvoltage margin; Scenario 2: Distributed photovoltaic optimization configuration scheme considering only overcurrent margin; Scenario 3: Distributed photovoltaic optimization configuration scheme considering only reverse power margin; Scenario 4: Distributed photovoltaic optimization configuration scheme considering multiple safety margins, that is, the above-mentioned method of the present invention.

[0207] The optimization results of the above four distributed photovoltaic optimization configuration scenarios are shown in Table 3.

[0208] Table 3 Optimization results of different scenarios

[0209]

[0210]

[0211] As can be seen from Table 3, among the four scenarios, scenario 4 has the highest degree of closeness, indicating that scenario 4 is the optimal configuration scheme for distributed photovoltaics. Compared with the other three scenarios, scenario 4 can maximize the multiple safety margins of the distribution network, including overvoltage margin, overcurrent margin and reverse power margin, while maintaining good economy. That is, it can ensure the safe and stable operation of the distribution network to the greatest extent, and is not prone to node overvoltage, line overcurrent, transformer overload and other risks. Scenarios 1 to 3 all have a single or multiple safety margin of 0, which is not conducive to the safe and stable operation of the distribution network. The above results fully verify the effectiveness of the above-mentioned distribution network distributed photovoltaic optimization configuration method of the present invention.

[0212] The specific configuration results of scenario 4 are as follows Figure 8 As shown in Table 4, the data format is node number / distributed photovoltaic configuration capacity (kW). In order to further analyze the improvement effect of the coordination of adjustable measures on both sides of the source and load on the distributed photovoltaic optimization configuration scheme, the optimization configuration results considering different adjustable measures are compared and analyzed based on scenario 4, as shown in Table 4.

[0213] Table 4 Optimization results considering different adjustable measures

[0214]

[0215]

[0216] As can be seen from Table 4, among the different adjustable measures, the scheme considering inverter power angle adjustment and demand response coordination measures has the highest degree of closeness, indicating that it is the optimal scheme, which is specifically manifested in the lowest total cost, the largest multiple safety margins, and the lowest total voltage offset and total network loss. Among them, the total network loss is reduced by 16.39% compared with the scheme without considering adjustable measures (i.e., without considering inverter power angle adjustment + demand response), and the effect of improving the overcurrent margin is the most obvious, effectively avoiding the risk of current exceeding the limit of the distribution network line. The above results fully prove that considering inverter power angle adjustment and demand response coordination measures can effectively improve the economy and safety of distributed photovoltaic optimization configuration schemes, so that the distribution network can operate safely and economically.

[0217] The node voltage of the distribution network considering different adjustable measures is analyzed, and the node voltage mean curve of the distribution network under each case is drawn, such as Fig. 9 As shown. Fig. 9 It can be seen that considering the inverter power angle adjustment and demand response coordination measures can effectively improve the voltage mean of each node in the distribution network, which is the highest among different adjustable measures. This shows that considering the inverter power angle adjustment and demand response coordination measures can reduce the voltage offset of each node in the distribution network, thereby improving the power quality, so its total voltage offset is the lowest.

[0218] The average power angle of distributed photovoltaic inverters considering different adjustable measures is as follows: Fig.10 As shown. Fig.10 It can be seen that compared with a single inverter power angle adjustment measure, considering inverter power angle adjustment and demand response coordination measures can effectively reduce the average power angle of distributed photovoltaic inverters, that is, reduce the number of adjustments to the inverter power angle, prevent the inverter from being damaged due to too frequent adjustment of the power angle, and improve the safety of distributed photovoltaic operation, thereby ensuring the safe and stable operation of the distribution network.

[0219] Please refer to Figure 2 , Embodiment 2 of the present invention is:

[0220] A distributed photovoltaic optimization configuration terminal for a distribution network comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step of the above-mentioned distributed photovoltaic optimization configuration method for a distribution network is implemented.

[0221] In summary, the present invention provides a method and terminal for optimizing the configuration of distributed photovoltaic power in a distribution network, which uses a combined weighting method based on hierarchical analysis and grey correlation analysis to determine the combined weights of a first group of indicators including distributed photovoltaic configuration capacity, total cost and multiple safety margins, and establishes an objective function based on the combined weights of the first group of indicators with the maximum distributed photovoltaic configuration capacity, the minimum total cost and the maximum multiple safety margins, and establishes corresponding constraint conditions, traverses all adjustable measures, and the adjustable measures are determined from inverter power angle adjustment and demand response. For the traversed adjustable measures, the traversed adjustable measures are implemented based on the obtained distribution network grid parameters, typical daily load data and distributed photovoltaic power generation efficiency data, and the distributed photovoltaic optimization configuration model is solved to obtain the distributed photovoltaic configuration results corresponding to the traversed adjustable measures, and from all the distributed photovoltaic configuration results The optimal distributed photovoltaic configuration result is screened out. The distributed photovoltaic optimization configuration model takes into account multiple safety margins and source-load coordination, which is more comprehensive. The combined weighting method based on hierarchical analysis and grey correlation analysis is adopted for indicator weighting to avoid strong subjective color. The distributed photovoltaic configuration results solved by different adjustable measures are first obtained, and then the optimal distributed photovoltaic configuration results are screened out. The distributed photovoltaic can be reasonably and comprehensively optimized, thereby effectively ensuring the safe and stable operation of the distribution network, which has a reference significance for the orderly development of the distributed photovoltaic industry. In addition, different adjustable measures are determined, including the fourth adjustable measure of inverter power angle adjustment and demand response, which can further analyze the synergy of the adjustable measures on both sides of the source and load on the distributed photovoltaic optimization configuration scheme. Improvement effect, thereby achieving more effective distributed photovoltaic optimization configuration.

[0222] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for optimizing the configuration of distributed photovoltaic power generation in a distribution network, characterized in that: Includes steps: Determine the combined weights of the first group of indicators using a combined weighting method based on hierarchical analysis and grey correlation analysis, wherein the first group of indicators includes distributed photovoltaic configuration capacity, total cost and multiple safety margins; Based on the combined weight of the first group of indicators, an objective function is established with the distributed photovoltaic configuration capacity being maximized, the total cost being minimized, and the multiple safety margins being maximized, and constraints corresponding to the objective function are established; Generate a distributed photovoltaic optimization configuration model according to the objective function and the constraint conditions; Obtaining distribution network parameters, typical daily load data and distributed photovoltaic power generation efficiency data, and traversing all adjustable measures, wherein the adjustable measures are determined from inverter power angle adjustment and demand response; For the traversed adjustable measures, based on the distribution network grid parameters, the typical daily load data and the distributed photovoltaic power generation efficiency data, the traversed adjustable measures are implemented, and the distributed photovoltaic optimization configuration model is solved to obtain the distributed photovoltaic configuration results corresponding to the traversed adjustable measures; The optimal distributed photovoltaic configuration result is selected from all distributed photovoltaic configuration results.

2. A method for optimizing the configuration of distributed photovoltaic power generation in a power distribution network according to claim 1, characterized in that: The method of using a combined weighting method based on hierarchical analysis and grey correlation analysis to determine the combined weights of the first group of indicators comprises: Use the 1-9 scale method to make quantitative comparisons between the indicators in the first group and construct a judgment matrix; Obtaining the eigenvector of the judgment matrix according to the square root method; Calculating a consistency index of the judgment matrix, and performing a consistency test on the judgment matrix based on the consistency index to obtain a test result; If the test result is passed, the weighting result of each indicator in the first group of indicators is obtained to obtain the indicator weight matrix of the expert group; Determine a reference sequence of weights according to the indicator weight matrix of the expert group; Calculating the relative distance between the indicator weight matrix of the expert group and the reference sequence; The combined weight of the first group of indicators is calculated according to the relative distance.

3. A method for optimizing the configuration of distributed photovoltaic power generation in a power distribution network according to claim 1, characterized in that: The method of using a combined weighting method based on hierarchical analysis and grey correlation analysis to determine the combined weights of the first group of indicators also includes: Determine the combined weights of the second group of indicators using a combined weighting method based on hierarchical analysis and grey relational analysis, wherein the second group of indicators includes overvoltage margin, overcurrent margin and reverse power supply margin; The objective function established based on the combined weight of the first group of indicators with the maximum distributed photovoltaic configuration capacity, the minimum total cost and the maximum multiple safety margins includes: Determining the distributed photovoltaic configuration capacity; The total cost is determined based on the investment cost, operation and maintenance cost, interaction cost between the distribution network and the upper power grid, and demand response cost generated in the distributed photovoltaic configuration; Determining the overvoltage margin, the overcurrent margin, and the reverse power margin, and determining the multiple safety margins based on the combined weight of the second group of indicators, the overvoltage margin, the overcurrent margin, and the reverse power margin; An objective function is established based on the combined weights of the first group of indicators with the distributed photovoltaic configuration capacity being maximized, the total cost being minimized, and the multiple safety margins being maximized.

4. A method for optimizing the configuration of distributed photovoltaic power generation in a distribution network according to claim 3, characterized in that: The determining of the overvoltage margin, the overcurrent margin, and the reverse power margin, and determining the multiple safety margins based on the combined weight of the second group of indicators, the overvoltage margin, the overcurrent margin, and the reverse power margin comprises: In the formula, D represents multiple safety margins, λ1 represents the combined weight of overvoltage margins, and U out Indicates the overvoltage margin, U out,min Indicates the minimum value of the overvoltage margin, U out,max represents the maximum value of the overvoltage margin, λ2 represents the combined weight of the overcurrent margin, I out Indicates the overcurrent margin, I out,min Indicates the minimum value of the overcurrent margin, I out,max represents the maximum value of the overcurrent margin, λ3 represents the combined weight of the reverse power margin, P back,out Represents the reverse power margin, P back,out,min Indicates the minimum value of the reverse power margin, P back,out,max Indicates the maximum value of the reverse power margin, U max Indicates the upper limit of the node voltage, represents the voltage value of node i at time t, I max Indicates the upper limit of the line current. represents the current value flowing through line j at time t, P back,max Indicates the upper limit of the reverse power. It represents the active power transmitted by the distribution network to the upper grid at time t.

5. A method for optimizing the configuration of distributed photovoltaic power generation in a distribution network according to claim 3, characterized in that: The objective function established based on the combined weight of the first group of indicators with the maximum distributed photovoltaic configuration capacity, the minimum total cost and the maximum multiple safety margins includes: In the formula, F represents the objective function, τ1 represents the combined weight of distributed photovoltaic configuration capacity, S represents the total capacity of distributed photovoltaic configuration, and S min represents the minimum total configuration capacity of distributed photovoltaics, S max represents the maximum total configuration capacity of distributed photovoltaics, τ2 represents the combined weight of the total cost, C represents the total cost, and C min represents the minimum value of the total cost, C max represents the maximum value of the total cost, τ3 represents the combined weight of multiple safety margins, D represents multiple safety margins, and D min Indicates the minimum value of multiple safety margins, D max Indicates the maximum value of the multiple safety margins.

6. A method for optimizing the configuration of distributed photovoltaic power generation in a power distribution network according to claim 1, characterized in that: The establishing of constraint conditions corresponding to the objective function comprises: Establish distributed photovoltaic configuration capacity constraints, distributed photovoltaic output constraints, distributed photovoltaic inverter power angle constraints, node voltage constraints, line current constraints, power flow balance constraints, distribution network power purchase and sales constraints and demand response constraints corresponding to the objective function.

7. A method for optimizing the configuration of distributed photovoltaic power generation in a power distribution network according to claim 1, characterized in that: The method of selecting the optimal distributed photovoltaic configuration result from all distributed photovoltaic configuration results includes: The approximate ideal solution sorting method is used to screen out the optimal distributed photovoltaic configuration result from all distributed photovoltaic configuration results.

8. A method for optimizing the configuration of distributed photovoltaic power generation in a power distribution network according to claim 6, characterized in that: Before solving the distributed photovoltaic optimization configuration model to obtain the distributed photovoltaic configuration result corresponding to the traversed adjustable measures, the method further includes: Linearizing the node voltage constraint, the line current constraint, and the power flow balance constraint in the distributed photovoltaic optimization configuration model to obtain a processed distributed photovoltaic optimization configuration model; The step of solving the distributed photovoltaic optimization configuration model to obtain a distributed photovoltaic configuration result corresponding to the traversed adjustable measures includes: Solve the processed distributed photovoltaic optimization configuration model to obtain a distributed photovoltaic configuration result corresponding to the traversed adjustable measures.

9. A method for optimizing the configuration of distributed photovoltaic power generation in a power distribution network according to claim 1, characterized in that: All the adjustable measures include a first adjustable measure, a second adjustable measure, a third adjustable measure and a fourth adjustable measure; Also includes: determining a first adjustable measure without considering the inverter power angle adjustment and the demand response; determining a second adjustable measure including adjusting a power angle of the inverter; determining a third adjustable measure including said demand response; A fourth adjustable measure including the inverter power angle adjustment and the demand response is determined.

10. A distributed photovoltaic optimization configuration terminal for a distribution network, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, each step of the method for optimizing the configuration of distributed photovoltaic power generation in a distribution network according to any one of claims 1 to 9 is implemented.

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